OpenRaiser/Intern-Atlas huggingface dataset:
Beyond Stereotypes: Exploring How Minority College Students Experience Stigma on Reddit
Venue: arXiv cs.SI 2025
Year: 2025

Minority college students face unique challenges shaped by their identities based on their gender/sexual orientation, race, religion, and academic institutions, which influence their academic and social experiences. Although research has highlighted the challenges faced by individual minority groups, the stigma process-labeling, stereotyping, separation, status loss, and discrimination-that underpin these experiences remains underexamined, particularly in the online spaces where college students are highly active. We address these gaps by examining posts on subreddit, r/college, as indicators for stigma processes, our approach applies a Stereotype-BERT model, including stance toward each stereotype. We extend the stereotype model to encompass status loss and discrimination by using semantic distance with their reference sentences. Our analyses show that professional indicated posts are primarily labeled under the stereotyping stage, whereas posts indicating racial are highly represented in status loss and discrimination. Intersectional identified posts are more frequently associated with status loss and discrimination. The findings of this study highlight the need for multifaceted intersectional approaches to identifying stigma, which subsequently serve as indicators to promote equity for minority groups, especially racial minorities and those experiencing compounded vulnerabilities due to intersecting identities.
Controlled Territory and Conflict Tracking (CONTACT): (Geo-)Mapping Occupied Territory from Open Source Intelligence
Venue: arXiv cs.CL 2025
Year: 2025

Open-source intelligence provides a stream of unstructured textual data that can inform assessments of territorial control. We present CONTACT, a framework for territorial control prediction using large language models (LLMs) and minimal supervision. We evaluate two approaches: SetFit, an embedding-based few-shot classifier, and a prompt tuning method applied to BLOOMZ-560m, a multilingual generative LLM. Our model is trained on a small hand-labeled dataset of news articles covering ISIS activity in Syria and Iraq, using prompt-conditioned extraction of control-relevant signals such as military operations, casualties, and location references. We show that the BLOOMZ-based model outperforms the SetFit baseline, and that prompt-based supervision improves generalization in low-resource settings. CONTACT demonstrates that LLMs fine-tuned using few-shot methods can reduce annotation burdens and support structured inference from open-ended OSINT streams. Our code is available at https://github.com/PaulKMandal/CONTACT/.
DAM-Net: Domain Adaptation Network with Micro-Labeled Fine-Tuning for Change Detection
Venue: arXiv cs.CV 2025
Year: 2025

Change detection (CD) in remote sensing imagery plays a crucial role in various applications such as urban planning, damage assessment, and resource management. While deep learning approaches have significantly advanced CD performance, current methods suffer from poor domain adaptability, requiring extensive labeled data for retraining when applied to new scenarios. This limitation severely restricts their practical applications across different datasets. In this work, we propose DAM-Net: a Domain Adaptation Network with Micro-Labeled Fine-Tuning for CD. Our network introduces adversarial domain adaptation to CD for, utilizing a specially designed segmentation-discriminator and alternating training strategy to enable effective transfer between domains. Additionally, we propose a novel Micro-Labeled Fine-Tuning approach that strategically selects and labels a minimal amount of samples (less than 1%) to enhance domain adaptation. The network incorporates a Multi-Temporal Transformer for feature fusion and optimized backbone structure based on previous research. Experiments conducted on the LEVIR-CD and WHU-CD datasets demonstrate that DAM-Net significantly outperforms existing domain adaptation methods, achieving comparable performance to semi-supervised approaches that require 10% labeled data while using only 0.3% labeled samples. Our approach significantly advances cross-dataset CD applications and provides a new paradigm for efficient domain adaptation in remote sensing. The source code of DAM-Net will be made publicly available upon publication.
Robust Distributed Arrays: Provably Secure Networking for Data Availability Sampling
Venue: arXiv cs.DS 2025
Year: 2025

Data Availability Sampling (DAS), a central component of Ethereum's roadmap, enables clients to verify data availability without requiring any single client to download the entire dataset. DAS operates by having clients randomly retrieve individual symbols of erasure-encoded data from a peer-to-peer network. While the cryptographic and encoding aspects of DAS have recently undergone formal analysis, the peer-to-peer networking layer remains underexplored, with a lack of security definitions and efficient, provably secure constructions. In this work, we address this gap by introducing a novel distributed data structure that can serve as the networking layer for DAS, which we call robust distributed arrays. That is, we rigorously define a robustness property of a distributed data structure in an open permissionless network, that mimics a collection of arrays. Then, we give a simple and efficient construction and formally prove its robustness. Notably, every individual node is required to store only small portions of the data, and accessing array positions incurs minimal latency. The robustness of our construction relies solely on the presence of a minimal absolute number of honest nodes in the network. In particular, we avoid any honest majority assumption. Beyond DAS, we anticipate that robust distributed arrays can have wider applications in distributed systems.
DP2Unlearning: An Efficient and Guaranteed Unlearning Framework for LLMs
Venue: arXiv cs.LG 2025
Year: 2025

