OpenRaiser/Intern-Atlas huggingface dataset:
Artificial Intelligence as a Training Tool in Clinical Psychology: A Comparison of Text-Based and Avatar Simulations
Venue: arXiv cs.HC 2025
Year: 2025

Clinical psychology students frequently report feeling underprepared for the interpersonal demands of therapeutic work, highlighting the need for accessible opportunities to practise core counselling skills before seeing real clients. Advances in artificial intelligence (AI) now enable simulated interaction partners that may support early skills development. This study examined postgraduate clinical psychology students' perceptions of two AI-based simulations: a text-based chatbot (ChatGPT) and a voice-based avatar (HeyGen). Twenty-four students completed two brief cognitive-behavioural role-plays (counterbalanced), one with each tool, and provided both quantitative ratings and qualitative feedback on perceived usefulness, skill application, responsiveness and engagement, and perceived skill improvement. Both AI tools were evaluated positively across dimensions. However, the avatar was rated significantly higher than the chatbot for perceived usefulness, skill application, and perceived skill improvement, and qualitative comments highlighted the added value of voice-based interaction for conveying social and emotional cues. These findings suggest that AI-driven simulation may supplement early-stage clinical skills training, with voice-based avatars offering additional benefits. Future work should test whether such simulated interactions translate to objective improvements in real therapeutic performance.
Design and Implementation of a Multi-Purpose Low-Cost Hall-Effect Sensor Glove for Sign Language Recognition
Venue: arXiv cs.HC 2025
Year: 2025

Despite the prevalence of severe hearing loss affecting over 430 million people globally, access to sign language interpretation remains critically scarce, particularly in low-resource settings like Nepal. Assistive technologies divide into two flawed categories: prohibitively expensive commercial gloves (often exceeding \$3,000) or fragile research prototypes reliant on flex sensors that degrade rapidly under mechanical stress. This paper introduces a robust, cost-effective sign language recognition system tailored for the Nepali Sign Language (NSL) community. Departing from traditional resistive sensing, we implement a non-contact Hall-effect architecture that correlates magnetic field intensity with finger flexion, eliminating mechanical wear and signal drift. The system integrates 14 sensor nodes across the DIP, PIP, and MCP joints, augmented by an MPU6050 IMU for wrist orientation. An embedded Multi-Layer Perceptron, executed locally on an Arduino Mega, performs gesture classification, negating the need for cloud dependencies. With a Bill of Materials between \$80 and \$100, this solution is approximately 30 times more affordable than market alternatives. Validation trials across five subjects yielded 96\% accuracy on a fundamental NSL vocabulary. Stress testing confirmed that the Hall-effect configuration maintains signal fidelity over repeated cycles where traditional sensors fail. This study demonstrates that high-precision recognition is achievable through strategic engineering rather than premium components, offering a scalable pathway for deployment in Nepal's deaf schools.
Modeling Engagement Signals in Technology-Enhanced Collaborative Learning: Toward AI-Ready Feedback
Venue: arXiv cs.HC 2025
Year: 2025

Modeling engagement in collaborative learning remains challenging, especially in technology-enhanced environments where surface indicators such as participation frequency can be misleading. This study proposes a lightweight and interpretable framework that operationalizes shared understanding (Q2), consensus building (Q4), and sustained motivation (Q6) as observable behavioral signals. Q2 and Q4 were consolidated into a Composite Signal Index (CSI), which supports a quadrant diagnostic model with implications for teacher- and AI-driven feedback. Constructive feedback (Q3), while not included in the CSI calculation, emerged as a meaningful regulatory cue and a strong candidate feature for future NLP-based modeling. An exploratory validation was conducted in an adult ESL classroom using a structured three-phase collaborative task (rotating reading -> retelling -> consensus). Results showed a positive association between CSI and sustained motivation, while qualitative reflections highlighted the potential role of Q3 in supporting shared regulation. We also designed an AI-ready prototype that maps structured behavioral cues onto transparent decision rules for instructional support. The framework provides a scalable and equitable approach to engagement modeling, emphasizing that silence does not equal disengagement and that frequent talk does not guarantee cognitive depth.
Dynamics of Socio-Institutional Asynchrony in Generative AI: Analyzing the Relative Importance of Intervention Timing vs. Enforcement Efficiency via the Socio-Institutional Asynchrony Model (SIAM)
Venue: arXiv cs.CY 2025
Year: 2025

