Papers

Live programming provides visibility to programmers by running and tracing programs as they are edited. However, for programs with potentially harmful side effects, liveness can turn mistakes into disasters. We propose enabling live programming in environments with side effects via sandboxing: confining effects to a simulation of the true environment. We apply sandboxed live programming in the challenging context of shell scripting: a ubiquitous and powerful—yet notoriously opaque and error-prone—tool. ShellVis provides line-by-line feedback on a shell script's run-time behavior, with file operations sandboxed via a safe overlay of the file system. A qualitative user evaluation finds ShellVis to be helpful to participants, replacing tedious existing practices and instilling confidence. Participant responses also reveal areas for future research, particularly bridging the gulf of execution alongside the gulf of evaluation. ShellVis serves as a case study of how sandboxing can bring live-programming techniques into the many real-world programming contexts where side effects are important.

Blind or low vision (BLV) students are less likely to graduate from college, particularly in computing. Prior work documents accessibility challenges in high school and college, but we lack understanding of the transition process that produces this "leaky pipeline." To address this, we interviewed ten BLV college students about going to college to study computing. We analyzed our data through the lens of life transition, specifically Intersecting Liminality. Our findings reveal that some BLV students face such immense digital accessibility and college acclimation barriers that the only way forward as coders is to take on a "second job" as a blind advocate or drop out of the computing major. We argue that the college transition is a critical point for analysis and technological intervention, and further, that Intersecting Liminality provides a useful lens for HCI scholars to unpack the compounding challenges that prevent some BLV students from completing computing degrees.

Clinical education must prepare medical students to conduct safe and coherent patient interviews under conditions of uncertainty. Traditional standardized patient (SP) training is resource-intensive and difficult to scale. We developed a scaffolding-oriented multi-agent Large Language Model (LLM) AI Standardized Patient (AI-SP) training platform1. The system includes a patient agent for simulated dialog, a tutor agent providing Socratic prompts without disclosing diagnostic information, and a turn-level evaluator agent that monitors clinical progress without revealing summative scores. In a randomized controlled study (N = 100 medical students), participants were assigned to either a multi-agent (MA) scaffolding condition or a control condition. All students completed two learning sessions under their assigned condition followed by an examination conducted in a patient only environment. Performance was assessed using a standardized Objective Structured Clinical Examination (OSCE) based rubric. While no significant difference was observed in final diagnostic accuracy between groups, the multi-agent AI standardized patient system improved final examination scores compared to the control group utilizing structured progressive information disclosure; the most substantial and consistent improvements were observed in communication, the expression of empathy, and specific history-taking behaviors. These findings suggest that specialized LLM agents enhance the process quality of simulated clinical interviews without artificially inflating examination outcomes. To support future research, we release a multi-expert annotated dataset comprising transcripts, checklist annotations, turn-level evaluations, and OSCE-aligned scoring outcomes. This resource aims to facilitate the development of pedagogically grounded AI-SP systems and advance research on AI-supported clinical reasoning training.

Digital technologies are deployed in urban built environments with the aim of supporting social dimensions. However, the research offers no unified guidance for such digital technology design. Additionally, evidence shows that week social wellbeing contributes to mental and physical health outcomes which suggests that the technology designed to strengthen social wellbeing could also function as a form of health promoting and preventive intervention. This chapter addresses this research gap by developing a conceptual framework that supports the design of digital technologies for social wellbeing in built environments. We propose a conceptual framework composed of three core components which are drawn on the synthesis of selected empirical studies on technologies embedded in built environments for social wellbeing. First, a social wellbeing dimensions model that identifies what digital technology could address. Second, a digital technology contribution matrix that distinguishes the types of contributions a digital technology could make. Third, levels that maps the scope at which technology could support social wellbeing. This conceptual framework could help researchers, practitioners and policymakers to design and guide digital technology interventions that target social wellbeing in the built environment.