Large language models (LLMs) have recently revolutionized language processing tasks but have also brought ethical and legal issues. LLMs have a tendency to memorize potentially private or copyrighted information present in the training data, which might then be delivered to end users at inference time. When this happens, a naive solution is to retrain the model from scratch after excluding the undesired data. Although this guarantees that the target data have been forgotten, it is also prohibitively expensive for LLMs. Approximate unlearning offers a more efficient alternative, as it consists of ex post modifications of the trained model itself to prevent undesirable results, but it lacks forgetting guarantees because it relies solely on empirical evidence. In this work, we present DP2Unlearning, a novel LLM unlearning framework that offers formal forgetting guarantees at a significantly lower cost than retraining from scratch on the data to be retained. DP2Unlearning involves training LLMs on textual data protected using ε-differential privacy (DP), which later enables efficient unlearning with the guarantees against disclosure associated with the chosen ε. Our experiments demonstrate that DP2Unlearning achieves similar model performance post-unlearning, compared to an LLM retraining from scratch on retained data -- the gold standard exact unlearning -- but at approximately half the unlearning cost. In addition, with a reasonable computational cost, it outperforms approximate unlearning methods at both preserving the utility of the model post-unlearning and effectively forgetting the targeted information.
Fighting Fires from Space: Leveraging Vision Transformers for Enhanced Wildfire Detection and Characterization
Venue: arXiv cs.CV 2025
Year: 2025

Wildfires are increasing in intensity, frequency, and duration across large parts of the world as a result of anthropogenic climate change. Modern hazard detection and response systems that deal with wildfires are under-equipped for sustained wildfire seasons. Recent work has proved automated wildfire detection using Convolutional Neural Networks (CNNs) trained on satellite imagery are capable of high-accuracy results. However, CNNs are computationally expensive to train and only incorporate local image context. Recently, Vision Transformers (ViTs) have gained popularity for their efficient training and their ability to include both local and global contextual information. In this work, we show that ViT can outperform well-trained and specialized CNNs to detect wildfires on a previously published dataset of LandSat-8 imagery. One of our ViTs outperforms the baseline CNN comparison by 0.92%. However, we find our own implementation of CNN-based UNet to perform best in every category, showing their sustained utility in image tasks. Overall, ViTs are comparably capable in detecting wildfires as CNNs, though well-tuned CNNs are still the best technique for detecting wildfire with our UNet providing an IoU of 93.58%, better than the baseline UNet by some 4.58%.
Four Bottomless Errors and the Collapse of Statistical Fairness
Venue: arXiv cs.CY 2025
Year: 2025

The AI ethics of statistical fairness is an error, the approach should be abandoned, and the accumulated academic work deleted. The argument proceeds by identifying four recurring mistakes within statistical fairness. One conflates fairness with equality, which confines thinking to similars being treated similarly. The second and third errors derive from a perspectival ethical view which functions by negating others and their viewpoints. The final mistake constrains fairness to work within predefined social groups instead of allowing unconstrained fairness to subsequently define group composition. From the nature of these misconceptions, the larger argument follows. Because the errors are integral to how statistical fairness works, attempting to resolve the difficulties only deepens them. Consequently, the errors cannot be corrected without undermining the larger project, and statistical fairness collapses from within. While the collapse ends a failure in ethics, it also provokes distinct possibilities for fairness, data, and algorithms. Quickly indicating some of these directions is a secondary aim of the paper, and one that aligns with what fairness has consistently meant and done since Aristotle.
ChatNekoHacker: Real-Time Fan Engagement with Conversational Agents
Venue: arXiv cs.HC 2025
Year: 2025

ChatNekoHacker is a real-time conversational agent system that strengthens fan engagement for musicians. It integrates Amazon Bedrock Agents for autonomous dialogue, Unity for immersive 3D livestream sets, and VOICEVOX for high quality Japanese text-to-speech, enabling two virtual personas to represent the music duo Neko Hacker. In a one-hour YouTube Live with 30 participants, we evaluated the impact of the system. Regression analysis showed that agent interaction significantly elevated fan interest, with perceived fun as the dominant predictor. The participants also expressed a stronger intention to listen to the duo's music and attend future concerts. These findings highlight entertaining, interactive broadcasts as pivotal to cultivating fandom. Our work offers actionable insights for the deployment of conversational agents in entertainment while pointing to next steps: broader response diversity, lower latency, and tighter fact-checking to curb potential misinformation.
Unified Manipulability and Compliance Analysis of Modular Soft-Rigid Hybrid Fingers
Venue: arXiv cs.RO 2025
Year: 2025