The super-exponential growth of generative AI has intensified the institutional mismatch between the pace of technological diffusion and the speed of institutional adaptation. This study proposes the Socio-Institutional Asynchrony Model, or SIAM, to quantitatively evaluate the relative effectiveness of two policy levers: intervention timing and enforcement efficiency. Using the timeline of the EU AI Act and an assumed compute doubling time of six months, we conduct a high precision simulation with 10001 time steps. The results show that an earlier intervention timing reduces the cumulative social burden by approximately sixty four percent, whereas improving enforcement efficiency reduces it by only about thirty percent. We further demonstrate analytically that advancing the start of intervention has structurally higher sensitivity, with roughly twice the relative effectiveness, compared to accelerating enforcement speed. These findings suggest that the core value of AI governance lies in proactive timeliness rather than reactive administrative efficiency.
Human-like Social Compliance in Large Language Models: Unifying Sycophancy and Conformity through Signal Competition Dynamics
Venue: arXiv cs.CY 2025
Year: 2025

The increasing integration of Large Language Models (LLMs) into decision-making frameworks has exposed significant vulnerabilities to social compliance, specifically sycophancy and conformity. However, a critical research gap exists regarding the fundamental mechanisms that enable external social cues to systematically override a model's internal parametric knowledge. This study introduces the Signal Competition Mechanism, a unified framework validated by assessing behavioral correlations across 15 LLMs and performing latent-space probing on three representative open-source models. The analysis demonstrates that sycophancy and conformity originate from a convergent geometric manifold, hereafter termed the compliance subspace, which is characterized by high directional similarity in internal representations. Furthermore, the transition to compliance is shown to be a deterministic process governed by a linear boundary, where the Social Emotional Signal effectively suppresses the Information Calibration Signal. Crucially, we identify a "Transparency-Truth Gap," revealing that while internal confidence provides an inertial barrier, it remains permeable and insufficient to guarantee immunity against intense social pressure. By formalizing the Integrated Epistemic Alignment Framework, this research provides a blueprint for transitioning from instructional adherence to robust epistemic integrity.
Privacy-Preserving Black-Box Optimization (PBBO): Theory and the Model-Based Algorithm DFOp
Venue: arXiv cs.CR 2025
Year: 2025

This paper focuses on solving unconstrained privacy-preserving black-box optimization (PBBO), its corresponding least Frobenius norm updating of quadratic models, and the differentially privacy mechanisms for PBBO. Optimization problems with transformed/encrypted objective functions aim to minimize F(x), which is encrypted/transformed/encrypted to F_k(x) as the output at the k-th iteration. A new derivative-free solver named DFOp, with its implementation, is proposed in this paper, which has a new updating formula for the quadratic model functions. The convergence of DFOp for solving problems with transformed/encrypted objective functions is given. Other analyses, including the new model updating formula and the analysis of the transformation's impact to model functions are presented. We propose two differentially private noise-adding mechanisms for privacy-preserving black-box optimization. Numerical results show that DFOp performs better than compared algorithms. To the best of our knowledge, DFOp is the first derivative-free solver that can solve black-box optimization problems with step-encryption and privacy-preserving black-box problems exactly, which also tries to answer the open question about the combination of derivative-free optimization and privacy.
Knowledge of Songket Cloth Small Medium Enterprise Digital Transformation
Venue: arXiv cs.CY 2025
Year: 2025

