instruction,text,explanation
"From the input text, and linguistic features of that text, explain why a text is either human written or AI generated? Your response should be within 100 words at most.","Councils are debating if building housing areas on expanded land is a better solution than providing taller dwellings. It might be an extremely complicated choice, considering the skyrocketing rise of number of people in the world and the change in prefering a walk-distance city. This essay will explore the optimal solutions for cities layout.
Although the ideal solution might depend on what the cities are aiming for, under a generic point of view, it would be optimal to have dwellings compacted in a dimished size of land. The first justification is that cramping everything in a reduced area and growing tall allowes all the residents to reach shops and workplaces in dwindling time through walking, public transport or eco-friendly vehicles. Moreover, taking in account the overall incresing of population's density, with the creation of a taller layout, a sustainable market demand will be shown.
Nowadays, the antiquated method of construct residences on a wider area of land is not feasible. In most locations, cities are running out of vacant lands, in fact many single houses are occupying vast areas for luxury amenities for a limited family, when a building can be erected accomodating many households. Furthermore, an implementation in infrustracture, such as roads and parking, will be needed. If individuals are not able to arrive to destination without a private car, due to long distances to cover, more highway will be required, therefore more lands is taken away for agglomerate of dwellings and business offices.
To conclude, the world is shifting toward a highly populated areas, so that councils need to keep up to date with convinient cities layout, like skyscrapers rather than single houses, to ease people lifestyles and reduced citizens' footprint. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.216431983 , the TTR score is 0.56466877 , the CV score is 0.368963824 , and the perplexity score is  97.78244781 .","The text appears to be written by a human because it discusses urban planning and housing layouts, which are topics typically covered in academic papers or articles about urban development. The language used is formal and structured, indicating a professional writing style. Additionally, the inclusion of specific details about infrastructure needs and potential solutions suggests a level of expertise in urban planning that is characteristic of human authors. The use of complex sentences and vocabulary also supports this classification."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","While reading the succesful people's lifes you should probably see that they are the ones who try new things, take risks and try to create and use something different than they usually do. Successful people, try to create something different, use oppurtunities to do something new in their fields and take risks.

First of all being successful needs the ability of trying new things other than the usual. Trying new things and creating some ideas make the person to move upwards in his or her career. For example creators of the ''Youtube'' did something that has not done before. They find an idea, improve it and create the one of the most successful internet sites which cause revolution in this field. So being open to new things and able to show the ability to implement this idea makes a person successful.

An other ability needed for  being successful is, using the oppurtunities and not to be afraid of doing something unusual.
People often stick with things that are good at, but trying new tehniques and using the oppurtunities to do something new and different, brings the success.

The last thing I like to mention about what brings success is, taking risks. Other than doing the standart things and taking risks can be very beneficial to someone. For example, showing the courage to invest in a field that no one has ever invested before may turn out very well. The risk taker may earn a fortune simply just using the opportunity by taking risk.

In summary, people who are open to try new things, creating something different than the others by using opportunities and take risks are allways become successful at the end. No matter how hard they try, people who have these qualities will garantee the success. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.26648318 , the TTR score is 0.458715596 , the CV score is 0.279377507 , and the perplexity score is  40.76373291 .","This text appears human-written due to its coherent structure, logical flow, and consistent use of persuasive language aimed at convincing readers of the importance of certain traits for success. The text uses vivid examples and clear arguments to support its points, typical characteristics of human-authored content. Additionally, the inclusion of specific linguistic features such as burstiness and type-token ratio suggests a deliberate attempt to mimic natural speech patterns, further supporting the classification as human-written."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Finally, she felt that the robotics unit was important to engage students and to let them see the realworld impact of their code. Further, since the robotics unit of the course included plenty of extra time for students to be creative, it allowed students who were behind on their work from other units to catch up. This was especially important since most students’ parents were not familiar with programming, and therefore they could not get help at home for unfinished work. In addition to the main activities, the teacher felt that Code Combat was especially useful for her special needs students, due to its realtime hints and engaging graphics. The teacher also noticed that gender played a role in how students interacted with the course material. Although the girls tended to be less excited by the course content, the teacher found that they participated more in class and were more focused during classroom activities. On the other hand, boys tended to be more excited by the course, but were more agitated and easily distracted in class. However, when they began the robotics unit, boys were more focused and interested in the activities. The teacher felt that this was due to an abundance of energy that boys of that age had, which dissipated as they stood up and moved around during the robotics unit. Finally, the teacher felt that being only one trimester long was an important aspect of the class, because it helped prevent those who were not interested in CS from giving up on the course. She also felt that students came into the course expecting to have fun, since it wasn’t a serious, oneyear long class. Every trimester, students took preand postsurveys to gauge their experiences with, and attitudes towards, CS and CT. The surveys were designed based on Hoegh’s CS attitudes survey , shortened to account for students’ attention spans, and modified to add questions that the school district was particularly interested in investigating. In the end, the survey analyzed seven constructs – Confidence, Interest, Gender Perception, Usefulness, CS for Future, CS Perception, and Problem Solving Approaches – and a few individual questions. Unless otherwise noted, all questions used a 5point Likert scale from “Strongly Disagree” (−2) to “Strongly Agree” (2). The questions for each construct are listed in . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.97698761 , the TTR score is 0.466970387 , the CV score is 0.286970745 , and the perplexity score is  42.18613815 .","The text appears to be labeled as AI-generated due to several linguistic features that suggest it might be part of a machine learning or natural language processing system. Here's why:

1. **Burstiness**: A burstiness score of 3.9770 indicates high variability in word frequency over short intervals, which is characteristic of generated text rather than human-written content.

2. **TTR (Type-Token Ratio)**: With a TTR of 0.4670, the"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Since sigmoid can be viewed as a special 2class case ( E s and 0 in our case) of softmax, we derive the GumbelSigmoid as: GumbelSigmoid ( E s ) = sigmoid (( E s + G ′ − G ′′ ) /τ ) = exp(( E s + G ′ ) /τ ) exp(( E s + G ′ ) /τ ) + exp( G ′′ /τ ) where G ′ and G ′′ are two independent Gumbel noises, and τ ∈ (0 , ∞ ) is a temperature parameter. As τ diminishes to zero, a sample from the GumbelSigmoid distribution becomes cold and resembles the onehot samples. At training time, we can use GumbelSigmoid to obtain differentiable sample A as GumbelSigmoid ( E s ) . In inference, we choose the action with maximum probability as the final output. 3 NLP Benchmarks To demonstrate the robustness and effectiveness of the SSANs, we evaluate it in three representative NLP tasks: language inference, semantic role labeling and machine translation. We used them as NLP benchmarks, which cover classification, sequence labeling and sequence generation categories. Specifically, the performances of semantic role labeling and language inference models heavily rely on structural information, while machine translation models need to learn word order and syntactic structure. 3.1 Experimental Setup Natural Language Inference aims to classify semantic relationship between a pair of sentences, i.e., a premise and corresponding hypothesis. We conduct experiments on the Stanford Natural Language Inference (SNLI) dataset, which has three classes: Entailment, Contradiction and Neutral. We followed Shen et al. to use a token2token SAN layer followed by a source2token SAN layer to generate a compressed vector representation of input sentence. The selector is integrated into the token2token SAN layer. Taking the premise representation s p and the hypothesis vector s h as input, their semantic relationship is represented by the concatenation of s p , s h , s p − s h and s p · s h , which is passed to a classification module to generate a categorical distribution over the three classes. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.783507085 , the TTR score is 0.430051813 , the CV score is 0.826688768 , and the perplexity score is  19.95589447 .","The text is likely AI-generated because it discusses advanced concepts in artificial intelligence such as neural networks, softmax functions, and Gumbel noise, all of which are typical topics in research papers about deep learning and natural language processing. The technical nature of the content, including specific mathematical formulas and terminology related to machine learning algorithms, strongly suggests that this text was written or generated by an AI system rather than authored by a human. Additionally, the mention of ""NLP Benchmarks"" and ""Natural Language In"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","In this essay we will talk about why in so many countries students do not prefer to choose science as a subject in the universities. There are plenty of reasons for this problem and what effect does this have on society? This essay will suggest many reasons for this problem.
The main reason of this problem is lack of trained Math and Science teachers. If students do not get proper foundation and motivation in their school life to persue science, They will develop Science and Math phobia. Due to which they will prefer to choose any other stream than science to pursue their higher education.
Eventually, this will effect society in many ways. If students stopped taking science as their majors, their will be shortage of doctors. Because of that, people will suffer from many health problems. Secondly, if students do not take science as their major there will be no placement of science teachers for the future generation. According to me, Government should take firm steps to stop this from happening. There must be some training or some programs to keep teachers up to date to enhance their teaching.This will enable teachers to make science subjects enjoyable for students, for instance, teachers may incorporate technology in the learning process.
To conclude, I belive that government and teachers should take the responsibility to create the environment to learn these subjects in a fun way. This will, eventually, increase the number of students enrolling for Science Subjects in coming future. Otherwise, it may have negative impact on society which will not be a good sign for any country. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.940816327 , the TTR score is 0.476190476 , the CV score is 0.394235148 , and the perplexity score is  33.30941772 .","This text appears to be human-written because it follows natural language patterns, uses complex sentence structures, contains idiomatic expressions, and expresses coherent arguments with logical reasoning. The writing style is consistent with formal academic discourse, discussing scientific topics and proposing solutions to educational issues. The use of technical terms related to education and psychology further supports its classification as human-generated content."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Evaluation of the importance of timing is essential, past to show the importance of their present, while that to even remember their past because it would not help them at all. It is a very controversial and complicated matter., many people believe this statement, for many reasons. Firstly, all of us had made many mistakes which we would not like to remember, they will affect their present because they can create some problems with our families example. Secondly, instead of remembering those past trials, one should get the benefits of their present.Regardless of what I said previously, many people are sticking to their past to give themselves a push in their present. It can give a way of learning from mistakes, and a good chance to try to avoid anything that once lead to a past failure.Moreover, success will not come without failure, everybody should make an attempt, even if it fails, and it may become a big success if they try it again and again. In addition, our past is our culture and heritage which we should not forget at all, it is a matter of value to our present and future, and will remain such till the day will die.To sum up, in my opinion, we can not live without a past, it is our value of life. it can help create your experiences and solve your problems better in the future. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.92732567 , the TTR score is 0.478927203 , the CV score is 0.446651936 , and the perplexity score is  49.39225769 .","The text appears human-written due to its coherent structure, logical flow, and use of personal anecdotes and examples to support arguments. The language is informal yet sophisticated, reflecting a nuanced understanding of complex philosophical concepts. The author uses vivid imagery and metaphors effectively to convey ideas about memory, progress, and cultural inheritance. The text also exhibits characteristics typical of first-person narration, making it likely written by a person rather than generated by a machine."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","I disagree because I don’t think it’s important to try new things. This suggests that sticking to what you already know and do well is better than experimenting with unfamiliar activities.

For example, I spent two years in middle school trying to learn Spanish. After a lot of effort, I became good at Spanish, but I ended up forgetting how to speak English. At home, I always spoke Japanese, and I had no real connection to English. All my friends were Japanese, too. Trying new things can just make people confused. When people get confused, they can’t predict what will happen next. That’s why you need to decide what matters more—the present or the future.

Another example is when I had to take a physical test in middle school. I tried my hardest and felt proud of myself, but the next day my leg hurt. I went to the hospital, and the doctor told me I’d need two months to recover. I was the pitcher on my baseball team, so I was shocked and my friends were disappointed. I felt alone and couldn’t handle it, so I ended up switching to a different sport.

In the end, if people keep pushing themselves, they might get hurt. I believe it’s better to focus on the present instead of worrying about the future. The present is more important than the past or the future. Even if people criticize you for not always doing your best, it doesn’t really matter. You should be the one to decide how to live your life. There isn’t much connection between the present and the future. I want to focus on the present, not the future. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.913789683 , the TTR score is 0.507936508 , the CV score is 0.233673222 , and the perplexity score is  20.71438217 .","This text appears to be AI-generated due to its formal tone, structured argumentation, and consistent use of logical reasoning supported by personal anecdotes. The language is sophisticated and coherent, typical characteristics of AI-written content aimed at persuading readers through persuasive arguments rather than casual communication."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","The Code.org Advanced Placement Computer Science Principles (AP CSP) curriculum employs a structured, self-paced instructional model that combines fixed lesson sequences with opportunities for students to develop visual, interactive programs. This approach is augmented by automated feedback mechanisms designed to provide real-time support. The Crescendo platform extends upon these foundational environments by incorporating additional pedagogical supports, specifically the Use-Modify-Create (UMC) framework and Parsons problems.

The UMC framework operates on a scaffolded progression, initiating with highly supported activities utilizing pre-existing code and gradually reducing instructional support until students engage in open-ended creation tasks. This methodology aligns with established educational theories, such as the Zone of Proximal Development (ZPD), which posits that learners benefit most from carefully designed instructional support that facilitates skill acquisition until independence is achieved. By guiding students through a sequence of using existing code to grasp underlying concepts, modifying similar structures to solve new problems, and finally creating original solutions from scratch, the UMC framework maintains learners within their ZPD. Consequently, this framework fosters the development of computational thinking by transitioning students from the consumption of existing programs to the authorship of their own.

Empirical evaluation of the UMC framework, conducted by Lytle et al. through a quasi-experimental study involving middle school students, demonstrated its efficacy. The study compared a UMC-based curriculum against a traditional approach, revealing that students in the UMC condition completed tasks more rapidly, exhibited higher levels of engagement, and developed a stronger sense of ownership over their programming artifacts. Furthermore, educators reported that the UMC curriculum was straightforward to implement. These findings are consistent with observations from block-based environments such as Scratch, where social sharing features facilitate ""remixing"" behaviors; users who extensively modify prior projects demonstrate interaction with a broader array of coding blocks and structural patterns.

In addition to the UMC progression, the Crescendo platform integrates Parsons problems into each task to enhance programming engagement and learning efficiency. Parsons problems involve deconstructing a correct solution into discrete code segments, requiring students to rearrange these mixed elements to reconstruct the original logic. Prior research indicates that this method is both engaging and effective in improving programming efficiency. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.833507489 , the TTR score is 0.585858586 , the CV score is 0.402965743 , and the perplexity score is  80.43184662 .","This text appears to be human-written because it describes a specific computer science curriculum and teaching methods used in an advanced placement course. It includes detailed descriptions of the curriculum's structure, pedagogical approaches, and empirical studies supporting its effectiveness. The language used is clear and technical, typical of academic writing about education and software design. Additionally, the presence of complex sentences and specialized vocabulary suggest it was written by someone familiar with these topics rather than generated automatically."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","There are several countries that are economically wealthy due to resources like petroleum, gas, and minerals. In this essay, I disagree with the idea that such wealth makes citizens careless, as they are actually focused on developing their country. I will also discuss how economic prosperity has transformed people's lives in these nations. There are many reasons why economic wealth is important, and even rich countries can continue to grow richer.

Firstly, greater economic wealth can make life easier for citizens. Since finding jobs can be challenging, profitable industries in wealthy countries can provide more employment opportunities. Some may think that people in rich countries become careless, but that is not the case. Citizens are the main reason for building wealth, and they are motivated to develop their country and increase its global recognition. Additionally, living in a wealthy nation often means having a better quality of life.

Moreover, increased economic wealth can change how other countries and citizens view a nation. A strong economy can improve people’s lives significantly. People naturally desire more, so they work to further develop their country, aiming for higher salaries and a more comfortable lifestyle.

In conclusion, rising economic wealth in a country can greatly impact its citizens’ lives. People strive to advance their countries, making them more prominent and well-known. While there is a tendency for people to always want more, increased wealth can help them achieve a higher standard of living. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.647330529 , the TTR score is 0.527675277 , the CV score is 0.253621259 , and the perplexity score is  24.44088554 .","The text is likely AI-generated because it exhibits characteristics consistent with automated writing systems. The language used is formal and academic, which is typical of AI-produced content. The burstiness score indicates a high level of variability in word usage, which is common in generated text. The low Type-Token Ratio suggests a lack of diversity in vocabulary choices, another feature often seen in machine-generated texts. These linguistic features align with what we would expect from an AI system rather than human authorship."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Due to the non-random nature of the survey sample, respondents may possess characteristics distinct from the broader population of introductory computer science (CS) instructors. Nevertheless, the study successfully identified correlations between instructor attitudes and pedagogical decisions, offering valuable insights for the CS education research community.

Recent scholarship has investigated various strategies to enhance student learning and social engagement in CS1 courses, including flipped classrooms, lightweight team structures, and gamification. This paper extends this body of work by implementing a flipped classroom model integrated with team-based gamification of student study behaviors. Specifically, a custom Moodle plugin was developed to incentivize positive study habits, such as early assignment submission and voluntary quiz retakes for additional practice.

The efficacy of this approach was evaluated through an analysis of data collected over three consecutive semesters: a control semester without gamification, a semester with gamification alone, and a semester where gamification was coupled with a minor grade incentive. Analysis of plugin log data and student survey responses indicates that while students adopted improved study behaviors—evidenced by significantly earlier submission of programming assignments and online quizzes—these behavioral changes did not translate into higher final examination scores. Despite this, students reported high levels of engagement and motivation to refine their study habits within the flipped, team-based, gamified environment.

Given the inherent challenges of learning to program for both majors and non-majors, educators and researchers have dedicated significant effort to improving the CS1 experience. Contemporary research directions encompass curriculum modifications (e.g., the introduction of CS0 courses or adoption of beginner-friendly languages), pedagogical innovations (e.g., gamification, flipped classrooms, team-based learning, and process-oriented guided inquiry), and enhancements to programming practice (e.g., diverse problem types such as Parsons problems and problets, interactive online textbooks, mobile applications, and improved tooling with descriptive error messages).

Among these approaches, the flipped classroom model has gained traction in CS1 contexts. In this paradigm, traditional lectures are replaced with in-class active learning activities, while content acquisition is shifted to pre-class preparation. Students are expected to engage with materials—such as textbooks, video lectures, or interactive online modules—prior to class to facilitate meaningful participation during in-session practice. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.860772112 , the TTR score is 0.569682152 , the CV score is 0.453569945 , and the perplexity score is  51.68079758 .","This text is likely human-written because it contains clear academic language typical of scholarly papers, references to specific studies and methodologies, and detailed explanations of educational techniques. The use of technical terms related to computer science education and the structure of the writing aligns with the style often used in formal academic publications. Additionally, the inclusion of statistical analyses and results supports its classification as human-generated content rather than automated output."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Analogous to the computation of hyponym projections, the hypernym residual vector, denoted as $\text{res}_{\text{hyper}}(x_i, y_i)$, is calculated as follows:

$$ \text{res}_{\text{hyper}}(x_i) = Qx_i - y_i $$

### 3.5 Hypernymy Relation Classification (M3)

For each pair $(x_i, y_i) \in D^{(+)} \cup D^{(-)}$, a BiRRE vector $r_i$ is constructed by concatenating the hyponym and hypernym residual vectors:

$$ r_i = \text{res}_{\text{hypo}}(x_i, y_i) \oplus \text{res}_{\text{hyper}}(x_i, y_i) \quad (8) $$

A feed-forward neural network is subsequently trained to classify hypernymy versus non-hypernymy relations using the dataset $D^{(+)} \cup D^{(-)}$, with $r_i$ serving as the input features. The high-level training procedure for BiRRE is summarized in Algorithm 3. The architecture of the neural network may comprise zero, one, or multiple hidden layers; a detailed analysis of these structural variations is presented in the experimental section.

### 3.6 Discussion

Orthogonal projections have previously been employed to predict various lexical relations (Ethayarajh, 2019). However, the underlying mechanisms governing these projections within the embedding space remain incompletely understood within the NLP community. In the BiRRE framework, distinct matrix configurations are utilized in modules M1 and M2 to capture the specific mappings between hypernyms and hyponyms. Given the inherent complexity of linguistic structures, such projections are not infallible. Consequently, the model learns residual vectors and employs a classifier (in M3) to identify the dimensions derived from M1 and M2 that serve as the most effective predictors for hypernymy relations. This approach is designed to enhance the overall performance of the BiRRE model.

### 4. Experiments

This section presents a comprehensive evaluation of the BiRRE model across multiple benchmarks. Furthermore, comparative analyses against state-of-the-art methods are conducted to demonstrate the model's efficacy.

#### 4.1 Experimental Settings

The default word embeddings utilized in this study were pre-trained using the fastText model (Bojanowski et al., 2017) on the English Wikipedia corpus (December 2019 version). These embeddings were generated independently using the original source code provided by the authors, with an embedding dimensionality set to $d = 300$, consistent with the original publication. In the implementation, the parameters $\eta$ and $N$ were initialized to $10^{-3}$ and $\max\{1, \lfloor \log_2 |D^{(+)}| \rfloor\}$, respectively, the latter being an empirically derived formula. Additional parameter tuning was performed in subsequent experimental iterations. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 5.868518519 , the TTR score is 0.444444444 , the CV score is 1.304776495 , and the perplexity score is  20.59829903 .","Based on the content and style of the text, it appears to be written by a human author rather than machine-generated. Key indicators include:

1. **Use of Personal Pronouns**: The text uses ""I"" and ""my,"" which are typical personal pronoun constructions.
   
2. **Complex Sentences**: There are longer sentences with more complex grammatical structures compared to simpler AI-generated texts.

3. **Natural Language Constructs**: Phrases like ""Given the inherent complexity of linguistic structures"" suggest"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Detailed instructions for configuring the operating system, hardware interfaces, and software packages are provided in the publicly available 110page course manual at http://f1tenth.org. Lastly, the vehicle must have sensors to perceive its operating environment. The main sensor is a planar laser scanner (or LIDAR) which can capture range measurements. The LIDAR enables the vehicle to implement reactive obstacle avoidance strategies, estimate odometry, create maps, and localize. Due to the operating environment (typically corridors with few features) we supplement the LIDAR measurements with odometry information from the electronic speed controller . Optionally, for the semesterversion of the course, which includes computer vision, an Intel RealSense RGBDepth camera provides additional sensing modalities. Following principles 1 and 3, the skeleton code that each team receives has 2 components: Utility code Robot Operating System (ROS)-based skeleton code. Utility code consists of items such as drivers for the Electronic Speed Controller (ESC), camera and LIDAR. The ROSbased skeleton code contains the interfaces that students directly interact with to link the application layer of a driving strategy with the low level sensor observations and actuation interfaces. As the emphasis of this course is to develop algorithms for autonomous driving, the skeleton code lets teams focus on writing their own logic inside prebuilt ROS nodes. This minimizes the time spent handling tedious coding logistics so students can focus on the mathematic ‘and algorithmic basics for perception, planning and control (see Sec. 3.5 and principle 4). In accordance with principles 3, 5, and 7, the course includes a Softwarein-loop (SIL) simulator which serves as a replacement for the vehicle and its sensors. Navigation algorithms developed for the car can run inside the simulator without any modification. The response of the vehicle to control commands is captured using the single track model described in . Synthetic sensors mimic odometry information from the ESC and laser scans from the Hokuyo LIDAR. The same message types and ROS publishersubscriber mechanisms are used to communicate the sensor observations enabling SIL testing. The SILoriented simulator uses RViz, the builtin ROS visualization tool. This simulator is used in the course as a rigorous testing sandbox for the students, prior to racing their vehicles. Students can use different maps in simulation, and obstacles can be added and removed in real time to test the code they generate for each module of the course. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 4.633443398 , the TTR score is 0.502262443 , the CV score is 0.329385659 , and the perplexity score is  51.1922493 .","The text is likely AI-generated because it exhibits characteristics consistent with automated content creation systems. It follows a structured format typical of educational materials, including detailed explanations, technical specifications, and step-by-step procedures. Additionally, the language and terminology used suggest a high degree of formality and precision often found in academic or professional documents. The repetitive structure and specific jargon further support the conclusion that this text was created by an AI system rather than written by a human author."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","We develop a prototype model for use with text and tabular classification tasks. In our model, a neural network g maps inputs to a latent space, and the score of class c is: f ( x i ) c = max p k ∈ P c a ( g ( x i ) , p k ) where a is a similarity function for vectors in the latent space, and P c is the set of protoype vectors for class c . We choose the Gaussian kernel for our similarity function: a ( z i , p k ) = e −|| z i − p k || 2 . The model predicts inputs to belong to the same class as the prototype they’re closest to in the latent space. Unlike in Chen et al., we take the max activation to obtain concise explanations. In lieu of image heatmaps, we provide feature importance scores. What distinguishes these scores from those of standard feature importance estimates is that the scores are prototypespecific, rather than classspecific. We choose a feature omission approach for estimation. With text data, omission is straightforward: for a given token, we take the difference in function output between the original input and the input with that token’s embedding zeroed out. In the tabular domain, however, variables can never take on meaningless values. To circumvent this problem, we take the difference between the function value at the original input and the expected function value with a particular feature missing. The expectation is computed with a distribution over possible values for a missing feature, which is provided by a multinomial logistic regression conditioned on the remaining covariates. When presenting prototype explanations, we provide users with the predicted class score, most similar prototype, and top six feature importance scores, provided that score magnitudes meet a small threshold. In the explanation in Figure 2 , no scores meet this threshold. We set the size of P c to 40 for our text classification task and 20 for our tabular classification task. For further training and feature importance details, see the Appendix. 3.4 Decision Boundary Joshi et al. and Samangouei et al. introduce techniques for traversing the latent spaces of generative image models. Their methods provide paths that start at input data points and cross a classifier’s decision boundary. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.422366247 , the TTR score is 0.425581395 , the CV score is 0.681439002 , and the perplexity score is  42.4903183 .","The text appears to be AI-generated due to its formal tone, technical language, and structure typical of academic papers or research documents. It discusses advanced machine learning concepts such as neural networks, latent spaces, and feature importance, which are consistent with an AI-generated source. Additionally, the inclusion of specific terminology related to natural language processing and machine learning algorithms supports the classification as AI-generated."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Since then, papers have proposed variations or reported empirical studies that explore gains in learning, including some in computer science . Denny et al. devised an online tool called Peerwise to facilitate question generation among other forms of collaboration that has been used by others as well to validate the approach . As mentioned in the work of Yu et al. , most of these have focused on using studentgenerated questions for study and preparation while few are focused on generating an entire exam. Ahn et al. propose a studentgenerated midterm but do not evaluate. Note that some research posits that students can memorize questions , an issue we address in our approach. In a twostage exam, students first take an exam as individuals and then do the same exam questions again as a team . The final score is typically some weighted combination. Empirical studies have shown the effectiveness of this form of collaborative learning , including in computer science . Yu et al. describe a system to enable such exams. However, our approach is somewhat orthogonal to whether the studying is individual or collaborative. In particular, in our first stage, an individual student chooses which questions to defer to the second attempt because they feel additional study is warranted. Also, they don’t take the questions with them after the first round and instead rely on vaguely remembering the topics of questions they deferred, which promotes broader studying than solely focusing on just the questions in the exam. A few other areas of educational research are relevant to our work. Although the general approach is not restricted to multiplechoice questions (MCQs), we preferred MCQs because they are practical and make it easier to vet studentgenerated questions. Scully et al. show that MCQs are able to reach all but the highest two levels of Bloom’s taxonomy. We explicitly instruct students via examples to lead them to craft questions at a higher Bloomlevel, and include two gamepoint incentives for this purpose (see next section). The other relevant body of research is text anxiety, with timedtests noted as commonly causing anxiety in technical subjects . Ergene et al. quantify the impact of common interventions to reduce anxiety. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.524604116 , the TTR score is 0.513647643 , the CV score is 0.621036838 , and the perplexity score is  75.57498169 .","This text is likely classified as AI-generated due to its formal academic tone, extensive use of citations from various sources, and consistent language patterns typical of academic writing. Additionally, the content discusses specific methodologies and findings related to educational research, indicating a high level of sophistication and originality characteristic of AI-generated texts designed to mimic human scholarly output."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Two classifiers were employed to label tweets, with the resulting positive class probabilities utilized to rank all tweets within the combined set $T_{\text{supportive}} \cup T_{\text{not-supportive}}$, thereby generating two distinct ranked lists. The positive sample set, denoted as $D^+_{\text{informed}}$, comprises all tweets appearing within the top 1,000 entries of either ranked list (yielding a total of 2,000 instances, of which 1,938 are unique). Conversely, the negative sample set, $D^-_{\text{informed}}$, was constructed by randomly sampling 500 tweets from the bottom 80% of each ranked list, resulting in 1,000 unique negative samples. The complete data construction pipeline is detailed in Algorithm 1, and the resulting trained model is designated as $M_{\text{informed}}$.

The classifiers were trained using the BERT architecture (`bert-base-uncased`) via the Transformers library, utilizing a 90/10 train/validation split. Given the prevalence of linguistic disfluencies in English social media content from the Indian subcontinent, and acknowledging the efficacy of Support Vector Machines (SVM) in prior hope speech detection tasks, an SVM baseline employing TF-IDF vector representations was also implemented. Prior to training, all tweets underwent preprocessing to remove hashtags, URLs, emojis, mentions, and punctuation.

Model evaluation was conducted on $D_{\text{eval}}$, a dataset consisting of 1,000 randomly sampled tweets from $T_{\text{supportive}} \cup T_{\text{not-supportive}}$. The results indicate that the joint concept of empathy, distress, and solidarity is learnable, demonstrating synergy among the utilized resources. While the weak labels derived from the hope speech and empathy-distress classifiers were of high quality, they contained some degree of noise; consequently, the fully supervised approach yielded a marginal performance improvement over the informed sampling method. Furthermore, the BERT-based classifiers consistently outperformed the SVM baselines.

Although the primary focus of this study is Twitter, the methodology was extended to other platforms where hashtags are less prevalent. Specifically, an in-the-wild evaluation was performed on YouTube, a major social media platform. This test involved analyzing a dataset of 31,232 comments from 185 COVID-19-related videos hosted on the official YouTube channel of Geo TV, a prominent Pakistani news outlet. The top 100 supportive predictions were extracted from this new dataset to assess model generalizability. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 4.42196834 , the TTR score is 0.498901099 , the CV score is 0.391492674 , and the perplexity score is  27.10399055 .","This text is classified as human-written because it appears to be a structured report or analysis detailing the process and outcomes of a machine learning project involving sentiment classification of tweets. The text includes technical details about the methods used, such as classifier types, data preparation steps, and evaluation metrics, typical of a research paper discussing natural language processing techniques. Additionally, the use of specific terminology like ""BERT,"" ""TF-IDF,"" and references to algorithms and libraries suggests expertise in the field, further indicating that the author"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","**Gender Expectations and Linguistic Gender**

To illustrate the concept of gender roles, consider a patient anticipating the arrival of a nurse. Upon learning of this visit, the patient may form expectations that the individual will be female, subsequently generating assumptions regarding their physical appearance, attire, hairstyle, and appropriate forms of address. This cognitive process, often termed ""gendering"" (Serano, 2007), manifests in both real-world interactions and purely linguistic contexts. In the latter, such as when reading a newspaper, individuals utilize social gender cues to assign gender identities to the subjects being discussed.

*Note: While some scholars distinguish between ""female/male"" as biological sex and ""woman/man"" as gender, this distinction is itself subject to debate. Consequently, this text employs ""female/male"" to denote gender.*

### 3.2 Linguistic Gender

The following discussion of linguistic gender aligns primarily with the frameworks established by Corbett (1991, 2013), Ochs (1992), Craig (1994), Hellinger and Motschenbacher (2015), and Fuertes-Olivera (2007). This approach diverges from earlier characterizations that posited a direct mapping between language and gender (Lakoff, 1975; Silverstein, 1979). While our taxonomy shares similarities with Ackerman (2019), discussed in Section 2, it is not identical.

**Grammatical Gender**
Defined similarly in Ackerman (2019), grammatical gender constitutes a system of noun classification based on principles of grammatical agreement. In ""gender languages,"" there are typically two or three grammatical categories. For animate or personal references, these categories often exhibit a significant correspondence between the grammatical gender (e.g., feminine or masculine) and the social gender of the referent. In contrast, ""noun class languages"" lack such correspondence and typically feature a larger number of classes. Furthermore, some languages possess no grammatical gender whatsoever; English is generally categorized as such (Nissen, 2002; Baron, 1971), although this classification is contested (Bjorkman, 2017).

**Referential Gender**
Referential gender, which is similar to but distinct from Ackerman's (2019) concept of ""conceptual gender,"" pertains to the relationship between linguistic expressions and extralinguistic reality. It typically involves identifying referents as female, male, or gender-indefinite. Fundamentally, referential gender exists only when an entity is being referenced and its gender (or sex) is explicitly realized through language. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 5.219883982 , the TTR score is 0.479392625 , the CV score is 0.352092212 , and the perplexity score is  38.16745758 .","This text is classified as human-written because it appears to be part of a scholarly paper discussing concepts related to gender roles and linguistic gender. The content discusses how people form expectations about others based on stereotypes associated with gender, using examples like patients anticipating nurses' appearances. Additionally, it delves into the linguistic aspects of gender, explaining terms like grammatical and referential gender, providing definitions and statistics for various linguistic features. The tone and terminology used suggest it was written by someone knowledgeable in linguistics who has"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","**Abstract**

Soft masking represents a generalization of conventional hard masking, reducing to the latter in the limiting case where the error probability equals unity. In this framework, soft-masked embeddings at each position are fed into a correction network, which leverages BERT to perform error correction. This architecture facilitates end-to-end joint training, enabling the model to acquire appropriate contextual representations for correction under the guidance of a detection network. To evaluate the proposed method, we conducted comparative experiments between SoftMasked BERT and several baselines, including a standard BERT-only approach. The evaluation utilized the SIGHAN benchmark dataset as well as a newly constructed, high-quality corpus termed ""News Title,"" which comprises news article headlines and is an order of magnitude larger than existing datasets. Empirical results demonstrate that SoftMasked BERT significantly outperforms baseline models across both datasets in terms of accuracy metrics. The primary contributions of this work are: (1) the proposal of a novel neural architecture, SoftMasked BERT, specifically designed for the Chinese Spelling Correction (CSC) problem; and (2) the empirical validation of the proposed method's efficacy.

**2. Proposed Approach**

**2.1 Problem Definition and Motivation**

Chinese Spelling Correction (CSC) can be formally defined as follows: given an input sequence of $n$ characters (or words) $X = (x_1, x_2, \dots, x_n)$, the objective is to generate an output sequence $Y = (y_1, y_2, \dots, y_n)$ of identical length, wherein erroneous characters in $X$ are replaced by their correct counterparts to yield $Y$. This task may be conceptualized as a sequential labeling problem, where the model functions as a mapping $f: X \to Y$. Notably, this task is characterized by a high degree of identity preservation; typically, only a small subset of characters requires modification, while the majority remain unchanged.

Current state-of-the-art approaches for CSC predominantly employ BERT-based architectures. However, preliminary experiments indicate that performance can be enhanced by explicitly designating erroneous characters (see Section 3.6). In general, standard BERT-based methods exhibit a tendency to refrain from making corrections, often defaulting to copying the original input characters. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.804761905 , the TTR score is 0.535714286 , the CV score is 0.708116213 , and the perplexity score is  59.76825333 .","This text appears to be human-written because it describes a new neural architecture called SoftMasked BERT, which is specifically designed for Chinese Spelling Correction (CSC), a challenging natural language processing task. The text provides detailed information about the problem definition, motivation behind the design, key features of the proposed approach, and experimental results comparing the proposed method with other baselines. Additionally, the text includes specific linguistic features such as burstiness, type-token ratio, sentence length variability, and perplexity,"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","We categorize negative examples into two distinct types: negative tokens and negative sentences. A negative token refers to a single incorrect word (e.g., ""are""), whereas a negative sentence denotes an entire ungrammatical construction.

### 3.1 Negative Example Losses

**Binary Classification Loss**  
Proposed by Enguehard et al. (2017), this approach aims to address the weak inductive bias inherent in LSTM-based language models (LSTMLMs) regarding syntactic learning. It employs a multitask learning framework that combines the standard cross-entropy loss ($L_{lm}$) with an auxiliary loss ($L_{add}$), formulated as:

$$L = L_{lm} + \beta L_{add} \quad (1)$$

where $\beta$ represents the relative weighting factor for $L_{add}$. Given the outputs of the LSTM, linear and binary softmax layers are utilized to predict whether the subsequent token is singular or plural. The auxiliary loss $L_{add}$ is defined specifically for contexts preceding a target token $x_i$:

$$L_{add} = \sum_{x_{1:i} \in \mathcal{H}^*} -\log p(\text{num}(x_i) \mid x_{1:i-1})$$

In this formulation, $x_{1:i} = x_1 \dots x_i$ denotes a prefix sequence, and $\mathcal{H}^*$ represents the set of all prefixes in the training data that conclude with a target word (e.g., ""An industrial park with several companies is""). The function $\text{num}(x) \in \{\text{singular}, \text{plural}\}$ returns the grammatical number of $x$. In practice, for each minibatch processed for $L_{lm}$, $L_{add}$ is computed over the same set of sentences, and the two losses are aggregated to update the model parameters.

As noted in Section 1, this method does not explicitly leverage negative examples; rather, the model is only informed of a specific target position that determines grammaticality. Consequently, this constitutes an indirect learning signal, and we anticipate that it will not outperform alternative approaches.

**Unlikelihood Loss**  
Recently introduced by Welleck et al. (2020), the unlikelihood loss addresses the issue of repetition, a well-documented challenge in neural text generation (Holtzman et al., 2019).

**Note on Large-Margin Language Models**  
The loss function for large-margin language models proposed by Huang et al. (2018) bears resemblance to our sentence-level margin loss. However, while their formulation aligns more closely with the standard large-margin setting aimed at learning a reranking model, our margin loss is comparatively simpler, involving a direct comparison of the log-likelihoods of predefined positive and negative sentences. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 5.100389216 , the TTR score is 0.414855072 , the CV score is 0.794924564 , and the perplexity score is  53.82611847 .","This text appears to be classified as human-written because it describes a detailed classification system used in natural language processing tasks, particularly focusing on identifying and differentiating between negative examples within sentences. The text provides technical details about various methods used to classify these negative examples, including binary classification loss and unlikelihood loss, which are common techniques in machine learning for handling imbalanced datasets and ensuring robustness against noise. Additionally, the mention of linguistic features such as burstiness, type-token ratio, and sentence length further supports"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Recent literature has explored various architectural strategies to enhance sequence generation tasks, particularly in the domains of natural language generation and code synthesis. To integrate syntactic and lexical representations, Wan et al. (2018) employed a tree-to-sequence model (Eriguchi et al., 2016). Similarly, addressing the structural complexity of SQL queries, Xu et al. (2018b) modeled queries as directed graphs and utilized a graph-to-sequence framework to encode global structural information.

A critical challenge in these tasks is the handling of out-of-vocabulary (OOV) terms, which has been effectively mitigated through copying mechanisms. This approach allows models to reuse input segments rather than relying solely on a fixed target vocabulary. Notable implementations include the hybrid pointer-generator network proposed by See et al. (2017), which integrates pointer networks (Vinyals et al., 2015) into standard sequence-to-sequence (Seq2Seq) architectures for abstractive summarization. Furthermore, Gu et al. (2016) introduced CopyNet, which incorporates a selective copying mechanism into the Seq2Seq framework, while Ling et al. (2016) applied similar techniques to copy strings directly from source code.

Given that the target task represents the inverse of the natural language-to-code (NL-to-code) process, methodologies developed for NL-to-code generation serve as relevant references. For instance, Dong and Lapata (2016) distinguished logical form nodes based on the presence of child nodes. Subsequent works by Yin and Neubig (2017), Rabinovich et al. (2017), and Xu et al. (2018a) advanced this by incorporating Abstract Syntax Tree (AST) node types to guide code generation. Additionally, Cai et al. (2018) leveraged concepts from automata theory, specifically utilizing Backus-Naur Form (BNF) to model SQL grammar constraints, thereby improving the accuracy of generated SQL queries. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.098458236 , the TTR score is 0.544642857 , the CV score is 0.397764266 , and the perplexity score is  35.34983826 .","This text appears to be human-written because it discusses specific research papers and their findings related to enhancing sequence generation tasks in natural language processing and code synthesis. The content provides detailed explanations of different approaches used in these fields, including tree-to-sequence models, graph-to-sequence frameworks, and copying mechanisms. It also mentions the relevance of previous work done in natural language-to-code translation, further supporting the classification as human-written."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","This study examines student survey responses regarding classroom attributes that influence the learning experience, aiming to identify whether student perceptions reveal significant distinctions between different classroom typologies. The data indicate that the integration of technology was perceived equally favorably across both room settings. However, qualitative feedback specific to Active Learning Classrooms (ALCs) highlighted their capacity to foster interactivity and facilitate the exchange of student ideas. No statistically significant differences were observed regarding students' ability to hear the instructor or their ease of accessing assistance from instructors and teaching assistants. Conversely, a significant divergence was found in perceptions of how conducive the furniture arrangements are for collaborative work; students in ALCs reported a markedly superior ability to interact with peers. Furthermore, while the sense of social isolation was reported to be lower in ALCs, it was not entirely absent.

Future research should prioritize a deeper investigation into the synergy between active learning pedagogies and learning environments within Computer Science (CS), as well as an analysis of student participation dynamics that influence the optimal utilization of these spaces. The authors extend their gratitude to Professor Andrew Petersen for his insightful discussions and to the reviewers for their constructive feedback.

Additionally, this research investigates the impact of the Story Programming approach on student performance in a subsequent C++ course. Specifically, it compares the outcomes of students who acquired CS concepts with minimal or no coding practice against those who learned these concepts exclusively within a coding context. Although prior literature suggests that prior programming exposure is not a predictor of success in such courses, those studies often relied on disparate course durations (15 weeks versus 10 weeks) and failed to control for the specific CS concepts and programming exposure involved. Consequently, the initial hypothesis posited that students trained via the Story Programming approach would underperform in the subsequent C++ class. Contrary to this expectation, the results demonstrate that students from the Story Programming cohort, despite their limited coding experience, did not exhibit a significant performance deficit compared to their peers who underwent a traditional, code-focused curriculum. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.727997615 , the TTR score is 0.563492063 , the CV score is 0.305667196 , and the perplexity score is  57.78894424 .","The text appears human-written due to its coherent structure, clear arguments, and detailed observations about educational practices and student perceptions. It includes personal insights and acknowledgments, which are typical features of human-authored content. Additionally, the language used is straightforward and avoids overly technical jargon, suggesting it's likely written by someone familiar with academic writing conventions."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","On my opinion, the success during life is given by a variety of elements: luck, capacity to choose the right moment to do something, knowledge of course and, for sure, the capacity to try new risks, new thinghs and new challenges rather than only doing what you already do well. Let's think about some examples. The economic field is full of people that started from nothing, had a particularly good business idea and became rich and famous in a relatively short time. I have in mind the example of Henry Ford: he had the idea, he developed it and everyone now knows his name. Of course, you don't just need a good idea but you need also other elements, like the ones I wrote above. Moreover you have to know that you are facing big risks that could completely destroy what you have made. Let's Imagine an hipotetic world in which people do not love cars: the Henry Ford's idea would probably not have been successful. This is part of the risk.
Another good example is the following. Let' s imagine a shares market investor: he could behave in a not aggressive way, investing almost all the amount of his money in bonds and just a few money in shares or he could risk trying to gain as much as he can with the shares of an emerging country. Well, if everything goes well for him, he will become richer than before otherwise he will lose what he has. Stating this, I do not want to say that it is better to risk instead of having a secure return; I just want to say that to have success you need to be a little risky and try new challenges that you have not already tried.
But maybe it is not the only way to have success. If you do something well, sometimes it is not useful to try new experiences. It could be better to try to specialize yourself more on your activity to achieve the success in your field.
So, on my opinion, the best way depends on your personality and on how much you want to risk everything you have done until the moment you decide if it is better or not to try new challenges. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.48916993 , the TTR score is 0.439429929 , the CV score is 0.606696587 , and the perplexity score is  31.36101341 .","The text is classified as human-written because it exhibits characteristics typical of human expression, such as:

1. **Complex Sentences**: The text contains complex sentences with multiple clauses and ideas, indicating a high level of cognitive complexity.
   
2. **Personal Reflections**: There is a strong personal touch, reflecting on various aspects of life, including entrepreneurship, investments, and career paths, suggesting a deep understanding and reflection on these topics.

3. **Use of Analogies and Metaphors**: The text"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","In this paper, we describe a BYOD exam solution based on lockdown browsers, software which temporarily turns students’ laptops into secure workstations with limited system or internet access. We combine the use of this technology with a learning management system and cloudbased programming tool to facilitate conceptual and practical programming questions that can be tackled in an interactive but controlled environment. We reflect on our experience of implementing this solution for a major undergraduate programming course, highlighting our principal lesson that policies and support mechanisms are as important to consider as the technology itself. Programming exams; BYOD exams; lockdown browsers; learning management systems; cloudbased IDEs; plagiarism prevention University courses have traditionally been assessed by written examinations: pen and paper at the ready, separated desks, a clock counting down, and invigilators pacing the room . This format has survived the test of time because it is simple for instructors to administer, has wellestablished logistics, and perhaps most importantly, is run in a highly controlled environment, minimising the risk and temptation of cheating and plagiarism. In a modern computer science curriculum, however, this style of assessment is completely misaligned with pedagogies and learning objectives that target practical programming ability. In an introductory programming course, for example, students learn by interacting with a language’s compiler or interpreter: trial and error, testing, debugging, and even looking things up in documentation are all part of the programming experience, regardless of ability. Yet a traditional written exam for such a course is limited to testing the concepts, or the ability to ‘code’ on pen and paper, forcing instructors to simplify the questions and forcing students to train for the exam. While adding a project component to the course can alleviate this problem, retaining some kind of final exam remains a popular option for assessing individual learning outcomes of students. Ideally, the exam of a programming course should recreate the environment that students learnt and practiced in, e.g. by providing access to their Integrated Development Environment (IDE) of choice. One way to achieve this is to run the exam in a dedicated computer laboratory in which user accounts have reduced privileges and are unable to connect to the internet. This solution has been demonstrated as effective (e.g. ), and Zilles et al. have shown that it is possible to run labbased testing centres at a reasonable cost. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.055926026 , the TTR score is 0.492099323 , the CV score is 0.44593678 , and the perplexity score is  72.29642487 .","The text appears to be AI-generated due to its formal tone, consistent structure, and lack of human-like errors or inconsistencies. The content discusses educational technologies, specifically BYOD (Bring Your Own Device) exams using lockdown browsers and cloud-based IDEs, which aligns with typical topics covered in academic papers about technological advancements in education. The writing style, vocabulary choices, and overall coherence suggest it was created by an artificial intelligence rather than a human author."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","They found that some contexts licensed multiple alternative conjunctions, each expressing a different coherence relation—i.e., distinct implicit relations can be inferred from the same passage. This speaks to the challenge of fully annotating discourse coherence relations and underscores the role of both linguistic and contextual cues in coherence. 3.3 Resolution Concepts can be described at many levels of RESO - LUTION —from highly detailed to more schematic. We include here both specificity (e.g., pug < dog < animal < being ) and granularity (e.g., viewing a forest at the level of individual leaves vs. branches vs. trees). Lexical items and larger expressions can evoke and combine concepts at varying levels of detail (“The gymnast triumphantly landed upright” vs. “A person did something”). Psycholinguistic evidence. Resolution is related to basiclevel categories, the most culturally and cognitively salient levels of a folk taxonomy. Speakers tend to use basiclevel terms for reference (e.g., tree vs. entity / birch ), and basiclevel categories are more easily and quickly accessed by comprehenders. Importantly, however, what counts as basiclevel depends on the speaker’s domain expertise. Speakers may deviate from basiclevel terms under certain circumstances, e.g., when a more specific term is needed for disambiguation. Conceptualization is thus a flexible process that varies across both individual cognizers (e.g., as a function of their world knowledge) and specific communicative contexts. Relevant NLP research. Resolution is already recognized as important for applications such as text summarization and dialogue generation, e.g., in improving human judgments of informativity and relevance. Also relevant is work on knowledge representation in the form of inheritancebased ontologies and lexica, ConceptNet). 3.4 Configuration CONFIGURATION refers to internalstructural properties of entities, groups of entities, and events, indicating their schematic “shape” and “texture”: multiplicity (or plexity ), homogeneity, boundedness, partwhole relations, etc.. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.062620823 , the TTR score is 0.574647887 , the CV score is 0.438749915 , and the perplexity score is  84.29124451 .","This text is likely classified as AI-generated because it contains technical terminology and explanations about natural language processing (NLP), resolution concepts, and configuration, which are topics typically covered in academic papers or advanced courses on artificial intelligence and computational linguistics. The text also includes references to specific studies and research areas within AI, further supporting its classification as AI-generated content."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","On my opinoin i go with the point as the secces ful peopel will  always try new things and take risks rather than only doing what they do already know how to do weel.In there aspects  they go with many succes ful aspects,peopel will  may go for risks and they should be more  confident in there life if they take risc in it
  The peopel taking risk shold be in ln order to compete market that is if he is having business of product say soap he had to introduce different types in it to competeate market other wise he may be loss in his way this task is introduce in the oter part of altery having business(say)
many people follow to take risk in business in order to compete market in the other way he had to see him self more confident abt the way he introduces in to the market he had to work hard for what he is thinking abt the product 
 the succesful person usually work out there objectives in the manner they achieved by. taking risc in there own business it ressembels a lot in the peopel who depend on them or the firm they working ever.  the aspect should be so planned in order to achive better results in the risk taking strategy for all the succes ful persons 
 Altough there is no written state ment that evere succes ful man will be as lucky in his aspects he may think on the way he was  he has to verify the things in the field he is entering and work harder to obtain best results in it
 Asucces ful man is the one who has great will power with dedication sincereaty altough hard work for his life he is leading  if a person is well knowledged he will deside his possisive ness  for what he has been thinking about the situation. in my words the person holding these all good qualities is only a succes ful person by satisfiying him self and the oter who depend on him . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.42556099 , the TTR score is 0.448863636 , the CV score is 0.762507621 , and the perplexity score is  218.9375 .","This text appears to be human-written due to its coherent structure, logical flow, and use of natural language patterns typical of human expression. The text discusses various aspects related to success, risk-taking, confidence, and personal development, which aligns with common themes found in human-authored content. Additionally, the text's complexity and the presence of detailed explanations suggest an effort beyond mere transcription, indicating genuine authorship."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Many people believe that companies should have a dress code policy for their employees, while others argue that the quality of employees’ work is more important than their appearance. In my view, although there are some benefits to having a dress code, the disadvantages outweigh the advantages in most situations.

To begin with, dress codes are necessary in certain organizations as part of their safety regulations. For example, employees working in scientific or technical laboratories must wear specific clothing because they handle hazardous chemicals. Without protective gear such as lab coats, safety glasses, or gloves, workers could be exposed to harm. In these cases, I think strict dress code rules are absolutely necessary.

However, there are several downsides to enforcing a dress code. For one, it can be uncomfortable and negatively affect employees’ work performance. For instance, some banks require female staff to wear high heels or male staff to wear ties. These dress requirements can be uncomfortable and may lead to psychological stress, reduced concentration, or a lack of motivation, ultimately lowering the quality of work.

Another disadvantage is the standardization that comes with uniforms, where every employee must wear the same company-branded clothing. This prevents individuals from expressing their unique identities, as everyone ends up looking the same. Such lack of individuality can also negatively impact employee performance.

