International Journal of Ethical AI Application
Transparent Governance in an Automated Age: Challenges and Solutions in Public Authorities AI Deployment
The integration of artificial intelligence into administrative governance has transformed public decision-making but simultaneously challenged the foundational principle of transparency in administrative law. Through a comparative legal analysis, this paper examines how automated decision-making systems—particularly the OCI (“Robo-Debt”) case—expose deficiencies in procedural fairness, explainability, and accountability. It identifies the algorithmic “black box” as a structural barrier to transparency, undermining the rule of law and citizens’ trust in government. The study argues that embedding Explainable Artificial Intelligence (XAI) within administrative processes offers a legal–technical solution to reconcile automation with transparency. By linking algorithmic governance to existing administrative review principles, XAI enables interpretability, justifiability, and contestability of automated decisions, thereby strengthening democratic legitimacy in the age of algorithmic administration.
Explainable Artificial Intelligence (XAI)
Copyright (c) 2025 Zijing Wang (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
A Hybrid Framework for Temporal Object Behavior Analysis Using LSTM and Real-Time Detection
Fall detection in surveillance videos is a critical task with widespread applications in healthcare and public safety. In this paper, we propose a hybrid framework that combines YOLOv8 for real-time object detection with an LSTM based temporal reasoning module to classify human activities across video sequences. Our method captures both spatial appearance and temporal motion patterns, allowing it to distinguish subtle differences between activities such as standing, walking, lying, and falling. We evaluate our model on the UR Fall Detection (URFD) dataset and achieve a validation accuracy of 92.0% and a mean Average Precision (mAP@0.5) of 91.2%. Qualitative results further demonstrate robust predictions even in challenging scenarios with occlusions and lighting variations. An ablation study confirms that integrating temporal reasoning significantly boosts performance over frame-based detection alone. The proposed approach offers a promising solution for real-time fall detection in intelligent surveillance and assistive monitoring systems.
Copyright (c) 2025 Subhan Uddin, Babar Hussain, Noman Ahmad, Adil Hussain, Sidra Fareed (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
AI-Enhanced Scholarly Communication: Transforming Peer Review, Knowledge Dissemination, and Academic Publishing Workflows in the Digital Era
The use of Artificial Intelligence (AI) in academic communication is changing the conventional methods of peer reviews, knowledge diffusion, and the publishing process in academic activities. This paper will discuss how AI-based tools are changing the speed, quality of scholarly outputs, and its accessibility whilst increasing ethical and equity issues. Based on the qualitative research framework, the paper will examine scholarly insights into the opportunities and challenges of AI to using AI in publishing. Examples of the major areas of impact include the transformation of peer review, in the form of automated quality assurance and natural language processing, improvement of knowledge distribution via smart indexing and recommendations, and streamlining of the publishing process with AI-enhanced editing and formatting. Its conclusions point to the positive values of efficiency, transparency, and greater accessibility on the one hand, and to the potential threats of algorithmic bias, transparency, and loss of human judgment on the other. In conclusion, the analysis arrives at the conclusions that hybrid human-AI systems, where transparency and ethical supervision play the key role, are the most sustainable direction. Recommendations are also placed on researchers, publishers, and policymakers so as to promote responsible and inclusive AI-enhanced scholarly communication.
Copyright (c) 2025 Muhammad Huzaifa Bin Salih, Tahir Gul, Syeda Sumblah Bukhari, Iffat Liaqat, Anam Majeed (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Legal Logic of AI Data Governance Based on Federated Learning: Institutional Evolution from Privacy Protection to Rights Distribution
This study focuses on the legal logic of data governance under federated learning technology in the field of artificial intelligence (AI). Through legal reasoning and literature analysis, it delves into the importance of federated learning as a key institutional approach that balances privacy and data utilization in the face of real-world challenges such as conflicts between data silos and privacy protection, disputes over the ownership of data rights in AI training works, and compliance pressures from the EU AI Act and GDPR. Starting from a legal logic inference framework and the legal value foundation of federated learning, this study reviews existing research findings and shortcomings, and constructs a logical chain of institutional evolution from privacy protection to rights allocation. The study finds that federated learning is not merely a technical tool but also an opportunity to drive legal institutional design innovation. The institutional chain linking privacy to interest allocation constitutes a new paradigm for AI data governance. By integrating legal logical inference into technical literature and fusing the three legal logics of privacy, ownership, and benefits, this research provides an innovative institutional evolution model and operational governance recommendations for AI data governance, demonstrating significant theoretical and practical significance in interdisciplinary research.