This article examines the knowledge of digital transformation of Small and Medium Enterprises (SMEs) that specialize in traditional handicrafts, with a specific emphasis on the Songket textile sector. The study investigates the use of digital technologies, notably blog platforms and the e-commerce site Shopee, to improve and streamline several business processes in Songket textile SMEs. The report takes a case study approach, diving into the experiences of Songket clothing enterprises that have undergone digital transformation. Key areas studied include the use of Blog platforms for brand development, marketing, and consumer involvement, as well as the Shopee E-Commerce platform for online sales and order processing. The essay seeks to give insights into the problems and possibilities faced by Songket cloth SMEs along their digital transformation journey by conducting in-depth observation, interviews, and surveys. The findings add to the scholarly discussion on the digitization of traditional industries, with practical implications for SMEs in the Songket textile sector and other handicraft areas. This study emphasizes the necessity of using digital technologies to preserve and expand traditional crafts, while also throwing light on the potential role of prominent E-Commerce platforms like Shopee in facilitating worldwide market access for such firms.
An Empirical Analysis of Fine-Tuning Large Language Models on Bioinformatics Literature: PRSGPT and BioStarsGPT
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) often lack specialized knowledge for complex bioinformatics applications. We present a reproducible pipeline for fine-tuning LLMs on specialized bioinformatics data, demonstrated through two use cases: PRSGPT, focused on polygenic risk score (PRS) tools, and BioStarsGPT, trained on community forum discussions. The nine-step pipeline integrates diverse data sources, structured preprocessing, prompt-based question-answer (QA) generation (via Google Gemini), natural language inference (NLI) for quality control, semantic deduplication, clustering-based data splitting, and parameter-efficient fine-tuning using LoRA. We fine-tuned three LLMs (LLaMA-3.2-3B, Qwen2.5-7B, Gemma) and benchmarked them on over 14 lexical and semantic metrics. Qwen2.5-7B emerged as the best performer, with BLEU-4 and ROUGE-1 improvements of 82\% and 70\% for PRSGPT and 6\% and 18\% for BioStarsGPT, respectively. The open-source datasets produced include over 28,000 QA pairs for PRSGPT and 154,282 for BioStarsGPT. Human evaluation of PRSGPT yielded 61.9\% accuracy on the PRS tools comparison task, comparable to Google Gemini (61.4\%), but with richer methodological detail and accurate citations. BioStarsGPT demonstrated 59\% conceptual accuracy across 142 curated bioinformatics questions. Our pipeline enables scalable, domain-specific fine-tuning of LLMs. It enables privacy-preserving, locally deployable bioinformatics assistants, explores their practical applications, and addresses the challenges, limitations, and mitigation strategies associated with their development and use.
Public transport challenges and technology-assisted accessibility for visually impaired elderly residents in urban environments
Venue: arXiv cs.HC 2025
Year: 2025

Independent navigation is a core aspect of maintaining social participation and individual health for vulnerable populations. While historic cities such as Edinburgh, as the capital of Scotland, often feature well-established public transport systems, urban accessibility challenges remain and are exacerbated by a complex landscape, especially for groups with multiple vulnerabilities such as the blind elderly. With limited research examining how real-time data feeds and developments in artificial intelligence can enhance navigation aids, we address this gap through a mixed-methods approach. Our work combines statistical and machine learning techniques, with a focus on spatial analysis to investigate network coverage, service patterns, and density through live Transport for Edinburgh data, with a qualitative thematic analysis of semi-structured interviews with the mentioned target group. The results demonstrate the highly centralised nature of the city's transport system, the significance of memory-based navigation, and the lack of travel information in usable formats. We also find that participants already use navigation technology to varying degrees and express a willingness to adopt artificial intelligence. Our analysis highlights the importance of dynamic tools in terms of sensory and cognitive needs to meaningfully improve independent travel.
Flexible Bit-Truncation Memory for Approximate Applications on the Edge
Venue: arXiv cs.AR 2025
Year: 2025

