| N270 | 0 | 200 | 5 Days | 10min | 0/1 | 0/2 | SHA-256 | 256 | \",\"reading_order\":2,\"tags\":[],\"alt_text\":\"This image is a detailed table displaying data across multiple columns and rows. The columns are labeled \\\"Participant\\\", \\\"Prompting Payment\\\", \\\"Working Time\\\", \\\"Include Security\\\", \\\"Secure\\\", \\\"Security Score\\\", \\\"Function\\\", \\\"Length in bits\\\", \\\"Iteration\\\", \\\"Salt\\\", \\\"C&P\\\", \\\"Study\\\", and \\\"SQ\\\". The table presents information for various participants (P1-P12, N1-N12), showing numerical values, time durations, security parameters, functions, and binary checkmark/cross indicators.\",\"figure_path\":\"tables/3290605-3300370_pdf_shortened_page_6_tab_2.png\"},{\"label\":\"table_cap\",\"bbox\":[274,572,536,588],\"text\":\"Table 2: Evaluation of Participants' Submissions\",\"reading_order\":3,\"tags\":[]},{\"label\":\"anno\",\"bbox\":[90,591,745,645],\"text\":\"Bold: Participants who at first delivered an insecure solution and received the additional security request. Working time participants took to submit\\ntheir first solution. Include Security: Time participants needed to add security after the request. C&P Security code was copied and pasted from the\\nInternet. Study: Freelancers who stated that they were aware the project might be a study. SQ: Freelancers who asked which hashing function they\\nshould use.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"half_para\",\"bbox\":[68,661,395,758],\"text\":\"receiving a security hint, 3 additional freelancers decided to\\nuse symmetric encryption. Interestingly, almost all of them\\nused a secret key, which was a combination of our social\\nnetworking website name \\\"SportSnapShare\\\" and some other\\nString (e.g., registration). As noted in the methodology, we\\ndid not consider this as a secure solution.\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,759,395,822],\"text\":\"Further, 5 of our participants decided to use Base64 to en-\\ncode the passwords in their first submission. After receiving\\na security instruction, 3 additional freelancers added Base64\\nto their solution. Naturally, this is not a secure solution.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,823,395,902],\"text\":\"Since some participants received a security request, we\\nchecked to see if the security scores of these participants\\ndiffered from those who did not (but who considered security\\nby themselves). We did not find such a difference (Wilcoxon\\nrank sum: W = 200, p-value = 0.83).\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,902,395,950],\"text\":\"Next we wanted to see the effects of any of our two\\nvariables task description (prompting vs. non-prompting)\\nand payment (€100 €100 vs. €200). For this we first conducted a\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,661,747,742],\"text\":\"Fisher's exact omnibus test on all four conditions (FET: $\\\\rho$ =\\n0.02). Further, we followed this up with three post-hoc tests\\n(regarding prompting, payment and password storage expe-\\nrience) with Bonferroni-Holm correction to test the variables\\nseparately.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,742,746,871],\"text\":\"Task Design. For the first solution, we received 17 non-\\nsecure solutions from the non-prompted and 8 from the\\nprompted participants (see Table 3 ). For the secure solu-\\ntions, 4 came from non-prompted participants and 13 from\\nprompted. Consequently, task design had a significant effect,\\nwith non-prompted participants delivering fewer initial se-\\ncure solutions (FET: $p = 0.01$ , $cor - p = 0.03$ , OR = 6.55, CI =\\n[1.44, 37.04], family = 3).\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,870,746,933],\"text\":\"Payment did not have a significant effect (FET, $p = 0.22$,\\ncor-p = 0.44, family = 3). However, more participants had\\nin secure initial solutions in the €200 conditions. Since we\\nhad a small sample size, we recommend not dismissing the\",\"reading_order\":11,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,127,1012],\"text\":\"Paper 140\",\"reading_order\":12,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[704,995,745,1012],\"text\":\"Page 7\",\"reading_order\":13,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300370/results/markdown/3290605-3300370_pdf_shortened_page_6.md"}
{"file_name": "images/test_data/3290605-3300370/3290605-3300370_pdf_shortened_page_7.png", "source_folder": "test_data", "document_id": "3290605-3300370", "source_image_path": "test_data/3290605-3300370/3290605-3300370_pdf_shortened_page_7.png", "source_json_path": "test_data/3290605-3300370/results/output_json/3290605-3300370_pdf_shortened_page_7.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"tab\",\"bbox\":[116,123,344,215],\"text\":\" | Secure | Insecure | | Prompted$_{100}$ | 5 | 4 | | Prompted$_{200}$ | 8 | 4 | | Non-Prompted$_{100}$ | 1 | 10 | | Non-Prompted$_{200}$ | 3 | 7 |
\",\"reading_order\":2,\"tags\":[],\"alt_text\":\"A table displays data categorized by \\\"Secure\\\" and \\\"Insecure\\\" columns. The rows are labeled \\\"Prompted₁₀₀\\\", \\\"Prompted₂₀₀\\\", \\\"Non-Prompted₁₀₀\\\", and \\\"Non-Prompted₂₀₀\\\". Numerical values are present in each cell.\",\"figure_path\":\"tables/3290605-3300370_pdf_shortened_page_7_tab_2.png\"},{\"label\":\"table_cap\",\"bbox\":[91,218,370,234],\"text\":\"Table 3: Number of Secure Solutions per Condition\",\"reading_order\":3,\"tags\":[]},{\"label\":\"half_para\",\"bbox\":[68,279,394,310],\"text\":\"different payment levels yet and looking at this in future \\nwork。\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,311,394,518],\"text\":\"Experience. Acar et al. [3] found a correlation between pro-\\ngramming experience, whereas Naikshina et al. [22] did not.\\nNaikshina postulated that the effect might not be visible\\ndue to the smaller range of experience seen in the student\\nsample. Thus, we also tested whether more Java experience\\nis correlated with higher security scores. We found no effect\\n(Kruskal-Wallis: $\\\\chi^2 = 13.31$ , df = 10, $p$ -value = 0.21). Fur-\\nther we investigated whether previous password experience\\nmight affect the security awareness. We found no significant\\neffect between whether the participants stated that they had\\nstored passwords before and whether their solutions were\\nsecure (FET: $p$ -value = 0.52, $cor - p = 0.52$ , OR = 0, CI = [0,\\n8.91], family = 3).\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,518,394,677],\"text\":\"Copy and Paste . Naiakshina et al. [22] found a significant\\npositive effect of copy and paste. Due to the fact that we\\nconducted a field study we could not gather the same level\\nof information on this behavior. We did, however, analyze\\nthe code to see if we could find online sources. We found\\n16 obviously copied examples. Out of these 16 participants,\\n6 copied MD5, 2 symmetric encryption, 4 bcrypt, 3 PBKDF2,\\nand 1 SHA-1 as security methods. We cannot say with cer-\\ntainty that the other solutions were not copied, so we did\\nnot perform any comparisons on this data.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,691,194,709],\"text\":\"Qualitative Analysis\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,712,395,792],\"text\":\"Next, we present a qualitative analysis of the data we gath-\\nered. Based on the inductive coding process we separate our\\nfindings into three phases: (1) the request phase; (2) the im-\\nplementation phase; and (3) the reflection phase. The coding\\noverview can be found in the supplemental material.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,804,197,823],\"text\":\"Phase One - Request\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,826,395,954],\"text\":\"Our participants mentioned several aspects from which they\\ndecided whether to implement what they thought was a\\nsecure solution. While N4 $_{100}$ stated that security is dependent\\non data sensitivity, most of our participants (N6 $_{100}$ , N10 $_{100}$ ,\\nP9 $_{200}$ , P7 $_{100}$ , P5 $_{100}$ , N10 $_{100}$ , N7 $_{100}$ , N2 $_{100}$ , N20 $_{100}$ , N250 $_{100}$ ) stated\\nthat security should be part of the task description from the\\nclient: \\\"I cannot find it in requirements, password encryption,\\ncan you tell me where is it written? I might have missed it\\\"\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,125,746,172],\"text\":\"(N11 $_{100}$ ). Interestingly, this was also the case for participants\\nin the prompted conditions, where a secure solution was\\nexplicitly required.\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,172,746,252],\"text\":\"In the non-prompted group we had an interesting case:\\nN10 $_{100}$ sent us a message asking whether he should store\\nuser passwords securely. However, before we could reply he\\nhad already handed in a plain text solution. This happened\\nwithin three hours during the night in our time zone.\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,252,746,381],\"text\":\"As mentioned above, we found a significant effect of prompt-\\ning and non-prompting with our freelancers. Four out of 22\\nnon-prompted developers did add security, which is more\\nthan the 0 out of 20 in the student lab study. $^*$ Yet the lesson\\nremains the same: Even for a task which - for security ex-\\nperts - is obviously security-critical, like storing passwords,\\none should not expect developers to know this or be willing\\nto spend time on it without explicit prompting:\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[436,395,735,412],\"text\":\"\\\"If you want, I can store the encrypted password.\\n\\\" (P2200)\",\"reading_order\":14,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,427,745,459],\"text\":\"So it is absolutely necessary to explicitly state that security\\nis desired.\",\"reading_order\":15,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[420,473,596,491],\"text\":\"Phase Two - Implementation\",\"reading_order\":16,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,493,746,764],\"text\":\"Misconceptions. While the task description had a similar ef-\\nfect on the freelancers as on the students, we found some\\ninteresting differences concerning misconceptions about se-\\ncure password storage. For example, some participants in the\\nstudent sample confused password storage security with data\\ntransmission security. This confusion was not found in our\\nfreelance sample. Instead, we found another phenomenon,\\nwhich was not observed in the student sample: the usage\\nof the binary-to-text encoding scheme base64 to \\\"securely\\\"\\nstore user passwords in a database. Eight of our freelancers\\nstored user passwords in the database by using base64 ; 4\\nof them were in the prompted condition and 4 in the non-\\nprompted condition. Of the 18 participants who received the\\nadditional security request, 3 (N 100 , N 100 , P 100 ) decided to\\nuse base64 and argued, for example: \\\"[I] encrypted it so the\\nclear password is not visible\\\" (N 100 ) and \\\"It is very tough to\\ndecrypt\\\" (N 100 ).\",\"reading_order\":17,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,764,746,909],\"text\":\"We also found misconceptions which were shared by stu-\\ndents and freelancers. Many participants used hashing and\\nencryption as synonyms (e.g., P 5200 , N7100 , N1100 , N1020 ,\\nP200 , N8100 , N10100 , N12100 ). However, this did not lead any\\nof the students to use symmetric encryption methods to store\\npasswords. This misconception was strongly reflected by the\\nf freelancers' code submissions. For instance, when asked how\\nhe stored user passwords, N7100 stated he \\\"used [a] hashing\\nalgorithm . In actual fact, he used symmetric encryption. Six\",\"reading_order\":18,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[420,924,745,953],\"text\":\"°The difference is not statistically significant (FET = 0.12). However, due\\nto the small sample size this should not be over-interpreted.\",\"reading_order\":19,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,127,1012],\"text\":\"Paper 140\",\"reading_order\":20,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[704,995,745,1012],\"text\":\"Page 8\",\"reading_order\":21,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300370/results/markdown/3290605-3300370_pdf_shortened_page_7.md"}
{"file_name": "images/test_data/3290605-3300371/3290605-3300371_pdf_shortened_page_0.png", "source_folder": "test_data", "document_id": "3290605-3300371", "source_image_path": "test_data/3290605-3300371/3290605-3300371_pdf_shortened_page_0.png", "source_json_path": "test_data/3290605-3300371/results/output_json/3290605-3300371_pdf_shortened_page_0.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[70,44,158,59],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,44,745,60],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\"]},{\"label\":\"icon\",\"bbox\":[0,92,38,132],\"reading_order\":2,\"tags\":[\"cross_marker\"]},{\"label\":\"sec_0\",\"bbox\":[105,102,708,197],\"text\":\"Virtual Showdown: An Accessible Virtual Reality\\nGame with Scaffolds for Youth with Visual\\nImpairments\",\"reading_order\":3,\"tags\":[]},{\"label\":\"author_information\",\"bbox\":[101,211,256,286],\"text\":\"Ryan Ledoff\\n\\nMixed Reality Applications\\nMicrosoft\\nRedmond, WA, USA\\nryan.ledoff@microsoft.com\",\"reading_order\":4,\"tags\":[\"author\",\"author_affili\",\"author_mail\"]},{\"label\":\"author_information\",\"bbox\":[318,211,499,317],\"text\":\"Lindsay Ball\\n\\nDepartment of Kinesiology, Sport\\nStudies & Physical Education\\nThe College at Brockport, State\\nUniversity of New York\\nBrockport, NY, USA\\n\\nlball@brockport.edu\",\"reading_order\":5,\"tags\":[\"author\",\"author_affili\",\"author_mail\"]},{\"label\":\"author_information\",\"bbox\":[564,211,713,300],\"text\":\"Amelia Wang\\n\\nHuman Centered Design &\\nEngineering\\nUniversity of Washington\\nSeattle, WA, USA\\n\\naw1998@uw.edu\",\"reading_order\":6,\"tags\":[\"author\",\"author_affili\",\"author_mail\"]},{\"label\":\"author_information\",\"bbox\":[85,336,270,411],\"text\":\"Yi Xuan Khoo\\nDepartment of Computer Science\\nUniversity of Iowa\\nIowa City, IA, USA\\nxiyuan-khoo@uiowa.edu\",\"reading_order\":7,\"tags\":[\"author\",\"author_affili\",\"author_mail\"]},{\"label\":\"author_information\",\"bbox\":[318,336,499,443],\"text\":\"Lauren Lieberman\\nDepartment of Kinesiology, Sport\\nStudies & Physical Education\\nThe College at Brockport, State\\nUniversity of New York\\nBrockport, NY, USA\\nllieberman@brockport.edu\",\"reading_order\":8,\"tags\":[\"author\",\"author_affili\",\"author_mail\"]},{\"label\":\"author_information\",\"bbox\":[548,336,730,411],\"text\":\"Kyle Rector\\n\\nDepartment of Computer Science\\n\\nUniversity of Iowa\\nIowa City, IA, USA\\nkyle.rector@uiowa.edu\",\"reading_order\":9,\"tags\":[\"author\",\"author_affili\",\"author_mail\"]},{\"label\":\"sec_1\",\"bbox\":[70,468,141,485],\"text\":\"ABSTRACT\",\"reading_order\":10,\"tags\":[\"paper_abstract\"]},{\"label\":\"para\",\"bbox\":[69,490,395,791],\"text\":\"Virtual Reality (VR) is a growing source of entertainment,\\nbut people who are visually impaired have not been\\neffectively included. Audio cues are motivated as a\\ncomplement to visuals, making experiences more\\nimmersive, but are not a primary cue. To address this, we\\nimplemented a VR game called Virtual Showdown. We\\nbased Virtual Showdown on an accessible real-world game\\ncalled Showdown, where people use their hearing to locate\\nand hit a ball against an opponent. Further, we developed\\nVerbal and Verbal/Vibration Scaffolds to teach people how to\\nplay Virtual Showdown. We assessed the acceptability of\\nVirtual Showdown and compared our scaffolds in an\\nempirical study with 34 youth who are visually impaired.\\nThirty-three participants wanted to play Virtual Showdown\\nagain, and we learned that participants scored higher with\\nthe Verbal Scaffold or if they had prior Showdown\\nexperience. Our empirical findings inform the design of\\nfuture accessible VR experiences.\",\"reading_order\":11,\"tags\":[\"paper_abstract\"]},{\"label\":\"sec_1\",\"bbox\":[421,466,523,483],\"text\":\"CCS CONCEPTS\",\"reading_order\":12,\"tags\":[]},{\"label\":\"list\",\"bbox\":[421,486,742,520],\"text\":\"• Human-centered computing → Accessibility systems and tools\\n• Human-centered computing → Empirical studies in accessibility\",\"reading_order\":13,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[422,533,502,547],\"text\":\"KEYWORDS\",\"reading_order\":14,\"tags\":[\"paper_keywords\"]},{\"label\":\"para\",\"bbox\":[422,549,732,564],\"text\":\"Virtual Reality; Blind; Low Vision; Youth; Spatial Audio; Haptics\",\"reading_order\":15,\"tags\":[\"paper_keywords\"]},{\"label\":\"sec_2\",\"bbox\":[422,579,537,592],\"text\":\"ACM Reference format:\",\"reading_order\":16,\"tags\":[]},{\"label\":\"para\",\"bbox\":[422,593,746,672],\"text\":\"Ryan Wedoff, Lindsay Ball, Amelia Wang, Yi Xuan Khoo, Lauren\\nLieberman, Kyle Rector. 2019. Virtual Showcase: An Accessible Virtual\\nReality Game with Scaffolds for Youth with Visual Impairments. In 2019\\nCHI Conference on Human Factors in Computing Systems Proceedings (CHI\\n2019), May 4-9, 2019, Glasgow, Scotland, UK . ACM, New York, NY, USA,\\nPaper 141, 15 pages. https://doi.org/10.1145/3290605.330371\",\"reading_order\":17,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[422,697,556,714],\"text\":\"1 INTRODUCTION\",\"reading_order\":18,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,719,746,887],\"text\":\"Virtual Reality (VR) has grown dramatically over the last\\nseveral years, with new commercial VR experiences such as\\nOculus Rift [41] , Google Cardboard [42] , Samsung Gear VR\\n[43] , and HTC VIVE [44] . VR, a computer-generated\\nsimulation of a 3D environment [45] , is a source of gaming,\\nentertainment, exploration of spaces, physical exertion [13] ,\\nand medical applications [6,12] . While gaming has an\\nimpact on people of all ages, youth are playing video\\ngames; according to Pew Research Center in 2015, 72 % of\\nteens are playing video games [23] .\",\"reading_order\":19,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,894,746,945],\"text\":\"As prolific as gaming is, and as VR is increasing in\\npopularity, the design of these experiences is excluding\\ncertain segments of the population. For youth who are\",\"reading_order\":20,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[70,826,388,902],\"text\":\"Permission to make digital or hard copies of all or part of this work for personal or\\nclassroom use is granted without fee provided that copies are not made or distributed\\nfor profit or commercial advantage and that copies bear this notice and the full\\ncitation on the first page. Copyrights for components of this work owned by others\\nthan thoseauthor(s) must be honored. Abstracting with credit is permitted. To copy\\notherwise, or republish, to post on servers or to redistribute to lists, requires\\nspecific permission and/or a fee. Request permissions from permissions@acm.org.\",\"reading_order\":21,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[71,902,250,912],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK.\",\"reading_order\":22,\"tags\":[\"meta_subject\",\"meta_pub_date\",\"meta_num\"]},{\"label\":\"fnote\",\"bbox\":[70,913,385,932],\"text\":\"© 2019 Copying is held by the owner/author(s). Publication rights licensed to ACM\\nACM ISBN 978-1-4503-5970-1/95. $15.00.\",\"reading_order\":23,\"tags\":[\"meta_pub_date\"]},{\"label\":\"fnote\",\"bbox\":[70,933,239,946],\"text\":\"DOI: https://doi.org/10.1145/3290605.3300371\",\"reading_order\":24,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[70,996,125,1012],\"text\":\"Paper 141\",\"reading_order\":25,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,996,744,1012],\"text\":\"Page 1\",\"reading_order\":26,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300371/results/markdown/3290605-3300371_pdf_shortened_page_0.md"}
{"file_name": "images/test_data/3290605-3300371/3290605-3300371_pdf_shortened_page_1.png", "source_folder": "test_data", "document_id": "3290605-3300371", "source_image_path": "test_data/3290605-3300371/3290605-3300371_pdf_shortened_page_1.png", "source_json_path": "test_data/3290605-3300371/results/output_json/3290605-3300371_pdf_shortened_page_1.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_pub_date\"]},{\"label\":\"half_para\",\"bbox\":[68,101,394,252],\"text\":\"visually impaired (VVI), there remain accessibility barriers\\nto VR experiences. There is prior research designing audio\\n(e.g., [7,13] ) and haptics [13] for 3D virtual experiences.\\nHowever, these experiences are motivated as a complement\\nto visuals, with goals of engagement, persuasion, and\\nimmersion [7,13] , as opposed to a primary cue of\\ninteraction. While there are existing 3D audio games [46–\\n50] , they involve keyboard input as opposed to using one's\\nbody like immersive VR games.\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,259,394,494],\"text\":\"To address this gap, we implemented a VR game (Virtual\\nShowdown) that is inspired by an accessible game called\\nShowdown, where people use their hearing to locate and\\nhit a ball against an opponent. We chose Showdown\\nintentionally to avoid designing the visuals (beyond\\ndebugging purposes) because players wear a blindfold. We\\ncould focus solely on audio, verbal, and vibration design in\\nVR. For many YVI this may be their first experience with\\nVR. We implemented the system, made design decisions for\\ngameplay, and designed scaffolds to help YVI learn how to\\nplay. We present our findings from an empirical user study\\nwith 34 YVI to assess the acceptability of Virtual\\nShowdown and the comparison between two types of\\nscaffolds:\",\"reading_order\":3,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,501,393,568],\"text\":\"1 Verbal Scaffolds that provide verbal previews of the ball\\ntrajectory across the table, verbal coaching as the ball\\ncrosses the halfway point of the table, and constructive\\nfeedback if a user misses the ball.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,570,393,636],\"text\":\"2 Verbal/Vibration Scaffolds that provide the same verbal\\ninformation and employ a vibration \\\"metal detector,\\\"\\nmeaning the closer one's hand gets to the right location\\nto hit the ball, the more intensely the vibration they feel.\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,636,393,787],\"text\":\"We compared the participants' final scores and levels\\nachieved between conditions. We assessed demographic\\nfactors for both conditions (e.g., age, gender, Showdown\\nexperience). We also determined body movement strategies\\nthat the participants employed while playing the game. We\\nhad participants compare the conditions and found no clear\\npreference. Our work addresses two research questions.\\nWhen YY play a 3D audio VR game that involves targeting\\nand hitting a moving virtual object:\",\"reading_order\":6,\"tags\":[]},{\"label\":\"list\",\"bbox\":[69,793,393,861],\"text\":\"1 How do they perform with Verbal Scaffolds versus\\nVerbal/Vibration Scaffolds?\\n2 What are their body movement strategies during\\ngameplay?\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[84,861,342,878],\"text\":\"From our work, we present three contributions:\",\"reading_order\":8,\"tags\":[]},{\"label\":\"list\",\"bbox\":[70,885,392,920],\"text\":\"1\\nA detailed system design of Virtual Showdown, a VR\\ngame accessible to YCI with 3D audio that encourages\",\"reading_order\":9,\"tags\":[]},{\"label\":\"list\",\"bbox\":[419,102,745,217],\"text\":\"body movement and interaction with moving virtual\\nobjects\\n2 A design of two scaffolds for learning to play Virtual\\nShowdown\\n3 Results of our empirical study with 34 YVI to assess the\\nacceptability of Virtual Showdown and a comparison\\nbetween the two scaffolds with respect to scores\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,219,746,369],\"text\":\"We discuss background and related work with the game\\nof Showdown, 3D audio design and localization, accessible\\ncomputing games and VR for people who are visually\\nimpaired. We describe our Virtual Showdown\\nimplementation and the design of the scaffolds. Then, we\\npresent our empirical user study with 34 participants, along\\nwith our findings from the user study. Finally, we discuss\\nour contribution and its implications making accessible VR\\nexperiences for YVI .\",\"reading_order\":11,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,394,691,410],\"text\":\"2 BACKGROUND AND RELATED WORK\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,415,746,515],\"text\":\"We discuss the game of Showdown, 3D audio design and\\nlocalization, accessible computing games, and VR for people\\nwho are visually impaired (PVI). While our project focuses\\non youth, instead most related work focuses on adults with\\nvisual impairments. Therefore, we will mention when the\\nrelated research is with YVI.\",\"reading_order\":13,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[421,528,518,543],\"text\":\"2.1 Showdown\",\"reading_order\":14,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,547,747,648],\"text\":\"Showdown [51] is a sport that is inclusive to players of all\\nvision levels because all players must wear a blindfold\\nduring the game. Two players stand at opposite sides of a\\nShowdown table 1 (Figure 1 ). Like air hockey, the objective\\nis to hit the ball with the bat into the opponent's goal across\\nthe table. The opponent attempts to block their goal. A\",\"reading_order\":15,\"tags\":[]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300371_pdf_shortened_page_1_figure_016.png\",\"bbox\":[440,655,727,894],\"reading_order\":16,\"tags\":[],\"alt_text\":\"A worn green game table with raised edges and a clear partition in the middle stands on black legs. On the table are a yellow ball, two orange paddles, and a black and yellow glove. The table is in a room with a red wall and a dark carpeted floor.\"},{\"label\":\"figure_cap\",\"bbox\":[429,901,735,946],\"text\":\"Figure 1. This is a Showdown table with plastic ball,\\nblindfold, and glove grasping a wooden bat at one of\\nour study sites.\",\"reading_order\":17,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,996,126,1013],\"text\":\"Paper 141\",\"reading_order\":18,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[706,996,745,1013],\"text\":\"Page 2\",\"reading_order\":19,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300371/results/markdown/3290605-3300371_pdf_shortened_page_1.md"}
{"file_name": "images/test_data/3290605-3300371/3290605-3300371_pdf_shortened_page_2.png", "source_folder": "test_data", "document_id": "3290605-3300371", "source_image_path": "test_data/3290605-3300371/3290605-3300371_pdf_shortened_page_2.png", "source_json_path": "test_data/3290605-3300371/results/output_json/3290605-3300371_pdf_shortened_page_2.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[68,44,159,61],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"half_para\",\"bbox\":[68,101,394,184],\"text\":\"player can score 2 points for a goal and 1 point if the ball\\nstops or becomes inaudible for $\\\\geq$ 2 seconds on the\\nopponent's side, or if the ball flies off the table. The\\nInternational Blind Spots Federation website has further\\ninformation [51] .\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,193,394,359],\"text\":\"We chose Showdown as the game to implement in VR\\nbecause of the fast-paced fun nature of the game. In fact, at\\none of our study sites (sports camp for YVI) VVI took\\nadvantage of their access to Showdown by creating\\ntournament brackets. Further, Showdown allows people to\\nmove their bodies during gameplay, which allows a player\\nto be physically active. The non-visual aspect of Showdown\\nallows us to focus solely on the audio, verbal, and vibration\\ndesign to make VR accessible. Finally, we can make\\nShowdown accessible to YVI who do not own a table.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[69,372,302,389],\"text\":\"2.2 3D Audio Design and Localization\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,392,394,493],\"text\":\"3D audio is used in VR to create realistic virtual\\nenvironments [28] . In fact, Oculus and Microsoft have\\nthorough documentation on spatialized 3D audio [52,53] .\\nDevelopers can use 3D audio to help people localize sounds,\\nwhich is the ability to determine where sound is coming\\nfrom in a virtual environment.\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,501,394,684],\"text\":\"Lateralization, the most important cue for localizing a\\nsound source's angular position, involves the difference in\\nthe sound waves coming between one's left and right ears\\n[7] . When listening to 3D audio, it is important to wear\\nheadphones (ideally on-ear or over-ear) to discern between\\nthe left and the right ears. Head-Related Transfer Functions\\n(HRTFs) are also important because they filter sounds based\\non the user's physical characteristics. Because we want\\nVirtual Showdown to work with a wide audience, we use a\\ngeneric HRTF with the Microsoft Audio Spatializer, bundled\\nwith Unity3D and Windows 10 [27] .\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,693,394,877],\"text\":\"3D audio does not just move horizontally or vertically, it\\nmust also convey distance. Research shows that computers\\ncan simulate the attenuation of a sound source using the\\nInverse Square Law by decreasing volume and intensity as\\nan object moves further away [7] . Sound reflections can\\nalso help convey distance in a virtual environment.\\nCompanies such as Oculus and Microsoft provide the\\nability to reflect sound in 3D environments [52,54] . Virtual\\nShowdown conveys subtle reflections based on the ball's\\nlocation to convey distance without sounding artificial or\\ndistracting.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,885,394,918],\"text\":\"A barrier in 3D sound localization is the cone of\\nconfusion where a person cannot distinguish sound\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,101,745,184],\"text\":\"between the ears $^2$ . To alleviate this issue, VR headsets have\\nhead tracking [27, 53] so that users can tilt or turn their\\nhead to hear the sound from different perspectives. Virtual\\nShowdown uses the Microsoft Kinect Face Tracking SDK\\n[37] to track a user's head.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,193,746,276],\"text\":\"Implementing 3D audio in a game can be\\ncomputationally expensive. Both research [10,22] and\\nindustry [52,53] have optimized acoustics in VR to work in\\nreal time. For instance, Unity3D excludes audio occlusions\\n[35] .\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,285,746,435],\"text\":\"The related work on 3D audio and localization provides\\nus best practices for Virtual Showdown. However, the goal\\nof the related works – that the sounds are “ complementary\\nto visuals ” or make it “ feel immersive ” in a virtual\\nenvironment [7] – is different than ours, which is to use\\naudio as a primary cue. There is an opportunity to explore\\nhow to implement 3D audio as a primary cue of gameplay\\nalong with verbal and vibration scaffolds to make VR\\naccessible to VVI.\",\"reading_order\":11,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[420,447,667,464],\"text\":\"2.3 Accessible Computer Games for PVI\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,469,746,702],\"text\":\"Several audio-based games exist for people who are blind\\nwhere they replace visual cues with binaural sound or 3D\\naudio. Audiogames.net has over 638 audio games [55] ,\\nincluding Shades of Doom, AudioQuake, Terraformers, and\\nPB-Games' Showdown [46–49] . PB-Games' Showdown\\nuses recorded stereo audio effects, including left and right\\naudio cues to locate the ball. Based on the sound of the ball,\\nplayers use the left or right arrow keys to move the bat and\\nthe enter key to hit the ball. Giannakopoulos et al. also\\ndeveloped an 3D audio-based tennis game that employs the\\nkeyboard [11] . However, these games are not immersive:\\nthey do not have head tracking to account for the cone of\\nconfusion and the keyboard, not the body, solely controls\\nthe game.\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,710,746,861],\"text\":\"VI-Bowling [21] and VI-Tennis [19] (tested with youth\\nand adults who are visually impaired) are accessible games\\nthat involve body movement and moving objects. Both\\ngames use a Wii-Mote along with audio and haptic\\nfeedback to communicate where to aim the ball or when to\\nswing the racket. In Virtual Showdown, we leave the\\ndecisions of aiming and swinging up to the player. We\\nexplore scaffolding to help players learn how to play the\\ngame.\",\"reading_order\":14,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[421,911,744,935],\"text\":\"2 The cornet of confusion [7] results from identical Interaural Intensity Differences\\n(IIID) and Interaural Time Difference (ITD).\",\"reading_order\":15,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,997,126,1013],\"text\":\"Paper 141\",\"reading_order\":16,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[706,997,744,1013],\"text\":\"Page 3\",\"reading_order\":17,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300371/results/markdown/3290605-3300371_pdf_shortened_page_2.md"}
{"file_name": "images/test_data/3290605-3300371/3290605-3300371_pdf_shortened_page_3.png", "source_folder": "test_data", "document_id": "3290605-3300371", "source_image_path": "test_data/3290605-3300371/3290605-3300371_pdf_shortened_page_3.png", "source_json_path": "test_data/3290605-3300371/results/output_json/3290605-3300371_pdf_shortened_page_3.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_pub_date\"]},{\"label\":\"para\",\"bbox\":[68,101,394,336],\"text\":\"Lastly, Smith and Nayar developed a Racing Auditory\\nDisplay (RAD), an accessible implementation of a racing\\ngame that uses 3D audio [3] . In their paper, they classified\\naccessible games for PVI. They found that most games\\neither felt intention-preserving and authentic but had slow\\nplaymap or were efficiency-preserving but felt contrived to\\nplay. The goal is to develop games that are both intention-\\npreserving and efficiency-preserving . The authors presented\\na racing game that used RAD as an example: the user\\ndecides when and how sharp to turn during a fast-paced\\nrace. We designed Virtual Showdown to be intention-\\npreserving in that players can locate the ball and efficiency-\\npreserving by having the ball move constantly during\\nplaymap.\",\"reading_order\":2,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[69,346,234,364],\"text\":\"2.4 Virtual Reality for PVI\",\"reading_order\":3,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,368,394,485],\"text\":\"Recently, people have developed accessible VR games. A\\npuzzle game called 'Blind' [56] includes audio cues and was\\nreleased September 18, 2018. Blind Swordsman (2015) [50]\\nuses 3D audio cues to locate enemies, and a player is to\\n\\\"swing\\\" the sword by pressing a button. Virtual Showdown\\npresents a more immersive VR experience by having the\\nplayer move their body to hit the ball.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,493,394,677],\"text\":\"There are research efforts exploring VR for PVI, but to\\nour knowledge, no research in accessible VR games. Zhao et\\nal. [36] created a VR experience to simulate white cane\\ninteractions in a virtual environment. A person uses a white\\ncane modified with haptics, and a VR headset with 3D\\naudio feedback. Torres-Gil et al. [15] created a VR\\nexperience where users walk through the environment\\nwith specialized equipment using 3D audio. There is an\\nopportunity to explore how to convey information about\\nmoving objects that are separate from the person (e.g. ball),\\nand how to scaffold interactions with these objects.\",\"reading_order\":5,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,701,333,734],\"text\":\"3\\nVIRTUAL SHOWDOWN DESIGN AND\\nIMPLEMENTATION\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,738,394,839],\"text\":\"Our goal was to develop an accessible VR game that\\nencourages body movement and interaction with moving\\nvirtual objects. We explain the design and implementation\\nof Virtual Showdown, addressing the challenges of: 1)\\ndimensions and physics emulation, 2) body interaction, 3)\\naudio, 4) vibration, and 5) scoring.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[69,850,353,867],\"text\":\"3.1 Dimensions and Physics Emulation Design\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,871,394,938],\"text\":\"To develop Virtual Showdown, we used Unity3D [38] to\\ncreate a 3D virtual environment, using a physical\\nShowdown table as a base for our virtual table design.\\nConsidering the challenges of adapting the physical game\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,101,745,151],\"text\":\"to a VR setting, we made several design decisions to create\\na balance between the ability to recognize the game and\\nplayability.\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,159,745,243],\"text\":\"First, we made the virtual table smaller in length and\\nwidth (100cm wide and 26cm long) than the physical table.\\nPlayers can reach both edges of the virtual table without\\nexperiencing fatigue, and we kept the aspect ratio the same\\nas the physical table.\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,251,745,369],\"text\":\"Second, we made the bat and ball size larger than their\\nphysical counterparts did (by 26 % and 66 % , respectively).\\nThis choice increases the likelihood that the players can\\nlocate and hit the ball with the bat. In a full-featured game,\\nthe size of the ball and bat could be made smaller to\\nincrease the game difficulty as players increase their skills\\nto fulfill the goals of GameFlow [26] .\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,376,746,510],\"text\":\"Third, we set the Unity 3D physics settings [31] to allow\\nthe ball to bounce easily and experience friction while on\\nthe table. We set the ball's dynamic friction so that it comes\\nto rest quickly so the ball would not roll infinitely,\\nconfusing players with endless spatial audio. We set the\\nbounciness with almost no loss in energy so that beginner\\nplayers could hit the ball at different speeds and still\\nexperience gameplay.\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,518,746,668],\"text\":\"Fourth, we controlled the speed range of the ball, which\\nis difficult to do during natural gameplay. Our computer\\nopponent (described in \\\"3.5 Game Design\\\") hits the ball at a\\nspeed range of 40 - 250 Unity3D units, which correlates to\\n1.87 - 8.2ft/s. We made this choice to allow for various\\nlevels of challenge for gameplay. We capped the speed at\\n250 (8.2 ft/s) so that the Unity3D physics engine could\\ncalculate collisions quickly enough to prevent the ball from\\nphasing through the walls and bat.\",\"reading_order\":14,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[420,679,597,697],\"text\":\"3.2 Body Interaction Design\",\"reading_order\":15,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,701,745,768],\"text\":\"Players can stand either in open space or in front of a table.\\nA table is preferred because it increases the realism of the\\ngame and decreases fatigue because players can rest their\\nbody on the table.\",\"reading_order\":16,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,776,746,943],\"text\":\"3.2.1 Body Tracking . To track the player's body, we used the\\nMicrosoft Kinect for Windows SDK version 2.0 [37] , C # ,\\nand a Unity3D plugin [57] . We used Kinect's Body Tracking\\n[18] to track the player moving in the game space as a form\\nof \\\"outside-in\\\" VR positional tracking, where the Kinect\\ntracks the position of the body in a space instead of with\\nuser-worn equipment [39] . Xbox One Kinect\\nrecommendations suggest that a player stands 1.4m away\\nfrom the Kinect [16] , but the game will work as long as the\\nKinect is properly tracking the player.\",\"reading_order\":17,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[70,997,126,1013],\"text\":\"Paper 141\",\"reading_order\":18,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[704,997,744,1013],\"text\":\"Page 4\",\"reading_order\":19,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300371/results/markdown/3290605-3300371_pdf_shortened_page_3.md"}
{"file_name": "images/test_data/3290605-3300371/3290605-3300371_pdf_shortened_page_4.png", "source_folder": "test_data", "document_id": "3290605-3300371", "source_image_path": "test_data/3290605-3300371/3290605-3300371_pdf_shortened_page_4.png", "source_json_path": "test_data/3290605-3300371/results/output_json/3290605-3300371_pdf_shortened_page_4.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[]},{\"label\":\"header\",\"bbox\":[474,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,102,394,301],\"text\":\"Because the table is optional for gameplay, the Kinect\\ndoes not track the table directly. Therefore, we calibrate the\\nlocation of the table via \\\"the center of the table edge on the\\nplayer's side,\\\" which we call the player's virtual table\\nposition (PVPT, Figure 2 ). The player stands at \\\"the center\\nof the table edge\\\" on their side during calibration. We\\ncalculate PVPT using Kinect's Body Tracking. The Z-axis of\\nPVPT is the minimum distance of the spine, hips, waist, and\\nmid-section. The X-axis of PVPT is the center point of the\\nchest. After calibration, the player can move around the\\ngame space. Without the table, the virtual table follows the\\ncenter of the player's body in case a player drifts over time.\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,309,394,409],\"text\":\"3.2.2 Bat Positioning. To determine the bat position, we\\ncalculate the difference between the PVTP and the user's\\ntracked hand position in the Z and X dimensions. We omit\\nthe Y-axis because we cannot assume the table height or\\nthat the player has a table. The user can place their hand as\\nhigh or low as preferred.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,417,394,652],\"text\":\"To calculate the rotation of the bat in the virtual world,\\nwe first detect the Z and X position of the wrist and the Z\\nand X position of the fingertips (Figure 3 ). We create a right\\ntriangle, where the line segment between the wrist and\\nfingertips is the hypotenuse, and the adjacent segment runs\\nparallel to the player's chest (Figure 3 ). Arctangent provides\\nthe angle between these segments. Because the real-time\\nposition of the wrist and hands introduce jitter, we used\\nbuilt-in Unity smoothing functions [52] . Further, we locked\\nthe rotation of the bat to the closest angle of -45, 0, 45, 135,\\nand 180° to keep the bat steady (Figure 3 ). These angles\\nallow the player to hit the ball back to the other side of the\\ntable using straight and angled forehand and backhand\\nshots. A player can use their right or left hand.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300371_pdf_shortened_page_4_figure_005.png\",\"bbox\":[132,680,331,869],\"reading_order\":5,\"tags\":[],\"alt_text\":\"A gray hand holding a controller is shown within a semicircular angular reference, ranging from -45 to +180 degrees. A right-angled triangle is overlaid, with its horizontal side labeled X, vertical side labeled Z, and an angle Phi. The 0-degree line aligns with the side X of the triangle.\"},{\"label\":\"figure_cap\",\"bbox\":[77,873,385,944],\"text\":\"Figure 3. This is a top down view of the available bat\\nrotations for the right hand. Photo attributions:\\nDetachable Switch Controller Armand Right by Chad\\nRemsing from the Noun Project CCBY 3.0, Hand by\\nAlexander Wielf from the Noun Project CCBY 3.0\",\"reading_order\":6,\"tags\":[]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300371_pdf_shortened_page_4_figure_007.png\",\"bbox\":[467,104,693,447],\"reading_order\":7,\"tags\":[],\"alt_text\":\"A diagram illustrates a human-computer interaction setup featuring a Kinect v2 sensor positioned above a physical table. A hand wearing a Joy-Con controller is shown near a semi-circular cardboard overlay, labeled 30cm, with a PVTP vector pointing upwards from its center. An X and Z axis coordinate system is also present.\"},{\"label\":\"figure_cap\",\"bbox\":[428,455,735,552],\"text\":\"Figure 2. This is a symbolic diagram with Kinect, Joy-\\ncon, PVT, and cardboard overlay. Photo attributions:\\nGame console by Vangos Pternes from the Noun\\nProject CCBY 3.0. Detachable Switch Controller\\nfrom the Noun Project CCBY 3.0. Text from the Noun\\nProject CCBY 3.0. Hand by Alexander Wielst from the\\nNoun Project CCBY 3.0\",\"reading_order\":8,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[420,564,533,581],\"text\":\"3.3 Audio Design\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,585,746,735],\"text\":\"3.3.1 Location of the Audio Listener via Head Tracking . To\\nease body movement, we do not use a VR headset to\\nperform the head tracking. Instead, we track the pitch, roll,\\nand yaw of the person's head with Kinect's Face Tracking\\n[17] . We have the player's head control Unity 3D's main\\ncamera with attached audio listener. In turn, the game\\nconveys the sound of the ball with spatial audio with\\nrespect to the player's head in the virtual world (Figure 4 ).\\nFor example, if the player turns their head to the right, the\",\"reading_order\":10,\"tags\":[]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300371_pdf_shortened_page_4_figure_011.png\",\"bbox\":[447,753,717,885],\"reading_order\":11,\"tags\":[],\"alt_text\":\"A 3D virtual environment featuring a \\\"Virtual Showdown\\\" sign is shown, likely from a game or simulation. Overlaid on the right side are user interface elements, including controls for an \\\"Experiment\\\" and a top-down perspective of the virtual space with various icons and sliders. A smaller preview window of the main environment is visible at the bottom right.\"},{\"label\":\"figure_cap\",\"bbox\":[429,889,735,946],\"text\":\"Figure 4. Screen shot of a visual debugging interface\\nshowing a person controlling the camera and audio\\nlistener. Left: camera view, Right: top-down view with\\ncamera and audio listener at the bottom\",\"reading_order\":12,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[70,996,126,1012],\"text\":\"Paper 141\",\"reading_order\":13,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[706,996,744,1012],\"text\":\"Page 5\",\"reading_order\":14,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300371/results/markdown/3290605-3300371_pdf_shortened_page_4.md"}
{"file_name": "images/test_data/3290605-3300371/3290605-3300371_pdf_shortened_page_5.png", "source_folder": "test_data", "document_id": "3290605-3300371", "source_image_path": "test_data/3290605-3300371/3290605-3300371_pdf_shortened_page_5.png", "source_json_path": "test_data/3290605-3300371/results/output_json/3290605-3300371_pdf_shortened_page_5.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[68,44,159,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,44,746,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"author_affili\"]},{\"label\":\"half_para\",\"bbox\":[69,102,394,133],\"text\":\"camera view and audio listener will turn and face the right\\nside of the table.\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,142,394,260],\"text\":\"However, we fixed the Y position of the camera at 25cm\\nabove the virtual table – the same height as the audio\\nsources. While this position is lower than a person's ears in\\nthe real world, we chose this height to exaggerate the left\\nand right audio cues. Lastly, we used Unity's built in\\nsmoothing functions [33] to smooth the Face Tracking data\\nso that the camera and audio listener movements are fluid.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,267,394,453],\"text\":\"3.3.2 Conveying the Location of the Ball. Like real-world\\nShowdown, Virtual Showdown employs audio as the\\nprimary cue in determining the location of the ball. The ball\\nhas a digital sound [30] that oscillates between low and\\nhigh frequencies (648.00 Hz – 735.00 Hz). We chose this type\\nof sound effect based on the Oculus Audio documentation\\n[53] ; a sound that oscillates in pitch is easier to locate than a\\nconstant tone. We used Unity3D to generate the 3D sound\\nbased of the ball with respect to the audio listener. For\\nlateralization, we used Microsoft Audio Spatializer with a\\ngeneric HRTF [27] .\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,460,395,660],\"text\":\"To help the player infer depth, we created a custom\\nvolume rolloff curve based on Microsoft's\\nrecommendations [29] . We added a Doppler Effect level of\\n0.25 to help convey the direction of the ball's movement.\\nWe also employed a dynamic Audio Mixer in Unity3D to\\nconvey when the ball is on the far side of the table. To\\nconvey when the ball is on the far side, we added slight\\nearly reflections using the Unity3D audio filter SFX Reverb\\neffect [34] . We also applied a low pass filter (cutoff of\\n2975.00 Hz) to exaggerate the sound. When the ball is on\\nthe player's side of the table, we programmatically remove\\nthe reflections and low pass filter to generate a clear sound.\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,667,394,768],\"text\":\"Our approach to audio effects is computationally cheap\\nbecause the game is not dynamically calculating occlusions,\\nreverb, and reflections for every location and 3D object in\\nthe virtual environment for every frame. Instead, we add\\nsimplified audio effects based on which side of the table the\\nball is on.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,777,394,927],\"text\":\"3.3.3 Other Sound Effects. The player constantly hears the\\nball sound, but we include other intermittent sound effects\\nwhen the ball collides with a wall, the bat hits the ball, and\\nthe player's hand goes out of bounds. When the ball hits a\\nwall, the ball makes a \\\"bloop\\\" sound effect [30] and then\\nbounces off the wall. The \\\"bloop\\\" has spatialized audio like\\nthe ball. When the bat hits the ball, the system plays a\\n\\\"hitting\\\" sound effect (self-recorded). If the player's hand\\nmoves out of the game boundaries, Virtual Showdown\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,102,746,134],\"text\":\"plays a warning sound effect [30] to notify the user so they\\nmove their hand in bounds.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,142,746,243],\"text\":\"Finally, we dynamically changed the pitch of the ball\\nbased on its velocity. The pitch would modulate on a scale\\nof 80 % to 125 % of the original pitch on a linear scale. At the\\nball's lowest velocity, the pitch was 80 % of the original\\nsound effect. At the maximum velocity, the pitch is higher\\nthan the original at 125 % .\",\"reading_order\":9,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[421,255,553,272],\"text\":\"3.4 Vibration Design\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,274,747,460],\"text\":\"To provide multimodal feedback (which is helpful during\\ngameplay [20,22] ), we used a Nintendo Switch Joy-Con [58]\\nbecause the controller is small enough for youth (10.21cm\\nhigh, 3.58cm long, and 2.84cm deep), light (1.7oz), and\\nincludes a wrist strap. In this way, players can make body\\ngestures without experiencing fatigue or losing the game\\ncontroller. Further, the Joy-Con supports HD-rumble,\\nwhich are different intensities of vibration [59] . We\\nconnected the Joy-Con to Virtual Showdown with a\\nUnity3D plugin that interfaced C # and the Bluetooth\\nconnection [40] .\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,466,747,719],\"text\":\"The Joy-Con vibrates with HD-rumble in complement to\\nthe sound effects. Vibrations convey when the player hits\\nthe ball with their bat and when the player's bat moves out\\nof bounds. Because Joy-con's HD-rumble has different\\nintensities [40] , Virtual Showdown uses different vibration\\nintensities based on how hard the player hits the ball. The\\nvibration was set to a low frequency of 160Hz and a high\\nfrequency of 320Hz. The amplitude changes on a linear\\nscale based on the squared magnitude of the collision\\nimpulse. The harder the player hits the ball, the Joy-con\\nvibrates more intensely. When the player's hand moves out\\nof bounds, the vibrations were set from 90Hz-270Hz. As the\\nplayer's hand moved further out of bounds, the frequency\\nincreased linearly to indicate that they needed to move\\ntheir hand back to the table.\",\"reading_order\":12,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[421,730,533,747],\"text\":\"3.5 Game Design\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,751,747,918],\"text\":\"To conduct a controlled lab study, we preselected the\\ndirections in which the virtual opponent hits the balls\\ntoward the player. Therefore, our game is a Showdown drill\\nsession. The player does not serve the ball. The computer\\nopponent sends 30 balls one at a time along 15 trajectories\\nin a random order. The balls start in three locations on the\\nopponent's side: left, center, and right. Dividing the width\\nof the table (100cm) by the width of the ball (20cm) gives us\\nfive destinations on the player's side: far right, center right,\\ncenter, center left and far left (Figure 5 ).\",\"reading_order\":14,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[70,996,125,1013],\"text\":\"Paper 141\",\"reading_order\":15,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[706,996,746,1013],\"text\":\"Page 6\",\"reading_order\":16,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300371/results/markdown/3290605-3300371_pdf_shortened_page_5.md"}
{"file_name": "images/test_data/3290605-3300371/3290605-3300371_pdf_shortened_page_6.png", "source_folder": "test_data", "document_id": "3290605-3300371", "source_image_path": "test_data/3290605-3300371/3290605-3300371_pdf_shortened_page_6.png", "source_json_path": "test_data/3290605-3300371/results/output_json/3290605-3300371_pdf_shortened_page_6.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"author_affili\"]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300371_pdf_shortened_page_6_figure_002.png\",\"bbox\":[140,104,321,419],\"reading_order\":2,\"tags\":[],\"alt_text\":\"This is a top-down view of a rectangular game or simulation board with various numbered sections. The board includes fields labeled \\\"Trajectory Starting #'s\\\" and \\\"Trajectory Ending #'s\\\" in the middle. At the top are numerical ranges \\\"1-5\\\", \\\"6-10\\\", \\\"11-15\\\", and at the bottom, a grid of numbers 1-15, followed by five circles labeled \\\"1\\\" through \\\"5\\\" with the text \\\"Endpoint #'s\\\".\"},{\"label\":\"figure_cap\",\"bbox\":[78,428,387,486],\"text\":\"Figure 5. Illustration of the trajectories and endpoints.\\nTwo examples: 1) Trajectory 1 starts in the upper left\\ncorner and finishes in the lower left corner; 2)\\nEndpoint 5 is located in the lower right corner.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,494,394,676],\"text\":\"The objective of Virtual Showdown is to earn as many\\npoints as possible, with the highest score being 90.\\nFollowing 'GameFlow's [26] and Brewer et al.'s\\nrecommendation [1] , we added points to keep VVI engaged\\nand give them a task to accomplish. If the player hit the ball\\ninto the opponent's goal, they receive 3 points. If the player\\nhits the ball and it made it at least halfway across the table\\nwithin 8 seconds, they receive 2 points. If the player tapped\\nthe ball, they receive 1 point. Lastly, if the player hit the ball\\ninto their own goal or they did not strike the ball within 8\\nseconds, they do not receive points.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[69,702,370,718],\"text\":\"4 VIRTUAL SHOWDOWN SCAFFOLD DESIGN\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,724,394,924],\"text\":\"To explore our research questions of how scaffolds with\\nverbal or verbal/vibration hints affect performance and\\nmovement, we included seven levels in Virtual Showdown\\nwith increasing difficulty. Over the play session of 30 balls,\\na player can reach level seven based on their personal\\nprogress. To advance to the next level, a player must hit\\nfour out of the last six balls in a sliding window. In Levels\\n1-3, we employ our scaffolds, while keeping the ball at a\\nconstant speed of 40 Unity units (1.87 fm/s). In Levels 4-7, we\\nremove our scaffolds, and the ball increases at a speed of 10\\nUnity units per level. We present the verbal and\\nverbal/vibration scaffolds explored in our user study.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[421,102,543,118],\"text\":\"4.1 Verbal Scaffold\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,123,746,405],\"text\":\"Levels 1-3 provide verbal scaffold support to help the player\\nlearn how to interact with Virtual Showdown (Figure 6 ). In\\nLevel 1, Virtual Showdown presents a verbal preview of the\\nball's trajectory. It reveals where the ball will depart from\\non the opponent's side (with respect to the player's right\\nand left side) and where it will arrive on the player's side in\\na complete grammatically correct sentence. Further, the\\nverbal preview uses spatial audio. In Level 2, we present the\\nsame information in Level 1 also using spatial audio, but\\nwith shorter incomplete sentences. The volume rolloff\\ncurve used in the verbal statements is the same as they are\\non the ball. This technique allows users to be acquainted\\nwith the distance before Virtual Showdown releases the\\nball. In Level 3, we present the same phrase as Level 2, but\\nwithout spatial audio. Virtual Showdown presents Level 3\\nprompts with a medium volume in the center of the virtual\\ntable.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,414,746,564],\"text\":\"Fisch's work on making educational games recommends\\nusing hints along with scaffolding when players are\\nincorrect [8] . Hints help lead players in the right direction\\nto help them understand the underlying context of game\\nplay. Therefore, we created midway audio prompts in\\nLevels 1-3. As the ball moves from the opponent's side and\\nacross the halfway point on the table, Virtual Showdown\\nassesses whether the player's hand is in the correct position\\nto hit the ball back. If the player's hand were not in the\",\"reading_order\":9,\"tags\":[]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300371_pdf_shortened_page_6_figure_010.png\",\"bbox\":[427,567,741,915],\"reading_order\":10,\"tags\":[],\"alt_text\":\"Three levels illustrate auditory processing with headphones. In Levels 1 and 2, speech bubbles from a left speaker, a central speaker (inside headphones), and a right speaker deliver separate parts of a sentence. In Level 3, a single integrated phrase is heard centrally through the headphones.\"},{\"label\":\"figure_cap\",\"bbox\":[429,919,736,947],\"text\":\"Figure 6. Presented is an example scenario of Levels 1-3\\nwith Verbal Scaffolds.\",\"reading_order\":11,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[70,996,125,1012],\"text\":\"Paper 141\",\"reading_order\":12,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[706,996,744,1012],\"text\":\"Page 7\",\"reading_order\":13,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300371/results/markdown/3290605-3300371_pdf_shortened_page_6.md"}
{"file_name": "images/test_data/3290605-3300371/3290605-3300371_pdf_shortened_page_7.png", "source_folder": "test_data", "document_id": "3290605-3300371", "source_image_path": "test_data/3290605-3300371/3290605-3300371_pdf_shortened_page_7.png", "source_json_path": "test_data/3290605-3300371/results/output_json/3290605-3300371_pdf_shortened_page_7.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,43,159,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,43,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_pub_date\",\"author_affili\"]},{\"label\":\"half_para\",\"bbox\":[68,101,394,150],\"text\":\"correct position, then the game would say, \\\"Move to the\\nright\\\" or \\\"Move to the left.\\\" These quick audio prompts last\\n1.012 seconds.\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,158,395,409],\"text\":\"Finally, we present constructive feedback after each ball\\nhit attempt. Previous research demonstrates that a game\\nneeds to not only have feedback on performance, but\\nexplanatory feedback to scaffold users toward doing the\\ncorrect action next time [8,26] . Following the\\nrecommendation of Giannakopoulos et al.'s tennis game,\\nwe provide guidance for all seven levels [11] . If a player\\nmisses the ball, the game provides feedback on how far and\\nin which direction the player had to move their hand. We\\ndivided the constructive feedback evenly between\\nquantifiable distances and metaphors that represent\\ndistance. The metaphors included are “ off by a foot ” , “ by\\nthe length of a pen ” , “ by the length of an arm ” , “ by a pinch ” ,\\nwhile; the quantifiable distances were all presented in\\ninches.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[69,421,254,439],\"text\":\"4.2 Verbal/Vibration Scaffold\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,443,395,660],\"text\":\"This condition is identical to the Verbal Scaffold, except\\nthat we add vibrations. We chose not to implement a\\nVibration Scaffold alone because we expected too much\\ndelay in players interpreting previews and constructive\\nfeedback encoded solely as vibrations. Virtual Showdown\\nwould no longer be `efficiency preserving' [3] , where the\\ngame is at a fast pace. We explore a tradeoff between two\\npossible effects: 1) providing multimodal feedback helps the\\nplayer understand the information faster or more clearly,\\nwhich has been shown to help users perform better in video\\ngames [20] ; and 2) providing vibration information will\\ncompete with the player's ability to listen to the moving\\nball due to extra cognitive load [24] .\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,666,395,886],\"text\":\"In Levels 1-3, we employed tactile dousing (similar to the\\nproximity-sensitive sound coming from a metal detector),\\nas utilized by the work done in Morelli et al.'s VI Bowling\\n[21] . For our tactile dousing, we used the Joy-con HD\\nRumble. In real time, as the player's hand moved closer to\\nthe ball's incoming destination, the Joy-con's vibration\\nincreased. First, if the hand was $\\\\geq$ 30cm away from the\\ndestination, the Joy-con did not vibrate. From 21-30cm, the\\ncontroller vibrated at 10 % its maximum capability. At 11-\\n20cm, the controller vibrated at 30 % its maximum\\ncapability. When the hand was closest (< 10cm), the\\ncontroller vibrated at 50 % . We chose to limit the vibration\\namplitude to 50 % to avoid fatigue from the vibrations [5] .\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,893,395,944],\"text\":\"While in the Verbal Scaffold, we decreased the detail as\\nthe players advanced from Levels 1-3, with tactile dousing,\\nwe could not decrease the intensities of the vibrations\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,101,745,149],\"text\":\"between levels. Otherwise, players would have to relearn\\nthe relationship between vibration intensity and proximity\\nof hand to the destination.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,175,533,193],\"text\":\"5 USER STUDY\",\"reading_order\":9,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[421,203,647,221],\"text\":\"5.1 Participants and Study Locations\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,224,746,424],\"text\":\"We had 34 participants in our study who are visually\\nimpaired (ages 8-20, 13 females, 21 males). Ten participants\\nreported no vision, totally blind, or light perception only.\\nNineteen reported having low vision, two have peripheral\\nvision, one has light sensitivity and color blindness, one has\\nvision in only one eye, and one participant did not know\\n(but used a cane). Twenty-eight had experience with video\\ngames, but only seven had experience with VR. Fifteen had\\nexperience with Showdown. We conducted the user studies\\nin separate rooms or areas during two different sports\\ncamps for YVI. We received permission from the directors\\nof both camps to conduct user studies.\",\"reading_order\":11,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[421,436,530,454],\"text\":\"5.2 Device Setup\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,458,746,660],\"text\":\"Participants stood at the head of the table to participate in\\nthe study. They held the Joy-con with wrist strap and wore\\non-ear style. Skullcandy Grind Wireless Headphones [25] .\\nAt the center of the head of the table, we taped a semi-circle\\ncardboard piece that matched the size of the virtual goal\\n(Figure 2 ). The cardboard semicircle has a 30-inch diameter.\\nWe told participants to stand in front of the semicircle for\\ncalibration to determine the PVT.P. On the other head of the\\ntable were the Microsoft Kinect, laptop, and experimenter.\\nThere was a video camera on a tripod next to the\\nexperimenter to video record the participant's motions and\\nreactions during gameplay.\",\"reading_order\":13,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[421,669,551,686],\"text\":\"5.3 Study Procedure\",\"reading_order\":14,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,691,746,858],\"text\":\"Our study was within-subjects, so participants experienced\\nboth the Verbal and Verbal/Vibration Scaffolds. We\\ncounterbalanced the participants, where 17 experienced\\nVerbal first and 17 experienced Verbal/Vibration first to\\nensure learning effects or fatigue did not bias the\\nconditions. Our independent variable was Verbal Scaffold\\nversus Verbal/Vibration Scaffold. Our dependent variables\\nincluded gameplay measures: final score and final level;\\nattitudinal measures of participant's preference and ease of\\nhitting the balls; and body movement strategies.\",\"reading_order\":15,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,865,746,950],\"text\":\"We gave a tutorial to participants to explain, \\\"If the ball\\nis louder in your right ear, it is on the right side, and if it is\\nlouder in your left ear, then it is on your left side.\\\" We also\\nexplained, \\\"Sometimes the ball is in the center, so it may\\nsound equal in both ears.\\\" We explained to participants that\",\"reading_order\":16,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,126,1013],\"text\":\"Paper 141\",\"reading_order\":17,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[706,995,745,1013],\"text\":\"Page 8\",\"reading_order\":18,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300371/results/markdown/3290605-3300371_pdf_shortened_page_7.md"}
{"file_name": "images/test_data/3290605-3300372/3290605-3300372_pdf_shortened_page_0.png", "source_folder": "test_data", "document_id": "3290605-3300372", "source_image_path": "test_data/3290605-3300372/3290605-3300372_pdf_shortened_page_0.png", "source_json_path": "test_data/3290605-3300372/results/output_json/3290605-3300372_pdf_shortened_page_0.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\"]},{\"label\":\"icon\",\"bbox\":[0,92,39,134],\"reading_order\":2,\"tags\":[\"cross_marker\"]},{\"label\":\"sec_0\",\"bbox\":[87,118,727,215],\"text\":\"Moderation Practices as Emotional Labor in\\nSustaining Online Communities: The Case of AAPI\\nIdentity Work on Reddit\",\"reading_order\":3,\"tags\":[]},{\"label\":\"author_information\",\"bbox\":[171,234,314,297],\"text\":\"Bryan Dosono\\n\\nSyracuse University\\nSyracuse, New York, USA\\nbdosono@syr.edu\",\"reading_order\":4,\"tags\":[\"author\",\"author_affili\",\"author_mail\"]},{\"label\":\"author_information\",\"bbox\":[498,234,643,295],\"text\":\"Bryan Semaan\\nSyracuse University\\nSyracuse, New York, USA\\nbsemaan@syr.edu\",\"reading_order\":5,\"tags\":[\"author\",\"author_affili\",\"author_mail\"]},{\"label\":\"sec_1\",\"bbox\":[69,307,141,324],\"text\":\"ABSTRACT\",\"reading_order\":6,\"tags\":[\"paper_abstract\"]},{\"label\":\"para\",\"bbox\":[68,329,395,600],\"text\":\"We examine how and why Asian American and Pacific Is-\\nlander (AAPI) moderators on Reddit shape the norms of their\\nonline communities through the analytic lens of emotional\\nlabor. We conduct interviews with 21 moderators who facili-\\ntate identity work discourse in AAPI subreddits and present\\na thematic analysis of their moderation practices. We report\\non their challenges to sustaining moderation, which include\\nburning out from volunteer work, navigating hierarchical\\nstructures, and balancing unfulfilled expectations. We then\\ndescribe strategies that moderators employ to manage emo-\\ntional labor, which involve distancing away from drama,\\nbuilding solidarity from shared struggles, and integrating\\nan ecology of tools for self-organized moderation. We pro-\\nvide recommendations for improving moderation in online\\ncommunities centered around identity work and discuss im-\\nplications of emotional labor in the design of Reddit and\\nsimilar platforms.\",\"reading_order\":7,\"tags\":[\"paper_abstract\"]},{\"label\":\"sec_1\",\"bbox\":[69,614,172,632],\"text\":\"CCS CONCEPTS\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,635,394,669],\"text\":\"• Human-centered computing → Computer supported\\ncooperative work;\",\"reading_order\":9,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[69,684,150,701],\"text\":\"KEYWORDS\",\"reading_order\":10,\"tags\":[\"paper_keywords\"]},{\"label\":\"para\",\"bbox\":[68,705,394,753],\"text\":\"Online Communities; Identity Work; Moderation; Emotional\\nLabor; Reddit; Harassment; Bot; Microaggressions, AAPI;\\nRace.\",\"reading_order\":11,\"tags\":[\"paper_keywords\"]},{\"label\":\"sec_1\",\"bbox\":[422,309,555,324],\"text\":\"ACM Reference Format:\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,324,747,411],\"text\":\"Bryan Dosona and Bryan Semaan. 2019. Moderation Practices as\\nEmotional Labor in Sustaining Online Communities: The Case\\nof AAPI Identity Work on Reddit. In CHI Conference on Human\\nFactors in Computing Systems Proceedings (CHI 2019) . May 4–9, 2019,\\nGlasgow, Scotland , UK. ACM, New York, NY, USA, 13 pages. https:\\n//doi.org/10.1145/3299665.303072\",\"reading_order\":13,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[422,421,549,439],\"text\":\"1 Introduction\",\"reading_order\":14,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,443,747,522],\"text\":\"While identity is often an individuated concept [18, 23] , for\\nmany people, identity is a social construct — that is, it is gen-\\nerated through perceived membership in a social group [91] .\\nIn this view, who we are can be defined across a range of\\ncollective factors, such as social class, race, and ethnicity.\",\"reading_order\":15,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,523,747,666],\"text\":\"The ways in which we come to define our collective, so-\\ncial identities, however, can be complex [87] . Here, we focus\\nour attention on the identity work—a collaborative process\\nthrough which people negotiate their identities—engaged in\\nby Asian American and Pacific Islanders (AAPIs). AAPIs live\\na rich and storied history through which their intersectional\\nidentities have developed over time [50, 72] and are inter-\\nconnected across a range of demographic categories, diverse\\nbackgrounds, and multi-generational perspectives [57] .\",\"reading_order\":16,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,666,747,906],\"text\":\"However, the composition of conflicting identities within\\nthe larger AAPI community is diverse and potentially prob-\\nlematic. For example, there have been well-documented ten-\\nsions between people from different countries that comprise\\nthe AAPI identity, such as China and Japan [56] . Further com-\\nplicating identity work is where AAPIs fall at the receiving\\nends of stereotypes that often manifest in inaccurate public\\nperceptions, such as the model minority stereotype [51]— the\\ncultural expectation that AAPIs are exemplary achievers and\\nattain a higher degree of education and socioeconomic sta-\\ntus in contrast to other minority groups [51]— and they are\\noften “othered” as perpetual foreigners [65] . This leads us to\\nquestioning how AAPIs engage in collective identity work\\nin service of managing inter-group tensions and outer-group\\nstereotypes.\",\"reading_order\":17,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,906,747,955],\"text\":\"Today, AAPIs have access to a range of online commu-\\nnities through which they can engage in collective identity\\nwork. In this paper, we explore the online identity work being\",\"reading_order\":18,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[68,787,395,894],\"text\":\"Permission to make digital or hard copies of all or part of this work for\\npersonal or classroom use is granted without fee provided that copies are not\\nmade or distributed for profit or commercial advantage and that copies bear\\nthis notice and the full citation on the first page. Copyrights for components\\nof this work owned by others than ACM must be honored. Abstracting with\\ncredit is permitted. To copy otherwise, or republish, to post on servers or to\\nredistribute to lists, requires prior specific permission and/or a fee. Request\\npermissions from permissions@acm.org.\",\"reading_order\":19,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[69,896,273,950],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland UK\\n\\n© 2019 Association for Computing Machinery,\\n\\n\\nACM ISBN 978-1-5403-5970-2/19/05...$15.00\\nhttps://doi.org/10.1145%2F960605.3300372\",\"reading_order\":20,\"tags\":[\"meta_pub_date\",\"meta_subject\"]},{\"label\":\"foot\",\"bbox\":[69,996,126,1012],\"text\":\"Paper 142\",\"reading_order\":21,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,996,744,1012],\"text\":\"Page 1\",\"reading_order\":22,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300372/results/markdown/3290605-3300372_pdf_shortened_page_0.md"}
{"file_name": "images/test_data/3290605-3300372/3290605-3300372_pdf_shortened_page_1.png", "source_folder": "test_data", "document_id": "3290605-3300372", "source_image_path": "test_data/3290605-3300372/3290605-3300372_pdf_shortened_page_1.png", "source_json_path": "test_data/3290605-3300372/results/output_json/3290605-3300372_pdf_shortened_page_1.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\"]},{\"label\":\"half_para\",\"bbox\":[68,125,394,252],\"text\":\"engaged in by AAPIs whereby they are working to redefine\\ntheir public identity, challenge existing stereotypes, and ne-\\ngotiate their diverse and often conflicting identities. Online\\ncommunities provide the socio-technical infrastructure for\\npeople to engage in identity work, yet given the complexity\\nof AAPI identity construction, understanding how these on-\\nline spaces sustain these activities beyond Western contexts\\nremains understudied.\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,252,395,507],\"text\":\"To explore how online identity work spaces are sustained,\\nwe examine the role and daily practices of moderators on\\nReddit—a category of users who, unlike traditional users, en-\\ngage in the work of mediating the interactions and activities\\nof other members of their online communities [47, 48] . More\\nspecifically, we explore how they work toward sustaining\\nthe online community spaces (subreddits) through which\\ncollective identity is formed. Importantly, the practices they\\nengage in are often in service of promoting pro-social behav-\\nior and limiting anti-social behavior. Their primary role is to\\ndevelop productive social norms and mediate the interactions\\nof users as a means of sustaining their online communities\\n[47, 48] . This leads to examining the role moderators play in\\ndeveloping sustainable online communities through which\\nidentity work can be accomplished. To guide our study, we\\nfocus on the following research questions:\",\"reading_order\":3,\"tags\":[]},{\"label\":\"list\",\"bbox\":[89,511,395,576],\"text\":\"• RQ1: How do moderators shape the identity work of\\nonline communities?\\n• RQ2: How does emotional labor sustain an online com-\\nmunity in AAPI contexts?\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,580,394,659],\"text\":\"Against this backdrop, we apply a lens that combines emo-\\ntional labor [34, 35] , identity work [18, 84] , and public sphere\\ndeliberation [28, 29] to examine the visible and invisible prac-\\ntices of AAPI moderators on Reddit in service of sustaining\\ntheir online communities.\",\"reading_order\":5,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,672,198,690],\"text\":\"2 RELATED WORK\",\"reading_order\":6,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[68,693,379,726],\"text\":\"Identity Work as a Collective Process in the Online\\nPublic Sphere\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,730,394,857],\"text\":\"Identity is constructed in relation to rules and norms in a\\nsocial setting [ 26 ] . Identity is also a multi-faceted construct\\nin that people can have several identities [ 8 ] . For example,\\npeople often identify with a certain race or ethnic group,\\nor they may identify with other affiliations, such as their\\nworkplace or school. When viewed as a social construction,\\nidentity is a moving target, and members of a particular\\ngroup may not agree with public perceptions of themselves.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,857,395,953],\"text\":\"As a means of making sense of or changing one's iden-\\ntity, people engage in identity work [84] . This process can\\nbe collaborative as people work together to make sense of\\nidentity for themselves or reconstruct collective identity in\\nan effort to change public perceptions. Studies of identity\\nwork have explored the uses of social media by a number\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,125,746,236],\"text\":\"of groups, including those undergoing gender transitions\\n[30], first-time fathers [1], underprivileged college students\\n[64], transnational migrants [54], and disability rights ac-\\ntivists [53]. Beyond scholarship focusing on individual iden-\\ntity work, Schwabe examined subcultural identity work as\\na group process, suggesting how identity work can be un-\\nderstood as part of a process of cultural struggle [79].\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,236,746,491],\"text\":\"Identity work is a process that can emerge in the public\\nsphere, which as defined by Habermas is a domain of our\\nsocial life through which public opinion is formed [28, 29] .\\nWithin the public sphere, people often engage in deliberation,\\nwhich comprises of the activities through which people share\\ninformation, form opinions, and participate in the political\\nprocess. A critique of Habermas' ideal concept of the public\\nsphere is that it is based on rationality rather than emotion-\\nality. According to Melucci [62] , emotion is an important\\nelement of collective action as \\\"there is no cognition without\\nfeeling and no meaning without emotion.\\\" In a modern net-\\nworked society, digital publics are mobilized and connected\\nor disconnected through affective statements that blend fact\\nwith opinion and emotion, demonstrating the importance of\\naffect in creating feelings of community to sustain involve-\\nment and connections [68] .\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,491,747,666],\"text\":\"It is well established that in the context of online political\\nactivities, certain communicative acts invite the formation\\nof an ad hoc public sphere — an online space where any in-\\ndividual is free to exchange political information and ideas\\nwith others [82] . For example, users of social media services\\nsuch as Facebook and Twitter can engage in political deliber-\\nation through private groups [5, 7, 82, 83] . Identity work in\\nthe context of AAPI community, then, constitutes a form of\\npolitical deliberation, and today, users have taken to online\\ncommunity platforms, including Reddit, to engage in identity\\nwork as a deliberative, political process [15] .\",\"reading_order\":12,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[421,677,674,694],\"text\":\"Moderation Practices as Emotional Labor\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,699,747,857],\"text\":\"Given the rich socio-historical context through which AAPIs\\nengage in identity work, we are motivated to understand\\nfactors that sustain online community spaces like Reddit in\\nthe face of potential adversity and conflict. To explore this\\nphenomenon, we draw on the conceptual framing of emo-\\ntional labor. Hochschild defines emotional labor as \\\"the silent\\nwork of evoking and suppressing feeling—in ourselves and in\\nothers\\\" [35] . Hochschild focused on the ways that workers in\\norganizations regulate their emotions while interacting with\\na range of stakeholders, such as customers and superiors.\",\"reading_order\":14,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,858,747,954],\"text\":\"Recent scholarship has found that participation in mod-\\nern sociotechnical systems involves various forms of emo-\\ntional labor. For instance, dominant patriarchal structures in\\npeer production systems like Wikipedia make it challenging\\nfor women editors to participate when they are constantly\\ntargeted by trolls or receiving unwanted sexual advances\",\"reading_order\":15,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,996,126,1013],\"text\":\"Paper 142\",\"reading_order\":16,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[706,996,745,1013],\"text\":\"Page 2\",\"reading_order\":17,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300372/results/markdown/3290605-3300372_pdf_shortened_page_1.md"}