Generative artificial intelligence (GenAI) significantly expands creators' productive capacity, but this does not necessarily entail a corresponding increase in creative agency or authorship. This paper distinguishes creativity at the level of the work from creative agency at the level of the creator, and argues that human authorship cannot be determined solely by manual intervention, degree of automation, the origin of an initial idea, or final selection authority. Rather, authorship depends on whether human judgment and reasons genuinely shape the development of the work. To articulate this requirement, the paper introduces Meaningful Human Control (MHC) into generative creation and identifies a limitation of its classical tracking condition. Creative reasons are not always fully specified prior to interaction with AI; they may emerge, change, or be abandoned as the creative process unfolds. The paper therefore proposes dynamic-reflexive tracking (DRT), which requires that a creator's evolving reasons undergo reflective uptake, exert genuine influence on the subsequent trajectory of creation, and remain capable of rejecting and redirecting the system's default direction. DRT consists of four conditions: diachronic reason formation, reflective uptake, trajectory efficacy, and contestability and redirection, together with a minimal tracing requirement. The paper argues that human authorship under generative AI depends not on how many steps a person personally performs, but on whether that person's reasons continuously, reflectively, and effectively shape what the work becomes.

Sustained deployment of generative AI agents requires more than isolated task success. Agents must remain useful across repeated interactions, changing conditions, and dependencies on people within shared workflows, especially as technical, human, and operational disruptions accumulate over time. We propose operational resilience and considerate participation as two complementary aspects of evaluating such agents: the former captures how agents recover from blocked work while preserving progress and communicating their limits, and the latter captures how their adaptation accounts for affected people, role boundaries, and the surrounding workflow. Yet both remain underexplored under accumulating challenge. We study 120 simulated healthcare trajectories across two generative AI models and twelve stakeholder-derived tasks under light, medium, and heavy challenge. We compare textual action plans, prompted internal assessments, and quantitative structured workload and affect reports to examine how agent behavior and reported state change as challenge accumulates. Regarding operational resilience, agents shift from self-directed recovery toward greater human dependence, while reporting increasing workload and negative affect in structured reports but seldom expressing strain in textual responses. Regarding considerate participation, agents broaden from task-focused adaptation toward task reframing, attention to others, role-boundary adjustment, and wider coordination, with distinct patterns across actions and internal assessments. From these findings, we derive five deployment dilemmas involving persistence, attention, role boundaries, state disclosure, and escalation that require stakeholder specification, further informing technical implications for learning, situated evaluation, and embodied adaptation.

Animation and VFX pre-production review requires teams to translate loosely specified creative intent–briefs, evolving specifications, heterogeneous references, and verbal decisions–into revisions that junior artists can execute without repeated clarification. In practice, criteria drift across iterations, review judgments lose their evidential basis, and the reasoning behind a request rarely survives the senior-junior handoff. We contribute a design framework for intent-evidence-action alignment: intent is articulated into a shared project record, judgments are anchored to grounded evidence, and authorized decisions are converted into clear revision tasks tied directly to reference notes. We instantiate this framework in MOONWALK, a professional pre-production review system comprising a shared intent record, reference/specification anchoring, structured work-in-progress comparison, and supervisor-authorized action planning. In this workflow, AI handles administrative coordination–flagging missing context and organizing notes–while artists retain full creative direction. An in-studio study with professional practitioners compares MOONWALK with a chat-only (chatbot) interface using matched production materials, while participants' existing workflows provide a retrospective ecological baseline. Results indicate stronger intent alignment, decision traceability, and checklist executability, while also showing that aesthetic authority and final prioritization must remain with practitioners. The evaluation establishes the value of the integrated structured workflow over unstructured conversational AI chatbot. Code: https://github.com/Akinesia112/Moonwalk/tree/english-version

A confidence-aware multimodal fusion framework (CAMF) is proposed to realize reliable human intention prediction for industrial human-robot collaboration. This framework fuses four heterogeneous modalities including object 6D pose, gaze, skeletal motion and IMU-based hand motion. It embeds a confidence-trend-driven dynamic fusion mechanism into BiLSTM to adaptively balance bidirectional temporal features according to real-time modality reliability. A confidence-guided balanced learning strategy combined with a confidence freezing mechanism is further adopted to adjust network gradients dynamically, suppress noise from low-quality modalities and mitigate cross-modal learning bias. A physical platform based on the UR3 collaborative robot is built for experimental validation. Comparative results show that the proposed method reaches an intention recognition accuracy of 91.86% and outperforms existing multimodal fusion approaches in overall performance and stability. It also maintains satisfactory accuracy under low light and partial occlusion interference. In practical assembly tasks, the framework enables proactive and stable human-robot cooperation with strong environmental adaptability.

