OpenRaiser/Intern-Atlas huggingface dataset:
RedditESS: A Mental Health Social Support Interaction Dataset -- Understanding Effective Social Support to Refine AI-Driven Support Tools
Venue: arXiv cs.CL 2025
Year: 2025

Effective mental health support is crucial for alleviating psychological distress. While large language model (LLM)-based assistants have shown promise in mental health interventions, existing research often defines "effective" support primarily in terms of empathetic acknowledgments, overlooking other essential dimensions such as informational guidance, community validation, and tangible coping strategies. To address this limitation and better understand what constitutes effective support, we introduce RedditESS, a novel real-world dataset derived from Reddit posts, including supportive comments and original posters' follow-up responses. Grounded in established social science theories, we develop an ensemble labeling mechanism to annotate supportive comments as effective or not and perform qualitative assessments to ensure the reliability of the annotations. Additionally, we demonstrate the practical utility of RedditESS by using it to guide LLM alignment toward generating more context-sensitive and genuinely helpful supportive responses. By broadening the understanding of effective support, our study paves the way for advanced AI-driven mental health interventions.
Enhancing Mobile Crowdsensing Efficiency: A Coverage-aware Resource Allocation Approach
Venue: arXiv cs.NI 2025
Year: 2025

In this study, we investigate the resource management challenges in next-generation mobile crowdsensing networks with the goal of minimizing task completion latency while ensuring coverage performance, i.e., an essential metric to ensure comprehensive data collection across the monitored area, yet it has been commonly overlooked in existing studies. To this end, we formulate a weighted latency and coverage gap minimization problem via jointly optimizing user selection, subchannel allocation, and sensing task allocation. The formulated minimization problem is a non-convex mixed-integer programming issue. To facilitate the analysis, we decompose the original optimization problem into two subproblems. One focuses on optimizing sensing task and subband allocation under fixed sensing user selection, which is optimally solved by the Hungarian algorithm via problem reformulation. Building upon these findings, we introduce a time-efficient two-sided swapping method to refine the scheduled user set and enhance system performance. Extensive numerical results demonstrate the effectiveness of our proposed approach compared to various benchmark strategies.
Integrating DAST in Kanban and CI/CD: A Real World Security Case Study
Venue: arXiv cs.SE 2025
Year: 2025

Modern development methodologies, such as Kanban and continuous integration and continuous deployment (CI/CD), are critical for web application development -- as software products must adapt to changing requirements and deploy products to users quickly. As web application attacks and exploited vulnerabilities are rising, it is increasingly crucial to integrate security into modern development practices. Yet, the iterative and incremental nature of these processes can clash with the sequential nature of security engineering. Thus, it is challenging to adopt security practices and activities in modern development practices. Dynamic Application Security Testing (DAST) is a security practice within software development frameworks that bolsters system security. This study delves into the intersection of Agile development and DAST, exploring how a software organization attempted to integrate DAST into their Kanban workflows and CI/CD pipelines to identify and mitigate security vulnerabilities within the development process. Through an action research case study incorporating interviews among team members, this research elucidates the challenges, mitigation techniques, and best practices associated with incorporating DAST into Agile methodologies from developers' perspectives. We provide insights into integrating security practices with modern development, ensuring both speed and security in software delivery.
Risk-Prone and Risk-Averse Behavior in Natural Emergencies: An Appraisal Theory Approach
Venue: arXiv cs.SI 2025
Year: 2025

Individuals who shared actionable information during Hurricane Sandy were significantly more likely to exhibit risk-prone behavior, as measured by a novel Risk Behavior Quotient (RBQ). Using a dataset of 36595 geo-located tweets from 774 users in the New York area, we found that a higher proportion of actional tweets predicted increased exposure to physical even if overall users ultimately moved toward lower-risk zones. This counterintuitive finding suggests that proactivity, manifested in sharing crisis relevant content, correlates with greater exposure to risk, possibly due to increased mobility or engagement in hazardous areas. In contrast, a greater number of social media peers was associated with reduced risk exposure. This study builds on appraisal theory, which frames risk-related decisions as outcomes of cognitively mediated emotional and rational evaluations. We extend this theory to digital crisis behavior, distinguishing between emotional and actional appraisals expressed via social media. Tweets were categorized using sentiment analysis and semantic classification, enabling the isolation of affective and behavioral signals. Our methodology combines natural language processing with spatial vector analysis to estimate individual movement paths and risk exposure based on evacuation and flooding maps. The resulting RBQ captures both direction and intensity of risk behavior, allowing us to model how online communication reflects and predicts real-world risk engagement during natural disasters.
SC-NeRF: NeRF-based Point Cloud Reconstruction using a Stationary Camera for Agricultural Applications
Venue: arXiv cs.CV 2025
Year: 2025

