Computational thinking (CT) is increasingly promoted as a core literacy, yet learners and teachers face challenges in connecting abstract program logic to meaningful outcomes. We design and evaluate RoboBlockly Studio, an integrated interactive system that combines block-based programming, a conversational AI teaching agent, and embodied robot execution. RoboBlockly Studio creates a tight iterative loop of authoring, running, observing, and revising. Informed by interviews with five programming teachers, the system was designed to support four goals: (1) preserving learner agency in computational thinking, (2) making program behavior transparent and interpretable, (3) grounding programming in embodied, classroom-aligned tasks, and (4) scaffolding reflection through pedagogically grounded AI dialogue. We deployed RoboBlockly Studio with 32 high school students, observing how robot and AI feedback influenced students’ interactions with code, reflections on problem-solving strategies, and understanding of CT concepts. We discuss design insights and implications for creating interactive, embodied learning environments that integrate AI and robotics to support CT learning in computing education.

Dialect bias is pervasive yet often unconscious, normalized, or obscured by masking. Existing HCI interventions primarily audit disparities and propose reactive fixes. We present CompassioMate, a dialect-aware serious game that nurtures perspective-taking through AI-mediated play. Players listen to audio samples to identify regional dialects, engage in simulated social interactions involving dialect discrimination, and explore branching narratives that reveal how changes in wording or stance can influence the outcomes. In a three-week field study with 20 university students, participants reported feeling comfortable when observing region-tailored dialogues; several described experiencing perspective change. We contribute: 1) a formative study identifying goals for safe action consequence modelling, 2) the design and evaluation of a serious game integrating dialect audio, region-mapping play, bias; and 3) design implications highlighting listener-side training, transparent evaluation, and narratives maintaining psychological well-being.

We present SmartWalkCoach, a mobile AI companion that supports the full walking journey: from pre-walk planning to in-walk guidance through to post-walk reflection. Addressing a gap between map navigation and motivational coaching, SmartWalkCoach orchestrates three lightweight agents: (1) GeographyAgent for conversational route curation from nearby points of interest and user preferences while delegating pathfinding to map APIs; (2) AccompanyAgent for context-aware, just-in-time prompts that blend informational cues with relational encouragement; and (3) SummaryAgent for concise reflection and next-step planning. This end-to-end, tool-using design aims to lower cognitive load in planning and sustain engagement and motivation during walking through delivering dynamic, cadence-aware interventions. We conducted an in-the-wild, two-period AB/BA crossover study (N=12), where each participant completed two comparable walks with counterbalanced conditions: Information-only versus Information+Motivation. Linear mixed models show that adding motivational, companion-like dialogue significantly improved outcomes: participants reported higher positive feelings and better user experience, with no evidence of carryover. Thematic analysis surfaced two design imperatives for mobile companions: supportive, relational expression and context-aware timing (e.g., avoiding high-load moments, intervening at fatigue/milestones). Our contributions are: (i) an end-to-end, tool-using agent architecture for everyday walking that reduces cognitive load during planning and accompaniment; (ii) a controlled field evaluation linking context-aware motivation to affect and UX gains; and (iii) actionable design guidance on expression, timing, and frequency for mHealth companions. We outline limitations and paths toward multimodal, voice-first companions, with adaptive personalization mechanisms.

Accurate emotion recognition from physiological signals is critical for applications in healthcare, autonomous systems, and human-computer interaction. However, prevailing methods often fail to model long-term dependencies and overlook periodic patterns inherent in physiological data. To address these challenges, we propose Phy-FusionNet, a novel memory-augmented transformer architecture for multimodal emotion recognition. Phy-FusionNet introduces a Memory Stream Module with FIFO-queue and decay-based updates to preserve long-term contextual information. It further integrates Fourier-based positional encoding and frequency-aware attention, enabling robust detection of periodic emotional cues. An Adaptive Temporal Attention Module enhances computational efficiency and enables dynamic relevance in temporal feature extraction. For cross-modal fusion, we employ a transformer-based Multimodal Binding Learning framework that balances modality-specific and shared features. Extensive experiments on five public datasets—WESAD, CL-Drive, PPB-Emo, PhyMER, and EEG-VUI—demonstrate that Phy-FusionNet outperforms state-of-the-art models, achieving up to 16.3% improvement in accuracy and superior robustness across diverse emotional states and noisy environments. Notably, the model maintains low performance variance across emotion classes, with F1-Score differences under 2.5%, indicating stable recognition even for subtle or overlapping emotions. Our results underscore the importance of integrating memory, frequency, and adaptive attention for effective affective computing. The code will be publicly available on GitHub.

