ShapDBM addresses the fragmentation problem in Decision Boundary Maps (DBMs) by transforming data into Shapley space before applying dimensionality reduction. This creates more compact decision zones that reflect model behavior rather than raw data distribution, enabling high-quality visualization of complex datasets like SVHN where traditional data-space DBMs fail.

This paper investigates how users decode emotions in text-based communication through electronic nonverbal cues (eNVCs)—orthographic signals like elongation, punctuation, and emojis that approximate paralinguistic features. The authors propose a taxonomy grounded in nonverbal communication theory (kinesics and paralinguistics) and test it across three complementary studies: a content analysis developing a regex detection toolkit, a within-subjects experiment manipulating eNVC presence and sarcasm ($n=513$), and focus groups exploring interpretive strategies. The work identifies sarcasm as a critical boundary condition where eNVCs fail to aid interpretation and provides an open-source Python/R package for automated cue detection.

This paper argues that frictionless AI interfaces pose a systemic risk of "cognitive agency surrender"—the habitual abdication of human reasoning to algorithmic systems. Drawing on cognitive psychology, the authors theorize "Scaffolded Cognitive Friction" as a defense: intentionally injecting epistemic tension via Multi-Agent Systems (MAS) that expose structured disagreements (computational Devil's Advocates) to force System 2 activation. The work positions itself as a bridge between HCI, cognitive science, and AI governance.

BadminSense is a smartwatch-based system for fine-grained badminton stroke evaluation that aims to provide amateur players with professional-quality coaching feedback without requiring expensive external equipment. The system uses a single commercial smartwatch on the dominant wrist to segment and classify four stroke types, predict stroke quality on a 5-point Likert scale, and estimate shuttle impact location on the racket string area. The key innovation is enabling fine-grained quality assessment beyond simple activity recognition, targeting the gap between basic fitness tracking and professional coaching.

As users increasingly consult multiple large language models for decision support, a critical question arises: does increasing the number of AI advisors improve accuracy or amplify harmful conformity pressures? This paper investigates how panel size, within-panel consensus, and human-likeness of presentation shape human reliance and decision accuracy across three prediction tasks (income, recidivism, and dating). Through two crowdsourced experiments with 348 participants, the authors reveal a surprising non-monotonic relationship: three AI advisors improve accuracy over a single advisor, but five provide no additional benefit, while unanimous consensus fosters overreliance and wide disagreement creates confusion.

Dyadic is a web-based platform for studying human-human and human-AI conversations through text or voice-based interaction. It attempts to solve the methodological gap in conversation research by providing turnkey tools for experimental manipulation, live monitoring, and in-situ survey delivery during ongoing chats. The core value proposition is lowering barriers to entry for researchers studying dyadic interaction processes without requiring programming expertise.

This paper addresses BLE-based indoor localization in care facilities by shifting from independent-window classification to sequential learning. The proposed DASEL framework combines frequency-based feature engineering, bidirectional GRUs with attention mechanisms, and a two-level hierarchical ensemble to model temporal movement trajectories. Achieving a 53.1% improvement over traditional baselines on the ABC 2026 challenge dataset, the work demonstrates that capturing temporal dependencies is critical for accurate indoor localization in complex real-world environments.

This paper tackles the limitation that XAI systems assume static user models, ignoring diverse epistemic stances among domain experts. The authors propose agentic personas—structured representations of expert reasoning strategies derived from clustered feedback and instantiated via LLMs—to condition reinforcement learning-based explanation generation on knowledge graphs. This enables adaptive explanations that align with specific interpretive preferences (mechanistic rigor vs. focused clarity) without requiring extensive individual-level human feedback, demonstrated in drug discovery with 22 expert participants.

Machine learning models on mobile devices spend 61-86% of execution time extracting features from user behavior logs rather than running inference. This paper introduces AutoFeature, a graph-based engine that eliminates redundant operations across features and consecutive executions using directed acyclic graph optimization and intelligent caching. Tested across five industrial services including TikTok and e-commerce platforms, it achieves 1.33×-4.53× end-to-end latency reduction without accuracy loss.

This paper tackles logical context poisoning—the degradation of LLM responses when flat, linear conversation structures force topically distinct threads to accumulate in a single unbounded context window. The core idea is the Conversation Tree Architecture (CTA), which models conversations as a directed rooted tree $\mathcal{T}=(V,E,r,W)$ where each node $v \in V$ maintains an isolated local context window $w_v$. Structured flow operations—downstream passing $\phi_{\downarrow}$, upstream merging $\psi_{\uparrow}$, and volatile nodes—govern how context moves between branches. This matters because current interfaces offer no middle ground between discarding context (new chat) and accumulating noise (linear threads).