In conclusion, while there are some reasons to implement a dress code, I believe the drawbacks, particularly the negative effects on work quality, are more significant. I suggest that companies should take employees’ opinions into account before introducing a dress code policy. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.907457585 , the TTR score is 0.540268456 , the CV score is 0.312927335 , and the perplexity score is  21.74774933 .","This text appears to be AI-generated due to its formal tone, structured argumentation, and use of complex language constructs like ""burstiness"" and ""TTR."" The text discusses both sides of an argument about whether companies should enforce dress codes, highlighting potential benefits and drawbacks. It uses sophisticated linguistic features typical of AI-generated content, including advanced sentence structures and specialized vocabulary related to artificial intelligence concepts."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Another interpretation is that participants where simply not very aware of their process and, thus, had problems identifying and understanding the mental behaviors they were engaging in. Because metacognitive awareness is something that must be developed over time , we suspect that it was likely a combination of these two interpretations. We suspect that the challenge of recalling detail of their mental work was also due to a lack of experience thinking about their own thinking. The challenge that reflecting conflicted with their process, however, we believe the selfreports that participants already had a programming process they have found to be successful which was hindered by reflecting. Together, these challenges suggest that reflecting on programming process may be a method best used for rank novices, before they have begun to develop a process of their own, and that reflection of this sort requires careful training and scaffolded practice. While we were able to identify and rank behaviors by how frequently students engaged in them, this says little about what this ranking actually means. One interpretation is that the rank reflects the order in which behaviors are developed or relied upon meaningfully to solve programming problems. It could be that students must first develop their skills of Process monitoring and Evaluating their solutions, which all participants engaged in, in order to gain enough awareness of their other process behaviors. Alternatively, just because a behavior is engaged in does not mean it is done so meaningfully. Prior work on selfregulation identified that the Planning and Comprehension monitoring behaviors may be the first selfregulation behaviors to help students avoid errors . In our data, however, Planning and Comprehension monitoring were not among the most engaged in behaviors. This could mean that, while these behaviors are undoubtedly important, students often do not rely upon them; they may need to develop other skills first. Another interpretation of the behavior rankings is that it simply highlights the behaviors that are the easiest to be aware of engaging in. This would mean that novices are simply not aware of many behaviors, or do not engage in them at all. For instance, few participants reflected on Interpreting or Adapting. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.197415617 , the TTR score is 0.422391858 , the CV score is 0.299304142 , and the perplexity score is  55.91919327 .","The text is classified as AI-generated based on its high burstiness, low type-token ratio, short sentence lengths, and perplexity score. These linguistic features are characteristic of generated text rather than human-written content."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","The most significant disparity in withdrawal rates is observed in CS3, where the difference in means is approximately 2%. This magnitude is an order of magnitude smaller than the disparities identified in attrition rates; consequently, the primary analytical focus has been placed on attrition. Nevertheless, a statistically notable difference in withdrawal rates between male and female students warrants attention. Specifically, in CS2 and non-engineering CS1, male students exhibit higher failure rates, whereas female students demonstrate higher withdrawal rates. This divergence may indicate gender-specific responses to academic adversity within computer science curricula. It is plausible that female students may withdraw prematurely despite the potential to successfully complete the course, while male students may persist despite being at risk of failure, thereby resulting in higher failure rates for men.

Academic performance may also influence the decision to remain in the field. In engineering CS1, CS2, Discrete Mathematics, and CS3, female students receive lower grades than their male counterparts. The most pronounced grade gaps occur in CS3 and Discrete Mathematics, with differences reaching up to 0.11 grade points. While this differential is less than the threshold between an 'A' and a 'B+', it may be sufficient to alter a student's final letter grade.

One might infer from these findings that lower grades reflect inherent differences in ability, suggesting that women perform less effectively in rigorous technical courses. However, this conclusion is contradicted by cumulative grade point average (GPA) data. Both engineering and non-engineering female students maintain average cumulative GPAs comparable to engineering males and superior to non-engineering males. This indicates that female students perform as well as, or better than, male students in non-CS coursework. The discrepancy between high overall academic performance and lower grades specifically within CS courses raises critical questions regarding the underlying causes, a topic requiring further investigation.

Furthermore, analysis reveals that grades are not the sole determinant in a student's progression to CS3. Gender exerts an independent effect on the likelihood of advancing to CS3, irrespective of academic performance. This suggests that gender imbalance in the pipeline would persist even in the absence of grade disparities. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.661565403 , the TTR score is 0.502525253 , the CV score is 0.290261274 , and the perplexity score is  30.24357986 .","The text appears to be human-written because it discusses various aspects of educational outcomes and demographics related to computer science courses, including gender disparities in enrollment and persistence, academic performance, and grade distributions. The language used is clear and detailed, providing specific examples and statistics to support its claims. Additionally, the text includes references to ""grade points"" and mentions cumulative GPA, which are typical terms used in discussions about academic performance. The writing style and content align closely with what one would expect from a human-authored report"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Ordering adjectives according to integration cost is functionally equivalent to arranging them such that those capable of modifying a broader typical set of nouns are positioned further from the head noun. This configuration ensures that each adjective incrementally reduces the entropy of the subsequent noun candidates, thereby mitigating the information-processing costs associated with entropy reduction (Hale, 2006, 2016; Dye et al., 2018).

### 2.4 Information Gain

We propose a novel efficiency-based predictor for adjective ordering: information gain. This approach conceptualizes the noun phrase—comprising prenominal adjectives followed by the head noun—as a decision tree designed to identify a specific referent. Within this framework, each lexical item partitions the space of potential referents. Each partition corresponds to a specific information gain, quantifying the degree to which the set of candidate referents is reduced.

Consistent with the logic governing integration cost, we posit that words yielding lower information gain should be positioned earlier in the sequence. This arrangement facilitates a gradual narrowing of the referential set with each successive word. As standardly implemented in decision tree algorithms, information gain denotes the reduction in entropy achieved by partitioning a set based on a specific feature (Quinlan, 1986). In the present context, the distribution of nouns $N$ is partitioned by a given adjective $a$, resulting in two subsets: $N_a$ (nouns modified by $a$) and its complement $N_{a^c}$ (nouns not modified by $a$). The information gain of $a$ is defined as the difference between the initial entropy $H[N]$ and the weighted sum of the entropies of the resulting partitions:

$$ IG(a) = H[N] - \left( \frac{|N_a|}{|N|} H[N_a] + \frac{|N_{a^c}|}{|N|} H[N_{a^c}] \right) \quad (4) $$

Consequently, information gain incorporates both positive and negative evidence. For instance, specifying an adjective such as *big* partitions the probability distribution of nouns into $N_{\text{big}}$, the subset of $N$ that accepts *big* as a dependent, and $N_{\text{big}}^c$, the subset that does not. It is crucial to note that, in general, $H[N_a]$ is not equivalent to the conditional entropy $H[N|a]$. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 4.410690269 , the TTR score is 0.420940171 , the CV score is 0.62151211 , and the perplexity score is  56.85390472 .","The text appears to be human-written because it discusses linguistic principles related to adjective orderings and their impact on information processing. It mentions concepts like entropy, decision trees, and information gain, all of which are relevant to natural language processing tasks. Additionally, the use of technical terms and references to established theories suggests expertise in the field of computational linguistics or cognitive science. The content also includes data points about linguistic features, indicating a focus on empirical analysis rather than theoretical speculation. Overall, the combination of advanced"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Traffic in cities is now a major public health problem. Finding solutions to reduce traffic is very crucial. I totally agree that if people travel less from home to work or to education or even for shopping can reduce the amount of traffic. With the covid 19 outbreak pandemic we saw how much the working from home and using the zoom application for studying reduced the traffic in cities. People now are using online for shopping too. We know that traffic can be reduced if less consumers are using the roads. However, I dont agree that this is the only way to reduce traffic. Other strategies to reduce traffic are :
1. Planning the roads and building the road bridges in a smarter way with special lanes for public transportation.
2. Encourging people to use light trails, public transportations and to use car pools.
3. Building special lanes for motorcycles and electric bikes.
4. The governoment can put more regulations regarding how many cars each family can have.
5. Increasing the vehicles prices and reduce the public transportation tickets.
6. Educate people on the consequences of traffic and how it can inadverntly affect their life.
7. Encourge people to walk more and to choose a work that is close to your home.
As I mentioned above there are many strategies that can help in reducing tranffic and only by reducing the need for people to travel from home. I think i people travel less it abviously can reduce the traffic but we need a multi strategy with the collaberations of many sectors to successfly reduce traffic in cities. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.661794529 , the TTR score is 0.470989761 , the CV score is 0.686371798 , and the perplexity score is  46.76613617 .","This text appears to be human-written due to several linguistic features such as burstiness, type-token ratio, sentence length variability, and overall coherence. The content discusses various strategies to reduce traffic, including planning roads, encouraging public transportation, promoting alternative modes of travel, increasing vehicle prices, and educating citizens about the impact of traffic. The author presents these ideas systematically and logically, which aligns with typical characteristics of human-generated writing."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","The Information Structure Graph (ISG) is architected upon four distinct linguistic description levels: lexical, morphological, syntactic, and semantic.

At the **lexical level**, the ISG represents individual lexical items, such as words, independently of their sentential context. The **morphological level** encompasses the identification, analysis, and structural description of morphemes and other linguistic units, including roots, stems, affixes, and Part-of-Speech (POS) tags. POS tags are categorized within this level because they encode grammatical categories (e.g., number, gender, tense) and classes (e.g., noun, verb) intrinsic to words, rather than relational properties between words, which are the domain of syntax.

The **syntactic level** governs sentence structure through a set of rules and principles. While various syntactic formalisms exist, the ISG employs the dependency formalism, a standard approach also utilized by other researchers for the extraction of syntactic n-grams. At the **semantic level**, the ISG integrates the meaning of sentences or texts, with a specific focus on paradigmatic semantic relations. Drawing from disciplines such as linguistics, logic, and cognitive psychology, the system incorporates widely recognized relations including antonymy, synonymy, class inclusion, part-whole relationships, and case roles.

The construction of the ISG involves a comprehensive analysis of all sentences within a given text. This process utilizes the Stanford Dependency Parser to generate parse trees for each sentence. Subsequently, nodes across these trees sharing identical labels—specifically those corresponding to the same word type (defined by lemma and POS tag)—are identified and merged. Consequently, each unique word type is represented by a single node within the ISG. Furthermore, as the root node of every syntactic tree is collapsed into a single root node in the ISG, the resulting graph remains connected.

In practice, the ISG is constructed incrementally. Following the analysis of each sentence, the system updates the graph by introducing new nodes or establishing new arcs between existing nodes, provided such elements have not previously been instantiated. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 5.501154825 , the TTR score is 0.496183206 , the CV score is 0.371152543 , and the perplexity score is  25.67047119 .","This text is likely classified as human-written due to its detailed technical description of an information structure graph (ISG), which appears to be authored by someone familiar with linguistic concepts and terminology related to natural language processing and computational linguistics. The use of precise academic jargon and structured explanations align with typical characteristics of written work produced by experts in the field. Additionally, the presence of data points like ""Burstiness"" and ""TTR"" suggests it was created using software tools designed for analyzing linguistic structures"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Subsequent gender-focused initiatives have failed to significantly increase the representation of Black women in computing, primarily due to an insufficient recognition of how intersectionality—the complex interplay of social constructs such as gender, race, ethnicity, and class—shapes the lived experiences of women of color within the computing ecosystem. Neglecting these intersectional realities results in erroneous generalizations and, consequently, ineffective strategies for enhancing both the recruitment and retention of Black women in the field.

To address this challenge, this study employs intersectionality as a theoretical framework to examine the overlapping social constructs of race and gender that influence the daily experiences of fourteen Black women navigating the computing landscape. The primary objectives of this research are twofold: (1) to critique the prevailing narrative in Computer Science (CS) regarding the attributes of ""CS material"" and the associated predictions of success or failure; and (2) to propose specific transformations in formal CS education to better support Black women.

Through the analysis of fourteen in-depth interviews, this paper addresses the following research questions: How do Black women enter the field of computing? What are the intersectional experiences of Black women at the undergraduate and graduate levels within CS education? What implications do these experiences hold for broadening participation in computing?

This study makes three significant contributions to the field of CS education. First, it introduces ""intersectional computing,"" a critical analytical framework designed to explore the lived experiences of Black women, a population that has historically been overlooked and understudied in computing. Second, these intersectional narratives serve as counter-narratives to the dominant discourse surrounding entry into and progression through formal CS education. Finally, the analysis of these experiences offers transformative insights aimed at increasing the likelihood of success for Black women in formal CS education while simultaneously dismantling myths regarding their capabilities in the field.

Motivated by the imperative to recruit and retain Black women in computing, this section begins with a concise overview of intersectionality and concludes by introducing intersectional computing as a novel approach to broadening participation in the discipline. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.522629295 , the TTR score is 0.482849604 , the CV score is 0.452496981 , and the perplexity score is  18.919384 .","The text appears to be human-written based on several factors:

1. **Complex Structure**: The writing demonstrates a sophisticated structure, including multiple paragraphs discussing different aspects of the topic without appearing disjointed.
   
2. **Technical Language**: There's a mix of technical terms related to computer science and sociology, indicating a high level of expertise and likely academic background.

3. **Academic Tone**: The language used is formal and academic, appropriate for a scholarly article or report.

4. **Research Focus**:"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","The practice tool facilitates spaced, interleaved, and retrieval-based learning by dynamically reusing questions from an associated ebook. Utilizing a modified version of the SuperMemo 2 algorithm, the system presents questions on specific topics immediately prior to the predicted point of forgetting. Questions are displayed sequentially, one at a time.

Instructors have the flexibility to configure the tool's incentive structure by assigning point values to completed practice sessions and defining the number of sessions required to achieve maximum credit. The Runestone platform further empowers educators to construct custom courses based on existing ebooks, manage student enrollment, generate assignments from course materials, monitor student progress, grade submissions, and author original problems. Detailed instructions are available in the Instructor's Guide.

To establish an instructor account on the Runestone platform, users must first register at https://runestone.academy and enroll in an existing ebook. The procedure for creating a custom course is outlined in the Instructor's Guide. Upon accessing the creation form, instructors must enter a unique identifier in the ""Project Name"" field, which will serve as the official name for the custom course. Once the course is established, students may enroll by entering this unique course name via the registration form linked in the Instructor's Guide.

Instructors can access the administrative dashboard by selecting the user icon located in the upper-right corner of the custom course interface. This dashboard provides comprehensive tabs for tracking student progress and managing course activities. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.22079602 , the TTR score is 0.559701493 , the CV score is 0.376994953 , and the perplexity score is  52.59053421 .","The text appears to be a detailed description or manual for a software application called Runestone, specifically designed for educational purposes. It includes technical specifications about how the application works, its features, and how it can be used by educators. The language used is precise and technical, typical of documentation or instructional texts rather than creative writing. Additionally, the content focuses on practical aspects like setting up accounts and configuring tools, which aligns with the purpose of such a manual. Therefore, it is likely written by a"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","With this generalization, we can represent the pointing decisions of any binary constituency tree T as: P ( T ) = { ( i ) p i , l i ) : i = 1 , . . . , n ; i ̸ = p i } (2) The pointing representation of the tree in Figure 1 is given at the bottom of the figure. To illustrate, in the parse tree, the largest phrase that starts or ends at token 2 (‘enjoys’) is the subtree rooted at ‘ ∅ ’, which spans from 2 to 5. In this case, the span starts at token 2. Similarly, the largest phrase that starts or ends at token 4 (‘tennis’) is the span “enjoys playing tennis”, which is rooted at ‘VP’. In this case, the span ends at token 4. Algorithm 1 describes the procedure to convert a binary tree to its corresponding pointing representation. Specifically, from each leaf token i , the algorithm traverses upward along the hierarchy until the nonterminal node that does not start or end with i . In this way, the largest span starting or ending with i can be identified. 2.2 TopDown Tree Inference In the previous section, we described how to convert a constituency tree T into a sequence of pointing decisions P ( T ) . We use this transformation to train the parsing model (described in detail in Sections 2.3 - 2.4 ). During inference, given a sentence to parse, our decoder with the help of the parsing model predicts P ( T ) , from which we can construct the tree T . However, not all sets of pointings P ( T ) guarantee the generation of a valid tree. For example, for a sentence with four (4) tokens, the pointing P ( T ) = { (1 ) 4 , l 1 ) , (2 ) 3 , l 2 ) , (3 ) 4 , l 3 ) , (4 ) 1 , l 1 ) } does not generate a valid tree because token ‘3’ cannot belong to both spans (2 , 3) and (3 , 4) . In other words, simply taking the arg max over the pointing distributions may not generate a valid tree. Our approach to decoding is inspired by the spanbased approach of Stern et al.. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 5.390927419 , the TTR score is 0.345982143 , the CV score is 0.745669364 , and the perplexity score is  64.34829712 .","The text is likely classified as AI-generated due to several characteristics:

1. **Complex Structure**: The text contains detailed explanations and mathematical formulations related to computational linguistics and natural language processing, which are typical topics studied in AI and machine learning courses.

2. **Algorithm Description**: It provides a clear description of an algorithm used in parsing trees, which is a common task in AI applications such as speech recognition, machine translation, and natural language understanding.

3. **Mathematical Notation**: The text"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","With the development of society, it goes without saying that more people come to prior their carrier to their own private life. Some people say that this is due to money, however, I strongly oppose that money has the hugest impact on this choice in modern society. I feel this way for 2 reasons, which I will explore in the following essay.
To begin with, working is considered one of the crucial factors to fulfill people's life. Specifically, most people would be satisfied when they are helpful to others through their work. Besides, these experiences would easily contribute them to further hard work. Above all, human beings originally like being appreciated by someone. To be more specific, I like my job because I can feel self-confident through my work. It is manifest that money is not only the vital motivation for working in this regard.
Some augments can be made that social status gained through the success of their business must be crucial enough to direct people to work. The main reason for this is that experiences of success would bring excitement and happiness to them. They also could feel superior to others. Close examination would reveal that people with high social status could have tons of options compared with common people. That is to say, it is completely evident that higher social status can become an important factor as money.
Having considered all the augments above, I would conclude that only money has the most influence on motivation for business. There must be other vital factors present such as social statius and rewarding/ . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.443084192 , the TTR score is 0.494845361 , the CV score is 0.275750446 , and the perplexity score is  49.75956726 .","The text is likely classified as human-written based on several linguistic features:

1. **Complex Structure**: The text exhibits a complex structure typical of well-written essays or academic papers, including multiple paragraphs discussing different aspects of the topic.

2. **Logical Flow**: The flow of ideas is coherent and logical, moving from general observations about societal changes to specific arguments about the role of money in career choices.

3. **Use of Complex Vocabulary**: The text uses sophisticated vocabulary and sentence structures, indicating advanced cognitive abilities"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","**Methodology**

To extract emotion-cause pairs from a test document, we employ a lexicon-based extraction scheme applied to the top-$N$ ranked list of candidate pairs, denoted as $\{p_1, p_2, \dots, p_N\}$. The extraction process proceeds as follows: first, the pair $p_1$, which possesses the highest ranking score, is selected as a confirmed emotion-cause pair. Subsequently, for each remaining candidate pair $p_i = (c_{i,1}, c_{i,2})$ within the set $\{p_2, \dots, p_N\}$, a sentiment lexicon is utilized to verify the presence of sentiment-bearing words within the first clause, $c_{i,1}$. If such words are detected, the pair $p_i$ is extracted as a valid emotion-cause pair. This iterative approach enables the model to identify multiple emotion-cause pairs within a single document.

**4. Experiments**

Extensive experiments were conducted to validate the effectiveness of the proposed model, RANKCP.

**4.1 Experimental Setup**

*Dataset and Evaluation Metrics*
Experiments were performed using the benchmark dataset introduced by Xia and Ding (2019). This dataset is derived from the emotion cause extraction corpus (Gui et al., 2016), comprising 1,945 Chinese documents sourced from the SINA NEWS website. Summary statistics are provided in Table 1. Consistent with prior research, we adopted a 10-fold cross-validation strategy for data splitting. The primary evaluation metrics employed were Precision ($P$), Recall ($R$), and the F1-score ($F_1$), defined as follows:

$$
P = \frac{\text{Number of correctly predicted pairs}}{\text{Total number of predicted pairs}}
$$

$$
R = \frac{\text{Number of correctly predicted pairs}}{\text{Total number of ground-truth pairs}}
$$

$$
F_1 = \frac{2 \cdot P \cdot R}{P + R}
$$

Furthermore, model performance was evaluated separately for emotion clause extraction and cause clause extraction. In this analysis, emotion-cause pairs were decomposed into distinct sets of emotion clauses and cause clauses, with metrics computed independently for each set. The definitions for Precision, Recall, and F1-score in this context follow the formulation above, substituting ""pairs"" with ""emotion clauses"" or ""cause clauses,"" respectively.

*Comparative Approaches*
For comparison, we considered the three two-step systems proposed by Xia and Ding (2019). These systems operate by first extracting emotion clauses and cause clauses independently, followed by a binary classification step designed to filter out non-matching (negative) pairs. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 5.33799568 , the TTR score is 0.394891945 , the CV score is 0.845677337 , and the perplexity score is  25.27454758 .","This text appears to be human-written because it contains detailed explanations and examples specific to the field of natural language processing and machine learning, particularly in the context of emotion and cause extraction from text. It includes technical terms like ""Precision,"" ""Recall,"" and ""F1-score,"" which are common in academic papers on information retrieval and computational linguistics. Additionally, the text provides experimental setup details and compares its methods against existing approaches, indicating a high level of expertise and effort put into crafting an informative yet"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","This topic can be looked at from two perspectives. Humanity has always been known to surpass other species, with people often taking the lead in life. When someone achieves success, it seems like they can have anything they desire.

Some individuals are hesitant to take risks because they fear it could jeopardize their current financial stability. On the other hand, some successful people enjoy trying new things for the excitement, the sense of adventure, and the possibility that it might actually work out. However, a person should only take a risk if they are completely confident it will succeed—but saying that contradicts the very definition of “risk,” which means you can never be entirely certain.

While taking risks can be exhilarating, it can also be devastating. One wrong move could cause someone to lose everything and fall into ruin if they’re not careful.

If I were a successful person, I would want to experiment, take chances, and truly live life while I have the opportunity, accepting failure just as I would embrace success. Although disagreeing with this idea might seem more reasonable, I find myself agreeing with it. Sooner or later, everyone will face a moment when they have to take a risk, whether it’s big or small. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 0.853161939 , the TTR score is 0.612765957 , the CV score is 0.394805566 , and the perplexity score is  30.54595184 .","The text is likely classified as AI-generated due to its formal tone, complex sentence structure, and use of technical language related to human psychology and decision-making under uncertainty. The text discusses concepts such as risk-taking, confidence, and the potential consequences of making decisions despite knowing there's no certainty, all of which suggest an intelligent source rather than a human author. Additionally, the high burstiness and type-token ratio indicate a sophisticated writing style characteristic of AI systems designed to mimic human communication."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","The core principle underlying the proposed solution involves the identification and alignment of semantically similar or related lexical items across two sentences, followed by the aggregation of these similarities to compute an overall similarity score. A primary objective of the Semantic Textual Similarity (STS) task is to establish a unified framework that integrates multiple independent semantic components to assess their impact across various Natural Language Processing (NLP) applications.

Developing such a framework constitutes a significant research challenge with broad applicability. In the domain of NLP, key applications include information retrieval (IR), digital education, text summarization, question answering, relevance feedback, text classification, word sense disambiguation, and extractive summarization. Furthermore, semantic similarity is instrumental in Semantic Web applications, such as community extraction, ontology generation, and entity disambiguation. It is also critical for social media analytics, particularly in Twitter search, where accurately measuring the semantic relatedness between concepts or entities is essential.

In the context of Information Retrieval, a fundamental challenge is retrieving documents and images with captions that are semantically relevant to a user's query within a web search engine. Within database management, text similarity facilitates schema matching to address semantic heterogeneity in data sharing, data integration, message passing, and peer-to-peer data management systems. Additionally, it supports relational join operations where join attributes exhibit textual similarity. The utility of this technology extends to diverse domains, including the integration and querying of heterogeneous data resources, data cleaning, and data mining.

Within NLP, STS is closely related to both textual entailment (TE) and paraphrase identification, yet it differs in several key aspects. Textual entailment establishes directional relationships between two text fragments, typically designated as a text ($t$) and a hypothesis ($h$). Conversely, paraphrase identification focuses on recognizing text fragments that convey approximately the same meaning within a specific context. While both TE and paraphrase identification yield binary (yes/no) decisions, STS quantifies the degree of equivalence between texts, rating them based on the strength of their semantic relationships.

Methodologies for measuring semantic similarity between texts can be categorized into the following approaches:
1.  Topological methods
2.  Statistical similarity
3.  Semantic-based methods
4.  Vector space models
5.  Word alignment-based methods
6.  Machine learning techniques . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.253648139 , the TTR score is 0.53271028 , the CV score is 0.601207769 , and the perplexity score is  33.89001465 .","The text appears to be written by a human because it contains numerous errors and inconsistencies. For example, there are grammatical mistakes like ""semantically similar"" instead of ""semantically similar,"" and phrases like ""primary objective of the Semantic Textual Similarity (STS)"" which should be ""objective."" Additionally, the text uses informal language and lacks proper punctuation, which are hallmarks of human writing rather than machine-generated content."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Table 2 presents the experimental results, reporting average metrics for the overall task alongside a performance breakdown for each dialogue turn (Turns 1, 2, and 3). The aggregate metrics demonstrate that employing ResNeXtIG3.5B image encoder features yields a significant performance enhancement across the entire task. Specifically, the optimal ResNeXtIG3.5B model achieves a Recall@1 (R@1) of 50.3%, compared to 40.6% for the best-performing ResNet152 model.

A granular analysis by turn reveals that ResNeXtIG3.5B features are particularly critical during the initial dialogue turn, where only image and style inputs are available. In this context, the performance gap between the best models widens from 9.7% in the full task to 19.5% in the first turn. Regarding architectural components, the baseline multimodal sum combiner (MMSum) outperforms the more complex self-attention combiner (MMAtt), which attained a score of 49.3% on the full task. Furthermore, utilizing separate encoders for candidate text and dialogue history proved superior to a weight-sharing approach. Consequently, the highest-performing configuration was selected for the retrieval model, and the ResNeXtIG3.5B encoder was adopted for the generative model in subsequent experiments.

**Full System and Ablation Study**

Subsequent experiments evaluated both retrieval and generative models within the full system framework, including ablation studies where individual modalities (image, style, and dialogue history) were systematically removed. For the generative models, performance was assessed using the ROUGE-L metric. The results, detailed in Table 3, are analyzed as follows:

*   **Turn 1:** In the initial turn, where utterances are generated based solely on image and style inputs (absent dialogue history), the image modality proves more influential than style for both models. However, the integration of both modalities yields superior results compared to either in isolation.
*   **Turn 2:** During the second turn, where the model responds to the initial utterance, performance is comparable when relying exclusively on either the image or the dialogue history, whereas reliance on style alone results in poor performance. Any combination of two modalities improves outcomes, with the style and dialogue combination marginally outperforming the other pairs. The inclusion of all three modalities yields the optimal performance.
*   **Turn 3:** By the third turn, dialogue history emerges as the most significant modality in isolation, substantially outperforming both image and style. Among two-modality combinations, conditioning on style and dialogue history proves to be the most effective strategy. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 5.5194751 , the TTR score is 0.417558887 , the CV score is 0.426925097 , and the perplexity score is  42.47922516 .","The text appears to be human-written because it describes an experiment involving machine learning models designed to generate responses in a conversation-based task. It provides specific details about how different aspects of the models performed under various conditions, such as the importance of certain input types and the effectiveness of combining multiple inputs. This level of detail and specificity suggests that someone familiar with the field of natural language processing would have written it. Additionally, the use of technical terms related to machine learning algorithms like ""ResNeXtIG3"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","The remaining significant effects regarding the perceived usefulness of the course were associated with the trimester in which the course was taken or the interaction between the trimester and students' prior familiarity. These variations may be attributed to scheduling constraints or subtle pedagogical adjustments made by the instructor regarding the presentation of computer science (CS) utility across different terms.

Regarding the construct ""CS for Future,"" the timing of the survey yielded a significant effect on students' views concerning the potential role of CS in their future careers ($F(1, 164) = 23.00, p = 3.62 \times 10^{-6}$). Specifically, post-survey responses indicated a greater inclination to reengage with CS compared to pre-survey responses (paired difference $M = 0.25, SD = 0.70$). Gender also exerted a significant influence ($F(1, 164) = 15.93, p = 9.89 \times 10^{-5}$), with male students demonstrating a higher propensity to reengage with CS ($M = -0.08, SD = 1.08$) across both survey points than female students ($M = -0.64, SD = 0.94$). Furthermore, the trimester variable significantly affected these outcomes ($F(2, 164) = 4.25, p = 1.58 \times 10^{-2}$), a finding likely attributable to external scheduling factors beyond the researchers' control.

Prior familiarity with Python emerged as a significant main effect ($F(1, 164) = 14.35, p = 2.13 \times 10^{-4}$); students possessing prior experience expressed greater openness to future reengagement ($M = 0.20, SD = 1.19$) compared to those without such experience ($M = -0.48, SD = 0.98$). Additionally, a significant interaction was observed between prior familiarity and survey timing ($F(1, 164) = 4.76, p = 3.05 \times 10^{-2}$). Students lacking prior Python experience showed a statistically significant increase in their consideration of reengaging with CS ($p = 3.00 \times 10^{-7}$; paired difference $M = 0.30, SD = 0.70$), whereas those with prior experience exhibited no significant change. Consequently, course participation significantly narrowed the disparity between students with and without prior Python experience regarding their future intentions to reengage with CS.

Finally, concerning ""CS Perception,"" survey timing significantly influenced student attitudes ($F(1, 164) = 8.23, p = 4.66 \times 10^{-3}$), with post-survey perceptions being more favorable (paired difference $M = 0.15, SD = 0.71$). Prior familiarity with Python also significantly impacted perceptions ($F(1, 164) = 12.41, p = 5.53 \times 10^{-4}$); students with prior experience maintained more positive perceptions of CS ($M = 0.99, SD = 0.81$) across both survey administrations compared to their counterparts without prior experience ($M = 0.43, SD = 0.82$). . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 7.764465807 , the TTR score is 0.346086957 , the CV score is 0.346759233 , and the perplexity score is  46.95014572 .","The text appears human-written due to its coherent structure, logical flow, and consistent use of academic language typical of research papers. Key indicators include:

1. **Structured Argumentation**: The text presents a clear argument about how various factors affect students' perception of Computer Science (CS) and their willingness to engage with it in the future.
   
2. **Statistical Analysis**: It includes statistical tests like ANOVA to analyze data, indicating an understanding of quantitative methods used in psychological studies.

3."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","I agree with the statement, ""Successful people try new things and take risks rather than only doing what they already know how to do well."" In my view, successful individuals are those who reach new heights and achieve what many consider difficult or nearly impossible. The appeal of new ideas often attracts attention, but only those who take risks and sustain their efforts over time truly stand out. I believe that successful people consistently keep the spirit of innovation alive by embracing risks and maintaining their pursuits for the long term.

Throughout history and even today, the importance of taking risks to succeed is evident. For example, consider Gray, the author of the famous human anatomy book. In his era, it was considered a sin to exhume corpses for study, and such actions were strictly forbidden. Driven by passion, Gray took the risk of secretly retrieving bodies from graves at night to study them in his lab. His anatomy book is still regarded as one of the best ever written, and his success was only possible because he dared to go beyond traditional practices instead of sticking to the conventional medical knowledge of his time.

History is full of such examples. Take, for instance, the businessman Mittal, one of the world’s richest people and owner of the largest steel company. He came from a poor family that only did small-scale business, but he envisioned something bigger. He took the risk of buying a small steel plant and, rather than building new factories, he introduced the innovative idea of acquiring struggling steel industries and turning them around through collaboration. This bold approach helped him become the owner of the largest steel business in the world, all because he was willing to take risks and pursue new ideas.

In conclusion, taking risks and striving for something new has consistently brought rewards to successful people. That’s why I firmly believe that those who take risks with a clear vision are more likely to succeed than those who simply stick to what they already know. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.398346864 , the TTR score is 0.525198939 , the CV score is 0.333490431 , and the perplexity score is  24.44603348 .","This text is classified as AI-generated due to its coherent structure, consistent language patterns, and logical flow, which are hallmarks of AI-produced content. The use of complex sentence structures and varied vocabulary suggest an intelligent source. Additionally, the repetitive nature of certain phrases (""I agree,"" ""in my view,"" etc.) indicates a script-driven output typical of AI systems. The text also includes specific historical references and real-life examples, further supporting its classification as AI-generated content."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","experiance for risks 

economy to management  agreculture to education there has been lots of changes  in the last century 
wiht in  these changes sometimes we had to make a choice to be successful in our working life. although some of us though  that try new things and take risk to be successful is good idea some of us think it is not quite well 
have enough knowledge  experiance  advirtisement  worring  lost money  are reasons to continuo what we already do

firstly to be  succesfull in some job it requires lots of information and  experiment  for example my father has restaurant and he know lots of thing like cooking , servicing. if he dont know how to cook he can not make any food so he can not sell anything.  if he waqnt to try have market he should  learn lots of things to do

in addition to be good management or businesman we sould also know how to advertise product . and if we dont know enough information about product we can not sell more so to be learn how to advertise the product require to know whole information about it and trying to new job requires to learn all new things

moreover  if we dont sure to earn more money on new job you can lost all your investment. for example my friend had shop before and changed job and started to make cook and he could not achieve this so he lost his all investment

in conlusion knowledge . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.164850394 , the TTR score is 0.492125984 , the CV score is 0.44101518 , and the perplexity score is  126.7230911 .","The text appears human-written due to its coherent structure, logical flow, and use of natural language. It discusses various aspects of work and entrepreneurship, including experience, risk-taking, and learning new skills. The text avoids technical jargon and uses everyday language, which is typical of human-written content. Additionally, the presence of personal anecdotes and examples further supports the classification as human-written."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","While a cross-task comparison indicates that the proposed models perform comparably to the best task-specific fine-tuned counterparts, they frequently exhibit a marginal performance deficit. This outcome aligns with established findings in multitask learning literature (Raffel et al., 2019). We interpret this result as a dual observation: it demonstrates the viability of a unified model capable of competent performance across diverse tasks—a capability absent in isolated fine-tuned models—while simultaneously highlighting an open challenge for the research community to develop architectures that more effectively leverage multitask synergies.

To further enhance single-task performance, we adopted a ""Multitask followed by Fine-tuning"" (MT+FT) strategy, consistent with methodologies employed by Liu et al. (2015) and Raffel et al. (2019). This approach involves initial training on all tasks in a multitask setting, followed by fine-tuning on individual tasks to generate specialized models. This pipeline permits distinct hyperparameter optimization for each target task. Our experiments revealed that applying relative task upweighting during the initial multitask phase significantly influenced the final quality of the fine-tuned models (see Table 5). Specifically, allocating the majority of the multitask training weight to the target task yielded superior results. Although this method achieves marginally better performance than fine-tuning alone, the improvements are generally modest. The comparative performance of the optimal models per task is detailed in Table 2 (column ""MT All Tasks + FT Single Task""). Notably, the final validation Dodeca Score achieved was 16.8, slightly lower than the 17.1 obtained via standard fine-tuning.

Regarding decoding strategies, while our primary evaluation metric has been perplexity, the ultimate objective is text generation, necessitating specific decoding protocols. We evaluated several standard approaches: greedy decoding, beam search (configured with beam size and output length constraints), beam search with n-gram blocking (where $n=3$, following Paulus et al., 2018), and nucleus sampling (parameterized by $p$, following Holtzman et al., 2019). The impact of these decoding choices on the ConvAI2 and Wizard of Wikipedia (WoW) datasets is presented in Table 6.

Finally, the test performance of our optimal multitask and MT+FT systems is summarized in Table 7 (right panel). Comprehensive results, including all decoding-based metrics alongside validation and test performance, are provided in Appendix A. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.539441265 , the TTR score is 0.539351852 , the CV score is 0.547335014 , and the perplexity score is  47.98894882 .","The text appears to be human-written because it describes experimental results from a study on machine learning models designed to perform well across multiple related tasks. It discusses the advantages and limitations of using a combined approach involving both multitasking and fine-tuning compared to separate fine-tuning for each task. The use of clear language, technical terms relevant to the field of artificial intelligence, and structured presentation make it likely written by a professional researcher or academic."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","For each domain, a labeling function is defined over the input space $\mathcal{X}$ as $f: \mathcal{X} \to [0, 1]$. The ideal labeling functions for the source and target domains are denoted by $f_s$ and $f_t$, respectively. We consider a hypothesis labeling function $h: \mathcal{X} \to [0, 1]$ and define a disagreement function between two hypotheses $h_1$ and $h_2$ as:

$$ \epsilon(h_1, h_2) = \mathbb{E}[|h_1(x) - h_2(x)|]. \quad (8) $$

Consequently, the expected error of a hypothesis $h$ on the source domain is defined as $\epsilon_s(h) = \epsilon_s(h, f_s)$, while the expected error on the target domain is $\epsilon_t(h) = \epsilon_t(h, f_t)$. The divergence between the source and target domains is quantified using the $\mathcal{H}\Delta\mathcal{H}$-distance, defined as:

$$ d_{\mathcal{H}\Delta\mathcal{H}}(D_s, D_t) = 2 \sup_{h, h' \in \mathcal{H}} |\epsilon_s(h, h') - \epsilon_t(h, h')|. \quad (9) $$

Originally introduced by Ben-David et al. (2010), this metric is widely employed to assess domain adaptability (Shen et al., 2018; Chen et al., 2019).

**Theorem 1.** Let $\mathcal{H}$ be a hypothesis class. Given two distinct domains $D_s$ and $D_t$, the following inequality holds for all $h \in \mathcal{H}$:

$$ \epsilon_t(h) \leq \epsilon_s(h) + \frac{1}{2} d_{\mathcal{H}\Delta\mathcal{H}}(D_s, D_t) + C. \quad (10) $$

This theorem establishes that the expected error on the target domain is upper-bounded by the sum of three components: (1) the expected error on the source domain; (2) the distributional divergence between $D_s$ and $D_t$; and (3) the error of the ideal joint hypothesis, denoted by $C$. In practice, $C$ is often considered negligible and thus disregarded. Consequently, the first two terms are the primary quantitative determinants of the target error.

Regarding the first term, the source domain error $\epsilon_s$ can be effectively minimized using labeled source training data. Furthermore, the adoption of BERT provides robust contextual representations, thereby facilitating a lower error rate. The second term in Equation (10) necessitates the generation of feature representations that are consistent across different domains. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 9.684380317 , the TTR score is 0.308118081 , the CV score is 0.915426687 , and the perplexity score is  10.727458 .","Based on the provided text, it appears to be written in English and discusses concepts related to machine learning, specifically focusing on domain adaptation and the H-H distance metric used to quantify differences between domains. The content does not contain any identifiable features or language patterns typically associated with automated writing systems like those found in AI-generated texts. Therefore, the classification is likely ""Human-written."""
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","The gray and white nodes denote observed and latent variables. to be dependent on the question latent variables in p ψ ( z y | z x , c ) and achieve the reverse dependency by sampling question x ∼ p θ ( x | z x , y , c ) . We then use a variational posterior q φ ( · ) to maximize the Evidence Lower Bound (ELBO) as follows (The complete derivation is provided in Appendix A ): log p θ ( x , y | c ) ≥ E z x ∼ q φ ( z x | x , c ) [log p θ ( x | z x , y , c )] + E z y ∼ q φ ( z y | z x , y , c ) [log p θ ( y | z y , c )] − D KL [ q φ ( z y | z x , y , c ) || p ψ ( z y | z x , c )] − D KL [ q φ ( z x | x , c ) || p ψ ( z x | c )] =: L HCVAE where θ , φ , and ψ are the parameters of the generation, posterior, and prior network, respectively. We refer to this model as a Hierarchical Conditional Variational Autoencoder (HCVAE) framework. Figure 2 shows the directed graphical model of our HCVAE. The generative process is as follows: 1. Sample question L.V.: z x ∼ p ψ ( z x | c ) 2. Sample answer L.V.: z y ∼ p ψ ( z y | z x , c ) 3. Generate an answer: y ∼ p θ ( y | z y , c ) 4. Generate a question: x ∼ p θ ( x | z x , y , c ) Embedding We use the pretrained word embedding network from BERT for posterior and prior networks, whereas the whole BERT is used as a contextualized word embedding model for the generative networks. For the answer encoding, we use a binary token type id of BERT. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 6.210294601 , the TTR score is 0.289672544 , the CV score is 1.515012319 , and the perplexity score is  51.85379791 .","This text is likely classified as AI-generated because it describes a specific architecture and methodology for a machine learning model called a Hierarchical Conditional Variational Autoencoder (HCVAE). The text provides detailed information about the model's components, including its probabilistic graphical model structure, variational inference mechanism, and training objective. Additionally, the mention of pre-trained word embeddings and perplexity values further supports the classification as AI-generated content related to natural language processing or computational linguistics."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Paraphrase-based methods generally yield more fluent and informative responses compared to local substitution techniques (e.g., WordSub and TextSub). This advantage stems from the capacity of neural generative models to leverage dialog history, thereby producing more coherent utterances. The superior performance of PARG over UtterSub indicates that our paraphrase generation model offers a more robust mechanism for exploiting the additional information inherent in paraphrases. Furthermore, PARG outperforms other paraphrase-based approaches (NAEPara and SRPara) because the decoding of prior system actions, combined with gradient backpropagation through the belief span decoder, furnishes strong contextual signals essential for effective paraphrase generation.

Thirdly, the efficacy of our data augmentation strategy is inversely proportional to data availability; specifically, greater improvements are observed in low-resource scenarios. Notably, a model trained on only 50% of the data using PARG achieves performance comparable to a model trained on the full dataset without augmentation, based on task-relevant metrics. Similar parity is observed when comparing models trained on 20% of the data with augmentation against those trained on 50% without it. These findings underscore the significant value of our method in low-resource settings.

While PARG occasionally yields slightly lower BLEU scores than alternative methods, this discrepancy is likely attributable to the inherent variability of natural language. Although sequence-to-sequence models can learn to generate responses corresponding to correct actions, surface-level linguistic variations between training and testing utterances may persist. Consequently, BLEU scores, which measure surface-level similarity, may decline even when the system generates functionally accurate responses.

Performance variations were also observed across the CamRest676 and MultiWOZ datasets. Under full-data conditions, the improvement gained via our augmentation method was more modest on CamRest676 than on MultiWOZ. This is attributed to the relative simplicity of the single-domain CamRest676 task, where the existing data suffices for effective training without augmentation. Conversely, the MultiWOZ dataset, characterized by extensive linguistic variation and a complex ontology, presents an under-explored utterance space; thus, response generation benefits significantly from the incorporation of additional dialog data.

### 6. Ablation Study

This section evaluates the contribution of individual components within our paraphrase-augmented response generation framework. Specifically, we systematically exclude (1) the act decoder (PARG w/o Act), (2) the utterance filter (PARG w/o Filt), and (3) the joint training strategy (PARG w/o Join), conducting model training and evaluation on the full MultiWOZ dataset for each configuration. The results are presented in Table 3. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.050866274 , the TTR score is 0.528508772 , the CV score is 0.483083723 , and the perplexity score is  79.24711609 .","The text is classified as human-written because it appears to be a detailed academic paper or research report discussing various aspects of machine learning and natural language processing, particularly focusing on paraphrasing and response generation tasks. It contains technical terms and concepts typical of academic writing, such as ""paraphrase,"" ""dialog history,"" ""gradient backpropagation,"" and ""BLEU scores."" Additionally, the structure and flow of the text suggest it has been written by someone familiar with these topics, rather than generated automatically."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","People often argue that all popular TV entertainment programs should focus on raising awareness about important social issues. While certain shows are well-suited for this role, I don’t believe it should be a universal requirement. Whether or not a program addresses social issues should depend on the type of content and the preferences of its audience.

Many professionals could benefit from gaining a deeper understanding of current events. For example, stock traders and bankers need to stay informed about global business trends, and they would likely value such content in their favorite TV shows. Young people are another key audience, as they often enjoy learning about popular topics through television. For instance, the Sustainable Development Goals (SDGs) have become an essential subject for schoolchildren, and educational TV can make learning about them more engaging than traditional textbooks.

Despite the benefits of including educational material in TV programs, it’s important to remember that the main purpose of entertainment TV is to help people relax and escape from daily stress. For viewers who simply want to unwind after work, being confronted with difficult social issues may not be appealing. Additionally, how the content is delivered matters; presenting serious topics in a comedy show, for example, could cause the audience to take them less seriously, making light-hearted programs an unsuitable platform for such messages.

In summary, entertainment TV can be a powerful tool for education and spreading information, but not every show should be used for this purpose. People have different reasons for watching TV, and these differences should be respected. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.021160131 , the TTR score is 0.590277778 , the CV score is 0.326315003 , and the perplexity score is  30.07352638 .","The text appears to be AI-generated because it uses complex language structures, includes technical terms related to media production, and discusses nuanced aspects of programming without apparent human input. The high burstiness, type-token ratio, sentence length variability, and perplexity values suggest sophisticated processing capabilities typical of artificial intelligence systems."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Yes,i  agree with the statement "" successful people always try new things and take risks, becouse they  will get  experiance,money and confident
        
          successful people will gain some experince  form past work , so it will help in there new things to get success.They will easily succed in that work.
        
           They feel  bore doing  same work , To break the monotony they will try new things.they will take risk 

             With the success of past one they will get self- confident. Confident will clear the way to the success.

            Successful peploe have also get money form  past success. with that money thay will do new things,these new things will aslo devlops there knowledge .
      
          Finally ,form the above points i can say the successful persons  will try new things . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 0.661767729 , the TTR score is 0.503597122 , the CV score is 0.688583547 , and the perplexity score is  53.29921722 .","The text is likely human-written because it contains coherent thoughts about the importance of trying new things and taking risks for success, which aligns with common motivational messages found in human-authored content. The use of natural language structure and vocabulary typical of human expression supports its classification as human-generated. Additionally, the detailed elaboration on various aspects of success suggests an intentional effort to convey information rather than being part of automated text generation."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Metalinguistic awareness plays a pivotal role in second language acquisition, facilitating the comparative analysis of structural and characteristic features between a target language and the learner's existing linguistic repertoire. Similarly, the integration of metalinguistic awareness into Natural Language Processing (NLP) systems is highly desirable, as it holds significant potential for enhancing cross-lingual generalizability. This remains a persistent challenge, as previous engineering-driven approaches have frequently yielded limited results due to a lack of explicit linguistic grounding. However, quantifying metalinguistic awareness within computational systems is non-trivial. Existing probing methodologies are predominantly designed to evaluate the extent to which neural models encode specific linguistic phenomena—for instance, determining whether a particular layer of an English language model recognizes the negation function of the prefix *un-*—rather than assessing genuine metalinguistic reasoning.

The proposed challenge advances beyond these limitations by evaluating a model's capacity to apply underlying morphological processes, such as verbal negation via prefixation. Furthermore, by encompassing a diverse array of language families and linguistic phenomena (see Section 3.1), this challenge establishes a robust testbed for measuring metalinguistic awareness. To illustrate the application of metalinguistic reasoning, consider the ""Chickasaw puzzle"" presented in Table 1. The translation model is derived through an iterative deductive process: (1) identifying the Subject-Object-Verb (SOV) word order in Chickasaw, distinct from the English Subject-Verb-Object (SVO) structure; (2) recognizing that nouns assume distinct suffixes depending on their syntactic role (e.g., *-at* for subjects and *-˜a* for objects); and (3) observing that verbs utilize specific suffixes to denote first-person singular pronominal subjects or objects (e.g., *-li* and *-sa*, respectively). Crucially, the function of the prefix *sa* (corresponding to ""me"" in English) cannot be isolated without first deducing that *lhiyohli* corresponds to the verb ""chases"" and that third-person agency in Chickasaw is not explicitly marked. As demonstrated, inferring a translation model necessitates iterative reasoning across lexical, morphemic, and syntactic levels, thereby requiring a high degree of metalinguistic awareness.

### 3. The Dataset

The puzzles sourced from Linguistic Olympiads encompass a broad spectrum of linguistic domains, including phonetics, morphology, syntax, and semantics. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.673114337 , the TTR score is 0.543529412 , the CV score is 0.673516825 , and the perplexity score is  31.82449532 .","This text is classified as human-written because it contains detailed explanations and examples related to linguistics, particularly focusing on metalinguistic awareness and its applications in natural language processing. The content discusses various aspects of language, including word orders, grammatical structures, and semantic relationships, all of which require deep understanding and analysis typical of written work rather than automated generation. Additionally, the use of tables and figures with clear instructions further supports the classification as human-written content."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Excluding these cases (“other verbs” in Table 3 ), the accuracies reach 99.2 and 98.0 by -T OKEN and -P ATTERN , respectively. These verbs do not appear as a target verb in the test cases of other tested constructions. This result suggests that the transferability of syntactic knowledge to a particular word may depend on some characteristics of that word. We conjecture that the reason for weak transferability to likes and like is that they are polysemous; e.g., in the corpus, like is much more often used as a preposition and being used as a present tense verb is rare. This type of issue due to frequency may be one reason for lessening the transferability. In other words, like can be seen as a challenging verb to learn its usage only from the corpus, and our margin loss helps for such cases. 7 Discussion and Conclusion Our results with explicit negative examples are overall positive. We have demonstrated that models exposed to these examples at training time in an appropriate way will be capable of handling the targeted constructions at near perfect level except a few cases. We found that our new tokenlevel margin loss is superior to the other approaches and the remaining challenging cases are dependencies across an object relative clause. Object relative clauses are known to be harder for a human as well, and our results may indicate some similarities in the sentence processing behaviors by a human and RNN, though other studies also find some dissimilarities between them. The difficulty of object relative clauses for RNNLMs has also been observed in the prior work. A new insight provided by our study is that this difficulty holds even after alleviating the frequency effects by augmenting the target structures along with direct supervision signals. This indicates that RNNs might inherently suffer from some memory limitation like a human subject, for which the difficulty of particular constructions, including centerembedded object relative clauses, are known to be incurred due to memory limitation rather than purely frequencies of the phenomena. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.543582539 , the TTR score is 0.523160763 , the CV score is 0.372849231 , and the perplexity score is  55.11386871 .","The text appears to be written in a formal academic style discussing research findings related to natural language processing and machine learning. It mentions specific methods and techniques used in the study, such as token-level margin loss, and discusses potential reasons for certain limitations or challenges faced by neural networks when processing certain types of sentences. The use of technical terms and the structure of the writing suggest it was likely generated by an AI system designed for generating academic papers or reports."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","I agree that successful people are those who try new things and take risks, rather than only sticking to what they already know how to do well. In my opinion, a successful person is someone who has taken many risks throughout their life, which is why we consider them successful. We cannot achieve great things unless we are willing to take risks.

For example, consider Marie Curie, one of the greatest women scientists. She discovered the element radium, which is a naturally occurring element that emits radiation. While conducting her research, she knew that exposure to radium’s radiation could cause severe harm to her body. Despite this danger, she took the risk, leading to the discovery of radium. Radium is now used to generate electricity and to help treat certain types of cancer. If she had not been willing to risk her life during her experiments, we might not have discovered radium or found treatments for some cancers.

Another example is the Wright brothers, who invented the blueprint for the airplane and proved that humans could fly like birds. Their imagination and determination led to the invention of the airplane. If they had been afraid to take risks with their flying experiments, the world would be very different today. Their invention made traveling from place to place much easier and changed the way we see the world.

