OpenRaiser/Intern-Atlas huggingface dataset:
World of ScoreCraft: Novel Multi Scorer Experiment on the Impact of a Decision Support System in Sleep Staging
Venue: arXiv cs.HC 2025
Year: 2025

Manual scoring of polysomnography (PSG) is a time intensive task, prone to inter scorer variability that can impact diagnostic reliability. This study investigates the integration of decision support systems (DSS) into PSG scoring workflows, focusing on their effects on accuracy, scoring time, and potential biases toward recommendations from artificial intelligence (AI) compared to human generated recommendations. Using a novel online scoring platform, we conducted a repeated measures study with sleep technologists, who scored traditional and self applied PSGs. Participants were occasionally presented with recommendations labeled as either human or AI generated. We found that traditional PSGs tended to be scored slightly more accurately than self applied PSGs, but this difference was not statistically significant. Correct recommendations significantly improved scoring accuracy for both PSG types, while incorrect recommendations reduced accuracy. No significant bias was observed toward or against AI generated recommendations compared to human generated recommendations. These findings highlight the potential of AI to enhance PSG scoring reliability. However, ensuring the accuracy of AI outputs is critical to maximizing its benefits. Future research should explore the long term impacts of DSS on scoring workflows and strategies for integrating AI in clinical practice.
AI-Powered Assistive Technologies for Visual Impairment
Venue: arXiv cs.HC 2025
Year: 2025

Artificial Intelligence (AI) is revolutionizing assistive technologies. It offers innovative solutions to enhance the quality of life for individuals with visual impairments. This review examines the development, applications, and impact of AI-powered tools in key domains, such as computer vision, natural language processing (NLP), and wearable devices. Specific advancements include object recognition for identifying everyday items, scene description for understanding surroundings, and NLP-driven text-to-speech systems for accessing digital information. Assistive technologies like smart glasses, smartphone applications, and AI-enabled navigation aids are discussed, demonstrating their ability to support independent travel, facilitate social interaction, and increase access to education and employment opportunities. The integration of deep learning models, multimodal interfaces, and real-time data processing has transformed the functionality and usability of these tools, fostering inclusivity and empowerment. This article also addresses critical challenges, including ethical considerations, affordability, and adaptability in diverse environments. Future directions highlight the need for interdisciplinary collaboration to refine these technologies, ensuring equitable access and sustainable innovation. By providing a comprehensive overview, this review underscores AI's transformative potential in promoting independence, enhancing accessibility, and fostering social inclusion for visually impaired individuals.
Entwicklung einer Webanwendung zur Generierung von skolemisierten RDF Daten für die Verwaltung von Lieferketten
Venue: arXiv cs.HC 2025
Year: 2025

Für eine frühzeitige Erkennung von Lieferengpässen müssen Lieferketten in einer geeigneten digitalen Form vorliegen, damit sie verarbeitet werden können. Der für die Datenmodellierung benötigte Arbeitsaufwand ist jedoch, gerade IT-fremden Personen, nicht zuzumuten. Es wurde deshalb im Rahmen dieser Arbeit eine Webanwendung entwickelt, welche die zugrunde liegende Komplexität für den Benutzer verschleiern soll. Konkret handelt es sich dabei um eine grafische Benutzeroberfläche, auf welcher Templates instanziiert und miteinander verknüpft werden können. Für die Definition dieser Templates wurden in dieser Arbeit geeignete Konzepte erarbeitet und erweitert. Zur Erhebung der Benutzerfreundlichkeit der Webanwendung wurde abschließend eine Nutzerstudie mit mehreren Testpersonen durchgeführt. Diese legte eine Vielzahl von nützlichen Verbesserungsvorschlägen offen. -- For early detection of supply bottlenecks, supply chains must be available in a suitable digital form so that they can be processed. However, the amount of work required for data modeling cannot be expected of people who are not familiar with IT topics. Therefore, a web application was developed in the context of this thesis, which is supposed to disguise the underlying complexity for the user. Specifically, this is a graphical user interface on which templates can be instantiated and linked to each other. Suitable concepts for the definition of these templates were developed and extended in this thesis. Finally, a user study with several test persons was conducted to determine the usability of the web application. This revealed a large number of useful suggestions for improvement.
Revival: Collaborative Artistic Creation through Human-AI Interactions in Musical Creativity
Venue: arXiv cs.HC 2025
Year: 2025

Revival is an innovative live audiovisual performance and music improvisation by our artist collective K-Phi-A, blending human and AI musicianship to create electronic music with audio-reactive visuals. The performance features real-time co-creative improvisation between a percussionist, an electronic music artist, and AI musical agents. Trained in works by deceased composers and the collective's compositions, these agents dynamically respond to human input and emulate complex musical styles. An AI-driven visual synthesizer, guided by a human VJ, produces visuals that evolve with the musical landscape. Revival showcases the potential of AI and human collaboration in improvisational artistic creation.
ImageInThat: Manipulating Images to Convey User Instructions to Robots
Venue: arXiv cs.HC 2025
Year: 2025

