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.

Introduction: Algorithmic 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.Methods: The 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.Results: We 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.Conclusion: Real-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.

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.

The rapid progression of Decentralized Finance (DeFi) has established Decentralized Exchanges (DEX) as critical elements in the financial landscape. Nevertheless, the open and transparent nature of DEX makes them susceptible to strategic manipulations, especially the sandwich attack. During such maneuvers, ill-intentioned actors exploit price slippage by positioning their transactions strategically around a target’s order to reap unfair profits. This paper introduces a ground-breaking framework rooted in mechanism design game theory to lessen the impact of sandwich attacks. The framework delineates the precise strategy of the sandwich attack and its repercussions, shedding light on the tactical aspects and utility functions pertinent to both the attackers and the ordinary traders, subsequently referred to as workers. The discussion extends to defining utility functions for both the market and the workers, emphasizing the benefits of liquidity provision for the market and the potential profits and losses for the workers. The proposal encourages adopting a market-centric mechanism design grounded in game theory, wherein the market, operating as the designer, creates rules to maximize its utility while considering the workers’ utility. Through a meticulous analysis of this game-theoretic approach, the study identifies optimum strategies for all the involved parties, demonstrating that these strategies can reach a balanced state. Further, this study presents a comparative view against existing research, highlighting the limitations of contemporary solutions and asserting the effectiveness of the proposed model in protecting the interests of both the market and the workers. Ultimately, this research furnishes stakeholders with new perspectives and instruments to thwart sandwich attacks and lays a foundation for creating resilient and fair decentralized trading infrastructures.