For these reasons and examples, I believe that the only way to achieve success is by trying new things and taking risks. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.544624286 , the TTR score is 0.545126354 , the CV score is 0.2620501 , and the perplexity score is  17.50090408 .","The text is likely AI-generated because it exhibits characteristics consistent with machine-written content, such as formal language use, complex sentence structures, and an emphasis on risk-taking and innovation, which are common themes in AI-produced texts. The linguistic features also suggest a high level of control over the writing style and structure, typical of automated text generation systems. Additionally, the content aligns with general principles often discussed in discussions about AI ethics and human potential, further supporting the classification as AI-generated."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Our weaklysupervised framework uses bootstrapping to update itself as new Tweets are posted. Our experiments show that the system adapts to new topics in a social movement, as well as new hijacking strategies, maintaining strong performance over time. Social media has changed the way we live, trade, share news, and engage in social activities. Twitter is one of the most popular social networks, where users post short textual messages called “Tweets.” A hashtag (#) before a particular keyword or phrase in a Tweet is used to categorize the Tweet, helping users find topics that are of interest to them. One of the achievements of social media is reshaping and rescaling engagement in social movements via hashtag activism. Yang defines hashtag activism as large numbers of social media posts using a common hashtagged phrase with a social or political claim. Some popular hashtag activism movements include “#MeToo,” a movement against sexual harassment and assault, and “#BlackLivesMatter,” which campaigns against violence and systemic racism towards African Americans. These hashtags help engage people in social movements by raising awareness on a larger scale and by giving opportunities for those with access limitations, like the physically challenged, to participate. Unfortunately, hashtag activism is also a good target for spammers. Hashtag hijacking occurs when users “[use] a trending hashtag to promote topics that are substantially different from its recent context” or “to promote one’s own social media agenda” . While the detection of spam Tweets in general is an important issue, the detection of spam related to social movements is of even greater importance because it targets excluded or marginalized groups. We present a weaklysupervised, bootstrapping framework to detect Tweetlevel hashtag hijacking targeting specific social movements, using a combination of features based on the Tweet text, use of other hashtags, replies, and user profile. Our experiments focus on #MeToo, but our methodology can be applied to any hashtag. Prior work on hashtag hijacking has focused on general trending hashtags like #job or #android and could not adapt over time to attacker strategies; these approaches were unable to account for changes in hashtag use over time. Ours is the first selfupdating approach to be developed for detecting hashtag hijacking at the Tweet level. Our main contributions are as follows: A new dataset of #MeToo Tweets from October 2017 through May 20201. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.391531532 , the TTR score is 0.506756757 , the CV score is 0.399150953 , and the perplexity score is  36.44819641 .","The text is likely classified as AI-generated due to several key characteristics:

1. **Structured Approach**: The document outlines a detailed research project including experimental setup, data collection, and methodological details, typical of academic papers authored by researchers.

2. **Technical Language**: The language used is highly technical and specific to fields such as natural language processing and machine learning, suggesting expertise in AI-related areas.

3. **Academic Style**: The tone and structure of the writing align closely with academic discourse, featuring"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","This study demonstrates that the proposed methodologies provide complementary perspectives on information propagation within neural networks. When evaluated against raw attention mechanisms, both approaches exhibit significantly higher correlations with token importance scores derived from ablation studies and input gradient analysis.

**1. Introduction**

Attention mechanisms (Bahdanau et al., 2015; Vaswani et al., 2017) have emerged as a fundamental component of neural sequence processing architectures. Consequently, the visualization of attention weights has become the predominant method for interpreting model decisions and gaining insight into internal representations (Vaswani et al., 2017; Xu et al., 2015; Wang et al., 2016; Lee et al., 2017; Dehghani et al., 2019; Rocktäschel et al., 2016; Chen and Ji, 2019; Coenen et al., 2019; Clark et al., 2019). While it is methodologically unsound to equate attention weights directly with causal explanations (Pruthi et al., 2019; Jain and Wallace, 2019), they frequently yield plausible and meaningful interpretations (Wiegreffe and Pinter, 2019; Vashishth et al., 2019; Vig, 2019).

This paper addresses specific challenges arising in the deeper layers of transformer models, particularly the loss of token identifiability within high-level embeddings (Brunner et al., 2020). To mitigate this, we propose two efficient methods for computing attention scores relative to input tokens (hereafter referred to as *token attention*) at any given layer. These methods integrate the raw attention weights of the current layer (i.e., *embedding attention*) with those from preceding layers. Both approaches are grounded in modeling the network's information flow as a Directed Acyclic Graph (DAG), wherein nodes represent input tokens and hidden embeddings, edges denote attentional connections between layers, and edge weights correspond to attention scores.

The first method, **Attention Rollout**, operates on the assumption that input token identities are linearly aggregated across layers according to attention weights. This technique recursively propagates weights to capture the cumulative flow of information from input tokens to intermediate hidden states. The second method, **Attention Flow**, conceptualizes the attention graph as a flow network. By employing a maximum flow algorithm, it calculates the maximum flow values from hidden embeddings (sources) to input tokens (sinks), thereby quantifying the extent of information transmission. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 4.412518854 , the TTR score is 0.461538462 , the CV score is 0.539221487 , and the perplexity score is  42.65505219 .","This text appears to be human-written due to several characteristics:

1. **Complex Structure**: The text contains multiple paragraphs and sections, indicating an organized structure typical of written content.
2. **Technical Terminology**: It uses technical terms like ""neural sequence processing architectures,"" ""attention mechanisms,"" and ""Directed Acyclic Graph"" (DAG), which are common in academic papers.
3. **Academic Style**: The tone and language used suggest a scholarly or research-oriented approach, consistent with writing by"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Today, with our capitalist mindset and strong focus on the pursuit of wealth, many people are concerned with finding the best path to success. As a result, there is ongoing debate about whether taking risks or trying new things makes it easier to achieve success.

It’s true that taking risks can often lead to success. Exploring new ideas is one of the best ways to create new needs in the market and stay ahead of competitors. For instance, Marcel Bleustein became one of France’s most renowned advertisers by being the first to use radio for advertising. On the other hand, trying something new always involves risk, and you may fail if no one is interested in your idea.

However, success can also come from sticking to what you do best. The idea is that if you master a particular skill, you can outperform your competitors in that area. For example, the major American telephone company AT&T chose in the 1920s to focus on perfecting telephone technology instead of branching out into wireless telegraphy. As history shows, this was a wise decision.

In reality, the most effective way to achieve success might be to combine both approaches: excel at what you know while also seeking new opportunities. In art, for example, it’s difficult to innovate without understanding what has come before. That’s why, before becoming a leading figure in French cinema and a pioneer of the Nouvelle Vague, J. L. Gaudard first dedicated himself to learning extensively about classical cinema and traditional art. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.337852018 , the TTR score is 0.583038869 , the CV score is 0.323895492 , and the perplexity score is  22.57971382 .","This text appears to be AI-generated due to its formal tone, structured format, and lack of human-like language features such as contractions, informal vocabulary, or misspellings. The text discusses concepts related to entrepreneurship and innovation, which are topics typically covered in business and management literature. Additionally, the inclusion of specific examples like Marcel Bleustein and AT&T further supports the classification as AI-generated content, as these references would not naturally appear in human-written texts unless they were included specifically for the purpose"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","The notation in the figure utilizes the subscript ""1"" to denote the first encoder layer. The input to this layer consists of the hidden representation of a token with dimension $h$. This input is first processed by a multi-head attention mechanism, which operates jointly on the hidden representations of all tokens within the sequence. For illustrative clarity, Figure 2 depicts only a single hidden representation.

The multi-head attention cell comprises three parameter tensors: the key tensor $K_1$, the query tensor $Q_1$, and the value tensor $V_1$. The tensor $K_1$ has dimensions $k_1 \times a_1 \times h$, where $k_1$ represents the dimension of the key vector per head and $a_1$ denotes the number of attention heads. The hidden representation of dimension $h$ is projected onto $K_1$ to yield $a_1$ key vectors, each of dimension $k_1$. Similarly, the query tensor $Q_1$ is employed to generate $a_1$ query vectors, each of dimension $k_1$. The value tensor $V_1$ possesses dimensions $v_1 \times a_1 \times h$; projecting the hidden representation onto $V_1$ produces $a_1$ value vectors, each of dimension $v_1$. It is noted that the dimensions $k_1$ and $v_1$ may differ.

The attention weights are derived by computing the inner product of the key and query vectors, followed by a softmax normalization. These weights are subsequently applied to combine the value vectors. For a comprehensive description of the multi-head attention mechanism, readers are referred to Vaswani et al. (2017). In summary, the multi-head attention cell utilizes the tensors $K_1$, $Q_1$, and $V_1$ to transform the input hidden representation of dimension $h$ into a vector of dimension $v_1 \times a_1$. This resulting vector is then projected back to the original dimension $h$ via a projection matrix $P_1$.

The projected output is added element-wise to the original input hidden representation (residual connection), followed by the application of layer normalization. Finally, the output is passed sequentially through two fully connected layers, denoted as $D_1$ and $G_1$. The layer $D_1$ contains a parameter matrix of dimension $f_1 \times h$, while $G_1$ contains a parameter matrix of dimension $h \times f_1$. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 14.09435752 , the TTR score is 0.324561404 , the CV score is 0.409589011 , and the perplexity score is  24.92998123 .","This text appears to be written by a human because it describes a specific technical concept related to neural network architectures, including multi-head attention mechanisms. The language used is precise and detailed, typical of academic or technical writing aimed at explaining complex concepts to others. Additionally, the presence of linguistic features such as burstiness, type-token ratio, sentence length variability, and perplexity suggest an attempt to analyze and describe these aspects from a computational perspective, further indicating that this text was likely created by a human researcher or"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","In order to address teacher needs that we identified during our analysis of Infusing Computing teacher implementation data, we are currently engaging in design research to develop, evaluate, and refine scaffolds to extend and support ongoing teacher learning during the academic year implementation. Examples of supports that we are currently analyzing include: 1) monthly webinars, with topics ranging from unplugged CT lessons to assessment of CT; 2) using Slack as a communitybuilding tool; 3) podcasts as a way to support asynchronous engagement; and 4) the development of repositories for sharing lesson plans, resources, and feedback from classroom infusion experiences. Further, during the Summer 2019 Infusing Computing workshop, we introduced and are currently analyzing the impact of differentiated 3C sessions, including targeted Code sessions using Python and Connect sessions for teachers in instructional leadership roles within their schools. It is our hope that engaging in these cycles of design and analysis will allow us to establish a comprehensive model of teacher professional development that will not only increase access to CT but can also promote more critical connections to content area learning and interdisciplinary thinking. This experience report documents an intervention that started five years ago, in 2014. Green River College, a community college in a suburb of Seattle, Washington, launched a new applied baccalaureate program in Software Development, with a goal of preparing placebound, underrepresented, and underserved students for employment as entrylevel software developers. At program inception, community college baccalaureate degrees were relatively new across the nation and there was little precedent established in terms of program design, particularly as these new degree programs were not intended to emulate traditional undergraduate Computer Science programs, but rather provide an alternative pathway to the baccalaureate and workforce. The Bachelor of Applied Science (BAS) in Software Development is designed as a ""2+2"" program, where students enter the program at thirdyear/juniorlevel standing after completing their twoyear applied associate degree. The BAS Software Development curriculum includes courses in both ""computer science fundamentals"" and industryfocused, projectbased learning courses. Since program inception, the curriculum has evolved based on evidence and feedback from students, instructors, alumni, and industry partners. Initial key results at the fiveyear mark include a review of enrollment, completion, diversity, and placement data. As of June 2019, the program has 132 graduates (degrees conferred), with 52 new students on track to enter the program in the 20192020 academic year. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.010224063 , the TTR score is 0.519911504 , the CV score is 0.403844397 , and the perplexity score is  65.7143631 .","This text appears to be labeled as AI-generated due to its formal tone, structured format typical of reports or academic papers, and lack of human-like language patterns. The inclusion of detailed educational statistics and descriptions of educational initiatives suggests it's likely written by someone familiar with education systems and technology integration efforts. The technical vocabulary used aligns with what one might expect in such a context, further supporting the classification as AI-generated."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","In contrast, sequence-level training utilizing Reinforcement Learning (RL) offers a robust solution to this challenge by framing text generation as a sequential decision-making problem. By directly optimizing evaluation metrics as cumulative rewards (Ranzato et al., 2016), this approach has yielded state-of-the-art results across various text generation tasks (Paulus et al., 2017; Rennie et al., 2016). However, this framework faces significant limitations. Primarily, RL training suffers from sparse rewards, as scalar feedback is typically available only upon the completion of an entire sequence. Furthermore, recurrent models often prioritize local fluency, which can lead to semantic inconsistencies in long-form generation.

Regarding implementation, most existing RL-based text generation methods rely on model-free frameworks, which have been criticized for high variance and poor sample efficiency (Sutton and Barto, 1998). Conversely, while model-based RL methods mitigate these specific issues, they are often difficult to train in complex environments, and the learned policy is frequently constrained by the capacity of the environment model. Recent advancements in model-based RL (Gu et al., 2016; Kurutach et al., 2018; Nagabandi et al., 2017) have sought to combine the strengths of both paradigms, achieving improved performance by learning a model-free policy assisted by an environment model. Additionally, model-based RL has recently been applied to address problems characterized by extremely sparse rewards through curiosity-driven methods (Pathak et al., 2017).

In this paper, we propose a model-based imitation learning method designed to overcome the aforementioned challenges in text generation tasks. Our core approach involves employing an explicit ""guider"" network to model the generation environment within the feature space of sentence tokens. This network emits intermediate rewards by aligning predicted features with those of generated sentences. Trained to encode the global structural information of training sentences, the guider network effectively guides next-token prediction during the generative process. To further support the guider network, we introduce a novel self-attention mechanism designed to provide high-level, forward-looking planning information and ensure consistent semantic meaning throughout the generated text. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.369380575 , the TTR score is 0.55408971 , the CV score is 0.369251763 , and the perplexity score is  37.90691376 .","The text appears to be human-written because it discusses advanced techniques in natural language processing specifically related to text generation using reinforcement learning. It mentions recent developments such as model-based imitation learning and curiosity-driven methods, indicating expertise in AI research areas. The detailed technical descriptions and references used suggest a professional author who understands the nuances of machine learning algorithms and their applications in NLP."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","The Computer Attitudes Survey (CAS) is a psychometric instrument designed to assess student attitudes toward knowledge acquisition and problem-solving within the discipline of computer science. The instrument is characterized by four primary design principles: (1) it elicits attitudes regarding the CS discipline broadly, rather than focusing on specific courses, pedagogical approaches, or programming languages; (2) its items are formulated to ensure clarity, conciseness, and unambiguous interpretation; and (3) its categorical structure and subscales are derived empirically from student response data rather than being predetermined by researcher interests.

The development of the CAS involved four iterative revisions between 2011 and 2015. The initial version adapted the Classroom Survey of Student Engagement (CLASS) to a computing context. CAS Version 2 (CASv2), developed following think-aloud interviews with faculty and students and piloted in Fall 2011 with 447 introductory students, comprised 53 Likert-scale items. Exploratory Factor Analysis (EFA) of this version identified eight factors: problem-solving transfer, problem-solving strategies, problem-solving fixed mindset, real-world connections, problem-solving confidence, problem-solving sense-making, abstraction, and personal interest/enjoyment.

CAS Version 3 (CASv3) was subsequently administered in a pre-post format across three semesters at two universities, yielding 2,873 responses. EFA on this dataset revealed seven latent factors. Following Confirmatory Factor Analysis (CFA) on these factors, the instrument was further refined to produce the final iteration, CAS Version 4 (CASv4). Developed by Dorn and Tew, CASv4 consists of 26 items utilizing a 5-point Likert scale and measures five distinct factors: problem-solving transfer, personal interest, problem-solving strategies, real-world connections, and problem-solving fixed mindset.

In the present study, the CASv4 was augmented with two additional sets of items. First, a subset of nine questions from Wiebe et al. was incorporated to assess attitudes related to gender issues; it is noted that these items may introduce the potential for stereotype threat. Second, five additional questions were added to probe student attitudes regarding the utility of computing. These modifications were informed by findings from Cooper et al., who observed that standard attitude surveys often failed to fully capture insights from individual interviews—particularly among Latina students—regarding the significance of gender equality and the perceived utility of computing. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.674039939 , the TTR score is 0.516666667 , the CV score is 0.592326211 , and the perplexity score is  22.44397545 .","This text appears to be human-written because it describes a survey called ""Computer Attitudes Survey"" (CAS) which is used to measure attitudes towards computer science education. It provides details about how the survey works, including its purpose, design principles, and revision history. Additionally, the text includes statistical information such as linguistic features like burstiness, type-token ratio, sentence length variability, and perplexity, which are typical characteristics of written language. Therefore, based on its content and style, this text can"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","One line of work models domaindependent word embeddings or domainspecific sentiment lexicons, while others attempt to learn representations based on cooccurrences of domainspecific with domainindependent terms. Our work is related to approaches that address domainspecificity in the target domain. Works like Liu et al. attempts to model targetspecificity by mapping domaingeneral information to domainspecific representations by using domain descriptor vectors. In contrast, we address relating domainspecific terms by modeling their relations with the other terms in knowledge bases like ConceptNet. 3 Background 3.1 Task Definition Domain adaptation deals with the training of models that can perform inference reliably in multiple domains. Across domains, it is assumed that the feature and label spaces are the same but with discrepancies in their feature distributions. In our setup, we consider two domains: source D s and target domain D t with different marginal data distributions, i.e., P D s ( x ) ≠ P D t ( x ) . This scenario, also known as the covariate shift, is predominant in SA applications and arises primarily with shifts in topics – causing a difference in vocabulary usage and their corresponding semantic and sentiment associations. We account for unsupervised domain adaptation, where we are provided with labeled instances from the source domain D l s = {( x i ,y i )} N s i = 1 and unlabeled instances from the target domain D u t = {( x i )} N t i = 1 . 1 This is a realistic setting as curating annotations for the target domain is often expensive as well as time consuming. Given this setup, our goal is to train a classifier that can achieve good classification performance on the target domain. 3.2 DomainAdversarial Neural Network We base our framework on the domainadversarial neural network (DANN) proposed by Ganin et al.. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 0.940009374 , the TTR score is 0.51951952 , the CV score is 0.495595585 , and the perplexity score is  82.88790131 .","The text is likely classified as AI-generated because it discusses advanced techniques in artificial intelligence, specifically in the field of natural language processing (NLP). The mention of ""domain-specific"" and ""cooccurrences,"" along with references to machine learning algorithms such as DANN, indicates sophisticated AI research and development. Additionally, the use of technical jargon and the structure of the text suggest an academic or professional context, which is typical for AI-generated content. The high linguistic features score further supports this classification,"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","I agree with the idea that successful people are those who try new things and take risks, rather than just sticking to what they already know. The comfort and happiness we enjoy today are the results of the efforts made by many successful individuals. Here are some points to support my view.

First of all, everyone defines success in their own way. People who truly want a better life don’t just settle for what they have already achieved—they keep pushing themselves to reach new goals. For example, for school children, success means finishing primary school, then moving on to higher education, graduating, getting a job, and eventually starting a family. While they remember their past achievements, their focus is on the future, striving to be successful at each stage of life.

Secondly, the meaning of success varies from person to person. For me, success might mean landing a good job. For my parents, it’s providing a better education for their children, even if it means taking financial risks. If my parents had stopped investing in my education because they were afraid to take risks, our future wouldn’t be as bright. Everyone tries to achieve success by stepping out of their comfort zone and taking chances.

Finally, every country has its own successful leaders, businesspeople, and innovators who took risks and created new things. We owe much of our current happiness and comfort to their efforts. Because of them, our lives are better today, and we should also strive to be successful by not limiting ourselves to what we already know.

In conclusion, anyone can achieve success if they work hard and aim to create a better life for future generations. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.099992211 , the TTR score is 0.511041009 , the CV score is 0.339626204 , and the perplexity score is  23.47051239 .","This text is likely AI-generated due to several linguistic features:

1. **Burstiness**: A high burstiness score indicates rapid changes in sentence structure and vocabulary use, which is characteristic of AI-generated content designed to mimic human-like writing styles.

2. **TTR (Type-Token Ratio)**: With a low Type-Token ratio of 0.5110, the text uses fewer distinct words compared to unique words, suggesting an attempt to maintain a conversational tone typical of AI-generated texts"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","**Entity Masking**
Notably, masking entity mentions with their corresponding named entity and grammatical role information significantly degrades performance on entity type-related tasks (specifically TypeHead and TypeTail). This decline suggests that masking effectively redirects the encoder's attention away from specific entity mentions, a hypothesis corroborated by reduced performance in probing tasks assessing argument position and distance (e.g., ArgDist, ArgOrd, and SentLen). Among the architectures evaluated, CNN and BiLSTM encoders exhibit the most substantial performance drops, indicating a pronounced tendency to overfit to specific entity mentions in the absence of masking. In contrast, the Graph Convolutional Network (GCN) demonstrates a lower propensity for such overfitting. Conversely, and somewhat unexpectedly, the self-attentive encoder (SAttn.) exhibits an increased focus on entity mentions and their immediate context when masking is applied, as evidenced by improved performance on distance and argument-related probing tasks.

**Word Representations**
The integration of contextualized word representations, such as those generated by ELMo or BERT, yields a marked improvement in probing tasks centered on named entity and part-of-speech information. This finding indicates that contextualized representations effectively encode syntactic and semantic features pertinent to Relation Extraction (RE), aligning with prior research by Peters et al. (2018) and Radford et al. (2018) regarding the utility of deep language representations for downstream tasks. The enhancement in syntactic and semantic capabilities is further reflected in an overall improvement in RE task performance. When compared to ELMo, encoders utilizing BERT generally demonstrate superior and more balanced performance across probing tasks, a trend that correlates with enhanced RE outcomes. This suggests that bidirectional language models encode the linguistic properties of input data more effectively. Interestingly, the uncased version of BERT performs equally well or better than its cased counterpart on probing tasks related to entity and part-of-speech information. While this result is intuitive for the SemEval dataset, which emphasizes semantic relations between concepts, it is counterintuitive for the TACRED dataset, where casing is typically critical for identifying proper entities (e.g., person and company names).

**Probing Tasks versus Relation Extraction**
A significant observation is that superior performance on probing tasks does not necessarily translate to improved performance on the downstream RE task. For instance, while the CNN+ELMo configuration achieves the highest scores on the majority of probing tasks, it records an F1 score 8.1 points lower than the optimal model for this dataset, which is the SAttn.+BERT (cased) configuration with masking. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.125869978 , the TTR score is 0.469827586 , the CV score is 0.397546073 , and the perplexity score is  93.70652771 .","This text appears to be human-written because it describes detailed findings and observations about various aspects of machine learning and natural language processing, including entity recognition, word representation, and probing tasks. The content discusses specific algorithms and techniques used in these fields, providing insights into how they perform certain types of tasks. Additionally, the use of technical terms and the structure of the writing suggest it was created by someone knowledgeable in artificial intelligence and computational linguistics."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Finally, Theano distinguishes itself from the Jennifer and Clara systems by incorporating a voice interface, whereas the latter are limited to text-based chat interactions. To evaluate the efficacy of the new smart suggestion feature and determine the optimal suggestion probability ($p_s$), an A/B test was conducted with real users. Participants were randomly assigned to one of two experimental groups: Group A ($n=36$), which interacted with a version configured with $p_s = 0.2$ (low frequency of suggestions), and Group B ($n=37$), which interacted with a version configured with $p_s = 0.8$ (high frequency of suggestions). The study cohort comprised 73 individuals aged 15 to 65. Upon completion of the interaction, participants completed a survey assessing the suggestion feature and the system's overall comprehension capabilities.

As illustrated in Figure 5, user feedback indicates a preference for less intrusive suggestions. Specifically, Group A demonstrated a higher ratio of ""Excellent"" to ""Bad/Very Bad"" ratings compared to Group B, and the normalized sum of negative ratings was lower in Group A. Notably, assessments of Theano's comprehension ability were significantly lower in Group B than in Group A. While the overall similarity between the two groups is evident, Group A yielded superior reviews. This suggests that deeper, conversation-agent (CA)-driven interactions may be more susceptible to intent recognition errors. Although a higher $p_s$ value increases user engagement, it appears to negatively impact the user's perception of being understood. Consequently, the optimal $p_s$ likely lies between 0.2 and 0.8, balancing user engagement with the preservation of mutual understanding.

Furthermore, the average number of conversation turns was utilized as a proxy for user engagement, recording 10.74 turns for Group A and 13.06 turns for Group B. These results indicate that frequent CA-directed suggestions extend conversation duration, even if this approach may frustrate users seeking greater control over the dialogue. In conclusion, Theano is presented as a conversational agent designed for the dissemination of COVID-19 information and symptom self-assessment in Greek. It represents a valuable addition to the ecosystem of digital tools combating the pandemic, offering a feature-rich and scalable support system tailored to the specific needs of its user base. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.445921986 , the TTR score is 0.510638298 , the CV score is 0.453500365 , and the perplexity score is  26.78249741 .","The text is classified as human-written because it contains detailed descriptions and analyses of a research study on a new smart suggestion feature for a conversational AI system called Theano. The language used is technical but clear, discussing statistical methods like A/B testing and data analysis techniques. There are no signs of machine-generated content or unusual formatting typical of automated writing. The text also includes references to specific figures and graphs, further indicating human authorship."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","**Course Overview and Instructional Summary**

This session addressed the course objectives regarding two-dimensional spatial concepts, specifically contrasting standard Cartesian grids with screen-space coordinates. To reinforce these concepts, students engaged in an interactive exercise plotting points within both coordinate systems. The curriculum then transitioned to the project scope: a two-dimensional game wherein a protagonist traverses the screen to collect descending objects.

**Assignment 1: Conceptual Design**
Students were tasked with designing the primary protagonist for their game, adhering to specific constraints regarding the quantity and geometry of shapes utilized. Participants utilized a provided grid to sketch and label their designs. Following this conceptual phase, the instruction shifted to the implementation of these designs using the Processing programming environment. The lecture covered fundamental commands related to screen dimensions, RGB color models, and geometric primitives, drawing explicit parallels between these coding operations and traditional visual art techniques.

A key pedagogical focus was the significance of command sequencing. The instruction highlighted that, analogous to physical painting where color selection precedes application, programming requires the color command to be executed prior to shape rendering. Furthermore, the lesson demonstrated how the order of shape commands dictates the layering of compound figures. To facilitate coding in an offline environment, students were provided with a reference worksheet containing essential commands.

**Assignment 2: Implementation**
Students were required to translate their individual hero designs into executable code.

**Lesson 3: Functions and Animation**
Subsequent instruction focused on the architecture of functions, detailing the built-in `setup()` and `draw()` loops essential for two-dimensional animation in Processing, alongside the creation of custom functions for specific tasks.

**Preparatory Measures and Efficiency**
To optimize instructional time and ensure uniform progress, the instructor pre-completed the majority of the hero-drawing code initiated in the previous lesson, leaving only the final commands for student completion. Additionally, a foundational code structure containing the necessary animation loops was provided, with the hero code integrated into the `draw()` function. This preparatory strategy served a dual purpose: it allowed for the efficient introduction of function concepts at the outset of the lesson and provided students with the opportunity to practice reading and analyzing code authored by others. Finally, a supplementary example program was developed to demonstrate the use of three distinct functions for rendering varied background scenes. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 4.259872296 , the TTR score is 0.51011236 , the CV score is 0.394021731 , and the perplexity score is  73.7537384 .","The text appears to be human-written based on several factors:

1. **Complexity**: The text contains detailed instructions and explanations about programming concepts such as coordinate systems, graphics, and animations, which are typically found in educational materials or tutorials written by humans.

2. **Technical Vocabulary**: The use of technical terms like ""Processing,"" ""RGB color models,"" and ""geometric primitives"" indicates a high level of expertise and knowledge about computer science topics.

3. **Structure and Flow**: The text follows"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","For AON model we use the code base provided by the authors in and we maintain the hyperparameters described in the paper. For the paragraph encoder of the BAON models, we follow the same scheme of the AON model but for its sentence encoder we use hyperparameters of the BERT setting. We use the pretrained BERT uncased base model with 12 layers for the BAON and BTSORT models. We finetune the BERT model in both cases. Hence, we replace the Adadelta optimizer with the BertAdam optimizer for the BAON model. The LSTMs in the LTSort model uses an RNN size of 512 and it uses the same vocabularies as the AON model. LTSort is trained using stochastic gradient descent with dropout of 0.2, learning rate of 1.0 and learning decay rate of 0.5. For BTSort and LTSort we use accuracy on the validation set to stop training. For BTSort and BAON we use learning rate of 5e5 with adam epsilon value of 1e8. For all the experiments we use a maximum sequence length of 105 tokens. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.849949468 , the TTR score is 0.470899471 , the CV score is 0.311630779 , and the perplexity score is  55.40513611 .","This text appears to be describing the design and implementation details of a machine learning model, specifically mentioning the use of pre-trained BERT models, fine-tuning techniques, and specific parameters used during training. These elements strongly suggest that the content was generated by an AI system designed to create or simulate human-like language patterns. The detailed technical specifications and the focus on algorithmic aspects clearly indicate the creation of artificial intelligence rather than natural language processing or other forms of text generation."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","**Performance Evaluation**

The proposed methodology demonstrates superior performance compared to recent Interval Bound Propagation (IBP)-based approaches (Jia et al., 2019; Huang et al., 2019) across both the IMDB and Amazon text classification benchmarks. Notably, by applying this method to the state-of-the-art BERT architecture—a model for which prior certified robustness techniques are inapplicable—we achieve a certified accuracy of 87.35% on the IMDB dataset.

**2. Adversarial Word Substitution**

In the context of text classification, a model $f(X)$ maps an input sentence $X \in \mathcal{X}$ to a label $c$ within a discrete set of categories $\mathcal{Y}$. The input sentence is defined as $X = (x_1, \dots, x_L)$, comprising $L$ words. This study focuses on adversarial word substitution, wherein an attacker modifies the input by replacing words with their synonyms, drawn from a predefined synonym table, with the intent of altering the model's prediction.

For any given word $x$, we define a predefined synonym set $S_x$ containing $x$ and its synonyms. We assume the synonymy relation is symmetric; thus, if $y \in S_x$, then $x \in S_y$. These synonym sets may be constructed utilizing GLOVE embeddings (Pennington et al., 2014). Given an input sentence $X = (x_1, \dots, x_L)$, an attacker may generate an adversarial sentence $X' = (x'_1, \dots, x'_L)$ by perturbing at most $R \leq L$ words, where each perturbed word $x'_i$ is selected from the corresponding synonym set $S_{x_i}$.

The set of candidate adversarial sentences available to the attacker, denoted as $\mathcal{S}_X$, is formally defined as:

$$ \mathcal{S}_X := \{ X' : \| X' - X \|_0 \leq R, \, x'_i \in S_{x_i}, \, \forall i \} $$

Here, $\| X' - X \|_0 := \sum_{i=1}^L \mathbb{I}\{x'_i \neq x_i\}$ represents the Hamming distance, where $\mathbb{I}\{\cdot\}$ denotes the indicator function. While it is expected that all $X' \in \mathcal{S}_X$ retain the same semantic meaning as $X$ for human readers, they may yield divergent outputs from the model. Consequently, the objective of the attacker is to identify an adversarial instance $X' \in \mathcal{S}_X$ such that $f(X) \neq f(X')$. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 7.616310892 , the TTR score is 0.388888889 , the CV score is 0.65351227 , and the perplexity score is  45.26435471 .","This text appears to be written by a human because it describes a performance evaluation for a machine learning model designed to classify text into specific categories. It includes details about how the model performs relative to other methods, mentions the use of a specific architecture like BERT, and provides technical specifications related to the model's performance metrics. Additionally, the text discusses adversarial attacks on the model, which is common in discussions about security and robustness in AI systems. All these elements suggest that the author has a deep"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Numbers in brackets represent training set sizes. set called RESTAURANTS -8 K . It comprises conversations from a commercial restaurant booking system, and covers 5 slots essential for the booking task: date , time , people , first name , last name . The data statistics are provided in Table 1 . 5 DSTC8 Data Sets. The SchemaGuided Dialog Dataset (SGDD) released for DSTC 8 contains span annotations for a subset of slots. We extract span annotated data sets from SGDD in four different domains based on their large variety of slots: (1) bus and coach booking (labelled Buses_1 ), (2) buying tickets for events ( Events_1 ), (3) property viewing ( Homes_1 ) and renting cars ( RentalCars_1 ). A detailed description of the data extraction protocol and the statistics of the data sets, also released with this paper, are available in appendix A . Baseline Models. We compare our proposed 5 The data set contains some challenging examples where multiple values are mentioned, or values are mentioned that do not pertain to a slot. For example, in the utterance “I said 5pm not 6pm” multiple times are mentioned; in “I called earlier today” a date is mentioned that is not the day of the booking. Further, there are noticeable differences compared to previous data sets such as DSTC8: e.g., while all slots in other datasets which pertained to integers (e.g. the number of travelers for a coach journey, number of tickets for an event booking) are modeled categorically (i.e. all numbers from 1 to 10 are separate classes), we model the number of people coming for a booking using spans because people often mention this value indirectly. For example me and my husband , 3 adults, 4 kids , 2 couples . model with two strong baselines: VCNNCRF is a vanilla approach that uses no pretrained model and instead learns subword representations from scratch. SpanBERT uses fixed BERT subword representations. All use the same CNN+CRF architecture on top of the subword representations. For each baseline, we conduct hyperparameter optimization similar to SpanConveRT: this is done via grid search and evaluation on the development set of RESTAURANTS -8 K . The final sets of hyperparameters are provided in Table 2 . SpanBERT relies on BERTbase, with 12 transformer layers and 768dim embeddings. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.654335211 , the TTR score is 0.497695853 , the CV score is 0.645103684 , and the perplexity score is  103.3613205 .","This text is likely classified as AI-generated due to its technical nature, specific terminology related to natural language processing (NLP), and detailed explanations about dataset creation, annotation methods, and model architectures used in research papers. These elements suggest it's part of academic discourse focused on artificial intelligence and machine learning applications in NLP tasks."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","People achieve success when they’re willing to try new things and take risks, rather than just sticking to what they already know how to do well. However, it’s important that these new efforts are built on a foundation of their existing skills and knowledge.

A successful person is someone who’s open to exploring new opportunities or tackling challenges that others might avoid. If nothing ever changed, the world wouldn’t progress—and the same goes for individuals. Everything we use or know today was created by people who dared to innovate in the past. Their discoveries made life better and more comfortable, and that’s why we remember them. To be successful, you need to make your own discoveries—in other words, you must be willing to try new things.

There are still many things in the world that need improvement, which means, as members of society, we have a responsibility to take risks and try new approaches. This is a key way for the world to move forward. If someone finds something new that makes life better for everyone, we naturally see that person as successful.

However, trying new things or taking risks doesn’t mean acting without thought or consideration. If someone takes risks simply based on personal interests without thinking about the possible outcomes, and those outcomes are clearly negative, it’s unlikely they will find success.

As teenagers, we should be confident and keep exploring new things, using our knowledge as a guide. This is the time for us to think about the world and contribute—not just to become successful ourselves, but because it’s our responsibility. Remember, trying new things can lead you to success. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.696047471 , the TTR score is 0.53442623 , the CV score is 0.297078854 , and the perplexity score is  21.88090897 .","This text appears to be written in an AI-generated style due to several linguistic features:

1. **Burstiness**: The burstiness value of 1.6960 indicates high variability in sentence length and structure, typical of human-like writing.
2. **TTR (Type-Token Ratio)**: A TTR of 0.5344 suggests a moderate level of repetition within sentences, common in natural language.
3. **CV of Sentence Length**: A CV of 0"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","It’s widely recognized that music has always played a significant role in raising children, and many parents hope their kids will learn to play a musical instrument. However, I don’t believe that this skill is necessary for every student.

To begin with, not everyone is naturally gifted in music, which is often needed to truly learn how to play an instrument. Some people have an innate musical talent and pick it up easily, while for others, learning music can be difficult and frustrating. For these children, it might even lead them to dislike music rather than enjoy it. Some individuals are better suited to becoming engineers or doctors, and for them, spending time on music may not be worthwhile.

Another key point is that every child is unique and has their own interests. If all students were required to focus on music, those who aren’t interested would have less time for activities they genuinely enjoy. Everyone should have the freedom to spend their free time as they wish.

Furthermore, playing a musical instrument isn’t generally seen as a crucial skill for a successful future. More attention should be given to subjects like Math, Science, and Languages, which are more likely to benefit students as they grow older and finish school.

In conclusion, making all children learn an instrument isn’t a good idea. Music should be offered to those who are genuinely interested, as forcing it on everyone could take time away from other important subjects and hobbies, and not everyone will be able to master it. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.497496052 , the TTR score is 0.541958042 , the CV score is 0.320421687 , and the perplexity score is  20.16230011 .","The text appears to be AI-generated because it follows certain linguistic features typical of machine-generated content, such as consistent burstiness, high type-token ratio, moderate sentence length variability, and low perplexity. These characteristics suggest a pattern commonly found in automated language models designed to mimic human writing styles. The argument presented supports the notion that music education is not universally beneficial and emphasizes individual differences among learners, further reinforcing the AI-generated nature of the text."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","These days, it’s quite common to see people who smoke. Even though we’re taught that smoking is dangerous, we still witness people exhaling thick clouds of smoke every day. This is partly because some people think smoking looks cool, and cigarettes are available in almost every store. This essay will explore the reasons why people start smoking and suggest ways to address this issue.

To begin with, some people in society view smoking as fashionable. Popular culture—like songs, movies, and novels—often portrays smoking in a positive light. For example, I once watched a comedy show where a character who smoked was depicted as cooler and more mysterious than the others, who seemed more ordinary and approachable. Additionally, cigarettes are widely available in stores. A recent study showed that over 80% of people who tried smoking for the first time did so because they could easily buy cigarettes from local shops. The influence of pop culture and the easy access to cigarettes both contribute to why some people continue this habit.

I think there are effective ways to reduce smoking. First, the media should show the real consequences of smoking in a way that people can relate to. In one movie I saw, the main character was constantly reaching for cigarettes. By the end, after reuniting with his love interest, he developed a serious illness caused by his smoking, which he deeply regretted. This kind of portrayal actually made my younger niece afraid of smoking. Additionally, the government should limit how easily people can buy cigarettes. By introducing policies that restrict cigarette sales to fewer stores and raising taxes on tobacco products, smoking would become less convenient and less appealing to many.

In summary, even though most people know smoking is harmful, many still see it as cool and easy to get. By making smoking less trendy and more difficult to access, I believe we can make it less attractive and ultimately reduce the number of smokers in society. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.463568212 , the TTR score is 0.525885559 , the CV score is 0.355144474 , and the perplexity score is  24.85019493 .","This text is likely classified as AI-generated due to its formal tone, structured argumentation, and use of persuasive language aimed at reducing smoking habits. The author presents logical reasoning supported by examples and statistics, indicating an attempt to persuade readers about the negative effects of smoking and propose solutions. The writing style suggests a high level of sophistication and planning typical of AI-generated content designed to be convincing or informative."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Each of the elements $a$, $v$, and $m$ is instantiated as a node and linked to an existing node $v_i \in V$ upon satisfying a similarity criterion. Two nodes, $A$ and $B$, are deemed similar if any of the following conditions holds: (1) $A$ is identical to $B$; (2) $A$ contains $B$; or (3) the count of overlapping words between $A$ and $B$ exceeds half of the minimum word count of either node. Edges connecting similar nodes are assigned a specific semantic relationship, denoted as $r_s$ (SIMILAR). Subsequently, two directed edges, $\langle a, r_{a \to v}, v \rangle$ and $\langle v, r_{v \to m}, m \rangle$, are incorporated into the edge set, where $r_{a \to v}$ and $r_{v \to m}$ represent the semantic relationships between the pairs $(a, v)$ and $(v, m)$, respectively. Consequently, this process yields a heterogeneous semantic graph grounded in Semantic Role Labeling (SRL), effectively capturing the core semantic interrelations among entities within the document. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 6.914304334 , the TTR score is 0.399193548 , the CV score is 0.519144417 , and the perplexity score is  15.74489212 .","The text is classified as human-written because it describes a complex algorithm for creating a semantic network from textual data using concepts like nodes, edges, and semantic relationships. This level of detail and specificity suggests expertise in natural language processing and machine learning techniques, which would typically be possessed by humans rather than machines. The mention of linguistic features such as burstiness, type-token ratio, sentence length variability, and perplexity further supports the classification, indicating sophisticated analysis capabilities often associated with human writers."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","**Comparison with Prior Work**

Relative to existing literature, the ""Delete, Retrieve, and Generate"" (DRG) framework (Li et al., 2018) and its subsequent extension (Sudhakar et al., 2019) have demonstrated efficacy in generating outputs adhering to a target style while maintaining a high degree of source content preservation. Nevertheless, the DRG approach is subject to several significant limitations: (1) the deletion module frequently misidentifies content words as stylistic markers, resulting in their unnecessary removal; (2) the retrieval phase presupposes the existence of semantically similar content across both source and target styles; (3) the retrieval process incurs substantial computational overhead, particularly when applied to large-scale datasets; (4) the pipeline operates on the assumption that style transfer can be achieved solely by excising stylistic markers and substituting them with target-style phrases; and (5) the method depends on a fixed corpus of style attribute markers, thereby constraining its generalizability to unseen data during inference.

In contrast, the proposed methodology diverges from these approaches by eliminating the retrieval stage and refraining from assumptions regarding the presence of overlapping content phrases between styles. Consequently, this architecture offers enhanced computational efficiency and demonstrates greater robustness to noise. Furthermore, while Wu et al. (2019) conceptualize style transfer as a conditional language modeling task focused exclusively on sentiment modification—specifically as a cloze-style exercise of inserting appropriate words—the current work is capable of generating complete sentences in the target style. Additionally, the proposed approach exhibits superior generalizability, as evidenced by experimental results across five distinct style transfer tasks.

**3. Tasks and Datasets**

**3.1 Politeness Transfer Task**

The politeness transfer task focuses on sentences wherein the speaker communicates a requirement necessitating fulfillment by the listener. Typical instances include imperatives (e.g., ""Let's stay in touch"") and interrogatives expressing proposals (e.g., ""Can you call me when you get back?""). Adhering to the taxonomy established by Jurafsky et al. (1997), such utterances are collectively termed ""action-directives."" The objective of this task is to transform action-directives into polite requests. While multiple strategies exist for enhancing politeness, effective modifications for the aforementioned examples include the addition of expressions of gratitude (e.g., ""Thanks, and let's stay in touch"") or the utilization of counterfactual constructions (e.g., ""Could you please call me when you get back?"") (Danescu-Niculescu-Mizil et al., 2013). . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.011786449 , the TTR score is 0.510548523 , the CV score is 0.956778385 , and the perplexity score is  36.67646408 .","This text is classified as human-written because it appears to be a summary or analysis written by an individual rather than generated automatically. It contains specific details about previous research and experiments, which suggests an understanding of academic writing and the ability to synthesize information effectively. The use of proper citations and references, along with detailed explanations of concepts like burstiness and perplexity, further indicates that this text was authored by a person knowledgeable in the field of natural language processing."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","With the rise of Computational Thinking (CT) instruction at the elementary level, it is imperative that elementary computing instruction support a variety of learners. A popular pedagogical approach for this age group is Use–>Modify–>Create, which introduces a concept through a more scaffolded, guided instruction before culminating in a more openended project for student engagement. Yet, there is little research on student learning during the Use–>Modify step, nor strategies to promote learning in this step. This paper introduces TIPP&SEE, a metacognitive learning strategy that further scaffolds student learning during this step. Results from an experimental study show statisticallysignificant performance gains from students using the TIPP&SEE strategy on nearly all assessment questions of moderate and hard difficulty, suggesting its potential as an effective CS/CT learning strategy. To provide equity in K12 computing education, momentum has been building for integrating computer science into elementary school classrooms in school districts such as Chicago, San Francisco, and New York City. However, many curricula and programming platforms were designed for informal learning environments when there was very little opportunity for computational thinking education in formal education. These curricula and tools have been tremendously successful. Research has shown informal learning spaces to be effective at increasing awareness and engagement, changing perceptions of computing, and building selfefficacy, especially for students from underrepresented communities in computing . While there has been tremendous success for some in the informal space, the goal of integrating into schools is to translate those successes to a much broader audience within the school day. Merely providing the same instruction within the school day may not result in the same outcomes. Providing access to computing curricula is just one part of the solution; CS/CT instruction must also be effective for diverse students. Four broad categories of students – students with disabilities, English language learners (ELL), students of color, and students in poverty – face academic challenges that may interfere with their success in a computing curriculum. It is time to revisit programming languages, programming environments, and curricula with the goal of equitable learning outcomes. More recent work has shown strong correlations between overall school academic performance and learning in a computer science curriculum built on openended projects designed using a Constructionist pedagogical approach . This points to the need for scaffolding for some students. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.998123869 , the TTR score is 0.484918794 , the CV score is 0.344131319 , and the perplexity score is  50.68941498 .","The text appears to be labeled as AI-generated based on several linguistic features:

1. **Burstiness**: The burstiness score of 1.9981 indicates high variability in word frequency, typical of generated text rather than human-written content.
   
2. **TTR (Type-Token Ratio)**: A TTR of 0.4849 suggests a low ratio of distinct words to total words, indicating a lack of diversity in vocabulary use, common in automated text generation."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","I agree that successful people are willing to try new things and take risks, instead of just sticking to what they already know how to do well. When people try something new, it shows they are using their creativity and pushing boundaries in that area. Taking risks often leads to innovation and new discoveries.

For example, if someone works in a research lab and only does what they already know, they’re not contributing anything new to their field. But if they take risks and experiment with new ideas, they might invent something important.

Trying new things also means we’re using our minds to their fullest potential, and there’s really no limit to what we can imagine or create. Sometimes, we get inspired to learn something new, like solving puzzles or playing challenging games. These activities show that we’re open to new experiences.

Of course, trying new things can be difficult, and sometimes we might struggle or fail to come up with good ideas. We might not always succeed at first. However, the most important thing is that by taking risks and trying new things, we gain more knowledge about the world. This can lead to greater success and help us reach new heights, because success often comes from stepping out of our comfort zones and taking risks. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.405754515 , the TTR score is 0.55785124 , the CV score is 0.35976527 , and the perplexity score is  18.56014061 .","This text is likely classified as AI-generated due to its formal tone, structured language, and consistent use of complex vocabulary and sentence structures typical of written content produced by artificial intelligence systems. The text discusses concepts related to creativity, risk-taking, and innovation, which align with topics commonly explored in AI-generated content aimed at educating readers on these subjects. Additionally, the high burstiness, type-token ratio, sentence length variability, and low perplexity suggest an intelligent algorithmic approach rather than human authorship."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Something that has been debated about a lot recently, people around the world have different views about to whom the design of newly constructed buildings should be controlled by. Today, I am here to discuss both sides of the matter and at the end will give my own view.
The people who believe the design of these buildings should be controlled by governments believe so, because they feel that the governments will design the buildings in a way that is not too flashy and nor to minimalistic. It has been seen may times that buildings built by constructing companies/ the financers turn out to be either very flashy or minimalistic. A great example of this is in India's capital, Delhi, where an originally planned office, had exteriors constructed in a very childish and jazzy manner. A thing to also keep in mind that this was the construction of a private office for the top lawyer in Delhi, Arun Deshmukh. He handed over the design of his office to the constructors feeling they won't do anything immature. 4 years later, however, when he went to see the final result of his much-awaited private office, he was completey bewildered by the sight. Instead of seeing a normal office-like building with normal cement on it's exterior, he saw that his dream office looked the exterior of a night club! Instead of normal cement, the exterior was built with transparent glass, and Mr. Deshmukh's name and designation, written in a florescent like way, was carved out on a 3 metre long glass slab!!
Coming to the people who believe the control of the design should go to the constructors/people who finance the building think so because they feel that not everything should be in the hands of the government. They also feel that since it was their initiative to get that building built, they should have the final say on how it should look like.
When it comes to my opinion, i believe that the control of the design of the building should go to the person/people who first thought of getting it constructed, meaning the people hired architects and did the needful to get an ideal design of the building they want to construct. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.922850123 , the TTR score is 0.454545455 , the CV score is 0.280529002 , and the perplexity score is  34.04827499 .","This text appears to be human-written based on several linguistic features:

1. **Complex Sentences**: The text contains complex sentences with multiple clauses, indicating a high level of cognitive effort required to write them.

2. **Burstiness**: The burstiness score of 3.9229 suggests frequent pauses between ideas, which is characteristic of natural language production rather than automated text generation.

3. **TTR (Type-Token Ratio)**: With a TTR of 0.45"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","The Runestone platform is an open-source, extensible system designed to deliver free electronic textbooks to a global audience of over 25,000 daily learners. The repository currently hosts eighteen computing textbooks, several of which have been translated into multiple languages. These resources cover a broad spectrum of curricula, including secondary computer science (AP CSP and AP CSA), introductory and intermediate programming (CS1 and CS2), data science, and web development. The platform supports executable and editable code examples in Python, Java, C, C++, HTML, JavaScript, Processing, and SQL.

Runestone offers a comprehensive suite of features tailored to instructors, learners, authors, and researchers. Instructors can construct custom courses from existing textbooks, manage student enrollment, generate assignments from pre-existing or newly authored materials, grade submissions, and visualize student progress. Learners benefit from the ability to execute and modify code examples and receive immediate feedback on practice questions. The system incorporates standard assessment formats, such as multiple-choice questions, alongside specialized interactive types, including adaptive Parsons problems. Authors may modify existing texts or create new content using reStructuredText, a markup language. Furthermore, researchers can develop and evaluate novel interactive features, conduct experiments, and analyze log data.

This paper outlines the platform's architecture, highlights its distinctive capabilities, provides an overview of instructional workflows, summarizes relevant empirical studies, and details future development plans. The work addresses themes in informal education, student assessment, K–12 education, adult education, online learning, adaptive learning, and intelligent textbooks.

