Handshapes serve a fundamental phonological role in signed languages, with American Sign Language employing approximately 50 distinct shapes. However,computational approaches rarely model handshapes explicitly, limiting both recognition accuracy and linguistic analysis.We introduce a novel graph neural network that separates temporal dynamics from static handshape configurations. Our approach combines anatomically-informed graph structures with contrastive learning to address key challenges in handshape recognition, including subtle interclass distinctions and temporal variations. We establish the first benchmark for structured handshape recognition in signing sequences, achieving 46% accuracy across 37 handshape classes (with baseline methods achieving 25%).

Even though current generation of AI is underpinned by a common technology - namely machine learning, especially in the form of deep learning - in the public eye it has not emerged as a single solution. Rather, it has taken shape through multiple and overlapping applications - ranging from predictive diagnostics in healthcare and algorithmic hiring systems in HR to autonomous weapons and generative language models. As AI becomes increasingly embedded in sector - specific infrastructures, the question of how publics perceive its us e is gaining urgency. Existing literature on public perception of AI suggests that attitudes are highly sensitive to the application domain . People tend to be more supportive of AI in domains where it is perceived to augment human capacity (e.g., in medical diagnostics) and more sceptical when AI is seen as replacing judg e ment or threatening civil liberties or rights (e.g., in security or surveillance). These perceptions are shaped not only by technical features of the AI system but also by institutional trust, cultural attitude s toward risk, and the moral economy of the domain in question. Despite this, few reviews have systematically compared public perceptions across sectors and explored the cross - domain patterns and differences in attitudes.

Knowledge Tracing (KT) plays a central role in assessing students' skill mastery and predicting their future performance. While deep learning-based KT models achieve superior predictive accuracy compared to traditional methods, their complexity and opacity hinder their ability to provide psychologically meaningful explanations. This disconnect between model parameters and cognitive theory poses challenges for understanding and enhancing the learning process, limiting their trustworthiness in educational applications. To address these challenges, we enhance interpretable KT models by exploring human-understandable features derived from students' interaction data. By incorporating additional features, particularly those reflecting students' learning abilities, our enhanced approach improves predictive accuracy while maintaining alignment with cognitive theory. Our contributions aim to balance predictive power with interpretability, advancing the utility of adaptive learning systems.

Generative AI, and specifically GPT, can produce a remarkable solution to a mechanical engineering analysis problem - but also, on occasion, a flawed solution. For example, an elementary mechanics problem is solved flawlessly in one GPT instance and incorrectly in a subsequent GPT instance, with a success probability of only 85%. This unreliability renders "out-of-the-box" GPT unsuitable for deployment in education or engineering practice. We introduce an "N-Plus-1" GPT Agency for Initial (Low-Cost) Analysis of mechanical engineering Problem Statements. Agency first launches N instantiations of Agent Solve to yield N independent Proposed Problem Solution Realizations; Agency then invokes Agent Compare to summarize and compare the N Proposed Problem Solution Realizations and to provide a Recommended Problem Solution. We argue from Condorcet's Jury Theorem that, for a Problem Statement characterized by per-Solve success probability greater than 1/2 (and N sufficiently large), the Predominant (Agent Compare) Proposed Problem Solution will, with high probability, correspond to a Correct Proposed Problem Solution. Furthermore, Agent Compare can also incorporate aspects of Secondary (Agent Compare) Proposed Problem Solutions, in particular when the latter represent alternative Problem Statement interpretations - different Mathematical Models - or alternative Mathematical Solution Procedures. Comparisons to Grok Heavy, a commercial multi-agent model, show similarities in design and performance, but also important differences in emphasis: our Agency focuses on transparency and pedagogical value.

Conversational agents must translate egocentric utterances (e.g., "on my right") into allocentric orientations (N/E/S/W). This challenge is particularly critical in indoor or complex facilities where GPS signals are weak and detailed maps are unavailable. While chain-of-thought (CoT) prompting has advanced reasoning in language and vision tasks, its application to multimodal spatial orientation remains underexplored. We introduce Conversational Orientation Reasoning (COR), a new benchmark designed for Traditional Chinese conversational navigation projected from real-world environments, addressing egocentric-to-allocentric reasoning in non-English and ASR-transcribed scenarios. We propose a multimodal chain-of-thought (MCoT) framework, which integrates ASR-transcribed speech with landmark coordinates through a structured three-step reasoning process: (1) extracting spatial relations, (2) mapping coordinates to absolute directions, and (3) inferring user orientation. A curriculum learning strategy progressively builds these capabilities on Taiwan-LLM-13B-v2.0-Chat, a mid-sized model representative of resource-constrained settings. Experiments show that MCoT achieves 100% orientation accuracy on clean transcripts and 98.1% with ASR transcripts, substantially outperforming unimodal and non-structured baselines. Moreover, MCoT demonstrates robustness under noisy conversational conditions, including ASR recognition errors and multilingual code-switching. The model also maintains high accuracy in cross-domain evaluation and resilience to linguistic variation, domain shift, and referential ambiguity. These findings highlight the potential of structured MCoT spatial reasoning as a path toward interpretable and resource-efficient embodied navigation.