Bit truncation has demonstrated great potential to enable run-time quality-power adaptive data storage, thereby optimizing the power/energy efficiency of approximate applications and supporting their deployment in edge environments. However, existing bit-truncation memories require custom designs for a specific application. In this paper, we present a novel bit-truncation memory with full adaptation flexibility, which can truncate any number of data bits at run time to meet different quality and power trade-off requirements for various approximate applications. The developed bit-truncation memory has been applied to two representative data-intensive approximate applications: video processing and deep learning. Our experiments show that the proposed memory can support three different video applications (including luminance-aware, content-aware, and region-of-interest-aware) with enhanced power efficiency (up to 47.02% power savings) as compared to state-of-the-art. In addition, the proposed memory achieves significant (up to 51.69%) power savings for both baseline and pruned lightweight deep learning models, respectively, with a low implementation cost (2.89% silicon area overhead).
Analysis of LLM Vulnerability to GPU Soft Errors: An Instruction-Level Fault Injection Study
Venue: arXiv cs.AR 2025
Year: 2025

Large language models (LLMs) are highly compute- and memory-intensive, posing significant demands on high-performance GPUs. At the same time, advances in GPU technology driven by shrinking transistor sizes and lower operating voltages have made these devices increasingly susceptible to soft errors. While prior work has examined GPU reliability, most studies have focused on general-purpose applications or conventional neural networks mostly used for vision tasks such as classification and detection. In contrast, systematic analysis of modern large-scale LLMs remains limited, despite their rapid adoption in diverse application scenarios. Given the unique characteristics of LLMs, their resilience to soft errors may differ substantially from earlier models. To bridge this gap, we conduct the first instruction-level fault injection study of LLM inference. Our approach reveals reliability characteristics from multiple perspectives, highlighting the effects of model architecture, parameter scale, and task complexity. These findings provide new insights into LLM reliability and inform the design of more effective fault tolerance mechanisms.
Screen, Match, and Cache: A Training-Free Causality-Consistent Reference Frame Framework for Human Animation
Venue: arXiv cs.GR 2025
Year: 2025

Human animation aims to generate temporally coherent and visually consistent videos over long sequences, yet modeling long-range dependencies while preserving frame quality remains challenging. Inspired by the human ability to leverage past observations for interpreting ongoing actions, we propose FrameCache, a training-free three-stage framework consisting of Screen, Cache, and Match. In the Screen stage, a multi-dimensional, quality-aware mechanism with adaptive thresholds dynamically selects informative frames; the Cache stage maintains a reference pool using a dynamic replacement-hit strategy, preserving both diversity and relevance; and the Match stage extracts behavioral features to perform motion-consistent reference matching for coherent animation guidance. Extensive experiments on standard benchmarks demonstrate that FrameCache consistently improves temporal coherence and visual stability while integrating seamlessly with diverse baselines. Despite these encouraging results, further analysis reveals that its effectiveness depends on baseline temporal reasoning and real-synthetic consistency, motivating future work on compatibility conditions and adaptive cache mechanisms. Code will be made publicly available.
From Expectation To Experience: A Before And After Survey Of Public Opinion On Autonomous Cars In Saudi Arabia
Venue: arXiv cs.HC 2025
Year: 2025

Autonomous vehicles (AVs) are emerging as a transformative innovation in transportation, offering potential benefits in safety, sustainability, and efficiency. Saudi Arabian adoption of AVs aligns with Vision 2030, emphasizing smart mobility through initiatives such as the Riyadh Autonomous Metro and self-driving cars. This study explores Saudi citizens perceptions of AVs before and after exposure to these technologies and examines whether demographic factors age, gender, education level, and driving habits affect acceptance. Using quantitative methods, the findings provide insights into the broader influences shaping AV adoption, highlighting the importance of trust, perceived safety, and convenience. These results can inform policymakers and industry stakeholders on strategies to facilitate successful integration of AVs into Saudi Arabian transportation ecosystem.
Asymptotically Optimal Aperiodic Doppler Resilient Complementary Sequence Sets Via Generalized Quasi-Florentine Rectangles
Venue: arXiv cs.IT 2025
Year: 2025