{"file_name": "images/test_data/3290605-3300372/3290605-3300372_pdf_shortened_page_2.png", "source_folder": "test_data", "document_id": "3290605-3300372", "source_image_path": "test_data/3290605-3300372/3290605-3300372_pdf_shortened_page_2.png", "source_json_path": "test_data/3290605-3300372/results/output_json/3290605-3300372_pdf_shortened_page_2.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_pub_date\"]},{\"label\":\"half_para\",\"bbox\":[69,125,394,298],\"text\":\"[63] . psychological constraints embedded in crowdfunding\\nplatforms like Kickstarter disadvantage introverted person-\\nality types [13] ; and demands to improve reputation ratings\\nin ridesharing services like Uber and Lyft result in added\\nwork for drivers to please their passengers [73] . The proac-\\ntive socialization of online volunteers remains essential for\\nretaining their engagement [31] . In building on this schol-\\narship, we study how moderators of online communities\\nengage in emotional labor in the context of identity work,\\nand how such practices might serve as a way to sustain their\\ncommunities.\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,299,395,506],\"text\":\"Reddit relies on volunteer moderators to keep its platform\\nactive. Similar to users of other large-scale online commu-\\nnities, redditors that continue to visit the platform are in-\\ncreasingly exposed to sponsored advertisements that keep\\nthe company profitable [93] . In Hochschild's words, “a com-\\nmercial logic will penetrate deeper and deeper into what we\\nused to think of as a private, psychological, sacred part of a\\nperson's self and soul ” [35] , the platform has been criticized\\nfor providing a fertile ground for breeding toxic online be-\\nhavior that promotes misogyny and racism [59] . In its visible\\nefforts to ban hate speech, Reddit closed down a number\\nof prominent subreddits that violated its anti-harassment\\npolicy in 2015 [11] .\",\"reading_order\":3,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,507,394,730],\"text\":\"Hochschild argues that during times of uncertainty, emo-\\ntional laborers are most likely to sense an organization's\\n\\\"feeling rule\\\" as socially shared norms that guide how people\\nshould try to feel [33] . Hochschild uses the example of flight\\nattendants and the outward positive performance they must\\nuphold in front of airplane passengers [35] . Based on the\\nadvertisements of airline companies that promise enjoyment\\nto their clients, flight attendants are expected to greet pas-\\nsengers with a smile when they board a plane to prompt the\\nway that \\\"passengers ought to feel\\\" [35] . Hochschild draws\\non Goffman's work of the presentation of self [26] to explain\\nthe dramaturgical demands of frontstage (visible to the audi-\\nence) and backstage (invisible to the audience) behavior in\\nthe context of service sector jobs.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,731,395,906],\"text\":\"Emotional labor can be a highly visible, frontstage practice.\\nIn the case of Reddit, as feeling rules reflect patterns of social\\nmembership, moderators are taxed with the emotional labor\\ninvolved in generating and enforcing shared norms with\\nrespect to how redditors should interact with each other. For\\nexample, most subreddits have a visible set of rules typically\\nreferenced in the sidebar of the subreddit, and moderators\\noften engage with their communities in service of enforcing\\nthose rules. Thus, we see that the rules that become institu-\\ntionalized within Reddit are actually institutionalized feeling\\nrules.\",\"reading_order\":5,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[422,124,558,139],\"text\":\"Moderation on Reddit\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[422,144,747,270],\"text\":\"Moderators play integral roles as volunteer stewards in shap-\\ning and enforcing the rules and norms of their community\\n[20, 60] . Any redditor can become a moderator by either\\ncreating their own subreddit or being invited by an existing\\nmoderator to moderate a subreddit. The latter may involve\\na vetting process which includes applying for a moderator\\nposition within the community and having one's post history\\nreviewed by the moderation team.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[422,271,747,414],\"text\":\"Moderators have a range of controls for configuring the\\nsettings of their subreddits. They may edit stylesheets to\\nchange the subreddit's aesthetic, edit the rules page view-\\nable by redditors, view traffic statistics of their subreddits\\nover time, and manage items that are reported for review.\\nModerators are listed on the sidebar of their respective sub-\\nreddit communities. Redditors may reach out to moderators\\nthrough ModMail, a personal messaging tool that forwards\\ninquiries to all moderators of a subreddit.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[422,414,747,814],\"text\":\"HCI and CSCW scholarship exploring moderation prac-\\ntices have looked at the quality control of user-generated\\ncontent in health discussion boards like WebMD.com [38, 39] ,\\ntools for both deterring antisocial behavior and promoting\\ncontent literary practices on the video streaming site Twitch\\n[22, 80] , and blocklist subscriptions for preventing online ha-\\nrassment on Twitter [21, 44] . More specific to Reddit, studies\\nhave also been conducted with Reddit moderators, exploring\\nhow they took part in collective action against the platform\\nin the 2015 Reddit Blackout [61] , how they negotiated their\\ncontributions as civic participation [60] , how they foster dis-\\ncussion on topics of debate [58] , and how they view their\\nrole in welcoming newcomers to rapidly growing subreddits\\n[45] . Additional studies on Reddit have examined how its\\nsocial voting design contributes to users overlooking high-\\nquality content [24] , how users create throwaway accounts\\nto protect their anonymity in sharing controversial opinions\\n[49] , and how it has become a place for users to self-disclose\\nstigmatized issues [2, 14, 71, 92] . Less is known about the\\nday-to-day emotional costs involved with moderating be-\\nhavior and the resulting practices, or labor, that contribute\\nto the longevity or stagnation of an online community. To\\naddress this gap in scholarship, we investigate how moder-\\nators engage in the emotional labor that sets the stage for\\npro-social behavior.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[422,844,511,860],\"text\":\"3 METHODS\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[422,864,747,944],\"text\":\"This research is part of a broad, multi-sided investigation of\\nthe use of ICTs within and among populations engaging in\\nidentity work. This particular sub-study draws from a set of\\n21 semi-structured interviews with Reddit moderators from\\na diverse range of API subreddits.\",\"reading_order\":11,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,996,125,1013],\"text\":\"Paper 142\",\"reading_order\":12,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[706,996,745,1013],\"text\":\"Page 3\",\"reading_order\":13,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300372/results/markdown/3290605-3300372_pdf_shortened_page_2.md"}
{"file_name": "images/test_data/3290605-3300372/3290605-3300372_pdf_shortened_page_3.png", "source_folder": "test_data", "document_id": "3290605-3300372", "source_image_path": "test_data/3290605-3300372/3290605-3300372_pdf_shortened_page_3.png", "source_json_path": "test_data/3290605-3300372/results/output_json/3290605-3300372_pdf_shortened_page_3.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_pub_date\",\"author_affili\"]},{\"label\":\"table_cap\",\"bbox\":[90,122,370,138],\"text\":\"Table 1: Summary of AAPI subreddits studied.\",\"reading_order\":2,\"tags\":[]},{\"label\":\"tab\",\"bbox\":[68,154,390,329],\"text\":\"| Subscriber Count | Subreadit Name | Creation Date | Moderator Count | | 18,847 | r/asianamerican | Jan 2010 | *11 | | 11,719 | r/abedesis | Sep 2013 | *6 | | 11,382 | r/asianidentity | Nov 2015 | *13 | | 10,614 | r/asianwox | Jan 2017 | *2 | | 9,305 | r/speake | Sep 2014 | *33 | | 6,029 | r/gaysian | Jan 2012 | *4 | | 4,047 | r/asianbeautyadvice | May 2017 | *7 | | 2,875 | r/asiandrama | Jun 2011 | 6 | | 2,781 | r/asianfeminism | Aug 2013 | 2 | | 1,463 | r/asianamericanfeatures | Feb 2019 | 1 | | 1,311 | r/asianfilms | Nov 2014 | *3 | | 550 | r/deistwox | Jan 2012 | *5 | | 63 | r/asianamericanallentiles | Sep 2017 | 1 |
\",\"reading_order\":3,\"tags\":[],\"alt_text\":\"This is a table displaying data for various subreddits. It lists the \\\"Subscriber Count,\\\" \\\"Subreddit Name,\\\" \\\"Creation Date,\\\" and \\\"Moderator Count\\\" for thirteen subreddits, primarily focused on Asian American communities, sorted by subscriber count in descending order.\",\"figure_path\":\"tables/3290605-3300372_pdf_shortened_page_3_tab_3.png\"},{\"label\":\"anno\",\"bbox\":[74,334,388,346],\"text\":\"* u/AutoModerator (hot) is listed as a subreddit moderator. Updated 18 April 2018.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[69,388,219,404],\"text\":\"Participant Recruitment\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,407,395,631],\"text\":\"Between February and April 2018, we recruited 21 partici-\\npants who were listed as moderators on public API sub-\\nreddits. Using Reddit's internal search engine, we used key-\\nwords such as “ Asian , ” “ Asian American , ” “ Pacific Islander , ”\\nand “ AAPI ” to identify subreddits where AAPIs engaged in\\nidentity work through which we could recruit moderators.\\nTopical subreddits were chosen based on relevance to issues\\npertaining to Asian American identity as self-described in\\ntheir respective subreddit descriptions. We employed maxi-\\nmum variation sampling as a purposeful sampling strategy\\n[70] for observing how a phenomenon is understood among\\ndifferent groups of people across a variety of contexts. Thus,\\nour eligibility criteria included anyone who served as a cur-\\nrent or previous moderator for an AAPI-focused subreddit.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,631,395,854],\"text\":\"Moderators were recruited from 13 different APIA subred-\\ndits, 11 of which had a minimum 1,000 subscribers during\\nthe time of data collection. Listed by subscriber count in Table 1 ,\\nthe subreddits varied in the kind of identity work being en-\\ngaged in a public subreddit setting. Private subreddits were\\nexcluded from the study due to their discretionary nature and\\nstrict membership requirements. The first author directly\\nmessaged every single redder listed as a moderator in the\\nsidebar of targeted subreddits. A total of 118 Reddit direct\\nmessage solicitations were sent to moderators of our final list\\nof 18 AAPI subreddits (in addition to the subreddits reflected\\nin Table 1 , the first author reached out to moderators from\\nr/asianparentstories, r/asianmasculinity, r/easternsunrising,\\nr/asianpeoplegis, r/asianeats).\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,854,395,951],\"text\":\"The human moderator count for the targeted subreddits\\nvaried greatly (average = 7.15, standard deviation = 8.44; docs\\nwere removed from calculation), with the largest moderation\\nteam count of 33 belonging to r/hapas, and the smallest\\nmoderator count of 1 on both r/asianamericanissues and\\nr/asianamericanathletes. A moderator listed on a specific\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,124,746,155],\"text\":\"subreddit does not necessarily imply that the moderator is\\nstill active on the subreddit.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,167,537,184],\"text\":\"Interview Protocol\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,189,747,331],\"text\":\"The first author conducted semi-structured interviews via\\nvoice and video calling applications like Skype, Google Hang-\\nout, and Discord. One participant (P10) requested that the\\ninterview was done purely through textual exchanges to pre-\\nserve his anonymity. Moderators were required to be at least\\n18 years old to participate in the study. Interviews ranged\\nfrom 30 minutes to more than 2 hours. Recordings were tran-\\nscribed within 24 hours of conversing with each participant.\\nOur Institutional Review Board approved the study.\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,332,747,476],\"text\":\"The interview guide comprised of eight sections: (1) col-\\nlecting demographic information of the moderator, (2) diving\\ninto their motivations behind using Reddit, (3) understanding\\ntheir sense of identity within their community, (4) explor-\\ning factors that influence their deliberation on Reddit, (5)\\nuncovering their moderation practices, (6) unpacking their\\nunderstanding of anonymity, (7) inquiring about the ways\\nthey seek social support, and (8) their perceptions on the\\nReddit platform at large.\",\"reading_order\":12,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,486,507,503],\"text\":\"Data Analysis\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,506,747,699],\"text\":\"The first author analyzed interview transcripts iteratively by\\nusing coding [77] and memoing [25] techniques. He induc-\\ntively developed a codebook that maintained an inventory of\\ncodes with their descriptions [86] . A total of 18 reoccurring\\ncodes emerged related to the challenges moderators faced in\\nmanaging their subreddits, working with their moderation\\nteams, and developing strategies for moderating AAPI com-\\nmunities. The codes were reduced under axial coding into\\nthe 6 themes presented in this paper. We reached a point\\nof saturation after our interview with our fifteenth partic-\\nipant, as no new themes emerged, and the remaining six\\nparticipants confirmed the identified themes.\",\"reading_order\":14,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,709,583,727],\"text\":\"Participant Demographics\",\"reading_order\":15,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,730,747,954],\"text\":\"As illustrated in Table 2 , a total of 12 participants identified\\nas cis male (57 % ) and 9 identified as cis female (43 % ). The\\nmajority of moderators were aged in their twenties (62 % ).\\nAll but one moderator (P3) identified with having Asian or\\nMixed (with Asian) heritage (95 % ). Moderators were highly\\neducated, as 19 participants attained at least a bachelor's\\ndegree (90 % ), and 8 participants completed an advanced aca-\\ndemic degree (38 % ). We also collected self-reported data on\\nthe average amount of time moderators spent on Reddit dur-\\ning the participant recruitment period: 7 participants spent\\n0-1 hours on Reddit per day, 1 spent 1-2 hours on Reddit per\\nday, 8 spent 2-3 hours on Reddit per day, 2 spent 3-4 hours\\non Reddit per day, 2 spent 4-5 hours on Reddit per day, and\\n1 spent over 5 hours on Reddit per day.\",\"reading_order\":16,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,125,1013],\"text\":\"Paper 142\",\"reading_order\":17,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,995,745,1013],\"text\":\"Page 4\",\"reading_order\":18,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300372/results/markdown/3290605-3300372_pdf_shortened_page_3.md"}
{"file_name": "images/test_data/3290605-3300372/3290605-3300372_pdf_shortened_page_4.png", "source_folder": "test_data", "document_id": "3290605-3300372", "source_image_path": "test_data/3290605-3300372/3290605-3300372_pdf_shortened_page_4.png", "source_json_path": "test_data/3290605-3300372/results/output_json/3290605-3300372_pdf_shortened_page_4.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[]},{\"label\":\"header\",\"bbox\":[475,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[]},{\"label\":\"table_cap\",\"bbox\":[86,122,374,139],\"text\":\"Table 2: Summary of participant demographics.\",\"reading_order\":2,\"tags\":[]},{\"label\":\"tab\",\"bbox\":[70,154,391,376],\"text\":\"| ID | Sex | Age | Race | Ethnicity | Degree Earned | Subcredit Modeled | | P1 | M | 20-29 | Asian | Indian American | Bachelor | r/alcedin | | P2 | M | 20-29 | Asian | Pakistanese | Doctor | r/alcedin | | P3 | M | 18-19 | Asian | Cambodian | High School | r/alcedin | | P4 | M | 20-29 | Asian | Vietnamese American | Master | r/gayan | | P5 | F | 20-29 | Asian | Taiwanese American | Bachelor | r/taiwanian/byte-dive | | P6 | M | 20-29 | Asian | Chinese American | Bachelor | r/asian/american | | P7 | M | 20-29 | Asian | Chinese American | Bachelor | r/asian/american | | P8 | M | 20-29 | Asian | Chinese Canadian | Bachelor | r/amendity | | P9 | M | 20-29 | Asian | Japanese | Bachelor | r/amendity | | P10 | M | 30-39 | Asian | Chinese | Bachelor | r/asian/american | | P11 | F | 20-29 | Asian | Chinese American | Master | r/asian/american | | P12 | M | 20-29 | Afaced | Chinese Indian | Bachelor | r/asian/american | | P13 | M | 20-29 | Asian | Horn American | Doctor | r/asian/american | | P14 | F | 30-39 | Asian | Thai American | Postgraduate | r/asian/exo | | P15 | M | 30-39 | Asian | Korean American | Master | r/asian/american | | P16 | M | 30-39 | Asian | Indian | Doctor | r/alcedin | | P17 | M | 40-49 | Afaced | Chinese, Caucasian | Bachelor | r/japan | | P18 | F | 20-29 | Asian | Chinese American | Bachelor | r/asian/exo | | P19 | M | 30-39 | Asian | Korean Canadian | Master | r/asian/american/athletes | | P20 | M | 30-39 | Asian | Japanese | High School | r/asian/exo | | P21 | F | 20-29 | Asian | Bengali American | Bachelor | r/desixox |
\",\"reading_order\":3,\"tags\":[],\"alt_text\":\"This is a table displaying participant data across several columns: ID, Sex, Age, Race, Ethnicity, Degree Earned, and Subreddit Moderated. Each row represents a unique participant, detailing their demographic information and the subreddit they moderate, such as 'r/abedesis' or 'r/asianamerican'. The table lists 21 participants, identified as P1 through P21.\",\"figure_path\":\"tables/3290605-3300372_pdf_shortened_page_4_tab_3.png\"},{\"label\":\"sec_2\",\"bbox\":[69,425,206,443],\"text\":\"Reflexivity Statement\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,447,394,541],\"text\":\"In examining how marginalized communities are studied,\\nreflexively understanding the authors' race and ethnicity\\nmay bring certain affinities into perspective [78] . The first\\nauthor identifies as AAPI and has previously engaged in\\nadvocacy work for the AAPI community. He has also been\\nsubscribed to AAPI subreddits for more than four years.\",\"reading_order\":5,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[69,558,155,575],\"text\":\"4 FINDINGS\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,580,395,692],\"text\":\"We center our results on the emergent themes of emotional\\nlabor involved in the moderation practices of our participants.\\nTo answer RQ1, we report on challenges that arise in their\\nmeta moderation that shape the identity work of their online\\ncommunities. To answer RQ2, we describe their moderation\\nstrategies for managing emotional labor and the underlying\\nworkarounds they employ to sustain their AAPI subreddits.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[69,709,263,726],\"text\":\"Challenges of Meta Moderation\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,729,395,857],\"text\":\"Here we describe challenges moderators face as manifested\\nin emotional labor from meta moderation. Moderators en-\\ngage in a range of meta practices—practices that exist outside\\nof Reddit itself—as a means for fulfilling their duties as mod-\\nerators, and in managing tensions within moderating teams.\\nThese challenges include: (1) burning out from volunteer\\nwork, (2) navigating hierarchical structures, and (3) balanc-\\ning unfulfilled expectations.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,870,395,951],\"text\":\"Burning out from volunteer work. Six participants (P5, P7, P12,\\nP13, P16, P18) disclosed feeling burned out, or exhausted,\\nfrom their volunteer service as moderators at some point in\\ntheir service. P12 notes how burnout caused by overwork or\\nstress is a gendered issue for her moderation team.\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[453,125,714,190],\"text\":\"\\\"Sadly, we do get a lot of burnout, mostly through\\nour female moderators. As far as keeping every-\\nthing alive, we just keep moving forward. Try and\\nencourage them. But, you know, it's tough.\\n\\\" (P12)\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,192,747,320],\"text\":\"While moderators talk about burnout in different ways,\\nsome continued to serve their volunteer roles for the benefit\\nof others. P16, an Indian American female in her twenties\\nwho moderates for r/destivox (a subreddit devoted for Desi\\ngirls of the South Asian diaspora to talk about life, culture,\\nfashion, and community) underpins the gravitas of keeping\\nher community accessible for people like her, despite the\\nemotional toll she endures to keep her subreddit alive.\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[450,323,715,419],\"text\":\"\\\"It's been a tiring job doing this for free and deal-\\ning with a lot of the shit that we've dealt with, and\\nso now I kind of just mostly focus on r'destwox\\nand my private sub and just keeping the commu-\\nnity going and keeping that space open for other\\ngirls who may be like me.\\\" (P16)\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,422,748,551],\"text\":\"The majority of participants who have experienced burnout\\nin this study identified as female. These participants shared\\nhow they encountered harassment and misogynist comments\\ndirected either towards themselves or the redditors of the\\ncommunities they moderate. Similar outcomes in volunteer\\nroles have been found among female Wikipedia editors,\\nwhich eventually influence the type of work the editors con-\\nmit to serving in their volunteer roles [63] .\",\"reading_order\":14,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,558,748,719],\"text\":\"Navigating hierarchical structures. AAPIs hail from high con-\\ntext societies, such as those in the East, which are hierarchical\\nin nature and emphasize honor and respect-based practices\\nthat maintain positive impressions both within interpersonal\\nnetworks and outwardly in public [32, 81, 96] . We continue to\\nsee hierarchical structures reinforced in the moderation prac-\\ntices of AAPI subreddits, where moderators may be provided\\ndifferent levels of access contingent on the roles they fulfill\\nin each subreddit they moderate. P12 illustrates this point\\nfurther among the moderation team of n-asianamerican .\",\"reading_order\":15,\"tags\":[]},{\"label\":\"para\",\"bbox\":[453,722,715,834],\"text\":\"\\\"Yes, there's actually some hierarchy. The more\\ntenured ones have full moderator powers that in-\\nclude banning. They have a lot more say in what\\nwe wanna do as a whole, from a meta standpoint.\\nThen we have the newer mod permissions. It's\\nmore a support role. Mostly they're in charge of\\njust looking out for threads.\\\" (P12)\",\"reading_order\":16,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,837,747,902],\"text\":\"Hierarchical structures vary across moderation teams, and\\nsome teams found the hierarchy challenging to subvert. P7\\nnotes how uneven levels of moderation access prevent mod-\\nerators from gaining complete control of a subreddit.\",\"reading_order\":17,\"tags\":[]},{\"label\":\"para\",\"bbox\":[453,905,714,953],\"text\":\"\\\"Reddit's moderating structure makes it difficult to\\nusurp power... The only way to usurp a moderator\\nis by literally hacking their account.\\\" (P7)\",\"reading_order\":18,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,996,126,1013],\"text\":\"Paper 142\",\"reading_order\":19,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,996,745,1013],\"text\":\"Page 5\",\"reading_order\":20,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300372/results/markdown/3290605-3300372_pdf_shortened_page_4.md"}
{"file_name": "images/test_data/3290605-3300372/3290605-3300372_pdf_shortened_page_5.png", "source_folder": "test_data", "document_id": "3290605-3300372", "source_image_path": "test_data/3290605-3300372/3290605-3300372_pdf_shortened_page_5.png", "source_json_path": "test_data/3290605-3300372/results/output_json/3290605-3300372_pdf_shortened_page_5.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,43,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[]},{\"label\":\"header\",\"bbox\":[474,43,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,125,394,189],\"text\":\"The power dynamics stemming from hierarchical moder-\\nation structures influence how moderators interact with one\\nanother. P9 reveals how hierarchical clashes between mod-\\nerators may contribute to the formation of new subreddits.\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[99,191,363,272],\"text\":\"\\\"There's four [main moderators] who split from\\nrasianmasculinity [to create r'azidentiality] be-\\ncause we disagree with the moderation tactics of\\nthe top mod. We slowly moved the conversation\\nfrom sexual dynamics into racial politics.\\\" (P9)\",\"reading_order\":3,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,275,395,435],\"text\":\"Seven participants (P4, P5, P8, P9, P11, P13, P15) elaborated\\non their observations of AAPI subreddits and their history\\nof fragmentation. According to the creation dates of AAPI\\nsubreddits in Table 1 , r/asaianamerican (a subreddit that aims\\nto foster a sense of community among Asian Americans and\\ntheir respective counterparts in the Asian diaspora) is one\\nof the oldest subreddits created for AAPI identity work. P11,\\na Chinese American female in her twenties who moderates\\nfor r/asaianamerican, provides a brief overview of allegiances\\nand defections among AAPI subreddits.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[100,439,363,584],\"text\":\"\\\"On our side of the issue would be r'asianamerican,\\nr'asianfeminism, r'asiantwox, r'asianparentstories.\\nThen all the other Asian communities on Red-\\ndit are some variation of r'asianmasculinity and\\nr'azinidentity—those were actually splinter com-\\nmunities of r'asianamerican because they felt that\\nthe moderation of r'asianamerican was too harsh.\\nThose people moved out of the community and\\ncreated their own.\\\" (P11)\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,588,395,700],\"text\":\"As confirmed in our interviews, the r/asianamerican subred-\\ndit served as the central space for AAPIs to discuss a wide\\nrange of topics on Reddit, which later splintered off into\\nsmaller communities. Those who repeatedly shared philo-\\nsophical disagreements on the direction of r/asianamerican\\nwere banned or censored from the subreddit, thus creating\\nsplintered spin-offs for alternative discussion.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,708,395,916],\"text\":\"Balancing unfulfilled expectations. Three participants (P1,\\nP4, P8) have experienced challenges in keeping pace with\\nthe demands redditors expect out of their subreddit modera-\\ntors. Redditors grapple with maintaining values of the public\\nsphere: inclusion, civility, and rationality [28, 29]. Inclusion\\nmeans that anyone can participate. Civility means that every-\\none must treat others with respect. Rationality means that\\npeople engage in reasoned argumentation and provide facts\\nto support their claims. P1, an Indian American male in his\\ntwenties who moderates for r/abcesido (a subreddit for mem-\\nbers of the South Asian diaspora, colloquially referred to as\\nAmerican Born Confused Desis), feels caught in a dichotomy\\nof doing both too much and too little moderation.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[101,919,362,953],\"text\":\"\\\"It's always been a fine line to find balance. Some-\\ntimes we had users in the past they say we're 'Not\\nworking'.\\\"\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[453,125,715,156],\"text\":\"mads, and then there's sometimes where we get\\ncriticized for not doing enough.\\\" (P1)\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,159,767,287],\"text\":\"Political differences among influential AAPI redditors also\\nplayed a factor in the splintering of subreddits. P15, a Korean\\nAmerican male in his thirties who moderates for r/asianamerican,\\nattributes political ideology as a factor for subreddits to splin-\\nter into smaller fringe groups. Previous research has shown\\nthat AAPI redditors engage differently between conserva-\\ntive and progressive online communities through a process\\nconceptualized as identity work as deliberation [16] .\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[453,289,716,371],\"text\":\"\\\"There was a split within r/asianmasculinity where\\na bunch of people were kicked out. Those people\\nwent on to form r/asianidentity. It had to do with the\\namount of labor that was being invested and per-\\nsonal vendetas, also political differences.\\\" [P15]\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,374,747,470],\"text\":\"In light of the evident splintering within AAPI subreddits,\\nP8, a Chinese Canadian male in his twenties who moderates\\nfor r:Anzidensity (a subreddit that prioritizes the discussion\\nof issues, ideas, and policy that affects the lives of Asians who\\nlive in Western society), recognized how newer moderators\\nwork to change the culture left behind by older moderators.\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[453,473,722,536],\"text\":\"\\\"Our older moderators were in favor of more fringe\\ntopics that might not really get much praise, whereas\\nnewer moderators are more in favor of more mod-\\nerate topics that everybody likes to discuss.\\\" (P5)\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,540,746,621],\"text\":\"Moderators are also aware of the increasing importance\\nemotionality plays in online discourse, particularly when\\nfacilitating contentious deliberation. P8 became conscien-\\ntious of how moderation is interconnected with the moods\\nof people browsing the subreddit at that point in time.\",\"reading_order\":14,\"tags\":[]},{\"label\":\"para\",\"bbox\":[453,623,715,705],\"text\":\"[\\\"We are] balancing the type of extremist posts\\nwith moderate posts and positive and negative\\nkind of post material. Because I realize as a large\\nsubreddit, the type of topics that we have on our\\nfront page can affect people's moods.\\n\\\" (P8)\",\"reading_order\":15,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,707,748,851],\"text\":\"Despite their shortcomings, moderators have come to ac-\\ncept the thankless nature of their roles. Outside of their\\nmoderation team, moderators hardly receive any gratitude\\nfor the emotional labor they invest in their community. P4,\\na Vietnamese American male in his twenties who moderates\\nfor r/gaysian (a subreddit for LGBTQ members of the AAPI\\ncommunity) compares redditors as children of a subreddit\\nfamily, with moderators serving a largely unappreciated—yet\\nimportant—parental role in the development of the subreddit.\",\"reading_order\":16,\"tags\":[]},{\"label\":\"para\",\"bbox\":[453,853,715,902],\"text\":\"\\\"You can't please everybody and everybody wants\\nsomething different, but they\\n're all children about\\nit.\\n\\\" (P4)\",\"reading_order\":17,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,905,747,953],\"text\":\"While moderators play a key role in maintaining and up-\\nholding values of the public sphere through meta moderation,\\nwe find that they do not feel fully appreciated by members of\",\"reading_order\":18,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,126,1013],\"text\":\"Paper 142\",\"reading_order\":19,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[704,995,745,1013],\"text\":\"Page 6\",\"reading_order\":20,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300372/results/markdown/3290605-3300372_pdf_shortened_page_5.md"}
{"file_name": "images/test_data/3290605-3300372/3290605-3300372_pdf_shortened_page_6.png", "source_folder": "test_data", "document_id": "3290605-3300372", "source_image_path": "test_data/3290605-3300372/3290605-3300372_pdf_shortened_page_6.png", "source_json_path": "test_data/3290605-3300372/results/output_json/3290605-3300372_pdf_shortened_page_6.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,61],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[]},{\"label\":\"header\",\"bbox\":[475,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[]},{\"label\":\"half_para\",\"bbox\":[69,125,394,156],\"text\":\"their subreddits, which may negatively affect the recruitment\\nand retention of moderation teams.\",\"reading_order\":2,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[69,169,352,203],\"text\":\"Moderation Strategies to Manage the Stress of\\nEmotional Labor\",\"reading_order\":3,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,207,394,286],\"text\":\"In this section, we describe the strategies moderators employ\\nto manage the emotional stress manifest in moderation work.\\nThese strategies include: (1) distancing away from drama, (2)\\nbuilding solidarity from shared struggles, and (3) integrating\\nan ecology of tools for self-organized moderation.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,295,394,440],\"text\":\"Distancing away from drama. Twelve participants (P3, P4,\\nP5, P7, P9, P11, P13, P14, P15, P16, P20, P21) shared their\\nexperiences of observing drama unfold within their online\\ncommunities. P18, a Chinese American female in her twen-\\nties who moderates for r/asiantwox (a subreddit dedicated\\nto Asian women—the TwoX refers to the XX chromosomes\\nthat determine the sex of an individual), describes how she\\nhas become desensitized to threatening messages over the\\ntenure of her moderation.\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[101,445,363,590],\"text\":\"\\\"It is a ton of emotional labor, but I've forced my-\\nself to detach a lot. For example, we got like a\\nsuper, super threatening message in the ModMail\\nthe other day, and I thought it was kind of funny,\\nso I shared it with a friend. She was horrified. She\\nwas like,\\n'I'm so sorry. Wow, I can't believe you\\nhad to read that.\\n' I was like,\\n'Oh... I thought it was\\nkind of funny.\\n' I just have to emotionally divino\\nmyself, otherwise, it'd be too much.\\n\\\" (P115)\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,595,394,628],\"text\":\"P11 also reveals how she separated herself from drama\\nthat unfolds in her subreddit by not intervening in drama.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[101,633,363,697],\"text\":\"Lots of drama happened in these subreddits, of\\nwhich I wasn't that related to, but it just convinced\\nme that it was not a community that I wanted to\\ninvest more time in...I needed to step back.\\\" (P11)\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,702,394,796],\"text\":\"While discussions on Reddit have the capacity to be in-\\nteresting and meaningful, not all interactions on Reddit are\\nproductive. P12 has repeated observed the futility of arguing\\nwith anonymous people online, particularly redditors who\\nare not open to listening to opposing perspectives or having\\ntheir worldviews challenged.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[101,801,363,883],\"text\":\"\\\"I don't really directly respond because I know,\\nlike at this point... some of those guys' minds are\\nalready made up and they're not gonna change\\nthem. I just kinda, like, downvote, roll my eyes, get\\na little annoyed, and then just move on.\\n\\\" (P12)\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,888,394,952],\"text\":\"Moderators are acutely aware of how their responses to\\nincendiary dialogue can impact the emotional labor behind\\ntheir moderation. P14, a Thai American female in her thirties\\nwho moderates for r/asianxtoʊ, illustrates that avoiding\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,125,745,156],\"text\":\"antagonistic redditors serve as an effective means of strategic\\ndistancing.\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[453,159,714,224],\"text\":\"\\\"There some very aggressive antagonistic people\\nand I don't want to attack them, I don't want them\\ntracking me down because I'm not high profile, but\\nI can be found and I don't want that.\\n\\\" (P14)\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,227,746,323],\"text\":\"Experienced moderators tend to distance themselves from\\nunnecessary drama that pervades their subreddits, as the\\nvisible work that comes from dealing with drama is not\\nworth their emotional labor. Our empirical evidence draws\\nparallels to Hochschild's work on emotional labor, whereby\\nthe suppression of emotions are felt but not expressed [35] .\",\"reading_order\":14,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,330,746,442],\"text\":\"Building solidarity from shared struggles. All 21 interview\\nparticipants experienced microaggressions—indirect, subtle,\\nor unintentional discriminatory exchanges against members\\nof a marginalized group [ 88 , 89 ]— on Reddit targeted either\\ntoward them or members of their community. P8 describes\\nhow microaggressions have a long-term effect on how AAPIs\\nmay view themselves and those similar to them.\",\"reading_order\":15,\"tags\":[]},{\"label\":\"para\",\"bbox\":[453,445,715,557],\"text\":\"\\\"Think about the huge and ever-growing list of lies\\nagainst Asians: dog eaters, small penises, hated by\\nwomen, no human rights, organ harvesting, Ti-\\nbetan genocide, Mao's genocide, Tiananmen Square\\nmassacre, uncreative copycats, etc. What is there\\nto be proud of? These Asians get brainwashed into\\nhating themselves.\\\" (P8)\",\"reading_order\":16,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,560,747,689],\"text\":\"Beyond comments that hint at casual slights, moderators\\nalso expressed their interactions with redditors who spew\\nblatant racial biases, discriminatory remarks, and other forms\\nof harassment. Instead of directly responding to the attacks\\ntargeted toward them, moderators vented their frustrations\\ninternally to their moderation teams as a means of seeking\\nsocial support. P16 illustrates how this process of support\\nseeking develops friendship among a team of moderators,\",\"reading_order\":17,\"tags\":[]},{\"label\":\"para\",\"bbox\":[452,691,715,787],\"text\":\"\\\"I kind of started meeting some other Asian women\\nand we kind of started realizing that, 'Hey, this\\nharassment that we're facing, we're not the only\\nones.' We reached out to each other, 'Oh, you're\\ngetting this, too.' Oh, these are the same people.\\nI've formed friendships.\\\" (P16)\",\"reading_order\":18,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,791,746,902],\"text\":\"In response to microaggressions and harassment, modera-\\ntors form solidarity with others in the larger AAPI commu-\\nnity by connecting and welcoming redditors to newly created\\nsubreddits that address a community need. P16 shares the\\nmotivations behind her creation of r/deistvox and how mod-\\nerators who believed in her vision rallied around her and\\nvolunteered to grow her subreddit.\",\"reading_order\":19,\"tags\":[]},{\"label\":\"para\",\"bbox\":[453,905,715,954],\"text\":\"\\\"With radesitwor, one of the mods was like,\\n'Hey, I\\nreally want space for Desi girls, because radesis\\nis very male-dominated.\\n' We were like,\\n'Why don't\",\"reading_order\":20,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[70,996,125,1013],\"text\":\"Paper 142\",\"reading_order\":21,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,996,745,1013],\"text\":\"Page 7\",\"reading_order\":22,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300372/results/markdown/3290605-3300372_pdf_shortened_page_6.md"}
{"file_name": "images/test_data/3290605-3300372/3290605-3300372_pdf_shortened_page_7.png", "source_folder": "test_data", "document_id": "3290605-3300372", "source_image_path": "test_data/3290605-3300372/3290605-3300372_pdf_shortened_page_7.png", "source_json_path": "test_data/3290605-3300372/results/output_json/3290605-3300372_pdf_shortened_page_7.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"half_para\",\"bbox\":[100,125,363,189],\"text\":\"we carve out a space for ourselves? ” By that time,\\nI had already had experience with the other sub-\\nso I was like, 'Oh, I'll help run some of the auto-\\nmoderator' and that's how it came about. “P64”\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,193,395,289],\"text\":\"P17, a mixed-race male in his forties who moderates for\\nr/hapas (a subreddit with a reputation for sparking contro-\\nversial topics among those who identify with half-Asian\\nancestry), articulates the way he intervenes in guiding con-\\ntentious and gendered microaggressions surrounding dating\\nwithin the AAPI community.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"para\",\"bbox\":[101,291,363,404],\"text\":\"\\\"I didn't like banning the Asian males because I\\nkind of had a sense of camaraderie for them. But\\nthen I also understood, I could kind of see how\\nthey were, it was a little bit of a crowd out and\\nsometimes we had to just cut down the numbers\\nsomehow tactfully so that actual Hapas had more\\nof a voice in their own sub.\\n\\\" (P17)\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,408,395,488],\"text\":\"Here we see P17 engage in emotional labor in negotiating the\\nneed for camaraderie with the need for representation, per-\\nforming invisible work in banning Asian male redditors who\\nwere drowning out the voices of Hapa individuals through\\nmicroaggressive comments.\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,489,394,551],\"text\":\"Moderators like P9 are willing to put aside their personal\\ndifferences to see AAPI subreddits more united than divided,\\nunderscoring the need for the community to stand in solidar-\\nity for issues larger than the feuds between AAPI subreddits.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[101,555,362,605],\"text\":\"\\\"We all want the same thing, no matter how much\\nwe hate each other, we should just squash the beef.\\nI forgive.\\n\\\" (P9)\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,608,395,752],\"text\":\"As microaggressions have the potential to be internalized\\nby those that encounter them on a daily basis [89] , moder-\\nators play an important role in creating opportunities for\\nsolidarity by building online spaces that counter disparaging\\nstereotypes surrounding their AAPI identity. These spaces\\nallow AAPIs to reframe how they perceive themselves in the\\nlarger narrative of society and allow moderators to reclaim\\ntheir sense of purpose from the emotional labor invested in\\ntheir online communities.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,761,395,953],\"text\":\"Integrating an ecology of tools for self-organized moderation.\\nBricolage is the act of choosing tools at hand in order to\\ndevelop solutions that enable agency [94] . Twelve partici-\\npants (P2, P3, P4, P7, P8, P9, P11, P13, P16, P18, P19, P21)\\nengaged in bricolage whereby they assembled an ecology\\nof technologies together as a means of building capacity for\\nmoderation work. The Reddit platform lacks a range of fea-\\ntures that moderators need to perform their activities, and\\nwe find that moderators self-organize to develop agency for\\nmoderation work. A common strategy among moderators\\nis to relegate private communication channels for steering\\nthe direction of the subreddit to platforms like GroupMe and\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,125,746,188],\"text\":\"Discord, as Reddit's private messaging platform has been\\ndescribed as less usable. P11 shares how she works with her\\nmoderation team to make decisions backstage in a private\\nSlack channel invisible to their subreddit.\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[453,194,714,307],\"text\":\"\\\"We have a team of five to seven people who are\\nsomewhat active. We all sit in a Slack channel.\\nUsually, if people don't know what to do about\\na comment, whether they should allow it or not,\\nthey'll ask other mods. We kind of go on a consen-\\nsus basis, although we sort of divide our team into\\nfull mods and support mods.\\\" (P11)\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,313,746,472],\"text\":\"As illustrated by P11, moderation teams piece together\\nsubtasks (i.e., remove comments, ban users) in their coordi-\\nnated practices that require human intervention. For tasks\\nthat can be automated, moderators may resort to using Au-\\ntoModerator , 1 a customizable bot that monitors new and\\nedited comments in subreddits and acts on them as needed.\\nBots can be configured to detect, flag, and remove content\\nregarded as offensive, inappropriate, or rule-breaking; post\\nregular community discussion threads; and alert moderators\\nif certain types of content are posted.\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[453,477,714,559],\"text\":\"“I would say 50% to 70% of the work is done by\\nw/automoderator. When there’s a post that auto-\\nmod took down, it’ll message us, and we can look\\nat it and be like, ‘This is actually fine,’ or, No. This\\ndoes need to go.” (P11)\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,565,746,660],\"text\":\"Our participants also described ways in which they have\\ncustomized their Reddit routines to improve their ability to\\nmoderate their subreddits more effectively. P2 explains how\\nhe uses a third party browser extension to annotate private\\nnotes about a redditor's post history, which remains unseen\\nby the redditor.\",\"reading_order\":14,\"tags\":[]},{\"label\":\"para\",\"bbox\":[453,666,714,731],\"text\":\"\\\"People don't realize that as mods we have this\\ntoolbar where we can post notes about individual\\nusers. We can say, 'Over here he was trolling, or\\nover here, he was trying to bait someone.'\\\" (P2)\",\"reading_order\":15,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,738,745,771],\"text\":\"P16 notes how the users of its subreddit took it upon\\nthemselves to improve their experience with using Reddit.\",\"reading_order\":16,\"tags\":[]},{\"label\":\"para\",\"bbox\":[453,775,714,856],\"text\":\"“I wish Reddit itself would have those kinds of tools\\nintegrated into the platform, but they don’t. The\\ntoolbar, auto mod, all that stuff, it’s not actually\\nReddit created those kinds of things. It’s users who\\ncreated it and made it better.” (P16)\",\"reading_order\":17,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,863,746,911],\"text\":\"In implementing a bricolage of tools to facilitate their\\nmoderation practices, moderators engage in invisible work\\nthat does not get seen by the broader subreddit.\",\"reading_order\":18,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[421,938,624,952],\"text\":\"$^{1}$ https://www.reddit.com/wiki/automoderator\",\"reading_order\":19,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,996,126,1013],\"text\":\"Paper 142\",\"reading_order\":20,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[706,996,745,1013],\"text\":\"Page 8\",\"reading_order\":21,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300372/results/markdown/3290605-3300372_pdf_shortened_page_7.md"}
{"file_name": "images/test_data/3290605-3300374/3290605-3300374_pdf_shortened_page_0.png", "source_folder": "test_data", "document_id": "3290605-3300374", "source_image_path": "test_data/3290605-3300374/3290605-3300374_pdf_shortened_page_0.png", "source_json_path": "test_data/3290605-3300374/results/output_json/3290605-3300374_pdf_shortened_page_0.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,59],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,44,745,60],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"icon\",\"bbox\":[0,92,39,133],\"reading_order\":2,\"tags\":[\"cross_marker\"]},{\"label\":\"sec_0\",\"bbox\":[309,118,505,146],\"text\":\"In UX We Trust\",\"reading_order\":3,\"tags\":[]},{\"label\":\"sec_0\",\"bbox\":[95,156,719,213],\"text\":\"Investigation of Aesthetics and Usability of Driver-Vehicle Interfaces and Their\\n\\nImpact on the Perception of Automated Driving\",\"reading_order\":4,\"tags\":[]},{\"label\":\"author_information\",\"bbox\":[90,231,282,293],\"text\":\"Anna-Katharina Frison †\\n\\nTechnische Hochschule Ingolstadt\\nIngolstadt, Germany\\nanna-katharina.frison@thi.de\",\"reading_order\":5,\"tags\":[\"author\",\"author_affili\",\"author_mail\"]},{\"label\":\"author_information\",\"bbox\":[307,231,500,294],\"text\":\"Philipp Wintersberger †\\nTechnische Hochschule Ingolstadt\\nIngolstadt, Germany\\nphilipp.wintersberger@thi.de\",\"reading_order\":6,\"tags\":[\"author\",\"author_affili\",\"author_mail\"]},{\"label\":\"author_information\",\"bbox\":[530,231,721,292],\"text\":\"Andreas Riener†\\n\\nTechnische Hochschule Ingolstadt\\nIngolstadt, Germany\\nandreas.riener@thi.de\",\"reading_order\":7,\"tags\":[\"author\",\"author_affili\",\"author_mail\"]},{\"label\":\"author_information\",\"bbox\":[93,308,283,371],\"text\":\"Clemens Schartmüller†\\n\\nTechnische Hochschule Ingolstadt\\nIngolstadt, Germany\\n\\nclemens.schartmuller@thi.de\",\"reading_order\":8,\"tags\":[\"author\",\"author_affili\",\"author_mail\"]},{\"label\":\"author_information\",\"bbox\":[312,308,479,370],\"text\":\"Linda Ng Boyle\\n\\nUniversity of Washington\\nSeattle, WA, USA\\n\\nlinda@uw.edu\",\"reading_order\":9,\"tags\":[\"author\",\"author_affili\",\"author_mail\"]},{\"label\":\"author_information\",\"bbox\":[553,308,698,372],\"text\":\"Erika Miller\\nColorado State University\\nFort Collins, CO, USA\\nerika.miller@colostate.edu\",\"reading_order\":10,\"tags\":[\"author\",\"author_affili\",\"author_mail\"]},{\"label\":\"author_information\",\"bbox\":[312,387,501,448],\"text\":\"Klemens Weigl*\\nTechnische Hochschule Ingolstadt\\nIngolstadt, Germany\\nklemens.weigl@thi.de\",\"reading_order\":11,\"tags\":[\"author\",\"author_affili\",\"author_mail\"]},{\"label\":\"sec_1\",\"bbox\":[69,460,141,477],\"text\":\"ABSTRACT\",\"reading_order\":12,\"tags\":[\"paper_abstract\"]},{\"label\":\"para\",\"bbox\":[68,481,395,689],\"text\":\"In the evolution of technical systems, freedom from error\\nand early adoption plays a major role for market success\\nand to maintain competitiveness. In the case of automated\\ndriving, we see that faulty systems are put into operation\\nand users trust these systems, often without any restrictions.\\nTrust and use are often associated with users' experience of\\nthe driver-vehicle interfaces and interior design. In this work,\\nwe present the results of our investigations on factors that\\ninfluence the perception of automated driving. In a simulator\\nstudy, N=48 participants had to drive a SAE level 2 vehicle\\nwith either perfect or faulty driving function. As a secondary\\nactivity, participants had to solve tasks on an infotainment\\nsystem with varying aesthetics and usability (2x2). Results\",\"reading_order\":13,\"tags\":[\"paper_abstract\"]},{\"label\":\"para\",\"bbox\":[421,460,747,526],\"text\":\"reveal that the interaction of conditions significantly influ-\\nences trust and UX of the vehicle system. Our conclusion\\nis that all aspects of vehicle design cumulate to system and\\ntrust perception.\",\"reading_order\":14,\"tags\":[\"paper_abstract\"]},{\"label\":\"sec_1\",\"bbox\":[421,537,524,554],\"text\":\"CCS CONCEPTS\",\"reading_order\":15,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,558,746,592],\"text\":\"• Human-centered computing → Empirical studies in\\nHCI: Interactive systems and tools;\",\"reading_order\":16,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,604,502,621],\"text\":\"KEYWORDS\",\"reading_order\":17,\"tags\":[\"paper_keywords\"]},{\"label\":\"para\",\"bbox\":[421,625,746,658],\"text\":\"automated driving systems; user experience; UX; trust; dis-\\ntrust; SAE J5016; aesthetic; reliability\",\"reading_order\":18,\"tags\":[\"paper_keywords\"]},{\"label\":\"sec_1\",\"bbox\":[421,666,555,682],\"text\":\"ACM Reference Format:\",\"reading_order\":19,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,684,749,799],\"text\":\"Anna-Katharina Frison, Philipp Wintersberger, Andreas Riener,\\nClemens Schärmüller, Linda Ng Boyle, Erika Miller, and Klemens\\nWeig. 2019. In UX We Trust: Investigation of Aesthetics and Us-\\nability of Driver-Vehicle Interfaces and Their Impact on the Per-\\nception of Automated Driving. In CHI Conference on Human Fac-\\ntors in Computing Systems Proceedings (CHI 2019), May 4–9, 2019,\\nGlasgow, Scotland, UK. ACM, New York, NY, USA, 13 pages. https:\\n//doi.org/10.1145/3290653.303074\",\"reading_order\":20,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,819,549,836],\"text\":\"1 INTRODUCTION\",\"reading_order\":21,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,840,747,953],\"text\":\"The development of technology has focused on supporting\\nindividual mobility and satisfying human desire for auton-\\nomy, which is one of the most important psychological needs\\n[65]. Each new invention – from the wheel over horse car-\\nriages, steam driven railways, up to the automobile – fun-\\ndamentally changed our daily life and the societies we live\\nin. Automated vehicles (AVs) are a major next step in this\",\"reading_order\":22,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[70,712,287,724],\"text\":\"$^{*}$Both first and second author contributed equally\",\"reading_order\":23,\"tags\":[]},{\"label\":\"author_information\",\"bbox\":[69,725,243,738],\"text\":\"†Also with Johannes Kepler University.\",\"reading_order\":24,\"tags\":[\"author_affili\"]},{\"label\":\"author_information\",\"bbox\":[69,740,313,751],\"text\":\"‡Also with Katholische Universität Eichstätt-Ingolstadt.\",\"reading_order\":25,\"tags\":[\"author_affili\"]},{\"label\":\"fnote\",\"bbox\":[69,776,394,882],\"text\":\"Permission to make digital or hard copies of all or part of this work for\\npersonal or classroom use is granted without fee provided that copies\\nare not made or distributed for profit or commercial advantage and that\\ncopies bear this notice and the full citation on the first page. Copyrights\\nfor components of this work owned by others than the author(s) must\\nbe honored. Abstracting with credit is permitted. To copy otherwise, or\\nrepublish, to post on servers or to redistribute to lists, requires prior specific\\npermission and/or a fee. Request permissions from permissions@acm.org.\",\"reading_order\":26,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[70,884,273,896],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland Uk\",\"reading_order\":27,\"tags\":[\"meta_pub_date\"]},{\"label\":\"fnote\",\"bbox\":[70,898,394,924],\"text\":\"© 2019 Copyright held by the owner/author(s). Publication rights licensed\\nto ACM.\",\"reading_order\":28,\"tags\":[\"meta_pub_date\"]},{\"label\":\"fnote\",\"bbox\":[70,924,266,952],\"text\":\"ACM ISBN 978-1-4603-5970/2-019-03...3150.\\nhttps://doi.org/10.1145/3200605.330374\",\"reading_order\":29,\"tags\":[\"meta_num\"]},{\"label\":\"foot\",\"bbox\":[70,996,126,1012],\"text\":\"Paper 144\",\"reading_order\":30,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,996,744,1012],\"text\":\"Page 1\",\"reading_order\":31,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300374/results/markdown/3290605-3300374_pdf_shortened_page_0.md"}
{"file_name": "images/test_data/3290605-3300374/3290605-3300374_pdf_shortened_page_1.png", "source_folder": "test_data", "document_id": "3290605-3300374", "source_image_path": "test_data/3290605-3300374/3290605-3300374_pdf_shortened_page_1.png", "source_json_path": "test_data/3290605-3300374/results/output_json/3290605-3300374_pdf_shortened_page_1.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,43,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,43,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_pub_date\",\"author_affili\"]},{\"label\":\"half_para\",\"bbox\":[68,125,395,715],\"text\":\"evolution. AVs promise several benefits, such as less conges-\\ntion and pollution, higher safety, as well as more leisure time\\nand enhanced mobility for diverse target groups (children,\\nelderly, impaired) [51] . However, all these advantages can-\\nnot be delivered instantly. Fully automated “level 5” vehicles\\n[12] that can operate in all circumstances are not expected\\non the market before 2030 or 2040 [50] . In the meantime,\\nautomated driving systems (ADSs) with lower levels of au-\\ntomation are being gradually introduced. During this era of\\nmixed traffic (co-existence of vehicles at different levels of\\nautomation), drivers must be able to cope with automation\\nlimitations and act either as monitoring (level 2) or fallback\\n(level 3) authority [13] . Monitoring over extended periods\\nof time is a challenge, even for “highly motivated human\\nbeings” (c.f., irony of automation [1] ). This is particularly rel-\\nevant as drivers, who are not necessarily well trained domain\\nexperts, are expected to operate a safety critical system in\\npotentially dangerous environments [76] . Recent incidents\\nwith AVs (such as the fatal accidents with Tesla Autopilot or\\nthe Uber self-driving taxi [73] ) confirm that AV technology\\nis highly susceptible to overreliance/overtrust [19] . Since\\nthis problem is already well known from research on dri-\\nver assistance systems such as adaptive cruise control [16] ,\\ntrust calibration for AVs is an issue receiving a great deal of\\nattention [31, 40, 58, 77] . Ideally, users’ trust levels should ap-\\npropriately “match an objective measure of trustworthiness”;\\ne.g., system performance (reliability/ability/predictability to\\nachieve its goals) [45, 57] . However, reality is much more\\ncomplex: trust in automation has many dimensions and is in-\\nfluenced by a variety of aspects, including aesthetics, design,\\nand other factors of user experience (UX) [32] . Conversely,\\nalso UX is impacted by users’ trust in a system. For vehicle\\nmanufacturers, this results in a nearly unolvable paradox.\\nOn the one hand, they should design their systems in a way\\nthat can prevent overtrust/overreliance. On the other hand,\\nthey must maximize UX qualities of their vehicles to main-\\ntain competitiveness.\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,730,395,954],\"text\":\"In the context of AVs, how the partly overlapping constructs\\nof UX and trust actually influence each other is widely un-\\nknown. Vehicles consist of many subsystems and design\\naspects that may all contribute to users' overall assessment\\nof both constructs and, further on, may cause so called \\\"halo\\neffects\\\" [28, 49, 53, 67, 69, 71] . Hence, this paper aims at re-\\nvealing how both UX and trust influence each other, and\\nhow proper design could simultaneously support UX quali-\\nties while preventing miscalibrated trust. To evaluate this, we\\nconducted a driving simulator study, that, to the best of our\\nknowledge, combines for the first time relevant parameters\\nof both UX (usability, aesthetics) and trust (system perfor-\\nmance/reliability) in a single experiment. In our study, users\\nhad to complete several tasks in an in-vehicle infotainment\",\"reading_order\":3,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,442,747,697],\"text\":\"system (IVIS) while safely operating an AV in SAE level 2. We\\nutilized a mixed-model design that varies the reliability of\\nan ADS as a between-subjects factor. For the within-subjects\\nfactors, we varied pragmatic (representing usability) and\\nhedonic (representing aesthetics) qualities of the IVIS. For\\na holistic evaluation of UX, trust, affect, and psychological\\nneed fulfillment, we applied a triangulation of subjective\\n(AttractDiff mini [29] , PANAS short [74] , Trust Scale [34] ,\\nNeed Scale [64] , semi-structured interviews) and objective\\n(galvanic skin response, braking behavior) measures. For all\\nsubjective measures, we emphasized participants to assess\\nthe AV as a whole and not distinguish between subsystems.\\nThe results of our study give insights in how the stream of\\nexperiences combining performance, usability, and aesthet-\\nics of different vehicle subsystems correlate and influence\\neach other.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300374_pdf_shortened_page_1_figure_005.png\",\"bbox\":[419,117,749,367],\"reading_order\":5,\"tags\":[],\"alt_text\":\"The image shows the interior view from a car's driver's seat, featuring the steering wheel, instrument panel, and a central infotainment screen. A large projection in front of the vehicle displays a driving simulation of a highway lined with trees and mountains in the background, which is also visible in the rearview mirror.\"},{\"label\":\"figure_cap\",\"bbox\":[420,375,746,419],\"text\":\"Figure 1: Study setup showing the driving scenario and an\\nexample of the IVIS used to investigate the interaction be-\\ntween UX and trust.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[420,708,703,725],\"text\":\"2 UX AND TRUST: THEORY AND PRACTICE\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,729,746,794],\"text\":\"In the HCI community, UX and trust research are two areas\\nwhich are often considered in isolation. However, similarities\\nbetween both constructs cannot be denied and, thus, we\\npropose to consider them in a holistic way.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[420,803,494,821],\"text\":\"Similarities\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,825,747,954],\"text\":\"Trust in automation can be defined as \\\"attitude that an agent\\nwill achieve an individual's goals in a situation characterized\\nby uncertainty and vulnerability\\\", and is built upon analytic,\\nanalogical, and affective processes [45] . Trust is sensitive to\\nindividual traits (such as age, personality, etc.) and states\\n(self-confidence, emotional state, etc.), properties of the au-\\ntomation (complexity, task difficulty, etc.), as well as design\\nfeatures (appearance, ease of use, communication style, etc.).\",\"reading_order\":10,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,126,1012],\"text\":\"Paper 144\",\"reading_order\":11,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[706,995,745,1012],\"text\":\"Page 2\",\"reading_order\":12,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300374/results/markdown/3290605-3300374_pdf_shortened_page_1.md"}
{"file_name": "images/test_data/3290605-3300374/3290605-3300374_pdf_shortened_page_2.png", "source_folder": "test_data", "document_id": "3290605-3300374", "source_image_path": "test_data/3290605-3300374/3290605-3300374_pdf_shortened_page_2.png", "source_json_path": "test_data/3290605-3300374/results/output_json/3290605-3300374_pdf_shortened_page_2.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,61],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"half_para\",\"bbox\":[68,125,395,315],\"text\":\"[32] , and is the result of processes happening before ( “dispo-\\nsitional trust” ), during ( “situational trust” ), and after ( “learned\\ntrust” ) system interaction [32] . In contrast, UX can (accord-\\ning to ISO 9241/210) be defined as a “person’s perceptions and\\nresponses resulting from the use and/or anticipated use of a\\nproduct, system or service”. Thereby, experience can “occur\\nbefore, during and after use”, and “[...] is a consequence of\\nbrand image, presentation, functionality, system performance,\\ninteractive behaviour and assistive capabilities of the inter-\\nactive system, the user’s internal and physical state resulting\\nfrom prior experiences, attitudes, skills and personality, and\\nthe context of use”.\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,316,395,826],\"text\":\"Although trust and UX have separate definitions, they seem\\nto be influenced by similar factors and processes. Hence,\\nit is not surprising that trust is a mentioned (however, not\\nyet focused) construct in UX theory literature. The term\\ntrust is regarded as a component of UX [44] , users' personal\\nquality of experience [79] or as (context-dependent [35] ) per-\\nceived value [66] . Desmet et al. [15] mention trust within\\ntheir general set of 25 emotions relevant in human-product\\ninteraction. Although trust is not an emotion itself, a product\\ncan help users to feel confident and courageous if it is per-\\nceived as trustworthy. Thus, designers need to decide which\\npsychological needs they want to fulfill. Distler et al. [17]\\nrevealed the need of security as one of the most important\\nneeds for AVs (in the driving domain, the term \\\"need for\\nsafety\\\" would presumably fit better than \\\"security\\\", but for\\nreasons of consistency we stick to the original formulation\\nprovided by [64] throughout the paper). In order to fulfill\\nthis need, a specific form of interaction has to be selected\\nwhich aims at expressing trustworthiness and thereby trig-\\ngers trust [26, 37, 41, 46] . In this sense, trust can be regarded\\nas subjective sentiment and evaluative feeling dependent on\\nthe fulfillment of users' higher goals, such as the psychologi-\\ncal need of security [23, 27] . To provide examples, Väätäjä\\net al. [70] include trust as item in the AttrakWork question-\\nnaire to measure a products' hedonic quality and Roedel et\\nal. [62] chose trust as relevant UX factor when evaluating\\nuser acceptance and experience of ADSs at different levels of\\nautomation. Hence, a question that could arise in this regard,\\nespecially considering that both constructs are discussed as\\nvery broad, fuzzy and hard to understand [52, 43, 60, 78] , is,\\nwhether or not trust and UX can be considered the same in\\na specific context?\",\"reading_order\":3,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[69,837,140,854],\"text\":\"Differences\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,857,394,954],\"text\":\"The main difference becomes visible when looking at the\\ngoals both constructs aim to achieve. UX research tries to\\nmaximize the quality of interaction by satisfying psycho-\\nlogical needs and thereby providing pragmatic and hedonic\\nquality [23, 25] . For designers, there is no upper limit – the\\nmore these qualities are supported, the better. Thus, previous\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,125,747,572],\"text\":\"research focused on the impact of visual aesthetics, usability,\\nand branding on users' perceived trustworthiness, predom-\\ninantly in the area of e-commerce systems [18, 37, 47] and\\nwebsites [49] . These studies aimed to increase users' per-\\nceived trustworthiness and, consequently, enhance UX. In\\ntrust research however, maximizing trust is not the major\\ngoal. Here, the challenge is to precisely adjust users' subjec-\\ntive trust levels to a systems' actual performance ( “ calibration\\nof trust ” [57] ) while taking the operational and environmen-\\ntal context into account [45] . Thus, although trust may need\\nto be raised in many situations, an upper limit should not be\\nexceeded to prevent users from overreliance. In the domain\\nof automated driving, recent studies addressing trust can\\nbroadly be divided into two areas. Those dealing with dis-\\ntrust to reduce automation disuse, and those that address the\\nproblem of overtrust/overreliance to prevent misuse [57] . For\\nboth issues, various resolution strategies have been proposed.\\nTrust may be raised by increasing system transparency, us-\\ning various techniques such as why-and-how information\\n[38] , symbolic representation [31] , augmented reality [77]\\nor anthropomorphic agents [39, 75] . An often proposed so-\\nlution to deal with overtrust is the provision of uncertainty\\ndisplays in different forms and modalities [34, 40, 58] . In this\\ncontext, a problem that we see in many trust studies is that a\\ndistinction between the two constructs (trust and UX) is not\\nmade. For example, was the aim of an experiment actually to\\naddress trust/reliance or were mainly UX aspects evaluated\\nwhich potentially overlap with trust?\",\"reading_order\":6,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,582,568,599],\"text\":\"Research Opportunities\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,603,747,954],\"text\":\"Lindgaard et al. [49] claim that so called “halo effects” are a\\nreason for the interrelation of usability, aesthetics, and trust\\nin websites. These effects emerge from the paradox of “what\\nis beautiful is usable” [67] or “I like it, it must be good on all\\nattributes” [71] , already mentioned by [24, 28, 59] . The in-\\nterference model [28, 71] proved the existence of evaluative\\nconsistency (i.e., “halo effects”), which assumes that users\\ninterfere unavailable attributes from a general value to keep\\ntheir overall judgment consistent. Hence, there is an indirect\\nlink between beauty which leads to goodness and, with it,\\npragmatic quality. In contrast, a probabilistic consistency is\\na conceptually or causally linked judgment (high aesthetics\\nexpects a high perceived hedonic quality). According to this,\\nTuch et al. [69] identified negative affects, such as frustration\\nfrom poor usability, as a mediator variable that potentially\\ndecreases perceived aesthetics. Further, Minge et al. [55] dif-\\nferentiate between pragmatic “halo effects”, where usability\\nimpacts perceived visual attractiveness, and hedonic “halo\\neffects”, where visual aesthetics influences perceived usabil-\\nity. Consequently, we wonder if trust in automation can be\\ninvestigated in the absence of UX to draw useful conclusions.\\nA central question that arises is, how the two constructs\",\"reading_order\":8,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,126,1012],\"text\":\"Paper 144\",\"reading_order\":9,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,995,744,1012],\"text\":\"Page 3\",\"reading_order\":10,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300374/results/markdown/3290605-3300374_pdf_shortened_page_2.md"}
{"file_name": "images/test_data/3290605-3300374/3290605-3300374_pdf_shortened_page_3.png", "source_folder": "test_data", "document_id": "3290605-3300374", "source_image_path": "test_data/3290605-3300374/3290605-3300374_pdf_shortened_page_3.png", "source_json_path": "test_data/3290605-3300374/results/output_json/3290605-3300374_pdf_shortened_page_3.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[68,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"author_affili\"]},{\"label\":\"half_para\",\"bbox\":[68,125,394,189],\"text\":\"are correlated in the context of AVs? Similar to [49] , we ex-\\npect halo effects of aesthetics and usability as biasing factors\\nfor trust, what could become highly relevant for the future\\nimplementation of automated driving technology.\",\"reading_order\":2,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,200,173,217],\"text\":\"3 USER STUDY\",\"reading_order\":3,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,220,394,315],\"text\":\"We conducted a driving simulator study to investigate the\\ninteraction (potential correlation and \\\"halo effects\\\" of UX and\\ntrust) between an ADS's performance/reliability and relevant\\nUX factors (usability/aesthetics) of in-vehicle interfaces, as\\nwell as their effect on the perception of AVs in general; aiming\\nto answer the following research questions:\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,315,393,347],\"text\":\"Q11: How does IVIS design (usability and aesthetics) affect\\nUX of AVs with varying system performance?\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,348,393,379],\"text\":\"RQ2: How does IVIS design (usability and aesthetics) affect\\nusers' trust in AVs with varying system performance?\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,380,384,395],\"text\":\"RQ3: Is there a correlation between UX and trust in AVs?\",\"reading_order\":7,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[68,406,199,423],\"text\":\"Experimental Design\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,426,394,523],\"text\":\"We applied a full factorial mixed-model design varying the\\nperformance of the ADS as between-subjects factor, and aes-\\nthetics and usability of the IVIS as within-subjects factor\\n(each on two levels). Each participant had to perform various\\ntasks on four different IVISs that represented all combina-\\ntions of usability (good/bad) and aesthetics (nice/ugly).\",\"reading_order\":9,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[68,534,145,551],\"text\":\"Study Setup\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,554,394,619],\"text\":\"The experiment was conducted in a high-fidelity driving\\nsimulator (remodeled VW Golf on hexapod platform) and\\nan IVIS on a tablet PC installed on top of the center console\\n(see Figure 1 ).\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,626,394,914],\"text\":\"Driving Scenario. We simulated an AV at SAE level 2 (i.e.,\\ncombination of longitudinal and lateral control) driving on a\\n2-lane highway using IPG CarMaker, inspired by the setting\\nused in [3] . The AV drove with a constant speed of 120km/h\\non the left lane and was confronted with 12 lead vehicles\\ndriving at lower speed (70km/h). In such a situation, the ADS\\ndetected the lead vehicle and reduced the speed to prevent a\\ncrash (similar to an ACC system). As soon as the ego vehicle\\nslowed down to 70km/h, the lead vehicle performed a lane\\nchange to the right, allowing the ego vehicle to accelerate\\nagain to the target speed. In the high-performance condition\\n(group A), all 12 lead vehicles were successfully detected\\n(thus, no manual interventions were necessary). In the low-\\nperformance condition (group B), the ADS (randomly) failed\\nto detect the lead vehicle in 3 out of the 12 cases (75 % relia-\\nbility), generating the need for interventions – participants\\nthus had to brake manually to prevent a crash (however, they\\nnever had to manually engage in lateral control).\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,922,394,954],\"text\":\"In-Vehicle Infotainment System. We implemented four vari-\\nants of IVIS in HTML/Javascript on a 10.2\\\" table (Google\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,125,746,205],\"text\":\"Pixel C). The IVISs consisted of a main navigation and three\\ntypically available subsystems (a phone/call screen including\\na list of contacts, a media player including a collection of al-\\nbums/songs as well as different radio stations, and a climate\\ncontrol), see Figure 3 .\",\"reading_order\":14,\"tags\":[]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300374_pdf_shortened_page_3_figure_015.png\",\"bbox\":[423,221,743,339],\"reading_order\":15,\"tags\":[],\"alt_text\":\"Two car infotainment screens display a climate control interface with sliders for \\\"Temperatur\\\" (24) and \\\"Lüftung\\\" (5), alongside buttons for A/C, FRONT, and REAR defrost/airflow. The screens differ in their bottom navigation bars, with the left showing phone and music icons, and the right showing a green phone, red square, and blue cloud icon.\"},{\"label\":\"figure_cap\",\"bbox\":[426,348,740,365],\"text\":\"Figure 3: A/C menu of the nice (left) and ugly (right) IVIS.\",\"reading_order\":16,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,379,747,745],\"text\":\"The visual design was selected from a set of examples\\ncreated by groups of undergraduate students during a design\\nclass. Students were provided a specific menu/navigation\\nstructure and instructed to create an IVIS skin. All designs\\nwere evaluated using the UEQ [42] on a 7-point semantic dif-\\nferential scale from -3 (negative) to +3 (positive) with at least\\n5 participants. We utilized the results of the subscale “At-\\ntractiveness(Att-UEQ)” and selected the IVISs with the best\\nand worst values. While the nice design has a mean value\\nof ATT-UEQ=1.92 (excellent with respect to the UEQ bench-\\nmark dataset [63] ), the ugly design shows mean value of only\\nATT-UEQ=0.45 (bad compared to the benchmark). This pro-\\ncess aimed as guidance to confirm our subjective selection\\nof a nice and ugly IVISs, however, was no controlled experi-\\nment. To provide a potentially “bad” usability, we followed\\nthe definition provided in ISO 9241-11 that states usability\\nto be the “extent to which a product can be used by specified\\nusers to achieve specified goals with effectiveness, efficiency\\nand satisfaction in a specified context of use” [33] . Thus, we\\nchose to manipulate the IVISs reliability by semi-randomly\\ncalculating the chance for a successful button-press action,\\nwhere at least two and at most 8 clicks were required for a\\nsuccessful action.\",\"reading_order\":17,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[422,757,588,773],\"text\":\"Participants and Procedure\",\"reading_order\":18,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,777,747,954],\"text\":\"In total, 48 participants (16 female, 32 male) aged between\\n19 and 26 ( $M_{age}=22.09$ , $SD_{age}=1.89$ ) years, all undergrad-\\nuate students, voluntarily participated in the experiment.\\nEach participant was assigned to either group A (high ADS\\nperformance) or group B (low ADS performance), potential\\ndifferences between the groups considering gender and age\\nwere counterbalanced. No participant had to be excluded\\ndue to simulator sickness or technical problems. After com-\\npleting a short questionnaire assessing demographics, each\\nsubject conducted a 3-minute test drive to become famil-\\niar with the AV. Then, we instructed participants that they\",\"reading_order\":19,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,996,126,1012],\"text\":\"Paper 144\",\"reading_order\":20,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,996,745,1012],\"text\":\"Page 4\",\"reading_order\":21,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300374/results/markdown/3290605-3300374_pdf_shortened_page_3.md"}
{"file_name": "images/test_data/3290605-3300374/3290605-3300374_pdf_shortened_page_4.png", "source_folder": "test_data", "document_id": "3290605-3300374", "source_image_path": "test_data/3290605-3300374/3290605-3300374_pdf_shortened_page_4.png", "source_json_path": "test_data/3290605-3300374/results/output_json/3290605-3300374_pdf_shortened_page_4.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"author_affili\"]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300374_pdf_shortened_page_4_figure_002.png\",\"bbox\":[70,92,743,313],\"reading_order\":2,\"tags\":[],\"alt_text\":\"A horizontal timeline depicts a research study flow, starting with demographics and a test drive. This is followed by repeated data collection stages arranged in two parallel rows, one featuring a red car icon and the other a black car icon, each stage measuring physiological responses, braking behavior, and subjective scales like AttrakDiff and PANAS. The timeline concludes with a semi-structured interview.\"},{\"label\":\"figure_cap\",\"bbox\":[68,321,746,353],\"text\":\"Figure 2: Study procedure: The top row represents the drives with low, the bottom row drives with high ADS performance. The\\nred color indicates decreased qualities (finger usability, tablet aesthetics, driving simulator performance).\",\"reading_order\":3,\"tags\":[]},{\"label\":\"half_para\",\"bbox\":[68,365,395,829],\"text\":\"will experience four different types of AVs with different\\nIVISs. We further told them that manual braking interven-\\ntions could be necessary due to automation failures, and that\\nsafely completing the drive has the highest priority. After-\\nwards, participants experienced four consecutive 5-minute\\nlasting trips while experiencing the 4 different IVISs (in ran-\\ndomized order). Within each condition, participants had to\\ncomplete seven tasks on the IVISs with two levels of complex-\\nity. Easy tasks consisted of a single instruction only (such\\nas \\\"call John\\\"), while complex tasks required participants to\\nremember multiple steps (such as \\\"switch to Radio Disney\\nChannel and adjust the volume to 8\\\"). The task instructions\\nwere presented auditory (pre-recorded sound files). Success-\\nful completion of a task was indicated with a notification\\nsound and the next task was issued 35 seconds afterwards.\\nIn case all seven tasks were completed before finishing the 5-\\nminute lasting drive, the experimental condition was stopped\\nearlier. The selection of tasks from the set was randomized\\nover the conditions, and quasi-randomized within the sce-\\nnarios (each task was only presented once during the entire\\nexperiment). After each condition, participants had to com-\\nplete a survey including a set of different standardized scales\\nto assess trust and UX in the AV (see Figure 2), whereby\\nwe repeatedly instructed them to assess the AV as a whole,\\nsingle system based on their experiences. Additionally, a\\nshort semi-structured interview with all participants was\\nconducted after the experiment to reveal further insights\\ninto their thoughts and attitudes. The whole experiment\\nlaster approx. 90 minutes for each participant.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[69,842,167,858],\"text\":\"Data Collection\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,862,394,911],\"text\":\"To be able to evaluate the proposed research questions, we\\ntriangulate a set of subjective and objective measures derived\\nfrom established theory as emphasized in the following.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,920,394,953],\"text\":\"Subjective Measures: To assess UX and trust, we utilized mul-\\ntiple subjective scales. We used the AttrAidDiff mini [29] with\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,365,747,733],\"text\":\"a 7-point semantic differential scale ranging from 0 (low)\\nto 6 (high). Thereby, the subscale attractiveness (ATT), con-\\nsisting of two items for beauty and goodness, assesses the\\noverall perception combining both pragmatic (PQ) and hedo-\\nnic quality (HQ). Since for all subscales Cronbach's $\\\\alpha$ resulted\\nin acceptable values ( $> .60$ , see Table 1 ), we calculated mean\\nscale values. All UX qualities are intercorrelated (Pearson's\\ncorrelation coefficient), ranging from r=.412 to r=.880 across\\nall conditions. HQ and PQ showed least (r=.60, HQ) and ATT\\nhighest intercorrelations (r=.60). As UX is also dependent on\\nthe satisfaction of psychological needs [27, 30], we further\\nutilized the need scale (same version as used in [27] with\\n7-point Likert scale) and focused on the needs most relevant\\nin the context of AVs: autonomy (AUT), competence (COM),\\nstimulation (STI), and security (SEC) [21]. Also here, rela-\\nbility of all subscales was acceptable ( $\\\\alpha > .70$ , see Table 1 ).\\nIntercorrelation between the subscales across all conditions\\nranged from r=.26 to r=.81. Further, system interaction leads\\nto particular (positive and negative) emotion [14] resulting\\nfrom need fulfillment [27, 30]. Thus, we included the short\\nversion of PANAS also with a 7-point Likert scale [53, 74].\\nPA and NA did not correlate (r < .12) and reliability of all\\nsubscales was acceptable ( $\\\\alpha > .70$ , see Table 1 ).\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,733,747,829],\"text\":\"To evaluate subjective trust we used the trust scale provided\\nby Jian et al. [34] . This scale consists of two subscales for\\ntrust (T) and distrust (DT) (7-point Likert) and is widely used\\nto assess trust in automation or robotic systems [36, 58] . Also\\nhere, Cronbach's $\\\\alpha$ resulted in acceptable values while T and\\nDT showed a negative correlation $(r > -0.80)$ .\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,838,747,952],\"text\":\"Objective Measures. Galvanic Skin Response (GSR) is com-\\nmonly used as an indicator for the sympathetic nervous\\nsystem. Changes in skin conductance have been linked to\\narousal [10, 11] , (cognitive) workload [6, 9] , usability [48] ,\\nuser experience [22] , but also trust [56] . Signal peaks, so\\ncalled Skin Conductance Responses (SCRs), indicate such\\nactivation while the general signal level is subject to bias\",\"reading_order\":10,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,996,126,1012],\"text\":\"Paper 144\",\"reading_order\":11,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,996,745,1012],\"text\":\"Page 5\",\"reading_order\":12,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300374/results/markdown/3290605-3300374_pdf_shortened_page_4.md"}