Foundation models are rapidly improving the capability of robots in performing everyday tasks autonomously such as meal preparation, yet robots will still need to be instructed by humans due to model performance, the difficulty of capturing user preferences, and the need for user agency. Robots can be instructed using various methods-natural language conveys immediate instructions but can be abstract or ambiguous, whereas end-user programming supports longer horizon tasks but interfaces face difficulties in capturing user intent. In this work, we propose using direct manipulation of images as an alternative paradigm to instruct robots, and introduce a specific instantiation called ImageInThat which allows users to perform direct manipulation on images in a timeline-style interface to generate robot instructions. Through a user study, we demonstrate the efficacy of ImageInThat to instruct robots in kitchen manipulation tasks, comparing it to a text-based natural language instruction method. The results show that participants were faster with ImageInThat and preferred to use it over the text-based method. Supplementary material including code can be found at: https://image-in-that.github.io/.
The Trust Calibration Maturity Model for Characterizing and Communicating Trustworthiness of AI Systems
Venue: arXiv cs.HC 2025
Year: 2025

Recent proliferation of powerful AI systems has created a strong need for capabilities that help users to calibrate trust in those systems. As AI systems grow in scale, information required to evaluate their trustworthiness becomes less accessible, presenting a growing risk of using these systems inappropriately. We propose the Trust Calibration Maturity Model (TCMM) to characterize and communicate information about AI system trustworthiness. The TCMM incorporates five dimensions of analytic maturity: Performance Characterization, Bias & Robustness Quantification, Transparency, Safety & Security, and Usability. The TCMM can be presented along with system performance information to (1) help a user to appropriately calibrate trust, (2) establish requirements and track progress, and (3) identify research needs. Here, we discuss the TCMM and demonstrate it on two target tasks: using ChatGPT for high consequence nuclear science determinations, and using PhaseNet (an ensemble of seismic models) for categorizing sources of seismic events.
Superhuman Game AI Disclosure: Expertise and Context Moderate Effects on Trust and Fairness
Venue: arXiv cs.HC 2025
Year: 2025

As artificial intelligence surpasses human performance in select tasks, disclosing superhuman capabilities poses distinct challenges for fairness, accountability, and trust. However, the impact of such disclosures on diverse user attitudes and behaviors remains unclear, particularly concerning potential negative reactions like discouragement or overreliance. This paper investigates these effects by utilizing Persona Cards: a validated, standardized set of synthetic personas designed to simulate diverse user reactions and fairness perspectives. We conducted an ethics board-approved study (N=32), utilizing these personas to investigate how capability disclosure influenced behaviors with a superhuman game AI in competitive StarCraft II scenarios. Our results reveal transparency is double-edged: while disclosure could alleviate suspicion, it also provoked frustration and strategic defeatism among novices in cooperative scenarios, as well as overreliance in competitive contexts. Experienced and competitive players interpreted disclosure as confirmation of an unbeatable opponent, shifting to suboptimal goals. We release the Persona Cards Dataset, including profiles, prompts, interaction logs, and protocols, to foster reproducible research into human alignment AI design. This work demonstrates that transparency is not a cure-all; successfully leveraging disclosure to enhance trust and accountability requires careful tailoring to user characteristics, domain norms, and specific fairness objectives.
Towards Computer-Using Personal Agents
Venue: arXiv cs.HC 2025
Year: 2025

Computer-Using Agents (CUA) enable users to automate increasingly-complex tasks using graphical interfaces such as browsers. As many potential tasks require personal data, we propose Computer-Using Personal Agents (CUPAs) that have access to an external repository of the user's personal data. Compared with CUAs, CUPAs offer users better control of their personal data, the potential to automate more tasks involving personal data, better interoperability with external sources of data, and better capabilities to coordinate with other CUPAs in order to solve collaborative tasks involving the personal data of multiple users.
Robot Character Generation and Adaptive Human-Robot Interaction with Personality Shaping
Venue: arXiv cs.HC 2025
Year: 2025