Copyright (c) 2025 Wenzhou Shu (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Privacy-Preserving Weight Reconstruction in Federated Learning: A Legal Governance Framework Based on Algorithmic Justice
This study takes the "right to be forgotten" established by the European Union's General Data Protection Regulation (GDPR) and the compliance requirements of China's Personal Information Protection Law as its theoretical basis. It comprehensively employs a cross-disciplinary research method combining normative analysis and technical deconstruction to deeply explore the legal dilemmas inherent in the technical architecture of federated learning. The study reveals that in the operation of the federated learning system, there is a profound legal conflict between "algorithmic shadowing" and data control rights. Traditional privacy rights theories face increasing limitations in their explanatory power when confronted with this new data processing model. Based on this, this paper innovatively proposes a "dynamic responsibility allocation" legal governance paradigm. By precisely defining the rights and obligations of various participants in the federated learning ecosystem, such as data providers, model trainers, and platform operators, this paper constructs a legal governance framework that meets the requirements of algorithmic justice. The framework aims to achieve a dynamic balance between technological innovation and privacy protection, providing a solid theoretical foundation and feasible institutional guidance for the compliant and robust development of federated learning technology.
Copyright (c) 2025 Zikang Zhang (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Artificial Intelligence in Education: Applications, Challenges, and Future Directions–a Critical Review
Artificial Intelligence (AI) has emerged as a transformative force in education, reshaping how learning is delivered, assessed, and managed. This review critically examines the evolution, current applications, challenges, and future opportunities of AI in educational systems. By synthesizing developments from early rule-based and intelligent tutoring systems to contemporary adaptive, data-driven, and generative AI solutions, the paper highlights how AI enhances personalized learning, predictive analytics, automated assessment, and immersive learning environments. Furthermore, the review underscores key ethical, technical, and pedagogical challenges, including digital inequality, data privacy concerns, algorithmic bias, and overreliance on AI, while presenting tables and visual frameworks to clarify their interconnections and implications. Finally, the paper explores future directions, emphasizing human-AI collaboration, explainable AI (XAI), multimodal personalization, and immersive experiential learning as pathways toward inclusive, ethical, and future-ready educational systems. This work offers a strategic and holistic perspective, serving as both an academic reference and a practical roadmap for researchers, educators, and policymakers seeking to responsibly harness AI in education.
Copyright (c) 2025 Zahraa Ahmed Ali (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Decentralized Federated Learning with Blockchain for Privacy-Preserving Edge AI in Smart Cities
With all the new IoT devices and smart setups popping up in cities, we're generating a ton of data right at the network's edge. Now, while Edge AI can make decisions on the spot, training those AI models in a central way brings up issues around governance and privacy. So, this paper shares a pretty cool framework that mixes Federated Learning (FL) with Blockchain to let us train models on different devices in smart cities while keeping privacy intact. The idea with FL is that data stays put, and only the updates to the models get shared, which cuts down the risk of data leaks a lot. We also throw in a lightweight blockchain for keeping everything trustworthy and making sure the models are solid, even if some devices aren’t super reliable. This system not only scales well but also lower the amount of communication needed, all while keeping data secure and private. We used smart city data for our tests, and the results are promising better user privacy, tracking of data origins, and more accurate models, which all help in building secure and scalable Edge AI systems in our urban environments.
