OpenRaiser/Intern-Atlas huggingface dataset:
Artificial Intelligence in the Food Industry: Food Waste Estimation based on Computer Vision, a Brief Case Study in a University Dining Hall
Venue: arXiv cs.CV 2025
Year: 2025

Quantifying post-consumer food waste in institutional dining settings is essential for supporting data-driven sustainability strategies. This study presents a cost-effective computer vision framework that estimates plate-level food waste by utilizing semantic segmentation of RGB images taken before and after meal consumption across five Iranian dishes. Four fully supervised models (U-Net, U-Net++, and their lightweight variants) were trained using a capped dynamic inverse-frequency loss and AdamW optimizer, then evaluated through a comprehensive set of metrics, including Pixel Accuracy, Dice, IoU, and a custom-defined Distributional Pixel Agreement (DPA) metric tailored to the task. All models achieved satisfying performance, and for each food type, at least one model approached or surpassed 90% DPA, demonstrating strong alignment in pixel-wise proportion estimates. Lighter models with reduced parameter counts offered faster inference, achieving real-time throughput on an NVIDIA T4 GPU. Further analysis showed superior segmentation performance for dry and more rigid components (e.g., rice and fries), while more complex, fragmented, or viscous dishes, such as stews, showed reduced performance, specifically post-consumption. Despite limitations such as reliance on 2D imaging, constrained food variety, and manual data collection, the proposed framework is pioneering and represents a scalable, contactless solution for continuous monitoring of food consumption. This research lays foundational groundwork for automated, real-time waste tracking systems in large-scale food service environments and offers actionable insights and outlines feasible future directions for dining hall management and policymakers aiming to reduce institutional food waste.
Mind the Gap: A Review of Arabic Post-Training Datasets and Their Limitations
Venue: arXiv cs.CL 2025
Year: 2025

Post-training has emerged as a crucial technique for aligning pre-trained Large Language Models (LLMs) with human instructions, significantly enhancing their performance across a wide range of tasks. Central to this process is the quality and diversity of post-training datasets. This paper presents a review of publicly available Arabic post-training datasets on the Hugging Face Hub, organized along four key dimensions: (1) LLM Capabilities (e.g., Question Answering, Translation, Reasoning, Summarization, Dialogue, Code Generation, and Function Calling); (2) Steerability (e.g., Persona and System Prompts); (3) Alignment (e.g., Cultural, Safety, Ethics, and Fairness); and (4) Robustness. Each dataset is rigorously evaluated based on popularity, practical adoption, recency and maintenance, documentation and annotation quality, licensing transparency, and scientific contribution. Our review revealed critical gaps in the development of Arabic post-training datasets, including limited task diversity, inconsistent or missing documentation and annotation, and low adoption across the community. Finally, the paper discusses the implications of these gaps on the progress of Arabic-centric LLMs and applications while providing concrete recommendations for future efforts in Arabic post-training dataset development.
Forecasting Faculty Placement from Patterns in Co-authorship Networks
Venue: arXiv cs.SI 2025
Year: 2025

Faculty hiring shapes the flow of ideas, resources, and opportunities in academia, influencing not only individual career trajectories but also broader patterns of institutional prestige and scientific progress. While traditional studies have found strong correlations between faculty hiring and attributes such as doctoral department prestige and publication record, they rarely assess whether these associations generalize to individual hiring outcomes, particularly for future candidates outside the original sample. Here, we consider faculty placement as an individual-level prediction task. Our data consist of temporal co-authorship networks with conventional attributes such as doctoral department prestige and bibliometric features. We observe that using the co-authorship network significantly improves predictive accuracy by up to 10% over traditional indicators alone, with the largest gains observed for placements at the most elite (top-10) departments. Our results underscore the role that social networks, professional endorsements, and implicit advocacy play in faculty hiring beyond traditional measures of scholarly productivity and institutional prestige. By introducing a predictive framing of faculty placement and establishing the benefit of considering co-authorship networks, this work provides a new lens for understanding structural biases in academia that could inform targeted interventions aimed at increasing transparency, fairness, and equity in academic hiring practices.
Fraud is Not Just Rarity: A Causal Prototype Attention Approach to Realistic Synthetic Oversampling
Venue: arXiv cs.LG 2025
Year: 2025