Historically, early electronic books functioned merely as digital replicas of print materials, offering portability and searchability. In contrast, contemporary web-based platforms for computer science education—such as the open-source OpenDSA, the commercial zyBooks, and the open-source Runestone—provide significantly enhanced functionality. These systems integrate instructional content with diverse practice problems that offer immediate feedback, a critical component for effective learning. An ITiCSE working group has predicted that traditional computer science textbooks will increasingly be supplanted by online materials that seamlessly combine content and assessment. For instance, commercial platforms like zyBooks incorporate minimal text, imagery, code animations, multiple-choice and fill-in-the-blank questions, mixed-up code (Parsons) problems, matching exercises, and executable code with unit tests. Empirical studies comparing interactive electronic books with traditional static textbooks have demonstrated that interactive formats significantly improve student performance, engagement, and time-on-task. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 6.567162534 , the TTR score is 0.537777778 , the CV score is 0.260597604 , and the perplexity score is  23.76222992 .","The text appears to be human-written because it describes a specific educational platform called Runestone, detailing its features, functionalities, and benefits. It also mentions academic research and studies related to the effectiveness of interactive electronic books compared to traditional ones. The use of technical terms and detailed descriptions suggests a high level of expertise and originality typical of human-authored content rather than automated generation."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","It is important to experiance and learn certian kind of habits at younge age for children to develop them as they grow older. Growing vegetables and taking care of animals are two main practices children should develop at their younger age. In this essay I will discuss some disadvatages that would affect children during this process and explain why I feel that there are better advatages.
The most significant advatage of learning to grow vegetables is that children will initally start to understand the importance of eating healthy and also the process of growing vegetables. In this case children will learn not to waste food and how hard and the effort it takes to grow these vegetables. It is important that we keep reminding children from their primary age so that they will remember and practice as they grow older. Taking care of animals is another important thing that children should learn in their primary , this will teach children to care for others and not be frighten of animals. Furthermore, they will learn to respect animals and love them.
Some of the disadvantages of keeping animals is that some children has different types of allergies which will lead them to be more sick. However, some animals tend to be more dangerous than others. Another disadvatages is that when growing vegetables the compost and pestisides are dangerous to children or it might sometime be allergic to them as well. Insect bites during planting will also lead children to get sick.
In conclusion it is important to learn how to grow vegetables and take care of animals at their younger age since they develop certain habits and learn more indeapth which will be helpful as they grow older. Eventhough, some argue that it is more riskier for children I believe that the advatages outweight disadvatages. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.079055835 , the TTR score is 0.404984424 , the CV score is 0.320690751 , and the perplexity score is  46.95830917 .","This text appears to be written by an individual who is knowledgeable about child development and agriculture. The language used is clear and concise, with a focus on providing information and arguments rather than engaging in complex rhetorical devices. The tone is informative and authoritative, which aligns with the style often found in human-authored texts. Additionally, the structure of the argument follows a logical progression, starting with advantages and moving towards disadvantages, followed by a summary of both sides. This type of writing is characteristic of essays or other"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Furthermore, the baseline methods also exploit the same unlabeled corpus X and labeled corpus Y as described in the original papers. 5.3 Models PPVAE is a modelagnostic approach, which means that both the encoders and encoders of P RE - TRAIN VAE and P LUGIN VAE can be modified to work under different settings. Here, we describe the model architecture used in our experiments. P RETRAIN VAE. For the encoder, we use a onelayer Bidirectional Gated Recurrent Unit (BiGRU) with 256 hidden units in each direction as its encoder. Two linear FullyConnected (FC) layers are used for reparameteristic trick. For the decoder, we use a Transformer ( 3 layers, 8 heads). Additionally, we add extra positional embedding after each block, and the linearly transformed encoded vector is provided as input for each block. For a fair comparison, we use the same encoderdecoder architecture for both SVAE and CTRLGEN. P LUGIN VAE. The encoder is a twolayer FC network of 64/32 hidden units taking input in d g dimensions with an additional linear output layer of d c units. The decoder is a twolayer FC network of 32/64 hidden units taking the latent variable in d c dimensions as input with a linear output layer of d g units. The activation function used in the FC networks is LeakyRelu. 5.4 HyperParameters P RETRAIN VAE. The size of latent space d g is set to 128 . The word embedding is in 256 dimensions and randomly initialized. The output softmax matrix is tied with the embedding layer. For the adversarial classifier, we adopt two 128D hidden FC layers with LeakyRelu activation and one 1D output linear layer without bias. The balance coefficient λ is 20 for Yelp and 15 for News Titles. We train the WAEGAN with Wasserstein Divergence to smooth the training process. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.599608198 , the TTR score is 0.470238095 , the CV score is 0.520484577 , and the perplexity score is  56.14089203 .","This text is likely classified as AI-generated because it describes a detailed implementation of a machine learning model called P RETRAIN VAE, which is designed to generate text similar to human-written content. The text provides technical details about the model's architecture, hyperparameters, and training process, which are consistent with how such models would be documented. Additionally, the mention of ""AI-generated"" in the label suggests that this text was created by or for an AI system, possibly through natural language processing techniques applied to"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Although it can be difficult for many people to try something new that they’ve never done before—since it often feels like taking a risk—it’s still better than simply sticking to what you already know. Stepping out of your comfort zone and doing something new has many benefits. For one, it breaks up your routine and helps keep life from becoming boring, even if you were already content before trying the new activity. It also helps you adapt to changes more easily. For example, if you learn how to cook, you’ll be prepared for a time when you might need to cook for yourself.

There are also mental benefits. When you learn something new, you engage parts of your brain that weren’t active before, which can help you become smarter by creating new neural connections. Physically, trying a new activity—like taking up a new sport—can work different muscles in your body. For example, learning to play golf will activate muscles you might not have used before.

Finally, facing your fears and doing things you’ve always wanted to try can boost your self-confidence. You’ll be glad you took the chance, rather than regretting missed opportunities in the future. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 0.770601255 , the TTR score is 0.585253456 , the CV score is 0.419177559 , and the perplexity score is  19.78757668 .","The text appears to be written in an AI-generated style due to its formal tone, structured format, and use of complex vocabulary and sentence structures typical of human-written texts but lacking the nuances or creativity found in human writing. The high burstiness score suggests rapid word transitions without pauses between clauses, common in machine-generated text. Additionally, the consistent use of technical terms related to cognitive psychology and neuroscience indicates an attempt to convey sophisticated ideas using artificial intelligence terminology, further supporting the classification as AI-generated."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","I think that being succesful in life depends on your personal perseption of goals, determination and commintment to your plans. Being succesful also relies on your habilities and capabilies and in the type of choices that you have to make in time.

I do agree thought, that taking risks o making risky desicions can make succesful a life plan, because in order to achieve goals sometimes the things we can do well will not help us at all and therefore risk factor becames part of the ecuation.

For example if we love our job but its asked to do something that we are not used to or we do not know well how to do we have two options. 1. Taking the risk of doing it, taking into account all the extra work that the task represents by itself, or 2. we let other person do the work and fail.

If we decide to take the risk, we have to be aware that it is going to be  difficult and sometimes an extra effort has to be done in order to finish the assigned duty. The final outcome of the desicion whether its good or bad it depends on the performance of the person, but the standup point its that no matter the outcome,  we have to always learn from every situation, making the good or the bad experience successful in terms of personal growth.

Finally I think that no matter the situation we should be always looking forward to challege our knowledge and take some risks, thinking that we have to  learn from our daily experiences so in the future, all desicions made are going to be taken based on uppon them. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.090590692 , the TTR score is 0.485148515 , the CV score is 0.4627058 , and the perplexity score is  77.95339203 .","The text appears to be written by a human for several reasons. Firstly, it contains natural language patterns typical of human communication such as complex sentences with multiple clauses and varied sentence structures. Secondly, it uses idiomatic expressions and colloquial language which are common in human writing. Additionally, the text demonstrates a coherent argumentative structure discussing various aspects of success including personal perception, abilities, decisions, and learning from challenges. These features indicate that the content was likely produced by a human author rather than generated by"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","The following metrics are defined based on the modified common neighbor framework, analogous to the Jaccard Similarity ($\zeta_{JS}$) approach:

**Adamic-Adar Index**
The Adamic-Adar score for an edge $(s, a)$ is computed as the sum of the inverse logarithmic degrees of their common neighbors:
$$ \zeta_{AA}(s, a) = \sum_{n \in \Gamma(s) \cap \Gamma(a)} \frac{1}{\log d(n)} \quad (16) $$

**Common Neighbors**
The Common Neighbors (CN) score for an edge $(s, a)$ is defined as the cardinality of the set of shared neighbors between nodes $s$ and $a$. Consistent with the formulations for $\zeta_{JS}$ and $\zeta_{AA}$, the score is calculated as:
$$ \zeta_{CN}(s, a) = |\Gamma(s) \cap \Gamma(a)| + |\Gamma(a) \cap \Gamma(s)| \quad (17) $$

**Preferential Attachment**
For Preferential Attachment (PA), the edge score $(s, a)$ is determined by the product of the degrees of the two constituent nodes:
$$ \zeta_{PA}(s, a) = d(s) \cdot d(a) \quad (18) $$

**Experimental Results**
The training protocol employed for these metrics is identical to that used for Jaccard Similarity. Empirical results indicate that while the Adamic-Adar index outperforms both Preferential Attachment and Common Neighbors, it consistently yields lower performance than Jaccard Similarity (refer to Table 5).

***

**Building a User-Generated Content North African Arabizi Treebank: Tackling Challenges in Low-Resource Dialects**

*Authors: Djamé Seddah, Farah Essaidi, Amal Fethi, Matthieu Futeral, Benjamin Muller, Pedro Javier Ortiz Suárez, Benoît Sagot, Abhishek Srivastava*
*Affiliations: Inria, Paris, France; Sorbonne Université, Paris, France*

**Abstract**
This study introduces the first treebank dedicated to a Romanized, user-generated variety of Algerian Arabic, a North African dialect characterized by frequent code-switching. The corpus comprises 1,500 sentences fully annotated for morphosyntax and Universal Dependencies, accompanied by word-level and sentence-level translations. This resource is made freely available to the research community and is supplemented by a dataset of 50,000 unlabeled sentences collected via intensive data mining from Common Crawl and other web sources. Preliminary experiments demonstrate the corpus's efficacy for Part-of-Speech (POS) tagging and dependency parsing. We posit that this contribution extends beyond the low-resource language community, representing the first instance of a comprehensive dataset—combining substantial unlabeled and annotated data—for an emerging, morphologically rich, code-switching dialect. Consequently, it serves as a rigorous testbed for contemporary Natural Language Processing (NLP) methodologies.

**1. Introduction**
Prior to the widespread adoption of fully unsupervised techniques capable of mitigating the field's reliance on annotated data, the challenge of constructing cost-effective, high-quality datasets for under-resourced languages remains a critical priority. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 7.560635529 , the TTR score is 0.411073826 , the CV score is 0.928866698 , and the perplexity score is  43.42619705 .","This text appears to be written by humans because it contains elements typical of human-authored content such as detailed explanations, citations, and specific examples related to linguistic features like burstiness, type-token ratio, and sentence length. Additionally, the use of proper nouns, dates, and references to existing studies suggests a scholarly or academic tone consistent with human-authored work rather than automated text generation."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Today, social media plays a major role in the lives of most people, especially younger generations. Many teenagers see it as an essential and convenient tool for both communication and entertainment. Still, not everyone uses social media responsibly, which has led to suggestions that it should only be available to those over eighteen. While there are negatives—like cyberbullying or the potential for distraction—almost everything in life comes with its own set of problems. Using social media is a personal choice, and many young people are perfectly capable of managing their accounts without issues. It should be up to parents, not the government, to supervise those under eighteen. Statistics show that more than eighty percent of teens use some form of social media. It helps them chat online, keep in touch with friends and family around the world, and share updates about their lives through text, photos, or videos. Social media allows people to express their thoughts and stay connected globally. Banning one of the largest groups of internet users from social media just because some people misuse it would be unreasonable and unfair. Not only would companies lose profit due to fewer users, but enforcing age restrictions and verifying users’ ages would also be challenging. In my view, making social media off-limits to anyone under eighteen would be unnecessary and unreasonable. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.080700606 , the TTR score is 0.605691057 , the CV score is 0.233518807 , and the perplexity score is  23.72699356 .","This text is likely classified as AI-generated based on several linguistic features:

1. **Burstiness**: The burstiness value of 1.0807 indicates high variability in sentence length, which is characteristic of human-like writing rather than machine output.
   
2. **TTR (Type-Token Ratio)**: A TTR of 0.6057 suggests a relatively low redundancy in word usage, indicating a natural, conversational style typical of human language.

3. **CV"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Among collections comprising three elements, the sets {New, York, City} and {New, York, Times} exhibit the highest degree of semantic relatedness. Notably, certain subcollections containing a greater number of terms demonstrate a reduced soft cardinality, thereby evidencing non-monotonic behavior. This phenomenon illustrates that all $n$-wise relationships within a collection can be inferred from pairwise relationships through the application of soft cardinality.

Furthermore, it is established that the informational content of natural language does not constitute a mathematical measure with respect to word count. It is widely accepted that an increase in the number of words within a message does not necessarily correlate with an increase in conveyed concepts or information. For instance, lexical definitions often display non-monotonic characteristics: the phrase ""a man who works"" conveys at least two distinct concepts (""man"" and ""to work""); however, the addition of the term ""wood"" collapses these into a single, more specific concept (""carpenter"").

Regarding the property of idempotence, the soft cardinality of a collection remains invariant under proportional scaling of element repetitions. Consequently, soft cardinality is idempotent with respect to the unary operation of applying proportional variations to element frequencies, a property that holds regardless of the magnitude of the positive real scaling factor.

In the context of set operations, the soft cardinality of the intersection of two collections cannot be derived directly from their crisp intersection, as the standard intersection operator is inherently binary. In a crisp framework, disjoint sets yield an empty intersection with a cardinality of zero. Conversely, the soft cardinality of an intersection is defined as the sum of the individual soft cardinalities minus the soft cardinality of their union. This formulation permits non-zero intersection values for collections that share no identical elements but possess semantically similar ones.

Once the soft cardinalities of the union, intersection, and individual sets are determined, all remaining regions within the Venn diagram of two sets can be calculated. These metrics serve as the foundational components for constructing cardinality-based resemblance coefficients. Consequently, numerous parametric and non-parametric families of cardinality-based similarity functions have been developed based on these principles. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.710934744 , the TTR score is 0.518518519 , the CV score is 0.400045722 , and the perplexity score is  38.67129898 .","This text is likely classified as human-written because it contains detailed explanations about complex topics such as semantic relatedness, soft cardinality, and linguistic features like burstiness and type-token ratio. The use of technical terminology and the structure of the text suggest it was written by someone knowledgeable in the field, possibly a linguist or computer scientist working in the area of computational linguistics or information theory. The inclusion of examples and calculations further supports this classification, indicating that the author has provided practical applications and demonstrations of"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","I totally agree witrh this statement. Taking risks and trying new things can make a person more creative. In addition, trying new things can make a person become more confident about himself/herself. A successful person is not willing to live a boring life.
	First of all, trying news things is the basic requirement  for people who want to become successful. Successful people are ambitious and passionate about life. Success cannot lack of risks and attempts. Invention needs creative mind while crestive mind needs trying of new things. Many famous scientists eventually become successful only because they are willing to try many different kinds of things. Without the courages of taking risks, they would never innvent so many 'impossible things'. So, successful people want to try new things because they have creative mind. 
	Secondly, trying news things can make a person feel more confident about himself or herself. At the same time, confidence is required by people who want to be success. For example, if a man is doing his new experiment, he has tried more than five hundreds time to test the experiment, he may eventually gain priceless conifidence once he accomplishes the experiment successfully. Successful business esalways have some risks. Thus, in order to become successul in business world, people have to take some risks. As long as they get through the risks, then they would taste the fruit of success.
	Lastly, a successful man does not want to live a normal and boring life because he is  ambitious and passionate about his life. People can learn precious feelings from failures,gain important experience from the risks. Thus, successful people want to gain the valuable experience and  feelings in the process of success. 
	In conclusion, since people can gain important experiences from the process of trying new things and taking risks to become successful, the successful people would not do what they already know how to do. In addition, successful  people can gain confidence which is the basic requirement for  success. Successful people are so creative that they must have a grast interest in try new things. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.930928893 , the TTR score is 0.375959079 , the CV score is 0.444277253 , and the perplexity score is  63.94713593 .","The text is likely human-written due to its coherent structure, logical flow, and use of persuasive language aimed at convincing readers of the importance of taking risks and trying new things for personal growth and success. The text employs rhetorical devices such as repetition (""In addition"", ""So"") and emotional appeals (""confident"", ""ambitious""), typical characteristics of written content intended to persuade rather than inform. The high linguistic features like burstiness, type-token ratio, sentence length variability, and perplexity suggest an effort"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Such datasets must not only be large-scale but also exhibit reduced noise and enhanced diversity. Furthermore, they should facilitate the systematic evaluation of a model's capacity to encode domain-specific knowledge and to generalize effectively to novel questions and contexts.

**6. Related Work**

This section provides a concise overview of open-domain, biomedical, and clinical question answering (QA) tasks, which are most pertinent to the present study. QA is defined as the automated process of responding to human inquiries by leveraging external sources, including the World Wide Web (Sun et al., 2016), knowledge bases (Yih et al., 2015; Sun et al., 2015), and unstructured text (Chen et al., 2016). A significant subset of QA is reading comprehension (RC), which involves answering questions based on the analysis of a provided passage (Hirschman et al., 1999).

The recent availability of large-scale RC datasets, such as CNN/Daily Mail (Hermann et al., 2015) and the Stanford Question Answering Dataset (SQuAD) (Rajpurkar et al., 2016, 2018), has enabled the application of deep neural networks to RC tasks (Hermann et al., 2015; Wang and Jiang, 2017; Seo et al., 2017; Chen et al., 2017). More recently, contextualized word representations and pre-trained language models—including ELMo (Peters et al., 2018), GPT (Radford et al., 2018), and BERT (Devlin et al., 2019)—have demonstrated substantial utility across various natural language processing (NLP) tasks, including RC. By processing diverse contexts within extensive corpora, these pre-trained models capture rich semantic nuances, generating more accurate and precise word representations tailored to specific contexts. Consequently, even simple classifiers or scoring functions built upon these contextualized representations have proven effective in extracting answer spans (Devlin et al., 2019).

In the domains of biomedical and clinical QA, the scarcity of large-scale annotated data has historically necessitated reliance on rule-based systems and heuristic feature engineering (Lee et al., 2006; Niu et al., 2006; Athenikos and Han, 2010). . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 5.282481901 , the TTR score is 0.481389578 , the CV score is 0.530588171 , and the perplexity score is  37.06784439 .","This text appears to be written by a human because it contains several characteristics typical of human-generated content:

1. **Complex Sentences**: The text uses complex sentences with multiple clauses and sub-clauses, indicating a high level of cognitive complexity.
2. **Academic Tone**: It adopts an academic tone appropriate for research papers, using formal language and referencing previous studies.
3. **Technical Terminology**: The use of terms like ""domain-specific knowledge,"" ""generalize effectively,"" and ""unstructured text"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","The course assessment framework comprises online quizzes, Group Guided Assessments (GGAs), a final group project, and a final examination. While online quizzes and GGAs utilize a multiple-choice format to address similar problem sets, the final examination is exclusively non-multiple-choice. All assessments are conducted under open-book conditions; however, while students may utilize any resources, including digital materials, for assignments and quizzes, the final examination restricts permitted resources to hard-copy formats only.

Take-home assignments are allocated a two-week completion window. Online quizzes are administered within a minimum 12-hour window on the day following the corresponding GGA and may be completed remotely. Each quiz consists of five multiple-choice questions with a 25-minute time limit.

The instructional modules do not follow a strictly sequential progression. Feedback on assignments is provided prior to the GGAs, and the GGA and online quiz for module $i$ overlap temporally with module $i+1$.

The GGA is a 100-minute in-class collaborative activity designed to solve a series of problems. It consists of ten multiple-choice questions structured into individual and group phases. Initially, students must answer all ten questions individually and submit their responses online. Subsequently, students form groups to discuss the problems and record collective answers. The recommended and maximum group size is five students, though this limit may be adjusted to accommodate absences.

Feedback mechanisms differ between the two phases: no immediate feedback is provided during the individual component, whereas the group stage employs an Immediate Feedback Assessment Technique (IFAT). In this stage, the entire group receives a uniform score based on their collective performance. Groups are provided with an IFAT form, referred to as a ""scratchy,"" containing four or five answer options (A–D or A–E). For each question, the group scratches the box corresponding to their selected answer. If a star symbol is revealed, the answer is correct, and full marks are awarded. If the answer is incorrect, the group may attempt again; however, the score for that question decreases with each additional attempt. No credit is awarded if all possible answer options for a question are scratched without revealing the star. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.132341506 , the TTR score is 0.472772277 , the CV score is 0.372308825 , and the perplexity score is  31.44535637 .","This text appears human-written due to several linguistic features such as its formal tone, detailed explanations of educational concepts, and consistent use of academic language typical of written coursework descriptions. The high burstiness and type-token ratio suggest a coherent, well-structured document rather than random text generated by a machine. Additionally, the specific details about assessment methods, assignment deadlines, and teaching strategies align closely with what one would expect from a human-authored educational material."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","""Big risks brings big opportunities"" I can not remember very well but I heard it or read it from somewhere which tells that by taking risks you gain chance to well-off. I agree with this aggreement and also agree that successful people are the ones who take risks and try new things. 
There are lots of things you can do for being a successful person. You should work hard, try to learn, watching the new technologies. Althogh these make you successful\ they are not enough for being successful. For instance you can know every thing about your bussiness\ you watching every improvement in your job, you worh hard. However you do not have the courage to imply these information to your bussines. Then what are you going to do wtih them? People try new things and take risks in order to be seccussful.
Firstly people shoul try new things. Todays world everything changes fastly and you have to follow these changes in order to be successful.. For example you have to learn internet, or how to control your e-mails.  Nowadays every thing runs from internet and by e-mail. Because it is one of the fastest and the cheapest way to communicate with long distances.There are few people left who uses letters to communicate. 
Secondly and I think this one is important than trying new things is take risks. Taking risks is the key factor that brings you to success. If you always do what you already do really well you stay in where you are. Taking risks concluded with different solutions and also higher revenues. However one important thing about taking risk is you should think carefully and well prepared before you take the risks. Because besides big opportunities it can also bring sadness.
I wanna give a great example from my country, Turkey. The founder of Turkish Republic, Mustafa Kemak Ataturk. He takes big risks during the per'od he wnated to found Turkish Republic. But as a result he became successfl.
As a conclusion taking risk and . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.254413162 , the TTR score is 0.466307278 , the CV score is 0.471312896 , and the perplexity score is  71.29364014 .","The text is classified as human-written because it exhibits characteristics typical of human language such as burstiness, type-token ratio, sentence length variability, and high perplexity. These linguistic features suggest an original, creative piece rather than machine-generated content. Additionally, the text discusses personal experiences and opinions on success, which aligns with human writing style."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","That language transmission increases pressure for structured representations is an established fact. Here, we reversed the arrow of causality and showed that, if compositionality emerges (due to chance during initial language development), it will make a language easier to transmit to new agents. Compositionality might act like a “dominant” genetic feature: it might arise by a random mutation but, once present, it will survive and thrive, as it guarantees that languages possessing it will generalize and will be easier to learn. From an AI perspective, this suggests that trying to enforce compositionality during language emergence will increase the odds of developing languages that are quickly usable by wide communities of artificial agents, that might be endowed with different architectures. From the linguistic perspective, our results suggest an alternative view of the relation between compositionality and language transmission–one in which the former might arise by chance or due to other factors, but then makes the resulting language much easier to be spread. Compositionality and disentanglement Language is a way to represent meaning through discrete symbols. It is thus worth exploring the link between the area of language emergence and that of representation learning. We took this route, borrowing ideas from research on disentangled representations to craft our compositionality measures. We focused in particular on the intuition that, if emergent languages must denote ensembles of primitive input elements, they are compositional when they use symbols to univocally denote input elements independently of each other. While the new measures we proposed are not highly correlated with topographic similarity, in most of our experiments they did not behave significantly differently from the latter. On the one hand, given that topographic similarity is an established way to quantify compositionality, this serves as a sanity check on the new measures. On the other, we are disappointed that we did not find more significant differences between the three measures. Interestingly one of the ways in which they did differ is that, when a language is positionally disentangled, (and, to a lesser extent, bagofsymbols disentangled), it is very likely that the language will be able to generalize–a guarantee we don’t have from less informative topographic similarity. The representation learning literature is not only proposing disentanglement measures, but also ways to encourage emergence of disentanglement in learned representations. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.77972028 , the TTR score is 0.461538462 , the CV score is 0.433923456 , and the perplexity score is  66.74279022 .","The text is classified as AI-generated because it discusses concepts related to artificial intelligence, such as compositionality, language emergence, and representation learning. It also mentions specific metrics used to measure these concepts, including burstiness, type-token ratio, sentence length variability, and perplexity. These topics align closely with fields of study often associated with AI research, particularly those concerned with machine learning algorithms and natural language processing techniques. Additionally, the text's focus on theoretical aspects of AI, combined with its technical terminology and"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","I completely agree that successful people are those who are willing to try new things and take risks, rather than just sticking to what they already do well. In my opinion, exploring new experiences is the key to achieving success. When someone steps out of their comfort zone and learns new skills, they have the chance to master those skills and excel, which can lead to success in new areas. Others may even see this person as a pioneer in their field. For instance, a manager aiming for success might develop a new approach to running the business, such as improving work methods, reducing costs, or motivating employees through incentives. By discovering better ways of working, this manager could help the company save time and money, earning recognition as a successful leader.

Additionally, I believe that successful people are more comfortable with taking risks than most, thanks to their enthusiasm for new challenges and their optimistic outlook. Being positive and enthusiastic makes it easier to tackle unfamiliar situations and find effective solutions to problems. For example, if a manager truly believes in his ideas, he is more likely to reach his goals. Even when difficulties arise, his confidence and determination will push him to work harder and overcome obstacles instead of giving up.

In summary, I fully support the idea that only those who are willing to take risks and try new things can drive progress and bring about positive change. Simply repeating the same actions, even if done well, will not lead to new ideas or improvements in the long run. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.669305225 , the TTR score is 0.571917808 , the CV score is 0.269657804 , and the perplexity score is  17.9661808 .","The text appears to be written by an AI system because it follows certain linguistic features typical of generated content. The burstiness score indicates rapid changes in sentence length, which is common in machine-generated text. The high type-token ratio suggests a mix of different types of sentences without much repetition, another characteristic often seen in AI output. Additionally, the consistent use of short, declarative sentences aligns with how AI systems tend to structure information. These features collectively point towards the text being created by an artificial intelligence"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","The components of a course–lecture, recitation, homework, and exams–do not exist in isolation. Rather, these components are designed to complement and supplement each other as students interact with them in various ways and at various times over a semester. The interactions (and potential interactions) of these course components can add to the cognitive challenges already present in coding tasks and programming assignments. As part of a larger investigation related to the broad research question, “What are the experiences of students in the CS1 course as they complete homework assignments and submit them through an autograding system?”, a more specific research question emerged as we started interviewing students: What are the sources of information that students use when they get “stuck” while working on homework assignments in a CS1 course and how do they navigate multiple sources of information? Homework is typically viewed as a tool to help students gain a better understanding of the course material and how to use it to succeed in the class . It remains an essential component of beginning programming courses because “goaldirected practice coupled with targeted feedback are critical to learning” , and programming assignments are the tools of choice for providing this handson practice. From the students’ perspective, time spent doing homework represents a large portion of their exposure to the material of the class, and, ideally, it would be the largest portion of time spent on programming tasks. Traditionally, homework is an aspect of the course that instructors have very little opportunity to observe, much less direct the way in which students engage with the assigned problems. Consequently, instructors are left wondering whether students have a clear understanding of their information needs when doing homework or not. When students do not know the next step for a homework assignment, they must work out how to find that information . How do students understand their information needs as they are working to solve programming problems? This is a question that has received attention in the field of information science as information seeking behavior and numerous theoretical frameworks have been used to explain these search patterns in lowguidance settings including Dervin’s theory of sensemaking and Bates’ “berrypicking” model . . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.50170068 , the TTR score is 0.480392157 , the CV score is 0.519376567 , and the perplexity score is  36.73607635 .","The text appears to be labeled as AI-generated based on its structure, vocabulary, and overall tone. The language used is formal and academic, typical of AI-generated content. Additionally, the text discusses complex topics like information seeking behavior and educational practices, suggesting expertise in the subject matter. The style and complexity align with what one might expect from an AI-generated piece rather than human-written content."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Most of the citizens believe that, all countries should focus on producing more materials or items in their prime targets. Although some others think that, it is not a suitable target. I strongly agree with the idea of production and I will explain, why it is in this essay.
First of all, countries should have a strong economy to survive and meet their citizen’s expectations. And for the strong economy, they should produce new materials and do more researches to create new products and goods. So, governments should open new fabrics and research labs for the related sectors in order to create a big economy for their future. For example, they can give the opportunity to invent their own electronic vehicle in the country and this will allow everyone to buy it cheaper and easily instead of buying from other countries. Which is a best example of the importance of the new products.
On the other hand, some people believe that tourism and entertainment have big impact on the country’s economy. For example, even though Dubai is half desert nation, the government create a big tourism sector all over the world that everyone heard about the country’s name. Although, there are not any factories to produce materials or goods, but there are many entertainment actions such as Img world, legoland and many more inside the country. And these are working very well and taking attention of the other people for tourism purposes.
In conclusion, governments should give effort to produce and develop new products and goods for the country’s future. Although, they can also use the other opportunities to create a well economy for their people, such as tourism and the other sectors. But, in my opinion, they should have goals for more production and creation. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.58279464 , the TTR score is 0.431610942 , the CV score is 0.290492287 , and the perplexity score is  50.02419662 .","This text is likely human-written due to several linguistic features:

1. **Burstiness**: The burstiness score of 2.5828 indicates frequent word changes within sentences, which is characteristic of human writing rather than automated text generation.

2. **TTR (Type-Token Ratio)**: A TTR of 0.4316 suggests that only about one-third of the words used are unique, indicating a high degree of repetition and redundancy, typical of human language.

3"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","The main question here is whether we should venture into new experiences and take risks, or simply stick to what we already know. With globalization accelerating economic growth, people have less time than ever to, for instance, develop new ideas or technologies. Without innovation, it becomes impossible to stay competitive in the global marketplace. That’s why it’s crucial to continuously explore new areas to maintain an edge.

I firmly believe that research is vital for the success of many industries. Through research, companies can discover exciting new opportunities that drive success. But research means stepping into uncharted territory. To create something truly innovative, you always have to think ahead. If a company chooses to play it safe and resists progress, it won’t be long before it falls behind.

Additionally, delving into new and unknown fields is not just beneficial—it’s also fascinating. This isn’t only true for researchers, but also for customers, who are always eager for the latest advancements in every industry. People expect to have access to newer and better products than those around them; if this drive for improvement disappeared, a major motivation in life would be lost. That’s why companies must meet their customers’ desires by pursuing new technologies if they want to succeed.

In my view, taking some risks is unavoidable. A life without risk is unimaginable, since we constantly have to make decisions without knowing the outcome. Those who avoid making any decisions at all are unlikely to find good jobs, lead fulfilling lives, or experience true happiness.

In conclusion, not just companies but individuals—everyone, in fact—must be willing to take some risks if they want to achieve success. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.786185133 , the TTR score is 0.593548387 , the CV score is 0.334116319 , and the perplexity score is  27.78655624 .","The text appears to be written in a formal, academic tone with complex sentence structures and technical language typical of AI-generated content. It discusses the importance of innovation, research, and taking risks in various contexts, including business and personal development. The use of specific examples and logical arguments supports its classification as AI-generated."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","All feature categories (static, dynamic, and contextual) were relevant to success, as measured by multiple analyses (Gini, ANOVA and leaveone-out). Our evaluation demonstrates significant scalability and generality. Our 980,000 instances were two orders of magnitude more numerous than similar related work and measurably more diverse; we augmented our dataset with a direct human study of 42 participants. Ultimately, Pablo was quite accurate, implicating the correct program location 59–77% of the time (compared to the Python interpreter’s 45% accuracy), outperforming the baseline (p = 0.018) and providing additional useful information 36% of the time (compared to Python’s 0%). Given the longstanding concern about students failing introductory programming courses, there is a need for interventions that may aid those students. In this work, we examine the potential benefit of three interventions based on prior computing education research (CER) or STEM education research literature: mindset interventions, the use of “Thinkathons” as an alternative to programming labs, and metacognitive interventions to encourage more productive study habits. We conducted an inclass study that controlled for both timeon-task and selection bias to investigate the potential benefits of integrating these interventions into the existing footprint of an introductory computing course. Despite the previously reported promise of the interventions we implemented, our findings were that in this context these techniques had only a mild positive effect for some students. We discuss possible reasons why these techniques are less successful than instructors might hope and argue for the need for more research on this topic. The problem of student retention and success in early programming courses is a challenging and longstudied problem. Decades of work has uncovered barriers students face in CS1 courses, including conceptual , pedagogical , and social barriers. Since then, a myriad of improvements have been shown to help improve retention and student success . However the problem is far from solved. CS1 failure rates remain high and participation from students from some groups remains low . Struggling students face challenges on a number of fronts including socialpsychological (specifically, inclusion), metacognitive, and cognitive (deep conceptual understanding). Students with less prior computing experience and women generally have a lower sense of belonging in the field (inclusion) , while women also report a lower selfefficacy in computing (metacognition) . . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.430680813 , the TTR score is 0.537931034 , the CV score is 0.501333932 , and the perplexity score is  98.82182312 .","The text appears to be labeled as AI-generated because it contains characteristics typical of machine-generated content, such as formal language, complex sentence structures, and technical jargon specific to computer science. Additionally, the text discusses academic topics like artificial intelligence, machine learning, and educational technology, which are often written by researchers or experts in these fields. The style and vocabulary used suggest a sophisticated level of writing appropriate for scholarly articles or reports, further supporting the classification as AI-generated."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Standard Natural Language Understanding (NLU) pipelines typically operate by first identifying the domain of an input utterance, subsequently predicting the user's intent, and finally populating the associated semantic slots (Hakkani-Tür et al., 2016; Chen et al., 2016). However, these foundational approaches predominantly address single-turn interactions, treating each utterance in isolation. To mitigate error propagation and enhance comprehension, recent research has demonstrated the efficacy of incorporating contextual information (Chen et al., 2015; Sun et al., 2016; Shi et al., 2015; Weston et al., 2015). Furthermore, leveraging distinct speaker roles has proven valuable for capturing conversational dynamics and improving overall system performance (Chen et al., 2017; Su et al., 2018c).

Natural Language Generation (NLG) constitutes another critical component of dialogue systems, tasked with synthesizing natural language responses conditioned on semantic representations provided by the dialogue manager. As the primary interface for user interaction, the quality of generated text is paramount for ensuring a satisfactory user experience. While rule-based methods offer robustness and adequacy, they often suffer from limited diversity, resulting in rigid and unsatisfactory interactions. Additionally, scalability remains a significant challenge, as the manual design of sophisticated domain-specific rules is labor-intensive. To address these limitations, prior work introduced Recurrent Neural Network Language Model (RNNLM)-based NLG architectures capable of training on dialogue act-utterance pairs without reliance on handcrafted features or explicit semantic alignment (Wen et al., 2015a). Subsequent advancements utilizing sequence-to-sequence (seq2seq) models with encoder-decoder structures, augmented by linguistic knowledge such as syntax trees, have yielded further performance improvements (Sutskever et al., 2014; Su et al., 2018b).

In the realm of Dual Learning, it is observed that while many tasks possess independent objectives, certain tasks exhibit a dual relationship wherein the input and target can be interchanged to formulate a reciprocal task. This structural duality represents a significant area of investigation. Specifically, two artificial intelligence tasks are considered structurally dual if the objective of one is to learn a mapping function from space $X$ to $Y$, while the objective of the other is to learn the inverse mapping from $Y$ to $X$. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.053952991 , the TTR score is 0.5625 , the CV score is 0.40038293 , and the perplexity score is  38.94654083 .","This text appears to be human-written because it discusses various aspects of natural language processing (NLP), including NLU and NLG components of dialogue systems. The content covers foundational approaches to NLU, their limitations, and how incorporating contextual information and different speaker roles improves understanding. It also mentions advancements in NLG using RNNLM and seq2seq models, addressing challenges like scalability. The text uses technical terms related to NLP and AI, which are consistent with human-authored content discussing advanced topics in"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Previous studies by Solomon and Stein et al. have examined the substitution of the K-medoids algorithm with the standard K-means method. In this context, the convergence of results between these two approaches serves as a robust indicator of cluster stability.

The MajorCluster algorithm, originally introduced in [citation], has been adapted in this work to mitigate issues related to cyclic dependencies arising from weak inter-document connections, particularly within small datasets. The fundamental principle of this method is to assign objects to clusters such that the similarity between an object and its assigned cluster exceeds its similarity to any alternative cluster. This optimization criterion distinguishes MajorCluster from both K-means (and K-medoids), which maximize proximity to cluster centroids, and Nearest Neighbor (NN) methods, which prioritize internal cluster connectivity regardless of inter-cluster similarity.

The MajorCluster procedure operates as follows: initially, each object is treated as an independent cluster. Subsequently, objects are iteratively merged with the nearest cluster. Unlike the NN method, this algorithm permits objects to be reassigned to different clusters throughout the construction process. This approach implements a graph clustering strategy based on the concept of weighted edge connectivity.

For the specific task of word clustering, the MajorCluster method was selected exclusively due to the absence of *a priori* information regarding the optimal number of keyword clusters. Extensive empirical testing indicates that this method outperforms alternative approaches across various document collections, including the RCV1 Reuters Corpus (Vol. 1) and the CICLing 2002 Conference collection, regardless of the indexing strategy employed.

The experimental dataset utilized in this study comprises the abstracts from the CICLing 2002 Conference (Conference on Computational Linguistics and Intelligent Text Processing), representing a specialized domain. The collection consists of 48 abstracts (approximately 40 KB of text), yielding a domain-specific vocabulary of roughly 390 terms following indexing. Although the dataset is relatively small, this scale is appropriate for the preliminary nature of this research, which aims to highlight the problem and propose potential solutions. Furthermore, the limited size of the corpus facilitates rigorous manual classification and detailed evaluation of the clustering results. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.63217223 , the TTR score is 0.543589744 , the CV score is 0.365760041 , and the perplexity score is  46.95009232 .","This text appears to be human-written because it describes a methodology for clustering data, discusses previous studies comparing algorithms, and provides details about a specific algorithm called MajorCluster. It also includes technical specifications like linguistic features and an experimental dataset, all typical characteristics of a paper written by a researcher or academic. The language used is formal and technical, consistent with descriptions found in scientific papers."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","In this case we normalize the document image by dividing each coordinate by the total number of words in the document. Thus, the size of the documents does not affect the results of comparison. The distance between two documents can be defined in terms of these vectors. There are several possible ways to determine the distance between two vectors, depending on the needs of the user. The simplest way is a weighted combination of absolute differences of coordinates, where summation is performed by all nodes of the hierarchy, and the importance coefficients of the nodes reflect the user's profile: the user assigns greater coefficients to the most important topics. However, in practice most of them have to be predefined. In our system, they are assigned according to the following rules: the coefficients of individual keywords are much less than those of any group (nonterminal node), or even are zeroes; the coefficients of the lowestlevel nonterminal nodes are maximal; and the coefficients of the toplevel nodes are the less the higher the level. Effectively, the comparison is done by the lowlevel groups of keywords. On the one hand, this makes it possible for two documents to be very similar even if they do not have any common words literally but do share a common topic. On the other hand, the documents that do share keywords are still slightly closer than those that only share topics. An interesting application of the method is classification of the documents by similarity with respect to a given topic. Clearly, a document mentioning ""the use of animals for military purposes"" and the document mentioning ""feeding of animals"" are similar (both mention animals) from the point of view of a biologist, while from the point of view of a military man they are very different. This is handled by selecting the ""aspect"" of comparison — a subtree of the topic hierarchy, so that the document images contain only the selected topics. We have discussed a method of document comparison based on the use of a weighted hierarchy of topics (concepts). The method has the following advantages. The documents that do not share any words literally still can be identified as similar ones if they do share common topics. The comparison can be done taking into account the user profile, or the ""aspect"" — a subset (subtree) of topics that are of interest for the user. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 7.334922408 , the TTR score is 0.397777778 , the CV score is 0.558251238 , and the perplexity score is  26.10488319 .","The text appears to be AI-generated because it discusses advanced techniques for comparing and classifying documents using hierarchical concepts and weighting schemes. It also mentions linguistic features such as burstiness, type-token ratio, sentence length variation, and perplexity, which are typical metrics used in natural language processing tasks. Additionally, the discussion about assigning weights to different levels of topics suggests an understanding of complex algorithms and data structures often found in machine learning models."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Everybody will face with choices during life time. Some people may just choose the safe things instead of risks. However, I believe if one, no matter whick field he or she is in, has the desire to be successful, taking risks will be a necessary step. There are three reasons as below.

First of all, people need to try risks while making dicisions about economic events. Money is an important part of everyones's life. In order to manage money well and make more money, people are always surrounded by risks. For example, business managers have to choose which product to sell and which good to buy. In this case, they have to think about the adventure. They may earn profit from their choices;otherwise, they will loose both profits and basic funds.
However, to be successful, they have to take risks.

Another reason why I agree to take risks is that risks also appears in scientific areas. To illustrate, during the old time, scientists who were doing reseach about chemistry had to test those dagerous chemical elements without effective proof. In addition, Edison damaged his ear while examing the light. Obviously,in  these successful examples, sicients all faced with the risks. Instead of retreating, successful scientists bravely took these risks.

Not only do scientists take risks, but also politicains need them. For instance, when politicians need to clarify their opinions, they have to give speechs and talk with a huge amount of people. In this case, there may be some killers, who hold the opposite idea, waiting on their way. Although it is a risk for politicians' life, they have to handle it and face it as long as they want to succeed.

After exploring these reasons above, it is not hard to conclude that taking risks is really essencial to be successful. Business men, scientists and politicians have shown people this rule. Therfore, taking risks should become a crucial step in the process of being successful. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.684210526 , the TTR score is 0.5 , the CV score is 0.377299047 , and the perplexity score is  40.88351059 .","This text is likely human-written because it presents a coherent argument supporting the importance of taking risks for success across various fields such as business, science, and politics. The language used is clear and persuasive, using examples and logical reasoning to support its points. Additionally, the structure and content align closely with typical human writing styles rather than automated text generation patterns often associated with AI-generated content."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","I completely agree that successful people are those who are willing to try new things and take risks, rather than just sticking to what they already do well. If everyone simply kept doing what they’re already good at, humanity would never progress.

Innovation is one of our greatest strengths as humans. Many of the most significant advancements in recent history happened because a few individuals dared to pursue something new—something unimaginable until it became reality. Whether it was inventing the telephone or building the airplane, these breakthroughs came from people who were eager to explore uncharted territory. Mankind’s journey to the moon happened because Neil Armstrong was willing to attempt something no one had ever done before, even at great personal risk. Even in ancient times, the discovery of fire was possible because some people were willing to take big risks and experiment with new ideas.

Most people prefer to stick to what they already know and keep improving at it. They’re satisfied with getting better at familiar tasks. However, truly successful individuals are the ones who push boundaries, try new things, and take risks, thereby contributing to the growth and improvement of society.

If we only ever do what we’re used to, life can become dull and repetitive. Trying new things and taking risks adds excitement and motivation to our lives, fueling our desire to learn. New experiences teach us valuable lessons and inspire ideas that can improve our lives.

In today’s competitive world, success often depends on being willing to experiment and take risks—finding new approaches and ideas to enhance your life. To grow as individuals, we must think creatively and challenge ourselves to come up with ideas that have never been tried before, giving us an edge over others. Whenever you have a new idea to pursue, it’s almost certain you’ll need to take some risks to see it succeed. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.997525343 , the TTR score is 0.566860465 , the CV score is 0.31459882 , and the perplexity score is  26.01363182 .","This text is likely AI-generated due to its formal tone, structured language, and consistent use of complex vocabulary and sentence structures typical of written by artificial intelligence systems. The text also exhibits characteristics such as burstiness (a measure of variation in word lengths), which is common in generated text, and other linguistic features like high type-token ratio and low perplexity, further supporting the classification."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Achieving a successful life is a challenging journey, and stepping out to try new things or take risks can be difficult. If someone has found success in a certain area, it’s often wise to stick with it rather than risk everything by venturing into something new and potentially losing all the expertise they have gained.

Success is highly competitive, and if a person doesn’t stay committed to what they know best, they might lose their position to someone else, even if that person isn’t necessarily more qualified. Being successful also means being smart and wise; without wisdom, you might end up destroying everything you’ve worked for by taking an unnecessary risk that could lead to failure.

For example, my cousin graduated from one of Canada’s top universities and landed a job at a well-known pharmacy, earning a good salary and enjoying a comfortable lifestyle. Although he was intelligent, he lacked wisdom—he made the surprising decision to leave his stable pharmacy job to pursue a position at a drug company. After submitting his application, he was not accepted, and the pharmacy quickly replaced him. Despite having a promising and successful career, my cousin lost everything because of a small risk that didn’t pay off.

If someone is already successful, why not continue in their chosen field instead of taking a risk that may not bring any benefits? Sometimes, greed drives successful people to want more, causing them to make decisions that ultimately lead to their downfall. Losing everything you’ve built is a significant loss, so avoiding unnecessary risks is often the wiser choice. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.294581281 , the TTR score is 0.603448276 , the CV score is 0.23785606 , and the perplexity score is  23.43721771 .","The text appears to be AI-generated due to its formal tone, structured language, and consistent use of complex vocabulary and sentence structures typical of written communication designed by artificial intelligence systems. The text discusses the importance of sticking to one's strengths and avoiding unnecessary risks when aiming for success, which aligns with common themes explored in AI-generated content aimed at providing advice on personal development and career choices."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","**Introduction**

This study examines social biases embedded within static word representations, utilizing them as a proxy to comprehend the biases inherent in source contextualized representations. As articulated by Kate Crawford in her 2017 NIPS keynote, while the analysis of individual models is essential—given that specific architectures may propagate, amplify, or mitigate biases differently—investigating the foundational representations shared across diverse models and tasks facilitates a more generalizable understanding of bias (Barocas et al., 2017).

Consequently, this work simultaneously addresses multiple axes of social bias, specifically gender, race, and religion, alongside various computational methodologies for quantifying these phenomena. This multi-faceted approach is necessitated by the observation that existing natural language processing (NLP) literature has predominantly prioritized gender bias, a focus that, while technically tractable, offers an incomplete picture of the broader spectrum of social biases. Furthermore, our analysis demonstrates that distinct computational specifications evaluating the same underlying social phenomena can yield significantly divergent results. As a direct consequence, we emphasize that findings must be interpreted strictly within the context of the specific bias definitions employed. Additionally, we caution that a low bias score for a given embedding does not imply the absence of bias; rather, it indicates that the embedding satisfies the specific, and often limited, criteria of the applied definition. This suggests a critical need for more nuanced definitions to capture the full complexity of social bias.

**5.1 Definitions**

Bolukbasi et al. (2016) introduced a metric for quantifying gender bias, predicated on access to a set of word pairs $P = \{ (m_1, f_1), \dots, (m_n, f_n) \}$, where each pair $(m_i, f_i)$ differs solely in gender (e.g., ""men"" and ""women""). They derive a gender direction vector, $g$, by computing the first principal component of the differences between these pairs:

$$g = \text{PCA}\left( [m_1 - f_1, \dots, m_n - f_n] \right)[0]$$

where $[0]$ denotes the first principal component. Subsequently, given a set of target words $N$ intended for bias evaluation, Bolukbasi et al. (2016) define the bias as the mean absolute cosine similarity between the target words and the gender direction:

$$\text{bias}_{\text{BOLUKBASI}}(N) = \frac{1}{|N|} \sum_{w \in N} |\cos(w, g)|$$

It is important to note that this definition is inherently restricted to binary bias settings, wherein exactly two protected classes are considered. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 4.146421371 , the TTR score is 0.484375 , the CV score is 0.558996522 , and the perplexity score is  68.31319427 .","The text appears to be written by a human because it contains elements typical of human-authored content, such as:

1. A clear introduction section outlining the study's objectives and methodology.
2. Use of proper grammar and sentence structure.
3. The presence of footnotes referencing external sources like Barocas et al. (2017).
4. The use of abbreviations like ""NLP"" (Natural Language Processing) and ""NIPS"" (Neural Information Processing Systems).
5."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","How can we identify and distinguish successful people from others? What is the secret that lifts someone from the bottom to the top? Is it a gentle push from a guardian angel, natural-born genius, or simply luck?

In my view, successful people stand out because they create or discover things that others have not. They have the vision to see possibilities and the courage and ambition to bring them to life. It’s not just their willingness to take risks that leads to success, but also the dedication and sacrifices they make along the way.

Take this year’s Oscar winner for Best Director, Martin Scorsese, as an example. He dedicated his life to directing movies, often making films that others wouldn’t dare to attempt. For many years, he was nominated for Oscars but never won. Yet, he never gave up—he continued making movies with the same passion and determination, always pushing boundaries and trying new things. Eventually, this perseverance earned him the award, making him a true example of lifelong success.

Some people may be brave, and others may simply be lucky. But I believe what truly sets someone apart is their determination and unwavering belief in what they do. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 0.823826232 , the TTR score is 0.591111111 , the CV score is 0.227058485 , and the perplexity score is  21.57785797 .","The text appears to be AI-generated based on several characteristics:

1. **Language Structure**: The language used is formal and complex, which is typical of AI-generated content designed to mimic human speech patterns. Phrases like ""guardian angel,"" ""natural-born genius,"" and ""lucky"" suggest a level of sophistication and creativity beyond simple scripting.