{"file_name": "images/test_data/3290605-3300374/3290605-3300374_pdf_shortened_page_5.png", "source_folder": "test_data", "document_id": "3290605-3300374", "source_image_path": "test_data/3290605-3300374/3290605-3300374_pdf_shortened_page_5.png", "source_json_path": "test_data/3290605-3300374/results/output_json/3290605-3300374_pdf_shortened_page_5.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[68,42,159,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,42,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"tab\",\"bbox\":[75,123,384,303],\"text\":\"| Dep. Variable | Items | Cronbach's $\\\\alpha$ | Ref. | | UX Quality | | | | | Attractiveness (ATT) | 2 (Beauty and Goodness) | .65 | [29] | | Affordability (AQ) | 4 | .77 | [29] | | Hedonic Q (HQ) | 4 | .79 | [29] | | Competence | | | | | Autonomy (AUT) | 3 | .84 | [27, 64] | | Competence (COM) | 3 | .86 | [27, 64] | | Stimulation (STI) | 3 | .83 | [27, 64] | | Security (SEC) | 2 | .77 | [27, 64] | | FA | | | | | Positive (PA) | 5 | .75 | [74] | | Negative (NA) | 5 | .85 | [74] | | Trust | | | | | Trust (T) | 6 | .91 | [34] | | Distrust (DT) | 5 | .87 | [34] |
\",\"reading_order\":2,\"tags\":[],\"alt_text\":\"This table presents measurement properties for various dependent variables, categorized under UX Qualities, Needs, Affect, and Trust. For each variable, the table lists the number of items, its Cronbach's alpha reliability coefficient, and relevant references.\",\"figure_path\":\"tables/3290605-3300374_pdf_shortened_page_5_tab_2.png\"},{\"label\":\"table_cap\",\"bbox\":[75,304,386,323],\"text\":\"Table 1: Summary of subjective methods employed.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"half_para\",\"bbox\":[68,345,395,650],\"text\":\"by individual differences, room temperature, etc. [6] . We\\nutilized a professional 500 Hz physiological measurement\\nsystem from g.tec medical engineering (www.gtec.at) and\\nattached two skin electrodes to the volar (inner) middle pha-\\nnges (muscle limbs) of the non-dominant hand's middle\\nand ring fingers (see guidelines by [7, 8] ). Since GSR is sensi-\\ntive to motion artifacts, we instructed participants to behave\\nnaturally but also to prevent waving their hand excessively.\\nWe used Ledab for Matlab [4] to extract all SCRs since the\\nimplemented Continuous Decomposition Analysis (CDA) is\\nsupposed to be more robust at discriminating single SCRs\\nthan traditional peak-detection methods [5] . For the evalua-\\ntion, we utilized the number of SCRs, which is argued to be\\nless affected by individual differences and other forms of bias\\n[6] . To evaluate driving behavior, we recorded participants'\\nbrake pedal actuation and calculated three parameters – the\\nnumber of brakes representing the quantity of manual in-\\nterventions, the average duration of a brake pedal actuation,\\nand the average brake intensity (on a scale from 0 to 1).\",\"reading_order\":4,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,660,150,677],\"text\":\"4 RESULTS\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,680,395,953],\"text\":\"In the following we present a detailed analysis of the col-\\nlected data with respect to our research questions (all re-\\nsults with $p < .05$ are reported as statistically significant).\\nSince tests for normality (Shapiro-Wilk's $p > .05$ ), marginal\\nexistence of outliers, and homogeneity of error variances\\nassessed by Levene's test ( $p > .05$ ) were passed for all de-\\npendent variables (except for driving performance, see Table\\n1), parametric tests were applied. We performed three-way\\nmixed ANOVAs with the independent variables ADS perfor-\\nmance as between-subjects, and IVIS usability and aesthetics\\nas within-subjects factors. As the collected driving perfor-\\nmance measures did not follow a normal distribution, non-\\nparametric tests (Mann-Whitney-U tests for the between-,\\nand Wilcoxon Signed-Rank tests for the within-subject fac-\\ntors) were applied. To analyze correlations between the sub-\\njective constructs of UX and trust, we conducted Pearson's\\nbivariate correlation analyses.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[420,122,562,140],\"text\":\"User Experience (RQ1)\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,143,747,209],\"text\":\"To answer RQ1, we analyzed the data of UX (UX Qualities,\\nNeeds and Affect) scales as well as the objective data on\\nparticipants' arousal given by GSR. Concerning multivariate\\ntests statistic, we utilized Pillai's Trace.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,219,748,778],\"text\":\"UX Qualities. Multivariate tests evaluating the impact of ADS\\nperformance, IVIS aesthetics and usability on participants'\\nperception of product quality (measured by AttrakDiff) re-\\nveals no significant main effect for the between-subject f\\nator ADS performance $(v = .21, F(5, 42) = 2.24, p = .068)$. How-\\never, separate univariate ANOVAs on the outcome vari-\\nables show a significant effect for pragmatic quality (PQ,\\n$F(1, 46) = 8.62$, $p = .005$, $\\\\eta^2 = .16$). Results for high ADS perfor-\\nmance were perceived as significantly better than for low\\nperformance conditions. Ratings for attractiveness (ATT,\\nGoodness and Beauty) and hedonic quality (HQ) did not\\ndiffer significantly (see Table 2 ). Additionally, multivariate\\ntests reveal that the overall perceived system quality sig-\\nnificantly differs regarding IVIS usability $(v = .44, F(5, 42) =$\\n$6.47, p < .001)$. Univariate tests confirm a significant effect\\nfor ATT $(F(1, 46) = 14.67, p = .001$, $\\\\eta^2 = .24)$. Regarding the items\\n\\\"Goodness\\\" and \\\"Beauty\\\" separately, there is only a signi-\\nficant effect on \\\"Goodness\\\" $(F(1, 46) = 25.45, p < .001$, $\\\\eta^2 = .36)$. Also\\nPQ $(F(1, 46) = 25.36, p < .001$, $\\\\eta^2 = .36)$ and HQ $(F(1, 46) = 10.60, p =$\\n$.002$, $\\\\eta^2 = .19)$ differed significantly. Thus, systems with good\\nIVIS usability were perceived better than those with bad\\nIVIS usability across all conditions. Further, we can report\\na significant main effect for the within-subject factor IVIS\\naesthetics $(V = .37, F(5, 42) = 11.24, p < .001)$. Univariate tests re-\\nveal significant effects for ATT $(F(1, 46) = 50.22, p < .001$, $\\\\eta^2 = .52)$. \\nHere, both items, \\\"Goodness\\\" $(F(1, 45) = 20.22, p < .001$, $\\\\eta^2 = .30) $\\nand \\\"Beauty\\\" $(F(1, 46) = 58.23, p < .001$, $\\\\eta^2 = .36)$, show signifi-\\ncant effects. Also PQ $(F(1, 46) = 28.29, p < .001$, $\\\\eta^2 = .38)$ and HQ\\n$(F(1, 46) = 52.49, p < .001$, $\\\\eta^2 = .53)$. Thus, across all conditions the\\nnice IVIS was rated better than the ugly IVIS. Moreover, our\\ndata confirms the inference model [28, 71] – better aesthet-\\nics leads to a significantly higher ratings for goodness and\\ntherewith higher ratings for PQ, and not only beauty (evalua-\\ntive consistency). However, no two or three-way interaction\\neffects could be revealed.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,790,747,951],\"text\":\"Needs. For users' need fulfillment of Autonomy (AUT), Com-\\npetence (COM), Stimulation (ST), and Security (SEC), we\\ncan report a significant effect for ADS performance regard-\\ning the multivariate test statistic $(V = .22, F(4, 43) = 8.09, p = .025\\n)$. Univariate tests reveal only significant differences in par-\\nticipants' need of SEC $(F(1, 46) = 12.88, p = .001, \\\\eta^2 = .22)$, which\\nwas less fulfilled in the group with the low ADS perfor-\\nmance. Multivariate tests show a significant main effect\\nfor IVIS usability $(V = .024, F(4, 43) = 3.33, p = .018)$, unvari-\\nate tests resulted in a significant decrease of SEC in case\",\"reading_order\":10,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,127,1013],\"text\":\"Paper 144\",\"reading_order\":11,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[677,995,746,1013],\"text\":\"Page 6\",\"reading_order\":12,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300374/results/markdown/3290605-3300374_pdf_shortened_page_5.md"}
{"file_name": "images/test_data/3290605-3300374/3290605-3300374_pdf_shortened_page_6.png", "source_folder": "test_data", "document_id": "3290605-3300374", "source_image_path": "test_data/3290605-3300374/3290605-3300374_pdf_shortened_page_6.png", "source_json_path": "test_data/3290605-3300374/results/output_json/3290605-3300374_pdf_shortened_page_6.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,157,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"tab\",\"bbox\":[71,123,388,409],\"text\":\"| Dep. Variable | Ind. Variable | M | SD | 95% Confidence Interval | | lower | upper | | PQ | ADS performance | | | | | | high | 4.17 | 0.18 | 3.81 | 4.53 | | low | 3.42 | 0.18 | 3.06 | 3.79 | | ATT | IVIS usability | | | | | | good | 3.36 | 1.01 | 3.07 | 3.65 | | bad | 2.96 | 1.14 | 2.62 | 3.30 | | $\\\\leftarrow$ Goodness | good | 3.74 | 1.24 | 3.39 | 4.09 | | bad | 3.06 | 1.45 | 2.44 | 3.68 | | PQ | good | 4.10 | 0.92 | 3.86 | 4.34 | | bad | 3.49 | 1.14 | 3.17 | 3.81 | | HQ | good | 2.97 | 0.83 | 2.72 | 3.21 | | bad | 2.68 | 0.89 | 2.42 | 2.94 | | ATT | IVIS aesthetics | | | | | | nice | 3.72 | 1.06 | 3.40 | 4.04 | | ugly | 2.60 | 1.10 | 2.24 | 2.97 | | $\\\\leftarrow$ Beauty | nice | 3.72 | 1.34 | 3.39 | 4.05 | | ugly | 2.84 | 1.14 | 2.49 | 3.19 | | $\\\\leftarrow$ Goodness | nice | 3.72 | 1.34 | 3.34 | 4.10 | | ugly | 2.98 | 1.38 | 2.58 | 3.38 | | PQ | nice | 4.05 | 0.97 | 3.78 | 4.32 | | ugly | 3.34 | 1.04 | 3.06 | 3.62 | | HQ | nice | 3.38 | 1.14 | 3.15 | 3.61 | | ugly | 2.27 | 1.10 | 1.95 | 2.59 |
\",\"reading_order\":2,\"tags\":[],\"alt_text\":\"This is a table presenting statistical results, likely from an analysis of variance or similar comparison. It shows mean (M), standard deviation (SD), and 95% confidence intervals for various dependent variables (e.g., PQ, ATT, HQ) grouped by independent variable categories such as \\\"ADS performance,\\\" \\\"IVIS usability,\\\" and \\\"IVIS aesthetics.\\\"\",\"figure_path\":\"tables/3290605-3300374_pdf_shortened_page_6_tab_2.png\"},{\"label\":\"table_cap\",\"bbox\":[113,410,349,427],\"text\":\"Table 2: Significant UX Quality Values.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"half_para\",\"bbox\":[68,443,394,620],\"text\":\"of bad IVIS usability ($F(1,46) = 7.43, p = .009, \\\\eta^2 = .14$) and COM\\n($F(1,46) = 9.54, p = .003, \\\\eta^2 = .17$). Further, also for the within-\\nsubject factor IVIS aesthetics, a significant main effect could\\nbe revealed ($v = .24, F(4,6) = 5.38, p = .017$). Regarding univari-\\nate tests we can observe effects for the need of STI ($v_1,46) =$\\n.12.12, $p = .001, \\\\eta^2 = .21$), AUT ($F(1,46) = 5.22, p = .027, \\\\eta^2 = .10$), SEC\\n($F(1,46) = 6.26, p = .016, \\\\eta^2 = .12$) and COM ($F(1,46) = 6.09, p =$\\n.017, $\\\\eta^2 = .12$). Thereby, all these needs are significantly less\\nfulfilled when driving in an AV with ugly IVIS (see Table 3\\nfor means). Here, data analysis did not reveal any two- or\\nthree-way interaction effects.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"tab\",\"bbox\":[71,635,388,856],\"text\":\"| Dep. Variable | Ind. Variable | M | SD | 95% Confidence Interval | | lower | upper | | SEC | ADS performance | | | | | | high | 3.24 | 1.20 | 2.79 | 3.69 | | low | 2.10 | 0.25 | 1.65 | 2.56 | | SEC | IVIS usability | | | | | | good | 2.82 | 1.34 | 2.47 | 3.18 | | bad | 2.52 | 1.23 | 2.20 | 2.84 | | COM | good | 3.13 | 1.37 | 2.75 | 3.52 | | bad | 2.79 | 1.36 | 2.40 | 3.18 | | TUTS aesthetics | | | | | | | AUT | nice | 2.48 | 1.34 | 2.09 | 2.86 | | ugly | 2.30 | 1.41 | 1.89 | 2.71 | | STI | nice | 2.77 | 1.12 | 2.45 | 3.09 | | ugly | 2.40 | 1.27 | 2.04 | 2.76 | | SEC | nice | 2.82 | 1.28 | 2.48 | 3.16 | | ugly | 2.55 | 1.34 | 2.19 | 2.91 | | COM | nice | 3.07 | 1.26 | 2.71 | 3.44 | | ugly | 2.84 | 1.43 | 2.43 | 3.26 |
\",\"reading_order\":5,\"tags\":[],\"alt_text\":\"This table presents statistical data, including means, standard deviations, and 95% confidence intervals, for various dependent variables. Data is categorized by independent variables such as \\\"ADS performance,\\\" \\\"IVIS usability,\\\" and \\\"IVIS aesthetics,\\\" each with distinct conditions like \\\"high/low,\\\" \\\"good/bad,\\\" or \\\"nice/ugly.\\\"\",\"figure_path\":\"tables/3290605-3300374_pdf_shortened_page_6_tab_5.png\"},{\"label\":\"table_cap\",\"bbox\":[131,857,329,874],\"text\":\"Table 3: Significant Need Values.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,904,393,954],\"text\":\"Affect. Participants' positive (PA) and negative affect (NA)\\nrevealed a significant main effect for ADS performance. $(V =$\\n36, $F(2, 45) = 11.3, p < .001$ ). A look at univariate tests revealed\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,125,757,348],\"text\":\"that NA for the low ADS performance is significantly higher\\nthan for the high ADS performance condition (F(1,46) = 23.14, p <\\n.001, $\\\\eta^2 = .34$ ), while PA was not affected. Regarding the within-\\nsubject factor IVIS usability, we can observe similar results.\\nMultivariate tests reveal a significant main effect (V = .16, F(2,45) =\\n4.38, $p = .018$ ), however, also here only NA showed differences\\nin case IVIS usability is bad (F(1,46) = 7.26, $p = .010$, $\\\\eta^2 = .14$ ).\\nContrarily, IVIS aesthetics, which also has a significant main\\neffect (V = .21, F(2,45) = 6.007, $p = .005$ ), shows significant dif-\\nferences for both PA (F(1,46) = 4.24, $p = .045$, $\\\\eta^2 = .08$ ) and NA\\n(F(1,46) = 8.63, $p = .005$, $\\\\eta^2 = .16$ ). Thereby, PA is slightly (but\\nstill significantly) higher for the nice IVIS aesthetics in con-\\ntrast to the ugly IVIS variants. Again, no two or three-way\\ninteraction effects could be revealed (see Table 4 for means).\",\"reading_order\":8,\"tags\":[]},{\"label\":\"tab\",\"bbox\":[423,358,743,512],\"text\":\"| Dep. Variable | Ind. Variable | M | SD | 95% Confidence Interval | | lower | upper | | AIDS performance | | | | | | NA | high | 1.04 | 0.16 | 0.64 | 1.44 | | low | 2.40 | 0.23 | 2.00 | 2.80 | | IVIS usability | | | | | | NA | good | 1.58 | 1.26 | 1.27 | 1.89 | | bad | 1.46 | 1.22 | 1.37 | 2.15 | | IVIS aesthetics | | | | | | PA | nice | 2.98 | 0.08 | 2.69 | 3.27 | | ugly | 2.40 | 1.05 | 2.49 | 3.11 | | NA | nice | 1.57 | 1.21 | 1.27 | 1.86 | | ugly | 1.87 | 1.27 | 1.56 | 2.18 |
\",\"reading_order\":9,\"tags\":[],\"alt_text\":\"A table presents statistical data, including means (M), standard deviations (SD), and 95% confidence intervals (lower and upper bounds). The data is organized by 'Dep. Variable' (NA or PA) and 'Ind. Variable' for categories like 'ADS performance' (high/low), 'IVIS usability' (good/bad), and 'IVIS aesthetics' (nice/ugly).\",\"figure_path\":\"tables/3290605-3300374_pdf_shortened_page_6_tab_9.png\"},{\"label\":\"table_cap\",\"bbox\":[482,514,684,529],\"text\":\"Table 4: Significant Affect Values.\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,544,747,784],\"text\":\"Arousal. Analysis of GSR data revealed a significant main\\neffect for the within-subject factor IVIS usability ($f(1,38) =$\\n9.85, $p = .003$, $\\\\eta^{2} = .21$ ). Bad IVIS usability leads to significantly\\nmore peaks, thus arousal, than good usability. We further\\ncan observe a two-way interaction effect for IVIS usability\\nand ADS performance ($f(1,38) = 4.98$, $p = .032$, $\\\\eta^{2} = .12$ ). Descrip-\\ntive statistic show that if ADS performance is low and IVIS\\nusability is bad, participants are significantly more aroused\\nthan if ADS performance is high and IVIS usability is good.\\nHowever, when ADS performance is low although the IVIS\\nusability is good, the number of GSR peaks is also increasing.\\nNo further main effects for ADS performance or IVIS aes-\\nthetics, and also no further two- and three-way interaction\\neffects could be revealed by our statistical analysis (see Table\\n5 for descriptive statistics).\",\"reading_order\":11,\"tags\":[]},{\"label\":\"tab\",\"bbox\":[428,795,738,913],\"text\":\"| Dep. Variable | Ind. Variable | M | SD | 95% Confidence Interval | | lower | upper | | Peaks | IVIS usability | 203.06 | 65.66 | 183.99 | 222.14 | | good | 220.45 | 78.46 | 196.84 | 244.07 | | bad | 220.45 | 78.46 | 196.84 | 244.07 | | Peaks | ADS performance x IVIS usability | 194.20 | 67.20 | 167.22 | 221.18 | | high & good | 222.95 | 81.00 | 190.55 | 257.35 | | high & bad | 211.95 | 77.71 | 183.55 | 240.35 | | low & good | 216.95 | 77.71 | 183.55 | 250.35 | | low & bad | 216.95 | 77.71 | 183.55 | 250.35 |
\",\"reading_order\":12,\"tags\":[],\"alt_text\":\"A statistical table displays mean (M), standard deviation (SD), and 95% confidence intervals (lower and upper bounds) for \\\"Peaks\\\" as a dependent variable. Data is presented for \\\"IVIS usability\\\" (good and bad) and for an interaction of \\\"ADS performance x IVIS usability\\\" (high & good, high & bad, low & good, low & bad).\",\"figure_path\":\"tables/3290605-3300374_pdf_shortened_page_6_tab_12.png\"},{\"label\":\"table_cap\",\"bbox\":[474,915,690,930],\"text\":\"Table 5: Significant Arousal Values.\",\"reading_order\":13,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,997,126,1013],\"text\":\"Paper 144\",\"reading_order\":14,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[704,997,744,1013],\"text\":\"Page 7\",\"reading_order\":15,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300374/results/markdown/3290605-3300374_pdf_shortened_page_6.md"}
{"file_name": "images/test_data/3290605-3300374/3290605-3300374_pdf_shortened_page_7.png", "source_folder": "test_data", "document_id": "3290605-3300374", "source_image_path": "test_data/3290605-3300374/3290605-3300374_pdf_shortened_page_7.png", "source_json_path": "test_data/3290605-3300374/results/output_json/3290605-3300374_pdf_shortened_page_7.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"sec_1\",\"bbox\":[69,124,143,140],\"text\":\"Trust (RQ2)\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,144,394,176],\"text\":\"To answer RQ2, we analyzed the subjective trust ratings as\\nwell as participants' braking behavior.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,184,395,458],\"text\":\"Trust Scale. Multivariate data analysis (using Pillai's Trace)\\nof users' trust (T) and distrust (DT) revealed a significant\\nmain effect for ADS performance $(\\\\nu = .29, F(2,45) = 9.02, p = .001)$ .\\nUnivariate tests on the dependent variables show signif-\\nicant effects for T $(F(1,46) = 18.07, p < .001, \\\\eta^2 = .28)$ and DT\\n$(F(1,46) = 15.09, p < .001, \\\\eta^2 = .25).$ While T is decreasing in condi-\\ntions of low ADS performance, DT is increasing. Contrarily,\\nT is increasing for high ADS performance and DT decreas-\\ning. We can report another main effect for IVIS usability\\n$(\\\\nu = .24, F(2,45) = 7.12, p = .002)$ . Also here, significant effects\\nfor T $(F(1,46) = 14.54, p < .001, \\\\eta^2 = .24)$ and DT $(F(1,46) = 9.48, p =$\\n.003, $\\\\eta^2 = .17)$ are visible. Descriptive data shows similar ef-\\nfects like for the between-subject factor ADS performance.\\nFurther, also IVIS aesthetics shows a significant main ef-\\nfect $(\\\\nu = .22, F(2,45) = 6.17, p = .004)$ . However, here only DT\\ncould be significantly decreased by a nice IVIS interface,\\n$(F(1,46) = 12.58, p = .001, \\\\eta^2 = .22), see Table 6).$\",\"reading_order\":4,\"tags\":[]},{\"label\":\"tab\",\"bbox\":[71,474,390,651],\"text\":\"| Dep. Variable | Ind. Variable | M | SD | 95% Confidence Interval | | lower | upper | | ADS performance | | | | | | T | high | 3.91 | 0.21 | 3.34 | 4.36 | | low | 2.55 | 0.24 | 2.09 | 3.00 | | DT | high | 2.34 | 0.20 | 1.90 | 2.79 | | low | 3.56 | 0.24 | 3.11 | 4.01 | | IVIS usability | | | | | | T | good | 3.40 | 1.31 | 3.08 | 3.72 | | bad | 3.06 | 1.35 | 2.71 | 3.40 | | DT | good | 2.80 | 1.26 | 2.48 | 3.12 | | bad | 3.10 | 1.31 | 2.76 | 3.44 | | IVIS aesthetics | | | | | | DT | strict | 2.81 | 1.26 | 2.49 | 3.13 | | ugly | 3.09 | 1.28 | 2.76 | 3.43 |
\",\"reading_order\":5,\"tags\":[],\"alt_text\":\"A statistical table displays means (M), standard deviations (SD), and 95% confidence intervals (lower and upper bounds) for dependent variables T and DT. These statistics are presented for independent variables ADS performance (high/low), IVIS usability (good/bad), and IVIS aesthetics (nice/ugly).\",\"figure_path\":\"tables/3290605-3300374_pdf_shortened_page_7_tab_5.png\"},{\"label\":\"table_cap\",\"bbox\":[131,653,330,669],\"text\":\"Table 6: Significant Trust Values.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,697,395,808],\"text\":\"Braking Behavior. Since braking data was not normal dis-\\ntributed we performed non-parametric tests. Mann-Whitney\\nU tests with Bonferroni correction ( $\\\\alpha$ = 0.025) were conducted\\nto confirm expected differences in braking behavior between\\nlow and high ADS performance. All braking parameters are,\\nacross all IVIS conditions, significantly higher in conditions\\nwith low than with high ADS performance (see Table 7 ).\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,808,395,954],\"text\":\"To compare the impact of the IVIS on braking behavior,\\nwe calculated separate Friedman tests for low and high ADS\\nperformance with Bonferroni correction ( $\\\\alpha$ = .008). The num-\\nber of brake actions differs only significantly for the group\\nof the low ADS performance $(x^2(3) = 11.04, p = .012)$ . Post-hoc\\nanalysis revealed significant differences only between good\\n& nice and bad & nice ( $p = .022$ ), which led to more brake ac-\\ntions. Further, also, braking duration is significantly different\\nin conditions with low ADS performance $(x^2(1) = 13.40, p = .004)$ .\",\"reading_order\":8,\"tags\":[]},{\"label\":\"tab\",\"bbox\":[423,121,747,286],\"text\":\" | ADS performance | Test Statistic | | IVIS | Mdn (high) | Mdn (low) | Mann-Whitney-U test | | Number | bad & nice | 0 | 5* | U = 510, z = 4.73, p < .001 | | bad & ugly | 0 | 5 | U = 515, z = 4.83, p < .001 | | good & nice | 0 | 7 | U = 530, z = 5.12, p < .001 | | good & ugly | 0 | 6 | U = 510, z = 4.93, p < .001 | | Duration | bad & nice | 0 | 2.65* | | bad & ugly | 0 | 2.47 | U = 491, z = 4.39, p < .001 | | good & nice | 0 | 1.99* | U = 438, z = 5.16, p < .002 | | good & ugly | 0 | 2.42* | U = 511, z = 4.70, p < .001 | | bad & nice | 0 | .72 | U = 535, z = 5.19, p < .001 | | Intensity | good & nice | 0 | .72 | U = 511, z = 4.70, p < .001 | | good & ugly | 0 | .55 | U = 515, z = 4.79, p < .001 | | good & ugly | 0 | .65 | U = 519, z = 5.19, p < .001 |
\",\"reading_order\":9,\"tags\":[],\"alt_text\":\"This is a statistical table displaying \\\"ADS performance\\\" medians for \\\"high\\\" and \\\"low\\\" conditions, categorized by \\\"IVIS\\\" (bad/good and nice/ugly). It also presents \\\"Test Statistic\\\" results from Mann-Whitney-U tests, including U-values, z-scores, and p-values. The data is organized by \\\"Number\\\", \\\"Duration\\\", and \\\"Intensity\\\" metrics.\",\"figure_path\":\"tables/3290605-3300374_pdf_shortened_page_7_tab_9.png\"},{\"label\":\"table_cap\",\"bbox\":[420,288,747,323],\"text\":\"Table 7: Braking Behavior. Significances between vari-\\nables are indicated by '.\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,340,747,434],\"text\":\"Post-hoc analysis revealed a significant difference only be-\\ntween good & nice, which shows lowest braking duration\\nmedian and bad & nice with the highest braking duration\\nmedian (p = .005), and additionally between good & nice and\\ngood & ugly (p = .022). For braking intensity, no significant\\neffects could be revealed.\",\"reading_order\":11,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[420,447,606,464],\"text\":\"User Experience x Trust (RQ3)\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,467,747,547],\"text\":\"To evaluate a potential correlation between the constructs\\nUX and trust we ran bivariate Pearson correlation anal-\\nyses of averaged correlation coefficients after Fisher's Z-\\nTransformation (see Table 8 ). Thereby, we applied Bonferroni\\nCorrection and adjust the significance level to $\\\\alpha = .016$ .\",\"reading_order\":13,\"tags\":[]},{\"label\":\"tab\",\"bbox\":[490,571,676,746],\"text\":\" | Trust (T) | Distrust (DT) | | UX Qualities | | AUT | 5* | -48* | | → Beauty | 27 | -25 | | → Goodness | 59* | -57* | | HQ | 37* | -36* | | PQ | 68* | -66* | | Needs | | AUT | 25 | -16 | | COM | 30* | -18 | | STI | 29 | -25 | | SEC | 75* | -74* | | Affect | | NA | 06 | 04 | | NA | -79* | 8* |
\",\"reading_order\":14,\"tags\":[],\"alt_text\":\"A table presents numerical correlation values for \\\"Trust (T)\\\" and \\\"Distrust (DT)\\\" across three main categories: \\\"UX Qualities,\\\" \\\"Needs,\\\" and \\\"Affect.\\\" Each category lists several sub-items, with numerical values and asterisks indicating statistical significance in the respective Trust and Distrust columns.\",\"figure_path\":\"tables/3290605-3300374_pdf_shortened_page_7_tab_14.png\"},{\"label\":\"table_cap\",\"bbox\":[420,747,747,794],\"text\":\"Table 8: Averaged correlations between measures af-\\nter z-transformation. Significances are indicated by $^*$\\n(Bonferroni-corrected).\",\"reading_order\":15,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,808,747,954],\"text\":\"Correlations. Participants' product quality perceptions show\\ncorrelations with the constructs trust (T) and distrust (DT, s.\\nTable 8 ). Although the overall perceived attractiveness (ATT)\\nand almost all sub components correlate positively with T and\\nnegative with DT, the sole perception of beauty does not\\ncorrelate significantly with T or DT. Regarding correlations\\nof participants' psychological needs, we can observe a signifi-\\ncant positive correlation of the need for security (SEC) and T,\\nand a negative correlation with DT. The need of competence\",\"reading_order\":16,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,126,1012],\"text\":\"Paper 144\",\"reading_order\":17,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,995,744,1012],\"text\":\"Page 8\",\"reading_order\":18,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300374/results/markdown/3290605-3300374_pdf_shortened_page_7.md"}
{"file_name": "images/test_data/3290605-3300376/3290605-3300376_pdf_shortened_page_0.png", "source_folder": "test_data", "document_id": "3290605-3300376", "source_image_path": "test_data/3290605-3300376/3290605-3300376_pdf_shortened_page_0.png", "source_json_path": "test_data/3290605-3300376/results/output_json/3290605-3300376_pdf_shortened_page_0.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[68,44,157,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"icon\",\"bbox\":[0,93,38,132],\"reading_order\":2,\"tags\":[\"cross_marker\"]},{\"label\":\"sec_0\",\"bbox\":[100,117,713,183],\"text\":\"SottoVoce: An Ultrasound Imaging-Based Silent\\nSpeech Interaction Using Deep Neural Networks\",\"reading_order\":3,\"tags\":[]},{\"label\":\"author_information\",\"bbox\":[91,201,284,266],\"text\":\"Naoki Kimura\\n\\nThe University of Tokyo\\nTokyo, Japan\\n\\nkimura-naoki@g.cc.u-tokyo.ac.jp\",\"reading_order\":4,\"tags\":[\"author\",\"author_affili\",\"author_mail\"]},{\"label\":\"author_information\",\"bbox\":[331,201,477,266],\"text\":\"Michinari Koro\\n\\nThe University of Tokyo\\nTokyo, Japan\\nmchkono@acm.org\",\"reading_order\":5,\"tags\":[\"author\",\"author_affili\",\"author_mail\"]},{\"label\":\"author_information\",\"bbox\":[525,201,727,281],\"text\":\"Jun Rekimoto\\n\\nThe University of Tokyo\\nSony Computer Science Laboratories\\n\\nTokyo, Japan\\nrekimoto@acm.org\",\"reading_order\":6,\"tags\":[\"author\",\"author_affili\",\"author_mail\"]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300376_pdf_shortened_page_0_figure_007.png\",\"bbox\":[67,300,750,409],\"reading_order\":7,\"tags\":[],\"alt_text\":\"A multi-panel diagram illustrates a process beginning with a person's neck being scanned by an ultrasound transducer, which generates an ultrasound image. This image is then processed through a neural network architecture, producing a spectrogram, which subsequently appears on a smart display device.\"},{\"label\":\"figure_cap\",\"bbox\":[68,419,746,479],\"text\":\"Figure 1: SottoVoce silent voice system: an ultrasonic echo probe attached under the jaw that reads the internal situation\\nwhile the user is speaking without actually emitting a voice. By recognizing ultrasound images using deep convolutional\\nneural networks, the user's voice is resynthesized and can be used to control the existing speech interaction systems such as\\nsmart speakers.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,485,141,502],\"text\":\"ABSTRACT\",\"reading_order\":9,\"tags\":[\"paper_abstract\"]},{\"label\":\"para\",\"bbox\":[68,506,395,778],\"text\":\"The availability of digital devices operated by voice is expand-\\ning rapidly. However, the applications of voice interfaces are\\nstill restricted. For example, speaking in public places be-\\ncomes an annoyance to the surrounding people, and secret\\ninformation should not be uttered. Environmental noise may\\nreduce the accuracy of speech recognition. To address these\\nlimitations, a system to detect a user's unvoiced utterance\\nis proposed. From internal information observed by an ul-\\ntrasonic imaging sensor attached to the underside of the\\njaw, our proposed system recognizes the utterance contents\\nwithout the user's uttering voice. Our proposed deep neural\\nnetwork model is used to obtain acoustic features from a\\nsequence of ultrasound images. We confirmed that audio sig-\\nnals generated by our system can control the existing smart\\nspeakers. We also observed that a user can adjust their oral\\nmovement to learn and improve the accuracy of their voice\\nrecognition.\",\"reading_order\":10,\"tags\":[\"paper_abstract\"]},{\"label\":\"sec_1\",\"bbox\":[421,484,525,502],\"text\":\"CCS CONCEPTS\",\"reading_order\":11,\"tags\":[]},{\"label\":\"list\",\"bbox\":[421,506,746,572],\"text\":\"• Computing methodologies → Neural networks; • Human-\\ncentered computing → Interface design prototyping; Mo-\\nbile devices; Sound-based input / output; • Social and pro-\\nfessional topics → People with disabilities;\",\"reading_order\":12,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,585,501,601],\"text\":\"KEYWORDS\",\"reading_order\":13,\"tags\":[\"paper_keywords\"]},{\"label\":\"para\",\"bbox\":[421,605,746,638],\"text\":\"silent speech, ultrasonic imaging, deep neural networks,\\nhuman-AI integration\",\"reading_order\":14,\"tags\":[\"paper_keywords\"]},{\"label\":\"sec_1\",\"bbox\":[421,647,554,661],\"text\":\"ACM Reference Format:\",\"reading_order\":15,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,661,746,750],\"text\":\"Naoki Kimura, Michitera Kono, and Jun Rekimoto. 2019. SottoVec:\\nAn Ultrasound Imaging-Based Silent Speech Interaction Using Deep\\nNeural Networks. In CHI Conference on Human Factors in Computing\\nSystems Proceedings (CHI 2019), May 4–9, 2019, Glasgow, Scotland,\\nUK . ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/\\n3290605.3300376\",\"reading_order\":16,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,771,550,789],\"text\":\"1 INTRODUCTION\",\"reading_order\":17,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,792,747,952],\"text\":\"Smart devices that are controllable by speech (i.e., speech\\ninteraction) are being used in many situations. Smartphones,\\nsmart speakers, car-navigation systems, and various home\\nappliances can be controlled by speech and have presented\\nmany interactive potentials [47] . Speech interaction does not\\nrequire visual attention, and it can be used in a dark envi-\\nronment such as a bedroom. Owing to the recent progress of\\nspeech-recognition technology and the naturalness of speech\\nsynthesis, speech interaction is becoming an irreplaceable\\nprocess in human-computer interaction. Speech can also\",\"reading_order\":18,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[68,790,395,896],\"text\":\"Permission to make digital or hard copies of all or part of this work for\\npersonal or classroom use is granted without fee provided that copies are not\\nmade or distributed for profit or commercial advantage and that copies bear\\nthis notice and the full citation on the first page. Copyrights for components\\nof this work owned by others than ACM must be honored. Abstracting with\\ncredit is permitted. To copy otherwise, or republish, to post on servers or to\\nredistribute to lists, requires prior specific permission and/or a fee. Request\\npermissions from permissions@acm.org.\",\"reading_order\":19,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[70,898,273,910],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland Uk\",\"reading_order\":20,\"tags\":[\"meta_pub_date\"]},{\"label\":\"fnote\",\"bbox\":[69,913,274,924],\"text\":\"© 2019 Association for Computing Machinery.\",\"reading_order\":21,\"tags\":[\"meta_pub_date\"]},{\"label\":\"fnote\",\"bbox\":[70,926,265,938],\"text\":\"ACM ISBN 978-1-4503-5970-2/19/05...$15.00\",\"reading_order\":22,\"tags\":[\"meta_pub_date\"]},{\"label\":\"fnote\",\"bbox\":[69,940,244,953],\"text\":\"https://doi.org/10.1145/3290605.3300376\",\"reading_order\":23,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[70,995,127,1012],\"text\":\"Paper 146\",\"reading_order\":24,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,995,744,1012],\"text\":\"Page 1\",\"reading_order\":25,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300376/results/markdown/3290605-3300376_pdf_shortened_page_0.md"}
{"file_name": "images/test_data/3290605-3300376/3290605-3300376_pdf_shortened_page_1.png", "source_folder": "test_data", "document_id": "3290605-3300376", "source_image_path": "test_data/3290605-3300376/3290605-3300376_pdf_shortened_page_1.png", "source_json_path": "test_data/3290605-3300376/results/output_json/3290605-3300376_pdf_shortened_page_1.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[68,44,159,61],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,44,746,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_pub_date\"]},{\"label\":\"half_para\",\"bbox\":[68,125,394,220],\"text\":\"be used while the user of a speech-interaction device is per-\\nforming other tasks, such as driving, cooking, homeworking,\\nor using a traditional personal computer. For example, while\\nthe user is concentrating on a computer-screen and interac-\\ntion devices such as a keyboard and a mouse, they can still\\noperate other devices with voice interaction.\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,221,395,380],\"text\":\"However, as for the speech interface, two challenges must\\nbe overcome. First, using the interface in public places presents\\nlimitations. In addition to being an annoyance to the sur-\\nrounding people, disclosing personal information or secret\\ninformation by uttering it in public is risky in terms of infor-\\nmation security. Next, it cannot be used in a noisy environ-\\nment, because the accuracy of speech recognition may be\\ndeclined. These issues are particularly acute when trying to\\nuse a speech interface to interact with wearables or mobile\\ncomputers.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,380,395,792],\"text\":\"To overcome these challenges, research on \\\"silent-speech\\nrecognition\\\" has been conducted [8] . For example, by apply-\\ning a method known as lip reading, images of the mouth\\nof the speaker or the entire face are captured by a camera,\\nand the content of the utterance is estimated from those\\nimages [52] . If the user could simply mouth the utterance\\nwithout actually voicing it, it would be possible to use such\\na voice interaction in public places. However, with the cam-\\nera method, it is necessary to install a camera in front of\\nthe face, and its form factor renders it unsuitable for wear-\\nables or mobile applications. Other approaches of the studies\\non \\\"non- audioizable murmur\\\" (NAM) [22, 42] attempts to rec-\\nognize utterances with a microphone or an accelerometer\\nworn on the skin or throat of the user. In this case, the user\\nspeaks with articulated respiratory sound without vocal-fold\\nvibration (namely, whispering). However, to be recognized\\naccurately by the system, a user's whispering voice tends\\nto be noticeable by other people nearby. Furthermore, some\\nstudies attempt to estimate speech by estimating the move-\\nment of muscles near the oral cavity by electromyography\\n(EMG) [33, 49] . However, the estimation of free utterances\\nwith EMG is still difficult; instead, it is a type of gesture\\nrecognition using the movement of the oral cavity. Thus, the\\nnumber of detectable commands is limited, and the user has\\nto learn new gesture skills instead of using their existing\\nspeaking skills.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,793,395,954],\"text\":\"Instead of using the above-described approaches, we fo-\\ncus on using ultrasonic imaging [11] . Ultrasonic-imaging\\ntechnology recognizes the internal status in the body by\\nmeasuring the reflection time of ultrasonic waves radiated\\ninto the body. This technology is widely used to grasp the\\ncondition of the internal organs for medical purposes. In re-\\ncent years, small and lightweight systems that can be directly\\nconnected to smartphones (e.g., Vscan Extend, General Elec-\\ntronic Company) have appeared. If it were possible to attach\\na small ultrasonic imaging head around the neck to sense\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,125,746,189],\"text\":\"the situation in the oral cavity and convert it to acoustic\\ninformation, it would be a useful device for communicat-\\ning with speech-capable devices without actually speaking\\naloud; namely, “silent voice interaction” would be possible.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,190,746,331],\"text\":\"Silent voice interaction by ultrasonic imaging demon-\\nstrates two potential advantages over other approaches. First,\\nthe ultrasonic-imaging head can be miniaturized, and it can\\nconstitute a device with an inconspicuous shape such as a col-\\nlar. It would be an important feature for designing wearable\\nsilent voice systems. Next, by recognizing the situation in\\nthe oral cavity, it would be possible to measure the movement\\nof the tongue, which cannot be observed from the outside. It\\nwould thus be possible to reproduce sound more accurately.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,332,747,553],\"text\":\"Studies have been conducted on silent speech using ultra-\\nsound imaging, but many of them are used in combinations\\nwith lip or face images; therefore, a camera must be placed\\nin front of the user [26] . This configuration presents a limi-\\ntation when it is used as a wearable interface device. Recent\\nresearchers have challenged to use deep neural networks\\nwith ultrasound imaging for silent speech [7, 51] ; however,\\nthey are not based on convolutional neural networks and are\\nnot validated with data retrieved from a mouth movement\\nwithout speaking. They are only validated with mouth move-\\nment when a user is actually emitting a voice. We present a\\nsignificant step forward using convolutional neural networks\\nand proof-of-concept validations via actual silent speech, in-\\nteracting with an unchanged smart speaker (Amazon Alexa).\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,554,747,650],\"text\":\"Herein, a silent-voice interaction system called “SottoVoce”\\nbased only on ultrasonic images is described (Figure 1 ). By\\ncombining two types of deep neural networks, this system\\ncan be trained to generate voice signals from a sequence\\nof images captured from an ultrasonic-imaging device. Our\\ncontributions can be summarized as the following two topics:\",\"reading_order\":9,\"tags\":[]},{\"label\":\"list\",\"bbox\":[440,656,747,769],\"text\":\"• A two-level model of deep convolutional neural net-\\nworks to convert ultrasonic images to actual sounds is\\nproposed.\\n• As a proof of concept, a silent-voice system was devel-\\noped, and it was shown that the system could control\\na voice-controlled device (in this case, Amazon Echo)\\nwithout modifications.\",\"reading_order\":10,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[420,783,550,800],\"text\":\"2 RELATED WORK\",\"reading_order\":11,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[420,802,506,820],\"text\":\"Silent Speech\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,823,746,887],\"text\":\"Silent-speech interfaces have been studied using various\\ntechnologies and methods [8, 31] . Lip reading [52] or facial\\nimages [12] can be used to estimate speech uttered by a\\nsubject without using audio information.\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,888,746,953],\"text\":\"SilentVoice [17] is an ingressive speech approach that cap-\\ntures extremely soft speech. Electromagnetic articulogra-\\nphy (EMA) has been used to develop brain–computer inter-\\nfaces [5] and other interactive applications [53] . Magnets\",\"reading_order\":14,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,126,1012],\"text\":\"Paper 146\",\"reading_order\":15,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[706,995,746,1012],\"text\":\"Page 2\",\"reading_order\":16,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300376/results/markdown/3290605-3300376_pdf_shortened_page_1.md"}
{"file_name": "images/test_data/3290605-3300376/3290605-3300376_pdf_shortened_page_2.png", "source_folder": "test_data", "document_id": "3290605-3300376", "source_image_path": "test_data/3290605-3300376/3290605-3300376_pdf_shortened_page_2.png", "source_json_path": "test_data/3290605-3300376/results/output_json/3290605-3300376_pdf_shortened_page_2.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[]},{\"label\":\"header\",\"bbox\":[474,43,745,60],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[]},{\"label\":\"half_para\",\"bbox\":[68,125,395,298],\"text\":\"can be attached to the subject to detect silent speech [14,\\n19, 23] . Electroencephalogram (EEG) [44] and Electromyo-\\ngraphy (EMG) [37, 49] are also typical methods for silent-\\nspeech recognition. Particularly, EMG has been applied for\\ninteractive purposes and applications concerning human–\\ncomputer interaction (HCI) [38] ; for example, controlling\\na web browser [32] . A recent example proposed by Kapur\\net al. [33] used multiple electrodes to sense neuromuscular\\nsignals of a subject in an internal speech. In addition to the\\nmethods introduced above, other human – facial electrical\\npotentials have been combined and measured [22, 48] .\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,299,395,443],\"text\":\"Ultrasound imaging has also been used for silent-speech\\nrecognition in the field of speech processing [9, 27] . In one\\nstudy, which focused on singing, sung vowels could be syn-\\nthesized based on the ultrasound and video of the lips [30] .\\nIn a similar study, a silent-speech interface using ultrasound\\nand optical images of the tongue and lips was developed [26,\\n28] . In another study, a mapping technique for automatically\\ngenerating animations of the tongue movement from raw\\nultrasound images was created [13] .\",\"reading_order\":3,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,443,395,731],\"text\":\"Other approaches combine the techniques described above\\nwith deep neural networks, such as BCI applications [4] ,\\nmyoelectric signals [10] , and lip reading with long short-\\nterm memory (LSTM) [52] . Combining deep neural networks\\nand ultrasound imaging for silent-speech interfaces has also\\nbeen considered. For example, ultrasound imaging was used\\nto capture the tongue movement with such a combination\\nwith deep neural networks [7] . Moreover, the fundamental\\nfrequency (F0) curve, which had been considered unpre-\\ndictable, was estimated using a deep neural network [21] .\\nIn addition, it has been suggested that a global and visuo-\\nacoustic modeling approach called \\\"Eigentongues\\\" performs\\nbetter than tongue-contour modeling when using neural\\nnetworks [25] . A new benchmark for silent-speech research\\nbased on deep neural networks has also been proposed [31] .\\nThese approaches are still at the basic proof-of-concept level,\\nand none have been evaluated in terms of controlling the ex-\\nisting speech-interaction appliances such as smart speakers.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,731,395,826],\"text\":\"Following the prior approaches described above, we aim\\nto develop a system that allows silent speech to interact\\nwith voice-controlled devices and interactive purposes in a\\nHCI context. Furthermore, to improve the performance of the\\ndeveloped system, we aim for the interaction between human\\nand artificial neural networks for performance improvement.\",\"reading_order\":5,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[68,847,294,864],\"text\":\"Non-Auditory Inputs and Interaction\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,867,395,949],\"text\":\"EarFieldSensing [39] is a gesture-recognition technology\\nbased on electric-field sensing. CanalSense [3] senses changes\\nin air pressure in the ear canals that occur when the face is\\nmoved. Tongue-in-Cheek [18] senses the movement of the\\ntongue using the X-band Doppler radar for facial-gesture\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,125,746,156],\"text\":\"recognition. Other methods that use EMG [55] or combina-\\ntions of brain and muscle signal sensing [43] are also notable.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,157,747,316],\"text\":\"The techniques above have been utilized for arm-gesture\\nrecognition; however, an approach using ultrasound imaging\\nfor that purpose has been demonstrated, i.e., EchoFlex [40] .\\nIt is an interaction sensor that recognizes movements of the\\nforearm muscle using ultrasound imaging. The results of\\nthat study indicated that the sensor performed well and can\\npotentially be supplanted to other prior approaches. They\\nalso inspired the authors to consider using ultrasound imag-\\ning for silent-voice interaction using the inner part of the\\nmouth.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[420,333,610,350],\"text\":\"Interacting with Smart Devices\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,353,747,514],\"text\":\"Owing to the development of mobile and smart devices, we\\ncan now interact with them frequently through various meth-\\nods [35] . We may use our voice as an input method [47] , and\\nstudies to overcome issues concerning such voice-controlled\\nuser interfaces have been presented [41] . One typical inter-\\nface (a personal “ agent ” hereafter) is Amazon Echo, typically\\nknown as “ Alexa, ” with which people can communicate and\\nchat. The effect of this agent on people has been researched\\nextensively, and the results show that it is an effective agent\\nfor satisfying or influencing our lives [36, 45, 50] .\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,514,747,769],\"text\":\"The role of artificial intelligence (AI) has become more\\nimportant for tasks other than personal agents like Alexa,\\nand we now interact and collaborate with AIs; for example,\\nwhen sending text messages [24] and designing objects [16] .\\nThe hybrid existence and interaction of humans and AI is an\\nimpressive topic, which can overcome social issues and im-\\nprove the quality of our lives. Glove Talk II [15] , which was\\npresented in 1995, is a gesture-to-speech system that trans-\\nlates hand gestures into 10 control parameters of a speech\\nsynthesizer using neural networks. However, to use the sys-\\ntem, the user requires a long-term training of approximately\\n100 hours. In the present study, referring to this work, we\\napply the recently improved technologies of deep-neural\\nnetworks to create the hybrid interaction of humans and AI,\\nwhere the user (a human) learns to adapt and utilize the AI\\nsystem to achieve a better interaction.\",\"reading_order\":12,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[420,785,709,801],\"text\":\"3 SYSTEM ARCHITECTURE OF SOTTOVOCÉ\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,805,747,950],\"text\":\"The architecture of the proposed system for generating sound\\nfrom ultrasonic images is shown in Figure 2 . In general, the\\ngoal of the system is to transfer certain sequence represen-\\ntations (in this case, ultrasonic images) into other sequence\\nrepresentations (in this case, speech). This goal is similar\\nto that of text-to-speech systems [54] , voice-transfer sys-\\ntems [2] , and lip reading or face-to-voice systems [12] . In-\\nspired by these systems, the proposed system uses two neural\\nnetworks.\",\"reading_order\":14,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,126,1012],\"text\":\"Paper 146\",\"reading_order\":15,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[706,995,745,1012],\"text\":\"Page 3\",\"reading_order\":16,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300376/results/markdown/3290605-3300376_pdf_shortened_page_2.md"}
{"file_name": "images/test_data/3290605-3300376/3290605-3300376_pdf_shortened_page_3.png", "source_folder": "test_data", "document_id": "3290605-3300376", "source_image_path": "test_data/3290605-3300376/3290605-3300376_pdf_shortened_page_3.png", "source_json_path": "test_data/3290605-3300376/results/output_json/3290605-3300376_pdf_shortened_page_3.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[68,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,43,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300376_pdf_shortened_page_3_figure_002.png\",\"bbox\":[105,121,714,361],\"reading_order\":2,\"tags\":[],\"alt_text\":\"This diagram illustrates a system that translates an ultrasonic image sequence of the throat into a synthetic wave signal for voice-controllable devices. Neural Network 1 processes the image sequence to generate an audio feature vector (Mel-scale spectrum), which is then refined by Neural Network 2. The final output is a wave signal and audio feature vectors that can control devices like smart speakers, with the system trained using combined ultrasonic image movies and audio data.\"},{\"label\":\"figure_cap\",\"bbox\":[306,375,507,391],\"text\":\"Figure 2: SottoVoce system overview\",\"reading_order\":3,\"tags\":[]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300376_pdf_shortened_page_3_figure_004.png\",\"bbox\":[74,403,386,519],\"reading_order\":4,\"tags\":[],\"alt_text\":\"A person in a blue shirt holds a white, handheld device against their throat or chin area. The image presents two views of this action: a side profile on the left and a front-facing view on the right, focusing on the person's upper torso and head.\"},{\"label\":\"figure_cap\",\"bbox\":[131,525,330,541],\"text\":\"Figure 3: Ultrasonic imaging probes\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,551,395,775],\"text\":\"The first neural network ('Network 1' in Figure 2) transfers\\na time series of ultrasonic images to a sound-representation\\nvector. We used a 64-dimensional Mel-scale spectrum with\\nthe frequency range of 300 Hz to 8,000 Hz , sampled every\\n20 ms , as a sound-representation vector. Subsequently, the\\ntranslated sound representations constitute a series of sound-\\nrepresentation vectors (i.e., a spectrogram). This sound spec-\\ntrogram can be converted to an audio signal. In addition,\\nto refine the quality of those vectors, they are also trans-\\nferred by the next neural network ('Network 2' in Figure 2),\\nwhich generates a series of sound-representation vectors of\\nthe same length as that of the input sound-representation\\nvectors. Finally, the output vectors are converted to an actual\\naudio signal.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,776,395,839],\"text\":\"These two networks are speaker dependent; accordingly,\\nto train them, the system requires a set of ultrasonic-imaging\\nvideos captured while the user speaks various speech com-\\nmands.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[69,852,233,869],\"text\":\"Ultrasonic Imaging Device\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,872,395,953],\"text\":\"The CONTEC MS600P2 Full Digital B-Ultrasonic Diagnos-\\ntic System was used as the ultrasonic-imaging device. A user\\nattaches a 3.5-MHz convex-type ultrasonic imaging probe\\nunder the jaw (Figure 3). This system provides a screen out-\\nput port to be connected to the display monitor. In addition,\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,664,747,713],\"text\":\"a display-digitizing unit was used for converting the signal\\nsent to the display to an MPEG-4 movie file. Figure 4 shows\\nthe obtained ultrasonic image.\",\"reading_order\":10,\"tags\":[]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300376_pdf_shortened_page_3_figure_011.png\",\"bbox\":[476,405,690,618],\"reading_order\":11,\"tags\":[],\"alt_text\":\"This is an ultrasound image displaying a cross-section of the human tongue. The image highlights the tongue surface, tongue root, and tongue tip, with a hyoid shadow visible below. The tongue appears as a curved, lighter gray structure against a darker background.\"},{\"label\":\"figure_cap\",\"bbox\":[420,627,745,657],\"text\":\"Figure 4: Obtained ultrasonic image from probes attached to\\nthe jaw from underneath.\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,713,746,793],\"text\":\"We found a delay of ultrasonic images and sound captur-\\ning. To compensate for this delay, we examined the corre-\\nsponding utterance and tongue movement in the video and\\nestimated a delay of 300 ms. We subsequently adjusted to\\nthis delay in the training data.\",\"reading_order\":13,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,804,488,821],\"text\":\"Network 1\",\"reading_order\":14,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,825,748,953],\"text\":\"Network 1 uses a series of $K$ ultrasonic images (size of\\n$128 \\\\times 128$ , monochrome) as the input and generates an $n$ -\\ndimensional sound representation (Mel-scale spectrum) as\\nthe output. Currently, $K$ of 13 and $n$ of 64 are used. Because\\nthe frame rate of the ultrasonic images is 30 frames per sec-\\nond, the duration of $K$ ultrasonic images is thus 400 ms. This\\ntime duration covers the static and motion features of the\\nutterance. Samples of the ultrasonic images are shown in\",\"reading_order\":15,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,996,127,1012],\"text\":\"Paper 146\",\"reading_order\":16,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,996,745,1012],\"text\":\"Page 4\",\"reading_order\":17,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300376/results/markdown/3290605-3300376_pdf_shortened_page_3.md"}
{"file_name": "images/test_data/3290605-3300376/3290605-3300376_pdf_shortened_page_4.png", "source_folder": "test_data", "document_id": "3290605-3300376", "source_image_path": "test_data/3290605-3300376/3290605-3300376_pdf_shortened_page_4.png", "source_json_path": "test_data/3290605-3300376/results/output_json/3290605-3300376_pdf_shortened_page_4.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[68,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,43,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"author_affili\",\"meta_pub_date\"]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300376_pdf_shortened_page_4_figure_002.png\",\"bbox\":[90,117,726,237],\"reading_order\":2,\"tags\":[],\"alt_text\":\"A horizontal sequence of seven grayscale images illustrates a dynamic process over a time interval from 0 to 250 milliseconds. The frames show a light, textured area that changes shape, with a darker region appearing to move in from the left. A partial circular feature is visible at the bottom of each frame.\"},{\"label\":\"figure_cap\",\"bbox\":[156,257,657,273],\"text\":\"Figure 5: A series of ultrasonic images of the throat of a subject about to pronounce \\\"Alexa.\\\"\",\"reading_order\":3,\"tags\":[]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300376_pdf_shortened_page_4_figure_004.png\",\"bbox\":[94,291,363,458],\"reading_order\":4,\"tags\":[],\"alt_text\":\"The figure displays two time-aligned data streams: a sequence of ultrasonic tongue images at the top and an audio spectrogram at the bottom. A red box highlights one ultrasonic image (representing 1/30 second) and its corresponding 20ms segments within the audio spectrogram. The spectrogram illustrates 64 frequency bands over time.\"},{\"label\":\"figure_cap\",\"bbox\":[68,471,394,516],\"text\":\"Figure 6: Representation of training data. K-sized ultra-\\nsonic images are paired with the corresponding sounds (Mel-\\nsaled sound vectors).\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,538,394,587],\"text\":\"Figure 5. The K-size image sequence is prepared repeatedly\\nsuch that one Mel-scale spectrum is created every 20 ms (i.e.,\\nthe hop size is 20 ms) (Figure 6 ).\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,587,395,651],\"text\":\"A sound-representation vector corresponding to the time\\nposition at the center of each ultrasonic-image sequence\\nis extracted from the audio signal data, and Network 1 is\\ntrained to generate it.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,650,398,794],\"text\":\"Network 1 is based on a convolutional neural network\\n(CNN). It comprises four layers: Conv2D - LeakyReLU - Dropout\\n- BatchNormalization , followed by six layers: Flatten - Dense\\n- LeakyReLU - Dropout - Dense - LeakyReLU . The output\\nsize of Network 1 is the same as the length of the sound-\\nrepresentation vector (i.e., 64). Both input images and output\\nvectors are normalized to 0 to one, respectively. The loss\\nfunction is the mean-squared error, and the optimizer is\\nAdam [34] .\",\"reading_order\":8,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,805,136,822],\"text\":\"Network 2\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,825,395,954],\"text\":\"To improve the sound quality, Network 2 uses a sequence\\nof sound-representation vectors and generates a sequence\\nof sound-representation vectors with the same length as the\\ninput. This model comprises a bank of one-dimensional (1-D)\\nconvolutional filters (Conv1D), with a kernel size from 1 to\\n$M$ ( $M=8$ is currently used), followed by the U-Network [46]\\nwith the three layers of Conv1D-MaxPooling(strides=2)-\\nLeakyReLU-Dropout , and thrice of DeConv1D-Concatenate\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,535,747,615],\"text\":\"(Figure 7). The 1-D convolutional bank explicitly models\\nthe local and contextual information of the input sequence.\\nThe following U-Network also improves the quality of the\\naudio sequence with precise localization. Finally, the network\\ngenerates Mel-scale spectrum vectors.\",\"reading_order\":11,\"tags\":[]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300376_pdf_shortened_page_4_figure_012.png\",\"bbox\":[423,291,743,435],\"reading_order\":12,\"tags\":[],\"alt_text\":\"This diagram illustrates a U-Net architecture designed for processing audio feature vectors, specifically Mel-Spectrum sequences. It shows an input Mel-spectrum sequence passing through a 1D-Convolution Bank, then into an encoder-decoder structure with 1D-Convolution Networks and 1D-Deconvolution Networks, including skip connections, to produce an output Mel-spectrum sequence.\"},{\"label\":\"figure_cap\",\"bbox\":[421,448,746,508],\"text\":\"Figure 7: Network 2 improves the quality of generated Mel-\\nsaled spectrum sequences. (Note: for the consistency with\\nthe illustration of the neural networks, the time axis of the\\naudio-feature vector is shown as the vertical axis.)\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,615,747,727],\"text\":\"To train Network 2, Network 1 was used to create Mel-\\nscale spectrum vectors from the images of a training ultra-\\nsonic video clip as the input, and the same length Mel-scale\\nspectrum vectors from the audio of the same training video\\nclip as the output. Similarly, in the case of Network 1, the\\nmean-squared error was used as a loss function, and Adam\\nwas used as an optimizer.\",\"reading_order\":14,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,728,747,776],\"text\":\"For simplicity, the time durations of the input and output\\nwere fixed to the same value (currently, 3.68 s is used). This\\nduration encompasses many typical speech commands.\",\"reading_order\":15,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[422,789,533,804],\"text\":\"Sound generation\",\"reading_order\":16,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,807,747,888],\"text\":\"Following the neural-networks processing, a sequence of\\nMel-scale-spectrum sound-representation vectors is converted\\nto an audio signal using the Griffin Lim algorithm [20] . This\\nconversion is possible from the output of Network 1 or the\\noutput of Network 2.\",\"reading_order\":17,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,889,747,953],\"text\":\"For testing, the generated audio signals are transmitted\\nfrom an audio speaker, and they can be used to control nearby\\nsound-controlled devices such as a smart speaker. We are\\nalso considering taking the audio-waveform signal directly\",\"reading_order\":18,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,996,126,1012],\"text\":\"Paper 146\",\"reading_order\":19,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,996,745,1012],\"text\":\"Page 5\",\"reading_order\":20,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300376/results/markdown/3290605-3300376_pdf_shortened_page_4.md"}
{"file_name": "images/test_data/3290605-3300376/3290605-3300376_pdf_shortened_page_5.png", "source_folder": "test_data", "document_id": "3290605-3300376", "source_image_path": "test_data/3290605-3300376/3290605-3300376_pdf_shortened_page_5.png", "source_json_path": "test_data/3290605-3300376/results/output_json/3290605-3300376_pdf_shortened_page_5.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[68,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_pub_date\",\"meta_subject\"]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300376_pdf_shortened_page_5_figure_002.png\",\"bbox\":[77,120,385,268],\"reading_order\":2,\"tags\":[],\"alt_text\":\"A young East Asian male wearing glasses and a blue shirt holds an ultrasound transducer to his chin. He is seated in front of a white wall, with a laptop to his left and a large computer monitor displaying scientific graphs and medical imaging data to his right.\"},{\"label\":\"figure_cap\",\"bbox\":[85,278,375,295],\"text\":\"Figure 8: Apparatus used for training and evaluating.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"half_para\",\"bbox\":[68,325,394,357],\"text\":\"as the audio input information of the speech-controllable\\ndevice without actually reproducing it as a sound wave.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,371,124,388],\"text\":\"Training\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,391,395,534],\"text\":\"As for preparing the training data, two collaborators, (28-year\\nold male and 24-year old male) were attached with an ultra-\\nsonic imaging probe under their jaws, and were instructed to\\nutter various speech commands. Approximately 500 speech\\ncommands were collected from each collaborator (Table ?? ).\\nFor each command, as well as the voice utterance, a video of\\nthe ultrasonic images was recorded. The training session was\\napproved by the research ethics committee of the author's\\ninstitution.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,534,394,599],\"text\":\"The recorded video was used to train Network 1. The\\nultrasonic images were rescaled to $128\\\\times128$ and used as\\ninputs. The corresponding utterance voice was converted to\\na Mel-scale spectrum and used as outputs.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,599,394,677],\"text\":\"The number of test sets for Network 2 was the same as\\nthe number of trained video files (approximately 500). To\\nincrease the number of test sets, data augmentation by apply-\\ning Gaussian noise to the input Mel-scale spectrum vectors\\nwas used.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,677,394,742],\"text\":\"As our model is speaker dependent, both Network 1 and\\nNetwork 2 are trained for each speaker. Network 1 is trained\\nfirst and subsequently used to create the dataset for training\\nNetwork 2.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,755,216,773],\"text\":\"Implementation Details\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,777,394,888],\"text\":\"The above-described network models were implemented\\nbased on the Keras [6] deep-learning platform with Tensor-\\nflow [1] as the backend, and an NVIDIA GeForce 1080ti of the\\nGPU board. Training Network 1 with 500 speech commands\\n(which creates 35,000 training data pairs for Network 1) re-\\nquired approximately 4 h. Training Network 2 required less\\nthan an hour.\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,889,394,953],\"text\":\"As the ultrasonic-imaging device cannot be connected di-\\nrectly to the Ubuntu machine that runs the neural networks,\\na simple server-client program was developed. It connects\\nthe computer that controls the ultrasonic imaging device to\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,125,746,172],\"text\":\"the computer that operates the neural networks. The gen-\\nerated audio signals are sent back to the computer with the\\nultrasonic-imaging device.\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,172,746,252],\"text\":\"To process ultrasonic images at/with duration/intervals\\nof 3.68 s, 2.36 s is required for the neural networks to pro-\\ncess the images. The total processing time (including video\\nprocessing, neural networks processing, and conversion of\\nthe Mel-scale spectrum to an audio wave) was 2.61 s.\",\"reading_order\":14,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,263,502,278],\"text\":\"4 RESULTS\",\"reading_order\":15,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,283,746,506],\"text\":\"The results of converting the ultrasonic images to sound are\\nshown in Figure 9 . In the figure, the top-row graphs show the\\nsound-representation vectors (a Mel-scale spectrogram), and\\nthe bottom-row graphs are the corresponding waveforms.\\nThe graph labeled Net1 is the result of Network 1, that la-\\nbeled Net2 is the result of Network 1 + Network 2, and that\\nlabeled \\\"original\\\" is the wave data encoded as a Mel-scale\\nspectrogram and decoded back to the waveform. Thus, the\\nlast one is regarded as the ground-truth of the training. Al-\\nthough the difference between the outputs of Network 1\\nand Network 2 was unclear, we observed that the sound\\ngenerated by Network 2 was better than that generated by\\nNetwork 1 (Examples of the output audio signals are given\\nin the supplemental video.)\",\"reading_order\":16,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,507,746,586],\"text\":\"It is noteworthy that both Network 1 and Network 2 gen-\\nerate natural intonation, which is typically considered to be\\ncaused by vocal-fold vibration, not by the internal situation.\\nThis result might suggest that the neural networks learn the\\ncontext of the speech.\",\"reading_order\":17,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,587,746,745],\"text\":\"The generated sounds emitted from the computer's speaker\\nwere subsequently tested with an existing (unchanged) smart\\nspeaker (Amazon Echo and Amazon Echo Show), and this\\ntest confirmed that the generated sounds can control smart\\nspeakers. The speech commands used for training and test-\\ning were typical Amazon Alexa commands. For this test, the\\nparticipants spoke the following four commands, five times\\neach (20 utterances in total): “Alexa, play music,” “Alexa,\\nwhat's the weather like,” “Alexa, what time is it,” and “Alexa,\\nplay jazz.”\",\"reading_order\":18,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,746,746,857],\"text\":\"Table 1 lists the recognition success ratio of Network 1,\\nNetwork 1 + Network 2, and the original (Mel-scale encoded\\nand decoded) as a ground truth. We confirmed that the combi-\\nnation of Network 1 and Network 2 improves the recognition\\nrate. We also noticed that the trigger word ( “ Alexa ” ) is always\\nregenerated clearly. This may because that word is simply\\nthe most pronounced word in the training set.\",\"reading_order\":19,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,858,746,953],\"text\":\"We also performed the word error rate (WER) measure test\\nusing Google's cloud speech-to-text engine [29] and the same\\nenvironment used for the recognition measurement of smart\\nspeakers. The WER was 20.61 % , 41.03 % , and 33.56 % for GT,\\nNetwork 1, and Network 2, respectively (mean of a total of 40\\nspeech commands from two users from Table 1 ). We believe\",\"reading_order\":20,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,996,126,1012],\"text\":\"Paper 146\",\"reading_order\":21,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,996,745,1012],\"text\":\"Page 6\",\"reading_order\":22,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300376/results/markdown/3290605-3300376_pdf_shortened_page_5.md"}
{"file_name": "images/test_data/3290605-3300376/3290605-3300376_pdf_shortened_page_6.png", "source_folder": "test_data", "document_id": "3290605-3300376", "source_image_path": "test_data/3290605-3300376/3290605-3300376_pdf_shortened_page_6.png", "source_json_path": "test_data/3290605-3300376/results/output_json/3290605-3300376_pdf_shortened_page_6.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,43,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300376_pdf_shortened_page_6_figure_002.png\",\"bbox\":[106,129,702,391],\"reading_order\":2,\"tags\":[],\"alt_text\":\"This image displays a 2x3 grid of audio visualizations, with spectrograms in the top row and waveforms in the bottom row. The columns are labeled \\\"Net1\\\", \\\"Net2\\\", and \\\"original\\\" (top right) or \\\"mel encidec\\\" (bottom right), showing distinct patterns over time and frequency or amplitude. All plots span approximately 3.5 seconds.\"},{\"label\":\"figure_cap\",\"bbox\":[68,405,747,453],\"text\":\"Figure 9: Results of the training (shown as waves and Mel-scale spectrogram): Net1: Network 1 results; Net2: Network 2 results;\\noriginal: original voice encoded by Mel-scale spectrogram and decoded to an audio signal. This is the “ ground-truth ” of this\\ntraining.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300376_pdf_shortened_page_6_figure_004.png\",\"bbox\":[109,466,703,731],\"reading_order\":4,\"tags\":[],\"alt_text\":\"The image presents a 2x3 grid of plots comparing generated audio signals (\\\"Net2\\\") with an \\\"original\\\" signal. The top row displays spectrograms showing frequency (0-6000 Hz) over time (0-3.5 sec), while the bottom row shows corresponding audio waveforms depicting amplitude over the same time duration. The plots labeled \\\"Net2\\\" show visual differences from the \\\"original\\\" plots in both their spectrogram and waveform representations.\"},{\"label\":\"figure_cap\",\"bbox\":[68,745,746,791],\"text\":\"Figure 10: Comparison of Generated Sounds. Left: Network 2 results from ultrasonic images while a user is emitting a voice;\\nMiddle: Network 2 results from ultrasonic images without emitting a voice; Right: the original (Mel-scale spectrogram en-\\ncoded/decoded) voice\",\"reading_order\":5,\"tags\":[]},{\"label\":\"half_para\",\"bbox\":[69,802,394,916],\"text\":\"that this will also serve as evidence for the effectiveness of\\nNetwork 2. Through these studies, we found that commands\\nsuch as “what’s the weather like?” had a high recognition\\nrate for all conditions, while commands with shorter terms\\nsuch as “play jazz” performed much worse. This may suggest\\nthat the longer commands are easier for Network 2 to obtain\\nthe contexts.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"tab\",\"bbox\":[440,800,726,868],\"text\":\" | User A | User B | ave. | | Network 1 | 60.0% | 25.0% | 42.5% | | Network 1 + Network 2 | 65.0% | 65.0% | 65.0% | | GT | 90.0% | 90.0% | 90.0% |
\",\"reading_order\":7,\"tags\":[],\"alt_text\":\"This image is a table displaying performance percentages for \\\"User A\\\", \\\"User B\\\", and their \\\"ave.\\\" (average). The rows represent \\\"Network 1\\\", \\\"Network 1 + Network 2\\\", and \\\"GT\\\", with corresponding percentage values for each.\",\"figure_path\":\"tables/3290605-3300376_pdf_shortened_page_6_tab_7.png\"},{\"label\":\"table_cap\",\"bbox\":[420,868,746,933],\"text\":\"Table 1: Speech-recognition success ratio in tests with\\nan unchanged existing smart speaker ( “ GT ” means\\nMel-scale encoded/decoded from the original voice\\ndata, regarded as the ground truth of the training).\",\"reading_order\":8,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[70,995,127,1012],\"text\":\"Paper 146\",\"reading_order\":9,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[706,995,745,1012],\"text\":\"Page 7\",\"reading_order\":10,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300376/results/markdown/3290605-3300376_pdf_shortened_page_6.md"}
{"file_name": "images/test_data/3290605-3300376/3290605-3300376_pdf_shortened_page_7.png", "source_folder": "test_data", "document_id": "3290605-3300376", "source_image_path": "test_data/3290605-3300376/3290605-3300376_pdf_shortened_page_7.png", "source_json_path": "test_data/3290605-3300376/results/output_json/3290605-3300376_pdf_shortened_page_7.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_pub_date\",\"meta_subject\"]},{\"label\":\"sec_1\",\"bbox\":[69,123,295,155],\"text\":\"5 END-TO-END EVALUATION AND\\nOBSERVATIONS\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,160,394,256],\"text\":\"The real end-to-end silent voice to audio conversion was\\nexamined. In this case, a user is asked to mouth a speech\\ncommand without actually emitting a sound, and the oral\\ncavity movement is record by an ultrasonic imaging probe.\\nThe obtained image sequence is subsequently translated to\\na voice by the proposed system.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,256,395,463],\"text\":\"We asked the participants to speak as silently as possi-\\nble (not to vibrate their vocal cords) and to try to speak as\\nsimilar as possible when they speak with sound. However,\\nwe did not ask them to hold their breaths. Consequently, a\\ncase occurred where a small leaked sound was audible. To\\nclarify this situation, we measured the level of the emitted\\nsound level from the participant by following the evalua-\\ntion method of SilentVoice [17] . We used a noise meter that\\nhas the same specifications (min range 30 dB, 1.5 dB error)\\nplaced 30 cm away, in a room with a background noise level\\nof 31.0 dB(A) . The mean of the peak sound level of 20 mea-\\nsurements (typical Alexa speech commands) was 37.14 dB(A),\\nwhich is lower than that of soft whispering.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,463,394,526],\"text\":\"Figure 10 shows the comparison of the generated sound\\nwhen a user is emitting a voice and when a user is not emit-\\nting a voice (please refer to the supplemental video for the\\nactual generated sounds).\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,527,395,638],\"text\":\"Initially, the result was unsatisfactory. We first expected\\nthat the movement in the oral cavity without emitting a\\nvoice (Figure 10 (middle)) is the same as that when the user\\nactually emits a voice (Figure 10 (left)); however, a subtle\\ndifference was found between them, and the sound quality\\ngenerated by the image-without-voice was not as good as\\nthat generated by the image-with-voice.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,639,394,751],\"text\":\"However, the following interesting phenomenon was ob-\\nserved. As the user can also listen to the generated sound\\ngenerated by the image-without-voice, the user attempted\\nto change the mouth movement to obtain a slightly better\\nresult. After several trials, the quality of generated sound im-\\nproved. In this case, it is considered that the users improved\\ntheir own skills in silent voicing.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[69,776,179,792],\"text\":\"6 DISCUSSIONS\",\"reading_order\":8,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[69,796,253,814],\"text\":\"Incremental Voice Generation\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,817,395,946],\"text\":\"In our current design, the obtained ultrasonic-image se-\\nquences were converted to a voice at the granularity of the\\nspeech command (approximately 3.6 s). This is because Net-\\nwork 2 uses a fixed-length voice-representation sequence.\\nHowever, based on the observation of the users' practice, it\\nshould be better to generate sounds incrementally, such that\\nthe user can have a tighter feedback loop for learning oral\\nmovements for generating a better voice.\",\"reading_order\":10,\"tags\":[]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300376_pdf_shortened_page_7_figure_011.png\",\"bbox\":[445,99,723,334],\"reading_order\":11,\"tags\":[],\"alt_text\":\"A person with dark, shaggy hair and glasses wears a light blue collared shirt. A white device is strapped under their chin, with a blue earpiece and wires visible on their right ear. The background is plain white.\"},{\"label\":\"figure_cap\",\"bbox\":[421,340,746,372],\"text\":\"Figure 11: Future Image: possible configuration of an ultra-\\nsonic probe and an open earphone\",\"reading_order\":12,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[421,385,741,401],\"text\":\"Continuous Ultrasonic-Wave Emission into the Body\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,404,747,517],\"text\":\"The effect on human organs when ultrasonic waves are emit-\\nted continuously into the body is unknown. However, we\\nmay be able to combine a simple triggering mechanism to\\nstart and stop the emission of ultrasonic waves. For example,\\nthe combination of an accelerometer and a microphone in\\nthe device can detect jaw movements for starting the (silent)\\nvoice command without actually emitting a voice.\",\"reading_order\":14,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[421,529,618,545],\"text\":\"Cure for Vocal Cord Disabilities\",\"reading_order\":15,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,549,747,629],\"text\":\"We also expect people with damaged vocal cords to use our\\nresearch. As described in the Human-AI integration section,\\npeople will be able to learn how to correctly control their\\nmouth and tongue to generate sound, even though their\\nvocal cords do not work.\",\"reading_order\":16,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[421,642,630,660],\"text\":\"Combining with Other Modalities\",\"reading_order\":17,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,662,747,744],\"text\":\"Finally, it should be mentioned that this research is not in-\\ntended to exclude other modalities. Combining the informa-\\ntion of EMG, accelerometers, and NAM microphones may\\nimprove the quality of speech recognition. Investigating the\\ncombination of these modalities is subject of future research.\",\"reading_order\":18,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[421,757,560,774],\"text\":\"Human-AI integration\",\"reading_order\":19,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,777,747,888],\"text\":\"The above-described observation suggests that an interesting\\nrelationship exists between humans and AI. Rather than\\nconsidering AI as an autonomous or separated entity, we may\\nbe able to regard AI as a part of humans. Hence, even when\\nthe initial performance of the (artificial) neural networks is\\nnot perfect, a user can gradually learn and improve their\\nperformance.\",\"reading_order\":20,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,888,747,953],\"text\":\"This is similar to how people learn fundamental skills.\\nWhen we learn utterance, the coordination of the motor\\ncortex that drives the oral cavity, tongue, and vocal folds,\\nand the auditory cortex forms a tight loop to obtain better\",\"reading_order\":21,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[70,996,126,1012],\"text\":\"Paper 146\",\"reading_order\":22,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,996,745,1012],\"text\":\"Page 8\",\"reading_order\":23,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300376/results/markdown/3290605-3300376_pdf_shortened_page_7.md"}
