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Few-Shot Learning for IoT Intrusion Detection: An Attention-Based Siamese Network Approach
Get PDFThe Internet of Things (IoT) has garnered significant attention from both industries and the research community. The diverse nature of IoT devices makes them a prime target for cybercriminals. An intelligent intrusion detection system (IDS) can quickly identify multiple types of cyberattacks within an IoT system. However, operational efficiency and safety become challenging issues when managing limited labeled data in IoT networks. This article proposes a novel cyberattack detection scheme using few-shot learning (FSL). The proposed scheme employs a self-attention mechanism with a Siamese network that learns to recognize normal and malicious network traffic patterns through FSL. The Siamese network architecture facilitates efficient sample comparison by learning a shared representation. Incorporating an attention mechanism further enhances its ability to focus on discriminative features, improving attack detection accuracy for rapidly evolving intrusion patterns. The effectiveness of the proposed framework is evaluated through extensive experiments on the latest Edge-IIoTset dataset. The experimental findings demonstrate that the proposed IDS achieved the best accuracy of 99.75% with the complete dataset. In a few-shot performance evaluation, the designed architecture achieved the best accuracy of 78.69%, 81.19%, and 81.83% for 1 shot, 5 shots, and 10 shots, respectively
Criminal Justice in Scotland
No full textProviding a comprehensive and up-to-date introduction to the contemporary Scottish criminal justice system, this book focuses on its key processes (from arrest to post-sentence) and institutions, as well as its history and some of the key challenges and critical issues facing Scottish criminal justice today.Highly attentive to procedural and institutional detail, the book is underpinned by a critical sociological perspective. The opening chapter considers Scotland in its post-devolutionary context within the UK, setting the scene for Scottish criminal justice institutions. Subsequent chapters deal in turn with different institutions and processes of Scottish criminal justice: surveillance and crime prevention, policing, prosecution and the courts, criminal fines and other financial penalties, community justice, electronic monitoring, prisons, youth justice, and parole and post-sentence reintegration and supervision. The final chapter draws on comparative and international criminology to look at Scottish criminal justice in changing international contexts and its response to new global crimes. Designed to support learning, it includes:Key ideas at the start of each chapter, outlining expectations, and providing a very high-level summary of the most important points.Text boxes covering key issues, controversies and/or key populations in focus.Case studies of specific problems and events in Scottish criminal justice. Critical questions to challenge students to develop their own thinking and ideas about key issues in Scottish criminal justice.Short, informal interviews with key practitioners and researchers. Essential Readings and Key Resources at the end of each chapter. Criminal Justice in Scotland will be a key text for students of criminology and criminal justice, particularly those with an interest in the Scottish criminal justice system. It will also be of value to students of law, social work and social policy
Intentional Misrepresentations of Project Information
No full textThe intentional misrepresentation of project information can lead to project failure while also exposing practitioners and stakeholders to significant legal risks. This has driven scholarly interest in examining the existing research on the topic. However, effectively generating and applying knowledge from this body of work remains challenging due to a lack of clarity regarding its overall structure. To address this, we conducted a bibliometric analysis of research on intentional misrepresentation published in peer-reviewed journals between 1991 and 2024. Our analysis explored key structural elements, including publication trends over time, dissemination channels, frequently cited papers, common and co-occurring keywords, and the interconnections among relevant studies. We identified five dominant themes in the literature: bias and strategic misrepresentation, self-committed errors, silence, and whistleblowing, theoretical applications, organizational impact of intentional misrepresentations, and information asymmetry and distortion. This study makes two key contributions. First, it consolidates and synthesizes existing knowledge, offering a comprehensive structural characterization of the field in a single study. Second, it extends previous bibliometric analyses and literature reviews, which have primarily focused on broader corrupt and fraudulent practices in engineering and construction
Machine Learning-Based Position Detection Using Hall-Effect Sensor Arrays on Resource-Constrained Microcontroller
Get PDFThis paper presents an electromagnetic levitation system that stabilizes a magnetic body using an array of electromagnets controlled by a Hall-effect sensor array and TinyML-based position detection. Departing from conventional optical tracking methods, the proposed design combines finite-element-optimized electromagnets with a microcontroller-optimized neural network that processes sensor data to predict the levitated object’s position with 0.0263–0.0381 mm mean absolute error. The system employs both quantized and full-precision implementations of a supervised multi-output regression model trained on spatially sampled data (40 × 40 × 15 mm volume at 5 mm intervals). Comprehensive benchmarking demonstrates stable operation at 850–1000 Hz control frequencies, matching optical systems’ performance while eliminating their cost and complexity. The integrated solution performs real-time position detection and current calculation entirely on-board, requiring no external tracking devices or high-performance computing. By achieving sub 30 μm accuracy with standard microcontrollers and minimal hardware, this work validates machine learning as a viable alternative to optical position detection in magnetic levitation systems, reducing implementation barriers for research and industrial applications. The complete system design, including electromagnetic array characterization, neural network architecture selection, and real-time implementation challenges, is presented alongside performance comparisons with conventional approaches