Copyright (c) 2025 Sayma Nasrin Shompa (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
A Hybrid Matrix Factorization Framework for Balancing Personalization, Diversity, and Coverage in Ad Recommendation Systems
This paper presents a comprehensive framework for addressing the inherent trade-offs among personalization, diversity, and coverage in hybrid advertisement recommendation systems. In response to the growing complexity of online advertising, the proposed system integrates collaborative filtering via Singular Value Decomposition (SVD) with content-based filtering using Non-negative Matrix Factorization (NMF), enhanced by a re-ranking mechanism based on coverage profiles. This hybrid design aims to deliver relevant, diverse, and widely distributed ads, thereby improving user engagement and fairness among content providers. To quantitatively evaluate these objectives, three novel metrics—Ad User Profile (AUP), Ad Candidate Set (ACS), and Ad Matching Degree (AMD)—are introduced. Through empirical analysis, including visualization and comparative experiments, the study demonstrates that the proposed system outperforms traditional models in achieving a more balanced recommendation outcome. Additionally, real-world case studies and optimization formulations are explored to support scalable deployment. This work contributes to the evolving landscape of recommender systems by proposing a scalable, user-centric approach that harmonizes personalization with fairness and discovery in digital advertising.
Copyright (c) 2025 Yaqeen Mousa Abid, Ali Fahem Neamah (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Pattern-Based Architecture for Adaptive Multimedia Processing in Heterogeneous Environments: A Modular Approach with UML Modeling
Multimedia systems increasingly operate across heterogeneous devices and fluctuating network conditions, requiring adaptable and modular architectures to ensure real-time performance and cross-platform compatibility. Traditional multimedia frameworks such as FFmpeg and GStreamer, though powerful, often suffer from tightly coupled designs that limit reuse and dynamic reconfiguration. This paper addresses these limitations by introducing a set of reusable Multimedia Design Patterns tailored to the challenges of adaptive video processing, codec interoperability, and system heterogeneity. The proposed framework is grounded in pattern-based architectural modeling and formalized using UML class and component diagrams. Key patterns include the Stream Handler Pattern, Adaptive Codec Selector, Filter Chain, and Media Buffer Pattern—each designed to abstract core multimedia tasks such as input/output management, codec switching, filter composition, and buffer synchronization. To validate the effectiveness of these patterns, we implement a real-time cross-platform video streaming client that dynamically adjusts its encoding strategy and processing pipeline based on runtime conditions. The case study demonstrates how pattern-driven modularization improves reusability, reduces latency, and enables runtime pipeline reconfiguration with minimal code dependencies. Performance evaluation compares the pattern-based solution to a monolithic baseline using metrics such as frame processing time, resource consumption, and switching delay. Results show up to 38% improvement in responsiveness and 29% reduction in memory overhead. This work contributes a practical, scalable approach to multimedia system design, supporting both academic advancement and industrial deployment in smart streaming, edge rendering, and IoT media delivery contexts.
Copyright (c) 2025 Hasanain Hazim Azeez (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Comprehensive Review of Machine Learning Approaches for Alzheimer’s Disease Diagnosis and Prognosis
Alzheimer’s disease (AD) represents one of the most pressing challenges in modern healthcare, owing to its progressive nature, lack of curative treatments, and increasing global prevalence. In recent years, machine learning (ML) has emerged as a powerful tool to aid in the early diagnosis and prognosis of AD, offering data-driven approaches capable of managing high-dimensional, heterogeneous, and multimodal data. This review provides a comprehensive synthesis of ML techniques applied to AD, including supervised, unsupervised, and reinforcement learning algorithms. Particular emphasis is placed on models such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Recurrent Neural Networks (RNNs), which demonstrate strong performance in classifying disease stages and predicting cognitive decline. The review systematically analyzes studies published between 2014 and 2024, outlining prevailing approaches in feature selection, data preprocessing, and model evaluation. Major datasets—including ADNI, NACC, and OASIS—are discussed in terms of accessibility, modality, and clinical relevance. The paper also highlights challenges related to data imbalance, interpretability, and generalizability across clinical settings. Despite promising advances, the integration of explainable AI (XAI) frameworks remains limited. Future work must prioritize the development of balanced models that combine predictive accuracy with clinical interpretability to foster real-world deployment and personalized healthcare in AD management.