This paper presents a unified framework to analyze the manipulability and compliance of modular soft-rigid hybrid robotic fingers. The approach applies to both hydraulic and pneumatic actuation systems. A Jacobian-based formulation maps actuator inputs to joint and task-space responses. Hydraulic actuators are modeled under incompressible assumptions, while pneumatic actuators are described using nonlinear pressure-volume relations. The framework enables consistent evaluation of manipulability ellipsoids and compliance matrices across actuation modes. We validate the analysis using two representative hands: DexCo (hydraulic) and Edgy-2 (pneumatic). Results highlight actuation-dependent trade-offs in dexterity and passive stiffness. These findings provide insights for structure-aware design and actuator selection in soft-rigid robotic fingers.
MIG: Automatic Data Selection for Instruction Tuning by Maximizing Information Gain in Semantic Space
Venue: arXiv cs.CL 2025
Year: 2025

Data quality and diversity are key to the construction of effective instruction-tuning datasets. % With the increasing availability of open-source instruction-tuning datasets, it is advantageous to automatically select high-quality and diverse subsets from a vast amount of data. % Existing methods typically prioritize instance quality and use heuristic rules to maintain diversity. % However, this absence of a comprehensive view of the entire collection often leads to suboptimal results. % Moreover, heuristic rules generally focus on distance or clustering within the embedding space, which fails to accurately capture the intent of complex instructions in the semantic space. % To bridge this gap, we propose a unified method for quantifying the information content of datasets. This method models the semantic space by constructing a label graph and quantifies diversity based on the distribution of information within the graph. % Based on such a measurement, we further introduce an efficient sampling method that selects data samples iteratively to \textbf{M}aximize the \textbf{I}nformation \textbf{G}ain (MIG) in semantic space. % Experiments on various datasets and base models demonstrate that MIG consistently outperforms state-of-the-art methods. % Notably, the model fine-tuned with 5\% Tulu3 data sampled by MIG achieves comparable performance to the official SFT model trained on the full dataset, with improvements of +5.73\% on AlpacaEval and +6.89\% on Wildbench.
Interview AI-ssistant: Designing for Real-Time Human-AI Collaboration in Interview Preparation and Execution
Venue: arXiv cs.HC 2025
Year: 2025

Recent advances in large language models (LLMs) offer unprecedented opportunities to enhance human-AI collaboration in qualitative research methods, including interviews. While interviews are highly valued for gathering deep, contextualized insights, interviewers often face significant cognitive challenges, such as real-time information processing, question adaptation, and rapport maintenance. My doctoral research introduces Interview AI-ssistant, a system designed for real-time interviewer-AI collaboration during both the preparation and execution phases. Through four interconnected studies, this research investigates the design of effective human-AI collaboration in interviewing contexts, beginning with a formative study of interviewers' needs, followed by a prototype development study focused on AI-assisted interview preparation, an experimental evaluation of real-time AI assistance during interviews, and a field study deploying the system in a real-world research setting. Beyond informing practical implementations of intelligent interview support systems, this work contributes to the Intelligent User Interfaces (IUI) community by advancing the understanding of human-AI collaborative interfaces in complex social tasks and establishing design guidelines for AI-enhanced qualitative research tools.
Personal Data Protection in Smart Home Activity Monitoring for Digital Health: A Case Study
Venue: arXiv cs.HC 2025
Year: 2025

Researchers in pervasive computing have worked for decades on sensor-based human activity recognition (HAR). Among the digital health applications, the recognition of activities of daily living (ADL) in smart home environments enables the identification of behavioral changes that clinicians consider as a digital bio-marker of early stages of cognitive decline. The real deployment of sensor-based HAR systems in the homes of elderly subjects poses several challenges, with privacy and ethical concerns being major ones. This paper reports our experience applying privacy by design principles to develop and deploy one of these systems.
The Evolving Role of Programming and LLMs in the Development of Self-Driving Laboratories
Venue: arXiv cs.HC 2025
Year: 2025