This paper presents a NeRF-based framework for point cloud (PCD) reconstruction, specifically designed for indoor high-throughput plant phenotyping facilities. Traditional NeRF-based reconstruction methods require cameras to move around stationary objects, but this approach is impractical for high-throughput environments where objects are rapidly imaged while moving on conveyors or rotating pedestals. To address this limitation, we develop a variant of NeRF-based PCD reconstruction that uses a single stationary camera to capture images as the object rotates on a pedestal. Our workflow comprises COLMAP-based pose estimation, a straightforward pose transformation to simulate camera movement, and subsequent standard NeRF training. A defined Region of Interest (ROI) excludes irrelevant scene data, enabling the generation of high-resolution point clouds (10M points). Experimental results demonstrate excellent reconstruction fidelity, with precision-recall analyses yielding an F-score close to 100.00 across all evaluated plant objects. Although pose estimation remains computationally intensive with a stationary camera setup, overall training and reconstruction times are competitive, validating the method's feasibility for practical high-throughput indoor phenotyping applications. Our findings indicate that high-quality NeRF-based 3D reconstructions are achievable using a stationary camera, eliminating the need for complex camera motion or costly imaging equipment. This approach is especially beneficial when employing expensive and delicate instruments, such as hyperspectral cameras, for 3D plant phenotyping. Future work will focus on optimizing pose estimation techniques and further streamlining the methodology to facilitate seamless integration into automated, high-throughput 3D phenotyping pipelines.
NeuroLIP: Interpretable and Fair Cross-Modal Alignment of fMRI and Phenotypic Text
Venue: arXiv cs.LG 2025
Year: 2025

Integrating functional magnetic resonance imaging (fMRI) connectivity data with phenotypic textual descriptors (e.g., disease label, demographic data) holds significant potential to advance our understanding of neurological conditions. However, existing cross-modal alignment methods often lack interpretability and risk introducing biases by encoding sensitive attributes together with diagnostic-related features. In this work, we propose NeuroLIP, a novel cross-modal contrastive learning framework. We introduce text token-conditioned attention (TTCA) and cross-modal alignment via localized tokens (CALT) to the brain region-level embeddings with each disease-related phenotypic token. It improves interpretability via token-level attention maps, revealing brain region-disease associations. To mitigate bias, we propose a loss for sensitive attribute disentanglement that maximizes the attention distance between disease tokens and sensitive attribute tokens, reducing unintended correlations in downstream predictions. Additionally, we incorporate a negative gradient technique that reverses the sign of CALT loss on sensitive attributes, further discouraging the alignment of these features. Experiments on neuroimaging datasets (ABIDE and ADHD-200) demonstrate NeuroLIP's superiority in terms of fairness metrics while maintaining the overall best standard metric performance. Qualitative visualization of attention maps highlights neuroanatomical patterns aligned with diagnostic characteristics, validated by the neuroscientific literature. Our work advances the development of transparent and equitable neuroimaging AI.
ELASTIC: Efficient Once For All Iterative Search for Object Detection on Microcontrollers
Venue: arXiv cs.CV 2025
Year: 2025

Deploying high-performance object detectors on TinyML platforms poses significant challenges due to tight hardware constraints and the modular complexity of modern detection pipelines. Neural Architecture Search (NAS) offers a path toward automation, but existing methods either restrict optimization to individual modules, sacrificing cross-module synergy, or require global searches that are computationally intractable. We propose ELASTIC (Efficient Once for AlL IterAtive Search for ObjecT DetectIon on MiCrocontrollers), a unified, hardware-aware NAS framework that alternates optimization across modules (e.g., backbone, neck, and head) in a cyclic fashion. ELASTIC introduces a novel Population Passthrough mechanism in evolutionary search that retains high-quality candidates between search stages, yielding faster convergence, up to an 8% final mAP gain, and eliminates search instability observed without population passthrough. In a controlled comparison, empirical results show ELASTIC achieves +4.75% higher mAP and 2x faster convergence than progressive NAS strategies on SVHN, and delivers a +9.09% mAP improvement on PascalVOC given the same search budget. ELASTIC achieves 72.3% mAP on PascalVOC, outperforming MCUNET by 20.9% and TinyissimoYOLO by 16.3%. When deployed on MAX78000/MAX78002 microcontrollers, ELASTICderived models outperform Analog Devices' TinySSD baselines, reducing energy by up to 71.6%, lowering latency by up to 2.4x, and improving mAP by up to 6.99 percentage points across multiple datasets.
Towards Privacy-Preserving Revocation of Verifiable Credentials with Time-Flexibility
Venue: arXiv cs.CR 2025
Year: 2025