Copyright (c) 2025 Zainab Abdalhussain kareem, Ahmad Shaker Abdalrada (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Artificial Intelligence in Speech Emotion Detection: Trends, Challenges, and Future Directions
Speech Emotion Detection (SED) has become a pivotal component in the development of emotionally aware artificial intelligence systems. This paper presents a comprehensive review of recent advancements in the field, focusing on signal processing techniques, machine learning and deep learning approaches, real-time implementation, and multimodal integration. The study highlights the critical role of feature extraction and classification methods in improving emotion recognition accuracy and robustness. Additionally, it discusses emerging trends such as personalization, explainable AI (XAI), and adaptation to noisy and culturally diverse environments. Ethical considerations and legal implications surrounding emotion-aware systems are examined, along with practical applications in healthcare, education, customer support, and entertainment. The review concludes by outlining the unresolved challenges and proposing future research directions to bridge existing gaps and enable more human-centric and trustworthy emotion recognition technologies.
Copyright (c) 2025 Noor Alwan Malk, Sinan Adnan Diwan (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Advanced Machine Learning in Secure Authentication for Users in Healthcare Application
Networking and data communications technologies have evolved more rapidly thanks to sustainable computing. The idea of developing intelligent health systems is currently taking shape as the Sustainable Healthcare Systems. The study of security for Sustainable Healthcare Systems-based application systems, including such e-healthcare systems, industry automation systems, tactical surveillance systems, and so on, has recently made considerable achievements in the academic world. Chaotic Map assisted SHA-3 algorithm is discovered as a crucial security-control method to the design of Smart Environments. The S-USI assigns a unary-token to the authorised users so they can access the various services offered by a service provider across an IP-enabled distributed system in order to guarantee fidelity. There are many authenticating methods available for cloud-based decentralized systems. The majority of the techniques are still susceptible to security risks like replay attacks, powerful intrusion attempts, user request, and authentication protocols. In order to provide security and privacy, the intelligent healthcare industry described in this study help of sensors and sensor-tag technology. A strong secured based mechanism and well-formed cohabitation protocols proof for ubiquitous cloud services are suggested in order to bolster the authentication process. The significance of the proposed measures is demonstrated to demonstrate the security effectiveness of the suggested method using a formal security analysis. The comparison has been done from the formal verification show that the presented method uses less overhead processing, making it more appropriate for telemedicine hospital information systems.
Copyright (c) 2025 Sumit Kushwaha (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Enhancing Spatial Awareness via Multi-Modal Fusion of CNN-Based Visual and Depth Features
Achieving accurate spatial awareness is a fundamental requirement for intelligent vision systems operating in complex and dynamic environments, such as autonomous navigation, robotic manipulation, and augmented reality. While Convolutional Neural Networks (CNNs) have demonstrated exceptional performance in tasks such as image classification and semantic segmentation, their inherently two-dimensional structure limits their ability to model and reason about three-dimensional spatial relationships. Specifically, CNNs are constrained by local receptive fields, a lack of explicit geometric context, and their dependence on appearance-based cues, which often results in inaccurate understanding of object boundaries, depth discontinuities, and occlusions in real-world scenes. To address these limitations, this paper investigates the fusion of RGB visual data with depth information through a multi-modal intermediate fusion framework. We propose a lightweight experimental prototype that integrates parallel feature extraction pipelines for RGB images and corresponding depth maps, followed by feature-level fusion to enhance semantic and geometric understanding. The experiment is conducted on the NYU Depth V2 dataset, which provides densely labeled indoor scenes with aligned RGB and depth data. A comparative analysis is performed between a baseline CNN model trained solely on RGB input and a modified model utilizing intermediate fusion of RGB and depth features. Experimental results indicate that the inclusion of depth information significantly improves the model’s ability to delineate object boundaries, resolve foreground-background ambiguities, and maintain semantic coherence across varying spatial scales. The depth-enhanced model demonstrates increased robustness to occlusions and illumination changes, highlighting the practical benefits of integrating geometric cues into visual perception pipelines. These findings provide empirical support for the theoretical premise that multi-modal feature fusion can substantially enhance spatial reasoning in CNN-based architectures. This study contributes both a conceptual understanding and an applied perspective on the design of multi-modal spatial systems. The results serve as a foundation for further development of robust, depth-aware visual perception models with applications in real-time robotics, autonomous systems, and immersive AR/VR environments.
