Erick Purwanto

Liye Jia, Fengyufan Yang, Ka Lok Man et al.

Beneficial to advanced computing devices, models with massive parameters are increasingly employed to extract more information to enhance the precision in describing and predicting the patterns of objective systems. This phenomenon is particularly pronounced in research domains associated with deep learning. However, investigations of causal relationships based on statistical and informational theories have posed an interesting and valuable challenge to large-scale models in the recent decade. Macroscopic models with fewer parameters can outperform their microscopic counterparts with more parameters in effectively representing the system. This valuable situation is called "Causal Emergence." This paper introduces a quantification framework, according to the Effective Information and Transition Probability Matrix, for assessing numerical conditions of Causal Emergence as theoretical constraints of its occurrence. Specifically, our results quantitatively prove the cause of Causal Emergence. By a particular coarse-graining strategy, optimizing uncertainty and asymmetry within the model's causal structure is significantly more influential than losing maximum information due to variations in model scales. Moreover, by delving into the potential exhibited by Partial Information Decomposition and Deep Learning networks in the study of Causal Emergence, we discuss potential application scenarios where our quantification framework could play a role in future investigations of Causal Emergence.

Xianzhe Zhang, Mingxuan Hu, Bufan Xue et al.

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.

Leyi Li, Chenyu Du, Jiafei Sun et al.

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.

Sicheng Lu, Erick Purwanto, Hong Liu et al.

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.

Runwei Guan, Liye Jia, Fengyufan Yang et al.

The perception of waterways based on human intent is significant for autonomous navigation and operations of Unmanned Surface Vehicles (USVs) in water environments. Inspired by visual grounding, we introduce WaterVG, the first visual grounding dataset designed for USV-based waterway perception based on human prompts. WaterVG encompasses prompts describing multiple targets, with annotations at the instance level including bounding boxes and masks. Notably, WaterVG includes 11,568 samples with 34,987 referred targets, whose prompts integrates both visual and radar characteristics. The pattern of text-guided two sensors equips a finer granularity of text prompts with visual and radar features of referred targets. Moreover, we propose a low-power visual grounding model, Potamoi, which is a multi-task model with a well-designed Phased Heterogeneous Modality Fusion (PHMF) mode, including Adaptive Radar Weighting (ARW) and Multi-Head Slim Cross Attention (MHSCA). Exactly, ARW extracts required radar features to fuse with vision for prompt alignment. MHSCA is an efficient fusion module with a remarkably small parameter count and FLOPs, elegantly fusing scenario context captured by two sensors with linguistic features, which performs expressively on visual grounding tasks. Comprehensive experiments and evaluations have been conducted on WaterVG, where our Potamoi archives state-of-the-art performances compared with counterparts.