Detecting fraudulent credit card transactions remains a significant challenge, due to the extreme class imbalance in real-world data and the often subtle patterns that separate fraud from legitimate activity. Existing research commonly attempts to address this by generating synthetic samples for the minority class using approaches such as GANs, VAEs, or hybrid generative models. However, these techniques, particularly when applied only to minority-class data, tend to result in overconfident classifiers and poor latent cluster separation, ultimately limiting real-world detection performance. In this study, we propose the Causal Prototype Attention Classifier (CPAC), an interpretable architecture that promotes class-aware clustering and improved latent space structure through prototype-based attention mechanisms and we will couple it with the encoder in a VAE-GAN allowing it to offer a better cluster separation moving beyond post-hoc sample augmentation. We compared CPAC-augmented models to traditional oversamplers, such as SMOTE, as well as to state-of-the-art generative models, both with and without CPAC-based latent classifiers. Our results show that classifier-guided latent shaping with CPAC delivers superior performance, achieving an F1-score of 93.14\% percent and recall of 90.18\%, along with improved latent cluster separation. Further ablation studies and visualizations provide deeper insight into the benefits and limitations of classifier-driven representation learning for fraud detection. The codebase for this work will be available at final submission.
Privacy-Preserving Drone Navigation Through Homomorphic Encryption for Collision Avoidance
Venue: arXiv cs.ET 2025
Year: 2025

As drones increasingly deliver packages in neighborhoods, concerns about collisions arise. One solution is to share flight paths within a specific zip code, but this compromises business privacy by revealing delivery routes. For example, it could disclose which stores send packages to certain addresses. To avoid exposing path information, we propose using homomorphic encryption-based comparison to compute path intersections. This allows drones to identify potential collisions without revealing path and destination details, allowing them to adjust altitude to avoid crashes. We implemented and tested our approach on resource-limited virtual machines to mimic the computational power of drones. Our results demonstrate that our method is significantly faster and requires less network communication compared to a garbled circuit-based approach. We also provide a security analysis of the approach against potential attacks.
Disparities in Peer Review Tone and the Role of Reviewer Anonymity
Venue: arXiv cs.CL 2025
Year: 2025

The peer review process is often regarded as the gatekeeper of scientific integrity, yet increasing evidence suggests that it is not immune to bias. Although structural inequities in peer review have been widely debated, much less attention has been paid to the subtle ways in which language itself may reinforce disparities. This study undertakes one of the most comprehensive linguistic analyses of peer review to date, examining more than 80,000 reviews in two major journals. Using natural language processing and large-scale statistical modeling, it uncovers how review tone, sentiment, and supportive language vary across author demographics, including gender, race, and institutional affiliation. Using a data set that includes both anonymous and signed reviews, this research also reveals how the disclosure of reviewer identity shapes the language of evaluation. The findings not only expose hidden biases in peer feedback, but also challenge conventional assumptions about anonymity's role in fairness. As academic publishing grapples with reform, these insights raise critical questions about how review policies shape career trajectories and scientific progress.
InterAct-Video: Reasoning-Rich Video QA for Urban Traffic
Venue: arXiv cs.CV 2025
Year: 2025

Traffic monitoring is crucial for urban mobility, road safety, and intelligent transportation systems (ITS). Deep learning has advanced video-based traffic monitoring through video question answering (VideoQA) models, enabling structured insight extraction from traffic videos. However, existing VideoQA models struggle with the complexity of real-world traffic scenes, where multiple concurrent events unfold across spatiotemporal dimensions. To address these challenges, this paper introduces \textbf{InterAct VideoQA}, a curated dataset designed to benchmark and enhance VideoQA models for traffic monitoring tasks. The InterAct VideoQA dataset comprises 8 hours of real-world traffic footage collected from diverse intersections, segmented into 10-second video clips, with over 25,000 question-answer (QA) pairs covering spatiotemporal dynamics, vehicle interactions, incident detection, and other critical traffic attributes. State-of-the-art VideoQA models are evaluated on InterAct VideoQA, exposing challenges in reasoning over fine-grained spatiotemporal dependencies within complex traffic scenarios. Additionally, fine-tuning these models on InterAct VideoQA yields notable performance improvements, demonstrating the necessity of domain-specific datasets for VideoQA. InterAct VideoQA is publicly available as a benchmark dataset to facilitate future research in real-world deployable VideoQA models for intelligent transportation systems. GitHub Repo: https://github.com/joe-rabbit/InterAct_VideoQA
A Risk Assessment Framework for Digital Identification Systems
Venue: arXiv cs.CY 2025
Year: 2025

We introduce a risk assessment framework for digital identification systems, as well as recommended best practices to enhance privacy, security, and other desirable properties in these systems. To generate these resources, we created a casebook of a wide range of digital identification systems, and we then applied expert analysis and critique to identify patterns. We piloted the framework on several reviews within our organization over a period of approximately one year, and found it to be robust and helpful for those reviews. This work is intended to inform product review and development, product policy, and standards efforts, and to help guide a consistent responsible approach to digital identification across the broader digital identification ecosystem.
CXR-TFT: Multi-Modal Temporal Fusion Transformer for Predicting Chest X-ray Trajectories
Venue: arXiv cs.LG 2025
Year: 2025

