This paper introduces the eLasmobranc Dataset, a new, curated image collection designed to improve fine-grained identification of elasmobranch species (sharks and rays) for conservation and biodiversity monitoring. It addresses limitations of existing datasets by providing high-quality, out-of-water images with expert-validated annotations and detailed metadata to support AI system development.

This paper explores how humans' pre-existing beliefs about their own abilities and AI competence influence their decisions to delegate tasks to AI. It uncovers a systematic "AI optimism" bias and a disconnect between perceived efficacy and actual collaborative outcomes, suggesting that merely providing contextual information may not improve human-AI team performance.

This paper introduces Sign-Prioritized Federated Learning (SP-FL), a novel framework that improves wireless federated learning by prioritizing the transmission of critical gradient information, specifically gradient signs. It addresses unreliable communication in resource-constrained wireless networks by implementing an importance-aware hierarchical resource allocation strategy, demonstrating significant accuracy improvements.

This paper introduces an AI-driven, data-driven methodology for estimating spectrum demand by leveraging both site license and crowdsourced data. The approach uses an enhanced combined proxy, validated against real-world mobile network traffic, to achieve high predictive accuracy, demonstrating its robustness across multiple major Canadian cities for improved spectrum planning.

This paper introduces a decision-theory framework and a novel blended AI/statistical model for generating tailored, probabilistic seasonal monsoon onset forecasts. The system significantly improves forecast skill at longer lead times, enabling heterogeneous farmers to make more informed agricultural decisions under weather uncertainty, and was operationally deployed to 38 million Indian farmers in 2025.