{"file_name": "images/test_data/3290605-3300377/3290605-3300377_pdf_shortened_page_0.png", "source_folder": "test_data", "document_id": "3290605-3300377", "source_image_path": "test_data/3290605-3300377/3290605-3300377_pdf_shortened_page_0.png", "source_json_path": "test_data/3290605-3300377/results/output_json/3290605-3300377_pdf_shortened_page_0.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[70,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"icon\",\"bbox\":[0,92,39,135],\"reading_order\":2,\"tags\":[\"cross_marker\"]},{\"label\":\"sec_0\",\"bbox\":[82,118,731,215],\"text\":\"Assessing the Accuracy of Point & Teleport\\nLocomotion with Orientation Indication for Virtual\\nReality using Curved Trajectories\",\"reading_order\":3,\"tags\":[]},{\"label\":\"author_information\",\"bbox\":[109,234,707,313],\"text\":\"Markus Funk, Florian Müller, Marco Fendrich, Megan Shene, Moritz Kolvenbach, Niclas Dobbertin,\\nSebastian Günther, Max Mühlhäuser\\nTU Darmstadt\\nHochschulstraße 10, 64289 Darmstadt\\nlastname@tk.tu-darmstadt.de\",\"reading_order\":4,\"tags\":[\"author\",\"author_affili\",\"author_mail\"]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300377_pdf_shortened_page_0_figure_005.png\",\"bbox\":[65,328,750,516],\"reading_order\":5,\"tags\":[],\"alt_text\":\"A split panoramic image shows a person from behind wearing a VR headset and holding a controller, casting a blue beam into a low-poly virtual world with floating islands and trees. The virtual environment extends to the right, where another person, also in a VR headset, stands facing a virtual building, with a blue line connecting the two perspectives in the scene.\"},{\"label\":\"figure_cap\",\"bbox\":[68,526,746,571],\"text\":\"Figure 1: A user is teleporting herself in a Virtual Environment using the Curved Teleport . It allows her to teleport around an\\nobstacle and graphically choose the orientation, which she wants to face after teleportation only by using the curved trajectory\\nvisualization with orientation indication, and without having to turn her body in the physical world.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[69,577,141,595],\"text\":\"ABSTRACT\",\"reading_order\":7,\"tags\":[\"paper_abstract\"]},{\"label\":\"para\",\"bbox\":[68,598,394,758],\"text\":\"Room-scale Virtual Reality (VR) systems have arrived in\\nusers' homes where tracked environments are set up in lim-\\nited physical spaces. As most Virtual Environments (VEs)\\nare larger than the tracked physical space, locomotion tech-\\nniques are used to navigate in VEs. Currently, in recent VR\\ngames, point & teleport is the most popular locomotion tech-\\nnique. However, it only allows users to select the position\\nof the teleportation and not the orientation that the user is\\nfacing after the teleport. This results in users having to man-\\nually correct their orientation after teleporting and possibly\",\"reading_order\":8,\"tags\":[\"paper_abstract\"]},{\"label\":\"para\",\"bbox\":[421,579,747,689],\"text\":\"getting entangled by the cable of the headset. In this paper,\\nwe introduce and evaluate three different point & teleport\\ntechniques that enable users to specify the target orientation\\nwhile teleporting. The results show that, although the three\\nteleportation techniques with orientation indication increase\\nthe average teleportation time, they lead to a decreased need\\nfor correcting the orientation after teleportation.\",\"reading_order\":9,\"tags\":[\"paper_abstract\"]},{\"label\":\"sec_1\",\"bbox\":[421,703,525,720],\"text\":\"CCS CONCEPTS\",\"reading_order\":10,\"tags\":[]},{\"label\":\"list\",\"bbox\":[421,724,748,757],\"text\":\"• Human-centered computing → Virtual reality: Point-\\ning . User studies;\",\"reading_order\":11,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,771,502,788],\"text\":\"KEYWORDS\",\"reading_order\":12,\"tags\":[\"paper_keywords\"]},{\"label\":\"para\",\"bbox\":[421,792,746,824],\"text\":\"Virtual Reality, Locomotion, Teleportation, Orientation Indi-\\ncation, Virtual Environments, Point & Teleport\",\"reading_order\":13,\"tags\":[\"paper_keywords\"]},{\"label\":\"sec_1\",\"bbox\":[421,835,554,850],\"text\":\"ACM Reference Format:\",\"reading_order\":14,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,851,747,952],\"text\":\"Markus Funk, Florian Müller, Marco Frendich, Megan Shene, Moritz\\nKolvenbach, Nicolas Dobbert, Sebastian Günther, Max Mülhäuser.\\n2019. Assessing the Accuracy of Point & Teleport Locomotion with\\nOrientation Indication for Virtual Reality using Curved Trajectories.\\nIn CHI Conference on Human Factors in Computing Systems Proceed-\\nings (CHI 2019), May 4–9, 2019, Glasgow, Scotland UK . ACM, New\\nYork, NY, USA. 12 pages. https://doi.org/10.1145/3290605.330377\",\"reading_order\":15,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[69,777,394,883],\"text\":\"Permission to make digital or hard copies of all or part of this work for\\npersonal or classroom use is granted without fee provided that copies\\nare not made or distributed for profit or commercial advantage and that\\ncopies bear this notice and the full citation on the first page. Copyrights\\nfor components of this work owned by others than the author(s) must\\nbe honored. Abstracting with credit is permitted. To copy otherwise, or\\nrepublish, to post on servers or to redistribute to lists, requires prior specific\\npermission and/or a fee. Request permissions from permissions@acm.org.\",\"reading_order\":16,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[69,884,274,896],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland UK\",\"reading_order\":17,\"tags\":[\"meta_pub_date\"]},{\"label\":\"fnote\",\"bbox\":[69,898,392,924],\"text\":\"© 2019 Copyright held by the owner/author(s). Publication rights licensed\\nto ACM.\",\"reading_order\":18,\"tags\":[\"meta_pub_date\"]},{\"label\":\"fnote\",\"bbox\":[70,925,265,938],\"text\":\"ACM ISBN 978-1-4503-5970-2/19/05...$15.00\",\"reading_order\":19,\"tags\":[\"meta_num\"]},{\"label\":\"fnote\",\"bbox\":[70,940,244,952],\"text\":\"https://doi.org/10.1145/3290605.3300377\",\"reading_order\":20,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[70,996,126,1013],\"text\":\"Paper 147\",\"reading_order\":21,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,996,744,1013],\"text\":\"Page 1\",\"reading_order\":22,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300377/results/markdown/3290605-3300377_pdf_shortened_page_0.md"}
{"file_name": "images/test_data/3290605-3300377/3290605-3300377_pdf_shortened_page_1.png", "source_folder": "test_data", "document_id": "3290605-3300377", "source_image_path": "test_data/3290605-3300377/3290605-3300377_pdf_shortened_page_1.png", "source_json_path": "test_data/3290605-3300377/results/output_json/3290605-3300377_pdf_shortened_page_1.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,123,197,139],\"text\":\"1 Introduction\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,144,394,271],\"text\":\"While walking naturally has been known to be the most\\nimmersive [33, 35] and least discomforting [21, 27] active\\nlocomotion technique [17] for navigating through VEs, walk-\\ning naturally is challenging as VR systems have a limited\\ntracking space. Thus, research in VR locomotion focused on\\ncreating locomotion techniques that account for these spa-\\ntial mismatches between wide and spacious VEs and limited\\nphysical spaces [43] .\",\"reading_order\":3,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,271,395,541],\"text\":\"Teleportation, especially point & teleport [11] , is a very\\npopular locomotion technique, that overcomes the problem\\nof confined physical spaces by enabling users to teleport to\\nselected target positions using a hand-held controller. While\\nit has been argued that teleportation is an unnatural type\\nof movement [10] and that users do not gain spatial knowl-\\nedge of the VE when teleporting [3] , combining teleportation\\nand real walking does not lead to a mismatch of physical\\nmovement and virtual movement [25] . Traditionally, the\\nstate-of-the-art point & teleport locomotion technique uses\\na parabola shaped visualization for selecting the target posi-\\ntion and does not allow for selecting the user's orientation\\nwhile teleporting. Therefore, the users are required to physi-\\ncally turn their bodies to adjust their orientation after each\\nteleport. However, with the currently still tethered head-\\nmounted displays (HMDs) , this can result in users getting\\nentangled in their HMDs' cables.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,542,394,797],\"text\":\"To overcome this, Bozgeyikli et al. [ 11 ] introduced a point\\n& teleport technique that allows users to specify their orien-\\ntation while teleporting, and compared it to the traditional\\npoint & teleport technique. While their findings from a study\\nusing a maze escape task favor the traditional point & tele-\\nport approach, in this paper, we aim to revisit the idea of\\nincluding orientation indication into point & teleport loco-\\nmotion using a target acquisition task. This task requires\\nusers to precisely control their orientation, to compare five\\npoint & teleport techniques: two traditional point & teleport\\ntechniques ( Linear Teleport and Parabola Teleport ), the origi-\\nnal orientation indication teleport by Bozgeyikli et al. [ 11 ]\\n( AngleSelect Teleport ), and two novel teleportation techniques\\n( Curved Teleport and HPCurved Teleport ) using curved tra-\\njectories to select and indicate the user's orientation after\\nteleporting (see Figure 1 ).\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,797,394,909],\"text\":\"The contribution of this paper is two-fold. First, we intro-\\nduce two novel point & teleport techniques with orientation\\nindication using curved trajectories. Second, through a con-\\ntrolled lab study, we evaluate the two proposed teleportation\\ntechniques and compare them with two baseline teleporta-\\ntion techniques and one point & teleport technique with\\norientation indication from the literature.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[420,123,550,139],\"text\":\"2 RELATED WORK\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,144,746,209],\"text\":\"In this section, we provide an overview about relevant re-\\nlated locomotion techniques for navigating through VEs. We\\nassign the approaches to two groups: general locomotion\\ntechniques for VEs and point & teleport locomotion.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[420,223,588,240],\"text\":\"Virtual Reality Locomotion\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,244,768,339],\"text\":\"From walking in place [39] , moving tiles [19] , powered shoes [20] ,\\nleaning in chairs [23] , using fingers [22, 47] or controllers\\nto simulate walking [32] - many locomotion techniques for\\nnavigating through VEs have been introduced. They all have\\nthe goals of efficiently enabling locomotion for users in VR\\nwithout causing discomfort or simulator sickness [27] .\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,340,747,545],\"text\":\"As it is known that real walking is more immersive com-\\npared to other locomotion techniques [33, 35, 40] , more and\\nmore locomotion techniques for navigating through VEs\\nfocused on enabling the users to walk naturally, while con-\\nsidering the space constraints of the physical environment.\\nA prominent and well researched [29] example is the redi-\\nrected walking technique [34] that was first introduced by\\nRazzaque et al. [31] . Here users are made believe to walk a\\nstraight line but are subconsciously steered to walk in circles\\nto make the physical space feel endless. This has been used\\nin many demonstrations [37] and has even been validated\\nfor curved virtual paths [24] and for giving impulses with\\nelectronic muscle stimulation [1] .\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,546,746,673],\"text\":\"In contrast, step-to-place [9] , tap-in-place [30] , or walk-in-\\nplace [39] systems enable users to walk, while constraining\\nthem to stay in the same physical position [18] . The most\\nprominent example is treadmill systems that are meanwhile\\navailable commercially (e.g. the Cyberth Virtualizer $^1$ or the\\nVirtuix Omni $^2$ ). However, these systems cannot meet the\\nfeeling of real walking [40, 42] , require space in a user's\\nhome, and are still expensive.\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,673,746,833],\"text\":\"Other techniques make intelligent use of the available\\nphysical space. For instance, Wilson et al. [44] are explor-\\ning different translational gains for moving in VEs to make\\na room seem larger. Another technique to trick users into\\nbelieving that their interaction space is much larger, is the im-\\npossible spaces technique [38] . Here, multiple virtual rooms\\nhave self-overlapping architecture using the same physical\\nspace multiple times. This technique can be used to generate\\ndynamic layouts for infinite walking in VEs [41] and further\\nbe improved by predicting a user's walking direction [28] .\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,834,746,899],\"text\":\"Similar to a minimap navigation, the Worlds in Minia-\\nture (WIM) locomotion technique that was introduced by\\nStoakley et al. [36] in 1995 lets users move in a miniature\\nmodel of the VE without motion sickness [4] . It was equipped\",\"reading_order\":14,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[421,923,744,938],\"text\":\"$^{1}$Cyberith Virtualizer - https://www.cyberith.com/ (last access 09-21-2018)\",\"reading_order\":15,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[421,938,679,951],\"text\":\"$^{2}$Virtuix Omni - www.virtuix.com/ (last access 09-21-2018)\",\"reading_order\":16,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,968,126,984],\"text\":\"Paper 147\",\"reading_order\":17,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[706,968,745,984],\"text\":\"Page 2\",\"reading_order\":18,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300377/results/markdown/3290605-3300377_pdf_shortened_page_1.md"}
{"file_name": "images/test_data/3290605-3300377/3290605-3300377_pdf_shortened_page_2.png", "source_folder": "test_data", "document_id": "3290605-3300377", "source_image_path": "test_data/3290605-3300377/3290605-3300377_pdf_shortened_page_2.png", "source_json_path": "test_data/3290605-3300377/results/output_json/3290605-3300377_pdf_shortened_page_2.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300377_pdf_shortened_page_2_figure_002.png\",\"bbox\":[75,123,382,207],\"reading_order\":2,\"tags\":[],\"alt_text\":\"The image illustrates a VR controller being used for pointing. Panel (a) shows a hand holding a VR controller with a blue ray extending from it and arrows indicating rotational movement, highlighting a blue button. Panel (b) depicts a simplified human figure wearing a VR headset, using the controller to point a blue dashed ray towards a distant orange target.\"},{\"label\":\"figure_cap\",\"bbox\":[68,221,394,266],\"text\":\"Figure 2: A user is using the Linear Teleport to move onto a\\ntarget. The orientation that the user is facing is the forward\\nvector of the straight teleport line.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"half_para\",\"bbox\":[68,295,395,377],\"text\":\"with scaling and scrolling to become more scalable [45] . Re-\\ncently, similar to our approach, Elvezio et al. [12] combined\\nthe WIM locomotion technique with enabling the user to\\nadjust their orientation through choosing head pitch and\\nyaw before the teleportation.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,376,395,456],\"text\":\"Summing up, previous related approaches for locomotion\\nin VR focused on walking-in-place, redirected walking, cre-\\nating overlapping VEs, or using a minimap navigation. A\\ncomprehensive overview about VR locomotion techniques\\nis also presented by Boletis [7] .\",\"reading_order\":5,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,469,248,486],\"text\":\"Point & Teleport Locomotion\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,489,395,633],\"text\":\"The point & teleport locomotion technique is implemented\\nin more and more state-of-the-art VR games. Although, with\\nthe proliferation of room-scale VR systems this locomotion\\ntechnique only recently gained popularity, it was already in\\n1997, when Bowman et al. [10] found that pointing-based\\ntechniques outperform gaze-based locomotion techniques. In\\nthe VR literature, Bogeevi ́ c et al. [11] were the first in 2016\\nto introduce the point & teleport technique. They compare it\\nto a traditional walk-in-place and joystick based movement.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,633,395,760],\"text\":\"However, teleportation has been used for enhancing loco-\\nmotion in VEs previously. One example is Freitag et al. [13] ,\\nwho used teleportation after users walk through interactive\\nportals in a CAVE system. Using the teleportation, the users\\nare reoriented in the VE. Another example using telepor-\\ntation is the jumper metaphor [8] , which enables users to\\ncombine real walking and teleporting to a predicted position\\njumping.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,760,395,905],\"text\":\"Recently, point & teleport locomotion became more popu-\\nlar and was included in recent research. Frommel et al. [14]\\ninvestigate the effects of different controller-based locomo-\\ntion techniques on the user. They conclude that a free point\\n& teleport technique leads to the least discomfort. Further,\\nXu et al. [46] compare the point & teleport locomotion tech-\\nnique to joystick and walk-in-place locomotion. They could\\nnot find a significant difference in the spacial knowledge\\ngain between the conditions.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,905,395,953],\"text\":\"However also new locomotion techniques using point &\\nteleport were recently introduced. Bhandari et al. [5] present\\nDash, which lets users estimate the travelled distance during\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,307,747,355],\"text\":\"point & teleport locomotion. Further, Liu et al. [26] intro-\\nduced redirected teleportation, which spawns a portal in\\ndifferent directions to prevent users from walking into walls.\",\"reading_order\":11,\"tags\":[]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300377_pdf_shortened_page_2_figure_012.png\",\"bbox\":[425,123,734,204],\"reading_order\":12,\"tags\":[],\"alt_text\":\"A two-panel illustration demonstrates VR interaction with a controller. Panel 'a' shows a hand holding a VR controller with rotational arrows and a blue dashed arc originating from it. Panel 'b' shows a person wearing a VR headset, holding the controller, with the blue dashed arc extending from the controller to a circular target on the ground.\"},{\"label\":\"figure_cap\",\"bbox\":[421,221,747,266],\"text\":\"Figure 3: The Parabola Teleport uses a parabola shaped visu-\\nalization to indicate the target position of the teleport. After\\nthe teleport, users face the forward vector of the teleport.\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,355,748,579],\"text\":\"Interestingly and most relevant for our paper, Bozgevki\\net al. [11] added an orientation indication that lets the users\\ncontrol the orientation which they want to face after the tele-\\nport. However, after an initial proof-of-concept study [11] ,\\nthe teleportation with orientation selection did not lead to\\na better performance compared to the traditional point &\\nteleport locomotion technique without orientation indica-\\ntion. The authors even conclude that “ the direction indication\\ndegraded the user experience ” . Despite Bozgevki et al. [11] 's\\nfindings, which we consider important and valid, orientation\\nindication was only evaluated using a maze-task that did not\\nrequire users to precisely control their orientation. Thus, we\\nclaim that the full potential of using orientation indication\\nin point & teleport locomotion is still unexplored.\",\"reading_order\":14,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,579,748,770],\"text\":\"Summing up, although point & teleport with orientation\\nselection has been suggested in previous research [11] , its\\npotentials have not been analyzed in a study that requires\\nusers to accurately use orientation indication for reaching\\ntargets precisely. In this paper, we re-implement the point &\\nteleport technique with orientation indication by Bezgeviili\\net al. and introduce two new point & teleport techniques\\nwith orientation indication using a curved teleportation tra-\\njectory. Finally, through a user study, we compare these three\\npoint & teleport techniques with orientation indication with\\ntwo traditional teleportation techniques without orientation\\nindication.\",\"reading_order\":15,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,787,658,803],\"text\":\"3 POINT & TELEPORT TECHNIQUES\",\"reading_order\":16,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,806,748,950],\"text\":\"In order to evaluate the different VR locomotion point & tele-\\nport techniques, we implemented three teleportation tech-\\nniques that were inspired by both state-of-the-art systems\\nand the literature. Additionally, we implemented two novel\\nteleportation techniques. All teleportation techniques are\\nimplemented in Unity version 2018.1.3f1 using an HTC Vive\\nVR system. For our hardware setup, we further use deluxe\\naudio headstraps and a leather VR Facecover for hygienic\\nreasons.\",\"reading_order\":17,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,126,1012],\"text\":\"Paper 147\",\"reading_order\":18,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[706,995,745,1012],\"text\":\"Page 3\",\"reading_order\":19,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300377/results/markdown/3290605-3300377_pdf_shortened_page_2.md"}
{"file_name": "images/test_data/3290605-3300377/3290605-3300377_pdf_shortened_page_3.png", "source_folder": "test_data", "document_id": "3290605-3300377", "source_image_path": "test_data/3290605-3300377/3290605-3300377_pdf_shortened_page_3.png", "source_json_path": "test_data/3290605-3300377/results/output_json/3290605-3300377_pdf_shortened_page_3.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300377_pdf_shortened_page_3_figure_002.png\",\"bbox\":[75,124,382,207],\"reading_order\":2,\"tags\":[],\"alt_text\":\"A two-part diagram illustrates a virtual reality interaction. Part 'a' shows a hand gripping a VR controller, from which a dashed blue arc extends, suggesting a throwing or gesturing motion. Part 'b' depicts a person wearing a VR headset and holding the controller, with the dashed blue arc indicating a trajectory towards a target marked by a red line within a yellow circle on the ground.\"},{\"label\":\"figure_cap\",\"bbox\":[69,222,394,281],\"text\":\"Figure 4: The AngleSelect Teleport uses a parabola visualiza-\\ntion to show the user the target position. Further it uses an\\norientation indicator that lets the user select the orientation\\nthat the user is facing after the teleport.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[69,304,165,322],\"text\":\"Linear T\\neleport\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,325,395,404],\"text\":\"Our first and most basic teleportation method is the Linear\\nTeleport. Using the Linear Teleport, the user can point at a\\ntarget location using a straight line as a visual representation\\nfor the teleport destination. The Linear Teleport is depicted\\nin Figure 2 .\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,404,395,581],\"text\":\"The Linear Teleport on our HTC Vive controller is imple-\\nmented as follows: Once the user presses and holds the touch-\\npad on the controller, the Linear Teleport is active. While the\\nLinear Teleport is active, the user can point at a target loca-\\ntion using its straight line visualization. The target position\\nis visualized with an orange circle that is displayed on the\\nground (see Figure 2 ). When the user releases the touchpad\\non the controller, the user is teleported to the target location.\\nThe orientation in which the user is facing after the tele-\\nport is the forward vector of the straight line of the Linear\\nTeleport 's visualization.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[69,593,180,610],\"text\":\"Parabola Teleport\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,613,395,710],\"text\":\"We consider the Parabola Teleport the most-common and\\nstate-of-the-art teleportation system as it is used in many\\nrecent VR applications (e.g. VRChat$^{3}$ or SteamVR Home$^{4}$). A\\nuser can point at a target location like in the Linear Teleport,\\nhowever the line that points to the target location is shaped\\nin a parabola (see Figure 3 ).\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,710,395,902],\"text\":\"A user can activate the Parabola Teleport by pressing and\\nholding the touchpad on the HTC Vive controller. The system\\nthen displays a parabola to select the target. A user can\\nfurther change the length of the parabola by changing the\\nangle on the pitch axis. Holding the controller in a steeper\\nangle will result in a steeper parabola, while holding the\\ncontroller straight line will result in a straight line (as in the\\nLinear Teleport). The Parabola Teleport also uses an orange\\ncircle for displaying the target location at the position where\\nthe parabola intersects with the ground. When a user releases\\nthe touchpad on the controller, the user will be teleported to\\nthe target location facing the forward vector of the parabola.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300377_pdf_shortened_page_3_figure_010.png\",\"bbox\":[423,125,736,210],\"reading_order\":10,\"tags\":[],\"alt_text\":\"A two-part diagram illustrates VR controller interaction. Panel (a) shows a hand holding a VR controller with arrows indicating rotational and tilting movements. Panel (b) depicts a person wearing a VR headset, holding a controller from which a curved dotted blue line emanates, ending at a target represented by a red line inside an orange oval.\"},{\"label\":\"figure_cap\",\"bbox\":[420,222,746,281],\"text\":\"Figure 5: The Curved Teleport visualizes the trajectory of the\\nteleportation in a curved line. The orientation that the user\\nis facing at the target location can be influenced by adjusting\\nthe steepness of the curve.\",\"reading_order\":11,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,320,550,337],\"text\":\"AngleSelect T\\neleport\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,340,746,419],\"text\":\"The AngleSelect Teleport is a not very common teleportation\\ntechnique that was previously introduced by Bozgeyikli et\\nal. [11] . It is similar to the Parabola Teleport , however, it\\nadditionally lets a user select a target orientation that the\\nuser is facing after the teleport (see Figure 4 ).\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,420,747,645],\"text\":\"Similar to the Parabola Teleport , the users can activate the\\nAngleSelect Teleport by pressing the touchpad on the HTC\\nVive Controller and change the length of the parabola by\\nholding the controller in a different angle on the pitch axis.\\nAdditionally, by moving the finger on the round touchpad, a\\nuser can select the orientation which should be faced after\\nthe teleport. The target location of the teleport is displayed\\nusing an orange circle. In the AngleSelect Teleport , the target\\ncircle is extended with a red orientation indicator, which\\npoints towards the currently selected target orientation. As\\nsoon as the user releases the touchpad, the user is teleported\\nto the target location facing the selected orientation. This\\nextends the state-of-the-art Parabola Teleport by including\\norientation selection into the teleport.\",\"reading_order\":14,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,660,523,677],\"text\":\"Curved Teleport\",\"reading_order\":15,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,679,747,776],\"text\":\"The Curved Teleport is a teleportation technique that is in-\\nspired by the attention funnel visualization by Biocca et\\nal. [6] . It uses a curved shaped trajectory visualization that is\\nsimilar to rotating the visualization of the Parabola Teleport\\nin a 90° angle. The Curved Teleport lets a user combine select-\\ning a target position and a target orientation (see Figure 5 ).\",\"reading_order\":16,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,777,747,952],\"text\":\"As the previous teleportation techniques, the Curved Telep-\\nort is also activated by pressing and holding the touchpad\\non the HTC Vive controller. However, the parameters of the\\nparabola are controlled by moving the controller along the\\nroll axis to adjust the steepness of the curve. Further, the\\nusers are able to adjust the length of the curve by sliding\\ntheir finger on the touchpad forward to increase the length\\nor backwards to reduce the length. Similar to the previous\\nteleportation methods, the target location is indicated by\\nan orange circle and a red orientation indicator pointing\\ntowards the forward direction of the curve. In contrast to the\",\"reading_order\":17,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[70,911,307,924],\"text\":\"$^{3}$VRChat - https://vrchat.net (last accessed 09-21-2018)\",\"reading_order\":18,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[69,925,392,952],\"text\":\"*SteamVR Home - https://steamcommunity.com/steamvr (last accessed 09-\\n21-2018)\",\"reading_order\":19,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[70,996,127,1012],\"text\":\"Paper 147\",\"reading_order\":20,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,996,745,1012],\"text\":\"Page 4\",\"reading_order\":21,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300377/results/markdown/3290605-3300377_pdf_shortened_page_3.md"}
{"file_name": "images/test_data/3290605-3300377/3290605-3300377_pdf_shortened_page_4.png", "source_folder": "test_data", "document_id": "3290605-3300377", "source_image_path": "test_data/3290605-3300377/3290605-3300377_pdf_shortened_page_4.png", "source_json_path": "test_data/3290605-3300377/results/output_json/3290605-3300377_pdf_shortened_page_4.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[68,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300377_pdf_shortened_page_4_figure_002.png\",\"bbox\":[70,123,385,207],\"reading_order\":2,\"tags\":[],\"alt_text\":\"The image illustrates virtual reality interaction, showing a hand manipulating a VR controller with a joystick, indicated by movement arrows and an emanating dotted blue line in panel (a). Panel (b) depicts a person wearing a VR headset using a VR controller, from which a similar dotted blue line extends to a red segment highlighted by a yellow oval at its end.\"},{\"label\":\"figure_cap\",\"bbox\":[68,222,395,295],\"text\":\"Figure 6: The visualization of the HPCurvede Teleport first\\nuses a parabola that behaves like a state-of-the-art Parabola\\nTeleport , but at the high point of the parabola turns into a\\nCurved Teleport that lets the users chose the target orienta-\\ntion.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"half_para\",\"bbox\":[68,340,395,452],\"text\":\"AngleSelect Teleport, in the Curved Teleport this red orienta-\\ntion indicator cannot be adjusted and is only there to display\\nthe forward direction to the user. The target orientation can\\nbe manipulated by adjusting the steepness of the curved tra-\\njectory. Finally, when the user releases the touchpad, the user\\nis teleported to the selected position facing the orientation\\nthat is shown by the red indicator.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[68,469,234,487],\"text\":\"HighPointCurved T\\neleport\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,490,395,586],\"text\":\"The HighPointCurved Teleport (further abbreviated as HPCurve\\nTeleport ) is a mix between the Parabola Teleport and the\\nCurved Teleport . Until the parabola's high point, its visual-\\nization is identical to the Parabola Teleport . However, after\\nthe high point of the parabola, the visualization turns into a\\ncurve just like in the Curved Teleport (see Figure 6 ).\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,586,395,808],\"text\":\"The users can activate the HPCurved Teleport by pressing\\nand holding the touchpad on the HTC Vive controller. In\\ncontrast to the Curved Teleport or the Parahola Teleport , in\\nthe HPCurved Teleport the users can adjust two parameters\\nwhile doing the teleport. Using the roll axis, the users can\\nalter the steepness of the curve that it has after the high point.\\nA higher roll movement results in a higher steepness of the\\ncurve. Similar to the Curved Teleport , the users can adjust the\\nlength of the curve by sliding their finger on the touchpad\\nforwards and backwards. The orange target indicator and\\nthe red orientation indicator are the same as in the Curved\\nTeleport . Again, when the user releases the touchpad, the\\nteleportation is performed and the user is teleported to the\\nchosen position facing the indicated orientation.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,826,175,841],\"text\":\"4 EVALUATION\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,846,395,895],\"text\":\"We conduct a user study to evaluate the five different point\\n& teleport methods for VR. By conducting this study, we aim\\nto proof or disproof the following five hypotheses:\",\"reading_order\":9,\"tags\":[]},{\"label\":\"list\",\"bbox\":[79,902,395,951],\"text\":\"H $_0$ The different teleportation methods in combination\\nwith natural walking do not influence the participants'\\nability to reach target positions and target orientations.\",\"reading_order\":10,\"tags\":[]},{\"label\":\"list\",\"bbox\":[432,355,748,517],\"text\":\"H1 The different teleportation methods lead to a different\\namount of using natural walking for correcting the\\ntarget position.\\nH2 The teleportation methods with orientation indication\\nlead to a lower amount of using natural walking for\\ncorrecting the target angle .\\nH3 Teleportation methods with orientation indication re-\\nquire more time to use.\\nH4 Curved trajectories induce an increased perceived cog-\\nnitive load compared to parabola-shaped trajectories.\",\"reading_order\":11,\"tags\":[]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300377_pdf_shortened_page_4_figure_012.png\",\"bbox\":[420,121,745,213],\"reading_order\":12,\"tags\":[],\"alt_text\":\"A person wearing a VR headset and holding controllers is aiming a glowing blue dotted line, which forms an arc, towards a circular target on a green, textured ground. The target is divided into large red, orange, and small green sections, with a yellow center.\"},{\"label\":\"figure_cap\",\"bbox\":[421,225,746,314],\"text\":\"Figure 7: We used round targets in our accuracy study. The\\ndirection of the target is indicated by the green target area\\nand supported by the yellow target area. The rest of the tar-\\nget is colored in red indicating the wrong direction. In this\\nscreenshot of our accuracy study, the participant is using the\\nParabola Teleport. (Picture of participant added for clarity).\",\"reading_order\":13,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[421,531,467,548],\"text\":\"Design\",\"reading_order\":14,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,552,747,823],\"text\":\"We designed the user study following a repeated measures de-\\nsign with the used teleportation method as only independent\\nvariable with five levels ( Linear Teleport, Parabola Teleport,\\nAngleSelect Teleport, Curved Teleport and HCurved Teleport) .\\nAs dependent variables, we measured the distance to the tar-\\nget ( $d_{target}$ ), the physical distance correction using natural\\nwalking after the teleport ( $d_{corrected}$ ), the rotation offset to\\nthe target angle ( $\\\\alpha_{target}$ ), the physical orientation correc-\\ntion using natural walking after the teleport ( $\\\\alpha_{corrected}$ ), the\\naverage time needed to teleport ( $t_{teleport}$ ), the time spent\\nfor using natural walking to correct the position and ori-\\nentation after the last teleport ( $t_{corrected}$ ), the amount of\\nteleports needed ( $n_{teleports}$ ), and the perceived cognitive\\nload for each teleportation method using the Raw NASA\\nTask Load Index (RTLX) [15] score. To avoid learning effects,\\nwe used a 5 × 5 balanced Latin Square to counterbalance the\\nconditions.\",\"reading_order\":15,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,824,747,951],\"text\":\"We defined $d_{target}$ as the distance from the position of\\nthe user's HMD to the middle of the target and $d_{corrected}$\\nas the distance that the user corrected by walking naturally\\nafter the last teleport was made. Further, $\\\\alpha_{target}$ is defined\\nas the angle between the forward vector of the participant's\\nHMD and the target's direction vector. Finally, we defined\\n$\\\\alpha_{corrected}$ as the angle, that the participant corrected using\\nnatural walking after the last teleport was made.\",\"reading_order\":16,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,996,126,1012],\"text\":\"Paper 147\",\"reading_order\":17,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[706,996,745,1012],\"text\":\"Page 5\",\"reading_order\":18,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300377/results/markdown/3290605-3300377_pdf_shortened_page_4.md"}
{"file_name": "images/test_data/3290605-3300377/3290605-3300377_pdf_shortened_page_5.png", "source_folder": "test_data", "document_id": "3290605-3300377", "source_image_path": "test_data/3290605-3300377/3290605-3300377_pdf_shortened_page_5.png", "source_json_path": "test_data/3290605-3300377/results/output_json/3290605-3300377_pdf_shortened_page_5.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[68,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300377_pdf_shortened_page_5_figure_002.png\",\"bbox\":[68,120,393,295],\"reading_order\":2,\"tags\":[],\"alt_text\":\"A low-poly 3D landscape shows a central green plain with a grid and a small gray castle outline. This area is surrounded by brown mountains, some with snowy peaks, and floating islands featuring green patches, trees, a house, and a pond. The scene has a stylized, game-like appearance.\"},{\"label\":\"figure_cap\",\"bbox\":[68,308,394,398],\"text\":\"Figure 8: The VE that we created as the environment for con-\\nducting the study. The castle in the middle of the environ-\\nment is the tutorial area, where participants can practice the\\nteleportation method before starting the study. Once partic-\\nipants leave the tutorial castle, it disappears and becomes a\\ngreen lawn area.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,421,192,440],\"text\":\"Apparatus and Task\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,442,395,841],\"text\":\"We created a VE, which consists of a green lawn area that is\\nlimited by rocks around it. At the beginning of each telepor-\\ntation method, the user starts in a castle that represents a\\ntutorial and training environment (see Figure 8 ). In this tuto-\\nrial castle the users can try the current teleportation method\\nuntil they feel comfortable using the teleportation method.\\nOnce the users teleport out of the castle, the castle disap-\\npears and our VE starts displaying the target acquisition task.\\nOur VE spawns targets on the lawn in a distance range from\\n3m-10.5m away from the user and an angle of $10^{\\\\circ}$ from the\\nlast target. The rotation of the targets is randomly assigned\\nfrom a pool of 50 available rotations, making sure that all\\nrotations were used. The targets have a diameter of 2.5m.\\nWe specified the target direction that the user should face\\nusing a green color with a $20^{\\\\circ}$ beam width that is supported\\nby a yellow area having a $100^{\\\\circ}$ beam width. A graphical rep-\\nresentation of the targets we used in the study can be seen\\nin Figure 7 . It is ensured that no targets are spawned outside\\nthe lawn area of the VE and that all targets are reachable. As\\na task, we defined that a user has to reach and confirm 50\\ntargets. A target is confirmed by pressing the trigger button\\non the HTC Vive controller. We started counting $t_{teleport}$\\nwhen the user initiated the first teleport of the current target.\\nTherefore, the time it takes the user to visually locate the\\nnext target can be neglected.\",\"reading_order\":5,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,853,134,869],\"text\":\"Procedure\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,873,395,953],\"text\":\"Before the Study. After welcoming the participants and ex-\\nplaining the aim of the study, we told the participants that\\nthe data is anonymized and that the position and accuracy of\\nthe teleport will be recorded. However, we did not fully dis-\\nclose all dependent variables to the participants (especially\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,125,746,253],\"text\":\"not the physical movement and correction variables), as this\\nmight have influenced the participants' behavior. Once the\\nparticipants agreed to take part in the study, we started with\\nasking them to fill in a consent form and a demographic\\nquestionnaire. After that, we equipped the participants with\\nthe HTC Vive and started our VE. To get familiar with the\\nVE and the mechanism to confirm the targets, we always\\nstarted with 10 targets using the baseline Linear Teleport\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,259,747,564],\"text\":\"Study. After the participants were familiar with the system,\\nwe switched the teleportation method to the current con-\\ndition according to a Balanced Latin Square randomization.\\nAgain, the participants started in the tutorial castle to get fa-\\nmiliar with the current teleportation method. When the par-\\nticipants felt familiar with the current teleportation method,\\nthey left the castle and started moving towards the desig-\\nnated targets. Once the participants teleported onto a target,\\nthe participants were able to adjust the position and orienta-\\ntion by moving or turning naturally. However, we told the\\nparticipants to use this adjustment through natural walking\\nonly as a secondary option. As a first priority, they were in-\\nstructed to use the orientation indication mechanism of the\\ncurrent teleportation method (if applicable). After the partici-\\npants reached and confirmed all 50 targets, we removed their\\nheadset again and asked them to fill in a NASA-TLX question-\\nnaire [15] . Afterwards, we asked them to provide comments\\nabout the current teleportation method. We repeated this\\nprocedure for all five teleportation methods.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,570,746,619],\"text\":\"After the Study. After completing all five conditions of the\\nuser study, we asked the participants for additional qualita-\\ntive feedback through a semi-structured interview.\",\"reading_order\":10,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,629,498,646],\"text\":\"Participants\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,650,747,809],\"text\":\"For our user study, we recruited 20 participants (8 female, 12\\nmale) with an age range between 21 and 35 years ( $M = 26.5$ ,\\n$SD = 4.45$ ). All participants were students or employees of\\nour university with different majors. Considering the previ-\\nous VR experience of the participants, 11 participants stated\\nthat they had very little experience, 7 participants stated that\\nthey had medium VR experience, and 3 identified themselves\\nas VR experts. All participants took part in the study volun-\\ntarily. We did not provide any compensation for participating\\nin the user study.\",\"reading_order\":12,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,820,469,836],\"text\":\"Results\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,842,762,955],\"text\":\"We statistically compared $d_{target}, c_{corrected}, \\\\alpha_{target}, \\\\alpha_{corrected}$,\\n$t_{teleport}$, $t_{corrected}$ to $t_{teleport}$, and the RTX score between\\nthe five teleportation methods using a one-way repeated mea-\\nsures ANOVA test. Mauchly's test showed that the spheri-\\ncity assumption was violated for $\\\\alpha_{corrected}$ ($\\\\chi^2(9) = 39.731$,\\n$p < .001$), $t_{teleport}$ ($\\\\chi^2(9) = 21.793$, $p = .01$), $t_{corrected}$\\n($\\\\chi^2(9) = 45.403$, $p < .001$) and RTX ($\\\\chi^2(9) = 28.439$,\\n$p < .001$).\",\"reading_order\":14,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,126,1012],\"text\":\"Paper 147\",\"reading_order\":15,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,995,745,1012],\"text\":\"Page 6\",\"reading_order\":16,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300377/results/markdown/3290605-3300377_pdf_shortened_page_5.md"}
{"file_name": "images/test_data/3290605-3300377/3290605-3300377_pdf_shortened_page_6.png", "source_folder": "test_data", "document_id": "3290605-3300377", "source_image_path": "test_data/3290605-3300377/3290605-3300377_pdf_shortened_page_6.png", "source_json_path": "test_data/3290605-3300377/results/output_json/3290605-3300377_pdf_shortened_page_6.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[68,44,159,61],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[]},{\"label\":\"header\",\"bbox\":[474,44,746,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300377_pdf_shortened_page_6_figure_002.png\",\"bbox\":[70,126,380,276],\"reading_order\":2,\"tags\":[],\"alt_text\":\"A bar chart displays \\\"α corrected\\\" on the y-axis, ranging from 0 to 100, for five categories on the x-axis: Linear, Parable, Angle, Curved, and HPCurved. Linear and Parable bars are high and similar, marked as \\\"n.s.\\\", while Angle is very low, Curved is moderate, and HPCurved is low, with an \\\"all other *\\\" label above the Curved bar. Error bars are visible on all bars.\"},{\"label\":\"figure_cap\",\"bbox\":[68,293,395,368],\"text\":\"Figure 9: The average angle $\\\\alpha_{corrected}$ that was corrected by\\nthe participants using each teleportation method. All error\\nbars indicate the standard error. All conditions are signif-\\nicantly different, except the two baseline conditions (indi-\\ncated with n.s. ).\",\"reading_order\":3,\"tags\":[]},{\"label\":\"half_para\",\"bbox\":[68,395,395,490],\"text\":\"$p < .001$). Therefore, we used the Greenhouse-Geisser correc-\\ntion to adjust the degrees of freedom $(\\\\epsilon = .497$ for $t_{corrected},$\\n$\\\\epsilon = .829$ for $t_{report1}, \\\\epsilon = .49$ for $t_{corrected}$ and $\\\\epsilon = .676$ for\\nthe RTX score). We further used the Bonferroni correction\\nfor all post-hoc tests, which is a pessimistic correction to\\nreduce the inflation of the type-I error.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,490,395,664],\"text\":\"The average distance to the target, $d_{target}$ , for according\\nto the teleportation methods was the following: AngleSelect\\nTeleport ( $M = 16.89cm$ , $SD = 5.20cm$ ), Parabola Teleport ( $M =\\n17.57cm$ , $SD = 5.46cm$ ), HP Curved Teleport ( $M = 18.37cm$ , $SD$\\n$= 4.54cm$ ), Linear Teleport ( $M = 19.3cm$ , $SD = 6.31cm$ ), and\\nCurved Teleport ( $M = 21.55cm$ , $SD = 8.46cm$ ). A repeated\\nmeasures ANOVA revealed a significant difference between\\nthe conditions $F(4,76) = 2.572$ , $p = .044$ . However, post-hoc\\npairwise comparisons did not show any significant effect\\nbetween the different teleportation methods. The effect size\\nestimate shows a medium effect ( $\\\\eta^2 = .119$ )\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,665,395,841],\"text\":\"Considering the distance that the users corrected man-\\nually after using the teleport, $d_{corrected}$ , the teleportation\\nmethods led to the following amount of manual correction:\\nCurved Teleport ( $M=3.79$ cm, $SD=3.47$ cm), AngleSelect\\nTeleport ( $M=4.86$ cm, $SD=8.68$ cm), Parabola Teleport\\n( $M=5.08$ cm, $SD=4.64$ cm), HPCurved Teleport ( $M=5.29$ cm,\\n$SD=4.85$ cm), and Linear Teleport ( $M=6.7$ cm, $SD=6.05$ cm).\\nHowever, a repeated measures ANOVA could not find a sig-\\nnificant difference regarding the $d_{corrected}$ between the tele-\\nportation methods ( $p>0.05$ ). The effect size estimate shows\\na small effect $(\\\\eta^2=0.34)$ .\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,841,395,954],\"text\":\"For the rotation offset to the target angle, $\\\\alpha_{target}$ , the\\nteleportation methods performed the following: Parabola\\nTeleport ( $M = 4.86^{\\\\circ}$ , $SD = 2.50^{\\\\circ}$ ) Linear Teleport ( $M = 5.36^{\\\\circ}$ ,\\n$SD = 1.76^{\\\\circ}$ ), Curved Teleport ( $M = 5.56$ , $SD = 2.49^{\\\\circ}$ ), AngleSe-\\nlect Teleport ( $M = 5.62^{\\\\circ}$ , $SD = 2.64^{\\\\circ}$ ), and HPCurved Teleport\\n( $M = 5.64^{\\\\circ}$ , $SD = 2.67^{\\\\circ}$ ). A repeated measures ANOVA could\\nnot find a significant difference regarding $\\\\alpha_{target}$ between\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,378,746,410],\"text\":\"the teleportation methods ($\\\\rho < 0.05$). The effect size estimate\\nshows a small effect ($\\\\rho^2 = .051$).\",\"reading_order\":8,\"tags\":[]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300377_pdf_shortened_page_6_figure_009.png\",\"bbox\":[421,125,730,274],\"reading_order\":9,\"tags\":[],\"alt_text\":\"A bar chart titled \\\"all other *\\\" displays 't_teleport' on the y-axis, with values ranging from 0 to 5. The x-axis shows five categories: Linear, Parable, Angle, Curved, and HPCurved, each represented by a bar with error bars. \\\"n.s.\\\" labels indicate non-significant differences between Linear and Parable, and among Angle, Curved, and HPCurved.\"},{\"label\":\"figure_cap\",\"bbox\":[421,293,746,355],\"text\":\"Figure 10: The average time that participants took to tele-\\nport $t_{teleport}$ using each teleportation method. All error bars\\nindicate the standard error. All teleportation methods were\\nsignificantly different, except the ones indicated with n.s.\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,410,747,618],\"text\":\"Considering the angle that the users manually corrected\\nafter the teleport, $acc_{corrected}$ , the teleportation methods per-\\nformed the following: AngleSelect Teleport ( $M=6.23^\\\\circ$ , $SD=$\\n4.30\\\") , HCurved Teleport ( $M=30.46^\\\\circ$ , $SD=10.5^\\\\circ$ ) and\\nthe Curved Teleport ( $M=57.96^\\\\circ$ , $SD=19.59^\\\\circ$ ). The two\\nbaseline conditions led to an increased manual angle correc-\\ntion: Linear Teleport ( $M=77.26^\\\\circ$ , $SD=8.59^\\\\circ$ ) and Parabola\\nTeleport ( $M=81.82^\\\\circ$ , $SD=5.2^\\\\circ$ ). A repeated measures\\nANOVA revealed a significant difference between the condi-\\ntions $F(1.809,34.377)=168.532$ , $p<.001$ . A post-hoc test\\nshows significant differences between all conditions except\\nthe two baseline conditions Linear Teleport and Parabola Tele-\\nport (all $p<0.05$ ). The results are also depicted in Figure 9 .\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,618,747,857],\"text\":\"Analyzing the time the users spend per teleport, 𝑡𝑒𝑙𝑒𝑝𝑜𝑟𝑡 ,\\nthe two baseline conditions led to the lowest amount of time\\nspend per teleport: Parabola Teleport ( 𝑀 = 1.31s, 𝑆 D = 0.36s)\\nand Linear Teleport ( 𝑀 = 1.56s, 𝑆 D = 0.60s). The rotation-\\naware teleportation methods led to a higher 𝑡𝑒𝑙𝑒𝑝𝑜𝑟𝑡 : Curved\\nTeleport ( 𝑀 = 2.51s, 𝑆 D = 1.13s), AngleSelect Teleport ( 𝑀 =\\n2.73s, 𝑆 D = 0.86s), and HPCurved Teleport ( 𝑀 = 3.16s, 𝑆 D\\n= 0.89s). A repeated measures ANOVA revealed a significant\\ndifference between the conditions F( 2,787, 52.951) = 46.108,\\n$p < .001$ . Post-hoc pairwise comparisons revealed a sig-\\nnificant difference (all $p < 0.05$ ) between all teleportation\\nmethods except the two baseline methods Linear Teleport vs.\\nParabola Teleport , AngleSelect Teleport vs. Curved Teleport ,\\nand AngleSelect Teleport vs. HPCurved Teleport . The results\\nare also depicted in Figure 10 .\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,857,747,953],\"text\":\"Regarding the time the users took to physically correct\\ntheir position after their last teleport, $t_{corrected}$ , the tele-\\nportation methods led to the following times: AngleSelect\\nTeleport $(M = 1.61s, SD = 0.59s)$ , HPCurved Teleport $(M =\\n1.69s, SD = 0.38s)$ , Linear Teleport $(M = 1.94s, SD = 0.34s)$ ,\\nParabola Teleport $(M = 1.99s, SD = 0.54s)$ , and Curved\",\"reading_order\":13,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,126,1013],\"text\":\"Paper 147\",\"reading_order\":14,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[704,995,745,1013],\"text\":\"Page 7\",\"reading_order\":15,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300377/results/markdown/3290605-3300377_pdf_shortened_page_6.md"}
{"file_name": "images/test_data/3290605-3300377/3290605-3300377_pdf_shortened_page_7.png", "source_folder": "test_data", "document_id": "3290605-3300377", "source_image_path": "test_data/3290605-3300377/3290605-3300377_pdf_shortened_page_7.png", "source_json_path": "test_data/3290605-3300377/results/output_json/3290605-3300377_pdf_shortened_page_7.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,43,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,43,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300377_pdf_shortened_page_7_figure_002.png\",\"bbox\":[68,125,378,276],\"reading_order\":2,\"tags\":[],\"alt_text\":\"This is a bar chart displaying \\\"t_corrected\\\" values on the y-axis for five categories on the x-axis: Linear, Parable, Angle, Curved, and HPCurved. Each bar has an error bar, and asterisks above horizontal lines indicate significant differences between several pairs of categories, including Linear vs. Angle, Linear vs. Curved, Parable vs. Angle, and Angle vs. Curved.\"},{\"label\":\"figure_cap\",\"bbox\":[68,293,395,370],\"text\":\"Figure 11: The average time that participants took to correct\\ntheir position, $t_{corrected}$ , using natural walking for each tele-\\nportation method. All error bars indicate the standard error.\\nThe asterisk ( $^*$ ) indicates a statistically significant difference\\nbetween the teleportation methods.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"half_para\",\"bbox\":[69,404,395,532],\"text\":\"Teleport (M = 2.07s, SD = 0.67s). A repeated measures\\nANOVA revealed a significant difference between the con-\\nditions F(1,787, 33.95) = 8.171, p < .001. A post-hoc test\\nusing pairwise comparisons revealed a significant differ-\\nence between Linear Teleport vs. HP Curved Teleport, Parabola\\nTeleport vs. AngleSelect Teleport, and AngleSelect Teleport vs.\\nCurved Teleport Teleport (all p < 0.05). The results are also\\ndepicted in Figure 11.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,532,395,660],\"text\":\"Lastly, considering the average number of teleportants that\\nwere needed per target according to each teleportation method\\n$n_{teleports}$ , the methods performed the following: The Lin-\\near Teleport (M=1.17, SD=0.23), Parabola Teleport (M=1.08,\\nSD=0.09), AngleSelect Teleport (M=1.13, SD=0.13), Curved\\nTeleport (M=1.1, SD=0.13), and the HPCurved Teleport (M=1.11,\\nSD=0.15). A one-way repeated measures ANOVA did not find\\nany significant effect between the teleport methods.\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,660,395,869],\"text\":\"Regarding the RTLX score measuring the perceived cogni-\\ntive load, the teleportation methods performed the following:\\nLinear Teleport ( $M = 23.9$ , $SD = 12.23$ ), Parabola Teleport\\n( $M = 28.05$ , $SD = 15.24$ ), AngleSelect Teleport ( $M = 30.15$ , $SD$\\n$= 14.12$ ), HCruzed Teleport ( $M = 40.55$ , $SD = 16.94$ ), and\\nCurved Teleport ( $M = 46.15$ , $SD = 18.56$ ). A repeated mea-\\nsures ANOVA revealed a significant difference between the\\nconditions F(2,351, 44.664) = 13.071, p < .001. The post-hoc\\ntest showed a significant difference (all p < .05) between the\\nCurved Teleport vs. the Linear Teleport , Parabola Teleport , and\\nthe Angle Teleport . Further, between the HCruzed Teleport\\nvs. the Linear Teleport and the Parabola Teleport . The results\\nare depicted graphically in Figure 12 .\",\"reading_order\":6,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,882,201,900],\"text\":\"Qualitative Feedback\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,904,395,952],\"text\":\"During our user study and through a semi-structured inter-\\nview after the study, we further collected qualitative feedback\\nabout the five teleportation techniques.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300377_pdf_shortened_page_7_figure_009.png\",\"bbox\":[420,123,730,275],\"reading_order\":9,\"tags\":[],\"alt_text\":\"A bar chart displays \\\"TLX Score\\\" on the y-axis against five categories (\\\"Linear\\\", \\\"Parable\\\", \\\"Angle\\\", \\\"Curved\\\", \\\"HPCurved\\\") on the x-axis. Each category has a colored bar with an associated error bar, indicating the measured score. Lines with asterisks above the bars show statistically significant differences between several pairs of categories.\"},{\"label\":\"figure_cap\",\"bbox\":[420,293,747,355],\"text\":\"Figure 12: The NASA-TLX scores of the VR locomotion tech-\\nniques that were assessed in the user study. All error bars\\nindicate the standard error. The asterisk (') indicates a sig-\\nnificant difference between the locomotion techniques.\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,378,747,554],\"text\":\"The participants generally liked all point & teleport lo-\\ncomotion techniques. Especially the novel techniques were\\npopular: the Curved Teleport and the HPCurved Teleport \\\"felt\\nnatural\\\" (P1), \\\"were fun\\\" (P15) and \\\"the way they work [was\\nperceived as] fancy\\\" (P14). Participants enjoyed the new fea-\\ntures of the teleportation methods. One participant stated\\nthat \\\"the HPCurved Teleport was interesting, especially when\\nmaking short distance teleports with a lot of rotation\\\" (P2).\\nHowever, also the other teleportation techniques were per-\\nceived well. P5 stated that \\\"the Parabola Teleport and Angle-\\nelect Teleport felt simple and intuitive to use\\\".\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,554,747,761],\"text\":\"Considering the baseline teleportation techniques, two\\nparticipants stated that \\\"[they] preferred the Linear Teleport\\nas it was very easy to use\\\" (P6, P15). However, another partici-\\npant stated that \\\"there was no difference between the Linear\\nTeleport and the Parabola Teleport\\\" (P9). Three participants\\n(P9, P16, P17) preferred the AngleSelect Teleport over other\\nteleportation techniques. A user stated that \\\"[he] liked the\\nAngleSelect Teleport, but [he] took longer to use it\\\" (P9). P17\\nenjoyed the novel features as \\\"[she] likes that with the Angle-\\nSelect Teleport a 180° turn becomes possible\\\". Further, another\\nuser stated that \\\"[he] likes using the AngleSelect Teleport, as\\n[he] does not need to move that much in the physical world\\nanymore\\\" (P18).\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,761,747,954],\"text\":\"Considering the design of the experimental environment,\\none participant (P1) mentioned, that she would have pre-\\nferred it when the targets would have spawned directly in\\nfront of her instead of the 10 degrees that was our spawn-\\ning radius. Four participants had problems with the cable of\\nthe HTC Vive. Two participants stated that \\\"they needed\\nto move the HTC Vive cable as it was in the way\\\" (P3, P12).\\nAnother two stated that \\\"they got entangled in the HTC Vive\\ncable\\\" (P8, P13). Interestingly, the qualitative feedback also\\nrevealed that \\\"using the HPCurved, Curved, and AngleSelect\\nTeleport, [users] completely lose track of the orientation in the\\nphysical world\\\" (P11, P12).\",\"reading_order\":13,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,125,1012],\"text\":\"Paper 147\",\"reading_order\":14,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[704,995,744,1012],\"text\":\"Page 8\",\"reading_order\":15,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300377/results/markdown/3290605-3300377_pdf_shortened_page_7.md"}
{"file_name": "images/test_data/3290605-3300381/3290605-3300381_pdf_shortened_page_0.png", "source_folder": "test_data", "document_id": "3290605-3300381", "source_image_path": "test_data/3290605-3300381/3290605-3300381_pdf_shortened_page_0.png", "source_json_path": "test_data/3290605-3300381/results/output_json/3290605-3300381_pdf_shortened_page_0.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\"]},{\"label\":\"icon\",\"bbox\":[0,93,39,133],\"reading_order\":2,\"tags\":[\"cross_marker\"]},{\"label\":\"sec_0\",\"bbox\":[87,119,733,185],\"text\":\"“Pretty Close to a Must-Have:” Balancing Usability\\nDesire and Security Concern in Biometric Adoption\",\"reading_order\":3,\"tags\":[]},{\"label\":\"author_information\",\"bbox\":[127,203,249,252],\"text\":\"Flynn Wolf\\n\\nUMBC\\n\\nflynn.wolf@umbc.edu\",\"reading_order\":4,\"tags\":[\"author\",\"author_mail\"]},{\"label\":\"author_information\",\"bbox\":[354,203,459,251],\"text\":\"Ravi Kuber\\nUMBC\\nrkuber@umbc.edu\",\"reading_order\":5,\"tags\":[\"author\",\"author_mail\"]},{\"label\":\"author_information\",\"bbox\":[542,204,708,252],\"text\":\"Adam J. Aviv\\n\\nUnited States Naval Academy\\n\\naviv@usna.edu\",\"reading_order\":6,\"tags\":[\"author\",\"author_mail\"]},{\"label\":\"sec_1\",\"bbox\":[69,262,141,278],\"text\":\"ABSTRACT\",\"reading_order\":7,\"tags\":[\"paper_abstract\"]},{\"label\":\"para\",\"bbox\":[68,282,395,570],\"text\":\"We report on a qualitative inquiry among security-expert\\nand non-expert mobile device users about the adoption of\\nbiometric authentication using semi-structured interviews\\n(n=38, 19/19 expert/non-expert). Security experts more read-\\nily adopted biometrics than non-experts but also harbored\\ngreater distrust towards its use for sensitive transactions,\\nfeared biometric signature compromise, and in some cases\\ndistrusted newer facial recognition methods. Both groups\\nharbored misconceptions, such as misunderstanding of the\\nfunctional role of biometrics in authentication, and were\\nabout equally likely to have stopped using biometrics due\\nto usability. Implications include the need for tailored train-\\ning for security-informed advocates, better design for device\\nsharing and co-registration, and consideration for usability\\nneeds in work environments. Refinement of these features\\nwould remove perceived obstacles to ubiquitous computing\\namong the growing population of mobile technology users\\nsensitized to security risk.\",\"reading_order\":8,\"tags\":[\"paper_abstract\"]},{\"label\":\"sec_1\",\"bbox\":[69,582,172,600],\"text\":\"CCS CONCEPTS\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,603,393,636],\"text\":\"• Security and privacy → Authentication; • Human-\\ncentered computing → Empirical studies in HCI;\",\"reading_order\":10,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[69,648,149,663],\"text\":\"KEYWORDS\",\"reading_order\":11,\"tags\":[\"paper_keywords\"]},{\"label\":\"para\",\"bbox\":[69,668,394,701],\"text\":\"Authentication, Biometric adoption, Mobile device, Security\\nexpertise\",\"reading_order\":12,\"tags\":[\"paper_keywords\"]},{\"label\":\"sec_1\",\"bbox\":[69,709,202,724],\"text\":\"ACM Reference Format:\",\"reading_order\":13,\"tags\":[]},{\"label\":\"author_information\",\"bbox\":[69,725,394,811],\"text\":\"Flynn Wolf, Ravi Kuher, and Adam J. Aviv. 2019. \\\"Pretty Close to\\na Must-Have.\\\" Balancing Usability Desire and Security Concern\\nin Biometric Adoption. In CHI Conference on Human Factors in\\nComputing Systems Proceedings (CHI 2019), May 4-9, 2019, Glasgow,\\nScotland, UK . ACM, New York, NY, USA, Article 4.12 pages. https:\\n//doi.org/10.1145/3299605.3300381\",\"reading_order\":14,\"tags\":[\"author\",\"meta_pub_date\",\"meta_subject\",\"meta_num\"]},{\"label\":\"sec_1\",\"bbox\":[421,262,549,278],\"text\":\"1 INTRODUCTION\",\"reading_order\":15,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,282,747,395],\"text\":\"Biometric authentication has the potential to increase the us-\\nability of mobile devices. Frequent screen unlocking and ap-\\nplication authorization is accomplished with a quick glance\\nor touch rather than recalling and entering long/complex\\npasscodes [19] . Despite the benefits, adoption can be uneven\\ndue to usability issues [9, 29] and user misunderstanding or\\nsecurity concern [14] .\",\"reading_order\":16,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,395,747,602],\"text\":\"From a security-conscious perspective, allowing a new\\ntechnology to record and store a permanent signature of\\none's self and use it to control access to sensitive data trans-\\nactions might cause deep concern. Research has documented\\nbiometric adoption [2, 25] , experts' sophisticated mental\\nmodels of network security that are distinct from those of\\neveryday users [1, 4, 5, 26, 30, 33] , and the influence that\\nusability [12, 15, 16] and similar models of security have\\non outlook and behavior [8, 12, 14, 16, 27] . However, these\\nstudies have not specifically addressed the overlap of bio-\\nmetric adoption and expert understanding of the underlying\\ntechnologies and threats involved with mobile connectivity,\\nwhich we have investigated in this study.\",\"reading_order\":17,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,602,747,777],\"text\":\"We addressed this knowledge gap through a qualitative\\nstudy comparing biometric adoption for a group of security\\nexpert and non-expert users (n=38, 19/19 expert/non-expert).\\nBased upon criteria from prior research, we defined security\\nawareness for the expert cohort as one year of full-time ex-\\nposure to network security issues through work or academic\\nresearch. Using semi-structured interviews, we found key\\ndifferences and commonalities between these groups, which\\ncan inform design trade-offs between usability and security,\\nas well as how security adoption of mobile technologies more\\ngenerally can affect a broad array of systems.\",\"reading_order\":18,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,777,747,953],\"text\":\"We were further motivated to examine adoption of bio-\\nmetric unlocking because fingerprint recognition has gained\\nsignificant consumer acceptance, but is also a relatively new\\noffering by major technology providers (iOS TouchID is the\\nmost common example in our cohort, and is 5 years old at\\nthe time of this study). As a result, many security conscious\\nusers will have recent memory of how they struck their own\\nbargain between convenience and any doubts about its use.\\nAs facial and voice-based authentication also gain acceptance\\nby mobile device users, our aim is to compare the security\\nbargains made with those of fingerprints.\",\"reading_order\":19,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[69,828,394,896],\"text\":\"ACM acknowledges that this contribution was authored or co-authored\\nby an employer, contractor, or affiliate of the United States government.\\nAs such, the United States government retains a nonexclusive, royalty-free\\nlicense to reproduce this article, or to allow others to do so, for\\ngovernment purposes only.\",\"reading_order\":20,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[69,897,273,952],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland UK\\n© 2019 Association for Computing Machinery\\nACM ISBN 978-1-4503-5970-2/19/05...$15.00\\nhttps://doi.org/10.1145%2F960605.3300381\",\"reading_order\":21,\"tags\":[\"meta_pub_date\",\"meta_subject\",\"meta_num\"]},{\"label\":\"foot\",\"bbox\":[69,996,127,1012],\"text\":\"Paper 151\",\"reading_order\":22,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,996,744,1012],\"text\":\"Page 1\",\"reading_order\":23,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300381/results/markdown/3290605-3300381_pdf_shortened_page_0.md"}
{"file_name": "images/test_data/3290605-3300381/3290605-3300381_pdf_shortened_page_1.png", "source_folder": "test_data", "document_id": "3290605-3300381", "source_image_path": "test_data/3290605-3300381/3290605-3300381_pdf_shortened_page_1.png", "source_json_path": "test_data/3290605-3300381/results/output_json/3290605-3300381_pdf_shortened_page_1.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[68,43,159,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,43,746,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_pub_date\",\"author_affili\"]},{\"label\":\"para\",\"bbox\":[68,125,395,221],\"text\":\"We present six main findings that comparatively describe\\nthe outlook of two important populations of mobile users and\\nhighlight how perspectives on adoption have evolved over\\ntime. We also examine how factors including work require-\\nments and device sharing have impacted users' behavior.\\nOur findings include:\",\"reading_order\":2,\"tags\":[]},{\"label\":\"list\",\"bbox\":[87,223,396,495],\"text\":\"• Both experts and non-experts harbored misunderstand-\\ning about biometric implementation, such as its pri-\\nmary vs. secondary role.\\n• Both groups were subject to usability problems that\\nled to abandoning biometric use.\\n• Experts were more influenced by work/bring-your-\\nown-device (BYOD) authentication requirements than\\nnon-experts.\\n• Experts demonstrated more concern about securing\\ntheir mobile devices.\\n• Experts were more likely to try biometrics immedi-\\nately once available, were slightly more likely to view\\nconsumer biometric security as a good idea in princi-\\nple, and were more likely to recommend the features\\nthan not.\\n• Non-experts were more trusting of biometric autho-\\nrization for financial applications.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,498,395,642],\"text\":\"Finally, we derive from these findings three insights, de-\\nscribed in Section 5 , that can improve the design considera-\\ntions for relevant authentication methods. Specifically, we\\ndiscuss alleviating experts' concern and misunderstanding\\nthrough tailored education for security-informed advocates .\\nSimilarly, we examine the device sharing and work-related\\nobstacles to satisfactory biometric adoption that call for bet-\\nter design for co-registration and usability consideration for\\nwork environments.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,652,198,668],\"text\":\"2 RELATED WORK\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,672,395,850],\"text\":\"Rise of Biometrics. Biometric authentication, including recog-\\nnition of a user's fingerprint, face, or voice, addresses the\\nneed for increased usability with security of mobile devices,\\nwith estimates suggesting all mobile devices will be biometrics\\nenabled by 2020 [21] . Other biometric methods include con-\\ntinuous authentication (e.g. keystroke dynamics over a pe-\\nriod of time) [20] . Research indicates user acceptance, includ-\\ning security and usability, is a major concern in biometric\\nadoption [25] . Recognizing this, manufacturers have pub-\\nlished descriptions of underlying technical protections such\\nas the Apple Secure Enclave to assure users [28] .\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,857,395,955],\"text\":\"Attitudes Towards and Adoption of Biometrics. Biometric\\nadoption has been studied from a number of perspectives.\\nLab-based studies have compared users' task performance\\nand perception of different unlocking methods between oper-\\nating systems, finding complex interaction between biomet-\\nric method and users' task accuracy and acceptance [2, 29] .\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,125,747,236],\"text\":\"Lab and focus group studies have also surveyed user opin-\\nion directly on biometric issues such as acceptance of contin-\\nuous authentication [7] and securing sensitive transactions\\n[17] . Additional facets of biometric adoption examined by\\nresearchers include capturing user motivation in specific geo-\\ngraphic regions [25] , and the impact of romantic relationship\\nstatus on password and account sharing [22] .\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,236,747,298],\"text\":\"The underlying technical integration of biometric unlock-\\ning has also been studied, in terms of potential attack points\\nand countermeasures [20] , and the security of API imple-\\nmentation [3] .\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,298,748,633],\"text\":\"Perceptions of Individuals with Security Expertise. Most di-\\nrectly related to this study, research has also examined the\\nprevalence of mobile security awareness and its influence\\non behaviors such as authentication adoption. These stud-\\nies indicate that security-informed users are a significant\\npopulation with needs worthy of investigation. For exam-\\nple, researchers have examined differences and similarities\\nin security expert and non-expert mental models of the in-\\nformation technology, and how these views may influence\\nbehavior. Kang et al. found advanced mental models of In-\\nternet processes in IT experts imparted more awareness of\\nprivacy risks, but did not translate into more secure behavior\\nversus non-experts [16] . Similarly, Friedman et al. surveyed\\nInternet users from rural, suburban, and high-tech sectors\\nabout web security, and found generally poor comprehension\\nof security features [12] . Stobert and Biddle used thematic\\nanalysis of semi-structured interviews to describe academic\\nand industrial security experts' (n=15) password manage-\\nment. These users were found to vary between laxness and\\ncaution based on perceived risk towards their sensitive ac-\\ntions [27] .\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,633,747,793],\"text\":\"Research has described several effects that security knowl-\\nedge may impart. Das et al. surveyed the influence of de-\\nmographics and security behavioral intention (SBI) on news\\nsharing after major security-related events. High SBI corre-\\nlated with consuming security news online and willingness\\nto share news generally, and specifically with colleagues and\\nsignificant others. The authors were surprised to report a\\ncorrelation between low SBI and willingness to share secu-\\nrity and privacy news when the participant noticed others\\nbehaving insecurely [8] .\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,793,748,954],\"text\":\"Huh et al. also found that prior security awareness played\\na role in adoption of mobile features. An online survey of 349\\nmobile tap-and-pay users and deliberate non-users found\\nusability to be the paramount consideration. However, de-\\nliberate non-users of the feature cited security as their main\\ndeterrent. In that group, there was often misunderstanding\\nof how credit card information was stored on the mobile\\nphone and shared during transactions. A correlation was\\nalso found between security knowledge and adoption of the\\ntap-to-pay feature [14] . Relative non-expertise with security\",\"reading_order\":12,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,127,1012],\"text\":\"Paper 151\",\"reading_order\":13,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[706,995,746,1012],\"text\":\"Page 2\",\"reading_order\":14,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300381/results/markdown/3290605-3300381_pdf_shortened_page_1.md"}
{"file_name": "images/test_data/3290605-3300381/3290605-3300381_pdf_shortened_page_2.png", "source_folder": "test_data", "document_id": "3290605-3300381", "source_image_path": "test_data/3290605-3300381/3290605-3300381_pdf_shortened_page_2.png", "source_json_path": "test_data/3290605-3300381/results/output_json/3290605-3300381_pdf_shortened_page_2.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,43,159,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,43,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"half_para\",\"bbox\":[68,125,394,172],\"text\":\"management has also been studied in terms of understanding\\nof security issues [31, 32] , along with sources that influence\\nnon-expert security decision making [23, 24] .\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,172,395,316],\"text\":\"Biometric adoption has been studied, but not with a com-\\nparative approach towards those with security training. These\\nexpert related studies indicate that security-informed mental\\nmodels may be expected to differentiate some mobile user\\nchoices, but experts and everyday users may both be subject\\nto misunderstanding and not adhere to rigorous security\\npractices. We have examined this cohort alongside everyday\\nusers and describe a number of contrasting themes in their\\nadoption practices.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,327,151,343],\"text\":\"3 METHOD\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,348,395,477],\"text\":\"Objectives. We conducted a study comparing attitudes and\\nexperiences with adoption of biometric unlocking by secu-\\nrity experts and non-expert users. We asked participants to\\ndescribe and reflect on their own adoption of biometrics on\\npersonal or work-issued devices, their opinions of biometric\\nusability and security during the adoption process, and if any\\nof those views have changed since adoption (or instances of\\ndeciding to defer adoption).\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,481,395,674],\"text\":\"Study Design and Procedure. To gather detailed responses\\nto questions regarding a fairly broad range of topics, we de-\\ncided to use semi-structured questioning. A set of research\\nquestions was used to generate a 41-question interview in-\\nstrument that was used with all participants, addressing basic\\nparticipant demographics, mobile usage, and experiences and\\nopinions on biometric authentication. Other topics included\\nexposure to biometrics, BYOD and work-related authentica-\\ntion requirements, and possible concern towards spoofing\\nor compromise of one's biometric signature. Questions were\\ngenerated iteratively through a set of pilot interviews. A list\\nof questions are in the Supplementary Materials.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,674,395,754],\"text\":\"All interviews were conducted by phone or audio Skype,\\nlasting on average 32 minutes. All participants were read\\nan IRB notification describing the study and the handling\\nprocedures for their data, and their consent was recorded.\\nInterviews were conducted, notated, and transcribed.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,761,395,905],\"text\":\"Participant Demographics and Sampling. In total, 38 partic-\\nipants were recruited. 19 security experts and 19 non-experts\\n(detailed in Table 1 ). Experts were recruited through personal\\nnetworking among security researchers and developers to\\nlocate biometrics users, and through attendances at venues\\nwhere experts would be present (e.g. security related con-\\nferences). Non-experts were recruited through a range of\\nmailing lists. Individuals were carefully screened to ensure\\nthat they met criteria defined for each category.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,905,395,954],\"text\":\"To fit under the category of 'expert', a review of compara-\\nble research standards for security expertise was conducted\\nto identify an effective definition [1, 4, 12, 15, 16, 27] . We\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,125,747,347],\"text\":\"adopted a standard of one year of professional or research\\nexposure to network security issues, which was intended\\nto identify those with sensitizing exposure to privacy and\\nnetwork security concerns. While this standard was not in-\\ntended to qualify full domain expertise, our expert cohort\\naveraged 16.9 years of related experience. Two experts had\\nnear-minimum duration experience, but were both highly\\nengaged over at least 2 years in either biometric software\\ndevelopment or network security. Experience with usage of\\nbiometrics on a mobile device (current or previously) was\\nalso a requirement for the study for both experts and non-\\nexperts. Non-experts included individuals with little or no\\nsecurity experience who were current or prior users of bio-\\nmetric unlocking on mobile devices.\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,348,747,506],\"text\":\"The expert group consisted of 15 men and 4 women, with\\nan average age of 50.7 years, an average of 16.9 years of\\nnetwork security experience, and an average of 3.4 years of\\ndirect experience with biometric authentication. The non-\\nexpert group consisted of 12 males and 7 females, with an\\naverage age of 33.6 years and average of 2.5 years of direct\\nexperience using biometric authentication (as described with\\nadditional detail of biometric experience and mobile device\\nuse in Table 2 ). The size of these two groups were similar to\\nthose of related qualitative security research [6, 10, 34] .\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,506,747,680],\"text\":\"Analysis of Transcripts. Inductive thematic analysis was\\nperformed using four reviewers to identify themes from\\nparticipant responses. Initial open coding was conducted\\nwith notes and audio from each interview to sensitize to any\\nthemes or observations, followed by axial coding. The codes\\nwere combined and deconflicted to produce a set of 76 mutu-\\nally exclusive, descriptive themes. These were then further\\niterated and clarified. A subset of the interview transcripts\\n(12.5 % ) were reviewed by a fourth coder, and good inter-rater\\nreliability was found (Cohen's Kappa coefficient ($\\\\kappa$) = 0.72).\\nA subset of codes are shown in Table 3 .\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,680,747,825],\"text\":\"Themes that emerged towards the end of the interview\\nprocess were re-addressed with a subset of expert partici-\\npants in short follow-up interviews. These included ques-\\ntions about using biometrics on work-related versus personal\\ndevices, passcode sharing and biometric co-registration, and\\ndifferences in concern regarding compromise of behavioral\\nversus biometric signature data. Sampling was concluded\\nwhen coding saturation became apparent and no new axial\\ncodes emerged from interviews.\",\"reading_order\":13,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[420,836,508,853],\"text\":\"4 ANALYSIS\",\"reading_order\":14,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,857,747,953],\"text\":\"We address six major findings and 10 sub-findings drawn\\nfrom themes derived from expert and non-expert participant\\nresponses of our expert and non-expert participants (summa-\\nrized in Table 6 ). As shown in Table 4 , users generally favored\\nfingerprint recognition, either iOS (22 total users, 10 experts),\\nAndroid (13 total users, 7 experts), or Windows (7 total users,\",\"reading_order\":15,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,126,1013],\"text\":\"Paper 151\",\"reading_order\":16,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[704,995,746,1013],\"text\":\"Page 3\",\"reading_order\":17,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300381/results/markdown/3290605-3300381_pdf_shortened_page_2.md"}