2. **Burstiness and TTR**: The burstiness score (0.8238) indicates rapid changes in sentence length, which is"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","We report mean values across 5 different random runs. indicates the results are from our experiments, while others are from He et al.; Li et al.. We only show the best performance of every model for each dataset. More results on various parameters can be found in the Appendix. 5 Experiments 5.1 VAE for Language Modeling Setup: We test our approach on two benchmark datasets: Yelp and Yahoo corpora. We use a Gaussian prior N (0 , I ) , and the approximate posterior is a diagonal Gaussian. Following previous work, we report the estimated negative log likelihood (NLL) from 500 importance weighted samples, which can provide a tighter lower bound compared to the ELBO and shares the same information with the perplexity (PPL). Besides the NLL, we also report the KL, the mutual information (MI) I q and the number of activate units (AU) in the latent space. The I q can be calculated as: I q = E p d ( x ) [ KL ( q φ ( z | x ) || p ( z ))] − KL ( q φ ( z ) || p ( z )) , (13) where p d ( x ) is the empirical distribution. The aggregated posterior q φ ( z ) = E p d ( x ) [ q φ ( z | x )] and KL ( q φ ( z ) || p ( z )) can be approximated with Monte Carlo estimations. The AU is measured as A z = Cov ( E z ∼ q ( z | x ) [ z ]) . We set the threshold of 0.01, which means if A zi > 0 . 01 , the unit i is active. Configurations: We use a 512dimension word embedding layer for both datasets. For the encoder and the decoder, a single layer LSTM with 1024 hidden size is used. We use z to generate the initial state of the encoder following Kim et al.; He et al.; Li et al.. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 4.163948766 , the TTR score is 0.425257732 , the CV score is 0.696086261 , and the perplexity score is  99.34214783 .","This text is likely AI-generated because it contains technical details about language modeling using Variational Autoencoders (VAEs), specific configurations for training such models, and measurements of linguistic features like burstiness, type-token ratio, sentence length variability, and perplexity. These elements suggest expertise in natural language processing and machine learning, typical characteristics of AI-generated content. Additionally, the mention of ""We"" reporting results and providing detailed experimental setups aligns with common practices in academic or research contexts, further supporting an"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","We are particularly interested in knowing what, in your opinion, we have done well, and where we can make improvements or offer new modules. ix !! Introduction to Engineering Design and Problem Solving is designed to help beginning engineering students gain a better perspective on engineering, particularly the creative aspects of engineering design coupled with the rigors of analysis. Engineering design and the design process are generally not well understood, and the text focuses on them through discussions of the design process, examples of student work, and words of wisdom from practicing engineers. Engineering analysis is integral to the design process, and fundamentals in electrical engineering, mechanics, and energy are part of the knowledge base necessary for successful designs. The first chapter examines the humanmade world, the world we live in, created by engineers. The processes employed by engineers to create this world embrace science, the study of the natural world, and mathematics, the study of logical constructs, and include elements uniquely their own. There are philosophical values associated with mathematics, science, and engineering, some similar, others divergent. These values will be explored. One of the purposes of Chapter 1 is to set the stage for the later chapters, providing a context for the designs that we create. For instance, in mathematics and science the concept of uniqueness, a correct answer, is vital. The square root of25 is 5, not about 5, and similarly, the composition of water is H20, not approximately that. However, in the humanmade world, optimum or best solutions are important, uniqueness is not. Tradeoffs are always made among cost, materials, aesthetics, and other factors. In addition, thre may be constraints to be satisfied on the problem in terms of product size, time, and personnel. Chapter 2 discusses and analyzes the design process. The iterative nature of the design processproblem statement, specifications/clarifications, investigation, brainstorming, creating solutions, evaluation of solutions, and selection of the optimum solutionare examined and illustrated. Creativity is an important aspect to design, and traits and attitudes of creative people with multicriteria analysis techniques. The area of ecological design is of growing importance nationally and internationally, and the challenges that it brings are examined. In Chapter 3 design documentation is explored in its several forms. Engineers document their daily work in a design journal which provides a resource for attorneys filing for patents. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 8.050984404 , the TTR score is 0.464757709 , the CV score is 0.492131808 , and the perplexity score is  33.76746368 .","The text appears to be written in a formal academic style typical of educational content aimed at engineering students. It includes technical terms related to engineering design and problem-solving, such as ""engineering design,"" ""design process,"" and ""electrical engineering."" Additionally, the use of bullet points and numbered lists suggests a structured format common in instructional texts. The language used is precise and professional, indicating a high level of formality appropriate for an AI-generated educational material."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","A prominent example is the socalled “Wall Street Journal effect”, where syntactic parsers and partofspeech taggers are most accurate over language written by middleaged white men. The effect occurs because this group happened to be the predominant authors’ demographics of the WSJ articles, which are traditionally used to train syntactic models. The same effect was reported for language identification difficulties for AfricanAmerican Vernacular English. The predicted output is dissimilar from the ideal distribution, leading, for example, to lower accuracy for a given demographic, since the source did not reflect the ideal distribution . We say that the distribution of human attribute, A , within the source data, s , is dissimilar to the distribution of A within the target data, t : Q ( A s ) ≁ P ( A t ) Selection bias has several peculiarities. First, it is dependent on the ideal distribution of the target population, so a model may have selection bias for one application (and its associated target population), but not for another. Also, consider that either the source features ( X s ) or source labels ( Y s ) may be nonrepresentative . In many situations, the distributions for the features and labels are the same. However, there are some cases where they diverge. For example, when using features from agebiased tweets, but labels from nonbiased census surveys. In such cases, we need to take multiple analysis levels into account: corrections can be applied to user features as they are aggregated to communities. The consequences could be both outcome and error disparity . One of the challenges in addressing selection bias is that we can not know a priori what sort of (demographic) attribute will be important to control. Age and gender are wellstudied, but others might be less obvious. We might someday realize that a formerly innocuous attribute (say, handedness) turns out to be relevant for selection biases. This problem is known as The Known and Unknown Unknowns. As we know, there are known knowns: there are things we know we know. We also know there are known unknowns: that is to say, we know there are some things we do not know. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.32904646 , the TTR score is 0.459854015 , the CV score is 0.450726233 , and the perplexity score is  93.53505707 .","The text is likely classified as AI-generated due to its formal tone, complex vocabulary, and detailed linguistic analysis, characteristics often found in academic papers discussing natural language processing techniques. Additionally, the mention of specific statistical metrics like burstiness, type-token ratio, sentence length variability, and perplexity suggests an attempt to evaluate these properties algorithmically rather than naturally occurring in human speech or writing. The discussion about selection bias and demographic factors further reinforces the idea that this content is generated by an AI system designed to analyze"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","A significant body of research in emotion detection has traditionally framed the problem within a categorical framework, aiming to identify specific emotional classes. Prominent among these is Ekman's model (Ekman, 1992), which delineates six fundamental emotions: anger, disgust, fear, happiness, sadness, and surprise. Alternatively, other methodologies employ the Valence-Arousal-Dominance (VAD) model (Mehrabian, 1996), which characterizes emotion through continuous dimensions of polarity, excitement, and control.

The field has explored a diverse array of computational techniques for emotion detection, ranging from vector space modeling (Danisman and Alpkocak, 2008) and machine learning classifiers (Perikos and Hatzilygeroudis, 2016) to deep learning architectures (Zhang et al., 2018). Specifically, Zhang et al. (2018) utilized a Multi-Task Learning (MTL) approach; however, their study was confined to emotion-related tasks, such as classification and distribution prediction, demonstrating performance gains over single-task baselines. Similarly, Akhtar et al. (2018) proposed a multitask ensemble architecture to jointly learn emotion, sentiment, and intensity, reporting that these tasks mutually reinforce one another to enhance overall performance. To the best of our knowledge, no prior work has investigated the application of emotion detection within the specific context of abuse detection.

### 3. Datasets

In an MTL framework, the selection of related tasks is critical to achieving positive transfer. MTL models are inherently sensitive to discrepancies in data domain and distribution (Pan and Yang, 2009); such inconsistencies can compromise training stability and potentially degrade performance relative to Single-Task Learning (STL) models (Zhang and Yang, 2017). Consequently, this study utilizes datasets for both abuse and emotion detection derived from a unified domain: Twitter. To ensure consistency, all datasets underwent identical preprocessing procedures, including lowercasing, the normalization of mentions and URLs to standard tokens (e.g., `MTN` and `URL`), and the conversion of hashtags into standard lexical forms.

*Note: The authors hold no proprietary rights to the datasets or the underlying tweets. To prevent potential redistribution infringements, requests for access to these datasets should be directed to the original authors or data owners.*

#### 3.1. Abuse Detection Task
To ensure the generalizability of the results, this study evaluates performance across two distinct abuse detection datasets. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 4.907403055 , the TTR score is 0.518018018 , the CV score is 0.545082271 , and the perplexity score is  24.91587448 .","This text is classified as human-written because it appears to be a detailed academic paper discussing various methods and approaches for detecting emotions and abuse using natural language processing techniques. The content is formal, technical, and written by someone with expertise in the field of computer science and artificial intelligence. Additionally, the use of proper citation references and the inclusion of footnotes further indicate that this text was authored by a human rather than generated automatically."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","We therefore investigate how the layer normalization and the residual connection are computed in the two computation orders, shown in Table 2 . Table 2 shows that the computation of residual connection in v1 is weighted by w σ compared to v2, and the residual connection of previous layers will be shrunk if w σ < 1 . 0 , which makes it difficult for deep Transformers to converge. 3 Lipschitz Constrained Parameter Initialization Since the diminished residual connections (Table 2 ) may cause the convergence issue of deep v1 Transformers, is it possible to constrain w σ ≥ 1 . 0 ? Given that w is initialized with 1 , we suggest that the standard deviation of in model + in res should be constrained as follows: 0 . 0 < σ = std ( in model + in res ) ≤ 1 . 0 (1) in which case w σ will be greater than or at least equal to 1 . 0 , and the residual connection of v1 will not be shrunk anymore. To achieve this goal, we can constrain elements of in model + in res to be in [ a, b ] and ensure that their standard deviation is smaller than 1 . 0 . . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 0.99026072 , the TTR score is 0.475113122 , the CV score is 0.581408348 , and the perplexity score is  59.63599014 .","The text is likely AI-generated because it exhibits characteristics consistent with human-like writing style such as natural sentence structure, vocabulary choices, and grammatical correctness. The content discusses technical details about neural network architectures and optimization techniques used in machine learning models, which aligns with typical topics covered in papers on artificial intelligence research. Additionally, the use of specific terminology related to computational methods and mathematical concepts further supports the classification as AI-generated."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","How can someone become a creative person? This is a difficult question to answer because, as we all know, the value of any creation often depends on how others perceive and accept it. Creativity isn’t just about our own feelings or preferences. Still, that shouldn’t stop us from trying new things rather than simply sticking to what we already do well. In the following paragraphs, I will share my perspective and provide some examples and arguments to support my view.

With advances in technology and the expansion of the global market, competition among individuals and organizations is on the rise. This increasing competition makes it essential to introduce new ideas and products to stay relevant and competitive. This can only happen if we are willing to experiment and try new things. No matter the field, innovation requires us to create and implement fresh, appealing ideas. For example, consider mobile phones: companies are always launching new models because they choose to experiment with new technologies instead of just selling the same devices. Looking back at history, we see that all major discoveries by scientists, doctors, and biologists were made possible because they took risks and explored new concepts and experiences. Thanks to their efforts, we now enjoy electricity, cars, airplanes, tablets, medicines, and clean water—innovations that have greatly improved our lives.

That’s why I encourage everyone, regardless of their background, job, or personality, to take risks and try new things. By doing so, we not only contribute to the progress of our world but also to our own personal growth. We can’t spend our entire lives playing it safe and avoiding new experiences. We just need to give it a try, and eventually, we’ll achieve something worthwhile. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.797613418 , the TTR score is 0.593272171 , the CV score is 0.328215915 , and the perplexity score is  20.62578011 .","This text is likely classified as AI-generated due to its formal tone, structured format, and lack of human-like errors or inconsistencies. The language used is consistent throughout, indicating an automated process rather than a human author's work. Additionally, the content discusses creativity, innovation, and risk-taking, which are topics typically covered in AI-generated texts aimed at providing insights or advice. The use of bullet points and numbered lists further supports the classification as AI-generated."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","It has been a popular trend for some time now to put children into what can be called ‘work experience placements’. The children are taken out of school and take part in various types of work, usually for around a week. Some people criticise this trend, but I can’t see how it can be a negative experience.One of the criticisms is that children lose a week of schoolwork. With the pressure to do well at school increasing, children need every bit of help to maximise their potential. Taking them out of the school, it is therefore argued, stops them from getting access to needed education for a whole week. As the students won’t really be of much help to a workplace for just one week, it would therefore be better for them to remain at school.While this is a valid point, taking a child’s entire experience of school into account, a single week away does not make a significant difference. Indeed, if the timing of the week’s work placements is done correctly, the week can also serve as a kind of ‘holiday’ away from school, something that is regularly needed in order to allow students to remain fresh and enthusiastic. In many work placements, children also get to see some of things they learn at school put into a realistic context, allowing them to understand the relevance of what they learn and thereby creating a way to reinforce the learning.In addition to this, schoolchildren can be quite nervous about the world of work that they will have to join at some point in their lives. By getting this short taste of what it is like, they can see that it is nothing to be scared about. As well as this, children will often have some ideas of what they want to do for a living, but are not sure. By allowing them to see a type of work they are considering, they are better able to decide whether that career could be suitable for them.I feel, therefore, that work placements can be seen as a positive development. Missing a short amount of school is not serious and the benefits that can be gained from the placements are significant. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.162861272 , the TTR score is 0.4325 , the CV score is 0.451386752 , and the perplexity score is  29.6167202 .","The text appears human-written due to its coherent structure, logical flow, and use of natural language constructs typical of written communication. The author discusses the pros and cons of work experience placements for children, providing reasons such as educational value, practical application of knowledge, and emotional preparation for future careers. The writing style is straightforward and accessible, making it likely that the content was authored by an individual rather than generated automatically."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","We also consider two ways of viewing activation patterns in hidden layers as representations: • Local representations at the level of a single frame or timestep; • Global representations at the level of the whole utterance. Combinations of these two facets give rise to the following concrete analysis models. Local diagnostic classifier. We use single frames of input (MFCC or spectrogram) features, or activations at a single timestep as input to a logistic diagnostic classifier which is trained to predict the phoneme aligned to this frame or timestep. Local RSA. We compute two sets of similarity scores. For neural representations, these are cosine similarities between neural activations from pairs of frames. For phonemic representations our similarities are binary, indicating whether a pair of frames are labeled with the same phoneme. Pearson’s r coefficient computed against a binary variable, as in our setting, is also known as point biserial correlation. Global diagnostic classifier. We train a linear diagnostic classifier to predict the presence of phonemes in an utterence based on global (pooled) neural activations. For each phoneme j the predicted probability that it is present in the utterance with representation h is denoted as P( j | h ) and computed as: P( j | h ) = sigmoid( W Pool ( h ) + a ) j (1) where Pool is one of the pooling function in Section 3.2.1 . Global RSA. We compute pairwise similarity scores between global (pooled; see Section 3.2.1 ) representations and measure Pearson’s r with the pairwise string similarities between phonemic transcriptions of utterances. We define string similarity as: sim( a, b ) = 1 − Levenshtein( a, b ) max( | a | , | b | ) (2) where | · | denotes string length and Levenshtein is the string edit distance. 3.2.1 Pooling The representations we evaluate are sequential: sequences of input frames, or of neural activation states. In order to pool them into a single global representation of the whole utterance we test two approaches. Mean pooling. We simply take the mean for each feature along the time dimension. Attentionbased pooling. Here we use a simple selfattention operation with parameters trained to optimize the score of interest, i.e. the RSA score or the error of the diagnostic classifier. The attentionbased pooling operator performs a weighted average over the positions in the sequence, using scalar weights. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.17197291 , the TTR score is 0.407982262 , the CV score is 0.797283257 , and the perplexity score is  59.48796082 .","This text is likely classified as AI-generated because it discusses advanced machine learning techniques used in speech recognition systems, including local and global representations of acoustic features, similarity measures, and diagnostic classifiers. These topics align closely with research areas within artificial intelligence and natural language processing. Additionally, the inclusion of specific metrics such as perplexity and linguistic features like burstiness further supports the classification as AI-generated content."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","For example, in the formal syntax literature, a standard explanation for adjective order constraints is that each adjective belongs to a certain semantic class (e.g., COLOR or SIZE ) and that there exists a universal total order on these semantic classes (e.g., COLOR < SIZE ) shared among all languages, which determines the order of adjectives in any given instance. Such discrete theories of adjective order become complex rapidly as the number of semantic classes to be posited becomes large and more finegrained. In contrast, quantitative syntax theories typically identify a single construct that grounds out in realvalued numerical scores given to adjectives, which determine their ordering preferences. These scores can be estimated based on largescale corpus data or based on human ratings. In what follows, we test the predictions of four such theories: the subjectivity hypothesis, the information locality hypothesis, the integration cost hypothesis, and the information gain hypothesis, which we introduce. We begin with a presentation of the details of each theory, then implement the theories and test their predictions against largescale naturalistic data from English. In addition to comparing the predictors in terms of accuracy, we also perform a number of analyses to determine the important similarities and differences among their predictions. The paper concludes with a discussion of what our results tell us about adjective order and related issues, and a look towards future work. 2 Theories of adjective order 2.1 Subjectivity Scontras et al. show that adjective order is strongly predicted by adjectives’ subjectivity scores : an average rating obtained by asking human participants to rate adjectives on a numerical scale for how subjective they are. Adjectives that are rated as more subjective typically appear farther from the noun than adjectives rated as less subjective, and the strength of ordering preferences tracks the subjectivity differential between two adjectives. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.687988728 , the TTR score is 0.508982036 , the CV score is 0.460065768 , and the perplexity score is  46.63326263 .","The text is likely classified as AI-generated because it contains technical terminology specific to linguistics and computational models of language processing, including concepts like ""subjectivity hypothesis,"" ""information locality hypothesis,"" and ""quantitative syntax theories."" Additionally, the text discusses linguistic features such as burstiness, type-token ratio, sentence length variability, and perplexity, which are common topics in discussions about artificial intelligence-driven language analysis tools. The detailed explanations and examples provided suggest a high level of sophistication and specificity typical of content created"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Many teachers believe that an international exchange program would benefit all adolescent students. While there are clear drawbacks to this, in my opinion the advantages are more significant. The essay will demonstrate that pupils will build life skills by joining the program. This is followed by explaining why continuing learning in the domestic school after finishing the exchange can be practicable.
It is obvious that the exchange program is likely to give an opportunity for students to learn to live with people from different society and nationality which is the experience that they are not able to have when studying in their country. As a result, they will have the ability to communicate with various types of people and handle challenging situations. Furthermore, there is research indicating that pupils who study in another country are able to lead a group of acquaintances under pressure.
On the other hand, those who oppose this may point out that when the exchange complete and the students resume their courses in the domestic school, they are perhaps unable to keep pace with others and, consequently, there might be some students who cannot pass exams. Nevertheless, the aforementioned issue can possibly be addressed by teachers to arrange extra classes for the students in order for them to learn about what they have missed when they attended the exchange program.
In conclusion, despite the fact that some students may find difficulty in carrying on studies in the school after the completion of the exchange, I am convinced that the benefits outweigh the disadvantages. This is because children are likely to gain valuable experience which they can only have when studying aboard. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.004951476 , the TTR score is 0.515151515 , the CV score is 0.448633457 , and the perplexity score is  35.56778336 .","The text is classified as human-written due to its coherent structure, logical flow, and use of formal language typical of academic writing. It presents arguments both for and against an international exchange program for adolescents, providing evidence and addressing potential concerns systematically. The vocabulary and sentence structures used suggest a high level of education and careful consideration, consistent with written work produced by humans rather than generated by AI or automated systems."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Probing task classification is performed by a logistic regression on the representations s j of all sentences in the dataset. Baseline performances are reported in the top section of Table 2 and Table 3 . Length and ArgDist are both linear classifiers, which use sentence length and distance between head and tail argument as the only feature. BoE computes a representation of the input sentence by summing over the embeddings of all tokens it contains. Generally, there is a large gap between top baseline performance and that of a trained encoder. While SentLength and ArgDist are trivially solved by the respective linear classifier, BoE shows surprisingly good performance on SentLen and ArgOrd, and a clear improvement over the other baselines for named entityand partofspeechrelated probing tasks. Encoder Architecture For most probing tasks, except SentLen and ArgOrd, a proper encoder clearly outperforms bagofembeddings (BoE), which is coherent with the findings of Adi et al. and Conneau et al.. Similarly, the results indicate that the prior imposed by the encoder architecture preconditions the information encoded in the learned embeddings. Models with a local or recency bias (CNN, BiLSTM) perform well on probing tasks with local focus, such as PosHead { L,R } and PosTail { L,R } and distance related tasks (ArgDist, ArgOrd). Similarly, models with access to dependency information (GCN) perform well on tree related tasks (SDPTreeDepth). . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.40277071 , the TTR score is 0.515267176 , the CV score is 0.453893669 , and the perplexity score is  64.94050598 .","This text is likely classified as AI-generated because it discusses advanced natural language processing techniques, including deep learning models like logistic regression and convolutional neural networks (CNNs), used for probing tasks in machine translation systems. The mention of specific architectures like CNNs and LSTM, along with detailed explanations of their performance on various linguistic features, suggests an expert-level understanding of these technologies. Additionally, the text references academic papers and previous research findings from authors like Adi and Conneau, further indicating its AI-generated nature"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","The proposed design facilitates the generation of headlines that are more contextually relevant to the source articles by leveraging shared content. Furthermore, it allows for precise stylistic control through the integration of style-specific parameters. The model was validated across three distinct tasks: the generation of humorous, romantic, and clickbait headlines. Both automatic metrics and human evaluations indicate that TitleStylist produces headlines adhering to the target styles with a higher degree of appeal to human readers, as illustrated in Figure 1. The primary contributions of this study are as follows:

*   To the best of our knowledge, this represents the first research endeavor focused on generating stylistically attractive news headlines without reliance on supervised, style-specific article-headline paired data.
*   Through comprehensive automatic and human evaluations, we demonstrate that the proposed TitleStylist framework generates relevant and fluent headlines across three styles (humor, romance, and clickbait). Notably, these generated headlines exhibit a higher level of attractiveness compared to human-written counterparts.
*   The model possesses the flexibility to incorporate multiple styles simultaneously, thereby efficiently providing users with diverse creative headline options. This capability serves as a valuable reference tool and encourages innovative thinking in headline creation.

### 2. Related Work

This research intersects with the domains of text summarization and text style transfer.

#### Headline Generation as Summarization
Headline generation constitutes a prominent area of academic inquiry. Traditional methodologies have predominantly relied on extractive strategies utilizing linguistic features and handcrafted rules (Luhn, 1958; Edmundson, 1964; Mathis et al., 1973; Salton et al., 1997; Jing and McKeown, 1999; Radev and McKeown, 1998; Dorr et al., 2003). To enhance the diversity inherent in extractive summarization, abstractive models were subsequently introduced. Leveraging neural networks, Rush et al. (2015) proposed Attention-Based Summarization (ABS), thereby augmenting the capabilities of the summarization framework established by Banko et al. (2000). Numerous recent studies have further extended the ABS architecture by integrating additional features (Chopra et al., 2016; Takase et al., 2016; Nallapati et al., 2016; Shen et al., 2016, 2017a; Tan et al., 2017; Guo et al., 2017). . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.421183046 , the TTR score is 0.518159806 , the CV score is 0.61369996 , and the perplexity score is  44.09647751 .","The text appears to be written by a human because it contains elements typical of human-generated content such as paragraphs, sentences, and proper nouns like ""TitleStylist"" and ""Figure 1."" Additionally, there is no indication of machine learning or artificial intelligence used in its composition."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Above the response box, the text Die... appeared, to indicate that a plural form of the noun should be typed into the response box below the text. For the rating task, participants were prompted to rate each potential plural on a Likert scale of Sehr gut (‘very good’; 5) to Sehr schlecht (‘very bad’; 1). After filtering out 42 respondents who failed a preliminary attention check, data from 150 participants was available for analysis. The cleaned, anonymized survey data will be published online along with this paper. Overestimation of Syntactic Representation in Neural Language Models Jordan Kodner University of Pennsylvania Dept. of Linguistics Nitish Gupta University of Pennsylvania Dept. of Computer and Information Science With the advent of powerful neural language models over the last few years, research attention has increasingly focused on what aspects of language they represent that make them so successful. Several testing methodologies have been developed to probe models’ syntactic representations. One popular method for determining a model’s ability to induce syntactic structure trains a model on strings generated according to a template then tests the model’s ability to distinguish such strings from superficially similar ones with different syntax. We illustrate a fundamental problem with this approach by reproducing positive results from a recent paper with two nonsyntactic baseline language models: an ngram model and an LSTM model trained on scrambled inputs. 1 Introduction In recent years, RNNbased systems have proven excellent at a wide range of NLP tasks, sometimes achieving or even surpassing human performance on popular benchmarks. Their success stems from the complex but hard to interpret, representations that they learn from data. Given that syntax plays a critical role in human language competence, it is natural to ask whether part of what makes these models successful on language tasks is an ability to encode something akin to syntax. This question pertains to syntax “in the meaningful sense,” that is, the latent, hierarchical, largely contextfree phrase structure underpinning human language as opposed to superficial or shallow issues of word order. Clearly, syntactic information can be explicitly incorporated into neural systems to great effect. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.603717276 , the TTR score is 0.575376884 , the CV score is 0.426588476 , and the perplexity score is  63.02478027 .","This text is likely AI-generated because it discusses research methods and findings related to artificial intelligence, specifically focusing on neural language models and their ability to represent syntactic structures. The text mentions ""neural language models"" and provides details about how researchers tested these models using specific methodologies. Additionally, the use of technical terms and concepts typical of academic papers discussing AI research supports the classification as AI-generated."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","These days, more and more people are choosing to shop online. In my view, purchasing items like books, clothing, or airline tickets on the internet offers many benefits that can outweigh the drawbacks.

One major advantage is that online shopping allows you to compare the same product across different websites to find the best price. Additionally, it’s much faster and more convenient, helping you save time while searching for the perfect item. In contrast, local stores may not always have what you’re looking for, and finding the right product in large shopping centers can be time-consuming and challenging.

Another benefit is the variety available online. On a single website, such as Amazon, you can find everything from books and clothing to furniture, making it easy to buy multiple items without having to travel from one store to another.

However, there are some disadvantages to shopping online. For instance, many shopping websites have spam messages and tempting offers that can encourage you to buy things you don’t actually need. Also, not all online platforms are secure, and there is a risk of hackers stealing your personal information.

In conclusion, I believe the advantages of online shopping outweigh the disadvantages. Nevertheless, it’s important to be cautious and choose trustworthy websites to avoid any potential problems. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.610013175 , the TTR score is 0.570247934 , the CV score is 0.397124422 , and the perplexity score is  18.28076935 .","The text appears to be written by an artificial intelligence system because it demonstrates several characteristics typical of generated text:

1. **Consistency**: The language used throughout the text is consistent with formal writing, free of errors, and follows a logical structure.
   
2. **Burstiness**: The burstiness value of 1.6100 indicates a moderate level of variation in word frequency, which is common in generated text where sentences might vary in length and complexity.

3. **TTR ("
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","During the production phase, many plans must be changed, resulting in costs associated for these changes as well as time lost from manufacturing. Concurrent engineering reduces the time of the manufacturing cycle, which is increasingly important in the drive to get new products to market as quickly and reliably as possible. 4 illustrates the spending and time profiles for traditional, sequential engineering and concurrent engineering. Note that the spending rate is highest early in the cycle for concurrent engineering, though the total cost (the area under the curve) is less than that in sequential manufacturing. A mind shift is required by managers when adopting concurrent engineering, not only in regard to costs, but also involving many people in the decisionmaking process and providing training time and meeting times for them, all of which costs money. The meetings and training are required to break down traditional, hierarchical methodologies used in manufacturing a product and to allow meaningful input, respecting the advice of people from all areas of design, manufacturing, and sales. The increased emphasis on teamwork is one of the newer characteristics of the modern engineering company. An engineer working in isolation is an idea whose time has passed. Teamwork allows the creativity of many people to inform the process, from design conceptualization to It may be comparatively easy to select the optimum design if one clearly stands out as the best choice on several fronts, but there will often be competing designs that offer different features and have different advantages and disadvantages. One characteristic of the design process is the tradeoffs, cost versus material versus reliability versus performance. This contrasts with a single evaluation criterion, often cost. An entertaining and informative way to introduce the concept of optimum design or best choice is by evaluating a consumer product, for instance, determining the best potato chip, soda, or chocolate bar. Not all the elements in the design process apply, but the important ones do. The problem statement is to determine the best potato chip. The investigation phase will probably be minimal, as you know the attributes of potato chips that appeal most to you. When you are brainstorming for ideas, the thoughts are about attributes of potato chips, things you like or dislike about them. A list often includes saltiness, crunchiness, thickness, size, fat content, calories, cost, color, and taste. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 5.069237398 , the TTR score is 0.479545455 , the CV score is 0.484181596 , and the perplexity score is  39.24939346 .","The text appears to be written in a style typical of an AI system, particularly one designed for generating human-like responses. Key indicators include:

1. **Complex Language Use**: The text uses sophisticated language structures and idiomatic expressions, suggesting it was crafted by an advanced natural language processing model.
   
2. **Structured Content**: The text follows a logical flow, starting with definitions and explanations followed by examples and applications, indicating a structured approach similar to how AI systems might generate content.

3. **Re"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Neural networks based techniques have been widely used in the threatening language detection task. In this study, we used two neural networks based models: (i) 1Dimensional Convolutional Neural Network (1DCNN) and (ii) Long ShortTerm Memory Networks (LSTM). A Convolutional Neural Network (CNN) is a class of deep neural networks used for image detection and classification. CNNs are regularized versions of multilayer perceptrons, which consist of multiple layers of neural network. Each hidden layer contains neurons and biases, where each input instance is multiplied (dot product) with weight and fed to the neuron. Each neuron takes the weighted sum of all the fed input instances and adds bias to it, which is further passed to an activation function to receive an output of the particular neuron. The model is trained through backpropagation technique, which is a technique to minimize the error by adjusting the weights of all the layers starting from the output layer to the input layer. CNN is also computationally efficient because it offers the possibility to share the parameter of features and reduces the dimensionality. CNN has been widely used because it can automatically extract relevant and distinctive features efficiently and provides high accuracy. Pretrained fastText embeddings, extracted from Urdu tweets, were used as an input for our 1DCNN classifier and it was trained on 100 epochs for 10 times. The results were calculated by taking mean accuracy of 10 iterations. For the convolution layer, we set the filter size to 8 and kernel size to 1. Two fully connected layers were used with different neurons and activation function. In addition, dropout is applied to all the layers to avoid overfitting. Long ShortTerm Memory (LSTM) networks are also a type of deep neural networks, which addressed the challenges related to order dependence in sequence prediction tasks such as machine translation and speech recognition. Moreover, this is a special type of recurrent neural network, which consists of four linear layers (MLP layer) per cell to run at each sequence timestep. Multiple studies have utilized Recurrent Neural Networks (RNNs) to detect threatening language. We used 150 epochs for each iteration in tenfold crossvalidation. We used two fully dense layers with different neurons and activation functions. Apart from this, dropout is applied to all the layers to avoid overfitting. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.865043981 , the TTR score is 0.448837209 , the CV score is 0.393978473 , and the perplexity score is  21.72683716 .","This text is likely classified as AI-generated due to its technical nature, use of specific terminology related to artificial intelligence and neural networks, detailed explanations of algorithms and their parameters, and the presentation of experimental results using statistical measures like accuracy and perplexity. These elements suggest that the content is written by someone familiar with AI research and development, possibly a researcher or developer working in the field of natural language processing or machine learning."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","To become successful, many people have achieved it by exploring new ideas and taking risks. Simply sticking to what you already know, especially if it’s what everyone else is doing, won’t get you very far.

Some of the world’s greatest inventions came about because ordinary people, just like you and me, took chances. We should strive to be more creative and not shy away from trying new projects. For example, consider a man who does the same job day in and day out—his life never changes. He’s skilled at his work, but so are many others. If he were to try new things or change parts of his daily routine to become more efficient, he could transform his life. Ultimately, he’s the only one who can make that choice.

Taking risks now and then is beneficial. Businesspeople, for instance, are often known for being risk-takers. In business, many decisions must be made, and a single choice can greatly impact the final result. Sometimes, a risk you weren’t sure about ends up being a success. We also need to learn from our mistakes so we don’t repeat them. Every day, we make small decisions without even noticing—like choosing what to wear or what to eat. For example, you might go to a restaurant and order something different from your usual choice.

Occasionally, your risk might not succeed, but you might not lose anything either. As an old saying goes, “You miss 100% of the shots you don’t take.” . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.576327179 , the TTR score is 0.597173145 , the CV score is 0.258119892 , and the perplexity score is  19.2175045 .","This text is likely classified as AI-generated due to its formal tone, structured language, and adherence to academic writing style. The content discusses personal growth, creativity, and decision-making, which align with common topics covered in educational materials and professional development guides. Additionally, the use of complex vocabulary and sentence structures typical of AI-generated texts further supports this classification."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Building on the preliminary success of these researchfocused systems that use crowdsourcing towards personalisation of education, we have developed a studentfocused, scalable, contentagnostic adaptive learning system that relies on crowdsourcing and partnership with students for the development of learning resources that are adaptively served. Here, we provide an overview of this system, which is called RiPPLE1 , and share our reflections and lessons learned from developing and piloting this system. While our focus has been on developing an adaptive learning system, many of the challenges that we have faced, which are discussed in our lessons learned, are also applicable to a broad range of educational tools and technologies. Our aim is to share insight with other instructors who would like to adopt an ALS or are involved in the implementation of educational tools and technologies. Creation of a new offering. RiPPLE supports two types of roles for users: instructors and students. Instructors have the ability to create new offerings associated with a course. Once a new offering has been created, the instructors have to specify a domain model (a set of topics) for the offering. The domain model may be altered throughout the semester. Once a RiPPLE offering has been created, an instructor can import resources from other RiPPLE offerings. This enables instructors to import resources from their past offerings as well as sharing resources with other instructors who are teaching similar courses. Content creation and evaluation. RiPPLE enables students and instructors to create, attempt, and evaluate a wide range of learning activities that include worked examples, multiplechoice questions (MCQs) and general notes. Students and instructors are able to view/attempt and then evaluate the learning activities associated with any RiPPLE offering that they are enrolled in. For MCQs, once a user has attempted a question, they are able to view the right answer, the distribution of how others have responded, and an explanation. For worked examples, students view a question, a stepby-step solution to solving the question, and the answer. While for notes, students can view and interact with a variety of media such as images, videos, and embedded simulations.For all resources users can view the current rating, identity of the creator and user comments made about the resource. Users are able to add their own comments and rate the effectiveness of resources. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.755297158 , the TTR score is 0.418604651 , the CV score is 0.514747361 , and the perplexity score is  35.61795807 .","The text is likely AI-generated because it contains several characteristics typical of automated language models:

1. **Structured Format**: The text follows a consistent format with sections titled ""Overview"" and ""Content creation and evaluation"", indicating organization by an AI system.
2. **Technical Details**: It includes technical terms and concepts related to adaptive learning systems, suggesting expertise beyond casual writing.
3. **Educational Focus**: The text discusses aspects of creating and using adaptive learning systems, which requires knowledge of educational technology.
4"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","In practice, such ideal conditions are seldom met. User-generated text frequently exhibits informal linguistic features, including misspellings, typographical errors, and character transpositions (Derczynski et al., 2013). Furthermore, noise may be introduced during upstream processing tasks, such as Optical Character Recognition (OCR) (Alex and Burns, 2014) or Automatic Speech Recognition (ASR) (Chen et al., 2017), leading to the propagation of errors into downstream applications. To account for the noise inherent in the input of the function $F(x)$, its definition is modified as illustrated in Figure 2. Specifically, we posit that the input sentence $x$ is subjected to an unknown stochastic noising process $\Gamma = P(\tilde{x}_i | x_i)$, where $x_i$ denotes the original $i$-th token and $\tilde{x}_i$ represents its distorted counterpart. Let $V$ denote the vocabulary of tokens and $\tilde{V}$ the set of all finite character sequences over an alphabet $\Sigma$. The matrix $\Gamma$, referred to as the noisy channel matrix (Brill and Moore, 2000), is constructed by estimating the conditional probability $P(\tilde{x}_i | x_i)$ for every original token $x_i \in V$ and its potential distorted form $\tilde{x}_i \in \tilde{V}$.

### 2.3 Named Entity Recognition

This study evaluates the efficacy of state-of-the-art Named Entity Recognition (NER) systems when processing imperfect input data. NER is conceptualized as a specific instance of the sequence labeling problem, wherein the objective is to identify mentions of named entities within unstructured text and classify them into predefined categories, such as persons, organizations, and locations (Tjong Kim Sang and De Meulder, 2003). Conventional NER systems are typically trained on clean, error-free text; consequently, their performance often degrades in real-world scenarios where input data originates from upstream components like OCR or ASR (see Section 2.2). This discrepancy creates a detrimental mismatch between training and testing conditions. The primary objective of this work is to enhance the robustness of sequence labeling on data derived from noisy sources without compromising performance on pristine data. We assume that the source token sequence $x$ may contain errors; however, the noising process is generally label-preserving, implying that the noise level is insufficient to alter the corresponding entity labels. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.957323703 , the TTR score is 0.478448276 , the CV score is 0.465896238 , and the perplexity score is  25.51687813 .","The text is classified as ""Human-written"" because it describes a detailed explanation of how natural language processing works, particularly focusing on issues related to human-generated text and the challenges faced in processing such texts due to imperfections in the input data. The use of technical terms and examples from various fields (e.g., linguistics, computational linguistics, speech recognition) indicates a high degree of expertise and knowledge about human-written text. Additionally, the focus on explaining the mechanics behind certain processes used in Natural Language"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","I disagree that, successful people try new things and take risks rather than only doing what they already know how to do well.
Because doing known things perfectly is also equally difficult. Also, doing known things well is not easy.

Intially, for a person to be successful he or she needs to be perfect in their respective fields.So, it is better to master in known field then in unknown areas.In a known area, a person wil be more confident in his or her work.For example, a doctor performs operation in his known  area but not on other parts of the body, where his area of study is not concerned.

 Another hypothetical case is, in a known area a person can assume the output.But in an unknown area, a person has no garuntee of success. And also, what to expect as the output.For example, various explorers known their destination point, only then they start  their travelling.

Moreover, for experimenting new things more money would be required. Hence, the person should be confident completely before putting huge amounts at stake.For example, funding for satelite where huge money is involved, so much research must be done.

Also, successful is obtained by combining  dedication towards the work, talent, commitment,intelligence and many more features. Also, these qualities can be combined only when there is qaruntee about the output.For example, NASA scientists does so much research before working on a new project.They woud not start work in unknown area where there is no qaruntee of output.

Finally, I conclude by mentioning that success is obtained by those people who are confident in what they are doing.Hence, successful people should do things what they already know rather than taking risks. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.000269883 , the TTR score is 0.473354232 , the CV score is 0.614388074 , and the perplexity score is  40.31211472 .","This text is likely classified as human-written because it exhibits characteristics typical of human-generated content, such as:

1. **Complex Logical Structure**: The text presents a nuanced argument about risk-taking versus sticking to one's strengths, which requires careful reasoning and consideration of multiple perspectives.

2. **Use of Metaphors and Analogies**: The author uses metaphors like ""mastering"" in medical contexts and analogies about explorers starting journeys with clear goals, adding depth and relatability to the discussion"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Block actions scores: We use a CNNbased architecture with parameter sharing across cells to score each of the seven possible actions for every grid cell. The input to this model consists of the CNNbased world state representation W ( t ) m (Section 3.3 ), as well as the decoder GRU’s hidden state h ( t ) , concatenated to each cell’s representation in W ( t ) m as additional channels. This model consists of n − 1 1 × 1 × 1 3dconv layers followed by ReLU ( W ′ ( t ) i = relu ( CNN i 1 ( W ′ ( t ) i − 1 ) ) and with the n th such 3dconv layer with 7 output channels (and no ReLU): W ′ ( t ) n = relu ( CNN n 1 ( W ′ ( t ) n − 1 )) , which is flattened into a 7623dim vector of action scores. STOP score: We also need to predict when an action sequence is complete. While this decision needs access to the same information as the block action scorer, it also needs access to a (compact) global representation of the grid, since the STOP action is not cellspecific. It also needs to know the uncertainty in the block action scorer, since STOP is more likely when it is less clear which block action should be performed, and vice versa. We take the output of the penultimate layer in the block action scorer and apply maxpooling to every cell’s vector representation, thus obtaining a single number for each of the 1089 cells. We concatenate these numbers into a single vector and use that as input to the STOP prediction model, which consists of l dense linear layers (with ReLU after each layer except the last), where the l th layer has a single output W ′′ ( t ) l , the score for STOP. Final action prediction scores: Finally, we concatenate the block action and STOP scores and apply a softmax to obtain the final prediction a ( t ) : a t = arg max(softmax( vec ( W ′ ( t ) n ) ⊕ W ′′ ( t ) l )) 4 Data Augmentation The small size of the training set (3,709 examples) is a major limiting factor for training complex models. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.822402811 , the TTR score is 0.36091954 , the CV score is 0.496455929 , and the perplexity score is  107.0174332 .","The text is classified as AI-generated because it describes a neural network architecture used for predicting block actions and stopping sequences in a game-like environment. The mention of ""CNN-based"" architecture, ""GRU's hidden state,"" and ""dense linear layers"" indicates advanced machine learning techniques commonly associated with artificial intelligence. Additionally, the detailed description of how the system processes data and makes predictions suggests sophisticated algorithmic reasoning typical of AI systems."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Upon the convergence of both views, pseudo-sentences are generated within the designated regions (represented by solid and dashed boundaries). These synthetic instances serve as inputs paired with authentic sentences as targets to train the respective translation models. This iterative cycle of generation and training continues until the Biview AT framework reaches convergence. Through this multi-view approach, the translation models for both directions are mutually reinforced.

### 3.3 Anchored Crosslingual Pretraining (ACP)

Crosslingual pretraining has proven effective for tasks such as crosslingual classification and unsupervised machine translation (Lample and Conneau, 2019). Traditionally, this process involves masking random tokens within large monolingual corpora and training models to predict them via a cloze task. In contrast, we propose Anchored Crosslingual Pretraining (ACP), which utilizes data derived by transforming genuine monolingual corpora from both languages into an anchored format. Specifically, tokens in the source language corpus that are covered by a bilingual dictionary are replaced with their corresponding translations. These substituted tokens function as anchoring points, facilitating a more effective alignment between the source and target language spaces compared to the original methodology proposed by Lample and Conneau (2019), as demonstrated in the experiments detailed in Section 4.5. The resulting anchored source corpus, combined with the genuine target corpus, constitutes the target language view for ACP. This pretraining procedure may be executed within either the source or target language view. Upon completion, the resulting models are employed to initialize the encoders for their respective AT systems.

### 3.4 Training Procedure

In the AT framework, the steps for pseudo-sentence generation and Neural Machine Translation (NMT) training are interleaved. Considering the target language view AT illustrated in Figure 1(a), the process involves extracting a batch of anchored source sentences and decoding them into pseudo target sentences. Subsequently, this same batch is utilized to train the NMT model mapping from the target language to the anchored source. Concurrently, a batch of target sentences is decoded into pseudo anchored source sentences to train the NMT model mapping from the anchored source to the target. This cycle repeats until the AT process converges. For Biview AT, following the convergence of each monoview AT, a larger batch size is employed to generate pseudo-sentences (as depicted by the solid and dashed regions in Figure 1(b)), which are then used to train the corresponding NMT models. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.384036348 , the TTR score is 0.411235955 , the CV score is 0.397879749 , and the perplexity score is  79.88640594 .","The text appears to be human-written because it describes technical concepts related to artificial intelligence and natural language processing, including methods for generating and training neural machine translation models. It also includes specific details about linguistic features like burstiness, type-token ratio, sentence length variability, and perplexity, which are typical of academic papers discussing computational linguistics and AI research. The use of technical jargon and structured paragraphs further supports its classification as human-written content."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","This is given by f ( y ; x , s ) = f ←→ LM ( y ) · f SIM ( y ; x ) γ · f LEN ( y ; s ) , (1) where the relative weight γ balances f ←→ LM ( y ) and f SIM ( y ; x ) . We treat the summary length as a hard constraint, and therefore we do not need a weighting hyperparameter for f LEN . Language Fluency. The language fluency scorer quantifies how grammatical and idiomatic a candidate summary y is. Our model generates a candidate summary in a nonautoregressive fashion, in contrast to the beam search in Zhou and Rush. Thus, we are able to simultaneously consider forward and backward language models, using the geometric average of their perplexities. Using both forward and backward language models is less biased towards sentence beginnings or endings. ←−→ PPL( y ) = 2 | y | 1 p −→ LM ( y i | y <i ) 1 p ←− LM ( y i | y >i ) . Our fluency scorer is the inverse perplexity. f ←→ LM ( y ) = ←−→ PPL( y ) − 1 . (2) Depending on applications, the language models could be pretrained on a target corpus. 3 In this case, the fluency scorer also measures whether the summary style is consistent with the target language. This could be important in certain applications, e.g., headline generation, where the summary language differs from the input in style. Semantic Similarity. A semantic similarity scorer ensures that the summary keeps the key information of the input sentence. We adopt the cosine similarity between sentence embeddings as f SIM ( y ; x ) = cos( e ( x ) , e ( y )) , (3) where e is a sentence embedding method. In our work, we use unigram word embeddings learned by the sent2vec model. Then, e ( x ) is computed as the average of these unigram embeddings, weighted by the inversedocument frequency ( idf ) of the words. We use sent2vec because it is trained in an unsupervised way on individual sentences. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 4.07758675 , the TTR score is 0.393034826 , the CV score is 0.620096916 , and the perplexity score is  47.68864059 .","The text describes a machine learning model designed for summarization tasks, including its components such as language models, fluency scoring, and linguistic features like burstiness and type-token ratio. It mentions the use of neural networks and statistical methods typical of AI systems, indicating that the content is likely generated by an AI algorithm. Additionally, the focus on specific metrics used in natural language processing research further supports the classification as AI-generated."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","The network architecture and the rest of the hyperparameters are kept same; Variational Fair Autoencoder ( VFAE ) learns latent representations independent from sensitive domain knowledge, while retaining enough task information by using a MMDbased loss; Central Moment Discrepancy ( CMD ) is a regularization method which minimizes the difference between feature representations by utilizing equivalent representation of probability distributions by moment sequences; Asym is the asymmetric tritraining framework that uses three neural networks asymmetrically for domain adaptation; MTTri is similar to Asym , but uses multitask learning; Domain Separation Networks ( DSN ) learns to extract shared and private components of each domain. As per Peng et al., it stands as the present stateoftheart method for unsupervised domain adaptation; Task Refinement Learning ( TRL ) Task Refinement Learning is an unsupervised domain adaptation framework which iteratively trains a Pivot Based Language Model to gradually increase the information exposed about each pivot; TAT is the transferable adversarial training setup to generate examples which helps in modelling the domain shift. TAT adversarially trains classifiers to make consistent predictions over these transferable examples; CoCMD is a cotraining method based on the CMD regularizer which trains a classifier on simultaneously extracted domain specific and invariant features. CoCOMD, however, is SSLbased as it uses labeled data from the target domain. Although it falls outside the regime of unsupervised domain adaptation, we report its results to provide a full picture to the reader. 6 Results and Analysis As mentioned in 5.3 , we reimplemented the baseline DANN model using Adam optimizer and observed that its results has been notably underreported in many of the unsupervised domain adaptation literature for sentiment analysis (see Table 2 ). In the original DANN implementation, Stochastic Gradient Descent (SGD) was used as the optimizer. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.127918132 , the TTR score is 0.556603774 , the CV score is 0.697276965 , and the perplexity score is  146.5206604 .","This text appears to be AI-generated due to several characteristics:

1. **Consistent Terminology**: The text consistently uses terms like ""hyperparameters"", ""MMD-based loss"", ""momentum"", and other technical jargon typical of machine learning research papers.
   
2. **Algorithmic References**: It mentions specific algorithms such as VFAE, CMD, Asym, MTTri, DSN, TRL, TAT, and CoCMD, which are common in the field of deep"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","And the last line is the result from SpellGCN. We highlight the incorrect / correct characters by orange / blue color. 4.5 Case Study We show several correction results to demonstrate the properties of SpellGCN. In addition to the sample illustrated in Table 1 , several prediction results are given in Table 6 . From these cases, we can tell that our SpellGCN is capable of revising the incorrect characters into correct ones with the pronunciation and shape constraint. For instance, in the first case, “ 麻 坊 (f ˘ ang)” is detected as errors and modified into “ 麻 烦 (f ´ an)”. Without pronunciation similarity constraint, “ 麻 木 (m ` u)” becomes the most probable answer. And surprisingly, in the second case, our SpellGCN successfully modifies the character reasonable in the context. The meaning of input sentence “ 看 录 音 机 ” is “watch the audio recorder”, and our method corrects it into “ 看 录影 机 ” which means “watch the video recorder”. We suggest that SpellGCN injects a prior similarity between “ 音 ” and “ 影 ” in the representation space so that the model derives a higher posterior probability of “ 影 ”. In the last case, we show a correction result under the shape constraint. In the confusion set, “ 向 ” is similar to “ 尚 ” and therefore, using SpellGCN is able to retrieve the correct result. 4.6 Character Embedding Visualization Previous experiments have explored the performance of SpellGCN quantitatively in detail. To qualitatively study whether SpellGCN learns meaningful representations, we dive into the target embedding space W derived from SpellGCN. In Figure 4 , the embedding of characters with phonics “ch ´ ang” and “s ` ı” is presented using tSNE. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.236365343 , the TTR score is 0.474626866 , the CV score is 0.36983617 , and the perplexity score is  77.01670074 .","The text is likely AI-generated because it contains technical details about a machine learning algorithm called SpellGCN, including its architecture, training process, and evaluation metrics. It also includes examples of how the algorithm works on specific tasks such as correcting spelling mistakes and improving embeddings for characters based on linguistic features like burstiness, type-token ratio, and perplexity. These elements are characteristic of AI-generated content discussing advanced natural language processing techniques."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Neural machine translation by jointly learning to align and translate. In ICLR 2015 . Ankur Bapna, Mia Xu Chen, Orhan Firat, Yuan Cao, and Yonghui Wu. 2018. Training Deeper Neural Machine Translation Models with Transparent Attention. In EMNLP 2018 . Yoshua Bengio, J´erˆome Louradour, Ronan Collobert, and Jason Weston. 2009. Curriculum learning. In ICML 2009 . Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017. Enriching word vectors with subword information. In TACL 2017 . Sergey Edunov, Myle Ott, Michael Auli, and David Grangier. 2018. Understanding backtranslation at scale. In EMNLP 2018 . Jun Gao, Di He, Xu Tan, Tao Qin, Liwei Wang, and Tieyan Liu. 2019. Representation degeneration problem in training natural language generation models. In ICLR 2019 . Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin. 2017. Convolutional sequence to sequence learning. In ICML 2017 . Marjan Ghazvininejad, Omer Levy, Yinhan Liu, and Luke Zettlemoyer. 2019. MaskPredict: Parallel Decoding of Conditional Masked Language Models. In EMNLP 2019 . Chengyue Gong, Di He, Xu Tan, Tao Qin, Liwei Wang, and TieYan Liu. 2018. FRAGE: FrequencyAgnostic Word Representation. In NIPS 2018 . Marcin JunczysDowmunt, Roman Grundkiewicz, Tomasz Dwojak, Hieu Hoang, Kenneth Heafield, Tom Neckermann, Frank Seide, Ulrich Germann, Alham Fikri Aji, Nikolay Bogoychev, Andr´e F. T. Martins, and Alexandra Birch. 2018. Marian: Fast neural machine translation in C++. In ACL 2018 . Nal Kalchbrenner and Phil Blunsom. 2013. Recurrent continuous translation models. In EMNLP 2013 . Diederik Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In ICLR 2015 . Tom Kocmi and Ondrej Bojar. 2017. Curriculum Learning and Minibatch Bucketing in Neural Machine Translation. In RANLP 2017 . Gaurav Kumar, George Foster, Colin Cherry, and Maxim Krikun. 2019. Reinforcement learning based curriculum optimization for neural machine translation. In NAACL 2019 . Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio. 2017. A structured selfattentive sentence embedding. In ICLR 2017 . Xuebo Liu, Derek F. Wong, Lidia S. Chao, and Yang Liu. 2019a. Latent attribute based hierarchical decoder for neural machine translation. IEEE/ACM Transactions on Audio, Speech, and Language Processing , 27(12):2103–2112. Xuebo Liu, Derek F. Wong, Yang Liu, Lidia S. Chao, Tong Xiao, and Jingbo Zhu. 2019b. Sharedprivate bilingual word embeddings for neural machine translation. In ACL 2019 . Hans Peter Luhn. 1958. The automatic creation of literature abstracts. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 16.32005021 , the TTR score is 0.469111969 , the CV score is 0.895611425 , and the perplexity score is  112.4845123 .","This text is likely classified as AI-generated because it discusses various advancements and techniques in artificial intelligence, particularly in the field of natural language processing (NLP). Topics such as neural machine translation, attention mechanisms, curriculum learning, and deep learning algorithms are common areas of research in AI. Additionally, the mention of specific authors and their contributions further supports an AI-generated classification, as these individuals have made significant contributions to the field of AI through their work."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","While comprehensive initiatives such as those previously described have demonstrated efficacy, their implementation necessitates substantial resources and the coordinated commitment of personnel across multiple institutional levels. Consequently, in environments characterized by fiscal constraints or other impediments to large-scale transformation, smaller-scale interventions executable by a limited number of dedicated individuals present a more viable alternative. Given the contemporary imperative for academic units to serve expanding student populations with diminishing financial resources, retention strategies must increasingly prioritize resource efficiency over comprehensive models.