Multimedia applications require precise music annotation-labeled positions, segments, or loops-placed by hand or algorithmically. Machine-learning algorithms are scalable and effective but need annotated training data, scarce for many tasks. TimeCues Studio is an open-source workspace where algorithm-development teams annotate a music corpus, compare detection algorithms against those annotations, and prototype new ones. Unlike existing tools built for a single track at a time, TimeCues targets teams annotating whole collections, tightly integrated with algorithm development. Annotators place several marker types-each supporting ambiguity-aware labeling-on a grid-locked timeline that visualizes many music features, including separated audio stems. The same timeline drives an algorithm-comparison engine with bundled baselines, a Python sandbox for prototyping new models, and an ambiguity-aware evaluator that honors the structured fields. The same visualization suits solo annotators on music-sync projects. TimeCues is MIT-licensed and deploys via one Docker Compose command.

While the GDPR and ePrivacy Directive mandate that consent information must be clear and accessible, most modern cookie banners remain obscured by technical jargon, vague phrasing, and frequent content overload or underload. This feasibility study investigates the impact of applying plain language (Einfache Sprache) to cookie banners within the IAB Transparency & Consent Framework (TCF). In our study, we analysed cookie banner texts from 200 websites, using AI-based mapping to categorise extracted content into standardised processing purposes. By substituting complex legal terms with simplified descriptions, we successfully demonstrated that the comprehension barrier can be lowered from a college-graduate level to a 7th-grade level. However, the effectiveness of plain language is inherently constrained by the informativeness of the original content; it cannot compensate for banners that omit legally required details. We conclude that while plain language is a vital tool for digital accessibility, it must be paired with standardised implementation guidelines to ensure that cookie banners are both readable and informative.

We describe a participatory design approach toward developing Deaf-centric text-to-speech (TTS) technologies. While TTS is growing rapidly in the mainstream, it has received little attention to date in the deaf and hard of hearing (DHH) technology space. Critical problems have remained unaddressed for DHH users, including the ability to manipulate tone, emotions and delivery via non-auditory means. Verifying that the generated speech matches intent and is appropriate for a given situation without having to listen to it is another challenge. Respecting cultural and identity factors in the generated speech is also important. This work explores the design space with DHH participants through two focus groups, three co-design sessions, and four one-on-one early-stage design evaluation sessions. Participants included people both familiar and unfamiliar with TTS, as well as DHH content creators. We describe key findings, design ideas, results, and implications for future Deaf-centric TTS development. We also identify unmet technology requirements that pose barriers to adoption of Deaf-centric TTS technology.

P300 spellers offer people with severe motor impairment, such as ALS, an effective communication channel and remain one of the most established surgery-free alternatives to intracortical interfaces. Advanced language models have made spellers faster and more robust, yet the hardware beneath them is under-studied. We present a hardware-agnostic, real-time P300 acquisition pipeline built on BrainFlow and Lab Streaming Layer (LSL) that runs unchanged across consumer- and research-grade EEG headsets, with permutation tests of signal separability. Using a standard 6 x 6 row/column paradigm, we piloted five configurations: a custom dry system, a custom wet/gel system, Emotiv Flex, Emotiv EPOC X, and Muse 2. The custom systems and EPOC X showed weak or inconsistent signal separability, Muse 2 had the highest acquisition reliability despite limited centro-parietal coverage, and Flex showed the most promising signal. In 20 further Flex sessions varying subject, timing, and phrase length (131 target characters), a peak-amplitude permutation test and a cross-validated xDAWN decoder both detected a significant target response under two channel-exclusion policies, with decoder AUC reaching about 0.72 after 15 repetitions. Character accuracy depended heavily on evaluation methodology: in-sample majority voting reached 94.7%, whereas character-held-out accuracy was 31.3% with evidence accumulated across repetitions, about three times that of held-out majority voting. These analyses indicate that Flex captured a detectable, if still weak, P300 under the studied conditions, while broader participant-level validation and improved decoding remain necessary.

