Shaoping Xiao^1,*, Zhaoan Wang¹, Junchao Li², Caden Noeller¹, Jiefeng Jiang³, Jun Wang⁴

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-62, 2026, DOI:10.32604/cmc.2025.072146 - 09 December 2025

Abstract The integration of human factors into artificial intelligence (AI) systems has emerged as a critical research frontier, particularly in reinforcement learning (RL), where human-AI interaction (HAII) presents both opportunities and challenges. As RL continues to demonstrate remarkable success in model-free and partially observable environments, its real-world deployment increasingly requires effective collaboration with human operators and stakeholders. This article systematically examines HAII techniques in RL through both theoretical analysis and practical case studies. We establish a conceptual framework built upon three fundamental pillars of effective human-AI collaboration: computational trust modeling, system usability, and decision understandability. Our… More >

Sapan Pandya^*

Journal of Cyber Security, Vol.7, pp. 637-651, 2025, DOI:10.32604/jcs.2025.073670 - 24 December 2025

Abstract Ransomware has evolved from opportunistic malware into a global economic weapon, crippling critical services and extracting billions in illicit revenue. While most research has centered on enterprise networks and healthcare systems, an equally vulnerable frontier is emerging in lottery and betting kiosks—self-service financial Internet of Things (IoT) devices that handle billions of dollars annually. These terminals operate unattended, rely on legacy operating systems, and interact with sensitive transactional data, making them prime ransomware targets. This paper introduces a Resilient Security Framework (RSF) for kiosks under ransomware threat conditions. RSF integrates three defensive layers: (1) prevention… More >

Khulud Salem Alshudukhi^1,*, Sijjad Ali², Mamoona Humayun^3,*, Omar Alruwaili⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3029-3085, 2025, DOI:10.32604/cmes.2025.073705 - 23 December 2025

Abstract Problem: The integration of Artificial Intelligence (AI) into cybersecurity, while enhancing threat detection, is hampered by the “black box” nature of complex models, eroding trust, accountability, and regulatory compliance. Explainable AI (XAI) aims to resolve this opacity but introduces a critical new vulnerability: the adversarial exploitation of model explanations themselves. Gap: Current research lacks a comprehensive synthesis of this dual role of XAI in cybersecurity—as both a tool for transparency and a potential attack vector. There is a pressing need to systematically analyze the trade-offs between interpretability and security, evaluate defense mechanisms, and outline a… More >

Khalid Haseeb¹, Imran Qureshi^2,*, Naveed Abbas¹, Muhammad Ali³, Muhammad Arif Shah⁴, Qaisar Abbas²

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4349-4362, 2025, DOI:10.32604/cmes.2025.072326 - 23 December 2025

Abstract The rapid evolution of smart cities has led to the deployment of Cyber-Physical IoT Systems (CPS-IoT) for real-time monitoring, intelligent decision-making, and efficient resource management, particularly in intelligent transportation and vehicular networks. Edge intelligence plays a crucial role in these systems by enabling low-latency processing and localized optimization for dynamic, data-intensive, and vehicular environments. However, challenges such as high computational overhead, uneven load distribution, and inefficient utilization of communication resources significantly hinder scalability and responsiveness. Our research presents a robust framework that integrates artificial intelligence and edge-level traffic prediction for CPS-IoT systems. Distributed computing for More >

Sewwandi Tennakoon¹, Eric Danso¹, Zhenjie Zhao^2,*

Journal on Artificial Intelligence, Vol.7, pp. 517-547, 2025, DOI:10.32604/jai.2025.072628 - 01 December 2025

Abstract Generative Artificial Intelligence (GenAI) systems have achieved remarkable capabilities across text, code, and image generation; however, their outputs remain prone to errors, hallucinations, and biases. Users often overtrust these outputs due to limited transparency, which can lead to misuse and decision errors. This study addresses the challenge of calibrating trust in GenAI through a human centered testing framework enhanced with adaptive explainability. We introduce a methodology that adjusts explanations dynamically according to user expertise, model output confidence, and contextual risk factors, providing guidance that is informative but not overwhelming. The framework was evaluated using outputs… More >