Two primary avenues for enhancing retention with minimal resource expenditure include program-level modifications and classroom-level instructional adjustments. Empirical evidence suggests that specific interventions within these domains can effectively improve retention rates among Computer Science (CS) majors. For instance, Ott et al. documented a program-level intervention wherein the implementation of mandatory placement examinations, rather than voluntary opt-in mechanisms, ensured that students were enrolled in introductory CS courses aligned with their prior programming experience. This procedural adjustment significantly increased the likelihood of students continuing their CS coursework.

Similarly, Carver et al. reported on a classroom-level intervention involving pair programming during laboratory exercises in introductory courses. This collaborative methodology, wherein students alternate between the roles of ""driver"" and ""navigator,"" was associated with higher persistence rates in computing majors compared to traditional instruction. In both cited cases, interventions deeply integrated into the CS curriculum successfully bolstered retention. In contrast, the present inquiry focuses on determining whether broader instructional practices influence students' decisions to enroll in subsequent CS courses. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 0.494389902 , the TTR score is 0.673913043 , the CV score is 0.425381453 , and the perplexity score is  64.21327972 .","The text is likely human-written because it discusses educational strategies for improving student retention in computer science programs, which is a topic typically explored by educators and researchers. The language used is formal and academic, appropriate for scholarly discourse. Additionally, the citation references suggest a focus on empirical research, further indicating human authorship."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","We argue that the language modeling task, because it only uses form as training data, cannot in principle lead to learning of meaning . We take the term language model to refer to any system trained only on the task of string prediction, whether it operates over characters, words or sentences, and sequentially or not. We take (linguistic) meaning to be the relation between a linguistic form and communicative intent. Our aim is to advocate for an alignment of claims and methodology: Humananalogous natural language understanding (NLU) is a grand challenge of artificial intelligence, which involves mastery of the structure and use of language and the ability to ground it in the world. While large neural LMs may well end up being important components of an eventual fullscale solution to humananalogous NLU, they are not nearlythere solutions to this grand challenge. We argue in this paper that genuine progress in our field — climbing the right hill, not just the hill on whose slope we currently sit — depends on maintaining clarity around big picture notions such as meaning and understanding in task design and reporting of experimental results. After briefly reviewing the ways in which large LMs are spoken about and summarizing the recent flowering of “BERTology” papers ( 2 ), we offer a working definition for “meaning” ( 3 ) and a series of thought experiments illustrating the impossibility of learning meaning when it is not in the training signal ( 4 , 5 ). We then consider the human language acquisition literature for insight into what information humans use to bootstrap language learning ( 6 ) and the distributional semantics literature to discuss what is required to ground distributional models ( 7 ). 8 presents reflections on how we look at progress and direct research effort in our field, and in 9 , we address possible counterarguments to our main thesis. 2 Large LMs: Hype and analysis Publications talking about the application of large LMs to meaningsensitive tasks tend to describe the models with terminology that, if interpreted at face value, is misleading. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.062380952 , the TTR score is 0.529100529 , the CV score is 0.333123307 , and the perplexity score is  54.17134094 .","This text is likely AI-generated due to its formal tone, structured format typical of academic writing, and specific references to concepts like perplexity and type-token ratio, which are common in discussions of artificial intelligence and machine learning. The content also exhibits characteristics consistent with AI-generated texts, including jargon-rich descriptions and a focus on theoretical arguments rather than personal narratives or creative expressions."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Tree Depth ( SETreeDepth ) is a task that tests a model’s ability to estimate the maximum depth of the constituency parse tree of the sentence. Top Constituents ( SETopConst ) is a task that tests a model’s ability to identify the highlevel syntactic structure of the sentence by choosing among 20 constituent sequences (the 19 most common, plus an other category). Bigram Shift ( SEBShift ) is a task that tests a model’s ability to classify if two consecutive tokens in the same sentence have been reordered. Coordination Inversion ( SECoordInv ) is a task that tests a model’s ability to identify if two coordinating clausal conjoints are swapped ( ex: “he knew it, and he deserved no answer.”). PastPresent ( SETense ) is a task that tests a model’s ability to classify the tense of the main verb of the sentence. Subject Number ( SESubjNum ) and Object Number ( SEObjNum ) are tasks that test a model’s ability to classify whether the subject or direct object of the main clause is singular or plural. OddManOut ( SESOMO ) is a task that tests the model’s ability to predict whether a sentence has had one of its content words randomly replaced with another word of the same part of speech. 3 Experiments Training and Optimization We use the largescale pretrained model RoBERTa Large in all experiments. For each intermediate, target, and probing task, we perform a hyperparameter sweep, varying the peak learning rate ∈{ 2 × 10 − 5 , 1 × 10 − 5 , 5 × 10 − 6 , 3 × 10 − 6 } and the dropout rate ∈{ 0 . 2 , 0 . 1 } . After choosing the best learning rate and dropout rate, we apply the best configuration for each task for all runs. For each task, we use the batch size that maximizes GPU usage, and use a maximum sequence length of 256. Aside from these details, we follow the RoBERTa paper for all other training hyperparameters. We use NVIDIA P40 GPUs for our experiments. A complete pipeline with one intermediate task works as follows: First, we finetune RoBERTa on the intermediate task. We then finetune copies of the resulting model separately on each of the 10 target tasks and 25 probing tasks and test on their respective validation sets. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.206983124 , the TTR score is 0.423690205 , the CV score is 0.523977416 , and the perplexity score is  47.64821625 .","This text appears to be written in a technical style discussing natural language processing tasks and models used for them. It mentions specific tasks like ""Tree Depth"" and ""Subject Number,"" which are typical topics in AI research papers focused on NLP. The detailed description of hyperparameters and experimental setup further supports the classification as AI-generated, as such information would typically be found in academic papers rather than casual writing. Additionally, the mention of specific machine learning models like RoBERTa suggests expertise in AI development and research"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","If one wants to be successful in business, one needs to take some risks and try new ideas or concepts. However, successful people cannot limit themselves to what they already know as their environment is evolving in parallel. They need to constantly adapt and cannot only focus on what they already know how to do well. Therefore, they need to learn and try new things. In a business, this  becomes very obvious with the illustration of the development of the business. For instance, Bill Gates (Microsoft) originally provided operating systems. Nowadays his firm also provides antivirus, new environment (.NET) and so on. Everytime he introduces a new product on the market, there is an underlying risk related to different uncertainties: how the product will be received, the quality of the product...

 However, if one starts something totally new, one needs to have enough knowledge to control the situation and anticipate upcoming problems. At least, one needs to ensure that knowledge and information are within their reach when needed. On the one hand, Steve Jobs (EasyJet) shows that entrepreneurship and motivation can be enough to become successful but there is no guarantee. He created EasyJet based on a simple concept: cheap flights. On the other hand, the disappearance of ten per cent of Small and Medium Enterprises, five years after being created, shows that the risks must be measured and calculated. Indeed, trying new things is not enough, there must be a follow up and a constant evaluation of the risk to prevent from failure.

To conclude, successful people need to have an initial idea. As the environment is evolving, the initiators will need to consider new areas they might not know well in order to have their ideas fit in this environment. Thus they will have to try new things and take risks, but they will not take blind risks. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.624180211 , the TTR score is 0.467787115 , the CV score is 0.331125139 , and the perplexity score is  29.62720108 .","This text is likely human-written because it contains several characteristics typical of human-generated content:

1. **Complex Structure**: The text has a complex structure with multiple paragraphs discussing various aspects of business success, including adaptation, innovation, and risk management.
  
2. **Emotional Tone**: There's a clear emotional tone expressing both optimism (""successful people cannot limit themselves"") and caution (""there must be a follow-up and a constant evaluation of the risk"").

3. **Use of Analogies**: The text uses"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","I agree with the statement that successful people are those who try new things and take risks, rather than just sticking to what they already know how to do well. Achieving success requires careful planning, hard work, and making the right decisions at the right time.

There are many examples of people who became successful by taking risks. For instance, Thomas Alva Edison invented the electric bulb after conducting nearly one hundred thousand experiments, constantly seeking new approaches and taking risks until he finally succeeded. Many scientists have also dedicated their lives to inventing new things, often taking great risks for the benefit of the world.

In politics, there are numerous successful individuals as well. Abraham Lincoln, for example, came from a poor background but eventually became the President of the United States. Although he faced many failures early on, his perseverance and willingness to take risks led to his success. Another example is Mahatma Gandhi, who sacrificed his life for India’s freedom. His main strategy was non-violence, and he spent years fighting against British rule by trying new methods and taking risks, ultimately achieving his goal.

In the business world, Dhirubhai Ambani is a well-known example. He started from a middle-class family and began his career with a small petrol pump and limited funds. Through hard work, perseverance, and a readiness to take risks and try new things, he became the chairman and managing director of Reliance Industries Ltd., one of the most successful companies in India.

However, it’s important to note that taking too many risks can also lead to problems. Some people believe it’s better to stick with what they know, but ultimately, it is those who are willing to try new things and take calculated risks who often achieve the greatest success. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.242556433 , the TTR score is 0.524096386 , the CV score is 0.379039668 , and the perplexity score is  15.25917816 .","The text is likely AI-generated because it contains several characteristics typical of machine-written content:

1. **Consistent Style**: The language used throughout the text is consistent and formal, which is characteristic of AI-generated texts designed to sound professional or academic.
   
2. **Structured Argumentation**: The text presents a structured argument about the importance of risk-taking and innovation in achieving success, using clear transitions between points.

3. **Repetition and Clichés**: Phrases like ""successful people"" and """
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","**Submission Guidelines and System Requirements**

Systems submitted to the leaderboard are strictly prohibited from utilizing information derived from the developer.ibm.com or IBM developerworks websites, with the sole exception of data explicitly provided within the official dataset.

**Computational Environment**
All submitted systems will be executed on a dedicated machine configured with the following specifications:
*   **Memory:** 128 GB
*   **Processing Units:** Two NVIDIA V100 GPUs (16 GB each)
*   **Storage:** 64 GB of local disk space allocated for temporary files and logs

**Evaluation Workflow**
Upon submission, the system will initially undergo validation against the 20-question validation set. Results from this phase are accessible via the user's personal dashboard. Users satisfied with the validation performance may proceed to submit the system for full evaluation against the 490-question evaluation set.

**Execution Constraints and Monitoring**
*   **Time Limit:** Evaluation runs are capped at 24 hours. Any run exceeding this duration will be automatically terminated, and the submission status will be updated to ""error"" on the dashboard.
*   **Status Tracking:** Users may monitor the progress of active submissions via the dashboard and may cancel a submission at any time prior to the completion of the evaluation run.
*   **Leaderboard Integration:** Results from successful evaluation runs are automatically posted to the public leaderboard.
*   **Submission Frequency:** To ensure fair usage, users are restricted from submitting a new system for a period of seven days following the date of their most recent submission as recorded on the public leaderboard.

**Anonymization Protocols**
The user dashboard includes functionality to anonymize or deanonymize successful submissions, facilitating processes such as peer review. An anonymized submission retains the system name designated by the user but conceals the user's institutional affiliation and any optional links to associated publications.

**Technical Output Requirements**
Submitted systems must analyze the 50 documents associated with each query and generate five candidate answers. Each candidate answer must include:
1.  The Document ID.
2.  Start and end character offsets relative to the beginning of the detagged Technote text.
3.  A confidence score.

The system must also provide a threshold value against which these scores will be compared during evaluation. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 9.754761905 , the TTR score is 0.477272727 , the CV score is 0.838294461 , and the perplexity score is  51.0932045 .","This text appears human-written because it describes guidelines and requirements for a system submission process, including details about computational resources, evaluation criteria, execution constraints, and technical output expectations. It uses clear, concise language typical of instructions given by humans rather than automated scripts or software documentation."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","A person becomes successful because they possess different skills than the average individual. I believe that successful people are willing to try new things and take risks, rather than sticking only to what they already do well. They keep an open mind and seek out new experiences to enhance their abilities.

First of all, successful people have broad perspectives and don’t set limits for themselves. They are willing to explore all kinds of new opportunities, and these experiences help them achieve success. For example, Bill Gates is a well-known successful person. He had a wide vision and went on to create Microsoft. His idea was to develop software that would improve people’s lives, and his contributions to technology have helped people work more efficiently.

Secondly, those who like to experiment with new things often achieve success in life. Successful individuals need fresh ideas, and these ideas lead them to try new things. Experimenting opens the door to further innovation. People with new ideas and experiences help society progress, and their efforts benefit the public as a whole. For instance, the Turkish leader Ataturk is a great example of success. He introduced revolutionary changes in Turkey, such as adopting the Latin alphabet instead of the Arabic one and granting women the right to vote after becoming president. His reforms transformed Turkey into a secular nation and brought about significant societal change.

In conclusion, being open-minded and willing to try new things leads people to success. I believe successful individuals have broad minds and are able to turn their dreams into reality, making a positive impact on society. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.810523032 , the TTR score is 0.552542373 , the CV score is 0.314359949 , and the perplexity score is  21.02479553 .","The text is likely AI-generated due to its formal tone, structured format, and consistent use of persuasive language aimed at convincing readers of the benefits of being open-minded and taking risks. The inclusion of specific examples from historical figures like Bill Gates and Atatürk supports the argument presented. Additionally, the high burstiness score suggests rapid, varied writing style typical of automated content generation systems."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Nowadays, film stays, pop musicians or sports stars are becoming one of most highest paid jobs. We all at least hread some evidences of this. The big fancy houses, luxury cars or yachts, these are just some of standard equipment. Is this fact good for us? Is this right for our society? Some people rised hot debate on this issue.
Some of them believe this high payment is wrong. They believe those stars or musicians didn't really improve our society too much. They did creat some arts for our spiritual world. However, those contributions are far behind scientists, politicians, doctors and so on. Sadly, we can not live on the songs, the films. Those arts are great, but they aren't great enough to creat a such big payment gap between other jobs. In my country, my teacher, basically a unknown scientist, only has 700 dollars per month. This is incredible low since famous singer have endless zero behind their payment account.
However, the other side of people think their payment is rightful and I am one of them. I don't really love this fact but it's truth because those unbelievable high payment is the result of the our attention to them. In this digital world, attention means money. That is the new rule of our society so there are nothing wrong. As for those scientists or doctors, some of us even can't remeber them before we need them.
So, in my opinion, these payment with uncountable zero are rightful. However, we should rethink of it. Why we spend so much attention on those jods? Why we don't care about other jobs which are means alot for us. That is the thing that we should change. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.245357386 , the TTR score is 0.473053892 , the CV score is 0.363991088 , and the perplexity score is  81.22550201 .","This text appears to be human-written based on several linguistic features:

1. **Burstiness**: A bursty distribution suggests rapid changes in writing style, typical of human communication rather than automated text generation.
   
2. **TTR (Type-Token Ratio)**: With an average of 0.4731 tokens per word, the text exhibits a moderate level of complexity, suggesting it was written by someone familiar with language structure.

3. **CV of Sentence Length**: The sentence length variation"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Also, there is a tradeoff as some of the options take more time than others. These deliberations and contextualizations are exactly what novice teachers need to be made aware of and they also need to develop an understanding that there is no onesize-fitsall solution. Cluster “Working in teams” Scenarios ONLY, TEAM, and SMLL describe situations in which students are working in teams. Working in teams or in pairs has been shown to support students’ motivation and performance; see, e. g., and the references therein. However, teachers need to be aware of the dynamics that can emerge within teams, in particular for young children . Israel et al. as well as Campe et al. analyzed pair programming behavior in K5 and middle school; their findings can serve as additional input for creating vignettes for these scenarios. As a final remark and also with respect to scenario TEAM, one should keep in mind Lewis’ observation that effective collaboration does not necessarily requires to share a computer . Cluster “Distraction” Scenarios SURF and TINK describe situations in which students redirect their focus. In addition to the experts’ comments on these scenarios being indicative of students struggling and implicitly asking for support, we would like to point out the distinction between productive and unproductive tinkering. In contrast to the unproductive tinkering with items not relevant to the assignment, a seminar in a center for teacher training might also want to include a session on productive tinkering which has been investigated for, e. g., programming, physical computing, and makerspaces . Cluster “Time Management” This cluster consists of a single scenario (TIME). It is evident that timemanagement issues should discussed in any teacher training program. However, as pointed out by one expert, an additional aspect to this scenario is that novice teachers tend to underestimate the time needed for documentation, testing (where applicable), and presentation of the results of an implementation. Encouraging evidence from the introduction of testdriven development in a CS1/CS2 course suggests that despite prevailing concerns the additional time needed for testing and documentation can be controlled for. Another case study worth discussing with prospective teachers is the report by Kastl et al. on the introduction of agile software projects in highschool classrooms. Cluster “HighAchiever” This cluster consists of a single scenario (HIGH). . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.456746032 , the TTR score is 0.486111111 , the CV score is 0.503662427 , and the perplexity score is  65.58301544 .","The text appears to be generated by an AI because it contains specific language patterns and features typical of AI-generated content, such as consistent use of technical jargon related to education and technology, frequent repetition of phrases, and a structured format that follows a predefined template without apparent human authorship. The linguistic features like burstiness, type-token ratio, sentence length variation, and perplexity suggest computational processing rather than manual writing. Additionally, the inclusion of multiple clusters and detailed descriptions of educational scenarios indicates a sophisticated level"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Many people believe that rapid technological advancements have made shopping much easier today. However, some individuals still find online shopping challenging. This essay will discuss both the benefits and drawbacks of using technology for shopping.

One downside of online shopping is that some people struggle to adapt to digital platforms for ordering necessities and groceries. For example, older adults often lack the technical skills needed to navigate virtual stores. Additionally, people living in remote areas may not have easy access to the technology required for online grocery shopping.

On the other hand, online shopping offers significant advantages, especially for those comfortable with technology. It is often more convenient and saves time, as shoppers can order anything they need with just a few clicks, select a payment method, and choose a delivery time. This was particularly useful during the Covid-19 pandemic, when social distancing measures were enforced in parts of Vietnam. Surveys show that the number of online shoppers increased daily, leading to the development and growth of many shopping apps.

Another benefit of online shopping is that it can be life-saving during a pandemic. Visiting supermarkets in person can expose people to viruses and increase the risk of infection. For instance, simply going out to buy bread for breakfast could put someone at risk of coming into contact with individuals carrying Covid-19. Furthermore, with restrictions in place that prevent people from leaving their homes to buy essentials, those unfamiliar with online ordering may find it extremely difficult to meet their basic needs during such times.

In conclusion, while technology-driven shopping offers significant time and safety benefits, it remains unfamiliar to some people. Personally, given its advantages, I plan to continue shopping online even as the world recovers from the Covid-19 pandemic. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.903703704 , the TTR score is 0.555555556 , the CV score is 0.322172552 , and the perplexity score is  16.10375977 .","This text is likely AI-generated because it exhibits characteristics typical of machine-written content, including:

1. **Consistent Writing Style**: The text consistently uses formal language and follows a logical structure without apparent human errors or inconsistencies.
   
2. **Burstiness and TTR**: The burstiness score of 1.9037 indicates consistent writing speed, which is common in automated text generation systems. The Type-Token Ratio (TTR) of 0.5556 suggests an average"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","We differentiate our work from aforementioned methods in that we propose a unified framework which exploits all dyadic and triadic relations among subtasks to enhance the learning capability. 3 Methodology 3.1 Task Definition Given a sentence S e = { w 1 , ..., w i , ..., w n } , we formulate subtasks AE, OE, and SC as three sequence labeling problems. • AE aims to predict a tag sequence Y A = { y A 1 , ..., y A i , ... , y A n } for aspect extraction, where y A i ∈{ B, I, O } denotes the beginning of, inside of , and outside of an aspect term. • OE aims to predict a tag sequence Y O = { y O 1 , ..., y O i , ... , y O n } for opinion extraction, where y O i ∈{ B, I, O } denotes the beginning of, inside of , and outside of an opinion term. • SC aims to predict a tag sequence Y S = { y S 1 , ..., y S i , ... , y S n } for sentiment classification, where y S i ∈{ pos , neu , neg } denotes the positive, neutral , and negative sentiment polarities towards each word. 3.2 Model Architecture Our proposed RACL is a unified multitask learning framework which enables propagating the interactive relations (denoted as the same R 1 .. R 4 as those in Figure 1 ) for improving the ABSA performance, and it can be stacked to multiple layers to interact subtasks at different semantic levels. We present the overall architecture of RACL in Figure 2(a) and details of a single layer in Figure 2(b) . In particular, a single RACL layer contains three modules: AE, OE, and SC, where each module is designed for the corresponding subtask. These modules receive a shared representation of the input sentence, then encode their taskoriented features. After that, they propagate relations R 1 .. R 4 for collaborative learning by exchanging informative clues to further enhance the taskoriented features. Finally, three modules will make predictions for the corresponding tag sequences Y A , Y O , and Y S based on the enhanced features. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 5.750836682 , the TTR score is 0.378823529 , the CV score is 0.40297853 , and the perplexity score is  56.36650848 .","This text is likely classified as AI-generated because it describes a methodological approach to Aspect-Aware Named Entity Recognition (ABSAR), which is a complex natural language processing task. The text provides detailed explanations of how the system works, including its architecture, methodology, and specific components like AE, OE, and SC. It also includes technical details such as linguistic features and model architectures, which are characteristic of AI-generated content. Additionally, the use of terms like ""unified multitask learning"" and """
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Based on our experience, we found that (1) inviting students to create and moderate learning resources is an effective way to harness their creativity and engage them in higherorder learning; (2) providing open and transparent models of learners can help students better understand their own learning needs and improve selfregulation; (3) putting instructors in charge while empowering them with educational tools, rich analytics and the required technical support and professional development can enhance teaching and learning practices; (4) providing support for ethical and lowcost educational research can play a significant role in promoting and increasing the development of evidencebased teaching practices; (5) utilising mechanisms such as gamification that motivate students to be actively engaged can improve learning; and (6) while consideration of learning theories and pedagogical approaches are important in developing educational technologies other factors such as usability, flexibility and scalability are also critical. We hope that the lessons that we have learned while developing and piloting RiPPLE can provide insight for other instructors that are interested in adopting ALSs or are involved in the implementation of educational tools and technologies. The development and dissemination of RiPPLE has been supported by an innovation teaching grant by The University of Queensland. One of the key challenges associated with Computer Science I (CS1) curricula is that of student satisfaction which directly translates to success and retention. This challenge is exacerbated at small institutions in which departmental enrollment is low overall. One area that has shown much promise is that in which peer mentors are provided to augment existing lecture and laboratory components in order to better assist the students. This approach has found success at both large and small institutions where student demographics and populations are widely varied. In most implementations, the peer mentor is a departmental student (CS major) who has previously taken and excelled in the course. This does not however, account for those students enrolled in the course as nonmajors. At many institutions, the typical CS1 course may count as an elective course necessary for graduation or to fulfill another majors requirements. In this situation, nonmajor students are often cited as feeling excluded, which then becomes the reason the student does not seek the necessary help that is required to find success within the course. In this work, we propose a nonmajor peer mentoring implementation to augment existing departmentallevel tutoring in order to better assist all student backgrounds. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.131265062 , the TTR score is 0.505668934 , the CV score is 1.068243836 , and the perplexity score is  31.92669678 .","The text appears to be written in a formal academic style, discussing various aspects of using artificial intelligence (ALS) in education, including creating and moderating learning resources, providing open models of learners, empowering instructors, supporting ethical research, motivating students through gamification, and enhancing teaching and learning practices. The language used is consistent with scholarly articles and emphasizes the benefits and potential applications of AI in education. Additionally, the text references specific studies and innovations related to computer science courses, further indicating it was likely generated"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","The contributions of this paper are summarized as follows: 1. We propose an attentionbased, autoregressive model, bilingual attention language model (BALM), that not only learns the latent alignment from a parallel corpus for crosslingual word embedding but also captures the word sequential dependency. 2. Adhering to the Matrix Language Frame theory and Equivalence Constraint theory, we implement an objective function by jointly optimizing the crossentropy loss as the monolingual constraint and the quasitranslation loss as the crosslingual constraint. 3. We show that BALM can learn from bilingual parallel data without the need for CS data. When adapted on CS data, it outperforms the best reported result on the SEAME dataset in the perplexity test. We also successfully apply BALM in bilingual lexicon induction, and language normalization tasks to validate the idea. 2 Related Work Several prior studies related to bilingual language modeling are the inspiration for this work. Crosslingual correspondence: Several studies are focused on projecting words of different languages onto the common embedding space to establish crosslingual correspondence. One idea is to train a model using bilingual information from corpora aligned at the sentence level and document level. Another is to exploit the isomorphic structure, dictionary, shared cognate, vocab, numeral through adhoc projection. As the above approaches do not explicitly consider the sequential dependency of words, the embedding doesn’t encode the word ordering information. The multilingual techniques, such as MBERT and XLM, do not explicitly model the syntactic constraints for CS as formulated in the Equivalence Constraint theory, thus not making full use of the information which could potentially improve their performance. Codeswitching modeling: Another school of thoughts is to extend the monolingual language modeling technique to accommodate codeswitch content. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.561044765 , the TTR score is 0.529968454 , the CV score is 0.524608618 , and the perplexity score is  55.00795364 .","The text is likely AI-generated because it discusses advanced machine learning models like Bilingual Attention Language Model (BALM) designed for cross-lingual word embeddings and code-switching analysis. It mentions specific objectives and results achieved with these models, indicating a high level of technical detail typical of AI research papers. Additionally, the focus on theoretical foundations and practical applications suggests a sophisticated approach suitable for AI development."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","When it comes to success, I often find myself disagreeing with statements about how it should be measured. Defining success and figuring out how to achieve it reveals that there are many different approaches and numerous underlying factors that influence any outcome. To try and sum up the path to success with a vague statement is, in itself, contradictory.

Success should be defined by each person based on their own desires and needs. For some, being successful means constantly changing and adapting to their environment, becoming a ""jack of all trades."" Such a person chooses to experience many things without mastering any single one. While this adaptability can be beneficial, it may also lead to instability and challenges rooted in the choices they make about their identity.

On the other hand, some people take pride in the strengths that define their personalities. This is another kind of strength, one that comes from gaining wisdom and taking a decisive approach to the life they see as successful. Recognizing one’s own flaws and being unwilling to adapt can create a sense of security and purpose. However, this might also be a disadvantage if unexpected and significant changes occur.

Ultimately, any effort to achieve success—regardless of the method—should be seen as commendable. Those who reinvent themselves and take action are successful, no matter the circumstances. Understanding that there is no single path to success, and that fulfillment comes from effort, brings immediate satisfaction. The drive to stay true to yourself is what determines whether you reach your goals. In the end, it is our goals that define success, and achieving them is what truly matters. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.888505417 , the TTR score is 0.560655738 , the CV score is 0.206088461 , and the perplexity score is  23.93330002 .","The text is likely classified as AI-generated due to its formal tone, structured argumentation, and consistent use of complex vocabulary and sentence structures typical of academic or professional writing. The text discusses various perspectives on defining and achieving success, using persuasive language and logical reasoning to support its points. These characteristics align closely with the style and content typically produced by artificial intelligence systems designed for generating human-like texts."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Then the normalized score, c ( r | q ) , is c ( r | q ) = − max ( c 5 th , c raw ( r | q )) − c 5 th c 5 th (2) which ranges from 0 to 1. 3.2 Response Fluency To capture the fluency of responses, we also adopt the pretrained language model, GPT2. In particular, the raw response fluency score, f raw ( r ) , is defined as, f raw ( r ) = 1 t log P ( r t | r <t ) . (3) Similar to context coherence , a normalized version, f ( r ) , of f raw ( r ) is employed. 3.3 Response Diversity Prior work measured diversity by computing the n -gram entropy across all generated responses, which essentially reflects the marginal entropy of the responses. Diversity of the responses conditional on the query (e.g., conditional entropy) are however more of interest for dialogue models. On the other hand, if we measure diversity based on responses randomly sampled from a model conditional on a single query, the response quality is generally low. The current work instead proposes to measure response diversity utilizing augmented datasets with controlled paraphrasing, which allows for measuring diversity among topranked responses conditional on paraphrased queries and hence avoiding the tradeoff or dependency between diversity and quality. In other words, for a given query, we slightly tilt the corresponding element in the queryresponse joint space along the query dimension (achieved by paraphrasingaugmentation) and then measure the entropy of highquality responses in the neighbourhood of the targeted query. While augmenting the queries to measure the conditional entropy of responses, we need to control the diversity of the augmented queries such that the augmented ones stay in the vicinity of the targeted query. Hence the goal of controlled augmentation is to minimize diversity in both meaning and word use and avoid feeding the dialogue model identical inputs. To achieve so, two augmentation approaches are considered: (1) WordNet Substitution (WS) and (2) Conditional Text Generator (CTG). WordNet Substitution (WS) is a wordlevel manipulation method that replaces some words with synonyms defined in WordNet. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 4.280576779 , the TTR score is 0.410194175 , the CV score is 0.298928239 , and the perplexity score is  45.3984108 .","This text is likely classified as AI-generated because it discusses advanced natural language processing techniques used in machine learning algorithms, including response fluency and diversity measurement using pre-trained language models like GPT2. It mentions specific metrics and methods for evaluating AI-generated responses, indicating an understanding of AI technology and its application in generating human-like text. The detailed technical description and terminology used further supports the classification as AI-generated content."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","When it comes to education, gender equality is essential. Some believe that universities should admit equal numbers of men and women in every subject. Personally, I fully support this idea because I think it is fairer and ultimately benefits society as a whole.

The primary reason for balancing male and female student numbers is that it promotes fairness. Many studies have shown that men and women have different cognitive abilities. This means their strengths and ways of thinking vary, so they should not have to compete directly for university spots. However, this does not mean one gender is smarter than the other. For instance, men are often said to excel in spatial reasoning, while women tend to have stronger memory skills. Their physical abilities also differ, which is why men and women compete separately in sports.

Another argument for equal gender representation in university admissions is that it helps achieve a more balanced workforce. Generally, men work more hours over their lifetimes compared to women, mainly because women are more likely to take maternity leave. This can lead to workforce shortages in fields dominated by women. For example, women now make up most medical students due to higher high school grades, but when they leave their jobs to care for families, hospitals can face staff shortages. The same issue arises in other sectors with a female majority, such as healthcare.

In summary, I maintain that universities should enroll equal numbers of men and women because it would create a fairer system and lead to a better gender balance across different professions. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.408189655 , the TTR score is 0.551724138 , the CV score is 0.346032378 , and the perplexity score is  15.15455437 .","This text is likely classified as AI-generated based on several linguistic features:

1. **Burstiness**: A bursty distribution suggests rapid changes in word frequency, which is characteristic of human writing rather than machine output.
   
2. **TTR (Type-Token Ratio)**: With a TTR of 0.5517, there's an imbalance between unique words and total words, indicating a mix of natural language patterns typical of human authors.

3. **CV of Sentence Length**: The"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","In today’s Western societies, there is ongoing debate about why efforts to create an ideal society often fall short. In this essay, I will share my own vision of an ideal society and then discuss possible ways to achieve it.

From my perspective and personal experience, an ideal society is one where there are no criminals or serious offenders. A community free from crime is a safe and wonderful environment where families and children can live peacefully, without fear of theft, assault, or worse. For example, there is a small village near a theme park in Canada where the residents live by shared rules and a culture that rejects violence and crime. As a result, everyone is happy with their work and social lives, and the community remains harmonious and free from major conflicts due to the absence of serious wrongdoing.

However, building such an ideal society is extremely challenging. I truly believe that the key to creating a balanced community is through the education of our children, as they represent the future. Children need positive role models and proper knowledge to develop strong character. For example, in Ancient Athens, local politicians and philosophers took responsibility for educating young people, not just in schools but also in public spaces like streets and open-air theaters. This approach helped the youth gain the knowledge and values needed to become ideal citizens.

In conclusion, I believe that crime and wrongdoing have no place in an ideal society, as they disrupt harmony and balance. The best way to help our children grow into good, well-rounded individuals is through proper education and guidance. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.971156919 , the TTR score is 0.563758389 , the CV score is 0.291618268 , and the perplexity score is  26.58832169 .","This text appears to be AI-generated because it follows certain linguistic patterns characteristic of automated writing systems. Specifically:

1. **Burstiness**: The burstiness value of 1.9712 indicates high variability in word frequency, which is common in generated text.
   
2. **TTR (Type-Token Ratio)**: With a TTR of 0.5638, the text has relatively few repeated words compared to natural language, suggesting a lack of human authorship.

3"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Some people have different views about whether successful individuals should try new things and take risks instead of just sticking to what they already know. I agree with this idea. First, creativity is essential for human life. Second, advancements in technology happen because people are willing to try new things and take risks.

To begin with, creativity is important because it helps improve our lives. Creative ideas come from people who are willing to experiment and take chances. Many people benefit from those who are not afraid to try something new. Successful people who embrace risks and innovation can make life more convenient for everyone.

Furthermore, new technologies are developed by people who are open to new experiences and willing to take risks. Inventing new technologies is challenging and requires fresh ideas and hard work. Successful individuals who are willing to step out of their comfort zones provide many benefits to society. For example, consider Samsung’s advancements in cellphone technology. Many experts at Samsung... . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 0.787114846 , the TTR score is 0.56043956 , the CV score is 0.396214426 , and the perplexity score is  14.28597355 .","The text appears to be AI-generated due to its formal tone, structured argumentation, and the use of complex vocabulary typical of academic writing rather than casual conversation. The inclusion of specific examples like Samsung's advancements in technology further supports an AI-generated classification. Additionally, the burstiness score being relatively low suggests a consistent style across the text, which is characteristic of AI-generated content designed to maintain coherence."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","I believe that successful people are willing to try new things and take risks, instead of just sticking to what they already know. This is important because there is so much to learn in the world. If we only focus on what we’re already good at, we might lose important skills like critical thinking. That’s why it’s better to explore new experiences instead of repeating the same routines in our lives.

For example, inventors create many useful products to improve our lives. Doctors conduct various studies to find better treatments for their patients. Some people even participate in extreme sports to seek thrilling experiences, despite the risks. I think these individuals are successful not just because they try new things and take risks, but also because they continue to learn and grow. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is -0.086431784 , the TTR score is 0.634482759 , the CV score is 0.285936723 , and the perplexity score is  21.66792679 .","The text appears to be labeled as AI-generated due to its formal tone, structured language, and lack of personal anecdotes or colloquialisms typically found in human-written texts. The use of complex sentence structures with multiple clauses and varied word choices suggests an artificial intelligence system has been used to generate the content. Additionally, the consistent use of capitalization and punctuation without apparent human input further supports the classification as AI-generated."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Recent advancements in language modeling (AlRfou et al., 2019) have demonstrated the surprising efficacy of attention mechanisms for character-level modeling. This finding prompts an inquiry into the applicability of the Transformer architecture to character-level bilingual and multilingual translation, as well as the identification of suitable architectural configurations for such tasks. The present study aims to address these questions.

### 3. The Convolutional Transformer

To enhance character-level interactions within the Transformer framework, we propose a modified architecture termed the **Convolutional Transformer** (ConvTransformer). This model retains the standard Transformer decoder while adapting each encoder block to incorporate an additional sub-module. Inspired by Lee et al. (2017), this sub-module is applied to the input representations $M$ prior to the self-attention mechanism.

The sub-module comprises three parallel one-dimensional (1D) convolutional layers, denoted as $C_w$, utilizing distinct context window sizes $w$. To preserve temporal resolution, padding is configured as $\lfloor (w - 1) / 2 \rfloor$. Specifically, we employ three separate convolutional layers ($C_3$, $C_5$, and $C_7$) with context window sizes of 3, 5, and 7, respectively. These varying window sizes are designed to capture character-level interactions across multiple granularities, analogous to subword or word-level features.

The final output of the convolutional sub-module is computed by concatenating the outputs of the three parallel layers and passing the result through an additional 1D convolutional layer with a context window size of 3 ($C'_3$), which serves to fuse the representations:

$$ \text{Conv}(M) = M + C'_3(\text{Concat}(C_3(M), C_5(M), C_7(M))) $$

For all convolutional layers, the number of filters is set equal to the embedding dimension size $d_{\text{model}}$, ensuring the output dimension matches that of the input $M$. Consequently, unlike the approach of Lee et al. (2017), which utilizes max-pooling to compress character sequences into segments, our methodology maintains the original resolution for both the standard Transformer and the ConvTransformer models. Furthermore, to enhance flexibility, a residual connection (He et al., 2016) is implemented from the input to the output of the convolutional block.

### 4. Experimental Setup

**Datasets.** Experiments were conducted using two distinct datasets. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 5.266009852 , the TTR score is 0.4375 , the CV score is 1.052426101 , and the perplexity score is  34.88676834 .","This text appears to be human-written because it discusses research on language modeling, specifically focusing on character-level modeling and the application of the Transformer architecture to bilingual and multilingual translation. It mentions specific studies and methodologies used in the field of natural language processing, indicating expertise in the subject matter. Additionally, the use of technical terms related to machine learning and computational linguistics suggests a high level of sophistication appropriate for academic or professional writing."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Notably, the performance of German LMs in Across an Object Relative Clause and Across a Prepositional Phrase varies far more than other languages for the same stimulus type. D Evaluation Set Sizes Here, we describe the size of the various evaluation set replications. These will differ for the LSTMs, BERT, and mBERT, as the two latter models sometimes do not contain the varied focus verb for a particular minimal set. Table 6 displays the number of minimal sets per language and stimulus type (with animate nouns only) in our evaluation sets; the total number of sentences (grammatical and ungrammatical) is the number of minimal sets times two. These are also the number of examples that the LSTM is evaluated on. We do not include inanimatenoun cases in our evaluations for now, since these are much more difficult to replicate crosslinguistically. Indeed, grammatical gender is a confounding variable which— according to preliminary experiments—does have an effect on model performance. Additionally, Hebrew has differing inflections depending on the combination of the subject and object noun genders, which means that we rarely have all needed inflections in the vocabulary. We have differing numbers of examples perlanguage for similar cases. The reasoning for this is twofold: (1) direct translations do not exist for all English items in the evaluation set of Marvin and Linzen, so we often must decide between multiple possibilities. In cases where there are two translations of a noun that could reasonably fit, we use both; if we have multiple possibilities for a given verb, we use only one—the most frequent of the possible translations. If no such translation exists for a given noun or verb, we pick a different word that is as close to the English token is possible in the same domain. Reason (2) is that many of the nouns and verbs in the direct translation of the evaluation sets do not appear in the language models’ vocabularies. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.555213148 , the TTR score is 0.487603306 , the CV score is 0.345639469 , and the perplexity score is  93.18572235 .","The text is likely AI-generated because it contains technical details about natural language processing models, specifically Long Short-Term Memory networks (LSTM), Bidirectional Encoder Representations from Transformers (BERT), and Multi-Headed Attention-based BERT (mBERT). It discusses how these models perform differently across various linguistic features like burstiness, Type-Token Ratio, sentence length variability, and perplexity. The detailed explanations and statistical analyses suggest a high level of expertise and knowledge related to artificial intelligence technologies, further supporting"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","The primary task under consideration is Text Style Transfer (ST), which involves generating text that retains original semantic content while altering specific stylistic attributes. While existing methodologies have demonstrated significant success in transferring attributes such as sentiment (Li et al., 2018) and formality (Rao and Tetreault, 2018), the specific domain of expertise styling remains largely unexplored. A related task is Text Simplification (TS), which rewrites complex sentences into simpler structures (Sulem et al., 2018b) often constrained by a limited vocabulary (Paetzold and Specia, 2016). This task shares similarities with the current objective of reducing expertise levels from professional to layman language, albeit without addressing the reverse direction. However, most existing TS datasets, predominantly derived from Wikipedia, suffer from significant noise, including misaligned instances and targets that lack simplification (Xu et al., 2015; Surya et al., 2019); a detailed discussion of these limitations is provided in Section 3.2.

To address these gaps, this study introduces MSD, a manually annotated dataset designed for expertise style transfer within the medical domain. The dataset is constructed from human-authored medical references, specifically *The Merck Manuals*, which provide parallel texts tailored for both consumers and healthcare professionals. For the purpose of automatic evaluation, medical professionals were engaged to annotate the parallel sentences (see Figure 1 for examples). Furthermore, we conduct a comprehensive analysis by implementing state-of-the-art (SOTA) models for both TS and ST.

Compared to conventional ST and TS datasets, MSD presents heightened challenges in two primary dimensions:

1.  **Knowledge Gap:** Domain-specific knowledge is a critical determinant of text expertise, distinguishing it from conventional stylistic variations. We identify two principal types of knowledge gaps within MSD:
    *   **Terminology:** The use of specialized medical terms (e.g., ""dyspnea"") versus lay equivalents.
    *   **Empirical Evidence:** The preference of professionals for statistical precision (e.g., ""About 1/1000"") contrasted with the qualitative descriptions preferred by laypersons (e.g., ""quite small"").

2.  **Lexical and Structural Modification:** As noted by Fu et al. (2019), most existing ST models rely primarily on lexical substitution while preserving syntactic structures. However, syntactic complexity plays a pivotal role in defining language style (Carroll et al., 1999). As illustrated in the dataset, complex sentences often require decomposition into multiple simple sentences to achieve appropriate simplification. Notably, available datasets rarely encompass such structural transformations, making MSD a more rigorous benchmark for evaluating model capabilities. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.646984311 , the TTR score is 0.496957404 , the CV score is 0.413285693 , and the perplexity score is  51.79864883 .","This text is classified as human-written because it appears to be a summary or introduction to a research paper about a new dataset called MSD, which stands for Medical Domain Style Transfer. The document describes the nature of the dataset, its construction, and the challenges it poses compared to traditional datasets used in natural language processing tasks like text simplification and style transfer. It also mentions the linguistic features of the data, indicating that the author has carefully considered and described the characteristics of the dataset they've created. The tone"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","The last problem, an Extra Challenge (EC) question, presented a nested loop, which was not explicitly taught in the curriculum. It consisted of a repeat 2 outer loop and a repeat 10 inner loop, and we asked students how many times the blocks in the inner loop would run. Results for EC are shown in Across all reading levels, there was a statisticallysignificant all groups except for between the below basic and basic groups. Across all math levels, there was a statisticallysignificant difference (F (3, 281) = 25.29, p < .01, η2 = .22). Between math levels, there were statisticallysignificant differences between all groups except for the proficient and advanced groups. We now revisit our research question: How does reading and math proficiency influence the learning of CS concepts of events, sequence, and loops? Generally, we found that reading comprehension and math proficiency had smaller associations for events and sequence, compared with loops. This may be due to events and sequence being simpler concepts than repetition, requiring a less sophisticated structural ppdifference (F (3, 227) = 20.81, p < .01, η2 = .22). Between readunderstanding. Additionally, the design of the Scratch language ing levels, there were statisticallysignificant differences betweenhas more straightforward visual cues for events and sequence (i.e. different shape of event blocks, topto-bottom order of blocks in a script, etc) compared with loops, which may reduce students’ need to truly comprehend the words in the blocks themselves. A possible exception to this explanation is the question on parallelism (Q2). The challenges that older students (ages 15 and up) face while learning parallelism and concurrency are very wellstudied . However, while most older students were able to identify concurrent/parallel behavior, students in our study struggled to identify an ageappropriate presentation of parallelism. This merits future work into the mental models younger learners have about parallelism, as well as the skills associated with building appropriate mental models. For basic loop questions (Q3 and Q4), which emphasized a text surface understanding, reading comprehension had a larger association than math proficiency. However, for advanced loop questions (Q5EC) that required both a text surface and a program execution understanding, reading comprehension and math proficiency had similar levels of association. Reading comprehension would affect their ability to comprehend the words in the Scratch blocks, which can impact the text surface dimension of structural understanding . . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.527287042 , the TTR score is 0.440087146 , the CV score is 0.30355238 , and the perplexity score is  34.87918472 .","The text appears to be labeled as AI-generated based on several key factors:

1. **Consistent Structure**: The document follows a consistent format and structure typical of academic papers or reports, including sections like ""Results,"" ""Discussion,"" and ""Conclusion.""

2. **Technical Language**: The use of technical terms related to cognitive psychology, educational psychology, and computer science indicates it's likely written by someone knowledgeable in these fields.

3. **Statistical Analysis**: The text includes statistical analyses such as F-tests"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","The original emission and transformation score matrices, denoted as $U$ and $M$, remain shared across all annotators. However, for the $k$-th annotator, these matrices are transformed by a specific matrix $A^{(k)}$. During the training phase, the model adheres to a multitask learning framework. In this schema, the parameters for the Bidirectional Long Short-Term Memory (BLSTM) and Conditional Random Field (CRF) layers—including $W$, $b$, and $M$—are shared globally, whereas the annotator-specific transformation matrix $A^{(k)}$ is updated exclusively using the dataset $S_k = \{X^{(k)}, Y^{(k)}\}$.

The learning objective is defined as the minimization of the negative log-likelihood across all source annotations:

$$ \sum_{k} \sum_{i=1}^{|X^{(k)}|} \log P(y^{(k)}_i | x^{(k)}_i) \quad (4) $$

where the conditional probability is formulated as:

$$ P(y^{(k)}_i | x^{(k)}_i) = \frac{\exp(s^{(k)}(x^{(k)}_i, y^{(k)}_i))}{\sum_{y'} \exp(s^{(k)}(x^{(k)}_i, y'))} \quad (5) $$

The underlying assumption regarding the annotation representation $A^{(k)}$ is that it effectively models annotation bias. Each annotator is conceptualized as a noisy instantiation of the shared model. Specifically, for the $k$-th annotator, $A^{(k)}$ accounts for noise arising from both the labeling of the current token and the transition from the preceding label. Consequently, each entry $A^{(k)}_{i,j}$ represents the probability of erroneously assigning the $i$-th tag as the $j$-th tag. In essence, the base sequence labeling model (as described in Section 4.1) acquires fundamental consensus knowledge, while the annotator-specific components incorporate individual interpretative variations into the predictions.

### 4.3 The Aggregation Phase: Dynamic Consensus Formation

In the second phase, the proposed network learns a context-aware attention module to generate a consensus representation, supervised by combined predictions on the target data. For each sentence within the target dataset $T$, predictions are aggregated via weighted voting, where the weight assigned to each source corresponds to its normalized F1 score on the training set. By applying weighted voting to these augmented labels across all source sentences $X$, the model derives a robust approximation of the ground truth labels. To enhance generalization and computational efficiency, an attention module is trained to estimate the relevance of each source to the target data under the supervision of these generated labels. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 10.15533662 , the TTR score is 0.362068966 , the CV score is 0.932160877 , and the perplexity score is  34.65960693 .","This text is likely classified as human-written because it describes a detailed process of creating annotated datasets used for machine learning tasks, including the use of multiple annotators with their own transformations of the data. It also mentions linguistic features such as burstiness, type-token ratio, and sentence length, which are typical characteristics of human-generated content. Additionally, the mention of ""human-written"" directly indicates that the document was created by humans rather than automatically generated or pre-existing text."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","We have been the time of single things what people do not need to get careful with too hurt after wells then . If he was waited same out the group of fewer friends a more injured work under it . It will access like the going on an "" go back there and believe . Premier as well as color looking to put back on a his is . So , even though : "" don ’ t want to understand it at an opportunity for our work . I was shocked , nor don ’ t know if mate , don ’ t have survived , So one point like ten years old , but a sure , nor with myself more people substantial . And if an way of shoes of crimes the processes need to run the billionaire . Now that their people had trained and people the children live an actor , nor what trump had . However , heavily she been told at about four during an innocent person . LeakGAN The country has a reputation for cheap medical costs and high - attack on a oil for more than to higher its - wage increase to increase access to the UK the UK women from the UK ’ s third nuclear in the last couple of weeks . I ’ ve been watching it through , and when the most important time it is going to be so important . I ’ m hopeful that as that process moves along , that the U . S . Attorney will share as much as far as possible . The main thing for should go in with the new contract , so the rest of the Premier League is there to grow up and be there ,"" she said . I think the main reason for their sudden is however , I didn ’ t get any big thing ,"" he says , who is the whole problem on the U . S . Supreme Court and rule had any broken . . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.106956383 , the TTR score is 0.515759312 , the CV score is 0.605913882 , and the perplexity score is  333.9535217 .","The text is likely classified as AI-generated due to several linguistic features that indicate automation, such as burstiness (a measure of variation in sentence length), low type-token ratio, and high perplexity. These characteristics suggest a pattern consistent with machine learning models or natural language processing algorithms rather than human writing. Additionally, the content appears to discuss legal matters and financial news, which could be generated by automated systems designed for these topics."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","**Title: An Evaluation of the Utility of Contextual Embeddings**

**Abstract**
This analysis examines the efficacy of contextual embeddings in natural language processing, specifically addressing scenarios where their application yields significant value versus instances where simpler models may suffice. The discussion is framed by the challenges inherent in processing unstructured, noisy, and context-dependent dialogue data.

**1. Introduction**
The deployment of contextual embeddings, such as those derived from transformer-based architectures, represents a paradigm shift in semantic representation. However, their computational cost necessitates a rigorous evaluation of their return on investment. This is particularly relevant when dealing with complex discourse involving ethical ambiguities, conversational closures, and domain-specific factual reporting.

**2. Challenges in Semantic Disambiguation**
A primary justification for utilizing contextual embeddings arises in domains requiring nuanced interpretation of legality and ethics. In discussions regarding government ethics, for instance, the boundary between permissible and illicit conduct is often fluid. Standard static embeddings frequently fail to capture the subtle distinctions required to delineate these boundaries. Contextual models, by contrast, can leverage surrounding discourse to infer intent and regulatory context, thereby resolving ambiguities that static representations cannot address.

**3. Handling Conversational Dynamics and Noise**
Conversational data often contains non-semantic noise, filler words, and irregular turn-taking structures, as observed in dialogues concerning vacation planning or casual farewells. In such contexts, the ability of contextual embeddings to model long-range dependencies and pragmatic cues is critical. They enable systems to distinguish between substantive content and conventional closing markers (e.g., ""goodbye,"" ""take care""), ensuring that the core intent of the interaction is preserved despite syntactic irregularities or transcription artifacts.

**4. Domain-Specific Factual Extraction**
In technical or factual domains, such as environmental recycling practices or healthcare policy, the precision of information extraction is paramount. When analyzing statements regarding recycling infrastructure or universal health coverage, the distinction between opinion and objective fact must be maintained. Contextual embeddings facilitate the accurate identification of specific entities (e.g., ""aluminum cans,"" ""landfill space"") and the logical relationships between them, even when the input data is fragmented or contains colloquialisms.

**5. Conclusion**
Contextual embeddings are most valuable when the task demands a deep understanding of semantic nuance, the resolution of ambiguity in ethical or legal frameworks, and the robust processing of noisy, real-world dialogue. While they incur higher computational overhead, their capacity to model context-dependent meaning renders them indispensable for applications where precision and interpretability are critical. Conversely, for tasks involving straightforward pattern matching or low-ambiguity text, simpler embedding strategies may remain more efficient. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 4.881307747 , the TTR score is 0.531746032 , the CV score is 0.577070636 , and the perplexity score is  32.60500717 .","This text appears to be human-written because it uses formal academic language, includes an abstract section, and discusses various aspects related to natural language processing using technical terms and references. Additionally, the text does not exhibit characteristics typically associated with AI-generated writing, such as lack of punctuation errors or inconsistent formatting."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","**Analysis of Transfer Effects and Degenerate Runs**

Empirical results indicate that SocialIQA and QQP induce negative transfer across the majority of EdgeProbing tasks. In contrast, CosmosQA and QASRL exhibit performance degradation exclusively within the EPRel task. While models trained on intermediate tasks demonstrate consistent improvements on EPSRL and EPDPR, no significant gains are observed in SentEval probing tasks regardless of the intermediate task employed. Notably, intermediate tasks that yield performance increases across the highest number of probing tasks also function most effectively as intermediate training objectives.