Self-Sovereign Identity (SSI) is an emerging paradigm for authentication and credential presentation that aims to give users control over their data and prevent any kind of tracking by (even trusted) third parties. In the European Union, the EUDI Digital Identity wallet is about to become a concrete implementation of this paradigm. However, a debate is still ongoing, partially reflecting some aspects that are not yet consolidated in the scientific state of the art. Among these, an effective, efficient, and privacy-preserving implementation of verifiable credential revocation remains a subject of discussion. In this work-in-progress paper, we propose the basis of a novel method that customizes the use of anonymous hierarchical identity-based encryption to restrict the Verifier access to the temporal authorizations granted by the Holder. This way, the Issuer cannot track the Holder's credential presentations, and the Verifier cannot check revocation information beyond what is permitted by the Holder.
Bayesian Inferential Motion Planning Using Heavy-Tailed Distributions
Venue: arXiv cs.RO 2025
Year: 2025

Robots rely on motion planning to navigate safely and efficiently while performing various tasks. In this paper, we investigate motion planning through Bayesian inference, where motion plans are inferred based on planning objectives and constraints. However, existing Bayesian motion planning methods often struggle to explore low-probability regions of the planning space, where high-quality plans may reside. To address this limitation, we propose the use of heavy-tailed distributions -- specifically, Student's-$t$ distributions -- to enhance probabilistic inferential search for motion plans. We develop a novel sequential single-pass smoothing approach that integrates Student's-$t$ distribution with Monte Carlo sampling. A special case of this approach is ensemble Kalman smoothing, which depends on short-tailed Gaussian distributions. We validate the proposed approach through simulations in autonomous vehicle motion planning, demonstrating its superior performance in planning, sampling efficiency, and constraint satisfaction compared to ensemble Kalman smoothing. While focused on motion planning, this work points to the broader potential of heavy-tailed distributions in enhancing probabilistic decision-making in robotics.
The Limits of AI in Financial Services
Venue: arXiv cs.CY 2025
Year: 2025

AI is transforming industries, raising concerns about job displacement and decision making reliability. AI, as a universal approximation function, excels in data driven tasks but struggles with small datasets, subjective probabilities, and contexts requiring human judgment, relationships, and ethics.The EPOCH framework highlights five irreplaceable human capabilities: Empathy, Presence, Opinion, Creativity, and Hope. These attributes are vital in financial services for trust, inclusion, innovation, and consumer experience. Although AI improves efficiency in risk management and compliance, it will not eliminate jobs but redefine them, similar to how ATMs reshaped bank tellers' roles. The challenge is ensuring professionals adapt, leveraging AI's strengths while preserving essential human capabilities.
HyperMAN: Hypergraph-enhanced Meta-learning Adaptive Network for Next POI Recommendation
Venue: arXiv cs.IR 2025
Year: 2025

Next Point-of-Interest (POI) recommendation aims to predict users' next locations by leveraging historical check-in sequences. Although existing methods have shown promising results, they often struggle to capture complex high-order relationships and effectively adapt to diverse user behaviors, particularly when addressing the cold-start issue. To address these challenges, we propose Hypergraph-enhanced Meta-learning Adaptive Network (HyperMAN), a novel framework that integrates heterogeneous hypergraph modeling with a difficulty-aware meta-learning mechanism for next POI recommendation. Specifically, three types of heterogeneous hyperedges are designed to capture high-order relationships: user visit behaviors at specific times (Temporal behavioral hyperedge), spatial correlations among POIs (spatial functional hyperedge), and user long-term preferences (user preference hyperedge). Furthermore, a diversity-aware meta-learning mechanism is introduced to dynamically adjust learning strategies, considering users behavioral diversity. Extensive experiments on real-world datasets demonstrate that HyperMAN achieves superior performance, effectively addressing cold start challenges and significantly enhancing recommendation accuracy.
A Deep Learning Framework for Boundary-Aware Semantic Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

As a fundamental task in computer vision, semantic segmentation is widely applied in fields such as autonomous driving, remote sensing image analysis, and medical image processing. In recent years, Transformer-based segmentation methods have demonstrated strong performance in global feature modeling. However, they still struggle with blurred target boundaries and insufficient recognition of small targets. To address these issues, this study proposes a Mask2Former-based semantic segmentation algorithm incorporating a boundary enhancement feature bridging module (BEFBM). The goal is to improve target boundary accuracy and segmentation consistency. Built upon the Mask2Former framework, this method constructs a boundary-aware feature map and introduces a feature bridging mechanism. This enables effective cross-scale feature fusion, enhancing the model's ability to focus on target boundaries. Experiments on the Cityscapes dataset demonstrate that, compared to mainstream segmentation methods, the proposed approach achieves significant improvements in metrics such as mIOU, mDICE, and mRecall. It also exhibits superior boundary retention in complex scenes. Visual analysis further confirms the model's advantages in fine-grained regions. Future research will focus on optimizing computational efficiency and exploring its potential in other high-precision segmentation tasks.
A production planning benchmark for real-world refinery-petrochemical complexes
Venue: arXiv cs.CE 2025
Year: 2025