Mekhled Alharbi^1,*, Khalid Haseeb², Mamoona Humayun^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2601-2616, 2025, DOI:10.32604/cmes.2025.073039 - 26 November 2025

Abstract Emerging technologies and the Internet of Things (IoT) are integrating for the growth and development of heterogeneous networks. These systems are providing real-time devices to end users to deliver dynamic services and improve human lives. Most existing approaches have been proposed to improve energy efficiency and ensure reliable routing; however, trustworthiness and network scalability remain significant research challenges. In this research work, we introduce an AI-enabled Software-Defined Network (SDN)- driven framework to provide secure communication, trusted behavior, and effective route maintenance. By considering multiple parameters in the forwarder selection process, the proposed framework enhances network More >

Inam Ullah Khan¹, Fida Muhammad Khan^1,*, Zeeshan Ali Haider¹, Fahad Alturise^2,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4307-4344, 2025, DOI:10.32604/cmc.2025.070189 - 23 October 2025

Abstract The rapid expansion of the Internet of Things (IoT) has introduced significant security challenges due to the scale, complexity, and heterogeneity of interconnected devices. The current traditional centralized security models are deemed irrelevant in dealing with these threats, especially in decentralized applications where the IoT devices may at times operate on minimal resources. The emergence of new technologies, including Artificial Intelligence (AI), blockchain, edge computing, and Zero-Trust-Architecture (ZTA), is offering potential solutions as it helps with additional threat detection, data integrity, and system resilience in real-time. AI offers sophisticated anomaly detection and prediction analytics, and… More >

Lincong Zhao¹, Liandong Chen¹, Peipei Shen¹, Zizhou Liu¹, Chengzhu Li¹, Fanqin Zhou^2,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5057-5071, 2025, DOI:10.32604/cmc.2025.067521 - 23 October 2025

Abstract The rapid growth of Internet of things devices and the emergence of rapidly evolving network threats have made traditional security assessment methods inadequate. Federated learning offers a promising solution to expedite the training of security assessment models. However, ensuring the trustworthiness and robustness of federated learning under multi-party collaboration scenarios remains a challenge. To address these issues, this study proposes a shard aggregation network structure and a malicious node detection mechanism, along with improvements to the federated learning training process. First, we extract the data features of the participants by using spectral clustering methods combined… More >

Qinglin Wang^1,2, Hang Zhang², Junzhe Zhao², Wenfan Chao², Minghui Wang^2,*

Journal of Psychology in Africa, Vol.35, No.5, pp. 557-563, 2025, DOI:10.32604/jpa.2025.068969 - 24 October 2025

Abstract This study investigated the role of work role engagement and interpersonal perspectives mediation in the relationship between school principal moral leadership and teachers’ voice behavior. A sample comprising 315 middle school teachers from a central province in China participated in the research (females = 73.3%). These teachers completed surveys on moral leadership, work engagement, trust in superiors, and voice behavior. The results of dual mediation modeling indicated evidence of an indirect effect of moral leadership on teachers’ voice behavior through work engagement. The results also indicated evidence of mediating effect of trust between moral leadership More >

Jingfu Yan, Huachun Zhou^*, Weilin Wang

Computer Systems Science and Engineering, Vol.49, pp. 499-531, 2025, DOI:10.32604/csse.2025.068151 - 10 October 2025

Abstract The 6G network architecture introduces the paradigm of Trust + Security, representing a shift in network protection strategies from external defense mechanisms to endogenous security enforcement. While ZTNs (zero-trust networks) have demonstrated significant advancements in constructing trust-centric frameworks, most existing ZTN implementations lack comprehensive integration of security deployment and traffic monitoring capabilities. Furthermore, current ZTN designs generally do not facilitate dynamic assessment of user reputation. To address these limitations, this study proposes a DPZTN (Data-plane-based Zero Trust Network). DPZTN framework extends traditional ZTN models by incorporating security mechanisms directly into the data plane. Additionally, blockchain infrastructure… More > Graphic Abstract