Security verification of modern System-on-Chip (SoC) designs is becoming increasingly challenging due to the growing integration of third-party IPs and the complexity of hardware-software (HW/SW) interactions. This escalating complexity broadens the attack surface, leading to a higher number of potential vulnerabilities and longer detection times. Consequently, verification engineers face increasing pressure to ensure robust security within tight development schedules. Traditional techniques such as formal verification and information flow tracking often suffer from poor scalability, state space explosion, and significant manual effort, necessitating expert-level design knowledge. Fuzzing-based methodologies, while promising, typically rely on the availability of a golden reference model and struggle to scale effectively, which limits their applicability. Furthermore, the increasing intricacy of HW/SW stacks in modern SoCs introduces new classes of system-level vulnerabilities that remain largely unaddressed by existing approaches. To address these challenges, we propose GEmFuzz, a hardware emulation-based greybox fuzzing framework for SoC security verification. GEmFuzz uses a hardware emulation server to run the design under test (DUT) at near real-time speed, effectively addressing the scalability challenges. Also, it leverages a cost-function-guided fuzzer to generate intelligent input patterns for system-level vulnerability detection. We evaluate GEmFuzz on a RISC-V-based SoC and demonstrate its effectiveness in detecting a set of known system-level vulnerabilities. Additionally, it identifies two previously unknown vulnerabilities, highlighting the capability and promise of the proposed framework.

AI-driven educational technologies are expanding rapidly, yet their design rarely reflects the linguistic and infrastructural realities of Deaf learners in low-resource contexts. This qualitative study investigates how teachers, parents, and Deaf students in Bangladesh navigate fragile visual access, inconsistent Bangla Sign Language, and unreliable technology in everyday learning. Through interviews and focus groups with 13 teachers, eight parents, and five Deaf students, we show how small disruptions in sightlines, pacing, or sign clarity can quickly collapse comprehension, making accessibility a condition that must be constantly protected. We identify opportunities for AI to act as access support by stabilizing visual information, ensuring teacher-validated Bangla Sign Language, enabling offline use, and protecting emotional safety when seeking help. We contribute a model of learning continuity that explains how visual, linguistic, and affective stability interact in Deaf education and offer concrete design directions for AI-driven learning tools in the Global South.

The NBD (Network Block Device) protocol enhances the Android ecosystem's storage management and power efficiency, particularly on ARM devices. The study addresses storage limitations on ARM devices and explores how NBD can provide a solution. By leveraging NBD, Android devices can connect to remote storage resources and expand storage capacity without physical upgrades. This enables Android devices to be used as controllers for IoT networks and smart home appliances. The study includes statistical comparisons of NBD with alternative protocols like sFTP, iSCSI, NFS, SMB, and HTTP. Through read-and-write tests, NBD exhibits superior performance in bandwidth speed and operation duration, with speeds more than twice as fast as other protocols. Furthermore, the evaluation compares NBD's cloud storage capabilities with popular services such as Google Drive, Dropbox, OneDrive, and GCP. NBD outperforms these services regarding read and write speeds, completing operations much faster. The study also examines NBD's power consumption, demonstrating its energy efficiency compared to other protocols and cloud storage services. NBD's lower power consumption makes it ideal for energy-sensitive Android applications. In conclusion, the integration of NBD into the Android ecosystem enhances efficiency, storage flexibility, and adaptability. It empowers ARM-based Android devices with increased capabilities and versatility.

Fault-injection attacks (FIA) intentionally disrupt circuit behavior allowing adversaries to bypass safety mechanisms, disrupt system functionality, or extract sensitive information, thereby posing severe risks to the security and reliability of modern System-on-Chips (SoCs). However, pre-silicon security assessments targeting FIA predominantly rely on gate-level simulation or late-stage layout analysis, which are slow, limited in coverage, and often fail to capture realistic operating conditions—leaving critical vulnerabilities undetected until post-silicon stages. To address these limitations, we propose EmFIA, an emulation-driven register-transfer level (RTL) fault injection assessment framework designed to analyze security-critical vulnerabilities against FIA. EmFIA systematically analyzes securitycritical signals in a design by modeling the faults in hardware emulation platform, inserting SystemVerilog assertions, and monitoring security property violations. Demonstrated on a RISC-V SoC and standalone AES-128 and RSA-128 cores, EmFIA enables rapid exploration of fault scenarios, achieving speedups of several orders of magnitude compared to exhaustive gate-level simulation. EmFIA provides designers with fast, property-aware security insight early in the design cycle, significantly strengthening hardware resilience prior to fabrication.

With the rapid advancement of cloud technologies, cloud services have enormously contributed to the cloud community for application development life-cycle. In this context, Kubernetes has played a pivotal role as a cloud computing tool, enabling developers to adopt efficient and automated deployment strategies. Using Kubernetes as an orchestration tool and a cloud computing system as a manager of the infrastructures, developers can boost the development and deployment process. With cloud providers such as GCP, AWS, Azure, and Oracle offering Kubernetes services, the availability of both x86 and ARM platforms has become evident. However, while x86 currently dominates the market, ARM-based solutions have seen limited adoption, with only a few individuals actively working on ARM deployments. This study explores the efficiency and cost-effectiveness of implementing Kubernetes on different CPU platforms. By comparing the performance of x86 and ARM platforms, this research seeks to ascertain whether transitioning to ARM presents a more advantageous option for Kubernetes deployments. Through a comprehensive evaluation of scalability, cost, and overall performance, this study aims to shed light on the viability of leveraging ARM on different CPUs by providing valuable insights. • ARM architecture enables heterogeneous CPU design for enhanced performance. • Tests on x86 vs. ARM across clouds revealed varied performance outcomes. • ARM outperformed x86 in most performance comparisons with significant gains.

Real-time video surveillance, through CCTV camera systems has become essential for ensuring public safety which is a priority today. Although CCTV cameras help a lot in increasing security, these systems require constant human interaction and monitoring. To eradicate this issue, intelligent surveillance systems can be built using deep learning video classification techniques that can help us automate surveillance systems to detect violence as it happens. In this research, we explore deep learning video classification techniques to detect violence as they are happening. Traditional image classification techniques fall short when it comes to classifying videos as they attempt to classify each frame separately for which the predictions start to flicker. Therefore, many researchers are coming up with video classification techniques that consider spatiotemporal features while classifying. However, deploying these deep learning models with methods such as skeleton points obtained through pose estimation and optical flow obtained through depth sensors, are not always practical in an IoT environment. Although these techniques ensure a higher accuracy score, they are computationally heavier. Keeping these constraints in mind, we experimented with various video classification and action recognition techniques such as ConvLSTM, LRCN (with both custom CNN layers and VGG-16 as feature extractor) CNNTransformer and C3D. We achieved a test accuracy of 80% on ConvLSTM, 83.33% on CNN-BiLSTM, 70% on VGG16-BiLstm, 76.76% on CNN-Transformer and 80% on C3D.

This work proposes a novel fully distributed Ethernet over star coupler-based PON architecture. The architecture uses a collision-free DBA scheme in which the OLT is excluded from the implementation of the time slot assignment. To implement a distributed control plane, direct connectivity (communicability) between the ONUs should be in place without imposing any constraint on the PON topology. In addition to the added flexibility and reliability associated with a distributed architecture, the performance of the proposed decentralized Ethernet PON scheme and the associated bandwidth allocation algorithms are shown to be as efficient as their centralized counterparts.