http://hdl.handle.net/1893/216 Electronic copies of Computing Science and Mathematics journal articles. 2026-11-13T17:08:53Z http://hdl.handle.net/1893/38448 Title: The case for model-informed vaccine development Author(s): PF Bai, Jane; Beigmohammadi, Fatemeh; Craig, Morgan; Dalton, Mackenzie; Desikan, Rajat; Easlick, Terry; Gazeau, Sonia; Heffernan, Jane; Kirpichnikova, Anna; Padmanabhan, Pranesh; Sachs, Jeffrey R; Sher, Anna; Skibinski, David; Smith, Amber M Abstract: This perspective paper illustrates how model-informed vaccine development (MIVD) impacts vaccine discovery and development decisions, saving time and resources. It discusses the benefits of mechanistic and other mathematical models within MIVD. We outline key applications, opportunities, challenges, and future steps for MIVD as a call for the continued integration of mathematical modelling during vaccine development. 2026-10-24T00:00:00Z http://hdl.handle.net/1893/38444 Title: Foundation Models in Biomedical Imaging: Turning Hype into Reality Corresponding Author Author(s): Muneer, Amgad; Zhang, Kai; Hamdi, Ibraheem; Qureshi, Rizwan; Waqas, Muhammad; Fouad, Shereen; Ali, Hazrat; Anwar, Syed Muhammad; Wu, Jia Abstract: Foundation models (FMs) are driving a prominent shift in biomedical imaging from task-specific models to unified backbone models for diverse tasks. This opens an avenue to integrate imaging, pathology, clinical records, and genomics data into a composite system. However, this vision contrasts sharply with modern medicine's trajectory toward more granular sub-specialization. This tension, coupled with data scarcity, domain heterogeneity, and limited interpretability, creates a gap between benchmark success and real-world clinical value. We argue that the immediate role of FMs lies in augmenting, not replacing, clinical expertise. To separate hype from reality, we introduce REAL-FM (Real-world Evaluation and Assessment of Foundation Models), a multi-dimensional framework for assessing data, technical readiness, clinical value, workflow integration, and responsible AI. Using REAL-FM, we find that while FMs excel in pattern recognition, they fall short in causal reasoning, domain robustness, and safety. Clinical translation is hindered by scarce representative data for model training, unverified generalization beyond oversimplified benchmark settings, and a lack of prospective outcome-based validation. We further examine FM reasoning paradigms, including sequential logic, spatial understanding, and symbolic domain knowledge. We envision that the path forward lies not in a monolithic medical oracle, but in coordinated subspecialist AI systems that are transparent, safe, and clinically grounded. 2026-08-01T00:00:00Z 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. 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. 2026-07-22T00:00:00Z