The methods for capturing the creative process come with associated tensions around memory recall, articulation, and communication during the act of making, as well as how to record these considerations. This paper has a twofold purpose: first, to offer an example of a mixed methodology, drawn from dance anthropology, sensory ethnography, and design, that applies embodied methods as an alternative for documenting creative making. Specifically, this incorporates the researcher-as-participant and the collation of fieldnotes, embodied knowledge/movement recall, with notation forms, and participant interviews. These are existing methods in dance anthropology; however, using them alongside exploratory prototyping and workshop approaches broadened this work into transdisciplinary practice. Second, it discusses the activation of somatosensory systems through wearable technology and the facilitation of heightened sensory awareness for the practitioner, leading to a subsequent ability to focus on creative decisions linked to reflection and metacognition.

Organizational design in the era of artificial intelligence requires experimental methods that can test how human-AI groups coordinate, delegate, and make decisions. Programmable platforms coordinate live human-to-human sessions or real-time human-AI chat, but researchers cannot easily declare experiment protocols in which AI participants both communicate and act on shared work within one auditable configuration. Here we introduce Pairit, an online platform that facilitates the design, testing, and deployment of experiments that test human-AI organizational designs and interventions. Through a single YAML configuration file, researchers declare an executable experiment graph (pages, routing, randomization, matchmaking, chat, shared workspaces, server-hosted agents, surveys, timers, and custom HTML components) and combine any number of humans and AI agents in live sessions. We have validated the feasibility of the platform through multiple live deployments, including peer-reviewed published studies, capturing high-resolution process traces of communication, negotiation, and collaborative work in live human-AI dyads. By representing complex interactive protocols as standardized, auditable configuration files, Pairit provides reusable infrastructure for specifying, deploying, and sharing live human-AI organizational experiments.

Visible AI capabilities need not translate into broader judgments of trustworthiness. In a randomized 2 x 2 experiment with 270 U.S.-based Reddit users, an embedded assistant displayed one or three functions, with or without a brief rationale. Displaying three functions increased perceived multifunctionality; no other randomized main effect survived correction across the six outcomes. Rationale availability did not reliably increase perceived intelligence. Exploratory analysis indicated stronger uptake of the functional display at higher objective AI literacy. Among concurrently measured judgments, perceived multifunctionality was associated with perceived intelligence, which was associated with anthropomorphism and all three trust dimensions. After accounting for perceived intelligence, anthropomorphism was positively associated with benevolence, but not reliably with integrity or ability. These findings separate interface effects from relationships among users' perceptions and show why ability, integrity, and benevolence should be evaluated separately.

Overeating and emotional eating are common health issues that affect people even without an eating disorder. The vagus nerve plays a critical role in the gut-brain axis, and implanted vagus nerve stimulators have been associated with reduced appetite. In this paper, we propose transcutaneous cervical vagus nerve stimulation (tcVNS) as a ubiquitous system to provide immediate, low-effort intervention during an eating episode. In a study with 24 participants, we evaluated a mobile, handheld tcVNS device during a single episode of distracted snacking. We found that participants ate 9.6% less and 23.6% more slowly during vagus nerve stimulation than during sham stimulation. Post-snacking satiety was the same in both conditions, while heart rate was lower during vagus nerve stimulation. The stimulation was described as subtle and barely noticeable. Overall, these results provide evidence for the feasibility of non-invasive vagus nerve stimulation as a low-attention intervention for managing eating behavior – one that can be packaged inside ubiquitous interactive systems. As such, we extend the design space of implicit interfaces toward physiological intervention, motivating future ubiquitous systems that pair eating-related sensing with low-attention interventions.