We present a novel framework for designing emotionally agile robots with dynamic personalities and memory-based learning, with the aim of performing adaptive and non-deterministic interactions with humans while conforming to shared social understanding. While existing work has largely focused on emotion recognition and static response systems, many approaches rely on sentiment analysis and action mapping frameworks that are pre-defined with limited dimensionality and fixed configurations, lacking the flexibility of dynamic personality traits and memory-enabled adaptation. Other systems are often restricted to limited modes of expression and fail to develop a causal relationship between human behavior and the robot's proactive physical actions, resulting in constrained adaptability and reduced responsiveness in complex, dynamic interactions. Our methodology integrates the Big Five Personality Traits, Appraisal Theory, and abstracted memory layers through Large Language Models (LLMs). The LLM generates a parameterized robot personality based on the Big Five, processes human language and sentiments, evaluates human behavior using Appraisal Theory, and generates emotions and selects appropriate actions adapted by historical context over time. We validated the framework by testing three robots with distinct personalities in identical background contexts and found that personality, appraisal, and memory influence the adaptability of human-robot interactions. The impact of the individual components was further validated through ablation tests. We conclude that this system enables robots to engage in meaningful and personalized interactions with users, and holds significant potential for applications in domains such as pet robots, assistive robots, educational robots, and collaborative functional robots, where cultivating tailored relationships and enriching user experiences are essential.
InteractiveEdu: An Open-source Interactive Floor for Exergame as a Learning Platform
Venue: arXiv cs.HC 2025
Year: 2025

Children tend to be constantly exposed to technologies, such as smartphones, tablets, and gaming consoles, drawn by the interactive and visually stimulating nature of digital platforms. Thus, integrating the teaching process with technological gadgets may enhance engagement and foster interactive learning experiences, besides equipping students with the digital skills for today's increasingly technology-driven world. The main goal of this work is to provide an open-source and manageable tool that teachers can use as an everyday activity and as an exergame. For this, we present a prototype of an interactive platform that students use to answer a quiz by moving to segments available on an interactive floor. All the platform design and implementation directions are publicly available.
Reflections on the Use of Dashboards in the Covid-19 Pandemic
Venue: arXiv cs.HC 2025
Year: 2025

Dashboards have arguably been the most used visualizations during the COVID-19 pandemic. They were used to communicate its evolution to national governments for disaster mitigation, to the public domain to inform about its status, and to epidemiologists to comprehend and predict the evolution of the disease. Each design had to be tailored for different tasks and to varying audiences - in many cases set up in a very short time due to the urgent need. In this paper, we collect notable examples of dashboards and reflect on their use and design during the pandemic from a user-oriented perspective: we interview a group of researchers with varying visualization expertise who actively used dashboards during the pandemic as part of their daily workflow. We discuss our findings and compile a list of lessons learned to support future visualization researchers and dashboard designers.
Identifying Likely-Reputable Blockchain Projects on Ethereum
Venue: arXiv cs.CR 2025
Year: 2025

Identifying reputable Ethereum projects remains a critical challenge within the expanding blockchain ecosystem. The ability to distinguish between legitimate initiatives and potentially fraudulent schemes is non-trivial. This work presents a systematic approach that integrates multiple data sources with advanced analytics to evaluate credibility, transparency, and overall trustworthiness. The methodology applies machine learning techniques to analyse transaction histories on the Ethereum blockchain. The study classifies accounts based on a dataset comprising 2,179 entities linked to illicit activities and 3,977 associated with reputable projects. Using the LightGBM algorithm, the approach achieves an average accuracy of 0.984 and an average AUC of 0.999, validated through 10-fold cross-validation. Key influential factors include time differences between transactions and received_tnx. The proposed methodology provides a robust mechanism for identifying reputable Ethereum projects, fostering a more secure and transparent investment environment. By equipping stakeholders with data-driven insights, this research enables more informed decision-making, risk mitigation, and the promotion of legitimate blockchain initiatives. Furthermore, it lays the foundation for future advancements in trust assessment methodologies, contributing to the continued development and maturity of the Ethereum ecosystem.
Data-Driven Approximation of Binary-State Network Reliability Function: Algorithm Selection and Reliability Thresholds for Large-Scale Systems
Venue: arXiv cs.LG 2025
Year: 2025

Network reliability assessment is pivotal for ensuring the robustness of modern infrastructure systems, from power grids to communication networks. While exact reliability computation for binary-state networks is NP-hard, existing approximation methods face critical tradeoffs between accuracy, scalability, and data efficiency. This study evaluates 20 machine learning methods across three reliability regimes full range (0.0-1.0), high reliability (0.9-1.0), and ultra high reliability (0.99-1.0) to address these gaps. We demonstrate that large-scale networks with arc reliability larger than or equal to 0.9 exhibit near-unity system reliability, enabling computational simplifications. Further, we establish a dataset-scale-driven paradigm for algorithm selection: Artificial Neural Networks (ANN) excel with limited data, while Polynomial Regression (PR) achieves superior accuracy in data-rich environments. Our findings reveal ANN's Test-MSE of 7.24E-05 at 30,000 samples and PR's optimal performance (5.61E-05) at 40,000 samples, outperforming traditional Monte Carlo simulations. These insights provide actionable guidelines for balancing accuracy, interpretability, and computational efficiency in reliability engineering, with implications for infrastructure resilience and system optimization.
DPFAGA-Dynamic Power Flow Analysis and Fault Characteristics: A Graph Attention Neural Network
Venue: arXiv cs.LG 2025
Year: 2025