In intensive care units (ICUs), patients with complex clinical conditions require vigilant monitoring and prompt interventions. Chest X-rays (CXRs) are a vital diagnostic tool, providing insights into clinical trajectories, but their irregular acquisition limits their utility. Existing tools for CXR interpretation are constrained by cross-sectional analysis, failing to capture temporal dynamics. To address this, we introduce CXR-TFT, a novel multi-modal framework that integrates temporally sparse CXR imaging and radiology reports with high-frequency clinical data, such as vital signs, laboratory values, and respiratory flow sheets, to predict the trajectory of CXR findings in critically ill patients. CXR-TFT leverages latent embeddings from a vision encoder that are temporally aligned with hourly clinical data through interpolation. A transformer model is then trained to predict CXR embeddings at each hour, conditioned on previous embeddings and clinical measurements. In a retrospective study of 20,000 ICU patients, CXR-TFT demonstrated high accuracy in forecasting abnormal CXR findings up to 12 hours before they became radiographically evident. This predictive capability in clinical data holds significant potential for enhancing the management of time-sensitive conditions like acute respiratory distress syndrome, where early intervention is crucial and diagnoses are often delayed. By providing distinctive temporal resolution in prognostic CXR analysis, CXR-TFT offers actionable 'whole patient' insights that can directly improve clinical outcomes.
Toward Inclusive AI-Driven Development: Exploring Gender Differences in Code Generation Tool Interactions
Venue: arXiv cs.SE 2025
Year: 2025

Context: The increasing reliance on Code Generation Tools (CGTs), such as Windsurf and GitHub Copilot, are revamping programming workflows and raising critical questions about fairness and inclusivity. While CGTs offer potential productivity enhancements, their effectiveness across diverse user groups have not been sufficiently investigated. Objectives: We hypothesize that developers' interactions with CGTs vary based on gender, influencing task outcomes and cognitive load, as prior research suggests that gender differences can affect technology use and cognitive processing. Methods: The study will employ a mixed-subjects design with 54 participants, evenly divided by gender for a counterbalanced design. Participants will complete two programming tasks (medium to hard difficulty) with only CGT assistance and then with only internet access. Task orders and conditions will be counterbalanced to mitigate order effects. Data collection will include cognitive load surveys, screen recordings, and task performance metrics such as completion time, code correctness, and CGT interaction behaviors. Statistical analyses will be conducted to identify statistically significant differences in CGT usage. Expected Contributions: Our work can uncover gender differences in CGT interaction and performance among developers. Our findings can inform future CGT designs and help address usability and potential disparities in interaction patterns across diverse user groups. Conclusion: While results are not yet available, our proposal lays the groundwork for advancing fairness, accountability, transparency, and ethics (FATE) in CGT design. The outcomes are anticipated to contribute to inclusive AI practices and equitable tool development for all users.
Enhancing Resilience Against Jamming Attacks: A Cooperative Anti-Jamming Method Using Direction Estimation
Venue: arXiv cs.IT 2025
Year: 2025

The inherent vulnerability of wireless communication necessitates strategies to enhance its security, particularly in the face of jamming attacks. This paper uses the collaborations of multiple sensing nodes (SNs) in the wireless network to present a cooperative anti-jamming approach (CAJ) designed to neutralize the impact of jamming attacks. We propose an eigenvector (EV) method to estimate the direction of the channel vector from pilot symbols. Through our analysis, we demonstrate that with an adequate number of pilot symbols, the performance of the proposed EV method is comparable to the scenario where the perfect channel state information (CSI) is utilized. Both analytical formulas and simulations illustrate the excellent performance of the proposed EV-CAJ under strong jamming signals. Considering severe jamming, the proposed EV-CAJ method exhibits only a 0.7 dB degradation compared to the case without jamming especially when the number of SNs is significantly larger than the number of jamming nodes (JNs). Moreover, the extension of the proposed method can handle multiple jammers at the expense of degrees of freedom (DoF). We also investigate the method's ability to remain robust in fast-fading channels with different coherence times. Our proposed approach demonstrates good resilience, particularly when the ratio of the channel's coherence time to the time frame is small. This is especially important in the case of mobile jammers with large Doppler shifts.
Rethinking Memorization Measures and their Implications in Large Language Models
Venue: arXiv cs.LG 2025
Year: 2025