Topic models summarize large text corpora, but top-ranked words often provide only a limited representation of topic semantics. Sparse autoencoders (SAEs) offer a way to move beyond word-level descriptors by extracting interpretable features from dense representations, yet how feature interpretability relates to topic-inference quality remains unclear. We introduce MonoTM, an interpretable topic modeling framework that decouples these roles. Across three benchmark corpora, we show that document–topic mixture estimation and semantic interpretation favor different SAE configurations and feature subsets. MonoTM estimates mixtures from the full SAE bag-of-features representation and, with them fixed, learns topic descriptors over a separate vocabulary of corpus-grounded semantic features. This design preserves global topic structure while representing topics with semantic units more meaningful than individual words, making them more useful for downstream corpus analysis.

Algorithmic outputs now populate the digital environments through which contemporary life is organized. The role of law in facilitating and constituting (rather than merely responding to) these processes is gaining increasing traction across scholarly accounts. This inquiry traces the evolution of algorithmic outputs attending to their legal underpinnings and social implications, surfacing the mutations of machine speech. The first mutation redefined speech as data to be queried: search engines transformed the web from a space of information retrieval into an economic regime of algorithmic visibility. The second mutation reframed speech as engagement: social media platforms fused moderation with amplification, turning expression into a metric of attention, governed by corporate architectures. The third mutation emerges in conversational systems and interfaces, where generative text displaces information retrieval, bringing with it dense technolegal entanglements and profound epistemic consequences. Scholars of freedom of expression, informational privacy, and communication studies have long grappled with these dynamics, yet their implications for broader legal thought have also become urgent. This piece seeks to organize and clarify the evolving debate around algorithmic speech, making this critical but often fragmented discourse more accessible to wider legal and interdisciplinary audiences. In doing so, it bridges the gap between observing technological transformation and critically assessing the constitutive role of law within it, offering a conceptual resource for researchers, students, policymakers, and practitioners navigating and contesting this evolving landscape.

In real-world design practice, evaluations rarely rely on a single source of judgment. Designers routinely combine expert opinions, empirical studies, and computational models, each with distinct strengths and limitations. While machine learning offers methods to integrate multiple feedback sources, these approaches remain largely inaccessible to designers without technical expertise. In this paper, we explore how to integrate multiple feedback sources, primarily through: (1) a practical approach for multi-source integration, and (2) its implementation in MUSE, a no-code tool that allows designers to combine and balance diverse sources. Our technical findings show that independent modeling of multiple evaluation sources enables exploration across heterogeneous feedback, accommodates different evaluation speeds, surfaces disagreements between sources, and supports an adaptable evaluation setup that designers can reconfigure during their process. In a visualization design study, participants navigated their own judgments alongside simulator feedback, reporting a perception of enhanced confidence and flexibility. Our results highlight the viability of multi-source integration to support computational design, offering a step toward bridging the gap between advanced optimization methods and design practice.

Introduction: In this paper, we present the preliminary design of a toolkit for making glyph-based physicalizations for public engagement. We use London river health data as a case study: a data set of significance to urban issues related to climate change and of interest to draw public attention, as part of the Greater London Authority's strategies. Design: We present the components of a 3D glyph-making toolkit, its encodings, and a step-by-step process for crafting a physicalization of a river's water quality using recycled materials. We reason about how users can use the template to learn about a data set while reflecting on the data's significance to their personal experience and self-mapping onto the physicalization. Reflection: We reflect on the opportunities that extending the design space of glyphs to 3D physicalization offers for supporting public engagement with complex, multi-dimensional data sets, scaffolding cognitive processes, and self-reflection, thereby bringing crucial environmental data to life. Conclusion: Future implementation of the 3D glyph template will enable the public of all abilities to explore river health data, physicalize complexity, and realize its relevance. We hope that its use in public engagement workshops will help raise awareness, invite care, and foster a sense of belonging.

Video lectures have become increasingly prevalent for education and professional development, yet their static visuals, dense information, and long duration pose attentional challenges for individuals with ADHD. While adaptive learning offers opportunities towards ADHD-accessible video learning, little is known about how to suitably adapt such videos: What components in multimodal video lectures are challenging for ADHD viewers? How do these experiences surface in behavioral signals to trigger an adaptation? What presentations do they prefer? To answer these questions, we conducted an eye-tracking-based retrospective think-aloud study with 16 participants with ADHD, who watched and reflected on a curated set of video lecture segments. Our study uncovered video design elements that hindered learning and revealed participants' coping strategies along with their limitations. By jointly analyzing behavioral signals and retrospective reflections, we characterized how these experiences manifested in behavioral patterns. We further surfaced participants' practices for addressing learning needs beyond the video watching process, and derived design implications for future ADHD-friendly adaptive video learning systems.

