2026 September - 91 articles
Modern network infrastructures are under attack from increasingly sophisticated attacks that static, rule-based defenses cannot adequately mitigate. We introduce a multilayer anomaly detection and mitigation framework for Software-Defined Networking...
Monthly electricity demand forecasts are often affected by outliers and moving-holiday effects. This study proposes a forecasting framework that integrates regression with ARIMA errors (regARIMA), Hodrick–Prescott (HP) filter decomposition, and...
This research provides deep insights into the capabilities of encoder models, offering a unique, controlled benchmark of over 20 state-of-the-art models. The aim is to analyze the viability of a potentially advantageous recommender architecture that...
Cryptographic dependency updates can install cleanly and pass visible regression tests while changing project-facing behavior or leaving an application inside a vulnerable version range. This study presents VeriCrypt-Agent, an evidence-grounded workf...
Single-board computers (SBCs) are increasingly being used in embedded and edge computing applications, where power stability can affect computational reliability. This work experimentally characterizes an ARM-based SoC in a Raspberry Pi 5 under contr...
Background/Objectives: Real-time arthroscopic bone drilling in simulators requires high graphics and haptic update rates while deforming bone visually. We present a fully GPU-resident, shader-based pipeline for the Virtual Rotator Cuff Arthroscopic S...
Artificial Intelligence (AI) and Media and Information Literacy (MIL) are recognised as vital components of adult secondary education. However, we know little about how adult learners engage with AI in authentic educational contexts, given that AI ad...
Substation-level carbon-emission factors (CEFs) are operationally relevant because substations concentrate transformer losses, auxiliary consumption, and sulfur hexafluoride (SF6) leakage at the interface between transmission and distribution. Howeve...
Generative artificial intelligence (AI) can improve students’ work while assistance is available, but better AI-assisted performance does not necessarily show what students have learned or can do independently. The methodological gap is that ou...
Multimodal large language models (MLLMs) often struggle with open-ended questions requiring the integration of visual evidence, indirect textual clues, and background knowledge. We investigate whether team-based inference improves performance on Russ...
Adversarial patch attacks pose a tangible physical-world threat to traffic sign recognition in autonomous driving systems. Current state-of-the-art defenses based on image reconstruction require dual-model deployment and add per-frame inference laten...
Deep networks for optical coherence tomography (OCT) classification usually learn directly from image appearance, which leaves coherent speckle, depth-dependent attenuation, point-spread-function blur, and refractive distortion entangled with patholo...
Medical image classification models often struggle to generalise across heterogeneous clinical domains owing to variations in visual characteristics, acquisition conditions, and class imbalance. Existing studies largely address these challenges indep...
Log anomaly detection requires models that capture long-range event dependencies without the quadratic sequence-length cost of self-attention. We present LogMamba, a reconstruction-based model that combines a bidirectional selective state-space branc...
Multilingual code-switched language identification remains a challenging task due to frequent language alternation, lexical ambiguity, and the limited availability of annotated corpora for low-resource languages. While transformer-based language mode...
Pathology foundation models have recently emerged as powerful pretrained representations for computational pathology, yet whether complex downstream modeling is still necessary once frozen representations are evaluated under a common downstream frame...
Post-quantum migration is becoming binding for regulated systems, and delegating it to AI coding assistants is a plausible response. We measured what two open-weight coding models produced when asked to migrate a repository from RSA-based JSON Web To...
Financial loss, data theft, and user harm can result from phishing attacks, which are increasing in frequency and variety beyond traditional email-based attacks to include mobile forms like smishing, quishing, malicious mobile applications, and mobil...
Large language models (LLMs) are increasingly applied to software vulnerability detection, but evaluations report accuracy while ignoring inference cost and energy and under-represent open-weight models relative to proprietary systems. We benchmark e...
Image segmentation enables accurate object identification, a key requirement in computer vision applications. In aquaculture, this technology is essential for monitoring and management of species such as Oncorhynchus mykiss. In the Peruvian highlands...
Recent advances have been achieved in intelligent fault diagnosis of rolling bearings. However, noise interference and data imbalance remain critical challenges for achieving accurate and reliable fault diagnosis in practical industrial applications....
Europe’s ageing nuclear fleet creates a growing need for repeated radiological characterisation of contaminated, GPS-denied facilities where human access must be kept to a minimum. However, most deployed robotic systems remain limited to a sing...
Agile software development relies on frequent interaction, short feedback cycles, and adaptation, yet these practices do not automatically create shared understanding, visible assumptions, timely validation, or coordinated action. Relevant evidence i...
This paper presents a real-time browser-based platform for thermohydraulic characterization of a compact laboratory heating system, developed using the PolyVR research-grade virtual reality engine. Experimental measurements are retrieved at 1 Hz from...
DNS over HTTPS (DoH) protects name-resolution traffic but can also conceal command-and-control and data-exfiltration channels. Detection is challenging because benign and malicious flows share similar statistics, packet relationships span long sequen...
