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Facial recognition technology (FRT) has emerged as a powerful tool for public governance and security, but its rapid adoption has also raised significant concerns about privacy, civil liberties, and ethical implications. This paper critically examines the current rules and policies governing FRT, highlighting the tensions between state and corporate interests on one hand, and individual rights and ethical considerations on the other. The study also investigates international legal frameworks aimed at protecting individual rights and privacy, arguing that current legislative measures often fall short of robust scholarly standards and international human rights norms. The paper concludes with recommendations for developing principled and adaptable governance frameworks that harness the benefits of FRT while mitigating its risks and negative impacts, underscoring the importance of placing human rights and ethics at the center of regulating this transformative technology.

In the digital age, the rapid growth of web information has made it increasingly challenging for individuals and organizations to effectively explore and extract valuable insights from the vast amount of information available. This paper presents a novel approach to automated web text summarization that combines advanced natural language processing techniques with recent breakthroughs in deep learning. we propose a dual-faceted technique that leverages extensive pre-training on a broad dataset outside the domain, followed by a unique refined tuning process. We introduce a carefully curated dataset that captures the heterogeneous nature of web articles and propose an innovative pre-training and tuning approach that establishes a new state-of-the-art in news summarization. Through extensive experiments and rigorous comparisons against existing models, we demonstrate the superiority of our method, particularly highlighting the crucial role of the refined tuning process in achieving these results. Through rigorous experimentation against state-of-the-art models, we demonstrate the superior performance of our approach, highlighting the significance of their refined tuning process in achieving these results.

Abstract Active Learning has emerged as a viable solution for addressing the challenge of labeling extensive amounts of data in data-intensive applications such as computer vision and neural machine translation. The main objective of Active Learning is to automatically identify a subset of unlabeled data samples for annotation. This identification process is based on an acquisition function that assesses the value of each sample for model training. In the context of computer vision, image classification is a crucial task that typically requires a substantial training dataset. This research paper introduces innovative selection methods within the Active Learning framework, aiming to identify informative images from unlabeled datasets while minimizing the number of required training data. The proposed methods, namely Similari-ty-based Selection, Prediction Probability-based Selection, and Competence-based Active Learning, have been extensively evaluated through experiments conducted on popular datasets like Cifar10 and Cifar100. The experimental results demonstrate that the proposed methods outperform random selection and conventional selection techniques. The superior performance of the novel selection methods underscores their effectiveness in enhancing the Active Learning process for image classification tasks.

This research addresses the critical challenge of classifying surface defects in lithium electronic components, crucial for ensuring the reliability and safety of lithium batteries. With a scarcity of specific defect data, we introduce an innovative Cross-Domain Generalization (CDG) approach, incorporating Cross-domain Augmentation, Multi-task Learning, and Iteration Learning. Leveraging a steel surface defect dataset as foundational knowledge, our approach compensates for the limited lithium-specific data and enhances model generalization. We also introduce the Lithium Electronic Surface Defect Classification (IESDC) dataset, demonstrating significant accuracy improvements over baseline methods. Our comprehensive evaluation covers model interpretability, robustness, and adaptability. Beyond battery technology, this methodology offers a framework for data scarcity challenges in various industries, emphasizing the importance of adaptable learning methods.

IntroductionAlgorithmic decision-making systems are widely used in various sectors, including criminal justice, employment, and education. While these systems are celebrated for their potential to enhance efficiency and objectivity, they also pose risks of perpetuating and amplifying societal biases and discrimination. This paper aims to provide an indepth analysis of the types of algorithmic discrimination, exploring both the challenges and potential solutions.MethodsThe methodology includes a systematic literature review, analysis of legal documents, and comparative case studies across different geographic regions and sectors. This multifaceted approach allows for a thorough exploration of the complexity of algorithmic bias and its regulation.ResultsWe identify five primary types of algorithmic bias: bias by algorithmic agents, discrimination based on feature selection, proxy discrimination, disparate impact, and targeted advertising. The analysis of the U.S. legal and regulatory framework reveals a landscape of principled regulations, preventive controls, consequential liability, self-regulation, and heteronomy regulation. A comparative perspective is also provided by examining the status of algorithmic fairness in the EU, Canada, Australia, and Asia.ConclusionReal-world impacts are demonstrated through case studies focusing on criminal risk assessments and hiring algorithms, illustrating the tangible effects of algorithmic discrimination. The paper concludes with recommendations for interdisciplinary research, proactive policy development, public awareness, and ongoing monitoring to promote fairness and accountability in algorithmic decision-making. As the use of AI and automated systems expands globally, this work highlights the importance of developing comprehensive, adaptive approaches to combat algorithmic discrimination and ensure the socially responsible deployment of these powerful technologies.

