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University of North Texas

Academic institutionnorthamerica · us
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Research library242linked papers
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Selected work

Representative Papers

Learning-to-Explain through 20Q Gaming: An Explainable Recommender for Cybersecurity Education

Oct 29, 2025IEEE Annual Information Technology, Electronics and Mobile Communication Conference

The growing sophistication of contemporary cyber threats necessitates a more effective and adaptive approach to cybersecurity training. Intuitive and adaptive approaches to learning, which are often required, are not provided in traditional learning methods. In this article, we present a new educational framework, "Learning to Explain Cybersecurity with Q20 Game", based on explainable AI (XAI), an educational game to enhance interactivity in learning. We propose a novel, game-inspired framework - the Explainable Q20 Cybersecurity Recommender (EQ-20CR), that learns to elicit the minimal set of evidential facts needed to justify cybersecurity defensive action. By casting "Why should I execute this mitigation?" as a 20 questions (Q20) game, a policy-based reinforcement-learning (RL) agent actively queries an environment until it can both (i) recommend the optimal security education and (ii) explain that decision with a concise dialogue trace. The article draws from "Playing 20 Question Game with Policy-Based Reinforcement Learning" [1] and "Learning-to-Explain: Recommendation Reason Determination through Q20 Gaming" [2]. The framework uses a policy-based reinforcement learning (RL) agent that leads the user through a sequence of questions to recognize and articulate a targeted cybersecurity concept, attack vector, or defense strategy. Furthermore, users are gradually exposed to informative questions by the system, revealing complicated, structured way at an adaptive difficulty level. In this paper, we design the architecture, its application to various concepts of cybersecurity through illustrative case studies, and its transformative potential on the training and awareness of cybersecurity recommendations.

4 citationsRead paper

Optimal Rate Region for Multi-server Secure Aggregation with User Collusion

Jan 11, 2026arXiv.org

This work addresses the problem of information-theoretically secure aggregation in a multi-server two-hop network, where up to $T$ users may collude with any subset of servers. Users communicate exclusively with their assigned servers, which then collaborate to recover the global sum. The study provides the first complete characterization of the optimal rate region for this setting, uncovering a fundamental trade-off between security and key efficiency. It demonstrates that a multi-server architecture substantially reduces the required randomness for secret keys. Within an information-theoretic security framework, leveraging linear key constructions and tight entropy bounds, the authors establish that the minimum user-to-server communication rate, inter-server communication rate, and individual key rate are each one symbol per input symbol, while the optimal source key rate is $\min\{U+V+T-2, UV-1\}$, where $U$ denotes the number of servers and $V$ the number of users per server.

2 citationsRead paper

Do We Really Need to Design New Byzantine-robust Aggregation Rules?

Jan 29, 2025

In federated learning, Byzantine clients launching poisoning attacks can severely degrade the robustness of existing aggregation rules. To address this, we propose FoundationFL—a framework that preserves standard robust aggregators (e.g., Trimmed-mean, Median) without modifying their logic; instead, the server generates synthetic model updates, which are jointly aggregated with clients’ local updates. We provide the first theoretical proof that enhancing input quality alone—without designing new aggregation rules—significantly improves Byzantine resilience of classical robust aggregators. FoundationFL guarantees convergence under Byzantine threats and empirically demonstrates substantial improvements in poisoning resistance across multiple real-world datasets, while maintaining high model accuracy and low communication overhead. The framework thus achieves strong effectiveness, generalizability, and practicality.

1 citationsRead paper

Prioritizing Risk Factors in Media Entrepreneurship on Social Networks: Hybrid Fuzzy Z-Number Approaches for Strategic Budget Allocation and Risk Management in Advertising Construction Campaigns

Sep 13, 2024arXiv.org

This paper addresses the challenge of jointly optimizing advertising budget allocation and risk management in social media startups. To this end, it integrates Media Mix Modeling (MMM) with Failure Mode and Effects Analysis (FMEA), establishing a dynamic, risk-aware decision-making framework. Methodologically, it innovatively embeds Z-number theory into the FMEA process and proposes a hybrid approach—Z-SWARA for criterion weight determination and Z-WASPAS for multi-criteria ranking—overcoming the limitations of traditional Risk Priority Number (RPN) in modeling both fuzziness and reliability. The framework enables credibility-weighted assessment of risk factors across advertising channels. Empirical evaluation demonstrates a 12.7% improvement in ROI prediction accuracy and a 35% enhancement in risk response timeliness across three representative social media startup scenarios.

1 citationsRead paper
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