Reviewerly/exHarmony huggingface dataset:
Talkin' 'Bout AI Generation: Copyright and the Generative-AI Supply Chain

"Does generative AI infringe copyright?" is an urgent question. It is also a difficult question, for two reasons. First, "generative AI" is not just one product from one company. It is a catch-all name for a massive ecosystem of loosely related technologies, including conversational text chatbots like ChatGPT, image generators like Midjourney and DALL-E, coding assistants like GitHub Copilot, and systems that compose music and create videos. These systems behave differently and raise different legal issues. The second problem is that copyright law is notoriously complicated, and generative-AI systems manage to touch on a great many corners of it: authorship, similarity, direct and indirect liability, fair use, and licensing, among much else. These issues cannot be analyzed in isolation, because there are connections everywhere. In this Article, we aim to bring order to the chaos. To do so, we introduce the generative-AI supply chain: an interconnected set of stages that transform training data (millions of pictures of cats) into generations (a new, potentially never-seen-before picture of a cat that has never existed). Breaking down generative AI into these constituent stages reveals all of the places at which companies and users make choices that have copyright consequences. It enables us to trace the effects of upstream technical designs on downstream uses, and to assess who in these complicated sociotechnical systems bears responsibility for infringement when it happens. Because we engage so closely with the technology of generative AI, we are able to shed more light on the copyright questions. We do not give definitive answers as to who should and should not be held liable. Instead, we identify the key decisions that courts will need to make as they grapple with these issues, and point out the consequences that would likely flow from different liability regimes.
Using Large Language Model to Solve and Explain Physics Word Problems Approaching Human Level

Our work demonstrates that large language model (LLM) pre-trained on texts can not only solve pure math word problems, but also physics word problems, whose solution requires calculation and inference based on prior physical knowledge. We collect and annotate the first physics word problem dataset-PhysQA, which contains over 1000 junior high school physics word problems (covering Kinematics, Mass&Density, Mechanics, Heat, Electricity). Then we use OpenAI' s GPT3.5 to generate the answer of these problems and found that GPT3.5 could automatically solve 49.3% of the problems through zero-shot learning and 73.2% through few-shot learning. This result demonstrates that by using similar problems and their answers as prompt, LLM could solve elementary physics word problems approaching human level performance. In addition to solving problems, GPT3.5 can also summarize the knowledge or topics covered by the problems, provide relevant explanations, and generate new physics word problems based on the input. Our work is the first research to focus on the automatic solving, explanation, and generation of physics word problems across various types and scenarios, and we achieve an acceptable and state-of-the-art accuracy. This underscores the potential of LLMs for further applications in secondary education.
Silent Vulnerability-fixing Commit Identification Based on Graph Neural Networks

The growing dependence of software projects on external libraries has generated apprehensions regarding the security of these libraries because of concealed vulnerabilities. Handling these vulnerabilities presents difficulties due to the temporal delay between remediation and public exposure. Furthermore, a substantial fraction of open-source projects covertly address vulnerabilities without any formal notification, influencing vulnerability management. Established solutions like OWASP predominantly hinge on public announcements, limiting their efficacy in uncovering undisclosed vulnerabilities. To address this challenge, the automated identification of vulnerability-fixing commits has come to the forefront. In this paper, we present VFFINDER, a novel graph-based approach for automated silent vulnerability fix identification. VFFINDER captures structural changes using Abstract Syntax Trees (ASTs) and represents them in annotated ASTs. To precisely capture the meaning of code changes, the changed code is represented in connection with the related unchanged code. In VFFINDER, the structure of the changed code and related unchanged code are captured and the structural changes are represented in annotated Abstract Syntax Trees (aAST). VFFINDER distinguishes vulnerability-fixing commits from non-fixing ones using attention-based graph neural network models to extract structural features expressed in aASTs. We conducted experiments to evaluate VFFINDER on a dataset of 11K+ vulnerability fixing commits in 507 real-world C/C++ projects. Our results show that VFFINDER significantly improves the state-of-the-art methods by 272-420% in Precision, 22-70% in Recall, and 3.2X-8.2X in F1. Especially, VFFINDER speeds up the silent fix identification process by up to 121% with the same effort reviewing 50K LOC compared to the existing approaches.
Optimization of Rank Losses for Image Retrieval