Concerned with privacy threats, memorization in LLMs is often seen as undesirable, specifically for learning. In this paper, we study whether memorization can be avoided when optimally learning a language, and whether the privacy threat posed by memorization is exaggerated or not. To this end, we re-examine existing privacy-focused measures of memorization, namely recollection-based and counterfactual memorization, along with a newly proposed contextual memorization. Relating memorization to local over-fitting during learning, contextual memorization aims to disentangle memorization from the contextual learning ability of LLMs. Informally, a string is contextually memorized if its recollection due to training exceeds the optimal contextual recollection, a learned threshold denoting the best contextual learning without training. Conceptually, contextual recollection avoids the fallacy of recollection-based memorization, where any form of high recollection is a sign of memorization. Theoretically, contextual memorization relates to counterfactual memorization, but imposes stronger conditions. Memorization measures differ in outcomes and information requirements. Experimenting on 18 LLMs from 6 families and multiple formal languages of different entropy, we show that (a) memorization measures disagree on memorization order of varying frequent strings, (b) optimal learning of a language cannot avoid partial memorization of training strings, and (c) improved learning decreases contextual and counterfactual memorization but increases recollection-based memorization. Finally, (d) we revisit existing reports of memorized strings by recollection that neither pose a privacy threat nor are contextually or counterfactually memorized.
Exploring the In-Context Learning Capabilities of LLMs for Money Laundering Detection in Financial Graphs
Venue: arXiv cs.LG 2025
Year: 2025

The complexity and interconnectivity of entities involved in money laundering demand investigative reasoning over graph-structured data. This paper explores the use of large language models (LLMs) as reasoning engines over localized subgraphs extracted from a financial knowledge graph. We propose a lightweight pipeline that retrieves k-hop neighborhoods around entities of interest, serializes them into structured text, and prompts an LLM via few-shot in-context learning to assess suspiciousness and generate justifications. Using synthetic anti-money laundering (AML) scenarios that reflect common laundering behaviors, we show that LLMs can emulate analyst-style logic, highlight red flags, and provide coherent explanations. While this study is exploratory, it illustrates the potential of LLM-based graph reasoning in AML and lays groundwork for explainable, language-driven financial crime analytics.
FinChart-Bench: Benchmarking Financial Chart Comprehension in Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Large vision-language models (LVLMs) have made significant progress in chart understanding. However, financial charts, characterized by complex temporal structures and domain-specific terminology, remain notably underexplored. We introduce FinChart-Bench, the first benchmark specifically focused on real-world financial charts. FinChart-Bench comprises 1,200 financial chart images collected from 2015 to 2024, each annotated with True/False (TF), Multiple Choice (MC), and Question Answering (QA) questions, totaling 7,016 questions. We conduct a comprehensive evaluation of 25 state-of-the-art LVLMs on FinChart-Bench. Our evaluation reveals critical insights: (1) the performance gap between open-source and closed-source models is narrowing, (2) performance degradation occurs in upgraded models within families, (3) many models struggle with instruction following, (4) both advanced models show significant limitations in spatial reasoning abilities, and (5) current LVLMs are not reliable enough to serve as automated evaluators. These findings highlight important limitations in current LVLM capabilities for financial chart understanding. The FinChart-Bench dataset is available at https://huggingface.co/datasets/Tizzzzy/FinChart-Bench.
Variable Min-Cut Max-Flow Bounds and Algorithms in Finite Regime
Venue: arXiv cs.IT 2025
Year: 2025

The maximum achievable capacity from source to destination in a network is limited by the min-cut max-flow bound; this serves as a converse limit. In practice, link capacities often fluctuate due to dynamic network conditions. In this work, we introduce a novel analytical framework that leverages tools from computational geometry to analyze throughput in heterogeneous networks with variable link capacities in a finite regime. Within this model, we derive new performance bounds and demonstrate that increasing the number of links can reduce throughput variability by nearly $90\%$. We formally define a notion of network stability and show that an unstable graph can have an exponential number of different min-cut sets, up to $O(2^{|E|})$. To address this complexity, we propose an algorithm that enforces stability with time complexity $O(|E|^2 + |V|)$, and further suggest mitigating the delay-throughput tradeoff using adaptive rateless random linear network coding (AR-RLNC).
A Privacy-Centric Approach: Scalable and Secure Federated Learning Enabled by Hybrid Homomorphic Encryption
Venue: arXiv cs.CR 2025
Year: 2025

Federated Learning (FL) enables collaborative model training without sharing raw data, making it a promising approach for privacy-sensitive domains. Despite its potential, FL faces significant challenges, particularly in terms of communication overhead and data privacy. Privacy-preserving Techniques (PPTs) such as Homomorphic Encryption (HE) have been used to mitigate these concerns. However, these techniques introduce substantial computational and communication costs, limiting their practical deployment. In this work, we explore how Hybrid Homomorphic Encryption (HHE), a cryptographic protocol that combines symmetric encryption with HE, can be effectively integrated with FL to address both communication and privacy challenges, paving the way for scalable and secure decentralized learning system.