{"file_name": "images/test_data/3290605-3300381/3290605-3300381_pdf_shortened_page_3.png", "source_folder": "test_data", "document_id": "3290605-3300381", "source_image_path": "test_data/3290605-3300381/3290605-3300381_pdf_shortened_page_3.png", "source_json_path": "test_data/3290605-3300381/results/output_json/3290605-3300381_pdf_shortened_page_3.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_pub_date\"]},{\"label\":\"tab\",\"bbox\":[88,122,368,315],\"text\":\" | | Expert | Non-Expert | Total | | M | F | Total | M | F | Total | 3 | | Age | <21 | 0 | 0 | 0 | 2 | 1 | 3 | 3 | | 22-34 | 2 | 1 | 3 | 5 | 4 | 9 | 12 | | 35-44 | 3 | 1 | 4 | 3 | 1 | 4 | 8 | | 45-54 | 2 | 1 | 3 | 0 | 0 | 0 | 3 | | 55-64 | 4 | 0 | 4 | 1 | 1 | 2 | 6 | | >65 | 1 | 1 | 2 | 0 | 1 | 1 | 6 | | BAM use | <1 yrs | 2 | 1 | 3 | 0 | 1 | 1 | 1 | | 1 to 2 yrs | 4 | 1 | 5 | 2 | 2 | 4 | 9 | | >2 yrs | 8 | 1 | 9 | 7 | 3 | 10 | 19 | | Mobile OS | iOS | 6 | 3 | 9 | 6 | 3 | 9 | 18 | | Android | 7 | 0 | 7 | 1 | 3 | 4 | 11 | | Windows | 4 | 0 | 4 | 0 | 3 | 3 | 7 | | Total | 15 | 4 | 19 | 12 | 7 | 19 | 38 |
\",\"reading_order\":2,\"tags\":[],\"alt_text\":\"A detailed cross-tabulation table presents counts of participants categorized by gender (Male, Female) across two groups: Expert and Non-Expert. The data is further broken down by age ranges, years of BAM (Body Area Monitoring) use, and mobile operating system (iOS, Android, Windows), with sub-totals and grand totals for each category.\",\"figure_path\":\"tables/3290605-3300381_pdf_shortened_page_3_tab_2.png\"},{\"label\":\"table_cap\",\"bbox\":[124,324,334,342],\"text\":\"Table 1: Participant demographics.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"half_para\",\"bbox\":[68,352,394,448],\"text\":\"5 experts). Facial recognition use was still relatively scarce,\\nand its use was divided between Apple FaceID and older\\nWindows facial recognition. Several expert users also had\\npast experience using or developing advanced biometric se-\\ncurity controls, including body weight, iris scanning, and\\nhand size measures.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,448,394,623],\"text\":\"At a high level, we found that experts held an expected\\nhigher degree of concern towards data compromise than\\nnon-experts. This concern shaped both their willingness to\\ntry out new authentication methods and concurrently their\\ndistrust towards using mobile platforms for sensitive transac-\\ntions. However, we also found significant misunderstanding\\nin both groups about how biometric authentication is imple-\\nmented. Both groups feared compromise of their biometric\\nsignatures, and experts, in particular, tended to project their\\nprior knowledge of other network vulnerabilities onto this\\nfear.\",\"reading_order\":5,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[69,639,393,657],\"text\":\"Misunderstanding of primacy of biometric unlocking\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,660,394,788],\"text\":\"While biometrics may function in mobile operating systems\\nas a secondary method of authentication to back up other\\nwhat-you-know passcodes, half of both non-expert and ex-\\npert users misunderstood the biometric method to instead\\nbe the primary means of unlocking their mobile device. This\\nview was also evenly split between the two groups (held by\\n9 experts and 9 non-experts). This view was held at a higher\\nrate than any other code.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[69,802,281,821],\"text\":\"Device sharing and co-registration\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,824,394,952],\"text\":\"A number of participants (n=8, 4 experts) discussed how both\\nbiometrics and passcodes fit into their approach to physi-\\ncally sharing access to their mobile devices with others. For\\nexample, several non-experts reported cross-registering fin-\\ngerprints with partners on their phones to speed up routine\\ntasks and for potential emergencies. However, all expert par-\\nticipants reporting this type of device sharing with their\\nspouses, partners, or children (n=4) preferred only sharing\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,124,747,298],\"text\":\"conventional PIN passcodes, despite using biometric unlock-\\ning for themselves. One non-expert (p37) reported deliber-\\nately not sharing any passcodes or biometric co-registering\\nbecause he did not want his children accessing his devices (\\\"I\\ndon't want them buying stuff\\\"), and to avoid having to reset\\nthe authentication if a trusted relationship ended. Another\\nnon-expert (p38) felt that iOS FaceID was more secure for\\nsharing, on the assumption that faces seemed more unique,\\ndespite acknowledging not knowing the \\\"rhyme or reason\\n...behind the science,\\\" but like other participants chose bio-\\nmetric co-registration for convenience rather than security.\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,299,747,476],\"text\":\"Participants who only shared PINs with others, versus bio-\\nmetric co-registration, described their preference as simply\\nconvenience rather than reluctance to co-register because\\nof social or security concern. For example, an expert partici-\\npant (p25, an academic security researcher and application\\ndeveloper) described telling his PIN to his children while\\ndriving so they could control music in the car without dis-\\ntracting him. However, it is possible that co-registration is not a\\nfamiliar option, given that other expert participants described\\nsimply being unaware that multiple fingers could actually be\\nregistered.\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,476,747,714],\"text\":\"Participants who had tried and discarded biometric au-\\nthentication still shared passcodes, even if they did not use\\nPINs. A non-expert university professor, p32, had stopped\\nusing Android fingerprint recognition stating, \\\"I really hate\\nit, a lot\\\" because of frequent false negatives, but still shared\\ngrid pattern shapes that could be described verbally with\\nher husband. The instances of misunderstanding towards\\nbiometric co-registration and the likelihood of interaction\\nwith other usability and security concerns found here indi-\\ncate a need for more effective design for device co-registration,\\ndiscussed in Section 5 . Device sharing and co-registration\\noccurred within a number of relationship types (e.g. with\\nboth children, spouses, or romantic partners) and among a\\nwide range of age that suggests very limited potential for\\nage effects.\",\"reading_order\":12,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,725,717,743],\"text\":\"Experts more influenced by BYOD requirements\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,746,747,953],\"text\":\"A majority of those reporting that work-related requirements\\ninfluencing their authentication choices were experts (6 of\\n8). We acknowledge that this may also reflect the higher\\naverage age of the expert cohort (50 years old, versus 33\\nfor non-experts), which could place them further along in\\ntechnology-related careers that might contribute to this ef-\\nfect, rather than it being solely a product of their network\\nsecurity exposure. The average age of participants reporting\\nthis code was 46.6 years old. Of those experts, several were\\nrequired to add authentication through contract or employ-\\nment conditions. One expert (P04, a government security\\nresearcher) explained her caution towards authentication\\nchoices for work related devices, stating `I'm very cognizant\",\"reading_order\":14,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,127,1012],\"text\":\"Paper 151\",\"reading_order\":15,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[706,995,745,1012],\"text\":\"Page 4\",\"reading_order\":16,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300381/results/markdown/3290605-3300381_pdf_shortened_page_3.md"}
{"file_name": "images/test_data/3290605-3300381/3290605-3300381_pdf_shortened_page_4.png", "source_folder": "test_data", "document_id": "3290605-3300381", "source_image_path": "test_data/3290605-3300381/3290605-3300381_pdf_shortened_page_4.png", "source_json_path": "test_data/3290605-3300381/results/output_json/3290605-3300381_pdf_shortened_page_4.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,61],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,44,746,62],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_pub_date\"]},{\"label\":\"tab\",\"bbox\":[118,102,690,452],\"text\":\"| Participant # | Expertise | Expert Domain | Nature | Experience (Yrs) | Age | Gender | Current Biometric OS/Device | Biometer Use | | 1 | Expert | Expert Domain | 2 | 25 | M | Android | iPhone 6 | 2 yrs (Phone) 3 yrs (Laptop) | | 2 | Expert | Gov & research | 2 | 25 | M | Android | Android (Phone) & Win/Laptop | 2 yrs (Phone) 3 yrs (Laptop) | | 3 | Non-expert | Gov & research | 21 | 21 | M | iPhone | iPhone 6 | 3 yrs (phone) 1.5 yrs (iPad) | | 4 | Expert | Gov & Research | 21 | 21 | M | iPhone | Win/Laptop | 4 mos | | 5 | Expert | Industry | 12 | 42 | M | iPhone | iPhone 6 | 1 yrs | | 6 | Non-expert | Gov & research | 30 | 30 | F | iPhone | iPhone 6 | 1 yrs | | 7 | Non-expert | Gov & research | 22 | 22 | M | iPhone | iPhone 6 | 2 yrs (Phone) 3 yrs (Laptop) | | 8 | Non-expert | Gov & research | 21 | 21 | M | iPhone | iPhone 6 | 3 yrs (Phone) 6 yrs (Laptop) | | 9 | Non-expert | Gov & research | 22 | 22 | F | iPhone 6 | iPhone 6 | 3 yrs (Phone) 6 yrs (Laptop) | | 10 | Non-expert | Gov & research | 21 | 21 | M | iPhone | iPhone 6 | 3 yrs (Phone) 6 yrs (Laptop) | | 11 | Non-expert | Gov & research | 20 | 20 | M | Android | Android (Phone) | 2 yrs | | 12 | Non-expert | Gov & research | 20 | 20 | M | Android | Android (Phone) | 1.5 yrs | | 13 | Non-expert | Gov & research | 20 | 20 | M | Android | Android (Phone) | 1.5 yrs | | 14 | Non-expert | Gov & research | 20 | 20 | M | Android | Android (Phone) | 10 mos (no previous device) | | 15 | Non-expert | Gov & research | 25 | 25 | M | Android | Android (Phone) | 10 mos (no previous device) | | 16 | Non-expert | Gov & research | 27 | 27 | M | Android | Android (Phone) | 10 mos (no previous device) | | 17 | Non-expert | Gov & research | 26 | 26 | M | Android | Android (Phone) | 10 mos (no previous device) | | 18 | Non-expert | Gov & research | 26 | 26 | M | Android | Android (Phone) | 10 mos (no previous device) | | 19 | Non-expert | Gov & research | 26 | 26 | M | Android | Android (Phone) | 10 mos (no previous device) | | 20 | Non-expert | Gov & research | 26 | 26 | M | Android | Android (Phone) | 10 mos (no previous device) | | 21 | Non-expert | Gov & research | 2 | 2 | F | Android | Android (Phone) | 10 mos (no previous device) | | 22 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 23 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 24 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 25 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 26 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 27 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 28 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 29 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 30 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 31 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 32 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 33 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 34 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 35 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 36 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 37 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 38 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 39 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 40 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 41 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 42 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 43 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 44 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 45 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 46 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 47 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 48 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 49 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 50 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 51 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 52 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 53 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 54 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 55 | Non-expert | Gov & research | 2 | 2 | M | Android | Android (Phone) | 10 mos (no previous device) | | 56 | Non-expert | Gov & research | 2 | 2 | | Code | $\\\\kappa$ | n | Sample Quote | | BAM considered primary rather than secondary un-locking method | 86 | 19, 8 experts | "I would never use biometric alone as the primary on anything without backain, an anything hiding secure information" (p25) | | Would not use BAM on a work/high security device | 100 | 7, 5 experts | "If I think about it deeply I don't like it. I start to think about ridiculous murder mystery things that wouldn't happen" (p23) | | Would recommend BAM use to others | 95 | 22, 12 experts | "I’d say go for it... [but used] flug products" (p23) | | Work requirements influence personal device use | 69 | 6, 8 experts | "Try to be secure, but I had to use them [BAM] for work" (p05) | | Did not try BAM immediately when available | 84 | 11, 5 experts | "I'd [f]ound recommended that I let everyone throw everything at it first" (p23) | | When first heard of it, consumer BAM sounded like a good idea. | 80 | 11, 8 experts | "I thought its about time to allow biometrics" (p14) |
\",\"reading_order\":2,\"tags\":[],\"alt_text\":\"This is a table presenting qualitative data analysis with four columns: \\\"Code,\\\" \\\"κ\\\" (kappa coefficient), \\\"n\\\" (number of experts), and \\\"Sample Quote.\\\" It lists six different codes related to Biometric Authentication Methods (BAM), such as \\\"BAM considered primary rather than secondary unlocking method\\\" and \\\"Would not use BAM on a work/high security device,\\\" each with its corresponding statistical values and a supporting quote.\",\"figure_path\":\"tables/3290605-3300381_pdf_shortened_page_5_tab_2.png\"},{\"label\":\"table_cap\",\"bbox\":[334,281,478,298],\"text\":\"Table 3: Example codes\",\"reading_order\":3,\"tags\":[]},{\"label\":\"half_para\",\"bbox\":[69,317,394,350],\"text\":\"alone for unlocking a work device, unless it also had allowed\\nan alphanumeric password for disk encryption.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[69,363,192,380],\"text\":\"Biometric Adoption\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,383,395,703],\"text\":\"Experts are more likely to be early adopters of biometrics. We\\nexamined participants' descriptions of the interval between\\nacquiring a biometric-capable mobile device and setting up\\nthe feature (n=14, 11 experts). This is of interest assuming\\nthat users may have security-informed motivations for either\\nusing immediately or avoiding a new authentication method.\\nA preponderance of the experts interviewed reported at least\\ntrying out the biometric unlocking feature immediately when\\nit was first available. Given the even split between using An-\\ndroid and iOS devices for experts, this effect was likely a\\nresponse to several aspects of the expert cohort. Firstly, the\\nexpert cohort had a greater rate of direct familiarity with\\nbiometrics (21 % had prior direct experience with biometrics\\noutside of unlocking a personal mobile device, through pur-\\nposes such as using or developing building access controls).\\nSecondly, it might be assumed that experts, as a result of\\ntheir general training and experience with security, would\\nbe more likely to know the usability and security difficulties\\nassociated with recalling strong what-you-know passcodes\\nand want a better what-you-are biometric alternative.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,710,395,953],\"text\":\"Experts view biometrics positively post-adoption . We found\\nthat experts were almost evenly divided on whether they ini-\\ntially thought biometric authentication would be a positive\\nor negative feature to offer consumers, although a majority\\nof all participants favoring the idea were experts (n=13, 8\\nexperts). Prolonged exposure (primarily fingerprint recogni-\\ntion, see Table 4 ) appears to have put experts at ease. “ It's\\npretty close to a must-have [feature]: ” stated one (p30), “ it's\\njust so doggone convenient. ” When asked whether they now,\\nhaving used biometric unlocking, have or would recommend\\nit to others, experts were more favorable than not. It is also\\npossible that the higher rate of immediate use of biometrics\\nin experts is attributable to their slightly higher age and the\\ncommensurate exposure to technology that could support\\nexploring and trying out new features (the average age of\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,317,746,445],\"text\":\"participants reporting this code was 43.9 years old). One\\nexpert (P04) stated, \\\"I definitely went to the TouchID as a\\npreferred authorization method and that was just because of\\nmy own experience and knowledge of how passwords can\\nbecome co-opted very easily. That influenced me, once I had\\nthat available on my iPhone to just immediately enable that\\nfeature... That's based on my work experience and what I\\ncan see and the problems I see people having.\\\"\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,455,747,759],\"text\":\"Non-experts delay adoption of biometrics. A slightly higher\\nrate of non-experts also reported having deliberately waited\\nto try out their biometric unlocking features, compared to\\nthe rate of reported initial avoidance in experts. In the case\\nof experts this reluctance could be attributable to a generally\\nmore polarized opinion on security matters. Non-experts'\\nmay have a relative lack of technological experience (in part\\ndue to their lower average age) that produces caution or\\nindifference towards new and unfamiliar security features.\\nThe average age of participants reporting this code was 41.7\\nyears old. One non-expert (p27) felt it best to wait to try the\\nfingerprint unlocking features on both his Android phone\\nand iOS tablet out of a non-specific sense of security caution\\nto \\\"let everyone throw everything at it first.\\\" Another (p07)\\ninitially felt that the TouchID feature on her iPhone was an\\nunnecessary \\\"stupid rich person's feature.\\\" She eventually\\ntried the feature after a year, to \\\"keep in touch, in the same\\nboat with technology,\\\" but still wondered, \\\"is it really safe?\\\"\\nand would its use might \\\"make my brain lazy.\\\"\",\"reading_order\":9,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,772,710,789],\"text\":\"Expert caution with securing data biometrically\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,792,747,954],\"text\":\"At the same time that experts demonstrated more eagerness\\nto try out biometric unlocking, they also distrusted it to\\nsecure their most sensitive data. While only a portion of\\nthe total cohort reported avoiding biometrics entirely for\\nsecuring a highly sensitive device, that view was primarily\\nheld by experts (n=7, 5 experts). Similarly, concern over sale\\nor leakage of one's biometric signature was a view only\\nreported by experts (n=6 experts), often stating variations\\non “ you can't grow a new thumb (p15, a government and\\nindustry security developer). An expert (p04) stated, “ I don't\",\"reading_order\":11,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[70,995,127,1012],\"text\":\"Paper 151\",\"reading_order\":12,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[705,995,745,1012],\"text\":\"Page 6\",\"reading_order\":13,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300381/results/markdown/3290605-3300381_pdf_shortened_page_5.md"}
{"file_name": "images/test_data/3290605-3300381/3290605-3300381_pdf_shortened_page_6.png", "source_folder": "test_data", "document_id": "3290605-3300381", "source_image_path": "test_data/3290605-3300381/3290605-3300381_pdf_shortened_page_6.png", "source_json_path": "test_data/3290605-3300381/results/output_json/3290605-3300381_pdf_shortened_page_6.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,61],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\"]},{\"label\":\"half_para\",\"bbox\":[68,125,395,332],\"text\":\"know that people think about [trusting a device to store their\\nfingerprint data], but if it's a foreign government... I might\\nbe a little more concerned. There's some acceptability issues,\\ndepending on who has access to your info.\\\" \\\"The fact that\\nbiometrics can be forced out of you, against your will... I\\n shy away from those,\\\" another expert (P05) stated, adding \\\"I\\nwould never use a biometric method alone as the primary,\\nwithout backup, on anything holding secure information... if\\na fingerprint is the only thing between you and the keys to\\nthe kingdom, that's a bad design.\\\" Further, the user's trust\\nwas limited by knowledge of who was providing the device\\nand services, saying, \\\"Two companies I don't place my trust\\nin are Apple and Google.\\\"\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,339,395,723],\"text\":\"Experts much more reluctant to biometrically authorize fi-\\nnancial applications. In addition to these views, using bio-\\nmetrics to authorize financial applications (including bank\\napps and payment apps such as PayPal or Venmo) was much\\nless prevalent in experts. Many participants used biometrics\\nto authorize these types of financial applications, but non-\\nexperts outpaced experts in allowing this. Interestingly, only\\none non-expert (p09) explicitly pointed out that biometric\\nunlocking of his banking application allowed him to use a\\nlonger complex passcode for better security because he did\\nnot have to remember it or write it down. Demonstrating\\nthis added level of concern with financial transactions on a\\nmobile platform, expert p05 stated, “After having my wife’s\\nphone stolen and the credit card info extracted, I, you know,\\nwipe down any device I’d ever used for any type of transac-\\ntion... Basically reinstall the OS. Wipe it down. Remove any\\ntransaction history, and when that’s not possible I use in-\\ncreasing layers of disk encryption to protect the files that are\\nstored.” Participants were also asked if security had factored\\nin when choosing their current mobile devices. Participants\\nin general were almost twice as likely to have voiced affir-\\nmative responses (n=17, 12 experts) as negative responses\\n(n=9, 3 experts), but experts specifically were four times as\\nlikely to cite concern with security features.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,730,395,953],\"text\":\"Biometric adoption interacting with experts’ distrust towards\\nmobile platforms. These findings of both greater expert cu-\\nriosity and concern towards authentication suggest that ex-\\nperts view biometrics with a sort of “accept but restrict” ap-\\nproach, in which quick adoption based on prior familiarity\\nor principles of secure computing are balanced with doubt\\ntowards the trustworthiness of mobile platforms in general.\\nIndicative of this, when asked which was more secure, of\\ntheir laptop (n=11, 5 experts) or smartphone (n=4, 1 expert),\\nmore experts trusted the desktop operating systems over\\nmobile. There is “less idea of what’s going on in the back-\\nground” of his Motorola Android phone, versus a PC, one\\nexpert (p30) related. Some experts also expressed preferences\\nin mobile platform based on configurability for security. For\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,125,746,172],\"text\":\"example, p05 stated “I would pick one [Android] over the\\nother because I believe I understand how to secure its attack\\nsurfaces more efficiently.”\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,186,747,506],\"text\":\"Experts and non-experts both aware of biometric spoofing .\\nHowever, while experts offered detailed descriptions of threat\\nmodels of data leaks or hacking that amplified their distrust,\\nawareness of risks to biometrics was not exclusive to that\\ncohort at all. In response to late-added question specifically\\nabout biometric-spoofing stories, an almost equal number of\\nnon-experts and experts (n=7, 3 experts) could recall exam-\\nples. Experts could place these stories in context (for example,\\np30 stating, “we’re in an arms race with gummy bears [used\\nto spoof fingerprints]”), but macabre news reports of ampu-\\ntated fingers being used in criminal heists (p29, non-expert)\\nand YouTube videos of fake heads unlocking phones (p27,\\nnon-expert) appeared to have also made a mark with non-\\nexperts. More specifically, an equal number (n=4, 2 experts)\\nof expert and non-expert participants also stated in almost\\nexactly similar terms that they did not feel like they were a\\n“high value target” that would warrant elaborate biometric-\\nspoofing. One expert, (p28) stated, “I generally don’t hang\\naround those types of people [who might be targeted with\\nelaborate spoofing techniques]. They build rockets.”\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,519,747,790],\"text\":\"Apprehension towards facial recognition. The number of\\ncurrent facial recognition users was low compared to finger-\\nprint recognition. Participants mentioned two factors con-\\ntributing to this. Apple FaceID was only recently released\\nat the time of this inquiry, and some curious iOS users were\\nnot ready to upgrade to a compatible device. Other laptop-\\nbased facial recognition users had stopped use because of\\nfrequent false negatives. Participants who were current or\\nprior users of other forms of biometric unlocking (n=7, 5\\nexperts) expressed that they were already resolved to not\\nimmediately adopt facial recognition. A non-expert (p27) felt\\nthe approach was \\\"unnecessarily personal,\\\" and might be\\nexploitable if Facebook's algorithms for facial recognition\\nwere also hacked. One expert (p26, an academic security re-\\nsearcher) echoed a common sentiment that new technologies\\nwere generally untrustworthy, and he felt it \\\"would be better\\nto wait to see how things play out.\\\"\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,791,747,951],\"text\":\"Given this lack of recent and direct experience with facial\\nrecognition, it evoked doubt and concern about its secu-\\nrity that fingerprint recognition had largely overcome in\\nthe same users. The acceptance of one biometric method did\\nnot readily transfer to another, even on the same device . Ex-\\nperts who had overcome their doubts and become fingerprint\\nrecognition users still expressed similar concerns about fa-\\ncial recognition. Participant 05, a recent user of fingerprint\\nrecognition, saw facial recognition as “still not controllable.”\\nAnother expert (p35) thought it “strange for social norms;”\",\"reading_order\":8,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,996,127,1012],\"text\":\"Paper 151\",\"reading_order\":9,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[706,996,745,1012],\"text\":\"Page 7\",\"reading_order\":10,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300381/results/markdown/3290605-3300381_pdf_shortened_page_6.md"}
{"file_name": "images/test_data/3290605-3300381/3290605-3300381_pdf_shortened_page_7.png", "source_folder": "test_data", "document_id": "3290605-3300381", "source_image_path": "test_data/3290605-3300381/3290605-3300381_pdf_shortened_page_7.png", "source_json_path": "test_data/3290605-3300381/results/output_json/3290605-3300381_pdf_shortened_page_7.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,43,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[]},{\"label\":\"header\",\"bbox\":[474,43,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[]},{\"label\":\"half_para\",\"bbox\":[68,125,395,205],\"text\":\"easier to fake, and potentially discriminatory. Even partici-\\npants who had accepted facial recognition expressed doubt.\\nA non-expert (p09) was dubious of the feature “doing lots of\\ncrazy stuff,” and found the prospect of his facial signature\\nbeing compromised “terrifying.”\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,217,395,744],\"text\":\"Context on fear of biometric signature compromise. It is\\nworth observing that major concerns that security conscious\\nusers commonly expressed about face and fingerprint recog-\\nnition, namely that it could leak or be stolen off the device\\n(rendering their face or fingerprint forever unusable), are not\\nvery likely. For example, Apple's TouchID feature (the most\\ncommon biometric method among participants) is stated to\\nonly collect the scan of the user's finger in a format that\\nis not reconstructable as a fingerprint scan. The imagery\\nis also not tagged with that user's identity, not uploaded\\nanywhere off the device, and is only stored and encrypted\\nlocally. A secure boot chain, system software authorization\\nprocesses, and unique session keys also control how an Apple\\ndevice's processor and Secure Enclave exchange informa-\\ntion about the fingerprint scan, lessening the likelihood that\\nmalware could easily spoof a component to obtain sensitive\\nbiometric information [28] . Despite these safeguards, con-\\ncern with biometric signature compromise transferred to\\nexperts' approach to setting up device access. For example,\\nseveral experts (p15 and p30) deliberately limited the num-\\nber of fingers they would register, either to have a reserve\\nfingerprint if all others were compromised by a data leak or\\nto reduce the perceived possibility of false positives. This\\nfear of signature data leaks was acute for some experts. One\\nexpert (p30) explained, “I just assume it's going to get out\\nat some point [his biometric signature]... I do think I will\\nregret it. Hopefully not soon. Maybe it will only be iOS users.”\\nThese overlapping instances of expert distrust and misunder-\\nstanding towards features including biometric registration,\\nmobile financial transactions, and facial recognition and their\\nimpact on adoption and expert advocacy suggest a need for\\ntailored education for security-informed users , discussed in\\nSection 5 .\",\"reading_order\":3,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,757,395,951],\"text\":\"Impact of 2015 San Bernardino shooting story on both ex-\\nperts and non-experts. News accounts of law enforcement\\nefforts to unlock an iPhone belonging to a suspect in the\\n2015 San Bernardo shooting made a similar impression\\nto that of biometric spoofing. Respondents (n=4, 1 expert)\\nmentioned this as positively impacting their sense of security\\nwith their own device, since law enforcement appeared ini-\\ntially to be unable to unlock the phone. Of these participants,\\nthree of the four were non-experts. Other news stories drew\\nmention as influences on authentication behavior, including\\ncompromise of biometric signatures in the Indian Aadhaar\\ndatabase and the 2013 Snowden leaks (p24, expert).\",\"reading_order\":4,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[422,124,717,141],\"text\":\"Expert and non-expert biometric usability issues\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[422,144,747,271],\"text\":\"Participants described trying out but then abandoning some\\ntype of biometric unlocking (n=16, 9 experts), mostly at-\\ntributed to usability problems and nearly evenly distributed\\nbetween experts and everyday users (n=12, 7 experts). Many\\ninstances involved unreliable biometrics on older Windows\\nlaptops. One expert (P02) explained his frustration with the\\nfingerprint reader on his Windows laptop, stating, \\\"Nobody\\nis getting in there with a fingerprint, including me usually.\\\"\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[422,271,747,464],\"text\":\"Other users stopped using biometrics because they sim-\\nply felt that PIN entry was quicker (n=2). However, several\\nexperts described security concerns that caused them to\\nstop using a biometric unlocking method. One expert (p24)\\nwas alarmed enough by reported leaks of his phone's op-\\nerating system code to stop using fingerprint recognition,\\nwhile another (p05) disabled his laptop fingerprint reader\\nafter learning details of implementation in the operating\\nsystem that he considered untrustworthy. One non-expert\\n(p03) felt TouchID was too unreliable for regular unlocking\\nand switched back to PIN use, but liked its added security\\nenough to still use for authorizing purchases in applications.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[422,464,748,719],\"text\":\"Participants (n=17, 6 experts) also described other types\\nof biometric usability problems. Again, many had struggles\\nparticularly with older laptop biometric unlocking. The most\\ncommonly described problem was relatively simple: wet or\\noily fingers not being read well (n=12, 4 experts). However,\\nparticipants explained other theories for unlocking problems,\\nincluding speculation that winter weather changes skin to\\nmake it unrecognizable, or that the phone itself might work\\nless reliably when cold. Others guessed that the phone could\\nonly read fingerprints accurately at certain angles (n=2, 1\\nexpert). Frustrated laptop facial recognition users, including\\nexperts, speculated that non-facial factors such as hairstyle\\nor background lighting caused interference. A non-expert\\nfacial recognition user (p37) felt the feature very reliably\\nidentified him with different beard lengths, but failed if he\\nswitched from his regular sunglasses to his golf sunglasses.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[422,738,619,755],\"text\":\"Changing perceptions over time\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[422,758,747,951],\"text\":\"Users noted a number of ways that their authentication ap-\\nproach had changed over time (n=13, 10 experts), but this\\ntype of observation was most commonly shared by experts.\\nTheir adjustments intended to add more security or try new\\nmethods with better usability and the same level of secu-\\nrity. These security-enhancing actions included adding two-\\nfactor account authentication (p02), adding boot encryption\\nto devices before travel (p04), and changing duplicate pass-\\nwords (p15). One expert (p24) and non-expert (p37) also\\nnoted changes made after biometric adoption. p24 was com-\\nfortable enough with the security of fingerprint recognition\\non his phone to reduce his PIN from 6 to 4 digits for quicker\",\"reading_order\":10,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,127,1013],\"text\":\"Paper 151\",\"reading_order\":11,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[705,995,745,1013],\"text\":\"Page 8\",\"reading_order\":12,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300381/results/markdown/3290605-3300381_pdf_shortened_page_7.md"}
{"file_name": "images/test_data/3290605-3300388/3290605-3300388_pdf_shortened_page_0.png", "source_folder": "test_data", "document_id": "3290605-3300388", "source_image_path": "test_data/3290605-3300388/3290605-3300388_pdf_shortened_page_0.png", "source_json_path": "test_data/3290605-3300388/results/output_json/3290605-3300388_pdf_shortened_page_0.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"icon\",\"bbox\":[0,93,38,132],\"reading_order\":2,\"tags\":[\"cross_marker\"]},{\"label\":\"sec_0\",\"bbox\":[157,118,657,183],\"text\":\"Virtual Performance Augmentation\\nin an Immersive Jump & Run Exergame\",\"reading_order\":3,\"tags\":[]},{\"label\":\"author_information\",\"bbox\":[86,201,400,235],\"text\":\"Christos Ioannou, Patrick Archard, Eamonn O'Neill\\nUniversity of Bath, UK\",\"reading_order\":4,\"tags\":[\"author\",\"author_affili\"]},{\"label\":\"author_information\",\"bbox\":[467,201,677,249],\"text\":\"Christof Lutteroth*\\nUniversity of Bath, UK\\nUniversity of Auckland, New Zealand\",\"reading_order\":5,\"tags\":[\"author\",\"author_affili\"]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300388_pdf_shortened_page_0_figure_006.png\",\"bbox\":[87,269,727,413],\"reading_order\":6,\"tags\":[],\"alt_text\":\"This image illustrates a virtual reality system and its interaction mechanics, showing a person physically running and jumping in place while wearing a VR headset and sensors. Corresponding virtual environments display a road with trucks, and diagrams explain how real-world movements (running in-place, jumping in-place) are translated via a Virtual Perceptual Algorithm (VPA) into virtual forward motion and jumping, with specific scaling factors for speed and height.\"},{\"label\":\"figure_cap\",\"bbox\":[68,422,747,497],\"text\":\"Figure 1: a) The exergame is played by running and jumping in place while wearing a head-mounted display. b) A sense of\\nembodiment is created through visual-motor synchronicity with an avatar. c) Running, jumping over \\\"lava gaps\\\", and avoiding\\ntrucks during gameplay. d) In place running with estimated speed is used to simulate forward running in VR with the speed\\naugmented by a factor $\\\\alpha_{run}$ . d) In place jumping is used to simulate forward jumping in VR with the height augmented by a\\nfactor $\\\\alpha_{jump}$ .\",\"reading_order\":7,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[69,505,141,522],\"text\":\"ABSTRACT\",\"reading_order\":8,\"tags\":[\"paper_abstract\"]},{\"label\":\"para\",\"bbox\":[69,525,395,734],\"text\":\"Human performance augmentation through technology has\\nbeen a recurring theme in science and culture, aiming to\\nincrease human capabilities and accessibility. We investigate\\na related concept: virtual performance augmentation (VPA),\\nusing VR to give users the illusion of greater capabilities than\\nthey actually have. We propose a method for VPA of running\\nand jumping, based on in place movements, and studied its\\neffects in a VR exergame. We found that in place running\\nand jumping in VR can be used to create a somewhat natural\\nexperience and can elicit medium to high physical exertion\\nin an immersive and intrinsically motivating manner. We\\nalso found that virtually augmenting running and jumping\\ncan increase intrinsic motivation, perceived competence and\",\"reading_order\":9,\"tags\":[\"paper_abstract\"]},{\"label\":\"para\",\"bbox\":[421,506,747,585],\"text\":\"flow, and may also increase motivation for physical activity\\nin general. We discuss implications of VPA for safety and\\naccessibility, with initial evidence suggesting that VPA may\\nhelp users with physical impairments enjoy the benefits of\\nexergaming.\",\"reading_order\":10,\"tags\":[\"paper_abstract\"]},{\"label\":\"sec_1\",\"bbox\":[421,596,524,613],\"text\":\"CCS CONCEPTS\",\"reading_order\":11,\"tags\":[]},{\"label\":\"list\",\"bbox\":[421,617,747,665],\"text\":\"• Applied computing $\\\\rightarrow$ Computer games; Consumer\\nhealth; • Human-centered computing $\\\\rightarrow$ Virtual real-\\nity.\",\"reading_order\":12,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,676,502,693],\"text\":\"KEYWORDS\",\"reading_order\":13,\"tags\":[\"paper_keywords\"]},{\"label\":\"para\",\"bbox\":[422,697,737,714],\"text\":\"Exergame; virtual reality; performance; running; jumping\",\"reading_order\":14,\"tags\":[\"paper_keywords\"]},{\"label\":\"sec_1\",\"bbox\":[422,721,554,736],\"text\":\"ACM Reference Format:\",\"reading_order\":15,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,737,747,823],\"text\":\"Christos Ioannou, Patrick Archard, Eamonn O’Neil and Christof\\nLutteroth. 2019. Virtual Performance Augmentation in an Inter-\\nive Jump & Run Exergame. In CHI Conference on Human Fac-\\ntors in Computing Systems Proceedings (CHI 2019), May 4–9, 2019,\\nGlasgow, Scotland .UK: ACM, New York, NY, USA, 15 pages. https:\\n//doi.org/10.1145/3299665.330388\",\"reading_order\":16,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[422,836,549,853],\"text\":\"1 INTRODUCTION\",\"reading_order\":17,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,858,747,955],\"text\":\"Human performance augmentation through technology has\\nbeen a theme in science and culture for decades [37] . The idea\\nof augmenting human physical, mental and social capabilities\\nhas intrigued scientists from many disciplines [94] and led,\\nfor example, to the development of powered exoskeletons\\n[5, 33, 131] . Similarly, performance augmentation, especially\",\"reading_order\":18,\"tags\":[]},{\"label\":\"author_information\",\"bbox\":[70,748,250,761],\"text\":\"$^{*}$Correspondence: c.lutteroth@bath.ac.uk\",\"reading_order\":19,\"tags\":[\"author_mail\"]},{\"label\":\"fnote\",\"bbox\":[69,777,394,897],\"text\":\"Permission to make digital or hard copies of all or part of this work for\\npersonal or classroom use is granted without fee provided that copies\\nare not made or distributed for profit or commercial advantage and that\\ncopies bear this notice and the full citation on the first page. Copyrights\\nfor components of this work owned by others than the author(s) must\\nbe honored. Abstracting with credit is permitted. To copy otherwise, or\\nrepublish, to post on servers or to redistribute to lists, requires prior specific\\npermission and/or a fee. Request permissions from permissions@acm.org.\\nCH 2019, May 4-9, 2019, Glasgow, Scotland UK\",\"reading_order\":20,\"tags\":[\"meta_pub_date\"]},{\"label\":\"fnote\",\"bbox\":[69,900,394,924],\"text\":\"© 2019 Copyright held by the owner/author(s). Publication rights licensed\\nto ACM.\",\"reading_order\":21,\"tags\":[\"meta_pub_date\"]},{\"label\":\"fnote\",\"bbox\":[70,925,265,953],\"text\":\"ACM ISBN 978-1-4503-5970-2/19-05...$15.00\\nhttps://doi.org/10.1145/3209605.3300388\",\"reading_order\":22,\"tags\":[\"meta_num\"]},{\"label\":\"foot\",\"bbox\":[70,996,127,1012],\"text\":\"Paper 158\",\"reading_order\":23,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,996,744,1012],\"text\":\"Page 1\",\"reading_order\":24,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300388/results/markdown/3290605-3300388_pdf_shortened_page_0.md"}
{"file_name": "images/test_data/3290605-3300388/3290605-3300388_pdf_shortened_page_1.png", "source_folder": "test_data", "document_id": "3290605-3300388", "source_image_path": "test_data/3290605-3300388/3290605-3300388_pdf_shortened_page_1.png", "source_json_path": "test_data/3290605-3300388/results/output_json/3290605-3300388_pdf_shortened_page_1.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,43,159,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,43,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"half_para\",\"bbox\":[68,125,395,267],\"text\":\"physical augmentation, has been a common theme in many\\ncomputer games: players control characters that are more\\nskilled, can run faster and jump higher, often far beyond a\\nplayer's own realistic capabilities. Despite lack of realism,\\nplayers often identify with their game character and are\\nlikely to perceive attributes of their game character in them-\\nselves [10, 46, 58, 60, 89, 115, 127] . As a result, augmentation\\nof the character in the game becomes a virtual performance\\naugmentation (VPA) of the players themselves.\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,267,395,476],\"text\":\"Our focus here is on the use of VPA in a VR exergame,\\nwhere physical performance is a central concept [86] . With\\nimmersive VR and natural interaction technology, it has be-\\ncome possible to map a player's real physical performance\\nclosely to the in-game performance of an avatar [28, 71, 108,\\n109] . This visual-motor synchronicity strengthens the identi-\\nfication between the player and the avatar, inducing a feeling\\nof embodiment [59, 106] that may facilitate the creation of\\na convincing VPA experience. Exergames generally aim to\\nimprove the performance of the player, e.g. to improve their\\nfitness or help them rehabilitate from a medical condition.\\nAdding VPA to a VR exergame could have the following\\npositive implications.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,476,395,603],\"text\":\"Increased Motivation: VPA may help to motivate players\\nto engage with the exergame. It may give players a sense of\\nempowerment by adjusting the difficulty of an exergame\\n[15, 39] , help induce a sense of flow by balancing skills\\nand challenges [23, 74] , and increase perceived competence,\\nwhich is regarded as an important factor for intrinsic motiva-\\ntion [98, 114] . Increased intrinsic motivation is in turn linked\\nto an increased adherence to physical exercise [2, 34, 99] .\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,603,395,825],\"text\":\"Accessibility: VPA can enable players with reduced physi-\\ncal capabilities to play a VR exergame that would otherwise\\nbe too difficult for them. For example, a player with a mobility\\nimpairment could be enabled to control an avatar similarly to\\na non-impaired player by taking individual capabilities into\\naccount [118] . Non-immersive exergames with adjustable\\ndifficulty have shown some success for players with neuro-\\nlogical disability [76] and the rehabilitation of stroke [65] .\\nVPA may be used to achieve similar results using immersive\\nVR. Similarly, VPA could empower less capable players to\\novercome feelings of low self-efficacy [125] and enable them\\nto compete with more capable peers. Some form of VPA is of-\\nten used in game balancing methods, which adjust a player's\\nchance of winning relative to their capabilities [8, 39] .\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,825,395,955],\"text\":\"Feedforward: VPA could be used to create an experience\\nof an `improved self', i.e. a self model of the player exhibit-\\ning a performance exceeding the player's current ability, in\\norder to elicit a `feedforward effect'. Feedforward is an es-\\ntablished method that helps an individual improve a skill\\nor performance by exposing them to improved self models\\n[30, 31] . It has traditionally been applied by creating videos\\nof an individual showing an improved behaviour [29] , and\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,125,748,315],\"text\":\"more recently with interactive self models to improve per-\\nformance in a VR exergame [7] . Similarly, VPA may induce a\\nProteus effect, i.e. a change in behaviour that conforms with\\na player's digital self-representation [124] . Positive trans-\\nfer effects have been observed after embodying `improved'\\navatars in VR: for example, avatars with `superhero' abilities\\nmotivated consistent pro-social behaviour [95, 127] , `creative'\\navatars increased creativity [41] , and avatars perceived as\\nmore appropriate for a musical task led to increased musi-\\ncal performance [57] . Embodiment of a physically higher\\nperforming avatar may induce players to adopt more active\\nbehaviours.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,316,748,620],\"text\":\"Scope: We propose a method for VPA of running and\\njumping, based on in place movements, and describe empiri-\\ncal results on its effects in a VR exergame. Players walk, run\\nor jump in place according to their abilities while wearing a\\nVR head-mounted display (HMD) (Fig. 1a). Body movements\\nare tracked and a sense of embodiment is created through\\nvisual-motor synchronicity (Fig. 1b). Walking, running and\\njumping activities are recognised with a novel algorithm,\\nbased on a biomechanical analysis of human locomotion,\\nand used as input for a jump & run exergame (Fig. 1c). We\\npropose a VPA algorithm that processes the recognised in\\nplace activities in real-time to simulate corresponding ‘nat-\\nural’ locomotory gaits of a given augmented performance\\nin VR: walking, running and jumping in place are used to\\nsimulate 'natural' forward walking, running and jumping\\ngaits, with forward speed and jump height adjusted by per-\\nformance augmentation factors $\\\\alpha_{run}$ and $\\\\alpha_{jump}$ (Fig. 1d&e).\\nIn order to investigate VPA and its effects we pose the fol-\\nlowing research questions:\",\"reading_order\":8,\"tags\":[]},{\"label\":\"list\",\"bbox\":[440,634,748,732],\"text\":\"RQ1 How can running and jumping be virtually aug-\\nmented using consumer-level VR?\\nRQ2 Are in place running and jumping suitable as phys-\\nical activities in a VR exergame?\\nRQ3 Can VPA improve intrinsic motivation and per-\\nceived competence of the augmented activity?\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,745,748,955],\"text\":\"VPA has previously been described using the term \\\"mixed-\\nreality empowerment\\\" referring to both virtual and actual\\nperformance augmentation [43] . Players have been tracked\\nwhile jumping on a trampoline [48, 55] or performing martial\\narts [42] , showing exaggerated player movements on large\\nscreens. Player flexibility has been exaggerated using VR in\\na martial arts kicking task, by augmenting range of move-\\nment and kicking height [40] . VPA has also been utilised for\\nnavigation in VR by walking, allowing users to amplify their\\nvirtual speed [52] or enlarge their virtual body relative to\\nthe virtual world [62] . In addition, the effects of diminished\\noptical flow (i.e. diminished virtual performance) have been\\nstudied for treadmill-based walking using a large screen [92] .\",\"reading_order\":10,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,127,1013],\"text\":\"Paper 158\",\"reading_order\":11,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[706,995,745,1013],\"text\":\"Page 2\",\"reading_order\":12,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300388/results/markdown/3290605-3300388_pdf_shortened_page_1.md"}
{"file_name": "images/test_data/3290605-3300388/3290605-3300388_pdf_shortened_page_2.png", "source_folder": "test_data", "document_id": "3290605-3300388", "source_image_path": "test_data/3290605-3300388/3290605-3300388_pdf_shortened_page_2.png", "source_json_path": "test_data/3290605-3300388/results/output_json/3290605-3300388_pdf_shortened_page_2.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,43,159,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,43,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\"]},{\"label\":\"half_para\",\"bbox\":[68,125,394,189],\"text\":\"It is generally more difficult to create a usable VPA experi-\\nence in immersive VR compared to large screens as users of\\nimmersive VR are particularly susceptible to sensory conflict\\nand VR sickness [1, 102] .\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,189,394,365],\"text\":\"Exergame researchers have identified augmentation of\\nperceived physical performance as a largely underexplored\\narea [44, 81] . To the best of our knowledge, there has been\\nvery little research exploring recognition and augmentation\\nof in place running and jumping for exercise in immersive VR.\\nThe closest related work is VRun [126] , a VR exergame based\\non running in place that does not consider augmentation.\\nVRun identifies the accuracy of activity tracking as a major\\nchallenge; we address this by proposing a novel activity\\nrecognition algorithm based on biomechanics of locomotion.\\nIn summary, we make the following novel contributions:\",\"reading_order\":3,\"tags\":[]},{\"label\":\"list\",\"bbox\":[81,372,394,436],\"text\":\"(1) A method for recognition and augmentation of walk-\\ning, running and jumping in VR (Section 2).\\n(2) Empirical evidence about the effects of virtual perfor-\\nmance augmentation (Section 3).\",\"reading_order\":4,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,454,352,488],\"text\":\"2 AUGMENTING WALKING, RUNNING AND\\nJUMPING IN VR\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,491,394,538],\"text\":\"We first address RQ1, motivating why we use walking, run-\\nning and jumping in place and describing how these activities\\ncan be recognised and augmented in a VR exergame.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,538,394,808],\"text\":\"Motivation: We consider game mechanics for walking,\\nrunning and jumping in place to allow users to exercise at\\ndifferent levels of intensity and to offer a variety of biome-\\nchanical stimuli. Walking was chosen because it is the main\\ngait of human locomotion, fairly accessible and an essential\\nactivity of daily living [47] . It has well documented public\\nhealth benefits [66] and is likely important for maintaining\\nmobility and recovering from mobility disabilities [35, 105] .\\nRunning is the most popular type of cardiovascular exercise\\nafter walking, and more natural and economical for exertion\\nat high intensity [27, 80] . Jumping offers a biomechanical\\nstimulus with a greater magnitude than running and a dis-\\ntinct pattern of body acceleration [9] . Many people with\\nmobility impairments have difficulties running and jumping,\\nfor accessibility an exergame can be played using only slow\\nwalking motions, and jumping obstacles can be removed or\\nignored.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,808,394,954],\"text\":\"Are walking, running and jumping suitable mechanics for\\nan exergame when they are performed in place? Walking in\\nplace (WIP) methods [12, 14, 32, 63, 82, 83, 117, 119, 121, 123]\\nand jogging in place [67, 126] have been proposed for VR\\nlocomotion. The biomechanics of walking and running in\\nplace are similar to those of their locomatory counterparts,\\nso it is reasonable to expect similar physiological effects. The\\nhorizontal (i.e. forward force is greatly reduced, but the dom-\\ninant pattern of vertical force is similar, with peak vertical\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,125,746,364],\"text\":\"forces 4-6 times greater than peak horizontal forces during\\nnormal locomotion [22] . Running and jumping in place are\\npopular exercises in high intensity interval training [87] and\\nphyliometrics [24] . Running in place was touted as an effec-\\ntive method for improving running technique more than a\\ncentury ago [38] and rediscovered more recently [78] . It was\\nfound to cause exertion similar to the common treadmill-\\nbased Bruce Protocol maximal exercise test [88] . It can be\\nperformed fairly easily and has documented health benefits\\n[19] , also for injury rehabilitation [104] . Due to reduced hori-\\nzontal force, running in place may reduce shearing forces and\\nknee strain compared to normal running [128] . People are\\nmore likely to land on the forefoot, which generates smaller\\ncollision forces compared to the more common landing on\\nthe heel and may prevent impact-related injuries [69] .\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,364,746,634],\"text\":\"Would using a treadmill be more suitable than exercising\\nin place? Running on a treadmill facilitates a more natural\\ngait including horizontal force. However, there are limita-\\ntions with this setup. Walking on a treadmill poses safety\\nchallenges, which are typically mitigated by use of handle-\\nbars or a harness [36, 92, 107] . Running exacerbates these\\nchallenges and renders simple solutions such as handlebars\\ninfeasible, while jumping on a treadmill is very dangerous\\neven without wearing a HMD [56] . Treadmills for walking\\nin VR are bespoke systems that are not typically available\\nto consumers; they limit a player's range of motion making\\nvigorous exercise difficult, can take up considerable space,\\nand would add markedly to the cost of an exergame setup\\n[4, 91, 107] . Walking on a treadmill in VR can reduce VR sick-\\nness [53] ; however, it leads to distortions in the perception of\\nmovement [90] and different gait characteristics compared\\nto walking in the real world [93] .\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,634,746,778],\"text\":\"System Overview: We propose a system for walking,\\nrunning and jumping in VR based on consumer hardware\\nsuch as a HTC Vive HMD and a Kinect 2 depth camera for\\nbody tracking. Knee and foot positions are tracked by the\\nKinect at 30 Hz, while the HTC Vive is used to track the\\nhead position and orientation at a minimum of 50 Hz. The\\nbipedal gait cycle is well understood, therefore we base our\\nalgorithm for recognition of walking, running and jumping\\non a biomechanical and kinematic analysis of human gait.\",\"reading_order\":11,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[420,804,548,822],\"text\":\"Per-User Calibration\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,825,746,954],\"text\":\"In order to adapt the system to a user's physical capabilities,\\nwe record characteristic features of a user's activities in a\\ncalibration phase. To determine ground level, we track the\\nvertical position of the user's feet while standing and calcu-\\nlate an average value $groundY$ over three seconds and both\\nfeet. Then we ask the user to run in place for 10 seconds, start-\\ning slowly and building up to a run of maximum intensity\\nso that the full kinematic range is represented. We record:\",\"reading_order\":13,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,127,1013],\"text\":\"Paper 158\",\"reading_order\":14,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[704,995,745,1013],\"text\":\"Page 3\",\"reading_order\":15,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300388/results/markdown/3290605-3300388_pdf_shortened_page_2.md"}
{"file_name": "images/test_data/3290605-3300388/3290605-3300388_pdf_shortened_page_3.png", "source_folder": "test_data", "document_id": "3290605-3300388", "source_image_path": "test_data/3290605-3300388/3290605-3300388_pdf_shortened_page_3.png", "source_json_path": "test_data/3290605-3300388/results/output_json/3290605-3300388_pdf_shortened_page_3.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,61],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[]},{\"label\":\"header\",\"bbox\":[474,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[]},{\"label\":\"half_para\",\"bbox\":[68,124,394,236],\"text\":\"average avgHead $V_{run}$ vertical head position heady; mini\\nmin头Head $V_{run}$, average avgHead $V_{run}$ and maximum\\nmaxHead $V_{run}$ absolute vertical velocity of the head heady;\\nminimum minKnee $V_{run}$ and maximum maxKnee $V_{run}$ abso-\\nlute vertical velocity of the knees relative to the hip knee $V$\\n(average of both knees), and minimum minCadence and max-\\nimum maxCadence cadence.\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,236,395,538],\"text\":\"Cadence in steps per minute is estimated based on a func-\\ntion feetGrounded , which detects how many feet are cur-\\nrently touching the floor; a foot is considered grounded if\\n$footY \\\\leq groundY + \\\\epsilon$ , with $\\\\epsilon$ chosen to accommodate for\\nerrors in the tracked vertical position of the feet. During\\nwalking, feetGrounded alternates between two and one; dur-\\ning running, it alternates between zero and one (Figure 3 ).\\nSimilar to Wendt et al. [117] , we consider the phases of the\\ngait cycle in order to improve the time-resolution of the ca-\\ndence estimate: we measure the half-stride time $t_{step}$ (the\\ntime between steps in seconds) starting either at collision (in-\\ncrease of feetGrounded ) or toe-off (decrease of feetGrounded ),\\nwhatever occurred most recently. If the last change of feet-\\nGrounded was an increase, we calculate $t_{step}$ as the time to\\nthis increase from the increase before, and analogously for\\ndecreases. Based on this, we calculate cadence considering\\nthe time $\\\\Delta t$ that has passed since $t_{step}$ was last updated by\\na collision or toe-off, so that cadence drops immediately as\\nthe time between steps increases:\",\"reading_order\":3,\"tags\":[]},{\"label\":\"equ\",\"bbox\":[130,546,331,594],\"text\":\"$$ c a d e n c e = \\\\begin{cases} 6 0 t _ { s t e p } & f o r ~ \\\\Delta t \\\\leq t _ { s t e p } \\\\\\\\ 6 0 / \\\\Delta t & f o r ~ \\\\Delta t > t _ { s t e p } \\\\end{cases}$$\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,602,395,890],\"text\":\"Lastly, we ask the user to repeatedly jump in place for\\n10 seconds, recording $minHeadV_{jump}$ , $maxHeadV_{jump}$ and\\n$avgHeadV_{jump}$ similar to running. In order to estimate an\\n$avgHeadV_{jump}$ characteristic of the push-off and landing\\nphases of a jump, where the velocity is markedly higher\\nthan for running, we consider values of $headV$ only when\\n$headV>avgHeadV_{run}$ . We also record the minimum value\\n$minKneeA_{jump}$ and average $avgKneeA_{jump}$ of the angles at\\nthe knee joints $kneeA$ (average of both knees), and the min-\\nimum $minKneeD_{jump}$ and maximum $maxKneeD_{jump}$ verti-\\ncal distances of the knees from the hip $kneeD$ (average of\\nboth knees). $kneeD<0$ means the knees are level with\\nthe hip; $kneeD<0$ means the knees are below the hip.\\n$maxKneeD_{jump}$ , $minKneeA_{jump}$ and $minHeadV_{jump}$ are typ-\\nically reached at the end of the counter-movement phase\\nor during the landing phase, when users bend their knees\\nto prepare for or recover from a jump; $minKneeD_{jump}$ and\\n$maxHeadV_{jump}$ are typically reached in the aerial phase [70] .\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,889,395,955],\"text\":\"Taking the average of values measured in each leg for the\\ncalculation of $kneeV$ , $kneeA$ and $kneeD$ improves accuracy\\nand amplifies distinguishing characteristics of running, walk-\\ning and jumping. During walking, only one knee is moving\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,468,747,661],\"text\":\"at a time (Fig. 3 ), leading to a $kneeV$ lower than that of the\\nmoving knee. By contrast, during running both knees are\\nmoving simultaneously with similar absolute velocity in op-\\nposite directions, leading to a $kneeV$ that is markedly higher\\nthan for walking and likely more accurate than the measure-\\nment of a single knee alone. As mentioned, $kneeA$ and $kneeD$\\ntypically reach their extrema during jumping; both knees are\\nsimilarly bent making averages more accurate in this case.\\nBy contrast, running and walking are asymmetrical move-\\nments, reducing $kneeA$ and $kneeD$ compared to individual\\nmeasurements of each leg and making the difference to the\\nsymmetrical movements of jumping more pronounced.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300388_pdf_shortened_page_3_figure_008.png\",\"bbox\":[417,120,748,386],\"reading_order\":8,\"tags\":[],\"alt_text\":\"This is a decision tree diagram that classifies an action based on several conditions. It starts with \\\"feetGrounded\\\" and branches through various tests involving head and knee indices, head velocity, and head Y position, leading to classifications such as \\\"Run\\\", \\\"Pre/Post Jump\\\", \\\"Jump\\\", or \\\"Ambiguous\\\".\"},{\"label\":\"figure_cap\",\"bbox\":[420,395,746,424],\"text\":\"Figure 2: Decision tree for the classification \\\\u00b7 classification of locomotion\\nactivities.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,676,549,695],\"text\":\"Activity Recognition\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,698,747,955],\"text\":\"Our algorithm is a classification \\\\new{ } tree (a.k.a. decision tree)\\n[13] , illustrated in Fig. 2 , which is applied for every rendered\\nframe of the game based on the following tracked input vari-\\nables: feetGrounded , head $\\\\hat{y}$ , head $V$ , knee $V$ , knee $A$ and knee $D$\\n(Fig. 3 ). All input variables relate to features of the head and\\nlegs in order to include users with impaired mobility in the\\nupper limbs. The algorithm recognises the following classes:\\n'Run', 'Jump', 'Pre/Post Jump', and 'Ambiguous'. Class 'Run'\\nabstracts from the speed of locomotion and encompasses not\\njust running in place, but also walking in place and stand-\\ning on the spot. These activities are distinguished by their\\nestimated forward velocity as described in the next section.\\nBy classifying walking as 'Run', users with physical impair-\\nments can run virtually through VPA by walking in place.\\n'Pre/Post Jump' encompasses the counter-movement, early\\npush-off and late landing phases of a squat-jump movement\",\"reading_order\":11,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,127,1013],\"text\":\"Paper 158\",\"reading_order\":12,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[704,995,746,1013],\"text\":\"Page 4\",\"reading_order\":13,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300388/results/markdown/3290605-3300388_pdf_shortened_page_3.md"}
{"file_name": "images/test_data/3290605-3300388/3290605-3300388_pdf_shortened_page_4.png", "source_folder": "test_data", "document_id": "3290605-3300388", "source_image_path": "test_data/3290605-3300388/3290605-3300388_pdf_shortened_page_4.png", "source_json_path": "test_data/3290605-3300388/results/output_json/3290605-3300388_pdf_shortened_page_4.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,43,159,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[]},{\"label\":\"header\",\"bbox\":[474,43,746,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"author_affili\"]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300388_pdf_shortened_page_4_figure_002.png\",\"bbox\":[116,119,349,408],\"reading_order\":2,\"tags\":[],\"alt_text\":\"This image presents a sequence of human figures arranged in three rows, illustrating different phases of movement like walking, running, and jumping. Each figure shows specific body postures and is accompanied by labels indicating measurements such as average head height, knee angles, and foot contact, with arrows indicating vertical displacement.\"},{\"label\":\"figure_cap\",\"bbox\":[68,421,394,453],\"text\":\"Figure 3: Walking, running and jumping in place (top to bot-\\ntom).\",\"reading_order\":3,\"tags\":[]},{\"label\":\"half_para\",\"bbox\":[68,485,395,613],\"text\":\"[130] ; by recognizing it, the forward momentum of real jump-\\ning while running can be simulated when squat-jumping in\\nplace. The late push-off, aerial and early landing phases,\\nin which a user is committed to jumping, are classified as\\n'Jump'. The 'Ambiguous' class signifies transitions between\\njumping and running, which are resolved naturally as the\\nactivities progress. In the following we refer to the decision\\nnode numbers in Fig. 2 .\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,613,395,804],\"text\":\"The root node (1) is defined on function feetGrounded . If\\nonly one foot is grounded, this is characteristic of running\\nand classified as 'Run'. It may also happen for asymmetri-\\ncal push-off or landing during a jump, but this is quickly\\nresolved as the second foot leaves or hits the ground, with\\n'Run' ensuring that smooth forward momentum is simulated.\\nIn case both feet are grounded (1.1), we try to distinguish\\nthe squat-like movements of 'Pre/Post Jump' (i.e. counter-\\nmovement, early push-off and late landing) from running\\nby calculating an index representing the similarity of headY\\nto the relevant calibration parameters minHeadY $_{jump}$ and\\navgHeadY $_{run}$ :\",\"reading_order\":5,\"tags\":[]},{\"label\":\"equ\",\"bbox\":[93,813,364,849],\"text\":\"$$h e a d Y_{i n d e x p p r}=\\\\frac{a v g h e a d Y_{r u n}-h e a d Y}{a v g f e a d Y_{r u n}-m i n h e a d Y_{j m p}}$$\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,857,395,906],\"text\":\"Using this inverse linear interpolation, this index is 0 if the\\ncurrent head height is the average for running and 1 if it is\\nthe lowest observed during pre/post jumping phases.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,906,395,954],\"text\":\"With a similar intention and further differentiation of\\n'Pre/Post Jump' from 'Jump', we calculate an index repre-\\nsenting the similarity of kneeA to the calibration parameters\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,124,618,141],\"text\":\"$minKneeA_{jump}$ and $avgKneeA_{jump}$ :\",\"reading_order\":9,\"tags\":[]},{\"label\":\"equ\",\"bbox\":[442,145,723,185],\"text\":\"$$k_{n e e A i n d e x_{p p}}=\\\\frac{a v g K_{n e e A_{j u m p}}-k_{n e e A}}{a v g K_{n e e A_{j u m p}}-m i n K_{n e e A_{j u m p}}}$$\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,187,747,379],\"text\":\"This index is 0 if knee bend is average, signifying the relevant\\ntransitions to late push-off or from early landing, and 1 if it\\nis at the minimum observed during pre/post jumping phases,\\ni.e. at the end of the counter-movement or just before the\\nlanding recovery phase. The two indices are considered in\\ncombination $headYindex_{ppj} + kneeAindex_{ppj}$ and compared\\nto a threshold $\\\\theta_{ppj}$ to differentiate 'Pre/Post Jump' from 'Run'\\nand 'Jump'. For our experiments we chose $\\\\theta_{ppj} = 1$ , which\\nwe determined empirically in pilot tests. A similar concept\\nis used in node (1.3). The other nodes compare the tracked\\nvariables directly with the characteristic features recorded\\nin the calibration phase.\",\"reading_order\":11,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,391,540,407],\"text\":\"Run Augmentation\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,411,747,650],\"text\":\"In order to achieve a realistic experience, the way players con-\\ntrol their virtual movements varies based on the recognised\\nlocomotion class. During `Run', in place and lateral move-\\nments are applied directly based on head tracking, whereas\\nforward velocity speed can merely be estimated based on\\nthe tracked kinometric variables. We address requirements\\nsimilar to WIP such as low latency, smoothness and continu-\\nous control of estimated locomotion speed [14, 32, 117] . The\\n`Run' locomotion class includes WIP as well as running in\\nplace, therefore this estimation must accommodate the dif-\\nferences between the two gaits [22, 67, 73, 80] . We use a new,\\nintegrated approach to estimate both walking and running\\nspeed based on biomechanical considerations, related work\\non WIP [12, 14, 32, 63, 67, 82, 83, 111, 113, 117, 119–121, 123]\\nand empirical experimentation.\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,650,747,873],\"text\":\"Cadence is known to be correlated with walking speed [25,\\n50, 51] and is an important factor (sometimes the only factor)\\nin many WIP approaches [12, 63, 83, 117, 119, 121]. Head\\nvelocity has been found to be a good indicator of cadence\\n[113]; it is more continuous in nature than a step count and\\nhas been used exclusively for WIP [111] as well as jogging in\\nplace [67]. Knee velocity correlates with cadence and forward\\nvelocity [50, 51], as well as leg lift and foot velocity, both of\\nwhich have been used as exclusive factors for WIP [14, 32,\\n120, 123]. Hip flexion and hence knee displacement relative to\\nthe body correlate with running speed [75]; sprinters lift their\\nknees higher than long distance runners. This is also reflected\\nin a higher footstep amplitude, which has successfully been\\nused for WIP [14].\",\"reading_order\":14,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,873,747,955],\"text\":\"The variables cadence , headV , kneeV and kneeD correlate\\nwith running speed. To account for a player's individual phys-\\nical capabilities, we normalise them according to the range\\nof values exhibited during the running calibration phase, by\\nlinearly transforming [minimum, maximum] to [0, 1]. The\",\"reading_order\":15,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,127,1013],\"text\":\"Paper 158\",\"reading_order\":16,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[704,995,746,1013],\"text\":\"Page 5\",\"reading_order\":17,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300388/results/markdown/3290605-3300388_pdf_shortened_page_4.md"}
{"file_name": "images/test_data/3290605-3300388/3290605-3300388_pdf_shortened_page_5.png", "source_folder": "test_data", "document_id": "3290605-3300388", "source_image_path": "test_data/3290605-3300388/3290605-3300388_pdf_shortened_page_5.png", "source_json_path": "test_data/3290605-3300388/results/output_json/3290605-3300388_pdf_shortened_page_5.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,43,159,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,43,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"half_para\",\"bbox\":[68,124,394,156],\"text\":\"variables correlate positively with speed, therefore we esti-\\nmate speed linearly as a weighted sum:\",\"reading_order\":2,\"tags\":[]},{\"label\":\"equ\",\"bbox\":[80,161,379,310],\"text\":\"$$s p e e d ^ { \\\\prime } = s p e e d _ { a u g } \\\\left( w _ { 1 } \\\\frac { c a d e n c e - m i n C a d e n c e } { m a x C a d e n c e - m i n C a d e n c e } \\\\right. $$\\n\\n$$+ w _ { 2 } \\\\frac { k n e e V - m i n K n e e V _ { r u n } } { m a x K n e e V _ { r u n } - m i n K n e e V _ { r u n } } $$\\n\\n$$+ w _ { 3 } \\\\frac { k n e e D _ { r u n } - m i n K n e e D _ { r u n } } { m a x K n e e D _ { r u n } - m i n K n e e D _ { r u n } } $$\\n\\n$$+ w _ { 4 } \\\\frac { h e a d V - m i n H e a d V _ { r u n } } { m a x H e a d V _ { r u n } - m i n H e a d V _ { r u n } }$$\",\"reading_order\":3,\"tags\":[]},{\"label\":\"equ\",\"bbox\":[105,326,355,370],\"text\":\"$$s p e e d = \\\\left\\\\{ s p e e d ^ { \\\\prime } \\\\right. \\\\begin{array} { l l } { f o r ~ k n e e V \\\\geq m i n K n e e V _ { r u n } } \\\\\\\\ { 0 \\\\quad ~ o t h e r w i s e } \\\\end{array}$$\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,379,395,587],\"text\":\"The formula cannot easily be 'cheated', e.g. by bobbing only\\nthe head, as the linear combination speed' is applied in the\\nfinal estimate speed only if the knees are moving and the\\nplayer's movements have already been classified as 'Run'.\\n$speed_{run}$ is the augmented performance (speed in km/h) pro-\\nduced by the algorithm, i.e. the approximate running speed\\nperceived by the player during typical gameplay. We call\\nthis absolute VPA as the performance is augmented irrespec-\\ntive of a player's real performance such as their typical run-\\nning speed $speed_{real}$ . It is more realistic to augment perfor-\\nmance relative to a player's real capabilities – relative VPA –\\nand this can be achieved by using an augmentation factor\\n$a_{run} = speed_{run}/speed_{real}$ (Fig. ).\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,586,395,955],\"text\":\"We chose weights $w_1 = 0.07$ , $w_2 = 0.48$ , $w_3 = 0.48$ and\\n$w_4 = 0.24$ based on biomechanical considerations (see below)\\nand empirical tests. First, weights were estimated and ad-\\njusted in pilot tests. Then the game was tested with players\\n(6 female, 9 male) of various running abilities with estimated\\naverage speeds over 5 km ranging from 8 to 17 km/h. For\\neach participant we manually scaled the weights based on\\nfeedback, so that the participant's game experience approx-\\nimately matched their experience of real-world running at\\ntheir estimated real speed. Optical flows in VR are notori-\\nously hard to match with real-world locomotive optical flows,\\nwith most users underestimating their speed in VR [83] . We\\nmitigated this by making participants aware of this bias and\\nshowing them real-world first-person videos illustrating es-\\ntimated running speeds before running in VR, giving them\\na ground truth to compare to. In order to calculate the final\\nweights, a simple linear regression was performed to predict\\nthe scale of the weights based on estimated real running\\nspeed, without constant as all weights must be zero to guar-\\nantee an augmented speed of zero. A significant regression\\nequation was found (F(1, 14) = 251.2, p < .001), with $R^2 = .95$ .\\nThis resulted in weights that approximate a real running\\nexperience for a given $speed_{avg}$ .\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,124,747,507],\"text\":\"In contrast to many WIP approaches, our weight for ca-\\ndence is low because of differences between gaits and consid-\\nerations of accuracy. For walking, forward velocity is equal\\nto cadence times stride length [129] , and the latter can be\\nestimated from cadence and body height [25, 51] . By con-\\ntrast, for running, anthropometric variables such as height\\nor leg length cannot be used to predict stride length accu-\\nrately; the latter varies based on running speed [18] . For\\nwalking, cadence and stride length are strongly positively\\ncorrelated [51, 100] . By contrast, many runners hold their\\ncadence fairly constant irrespective of their speed [116] . As\\na result, the linear relationship of cadence to speed is weaker\\nin running compared to walking. Also for WIP, the accuracy\\nof cadence as a sole predictor of speed has been criticised be-\\ncause of the differences between stepping in place and actual\\nwalking [14, 82] – during WIP steps can be performed more\\nquickly due to the absence of forward velocity. Furthermore,\\ncadence measurements suffer from latency and discontinuity\\nas cadence may change between steps, potentially leading\\nto a delayed and abrupt change in speed [14, 32, 117] . As a\\nresult, VPA of running could not be implemented well using\\nonly sensor mats, which measure mainly cadence. In order to\\nprovide a smooth running experience, other variables such\\nas knee velocity and displacement need to be considered.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,506,747,890],\"text\":\"Relative knee velocity ( $kneeV$ ) contributes strongly to for-\\nward velocity, as power is largely generated by extension\\nof the hip, which accelerates the knee downward, while the\\nother knee is accelerated upward by hip flexion [50, 51] .\\nChanges in $kneeV$ , which are reflected in changes of for-\\nward velocity, can be accurately measured with low latency.\\nThe power generated by the hip is also strongly reflected\\nin the degree of hip flexion and hence knee displacement.\\n$kneeD$ relative to the body [14, 75] . As a result, both $kneeV$\\nand $kneeD$ are strong indicators of forward velocity and are\\nweighted highly. Movements of the legs cause correspond-\\ning movements of the head, and the latter can be measured\\nwith higher temporal resolution and accuracy by the HMD\\ncompared to the Kinect. Other WIP researchers found track-\\ning data from a single Kinect to be too noisy for robust WIP\\n[121] , and this can be mitigated by integrating measurements\\nfrom other sensors such as the HMD. Head velocity corre-\\nlates with cadence but can be measured more accurately and\\ncontinuously [113] . Although movements of the head are\\nonly indirectly related to walking/running speed, they have\\npreviously been found useful in estimating speed of jogging\\nin place [67] . We found they assisted in creating a smooth\\nand natural running experience, hence assigning $headV$ a\\nmedium weight.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[420,901,547,918],\"text\":\"Jump Augmentation\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,921,746,955],\"text\":\"During jumping, in place and lateral movements are again\\napplied directly based on head tracking. During \\\"Pre/Post\",\"reading_order\":10,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,127,1013],\"text\":\"Paper 158\",\"reading_order\":11,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[704,995,745,1013],\"text\":\"Page 6\",\"reading_order\":12,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300388/results/markdown/3290605-3300388_pdf_shortened_page_5.md"}