Copyright (c) 2025 Babar Hussain, Jiandong Guo , Fareed Sidra , Bohuan Fang, Luyao Chen , Subhan Uddin (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
VOXAIOT: A Voice-Activated IoT System for Real-Time Automation and Smart Control Using Google Assistant and Arduino
Supported by the Internet of Things (IoT) and voice assistance, the project VOXAIOT is about developing a new high-level automation system. Google Assistant is used to control and display feedback for an Arduino Uno project that is made up of a buzzer, LED, PIR sensor, ultrasonic sensor, resistor, breadboard, and jumper wires. Monitoring via voice commands is allowed with this smooth integration, contributing to the enhancement of user experience and system interaction. The system is based on different types of sensors performing motion detection and distance measurement, providing both security and automation features. The PIR sensor is used to motion detection, and the ultrasonic sensor is used to displacement measurement, which can provide precise data to the control system. Google Assistant serves as voice interface, through which the user can enable/disable the sensors, and do actions such as turning the LED light on/off, blow the buzzer, etc. The primary feature that differentiates VOXAIOT is the marriage of hardware with the cloud, which facilitates high-acuity voice control with very low lag time. This allows the system to quickly heeds voice commands, leaving the interaction uninterrupted. The experiment reveals the feasibility of using voice recognition and IoT to design a smart home/industrial automation based solution. The findings demonstrate the effectiveness of the framework to integrate low-level hardware with cloud services, yielding a novel way to command and monitor automation system by voice commands.
SDG 9 (Industry, Innovation, and Infrastructure)
Copyright Expectancy Right: Paradigm Reconstruction of AI Training Data Governance
This paper focuses on the intricate challenges in AI training data governance and innovatively introduces the theory of copyright expectancy right as a potential solution. It first dissects the existing "trilemma dilemma" in AI copyright governance, encompassing the ambiguous rights of data sources, the paradox in determining copyright for AI-generated content, and the lag in regulatory frameworks. Subsequently, the study conducts in-depth jurisprudential validation of the expectancy right theory, exploring its legal philosophical foundations—drawing on Locke's labor property theory (framing data contributions as "digital labor") and Rawls' principle of justice (ensuring fair data distribution under the "curtain of ignorance")—and highlighting its institutional comparative advantages, such as breaking the "all-or-nothing" logic of traditional copyright, compensating for the passivity of the unjust enrichment system, and compatibility with the EU Text Mining Exception. A three-stage governance model (technology, institution, and ethics) is constructed to propose a gradient implementation path, including blockchain traceability, Shapley value-based dynamic distribution, obligation configurations for different scenarios, and ethical guidelines like Habermasian interaction rationality and the "glass box" principle of algorithm transparency. Additionally, a formula (ER=(Q×0.6+V×0.4)×C) is developed for quantifying the realization of copyright expectancy right. Finally, the paper returns to the humanistic value of intellectual property law, reinterpreting the incentive theory and constructing a ternary balance paradigm of technological innovation, institutional protection, and humanistic care. The research aims to provide a new paradigm for AI training data governance, balance the relationships between technological progress, institutional fairness, and humanistic care, and contribute to improving the global AI governance system.
FedSegNet: A Federated Learning Framework for 3D Medical Image Segmentation
Medical image segmentation plays a vital role in diagnostic imaging and treatment planning, especially for volumetric modalities such as MRI and CT scans. However, training high-performance deep learning models for 3D medical image segmentation requires large, annotated datasets, which are often siloed due to strict privacy laws like HIPAA and GDPR. Federated Learning (FL) offers a decentralized solution that enables collaborative training without sharing raw patient data, but its effectiveness is hindered by challenges such as data heterogeneity, communication overhead, and model degradation on non-IID datasets. In this study, we propose FedSegNet, a novel federated learning framework tailored for 3D medical image segmentation. FedSegNet integrates a Transformer-based U-Net architecture for capturing both local and global spatial features and introduces an Adaptive Aggregation Mechanism (AAM) to dynamically weigh client updates based on data quality, performance, and divergence. To reduce communication costs, the framework employs gradient sparsification and quantization techniques. We evaluate FedSegNet on multi-institutional datasets including BraTS, LiTS, and ACDC, using metrics such as Dice Similarity Coefficient and Hausdorff Distance. Results show that FedSegNet achieves up to 7.2% improvement in segmentation accuracy and 38% reduction in communication cost compared to existing methods, demonstrating its potential for secure, decentralized medical AI applications.
