http://hdl.handle.net/1893/216 Electronic copies of Computing Science and Mathematics journal articles. Fri, 23 Oct 2026 11:34:27 GMT 2026-10-23T11:34:27Z http://hdl.handle.net/1893/38409 Title: Beyond Frame‐Level Accuracy: Analyzing Video‐Level Stability in Transformer‐Based Deepfake Forensics Author(s): Hasan, Syed Azmul; Hera, Mowmita Pervin; Mim, Afroza Lona; Jihad Hossain, M M; Ali, Hazrat; Bulbul, Mohammad Farhad Abstract: Deepfake videos generated by advanced generative models pose serious threats to digital security, misinformation control, and media forensics. Although numerous detection methods have been proposed, their reported performances are often based on heterogeneous preprocessing pipelines, inconsistent data splits, and model-specific training protocols, hindering fair architectural comparison. This work presents a controlled and reproducible comparative study of convolutional neural networks (ResNet-18 and EfficientNet-B0) and a Transformer-based architecture (ViT-B/16) within a unified, face-centric deepfake detection framework. All models are trained using identical preprocessing, augmentation, optimization settings, and data splits on Celeb-DF v2 and FaceForensics++, and results are validated across three independent random seeds with bootstrapped confidence intervals. Experimental results show that while CNN-based models perform competitively at the frame level, their performance degrades after video-level aggregation. In contrast, ViT-B/16 demonstrates more stable video-level performance, achieving 92.8% (95% CI: 91.7–94.0) video-level accuracy and an area under the curve (AUC) of 0.950 (95% CI: 0.934–0.964) on Celeb-DF v2 (3-seed bootstrapped). Cross-dataset evaluation across the same three random seeds reveals a large, asymmetric generalization gap (AUC 0.79 FF++ ⟶ Celeb-DF vs. 0.64 Celeb-DF ⟶ FF++), indicating that in-domain video-level stability does not transfer to unseen manipulation domains. These findings indicate that global self-attention improves robustness and video-level stability for deepfake detection within a given data domain, while highlighting the need for further work on cross-domain generalization. Mon, 19 Oct 2026 00:00:00 GMT http://hdl.handle.net/1893/38409 2026-10-19T00:00:00Z http://hdl.handle.net/1893/38404 Title: Hybrid Scalable Scheduling Reservation Protocol for Industrial IoT Networks Author(s): Kumar, K; Kolberg, M Abstract: In dense Wireless Sensor Networks (WSNs), communication fails due to multipath fading and external interferences (EI). In Industry 4.0 environments, low-power wireless standards such as ZigBee, Bluetooth Low Energy (BLE), WirelessHART, and ISA100.11a face challenges in terms of reliability. Recently, Time-Slotted Channel Hopping (TSCH) protocol has been adopted as a default mode in IEEE 802.15.4, providing resilience against EIs and multipath fading through time synchronization and channel hopping. Although WirelessHART also adopts TSCH, its centralized and non-IP-based architecture introduces significant control overhead, which limits scalability and seamless Internet integration. BLE, relying on distinct MAC and physical-layer mechanisms, remains susceptible to packet loss under harsh industrial conditions. Conversely, traditional IP-enabled solutions that do not employ TSCH often depend on primitive channel-access protocols, leading to internal collisions and reduced reliability in dense deployments. 6TiSCH (RFC 9030) standard extends TSCH by providing IPv6 connectivity through a lightweight 6LoWPAN stack and the 6top sublayer, enabling integration with the IPv6 protocol suite and improved architectural flexibility. Despite these advantages, network scalability remains an open challenge, as existing 6TiSCH Scheduling Functions (SFs) operating over the 6top sublayer impose trade-offs among reliability, latency, and energy consumption. In this paper, we present a Hybrid Scalable Scheduling Reservation Protocol (SSR), addressing important challenges with scalability, reliability, latency, and energy efficiency. The proposal, with its cake-slicing technique, network depth mapping, efficient traffic adaption, and congestion mitigation, shows potential for significant improvements over existing solutions like Minimal Scheduling Function (MSF) (RFC 9033). The experimental results show significant improvements over fixed hysteresis-based scheduling policies of MSF and demonstrate scalable performance using hundreds of IoT nodes within a subnet. Wed, 22 Jul 2026 00:00:00 GMT http://hdl.handle.net/1893/38404 2026-07-22T00:00:00Z http://hdl.handle.net/1893/38346 Title: A Comprehensive Analysis of Adversarial Attacks against Spam Filters Author(s): Hotoğlu, Esra; Sen, Sevil; Can, Burcu Abstract: Deep learning has revolutionized email filtering, which is critical to protect users from cyber threats such as spam, malware, and phishing. However, the increasing sophistication of adversarial attacks poses a significant challenge to the effectiveness of these filters. This study investigates the impact of adversarial attacks on deep learning-based spam detection systems using real-world datasets. Six prominent deep learning models are evaluated on these datasets, analyzing attacks at the word, character sentence, and AI-generated paragraph-levels. Novel scoring functions, including spam weights and attention weights, are introduced to improve attack effectiveness. A key contribution of this study is the analysis of spam-weight-and attention-weight-based scoring functions, highlighting their role in improving the effectiveness and efficiency of adversarial attacks. This comprehensive analysis sheds light on the vulnerabilities of spam filters and contributes to efforts to improve their security against evolving adversarial threats. Experimental results show that word-and sentence-level attacks markedly increase false negatives, while character-level perturbations disrupt token representations with minimal semantic change. AI-generated paragraph-level attacks remain challenging even for transformer-based models. In addition, spam-weight-based scoring consistently enables more effective adversarial attacks than alternative scoring strategies with lower computational cost. Tue, 01 Dec 2026 00:00:00 GMT http://hdl.handle.net/1893/38346 2026-12-01T00:00:00Z http://hdl.handle.net/1893/38322 Title: The Impact of Non-AKI eGFR Variability on CKD Progression in Individuals With Type 2 Diabetes and Preserved Kidney Function Author(s): Hapca, Simona; Yang, Qinbo; Li, Sheyu; McGurnaghan, Stuart J; Blackbourn, Luke A K; Pearson, Ewan R; Colhoun, Helen M; Bell, Samira Abstract: Introduction Variability in estimated glomerular filtration rate (eGFR) has been associated with increased risks of mortality and chronic kidney disease (CKD) progression in people with type 2 diabetes mellitus (T2DM) and impaired kidney function. However, its significance in individuals with preserved kidney function remains unclear. Methods In this nationwide retrospective population-based study of individuals with T2DM, eGFR variability was calculated by fitting a linear regression model to longitudinal data to estimate both the individual eGFR slope over the 5-year period as well as the variability in model residuals provided by the SD of the model residuals using longitudinal serum creatinine (SCr) measurements obtained during the first 5 years after diagnosis. Cox proportional hazards models were then applied to assess the association between eGFR variability and progression to stage G3b CKD among participants with preserved kidney function. Results This study included 98,322 participants who had an eGFR > 60 ml/min per 1.73 m2 at diagnosis, remained alive with an eGFR > 60 ml/min per 1.73 m2 5 years after diagnosis, and were subsequently followed for a mean of 5.1 years. Greater eGFR variability was associated with an increased risk of progression to stage G3b CKD- hazard ratios (HRs) for the second, third, and fourth quartiles of variability versus the first quartile were 1.56 (95% confidence interval [CI]: 1.38-1.75), 1.85 (95% CI: 1.65-2.08), and 2.56 (95% CI: 2.29-2.86), respectively. This association persisted after adjustment for multiple variables-HR: 1.57; 95% CI: 1.40-1.77 for the fourth quartiles of variability versus the first quartile. Conclusion eGFR variability in the absence of acute kidney injury (AKI) is associated with CKD progression in individuals with T2DM and preserved kidney function. Tue, 01 Sep 2026 00:00:00 GMT http://hdl.handle.net/1893/38322 2026-09-01T00:00:00Z