{"file_name": "images/test_data/3290605-3300388/3290605-3300388_pdf_shortened_page_6.png", "source_folder": "test_data", "document_id": "3290605-3300388", "source_image_path": "test_data/3290605-3300388/3290605-3300388_pdf_shortened_page_6.png", "source_json_path": "test_data/3290605-3300388/results/output_json/3290605-3300388_pdf_shortened_page_6.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,43,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\"]},{\"label\":\"half_para\",\"bbox\":[68,125,394,299],\"text\":\"Jump', the speed of the player estimated from immediately\\npreceding walking/running is preserved in order to create\\na smooth experience of the player's momentum carrying\\nforward. During 'Jump', the aerial phase is augmented along\\nboth the vertical and forward axes (Fig. 1 e): the vertical dis-\\ntance of the player from the ground is scaled proportionally\\naccording to $a_{jump}$ , while the augmented running speed esti-\\nmated immediately before the jump is preserved [45, 72, 101] .\\nAs a result, the avatar jumps higher but not longer than the\\nuser and lands on the ground at the same time as the user,\\nin order to prevent sensory disconnect and loss of balance.\",\"reading_order\":2,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[69,311,351,329],\"text\":\"Evaluation of Augmented Running & Jumping\",\"reading_order\":3,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,332,394,426],\"text\":\"We evaluated the proposed VPA method by testing how well\\nit is able to deliver an acceptable experience of running and\\njumping in VR across different levels of performance aug-\\nmentation. We chose evaluation criteria that have previously\\nbeen used to evaluate the quality of WIP [14, 82, 83, 121] :\\nnaturalness, realism and responsiveness.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,427,394,602],\"text\":\"We used a between-participants design, varying the aug-\\nmented speed of running $speed_{aug}$ and the augmentation\\nfactor for jumping $a_{jump}$ together from a 'realistic' perfor-\\nmance of $speed_{aug} = 9$ km/h and $a_{jump} = 1$ up to an ex-\\ntremely exaggerated performance of $speed_{aug} = 88$ km/h\\nand $a_{jump} = 25$ (see x-axes in Fig. 4 ). Jumping was aug-\\nmented more strongly in proportion to running as we found\\njumping augmentations were less noticeable. The between-\\nparticipants design was chosen in order to avoid fatigue, as\\nthe game was tiring and participants would otherwise have\\nhad to play many rounds.\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,602,395,890],\"text\":\"We recruited 80 participants (22 female, 58 male, average\\nage 23, 62 reporting previous experience with VB) from the\\ncampus of the University of Bath and randomly assigned each\\nto one of six augmentation groups. First we demonstrated\\nthe system to each participant and guided them through the\\ncalibration process. Then the participant had time to freely\\nplay the exergame for several minutes by running and jump-\\ning at the assigned level of augmentation. Finally, we asked\\nthe participant to complete a questionnaire about their ex-\\nperience, consistent with those used by other researchers to\\nevaluate WIP and immersive experiences [6, 14, 64, 68, 82] .\\nSingle 7-point Likert-scale items were used to measure per-\\nceived Naturalness, Consistency with real-world experiences,\\nand perceived Responsiveness of running and jumping, re-\\nspectively, the Natrualness (NATRL) subscale of Wimmer &\\nSinger's Presence Questionnaire (PQ) [122] and open feed-\\nback were used to evaluate the overall experience. The pro-\\ncedure took about 5-10 minutes.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,890,394,954],\"text\":\"We hypothesised a prior that quality scores would be at\\nleast above scale-midpoint, i.e. at least 'moderately natural,\\nconsistent and responsive based on the descriptive label in\\nthe PQ – a criterion consistent with results from studies of\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,513,747,625],\"text\":\"WIP and VR [64, 68, 82]. Based on a power analysis, the study\\nwas able to detect 'large' effects between augmentation levels\\n(Cohen's f≥0.42), 'medium' sized effects between the quality\\nof running and jumping (Cohen's f≥0.18), and 'medium'\\nsized interaction effects (Cohen's f≥0.18) at significance level\\n$\\\\alpha=0.05$ with a power of 0.8. This allows us to to better\\nunderstand uncertainty in the results.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300388_pdf_shortened_page_6_figure_009.png\",\"bbox\":[421,123,747,400],\"reading_order\":9,\"tags\":[],\"alt_text\":\"This image presents four line graphs with error bars, arranged in a 2x2 grid, each displaying scores (1-7) against varying experimental conditions. The top two graphs show scores for Naturalness, Consistency, and Responsiveness as a function of Running Speed (km/h) and Jump Augmentation, respectively. The bottom-left graph compares Run Avg. and Jump Avg. scores across an Augmentation Level, while the bottom-right graph plots PQ Naturalness (1-7) against Augmentation Level.\"},{\"label\":\"figure_cap\",\"bbox\":[420,410,746,486],\"text\":\"Figure 4: Naturalness, Consistency and Responsiveness\\nscores (1-7, higher is better) for Running (top-left) and jump-\\ning (top-right), average quality scores for running and jump-\\ning (bottom-left) and overall PQ Naturalness scores (bottom-\\nright) across different augmentation factors.\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,634,747,955],\"text\":\"Results. The variances in each group were similar enough\\nand the measurements' distributions close enough to normal,\\nwith sufficient sphericity, to warrant analyses of variance\\n(ANOVA). The level of significance used was $\\\\alpha = 0.05$ . All\\ngraphs show 95 % confidence intervals of the mean. Two-\\nway repeated measures ANOVAs were conducted on the\\neffects of Augmentation Level (1-6) and Activity (Running\\nand Jumping) on Naturalness, Consistency and Responsiv-\\neness (Fig. 4 top). The main effect of Activity on Naturalness\\nwas significant: $F(1,74) = 6.57, p = .01$ , indicating that Run-\\nning felt significantly more natural than Jumping with a\\n'medium' effect size (Cohen's d=0.27). All other main effects\\nof Augmentation Level ( $F(5,74) \\\\leq 1.46, p \\\\geq .22$ ) and Ac-\\ntivity ( $F(1,74) \\\\leq 0.17, p \\\\geq .68$ ), and all interaction effects\\n( $F(5,74) \\\\leq 2.09, p \\\\geq .08$ ) were not significant. Directed one-\\nsample t-tests of the marginal means showed that Natural-\\nness and Responsiveness scores for Running, Responsiveness\\nscores for Jumping, and the averages of Naturalness, Con-\\nsistency and Responsiveness scores for Running and Jump-\\ning (Fig. 4 bottom-left) were all significantly greater than\",\"reading_order\":11,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,127,1012],\"text\":\"Paper 158\",\"reading_order\":12,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[705,995,745,1012],\"text\":\"Page 7\",\"reading_order\":13,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300388/results/markdown/3290605-3300388_pdf_shortened_page_6.md"}
{"file_name": "images/test_data/3290605-3300388/3290605-3300388_pdf_shortened_page_7.png", "source_folder": "test_data", "document_id": "3290605-3300388", "source_image_path": "test_data/3290605-3300388/3290605-3300388_pdf_shortened_page_7.png", "source_json_path": "test_data/3290605-3300388/results/output_json/3290605-3300388_pdf_shortened_page_7.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[68,42,159,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,42,745,60],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"half_para\",\"bbox\":[68,124,394,299],\"text\":\"the scale mid-point 4, $t(79) \\\\geq 4.02, p < .001$ , with at least\\n'medium' effect sizes (Cohen's d $\\\\geq$ 0.45). Consistency of Run-\\nning ($t(79) = 1.78, p = .06$ ) and Jumping ($t(79) = 1.27, p = .10$ )\\nand Naturalness of Jumping ($t(79) = 1.51, p = .07$ ) were\\nnot significantly above 4. A one-way ANOVA showed that\\nthe main effect of Augmentation Level (1-6) on PQ Natu-\\nralness was not significant, $F(5, 74) = 1.16, p = .34$ (Fig. 4\\nbottom-right). A directed one-sample t-test of the marginal\\nmean showed that PQ Naturalness was significantly greater\\nthan the scale mid-point 4, $t(79) = 2.88, p = .003$ , with a\\n'medium' effect size (Cohen's d $\\\\geq$ 0.32).\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,310,394,775],\"text\":\"Discussion. Comparing running and jumping in place with\\nnormal running and forward jumping sets a high bar as they\\nhave different kinematics and kinetics [22, 45, 84] and can\\nnever feel exactly the same. Also, real running and jumping\\ngenerally cannot deliver a performance on par with aug-\\nmented activities, which are therefore unlikely to feel en-\\ntirely natural. All quality measures were on average above\\nscale mid-point and most were significantly above; how-\\never, naturalness of jumping and consistency of running\\nand jumping with real-world experiences were only close\\nto significant. Overall the results indicate that the proposed\\nVPA method produces experiences of at least moderate qual-\\nity, roughly on par with other WIP and VR experiences\\n[64, 68, 82] , with augmented jumping feeling less consis-\\ntent and natural than augmented running. In conclusion, the\\nVPA method works sufficiently well to warrant investiga-\\ntion of its use in a VR exergame, but it has some limitations.\\nWe observed that if the running/jumping behaviour in the\\ncalibration phase was different from the behaviour during\\ngameplay, then this could negatively affect recognition and\\naugmentation quality. In particular the participants unfamil-\\niar with VR had a tendency to move around in the room, and\\nwhen they got too close to the Kinect this negatively affected\\ntracking and augmentation quality. In this study participants\\nonly played the game for a few minutes as the exergame is\\nbased on a high-intensity interval training protocol and not\\nintended for longer periods of use. Our observations from\\nlonger use indicate that players became fatigued and had to\\nreduce the intensity of their movements.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,788,332,820],\"text\":\"3 EFFECTS OF AUGMENTATION IN A VR\\nEXERCAME\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,825,394,954],\"text\":\"In order to investigate the effects of VPA and answer RQs 2-3,\\nwe conducted an experiment with the degree of Augmenta-\\ntion and augmented Activity as independent variables (see\\nTable 1 ). For Augmentation we used a within-participants\\ndesign, with conditions \\\"no augmentation\\\" (\\\"N\\\", augmenta-\\ntion factor $\\\\alpha=1$ ), \\\"low augmentation\\\" (\\\"L\\\", $\\\\alpha=1.3$ ) and\\n\\\"high augmentation\\\" (\\\"H\\\", $\\\\alpha=2$ ). Augmentation factors were\\nchosen based on considerations of realism and perception. A\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,124,747,396],\"text\":\"performance increase of 30 % (L) in running speed and jump-\\ning height is high but not entirely unrealistic for average\\nindividuals [3, 21] . However, users tend to overestimate their\\nreal physical speed during VR walking (treadmill and WIP)\\nby a factor of 2 [83] , so may in fact perceive L as a reduction\\nof their real performance. A performance increase of 100 %\\n(H) is generally unrealistic, but would likely be perceived as\\nan increase given the bias observed in VR walking. We chose\\nL to explore subtle effects of VPA, and chose H to validate\\nhypothesised effects when users can consciously perceive\\nincreases in their own performance. For Activity we used a\\nbetween-participants design with one group for \\\"augmented\\nrunning and unaugmented jumping\\\" (R) and the other group\\nfor \\\"augmented running and augmented jumping\\\" (RJ). This\\nallowed us to explore differences caused by the augmentation\\nof jumping, which was perceived differently from running\\nduring our evaluation of the VPA method (Section 2 above).\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,409,747,955],\"text\":\"Outcome Variables. We measured participants' exertion based\\non heart rate (HR), using a Garmin 620 chest-strap moni-\\ntor. Average and peak HR was expressed as a percentage\\nof a participant's estimated maximum HR (HR Avg % and\\nHR Peak % ). Based on ACSM guidelines [85] , maximum HR\\nwas estimated as 220 minus age. This measure is commonly\\nused in exercise studies to confirm participants are work-\\ning at a required level of exertion. We also used the Borg\\nRating of Perceived Exertion Revised Category-Ratio Scale\\n(RPE), which has a range of 0 to 10. We measured immersive-\\nness of VPA with the Immersive Experience Questionnaire\\n(IEQ) [54] , which has been previously used for exergames\\n[7, 17] . We recorded IEQ scores as an average over item\\nscores between 1 to 7, with 7 representing the most immer-\\nsion. We measured intrinsic motivation of VPA with the\\nIntrinsic Motivation Inventory (IMI) scale [96] , which has\\nbeen validated for sports and exercise [20, 77] . We used only\\nthe Interest/Enjoyment subscale, which is considered the\\nmain self-report measure, and the Perceived Competence\\nsubscale. IMI scores range from 1 to 7, with 7 representing\\nthe highest enjoyment or perceived competence. We also\\nmeasured participants' motivation for physical activity in\\ngeneral using the RM 4-FM Motivation for Physical Activity\\nquestionnaire (RM 4-FM) [26, 97] , which has been used in\\nmany health and exercise studies [110] . RM 4-FM produces\\nan Autonomy Index score representing the relative impact\\nof intrinsic and extrinsic factors in a participant's motiva-\\ntion to be active. Negative numbers signify dominance of\\nextrinsic motivation in the regulation of active behaviours;\\npositive numbers reflect dominance of intrinsic motivation,\\nwhich is linked to an increased adherence to physical exercise\\n[2, 34, 99] . We measured flow experienced during VPA using\\nthe Positive Psychology Lab's Flow State Questionnaire (FSQ)\\n[74] , which has been validated with exergames, including\",\"reading_order\":7,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,127,1013],\"text\":\"Paper 158\",\"reading_order\":8,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,995,744,1013],\"text\":\"Page 8\",\"reading_order\":9,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300388/results/markdown/3290605-3300388_pdf_shortened_page_7.md"}
{"file_name": "images/test_data/3290605-3300390/3290605-3300390_pdf_shortened_page_0.png", "source_folder": "test_data", "document_id": "3290605-3300390", "source_image_path": "test_data/3290605-3300390/3290605-3300390_pdf_shortened_page_0.png", "source_json_path": "test_data/3290605-3300390/results/output_json/3290605-3300390_pdf_shortened_page_0.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[70,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\"]},{\"label\":\"icon\",\"bbox\":[0,93,39,134],\"reading_order\":2,\"tags\":[\"cross_marker\"]},{\"label\":\"sec_0\",\"bbox\":[73,118,744,215],\"text\":\"Volunteer Moderators in Twitch Micro Communities:\\nHow They Get Involved, the Roles They Play, and the\\nEmotional Labor They Experience\",\"reading_order\":3,\"tags\":[]},{\"label\":\"author_information\",\"bbox\":[310,234,503,295],\"text\":\"Donghee Yvette Wohn\\nNew Jersey Institute of Technology\\nNewark, New Jersey\\nwohn@njtu.edu\",\"reading_order\":4,\"tags\":[\"author\",\"author_mail\",\"author_affili\"]},{\"label\":\"sec_1\",\"bbox\":[70,306,141,324],\"text\":\"ABSTRACT\",\"reading_order\":5,\"tags\":[\"paper_abstract\"]},{\"label\":\"para\",\"bbox\":[69,329,395,535],\"text\":\"The ability to engage in real-time text conversations is an im-\\nportant feature on live streaming platforms. The moderation\\nof this text content relies heavily on the work of unpaid vol-\\nunteers. This study reports on interviews with 20 people who\\nmoderate for Twitch micro communities, defined as channels\\nthat are built around a single or group of streamers, rather\\nthan the broadcast of an event. The study identifies how peo-\\nple become moderators, their different styles of moderating,\\nand the difficulties that come with the job. In addition to the\\nhardships of dealing with negative content, moderators also\\nhave complex interpersonal relationships with the streamers\\nand viewers, where the boundaries between emotional labor,\\nphysical labor, and fun are intertwined.\",\"reading_order\":6,\"tags\":[\"paper_abstract\"]},{\"label\":\"sec_1\",\"bbox\":[70,548,173,566],\"text\":\"CCS CONCEPTS\",\"reading_order\":7,\"tags\":[]},{\"label\":\"list\",\"bbox\":[69,569,394,618],\"text\":\"• Human-centered computing $\\\\rightarrow$ Empirical studies in\\nHCI: Empirical studies in collaborative and social com-\\nputing.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[70,632,149,648],\"text\":\"KEYWORDS\",\"reading_order\":9,\"tags\":[\"paper_keywords\"]},{\"label\":\"para\",\"bbox\":[70,652,394,684],\"text\":\"Live streaming, moderation, online harassment, qualitative,\\nonline community, Twitch\",\"reading_order\":10,\"tags\":[\"paper_keywords\"]},{\"label\":\"sec_1\",\"bbox\":[70,697,202,711],\"text\":\"ACM Reference Format:\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[70,711,394,769],\"text\":\"Donghee Yvette Wohn. 2019. Volunteer Moderators in Twitch Micro\\nCommunities: How They Get Involved, the Roles They Play, and\\nthe Emotional Labor They Experience. In CHI Conference on Human\\nFactors in Computing Systems Proceedings (CHI 2019), May 4-9, 2019,\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,308,745,339],\"text\":\"Glasgow, Scotland UK. ACM. New York, NY, USA, 13 pages. https:\\n//doi.org/10.1145/3290655.330390\",\"reading_order\":13,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,359,549,377],\"text\":\"1 Introduction\",\"reading_order\":14,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,380,747,523],\"text\":\"Live streaming services are the latest form of social media\\nthat marries user-generated content with the traditional con-\\ncept of live television broadcasting. Twitch is a live streaming\\nplatform where people (henceforth described as \\\"streamers\\\")\\ncan broadcast live videos on their own channel and simulta-\\nneously interact with viewers who communicate via a text\\nchatting function (henceforth referred to as \\\"chat\\\"). This chat\\ninterface is displayed alongside the live broadcast, creating\\nan interactive near-synchronous media experience.\",\"reading_order\":15,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,523,746,618],\"text\":\"As is the case with most social media and online commu-\\nnities, Twitch has its share of people posting rude things\\nonline [3, 37] . The real-time interaction, however, makes it\\ndifficult for streamers for avoid reading negative comments,\\nsince the interaction between streamers and viewers is one\\nof the main features of live streaming [15, 17] .\",\"reading_order\":16,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,618,747,826],\"text\":\"Content moderation is \\\"the organized practice of screen-\\ning user-generated content posted to Internet sites, social\\nmedia and other online outlets, in order to determine the\\nappropriateness of the content for a given site, locality, or\\njurisdiction\\\" [35] . This content can be removed by modera-\\ntors, who are sometimes volunteers or paid individuals in a\\ncommercial context [35] . Most current forms of social media\\nrely on crowdsourced methods of moderation, where users\\nreport bad content that is ultimately reviewed by a human\\nmoderator [7, 21, 34] . Due to the time delay between the\\nreporting of bad content and the review of it, this method\\ndoes not work well in the context of real-time moderation,\\nposing greater social and technological challenges.\",\"reading_order\":17,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,826,746,955],\"text\":\"While there have been numerous studies on moderation\\npractices in online communities [2, 7, 12, 20, 21] , live streams\\nare an interesting new context because platforms like Twitch\\nsupport micro communities, where each channel operates\\nunder different norms, different audiences, an different ad-\\nministrators [37] . For example, there are over a million\\nstreamers on Twitch, yet because streamers are allowed to\\ncreate their own rules, the way channels operate greatly vary\",\"reading_order\":18,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[69,790,395,896],\"text\":\"Permission to make digital or hard copies of all or part of this work for\\npersonal or classroom use is granted without fee provided that copies are not\\nmade or distributed for profit or commercial advantage and that copies bear\\nthis notice and the full citation on the first page. Copyrights for components\\nof this work owned by others than ACM must be honored. Abstracting with\\ncredit is permitted. To copy otherwise, or republish, to post on servers or to\\nredistribute to lists, requires prior specific permission and/or a fee. Request\\npermissions from permissions@acm.org.\",\"reading_order\":19,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[70,899,273,954],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland UK\\n\\n© 2019 Association for Computing Machinery,\\nACM ISBN 978-1-4305-3970-2/19/05...$15.00\\n\\nhttps://doi.org/10.1145/3290605.3303091\",\"reading_order\":20,\"tags\":[\"meta_subject\",\"meta_pub_date\",\"meta_num\"]},{\"label\":\"foot\",\"bbox\":[70,996,127,1013],\"text\":\"Paper 160\",\"reading_order\":21,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,996,744,1013],\"text\":\"Page 1\",\"reading_order\":22,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300390/results/markdown/3290605-3300390_pdf_shortened_page_0.md"}
{"file_name": "images/test_data/3290605-3300390/3290605-3300390_pdf_shortened_page_1.png", "source_folder": "test_data", "document_id": "3290605-3300390", "source_image_path": "test_data/3290605-3300390/3290605-3300390_pdf_shortened_page_1.png", "source_json_path": "test_data/3290605-3300390/results/output_json/3290605-3300390_pdf_shortened_page_1.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,43,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,43,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"half_para\",\"bbox\":[69,125,394,156],\"text\":\"and what may be acceptable in one channel may not apply\\nto another channel [37] .\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,156,395,411],\"text\":\"This qualitative study shifts the focus from the technicali-\\nties of moderation practices to the psychology behind and\\nassociated with the volunteer moderators in live streaming\\nmicro communities. Taking an exploratory approach to un-\\nderstanding why people want to be volunteer moderators,\\nthe study identifies their motivations, the roles they play,\\nand the emotional difficulties they experience in the context\\nof the live streaming platform Twitch. As more HCI research\\nturns toward methods of augmenting and scaling worker\\ncapabilities with technology, it is important to understand\\nthe essence of the type of work volunteer moderators are\\nengaging in to better design appropriate tools. Also, in de-\\nsigning and operating systems that heavily rely on the work\\nof volunteer moderators, it is necessary to understand the\\ndifficulties they experience as not to exploit people who are\\nessential elements to system operation.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,423,265,440],\"text\":\"2 MODERATION ON TWITCH\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,443,395,554],\"text\":\"Twitch is a live streaming platform where anybody can broad-\\ncast a live video of themselves doing some kind of activ-\\nity [17, 37] . The type of content that people show on Twitch\\nis mostly related to video games, but is slowly diversifying\\nto include other things such as cooking, doing crafts, play-\\ning music, or other aspects of their offline lives, known (on\\nTwitch) as IRL. (In Real Life) [1] .\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,554,395,682],\"text\":\"In this study, we will focus on moderation related to chat,\\nnot moderation of the streaming content. Like most online\\nspaces that relies on user generated content [4] , Twitch is\\nsubject to disruptive behavior [3, 37] , which has led the\\ncompany's continuous updates to community guidelines [41] .\\nDiscouraged content in chat includes targeted harassment,\\nunauthorized sharing of private information, hate speech,\\nand spamming among others [8, 41] .\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,682,395,858],\"text\":\"To deal with bad content, the Twitch platform has many\\ndifferent \\\"layers\\\" of moderation [39] : some of it is crowd-\\nsourced, allowing viewers to report inappropriate or offen-\\nsive content. Some of it is algorithmic and preventative in\\nnature, where certain words or phrases can be blocked be-\\nfore they even appear in chat [37, 40] . This type of modera-\\ntion, however, has limited customizability and can always be\\nthwarted by people who invent new terms, creative spelling\\ncombinations, use emotoes, etc. Then, there is moderation\\nthat happens after the fact, in which content can be deleted\\nand people banned. The latter requires a human moderator:\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,858,395,954],\"text\":\"The streamer has moderating privileges on their own chan-\\nnel, but they can also give some of these privileges to others\\n[35] . When a user is appointed to be moderator, they have\\npartial access to the built-in moderation tools on the platform,\\nthat enables them to ban people or delete messages. Modera-\\ntors of Twitch channels have no control over the streaming\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,125,746,188],\"text\":\"content, just the comments that accompany the live video on\\na specific channel. Finally, there are moderators employed by\\nTwitch (global mods), who moderate everything on the site,\\nmainly processing issues escalated by channel moderators.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,189,746,315],\"text\":\"While most of the early online communities in the 1980s\\nand 1990s relied on volunteer moderators [20] , many of the\\ncontemporary social media platforms have a more closed\\nstructure. For example, social media systems such as Face-\\nbook, Twitter, and YouTube handle harassment reports cen-\\ntrally, which does not allow the creators of the user gener-\\nated content to have an active role in moderation aside from\\nreporting abusive people or negative content [12, 36] .\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,315,746,553],\"text\":\"Twitch is thus unique in that is a relatively new service\\n(launched in 2011) but does not follow moderation conven-\\ntions of other contemporary social media. The company\\nhandles the moderation of the content that people stream in\\na centralized manner but gives content producers autonomy\\nover how to moderate the real-time chat [39] . While the\\ncompany has some basic moderation features for streamers\\nto utilize and is increasingly trying to develop more in-house\\nmoderation tools, much of advanced moderation tools rely\\non third-party applications [27] . While this creates a bit of\\na learning curve for new streamers and places responsibil-\\nity on themselves for managing their channel, it allows for\\nstreamers to play an active role in governance and presents\\nopportunities for more \\\"grassroots\\\" moderation practices in\\ncomparison to centralized moderation.\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,553,747,762],\"text\":\"The decentralized structure of chat moderation on Twitch\\nis somewhat similar to the sub communities of sites like Red-\\ndit [11] , but is different in that each channel is based on the\\ncontent being generated by one individual or small group. It\\nis also different from online communities such as Slashdot\\nin which the entire community comes up with moderation\\nguidelines and votes up or down [20, 21] . The volunteer\\naspect of moderation is similar to Wikipedia, but the con-\\ntent on Twitch chat is not based on facts, thereby adding\\nmore subjectivity to moderation decisions. Thus Twitch is a\\nplatform that allows streamers to set their own moderation\\nguidelines for their channels, but the viewers do not play a\\nrole in the establishment of those guidelines.\",\"reading_order\":12,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[421,789,563,805],\"text\":\"Becoming a Moderator\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,808,746,906],\"text\":\"There has been much research about why people participate\\nin live streams such as Twitch, both from a streamer perspec-\\ntive [19, 25, 31] and from a viewer perspective [5, 14, 17] .\\nThere has also been research on how it forms communi-\\nties [16] and different types of interactions between streams\\nand viewers [14, 23, 33, 44] .\",\"reading_order\":14,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,906,746,954],\"text\":\"Missing from the scholarship, however, are narratives\\nabout the motivations of the (human) moderator in the live\\nstreaming context. Although moderators are also viewers,\",\"reading_order\":15,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,127,1013],\"text\":\"Paper 160\",\"reading_order\":16,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[706,995,745,1013],\"text\":\"Page 2\",\"reading_order\":17,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300390/results/markdown/3290605-3300390_pdf_shortened_page_1.md"}
{"file_name": "images/test_data/3290605-3300390/3290605-3300390_pdf_shortened_page_2.png", "source_folder": "test_data", "document_id": "3290605-3300390", "source_image_path": "test_data/3290605-3300390/3290605-3300390_pdf_shortened_page_2.png", "source_json_path": "test_data/3290605-3300390/results/output_json/3290605-3300390_pdf_shortened_page_2.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,43,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,43,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"author_affili\"]},{\"label\":\"half_para\",\"bbox\":[68,125,394,251],\"text\":\"they have an additional – and arguably big- role of maintain-\\ning the norms of channel. Who are these people and how\\ndo they end up with this responsibility? Literature review\\non volunteer moderators mostly yielded studies that were\\nconducted in the context of online learning [10, 22] and on-\\nline communities [2, 18, 21, 43] . Both of these contexts have\\ndifferent affordances than live streaming, however, so it is\\nunclear how much they will relate.\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,252,395,538],\"text\":\"Furthermore, it is important to note that there are many\\ntypes of content on Twitch. Live streams can range from\\npuppy cams (just a camera on a box of puppies) to a live\\nbroadcast of a national sports game. Streams like the NFL or\\nreruns of older television shows (e.g., marathon reruns of Bob\\nRoss 1 ) attract many viewers who can chat with each other,\\nbut do not have a human host that directly interacts with\\nviewers. Live events, such as streams of e-sports tournaments,\\nare usually professionally produced and have thousands to\\ntens of thousands of concurrent viewers, allowing for very\\nfew intimate interactions [24] . Also, once a person becomes\\nso popular that they have tens of thousands of viewers, the\\nchat becomes so fast that the content is unreadable. Hence,\\nthe moderators of focus in this paper are those who work for\\nmicro communities – defined as channels that are centered\\naround a single or small group of streamers who directly\\ninteract with the audience and where chat conversations are\\nslow enough that people can read what others are saying.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,538,394,602],\"text\":\"This paper thus poses a very broad and exploratory re-\\nsearch question to further understand the process of becom-\\ning a moderator in micro communities and the different\\nstyles of moderation:\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,602,393,632],\"text\":\"RQ1: How do people become a moderator in Twitch micro\\ncommunities?\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[82,634,331,652],\"text\":\"RQ2: What are the roles that moderators play?\",\"reading_order\":6,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,664,166,681],\"text\":\"Emotional Tolls\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,685,395,895],\"text\":\"Moderation can involve a lot of repetition, and when that\\nrepeated work involves handling negativity, this could lead\\nto secondary trauma and eventually burnout. Secondary\\ntrauma is the acute response to being exposed to someone\\nelse's traumatic experience whereas burnout is the ultimate\\nconsequence of “continual exposure to traumatic material”\\n(p.134) [29] . In the case of moderators, they are exposed to\\ntwo types of traumatic situations. The first is direct victim-\\nization of harassment from viewers, which may have similar\\neffects to online bullying. The second is vicarious experi-\\nences of negativity since they have to view “bad” content\\nbefore they delete it. This notion of the vicarious experiences\\nwith traumatic situations related to exposure to negativity\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,125,747,252],\"text\":\"is correlated with compassion fatigue and has been well doc-\\numented in mental health workers across numerous studies\\nas found in this literature review by Collins et al. [6] . Contin-\\nuous exposure to negativity would lead to higher stress: for\\nexample, Perez et al. [32] found that law enforcement agents\\nwho investigated Internet child pornography cases reported\\nhigher secondary traumatic stress disorder the more they\\nwere exposed to disturbing media.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,252,747,348],\"text\":\"While moderators are not mental health workers, they are\\nexposed to individuals expressing self-harm, anxiety, and\\ndepression [13] . Not being formally trained how to deal with\\nthese situations, however, can create secondary trauma and\\nfeelings of guilt. Thus, this paper asks a general research\\nquestion:\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,348,746,380],\"text\":\"RQ3: What are the emotional tolls of being a moderator in\\nTwitch micro communities?\",\"reading_order\":11,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[420,405,504,423],\"text\":\"3 METHOD\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,427,747,553],\"text\":\"The research was conducted using semi-structured inter-\\nviews with Twitch moderators. The interview protocol started\\nwith questions about who they moderated for, how long they\\nhad been moderating for, and how they became a modera-\\ntor. Participants were then asked about their motivations,\\ngeneral moderation practices, and their relationships with\\nstreamers and viewers. The protocol was reviewed and ap-\\nproved by IRB.\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,553,747,746],\"text\":\"Recruitment was focused on people who moderated in mi-\\ncro communities. This criteria was used because live stream-\\ning can refer to any live event that is broadcast via the In-\\nternet and can also involve the broadcasting of events or\\ntournaments. Moderating for events may be very different\\nfrom that of micro communities, because communication\\nthat happens in chat for events has large volume and makes\\nit difficult for people to read, leading to higher reliance on\\nemotes [24, 30] . Also, events are usually run by a professional\\nproduction team, thus the dynamics between the content pro-\\nducer and moderator may be different than channels where\\nit is just one streamer.\",\"reading_order\":14,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,746,747,889],\"text\":\"Convenience sampling was used to recruit moderators.\\nTwo moderators were contacted through personal connec-\\ntions of the research team while five were moderators of\\nstreamers who were interviewed for a separate research\\nproject (the streamers were those who said they were a\\nTwitch streamer on their Twitter profile and were contacted\\nvia Twitter or those that were recruited at TwitchCon, a con-\\nvention for Twitch streamers). The other moderators were\\nrecruited\",\"reading_order\":15,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,890,748,955],\"text\":\"through Twitter by searching for people who wrote that\\nthey were Twitch moderators in their profile and sending\\nthem direct messages. Participants were interviewed for 40-\\n60 minutes over the phone or through the audio function of\",\"reading_order\":16,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[68,924,393,954],\"text\":\"'Bob Ross was the host of The Joy of Painting, a program on PBS from\\n1983-94 where he taught oil painting.\",\"reading_order\":17,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,127,1013],\"text\":\"Paper 160\",\"reading_order\":18,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[706,995,745,1013],\"text\":\"Page 3\",\"reading_order\":19,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300390/results/markdown/3290605-3300390_pdf_shortened_page_2.md"}
{"file_name": "images/test_data/3290605-3300390/3290605-3300390_pdf_shortened_page_3.png", "source_folder": "test_data", "document_id": "3290605-3300390", "source_image_path": "test_data/3290605-3300390/3290605-3300390_pdf_shortened_page_3.png", "source_json_path": "test_data/3290605-3300390/results/output_json/3290605-3300390_pdf_shortened_page_3.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\"]},{\"label\":\"half_para\",\"bbox\":[69,124,394,156],\"text\":\"Skype or Discord? All interviews were audio recorded and\\ntranscribed for analysis.\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,156,394,411],\"text\":\"The author first went through all the transcripts and ex-\\ntracted all relevant text that pertained to the research ques-\\ntions. Some of the literal questions that asked about motiva-\\ntions and roles were: “How did you become a mod?” “What\\nmotivates you to be mod?” and “How serious do you con-\\nsider your role as moderator?” However, other questions that\\nasked more generally about their activities also contained\\ndiscussions on those topics. There were also questions that\\nasked moderators to recall toxic scenarios and describe how\\nthey handled the situations. The author conducted open\\ncoding and arranged the codes in groups and hierarchies\\nto determine emergent themes. High-level codes included\\ntopical codes such as “motivation, experience, role, decision,”\\nas well as emotional states such as “feelings, treatment, rela-\\ntionships” which broke down into more detailed categories\\nsuch as “racism, sexual harassment, loneliness.”\",\"reading_order\":3,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[69,431,185,450],\"text\":\"Participant Details\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,453,394,644],\"text\":\"The moderators who participated in this study were diverse\\nin age, ranging from 18 to 45, and were primarily in the\\nU.S. (see Table 1 ). They were mostly White males, although\\nthe streamers that they supported were diverse in gender.\\nParticipants were moderators for one to as many as 80 differ-\\nent channels for streamers who had as few as a handful of\\nviewers to those who were very popular and had thousands\\nof viewers. They spent as little as two hours per week up\\nto 70 hours per week as a volunteer moderator. On aver-\\nage, our participants had been moderators on Twitch for 2.4\\nyears. Table 1 contains basic information of our participants.\\nParticipant names are randomly assigned pseudonyms.\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,644,394,772],\"text\":\"None of the moderators that we interviewed received\\nmoney for their services from the streamer but some received\\nsmall gifts, such as subscriptions for the channel that they\\nmoderate, tickets to events, and branded merchandise from\\nthe streamer's online shop, such as mugs and T-shirts. Some\\nof these gifts came from the streamer, other times some-\\none in the community would give them a gift as a sign of\\nappreciation.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,772,394,868],\"text\":\"In the Twitch interface, moderators carry a green badge\\nwith white sword next to their username, which many con-\\nsidered a literal and figurative status symbol. Participants\\ndiscussed about how viewers recognize their moderator sta-\\ntus. \\\"Everyone knows who you are and kind of looks up to\\nyou in that certain way,\\\" Dave said.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,124,502,140],\"text\":\"4 RESULTS\",\"reading_order\":8,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[421,144,563,161],\"text\":\"Becoming a Moderator\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,164,746,356],\"text\":\"RQ1 inquired into the process of becoming a moderator.\\nWhile moderators had different motivations, their process\\nof becoming a moderator was not always aligned with their\\nmotivations – just because someone wanted to become a\\nmoderator did not mean that they could be one. In fact, there\\nwas almost a stigma attached to asking, “Never ask for a\\nmod,” Chuck said. Thus, 18 of our 20 moderators were asked\\nto be moderators by the streamer while two responded to\\nopen calls for moderators. For moderators who did not know\\ntheir streamer beforehand, only two had met their streamer\\nin person at various events before becoming a mod but most\\nothers had never met their streamer in person.\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,356,746,467],\"text\":\"Most of our volunteer moderators had no formal training\\non how to moderate, although some thought that certain\\nexperiences that they had gave them better justification for\\nwhy they should be one, such as studying law in college,\\nbeing a teacher, or having customer service experience. Sev-\\neral had had experience being moderators on online forums,\\naudio chat rooms, or were admins in gaming communities.\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,468,747,707],\"text\":\"Only a few moderators said that their channels had strict\\ninstructions with regards to moderation. “I think it's all\\ntrial and error, there were some moderation guidelines, but\\nTwitch themselves didn't really have a guideline for how to\\ndo moderation,” Barry said. Echoing this sentiment, Ivan said,\\n“I just learned a lot from for example when I first started I\\nwas really afraid of timing people out. I was so terrified of\\nmaking a mistake like banning someone who didn't deserve\\nto be banned or anything, but my friend he really just took\\nme under his wing and like, I have a lot of friends now who\\nare involved in moderation and it's usually who most of my\\nfriends online are. We really just learned from one another.\\nSo, I didn't really look anything up or take classes for how to\\nbe a mod I kind of just developed as a person and I learned\\nhow to do it as I [sic] wung it.”\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,707,746,818],\"text\":\"There was also a lot of building of informal guidelines\\nthrough cases, much like how the legal system relies on\\nprecedence. If there was a specific instance of an unusual sit-\\nuation, moderators would sometimes discuss this with other\\nmoderators and/or the streamer after the stream, usually on\\nDiscord. These discussions sometimes involved the streamer\\nbut not always.\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,825,747,955],\"text\":\"The Token Mod. Moderators sometimes became moderators\\nbecause they were already friends or family with the streamer.\\nSome referred to this as a \\\"token mod\\\" implying that the\\nperson did not become a moderator because of merits but\\nbecause of their personal connections. \\\"There's such thing\\nas what I consider a token mod. You're in a small stream,\\nyou know the person. They mod you because they know\\nyou, not because you know they expect you to actually do\",\"reading_order\":14,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[69,924,394,954],\"text\":\"5Skype and Discord are both free Voice over Internet Protocol (VoIP) appli-\\ncations that support both voice and audio calls.\",\"reading_order\":15,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,127,1013],\"text\":\"Paper 160\",\"reading_order\":16,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,995,745,1013],\"text\":\"Page 4\",\"reading_order\":17,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300390/results/markdown/3290605-3300390_pdf_shortened_page_3.md"}
{"file_name": "images/test_data/3290605-3300390/3290605-3300390_pdf_shortened_page_4.png", "source_folder": "test_data", "document_id": "3290605-3300390", "source_image_path": "test_data/3290605-3300390/3290605-3300390_pdf_shortened_page_4.png", "source_json_path": "test_data/3290605-3300390/results/output_json/3290605-3300390_pdf_shortened_page_4.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"author_affili\"]},{\"label\":\"table_cap\",\"bbox\":[331,122,482,137],\"text\":\"Table 1: Participant Details\",\"reading_order\":2,\"tags\":[]},{\"label\":\"tab\",\"bbox\":[89,152,723,499],\"text\":\"| Participant | Age | Race | Gender | Country | Hours spent weekly | No. of channels | Experience (yrs) | | Alex | 35 | White | Male | Netherlands | 30 | 4 | 2 | | Anne | 30s | White | Transfemale | USA | 6 | 1 | 2 | | Brandon | 31 | White | Male | USA | 10 | 6-7 | 5 | | Barry | 24 | White | Male | USA | 20 | 80 | 4 | | Chuck | 21 | White | Male | USA | Not sure | 30 | 3 | | Caleb | 43 | White | Male | USA | Depends | 2 | | | Diana | 33 | White | Female | UK | 20 | 2 | 1 | | Dave | 18 | White | Male | USA | 60-70 | 1 | 2 | | Evan | 25 | White | Male | France | 35-42 | 2 | | | Emily | 37 | Asian | Female | USA | 3 | 1 | 1.5 | | Frank | 20 | White | Male | USA | 21-28 | 2 | 1 | | Floyd | 21 | | Male | USA | Not sure | 1 | 1 | | George | 41 | White | Male | USA | 21-28 | 60 | 2.5 | | Greg | 29 | White | Male | USA | 12-16 | 2-3 | 1 | | Harry | 19 | White | Male | Scotland | 2-3 | 44 | 2 | | Hanna | 40 | White | Female | USA | 12 | 20 | 2 | | Isaac | 40 | White | Male | USA | 4-12 | 4 | 3-4 | | Ivan | | | Male | USA | 8-10 | 3 | 4 | | Jasmine | 27 | Black | Female | USA | 36-70 | 5 | 5 | | Jared | 45 | Black | Transmale | USA | 16-24 | 1 | 1 |
\",\"reading_order\":3,\"tags\":[],\"alt_text\":\"This is a table presenting participant data across various attributes. The columns include Participant, Age, Race, Gender, Country, Hours spent weekly, No. of channels, and Experience (yrs). Each row contains details for a specific participant, showing a mix of numerical and categorical data.\",\"figure_path\":\"tables/3290605-3300390_pdf_shortened_page_4_tab_3.png\"},{\"label\":\"half_para\",\"bbox\":[68,524,394,587],\"text\":\"anything. They also trust you enough to know that you're \\nnot going to go on a banning spree and just ban people just \\nbecause you can,\\\" explained Brandon, who said that he was \\na token mod on some channels.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,588,394,699],\"text\":\"Sometimes these family and friends were the most dedi-\\ncated supporter of the streamer in spirit but not necessarily\\ndoing any physical work of moderation despite carrying the\\nmoderator status. “Before I became a mod I thought all mods\\nwill actually moderate and it came off as I'm the only one\\nthat's doing a lot of the moderating and I thought that was\\nkind of weird,” Anne said.\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,700,395,907],\"text\":\"Token mod motivations varied, especially depending on\\nthe popularity of the streamer and how many viewers would\\nbe watching, but for the most part token mods did not have\\na strong sense of duty to be present all the time and of all\\nmoderator types were the least intrinsically motivated. Par-\\nticipants noted, however, that even if the token mod is not\\nactually doing anything, their presence in itself could help\\nthe streamer, especially for channels that are new or do not\\nhave many viewers, because the interface allows viewers to\\nsee the list of who is viewing the channel. Because modera-\\ntors have a green badge of a sword next to their user name,\\ntheir presence could potentially make people think twice\\nbefore saying something rude.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,920,394,954],\"text\":\"The Glorified Viewer. If not appointed because of an exist-\\ning relationship, moderators earned their status by being a\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,524,746,652],\"text\":\"positive influence in the channel and/or a frequent viewer,\\nwhich brought them to the streamer's attention and subse-\\nquent appointment as a moderator. This appeared to be the\\nmost common path to becoming a moderator and considered\\nequivalent to a recognition and promotion of sorts. \\\"You\\ndon't become a moderator without first being a member of\\nthe community. So you understand what that community\\nexpects and what it is like,\\\" Isaac said.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,652,746,794],\"text\":\"Oftentimes, the streamer would personally reach out and\\nask the person if they wanted to be a moderator. Floyd said he\\nviewed someone's stream for several years and then started\\nplaying games together. \\\"I started playing Overwatch after\\nthe stream and we grew closer and closer. I had been on\\nTwitch for a few years more than him so I knew about how\\nthings worked. He asked me if I would be a moderator for\\nhis chat and I gladly said yes, and that's how it happened,\\\"\\nhe recalled.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,794,746,907],\"text\":\"However, surprisingly, there were a few moderators who\\nwere not particularly intending to be a moderator but sud-\\ndenly found themselves one appointed by the streamer. All\\nof these participants said that because of their new found\\nrole, they felt a stronger obligation to be in the stream more\\noften. Emily described her thoughts on how she became a\\nmoderator:\",\"reading_order\":10,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,127,1012],\"text\":\"Paper 160\",\"reading_order\":11,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[706,995,745,1012],\"text\":\"Page 5\",\"reading_order\":12,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300390/results/markdown/3290605-3300390_pdf_shortened_page_4.md"}
{"file_name": "images/test_data/3290605-3300390/3290605-3300390_pdf_shortened_page_5.png", "source_folder": "test_data", "document_id": "3290605-3300390", "source_image_path": "test_data/3290605-3300390/3290605-3300390_pdf_shortened_page_5.png", "source_json_path": "test_data/3290605-3300390/results/output_json/3290605-3300390_pdf_shortened_page_5.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_pub_date\"]},{\"label\":\"para\",\"bbox\":[69,125,394,171],\"text\":\"Emily: \\\"Suddenly one day I log in and find out that I am\\na mod. They didn't even tell me, it was like, oh, I'm a mod-\\nuhh oh.\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[82,173,331,188],\"text\":\"Interviewer: and how did you feel about that.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,189,394,298],\"text\":\"Emily: It was weird. I had mixed feelings. I felt like being a \\nmod would give me responsibilities, and watching the stream \\nwas fun for me so getting a job was a little bit of a – well, I \\nwouldn't say burden, but yeah, responsibility that I wasn't \\nsure I wanted. At the same time it was flattering, I mean, in \\nhindsight I guess I deserved it in a sense but it was nice to \\nbe recognized.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[82,299,350,315],\"text\":\"Interviewer: What do you mean you deserved it?\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,316,394,395],\"text\":\"Emily: Well they stream once a week and I was there most\\nof the time, pretty consistently, to support them. I also was\\ninteracting with other people in chat because I wanted to\\nkeep up the conversation so in a way I was already being a\\nmoderator before I became a moderator.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,407,395,520],\"text\":\"The Recruit. Another method of becoming a moderator was\\nthrough open requests for moderators by the streamer. Un-\\nlike glorified viewers, who were usually approached directly\\nby the moderators, recruits were those who responded to the\\nstreamer's request and usually happened when a streamer\\nwho had a small viewership experienced a sudden influx of\\nviewers and needed the extra help.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,520,394,583],\"text\":\"Diana said that she frequented a certain channel of a\\nwoman who makes quilts, and that one day, the streamer\\nasked if any of the viewers would be interested in becoming\\na moderator, so she volunteered.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,584,394,711],\"text\":\"Moderators also received requests from streamers who\\nknew that they moderated for other streams and solicited\\ntheir help even if that moderator was not a viewer on their\\nchannel. In these cases usually the streamer would be familiar\\nof the person that the moderator already moderated for and\\nhad either seen the moderator in action on a different channel\\nor had heard of the moderator's reputation through other\\nstreamers.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[69,728,173,745],\"text\":\"Moderator Roles\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,749,394,908],\"text\":\"Our second research question was about the different roles\\nthat moderators play and their styles of moderation. Even\\nif the basic moderation tasks were fairly uniform across all\\nmoderators (e.g., deleting rude messages, banning people,\\nanswering questions posed by viewers) there were clear pref-\\nerences and priorities that moderators had. Moderators took\\non specific roles that reflected different approaches to mod-\\neration; these inclinations were completely self-imposed and\\nnot necessarily mutually exclusive – a moderator could take\\non one or more of the roles outlined below.\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,921,394,954],\"text\":\"The Helping Hand: The vast majority of moderators said that\\nthey moderate because they really wanted to help others.\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,125,746,236],\"text\":\"\\\"I'm somebody who really really likes to help people,\\\" ex-\\nplained Dave. \\\"I've been working customer service and retail\\nsince I turned 18, I'm a people's person, I like helping people\\nout, making sure that they're successful, I just really have\\na servant's heart and I really enjoy making sure people are\\nhappy and satisfied and can pursue their passions,\\n\\\" Barry\\nsaid.\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,237,745,283],\"text\":\"The desire to help was twofold: helping the streamer\\nand helping the community. Sometimes these interests over-\\nlapped but not always.\",\"reading_order\":14,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,283,746,443],\"text\":\"For those who cared about the community, it was all about\\nfacilitating a positive environment. Numerous participants\\ntalked about making the internet a better place. “ If it's a place\\nthat's trying to create a good healthy space on the internet\\nor is doing really good work that I believe in and I think I\\ncan be a positive contributor to, that is one of the things that\\nwould get me to say \\\"yes,\\\" said Jared, describing how he\\nagreed to become a moderator. Similarly, Ivan said that they\\n\\\"just love making communities a happier and healthier place\\nand building the friendships within the community.\\\"\",\"reading_order\":15,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,444,745,570],\"text\":\"While the desire to make online spaces nice was generic,\\nthere were a few moderators who were finding meaning\\nby making a difference—something that they did not think\\nthey did offline. Some mentioned being bullied as a child,\\nincluding Anne, a transgender woman, who said that there\\nis a \\\"trend of toxic that just comes out by being anonymous\\non the internet.\\\" Bullied individuals said that they wanted to\\nprevent others from having similar experiences.\",\"reading_order\":16,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,571,746,634],\"text\":\"Alex, who has autism, said,\\n\\\"Generally feel pretty useless\\nin the world. This is a great way to prove that I do still have\\nvalue even though I am on disability. I got to be useful, I got\\nto help, I got to make the world a better place.\\n\\\"\",\"reading_order\":17,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,635,746,746],\"text\":\"Moderators also wanted to help the channel because they\\nadmired the streamer, appreciated their content, and/or\\nwanted to ensure the streamer's success. \\\"It really is very\\nsatisfying to know like you can help a streamer out like\\nthat. There are people that donate or that subscribe, but\\nmoderating you don't really offer anything financially but\\nit's still a very a good way to help a streamer,\\\" said Harry.\",\"reading_order\":18,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,747,746,825],\"text\":\"\\\"Being able to make someone else's dream a reality is the\\nbiggest thing. Because if a streamer is happy, it makes all the\\nviewers happy and then everyone is smiling. Being able to\\nbe part of the team that makes that happen means the world,\\\"\\nsaid Chuck.\",\"reading_order\":19,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,826,746,954],\"text\":\"Sometimes the desire to support streamers and the view-\\ners intersected, but other times it didn't. Brandon said, \\\"Ulti-\\nmately, you know, I'm there for the streamer not the viewers.\\nSo if I do something, you know, if I time out a viewer and\\nthat pisses them off I could care less (laughs). I'm not there\\nto make them happy.\\\" There were also moderators who dif-\\nferentiated between wanting to help the community and\\nwanting to help a viewer by putting collective needs first.\",\"reading_order\":20,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[70,996,127,1012],\"text\":\"Paper 160\",\"reading_order\":21,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[704,996,745,1012],\"text\":\"Page 6\",\"reading_order\":22,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300390/results/markdown/3290605-3300390_pdf_shortened_page_5.md"}
{"file_name": "images/test_data/3290605-3300390/3290605-3300390_pdf_shortened_page_6.png", "source_folder": "test_data", "document_id": "3290605-3300390", "source_image_path": "test_data/3290605-3300390/3290605-3300390_pdf_shortened_page_6.png", "source_json_path": "test_data/3290605-3300390/results/output_json/3290605-3300390_pdf_shortened_page_6.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\"]},{\"label\":\"half_para\",\"bbox\":[69,125,395,299],\"text\":\"The Justice Enforcer. Seeking justice was a major theme among\\nmoderators who felt strongly about enforcing community\\ntandards. Some of these did not particularly enjoy this\\npower, whereas others explicated stated that they enjoyed\\npunishing bad behavior.\\n\\\"It is something I really like to do\\nsince I have strong sense of justice. I can understand when\\npeople behave badly, sexism, racism, all that stuff, so it feels\\nreally good to do something about it. In real life, you can only\\nleave them there, you can let them go away, but in a chat\\nroom, if I am moderating, saying 'You are terrible human\\nbeing, go away,\\n' that feels really good,\\n\\\" said Alex.\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,299,395,522],\"text\":\"For those who took moderation as a duty, some considered\\nit as a form of community service whereas others saw it as\\nbeing thee \\\"middle man\\\" or \\\"bad guy\\\" so that the streamer\\ndid not have to be. \\\"The streamer needs to get viewers and\\nneeds to be likeable, especially a streamer who does this\\nprofessionally, they are successful as far as people subscribe\\nto them and view them, so the streamer can't be the bad guy.\\nIt's easier for the streamer when the mods will take care of\\npeople who are problems. If we're doing the banning, the\\nmods are the bad guys, which is important for the health of\\nthe stream,\\\" said Jared. Caleb also said he was there to be\\nthe \\\"bad guy\\\": \\\"The streamer is there to stream... they don't\\nneed that kind of headaches. So you are there to be the one\\nto get all the hate,\\\" he said.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,522,395,667],\"text\":\"For others, it wasn't about taking the hit but about exer-\\ncising behavior that made them feel powerful, especially if\\nthey felt like they did not have much power in their offline\\nlife. \\\"Sometimes it feels good. I have a long day at work,\\nand you're like 'work sucks' and you come home and you\\nsee some idiot in chat yelling all the time- just click and\\nhe's done. It's satisfying,\\\" Chuck said. Brandon said, \\\"Being\\na moderator is just like being a regular viewer except you\\nhave power.\\\"\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,714,395,954],\"text\":\"The Surveillance Unit. A couple moderators described mod-\\neration as a form of surveillance, saying that they did not\\nsee their role as interacting with the viewers but watching\\nuntil it is time for them to step in. Sometimes, this was not\\nnecessarily because they did not want to talk to viewers\\nbut because they felt that their voice as a moderator should\\nnot overwhelm the chat. \\\"If there's a current conversation\\ngoing on and it is fine, I don't really feel the need to throw\\nmyself in the middle of it. I just kind of sit there and watch\\nthings quietly,\\\" said Hanna. She added that the moderators'\\nsword badge also discourages her to converse. \\\"They com-\\nplain about too many swords in chat so sometimes I just kind\\nof keep it to myself because I don't want to make people\\nuncomfortable or think that they can't have a conversation\\nbecause I'm watching,\\\" she said.\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,125,746,283],\"text\":\"The Conversationalist. On the opposite spectrum were the\\nconversationalists, who genuinely enjoyed the aspect of in-\\nteracting with viewers, building community, facilitating con-\\nversation, and keeping people engaged so that they have a\\ngood experience. \\\"Whenever there's no hatred in the chat,\\nit's a really fun interactive thing to watch and being able\\nto like mold it, for example whenever you're the mod for a\\nreally big channel you can just ask whatever you want and\\nthe chat is so eager to respond to you, like, you can ask them\\nanything and 50k people will just reply to you,\\\" said Frank.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,283,746,379],\"text\":\"There were a mix of participants who said they were \\\"nat-\\nurally extroverted\\\" and those who admitted to being more\\nsocial online then in person. In particular, this latter group\\nfound that it was easier for them to interact with people\\nthrough typing in chat than talking in person, and that do-\\ning so sometimes improved their social skills.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,380,746,538],\"text\":\"Jasmine pointed out that moderation is not just about \\nbanning but also about answering questions or just keeping \\nthe chat entertaining while the streamer is focused on the \\ngame. She talked about how she actively tries to say things \\nin chat so the streamers can see that an audience exists. “I \\ndo try to consciously welcome new people though and if \\nchat gets quiet I will talk more so it doesn't seem like chat \\nis empty. I think streamers could feel bad if there is nothing \\ngoing on in chat and seeing someone else in chat may get \\nlurkers3 to say something too,” she said.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,538,746,635],\"text\":\"Conversationalists also helped facilitate more meaningful \\ninteractions beyond general hellos. Alex, who lived in the \\nNetherlands, talked about a conversation with a viewer from \\nPuerto Rico right after the hurricane, and how people were \\ninterested in how they were doing and if everything was \\nokay.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,649,518,664],\"text\":\"Emotional Tolls\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,667,746,813],\"text\":\"While there are many difficulties in moderating that have\\nto do with the moderation practices themselves – some of\\nwhich include unclear moderation guidelines, different men-\\ntal models of moderation, and technical difficulties – RQ3\\nfocused on the emotional tolls that moderators experience.\\nThree main themes emerged along these lines: feelings of be-\\ning under appreciated, misaligned relationship expectations\\nwith both streamers and viewers, and difficulties in handling\\nnegativity and guilt.\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,822,746,920],\"text\":\"Lack of Appreciation. As much as many moderators said that\\nthey felt like a cherished member of the community and a\\ntrue collaborator of the streamer, there were also instances,\\neither observed or experienced directly, where moderators\\nthought they were not being valued as much as they should.\\nDiana talked about how, mostly in channels with higher\",\"reading_order\":12,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[422,938,664,953],\"text\":\"3People who are watching but not participating in chat\",\"reading_order\":13,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,127,1013],\"text\":\"Paper 160\",\"reading_order\":14,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,995,745,1013],\"text\":\"Page 7\",\"reading_order\":15,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300390/results/markdown/3290605-3300390_pdf_shortened_page_6.md"}
{"file_name": "images/test_data/3290605-3300390/3290605-3300390_pdf_shortened_page_7.png", "source_folder": "test_data", "document_id": "3290605-3300390", "source_image_path": "test_data/3290605-3300390/3290605-3300390_pdf_shortened_page_7.png", "source_json_path": "test_data/3290605-3300390/results/output_json/3290605-3300390_pdf_shortened_page_7.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[]},{\"label\":\"half_para\",\"bbox\":[69,125,394,252],\"text\":\"volume of chat, some streamers or viewers blame moder-\\nators for missing certain things, which they thought was\\nunfair given the amount of work that moderators have to\\ndo: “I understand that it’s a stressful environment for the\\nstreamer, but sometimes they won’t appreciate that it’s also\\na stressful time for the moderators. I’ve seen a couple of\\noccasions where the stream has snapped at the moderators\\nfor not reacting quickly enough,” she said.\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,252,394,380],\"text\":\"\\\"Having a healthy chat is a good part of a good live stream\\nin my opinion and moderators do most of the job to keeping\\nthe chat healthy,\\\" said Evan, \\\"A lot of live streamers don't\\nappreciate that, which is kind of sad. Some do, but I've like\\nmoderated for a lot of streamers. Sometimes you rarely get\\na thank you. Like alright, I'm just doing hours of work just\\nfor you, just for the chat, just for the stream. You're getting\\npaid for it. I don't even get a thank you.\\\"\",\"reading_order\":3,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,380,393,427],\"text\":\"This idea of \\\"just wanting thanks\\\" was echoed by many\\nmoderators. \\\"They recently had their stream anniversary\\nand they thanked everybody but their mods,\\\" said Emily.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,428,394,522],\"text\":\"Several moderators also said that they felt like an employee\\nor colleague rather than a member of a team. Many streamers\\nnever shared their schedule for streaming in advance, which\\nmade moderators feel uncomfortable because they felt like\\nthey had a responsibility to be there, even if they technically\\ndid not have to always be there.\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,523,394,666],\"text\":\"Brandon described a situation where he was moderating \\nfor someone for years and then the streamer just closed their \\nchannel without letting any of the moderators know, “If they \\njust stop and go dark without saying anything, it’s like, ‘OK, \\nwhy?’ It’s not cool that we put in all this free time and work \\nfor them– they were making money off the stream and I \\nwasn’t, I was doing this completely voluntary. The least they \\ncould do is tell me why they quit but when they don’t even \\ndo that, it’s like, basically a PU,” he said.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,666,394,891],\"text\":\"In a unique instance, Anne said that she felt like the stream-\\ners paid more attention to people who paid money to sub-\\nscribe to the channel and that monetary contributions were\\nfavored over time. She said that the streamer sometimes\\nlet viewers join in on playing games and that people who\\npaid the streamer got opportunities to do that whereas mod-\\nerators did not, even though some of the moderators had\\nbeen on the channel for a couple years. “Their personality\\nchanged when they got partnered 14 too. It became about the\\nmoney,” she added. Sometimes moderators removed them-\\nselves from the community because of these issues, but this\\nwas rare and something that the participants noted seeing in\\nother moderators but did not experience themselves. When\\nasked why moderators continue despite these feelings of\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,125,746,205],\"text\":\"disappointment – and sometimes resentment – they explained\\nthat either 1) they still enjoyed the content of the stream\\nregardless of how the streamer treated them, and 2) they\\nenjoyed interacting with the viewers, whom they considered\\nthemselves a part of the community with.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,219,747,363],\"text\":\"Misaligned Relationship Expectations. A second theme that\\nwas identified was misaligned relationship expectations with\\nregard to both streamers and viewers. For example, some\\nmoderators had a strong desire to become closer to the\\nstreamer but found out that the streamer did not share those\\ndesires. These examples led to disappointment. “I think friend-\\nship is a strong priority for most people but this relationship\\nis just going to stay where it's at. Some days it can be frus-\\ntrating but usually it just feels like it is what it is,” said Jared.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,364,746,476],\"text\":\"To be fair, there were moderators who had wonderful re-\\nlationships with their streamers and over time become close\\nfriends. For example, Floyd, who moderates for a streamer\\nwho is an artist, said that he has a very close relationship\\nwith the streamer, and that when they went to a convention,\\nthe streamer and his fiance picked him up from the airport\\nand they all shared a hotel room together.\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,476,746,570],\"text\":\"Similarly, Ivan also said that he became close with several\\nof streamers that he is a moderator for: \\\"I met them, I've been\\nto their house, I consider myself to be best friends with them,\\\"\\nhe said. The moderators who fell close to streamers usually\\nspent a lot of time with the streamer outside of the stream,\\nmostly playing games together or chatting in Discord.\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,571,747,730],\"text\":\"Some moderators, however, feel that it is necessary to\\nmaintain a professional distance, describing how they frowned\\nupon practices when moderators tried to use their moderator\\nstatus to seek special favors from the streamer or express\\ninterest to streamers in an inappropriate manner, such as\\nwanting sexual relations with the moderator. No one had an\\nexample that pertained to themselves but were able to recall\\ninstances in which they thought other moderators in the\\nchannel were being unprofessional. Jared had some strong\\nopinions on this issue:\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,731,746,873],\"text\":\"\\\"I try not to have too close of a relationship with my\\nstreamer because I know that streamers sometimes get stall-\\ners and people who are really needy or people who overstep\\nboundaries by trying to be too close and some people might\\nwant to mod because they want a too close relationship, but\\nI think that's creepy so I generally try to stay pretty profes-\\nsional and keep strong boundaries, like I don't contact her\\nvery often and I don't contact her much for things outside\\nof mod work.\\\"\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,874,747,955],\"text\":\"Anne discussed how meeting the streamer offline made\\nher realize where her expectations went wrong. A main\\nmoderator for a certain channel, she talked about how she\\nwent to TwitchCon to meet the streamer but aside from a\\ngreeting, did not get to spend any time with the streamer,\",\"reading_order\":14,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[69,899,394,954],\"text\":\"\\\"Twitter has a tiered system: users who stream regularly, have a certain\\nlevel of viewership, and agree to exclusively stream to Twitter (among other\\ncriteria) are made \\\"partners.\\\" Partners receive ad revenue and also can earn\\nmoney through viewer subscriptions and other methods.\",\"reading_order\":15,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,127,1013],\"text\":\"Paper 160\",\"reading_order\":16,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[705,995,745,1013],\"text\":\"Page 8\",\"reading_order\":17,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300390/results/markdown/3290605-3300390_pdf_shortened_page_7.md"}
{"file_name": "images/test_data/3290605-3300393/3290605-3300393_pdf_shortened_page_0.png", "source_folder": "test_data", "document_id": "3290605-3300393", "source_image_path": "test_data/3290605-3300393/3290605-3300393_pdf_shortened_page_0.png", "source_json_path": "test_data/3290605-3300393/results/output_json/3290605-3300393_pdf_shortened_page_0.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[68,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"icon\",\"bbox\":[0,92,39,134],\"reading_order\":2,\"tags\":[\"cross_marker\"]},{\"label\":\"sec_0\",\"bbox\":[117,118,697,182],\"text\":\"Towards Understanding the Link\\nBetween Age and Smartphone Authentication\",\"reading_order\":3,\"tags\":[]},{\"label\":\"author_information\",\"bbox\":[155,199,329,266],\"text\":\"Lina Qiu\\n\\nUniversity of British Columbia\\nVancouver, Canada\\n\\nlqiu@cee.ubc.ca\",\"reading_order\":4,\"tags\":[\"author\",\"author_affili\",\"author_mail\"]},{\"label\":\"author_information\",\"bbox\":[154,275,329,342],\"text\":\"Idar Mushukhov\\n\\nUniversity of British Columbia\\nVancouver, Canada\\n\\nidarm@ece.ubc.ca\",\"reading_order\":5,\"tags\":[\"author\",\"author_affili\",\"author_mail\"]},{\"label\":\"author_information\",\"bbox\":[399,199,633,266],\"text\":\"Alexander De Luca\\n\\nGoogle\\n\\nZürich, Switzerland\\n\\nadeluca@google.com\",\"reading_order\":6,\"tags\":[\"author\",\"author_affili\",\"author_mail\"]},{\"label\":\"author_information\",\"bbox\":[396,276,657,342],\"text\":\"Konstantin Beznosov\\n\\nUniversity of British Columbia\\nVancouver, Canada\\n\\nbeznosov@ece.ubc.ca\",\"reading_order\":7,\"tags\":[\"author\",\"author_affili\",\"author_mail\"]},{\"label\":\"sec_1\",\"bbox\":[68,350,141,367],\"text\":\"ABSTRACT\",\"reading_order\":8,\"tags\":[\"paper_abstract\"]},{\"label\":\"para\",\"bbox\":[68,372,395,529],\"text\":\"While previous work on smartphone (un)locking has re-\\nvealed real world usage patterns, several aspects still need\\nto be explored. In this paper, we fill one of these knowledge\\ngaps: the interplay between age and smartphone authen-\\ntication behavior. To do this, we performed a two-month\\nlong field study ( N = 134). Our results indicate that there\\nare indeed significant differences across age. For instance,\\nyounger participants were more likely to use biometric un-\\nlocking mechanisms and older participants relied more on\\nauto locks.\",\"reading_order\":9,\"tags\":[\"paper_abstract\"]},{\"label\":\"sec_1\",\"bbox\":[68,537,204,552],\"text\":\"ACM Reference Format:\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,553,394,641],\"text\":\"Lina Qiu, Alexander De Luca, Ildar Musshukov, and Konstantin\\nBeznosov. 2019. Towards Understanding the Link Between Age and\\nSmartphone Authentication. In CHI Conference on Human Factors in\\nComputing Systems Proceedings (CHI 2019), May 4-9, 2019, Glasgow,\\nScotland, UK . ACM, New York, NY, USA, 10 pages. https://doi.org/\\n10.1145/3290605.3300393\",\"reading_order\":11,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,656,197,672],\"text\":\"1 INTRODUCTION\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,676,395,804],\"text\":\"Advances in technical capabilities of modern smartphones\\nenable storing (and access to) large amounts of data, some\\nof which might be sensitive or private in nature [14] . Due to\\nthese devices' small sizes and high mobility, unauthorized\\naccess to sensitive data has become a realistic threat. For\\ninstance, it has been shown that 1 out of 5 users in the US\\nhas accessed someone else's mobile phone without permis-\\nsion [13] .\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,352,747,479],\"text\":\"To protect smartphones from unauthorized access, mobile\\noperating systems provide secure device locking, however,\\nmost smartphone users tend to choose no or easy-to-guess\\nunlocking secrets [3] . Inconvenience of the currently de-\\nployed unlocking methods, lack of motivation, and lack of\\nawareness about the sensitivity of the data stored on their\\ndevices are often used to justify why a secure lock is not\\nused [4, 7, 14] .\",\"reading_order\":14,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,480,747,669],\"text\":\"Recent studies [5, 6, 10, 12] have started to shed light on\\nhow and when smartphone users employ unlocking mech-\\nanisms, some of which were conducted in the wild. For in-\\nstance, Mahfouz et al. [12] investigated authentication pro-\\ncess parameters, such as time to unlock, authentication error\\nrates, and types of apps used within each session. Harbach\\net al. [5] focused on exact authentication speed, error counts,\\nand types of errors among different unlocking mechanisms,\\nwith a study sample containing 134 participants. While these\\nefforts have provided first valuable insights into studying\\nperformance of smartphone unlocking mechanisms in the\\nwild, there are still plenty of unknowns.\",\"reading_order\":15,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,670,747,862],\"text\":\"One of these unknowns is the relation between age and\\nauthentication behavior. A recent online survey on secure\\nsmartphone locking across eight countries suggested that age\\nmight be linked to differences in locking behavior of users [6]\\n(e.g., whether or not they use secure locks). It is, however,\\nstill unclear how age affects smartphone unlocking, because\\nreal world data is missing. In order to fill this knowledge gap\\nand to provide initial insights into how age correlates with\\nsmartphone authentication, we conducted a longitudinal\\nfield study with 134 participants of diverse backgrounds.\\nThe main goal was to investigate what behavioral patterns\\nare linked to age and which are not.\",\"reading_order\":16,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,863,748,925],\"text\":\"The results of our study reveal that age indeed signifi-\\ncantly correlates with unlocking behavior. For example, older\\nparticipants were less likely to use 'Fingerprint' authentica-\\ntion. They also interacted with their devices less frequently,\",\"reading_order\":17,\"tags\":[]},{\"label\":\"icon\",\"bbox\":[67,817,210,872],\"reading_order\":18,\"tags\":[\"cross_marker\"]},{\"label\":\"fnote\",\"bbox\":[69,874,339,902],\"text\":\"This work is licensed under a Creative Commons Attribution-\\nNonCommercial-NoDerivsInternational 4.0 License.\",\"reading_order\":19,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[68,913,273,953],\"text\":\"© 2019 Copyright held by the owner/author(s).\\nhttps://doi.org/10.1145/3296055.330391\",\"reading_order\":20,\"tags\":[\"meta_pub_date\",\"meta_num\"]},{\"label\":\"foot\",\"bbox\":[69,996,127,1012],\"text\":\"Paper 163\",\"reading_order\":21,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,996,744,1012],\"text\":\"Page 1\",\"reading_order\":22,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300393/results/markdown/3290605-3300393_pdf_shortened_page_0.md"}
{"file_name": "images/test_data/3290605-3300393/3290605-3300393_pdf_shortened_page_1.png", "source_folder": "test_data", "document_id": "3290605-3300393", "source_image_path": "test_data/3290605-3300393/3290605-3300393_pdf_shortened_page_1.png", "source_json_path": "test_data/3290605-3300393/results/output_json/3290605-3300393_pdf_shortened_page_1.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,43,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,43,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"half_para\",\"bbox\":[69,124,394,156],\"text\":\"which means that they are less frequently exposed to their\\nunlocking mechanism.\",\"reading_order\":2,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,167,198,184],\"text\":\"2 RELATED WORK\",\"reading_order\":3,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,189,394,426],\"text\":\"Despite the existence of a variety of smartphone unlocking\\nmethods (e.g., PIN, alphanumeric passwords, Android un-\\nlock patterns, biometrics), a recent study [4] showed that\\nmost smartphone users likely underestimate the sensitiv-\\nity of their data and how they access it with their devices.\\nThus, many users do not protect their devices, and with\\nit their data, properly [6] . One potential reason for this is\\nthe inconvenience caused by frequent unlocking and the\\nrespective time this takes [7] . As a consequence, many alter-\\nnative systems were proposed to make the authentication\\nprocess easier [1, 8, 9, 15] . SnapApp [1] , which provides a\\ntime-constrained quick-access option, is an example that re-\\nduces the authentication workload by keeping users logged-\\nin in a more secure way than having their device unlocked\\nall the time.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,427,394,491],\"text\":\"Researchers and designers need data on how people use\\ntheir devices in the real world, in order to design new mech-\\nanisms that are in line with users' real needs. A few studies\\nhave started to provide insights into this [5, 6, 10, 12] .\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,491,394,666],\"text\":\"Mahfouz et al. [12] studied how different smartphone use\\npatterns correlate with the time it takes users to unlock their\\ndevice, how often users make a mistake during authentica-\\ntion, and which authentication methods users choose for\\ndevice unlocking. The findings suggest that users who lock\\ntheir devices interact with them more frequently and for\\nlonger sessions than those who do not. In addition, the cost\\nof unlocking is low when compared to overall smartphone\\nusage and users do not mind adopting unlocking methods\\nwith a higher error rate (e.g., Android unlock pattern), as\\nlong as they allow faster input of the unlocking secret.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,666,394,808],\"text\":\"Hintze et al. [ 10 ] investigated the number of interactions\\nper day, the average interaction duration, and the total daily\\ndevice usage time by using a state machine based on screen\\non/off events. Here the authors analyzed mobile device data\\nlogs from 1,960 Android smartphones (from the Device Ana-\\nlyzer project [ 16 ] ). The authors report that on average, par-\\nticipants interacted with their devices 57 times a day, among\\nwhich 43 % were actual unlocks, and the daily device usage\\ntime was 117 minutes.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,808,395,954],\"text\":\"In a month-long field study done by Harbach et al. [5] , the\\nauthors collected data from a subset of PhoneLab users, all of\\nwhich were affiliated with a university. The authors instru-\\nmented LG Nexus 5 smartphones to study the performance\\nof Android unlocking mechanisms in situ. They found that\\n\\\"PIN\\\" users take longer to unlock while committing fewer\\nerrors than \\\"pattern\\\" users, who tend to unlock more fre-\\nquently and are more prone to errors. However, on average,\\n\\\"PIN\\\" and \\\"pattern\\\" users spend a similar amount of total\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,124,746,171],\"text\":\"unlocking time. In addition, the authors offer a benchmark\\nagainst which newly designed unlocking mechanisms can\\nbe evaluated.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,172,746,363],\"text\":\"Harbach et al. [6] also conducted a global-scale survey\\non Google Consumer Surveys (GCS) with 8,286 participants\\nfrom 8 countries to investigate whether users' attitudes to-\\nwards smartphone unlocking differed between various na-\\ntionalities. The findings pointed towards the need that de-\\nmographic differences, including both nationality and age,\\nshould be considered when designing new authentication\\nsystems for smartphones. The authors also conclude that de-\\nspite the differences between nationalities, inconvenience of\\nunlocking is still one of the major reasons for low adoption\\nrate of current authentication systems, especially for older\\nusers.\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,364,746,538],\"text\":\"While previous work provided manifold insights on smart-\\nphone authentication in situ, all the aforementioned real\\nworld studies suffer from samples skewed towards predomi-\\nnantly tech-savvy, young participants. That is, they do not\\nprovide data to understand whether and how age is linked\\nto smartphone authentication. To fill this gap, we conducted\\na field study with a more representative participant pool\\nwith regard to age and other factors. We focused our analy-\\nsis on how smartphone authentication behaviour correlates\\nwith participants' age, while considering the effect of other\\ndemographic covariates like gender.\",\"reading_order\":11,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[420,549,551,567],\"text\":\"3 METHODOLOGY\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,571,746,650],\"text\":\"At the beginning of the study, participants installed a custom-\\nbuilt application on their smartphones and ran it for 60 days\\nor more (the first 60 days were used for the analysis). The\\nstudy app ran in the background and collected relevant usage\\nstatistics.\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,651,746,746],\"text\":\"At first launch, the application presented the consent form\\nand allowed participants to opt out of providing certain types\\nof data (e.g., activity data). Afterwards, the app directed par-\\nticipants to an entry survey to collect basic demographic\\ndata. In addition, participants were asked to report which\\nunlocking mechanism they were currently using.\",\"reading_order\":14,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,746,746,872],\"text\":\"During the study, the app recorded all lock and unlock\\nevents, whether they were auto locks or manual locks, and\\nlogged the start and end time stamps of each user session. In\\naddition, the app collected user activity data such as whether\\nunlocking happened while being still or on the move (if\\nopted-in). We define a user session as the time between a\\ndevice unlock and the corresponding lock event (either auto\\nlock or manual).\",\"reading_order\":15,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,873,746,954],\"text\":\"In order to make sure that the data collection process\\nwas robust, we conducted a pilot study with six participants\\nfor 15 days and fixed the (few) bugs we found. We applied\\nand obtained approval from the research ethics board in our\\nuniversity before conducting the pilot.\",\"reading_order\":16,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,127,1012],\"text\":\"Paper 163\",\"reading_order\":17,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[706,995,745,1012],\"text\":\"Page 2\",\"reading_order\":18,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300393/results/markdown/3290605-3300393_pdf_shortened_page_1.md"}