Copyright (c) 2025 Sidra Fareed, Ding Yi, Babar Hussain, Subhan Uddin, Aqsa Arif, Amir Nazar Tajoor (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
A Review of Fault Tolerance Techniques in Generative Multi-Agent Systems for Real-Time Applications
Generative multi-agent systems are emerging as a powerful paradigm for simulating human-like behavior in real-time applications such as interactive storytelling, virtual reality environments, and autonomous decision-making. These agents, often powered by large language models and memory systems, act independently and adapt over time. However, a critical challenge in deploying such systems is ensuring their fault tolerance. The ability to maintain operation in the presence of faults such as communication failures, memory corruption, agent crashes, or behavioral inconsistencies. This paper presents a comprehensive review of fault tolerance techniques for generative agents, focusing on methods such as memory check pointing, agent replication, fusion-based resilience, and consistency protocols. We analyse these approaches, drawing parallels from distributed systems, and evaluate their effectiveness in maintaining operational integrity in large-scale, real-time environments. Our findings suggest that while no single technique offers a one-size-fits-all solution, a combination of methods can provide robust fault tolerance and support the scalability and reliability of generative agent systems in dynamic, fault-prone environments.
Copyright (c) 2025 Subhan Uddin, Babar Hussain, Sidra Fareed, Aqsa Arif, Babar Ali (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Language Death in the Digital Age: Can AI Save Endangered Tongues?
The swift decline of linguistic diversity poses a serious threat to our global cultural heritage, with almost 3,000 out of the 7,000 languages spoken around the world now at risk. Unfortunately, efforts to save these languages have often fallen short, as traditional linguistic tools just haven't cut it. This leaves the upcoming generation without any real hope of connecting with the visions shared by the indigenous elders they’ve looked up to. Faced with such overwhelming challenges, it’s no surprise that many young people choose to turn away from their heritage, feeling like the obstacles are just too great to overcome. But hope, like sunlight, in the form of artificial intelligence glimmers on the horizon for these would-be next generations. More computer power and better algorithms promise to finally deliver on the computational linguistics dream. That these (computational linguistics) tools so obviously and dangerously failed to deliver on in the past with a host of languages (some very 'well-documented' for those dangerous past tools in violation of human rights) need to be remembered as we move forward.
Fairness-Constrained Curriculum Adaptation for AI-Enhanced Education: A Multi-Objective Optimization Framework with NLP-Driven Cultural Analysis
We propose a fairness-constrained curriculum adaptation framework for AI-enhanced education that integrates natural language processing (NLP) with multi-objective optimization to dynamically tailor learning materials while ensuring cultural responsiveness and ethical alignment. The proposed method employs a Transformer-based language model to analyze student-generated text, extracting latent cultural and demographic features, which are then clustered using a fairness-sensitive algorithm to mitigate biased representations. These features are combined with conventional performance metrics to formulate a multi-objective optimization problem that simultaneously maximizes pedagogical effectiveness and minimizes disparities across demographic groups. The optimization framework incorporates differentiable fairness constraints, such as equalized odds, to ensure equitable learning outcomes while adhering to institutional guidelines. Moreover, the system interfaces with existing educational platforms through input and output substitution mechanisms, enabling seamless integration with conventional modules. A key novelty lies in the coupling of NLP-driven cultural analysis with constrained optimization, which provides a transparent and auditable approach to curriculum adaptation. Furthermore, the framework includes a human-in-the-loop component, allowing educators to override recommendations and refine the system iteratively. Experimental validation demonstrates that the method achieves superior fairness-performance trade-offs compared to baseline approaches, offering a scalable solution for equitable education. The work contributes to the growing discourse on ethical AI in education by addressing both technical and societal challenges through a mathematically rigorous yet interpretable framework.