Machine learning and automation are transforming scientific research, yet the implementation of self-driving laboratories (SDLs) remains costly and complex, and it remains difficult to learn how to use these facilities. To address this, we introduce Claude-Light, a lightweight, remotely accessible instrument designed for prototyping automation algorithms and machine learning workflows. Claude-Light integrates a REST API, a Raspberry Pi-based control system, and an RGB LED with a photometer that measures ten spectral outputs, providing a controlled but realistic experimental environment. This device enables users to explore automation at multiple levels, from basic programming and experimental design to machine learning-driven optimization. We demonstrate the application of Claude-Light in structured automation approaches, including traditional scripting, statistical design of experiments, and active learning methods. Additionally, we explore the role of large language models (LLMs) in laboratory automation, highlighting their use in instrument selection, structured data extraction, function calling, and code generation. While LLMs present new opportunities for streamlining automation, they also introduce challenges related to reproducibility, security, and reliability. We discuss strategies to mitigate these risks while leveraging LLMs for enhanced efficiency in self-driving laboratories. Claude-Light provides a practical and accessible platform for students and researchers to develop automation skills and test algorithms before deploying them in larger-scale SDLs. By lowering the barrier to entry for automation in scientific research, this tool facilitates broader adoption of AI-driven experimentation and fosters innovation in autonomous laboratories.
Human aversion? Do AI Agents Judge Identity More Harshly Than Performance
Venue: arXiv cs.HC 2025
Year: 2025

This study examines the understudied role of algorithmic evaluation of human judgment in hybrid decision-making systems, a critical gap in management research. While extant literature focuses on human reluctance to follow algorithmic advice, we reverse the perspective by investigating how AI agents based on large language models (LLMs) assess and integrate human input. Our work addresses a pressing managerial constraint: firms barred from deploying LLMs directly due to privacy concerns can still leverage them as mediating tools (for instance, anonymized outputs or decision pipelines) to guide high-stakes choices like pricing or discounts without exposing proprietary data. Through a controlled prediction task, we analyze how an LLM-based AI agent weights human versus algorithmic predictions. We find that the AI system systematically discounts human advice, penalizing human errors more severely than algorithmic errors--a bias exacerbated when the agent's identity (human vs AI) is disclosed and the human is positioned second. These results reveal a disconnect between AI-generated trust metrics and the actual influence of human judgment, challenging assumptions about equitable human-AI collaboration. Our findings offer three key contributions. First, we identify a reverse algorithm aversion phenomenon, where AI agents undervalue human input despite comparable error rates. Second, we demonstrate how disclosure and positional bias interact to amplify this effect, with implications for system design. Third, we provide a framework for indirect LLM deployment that balances predictive power with data privacy. For practitioners, this research emphasize the need to audit AI weighting mechanisms, calibrate trust dynamics, and strategically design decision sequences in human-AI systems.
Ambient Listening in Clinical Practice: Evaluating EPIC Signal Data Before and After Implementation and Its Impact on Physician Workload
Venue: arXiv cs.HC 2025
Year: 2025

The widespread adoption of EHRs following the HITECH Act has increased the clinician documentation burden, contributing to burnout. Emerging technologies, such as ambient listening tools powered by generative AI, offer real-time, scribe-like documentation capabilities to reduce physician workload. This study evaluates the impact of ambient listening tools implemented at UCI Health by analyzing EPIC Signal data to assess changes in note length and time spent on notes. Results show significant reductions in note-taking time and an increase in note length, particularly during the first-month post-implementation. Findings highlight the potential of AI-powered documentation tools to improve clinical efficiency. Future research should explore adoption barriers, long-term trends, and user experiences to enhance the scalability and sustainability of ambient listening technology in clinical practice.
Mozualization: Crafting Music and Visual Representation with Multimodal AI
Venue: arXiv cs.HC 2025
Year: 2025

In this work, we introduce Mozualization, a music generation and editing tool that creates multi-style embedded music by integrating diverse inputs, such as keywords, images, and sound clips (e.g., segments from various pieces of music or even a playful cat's meow). Our work is inspired by the ways people express their emotions -- writing mood-descriptive poems or articles, creating drawings with warm or cool tones, or listening to sad or uplifting music. Building on this concept, we developed a tool that transforms these emotional expressions into a cohesive and expressive song, allowing users to seamlessly incorporate their unique preferences and inspirations. To evaluate the tool and, more importantly, gather insights for its improvement, we conducted a user study involving nine music enthusiasts. The study assessed user experience, engagement, and the impact of interacting with and listening to the generated music.
State of the Art on Artificial Intelligence Resources for Interaction Media Design in Digital Cultural Heritage
Venue: arXiv cs.HC 2025
Year: 2025

This paper explores the integration of Artificial Intelligence (AI) in the design of interactive experiences for Cultural Heritage (CH). Previous studies indeed either miss to represent the specificity of the CH or mention possible tools without making a clear reference to a structured Interaction Design (IxD) workflow. The study also attempts to overcome one of the major limitations of traditional literature review, which may fail to capture proprietary tools whose release is rarely accompanied by academic publications. Besides the analysis of previous research, the study proposes a possible workflow for IxD in CH, subdivided into phases and tasks: for each of them, this paper proposes possible AI-based tools that can support the activity of designers, curators, and CH professionals. The review concludes with a final section outlining future paths for research and development in this domain.