Development and Validation of the International Skin Tear Advisory Panel Skin Tear Data Collection Tool
Get PDFOBJECTIVE: Numerous studies have examined the epidemiology of skin tears; however, inconsistent definitions, classification systems, and data collection methods have highlighted the need for a validated and standardized tool. The primary objective of this study was to validate a data collection tool for skin tears. A secondary aim was to provide a freely accessible tool for health care providers or researchers to collect consistent and reliable data on skin tears. METHODS: The development of the tool was guided by the 2018 International Skin Tear Advisory Panel (ISTAP) Best Practice Recommendations for the prevention, assessment, and management of skin tears in aged skin. Between June and October 2024, a multimethod validation process was undertaken. Content validity ratio and content validity index calculations were used to quantify content validity, supported by qualitative feedback from 15 experts to assess face validity and provide suggestions for refinement. RESULTS: The final tool consists of 22 questions addressing a patient’s demographics, clinical features of the skin tear, associated risk factors, and contextual data. The content validity index was calculated at 0.72, indicating an acceptable level of content validity. International experts reached consensus following a 2-round qualitative review, resulting in subsequent adjustments to the tool. CONCLUSIONS: The ISTAP DC-Tool was developed based on evidence-informed recommendations and validated by an international panel of experts. Its implementation will support health care providers and researchers in gathering standardized epidemiological data contributing to clinical practice improvements, quality initiatives, and further research in skin tear prevention and management
Few-Shot Class-Incremental SAR Target Recognition via Orthogonal Distributed Features
Get PDFAs synthetic aperture radar (SAR) imaging technology continues to evolve, the growing repository of SAR images depicting diverse types of observed targets has sparked rising interest in SAR target incremental recognition techniques. However, most of the existing SAR target incremental recognition algorithms typically require an ample amount of training data. In urgent scenarios, such as emergency response and disaster relief, there may be a necessity to identify targets for which a substantial amount of data has not been previously accumulated. Algorithms designed for general scenarios often fail to achieve satisfactory performance in such situations. To tackle the aforementioned issues, this article presents a few-shot incremental recognition algorithm for SAR targets based on orthogonal distributed features. Specifically, an orthogonal distribution optimization method for features is designed, which not only mitigates the feature confusion in few-shot incremental learning, but also reserves space for features of potential unseen classes. A random augmentation method for high-dimensional features is proposed to improve the overfitting problem while assisting in strengthening the boundaries between features of different classes. Furthermore, a joint decision criterion based on the Euclidean distance and the cosine distance is introduced, enabling the classifier to possess sufficient generalization ability and robustness in handling dynamic data. Experimental results on the MSTAR dataset show that the algorithm outlined in this article outperforms existing methods in SAR target few-shot incremental recognition tasks, demonstrating its superior effectiveness
Effects of Hearing Intervention on Physical Activity Measured by Accelerometry: a secondary analysis of the ACHIEVE Study
No full textBackground: Hearing loss is prevalent in older adults and associated with reduced daily physical activity, but whether hearing intervention attenuates declines in physical activity is unknown . We investigated the 3-year effect of a hearing intervention versus a health education control on accelerometer-measured physical activity in older adults with hearing loss. Methods: This secondary analysis of the ACHIEVE randomized controlled trial included 977 adults aged 70-84 years with hearing loss. Participants were randomized to either a hearing intervention group or a health education control group. Physical activity was measured using wrist-worn accelerometers at baseline, 1, 2, and 3 years. Linear mixed models assessed the impact of the intervention on changes in total activity counts, active minutes per day, and activity fragmentation. Results: Among 847 participants in the final analysis (mean age 76.2 years; 440 [52%] women; 87 [10%] Black; 5 [0.8%] Hispanic), total activity counts declined by 2.7% annually and active minutes/day declined by 2.1% annually over 3-years in both intervention and control groups. Activity patterns also became more fragmented over time. No appreciable differences were observed between hearing intervention and health education control in 3-year change in accelerometry-measured physical activity measures. Conclusions: Hearing intervention did not appreciably attenuate 3-year declines in physical activity compared to health education control in older adults with hearing loss. Alternative strategies beyond hearing treatment may be needed to enhance physical activity among older adults with hearing los
Towards a human-rights-based framework for assessing psychiatric intervention for children and young people
Get PDFDecisions around psychiatric interventions for children and young people involve balancing respect for the child’s wishes, the need to provide benefit and relevant risk factors. We recommend establishing a framework for assessment of interventions for children with mental disabilities, using a human-rights-based approach that can be applied across jurisdictions, alongside national laws
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