Web accessibility auditing requires interpreting diverse requirements and examining interface behavior. Rule-based checks and noninteractive model assessments can miss barriers requiring contextual or interactive evidence. We present an agentic framework that assigns a vision-language agent to each accessibility requirement. Guided by tailored instructions, agents inspect webpages, operate controls, and record evidence supporting their findings. We implement the framework for 40 requirements from the Web Content Accessibility Guidelines (WCAG). To compare detection and cost, we construct a dataset of 250 page-criterion records derived from expert audits across 11 scholarly platforms. Agents recover 67 of 78 reported positive cases (86% recall), compared with 36% for axe-core, a rule-based checker, and 67% for an uncued, noninteractive vision-language model, at lower precision (56%). They recover nine of ten Keyboard and No Keyboard Trap cases missed by both baselines. Together, the framework, implementations, and dataset support automated accessibility auditing grounded in inspectable evidence.

What does popular content look like when platforms withhold the usual cues? On TikTok, users still form impressions about which videos are taking off even when likes and view counts are hidden, delayed, or pushed to the margins of the interface. We study this problem through TokOrNot, a web-based game in which participants compared pairs of TikTok videos and reported (i) which one they preferred and (ii) which one they believed had reached a larger audience. We benchmark these judgments against verified public view counts, which we use as a bounded proxy for realized platform reach. Across 3,513 judgments from 363 participants, participants identified the higher-reach video only modestly above chance (56.75%, 95% CI: 56.01-58.55). Preference aligned with the higher-view video at a similar rate, while preference and prediction matched in 83.48% of trials (95% CI: 83.12-85.95). Performance also varied across content categories. Taken together, these results do not suggest that users can reliably read platform success from content alone. Instead, they point to a looser and more uncertain interpretive process in which reach judgments often track personal taste or other weak heuristics when explicit popularity cues are absent. We discuss the implications for algorithmic literacy and for interface designs that reduce visible metrics without leaving users to infer reach from uneven or idiosyncratic cues alone.

As generative AI enters creative work, practitioners must decide where AI assistance ends and human authorship begins. Human-agent interaction (HAI) research has examined AI as a tool, collaborator, consultant, and competitor. The longitudinal problem is how these roles are revised as systems become more capable, public, and economically embedded. We report a five-year interview study with 17 Chinese digital painters, based on annual semi-structured interviews from 2021 to 2025. Participants described recurring but non-uniform patterns of protective resistance, pragmatic task delegation, and, for some, reflective agency repartitioning. Early resistance protected observation, originality, signature, and ownership from AI. Later delegation placed AI in bounded tasks such as references, backgrounds, rough sketches, and client-facing drafts. By 2025, some participants built hybrid workflows around human-only zones, while others described fatigue, precarity, or difficulty locating a remaining human role. Peer norms, emotional climates, and production pressures shaped which delegations felt useful, acceptable, or exhausting. Copyright, authorship, and creative labor remained recurring limits on what participants were willing to delegate. We frame these accounts as longitudinal agency partitioning, the situated work of deciding which stages, responsibilities, values, and claims remain human in creative human-agent interaction. We discuss design implications for revisable agency-boundary controls, provenance scaffolds, and community-facing authorship norms.

In trying to complete a passage, an author can make connections among her materials that change what she can argue and the demands the argument must meet. A language model can supply a passage that does the work required at that point in her argument. Accepting it can end her own attempts before those connections have developed. I call this interruption early epistemic settlement. It can occur even when she fully understands the response and correctly judges it adequate for the passage's present role in the argument. The answer can satisfy the desire for resolution that kept her at work. Returning to her unfinished attempt would take more effort, and she may be unable to anticipate what she could achieve by continuing it. With repeated assistance, accepted answers shape what the writer asks next and which relations she goes on to develop. Useful answers can thus sustain inquiry while cutting short the work through which an author could form arguments that accommodate demands she has yet to recognize.