Industrial Internet of Things (IIoT) technologies have significantly improved the automation and monitoring of critical infrastructures such as water treatment facilities. However, the increasing interaction between human operators and cyber-physical...
Identifying the associations between non-coding RNAs and drug resistance (RDRAs) is crucial for uncovering resistance mechanisms and screening effective biomarkers. However, existing graph-based methods typically rely on purely data-driven learning a...
Although educational robotics is widely recognised as an effective approach to supporting curriculum-aligned learning in primary education, comparatively little attention has been paid to how teachers develop the learning resources needed for meaning...
Bearing fault diagnosis under variable operating conditions is challenging because changes in rotational speed and load alter the vibration response and cause substantial distribution shifts between operating domains. To improve cross-condition fault...
Autistic speech is highly heterogeneous, yet group-level comparisons may obscure meaningful individual acoustic patterns. This study used unsupervised machine learning to identify data-driven acoustic profiles in native speakers of Cypriot Greek, inc...
Processing-in-Memory (PIM) architectures are transforming system hierarchies by embedding computation within memory and collapsing traditional CPU–memory separation. This co-location enables measured gains in throughput and energy efficiency fo...
As the deployment of Internet of Things (IoT) ecosystems accelerates, safeguarding distributed networks against pervasive security and privacy threats has become a paramount concern. Integrating robust cryptographic protocols directly onto resource-l...
Bitcoin mining is a highly energy-intensive process whose economic sustainability depends on the complex interplay of electricity prices, Bitcoin prices, operating mode and technological efficiency. This paper analyses these relationships through sim...
Large language models can produce fluent requirements refinements and planning artifacts while still leaving information unresolved for implementation, testing, or planning commitment. This paper presents ReqPlan-Eval, an evidence-aware human-in-the-...
Delta compression is a promising technique for reducing the storage footprint of similar data. As the first step in delta compression, similarity detection has a significant impact on both the delta compression ratio (DCR) and the compression through...
Sequential object-oriented software remains difficult to parallelize because several important decisions are not entirely local. A useful decomposition should consider architectural structure, execution frequency, communication latency, and how much...
This study presents a leakage-proof hybrid machine learning framework for early flood risk forecasting using multimodal spatiotemporal tabular data. An event-based dataset covering Kazakhstan from 2001 to 2021 was created by integrating topographic c...
Facial biometric templates are permanent identifiers: once exposed, the underlying identity cannot be reissued, so single-copy storage is a critical single point of failure. This study protects facial templates by combining a non-invertible BioHashin...
Short-video platforms provide multimodal records of public affect during natural disasters and offer new opportunities for crisis-oriented social-media analysis. Using the 2021 Zhengzhou Flood as a single-event case study, we construct DMAC, which, t...
Online social networks increasingly serve as platforms for collective action, yet identifying minimal winning coalitions remains challenging due to heterogeneous user features, noisy behavioral traces, and the strategic ambiguity of swing members. Tr...
Ambient Internet-of-Things (IoT) deployments place large fleets of battery-less, energy-harvesting devices in environments where human operators cannot review every security event. Autonomous Artificial-Intelligence (AI) defenders can reduce this bur...
With the escalating frequency of sophisticated zero-day attacks, overcoming the critical limitations of signature-based Intrusion Detection Systems (IDSs) has become paramount. This study proposes a hybrid multi-layered intrusion detection framework...
The digital transformation of tourism has turned traveler reviews into a continuously expanding record of what visitors value, yet this evidence is fragmented across platforms and languages and is usually reduced to star ratings that obscure the unde...
Accurate differential diagnosis of Kawasaki disease (KD) from other febrile conditions remains challenging owing to nonspecific clinical presentation, absence of validated biomarkers, and reliance on post hoc explainability methods that cannot suppor...
Adaptive learning environments and large language models (LLMs) are increasingly combined, yet most LLM-based tutoring work addresses feedback generation in isolation rather than its integration into a controlled, auditable learning loop. This paper...
As professionals increasingly encounter artificial intelligence (AI) in their workplaces, questions around adoption barriers, ethical concerns, and job security have grown in prominence. This paper reports an exploratory cross-sectional descriptive s...
Accurate and subject-independent recognition of shoulder physiotherapy exercises from wrist-worn inertial measurement unit (IMU) signals is essential for automated home-based rehabilitation monitoring, yet existing deep learning models are too parame...
The exponential growth of scientific literature has amplified the need for ranking mechanisms that prioritize recent publications while ensuring the relevance and authenticity of cited information. The current recency-based metrics, like Price’...
Graphics processing units (GPUs) underpin high-performance computing, but device partitioning does not ensure that an in-context pointer remains within its allocation. We present a hardware–software co-design whose 16-bit tag uses odd parity an...
As we move further into a more digital supply chain, we have seen tremendous improvements in terms of efficiency, but we have also seen a rise in the risks that data vulnerabilities pose, particularly for intermediary supply chain nodes. As a result,...
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