{"file_name": "images/test_data/3290605-3300393/3290605-3300393_pdf_shortened_page_2.png", "source_folder": "test_data", "document_id": "3290605-3300393", "source_image_path": "test_data/3290605-3300393/3290605-3300393_pdf_shortened_page_2.png", "source_json_path": "test_data/3290605-3300393/results/output_json/3290605-3300393_pdf_shortened_page_2.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_pub_date\",\"meta_subject\"]},{\"label\":\"sec_2\",\"bbox\":[69,125,184,140],\"text\":\"Data Transmission\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,144,395,256],\"text\":\"For data confidentiality, we encrypted all data logged through-\\nout the day with a symmetric encryption key, generated at\\nrun time. We encrypted this key with a hard-coded public\\nkey, and then appended it to the encrypted data logs be-\\nfore submitting them to the back-end server. Encrypted logs\\nwere uploaded to our back-end server once a day, around\\nmidnight.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[69,269,154,285],\"text\":\"Data Analysis\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,289,395,625],\"text\":\"Our research objective was to investigate how (un)locking\\nbehaviours, such as choice of unlocking mechanism or error\\nlikelihood, correlate with age. For this, we also took into\\naccount other demographic covariates like gender. To an-\\nswer this research question, we conducted regression analy-\\nses among different variables. We applied (multiple) linear\\nregression models for continuous response variables, and\\nlogistic regression models for categorical response variables,\\nwith a p-value threshold of 0.05. We validated all required\\nassumptions, e.g. normality of residuals and no/little multi-\\ncollinearity, before applying the the regression models. We\\nperformed data transformation, e.g. natural log, square root,\\non the response variables, and data centralization on the inde-\\npendent variables when necessary, to assure the data meets\\nall assumptions required for applying corresponding regres-\\nsion models. For the analysis, we averaged participants' data\\nacross the 60 days whenever there were several data points.\\nFor example, we averaged session length across all recorded\\nsessions per participant. Details of all data metrics (e.g., how\\nwe computed the variables for each statistical test) can be\\nfound in Section 5 .\",\"reading_order\":5,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[69,636,186,653],\"text\":\"4 PARTICIPANTS\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,657,395,784],\"text\":\"We recruited participants from North America (US and Canada)\\nthrough Amazon Mechanical Turk (MTurk), Twitter, Face-\\nbook, our university mailing lists, and The Sample Network\\n(TSN). 1 etc. In order to be eligible for the study, participants\\nhad to be Android users and 19 years or older. To prevent\\npotential recruitment biases, we avoided using terms like\\n\\\"authentication\\\", \\\"privacy\\\" and \\\"security\\\" in our advertise-\\nment. 2\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,785,395,897],\"text\":\"Out of 185 people that completed the study, we had to\\nremove some for different reasons including a technical issue\\ndue to which our back-end server was silently failing for 10\\ndays. Another reason for removal was answer ambiguity\\nin the start and exit surveys. These exclusions resulted in\\na pool of 137 participants. In addition, we removed the 3\\npassword users from the statistical analysis due to being too\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,125,746,220],\"text\":\"small of a sample. We thus report our analysis of data from\\n134 participants. As shown in Table 1 , the majority of our\\nparticipants (57%) was recruited from TSN, while 27% were\\nrecruited through MTurk and the rest were from the other\\nfive platforms. All data used in the analysis was collected\\nbetween December 8, 2016 and August 10, 2017.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[422,221,746,298],\"text\":\"Each participant who ran the study app for 60 days re-\\nceived a compensation of USD 40 and was entered into a\\nraffle for an iPad Pro 2. We additionally provided a report\\nto each participant with a summary of how they used their\\nsmartphone during the study.\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,299,746,444],\"text\":\"We compared our study sample to the smartphone pop-\\nulation in the US reported by the Pew Research Center [2] .\\nStatistical results did not reveal any significant differences\\nbetween our participants' demographics and those presented\\nin the Pew Research Center report, in terms of age ( $\\\\chi^2 = 20$ ,\\n$p = .22$ ), gender ( $\\\\chi^2 = 2$ , $p = .16$ ), education levels ( $\\\\chi^2 = 15$ , $p$\\n= .24), and annual salary ( $\\\\chi^2 = 20$ , $p = .22$ ). Based on this, we\\nclaim that our study sample was relatively representative.\\nAll demographics are shown in Table 1 .\",\"reading_order\":11,\"tags\":[]},{\"label\":\"tab\",\"bbox\":[423,460,744,824],\"text\":\"| Parameter | Property | # of participants | | Gender | Female | 79 | | Male | 55 | | Age | 19-24 | 15 | | 25-34 | 82 | | 35-44 | 26 | | 45-54 | 30 | | 55-65 | 21 | | Education | Less than High School | 1 | | High School | 56 | | Professional School | 23 | | University (Bachelor's) | 33 | | Master or PhD | 18 | | Other | 5 | | Occupation | Managers | 8 | | Professionals | 29 | | Clerical Support Workers | 16 | | Service and Sales Workers | 19 | | Craft and Trades Workers | 7 | | Machine Operators | 1 | | Elementary Employees | 3 | | Students | 15 | | Unemployed/Retired/Disabled | 4 | | Annual salary | Less than$20,000 | 42 | | $30,000-$49,999 | 21 | | $50,000-$74,999 | 35 | | $75,000-$99,999 | 16 | | $100,000+ | 16 | | Recruiting platform | The Sample Network | 77 | | Amazon/Schrodersburg Park | 29 | | Martha Living in the University of British Columbia | 15 | | Other (e.g., Twitter) | 6 |
\",\"reading_order\":12,\"tags\":[],\"alt_text\":\"This table presents a summary of participant demographics and recruiting methods. It lists various parameters such as Gender, Age, Education, Occupation, Annual salary, and Recruiting platform, along with specific properties for each and the corresponding number of participants.\",\"figure_path\":\"tables/3290605-3300393_pdf_shortened_page_2_tab_12.png\"},{\"label\":\"table_cap\",\"bbox\":[466,827,700,842],\"text\":\"Table 1: Participant demographics, N = 134.\",\"reading_order\":13,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[422,885,502,901],\"text\":\"5 RESULTS\",\"reading_order\":14,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,906,746,954],\"text\":\"As mentioned before, we conducted regression analyses to\\nidentify usage patterns that were significantly correlated\\nwith age, while considering other demographic covariates\",\"reading_order\":15,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[70,912,345,924],\"text\":\"1The Sample Network was used for recruitment in the US only.\",\"reading_order\":16,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[69,924,394,950],\"text\":\"Full content of the advertisement can be found in the supplementary\\nmaterial.\",\"reading_order\":17,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[70,996,127,1013],\"text\":\"Paper 163\",\"reading_order\":18,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,996,745,1013],\"text\":\"Page 3\",\"reading_order\":19,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300393/results/markdown/3290605-3300393_pdf_shortened_page_2.md"}
{"file_name": "images/test_data/3290605-3300393/3290605-3300393_pdf_shortened_page_3.png", "source_folder": "test_data", "document_id": "3290605-3300393", "source_image_path": "test_data/3290605-3300393/3290605-3300393_pdf_shortened_page_3.png", "source_json_path": "test_data/3290605-3300393/results/output_json/3290605-3300393_pdf_shortened_page_3.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,43,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_pub_date\",\"author_affili\"]},{\"label\":\"half_para\",\"bbox\":[68,126,395,300],\"text\":\"(gender, education, occupation, annual salary, and unlocking\\nmechanism). For example, we checked whether there is a\\ncorrelation between used unlocking mechanism and age,\\nwith gender as a covariate. For each interaction pattern, we\\napplied different combinations of the independent variables\\n(e.g., age and unlocking mechanism, age and gender). We\\nreport the best fitting prediction models, based on metrics\\nincluding R-squared, adjusted R-squared, and predicted R-\\nsquared. Table 2 shows an overview of these results and the\\nadjustments used (e.g., for \\\"session length\\\" the best fit was\\nachieved by applying log).\",\"reading_order\":2,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[69,311,295,329],\"text\":\"Authentication Mechanism Selection\",\"reading_order\":3,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,333,395,603],\"text\":\"To understand how age correlates with used unlocking mech-\\nanism, we conducted a multinomial logistic regression analy-\\nsis of correlation between the types of unlocking mechanisms\\nand age, with gender as a covariate. We used “Swipe/None” as\\nthe reference category for lock type and “Female” for gender.\\nWe found that age significantly correlated with participants’\\nchoice of “Fingerprint” vs. “Swipe/None”, while there was no\\nsignificant difference in their choice of “Android Pattern” and\\n“PIN” vs. “Swipe/None”. With one-year increase in age, our\\nparticipants were 6.3% less likely to choose “Fingerprint” as\\ntheir unlocking mechanism, than “Swipe/None”. In addition,\\nwe found that gender significantly correlated with partici-\\npants’ choice of “PIN” vs. “Swipe/None”, but not their choice\\nof “Android Pattern” and “Fingerprint” vs. “Swipe/None”.\\nSpecifically, male participants were 230.8% more likely to\\nchoose “PIN” than “Swipe/None”. Table 3 presents the details\\nof the fitted model.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[69,616,141,632],\"text\":\"Error Rates\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,637,395,766],\"text\":\"Since “ Swipe/None ” users cannot make unlocking errors due\\nto the nature of the method, we removed these participants\\nfrom the error analysis, which reduced the analyzed sample\\nto 87 participants. We fitted a multiple linear regression\\nmodel of the square root of error rates to investigate if error\\nrates correlated with age . While applying the model, we\\nconsidered the correlation between the error rates and age,\\nthe type of unlocking mechanism, as well as the interaction\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,570,747,763],\"text\":\"effect between those two factors. We used “Android Pattern”\\nas the reference category. The results show that while the\\nmain effect of both age and unlocking mechanism were not\\nsignificant $(F(1,81)=2.07, p=.15$ vs. $F(2,81)=1.63, p=.20$\\nrespectively), and the interaction effect between age and\\nunlocking mechanism was significant $(F(2,81)=5.13, p=.008)$ . Specifically, “Fingerprint” participants were more likely\\nto have unlocking errors than “Android Pattern” participants,\\nwith the likelihood increasing for older participants. Overall,\\nthe model explained 10.98 % of the variance $(R2=.16, F(5,81)=3.12, p=.01)$ . Figure 1 shows the interaction effects and\\ngives an overview of the fitted model.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"tab\",\"bbox\":[70,786,393,901],\"text\":\"| Interaction Pattern | Age Matters? | | Used Unlocking Mechanism | Yes | | sqrt(Error Rate) | No | | Auto/Manual Locks | Yes | | log(Session Lengths) | Yes | | log(Number of Sessions Per Day) | Yes | | sqrt(Daily Usage Times) | Yes | | Whether authentication happened at still / on the move | Yes |
\",\"reading_order\":8,\"tags\":[],\"alt_text\":\"This image is a two-column table titled \\\"Interaction Pattern\\\" and \\\"Age Matters?\\\". The \\\"Interaction Pattern\\\" column lists various user behaviors and metrics like \\\"Used Unlocking Mechanism\\\" and \\\"log(Session Lengths)\\\". The \\\"Age Matters?\\\" column indicates \\\"Yes\\\" or \\\"No\\\" for each listed interaction pattern.\",\"figure_path\":\"tables/3290605-3300393_pdf_shortened_page_3_tab_8.png\"},{\"label\":\"table_cap\",\"bbox\":[68,903,394,932],\"text\":\"Table 2: Overview of the usage patterns that were and were\\nnot significantly correlated with age.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300393_pdf_shortened_page_3_figure_010.png\",\"bbox\":[432,123,724,364],\"reading_order\":10,\"tags\":[],\"alt_text\":\"This is a scatter plot showing the square root of error rates against centralized age. Data points are colored and grouped by unlocking mechanism: Android Pattern (green), PIN (magenta), and Fingerprint (cyan), each with a corresponding regression line and shaded confidence interval. The X-axis ranges from approximately -20 to 25, and the Y-axis ranges from 0 to about 6.\"},{\"label\":\"figure_cap\",\"bbox\":[420,376,747,538],\"text\":\"Figure 1: Multiple linear regression model of the square root\\nof error rates on age (centralized), unlocking mechanism\\n(base category \\\"Android Pattern\\\") and their interaction ef-\\nfect: sqrt( ErrorRates ) = 1.758±0.004 * CentralizedAge × 0.477 +\\nFingerprint×-0.302 + PIN×0.076 * CentralizedAge × Fingerprint×-\\n0.004 × CentralizedAge × PIN, N = 87 ( Fingerprint ) and PIN are\\nbinary variables with values { 0, 1 } . The black line represents\\nthe fitted model, and the grey area represents the 95 % confi-\\ndence interval. The other lines represent the simple linear\\nregression models of the square root of error rates on age\\nfor participants in each unlocking mechanism group.\",\"reading_order\":11,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[420,773,543,790],\"text\":\"Auto/Manual Locks\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,792,747,905],\"text\":\"We removed “Swipe/None” participants from the analysis\\non auto/manual locks, because in Android OS, the auto lock\\nsetting is not enabled for them. Furthermore, we removed\\n25 participants who manually disabled auto lock, either at\\nthe beginning of the study or during the study. Overall, we\\nanalyzed locking behavior based on data collected from 62\\nparticipants.\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,905,747,953],\"text\":\"By default, auto lock is set to 5 seconds. Our analysis\\nrevealed that out of the 62 participants, 26 kept the default\\nauto lock timeout, while 18 reduced the auto lock time to 0.\",\"reading_order\":14,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,127,1012],\"text\":\"Paper 163\",\"reading_order\":15,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[706,995,745,1012],\"text\":\"Page 4\",\"reading_order\":16,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300393/results/markdown/3290605-3300393_pdf_shortened_page_3.md"}
{"file_name": "images/test_data/3290605-3300393/3290605-3300393_pdf_shortened_page_4.png", "source_folder": "test_data", "document_id": "3290605-3300393", "source_image_path": "test_data/3290605-3300393/3290605-3300393_pdf_shortened_page_4.png", "source_json_path": "test_data/3290605-3300393/results/output_json/3290605-3300393_pdf_shortened_page_4.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"tab\",\"bbox\":[84,124,726,207],\"text\":\"| Variable | Estimate | Std. Err. | z value | Odds Ratio | Estimate | Std. Err. | z value | Odds Ratio | Estimate | Std. Err. | z value | Odds Ratio | | Swipe/None vs. Android Pattern | Swipe/None vs. Fingerprint | Swipe/None vs. PIN | | Intercept | 0.390 | 0.959 | 0.407 | 1.477 | 1.214 | 0.977 | 1.242 | 3.366 | -0.415 | 0.808 | -0.514 | 0.66 | | Age | -0.034 | 0.023 | -1.49 | 0.966 | -0.065 | 0.025 | 2.572$^{*}$ | 0.937 | -0.002 | 0.018 | -0.126 | 0.998 | | Gender=Male | 0.341 | 0.585 | 0.584 | 1.407 | 1.056 | 0.561 | 1.884 | 2.876 | 1.196 | 0.452 | 2.644$^{*}$ | 3.308 |
\",\"reading_order\":2,\"tags\":[],\"alt_text\":\"This is a statistical table displaying logistic regression results across three comparisons: \\\"Swipe/None vs. Android Pattern\\\", \\\"Swipe/None vs. Fingerprint\\\", and \\\"Swipe/None vs. PIN\\\". For each comparison, the table provides \\\"Estimate\\\", \\\"Std. Err.\\\", \\\"z value\\\", and \\\"Odds Ratio\\\" for the variables \\\"Intercept\\\", \\\"Age\\\", and \\\"Gender=Male\\\". Some z values are marked with an asterisk.\",\"figure_path\":\"tables/3290605-3300393_pdf_shortened_page_4_tab_2.png\"},{\"label\":\"table_cap\",\"bbox\":[68,210,745,241],\"text\":\"Table 3: Multinomial logistic regression model: how age and gender correlated with participants' choice of unlocking mecha-\\nnism. A *** denotes significance ( $p<.05$ ).\",\"reading_order\":3,\"tags\":[]},{\"label\":\"half_para\",\"bbox\":[68,277,394,357],\"text\":\"which locks the device immediately after it enters sleep mode.\\nSince we were unable to differentiate auto locks from manual\\nlocks when the timeout was set to 0, we further excluded\\nthose 18 participants from the analysis. This reduced our\\nparticipants pool for this analysis down to 44.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,357,395,614],\"text\":\"To evaluate whether participants relied more on auto locks\\nor manual locks, we calculated the percentages of auto locks\\nover the total number of locks per day per user. We fitted\\na multiple linear regression model to investigate if the av-\\nerage percentage of auto locks correlated with age, while\\nconsidering gender (with \\\"Female\\\" serving as the base cate-\\ngory) and its interaction with age as covariates. The results\\nin Figure 2 , show that only the main effect of age ($F(1,40)$\\n= 7.46, $p$ = .009) was significantly correlated with people's\\nlocking behaviors, but not the main effect of gender ($F(1,40)$\\n= 1.86, $p$ = .18) and the interaction effect between age and\\ngender ($F(1,40)$ = 2.26, $p$ = .14). Specifically, older people\\nrelied more on auto locks, with an increase of 5.09 % for fe-\\nmales and 16.06 % for males for every 10-year increment of\\nage. Overall, age explained 16.63 % of the variance ($R2$ = .22,\\n$F(3,40)$ = 3.86, $p$ = .02).\",\"reading_order\":5,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,627,170,644],\"text\":\"Session Lengths\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,648,395,887],\"text\":\"The analyzed dataset contained 257,437 user sessions in total.\\nOn average, a session lasted 10.89 minutes (SD = 12.80 min-\\nutes). We applied multiple linear regression analysis to test\\nif age, gender and the interaction effect between those two\\nfactors correlated with the log of averaged session length.\\nFigure 3 gives an overview of the fitted model, with “ Female ”\\nserving as the reference category. The results indicate that\\nthe main effect of both age and gender, as well as their in-\\nteraction effect were significantly correlated with the log\\nvalue of session length ( $F(1,129)=6.72$ , $p=.01$ vs. $F(1,129)$\\n$=6.91$ , $p=.01$ vs. $F(1,129)=6.31$ , $p=.01$ respectively), and\\nthose factors accounted for 11.37 % of the variance ( $R^2=.13$ ,\\n$F(3,129)=6.65$ , $p=.001$ ). Furthermore, a 10-year increase in\\nage corresponds to a growth of 0.9 % for female participants\\nand 41.76 % for male participants in session length.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,901,243,918],\"text\":\"Number of Sessions Per Day\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,922,395,954],\"text\":\"On average, participants had 32 smartphone sessions a day\\n(SD = 26). We applied multiple linear regression analysis to\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,745,747,954],\"text\":\"test if age and unlocking mechanism correlated with the aver-\\nage number of sessions each day. Figure 4 gives an overview\\nof the fitted model, with “ Swipe/None ” serving as the ref-\\nerence category. The results reveal that both age ( $F(1, 129)$\\n= 37.99, $p$ < .001) and unlocking mechanism ( $F(3, 129)$ =\\n4.14, $p$ = .008) were significant factors. Younger participants\\ninteracted with their devices more frequently than older\\nparticipants (i.e., a 10-year increase in age corresponds to\\na deduction of 25.17 % in total number of sessions per day),\\nand “ Swipe/None ” users had significantly more sessions (ap-\\nproximately 39.51 % ) than those using “ PIN ” . Overall, the two\\nindependent variables explained 25.87 % of the variance ( $R2$\\n= .28, $F(4, 129)$ = 12.60, $p$ < .001).\",\"reading_order\":10,\"tags\":[]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300393_pdf_shortened_page_4_figure_011.png\",\"bbox\":[437,272,730,513],\"reading_order\":11,\"tags\":[],\"alt_text\":\"This is a scatter plot showing \\\"Average Percentage of Auto Locks [%]\\\" on the y-axis against \\\"Age\\\" on the x-axis. Data points are color-coded by \\\"Gender\\\", with orange representing Female and cyan representing Male. The plot includes a black overall linear trend line and separate colored linear trend lines for each gender, all with shaded confidence intervals, indicating an increasing trend in auto locks percentage with age for both genders.\"},{\"label\":\"figure_cap\",\"bbox\":[420,528,747,662],\"text\":\"Figure 2: Multiple linear regression model of the per-\\ncentages of auto looks on age, gender (base category “Fe-\\nmale”) and their interaction effect: PercentageofAutoLooks =\\n13.303×0.509 × 0.509 + Age-28.906 × Male+1.097 = Age × Male, N = 44 (Male\\nis a binary variable with values [0, 1]). The black line repre-\\nsents the fitted model, and the grey area represents the 95 %\\nconfidence interval. The other lines represent the simple lin-\\near regression models of the percentage of auto looks on age\\nfor female and male participants.\",\"reading_order\":12,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,127,1013],\"text\":\"Paper 163\",\"reading_order\":13,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,995,745,1013],\"text\":\"Page 5\",\"reading_order\":14,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300393/results/markdown/3290605-3300393_pdf_shortened_page_4.md"}
{"file_name": "images/test_data/3290605-3300393/3290605-3300393_pdf_shortened_page_5.png", "source_folder": "test_data", "document_id": "3290605-3300393", "source_image_path": "test_data/3290605-3300393/3290605-3300393_pdf_shortened_page_5.png", "source_json_path": "test_data/3290605-3300393/results/output_json/3290605-3300393_pdf_shortened_page_5.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,43,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,43,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300393_pdf_shortened_page_5_figure_002.png\",\"bbox\":[84,121,375,358],\"reading_order\":2,\"tags\":[],\"alt_text\":\"This scatter plot displays the log of average session length versus age, with points colored by gender. Female users (orange) show a relatively flat trend line, while male users (teal) exhibit an increasing trend in log average session length with age. Both trend lines include confidence intervals.\"},{\"label\":\"figure_cap\",\"bbox\":[68,376,394,509],\"text\":\"Figure 3: Multiple linear regression model of log of av-\\nerage session length on age, gender (base category \\\"Fe-\\nmale\\\") and their interaction effect: $\\\\log(AverageSessionLength) = 2.073 \\\\times 0.0009 + Age - 1.715 + Male \\\\times 0.034 + Age + Male$ , $N = 133$\\n(Male is a binary variable with values (0, 1)). The black line\\nrepresents the fitted model, and the grey area represents the\\n95 % confidence interval. The other lines represent the sim-\\nple linear regression models of the percentage of auto locks\\non age for female and male participants.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300393_pdf_shortened_page_5_figure_004.png\",\"bbox\":[436,123,730,359],\"reading_order\":4,\"tags\":[],\"alt_text\":\"This scatter plot displays the relationship between Age and the logarithm of the Average Number of Sessions, categorized by different Unlocking Mechanisms. Each unlocking mechanism (Swipe/None, Fingerprint, Android Pattern, PIN) is represented by distinct colored data points and a corresponding linear regression line with a shaded confidence interval. All regression lines show a general downward trend, indicating a negative correlation between age and the logged average number of sessions across all unlocking mechanisms.\"},{\"label\":\"figure_cap\",\"bbox\":[420,376,747,524],\"text\":\"Figure 4: Multiple linear regression model of log of aver-\\nage number of sessions on age and unlocking mechanism\\n(base category “ Swipe/None ” ) $\\\\log$ ( AverageNumber of Sessions ) =\\n$4.338$ $0.029$ $+0.037$ $\\\\langle$ AndroidPattern $\\\\rangle$ $0.353$ + Fingerprint $\\\\rangle$\\n$0.333$ $+$ PIN , $N$ $134$ ( AndroidPattern , Fingerprint and PIN are\\nbinary variables with values [0, 1]). The black line represents\\nthe fitted model, and the grey area represents the $95\\\\%$ confi-\\ndence interval. The other lines represent the simple linear\\nregression models of the log of average number of sessions\\non age for participants in each unlocking mechanism group.\",\"reading_order\":5,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[69,574,184,591],\"text\":\"Daily Usage Times\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,594,395,818],\"text\":\"We fitted a multiple linear regression model among the\\nsquare root of the average amount of daily device usage\\ntime for participants and the three factors age, unlocking\\nmechanism, and gender, while using “Swipe/None” and ”Fe-\\nmale” as the reference categories. The results show that both\\nage and gender significantly correlated with daily usage time\\n( $F(1, 127) = 6.55$ , $p = .01$ vs. $F(1, 127) = 7.74$ , $p = .006$ respec-\\ntively), whereas unlocking mechanism did not ( $F(3, 127) =$\\n1.36, $p = .26$ ). In particular, older participants tended to inter-\\nact with their smartphones for a significant shorter amount\\nof time per day, while female participants tended to have\\nlonger daily usage times than male participants, as shown in\\nFigure 5 . Overall, the model explained 9.20 % of the variance\\n( $R^2 = 0.13$ , $F(5, 127) = 3.68$ , $p = .004$ ).\",\"reading_order\":7,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[69,836,227,854],\"text\":\"Activity While Unlocking\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,857,395,954],\"text\":\"Of the 134 study participants, 116 opted in to provide activity\\ndata. To identify user activities during unlocks, we mapped\\ntheir timestamps. We used one minute as a threshold, mean-\\ning that if there were no activity records that occurred one\\nminute before or after an unlock event, then we considered\\nthat we failed to detect the type of activity for this unlock.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,574,746,621],\"text\":\"When there were multiple activity records within the one-\\nminute timeframe, we selected the closest (time-wise) activ-\\nity as the one that user undertook during the unlock.\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,621,747,811],\"text\":\"Among all unlocking events collected for the 116 par-\\nticipants, we removed the unlocks that were tagged with\\n\\\"unknown\\\" activity and those in which we failed to detect\\nactivities for, which were around 50.1 % . Figure 6 shows the\\ndistribution of activity types over the remaining 49.9 % of\\nunlocks (106,030 in total). We found that more than half of\\nthese unlocks happened when participants' devices were\\nstill, whereas the other 43.8 % of unlocks happened while the\\ndevice was moving. Only 0.26 % of unlocks happened while\\nparticipants were on a bicycle. The distributions of activities\\nfor each unlocking mechanism did not differ notably from\\neach other.\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,811,747,909],\"text\":\"We removed the activity type \\\"tilling\\\", $^3$ which we con-\\nsidered as not significant enough of a move to influence\\nunlocking performance. Then we categorized all activities\\nexcept still as move . As shown in Table 2 , we found that age\\nwas a good predictor for whether authentication was used\\nwhile at still or on the move (details below).\",\"reading_order\":12,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[422,938,673,953],\"text\":\"$^{3}$Tilting the device around the horizontal or vertical axes.\",\"reading_order\":13,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,127,1013],\"text\":\"Paper 163\",\"reading_order\":14,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,995,746,1013],\"text\":\"Page 6\",\"reading_order\":15,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300393/results/markdown/3290605-3300393_pdf_shortened_page_5.md"}
{"file_name": "images/test_data/3290605-3300393/3290605-3300393_pdf_shortened_page_6.png", "source_folder": "test_data", "document_id": "3290605-3300393", "source_image_path": "test_data/3290605-3300393/3290605-3300393_pdf_shortened_page_6.png", "source_json_path": "test_data/3290605-3300393/results/output_json/3290605-3300393_pdf_shortened_page_6.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[68,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_pub_date\",\"meta_subject\"]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300393_pdf_shortened_page_6_figure_002.png\",\"bbox\":[86,120,378,358],\"reading_order\":2,\"tags\":[],\"alt_text\":\"This image presents four scatter plots showing the relationship between Age and the square root of Average Daily Usage Length, categorized by four different phone unlocking methods: Swipe/None, Android Pattern, Fingerprint, and PIN. Each panel displays individual data points color-coded by Gender (Female in orange, Male in blue), along with regression lines indicating trends.\"},{\"label\":\"figure_cap\",\"bbox\":[67,376,394,568],\"text\":\"Figure 5: Multiple linear regression model of the square\\nroot of average daily usage length on age, unlocking mech-\\nanism (base category “ Swipe/None ” ) and gender (base cate-\\ngory “ Female ” ): sqrt ( AverageDailyUsageLength )=18.869+0.108*\\n0Age+0.161 + (AndroidPattern)*1.019 × Fingerprint-1.256 + PIN-\\n2.867+Male × N + 133 (AndroidPattern, Fingerprint, PIN and Male\\nare binary variables with values [0, 1]). The black line rep-\\nresents the fitted model for participants in each unlocking\\nmechanism group, and the grey area represents the 95 % con-\\nfidence interval. The other colored lines represent the sim-\\nple linear regression models of the square root of average\\ndaily usage length on age for female participants and male\\nparticipants in each unlocking mechanism group.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300393_pdf_shortened_page_6_figure_004.png\",\"bbox\":[118,601,349,806],\"reading_order\":4,\"tags\":[],\"alt_text\":\"A pie chart displays the distribution across five categories, each represented by a slice. The largest segment, labeled \\\"Still,\\\" shows a value of 56.16, followed by \\\"In a vehicle\\\" at 21.98, \\\"Tilting\\\" at 13.87, \\\"On foot\\\" at 7.73, and \\\"On a bicycle\\\" at 0.26.\"},{\"label\":\"figure_cap\",\"bbox\":[73,844,388,860],\"text\":\"Figure 6: Distribution of activities during unlock, $N = 116$.\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[67,889,393,953],\"text\":\"Error Rates While At Still vs. On the Move. To assess how error\\nrates correlated with activity types, we again excluded par-\\nticipants who used “Swipe/None” , as errors are not possible\\nwith this unlocking method.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300393_pdf_shortened_page_6_figure_007.png\",\"bbox\":[439,123,726,358],\"reading_order\":7,\"tags\":[],\"alt_text\":\"This scatter plot illustrates the relationship between age and the percentage of unlocks that occurred while a user was either still or moving. Cyan data points and an upward trend line indicate unlocks while still, generally increasing with age, while red data points and a downward trend line show unlocks while moving, generally decreasing with age. Shaded areas around the trend lines represent confidence intervals.\"},{\"label\":\"figure_cap\",\"bbox\":[420,376,747,449],\"text\":\"Figure 7: Distribution of unlocks that happened while at\\nstill/move, $N = 116$ . The lines represent the fitted simple lin-\\near regression model of the percentage of unlocks on age for\\neach activity category, and the grey area represents the 95 %\\nconfidence interval.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,522,747,664],\"text\":\"We then counted the number of failed unlocking attempts\\nand total unlocking attempts that each participant performed,\\nwhile they were in \\\"still\\\" and \\\"move\\\" states. Afterwards, we\\ncalculated the likelihood of making an error, for each par-\\nticipant for each of the two activity categories. However, a\\npaired-samples t-test did not reveal any significant differ-\\nences in the likelihoods that participants would make an\\nerror, while their devices were still ( M = 4.37, SD = 7.16) and\\nmoving ( M = 4.78, SD = 7.27); (70) 1.08, p = .28.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,681,747,953],\"text\":\"How Age Predicts Whether Authentication is Used While At\\nStill vs On the Move. To evaluate how often participants\\nunlocked while they were in still and moving states, we cal-\\nculated the percentage of unlocks for each activity category\\nper user over the whole study period. Figure 7 shows the\\ndistribution of unlocks at still and on the move, with re-\\nspect to participants' age. To further understand how age\\ninteracted with activity types ( still/move ), we fitted a binary\\nlogistic regression model to predict whether authentication\\nwould be used when users were at still and on the move\\nbased on age, with move as the reference activity category.\\nThe analysis revealed that age had a significant effect on\\npredicting whether authentication happened while at still or\\non the move. Specifically, with one-year increase in age, par-\\nticipants were 1.5 % more likely to use authentication when\\nthey were still than moving. Table 4 gives an overview of\\nthe fitted model.\",\"reading_order\":10,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[68,995,127,1012],\"text\":\"Paper 163\",\"reading_order\":11,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,995,745,1012],\"text\":\"Page 7\",\"reading_order\":12,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300393/results/markdown/3290605-3300393_pdf_shortened_page_6.md"}
{"file_name": "images/test_data/3290605-3300393/3290605-3300393_pdf_shortened_page_7.png", "source_folder": "test_data", "document_id": "3290605-3300393", "source_image_path": "test_data/3290605-3300393/3290605-3300393_pdf_shortened_page_7.png", "source_json_path": "test_data/3290605-3300393/results/output_json/3290605-3300393_pdf_shortened_page_7.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_pub_date\",\"author_affili\"]},{\"label\":\"tab\",\"bbox\":[101,123,358,177],\"text\":\"| Variable | Estimate | Std. Err. | z value | Odds Ratio | | Intercept | .078 | .024 | 3.268$^{*}$ | 1.081 | | Age | .015 | .003 | 23.772$^{*}$ | 1.015 |
\",\"reading_order\":2,\"tags\":[],\"alt_text\":\"This table presents statistical results for an \\\"Intercept\\\" and \\\"Age\\\" variable. It includes columns for Estimate, Standard Error, z value, and Odds Ratio for each variable. The z values for both Intercept and Age are marked with an asterisk, indicating statistical significance.\",\"figure_path\":\"tables/3290605-3300393_pdf_shortened_page_7_tab_2.png\"},{\"label\":\"table_cap\",\"bbox\":[68,179,394,224],\"text\":\"Table 4: Binary logistic regression model: whether authen-\\ntication is used while at still vs on the move by age. An \\\"**\\\"\\ndenotes significance $(p<.05)$ .\",\"reading_order\":3,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,262,173,277],\"text\":\"6 DISCUSSION\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,282,395,475],\"text\":\"The results presented in this paper provide the first real\\nworld data and detailed insights on the link between indi-\\nvidual smartphone authentication patterns and age, thus,\\ncorroborating and extending on the previous study [6] that\\nhypothesized that age might be an important factor. In ad-\\ndition, we investigated a broader scope of unlocking mech-\\nanisms, than previous work [5, 6, 10, 12] , in terms of how\\nparticipants' in-situ (un)locking behaviour differs among un-\\nlocking mechanisms. We are the first to extend these findings\\nto \\\"Fingerprint\\\" users, despite that we had relatively smaller\\n\\\"Fingerprint\\\" sample size (about 56 % of participants' devices\\nsupporting such mechanism).\",\"reading_order\":5,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[69,486,216,503],\"text\":\"Age Makes a Difference\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,507,394,554],\"text\":\"First of all, and most importantly, our data suggests that\\nsmartphone usage patterns, indeed, in many cases signifi-\\ncantly correlate with age.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,554,395,681],\"text\":\"Specifically, we found a significant correlation of age with\\nthe following parameters: the choice of unlocking mecha-\\nnism, session length, number of sessions per day, daily usage\\nlength, auto/manual lock, state of movement (as still vs. on\\nthe move), the choice of unlocking mechanism at still vs. on\\nthe move. In addition, we found that participant's gender\\nalso significantly correlated with their session length, daily\\nusage length, and the choice of the unlocking mechanism.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,681,395,859],\"text\":\"That is, our results provide the first quantitative support\\n(based on field measurements of real-world behaviour) for\\nprevious recommendations that, while designing new au-\\nthentication systems for smartphones, demographic differ-\\nences, such as age, should be taken into account [6] . Further-\\nmore, we emphasize that our work not only confirms the link\\nbetween age and in-situ authentication behaviour, but also\\nextends the findings to gender. We thus invite more studies to\\ninvestigate (other) demographic factors, and suggest future\\nwork could focus on recruiting even more representative\\n(particularly age-wise) samples to provide further evidence.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[68,869,241,887],\"text\":\"Age and Unlocking Security\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,890,395,954],\"text\":\"One of our results related to age being a good predictor for\\nsome choices related to smartphone unlocking mechanism\\nselection, is that older participants were less likely to enable\\n\\\"Fingerprint\\\" authentication on their devices. In addition,\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,125,746,172],\"text\":\"older participants significantly more likely relied on the auto\\nlock feature, despite the used unlocking mechanisms, which\\nis in line with [5] .\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,172,746,252],\"text\":\"The auto lock feature very specifically represents a trade-\\noff between security and usability. The related question is\\nwhether users make educated choices, when making these\\ndecisions, or whether other factors are more prominent in\\nthe decision-making process.\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,252,746,299],\"text\":\"As a consequence, we argue that future research should\\nlook into the popularity of auto lock among older users and\\ntheir awareness of respective security implications.\",\"reading_order\":14,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[421,310,638,328],\"text\":\"Design and Evaluate for Movement\",\"reading_order\":15,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,332,746,379],\"text\":\"Another interesting result to highlight is that age was a good\\npredictor for whether device unlocking was used on the\\nmove compared to at still.\",\"reading_order\":16,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,380,746,460],\"text\":\"Our results show that, overall, about 44 % of unlocks hap-\\npened with the devices moving. Furthermore, despite a trend\\nin our data towards more authentication taking place in still\\nstates with increasing age, authentication on moving devices\\nwas a common task across all age groups.\",\"reading_order\":17,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,460,747,570],\"text\":\"While one might hypothesize that participants were more\\nlikely to make unlocking errors while moving, our findings\\ndo not support such a claim. The numbers of failed unlocking\\nattempts when participants' devices were still and when they\\nwere moving were very similar, also across age groups. This\\nmight indicate that current unlocking mechanisms are robust\\nagainst errors on the move.\",\"reading_order\":18,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,571,747,699],\"text\":\"A consequence of this and the fact that people do regularly\\nauthenticate themselves while moving, means that newly-\\ndesigned unlocking mechanisms should be as robust in the\\npresence of movement as existing mechanisms, in order to be\\nacceptable for smartphone unlocking. This requirement not\\nonly influences how unlocking systems should be designed\\n(e.g., constant eye contact might not be possible) but also\\nhow they are evaluated in studies (in-lab vs. in the wild).\",\"reading_order\":19,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[421,710,571,727],\"text\":\"No One-Fits-All Solution\",\"reading_order\":20,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,731,747,808],\"text\":\"As mentioned before, we showed that age significantly corre-\\nlated not only with the choice of unlocking mechanism but\\nalso whether and how features such as auto lock were used\\nand whether authentication took place on still or moving\\ndevices.\",\"reading_order\":21,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,809,746,890],\"text\":\"Previous research [7] has concluded that due to user pref-\\nerences, it is hard (or impossible) to find or build one authen-\\ntication system that caters to all user needs. Our results add\\nto these previous findings by providing evidence that these\\nusage differences are also linked to age and gender.\",\"reading_order\":22,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,890,747,954],\"text\":\"While we cannot make claims about the cause of these\\ndifferences, our findings suggest that these differences are\\nimportant factors to consider. The findings further our un-\\nderstanding of why offering different unlocking mechanisms\",\"reading_order\":23,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,127,1012],\"text\":\"Paper 163\",\"reading_order\":24,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[706,995,745,1012],\"text\":\"Page 8\",\"reading_order\":25,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300393/results/markdown/3290605-3300393_pdf_shortened_page_7.md"}
{"file_name": "images/test_data/3290605-3300395/3290605-3300395_pdf_shortened_page_0.png", "source_folder": "test_data", "document_id": "3290605-3300395", "source_image_path": "test_data/3290605-3300395/3290605-3300395_pdf_shortened_page_0.png", "source_json_path": "test_data/3290605-3300395/results/output_json/3290605-3300395_pdf_shortened_page_0.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[70,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"icon\",\"bbox\":[0,92,38,132],\"reading_order\":2,\"tags\":[\"cross_marker\"]},{\"label\":\"sec_0\",\"bbox\":[84,118,730,150],\"text\":\"HotStrokes: Word-Gesture Shortcuts on a Trackpad\",\"reading_order\":3,\"tags\":[]},{\"label\":\"author_information\",\"bbox\":[94,168,282,250],\"text\":\"Wenzhe Cui\\n\\nDepartment of Computer Science\\n\\nStony Brook University\\nStony Brook, New York, USA\\nwecui@cs.stonybrook.edu\",\"reading_order\":4,\"tags\":[\"author\",\"author_affili\",\"author_mail\"]},{\"label\":\"author_information\",\"bbox\":[328,168,483,237],\"text\":\"Jingjie Zheng\\n\\nGoogle\\n\\nKitchener, Ontario, Canada\\n\\njingjie@acm.org\",\"reading_order\":5,\"tags\":[\"author\",\"author_affili\",\"author_mail\"]},{\"label\":\"author_information\",\"bbox\":[523,168,729,250],\"text\":\"Blaine Lewis\\n\\nCheriton School of Computer Science\\nUniversity of Waterloo\\nWaterloo, Ontario, Canada\\nblaine.lewis@waterloo.ca\",\"reading_order\":6,\"tags\":[\"author\",\"author_affili\",\"author_mail\"]},{\"label\":\"author_information\",\"bbox\":[215,261,376,343],\"text\":\"Daniel Vogel\\n\\nSchool of Computer Science\\n\\nUniversity of Waterloo\\nWaterloo, Ontario, Canada\\ndvogel@uwaterloo.ca\",\"reading_order\":7,\"tags\":[\"author\",\"author_affili\",\"author_mail\"]},{\"label\":\"author_information\",\"bbox\":[421,261,610,343],\"text\":\"Xiaojun Bi\\n\\nDepartment of Computer Science\\n\\nStony Brook University\\nStony Brook, New York, USA\\nxiaojun@cstanburybrook.edu\",\"reading_order\":8,\"tags\":[\"author\",\"author_affili\",\"author_mail\"]},{\"label\":\"sec_1\",\"bbox\":[70,351,141,368],\"text\":\"ABSTRACT\",\"reading_order\":9,\"tags\":[\"paper_abstract\"]},{\"label\":\"para\",\"bbox\":[69,372,395,627],\"text\":\"Expert interaction techniques like hotkeys are efficient, but\\npoorly adopted because they are hard to learn. HotStrokes\\nremoves the need for learning arbitrary mappings of com-\\nmands to hotkeys. A user enters a HotStroke by holding a\\nmodifier key, then gesture typing a command name on a\\nlaptop trackpad as if on an imaginary virtual keyboard. The\\ngestures are recognized using an adaptation of the SHARK 2\\nalgorithm with a new spatial model and a refined method for\\ndynamic suggestions. A controlled experiment shows Hot-\\nStrokes effectively augments the existing \\\"menu and hotkey\\\"\\ncommand activation paradigm. Results show the method is\\nefficient by reducing command activation time by 43 % com-\\npared to linear menus. The method is also easy to learn with\\na high adoption rate, replacing 91 % of linear menu usage.\\nFinally, combining linear menus, hotkeys, and HotStrokes\\nleads to 24 % faster command activation overall.\",\"reading_order\":10,\"tags\":[\"paper_abstract\"]},{\"label\":\"sec_1\",\"bbox\":[70,639,172,657],\"text\":\"CCS CONCEPTS\",\"reading_order\":11,\"tags\":[]},{\"label\":\"list\",\"bbox\":[70,660,394,694],\"text\":\"• Human-centered computing $\\\\rightarrow$ Interaction devices;\\nInteraction techniques;\",\"reading_order\":12,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[70,701,202,717],\"text\":\"ACM Reference Format:\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[70,717,394,762],\"text\":\"Wenzhe Cui, Jingjie Zheng, Blaine Lewis, Daniel Vogel, and Xiaojun\\nBi. 2019. HotStrokes: Word-Gesture Shortcuts on a Trackpad. In\\nCHI Conference on Human Factors in Computing Systems Proceedings .\",\"reading_order\":14,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,405,549,423],\"text\":\"1 INTRODUCTION\",\"reading_order\":15,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,426,747,524],\"text\":\"It is a common belief that user interfaces should be easy\\nto learn, provide high-performance mechanisms for power\\nusers, and ideally, support the progression from ease to\\nefficiency [9, 23, 43] . In personal computing, menus and\\nhotkeys (or keyboard shortcuts) are the most common ways\\nof activating commands, representing the two opposite ends\",\"reading_order\":16,\"tags\":[]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300395_pdf_shortened_page_0_figure_022.png\",\"bbox\":[430,552,732,806],\"reading_order\":17,\"tags\":[],\"alt_text\":\"The figure illustrates a scenario where a user is attempting to open a file or folder in a graphical user interface (GUI) using a keyboard shortcut. The user's hands are shown interacting with the keyboard and a mouse, indicating the process of navigating through the GUI. In the image, the following elements are present: 1. **Laptop Screen**: The screen displays a window with the text \\\"open Ctrl+O\\\" in the center. The \\\"open\\\" text is in blue, and the \\\"Ctrl+O\\\" text is in black. The cursor is positioned above the \\\"open\\\" text, suggesting that the user is about to click on it. 2. **Keyboard**: Below the laptop screen, there is a standard QWERTY keyboard. The keys are depicted in a light gray color, and the user's left hand is positioned on the left side of the keyboard, pressing down on a key. 3. **Mouse**: The user's right hand is shown holding a mouse, which is positioned over the right side of the keyboard. The mouse is depicted in a light gray color, and the user's hand is pressing down on the mouse button. 4. **Arrows and Lines**: There are two yellow lines with arrows pointing towards the \\\"open\\\" text on the screen. One arrow points from the left side of the screen to the \\\"open\\\" text, and the other points from the right side of the screen to the \\\"open\\\" text. These lines likely represent the user's intended actions or the expected sequence of clicks. The overall scene demonstrates a common task in GUI interaction where a user might accidentally press the wrong key combination, leading to an undesired action. This scenario is relevant to the paper's focus on improving GUI design through user-centered design principles and automated testing.\"},{\"label\":\"figure_cap\",\"bbox\":[421,818,746,935],\"text\":\"Figure 1: HotStrokes activates a command ( “ Open ” ) by hold-\\ning down the Ctrl key, then tracing through letters of that\\ncommand on the trackpad ( “ O-P-E-N ” ) as if gesturing on\\na soft keyboard. The red trace on the trackpad shows the\\nfinger motion, the yellow trace on the keyboard shows the\\nequivalent gesture typing shape, and the blue trace on the\\ndisplay provides feedback. Red and yellow traces are for il-\\nlustration only and invisible in real use.\",\"reading_order\":18,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[70,776,394,884],\"text\":\"Permission to make digital or hard copies of all or part of this work for\\npersonal or classroom use is granted without fee provided that copies\\nare not made or distributed for profit or commercial advantage and that\\ncopies bear this notice and the full citation on the first page. Copyrights\\nfor components of this work owned by others than the author(s) must\\nbe honored. Abstracting with credit is permitted. To copy otherwise, or\\nrepublish, to post on servers or to redistribute to lists, requires prior specific\\npermission and/or a fee. Request permissions from permissions@acm.org.\",\"reading_order\":19,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[70,884,274,898],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland UK\",\"reading_order\":20,\"tags\":[\"meta_pub_date\"]},{\"label\":\"fnote\",\"bbox\":[70,899,394,924],\"text\":\"© 2019 Copyright held by the owner/author(s). Publication rights licensed\\nto ACM.\",\"reading_order\":21,\"tags\":[\"meta_pub_date\"]},{\"label\":\"fnote\",\"bbox\":[70,925,265,952],\"text\":\"ACM ISBN 978-1-4503-5970-2/19-05...$35.00\\nhttps://doi.org/10.1145/3206905.3303995\",\"reading_order\":22,\"tags\":[\"meta_pub_date\",\"meta_subject\",\"meta_num\"]},{\"label\":\"fnote\",\"bbox\":[421,354,745,384],\"text\":\"(CHI 2019), May 4–9, 2019, Glasgow, Scotland UK. ACM, New York,\\nNY, USA, 13 pages. https://doi.org/10.1145%2F966050.3300395\",\"reading_order\":23,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"foot\",\"bbox\":[70,996,127,1012],\"text\":\"Paper 165\",\"reading_order\":24,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,996,744,1012],\"text\":\"Page 1\",\"reading_order\":25,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300395/results/markdown/3290605-3300395_pdf_shortened_page_0.md"}
{"file_name": "images/test_data/3290605-3300395/3290605-3300395_pdf_shortened_page_1.png", "source_folder": "test_data", "document_id": "3290605-3300395", "source_image_path": "test_data/3290605-3300395/3290605-3300395_pdf_shortened_page_1.png", "source_json_path": "test_data/3290605-3300395/results/output_json/3290605-3300395_pdf_shortened_page_1.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,61],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\"]},{\"label\":\"half_para\",\"bbox\":[68,125,395,252],\"text\":\"of the ease-to-efficiency spectrum. On the one end, graphics-\\nbased menu interaction is easy for novices, but can be in-\\nefficient when performing repetitive actions [48] . On the\\nother, hotkeys are fast but require a conscious and effort-\\nful switch from menu interaction [15, 21] . Research shows\\nthat hotkeys are largely underused, and many attributes to\\nthe arbitrary command-to-key mappings caused by limited\\nkeyboard input space [9, 15, 27, 48, 49] .\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,252,395,427],\"text\":\"We present HotStrokes, a command activation method\\nthat removes the need for memorizing arbitrary key assign-\\nment (Figure 1 ). It works by transforming a laptop trackpad\\ninto an imaginary word-gesture keyboard: a user enters a\\ncommand by holding down a modifier key (e.g., Ctrl), then\\ninputs the command label on the trackpad by gesture typing.\\nThe user learns a word gesture by referring to the physical\\nkeyboard on the laptop for static visual guidance. Then, with\\nrepetitive use of the same command, they may gradually\\ntransition from depending on the visual guidance to directly\\nrecalling the gesture from memory.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,426,400,555],\"text\":\"We implement HotStrokes using an adaption of the SHAKE 2\\nalgorithm [19] with a new spatial model and a refined method\\nfor dynamic suggestions. Our evaluation shows that Hot-\\nStrokes is efficient by reducing command activation time\\nby 43 % compared to linear menus. The method is also easy\\nto learn with a high adoption rate, replacing 91 % of linear\\nmenu usage. Finally, combining linear menus, hotkeys, and\\nHotStrokes leads to 24 % faster command activation overall.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,570,197,588],\"text\":\"2 RELATED WORK\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,592,395,625],\"text\":\"HotStrokes refers to the word-gesture keyboard, techniques\\nfor promoting hotkey usage, and gestural command input.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[68,639,218,657],\"text\":\"Word-gesture Keyboard\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,659,395,868],\"text\":\"Word-gesture keyboard [19, 34, 42, 45] , also variably known\\nas gesture typing or shape writing, is a text entry method\\nthat has been widely adopted on touchscreen mobile devices.\\nUsers may trace through letters of a word as a single ges-\\nture instead of tapping on individual letters. As users gain\\nexpertise, they may gradually transition to directly recalling\\nthe gesture without relying on the visual guidance provided\\nby the keyboard. Past research has extended word-gesture\\nkeyboard to supporting bi-manual input [8] , mid-air text\\nentry [31] , head-movement based [41] , and device-tilt based\\ntext input [40] . Building on the success of word-gesture in-\\nput, HotStroke extends this input paradigm to command\\nactivation on a trackpad.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[68,882,224,900],\"text\":\"Promoting Hotkey Usage\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,903,395,952],\"text\":\"Hotkeys are viewed as the gold standard in efficient com-\\nmand input [27, 34, 35]. They enable parallel interaction by\\ncombining keyboard and mouse input, and allow users to\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,125,747,189],\"text\":\"focus on primary tasks by reducing visual distraction. How-\\never, research shows that except for very frequent commands\\nlike \\\"copy\\\" and \\\"paste\\\", users fail to transition from using\\ngraphical interfaces to using shortcuts [1, 18, 27, 39, 48] .\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,189,747,315],\"text\":\"The failure to adopt hotkeys may be ascribed to factors like\\npoor visibility [14, 15, 39] , cumbersome hand movement [22,\\n32] , lack of user motivation [9, 26, 38] , and the large cost\\nto learn more efficient interaction strategies [21, 23] . Yet,\\none primary issue, as identified by many [15, 18, 22, 27, 39]\\nand reiterated by a recent survey by Zheng et al. [48] , is\\nthe requirement for memorizing an often arbitrary mapping\\nfrom command labels to hotkeys.\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,315,747,491],\"text\":\"Various techniques have been proposed to promote hotkey\\nusage. ExposeHK [30] displays hotkey labels on UI elements\\nwhen a modifier key is pressed to enable rehearsing hotkey\\nactions with visual guidance. FingerArc and FingerChord [48]\\nprovide similar guidance, but reduce the need for pressing\\nmodifier keys by detecting special hand gestures. Grossman\\net al. [15] show providing auditory feedback about shortcut\\ninformation when interacting with menus may significantly\\nimprove hotkey adoption. IconHK [14] increases the visi-\\nbility of hotkeys by artistically incorporating hotkey letters\\ninto toolbar icons.\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,490,747,652],\"text\":\"Despite these techniques, requiring an arbitrary mapping\\nfrom a command to key presses is still a fundamental chal-\\nlenge for the wide adoption of hotkeys, partly due to the\\nlimited keyboard space compared to the relatively large num-\\nber of commands on desktop computing [49] . HotStrokes\\nproposes to alleviate this issue by leveraging the interac-\\ntion space of the trackpad for command activation. Because\\nHotStrokes directly associate the command with its word\\ngesture, it removes the need for learning arbitrary mappings,\\nwhich may substantially ease the learning procedure.\",\"reading_order\":14,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[421,660,578,678],\"text\":\"Gestural Command Input\",\"reading_order\":15,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,680,747,809],\"text\":\"Taking advantage of human's ability in well-remembering\\npictorial information [33] , many techniques have explored\\nusing gestures for command input [44] . Gestural commands\\nmay address the arbitrary mapping problem by using ges-\\ntures with semantic meanings. For example, Gesture Search\\n[28] triggers actions on a smartphone by drawing related\\ncharacters, and similarly, Gesture Avatar [29] activates on-\\nscreen UI widgets by drawing their icons.\",\"reading_order\":16,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,809,747,955],\"text\":\"Many other interfaces use somewhat arbitrary gestures,\\nbut provide visual guidance for novices to facilitate learning.\\nOn touchscreen devices, Marking Menu [21, 24, 25] and its\\nvariants [5, 6, 11, 12, 46, 47] allow novices to \\\"press and hold\\\"\\nto expand a hierarchical menu and trace through menu labels\\nto form a gesture, while experts may approximate the same\\ngesture without depending on any guidance. OctoPocus [7]\\nexpands this idea, but only shows what gestures are possible\\nwhen users pause their fingers. On trackpads, Markpad [13]\",\"reading_order\":17,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,127,1013],\"text\":\"Paper 165\",\"reading_order\":18,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[706,995,746,1013],\"text\":\"Page 2\",\"reading_order\":19,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300395/results/markdown/3290605-3300395_pdf_shortened_page_1.md"}
{"file_name": "images/test_data/3290605-3300395/3290605-3300395_pdf_shortened_page_2.png", "source_folder": "test_data", "document_id": "3290605-3300395", "source_image_path": "test_data/3290605-3300395/3290605-3300395_pdf_shortened_page_2.png", "source_json_path": "test_data/3290605-3300395/results/output_json/3290605-3300395_pdf_shortened_page_2.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"half_para\",\"bbox\":[69,125,394,157],\"text\":\"triggers commands by gesturing on a predefined grid while\\nreferring to the display for visual guidance.\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,157,394,348],\"text\":\"More relevant are Command Strokes [20] and Command-\\nBoard [2] , both supporting entering a command by gesture\\ntyping its label on a touchscreen keyboard. HotStrokes trans-\\nfers this idea to a laptop trackpad. It significantly deviates\\nfrom these techniques by letting users refer to the physical\\nkeyboard for static visual guidance and providing gesture\\nfeedback on a separate display. Anderson and Bischof [3]\\nshow that static guidance, in contrary to common belief, may\\nactually foster better motor performance when recalling ge-\\ntures, and the limited guidance with HotStrokes compared\\nto showing an on-screen keyboard directly under the finger\\nmay have certain benefits.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,358,378,375],\"text\":\"3 HOTSTROKES: WORD-GESTURE SHORTCUTS\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,380,394,587],\"text\":\"To strike a balance between efficiency and ease of use, we\\ndesign and implement HotStrokes to enable entering a com-\\nmand by gesture typing its label on a laptop trackpad as if on\\nan imaginary virtual keyboard, while also holding a modifier\\nkey (Figure 1 ). The gesture stroke is visualized on the laptop\\nscreen, providing real-time action feedback. The command\\nis executed as soon as the finger lifts off the trackpad. The\\nactivated command label, as well as the hotkey option, is\\ndisplayed on the screen after the execution. Since HotStrokes\\nis aimed to augment, rather than replace the existing com-\\nmand input paradigm, displaying hotkeys serves as calm\\nnotification [38] to remind users of the existence of another\\nefficient command input method.\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,587,394,747],\"text\":\"HotStrokes has the following theoretical advantages. First,\\na known command can be triggered directly without having\\nto search in linear menus or toolbars. Second, it inherits\\nthe same benefits of gesture typing, supporting a gradual\\ntransition from relying on visual guidance to direct memory\\nrecall. Third, it reduces the need for memorizing arbitrary\\nkey combinations by naturally mapping command labels to\\ncorresponding word gestures. The physical keyboard above\\nthe trackpad also serves as a reference to help users recall\\ngesture shapes and locations, easing the learning procedure.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,747,394,810],\"text\":\"In the rest of the paper, we first investigate how to decode\\nHotStrokes input, then study the pros and cons of using\\nHotStrokes in combination with the existing \\\"linear menu\\nand hotkey\\\" command input paradigm.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,821,301,837],\"text\":\"4 DECODING HOTSTROKES INPUT\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,841,394,955],\"text\":\"Decoding HotStrokes input on a trackpad is substantially dif-\\nferent from regular touchscreen keyboards. First, the input\\nsignal is expected to be noisy due to the lack of on-screen key-\\nboard visual where locating key positions can be challenging\\nto users. Second, no language context exists for command\\ninput, so the decoding algorithm can not leverage a language\\nmodel to disambiguate words like regular typing.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,124,746,189],\"text\":\"In this section, we review the SHARK 2 gesture decoding\\nalgorithm [19] , analyze users gesture typing behavior on a\\ntrackpad, and optimize SHARK 2 for decoding HotStrokes\\ninput.\",\"reading_order\":10,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[422,199,541,218],\"text\":\"Decoding Principle\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,221,746,301],\"text\":\"We developed a gesture decoding algorithm from scratch,\\nfollowing principles outlined in $SHARK^2$ [19] with two aug-\\nmentations explained later. Since command input has no\\nlanguage context, our algorithm relied on the shape channel\\nand location channel in $SHARK^2$ for decoding.\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,307,747,454],\"text\":\"Shape Channel. This channel classifies gestures based on the\\nshape information. It takes a gesture stroke $u$ as input, and\\noutputs the shape matching distance $D_{s}(u,v)$ between the\\ngesture input $u$ and the template pattern $v$ of a candidate\\ncommand $w$ . The template pattern is formed by connecting\\nkey centers of the command with straight lines on a Qwerly\\nlayout whose dimensions are identical with the trackpad. We\\nfollowed the same procedure outlined in [19] to calculate\\n$D_{s}(u,v)$ .\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,460,747,621],\"text\":\"Location Channel. This channel examines the absolute loca-\\ntion of the user's gesture trace on the keyboard. It outputs\\naverage the location distance $D_l(u,v)$ between the input ges-\\nture $u$ and the template pattern of a command candidate\\n$v$ . The procedure of calculating $D_l(u,v)$ is also detailed in\\nSHARK 2 [19] . We followed the same procedure. The only\\ndifference was that $D_l(u,v)$ was divided by the diagonal of a\\nkeyboard whose dimensions were identical to the trackpad,\\nwhich normalized its value between [0, 1], in the similar scale\\nwith $D_s(u,v)$ .\",\"reading_order\":14,\"tags\":[]},{\"label\":\"para\",\"bbox\":[422,620,745,668],\"text\":\"Finally, we combined the shape distance $D_s(u,v)$ and loca-\\ntion distance $D_l(u,v)$ to obtain the matching distance $D(u,v)$\\nbetween an input gesture $u$ and a template pattern $v$ as:\",\"reading_order\":15,\"tags\":[]},{\"label\":\"equ\",\"bbox\":[480,674,744,691],\"text\":\"$$D(u,v)=\\\\alpha D_{s}(u,v)+(1-\\\\alpha) D_{l}(u,v),\\\\quad(1)$$\",\"reading_order\":16,\"tags\":[]},{\"label\":\"para\",\"bbox\":[422,697,745,729],\"text\":\"where $a$ is the weight between $D_{s}$ and $D_{t}$ which was later\\ntrained from the collected data.\",\"reading_order\":17,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,729,746,825],\"text\":\"Applying Equation (1) , the algorithm obtains the matching\\ndistance $D(u,v)$ between an input gesture $u$ with every com-\\nmand candidate $v$ whose template pattern is denoted by $v$ .\\nThe one with the shortest $D(u,v)$ is the intended command\\nwhile the candidates with 2nd and 3rd shortest $D(u,v)$ are\\nthe 2nd and 3rd suggestions.\",\"reading_order\":18,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,825,746,889],\"text\":\"To investigate whether this algorithm can decode Hot-\\nStrokes and optimize its performance, we conducted a study\\nto collect HotStrokes input data, and used it to train and test\\nthe algorithm parameters.\",\"reading_order\":19,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[421,900,626,919],\"text\":\"Collecting HotStrokes Input Data\",\"reading_order\":20,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,921,746,954],\"text\":\"Task. The study was to study the users' HotStrokes gestur-\\ning behavior and collect HotStrokes input data. The subjects\",\"reading_order\":21,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[70,995,127,1012],\"text\":\"Paper 165\",\"reading_order\":22,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,995,745,1012],\"text\":\"Page 3\",\"reading_order\":23,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300395/results/markdown/3290605-3300395_pdf_shortened_page_2.md"}
{"file_name": "images/test_data/3290605-3300395/3290605-3300395_pdf_shortened_page_3.png", "source_folder": "test_data", "document_id": "3290605-3300395", "source_image_path": "test_data/3290605-3300395/3290605-3300395_pdf_shortened_page_3.png", "source_json_path": "test_data/3290605-3300395/results/output_json/3290605-3300395_pdf_shortened_page_3.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[68,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,43,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300395_pdf_shortened_page_3_figure_002.png\",\"bbox\":[115,118,348,311],\"reading_order\":2,\"tags\":[],\"alt_text\":\"A computer program window displays text in the upper left corner indicating \\\"Block: 1/16\\\" and \\\"Word: 2/5\\\". Centered on the screen is the blue word \\\"setup\\\" followed by \\\"( 2 to go )\\\", also in blue. Below the word, there is a handwritten-style blue line with a slight upward curve at its left end, flattening out to the right.\"},{\"label\":\"figure_cap\",\"bbox\":[68,325,394,370],\"text\":\"Figure 2: Screenshot of the data collection application. The\\nuser was instructed to gesture type setup three times in a\\nrow.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"half_para\",\"bbox\":[68,408,395,552],\"text\":\"were instructed to gesture type commands displayed on the\\nscreen via the trackpad of a laptop, imagining that there\\nwas an invisible keyboard superimposed on the trackpad\\nand could successfully decode their input. We used this Wiz-\\nard of Oz keyboard approach to collect neutral input data\\nwhich was not biased toward any algorithm. Participants\\nwere asked to gesture as naturally as possible. The keyboard\\nabove the trackpad worked as an input and to help partici-\\npants who were not familiar with QWERTY keyboard layout.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,576,395,737],\"text\":\"Design. The command set contained 80 single word com-\\nmands chosen from menu items from Apple OS X. These\\nwords covered all 26 English letters from 'a' to 'z'. These\\ncommands were extracted from Safari menus and systems\\napplications of Apple OS X. Most of the commands were sin-\\ngle words. For those commands containing multiple words\\nthat were hard to input in one continuous gesture, we picked\\neither the verb or the core word in the command phrase. An\\n80-word dictionary was set up using these commands for\\ngesture input recognition.\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,737,395,832],\"text\":\"Each command appeared 3 times consecutively, while the\\nCtrl key was pressed. In total, each participant input 240\\ngestures. The orders of all words were randomized across\\nparticipants. The gesture traces were recorded for later train-\\ning and testing. The gesture traces were displayed on the\\nlaptop screen as feedback.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,840,395,953],\"text\":\"Participants and Apparatus. We recruited 12 participants (3\\nfemales, 25 % ) aged from 22 to 39. All were right-handed.\\nThree of them had gesture input experiences. The others\\nhad never used gesture typing before. The subjects were\\ninstructed to use their preferred finger and hand. In the\\nexperiment, all the users used right hand and index finger.\\nAll the experiments conducted on a Lenovo Thinkpad X1\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,394,747,443],\"text\":\"Carbon 2017. (CPU:ti-7550U, RAM:8G) with Synaptics HID-\\nCompliant Touch pad Device. In total, 2880 gestures of 80\\nwords were collected from 12 participants.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300395_pdf_shortened_page_3_figure_009.png\",\"bbox\":[419,118,748,304],\"reading_order\":9,\"tags\":[],\"alt_text\":\"This visualization displays multiple overlapping, transparent, and multi-colored ellipses. Within or near these ellipses are various letters of the English alphabet, along with a few other characters like \\\"don\\\", \\\"Pse\\\", and a Hebrew letter. The arrangement of the letters broadly resembles a keyboard layout, but with significant clustering and distortion.\"},{\"label\":\"figure_cap\",\"bbox\":[421,315,746,360],\"text\":\"Figure 3: Imaginary key position distribution with 95 % confi-\\ndence ellipses. The black boundaries were the trackpad bor-\\nders.\",\"reading_order\":10,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[422,454,735,472],\"text\":\"Data Analysis – Imagined Key Position Distribution\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,475,747,554],\"text\":\"After collecting the input data, we first investigated whether\\nusers could recall key positions without key visuals, and\\nwhether the imagined key positions differed from a regular\\nQwerty keyboard. Such information would be critical in\\ndesigning the decoding algorithm.\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,554,747,906],\"text\":\"We located the imagined key positions of a command $w$\\nfrom its corresponding gesture input $u$ . It was straightfor-\\nward to locate the first and last letter positions: the first and\\nlast touch points in $u$ corresponded to the first and last letter\\nin $w$ , respectively. However, it was not easy to match letters\\nin the middle of $w$ with touch points in the gesture $u$ . We\\nachieved it using the Dynamic Time Warping (DTW) algo-\\nrithm [37] . We performed DTW between $u$ and the pattern\\ntemplate of $w$ (denoted by $v$ ). For a letter $c$ in $w$ , assuming\\nits key center in the template pattern is $c_v$ , the DTW algo-\\nrithm would output its matching point $u_c$ in $u$ , which is the\\nimagined key position of letter $c$ on the trackpad. Note that\\nDTW matched the template pattern ( $u$ ) with a user-entered\\ngesture ( $v$ ) based on touch point coordinates of these two\\ntraces, not the time stamp. So matching results were un-\\nlikely to be affected by the gesturing time from letter to\\nletter. More specifically, it first sampled $u$ and $v$ into 100\\nequidistant points, and obtained the optimal match between\\n$u$ and $v$ . This procedure resulted in good matches for letters\\nat the beginning and end of a word (they will be matched\\nto starting and ending points on a gesture trace), and also\\nmiddle letters where the gesture exhibited sharp turns.\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,907,747,954],\"text\":\"Figure 3 shows the distribution of imagined key positions\\nafter this procedure. As shown, the relative imagined key cen-\\nters approximately followed a Qwerty layout, which showed\",\"reading_order\":14,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,996,126,1013],\"text\":\"Paper 165\",\"reading_order\":15,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[706,996,745,1013],\"text\":\"Page 4\",\"reading_order\":16,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300395/results/markdown/3290605-3300395_pdf_shortened_page_3.md"}
{"file_name": "images/test_data/3290605-3300395/3290605-3300395_pdf_shortened_page_4.png", "source_folder": "test_data", "document_id": "3290605-3300395", "source_image_path": "test_data/3290605-3300395/3290605-3300395_pdf_shortened_page_4.png", "source_json_path": "test_data/3290605-3300395/results/output_json/3290605-3300395_pdf_shortened_page_4.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300395_pdf_shortened_page_4_figure_002.png\",\"bbox\":[81,140,366,347],\"reading_order\":2,\"tags\":[],\"alt_text\":\"A line graph plots Decoding Accuracy (%) on the y-axis, from 0 to 100, against α on the x-axis, from 0.0 to 1.0. The graph shows a peak accuracy of 77.71% at α = 0.67, indicated by a highlighted point and dashed gridlines.\"},{\"label\":\"figure_cap\",\"bbox\":[68,362,393,393],\"text\":\"Figure 4: Performing a linear search to obtain the optimal $\\\\alpha$\\nin Equation ( 1 ) on training data set.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"half_para\",\"bbox\":[68,439,394,582],\"text\":\"that users could to a certain degree recall relative Query\\nkey locations without key visuals shown on the trackpad.\\nPrevious research in imaginary interfaces [16, 17] has shown\\nthat users' memory on interface location is strong enough to\\nhelp them draw single-stroke characters and simple sketches\\nand perform interaction without interface visuals. Our work\\nadvances the understanding to word-gesture input on a track-\\npad, showing that such ability also holds for gesture typing\\non an imaginary keyboard on a trackpad.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,582,394,725],\"text\":\"Figure 3 also showed the touch point distribution clus-\\ntered near the central region of the trackpad, suggesting that\\nthe width of the imaginary keyboard was smaller than the\\nwidth of the trackpad, and the imagined key positions did\\nnot evenly distribute over the trackpad. This finding led us to\\nhypothesize that adapting the key positions to the imagined\\nkey positions (a.k.a, spatial model adaption) may improve the\\naccuracy of recognizing HotStrokes input. We investigate\\nthe effectiveness of this method as follows.\",\"reading_order\":5,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[69,740,358,758],\"text\":\"Enhancing Decoding 1. Spatial Model Adaption\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,761,394,825],\"text\":\"In this section, we evaluate the effectiveness of spatial model\\nadaption : we learned the key positions from the collected\\ndata, used them in decoding and compared its performance\\nwith a regular Qwerty layout.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,824,394,951],\"text\":\"To avoid over-fitting, we randomly selected 10 out of 12\\nusers' data as the training set, and the rest as the test set.\\nWe first computed the center of each key from the training\\ndata, following the procedure described in the previous sec-\\ntion. The new key locations were then used in both shape\\nand location channels [19] to form a template pattern for\\na command, and used in both shape and location distance\\ncalculation.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300395_pdf_shortened_page_4_figure_009.png\",\"bbox\":[429,144,716,337],\"reading_order\":9,\"tags\":[],\"alt_text\":\"This bar chart displays \\\"Input Accuracy (%)\\\" on the y-axis against \\\"Dictionary Size (word number)\\\" on the x-axis, ranging from 50% to 100% accuracy and 10 to 80 dictionary size. For each dictionary size, two bars compare accuracy: one for \\\"Without Spatial Model Adaptation\\\" (blue) and one for \\\"With Spatial Model Adaptation\\\" (orange). The orange bars consistently show higher accuracy than the blue bars across all dictionary sizes, with the difference becoming more pronounced as dictionary size increases.\"},{\"label\":\"figure_cap\",\"bbox\":[421,351,745,367],\"text\":\"Figure 5: Decoding accuracy by the size of the command set.\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,408,747,566],\"text\":\"We also performed a linear search to obtain the optimal\\n$\\\\alpha$ value in Equation (1) , to maximize the decoding accuracy\\non training data. It worked as follows. We first initiated $\\\\alpha$\\nto 0, and gradually increased it to 1, with 0.01 as the step\\nlength. For each $\\\\alpha$ value, we plugged it into Equation (1) and\\nobtained the overall decoding accuracy on the training data.\\nThe $\\\\alpha$ that led to the greatest accuracy was the optimal value.\\nFigure 4 showed how the accuracy varied as $\\\\alpha$ changed. The\\noptimal $\\\\alpha$ was 0.67 and the accuracy on the training data\\nwas 77.71 % .\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,567,746,678],\"text\":\"The algorithm was then evaluated over the test data and\\ncompared with a baseline condition. The baseline condition\\nwas using a regular Qwerty layout in decoding; we equally\\ndivided the trackpad into a $3\\\\times10$ grid, and superimposed a\\nQwerty layout onto it. We also performed an identical linear\\nsearch to obtain the optimal $\\\\alpha$ value for the baseline, which\\nwas 0.88. The accuracy on the training data was 74.17 % .\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,679,746,790],\"text\":\"To investigate how the decoding accuracy was affected by\\nthe size of the command set, we created 8 test command sets\\nfrom the test data, with the size of 10, 20, 30, $\\\\cdots$ , 80. For com-\\nmand set with size $X$ , we randomly chose $X$ commands from\\ntest data set, used these $X$ commands as the dictionary in\\nthe decoding algorithm, and fed the gestures corresponding\\nto these $X$ commands into the algorithm for decoding.\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,791,746,871],\"text\":\"Figure 5 shows the decoding accuracy by the size of the\\ncommand set, with and without spatial model adaption. As\\nshown, adapting key centers in general improved the de-\\ncoding accuracy, especially for command set greater than\\n30.\",\"reading_order\":14,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,884,689,918],\"text\":\"Enhancing Decoding 2. Suggesting Multiple\\nCommands When Necessary\",\"reading_order\":15,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,921,746,953],\"text\":\"Figure 5 showed that the top 1 decoding accuracy was above\\n90 % on small-sized command set, but the accuracy drop as\",\"reading_order\":16,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[70,995,127,1012],\"text\":\"Paper 165\",\"reading_order\":17,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[705,995,745,1012],\"text\":\"Page 5\",\"reading_order\":18,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300395/results/markdown/3290605-3300395_pdf_shortened_page_4.md"}