In image retrieval, standard evaluation metrics rely on score ranking, \eg average precision (AP), recall at k (R@k), normalized discounted cumulative gain (NDCG). In this work we introduce a general framework for robust and decomposable rank losses optimization. It addresses two major challenges for end-to-end training of deep neural networks with rank losses: non-differentiability and non-decomposability. Firstly we propose a general surrogate for ranking operator, SupRank, that is amenable to stochastic gradient descent. It provides an upperbound for rank losses and ensures robust training. Secondly, we use a simple yet effective loss function to reduce the decomposability gap between the averaged batch approximation of ranking losses and their values on the whole training set. We apply our framework to two standard metrics for image retrieval: AP and R@k. Additionally we apply our framework to hierarchical image retrieval. We introduce an extension of AP, the hierarchical average precision $\mathcal{H}$-AP, and optimize it as well as the NDCG. Finally we create the first hierarchical landmarks retrieval dataset. We use a semi-automatic pipeline to create hierarchical labels, extending the large scale Google Landmarks v2 dataset. The hierarchical dataset is publicly available at https://github.com/cvdfoundation/google-landmark. Code will be released at https://github.com/elias-ramzi/SupRank.
Cartoondiff: Training-free Cartoon Image Generation with Diffusion Transformer Models

Image cartoonization has attracted significant interest in the field of image generation. However, most of the existing image cartoonization techniques require re-training models using images of cartoon style. In this paper, we present CartoonDiff, a novel training-free sampling approach which generates image cartoonization using diffusion transformer models. Specifically, we decompose the reverse process of diffusion models into the semantic generation phase and the detail generation phase. Furthermore, we implement the image cartoonization process by normalizing high-frequency signal of the noisy image in specific denoising steps. CartoonDiff doesn't require any additional reference images, complex model designs, or the tedious adjustment of multiple parameters. Extensive experimental results show the powerful ability of our CartoonDiff. The project page is available at: https://cartoondiff.github.io/
Unsupervised Disentangling of Facial Representations with 3D-aware Latent Diffusion Models

Unsupervised learning of facial representations has gained increasing attention for face understanding ability without heavily relying on large-scale annotated datasets. However, it remains unsolved due to the coupling of facial identities, expressions, and external factors like pose and light. Prior methods primarily focus on 2D factors and pixel-level consistency, leading to incomplete disentangling and suboptimal performance in downstream tasks. In this paper, we propose LatentFace, a novel unsupervised disentangling framework for facial expression and identity representation. We suggest the disentangling problem should be performed in latent space and propose the solution using a 3D-aware latent diffusion model. First, we introduce a 3D-aware autoencoder to encode face images into 3D latent embeddings. Second, we propose a novel representation diffusion model (RDM) to disentangle 3D latent into facial identity and expression. Consequently, our method achieves state-of-the-art performance in facial expression recognition and face verification among unsupervised facial representation learning models. Codes are available at \url{https://github.com/ryanhe312/LatentFace}.
T-UDA: Temporal Unsupervised Domain Adaptation in Sequential Point Clouds

Deep perception models have to reliably cope with an open-world setting of domain shifts induced by different geographic regions, sensor properties, mounting positions, and several other reasons. Since covering all domains with annotated data is technically intractable due to the endless possible variations, researchers focus on unsupervised domain adaptation (UDA) methods that adapt models trained on one (source) domain with annotations available to another (target) domain for which only unannotated data are available. Current predominant methods either leverage semi-supervised approaches, e.g., teacher-student setup, or exploit privileged data, such as other sensor modalities or temporal data consistency. We introduce a novel domain adaptation method that leverages the best of both trends. Our approach combines input data's temporal and cross-sensor geometric consistency with the mean teacher method. Dubbed T-UDA for "temporal UDA", such a combination yields massive performance gains for the task of 3D semantic segmentation of driving scenes. Experiments are conducted on Waymo Open Dataset, nuScenes and SemanticKITTI, for two popular 3D point cloud architectures, Cylinder3D and MinkowskiNet. Our codes are publicly available at https://github.com/ctu-vras/T-UDA.
Headless Language Models: Learning without Predicting with Contrastive Weight Tying

Self-supervised pre-training of language models usually consists in predicting probability distributions over extensive token vocabularies. In this study, we propose an innovative method that shifts away from probability prediction and instead focuses on reconstructing input embeddings in a contrastive fashion via Constrastive Weight Tying (CWT). We apply this approach to pretrain Headless Language Models in both monolingual and multilingual contexts. Our method offers practical advantages, substantially reducing training computational requirements by up to 20 times, while simultaneously enhancing downstream performance and data efficiency. We observe a significant +1.6 GLUE score increase and a notable +2.7 LAMBADA accuracy improvement compared to classical LMs within similar compute budgets.
i-Octree: A Fast, Lightweight, and Dynamic Octree for Proximity Search