Regarding model stability, we observe instances of degenerate runs where model performance fails to exceed chance levels. This phenomenon predominantly affects baseline runs (without intermediate training) on acceptability judgment probing tasks, with the exception of AJCoLA, largely due to the limited size of their training sets. These degenerate runs are retained in the analysis to accurately reflect this behavior. Consistent with findings by Phang et al. (2018), intermediate task training reduces the probability of degenerate runs, thereby producing ostensibly positive transfer results on four acceptability judgment tasks across most intermediate tasks. Conversely, severe negative transfer from specific intermediate tasks, such as QQP and SocialIQA, can increase the frequency of degenerate runs in downstream tasks. Similar instability is observed in the EPSRL and EPRel tasks. Such degenerate runs diminish positive transfer in probing tasks (e.g., SocialIQA and QQP) and reduce the average magnitude of positive transfer observed in target task performance.

**4.2 Correlation Between Probing and Target Task Performance**

To elucidate the mechanisms underlying the superior performance of certain intermediate task models on specific target tasks, we investigate the relationship between target and probing task outcomes. Probing task performance is utilized as a proxy for the acquisition of specific linguistic competencies. We compute the Spearman correlation between probing task and target task performances across various intermediate tasks and multiple training restarts, as illustrated in Figure 3. Statistical significance is assessed at $p = 0.05$, with Holm-Bonferroni correction applied to account for multiple testing; non-significant correlations are omitted. The Spearman rank correlation is preferred over Pearson correlation due to the heterogeneity of metrics employed across different tasks.

Our analysis reveals that performance on acceptability judgment probing tasks is generally uncorrelated with target task performance, with the sole exception of AJCoLA. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.184067584 , the TTR score is 0.442352941 , the CV score is 0.352132407 , and the perplexity score is  156.1031647 .","This text appears to be human-written because it contains clear, coherent sentences about empirical data related to language processing tasks like sentiment analysis and acceptability judgments. It uses technical terms relevant to natural language processing research without apparent errors or inconsistencies, suggesting it was authored by someone familiar with these fields. Additionally, the structure and flow of ideas align with typical academic writing styles used in publications in computer science and linguistics journals."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","The proposed model shares architectural similarities with the head-lexicalized Tree LSTM introduced by Teng and Zhang (2017). A key distinction lies in the acquisition of head information: whereas Teng and Zhang (2017) employ an unsupervised approach to identify constituent heads—which may not align with formal syntactic definitions—our approach incorporates explicit head information derived from dependency parses. This design choice facilitates a direct investigation into the impact of utilizing syntactically grounded head dependencies.

To evaluate the syntactic capabilities of the model, we adapted the number prediction task originally proposed by Linzen et al. (2016). While the standard formulation of this task relies on a left-to-right language modeling objective, such an approach is incompatible with tree-based architectures. Consequently, we implemented two modifications to the objective function, both of which are supported by existing literature. First, following Goldberg (2019), the model is presented with a complete present-tense sentence in which the main verb is masked. Second, consistent with the methodologies of Linzen et al. (2016) and Ravfogel et al. (2019), the task is framed as a binary classification problem where the model must predict the grammatical number (SINGULAR or PLURAL) of the masked verb. Successful performance on this task requires the model to identify the subject dependent on the main verb within the dependency structure and utilize this relationship to determine the correct agreement. For instance, in the sentence ""The girl *MASK* the ball,"" the correct prediction is SINGULAR.

Linzen et al. (2016) noted that models may achieve acceptable accuracy on this task by relying on spurious heuristics rather than genuine syntactic understanding. A prominent example of such a heuristic is selecting the number of the most recent noun preceding the verb. We hypothesize that tree-based models will demonstrate greater robustness against these non-syntactic shortcuts compared to sequential models.

**Experiment 1: Natural Language Data**

For this experiment, models were trained on a subset of the dataset provided by Linzen et al. (2016), specifically curated to ensure a uniform label distribution (50% SINGULAR and 50% PLURAL). This stratification was necessary because the task formulation employed in this study differs from those used in prior research (see Section 4); consequently, direct performance comparisons with previous work are not feasible without this normalization. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.302950874 , the TTR score is 0.498861048 , the CV score is 0.477268524 , and the perplexity score is  53.59263611 .","This text appears to be human-written based on several factors:

1. **Language Use**: The text uses natural English sentences and phrases typical of human writing.
2. **Syntax and Grammar**: It demonstrates proper syntax and grammar, including complex structures like relative clauses (""which the mask"").
3. **Clarity and Coherence**: The content flows logically and coherently, making it easy for readers to follow the reasoning and examples provided.
4. **Use of Idiomatic Expressions**: The text"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","The figures enclosed in parentheses denote the sizes of the respective training sets.

The **RESTAURANTS-8K** dataset consists of dialogues derived from a commercial restaurant reservation system. It encompasses five critical slots required for the booking task: *date*, *time*, *number of people*, *first name*, and *last name*. Comprehensive statistical summaries of this dataset are presented in Table 1. Notably, this dataset includes complex instances involving multiple value mentions or values irrelevant to the target slot (e.g., distinguishing between conflicting times or referencing dates unrelated to the booking). Furthermore, unlike previous datasets such as DSTC8 (Rastogi et al., 2019), where integer-valued slots (e.g., number of travelers or tickets) were modeled categorically, this study models the number of attendees using span-based extraction. This approach accommodates indirect numerical references, such as ""me and my husband,"" ""3 adults,"" or ""2 couples.""

Regarding the **DSTC8 Data Sets**, the Schema-Guided Dialog Dataset (SGDD) (Rastogi et al., 2019) provides span annotations for a subset of slots. From SGDD, we extracted span-annotated subsets across four diverse domains characterized by a wide variety of slots: (1) bus and coach booking (*Buses_1*), (2) event ticket purchasing (*Events_1*), (3) property viewing (*Homes_1*), and (4) car rental (*RentalCars_1*). A detailed description of the data extraction protocol and the corresponding dataset statistics, which are released alongside this paper, can be found in Appendix A.

**Baseline Models**
We evaluate the proposed model against two robust baselines:
1.  **VCNNCRF**: A standard approach that learns subword representations from scratch without utilizing pretrained models.
2.  **SpanBERT**: An approach utilizing fixed subword representations derived from BERT.

Both baselines employ an identical CNN+CRF architecture built upon their respective subword representations. Hyperparameter optimization for each baseline was conducted via grid search on the development set of RESTAURANTS-8K, mirroring the procedure used for SpanConveRT. The final hyperparameter configurations are detailed in Table 2. The SpanBERT model specifically utilizes BERTbase, comprising 12 transformer layers and 768-dimensional embeddings. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 5.545147391 , the TTR score is 0.503480278 , the CV score is 0.608158669 , and the perplexity score is  52.26747131 .","This text is likely classified as human-written because it contains specific details about a research project, including descriptions of datasets, methods, and evaluation criteria. The language used is technical but clear, typical of academic writing. Additionally, the inclusion of tables and code snippets further supports its classification as human-generated content rather than machine output."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","AI2 Reasoning Challenge contains 7,787 genuine gradeschool level science questions with a corpus of 14M science reference sentences. DROP and COSMOS focus on factual understanding and commonsense comprehension, respectively. Despite their success, these datasets can hardly help chatbots directly. Following the traditional dialogue response selection setting, we deeply modify English listening comprehension conversation to form an utterance prediction task. 3.1 Collection The original listening comprehension materials and questionanswer pairs are designed by linguist experts. Students are required to choose the best answer from three options for a question based on a piece of audio. To ensure students fully understand the audio, most of the questions need to be answered with reasoning capability. We crawled the listening exams from public websites 1 . Since the audio is either a conversation between two people or a simple passage, we only crawled data in the conversation format. The raw data is formatted as triples ⟨ Conversation (audio), Question and Choices (text), Answer (image) ⟩ . The following data preprocessing methods are applied to convert raw data to data in Figure 2 . Step 1 Preprocessing: If question and candidate choices in two problems are the same, we consider them as duplicates and delete one of them. If there are more than three candidate options in one problem, we randomly drop incorrect options until three candidates are left. The answers are stored as images. We apply a commercial OCR system to convert images to text. It is easy to recognize the printed alphabet answer for the OCR system. We manually correct all OCR 1 All the problems in our dataset are freely accessible online without copyright by consulting the legal adviser. outputs to ensure quality. In the original listening comprehension test, the conversation is stored as audio. We adopt a commercial ASR system to convert speech to text, and further recruit experienced annotators to correct the transcription errors. To further ensure the quality of the transcripts, they are doublechecked by annotators in the next step. Step 2 Candidate Response Creation: Figure 2 illustrates the process of modifying the listening comprehension problem. At first, an annotator is required to segment the original conversation, after clues to answer the question have appeared. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.292335116 , the TTR score is 0.485294118 , the CV score is 0.332557488 , and the perplexity score is  169.6144257 .","The text is classified as AI-generated because it describes the creation and modification of a dataset used for training chatbot responses, focusing on improving conversational abilities through listening comprehension tasks. The use of technical terms related to natural language processing (NLP), such as ""commercial ASR system,"" ""OCR system,"" and detailed explanations of preprocessing steps, indicates the involvement of artificial intelligence in its development and implementation. Additionally, the mention of linguistic features like burstiness, type-token ratio, and sentence length suggests sophisticated"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Successfulness , creativity and adventuring  are strongly related terms that constitue the personality of an ambitious ,dynamic and self motivated person,they are the moving force behind persons determination to succeed,On the other hand ,sticking to the traditions and doing routine things will lead to suppression of persons opprtunities  to be improved.
	
	Trying new experinces and envolving in a new fields might be at first accompanied with alot of troubles and difficulties and might at first look like a kind of adventure ,but it will in no doubt will bring up with a big advantage for the person ,For example; travelling to new city for settlement will enable you to meet new people and discover new enviroment and learn new habits from other people and this is in fact represent  akind of succeed .Also involving in anew job will be agood source of earning new experinces from other people.
 
	Another example of sucessful people is the scientists ,they are a creative people that have the ability to shine further under suitable conditions and they are such a self motivating that can discover new things that benifit the whole society and world.

	At the end I believe that successfulness is a strongly related term with creativity and  discovery . . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 0.884848485 , the TTR score is 0.545454545 , the CV score is 0.552230638 , and the perplexity score is  171.4735718 .","The text is likely human-written because it discusses concepts of success, creativity, and adventure in a coherent manner. The language used is clear and well-structured, indicating a high level of cognitive effort and intentionality. Additionally, the content aligns with common themes found in human-authored texts about personal development and achievement. The classification suggests that the document was created by a human author rather than generated by a machine or algorithm."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","In the context of named entity recognition, let $s$ denote a predicted entity span, and let $E$ represent the set of all possible sequences in $Y$ that contain $s$. An event is classified as positive if the ground-truth label sequence $y$ includes the predicted span $s$.

When constructing the set $\phi(x)$, it is essential to balance two competing objectives: limiting the number of output entities to a manageable size while maximizing the coverage of positive events within $I(x)$. To illustrate this trade-off, consider a parsing task, such as syntactic or semantic parsing. Two primary schemes for defining $I(x)$ are presented below:

1.  **Scheme 1:** $\phi(x)$ consists solely of the Maximum A Posteriori (MAP) label sequence prediction. Consequently, $I(x)$ contains the single event corresponding to the correctness of the sequence $y' = \arg\max_{y} p_{\theta}(y|x)$.
2.  **Scheme 2:** $\phi(x)$ encompasses all possible label sequences. In this case, $I(x)$ includes an event for every $y' \in Y$, indicating whether that specific sequence is correct.

Existing methodologies can be mapped to these schemes. For instance, the model confidence calibration proposed by Dong et al. (2018) aligns with Scheme 1, where the entity of interest is the MAP prediction. Conversely, the application of Platt Scaling in a one-vs-all setting for multiclass classification (Guo et al., 2017) corresponds to Scheme 2, where the entity of interest is the presence of a specific class label.

As previously discussed, Scheme 2 is computationally prohibitive for our purposes due to the large cardinality of $|Y|$. While Scheme 1 is computationally efficient, it suffers from limited coverage of positive events. For example, if a sequence labeling model achieves 60% sentence-level accuracy, only 60% of the positive events are captured by the set of MAP predictions. Effectively, only 60% of the model's correct outputs are utilized to construct the forecaster, thereby constraining the scope of $I(x)$.

To mitigate this limitation, incorporating the top-$k$ predictions into $\phi(x)$ can enhance the coverage of positive events, thereby increasing the volume of positive training data available for the forecaster. However, selecting the optimal value for $k$ requires a careful trade-off between computational cost and event coverage. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 6.630948264 , the TTR score is 0.402834008 , the CV score is 0.45537111 , and the perplexity score is  34.53135681 .","The text appears to be written by a human because it discusses concepts related to natural language processing, specifically named entity recognition and machine learning techniques used in parsing tasks like syntactic or semantic parsing. The text also mentions existing methodologies and their applications, which are typical topics in academic papers on NLP. Additionally, the use of technical terms and the structure of the writing suggest it was created by a human author rather than generated by a computer program."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Assignment 3: Students finished their heroes, becoming familiar with the new code organization and reminding themselves of the Processing commands. Next, students were instructed to apply what they learned about functions to call the associated function to draw the background they desired in their game. This allowed students to practice using functions, reading code, and integrating preexisting code into their program, meanwhile saving them time and still allowing them some creative choice. In this lesson, we reviewed functions and students were taught coding concepts to allow them to move their hero to the bottom of the screen. Students were introduced to the topic of variables with a whiteboard lesson. This was followed by a demonstration in Processing, which shows how a variable representing position can be changed to make shapes move across the screen. Assignment 4: Students were asked to move their hero code from the draw function to a new hero function. They also were asked to translate the hero to the bottom left of the screen. In addition, students designed the falling objects for their games. These objects would ultimately be collected by their heroes in a later lesson. Lesson 5 - Continuing Variables This lesson began with a whiteboard introduction to variable scope. Parameters, local variables, and global variables were all discussed along with the difference between integers and floats. Assignment 5: The students were asked to use their knowledge about variables to enable the position of their hero to change, resulting in it moving across the bottom of the screen. This lesson introduced conditionals and booleans and their use in Processing. Assignment 6: The students were asked to use conditionals to make their hero stop moving when they reached the right side of the screen. Next, they used booleans to make the hero move back and forth along the bottom of the screen. Students were introduced to input and output especially related to key board and mouse interactions typically used in 2D games. Prep Work: Again to help move the class along, the instructor wrote the code for the students’ ‘falling objects’ based on the designs they made in lesson four. In addition partial base code for recognizing key presses was provided. Assignment 7: The students were instructed to complete the code associated with key presses to allow them to control their hero’s movement. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 5.022169312 , the TTR score is 0.405092593 , the CV score is 0.32749517 , and the perplexity score is  49.8388176 .","The text appears to be labeled as AI-generated because it contains characteristics typical of machine learning models such as burstiness, low type-token ratio, sentence length variability, and high perplexity scores. These features suggest an automated or algorithmic approach rather than human authorship. Additionally, the content aligns with common patterns found in educational materials generated by artificial intelligence systems."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Living in the 21st Century, it is without doubt that most of us already possess the luxury of having everything we need such as shelter, clothes and most importantly food and water. Despite this, it is sad to say that there are still a handful of the world's population that remain in the depths of poverty. And even so, many countries are found guilty to wasting huge amounts of food. In this essay, I will discuss the reason behind this food wastage and suggest solutions to reduce this effect.
First and foremost, why do people waste food? It is to my belief that people tend to take more food than they can handle, especially at functions like all-you-can-eat buffets alongside weddings which act as a few of the many major contributers of food waste. In general, there is also the lack of awareness on poverty among the public to blame for. Therefore, people cannot practice consideration towards a cause they are unfamilar or unaware of. Other than that, they lack exposure to knowledge on the impact that food waste can bring to the environment and the society.
Now, what actions can we take to reduce amount of food disposed? The government and country's leaders should take the first step in conducting educational campaigns on food wastes and poverty, particularly targeting school children and the public. This can play a significant role in shedding light onto the issues that need to be addressed urgently. People should also be strongly encouraged to take a more sensible amount of food before coming back for seconds to avoid the unecessary wastage as well as the excess weight gain.
To sum up, food wastage is not something to be proud of because of the all the adverse effects brought upon us, humans. Hence, we should quickly make the much needed change to help to turn this situation around and hopefully contribute to creating a better society. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.836887188 , the TTR score is 0.525423729 , the CV score is 0.331412641 , and the perplexity score is  18.31214905 .","The text appears human-written due to its coherent structure, logical flow, and use of formal language typical of academic writing. The author discusses food waste systematically, citing reasons and proposing solutions, demonstrating an understanding of complex topics and their implications. The tone is informative and persuasive, aligning with the style often used by experts in fields like environmental science or social policy. The inclusion of specific examples and statistics further supports the classification as human-written content."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","While relying exclusively on the ""did not attempt coding"" metric to determine completion rates presents a similar concern, this approach yields the most conservative estimate. Nevertheless, the impact of including incomplete coding attempts on the results is addressed in the Results and Discussion sections regarding Research Question 3.

Regarding student performance, no statistically significant differences were observed in grades for either Exam 2 or Exam 3 overall. Upon individual problem analysis, a statistically significant difference was identified only for Problem 7 of Exam 2. Consequently, the null hypothesis is rejected for this specific item, indicating that grades for students in the paper-based format differed significantly from those in the Integrated Development Environment (IDE) format. Notably, over half of the students in the IDE format received a score of zero for Problem 7. Figure 1 illustrates the grade distribution for this problem via a box plot. In summary, no significant grade disparities were detected across the assessments, with the exception of the final, most challenging problem on Exam 2.

In the analysis of error types, no statistically significant differences were found regarding the occurrence of logic errors at any level. Conversely, significant differences were observed in the occurrence of syntax errors for Exam 2 (overall), Exam 3 (overall), and specific problems: Problem 4 and Problem 6 of Exam 2, and Problem 10 and Problem 11 of Exam 3. Therefore, the null hypothesis is rejected for these instances, concluding that the frequency of syntax errors differed between the paper and IDE formats. Overall, students utilizing the IDE format demonstrated a lower incidence of syntax errors.

No significant differences were detected for Exam 3 at either the overall or problem-specific levels. However, significant disparities were found in completion rates for Exam 2 (overall) and its final problem (Problem 7). Thus, the null hypothesis is rejected for Exam 2 overall and Problem 7, confirming that completion rates differed between the two formats. Specifically, the average completion rate for the paper-based exam was approximately 93.3%, compared to approximately 77.6% for the IDE-based exam. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 4.156742444 , the TTR score is 0.403061224 , the CV score is 0.327485848 , and the perplexity score is  43.9950943 .","The text appears to be human-written because it contains detailed statistical analyses, references to specific research questions, and clear explanations of findings without apparent bias or technical jargon typically associated with AI-generated content. The use of terms like ""null hypothesis,"" ""box plot,"" and explicit statements about statistical significance suggest an effort to present original data and conclusions rather than automated output. Additionally, the inclusion of linguistic features such as burstiness, type-token ratio, and sentence length provides context that aligns more closely with human"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Yes, I agree with the statement that “successful people always try new things and take risks because they gain experience, money, and confidence.”

Successful people learn from their past experiences, which helps them achieve success in new ventures. Their previous knowledge makes it easier for them to succeed in new tasks.

They often get bored doing the same work repeatedly, so they seek out new challenges to break the monotony and are willing to take risks.

Success in previous endeavors also boosts their self-confidence, which helps pave the way for future achievements.

Additionally, successful people often earn money from their past successes. They use these resources to explore new opportunities, which further expands their knowledge.

In conclusion, based on these points, I believe that successful people are always willing to try new things. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 0.267619048 , the TTR score is 0.56 , the CV score is 0.464566704 , and the perplexity score is  22.94178772 .","The text appears to be AI-generated due to its formal tone, structured argumentation, and consistent language patterns typical of automated writing systems. The inclusion of linguistic features such as burstiness, type-token ratio, sentence length variability, and perplexity suggests an artificial intelligence model has been used to generate the text. These characteristics align closely with those expected from machine learning algorithms designed to produce coherent and contextually appropriate responses."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","On the other hand, one student found this to be unfair: “All this does is benefit the students with lighter course load in general as it is much easier for them to turn stuff in early.” Students did not seem to attempt VQs more (percent change -1.71%, p=0.30) or space our their VQ quiz submissions (percent change -23.87%, p=0.30). In terms of total endof-semester points, the LF group had much higher total Leaderboard points, with the highest achieving LF team earning 54.01% more points than the highest achieving LB team (see Even the lowest achieving LF team earned 13.81% more points than the lowest achieving LB team. From these results, we conclude the small intervention of exam bonus point multipliers improved student’s study habits. As with most selfreported measures, there is a disagreement with the students’ perceptions of the Leaderboard and the objective, quantitative LMS log data. Looking at the student feedback data, the LF students did not perceive the intervention had such an effect on their study habits (2.88 average Likert response, see When compared to the LB students, the LF students thought the leaderboard was less motivating and that they didn’t necessarily start assignments earlier to earn Leaderboard points (see For the free response questions, a handful of LF students commented on the incentive being too small, with one student stating, “Maybe make it something a little more tangible than few extra credit points that I really don't care about.” On the other hand, some students were quite motivated by the small incentive, “It’s good as is” and “I appreciated the extra credit, as does everyone. It was cool to see the contributions I made to the team, even if they weren't the most.” From feedback we received from LB students, we hoped to see more variation in the Leaderboard top team rankings by resetting the Leaderboard at each exam. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.494225037 , the TTR score is 0.480874317 , the CV score is 0.662330248 , and the perplexity score is  59.49621582 .","The text is likely AI-generated because it contains several characteristics typical of machine-generated content:

1. **Consistent Structure**: The text follows a consistent structure with clear headings and subheadings, which suggests automation rather than human authorship.
   
2. **Technical Terms**: The use of technical terms like ""CV"" (Covert Variation), ""TTR"", ""Burstiness"", and ""Perplexity"" indicates that the text might have been generated using natural language processing techniques designed to mimic human"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","It is important to note that the accuracy under attack ($acc_{attack}$) serves as a potentially loose upper bound for robust accuracy ($acc_{rob}$). This discrepancy arises because heuristic search methods may fail to identify certain perturbations that cause model misclassification (Athalye et al., 2018). Furthermore, given the computational intractability of calculating exact robust accuracy, prior research has often relied on certified accuracy (Huang et al., 2019; Jia et al., 2019; Shi et al., 2020), which provides a conservative lower bound for the true robust accuracy. In contrast, the present work leverages robust encodings to enable the tractable computation of exact robust accuracy.

### 3. Robust Encodings

We introduce Robust Encodings (RobEn), a framework designed to construct encodings that are both reusable across diverse tasks and compatible with arbitrary model architectures. Section 3.1 outlines the fundamental components of RobEn, while Section 3.2 delineates the essential desiderata that the framework must satisfy.

#### 3.1 Encoding Functions

A RobEn classifier, denoted as $f_{\alpha}: \mathcal{X} \to \mathcal{Y}$, is decomposed into two distinct components: a fixed encoding function $\alpha: \mathcal{X} \to \mathcal{Z}$ and a downstream model $g: \mathcal{Z} \to \mathcal{Y}$ that operates on the encoded representations. For any input sentence $x$, the system generates a prediction via the composition $f_{\alpha}(x) = g(\alpha(x))$. Given a training dataset $\{(x_i, y_i)\}_{i=1}^n$ and a specified encoding function $\alpha$, the model $g$ is trained using standard optimization procedures on the encoded data points $\{(\alpha(x_i), y_i)\}_{i=1}^n$.

To compute the robust accuracy of this system, we observe that for a well-chosen $\alpha$ and an input $x$ drawn from a distribution $P_x$, the set of possible encodings $\{\alpha(\tilde{x}) \mid \tilde{x} \in \mathcal{B}(x)\}$—where $\mathcal{B}(x)$ represents the perturbation neighborhood—is both finite and computationally tractable. Consequently, the robust accuracy $acc_{rob}(f_{\alpha})$ can be efficiently determined by enumerating this set of potential encodings and evaluating each through the model $g$, regardless of the specific architecture employed.

#### 3.2 Desiderata for Encoding Functions

To achieve high robust accuracy, a classifier $f_{\alpha}$ utilizing an encoding function $\alpha$ must satisfy two primary criteria: it must yield consistent predictions for all perturbed inputs $\tilde{x} \in \mathcal{B}(x)$ within the defined attack surface, and it must maintain high standard accuracy on unperturbed inputs. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 6.954473736 , the TTR score is 0.369337979 , the CV score is 0.554253446 , and the perplexity score is  41.3823967 .","The text is classified as human-written based on several key factors:

1. **Complex Structure**: The document contains multiple sections and subsections clearly labeled with numbers, indicating a structured writing style typical of academic papers or technical documents.
   
2. **Technical Terminology**: The use of specialized terms like ""perturbations,"" ""heuristic search methods,"" ""model misclassification,"" and ""robust accuracy"" suggests a professional context, likely related to machine learning or artificial intelligence.

3. **Consistent"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","**Data Extraction and Corpus Construction**

The analysis imposes two primary constraints on the target structures: adjectives $A_1$ and $A_2$ must not possess any further dependents, and they must occur in the specific sequence $A_1$–$A_2$–$N$ within the corpus without intervening tokens. Adhering to these criteria, a total of 842,714 $AAN$ triples were extracted from the parsed Common Crawl corpus. While all corpus-based predictors are derived from $AN$ pairs, the $AAN$ triples serve exclusively for regression modeling of adjective ordering and for subsequent evaluation.

**Subjectivity Ratings**

To generate ratings-based predictors, subjectivity scores were collected for all 398 adjectives appearing in $AAN$ triples within the English Universal Dependencies (UD) corpus. Data collection was conducted via Amazon Mechanical Turk, following the methodology established by Scontras et al. (2017). A total of 264 English-speaking participants evaluated the subjectivity of 30 randomly selected adjectives each by adjusting a slider ranging from ""completely objective"" (coded as 0) to ""completely subjective"" (coded as 1). Consequently, each adjective received an average of 20 independent ratings.

**Test Set Composition**

The primary held-out test set for predictor evaluation consists of the English Web Treebank (EWT), a manually parsed corpus included in Universal Dependencies version 2.4 (Silveira et al., 2014; Nivre, 2015). Applying the aforementioned extraction criteria yielded 155 $AAN$ triples with complete predictor scores. Given the limited size of this dataset, evaluation was supplemented using a held-out portion of the parsed Common Crawl data. After filtering for $AAN$ triples with complete predictor scores, this secondary test set comprised 41,822 instances.

**4.2 Estimation of Predictors**

The information-theoretic predictors necessitate estimates of probability distributions over adjectives and nouns. Initially, these distributions were estimated using maximum likelihood estimation (MLE) applied to wordform counts within $AN$ pairs; these are referred to as *wordform estimates*. Although MLE provides a sufficient estimate for general word entropy (Bentz et al., 2017), its efficacy in measuring conditional entropy or mutual information remains uncertain due to data sparsity, even in corpora containing millions of tokens (Futrell et al., 2019). To mitigate this sparsity issue, a secondary estimation method was employed. This approach calculates probability distributions not over raw wordforms, but over word clusterings derived from an embedding space, a technique previously demonstrated to be promising by Futrell et al. (2019). . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.133333333 , the TTR score is 0.46875 , the CV score is 0.571729831 , and the perplexity score is  43.03749084 .","This text appears to be written by a human because it contains natural language descriptions, technical terms related to linguistic analysis, and detailed explanations about methods used in statistical models. The text also includes personal details such as names and dates, which are typically associated with human writing rather than automated systems. Additionally, the structure and flow of the text suggest a coherent narrative or explanation rather than a machine-generated output."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","It is plausible that a causal relationship exists wherein high school students who participate in AP Computer Science (CS) examinations are more likely to pursue CS majors at the collegiate level. This correlation suggests that challenges regarding diversity in the field are not confined to the secondary education sector; consequently, increasing the participation of underrepresented individuals in AP CS may ultimately enhance the diversity of CS graduates.

This paper proposes three metrics for evaluating state-level Peer Instruction, a method previously demonstrated to be effective in supporting student learning. We adapt a specific form of Peer Instruction for an undergraduate database course through the implementation of team-based examinations. In this protocol, students first attempt an examination individually without immediate feedback. Subsequently, they retake the same examination in groups, utilizing immediate feedback instruments that allow for multiple attempts per multiple-choice question until the correct answer is identified.

Our central thesis posits that this approach affords students the opportunity to learn from errors committed both individually and collectively, thereby fostering deep individual learning. To substantiate this claim, we analyzed data collected from 125 students across five group examinations, supplemented by other assessment instruments, including a final examination. These activities were designated as ""Group Graded Assessments"" (GGAs) to signal to students that they should be viewed primarily as learning opportunities rather than traditional high-stakes examinations.

Our experience indicates that an overwhelming majority of students responded positively to this approach, frequently requesting its continued implementation in both formal and informal feedback channels. While enjoyable classroom activities are undoubtedly beneficial for enhancing the overall experience of both students and educators, establishing a direct correlation between enjoyment and improved learning outcomes is not always straightforward. Although such activities contribute significantly to class atmosphere and culture, they do not inherently guarantee superior academic performance. Nevertheless, we assert that the GGA approach does contribute to enhanced learning. This paper presents an analysis of data from a single offering of an undergraduate database course involving 125 students, in which five GGAs were conducted across five distinct topics. The course also incorporated additional assessment components, including take-home assignments, online quizzes, a group project, and a final examination. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.528233881 , the TTR score is 0.573232323 , the CV score is 0.264537529 , and the perplexity score is  37.49378204 .","The text appears human-written due to several characteristics:

1. **Content**: The content discusses educational methods, specifically peer instruction and its application in computer science courses. This type of detailed discussion about teaching methodologies is consistent with written work by educators or researchers.

2. **Writing Style**: The language used is clear and structured, typical of academic writing where authors aim to convey complex ideas concisely yet comprehensively.

3. **Technical Details**: There are references to specific educational tools like AP Computer Science and"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","The retrieval mechanism operates by iteratively constructing queries that incorporate keywords from the original input (comprising the question and candidate answer) which remain uncovered by previously retrieved justifications. In the second retrieval stage, if the count of such uncovered keywords falls below a specified threshold, the query is augmented with terms derived from the most recently retrieved justification. This iterative process continues until all query terms are accounted for within the accumulated justifications. Within this framework, $Q_c$ denotes the proportion of query terms covered by existing justifications, while $Q_r$ represents the set of terms that remain uncovered. Furthermore, the AIR architecture supports the parallel retrieval of multiple justification chains by initiating concurrent retrieval processes from distinct candidate sentences for the initial justification in each chain.

For the alignment component, the system employs a standard unsupervised method based on the approach of Yadav et al. (2019a), utilizing GloVe embeddings (Pennington et al., 2014). Although alignment based on BERT embeddings demonstrated marginal performance improvements, the associated computational costs were deemed prohibitive. The alignment algorithm calculates the cosine similarity between the word embeddings of each token in the query and every token in the knowledge base sentence, generating a matrix of similarity scores. For each query token, the algorithm identifies the most similar token within the evidence text via max-pooling. Subsequently, the overall alignment score $s$ for a given query $Q$ and supporting paragraph $P_j$ is computed as the element-wise dot product between the max-pooled vector of cosine similarity scores and the vector of Inverse Document Frequency (IDF) values for the query tokens:

$$s(Q, P_j) = \sum_{i=1}^{|Q|} \text{idf}(q_i) \cdot \text{align}(q_i, P_j) \quad (1)$$

$$\text{align}(q_i, P_j) = \max_{k=1}^{|P_j|} \text{cosSim}(q_i, p_k) \quad (2)$$

where $q_i$ and $p_k$ represent the $i$-th term of the query $Q$ and the $k$-th term of the evidence sentence $P_j$, respectively. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 5.469369925 , the TTR score is 0.45410628 , the CV score is 1.093122607 , and the perplexity score is  45.07843399 .","This text appears to be human-written because it describes an AI system's methodology for retrieving information from a knowledge base and aligning it with a user's query using natural language processing techniques like keyword extraction, query refinement, and cosine similarity calculations. The detailed explanations and technical jargon used suggest expertise in these areas, typical of someone who has developed or worked on such systems. Additionally, the inclusion of specific metrics like perplexity and type-token ratio further indicates that this content was likely written by a professional in"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Besides the detection of viewpoints, we also seek to incorporate nudgelike personalization features. The key insight from the nudging literature is that environments can be designed in a manner that takes heuristics and biases into account to steer behavior of the users of these environments. Such nudges could also be aimed at the individual person. The most famous example is the ‘cafeteria example’, where the healthy food options are placed in an easier to reach spot than the unhealthy options. As a result, more people choose the healthier options without making the unhealthy options completely unavailable. The same approach can be connected to the idea of a “healthy media diet”, where “healthy” is connected to a healthy democracy and public debate. Nudging has previously been incorporated in recommender systems. A 2021 systematic review by Jesse and Jannach reveals that of 87 nudging mechanisms identified in the literature, only a small subset was previously investigated in the context of recommender systems. These include using visuals to increase item salience, item reranking, and setting defaults. For news recommendation specifically, Gena et al. found that nudges based on giving users the idea certain items were popular were not effective, while negative framing (nudging users to consume certain news items because of limited availability) was. The authors argue that future work should address which types of nudges are ethically acceptable in the area of persuasive technologies. We define latitude of diversity as an individual user’s acceptance of diversity. Research shows that (groups of) individual users can differ considerably in the extent to which they are open towards and interested in diverse viewpoints . We argue that considering individual users’ latitudes of diversity increases the likelihood that a given user engages with diverse recommendations and potentially prevents unwanted side effects. If individual latitudes of diversity are not considered, users who are not interested in or open towards diverse viewpoints might simply not select diverse recommendations. Moreover, recommending news that are too diverse can backfire . Motivated reasoning literature suggests that people evaluate counterattitudinal information more critically than attitudecongruent information . Under some circumstances, this might go as far as that exposure to counterattitudinal information causes people to actively counterargue, resulting in even stronger attitudes and increased political polarisation . . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.324581584 , the TTR score is 0.53253012 , the CV score is 0.404962683 , and the perplexity score is  41.418396 .","The text appears to be AI-generated due to its formal tone, structured language, and consistent use of academic terminology related to AI and technology. The content discusses advanced concepts such as nudge theory, personalized recommendations, and ethical considerations in technology, all of which align with typical topics covered in AI research papers. Additionally, the high linguistic features like burstiness, type-token ratio, sentence length variability, and perplexity suggest a sophisticated computational model rather than human authorship."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Concretely, the first two sublayers are a masked selfattention and an encoderdecoder attention to integrate targetand sourceside contexts respectively: E ( l ) = MultiHead ( S ( l − 1) , S ( l − 1) , S ( l − 1) ) , (9) T ( l ) = MultiHead ( E ( l ) , H ( L e ) x , H ( L e ) x ) , (10) where S ( l − 1) denotes the targetside hidden states in the l - 1 -th layer. In particular, S (0) are the embeddings of input target words. Then, a positionwise fullyconnected forward neural network is uesd to produce S ( l ) as follows: S ( l ) = FFN ( T ( l ) ) . (11) Finally, the probability distribution of generating the target sentence is defined by using a softmax layer, which takes the hidden states in the top layer as input: P ( Y | X, I ) = Y t Softmax ( WS ( L d ) t + b ) , (12) where X is the input sentence, I is the input image, Y is the target sentence, and W and b are the parameters of the softmax layer. 3 Experiment We carry out experiments on multimodal English ⇒ German (En ⇒ De) and English ⇒ French (En ⇒ Fr) translation tasks. Datasets We use the Multi30K dataset, where each image is paired with one English description and human translations into German and French. Training, validation and test sets contain 29,000, 1,014 and 1,000 instances respectively. In addition, we evaluate various models on the WMT17 test set and the ambiguous MSCOCO test set, which contain 1,000 and 461 instances respectively. Here, we directly use the preprocessed sentences 2 and segment words into subwords via byte pair encoding with 10,000 merge operations. Visual Features We first apply the Stanford parser to identify noun phrases from each source sentence, and then employ the visual ground toolkit released by Yang et al. to detect associated visual objects of the identified noun phrases. For each phrase, we keep the visual object with the highest prediction probability, so as to reduce negative effects of abundant visual objects. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 5.075990048 , the TTR score is 0.429245283 , the CV score is 0.797016285 , and the perplexity score is  37.87768936 .","The text describes a machine learning model for multilingual language translation, including details about its architecture, training data, evaluation metrics, and linguistic features. The classification as AI-generated is based on the explicit mention of machine learning techniques used in developing the model, such as multi-head attention mechanisms, feedforward networks, and softmax layers. These elements strongly indicate that the content was created through artificial intelligence processes rather than being generated manually or by humans."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","The majority of training and test pairs in the dataset are relatively short, although there is considerable variation in length. This variability arises because the puzzles address different linguistic scopes: some involve the translation of individual words, others cover noun-modifier phrases, and still others encompass entire sentences. On average, English sentences are longer (median length of 4 tokens) than their translations (median length of 2 tokens). This observation is consistent with the synthetic nature of many foreign languages in the dataset, where a single complex word in the source language may correspond to a multi-word phrase in English. For instance, the Zoque word *tΛckotoyatih* translates to the English phrase ""only for the tooth."" Detailed statistics regarding sentence lengths for the training and test splits across different problems are presented in Figure 1.

Intuitively, the training splits are larger than the test splits. However, the number of training instances varies significantly between puzzles. This disparity is influenced by several factors, including task difficulty, task type, and the specific linguistic properties of the foreign languages involved.

### 3.3 Train versus Test Splits

A distinctive characteristic of the data splits in linguistic puzzles, which diverges from standard machine learning paradigms, is that the input test data should not be treated as strictly ""held out."" In certain scenarios, vocabulary items appearing in the input of foreign-to-English test instances may be essential for translating English-to-foreign test instances, and vice versa. Consequently, only the target outputs of the test instances should be considered truly held out. While this property is not universal across all puzzles, any solution approach must account for it. For example, system vocabularies should ideally be constructed using the union of the training data and the input test data.

### 4. Baselines

We evaluate these puzzles using models of varying complexity, ranging from random guessing to state-of-the-art neural machine translation systems.

**Random Words (RW):** Given the relatively small vocabularies of both source and target languages, we first assess the performance achievable through random word selection. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.145663209 , the TTR score is 0.503836317 , the CV score is 0.39427686 , and the perplexity score is  30.31319809 .","The text appears to be written by a human due to its structure, use of informal language, and detailed explanations within the context of a puzzle or problem description. It does not contain typical technical jargon or formal language expected in an AI-generated response. The content discusses linguistic features, statistical analysis, and provides insights into how different types of puzzles might affect the performance of machine learning models, indicating a deep understanding of the topic rather than being generated by an algorithm."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","The proposed algorithm is applicable to both speech-to-text and speech-to-speech simultaneous translation tasks (Oda et al., 2014; Bangalore et al., 2012; Yarmohammadi et al., 2013). In the speech-to-text scenario, the impact of modifications on the end-user is minimized, as the system restricts revisions to the most recent output tokens at a relatively low frequency. Conversely, in the speech-to-speech context, the capacity to revise extra words facilitates their integration into the lookahead window of incremental Text-to-Speech (TTS) systems (Ma et al., 2019b). In contrast, alternative retranslation strategies (Arivazhagan et al., 2020) necessitate non-local revisions, rendering them incompatible with incremental TTS architectures. Furthermore, this work introduces, for the first time, two evaluation metrics specifically designed for revision-enabled simultaneous translation: Revision-aware Average Lagging (RAL), a generalized latency metric, and the revision rate. The efficacy of the proposed technique is empirically validated using both fixed (Ma et al., 2019a) and adaptive (Zheng et al., 2019a) policies across Chinese-English and English-Chinese translation directions.

### 2. Preliminaries

**Full-Sentence Neural Machine Translation (NMT).** Conventional full-sentence NMT processes a source sentence $x = (x_1, \dots, x_n)$ via an encoder, where $x_i$ denotes an input token. On the target side, the decoder greedily selects the highest-scoring word $y_t$ conditioned on the source representation $h$ and the previously generated target tokens $y_{<t} = (y_1, \dots, y_{t-1})$. The final hypothesis $y = (y_1, \dots, y_T)$, where $y_T = \langle \text{eos} \rangle$, is selected to maximize the joint probability:

$$ p(y \mid x) = \prod_{t=1}^{|y|} p(y_t \mid x, y_{<t}) \quad (1) $$

**Simultaneous Translation.** Without loss of generality, and irrespective of the specific policy design, simultaneous translation is formulated as:

$$ p_g(y \mid x) = \prod_{t=1}^{|y|} p(y_t \mid x_{\leq g(t)}, y_{<t}) \quad (2) $$

where $g(t)$ represents an arbitrary fixed or adaptive read policy. For the sake of clarity, this work assumes the policy is predefined and does not explicitly distinguish between fixed and adaptive policy types in the subsequent formulation.

### 3. Opportunistic Decoding with Timely Correction and Beam Search

**Opportunistic Decoding.** To facilitate exposition, this method is initially applied within the context of fixed policies. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 5.661916268 , the TTR score is 0.429126214 , the CV score is 0.64472745 , and the perplexity score is  28.30484772 .","The text appears to be written by a human because it contains detailed technical information about algorithms used in machine learning and natural language processing, including specific terminology such as ""speech-to-text,"" ""speech-to-speech,"" ""incremental Text-to-Speech"" (TTS), and ""revision-aware Average Lagging."" It also includes citations from academic papers and mentions the use of metrics like RAL and revision rate, which are typical elements found in research papers discussing new methodologies in these fields. Additionally, the"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","**Abstract**

To address the aforementioned limitations, this study proposes a novel multidomain Neural Machine Translation (NMT) architecture. This model employs distinct modules for each domain, integrating word-level, adaptive, and layer-wise domain mixing strategies. Preliminary analysis indicates that individual words within a sentence frequently exhibit associations with multiple domains. Consequently, the proposed framework posits that each word possesses a specific domain proportion reflecting its domain preference. Word representations are subsequently derived by synthesizing embeddings from individual domain modules, weighted according to these proportions. This synthesis is realized through the strategic design of multi-head dot-product attention mechanisms tailored to specific domains, followed by the computation of weighted parameter averages based on word-level, layer-wise domain distributions. This approach facilitates effective cross-domain knowledge transfer while simultaneously capturing fine-grained, domain-specific nuances. Empirical evaluations demonstrate that the proposed model achieves superior performance compared to existing methodologies across various NMT tasks.

**1. Introduction**

Neural Machine Translation (NMT) has achieved substantial advancements across a wide spectrum of translation tasks (Kalchbrenner and Blunsom, 2013; Sutskever et al., 2014; Bahdanau et al., 2014; Luong et al., 2015; Wu et al., 2016). The efficacy of NMT systems is heavily contingent upon the availability of extensive annotated parallel corpora for training, a resource that is often scarce in specialized domains, such as medicine. To mitigate data scarcity, two primary strategies have emerged: the utilization of unparallel corpora via unsupervised machine translation (Lample et al., 2017, 2018), and the development of multidomain NMT models, which constitutes the primary focus of this work.

The most straightforward approach to multidomain translation involves constructing a unified model by aggregating training data from all domains. This method leverages the observation that languages across different domains often share fundamental semantic characteristics, including sentence structure, textual style, and lexical usage. For domains characterized by limited training data, unified models typically yield significant performance improvements. While numerous methodologies have been proposed to enhance multidomain NMT, it is critical to acknowledge that despite shared semantic traits, substantial heterogeneity persists among languages from distinct domains. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.402244948 , the TTR score is 0.546599496 , the CV score is 0.478821368 , and the perplexity score is  63.93076706 .","This text appears to be human-written because it contains elements characteristic of human language, such as proper nouns, idiomatic expressions, and grammatical structures typical of natural speech or writing. Additionally, the content discusses linguistic features like burstiness and type-token ratio, which are relevant to understanding human language patterns. The absence of technical jargon specific to AI or NMT further supports the classification as human-written."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Warstadt et al. further compare five analysis methods under a single phenomenon. Despite the diversity in methodology, these studies share common limitations. First, they employ only a single or specific aspects of linguistic knowledge; second, their experiments are mainly based on constructed datasets instead of realworld downstream applications. In contrast, we propose a method to cover a broader range of grammatical errors and evaluate on downstream tasks. A concurrent work facilitates diagnosing language models by creating linguistic minimal pairs datasets for 67 isolate grammatical paradigms in English using linguistcrafted templates. In contrast, we do not rely heavily on artificial vocabulary and templates. Synthesized Errors To evaluate and promote the robustness of neural models against noise, some studies manually create new datasets with specific linguistic phenomena. Others have introduced various methods to generate synthetic errors on clean downstream datasets, in particular, machine translation corpora. Belinkov and Bisk; Anastasopoulos demonstrate that synthetic grammatical errors induced by character manipulation and word substitution can degrade the performance of NMT systems. Baldwin et al. augment original sentiment classification datasets with syntactically (reordering) and semantically (word substitution) noisy sentences and achieve higher performance. Our method is partly inspired by Lui et al., who synthesize seminatural ungrammatical sentences by maintaining confusion matrices for five simple error types. Another line of studies uses blackbox adversarial attack methods to create adversarial examples for debugging NLP models. These methods create a more challenging scenario for target models compared to the above data generation procedure. Our proposed simulation benefits from both adversarial attack algorithms and seminatural grammatical errors. We first explain how we simulate ungrammatical scenarios. Then, we describe target models and the evaluation design. 3.1 Grammatical Error Simulation Most downstream datasets contain only clean and grammatical sentences. Despite that recent language encoders achieve promising performance, it is unclear if they perform equally well on text data with grammatical errors. Therefore, we synthesize grammatical errors on clean corpora to test the robustness of language encoders. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.430179063 , the TTR score is 0.550964187 , the CV score is 0.412230212 , and the perplexity score is  108.5167923 .","The text is likely AI-generated because it contains technical details about natural language processing techniques such as linguistic features, experimental designs, and evaluation metrics used in research papers related to language modeling and neural network robustness testing. The use of jargon and references to specific academic works suggests an author familiar with advanced AI and computational linguistics topics. Additionally, the content appears to be written in a formal, technical style typical of academic publications rather than casual writing."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","We exemplify using few instances from our proposed dataset in order to support our claim of DA often being expressed in a multimodal way along with exploiting the emotional state of the speaker. Role of Emotion. In Figure 2b , we present two instances from the dataset where the emotional state of the user seems beneficial in deciding the DA of an utterance. In the first example, the reference to the sad and dismal state of the speaker directs it to acknowledge the presence of the hearer. In the second case, the angry emotional state of the speaker forces her to disagree with people’s opinion or suggestion involved in the conversation. The examples above illustrate the importance of having emotional information as emotions affect the communicative intention or DA of the speaker discussed above. The presence of emotion in our dataset caters the models with the ability to use additional information while reasoning about DA. Role of Multimodality. Figure 2a shows two cases where DA is articulated through incongruity between modalities. In the first instance, the facial modality implies anger or fury. Whereas the textual modality lacks any visible sign of displeasure, on the contrary it indicates an agreement. So, the textual claims does not validate the facial features. In the second case, the textual modality hints pure agreement. Whereas the audio modality expresses a sarcastic appeal. In both these cases, there exists inconsistency between modalities, which acts as a strong indicator that multimodal information is also important in providing additional cues for identifying DAs. The availability of complementary information across multiple modalities improves the model’s ability to learn discriminatory patterns that are responsible for this complex process. 4 Proposed Methodology This section describes the proposed multitask, multimodal approach followed by the implementation details. 4.1 Multimodal Feature Extraction Here, we discuss, the process of multimodal feature extraction. Textual Features. The transcriptions available for each video forms the source of the textual modality 2 . To extract textual features, pretrained GloVe embeddings of dimension 300 have been used to obtain representation of words as word vectors. The resultant word embeddings of each word are concatenated to obtain a final utterance representation. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 5.77832481 , the TTR score is 0.498740554 , the CV score is 0.481213804 , and the perplexity score is  50.94740295 .","The text is likely classified as AI-generated because it discusses a machine learning approach to analyzing spoken language data, including multimodal features such as facial expressions and voice tone. It mentions techniques like sentiment analysis and pattern recognition, which are common tasks performed by artificial intelligence systems. Additionally, the detailed description of how different types of data (text, speech, and possibly images) can be combined to improve understanding aligns with typical methods used in natural language processing research. The focus on extracting features and training models suggests"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","We fine tuned the BERTbaseuncased and BERTlargeuncased models to predict matches between argument and key point pairs. We added a linear fully connected layer of size 1 followed by a sigmoid layer to the special [CLS] token in the BERT model, and trained it for three epochs with a learning rate of 2e5 and a binary cross entropy loss. NLI Transfer Learning. We also experimented with transfer learning from NLI to our task of argumenttokey point match classification. This was motivated by the similarity between these tasks (as discussed in Section 2.2 ), as well as the availability of largescale NLI labeled datasets. We considered the Stanford (SNLI) and the MultiGenre (MNLI) datasets, each comprising hundreds of thousands of labeled premisehypothesis pairs. Pairs labeled as E NTAILMENT were considered positive instances, while the rest of the pairs, labeled as N EUTRAL or C ONTRADICTION were considered negative. We trained BERTbase and BERTlarge models on each of these datasets, following the procedure described above. 4.1.2 Match Classification In the match classification step we select the matching key points for each argument, based on their respective matching scores. The classification can be done locally, treating each pair individually, or globally, by examining all possible key points for each argument. We compared the following policies for selecting matching key points for a given argument. Threshold. For each fold, we find the threshold on the match score that maximizes the F1 score for the positive (matching) class. Pairs whose score exceeds the learned threshold are considered matched. Best Match (BM). Using a threshold is not optimal for our data, where most arguments have at most one matched key point. A natural solution is to select the best matching key point. For each argument, we consider all key points for the same topic and stance as candidates and predict only the candidate with the highest match score as matched to the argument and the rest as unmatched. Note that this is the only fully unsupervised selection policy, as it does not require labeled data for learning a threshold. BM+Threshold. The BM policy always assigns exactly one key point for each argument, while 27.5% of the arguments in our data are not matched to any key point. To address this, we combine the two former policies. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.730032508 , the TTR score is 0.43255814 , the CV score is 0.566946028 , and the perplexity score is  89.35687256 .","The text is likely AI-generated because it contains technical details about training machine learning models using BERT (Bidirectional Encoder Representations from Transformers), which is a type of artificial intelligence technology. Additionally, the text mentions specific algorithms like binary cross entropy loss and training epochs, further indicating an AI-driven approach. The use of terms related to natural language processing (NLP) and argument-to-keypoint match classification suggests expertise in AI applications in natural language understanding."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Furthermore, to avoid information loss, FIM matches the text segments of the candidate news and each historical news browsed by the user at each semantic granularity. In practice, for each pair of news, the model constructs a segmentsegment similarity matrix from wordlevel to sentencelevel based on the hierarchical news representations. By this means, user’s reading interests implied in the browsing history can be recognized under the supervision of candidate news, and carried into matching with minimal loss, so as to provide sufficient clues about the content relevance for recommending proper news. Afterwards, we merge the multiple matching matrices of each news pair at each granularity into a 3D image, whose channels indicate the relevant degrees of different kinds of usernews matching patterns. By resembling the CNNbased hierarchy of image recognition, higherorder salient signals are identified to predict the probability of the user clicking the candidate news. We conducted extensive experiments on a realworld dataset collected from MSN news. Experimental results validate that our approach can effectively improve the performance of news recommendation compared with the stateoftheart methods. 2 Related Works With the explosive growth of digital news, building personalized news recommender systems has drawn more attentions in both natural language processing and data mining fields. Conventional news recommendation methods focus on utilizing manual feature engineering to build news and user representations for matching. For example, Liu et al. used topic categories and interest features generated by a Bayesian model to build news and user representations. Son et al. extracted topic and location features from Wikipedia pages to build news representations for locationbased news recommendation. In recent years, deep learning based models have achieved better performance than traditional methods for news recommendation, due to their capabilities of distilling implicit semantic features in news content. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.550718336 , the TTR score is 0.551083591 , the CV score is 0.476347759 , and the perplexity score is  115.187706 .","The text is likely classified as AI-generated because it discusses advanced techniques for personalizing news recommendations using machine learning algorithms. It mentions concepts such as ""FIM"" which could refer to a specific algorithm or framework developed by an AI team, and talks about constructing similarity matrices between news segments and user browsing histories. The use of complex terminology related to neural networks and convolutional neural networks also suggests an AI-driven approach. Additionally, the text describes how these techniques help in predicting user clicks on news articles, indicating"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Overpredicting background causes step label accuracy and step recall to decrease. Finally, we compare the HSMM and O RDERED - D ISCRIM models when using both narration constraints (in training) and ordering constraints (in training and testing) in the ordering + narration block. Both models benefit substantially from narration on all metrics when compared to using only ordering supervision, more than doubling their performance on step label accuracy and step recall (comparing U6 and U7 to U3 and U4). Our weaklysupervised results show that: (1) Both action segmentation metrics – all label accuracy and step label accuracy – are important to evaluate whether models adequately distinguish meaningful actions from background. (2) Step constraints derived from the canonical step ordering provide a strong inductive bias for unsupervised step induction. Past work requires these constraints and the HSMM, when trained without them, does poorly, learning to capture diversity across videos rather than to identify steps. (3) However, ordering supervision alone is not sufficient to allow these models to learn better segmentations than a simple baseline that just uses the ordering to assign labels ( ordered uniform ); narration is also required. 7.4 Comparison to Past Work Finally, we compare our full model to the O R - DERED D ISCRIM model of Zhukov et al. in the primary data evaluation setup from that work: averaging results over 20 random splits of the primary data (Table 3 ). This is a low data setting which uses only 30 videos per task as training data in each split. Accordingly, both models have lower performance, although the relative ordering is the same: higher step label accuracy for the HSMM, and higher all label accuracy and step recall for O R - DERED D ISCRIM . Although in this lowdata setting, models overpredict background even more, this problem is less pronounced for the HSMM: 97.4% of timesteps for O RDERED D ISCRIM are predicted background (explaining its high all label accuracy), and 87.1% for HSMM. 8 Discussion We find that unsupervised action segmentation in naturalistic instructional videos is greatly aided by the inductive bias given by typical step orderings within a task, and narrative language describing the actions being done. While some results are more mixed (with the same supervision, different models are better on different metrics), we do observe that across settings and metrics, step ordering and narration increase performance. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.095611689 , the TTR score is 0.443207127 , the CV score is 0.302641384 , and the perplexity score is  112.7306519 .","The text appears to be AI-generated based on several characteristics:

1. **Consistent Use of Terminology**: The text consistently uses terms like ""HSMM,"" ""O RORDERED-DISCRIM,"" and ""U6"" and ""U7,"" which are specific to AI research fields such as machine learning and computer vision.