{"file_name": "images/test_data/3290605-3300395/3290605-3300395_pdf_shortened_page_5.png", "source_folder": "test_data", "document_id": "3290605-3300395", "source_image_path": "test_data/3290605-3300395/3290605-3300395_pdf_shortened_page_5.png", "source_json_path": "test_data/3290605-3300395/results/output_json/3290605-3300395_pdf_shortened_page_5.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300395_pdf_shortened_page_5_figure_002.png\",\"bbox\":[79,135,379,357],\"reading_order\":2,\"tags\":[],\"alt_text\":\"Two interface elements display text suggestions with horizontal slider controls. The top element shows two suggestions, \\\"cut\\\" and \\\"copy\\\", with \\\"cut\\\" highlighted as if selected by a slider. The bottom element shows three suggestions, \\\"shut\\\", \\\"cut\\\", and \\\"copy\\\", with \\\"copy\\\" highlighted by a slider.\"},{\"label\":\"figure_cap\",\"bbox\":[68,400,394,476],\"text\":\"Figure 6: Dynamic suggestion shows 2 or 3 suggestions if the\\ninput command is ambiguous, determined by Equation (2) ,\\nand Equation (6) . Sliding the finger to the left, right, or up\\nselects the corresponding command. Sliding down cancels\\nthe input.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"half_para\",\"bbox\":[69,518,394,629],\"text\":\"the command set size increases, suggesting users will likely\\nbenefit from suggesting two or three commands especially\\non large command set. On the other hand, always providing\\nmultiple suggestions may introduce unnecessary cost for\\ngestures where the top 1 suggestion was already the intended\\nword, which was the majority across all the conditions (the\\ntop 1 accuracy was above 77 % for across command set sizes).\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,629,393,677],\"text\":\"To address this problem, we developed a dynamic sug-\\ngestion feature. The decoding algorithm will suggest two\\ncommand candidates if\",\"reading_order\":5,\"tags\":[]},{\"label\":\"equ\",\"bbox\":[177,695,391,712],\"text\":\"$$D _ { t o p 2 } - D _ { t o p 1 } < \\\\sigma _ { 1 } , ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 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~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,728,394,792],\"text\":\"where $D_{top1}$ is the matching distance between the top 1\\ncommand candidate and the input gesture, and $D_{top2}$ is the\\nmatching distance between the top 2 candidate and the input\\ngesture.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,792,394,952],\"text\":\"The rationale behind this feature is that if both top 1 and\\ntop 2 candidates have close matching distance to the input\\ngesture, it means both have high probabilities to be the in-\\ntended command and the algorithm should present both to\\nthe user, and request the user to select the suggestion by\\nperforming a sliding gesture (Figure 6 ). If none of the sugges-\\ntion is the intended command, sliding down will cancel the\\ncurrent input. If the algorithm produces only one suggestion,\\nthe command would automatically be executed upon the\\nfinger lifting off the trackpad.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,124,746,205],\"text\":\"To make the dynamic suggestion effective, we need to\\ndetermine an optimal $\\\\sigma_{1}$ value, which should produce 2 sug-\\ngestions only if the intended command is the 2nd suggestion,\\nand show one suggestion only if the intended command is\\nthe 1st suggestion.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,205,745,237],\"text\":\"We used recall and precision rates to quantify whether $\\\\sigma_1$\\nis optimal. The recall rate is defined as\",\"reading_order\":10,\"tags\":[]},{\"label\":\"equ\",\"bbox\":[546,244,744,276],\"text\":\"$$\\\\begin{array} { r } { \\\\frac { t p } { t p + f n } . \\\\quad( 3 ) } \\\\end{array}$$\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,282,746,363],\"text\":\"where $tp$ is the number of true positive (i.e. the intended\\ncommand was the 2nd suggestion and two suggestions were\\ndisplayed), and $fn$ is the number of false negative (i.e. the\\nintended command was the 2nd suggestion but the algorithm\\nonly showed 1 suggestion).\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[434,364,619,379],\"text\":\"The precision rate was defined as\",\"reading_order\":13,\"tags\":[]},{\"label\":\"equ\",\"bbox\":[546,386,744,419],\"text\":\"$$p = \\\\frac { t p } { t p + f p } . ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 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Missing a hit\\n(false negative) means the user needs to redraw the gesture,\\nwhile falsely reporting the 2nd suggestion (false positive)\\nmeans the user needs to do a follow-up sliding to select the\\nintended command.\",\"reading_order\":16,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,585,746,649],\"text\":\"To balance precision and recall rates, and weigh recall\\nhigher than precision, we performed a linear search to find\\nthe optimal $\\\\sigma_L$ value that maximized the commonly used $F_2$\\nscore [36] on the training data:\",\"reading_order\":17,\"tags\":[]},{\"label\":\"equ\",\"bbox\":[535,656,744,686],\"text\":\"$$F _ { 2 } = 5 \\\\cdot \\\\frac { P \\\\cdot r } { 4 \\\\cdot p \\\\cdot r ^ { \\\\prime } } . \\\\quad ( 5 ) $$\",\"reading_order\":18,\"tags\":[]},{\"label\":\"para\",\"bbox\":[422,695,702,711],\"text\":\"where $p$ is the precision rate and $r$ is the recall rate.\",\"reading_order\":19,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,711,746,774],\"text\":\"We first initiated $\\\\sigma_1$ to 0, gradually increased it to 0.25\\n(max value in experiment data) with an increment of 0.0001,\\nand calculated the $F_2$ score. $\\\\sigma_1 = 0.0468$ led to the highest $F_2$\\nscore of 0.6823.\",\"reading_order\":20,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,774,747,839],\"text\":\"Similarly, we expanded the dynamic suggestion feature\\nto include the 3rd suggestion. If the matching distance of\\ntop 1 and top 3 command candidates satisfy the following\\nequation, the decoding algorithm will show three candidates:\",\"reading_order\":21,\"tags\":[]},{\"label\":\"equ\",\"bbox\":[529,848,743,863],\"text\":\"$$D _ { t o p 3 } - D _ { t o p 1 } < \\\\sigma _ { 2 } , ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 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~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~\",\"reading_order\":22,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,872,746,953],\"text\":\"where $E_d$ is the matching distance between the 3rd command\\ncandidate and the input gesture. The user will then slide the\\nfinger in the corresponding direction to select the candidate\\ncommand (Figure 6 ). Similar to determining optimal $\\\\sigma_i$ , we\\nsearched for the optimal $\\\\sigma_2$ to maximize the corresponding\",\"reading_order\":23,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[70,996,126,1012],\"text\":\"Paper 165\",\"reading_order\":24,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[705,996,745,1012],\"text\":\"Page 6\",\"reading_order\":25,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300395/results/markdown/3290605-3300395_pdf_shortened_page_5.md"}
{"file_name": "images/test_data/3290605-3300395/3290605-3300395_pdf_shortened_page_6.png", "source_folder": "test_data", "document_id": "3290605-3300395", "source_image_path": "test_data/3290605-3300395/3290605-3300395_pdf_shortened_page_6.png", "source_json_path": "test_data/3290605-3300395/results/output_json/3290605-3300395_pdf_shortened_page_6.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"table_cap\",\"bbox\":[67,122,394,187],\"text\":\"Table 1: The intended command Coverage per com-\\nmand set size without and with dynamic suggestion,\\nand false positive rates for 2 suggestions (2-FP) and 3\\nsuggestions (3-FP) with dynamic suggestion.\",\"reading_order\":2,\"tags\":[]},{\"label\":\"tab\",\"bbox\":[102,199,363,368],\"text\":\"| size | Coverage (without) | Coverage (with) | 2-FP | 3-FP | | 10 | 92.31% | 96.00% | 13.05% | 6.92% | | 20 | 87.76% | 93.33% | 18.03% | 10.53% | | 30 | 85.20% | 92.11% | 22.33% | 15.89% | | 40 | 82.56% | 90.25% | 25.51% | 18.62% | | 50 | 81.52% | 89.18% | 29.52% | 22.60% | | 60 | 80.42% | 88.25% | 32.95% | 24.49% | | 70 | 78.81% | 87.71% | 35.94% | 25.30% | | 80 | 77.71% | 86.88% | 38.07% | 27.47% |
\",\"reading_order\":3,\"tags\":[],\"alt_text\":\"This is a table displaying five columns of numerical data. The columns are labeled \\\"size\\\", \\\"Coverage (without)\\\", \\\"Coverage (with)\\\", \\\"2-FP\\\", and \\\"3-FP\\\". The 'size' column lists values from 10 to 80, while the other columns show corresponding percentage values.\",\"figure_path\":\"tables/3290605-3300395_pdf_shortened_page_6_tab_3.png\"},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300395_pdf_shortened_page_6_figure_004.png\",\"bbox\":[77,421,363,609],\"reading_order\":4,\"tags\":[],\"alt_text\":\"This bar chart displays Input Accuracy (%) on the y-axis against Dictionary Size (word number) on the x-axis. It compares two conditions: \\\"Without Spatial Model Adaptation\\\" (orange bars) and \\\"With Spatial Model Adaptation\\\" (green bars). The green bars consistently show higher input accuracy than the orange bars across all dictionary sizes, with accuracy generally decreasing as dictionary size increases for both conditions.\"},{\"label\":\"figure_cap\",\"bbox\":[67,622,392,654],\"text\":\"Figure 7: Intended command coverage without and with dy-\\nnamic suggestions.\",\"reading_order\":5,\"tags\":[]},{\"label\":\"half_para\",\"bbox\":[68,696,393,728],\"text\":\"$F_2$ score, and found $\\\\alpha_2 = 0.0318$ led to the highest $F_2$ score\\nof 0.7155 on the training data set.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,729,394,792],\"text\":\"Equation (2) with $\\\\sigma_1=0.0468$ and Equation (6) with $\\\\sigma_2=0.0318$ were our solution for dynamically showing multiple\\ncommands when necessary. We applied it on the test data\\nset to evaluate its effectiveness.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,792,394,952],\"text\":\"Figure 7 shows the intended command coverage after\\nadopting the dynamic suggestion. Intended command cov-\\nerage is the percentage of gestures in which the intended\\ncommand was correctly recognized, or it is one of the pre-\\nsented suggestions when 2 or 3 suggestions are displayed.\\nAs shown in Figure 7 and Table 1 , using dynamic suggestion\\nbrought the intended command coverage to above or near\\n90 % for command set less or equal to 50. Table 1 showed the\\nintended command coverage, and the false positive rates for\\n2, and 3 suggestions.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,124,489,140],\"text\":\"Discussion\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,144,748,191],\"text\":\"Our investigation showed both spatial model adaption and\\ndynamic suggestions were effective in recognizing HotStrokes\\ninput.\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,192,746,303],\"text\":\"First, as shown in Figure 5 , adapting the spatial model\\nsubstantially improved the decoding accuracy, especially for\\ncommand set greater than 30 commands. It was probably\\nbecause users exhibited distinct gesturing behavior when\\ndrawing HotStrokes. For example, they underused the re-\\ngions near the left and right boundaries. Accounting for their\\ngesturing behavior improved the decoding performance.\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,303,746,431],\"text\":\"Second, the dynamic suggestion method was effective in\\nrecommending the necessary suggestion when there was\\nambiguity in decoding. If the algorithm only showed the\\ntop 1 suggestion, the recognition accuracy dropped from\\n92.21 % (10-command set) to around 80 % as the command set\\nsize increases to 60 or 70. Dynamic suggestions brought the\\nintended command coverage to above or near 90 % especially\\nfor command sets with 50 or fewer commands. (Figure 7 )\",\"reading_order\":12,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,446,527,461],\"text\":\"5 EVALUATION\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,465,746,545],\"text\":\"After investigating how to support HotStrokes, we studied\\nthe utility of HotStrokes. We conducted a user study to in-\\nvestigate whether and to what degree providing HotStrokes\\nas an alternative improved the performance of the existing\\ncommand input paradigm (e.g., menus and hotkeys).\",\"reading_order\":14,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[422,559,533,577],\"text\":\"Experiment Setup\",\"reading_order\":15,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,580,746,611],\"text\":\"Design. The study was a command activation task, and had\\ntwo conditions:\",\"reading_order\":16,\"tags\":[]},{\"label\":\"list\",\"bbox\":[442,617,749,729],\"text\":\"• Hotkeys + linear menus (KM). This represented the\\nexisting command activation paradigm. A user could\\nfreely choose either hotKey or use linear menus.\\n• HotStrokes + hotkeys + linear menus (SKM). HotStrokes\\nwere added as an option for triggering command. A\\nuser could choose any of these three methods for trig-\\ngering a command, according to her preference.\",\"reading_order\":17,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,734,747,814],\"text\":\"We adopted a between-subject design to eliminate the\\npotential carryover learning effect, since participants may\\nlearn hotkey combinations during the study. There was a\\nsingle independent variable, technique, with two levels (KM,\\nand SKM).\",\"reading_order\":18,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,824,747,952],\"text\":\"Task. The study consisted of multiple command activation\\ntrials. At the start of each trial, a start button appeared at\\nthe center of the screen. Participants moved the cursor to\\nclick on the start button, which would then be replaced by a\\nstimulus icon at the same position with a size of 200 × 200\\npixels. The icon was a highly familiar graphical object, such\\nas cat, apple, or hat. The associations among all the stimulus\\nobjects were trivial and unique. Choosing iconic stimuli was\",\"reading_order\":19,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,996,126,1013],\"text\":\"Paper 165\",\"reading_order\":20,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,996,745,1013],\"text\":\"Page 7\",\"reading_order\":21,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300395/results/markdown/3290605-3300395_pdf_shortened_page_6.md"}
{"file_name": "images/test_data/3290605-3300395/3290605-3300395_pdf_shortened_page_7.png", "source_folder": "test_data", "document_id": "3290605-3300395", "source_image_path": "test_data/3290605-3300395/3290605-3300395_pdf_shortened_page_7.png", "source_json_path": "test_data/3290605-3300395/results/output_json/3290605-3300395_pdf_shortened_page_7.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300395_pdf_shortened_page_7_figure_002.png\",\"bbox\":[83,120,379,336],\"reading_order\":2,\"tags\":[],\"alt_text\":\"A person's hands are positioned over the keyboard and trackpad of a black Lenovo laptop. The laptop screen displays a white background with a small illustration of a dog, the text \\\"dog ctrl+alt\\\", and a blue handwritten-style arrow pointing right.\"},{\"label\":\"figure_cap\",\"bbox\":[68,346,394,434],\"text\":\"Figure 8: A participant just finished inputting the command\\ndog with HotStrokes in Experiment. The green ✓ indicated\\nthe input was correct. The blue trace was the feedback for\\nHotStrokes input. The command label (e.g., dog ) and corre-\\nsponding hotkey option (e.g., ctrl + alt + d ) were displayed\\nafter the command was successfully entered.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"half_para\",\"bbox\":[69,475,394,524],\"text\":\"based on the fact that verbal or textural objects reduce the\\nconnections between the stimulus and the command. It is\\nthe same strategy used in previous research [4, 15] .\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,524,394,635],\"text\":\"Participants were then instructed to input the command\\naccording to the stimulus image on the icon. The participant\\ncould use either hotkeys or linear menus in the KM condi-\\ntion, and linear menus, hotkeys or HotStrokes in the SKM\\ncondition. In both conditions, participants were instructed\\nto only use the trackpad for cursor movement, a common\\nusage on laptop.\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,635,394,763],\"text\":\"If the input was correct, the command label and the corre-\\nsponding hotkey would appear under the stimulus image. It\\nwas consistent with the HotStrokes design: command feed-\\nback including hotkey option was provided after it was trig-\\ngered. If the input was incorrect, the window would freeze\\nfor 3 seconds as a penalty, preventing participants from ran-\\ndomly guessing hotkeys. The user then repeated the same\\ntrial until the command was correctly triggered.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,763,395,843],\"text\":\"A post-study questionnaire was administered at the end of\\nthe study to collect participants' subject rating about mental\\ndemand, physical demand, temporal demand, the effort of\\nperforming the task, and their overall preference of each\\ntechnique.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,854,394,951],\"text\":\"Command Set. The task included six categories of 72 com-\\nmands, each category containing 12 commands. A 72-word\\ndictionary was created based on these commands for recog-\\nnition. For each command, a hotkey was assigned after the\\nfollowing rules designed to simulate the common hotkey\\nassigning strategies:\",\"reading_order\":8,\"tags\":[]},{\"label\":\"list\",\"bbox\":[419,123,746,395],\"text\":\"1) If the first letter of a command word was the only letter\\nappears in initial letters of all the commands, it would be\\nassociated with 'Ctrl' button. For example, 'g' was the first\\nletter of 'gloves', and there was no other words in 30 com-\\nmands start with 'g', 'Ctrl + G' would be assigned to glove\\nas a hotkey.\\n\\n2) If there were several words with the same first letter, we\\nassigned the 7 combinations of 'Ctrl', 'Shift', 'Alt' buttons to\\neach of them. In an order as 'Ctrl', 'Shift', 'Alt', 'Ctrl + Shift',\\n'Ctrl + Alt', 'Alt + Shift', 'Ctrl + Alt + Shift'. For example,\\nif there are only three words 'pig', 'pea' and 'printer' in all\\n72 commands start with 'p', we will only use the first 3 of 72\\ncombinations, 'Ctrl + P', 'Alt + P' and 'Shift + P'. To ensure\\nall the hotkeys were designed with the first letter of the\\ncorresponding command, for all the words in our study, the\\nnumber of words that start with the same letter is less than\\nor equal to 7 in the study.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,396,746,459],\"text\":\"A command could also be activated via the menu bar. The\\nheight of the menu bar was 20 pixels (7mm on the laptop),\\napproximately the same size of menu bars in common appli-\\ncations.\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,460,746,586],\"text\":\"We randomly selected 14 commands from the 6 categories\\nas target commands. The same target commands were used\\nacross participants. Since previous research showed that the\\ndistribution of commands could be approximated with a\\nZipfian distribution, the frequencies of the command were\\ngenerated according to Zipfian distribution with exponent 1\\n(relative frequency = 1/rank) based on 30 random inputs of\\n7 items.\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,586,747,841],\"text\":\"The generated frequencies are (12, 6, 4, 3, 2, 2, 1). Since we\\nhad 14 target commands, each frequency value was assigned\\nto two commands. The frequencies of target commands in a\\n60-trial block were (12, 12, 6, 6, 4, 4, 3, 3, 2, 2, 2, 2, 1, 1). The\\ncommand to frequency mapping was counterbalanced across\\nall participants in each condition(KM and SKM). Therefore,\\neach command was mapped to each frequency an equal\\nnumber of times. This strategy was the same as used in the\\nprevious research [4, 15] . Each participant performed 20 tri-\\nals (in 3 minutes) as a warm-up prior to the formal study.\\nEach condition contained 2 blocks of 60 trials (120 trials in\\ntotal) for each participant. The orders of commands were\\nrandomized in each block, with the constraint on the fre-\\nquencies of each command. For instance, the two commands\\nmapped to the frequency 6, would each appear exactly 12\\ntimes over both blocks.\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,854,746,934],\"text\":\"Participants and Apparatus. 28 subjects (6 female), aged from\\n18 to 36 participated in this experiment. The median of self-\\nreported familiarity with the Qwerty keyboard (1: extremely\\nunfamiliar; 5: extremely familiar) was 5. The experiment was\\nconducted on a Thinkpad X1 2017 running Windows 10 OS.\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[434,934,621,951],\"text\":\"In total, our experiment included:\",\"reading_order\":14,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[70,996,127,1013],\"text\":\"Paper 165\",\"reading_order\":15,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,996,745,1013],\"text\":\"Page 8\",\"reading_order\":16,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300395/results/markdown/3290605-3300395_pdf_shortened_page_7.md"}
{"file_name": "images/test_data/3290605-3300398/3290605-3300398_pdf_shortened_page_0.png", "source_folder": "test_data", "document_id": "3290605-3300398", "source_image_path": "test_data/3290605-3300398/3290605-3300398_pdf_shortened_page_0.png", "source_json_path": "test_data/3290605-3300398/results/output_json/3290605-3300398_pdf_shortened_page_0.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[475,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\"]},{\"label\":\"icon\",\"bbox\":[0,93,39,132],\"reading_order\":2,\"tags\":[\"cross_marker\"]},{\"label\":\"sec_0\",\"bbox\":[73,118,741,151],\"text\":\"App Usage Predicts Cognitive Ability in Older Adults\",\"reading_order\":3,\"tags\":[]},{\"label\":\"author_information\",\"bbox\":[121,167,255,218],\"text\":\"Mitchell L. Gordon\\n\\nStanford University\\n\\nmgord@cs.stanford.edu\",\"reading_order\":4,\"tags\":[\"author\",\"author_affili\",\"author_mail\"]},{\"label\":\"author_information\",\"bbox\":[356,168,459,218],\"text\":\"Leon Gatys\\n\\nApple Inc\\n\\nlgatys@apple.com\",\"reading_order\":5,\"tags\":[\"author\",\"author_affili\",\"author_mail\"]},{\"label\":\"author_information\",\"bbox\":[568,168,684,218],\"text\":\"Carlos Guestrin\\n\\nApple Inc.\\n\\ngquestrin@apple.com\",\"reading_order\":6,\"tags\":[\"author\",\"author_affili\",\"author_mail\"]},{\"label\":\"author_information\",\"bbox\":[130,228,245,277],\"text\":\"Jeffrey P. Bigham\\n\\nApple Inc\\n\\nbjigham@app.apple.com\",\"reading_order\":7,\"tags\":[\"author\",\"author_mail\",\"author_affili\"]},{\"label\":\"author_information\",\"bbox\":[350,228,463,277],\"text\":\"Andrew Trister\\n\\nApple Inc\\n\\natrister@apple.com\",\"reading_order\":8,\"tags\":[\"author\",\"author_mail\",\"author_affili\"]},{\"label\":\"author_information\",\"bbox\":[571,228,679,278],\"text\":\"Kayur Patel\\n\\nApple Inc\\nkayur@apple.com\",\"reading_order\":9,\"tags\":[\"author\",\"author_mail\",\"author_affili\"]},{\"label\":\"sec_1\",\"bbox\":[69,288,141,305],\"text\":\"ABSTRACT\",\"reading_order\":10,\"tags\":[\"paper_abstract\"]},{\"label\":\"para\",\"bbox\":[68,309,394,597],\"text\":\"We have limited understanding of how older adults use smart-\\nphones, how their usage differs from younger users, and the\\ncauses for those differences. As a result, researchers and\\ndevelopers may miss promising opportunities to support\\nolder adults or offer solutions to unimportant problems. To\\ncharacterize smartphone usage among older adults, we col-\\nlected iPhone usage data from 84 healthy older adults over\\nthree months. We find that older adults use fewer apps, take\\nlonger to complete tasks, and send fewer messages. We use\\ncognitive test results from these same older adults to then\\nshow that up to 79 % of these differences can be explained by\\ncognitive decline, and that we can predict cognitive test per-\\nformance from smartphone usage with 83 % ROCAUC. While\\nolder adults differ from younger adults in app usage behavior,\\nthe “ cognitively young ” older adults use smartphones much\\nlike their younger counterparts. Our study suggests that to\\nbetter support all older adults, researchers and developers\\nshould consider the full spectrum of cognitive function.\",\"reading_order\":11,\"tags\":[\"paper_abstract\"]},{\"label\":\"sec_1\",\"bbox\":[69,601,202,616],\"text\":\"ACM Reference Format:\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,617,394,704],\"text\":\"Mitchell L, Gordon, Leon Gatys, Carlos Guestron, Jeffrey P. Bigham,\\nAndrew Trister, and Kayur Patel. 2019. App Usage Products Cog-\\nnitive Ability in Older Adults. In CHI Conference on Human Fac-\\ntors in Computing Systems Proceedings (CHI 2019), May 4-9, 2019,\\nGlasgow, Scotland. UK: ACM, New York, NY, USA, 12 pages. https:\\n//doi.org/10.1145/3290605.3303098\",\"reading_order\":13,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[69,716,197,733],\"text\":\"1 INTRODUCTION\",\"reading_order\":14,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,736,394,785],\"text\":\"Although more than 42 % of older adults use smartphones\\n[3] , relatively little is known about smartphone usage pat-\\nterns amongst older adults (65 or older), particularly when\",\"reading_order\":15,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,289,747,464],\"text\":\"compared to what we know about the younger population's\\nsmartphone usage patterns [27] . Understanding similarities\\nand differences in how older adults and the younger popula-\\ntion use smartphones is important for designing engaging\\nand helpful experiences. Emerging trends in health and well-\\nness applications delivered through smartphones seem espe-\\ncially well-suited to the needs of older adults, yet designers\\nmay fail to create applications that address the needs of older\\nadults without this understanding. For example, older adults\\noften experience cognitive decline while aging, but we do\\nnot know to what extent this affects smartphone use [26] .\",\"reading_order\":16,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,465,747,671],\"text\":\"In this paper, we present a quantitative study of smart-\\nphone usage by older adults (aged 61 to 76), utilizing a dataset\\nthat we created containing usage metadata logs of 84 iPhone\\nusers over a three month period. We characterize application\\nusage in a number of different ways, including by type and\\nnumber of applications used, amount of time spent on appli-\\ncations, and behavior in switching between applications. We\\ncompare with prior work that looked at similar behavior in\\nthe general population, noting that the usage behaviors of\\nolder adults are different than younger adults, but more simi-\\nlar than might have been previously assumed. This suggests\\nthat approaches developed for the general population might\\nusefully be applied for older smartphone users as well.\",\"reading_order\":17,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,671,747,864],\"text\":\"We also uncover important differences that may guide\\ndesign principles for application developers to better design\\nfor the older population. By reproducing analyses frequently\\nperformed on other smartphone usage data sets in prior\\nwork, we demonstrate that older adults display distinct app\\nusage characteristics from younger adults, such as using\\nfewer apps and keeping them open for longer. To further\\ncharacterize the usage patterns of older adults, we apply new\\nanalysis techniques for app-usage dynamics to reveal how\\napplications are used in sequence ( e.g., most switches into\\nPhotos comes from Camera, and most switches into Safari\\ncome from Mail).\",\"reading_order\":18,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,864,747,943],\"text\":\"While these differences likely result from a complex com-\\nbination of generational and age-related shifts in culture,\\nvalues, lifestyle, and cognitive / physical abilities, we focus\\non how cognitive function of participants is associated with\\ndifferences in use. Cognitive function is of particular interest\",\"reading_order\":19,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[69,792,392,807],\"text\":\"*This work was done while Mitchell L. Gordon was an intern at Apple Inc.\",\"reading_order\":20,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[69,816,394,896],\"text\":\"Permission to make digital or hard copies of part or all of this work for\\npersonal or classroom use is granted without fee provided that copies are\\nnot made or distributed for profit or commercial advantage and that copies\\nbear this notice and the full citation on the first page. Copyrights for third-\\nparty components of this work must be honored. For all other uses, contact\\nthe owner/author(s).\",\"reading_order\":21,\"tags\":[]},{\"label\":\"fnote\",\"bbox\":[69,898,274,954],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland UK\\n© 2019 Copyright held by the owner/author(s).\\nACM ISBN 978-1-4305-3970-2/19-05.\\nhttps://doi.org/10.1145/3290605.3303098\",\"reading_order\":22,\"tags\":[\"meta_pub_date\",\"meta_subject\",\"meta_num\"]},{\"label\":\"foot\",\"bbox\":[69,996,127,1013],\"text\":\"Paper 168\",\"reading_order\":23,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,996,744,1013],\"text\":\"Page 1\",\"reading_order\":24,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300398/results/markdown/3290605-3300398_pdf_shortened_page_0.md"}
{"file_name": "images/test_data/3290605-3300398/3290605-3300398_pdf_shortened_page_1.png", "source_folder": "test_data", "document_id": "3290605-3300398", "source_image_path": "test_data/3290605-3300398/3290605-3300398_pdf_shortened_page_1.png", "source_json_path": "test_data/3290605-3300398/results/output_json/3290605-3300398_pdf_shortened_page_1.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[68,44,159,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,44,746,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"half_para\",\"bbox\":[68,125,394,188],\"text\":\"in the context of older adults, as cognitive function has pre-\\nviously been found to be important across a wide array of\\nactivities, e.g., in technology usage [16] , office productivity\\n[29] , and avoiding motor vehicle accidents [55] .\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,189,394,476],\"text\":\"Each participant in our study also underwent neurocogni-\\ntive testing in two major cognitive domains: (i) perceptual\\nand motor, and (ii) learning and memory. To explore how dif-\\nferences in cognitive function might explain differences we\\nobserve in behavior, we constructed a quasi-experiment im-\\nplemented using our usage log data paired with the neurocog-\\nnitive testing results of our participants. Our 84 participants\\nhad assessed neurocognitive function in the normal range,\\nyet we observed a wide distribution of performance on neu-\\nrocognitive testing. Specifically, some healthy older adults\\nperform on neurocognitive testing at the level of younger\\nadults, while others did not. We compared app usage behav-\\nior of those who performed like younger adults with the\\nremainder of our participants, and found that the observed\\ndifferences in usage behavior can be largely explained by\\ncognitive function. We generalized these results into a pre-\\ndictive model capable of predicting neurocognitive testing\\nperformance from app usage patterns with 83 % ROC AUC.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,476,394,635],\"text\":\"Our research demonstrates that while older adults display\\nsome notable differences as compared to younger adults,\\n“ cognitively young ” older adults use smartphones much like\\ntheir younger counterparts. To better support all older adults,\\nresearchers and developers should especially consider how\\nto design applications that can accommodate users who are\\nno longer cognitively young. Older adults now form a sizable\\npercentage of the user base of smartphones. Our research\\ncontributes an important step toward understanding how to\\nbest support them.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,652,181,667],\"text\":\"2 PRIOR WORK\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,672,394,737],\"text\":\"Our work is informed by prior work on (i) studies of smart-\\nphone app usage patterns, (ii) how older adults use tech-\\nnology, and (iii) predicting personal characteristics from\\nsmartphone usage.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[68,754,269,772],\"text\":\"Smartphone App Usage Patterns\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,775,394,871],\"text\":\"Smartphone app usage patterns have been studied exten-\\nsively. Researchers have investigated how many apps people\\ninstall, which apps are most used at which time of day, and\\nhow long these apps are kept open for [10, 19, 24]. They've\\ninvestigated daily routines [20] and even predicted which\\napp a user will launch next [5].\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,871,394,952],\"text\":\"We note that two factors unify most of this work. The\\nfirst is that these studies have typically relied upon data\\nfrom younger adults. For instance: in Do et al.'s study, using\\ndata gathered from 77 volunteers, only 11 % of participants\\nwere over 40 [19] . Larger-scale studies, which rely on users\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,125,747,236],\"text\":\"deciding to download publicly available logging apps, typi-\\ncally did not collect demographic data but can be assumed\\nto contain primarily younger adults given the demographics\\nof Android users [10] . While some specific demographics\\nhave been investigated, such as a 14-participant study of\\nteenagers [9] , researchers have yet to characterize the usage\\npatterns of older adults.\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,236,747,443],\"text\":\"The second unifying factor is that, as pointed out by Mor-\\nison et al. [36] , nearly all of this work has been performed\\nusing Android phones rather than on iPhone and iOS, as\\nthe Android platform allows for the easy installation of log-\\nging software for OS-level events such as app launches and\\nphone unlocking. Morrison responded by reproducing on\\niPhone a variety of studies previously performed on An-\\ndroid. Morrison achieved this by focusing on 10,000 jailbro-\\nken users, which enabled them to install an app that could\\nlog OS-level events. While this study did not collect or re-\\nport demographic information, people who jailbreak their\\niPhones have been found to have a mean of 36 years old and\\nare and 65 % male [35] .\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,444,747,682],\"text\":\"In this paper, we analyze iPhone usage patterns – notably,\\nolder adults are more likely to use an iPhone than any other\\nage groups [22] . Therefore, when we compare usage statis-\\ntics reproduced on our population with other studies, we do\\nso primarily with Morrison et al. 's work, given that it is the\\nonly study reporting general usage patterns from iPhones.\\niOS is different than Android and may promote different us-\\nage patterns. Further, people who choose iPhone have been\\nfound to exhibit sociological differences [43] . One difficulty\\nwith this comparison is that, as noted by Morrison, users of\\njailbroken devices likely differ from the general population\\nin that they may be more engaged and familiar with their\\niPhones. Therefore, their usage patterns can likely be con-\\nsidered on the more 'extreme' end of what we may expect\\nfrom the general population [36] .\",\"reading_order\":12,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[421,693,602,711],\"text\":\"Technology and Older Adults\",\"reading_order\":13,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,715,747,808],\"text\":\"Older adult's relationship with technology is an important\\narea of research and has been studied extensively. Technol-\\nogy use tends to decline with age [46] . While older adults\\ngenerally use fewer technologies than younger adults, and\\nuse them less frequently, notable exceptions have been found\\nin domains such as healthcare [11, 40] .\",\"reading_order\":14,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,809,747,954],\"text\":\"While older adult's general relationship with technology\\nis well-documented, their specific usage patterns are not.\\nWork which quantifies how older adults use smartphones\\n– increasingly the most important and versatile pieces of\\ntechnology in our lives – is extremely limited. Many stud-\\nies have broadly surveyed older adults on their preferences\\nand attitudes towards mobile phones, but have not analyzed\\nactual usage data [ 38 ] . Some studies are centered around\\ninput. For instance, older adults have been found to input\",\"reading_order\":15,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,128,1013],\"text\":\"Paper 168\",\"reading_order\":16,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[706,995,746,1013],\"text\":\"Page 2\",\"reading_order\":17,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300398/results/markdown/3290605-3300398_pdf_shortened_page_1.md"}
{"file_name": "images/test_data/3290605-3300398/3290605-3300398_pdf_shortened_page_2.png", "source_folder": "test_data", "document_id": "3290605-3300398", "source_image_path": "test_data/3290605-3300398/3290605-3300398_pdf_shortened_page_2.png", "source_json_path": "test_data/3290605-3300398/results/output_json/3290605-3300398_pdf_shortened_page_2.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,43,159,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,43,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"author_affili\"]},{\"label\":\"half_para\",\"bbox\":[68,125,395,235],\"text\":\"text via voice equally as fast as younger adults, but slower in\\nother input methods [49] . While some work has sought to\\ncreate guidelines for designing mobile phone interfaces for\\nolder adults, typically recommending larger buttons and less\\ncomplex interfaces [1, 18, 47] , it relies primarily on surveys\\nof older adults and their preferences, rather than analysis of\\ntheir actual app usage data.\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,235,395,603],\"text\":\"Even more difficult than understanding the app usage pat-\\nterns of older adults is understanding why these patterns\\nmight be different than younger adults. One area of study is\\ncognitive ability. Older adults with cognitive difficulties may\\nperceive technology as more difficult to use [45] , and indi-\\nviduals with higher cognitive levels are likely to participate\\nin a wider variety of web-based tasks (i.e., Internet use and\\nemail) [23] . Perceptual speed, an area of cognitive ability,\\nhas been found to be a reasonable predictor of determining\\ndesktop browsing characteristics in older adults [15] . Poten-\\ntial cultural and lifestyle causes have also been explored. In\\na qualitative survey of 14 older adults, several reasons for\\na lack of technology use emerged: they were found to be\\nuncomfortable with having to take on responsibility for tasks\\npreviously handled by trained professionals, made deliberate\\ndecisions to not use technologies when they perceived them\\nas replacing or eroding something of value to them; and sup-\\nported by the fact that opting out of technology use fits with\\ncultural expectations and thus seems acceptable, despite be-\\ning increasingly limiting in digital society [31] . In our work,\\nwe aim to quantify how much of certain app-usage behav-\\nioral differences between older adults and younger adults\\ncan be explained by cognitive ability versus other factors.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[69,614,387,631],\"text\":\"Relating Personal Characteristics to Smartphone Use\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,635,395,842],\"text\":\"Smartphones can be a window into their users' minds. Early\\nwork in this area found that smartphone usage patterns\\ncan be used to predict personality traits [12] . More recently,\\nsmartphone usage patterns have been used to predict propen-\\nsity to trust others, their college GPA, depression dynamics,\\nsleep quality, and social capital [6, 8, 48, 51, 52] . Researchers\\nhave even shown that people exhibit unique app usage signa-\\ntures, and can be differentiated just from their usage patterns\\n[56] . Other work has focused on linking real-time physio-\\nlogical traits to usage patterns. In a study of 20 participants\\nover 40 days, Murnane et al. connected app usage patterns\\nwith biological rhythms, finding that usage patterns vary for\\nindividuals with different body clock types [37] .\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,842,395,954],\"text\":\"To our knowledge, our work is the first to focus on how\\nsmartphone app usage patterns can be explained by cog-\\nnitive ability. Other work has looked at how higher-level\\nsmartphone habits may have a lasting impact on cognitive\\nfunction and development [54] . We are aware of only one pa-\\nper which predicts cognitive ability from smartphone usage,\\nand its focus was on lower level physical touch events such\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,345,747,378],\"text\":\"as tapping on the space-bar followed by the first character of\\na word, rather than on higher level app usage behavior [17] .\",\"reading_order\":7,\"tags\":[]},{\"label\":\"tab\",\"bbox\":[437,122,730,298],\"text\":\"| Dataset statistics | | # of participants | 84 | | Age range | 61-76 | | Median age | 66 | | % female | 69.0 | | Observation period | Three months, mid 2018 | | Total app launches | 494,641 | | Total unlocks | 186,968 | | Total minutes of app use | 1,396,429.9 | | Total messages | 386,573 | | Total calls | 66,371 |
\",\"reading_order\":8,\"tags\":[],\"alt_text\":\"This image is a table titled \\\"Dataset statistics,\\\" presenting key figures about a study. It lists participant demographics, including number, age range, median age, and percentage female, along with mobile usage metrics such as observation period, total app launches, unlocks, minutes of app use, messages, and calls.\",\"figure_path\":\"tables/3290605-3300398_pdf_shortened_page_2_tab_8.png\"},{\"label\":\"table_cap\",\"bbox\":[420,300,745,333],\"text\":\"Table 1: Descriptive statistics from our dataset of\\nsmartphone usage log data from older adults.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[420,390,594,406],\"text\":\"3 DATASET DESCRIPTION\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,410,747,490],\"text\":\"We conducted a three-month observational study using smart-\\nphone data collected from 84 healthy older adults, ranging\\nin age from 61 to 76, with paired neurocognitive testing. Our\\ndataset contains 494,641 app launches and 186,968 phone\\nunlocks. Table 1 lists descriptive statistics from our dataset.\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,490,747,713],\"text\":\"All participants were existing iPhone users (iPhone 5S\\nor newer) for at least one year prior to joining the study.\\nParticipants were provided with an iPhone 7 Plus to use\\nas their primary personal phone. The iPhones were instru-\\nmented with logging software that recorded lock/unlock\\nevents, apps launched/closed, and anonymized metadata for\\neach text message and call sent/received. A full backup of\\ntheir personal pre-study iPhone was uploaded to the new\\niPhones 7. We gave participants new iPhones installed with a\\nbackup of their existing iPhone for two reasons: first, logging\\napp launches on iOS requires custom software that could not\\nbe installed on participants' existing phones. Second, doing\\nso removes confounders related to individual device quality,\\nspeed, size, or battery.\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,713,747,954],\"text\":\"At the start of the study, participants took a neurocogni-\\ntive screening exam called the Cogstate Brief Battery (CBB).\\nWe discuss the CBB in more detail in Section 5 . No partici-\\npants with diagnosed cognitive impairment were included\\nin our study. Participants were recruited at medical centers\\nin several locations around the United States. Participants\\ncompleted a survey to report diagnosed medical conditions.\\nWe removed participants diagnosed with cognitive impair-\\nment, deafness, blindness, motor impairment disorders, or\\ndepression. Participants were not given special instruction\\nas to how frequently or for what purpose they should use\\ntheir iPhones. We surveyed participants for their race, edu-\\ncational attainment, and employment status. 82 participants\\nidentified themselves as White Caucasian, 1 as African Amer-\\nican, and 7 as Hispanic. 34 % had achieved a graduate degree\",\"reading_order\":13,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,127,1013],\"text\":\"Paper 168\",\"reading_order\":14,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,995,745,1013],\"text\":\"Page 3\",\"reading_order\":15,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300398/results/markdown/3290605-3300398_pdf_shortened_page_2.md"}
{"file_name": "images/test_data/3290605-3300398/3290605-3300398_pdf_shortened_page_3.png", "source_folder": "test_data", "document_id": "3290605-3300398", "source_image_path": "test_data/3290605-3300398/3290605-3300398_pdf_shortened_page_3.png", "source_json_path": "test_data/3290605-3300398/results/output_json/3290605-3300398_pdf_shortened_page_3.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,44,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"half_para\",\"bbox\":[68,125,394,172],\"text\":\"as their highest level of education, 35% an undergraduate\\ndegree, and 31% a high school diploma. 26% were currently\\nemployed full time, 13% part-time, 53% retired, and 6% other.\",\"reading_order\":2,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,172,395,333],\"text\":\"For the purposes of our evaluation, we consider an app\\nlaunch to take place each time a user actively brings a new\\napp into focus. This includes when a user taps on a notifica-\\ntion to launch its corresponding app, but does not include\\nnotifications themselves, widgets, or other background ser-\\nvices. Following convention from prior work, when app cat-\\negories are discussed, we use the categories provided by app\\ndevelopers to the Apple App Store. For Apple's first party\\napps that are built into iOS and are thus not on the Store, we\\nmanually assigned apps to categories.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,343,362,360],\"text\":\"4 HOW OLDER ADULTS USE SMARTPHONES\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,363,395,603],\"text\":\"We begin by reproducing analyses by Morrison et al. [36] .\\nAs mentioned in our Prior Work section, this is the only\\nother paper to have characterized iPhone app-usage statis-\\ntics. Their analyses consist primarily of descriptive statistics\\ncentered around number of apps used, type of apps used,\\ntotal usage by time of day, and usage duration. In the follow-\\ning subsections, we report our results and discuss how they\\ncompare. In many cases, the scope of our analysis is limited\\nby available prior work. In many cases, we go beyond what\\nhas been done in prior work in order to present a more thor-\\nough picture of how older adults use smartphones, despite\\nthe inability to directly compare those results to younger\\nadults. Thus, our work combines replication of prior work\\nwith new analysis, focusing on older adults whose usage has\\nnot been studied in this way before.\",\"reading_order\":5,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[69,614,290,632],\"text\":\"Number of apps and duration of use\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,634,394,747],\"text\":\"Participants used an average of 50.3 unique apps (std. dev.\\n22.7). This is notably lower than Morrison et al., who found\\nan average of 64.0 (std. dev. 52.5) and the Boehmer et al.\\nAndroid study which found an average of of 74.37 (std. dev.\\n44.16) [10]. We find that older adults keep apps open for an\\naverage of 121.2 seconds, nearly 37 % longer than the 88.6\\nseconds found by Morrison et al.\",\"reading_order\":7,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[69,758,210,776],\"text\":\"App usage by category\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,778,395,955],\"text\":\"In Table 2 , we aggregate app usage by category. We list the\\npercent of app launches that fall into each category along\\nwith the mean duration of app usage. In a comparison to the\\nresults reported Morrison et al. 's work (who also used the App\\nStore's categories), we find that the Communication category\\ndominates for both populations, though with older adults at\\na slightly lower 46.3 % than Morrison's 49 % . However, Social\\nNetworking apps make up only half as many of launches for\\nolder adults as they do for younger adults, at 6.8 % compared\\nto 12.6 % . When older adults do launch a Social Networking\\napp, they keep it open for nearly twice as long, 207 vs. 115\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,474,747,553],\"text\":\"seconds. Perhaps the biggest difference in a single category\\nis Weather: the category makes up only 0.3 % of launches in\\nyounger adults, but is more than quadrupled for older adults\\nat 1.3 % . Duration of Weather apps is significantly longer for\\nolder adults as well, at 53.6 seconds vs. 28.9 seconds.\",\"reading_order\":10,\"tags\":[]},{\"label\":\"tab\",\"bbox\":[423,124,740,386],\"text\":\"| Category | Avg. du ration | Pct. launches | Sample apps | | Communication | 74.2 | 0.83 | Microsoft, Mail Phone, Messenger | | Entertainment | 143.7 | 1.52 | Safari, Google Chrome | | Productivity | 70.3 | 0.60 | Gmail, Calendar, Notes, Reminders | | Photo & Video | 100.3 | 0.55 | Instagram, Camera, Photo, YouTube | | Internet Networking | 207.3 | 1.91 | QQ, Facebook, Twitter, Pinterest | | Eduation | 107.1 | 0.63 | Google, Clip, App Store, Calculator | | Game | 36.4 | 0.04 | Weirds With Friends, Candy Crush | | Imagery | 220.5 | 2.02 | Apple, Microsoft, Google, WhatsApp | | News & Fittens | 196.6 | 1.48 | Apple News, BuzzFeed, iTunes | | Utility & Fitness | 177.4 | 1.57 | Fitbit, Health, Myfitness | | Settings | 68.1 | 0.61 | Settings | | Weather | 53.6 | 0.28 | Weather, The Weather Channel | | Health | 53.0 | 0.27 | Health, Google Health, Reddit, Wallet, Cut | | Music | 159.0 | 1.19 | Apple Music, Pandora, Spotify | | Travel | 73.2 | 0.98 | Yelp, Yelp, Lyft, Expedia | | Games | 36.2 | 0.05 | Games, Amazon, Kindle | | Shopping | 116.2 | 0.69 | Amazon, Postmeist, Groupon | | Books | 168.6 | 0.59 | Kindle, Kindle, Books | | Instrumental | 103.7 | 0.59 | Twitter, Facebook, Instagram, Store, Hulu | | Lifestyle | 96.0 | 0.55 | Reddit, Real Estate, Net, Zillow | | Food & Drink | 111.7 | 0.65 | Starbucks, Office, KFC, Cava | | Other | 228.9 | 2.01 | Amazon, Google, Bing, Bing+Golf | | Reference | 237.9 | 0.11 | Bible, Dictionary | | Education | 194.4 | 0.10 | Wikipedia, Yahoo!, Baby Brain Games, iTunes | | Medical | 100.2 | 0.07 | Mychan, Heart Disease, Morgan, Reddit |
\",\"reading_order\":11,\"tags\":[],\"alt_text\":\"A table displays app usage statistics categorized by type. It includes columns for Category, Average duration, Percentage of launches, and Sample apps for each entry. The categories range from Communication and Browser to Medical and Education.\",\"figure_path\":\"tables/3290605-3300398_pdf_shortened_page_3_tab_11.png\"},{\"label\":\"table_cap\",\"bbox\":[420,389,746,439],\"text\":\"Table 2: Average duration and percent launches for\\neach category, sorted by most launched. Categories for\\neach app are taken from the App Store.\",\"reading_order\":12,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[421,564,579,584],\"text\":\"App usage by hour of day\",\"reading_order\":13,\"tags\":[]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300398_pdf_shortened_page_3_figure_014.png\",\"bbox\":[442,619,731,792],\"reading_order\":14,\"tags\":[],\"alt_text\":\"This combined line and bar chart shows two distinct trends over a 24-hour period. The top panel features two lines, one for \\\"Younger Adults\\\" (blue circles) and one for \\\"Older Adults\\\" (orange dots), both representing percentages on the y-axis. The bottom panel displays a series of blue vertical bars, some positive and some negative, aligned with the same time axis as the lines.\"},{\"label\":\"figure_cap\",\"bbox\":[421,815,746,907],\"text\":\"Figure 1: Older adults use their phones earlier in the day\\nthan younger adults. We show app launches by hour of day\\nfrom our dataset of older adults overlayed on the results re-\\nported by Morrison et al [36] for younger adults. The y-axis\\nis normalized as percent of total usage to allow for compar-\\nison between populations with different sample sizes.\",\"reading_order\":15,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,921,747,954],\"text\":\"Figure 1 shows the total number of app launches per hour\\nof the day, overlayed atop the same plot from Morrison et\",\"reading_order\":16,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,127,1012],\"text\":\"Paper 168\",\"reading_order\":17,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,995,746,1012],\"text\":\"Page 4\",\"reading_order\":18,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300398/results/markdown/3290605-3300398_pdf_shortened_page_3.md"}
{"file_name": "images/test_data/3290605-3300398/3290605-3300398_pdf_shortened_page_4.png", "source_folder": "test_data", "document_id": "3290605-3300398", "source_image_path": "test_data/3290605-3300398/3290605-3300398_pdf_shortened_page_4.png", "source_json_path": "test_data/3290605-3300398/results/output_json/3290605-3300398_pdf_shortened_page_4.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,43,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,43,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\",\"meta_pub_date\"]},{\"label\":\"half_para\",\"bbox\":[69,124,394,206],\"text\":\"al's work among younger users. We note a distinct shift in\\nolder adult's usage to earlier in the day. This may be sugges-\\ntive of different sleep patterns between the populations, as\\nsmartphone usage has been found to provide a reasonable\\nmeasure of sleeping habits [14] .\",\"reading_order\":2,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[69,216,175,233],\"text\":\"App micro-usage\",\"reading_order\":3,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,236,394,492],\"text\":\"Ferreira et al. introduced the concept of a micro-usage [21] ,\\nwhich defines short bursts of interaction with apps versus\\nlonger, more involved interactions. Typically, micro-usage is\\nfound by partitioning app usage duration data into two clus-\\nters, which look for a natural break point that can separate\\na micro-usage from a longer usage. To compute this break-\\npoint, we follow prior work by applying k-means clustering\\nto log-scale app duration data, with k=2 . The micro-usage\\nbreakpoint we found was 27.5 seconds, 28.5 % higher than\\nthe 21.4 seconds found by Morrison et al. We propose two\\npossible explanations for this difference: that older adults\\nattempt to complete more tasks during micro-uses of apps,\\nor that older adults take longer to complete the same tasks\\nduring micro-uses as younger adults. Unfortunately, we can't\\nconclusively investigate this with our data, but it may be an\\ninteresting question for future work.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[69,502,184,519],\"text\":\"Multi-app sessions\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,522,394,730],\"text\":\"Following Morrison et al., a session is defined as the period\\nof activity between unlocking and locking a phone, with the\\nrequirement that a session is ended only after the phone has\\nbeen locked for at least 30 seconds. We found that partici-\\npants open an average of 2.9 apps per session, with 67.2%\\nof sessions consisting of 3 apps or fewer, and 73.5% of ses-\\nsions consisting of 4 apps or fewer. 36.1% of sessions contain\\nonly one app. By comparison, Morrison et al. found 76.7%\\ncontaining 3 apps or fewer, 83.5% containing 4 apps or fewer,\\nand 38% containing only one app. These results suggest that\\nolder adults open more apps per session than younger adults.\\nMorrison does not report average number of apps per ses-\\nsion.\",\"reading_order\":6,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[69,741,158,759],\"text\":\"App dynamics\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[69,761,394,954],\"text\":\"Inspired by work relating to behavioral patterns during web\\nsurfing sessions [53] , we believe that deeper exploration of\\nsequential app usage within and between sessions is an in-\\nteresting and important component in understanding the\\napp-usage behavior of users. Specifically, we focused on be-\\nhavior around how users switch from one app to another.\\nThough not studied in great detail in prior literature, some\\nwork has used sequential app usage patterns as a means to\\npredict or recommend future apps [33] . We aim to under-\\nstand and visualize these patterns, and call this area of study\\n“ app dynamics ” . In this paper, we limited our scope of ex-\\nploration to sequential app usage within single sessions. To\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,124,746,363],\"text\":\"analyze this data, we created bi-grams from each sequence.\\nFor instance, if within a single session a user opened Face-\\nbook, followed by Messages, followed by Weather, we would\\nthen have the bi-grams (Facebook, Messages) and (Messages,\\nWeather). We then treat these bi-grams as a bipartite graph,\\nwhere apps being switched out of and apps being switched\\ninto are treated as disjoint sets of nodes, with edges indicat-\\ning a switch between two apps. We find 289,771 resulting\\nswitches in our dataset. We chose to remove all self-switches\\nfrom our analysis. While likely an interesting and worthy\\narea of study, self-switches are complicated by unclear in-\\ntent (e.g., someone may have accidentally closed an app or\\nbe returning from a phone call). We believe that extracting\\nmeanings from these switches would require further logging\\nof OS-level behaviors.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,364,746,491],\"text\":\"Figure 2 shows the frequency and direction of app switches\\nfor the top twelve apps in our dataset (in terms of total\\nlaunches), created from the bi-grams discussed above. The\\nthickness of the lines indicate magnitude in terms of percent\\nof switches. While for clarity of visualization we only plot\\nthe top twelve apps, we note that 72 % of sessions start with\\none of the top twelve apps, and 70.7 % of switches are into\\none of the top 12 apps.\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,491,746,714],\"text\":\"We note a few interesting trends. A sizable percent of\\nswitches from every app are into Messages. Given that Mes-\\nsages displays a notification when a new message is received,\\nand that incoming messages are often of high interest to\\nsmartphone users [50] , this behavior makes sense – users\\nstop what they are doing when they receive a message to\\nattend to that message. On the opposite end of the spec-\\ntrum, we see that Contacts is rarely switched into, even from\\nPhone. We observe that Messenger (Facebook's messaging\\nproduct) shows a strong link to Facebook, while Facebook\\nhas a much wider distribution of potential switches. Future\\nwork, particularly with a larger sample size, should work\\nto understand and visualize how these switches may differ\\nbetween users.\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,727,747,952],\"text\":\"Switch duration. Above, we studied the origin and destina-\\ntion of app switches. We now investigate the time it takes\\nfor those switches to occur. Specifically, we are interested in\\nswitches where the user chooses to move directly from one\\napp to another app, without engaging in other iOS-related\\nfeatures such as widgets along the way, which adds noise\\nto the process of switching. For this analysis, we therefore\\nfiltered to only switches where there are two sequential apps\\nlogged as coming into focus with no other process coming\\ninto focus between them (the home screen itself is not logged\\nby the OS as coming into focus when it appears). To further\\nreduce the chance that a user is not directly switching from\\none app to another (as opposed closing an app, putting their\\nphone down for a little while without looking it, and picking\",\"reading_order\":12,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[70,996,127,1013],\"text\":\"Paper 168\",\"reading_order\":13,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[706,996,745,1013],\"text\":\"Page 5\",\"reading_order\":14,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300398/results/markdown/3290605-3300398_pdf_shortened_page_4.md"}
{"file_name": "images/test_data/3290605-3300398/3290605-3300398_pdf_shortened_page_5.png", "source_folder": "test_data", "document_id": "3290605-3300398", "source_image_path": "test_data/3290605-3300398/3290605-3300398_pdf_shortened_page_5.png", "source_json_path": "test_data/3290605-3300398/results/output_json/3290605-3300398_pdf_shortened_page_5.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[68,44,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,43,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_pub_date\",\"author_affili\"]},{\"label\":\"fig\",\"text\":\"\",\"figure_path\":\"figures/3290605-3300398_pdf_shortened_page_5_figure_002.png\",\"bbox\":[169,129,644,600],\"reading_order\":2,\"tags\":[],\"alt_text\":\"This is a colorful Sankey diagram illustrating flows between two sets of applications or functions on a device. On the left are source categories like Messages, Phone, and Mail, which connect via multiple colored ribbons to destination categories such as Messages, Mail, and Phone on the right. The width of each ribbon indicates the magnitude of the flow between specific source and destination pairs.\"},{\"label\":\"figure_cap\",\"bbox\":[68,612,746,658],\"text\":\"Figure 2: Frequency and direction of app switches, as percent of total switches, for the top twelve apps in our dataset. Messages\\nis the most frequently switched into app, making up 31 % of switches. We see clear connections between some apps, such as\\nCamera to Photos and Messenger to Facebook.\",\"reading_order\":3,\"tags\":[]},{\"label\":\"half_para\",\"bbox\":[68,679,394,727],\"text\":\"it back up again), we filtered to switches that took at most 30\\nseconds. This left us with 120,721 switches. We found that a\\nswitch takes an average of 10.79 seconds.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[68,739,185,757],\"text\":\"Messages and calls\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,761,394,857],\"text\":\"We found that our users send and receive an average of 37.2\\nmessages and 5.1 calls per day. While we are not aware of\\nany iPhone-specific studies reporting the messaging and\\ncalling behavior of younger adults, a platform-agnostic Pew\\nstudy found that teenagers send and receive a mean of 67\\ntext messages per day [32] .\",\"reading_order\":6,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,868,353,900],\"text\":\"5 EXPLAINING BEHAVIORAL DIFFERENCES\\nTHROUGH COGNITIVEABILITY\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,904,394,952],\"text\":\"We provide evidence above that older adults display notably\\ndifferent app usage patterns than younger adults. We now\\nask: how can these differences be explained? As mentioned\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,679,746,792],\"text\":\"earlier, in this paper we focused specifically on cognitive\\nfunction. We aim to quantify how much of the difference\\nbetween usage patterns of younger adults and older adults\\ncan be explained by cognitive decline. To do this, we defined\\na group of cognitively young older adults and investigated\\nhow these users differ from younger adults, as well as from\\ncognitively normal adults.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"sec_2\",\"bbox\":[421,803,633,821],\"text\":\"Cogstate Brief Battery Assessment\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[421,825,746,873],\"text\":\"To answer this question, we make use of our app usage\\ndata in combination with cognitive test results from each\\nparticipant.\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,873,746,953],\"text\":\"During study enrollment, the CogState Brief Battery as-\\nsessment (CBB) was administered to each participant. The\\nCBB consists of a simple reaction time task (Detection task),\\na choice reaction time task (Identification task), a one-back\\nworking memory task (One-Back task), and a continuous\",\"reading_order\":12,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,127,1012],\"text\":\"Paper 168\",\"reading_order\":13,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[705,995,745,1012],\"text\":\"Page 6\",\"reading_order\":14,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300398/results/markdown/3290605-3300398_pdf_shortened_page_5.md"}
{"file_name": "images/test_data/3290605-3300398/3290605-3300398_pdf_shortened_page_6.png", "source_folder": "test_data", "document_id": "3290605-3300398", "source_image_path": "test_data/3290605-3300398/3290605-3300398_pdf_shortened_page_6.png", "source_json_path": "test_data/3290605-3300398/results/output_json/3290605-3300398_pdf_shortened_page_6.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,43,159,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,43,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_pub_date\",\"author_affili\"]},{\"label\":\"half_para\",\"bbox\":[68,124,395,381],\"text\":\"recognition visual learning task (Learning Task). A full de-\\nscription of each task and their validity is available in Maruff\\net al. [34] . For each individual task, Cogstate reports many\\nassociated mw performance measures, often involving accu-\\nracy and speed of performance. Depending on the task, one\\nof the raw scores is designated by Cogstate as the “ primary\\noutcome ” of the task. Cogstate also provides two composite\\nscores computed from a mixture of these raw scores and\\nnormalized across ages. The two composites are described\\nas follows: Psychomotor Function/Attention composite, in-\\ncorporating the Detection and Identification tests, relative to\\nage-matched normative data, and Learning/Working Mem-\\nory composite, incorporating the One Card Learning and One\\nBack tests, relative to age-matched normative data. Within\\nour population of healthy older adults, we find that our CBB\\nscores are approximately normally distributed.\",\"reading_order\":2,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[69,396,196,414],\"text\":\"Experimental design\",\"reading_order\":3,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,416,395,593],\"text\":\"To determine whether differences in app usage patterns be-\\ntween younger adults and older adults can be explained by\\ncognitive function, we designed a quasi-experiment. Quasi-\\nexperiments are frequently used to estimate causal relation-\\nships in health-related observational studies where random-\\nized controlled studies would otherwise be infeasible, un-\\nethical, or impossible [28] . In our case, random assignment\\nwould be unethical and likely impossible since it would re-\\nquire recruiting a group of older adults with equal cognitive\\nability and then intentionally inducing cognitive decline to\\na random subset of those participants.\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,593,395,881],\"text\":\"For our quasi-experiment, we employed a posttest-only\\nnonequivalent control group design [13] . We created two\\ngroups. The first group consisted of older adults with the\\ncognitive ability of a typical younger adult. We denote this\\ngroup as YC. To determine which participants to assign to\\nthis group, we relied on normative data provided by Cogstate\\non their CBB test, which indicates the scores expected of a\\nhealthy younger adult. We selected all older adults in our\\nstudy whose CBBs scores are within one standard deviation\\nof the normative data, and assign them to the YC group.\\nWe then created a matched group, NC, from the remaining\\nparticipants, using an almost-exact matching strategy [44] ,\\nmatching on the age covariate. This is important because age\\nis known to be correlated with cognitive decline and, as we\\ndemonstrated earlier, app usage patterns. We created these\\ngroups for the two different composites mentioned above.\\nAfter creating our groups in this way, we had 16 participants\\nper group.\",\"reading_order\":5,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[68,897,116,914],\"text\":\"Results\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,918,394,951],\"text\":\"For each metric reported in the \\\"How Older Adults Use Smart-\\nphones\\\" section, we report the mean value from our YC and\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,124,747,317],\"text\":\"NC groups along with statistical significance from a Mann-\\nWhitney U test. We also report Pearson correlation values\\nfor each metric continuously over the entire dataset (not\\njust the matched users in the YC and NC groups). Table 3\\nshows the results from our quasi-experiment. For each app\\nusage metric, to test whether our result may be confounded\\nby socio-economic factors, we also ran an ANCOVA analy-\\nsis to look for a main effect of race, educational attainment,\\nor employment status on app usage patterns, after control-\\nling for cognitive scores. We do not find significant main\\neffects, meaning we don't find evidence these covariates are\\nconfounding the results of our quasi-experiment.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,329,602,347],\"text\":\"Quasi-experiment Discussion\",\"reading_order\":9,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,349,747,493],\"text\":\"The results of our quasi-experiment indicate that a surpris-\\ningly large amount of the difference in app usage behavior\\nbetween older adults and younger adults can be explained by\\ncognitive ability. In this section, we discuss the results from\\neach app-usage behavior tested in the experiment. We found\\nthat cognitive ability can explain over 67 % of the difference\\nbetween older adults and younger adults in terms of number\\nof apps used, over 76 % of the difference in duration, and 79 %\\nof the difference in micro-usage breakpoint.\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,501,747,694],\"text\":\"Number of apps used. We found that number of apps used\\nvaries significantly with the Psychomotor Function/Attention\\ncomposite, but not with Learning/Working Memory compos-\\nite. YCs in Psychomotor Function/Attention use 59.5 apps\\non average, while NCs use only 47.9. Compared to older\\nadults' overall mean of 50.3 reported earlier, Psychomotor\\nFunction/Attention can explain over 67 % of the difference\\nfrom the 64.0 found for Morrison et al.'s younger adults. This\\nresult agrees with findings from psychological literature: we\\nhypothesize that number of apps used may be a proxy for\\ninformation seeking and curiosity, which has been found to\\nbe strongly linked with attention [25] .\",\"reading_order\":11,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,702,747,880],\"text\":\"App duration. Duration varies significantly with the Learn-\\ning/Working Memory composite, with YCs averaging 96.2\\nseconds compared to NCs 122.4. Learning/Working Memory\\ncan explain over 76 % of the difference between the duration\\nfor younger adults, which Morrison et al. found to be 88.6\\nseconds, and older adults overall which we found to be 121.2\\nseconds. We hypothesize that how long a user keeps an app\\nopen may be a proxy for processing speed, and prior work\\nhas found that working memory influences processing speed\\nand reading fluency [30] . Therefore, we expect that YCs for\\nthis composite would experience this faster usage duration.\",\"reading_order\":12,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,887,747,953],\"text\":\"Micro-usage breakpoint. The micro-usage breakpoint for YCs\\nin the Learning/Working Memory composite is 7.2 seconds\\nlower. We did not find a significant difference for the Psy-\\nchomotor Function/Attention composite. Compared to our\",\"reading_order\":13,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,127,1013],\"text\":\"Paper 168\",\"reading_order\":14,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[704,995,744,1013],\"text\":\"Page 7\",\"reading_order\":15,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300398/results/markdown/3290605-3300398_pdf_shortened_page_6.md"}
{"file_name": "images/test_data/3290605-3300398/3290605-3300398_pdf_shortened_page_7.png", "source_folder": "test_data", "document_id": "3290605-3300398", "source_image_path": "test_data/3290605-3300398/3290605-3300398_pdf_shortened_page_7.png", "source_json_path": "test_data/3290605-3300398/results/output_json/3290605-3300398_pdf_shortened_page_7.json", "annotation_json": "[{\"label\":\"header\",\"bbox\":[69,43,158,60],\"text\":\"CHI 2019 Paper\",\"reading_order\":0,\"tags\":[\"meta_pub_date\"]},{\"label\":\"header\",\"bbox\":[474,43,745,61],\"text\":\"CHI 2019, May 4-9, 2019, Glasgow, Scotland, UK\",\"reading_order\":1,\"tags\":[\"meta_subject\"]},{\"label\":\"tab\",\"bbox\":[101,122,711,331],\"text\":\" | Learning/Working Memory | Psychomotor Function/Attention | | Behavior | YC mean | NC mean | Pearson R | YC mean | NC mean | Pearson R | | # apps used | 47.8 | 51.0 | .038 | 59.5$^{*}$ | 47.9$^{*}$ | .256$^{*}$ | | App duration (seconds) | 96.2$^{**}$ | 122.4$^{**}$ | -.220$^{*}$ | 119.6 | 116.3 | .033 | | Micro-usage breakpoint (seconds) | 22.7$^{***}$ | 29.9$^{***}$ | -.256$^{*}$ | 28.5 | 28.9 | .039 | | Apps per session | 2.6 | 3.1 | -.032 | 3.2 | 2.9 | .095 | | Messages per day | 43.7$^{*}$ | 33.8$^{*}$ | .143 | 36.5 | 37.6 | .070 | | Calls per day | 9.9$^{*}$ | 6.4$^{*}$ | .189 | 7.1 | 7.9 | .092 | | App switch speed | 10.09$^{\\\\dagger}$ | 10.74$^{\\\\dagger}$ | -.279$^{**}$ | 10.56 | 10.62 | .099 | | Early morning pct usage | 12.54 | 10.1 | 0.228$^{*}$ | 11.46 | 10.38 | 0.27$^{*}$ | | Mid morning pct usage | 29.56$^{**}$ | 26.54$^{**}$ | 0.136 | 27.88 | 26.98 | 0.162 | | Afternoon pct usage | 33.44 | 33.91 | -.033 | 34.77 | 33.57 | .008 | | Night pct usage | 22.08$^{**}$ | 27.12$^{**}$ | -.0346$^{**}$ | 24.46 | 26.51 | -.0253$^{*}$ |
\",\"reading_order\":2,\"tags\":[],\"alt_text\":\"A table presents various behavioral metrics related to smartphone usage, comparing \\\"YC mean\\\" and \\\"NC mean\\\" groups, and providing Pearson correlation coefficients for both Learning/Working Memory and Psychomotor Function/Attention. The table contains numerical values, with some bolded and marked with asterisks to indicate statistical significance.\",\"figure_path\":\"tables/3290605-3300398_pdf_shortened_page_7_tab_2.png\"},{\"label\":\"table_cap\",\"bbox\":[67,332,747,397],\"text\":\"Table 3: Results from our quasi-experiment. Statistically significant results are highlighted in bold. Older adults\\nwho are cognitively young in Learning/Working Memory keep app open for less time and use their phones earlier\\nin the morning. Older adults who are cognitively young in Psychomotor Function/Attention use more apps. Mann-\\nWhitney U test between YC and NC columns is used to test significance ( $p < 0.05, ^{**}p < 0.01, ^{***}p < 0.001$ ).\",\"reading_order\":3,\"tags\":[]},{\"label\":\"half_para\",\"bbox\":[68,433,394,500],\"text\":\"overall older adults micro-usage breakpoint of 27.5 seconds,\\nas reported earlier, cognitive ability can explain 79 % of the\\ndifference from the 21.4 second breakpoint in younger adults\\nreported by Morrison et al. [36].\",\"reading_order\":4,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,505,395,714],\"text\":\"Categorical/apps breakdown. As in the prior section of this\\npaper, we investigated both duration and percent of total\\nusage in terms of categories. We also analyzed potential dif-\\nferences in specific apps (only the top twenty apps in terms\\nof total launches were selected). Given there are 20 apps and\\n25 categories, each with two dimensions to evaluate them on,\\nthere is not room to list each result. Rather, we chose to list\\nnone of them, as no significant correlations were found after\\ncorrecting for multiple hypothesis testing (even before cor-\\nrection, only three significant correlations were found). One\\nlikely explanation is that as we evaluated granular behaviors\\nin our relatively small dataset, noise between participants\\nbecame too great to statistically overcome.\",\"reading_order\":5,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,720,394,834],\"text\":\"Messages. We found that the number of messages varies sig-\\nnificantly with Learning/Working Memory, with YCs send-\\ning and receiving 10 messages more per day. As mentioned\\nearlier, we are not aware of comparable data from younger\\niPhone users. This result is in agreement with psychology\\nliterature, which has found that social engagement has sig-\\nnificant links with cognitive ability among older adults [7] .\",\"reading_order\":6,\"tags\":[]},{\"label\":\"para\",\"bbox\":[68,840,395,954],\"text\":\"Switching speed. We found that YCs in the Learning/Working\\nMemory composite switch apps 2/3 of a second faster than\\nNCs. One might expect to also see a difference in the Pay-\\nchomotor Function composite, given that switching apps\\nquickly likely requires some level of quick physical tapping\\ncoordination. Surprisingly, we do not see a significant result\\nhere, indicating that the likely reason older adults switch\",\"reading_order\":7,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,433,746,483],\"text\":\"apps more slowly is due to their mental capacity to retain\\nthe information necessary to complete the tasks, rather than\\nthe physical ability to tap the correct areas.\",\"reading_order\":8,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,489,747,746],\"text\":\"Hour of day. Here we analyze the percent of app launches\\nthat fall into a given time of day. Daytime sleepiness has\\nbeen found to be highly correlated with cognitive ability in\\nolder adults [39] , and we hypothesized that daytime sleep-\\niness may manifest itself as relatively fewer launches during\\nthe morning and daytime versus night. We broke down app\\nusage by time into four components: early morning (5am-\\n7am), mid-morning (8am-12pm), afternoon (12pm-5pm), and\\nnight (5pm-10pm). Our results appear to confirm this re-\\nlationship, particularly when looking at learning/working\\nmemory. While 22.1 % of usage takes place at night for YCs,\\n27.1 % takes place at night for NCs. Interestingly, this is our\\nonly result in which YCs actually behave less like younger\\nusers: earlier, we observed that younger adults tend to use\\ntheir phones far more during late night hours than older\\nadults. Figure 3 shows this difference.\",\"reading_order\":9,\"tags\":[]},{\"label\":\"sec_1\",\"bbox\":[421,756,660,773],\"text\":\"6 PREDICTING COGNITIVE ABILITY\",\"reading_order\":10,\"tags\":[]},{\"label\":\"para\",\"bbox\":[420,777,747,954],\"text\":\"We designed a novel prediction task of predicting \\\"cogni-\\ntively young\\\" older adults – predicting whether an older\\nadult has the cognitive ability of a typical younger adult.\\nTo build such a model, we leveraged insights described in\\nprevious sections. We demonstrated that despite the large\\nvariation inherent to human behavior, and the relatively\\nsmall sample size in our dataset, we can achieve up to 83 %\\nROC AUC for learning/working memory . This prediction\\ntask is designed to validate our empirical findings, and show\\nthat the results we report above can be used as generalizable\\nfeatures in a predictive model.\",\"reading_order\":11,\"tags\":[]},{\"label\":\"foot\",\"bbox\":[69,995,127,1012],\"text\":\"Paper 168\",\"reading_order\":12,\"tags\":[\"page_num\"]},{\"label\":\"foot\",\"bbox\":[705,995,745,1012],\"text\":\"Page 8\",\"reading_order\":13,\"tags\":[\"page_num\"]}]", "source_markdown_path": "test_data/3290605-3300398/results/markdown/3290605-3300398_pdf_shortened_page_7.md"}
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