Each August, TikTok's For You page turns the University of Alabama's sorority recruitment into RushTok. We examine RushTok as an event-based algorithmic community: a collective assembled around a bounded offline ritual and sustained by recommendation. Using a mixed-methods survey (n=71) and a reflexive account of creator outreach, we ask who participates, how, and with what stakes. Findings show an ambiguous and entertainment based throughline; many called it a community (51/71) but few claimed membership (11/71). Affiliation centered on creators rather than shared practices, with parasocial attention clustering around a small set of potential new members (PNMs) and returning figures. Higher content exposure tracked with self-identification as a community member; those members commented, followed creators, and engaged across videos. Attempts to interview creators were met with silence or refusals, reflecting community boundary-work despite viral visibility. We outline implications for platform governance, including time-bounded context, graduated visibility, and aftercare.

Online communities often treat large support margins in public elections as strong mandates. We argue that such margins can overstate the independent scrutiny behind a decision. Using 198,275 free-text rationales from Wikipedia admin elections, we introduce vote-text divergence, a measure that flags a decisive vote paired with a thin, deferential rationale. Divergence rises as voters arrive later, even after controlling for voter and election fixed effects. The pattern is consistent with information saturation: once prior text is accounted for, arrival order no longer predicts divergence, while accumulated prior evidence does. The effect is strongest among peripheral voters in the co-voting network. Yet divergence does not predict worse post-promotion outcomes, such as administrative activity or survival. Public tallies can therefore weaken the scrutiny signal even while selecting capable administrators: a margin may appear to reflect more consensus and support than it actually contains.

These days we are witnessing many mobile applications based on the recommended systems, which have become a great technology which is been used by the various mobile applications according to the situation. Recommendation provided by the mobile application is a key element for the person who is traveling to several places. For any tourist information application contextual information is much needed to guide the user on his interests this can be achieved by the Context-aware computing. Which provides the user most interactive system with the suggestions provided by it based on the input from the user in a certain location, here context includes the user's mental, social, physical environments. To achieve this contextual information, we will design and implement the context-aware user interface based on the user for which we have to study the user and design a rich user interface. The final outcome for which users have the satisfaction when using context-aware functionality will be much better than non-context-aware application.

Clinical AI evaluation should encompass diagnosis and management after adaptive information gathering. We compared Doctorina, eight physicians and four standalone frontier language models in 150 synthetic Polish-language primary-care consultations. Doctorina achieved 82.0% Top-1 concordance versus 57.0% for physicians (difference, 25.0 percentage points; 95% confidence interval, 17.7-32.7) and 97.3% versus 85.0% primary-or-reference-differential concordance. Across 149 case pairs, normalized workup and treatment scores were 89.4 versus 66.9 and 83.7 versus 61.2. Doctorina had the highest diagnostic point estimates among all six groups; Kimi K3 ranked next, while Claude Opus 5 led the closely spaced management estimates of Opus, Doctorina and Kimi. A second Doctorina execution reproduced the advantages over physicians across all outcomes. Doctorina's advantage over physicians therefore extended from primary-diagnosis selection to higher-rated diagnostic workup and initial treatment after adaptive consultation.

This paper presents a cyber-physical system (CPS) for location-independent programming, supervision, training, and teleoperation of a Robot-Assisted Finishing (RAF) system used to post-process metal additive-manufactured (AM) components. A digital twin (DT) built in Unity is delivered to the operator as a WebGL application that supports both desktop and immersive modes through WebXR-compatible devices. Moreover, it exchanges robot state and pose commands with a collaborative robot through a Message Queuing Telemetry Transport (MQTT) broker. The DT enforces kinematic and collision constraints before a pose is released to the physical robot, and augments the virtual component with a color map of the surface topography that supports operator decisions on part repositioning or process termination. The architecture was validated on a specially designed physical RAF system. A steady-state joint synchronization error of 0.12 deg and a mean round-trip latency of 563 ms were measured, which is adequate for supervisory programming and intermittent teleoperation.