Large programming courses often use test-driven autograding systems (e.g., CodeRunner) for instant feedback, but these usually only show which tests failed without explaining why or how to fix the error. Novice students struggle with such minimal guidance and often resort to trial-and-error. Meanwhile, AI coding assistants (e.g., GitHub Copilot, ChatGPT) can provide hints and code suggestions, but novices may over-trust these outputs and lack the skills to verify them. To solve these issues, we present TraceMate, an IDE plugin that pairs the autograder’s tests with a conversational AI chatbot. TraceMate augments test feedback with context-aware explanations and inline suggestions for code modifications, and immediately validates each AI suggestion on the test suite. This workflow gives actionable hints while ensuring any AI-proposed changes are correct. In a user study with novice programmers, participants using TraceMate solved problems more effectively and reported higher confidence than those using only the autograder. These results suggest that pairing automated tests with an interactive AI assistant can enhance learning in introductory programming courses.

Waterway perception is critical for the special operations and autonomous navigation of Unmanned Surface Vessels (USVs), but current perception schemes are sensor-based, neglecting the interaction between humans and USVs for embodied perception in various operations. Therefore, inspired by visual grounding, we present WaterVG, the inaugural visual grounding dataset tailored for USV-based waterway perception guided by human prompts. WaterVG contains a wealth of prompts describing multiple targets, with instance-level annotations, including bounding boxes and masks. Specifically, WaterVG comprises 11,568 samples and 34,987 referred targets, integrating both visual and radar characteristics. The text-guided two-sensor pattern provides a fine granularity of text prompts aligned with the visual and radar features of the referent targets, containing both qualitative and numeric descriptions. To enhance the endurance and maintain the normal operations of USVs in open waterways, we propose Potamoi, a low-power visual grounding model. Potamoi is a multi-task model employing a sophisticated Phased Heterogeneous Modality Fusion (PHMF) mechanism, which includes Adaptive Radar Weighting (ARW) and Multi-Head Slim Cross Attention (MHSCA). The ARW module utilizes a gating mechanism to adaptively extract essential radar features for fusion with visual inputs, ensuring prompt alignment. MHSCA, characterized by its low parameter count and computational efficiency (FLOPs), effectively integrates contextual information from both sensors with linguistic features, delivering outstanding performance in visual grounding tasks. Comprehensive experiments and evaluations on WaterVG demonstrate that Potamoi achieves state-of-the-art results compared to existing methods. The project is available athttps://github.com/GuanRunwei/WaterVG.

While cloud storage services offer manifold benefits such as cost-effectiveness or elasticity, there also exist various security and privacy concerns. Among such concerns, we pay our primary attention to data residency -- a notion that requires outsourced data to be retrievable in its entirety from local drives of a storage server in-question. We formulate such notion under a security model called Proofs of Data Residency (PoDR). can be employed to check whether the data are replicated across different storage servers, or combined with storage server geolocation to "locate" the data in the cloud. We make key observations that the data residency checking protocol should exclude all server-side computation and that each challenge should ask for no more than a single atomic fetching operation. We illustrate challenges and subtleties in protocol design by showing potential attacks to naive constructions. Next, we present a secure PoDR scheme structured as a timed challenge-response protocol. Two implementation variants of the proposed solution, namely NVeri and EVeri, describe an interesting use-case of trusted computing, in particular the use of Intel SGX, in cryptographic timed challenge-response protocols whereby having the verifier co-locating with the prover offers security enhancement. Finally, we conduct extensive experiments to exhibit potential attacks to insecure constructions and validate the performance as well as the security of our solution.