http://hdl.handle.net/10453/148704 2026-06-06T09:27:33Z http://hdl.handle.net/10453/195234 Title: Tracking Academic Topic Evolution From the Perspective of Author Interest Shifts Authors: Jiang, W; Zhang, Y; Wang, M; Mo, H; Hussain, O; Dong, D; Zhang, W Abstract: Understanding how academic topics evolve over time is essential for tracking scientific progress and identifying emerging trends This study proposes a new framework for analyzing topic evolution based on the shifting interests of individual authors whose evolving research choices play a key role in shaping topic trajectories By leveraging a time sequenced learning approach that incorporates author information the framework captures how topics emerge grow decline and reemerge Improving traditional topic modeling approaches that rely solely on textual patterns our framework explicitly models the temporal dynamics of individual authors topic engagement providing a micro level lens on macro level topic changes As a case study we apply this framework to the Scientometrics field using data from 1978 to 2024 and construct a topic evolutionary map that reveals strong interconnections among bibliometrics research evaluation and related methodologies The analysis also reveals that despite the persistence of foundational topics like citation analysis and knowledge management which are reinforced by sustained author interest only two genuinely emerging topics were identified suggesting that while author interests evolve they tend to favor established areas over the exploration of entirely new directions 2025-12-22T00:00:00Z http://hdl.handle.net/10453/195192 Title: Student gaze target estimation based on depth transformation on dual-view classroom images Authors: Zhang, X; Miao, H; Zhao, P; Sun, Y; Nan, F; Morteza, S; Wu, Y; Tian, F Abstract: Existing gaze target estimation methods fail to adequately consider depth information, primarily focusing on 2D image features while neglecting the inherent 3D spatial context that could enhance global context modeling in classroom environments. To address this limitation, we propose a depth-aware gaze target estimation framework specifically designed for classroom scenarios. Our approach consists of three key components: First, a depth estimation module is developed to handle feature information degradation. Second, we design a dual-view depth transformation method to project students’ gaze cones onto the target frame. Third, we introduce a context-aware pyramid feature extraction (CPFE) module that generates multiscale high-level feature representations to strengthen global context modeling. We also construct two datasets (MPMOCS and DVSEG) for our tasks. Experimental results on these datasets demonstrate that our method achieves significant improvements in both single-view and dual-view gaze target estimation tasks. 2025-12-01T00:00:00Z http://hdl.handle.net/10453/195123 Title: Towards Sensor-Free Emission Monitoring for SMEs With Vision-Language Appliance Detection and Operational Context Inference Authors: Azeem, M; Hu, Y 2026-04-08T00:00:00Z http://hdl.handle.net/10453/195122 Title: AI for All: Identifying AI incidents Related to Diversity and Inclusion Authors: Shams, RA; Zowghi, D; Bano, M Abstract: The rapid expansion of Artificial Intelligence (AI) technologies has introduced both significant advancements and challenges, with diversity and inclusion (D&I) emerging as a critical concern. Addressing D&I in AI is essential to reduce biases and discrimination, enhance fairness, and prevent adverse societal impacts. Despite its importance, D&I considerations are often overlooked, resulting in incidents marked by built-in biases and ethical dilemmas. Analyzing AI incidents through a D&I lens is crucial for identifying causes of biases and developing strategies to mitigate them, ensuring fairer and more equitable AI technologies. However, systematic investigations of D&I-related AI incidents are scarce. This study addresses these challenges by identifying and understanding D&I issues within AI systems through a manual analysis of two AI incident databases, AI Incident Database (AIID) and AI, Algorithmic, and Automation Incidents and Controversies (AIAAIC). The research develops a decision tree to investigate D&I issues tied to AI incidents and populate a public repository of D&I-related AI incidents. The decision tree was validated through a card sorting exercise and focus group discussions. The research demonstrates that almost half of the analyzed AI incidents are related to D&I, with a notable predominance of racial, gender, and age discrimination. The decision tree and resulting public repository aim to foster further research and responsible AI practices, promoting the development of inclusive and equitable AI systems. 2025-01-01T00:00:00Z