We propose the joint graph attention neural network (GAT), clustering with adaptive neighbors (CAN) and probabilistic graphical model for dynamic power flow analysis and fault characteristics. In fact, computational efficiency is the main focus to enhance, whilst we ensure the performance accuracy at the accepted level. Note that Machine Learning (ML) based schemes have a requirement of sufficient labeled data during training, which is not easily satisfied in practical applications. Also, there are unknown data due to new arrived measurements or incompatible smart devices in complex smart grid systems. These problems would be resolved by our proposed GAT based framework, which models the label dependency between the network data and learns object representations such that it could achieve the semi-supervised fault diagnosis. To create the joint label dependency, we develop the graph construction from the raw acquired signals by using CAN. Next, we develop the probabilistic graphical model of Markov random field for graph representation, which supports for the GAT based framework. We then evaluate the proposed framework in the use-case application in smart grid and make a fair comparison to the existing methods.
A Bird Song Detector for improving bird identification through Deep Learning: a case study from Doñana
Venue: arXiv cs.SD 2025
Year: 2025

Passive Acoustic Monitoring is a key tool for biodiversity conservation, but the large volumes of unsupervised audio it generates present major challenges for extracting meaningful information. Deep Learning offers promising solutions. BirdNET, a widely used bird identification model, has shown success in many study systems but is limited at local scale due to biases in its training data, which focus on specific locations and target sounds rather than entire soundscapes. A key challenge in bird species identification is that many recordings either lack target species or contain overlapping vocalizations, complicating automatic identification. To address these problems, we developed a multi-stage pipeline for automatic bird vocalization identification in Doñana National Park (SW Spain), a wetland of high conservation concern. We deployed AudioMoth recorders in three main habitats across nine locations and manually annotated 461 minutes of audio, resulting in 3749 labeled segments spanning 34 classes. We first applied a Bird Song Detector to isolate bird vocalizations using spectrogram-based image processing. Then, species were classified using custom models trained at the local scale. Applying the Bird Song Detector before classification improved species identification, as all models performed better when analyzing only the segments where birds were detected. Specifically, the combination of detector and fine-tuned BirdNET outperformed the baseline without detection. This approach demonstrates the effectiveness of integrating a Bird Song Detector with local classification models. These findings highlight the need to adapt general-purpose tools to specific ecological challenges. Automatically detecting bird species helps track the health of this threatened ecosystem, given birds sensitivity to environmental change, and supports conservation planning to reduce biodiversity loss.
Navigating MLOps: Insights into Maturity, Lifecycle, Tools, and Careers
Venue: arXiv cs.SE 2025
Year: 2025

The adoption of Machine Learning Operations (MLOps) enables automation and reliable model deployments across industries. However, differing MLOps lifecycle frameworks and maturity models proposed by industry, academia, and organizations have led to confusion regarding standard adoption practices. This paper introduces a unified MLOps lifecycle framework, further incorporating Large Language Model Operations (LLMOps), to address this gap. Additionally, we outlines key roles, tools, and costs associated with MLOps adoption at various maturity levels. By providing a standardized framework, we aim to help organizations clearly define and allocate the resources needed to implement MLOps effectively.
MedSpaformer: a Transferable Transformer with Multi-granularity Token Sparsification for Medical Time Series Classification
Venue: arXiv cs.LG 2025
Year: 2025

Accurate medical time series (MedTS) classification is essential for effective clinical diagnosis, yet remains challenging due to complex multi-channel temporal dependencies, information redundancy, and label scarcity. While transformer-based models have shown promise in time series analysis, most are designed for forecasting tasks and fail to fully exploit the unique characteristics of MedTS. In this paper, we introduce MedSpaformer, a transformer-based framework tailored for MedTS classification. It incorporates a sparse token-based dual-attention mechanism that enables global context modeling and token sparsification, allowing dynamic feature refinement by focusing on informative tokens while reducing redundancy. This mechanism is integrated into a multi-granularity cross-channel encoding scheme to capture intra- and inter-granularity temporal dependencies and inter-channel correlations, enabling progressive refinement of task-relevant patterns in medical signals. The sparsification design allows our model to flexibly accommodate inputs with variable lengths and channel dimensions. We also introduce an adaptive label encoder to extract label semantics and address cross-dataset label space misalignment. Together, these components enhance the model's transferability across heterogeneous medical datasets, which helps alleviate the challenge of label scarcity. Our model outperforms 13 baselines across 7 medical datasets under supervised learning. It also excels in few-shot learning and demonstrates zero-shot capability in both in-domain and cross-domain diagnostics. These results highlight MedSpaformer's robustness and its potential as a unified solution for MedTS classification across diverse settings.