Abstract--Most educational recommender systems are tuned and judged on click-or rating-based relevance, leaving their true pedagogical impact unclear . We introduce OBER--an Outcome-Based Educational Recommender that embeds learning outcomes and assessment items directly into the data schema, so any algorithm can be evaluated on the mastery it fosters. OBER uses a minimalist entity-relation model, a log-driven mastery formula, and a plug-in architecture. Integrated into an e-learning system in non-formal domain, it was evaluated trough a two-week A/B/C test with over 5 700 learners across three methods: fixed expert trajectory, collaborative filtering (CF), and knowledge-based (KB) filtering. CF maximized retention, but the fixed path achieved the highest mastery. Because OBER derives business, relevance, and learning metrics from the same logs, it lets practitioners weigh relevance and engagement against outcome mastery with no extra testing overhead. The framework is method-agnostic and readily extensible to future adaptive or context-aware recommenders. Index T erms--recommendation systems, e-learning, evaluation, assessment, intended learning outcomes, constructive alingment, empirical software engineering.

Monitoring ground displacement is crucial for urban infrastructure stability and mitigating geological hazards. However, forecasting future deformation from sparse Interferometric Synthetic Aperture Radar (InSAR) time-series data remains a significant challenge. This paper introduces a novel deep learning framework that transforms these sparse point measurements into a dense spatio-temporal tensor. This methodological shift allows, for the first time, the direct application of advanced computer vision architectures to this forecasting problem. We design and implement a hybrid Convolutional Neural Network and Long-Short Term Memory (CNN-LSTM) model, specifically engineered to simultaneously learn spatial patterns and temporal dependencies from the generated data tensor. The model's performance is benchmarked against powerful machine learning baselines, Light Gradient Boosting Machine and LASSO regression, using Sentinel-1 data from eastern Ireland. Results demonstrate that the proposed architecture provides significantly more accurate and spatially coherent forecasts, establishing a new performance benchmark for this task. Furthermore, an interpretability analysis reveals that baseline models often default to simplistic persistence patterns, highlighting the necessity of our integrated spatio-temporal approach to capture the complex dynamics of ground deformation. Our findings confirm the efficacy and potential of spatio-temporal deep learning for high-resolution deformation forecasting.

Modern IoT devices increasingly rely on machine learning solutions to process data locally. However, the lack of graphics processing units (GPUs) or dedicated accelerators on most platforms makes on-device training largely infeasible, often requiring cloud-based services to perform this task. This procedure often raises privacy-related concerns, and creates dependency on reliable and always-on connectivity. Federated Learning (FL) is a new trend that addresses these issues by enabling decentralized and collaborative training directly on devices, but it requires highly efficient optimization algorithms. L-SGD, a lightweight variant of stochastic gradient descent, has enabled neural network training on Arm Cortex-M Microcontroller Units (MCUs). This work extends L-SGD to RISC-V-based MCUs, an open and emerging architecture that still lacks robust support for on-device training. L-SGD was evaluated on both Arm and RISC-V platforms using 32-bit floating-point arithmetic, highlighting the performance impact of the absence of Floating-Point Units (FPUs) in RISC-V MCUs. To mitigate these limitations, we introduce an 8-bit quantized version of L-SGD for RISC-V, which achieves nearly 4x reduction in memory usage and a 2.2x speedup in training time, with negligible accuracy degradation.

Optimizing and refining action execution through exploration and interaction is a promising way for robotic manipulation. However, practical approaches to interaction-driven robotic learning are still underexplored, particularly for long-horizon tasks where sequential decision-making, physical constraints, and perceptual uncertainties pose significant challenges. Motivated by embodied cognition theory, we propose RoboSeek, a framework for embodied action execution that leverages interactive experience to accomplish manipulation tasks. RoboSeek optimizes prior knowledge from high-level perception models through closed-loop training in simulation and achieves robust real-world execution via a real2sim2real transfer pipeline. Specifically, we first replicate real-world environments in simulation using 3D reconstruction to provide visually and physically consistent environments, then we train policies in simulation using reinforcement learning and the cross-entropy method leveraging visual priors. The learned policies are subsequently deployed on real robotic platforms for execution. RoboSeek is hardware-agnostic and is evaluated on multiple robotic platforms across eight long-horizon manipulation tasks involving sequential interactions, tool use, and object handling. Our approach achieves an average success rate of 79%, significantly outperforming baselines whose success rates remain below 50%, highlighting its generalization and robustness across tasks and platforms. Experimental results validate the effectiveness of our training framework in complex, dynamic real-world settings and demonstrate the stability of the proposed real2sim2real transfer mechanism, paving the way for more generalizable embodied robotic learning. Project Page: https://russderrick.github.io/Roboseek/

The field of Embodied AI predominantly relies on simulation for training and evaluation, often using either fully synthetic environments that lack photorealism or high-fidelity real-world reconstructions captured with expensive hardware. As a result, sim-to-real transfer remains a major challenge. In this paper, we introduce EmbodiedSplat, a novel approach that personalizes policy training by efficiently capturing the deployment environment and fine-tuning policies within the reconstructed scenes. Our method leverages 3D Gaussian Splatting (GS) and the Habitat-Sim simulator to bridge the gap between realistic scene capture and effective training environments. Using iPhone-captured deployment scenes, we reconstruct meshes via GS, enabling training in settings that closely approximate real-world conditions. W e conduct a comprehensive analysis of training strategies, pre-training datasets, and mesh reconstruction techniques, evaluating their impact on sim-to-real predictivity in real-world scenarios. Experimental results demonstrate that agents fine-tuned with EmbodiedSplat outperform both zero-shot baselines pre-trained on large-scale real-world datasets (HM3D) and synthetically generated datasets (HSSD), achieving absolute success rate improvements of 20% and 40% on real-world Image Navigation task. Moreover, our approach yields a high sim-vs-real correlation (0.87-0.97) for the reconstructed meshes, underscoring its effectiveness in adapting policies to diverse environments with minimal effort.