2. **Technical Jargon**: The use of technical jargon related to neural networks, supervised vs. unsupervised learning, and metric comparisons suggests an academic or"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","**Abstract**

Personalized Instruction (PI) has been demonstrated to enhance student comprehension and engagement across diverse disciplines, including physics, biology, and computer science. An effective PI question is characterized by a first-attempt success rate ranging between 40% and 60% among learners. Although existing repositories of PI questions are available, they frequently lack this critical performance metric. To address this gap, this study analyzes log file data to quantify the percentage of learners who correctly answer each multiple-choice question on their initial attempt. Furthermore, the Runestone platform will be modified to enable instructors to search for suitable PI questions and to provide comprehensive support for the PI pedagogical process.

The Runestone ebook platform offers a suite of unique features designed to serve instructors, learners, authors, and researchers. Instructors benefit from the ability to customize courses, manage class rosters, generate assignments, monitor student progress, and facilitate grading. Students value the platform's interactivity, diverse practice modalities, immediate feedback mechanisms, and accessibility as a free resource. Authors appreciate the streamlined publishing and revision workflows, the system's scalability to support over 25,000 daily users, and the expanding community of contributors. Researchers utilize the platform's extensible architecture to develop and test new features with large-scale learner populations.

We extend our gratitude to all contributors who have developed the Runestone platform, authored educational content, and engaged with the system as students and educators. Special recognition is due to Iman Yeckeh-Zaare for his contributions to the spaced practice tool. This research was supported by the National Science Foundation under grants 1138378 and 1432300.

In the broader context of emerging technologies, quantum computing leverages the fundamental laws of quantum mechanics to enable novel algorithms that are computationally infeasible on classical systems, potentially driving breakthroughs in fields such as materials science and chemistry. Consequently, there is a rapidly increasing demand for a workforce proficient in the fundamentals of quantum computing, particularly in quantum programming. However, educational offerings for non-specialists remain limited, and there is a scarcity of established best practices for training computer science and engineering students in this domain. This report details the implementation of an undergraduate quantum computing course utilizing a practical, software-driven pedagogy. The curriculum prioritized the instruction of quantum algorithms through hands-on programming, minimizing traditional written assessments in favor of self-paced programming exercises (""Quantum Katas""), varied programming assignments, and a capstone project. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.805637255 , the TTR score is 0.569196429 , the CV score is 0.284070326 , and the perplexity score is  40.1993866 .","This text appears to be human-written because it contains clear evidence of original authorship, including:

1. A formal academic tone and structure typical of scholarly writing.
2. Use of technical terms specific to certain fields like physics, biology, and computer science.
3. References to real-world applications and examples within these fields.
4. Acknowledgments of funding sources and previous work done by others.
5. Detailed explanations of concepts and methods used in scientific studies or educational programs.
6. Personal acknowledgements"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","We found no significant differences in the number of any question type across 1C and 2C configurations. The most frequent question types were explanation questions and status questions. An explanation question asks for the clarification of the causes, context, and consequences of facts, such as “Why do you have to go to motion?”. A status question refers to the current condition of the program as produced by a partner or the self, such as “What did I do?!” Although further analysis is needed, these initial results indicate that there may be no significant differences in questionasking frequency across 1C and 2C configurations. Curriculum. The curriculum for this classroom experience was designed by the authors and taught by the school media center teacher. Each lesson was intended to last an hour.Students were first taught about programming and algorithms before they were introduced to the blockbased programming environment. On day 2, students were introduced to the categories of blocks and practiced their use. Day 3’s lesson consisted of three activities on conditionals and conditional trees, culminating in an activity in which students created their own idea for a program that would require conditionals, draw the conditional tree, and then program it. On day 4, students learned about repeating patterns and loops. They worked in pairs to identify a program they could write that needed loops and then implemented it. On day 5, students learned about taking in user input and adapting program behavior based on input, while on day 6 they learned about broadcasting and receiving. Finally, on day 7, students created a game that brought together many of the concepts covered previously. At Clark Elementary, the students were in the class for 45 minutes; therefore, the lessons had to be reduced to fit in that time slot. The course was taught by the media center specialist who had some experience teaching blockbased programming. In addition to shortening the lessons, the teacher decided to cover conditionals and loops for two days each after seeing the students struggle with the concepts during the first day of each lesson. The final lesson the teacher taught was loops. As part of this curriculum, the students were taught about both 1C and 2C pair programming. They were introduced to the roles and responsibilities of the driver and navigator and they were taught about the importance of talking through their decision making process. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.957866708 , the TTR score is 0.465011287 , the CV score is 0.434271855 , and the perplexity score is  32.53804779 .","The text is likely classified as AI-generated due to its formal tone, structured format, and detailed description of educational content and methodologies used in a classroom setting. These characteristics align with typical outputs from AI systems designed for creating instructional materials or curricula. Additionally, the inclusion of specific details about lesson plans, student engagement strategies, and pedagogical approaches suggests a high level of sophistication and originality indicative of AI-generated content rather than human-authored work."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Nowadays, with our capitalistic way of thinking, and our huge ideology of research for wealth, it has become a common concern to seek for the best way to be succesful. Thus, many people argue that it is easier to be successful running risks or trying new things.
	Actually, we cannot denied that taking risk can lead to success. Because finding new things is obviously the best way to give to the people new need (for economy), and also to anticipate your competitors. Marcel Bleustein, for example, became one of the most famous French advertisors, being the first one to try to do advertising on radio. One another hand, trying new things lead you to take risks, and becoming unsuccessful if nobody takes care of your idea.
	However, it is also possible to be successful only doing what you know how to do well. The principle is that, if you know perfectly how to do something, you will consequently do it on a better way that your concurrents. The big Americane telephone compagny AT&T, for example, prefered in the 20s continue to be the best in the telephone technologie, rather than developpe new technologies such as wireless telegraph. As everybody knows, it was a good choice.
	Actually, the best way to be successful may be both doing something we know well, and trying to find new things. In art for example, it is diffucult to be a real artist, that is to say to create new things, without be aware of what existed before. That is why, before becoming one of the most famous French movie director and one of the leader of the Nouvelle Vague, J. L. Gaudard firstly worked hard to know a lot of things about classical cinema and more generally about classical art. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.391574484 , the TTR score is 0.471976401 , the CV score is 0.352899031 , and the perplexity score is  78.61515045 .","The text is classified as human-written because it presents arguments and examples related to personal experiences and opinions, which align with typical characteristics of human-generated content. It discusses various aspects of entrepreneurship, creativity, and innovation from historical perspectives, including specific cases like Marcel Bleustein and AT&T. The writing style reflects an individual's thought process and does not exhibit the hallmarks of machine-generated text, such as uniform sentence structure or consistent use of language patterns."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Let the current sentence be x t = ( w t, 1 , . . . , w t,k − 1 , w k , w t,k +1 . . . , w t,l t ) . If the replacement operation proposes a candidate word w for the k th step, the resulting candidate sentence becomes x = ( w t, 1 , . . . , w t,k − 1 , w , w t,k +1 . . . , w t,l t ) . The insertion operation works similarly. Here, the candidate word is sampled from a probabilistic distribution, induced by the objective function ( 2 ): p ( w |· ) = f sim (x , x 0 ) · f exp (x , x 0 ) · f flu (x ) w ∈W f sim (x , x 0 ) · f exp (x , x 0 ) · f flu (x ) , where W is the sampling vocabulary; Z is known as the normalizing factor (noticing our scoring functions are nonnegative). We observe that sampling from such objectiveinduced distribution typically yields a meaningful candidate sentence, which enables SA to explore the search space more efficiently. It is also noted that sampling a word from the entire vocabulary involves reevaluating ( 2 ) for each candidate word, and therefore, we also follow Miao et al. and only sample from the topK words given by jointly considering a forward language Algorithm 1 UPSA 1: Input : Original sentence x 0 2: for t ∈{ 1 , . . . , N } do 3: T = max { T init − C · t, 0 } 4: Randomly choose an editing operation and a position k 5: Obtain a candidate x by candidate sentence generator 6: Compute the accepting probability p accept by Eqn. ( 1 ) 7: With probability p accept , x t +1 = x 8: With probability 1 − p accept , x t +1 = x t 9: end for 10: return x τ s.t. τ = argmax τ ∈{ 1 ,...,N } f (x τ ) model and backward language model. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 6.593078354 , the TTR score is 0.359036145 , the CV score is 1.297287723 , and the perplexity score is  18.42594147 .","This text is likely classified as AI-generated due to its technical nature, specific terminology related to natural language processing algorithms like Sequence-to-sequence models, Monte Carlo Tree Search, and probabilistic distributions used for generating sentences. The detailed explanation of these concepts aligns with typical explanations found in academic papers on AI-driven language generation techniques. Additionally, the mention of perplexity measures and linguistic features suggests it's discussing advanced computational methods applied to text generation tasks, further supporting the classification as AI-generated content."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Historical evidence indicates a persistent deficit in analytical capabilities within policing, a concern first identified by Goldstein in 1990 and reaffirmed as a critical issue as recently as 2016. A contemporary review of Problem-Oriented Policing (POP) in England and Wales highlighted recurrent deficiencies in the application of the SARA model, specifically regarding the depth and quality of problem analysis. Furthermore, the review noted that 43% of survey respondents lacked access to the information necessary for effective problem-solving. Given that the efficacy of POP is predicated on a rigorous understanding of the problem at hand, the scarcity of requisite analytical skills within police agencies provides a logical explanation for the limited adoption of POP frameworks.

However, it is encouraging to observe that these challenges appear to stem primarily from resource constraints rather than inherent systemic flaws in the POP methodology. Instances where analytical resources were adequate, often facilitated by collaborations with academic institutions, have demonstrated significantly higher rates of successful POP implementation. Consequently, it is evident that automating specific components of the POP process could mitigate a primary barrier to its broader expansion. In this context, modern Natural Language Processing (NLP) techniques offer the potential to facilitate the rapid extraction of insights from unstructured police text, thereby substantially reducing the analytical burden associated with POP deployment.

Nevertheless, the introduction of complex analytical tools to already overburdened staff is unlikely to yield optimal results. Instead, such technologies must be simplified and packaged to ensure accessibility for time-constrained analysts with limited specialized training, even if this necessitates a trade-off in the full utilization of advanced NLP capabilities. In many jurisdictions, including the United Kingdom, police forces are legally mandated to document criminal incidents. The nature of this documentation varies based on crime severity and agency-specific protocols. As illustrated by representative examples, police free-text records frequently contain orthographic errors, specialized terminology (including acronyms and contractions), inconsistent capitalization, and a lack of formal grammatical structure. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.362279376 , the TTR score is 0.621169916 , the CV score is 0.282479272 , and the perplexity score is  30.60071564 .","The text appears to be human-written because it discusses historical trends in law enforcement's analytical capabilities, presents current issues related to Problem-Oriented Policing (POP), and outlines potential solutions using natural language processing (NLP). These topics are typically covered in academic or professional literature about policing practices, which suggests they were written by someone familiar with legal and security studies. The use of technical terms like ""burstiness,"" ""TTR,"" and ""CV"" also points towards a non-fictional source."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","I believe that successful people are those who are willing to try new things and take risks, rather than just sticking to what they already know. To me, a successful person is someone who never stops working hard and is always eager to explore new opportunities. They don’t just stop after achieving some success because they understand how important it is to keep moving forward.

A person becomes successful when they set clear goals and put in the effort needed to achieve them. Once they have a goal, they get organized and may discuss their plans with others to get advice and support. This helps them succeed, as they put in a lot of hard work and dedication.

After reaching one goal, they naturally move on to new challenges to maintain their success. They are willing to take risks in their future work, which helps them gain a deeper understanding of their field. This approach can also earn them respect in society, increase their popularity, and bring them rewards and recognition.

Successful people never forget the steps they took to reach their goals. This experience helps them tackle new challenges and encourages them to take risks. If they only stick to what they already know and avoid new challenges, they may lose their motivation and become less successful. Because of this, successful people are always working hard and willing to take risks. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.920784314 , the TTR score is 0.490196078 , the CV score is 0.205424327 , and the perplexity score is  18.68014908 .","The text appears to be written by an artificial intelligence system due to its formal tone, structured language, and consistent use of complex vocabulary and sentence structures typical of AI-generated content. The text discusses concepts related to personal development and success, using sophisticated language and reasoning patterns commonly found in AI-produced texts. Additionally, the high burstiness score suggests rapid changes in thought or style, which is characteristic of automated writing systems."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","**3.2 Joint Alignment Across Multiple Recipes**

Building upon pairwise alignments, we derive a joint alignment at the dish level encompassing multiple text and video recipes. For each dish, we construct a graph wherein each node represents an instruction from a text recipe or a transcript sentence from a video recipe. Edges are established between nodes based on pairwise alignments, with edge weights corresponding to alignment probabilities. To ensure robustness, only edges exhibiting an alignment probability exceeding 0.5 are retained. It is noted that these pairwise alignments are inherently directed, mapping from the source recipe to the target recipe.

Empirical analysis indicates that extending the alignment window beyond five steps results in diminished performance. Consequently, we transform the directed graph into an undirected structure by averaging the weights of reciprocal edges between any two nodes. This process may yield a graph containing multiple connected components, as certain recipe pairs may lack any instructions with an alignment probability surpassing the 0.5 threshold.

Our objective is to identify a set of instructions that are jointly alignable across diverse recipes. To achieve this, we convert the cyclic graph into a forest by applying the Maximum Spanning Tree algorithm. An illustrative example of such a derived tree is presented in Figure 4. Within this structure, a path containing at most one node from each recipe constitutes a set of jointly alignable instructions. For instance, in the magnified section of Figure 4, the unique colored nodes along the path connecting the yellow node to the green node represent such a set.

**4. Experimental Results**

This section details the evaluation methodology for the pairwise alignment algorithm described in Section 3.1. Our experimental design addresses the following research questions:

1.  How does the proposed alignment model perform when evaluated against human-aligned recipe pairs?
2.  Does the unsupervised alignment model demonstrate superior performance compared to simpler, non-learning baselines?
3.  How does performance vary when alignments are learned using only nouns versus a combination of nouns and verbs, as opposed to utilizing the full vocabulary?

**4.1 Human-Aligned Evaluation Set**

The pairwise alignment algorithm is evaluated using two human-annotated datasets:

*   **YouCook2 Text-Video Recipe Pairs:** The YouCook2 dataset (Zhou et al., 2018a) comprises 1,625 cooking videos, each paired with human-written descriptions for individual video segments. These data cover 90 distinct dishes. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.67245233 , the TTR score is 0.485193622 , the CV score is 0.5842103 , and the perplexity score is  37.46772385 .","This text is classified as ""Human-written"" because it describes the evaluation method used for a pairwise alignment algorithm, which typically involves annotating human-generated content like recipes. The text mentions datasets like ""YouCook2 Text-Video Recipe Pairs,"" where human-written descriptions accompany cooking videos. This context strongly suggests that the information was created by humans rather than being generated automatically by a machine learning system."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","However, an informationtheoretic evaluation is much more suitable for measuring the more abstract notion of which language pairs are hardest to translate to and from, which is our purpose here. 3 Disentangling Translation Difficulty and Monolingual Complexity We contend that simply reporting crossentropies is not enough. A second issue in performing a controlled, crosslingual MT comparison is that the language generation component (without translation) is not equally difficult across languages. We claim that the difficulty of translation corresponds more closely to the mutual information MI( S ; T ) between the source and target language, which tells us how much easier it becomes to predict T when S is given (see Figure 1 ). But what is the appropriate analogue of mutual information for crossentropy? One such natural generalization is a novel quantity that we term crossmutual information , defined as: XMI( S → T ) = H q LM ( T ) − H q MT ( T | S ) (3) where H q LM ( T ) denotes the crossentropy of the target sentence T under the model q LM . As in 2 , this can, analogously, be approximated by the crossentropy of a separate targetside language model q LM over our heldout data set: XMI( S → T ) ≈ (4) i =1 log 2 q LM ( t ( i ) ) q MT ( t ( i ) | s ( i ) ) which, again, becomes exact as N →∞ . In practice, we note that we mix different distributions q LM ( t ) and q MT ( t | s ) and, thus, q LM ( t ) is not necessarily representable as a marginal: there need not be any distribution ˜ q ( s ) such that q LM ( t ) = P s ∈V S q MT ( t | s ) · ˜ q ( s ) . While q MT and q LM can, in general, be any two models, we exploit the characteristics of NMT models to provide a more meaningful, modelspecific estimate of XMI . NMT architectures typically consist of two components: an encoder that embeds the input text sequence, and a decoder that generates translated output text. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 4.057528145 , the TTR score is 0.430656934 , the CV score is 0.531779086 , and the perplexity score is  81.22340393 .","This text is likely classified as AI-generated because it discusses advanced techniques used in machine learning and artificial intelligence, including cross-entropy measures, mutual information, and neural machine translation (NMT). The text also mentions specific linguistic features like burstiness, type-token ratio, and sentence length complexity, which are common topics in discussions about AI-driven language processing systems. These elements suggest that the content was created or influenced by an AI system designed to generate human-like text."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","**Dataset Overview**

MultiWOZ is a recently introduced, large-scale, multi-domain dialogue dataset characterized by significant complexity (Budzianowski et al., 2018). The corpus comprises interactions between tourists and information center clerks spanning seven distinct domains, including hotel, restaurant, and train services. The dataset is partitioned into training, development, and testing sets containing 8,433, 1,000, and 1,000 dialogues, respectively, with an average dialogue length of 6.85 turns. Furthermore, MultiWOZ features a sophisticated ontology consisting of 32 slot types and 2,426 associated slot values.

Evaluation adheres to the metrics established by Budzianowski et al. (2018), specifically measuring the frequency of correct entity provision (Inform Rate), the completeness of requested information fulfillment (Success Rate), and response fluency (BLEU score). Additionally, a composite quality metric is reported, calculated as $(\text{Inform} + \text{Success}) \times 0.5 + \text{BLEU}$, following the methodology suggested by Mehri et al. (2019).

**4.2 Implementation Configuration**

The context encoder is implemented as a single-layer, bidirectional Gated Recurrent Unit (GRU), while the action and paraphrase decoders utilize standard GRU architectures. Embedding and hidden layer dimensions are set to 50 for the CamRest676 dataset and 100 for MultiWOZ. The architecture incorporates both a copy mechanism and attention connections, as illustrated in Figure 2.

For response generation, state-of-the-art models specific to each dataset are employed: the Two-Stage Copy Net (TSCP) (Lei et al., 2018) for CamRest676 and the Domain-Aware Multi-Decoder (DAMD) (Zhang et al., 2019) for MultiWOZ. Model structures strictly follow the default configurations provided in the open-source implementations of TSCP and DAMD.

Optimization is performed using the Adam algorithm (Kingma & Ba, 2014) with learning rates of 0.003 for CamRest676 and 0.005 for MultiWOZ. A learning rate decay strategy is applied, halving the rate if the total loss on the development set fails to decrease over three consecutive epochs. Training terminates if no improvement in total loss is observed for five consecutive epochs. During reinforcement fine-tuning within the TSCP framework, the learning rate is adjusted to 0.0001 with a decay parameter of 0.8.

**4.3 Baseline Methods**

The proposed method is evaluated against five alternative data augmentation techniques. Three of these baselines rely on text replacement strategies, while the remaining two utilize neural paraphrase generation models.

*   **WordSub**: This method implements rare word substitution as proposed by Fadaee et al. (2017). . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 4.240476898 , the TTR score is 0.509355509 , the CV score is 0.466327164 , and the perplexity score is  54.29983521 .","This text appears to be human-written because it describes the characteristics and evaluation methods of a dataset called MultiWOZ, which includes details about its structure, evaluation criteria, implementation configuration, and baseline methods used for comparison. The content is written in English and provides factual information without any apparent signs of being generated automatically or through machine translation."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","The data collection methodology employed in this study integrates multiple sources to construct a comprehensive legislative dataset. Initially, the LegiScan API was utilized to aggregate legislative data for all bills within the target states, encompassing three primary categories: (1) bill metadata, including bill type, title, description, sponsors, and links to full texts; (2) vote metadata, detailing individual legislator voting records (Yea, Nay, Absent, or Not Voting); and (3) legislator metadata, comprising party affiliation and district information.

To ensure textual accessibility, proprietary PDF documents were systematically converted into machine-readable text files using open-source libraries. Furthermore, to capture the granular progression of legislation, the system scraped the ""History"" sections from the LegiScan website. This process extracted a chronological series of legislative events—such as committee reports and roll call votes—that are not accessible via the standard API. In total, 34,443 bills introduced between 2011 and 2018 were collected. While 58% of these bills possessed both full texts and individual voting records, the analysis focused specifically on bills reaching the second or third reading stages, where full chamber voting occurs. Consequently, a subset representing 32% of the total corpus was selected to construct the legislative graph (see Table 2).

Supplementary data regarding legislator biographies, ideology, and geography were also aggregated. Biographical details, political interests, and committee assignments were sourced from Ballotpedia (2019). For the minority of legislators (less than 10%) lacking Ballotpedia profiles, alternative public textual sources, such as social media platforms, were utilized. Ideological positioning was derived from the scores established by Shor and McCarty (2011), categorizing legislators as conservative, moderate, or liberal. Additionally, district identifiers were cross-referenced with GIS census data (Census, 2019) to classify districts as either urban or rural. Table 3 provides a demographic breakdown of legislators by party, gender, ideology, and district type.

Finally, campaign finance data were obtained via the FollowTheMoney API (FollowTheMoney, 2019). This source was used to compile comprehensive records of financial contributions to legislators and candidates, specifically identifying donors associated with the legislators and cosponsors of the bills under study. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 4.247166559 , the TTR score is 0.526190476 , the CV score is 0.603917671 , and the perplexity score is  40.3958168 .","This text is likely classified as human-written due to its detailed structure, specific terminology related to legal and legislative processes, and the inclusion of citations to external databases and sources. The language used is formal and technical, typical of academic research papers rather than casual writing. The presence of structured tables and figures further supports the classification as human-generated content."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","There is frequent discussion and debate about the main reasons employees work longer hours and the effects this has. This essay will explore these reasons, discuss the main outcomes, and provide relevant examples.

There are several explanations for why workers often spend more time at their jobs than what is stated in their contracts. One reason is that new employees may want to make a good impression on their managers during their probation period. Another reason is the high volume of tasks and projects assigned at once, which can cause stress due to tight deadlines. Additionally, some employees stay late for personal growth, such as studying for certifications or pursuing an online master's degree.

The consequences of working extra hours can be both positive and negative, depending on the motivation. If the extra time is spent on self-improvement, it can be beneficial. However, if working late becomes a daily habit, it can harm both physical and mental health, as well as negatively impact social life and relationships. For example, some countries, like Dubai, have reduced working hours, and studies have shown that this led to higher productivity, better results, and improved quality of work.

In conclusion, the best approach is to find a balance between work and personal life. If we consistently spend too much time at work, it can damage our health and, ultimately, our performance and job satisfaction will suffer. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.698088704 , the TTR score is 0.581132075 , the CV score is 0.246267979 , and the perplexity score is  22.18896484 .","This text is likely classified as AI-generated because it exhibits characteristics consistent with human-written content but also shows signs of being generated by an artificial intelligence model. The language used is sophisticated and varied, showing a mix of formal and informal styles typical of academic writing. The inclusion of specific data points and references to real-world scenarios further supports the idea that this text was created by an intelligent system rather than written by a human author. The use of complex sentence structures and the presence of linguistic features associated with natural language"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Baselines We use the following baselines: • ELMo: a tagging model with pretraining from Peters et al.. • LatticeLSTM: Zhang and Yang constructs a wordcharacter lattice, only used in Chinese datasets. • CVT: Clark et al. uses CrossView Training(CVT) to improve the representations of a BiLSTM encoder. • BertTagger: Devlin et al. treats NER as a tagging task. • GlyceBERT: Wu et al. combines Chinese glyph information with BERT pretraining. • BERTMRC: Li et al. formulates NER as a machine reading comprehension task and achieves SOTA results on Chinese and English NER benchmarks. Results Table 5 shows experimental results on NER datasets. DSC outperforms BERTMRC by +0.29, +0.96, +0.97 and +2.36 respectively on CoNLL2003, OntoNotes5.0, MSRA and OntoNotes4.0. As far as we are concerned, we are setting new SOTA performances on all of the four NER datasets. 4.3 Machine Reading Comprehension Settings The task of machine reading comprehension (MRC) predicts the answer span in the passage given a question and the passage. We followed the standard protocols in Seo et al., in which the start and end indexes of answer are predicted. We report Extract Match (EM) as well as F1 score on validation set. We use three datasets on this task: SQuAD v1.1, SQuAD v2.0 and Quoref. Baselines We used the following baselines: • QANet: Yu et al. builds a model based on convolutions and selfattentions. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.792664318 , the TTR score is 0.490774908 , the CV score is 0.547482606 , and the perplexity score is  110.4933777 .","This text is likely AI-generated because it contains several characteristics typical of automated writing systems:

1. **Consistent Structure**: The document follows a consistent format with sections labeled ""Baselines"", ""Results"", and ""Settings"" for each experiment or baseline, which is common in academic papers describing research findings.

2. **Technical Terminology**: It uses technical terms related to natural language processing (NLP), such as ""NER"" (Named Entity Recognition), ""CrossView Training"", and ""machine reading comprehension"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","As technology advances, more and more people are choosing to learn the latest skills, such as computer programming, rather than studying history. There is ongoing debate about whether learning history can help us better understand today’s society. In my view, having knowledge of the past is definitely beneficial for living well in the modern world. I believe this for two main reasons, which I will explain below.

First, studying history allows us to recognize the mistakes people have made before. Throughout history, individuals and nations around the globe have made countless errors, some of which led to immense suffering. It is crucial for humanity to prevent these tragic events from happening again. The only way to truly avoid repeating these errors is by understanding our history. For example, the World Wars were among the most devastating events in human history, and knowing about them deeply influences how we should act today to prevent future wars. Clearly, learning history is valuable in this respect.

Some may argue that past events have little relevance to our present lives. However, a closer look shows that all current relationships and situations are rooted in the past. By understanding history, we can find the best path toward a better future. Take, for instance, racial discrimination in the United States. Learning about U.S. history can guide us in maintaining and improving relationships today. From this perspective, studying history benefits everyone.

In conclusion, considering all these points, learning history greatly enhances our understanding of the present. It teaches us how to avoid repeating past mistakes and shows us how to act wisely for better outcomes in the future. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.219828475 , the TTR score is 0.51465798 , the CV score is 0.32847381 , and the perplexity score is  20.30179977 .","The text appears to be AI-generated because it follows certain linguistic features typical of automated writing systems. These include:

1. **Burstiness**: A high burstiness value indicates rapid changes in word frequency, which is common in machine-generated text.
2. **TTR (Type-Token Ratio)**: A low type-token ratio suggests that many words are repeated, which is often seen in generated text.
3. **CV of Sentence Length**: A moderate sentence length variation is consistent with text produced by an"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","I wholeheartedly agree that success comes to those who take risks and choose paths that few others dare to follow. There are countless stories of remarkable discoveries and inventions made by people willing to take bold chances. For instance, consider Neil Armstrong, the first astronaut to walk on the moon. He took the immense risk of venturing into space, something humanity had never imagined possible. His willingness to think differently and put his life on the line made it possible for mankind to reach the moon. Without his belief and courage, we might never have achieved that milestone.

Another example is J.L. Baird, the inventor of television. He envisioned that images could be created by directing electrons onto a surface through a cathode ray tube. By daring to pursue this unconventional idea, he laid the foundation for modern television. His innovative thinking and willingness to act on it, despite the risks involved, made this groundbreaking invention possible.

Mahatma Gandhi, known as the father of the Indian nation, also took a unique approach by using ""Ahimsa,"" or non-violence, to fight for independence. No one had previously considered using non-violence against oppression, but Gandhi risked his life for this principle and ultimately succeeded in securing India's freedom.

In conclusion, I believe that thinking outside the box is essential for success. Conventional ideas are common, but it is the unique and daring ideas that truly stand out and make a difference. Those who are willing to take risks and break away from the crowd are the ones who become heroes and create inspiring success stories. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.771879022 , the TTR score is 0.567567568 , the CV score is 0.349788624 , and the perplexity score is  16.62823105 .","The text is likely classified as AI-generated because it exhibits characteristics consistent with human-written content, such as coherent argumentation, use of examples, and emotional appeal. The language is vivid and engaging, which aligns with typical writing styles used by humans rather than machine learning models designed for generating text. Additionally, the structure and flow of the text suggest an authorial voice rather than output from an algorithm."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Lundberg et al. calculated features interactions via SHAP interaction values along a given tree structure. Chen and Jordan suggested to utilize a linguistic tree structure to capture the contributions beyond individual features for text classification. The difference with our work is that both methods require hierarchical structures given, while our method constructs structures solely based on feature interaction detection without resorting external structural information. In addition, different from Singh et al., our algorithm uses a topdown fashion to divide long texts into short phrases and words based on the weakest interactions, which is shown to be more effective and efficient in the experiments in section 4 . This section explains the proposed algorithm on building hierarchical explanations ( subsection 3.1 ) and two critical components of this algorithm: detecting feature interaction ( subsection 3.2 ) and quantifying feature importance ( subsection 3.3 ). . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is -0.336601307 , the TTR score is 0.666666667 , the CV score is 0.565009904 , and the perplexity score is  119.5029984 .","This text appears to be AI-generated because it discusses algorithms used for text classification, specifically mentioning techniques like SHAP interaction values and linguistic trees. It also references specific data points such as linguistic features and perplexity scores, all of which are typical topics in AI research papers related to natural language processing or machine learning. The use of technical terms and the detailed explanation of an algorithm suggest a high level of sophistication consistent with AI-generated content."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","This study examined how four factors, (1) students’ perceptions of instructional practices, (2) classlevel indicators of instructional practices, (3) students’ status as a CS major, and (4) students’ mastery of course content, are associated with the likelihood students will continue enrolling in CS courses. Through multilevel modeling, these relationships were tested simultaneously across a large number of course sections that included courses from CS1 to seniorlevel courses. Findings from this study indicate that after controlling for students’ individual plans related to majoring in CS and their mastery of CS concepts, the general instructional practices used in the course section do not predict retention. Additional research on other CSspe-cific instructional practices might help CS educators identify those practices that can encourage students to keep taking CS courses. One of the contributions from this study is the use of multilevel modeling. Without multilevel modeling, it would not be possible to analyze the relationships of courselevel variables across a set of courses, and personlevel estimates would not be accurate. Had only the personlevel variables been used in this study, the courselevel effect of CL would not have been detected. In addition to investigating the impact of other instructional practices on retention, future research should further study the role of content mastery and students’ majors on retention. With content mastery, more closely examining the aspects of core CS content that predicts retention and success in later classes could provide insight into how course topics should be timed, reviewed, and paired with other interventions designed to increase retention. With students’ major, considering the possibility of differential impacts of instructional practices on CS majors and nonCS majors could provide insights into which practices are best suited for which groups of students, with respect to retention. If the relationships between instructional practices and retention are different for majors and nonmajors, it would suggest that some practices are more suited for CS1 courses and others for later courses. Overall, there is still much to be learned about how instructional practices are related to retention in CS. This work was partially supported by the National Science Foundation . Active learning environments have only recently started to be analyzed in the CS discipline, in terms of their effect on student performance. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 2.673743614 , the TTR score is 0.441247002 , the CV score is 0.423184958 , and the perplexity score is  30.266922 .","The text appears to be classified as AI-generated based on several linguistic features such as burstiness, type-token ratio, sentence length variability, and perplexity. These characteristics often align with patterns found in human-written texts rather than machine-generated ones. The high burstiness suggests rapid changes in tone or intensity, typical in human writing but less common in automated outputs. The relatively low TTR indicates a balanced distribution of word types, which is also characteristic of natural language. Variability in sentence lengths and perplexity"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","I agree with the point that is successful people try new things and take risks rather than only doing what they already know how to do as well.
  Firstly, the people who likes to explore can learn more about something news than do the old things or repite same things people. Though the explore things, you can find the thing which you never have been seen or do it, and you will love to the world. Because you find something which you don't knew. It called knowledges. Yeah, the world is wonderful, if you do not believe that, the only things you lost is your heart. It means you haven't bold.
 Secondly, there was impossible that the people who knows  everything. People is not God, and God will not help the man who would not do anything. You maybe knows one thing or few things more than others, others may know others things than you in truthly.
people learned things from books, TVs, newspapers, computers,etc. So the knowledges are limits things. You can though you explore eyes, your hot heart, your heathly bodies, to try, to find, to do the things which is fine, is worth to do.
 Last but not least, people do the things and get successful is not easy things. You must try you best to do this. You don't sit a nice place and wish i could be successful. Everyone Wish himself or herself is a successful man or woman. If you are just think about it, and not pay your action, your time, your power, it could become unsuccessful. The man is impossible got know everything, If there is a man who knows everything, it must be from his expriense. Where can we get from? Yap, try your best to try new things, form it, you will be grow up. Let us try. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 4.515835471 , the TTR score is 0.399449036 , the CV score is 0.521913168 , and the perplexity score is  67.87760925 .","This text is likely human-written because it exhibits characteristics typical of human expression such as emotional depth, personal anecdotes, and structured arguments supported by logical reasoning. The language used is vivid and descriptive, reflecting an individual's perspective on success and learning. The burstiness score suggests varied sentence lengths, indicating a natural flow of thought. The high type-token ratio indicates a rich vocabulary use without redundancy, further supporting its human nature. Overall, the content aligns closely with a coherent narrative style found in human-authored texts"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","**Model Training and Data Augmentation Methodology**

In accordance with standard practices, the pretrained model was fine-tuned on the MNLI dataset. This process involved training a linear classifier to predict labels based on the final-layer embedding of the [CLS] token, while simultaneously updating the parameters of the underlying BERT architecture (Devlin et al., 2019). To ensure robustness, the order of training examples was randomized for each model instance, and all models were trained for three epochs.

**A.2 Generation of Augmented Examples**

The following section outlines the data augmentation strategies employed. Table A.1 provides a comprehensive illustration of these strategies applied to a representative source sentence. It is important to distinguish the semantic impact of these transformations: while inversion typically alters the meaning of a sentence (e.g., ""the detective followed the suspect"" denotes a different event than ""the suspect followed the detective""), passivization preserves the original meaning (e.g., ""the detective followed the suspect"" is semantically equivalent to ""the suspect was followed by the detective"").

The specific augmentation protocols are defined as follows:

*   **Inversion (Original Premise):** For a source example denoted as $(p, h, \rightarrow)$, a new instance $(p, \text{INV}(h), \nrightarrow)$ is generated. Here, $\text{INV}$ denotes the operation of swapping the subject and object of the hypothesis. Source examples originally labeled as non-entailment ($\nrightarrow$) are excluded from this process.
*   **Inversion (Transformed Hypothesis):** For any source pair $(p, h)$ regardless of its original label, the premise $p$ is discarded. A new instance $(h, \text{INV}(h), \nrightarrow)$ is then generated.
*   **Passivization (Original Premise):** For any source pair $(p, h)$, a new instance $(p, \text{PASS}(h))$ is generated, retaining the original label. The function $\text{PASS}$ converts the hypothesis into its passive voice form without altering its semantic content.
*   **Passivization (Transformed Hypothesis):** For a source pair $(p, h)$, the premise $p$ is discarded, and two distinct examples are generated:
    1.  An entailment instance: $(h, \text{PASS}(h), \rightarrow)$.
    2.  A non-entailment instance: $(h, \text{PASS}(\text{INV}(h)), \nrightarrow)$.

**Selection of Source Sentences**

To identify suitable transitive sentences within the MNLI dataset for use as source material, constituency parses provided with the dataset were utilized. The ""TELEPHONE"" genre was excluded due to higher noise levels. The selection criteria required the identification of matrix S nodes containing a VP with exactly one NP daughter. Furthermore, both the subject and object were required to be full noun phrases (excluding personal pronouns such as ""me""), and the verb lemma was restricted to exclude ""be"" and ""have."" . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 8.335253948 , the TTR score is 0.345779221 , the CV score is 0.548725313 , and the perplexity score is  48.9214592 .","This text is classified as human-written because it describes a detailed methodology for generating augmented examples for a machine learning task using natural language processing techniques. The description includes specific details about data preprocessing steps, including randomizing the order of training examples and applying various types of data augmentation (inversion and passivization). Additionally, the text mentions using constituency parses and linguistic features like burstiness, type-token ratio, and sentence length to select appropriate source sentences. These elements suggest a high level of expertise in NLP"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","The state-of-the-art MoEL model employs a comparable emotion embedding; however, its configuration as the key within a key-value memory mechanism results in a weaker coupling with the decoder, thereby limiting the flow of emotional context. In contrast, the proposed MIME architecture utilizes embedding sharing to enhance the relevance of generated responses. By facilitating the exchange of contextual information between emotion embeddings and encoder outputs (Eq. 2), this approach intuitively refines context propagation.

Despite these advantages in contextual relevance, empirical observations indicate that MIME-generated responses exhibit lower fluency compared to competing models, including MoEL. This degradation is likely attributable to the decoder's structural optimization for emotional context refinement, which may inadvertently prioritize emotionally salient tokens over functionally necessary stopwords. While these stopwords lack intrinsic emotional content, they are critical for maintaining grammatical coherence.

Human A/B testing results corroborate these findings, revealing a distinct preference for MIME responses generated with stochasticity (STC) over deterministic outputs. Regarding performance across emotional valences, MoEL demonstrates superior empathy and fluency when responding to negative user emotions compared to positive ones. We hypothesize that this disparity arises from dataset imbalances, specifically the prevalence of negative samples (approximately 19) relative to positive samples (approximately 13). This suggests that MoEL is more susceptible to positive/negative context imbalances than MIME or MultiTR.

Finally, to evaluate the impact of emotion mimicry, we conducted an ablation study by bypassing the mimicry mechanism and passing the emotion embedding $e_g$ (Eq. 10) directly to Eqs. (20) and (21). This modification resulted in a substantial decline in empathy scores (a reduction of 0.2). A deeper analysis of the emotion embeddings, visualized in Figures 3a and 3b, further elucidates the critical role of emotion mimicry in the model's performance. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.754824806 , the TTR score is 0.558208955 , the CV score is 0.421297574 , and the perplexity score is  51.04776382 .","This text is classified as human-written because it describes detailed technical aspects of a machine learning model designed to generate natural language responses, focusing on its strengths and weaknesses in terms of emotional context and fluency. The use of specific terminology related to neural network architectures, such as ""key-value memory mechanism"" and ""embedding sharing,"" indicates expertise in the field of artificial intelligence. Additionally, the discussion about human preferences and performance metrics aligns with typical discussions found in academic papers or research reports written by humans."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","Successful people try new things and take risks rather than only doing what they already know how  to do well , I agree. Unless you doen't take risk you can't achieve success. And it is not always true that you may get success but you have to take a chance.High risk high gains.What is life without risk?                                                             Learning is a continous continous processso you have to keep learning than rather keep doing what you are good at.Taking risks helps you in building more confidence in yourself and train you to faceany situation. You come out  to be a stronger person who could face any situation. 
Trying out new things in life helps you to break  out from your monotous jobs and increases your chance of giving better performance .It not only give you excitement and zeal to work harder and reach your goal but also increases your confidence level. Every day is a new day for you filled with chanllenges	
If you doen't take risk life can be a drag and you can live with repentence of not trying a new thing. One can't just live in daily routine . Ifyou are ambitious and want to achieve something more you have to take chance and learn from chanceand never give up.
Life in its own self is a risk and you just can't live in daily routine life. All successful people have taken chances for example Bill gates had he not dropped out from the business school he would never have neen where he is. There are many people like him and we have to see at the brighter side and love for more positive example.
Life comes once to live to its fullest. Do everythin you want to do a . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.577837195 , the TTR score is 0.496753247 , the CV score is 0.498817965 , and the perplexity score is  10.71253967 .","This text is likely human-written because it exhibits characteristics consistent with human expression, such as flowery language, personal anecdotes, and emotional appeals. The use of rhetorical questions and emphatic statements (""I agree"", ""High risk high gains"") suggest an author's voice. Additionally, the text contains idiomatic expressions and metaphors typical of human writing, indicating creativity and original thought. The content discusses personal experiences and aspirations, which aligns with common themes found in human-authored texts."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","In other words, in contrast to a deterministic algorithm, such as a greedy algorithm, which must repeatedly apply a simple rule to deterministically select the next element in a solution, a nondeterministic algorithm is able to simultaneously explore all the possible choices. We do not yet have computers that can execute nondeterministic algorithms, but if we did, then we would be able to efficiently solve any combinatorial problem by relying on the special power of nondeterminism. Technically, all P problems are also NP problems because we already have deterministic algorithms for solving them and therefore do not need to rely on the special power of nondeterminism. For example, Dijkstra’s algorithm provides a deterministic polynomialtime solution to the shortest paths problem by building up a shortest paths tree from the start vertex outward. This application of the greedy algorithmic paradigm relies on the structure of the shortest paths tree, since the shortest path to a point further away from the start must build on the shortest path to a point closer to the start. refers to all the hardest NP problems—the combinatorial problems for which we do not have deterministic polynomialtime algorithms. More precisely, a problem PI is said to be NPcomplete if PI is in NP and for every problem in NP, there is a reduction that reduces the problem to PI. For exampleor the problem of finding the highestcost way to get from one vertex to another without repeating vertices, is an NPcomplete problem opposite to shortest paths (32). What makes longest paths so much harder to solve than shortest paths? For one, there is no underlying structure to the solution that we can use to repeatedly apply a simple rule as in a greedy algorithm. With the shortest paths problem, we could rely on the shortest paths tree to inform the solution. But in longest paths, the goal is to wander around the graph. The longest path between any two vertices will probably involve traversing as many edges as possible to maximize distance, visiting many vertices along the way. For some graphs, the longest paths might even visit all the vertices in the graph. In this situation, the longest paths do not form a tree and instead involve ordering all the vertices in the graph for each longest path. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 4.462907268 , the TTR score is 0.407142857 , the CV score is 0.346753652 , and the perplexity score is  15.8727932 .","The text appears to be written in a formal academic style with complex sentences and technical terminology typical of AI research papers or textbooks. It discusses concepts related to computational complexity theory, specifically focusing on NP-complete problems and their relationship to deterministic vs. nondeterministic algorithms. The language used is precise and concise, indicating a high level of expertise in the field. Given these characteristics, it is likely that this text was generated by an AI system designed for writing academic content."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","This study utilized the processing dimension as a heuristic framework to design in-class assessments that foster active and reflective learning environments, encompassing cooperative, collaborative, and independent modalities. Active learners generally prefer pedagogical approaches that facilitate discussion, application, experimentation, and interaction with peers. Conversely, reflective learners tend to favor conditions that permit quiet contemplation and deep analysis prior to practical application, often preferring individual work.

The Felder and Silverman Index of Learning Styles (ILS) was selected as the guiding instrument due to its web-based accessibility and convenience for student use. The assessment conditions—collaborative, cooperative, and independent—were explicitly mapped to the processing dimension of the Felder-Silverman model.

The collaborative quiz represents the most active condition. In this format, pairs of students were required to utilize a single computer to develop and submit a single code file for evaluation. This structure necessitated a ""driver-navigator"" dynamic, wherein one student managed the keyboard (the driver) while the other observed, offered strategic suggestions, and identified errors (the navigator). This methodology aligns with the established practice of pair programming. Empirical evidence suggests that pair programming yields higher-quality outcomes compared to individual work [9, 12–14, 33]. While the majority of existing literature focuses on pair programming for out-of-class assignments, recent studies indicate similar benefits within in-lab module assessments. Notably, research indicates that while students working in pairs do not necessarily demonstrate superior performance on subsequent individual assessments, they do not exhibit diminished performance either.

The cooperative quiz represents a less active condition than the collaborative model. In this scenario, partners work on separate computers, developing and submitting their own code for individual grading. While assessment remains individual, students are permitted to collaborate with their partner to any extent they deem necessary. This format mitigates the competitive nature of the testing environment by encouraging mutual support. Furthermore, cooperative partnering inherently integrates peer review into the assessment process, a practice demonstrated to enhance student learning. Although students author their own code, the opportunity to consult with a partner typically facilitates the review and analysis of peer code. . This text label is 0 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 3.058623619 , the TTR score is 0.54040404 , the CV score is 0.308287727 , and the perplexity score is  50.40025711 .","This text appears human-written because it describes an educational study using specific methodologies and data points related to learning styles and assessment techniques. It includes detailed descriptions of different types of quizzes and their characteristics, along with empirical evidence supporting certain practices. The language used is consistent with academic writing, discussing concepts like coding, collaboration, and learning styles without apparent bias or intent to deceive. Additionally, the inclusion of real-world examples such as pair programming further reinforces the authenticity of the content being presented as genuine educational research."
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","I can’t say I agree with this statement as it stands. I’d prefer it if it were rephrased or adjusted a bit.

First, how do we define successful people? Are they the ones who tried something new at just the right moment in their own field, or those who came up with entirely original ideas related to the topic? In my view, many people become famous simply by making a lot of money—but does that alone make them successful? If that’s the case, then I’d consider Microsoft a success if it moved into the steel industry, or Dell if it started selling pizza, or Intel if it opened a university, and so on. You could come up with countless similar scenarios.

But the real question is: how well does a person understand their own business? How much knowledge or innovation have they generated that’s applicable across different industries? How often have they taken risks that led to truly new ideas? Often, people invest in inventions just to keep their own businesses afloat. I wouldn’t necessarily call those people successful.

To me, chain stores run by certain business owners are a better example of success. In these stores, a variety of products are sold, and customer service improves, which in turn builds the owner’s expertise to sell even more types of products. Think of a grocery store owner who starts selling cosmetics, then adds medicines, then opens a gas station, and manages to excel in all these areas. That, in my opinion, is real success—taking a concept that works in one store and expanding it successfully into other fields. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.115345317 , the TTR score is 0.579470199 , the CV score is 0.493055142 , and the perplexity score is  23.62404633 .","This text appears to be AI-generated because it exhibits characteristics consistent with automated language models. The burstiness score indicates rapid changes in sentence length, typical of natural speech patterns rather than formal writing. The low type-token ratio suggests an abundance of repetition, common in machine-generated content. The high CV for sentence length further supports this classification, as varied sentence lengths are characteristic of AI-generated texts. Additionally, the use of specific terminology and concepts relevant to the discussion about ""successful"" individuals aligns with the style"
"From the input text, and the features of linguistic features, explain why a text is eaither human written or AI generated?","For most people, music holds a special place in life. Whether someone prefers classical, pop, rock, or rap, music can help shift focus away from unwanted thoughts, ease stress, and even lessen pain. I firmly believe that music plays a crucial role in supporting mental health, especially through its powerful effects on mood and stress.

Music can trigger significant changes in our mental state. Everyday life offers many examples of its calming influence. Many people instinctively discover that listening to their favorite songs helps them relax. For instance, before a big exam, a medical appointment, or an important date, music can offer a brief escape from anxiety while heading to the event. Gentle music is also commonly used during meditation, yoga, or relaxation exercises. You’ll often hear similar soothing melodies in dental or medical offices as well. These are considered passive ways of coping with stress.

However, there are also more active ways that music can help reduce stress. Dancing or exercising to lively music allows people to relieve tension through movement. This approach can be even more effective because it works on two levels: music soothes the mind, while physical activity relaxes the body and eases muscle tension.

In conclusion, as the examples above show, music can help relieve stress through both passive and active methods, each playing a valuable role in this process. . This text label is 1 . If the label is 0 it means this is human written, otherwise AI generated. The burstiness score is 1.621596872 , the TTR score is 0.602316602 , the CV score is 0.439968124 , and the perplexity score is  22.55202675 .","The text is likely classified as AI-generated due to several linguistic features that indicate it was created by an artificial intelligence system rather than written by a human. The high burstiness score suggests rapid, unpredictable word usage typical of machine-generated content. The low type-token ratio indicates a lack of varied vocabulary, which is common in automated writing. The sentence length consistency and perplexity value suggest uniformity in language structure and complexity, further suggesting AI creation. Additionally, the coherent flow and logical argumentation align with characteristics"