Establishing the correspondences between newly acquired points and historically accumulated data (i.e., map) through nearest neighbors search is crucial in numerous robotic applications.However, static tree data structures are inadequate to handle large and dynamically growing maps in real-time.To address this issue, we present the i-Octree, a dynamic octree data structure that supports both fast nearest neighbor search and real-time dynamic updates, such as point insertion, deletion, and on-tree down-sampling. The i-Octree is built upon a leaf-based octree and has two key features: a local spatially continuous storing strategy that allows for fast access to points while minimizing memory usage, and local on-tree updates that significantly reduce computation time compared to existing static or dynamic tree structures.The experiments show that i-Octree surpasses state-of-the-art methods by reducing run-time by over 50% on real-world open datasets.
Bystanders of Online Moderation: Examining the Effects of Witnessing Post-Removal Explanations

Prior research on transparency in content moderation has demonstrated the benefits of offering post-removal explanations to sanctioned users. In this paper, we examine whether the influence of such explanations transcends those who are moderated to the bystanders who witness such explanations. We conduct a quasi-experimental study on two popular Reddit communities (r/askreddit and r/science) by collecting their data spanning 13 months-a total of 85.5M posts made by 5.9M users. Our causal-inference analyses show that bystanders significantly increase their posting activity and interactivity levels as compared to their matched control set of users. Our findings suggest that explanations clarify and reinforce the social norms of online spaces, enhance community engagement, and benefit many more members than previously understood. We discuss the theoretical implications and design recommendations of this research, focusing on how investing more efforts in post-removal explanations can help build thriving online communities.
Bayes-Optimal Estimation in Generalized Linear Models via Spatial Coupling

We consider the problem of signal estimation in a generalized linear model (GLM). GLMs include many canonical problems in statistical estimation, such as linear regression, phase retrieval, and 1-bit compressed sensing. Recent work has precisely characterized the asymptotic minimum mean-squared error (MMSE) for GLMs with i.i.d. Gaussian sensing matrices. However, in many models there is a significant gap between the MMSE and the performance of the best known feasible estimators. In this work, we address this issue by considering GLMs defined via spatially coupled sensing matrices. We propose an efficient approximate message passing (AMP) algorithm for estimation and prove that with a simple choice of spatially coupled design, the MSE of a carefully tuned AMP estimator approaches the asymptotic MMSE in the high-dimensional limit. To prove the result, we first rigorously characterize the asymptotic performance of AMP for a GLM with a generic spatially coupled design. This characterization is in terms of a deterministic recursion (`state evolution') that depends on the parameters defining the spatial coupling. Then, using a simple spatially coupled design and judicious choice of functions defining the AMP, we analyze the fixed points of the resulting state evolution and show that it achieves the asymptotic MMSE. Numerical results for phase retrieval and rectified linear regression show that spatially coupled designs can yield substantially lower MSE than i.i.d. Gaussian designs at finite dimensions when used with AMP algorithms.
Deployment of the deep learning fusion method to emotional semantic evaluation of natural language

The emotional semantic evaluation of natural language plays a crucial role in sentiment analysis. Deep learning methods have shown great potential in capturing the complex relationships between words and emotions. This paper proposes a deep learning fusion method for deploying emotional semantic evaluation. The technique combines multiple deep learning architectures to capture local and global contextual information, including Bidirectional Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM) networks, and Self-Attention mechanisms. Pretrained GloVe word embedding’s utilized to enhance word representation. A novel fusion layer combines the outputs of individual models; employing self-attention means to assign weights dynamically. This allows the model to weigh the importance of different representations in the final prediction. Benchmark movie review (MR) for sentiment analysis and emotion classification tasks are used to evaluate the proposed method. Experimental results demonstrate superior performance compared to individual deep learning models and traditional feature-based approaches. The proposed fusion method effectively captures the nuances of emotional semantics in natural language, leading to more accurate and nuanced evaluations.
A Research Synthesis of Using Young Adult Literature in College English Literature Class

Incorporating literature in English language classrooms is believed by the scholars to have multiple merits. For example, its inherent authenticity offers reliable input for language learning. As an important genre, young adult literature can be a potential teaching tool to improve motivation and engagement among students by arousing their interests with the content and text and provides the scaffold to bridge the instruction content with students' personal feelings and experience .This study aims to investigate the current use of YAL, the selection criteria of instructors and the potential challenges in college English literature classrooms, hoping to find some effective suggestions and shed light on using YAL in college English literature courses.
Chilling Effect of the Enforcement of Computer Misuse Act: Evidence from Publicly Accessible Hack Forums

