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Georgia State University

Academic institutionnorthamerica · us
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Research library213linked papers
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Selected work

Representative Papers

Conceptual Modeling: Topics, Themes, and Technology Trends

Mar 30, 2023ACM Computing Surveys

Despite rapid technological advances, the enduring value and evolutionary trajectory of conceptual modeling in digital-era information systems development remain inadequately understood. Method: Leveraging a corpus of 5,300+ multidisciplinary publications (1970–2023) from 35 journals/conferences, we conduct bibliometric analysis, LDA topic modeling, and temporal trend mining—delivering the first large-scale, cross-temporal, cross-disciplinary quantitative study of conceptual modeling evolution. Contribution/Results: We identify seven dominant modeling themes and their lifecycle patterns, pinpointing three major technology-driven inflection points (Web, Big Data, AI), and empirically confirm the paradigm’s robustness and adaptability. Contrary to assumptions of obsolescence, conceptual modeling remains foundational—enabling trustworthiness in AI, digital twins, and sustainable digital systems. We propose three new research directions: semantic interpretability, dynamic evolutionary capability, and human-AI collaborative trustworthiness.

16 citations1 influentialRead paper

Multi-Aggregator Time-Warping Heterogeneous Graph Neural Network for Personalized Micro-Video Recommendation

Oct 17, 2022International Conference on Information and Knowledge Management

To address the insufficient modeling of timeliness and the difficulty in capturing dynamic user interest evolution in news-oriented micro-video recommendation, this paper proposes a session-level graph neural network method that jointly incorporates temporal awareness and heterogeneous interaction. We construct a session-driven heterogeneous graph and design multi-granularity neighbor aggregators alongside a learnable time-warping function to jointly model content recency, interest drift, and interaction heterogeneity. This work is the first to integrate a multi-aggregator coordination mechanism with explicit time warping into a micro-video recommendation framework, enabling unified representation of dynamic preference evolution. Extensive experiments on multiple real-world micro-video datasets demonstrate that our approach achieves a 12.6% improvement in Recall@10 over state-of-the-art methods, validating its effectiveness and superiority for time-sensitive recommendation tasks.

4 citationsRead paper

Physics-Guided Multi-view Graph Neural Network for Schizophrenia Classification via Structural-Functional Coupling

May 21, 2025PRIME@MICCAI

Existing schizophrenia (SZ) classification methods often model structural connectivity (SC) and functional connectivity (FC) independently, neglecting their biophysically grounded dynamical coupling and underlying neurophysiological mechanisms. Method: We propose a physics-informed SC–FC coupling framework that explicitly models SC-driven FC dynamics via neural oscillation differential equations. A multi-view graph neural network integrates dual-channel graph convolutions over SC- and FC-derived graphs, while a coupling-aware joint loss function enables simultaneous learning of SC–FC coupling, cross-modal feature fusion, and disease discrimination. Contribution/Results: Evaluated on real clinical datasets, our method achieves significant improvements in SZ classification accuracy and model generalizability. Results demonstrate the efficacy and robustness of incorporating biophysical priors into multimodal coupling modeling for biomarker discovery in neuropsychiatric disorders.

2 citations1 influentialRead paper

From Reddit to Generative AI: Evaluating Large Language Models for Anxiety Support Fine-tuned on Social Media Data

May 24, 2025

The suitability and risks of large language models (LLMs) in anxiety support contexts remain poorly understood. Method: We systematically evaluated GPT-4 and Llama-2/3 using real r/Anxiety subreddit posts, applying prompt engineering and supervised fine-tuning, and introduced a multidimensional, interpretable evaluation framework assessing linguistic quality, safety (toxicity/bias), and supportive capacity (empathic expression, supportive discourse). Contribution/Results: We first demonstrate that fine-tuning on raw social media data improves fluency (+12%) but significantly degrades empathic responsiveness (−41% in empathic expression) and increases toxicity (+27%). GPT-series models consistently outperform Llama models in supportive capability. Based on these findings, we propose a dual-path optimization paradigm—“data purification + alignment constraints”—to enhance safety and trustworthiness. This work provides both methodological guidance and empirical evidence for the responsible deployment of LLMs in mental health applications.

1 citationsRead paper

Just KIDDIN: Knowledge Infusion and Distillation for Detection of INdecent Memes

Nov 19, 2024arXiv.org

This paper addresses the challenge of toxicity detection in multimodal hate memes—composite images with overlaid text. To this end, we propose a neuro-symbolic framework integrating knowledge distillation and explicit commonsense injection. Methodologically: (i) cross-modal knowledge distillation from a large vision-language model (LVLM) captures implicit toxic semantics; (ii) a ConceptNet subgraph is constructed and embedded into a multimodal alignment space to explicitly model commonsense-driven toxic associations between image and text; (iii) a relation-aware reasoning module orchestrates synergistic interaction between the two components. To our knowledge, this is the first work to jointly leverage LVLM-based distillation and structured knowledge graph infusion for hate speech detection. Evaluated on two benchmark datasets for hateful meme detection, our framework achieves absolute improvements of 1.1%, 7.0%, and 35.0% in AU-ROC, F1-score, and Recall, respectively—substantially outperforming existing state-of-the-art methods.

1 citationsRead paper
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