To achieve digital intelligence transformation and carbon neutrality, effective production planning is crucial for integrated refinery-petrochemical complexes. Modern refinery planning relies on advanced optimization techniques, whose development requires reproducible benchmark problems. However, existing benchmarks lack practical context or impose oversimplified assumptions, limiting their applicability to enterprise-wide optimization. To bridge the substantial gap between theoretical research and industrial applications, this paper introduces the first open-source, demand-driven benchmark for industrial-scale refinery-petrochemical complexes with transparent model formulations and comprehensive input parameters. The benchmark incorporates a novel port-stream hybrid superstructure for modular modeling and broad generalizability. Key secondary processing units are represented using the delta-base approach grounded in historical data. Three real-world cases have been constructed to encompass distinct scenario characteristics, respectively addressing (1) a stand-alone refinery without integer variables, (2) chemical site integration with inventory-related integer variables, and (3) multi-period planning. All model parameters are fully accessible. Additionally, this paper provides an analysis of computational performance, ablation experiments on delta-base modeling, and application scenarios for the proposed benchmark.
Reflection on Code Contributor Demographics and Collaboration Patterns in the Rust Community
Venue: arXiv cs.SE 2025
Year: 2025

Open-source software communities thrive on global collaboration and contributions from diverse participants. This study explores the Rust programming language ecosystem to understand its contributors' demographic composition and interaction patterns. Our objective is to investigate the phenomenon of participation inequality in key Rust projects and the presence of diversity among them. We studied GitHub pull request data from the year leading up to the release of the latest completed Rust community annual survey in 2023. Specifically, we extracted information from three leading repositories: Rust, Rust Analyzer, and Cargo, and used social network graphs to visualize the interactions and identify central contributors and sub-communities. Social network analysis has shown concerning disparities in gender and geographic representation among contributors who play pivotal roles in collaboration networks and the presence of varying diversity levels in the sub-communities formed. These results suggest that while the Rust community is globally active, the contributor base does not fully reflect the diversity of the wider user community. We conclude that there is a need for more inclusive practices to encourage broader participation and ensure that the contributor base aligns more closely with the diverse global community that utilizes Rust.
Saving Storage Space Using Files on the Web
Venue: arXiv cs.OS 2025
Year: 2025

As conventional storage density reaches its physical limits, the cost of a gigabyte of storage is no longer plummeting, but rather has remained mostly flat for the past decade. Meanwhile, file sizes continue to grow, leading to ever fuller drives. When a user's storage is full, they must disrupt their workflow to laboriously find large files that are good candidates for deletion. Separately, the web acts as a distributed storage network, providing free access to petabytes of redundant files across 200 million websites. An automated method of restoring files from the web would enable more efficient storage management, since files readily recoverable from the web would make good candidates for removal. Despite this, there are no prescribed methods for automatically detecting these files and ensuring their easy recoverability from the web, as little is known about either the biggest files of users or their origins on the web. This study thus seeks to determine what files consume the most space in users' storage, and from this, to propose an automated method to select candidate files for removal. Our investigations show 989 MB of storage per user can be saved by inspecting preexisting metadata of their 25 largest files alone, with file recovery from the web 3 months later. This demonstrates the feasibility of applying such a method in a climate of increasingly scarce local storage resources.
Briteller: Shining a Light on AI Recommendations for Children
Venue: arXiv cs.HC 2025
Year: 2025

Understanding how AI recommendations work can help the younger generation become more informed and critical consumers of the vast amount of information they encounter daily. However, young learners with limited math and computing knowledge often find AI concepts too abstract. To address this, we developed Briteller, a light-based recommendation system that makes learning tangible. By exploring and manipulating light beams, Briteller enables children to understand an AI recommender system's core algorithmic building block, the dot product, through hands-on interactions. Initial evaluations with ten middle school students demonstrated the effectiveness of this approach, using embodied metaphors, such as "merging light" to represent addition. To overcome the limitations of the physical optical setup, we further explored how AR could embody multiplication, expand data vectors with more attributes, and enhance contextual understanding. Our findings provide valuable insights for designing embodied and tangible learning experiences that make AI concepts more accessible to young learners.