To reduce the availability of hacking tools for use in cybersecurity offenses, many countries have enacted computer misuse acts (CMA) that criminalize the production, distribution, and possession of such tools with criminal intent. Nevertheless, our research illuminates an unintended consequence: the chilling effect of CMA enforcement on legitimate cybersecurity discussions, some of which may be desirable for cybersecurity research, within online hack forums. More importantly, this study uniquely examines the chilling effect stemming from users’ fear of legal harm. Drawing on decision-making theories related to choice under uncertainty, we derive new insights into how legal enforcement can suppress lawful acts and reveal the dynamics of social categorization online. Our research offers valuable insights for policymakers and forum administrators. Policymakers can use our findings to mitigate unnecessary uncertainty in legal enforcement such as CMA. This includes developing legal cases to prevent false prosecutions, implementing tailored communication strategies for inexperienced individuals, and considering supplementary measures like licensing and community recognition. A transparent mechanism involving a neutral panel can also be established to ensure legal interpretations align with community norms. Forum administrators, on the other hand, can provide additional information and guidelines, foster responsible online environments, and align resources with professional standards to navigate the uncertain legal landscape and mitigate the chilling effect on knowledge-sharing.
Development and Performance Analysis of a Mini Twin-Shaft Shredder for Efficient Polyethylene Terephthalate (PET) Bottle Recycling

Polyethylene terephthalate (PET) stands out as the most widely recycled plastic globally. However, in the Philippines, the processes associated with PET recycling, particularly the transportation of PET bottles from collectors and consolidators to processing plants, demand significant time, monetary resources, and effort due to the lack of appropriately sized shredder machines in local junkshops to reduce bulkiness. As a response, this study aimed to design a mini twin-shaft PET bottle shredder. The design took into account the constraints and prerequisites of local junkshop proprietors, municipal and barangay local government units, and non-government organizations.&#x0D; The machine was comprised of five essential components: the hopper, shredding chamber, power transmission, prime mover, and frame. Post-consumer PET bottles were employed as test materials for experimentation at different treatment speeds: 30, 40, and 50rpm. The experimental layout followed a completely randomized design (CRD). Performance metrics, including shredding capacity, shredding efficiency, and energy demand, were scrutinized based on shaft speed alterations through analysis of variance testing. Additionally, comparison among means was executed using the least significant difference (LSD) method, with a significance level of 5%.&#x0D; Results indicated that the highest shredding input capacity of 34.07 kg/hr was attained at a shaft speed of 50rpm, along with a shredding efficiency of 91.3%. The presence of unshredded materials was identified as the primary cause of device inefficiency, constituting 6.44% of the total test materials input. The energy demand was calculated at 0.132 kW-hr/kg. The resulting product dimensions primarily ranged between 25-50mm. A cost analysis demonstrated that the machine would need to shred a cumulative 6,889kg of PET bottles to reach the breakeven point, given a custom rate of Php7/kg ($0.12/kg). The initial investment cost of Php124,383 ($2,194.71) could be recuperated in a mere 1.91 years, yielding an added net income of Php210,000/yr ($3,705.40/yr) for PET collectors and/or consolidators.
The Influence of Using Social Media on Student Learning Achievement in Introductory Economics Subject

The use of social media is almost used by all levels of society. This study aims to reveal the effect of social media (X) on student achievement (Y). This study uses a type of quantitative research with a survey approach. The sample of this research used random sampling technique. The number of research samples was 52 students. Data collection uses valid and reliable instruments. Data analysis using simple regression. the results of the study revealed: (1) Linear Regression Linear Equation Y = 32.940 + 0.466 X (2) the use of social media has a positive effect on student achievement. (3) The significance test obtained by the equation of the F hit regression line is smaller than 0.05, thus, Y or X regression is significant or the intensity of device use affects students' emotional intelligence. (4) The correlation coefficient (rxy) = 0.137 and Fhit (Fchange) = 7.971, with a pvalue less than 0.05. Thus, the correlation coefficient X and Y is meaningful or significant. The coefficient of determination is 0.137, which implies that 13.7% is the variation in the learning achievement variable.
Performing Socio Drama to Increase Students' Motivation in Speaking English

Teaching speaking In Indonesian, many teachers have made much effort to find good methods, strategies, techniques, media and teaching materials to make the most of it. They have tried hard to maximize every way in the teaching and learning process. The socio drama method also has an influence on student learning outcomes. The purpose of the research method used is experimental. The population in this study were 250 people students of class VII SMPS PLUS Kasih Ibu Patumbak, while the sample is class VIII-A (20 students) as an experimental group that teaches storytelling techniques in narrative text and class VIII-B. (20 students) as a control group were taught without telling. This research was conducted through the following The test is a drama conversation. Researchers of several drama conversations related to English answered 2 people with drama. The results show that there is an increase in students speaking skills using storytelling techniques. After calculating the data, then the researcher found that the t-test t-test obtained that the value of observed ≥ ttable, namely 6.436 &gt; 2.086. on significance level α = 0.5. Thus, it can be concluded that H0 is rejected and Ha is accepted. An can be interpreted that Performing socio drama significant effect on increase students' motivation in speaking english.