Fairness-Constrained Curriculum Adaptation
Copyright (c) 2025 Yiming Wang (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Real-World Adaptation of Retinexformer for Low-Light Image Enhancement Using Unpaired Data
Low-light image enhancement remains a significant challenge in real-world computer vision applications, especially where lighting conditions vary drastically and paired training data is unavailable. While transformer-based models have shown promise in controlled environments, their ef-fectiveness often diminishes when applied to naturally degraded images. This paper presents a novel approach for adapting a transformer-based enhancement model to realworld low-light scenarios using unpaired datasets. We utilize real low-light images captured under uncontrolled conditions and propose a domain adaptation framework that enables effective transfer learning from synthetic to real domains. Our method integrates unsupervised reconstruction loss, perceptual optimization, and domain-invariant feature alignment to refine the model’s performance without requiring paired supervision. Experimental evaluations reveal notable improvements in both visual quality and quantitative metrics on real-world benchmarks. Compared to existing enhancement methods, our approach offers superior generalization, robustness to noise, and high-fidelity out-put. This demonstrates the potential of our domain-adapted transformer model in practical low-light imaging applications, including night photography, surveillance, and mobile vision systems.
Cross-Style Character Expression Mapping and Intelligent Retrieval Based on Deep Learning
This paper presents a novel deep learning framework for cross-style character expression mapping and intelligent retrieval in animation production. Traditional methods often produce exaggerated or unnatural expressions when transferring between realistic faces and stylized characters due to fundamental differences in parameter constraints. We propose a comprehensive solution that combines convolutional neural networks with generative adversarial networks to create a robust cross-style mapping system. Our approach introduces a disentangled latent space representation that separates identity, expression, and style-specific components, coupled with an attention mechanism that focuses on emotionally significant facial regions during style transfer. The mapping network is guided by multiple loss functions that balance style consistency with expression preservation. The intelligent retrieval system leverages multi-modal feature embedding to organize expression libraries based on both emotional content and stylistic attributes, employing a context-aware ranking algorithm that considers production requirements. Experimental results demonstrate significant performance improvements over state-of-the-art methods, achieving 45.95dB PSNR and 0.936 SSIM in expression mapping quality, along with 0.91 precision@10 in retrieval accuracy. The proposed framework enables efficient cross-style expression asset reuse while maintaining emotional fidelity, addressing critical challenges in modern animation production pipelines.
Copyright (c) 2025 Dai Li, Weishuo Lan (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Robotics for Space Exploration: From Mars Rovers to Lunar Missions
Robotics has become an indispensable element in the exploration of extraterrestrial environments, enabling scientific discoveries in domains that are otherwise inaccessible or hazardous to human presence. This survey paper provides a comprehensive overview of the development, deployment, and future direction of robotic systems specifically designed for space exploration, with a focus on missions to Mars and the Moon. Beginning with a historical perspective, the paper explores early robotic missions that laid the foundation for current technologies, including the Soviet Luna program and NASA’s Viking missions. It highlights the technical advancements and scientific achievements of iconic Mars rovers such as Spirit, Opportunity, Curiosity, and Perseverance, examining their mobility mechanisms, autonomous navigation systems, power sources, and onboard scientific instruments. Parallel to Martian exploration, the paper discusses lunar robotics, including legacy missions like Lunokhod and Apollo Lunar Rovers, as well as emerging platforms under NASA’s Artemis program. The survey further investigates key innovations in space robotics, such as autonomous decision-making, advanced locomotion systems, and energy efficiency in harsh environments, while addressing challenges like radiation exposure and communication latency. Finally, it explores future trends, including in-situ resource utilization (ISRU), robotic construction of habitats, and human-robot collaboration. By synthesizing past, present, and emerging trends, this paper serves as a detailed resource for understanding the critical role of robotics in advancing humanity’s reach beyond Earth, offering insights to guide future research and mission planning.
Space Robotics, Mars Rovers, Lunar Exploration, Autonomous Systems, In-Situ Resource Utilization (ISRU), Planetary Habitats.
Copyright (c) 2025 Babar Hussain, Jiandong Guo, Sidra Fareed, Subhan Uddin (Author) https://creativecommons.org/licenses/by-nc-sa/4.0