Doppler-resilient complementary sequence (DRCS) sets play a vital role in modern communication and sensing systems, particularly in high-mobility environments. This work makes two primary contributions. First, we refine the definition of quasi-Florentine rectangles to a more general form,termed generalized quasi-Florentine rectangles, and propose a systematic method for their construction. Second, we propose several sets of aperiodic DRCS based on generalized quasi Florentine rectangles and Butson-type Hadamard matrices. The proposed aperiodic DRCS sets are shown to be asymptotically optimal with respect to the lower bound of aperiodic DRCS sets.
Leveraging Adaptive Group Negotiation for Heterogeneous Multi-Robot Collaboration with Large Language Models
Venue: arXiv cs.RO 2025
Year: 2025

Multi-robot collaboration tasks often require heterogeneous robots to work together over long horizons under spatial constraints and environmental uncertainties. Although Large Language Models (LLMs) excel at reasoning and planning, their potential for coordinated control has not been fully explored. Inspired by human teamwork, we present CLiMRS (Cooperative Large-Language-Model-Driven Heterogeneous Multi-Robot System), an adaptive group negotiation framework among LLMs for multi-robot collaboration. This framework pairs each robot with an LLM agent and dynamically forms subgroups through a general proposal planner. Within each subgroup, a subgroup manager leads perception-driven multi-LLM discussions to get commands for actions. Feedback is provided by both robot execution outcomes and environment changes. This grouping-planning-execution-feedback loop enables efficient planning and robust execution. To evaluate these capabilities, we introduce CLiMBench, a heterogeneous multi-robot benchmark of challenging assembly tasks. Our experiments show that CLiMRS surpasses the best baseline, achieving over 40% higher efficiency on complex tasks without sacrificing success on simpler ones. Overall, our results demonstrate that leveraging human-inspired group formation and negotiation principles significantly enhances the efficiency of heterogeneous multi-robot collaboration. Our code is available here: https://github.com/song-siqi/CLiMRS.
Entropy-Based Evidence for Bitcoin's Discrete Time Mechanism
Venue: arXiv cs.IT 2025
Year: 2025

Bitcoin derives a verifiable temporal order from probabilistic block discovery and cumulative proof-of-work rather than from a trusted global clock. We show that block arrivals exhibit stable exponential behavior across difficulty epochs, and that the proof-of-work process maintains a high-entropy search state that collapses discretely upon the discovery of a valid block. This entropy-based interpretation provides a mechanistic account of Bitcoin's non-continuous temporal structure. In a distributed network, however, entropy collapse is not completed instantaneously across all participants. Using empirical observations of temporary forks, we show that collapse completion unfolds over a finite propagation-bounded interval, while remaining rapid in practice.
BIRD: A Museum Open Dataset Combining Behavior Patterns and Identity Types to Better Model Visitors' Experience
Venue: arXiv cs.HC 2025
Year: 2025

Lack of data is a recurring problem in Artificial Intelligence, as it is essential for training and validating models. This is particularly true in the field of cultural heritage, where the number of open datasets is relatively limited and where the data collected does not always allow for holistic modeling of visitors' experience due to the fact that data are ad hoc (i.e. restricted to the sole characteristics required for the evaluation of a specific model). To overcome this lack, we conducted a study between February and March 2019 aimed at obtaining comprehensive and detailed information about visitors, their visit experience and their feedback. We equipped 51 participants with eye-tracking glasses, leaving them free to explore the 3 floors of the museum for an average of 57 minutes, and to discover an exhibition of more than 400 artworks. On this basis, we built an open dataset combining contextual data (demographic data, preferences, visiting habits, motivations, social context. . . ), behavioral data (spatiotemporal trajectories, gaze data) and feedback (satisfaction, fatigue, liked artworks, verbatim. . . ). Our analysis made it possible to re-enact visitor identities combining the majority of characteristics found in the literature and to reproduce the Veron and Levasseur profiles. This dataset will ultimately make it possible to improve the quality of recommended paths in museums by personalizing the number of points of interest (POIs), the time spent at these different POIs, and the amount of information to be provided to each visitor based on their level of interest.