This paper explores the evidentiary significance of blockchain records and the procedural implications of integrating this technology into the U.S. judicial system, as several states have undertaken legislative measures to facilitate the admissibility of blockchain evidence. We employ a comprehensive methodological approach, including legislative analysis, comparative case law analysis, technical examination of blockchain mechanics, and stakeholder engagement. Our study suggests that blockchain evidence may be categorized as hearsay exceptions or non-hearsay, depending on the specific characteristics of the records. The paper proposes a specialized consensus mechanism for standardizing blockchain evidence authentication and outlines strategies to enhance the technology’s trustworthiness. It also highlights the importance of expert testimony in clarifying blockchain’s technical aspects for legal contexts. This study contributes to understanding blockchain’s integration into judicial systems, emphasizing the need for a comprehensive approach to its admissibility and reliability as evidence. It bridges the gap between technology and law, offering a blueprint for standardizing legal approaches to blockchain and urging ethical and transparent technology use.

Modern supply chain systems face significant challenges, including lack of transparency, inefficient inventory management, and vulnerability to disruptions and security threats. Traditional optimization methods often struggle to adapt to the complex and dynamic nature of these systems. This paper presents a novel blockchain-based zero-trust supply chain security framework integrated with deep reinforcement learning (SAC-rainbow) to address these challenges. The SAC-rainbow framework leverages the Soft Actor–Critic (SAC) algorithm with prioritized experience replay for inventory optimization and a blockchain-based zero-trust mechanism for secure supply chain management. The SAC-rainbow algorithm learns adaptive policies under demand uncertainty, while the blockchain architecture ensures secure, transparent, and traceable record-keeping and automated execution of supply chain transactions. An experiment using real-world supply chain data demonstrated the superior performance of the proposed framework in terms of reward maximization, inventory stability, and security metrics. The SAC-rainbow framework offers a promising solution for addressing the challenges of modern supply chains by leveraging blockchain, deep reinforcement learning, and zero-trust security principles. This research paves the way for developing secure, transparent, and efficient supply chain management systems in the face of growing complexity and security risks.

LGBTQ+ youth experience mental health disparities and higher rates of mental disorders due to barriers to accessing care, including insufficient services and the anticipated stigma of revealing their identities. This systematic review incorporated 15 empirical studies on digital interventions’ impact on LGBTQ+ youth mental health, examining their potential to address these inequities. This study innovatively categorized existing digital interventions into four streams: Structured Formal (telehealth, online programs), Structured Informal (serious games), Unstructured Formal (mobile applications), and Unstructured Informal (social media). We found that S&F and U&F effectively reduced symptoms. U&F showed potential but required enhancement, while U&I fostered resilience but posed risks. Further integration of emerging technologies like virtual reality may strengthen these interventions. This review identifies the characteristics of effective digital health interventions and evaluates the overall potential of digital technologies in improving LGBTQ+ youth mental health, uniquely contributing insights on digital solutions advancing LGBTQ+ youth mental healthcare.

The decline of sea ice in the Arctic region is a critical indicator of rapid global warming and can also influence the feedback processes in the Arctic, so the prediction of sea ice extent and thickness plays an important role in climate modeling and prediction. This paper uses machine learning methods to predict the sea ice extent, and by adjusting the methods and factors, which include the climate variables, the past sea ice extent, and the simple linear-regression-simulated sea ice extent, then we found the best combination to give the result with the highest R2 score. We noticed that with longer periods of past sea ice extent data and shorter periods of climate data, the results appeared to be better. This might be related to the difference in climate and ocean memory. The sub-region sea ice extent prediction shows that the regions with whole-year ice cover are easier to predict and that those regions with sudden weather changes and significant seasonal variability appear to have lower R2 scores in the sea ice extent prediction.