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arXiv 2025年

作者： Li, Yong Liu, Menglin Cui, Zhen Ding, Yi Zong, Yuan Zheng, Wenming Shan, Shiguang Guan, Cuntai School of Computer Science and Engineering The Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing210096 China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China School of Artificial Intelligence Beijing Normal University Beijing100875 China Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China School of Computer Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore Key Laboratory of Child Development and Learning Science of Ministry of Education School of Biological Science and Medical Engineering Nanjing210096 China

Despite the impressive performance of current vision-based facial action unit (AU) detection approaches, they are heavily susceptible to the variations across different domains and the cross-domain AU detection methods are under-explored. In response to this challenge, we propose a decoupled doubly contrastive adaptation (D2CA) approach to learn a purified AU representation that is semantically aligned for the source and target domains. Specifically, we decompose latent representations into AU-relevant and AU-irrelevant components, with the objective of exclusively facilitating adaptation within the AU-relevant subspace. To achieve the feature decoupling, D2CA is trained to disentangle AU and domain factors by assessing the quality of synthesized faces in cross-domain scenarios when either AU or domain attributes are modified. To further strengthen feature decoupling, particularly in scenarios with limited AU data diversity, D2CA employs a doubly contrastive learning mechanism comprising image and feature-level contrastive learning to ensure the quality of synthesized faces and mitigate feature ambiguities. This new framework leads to an automatically learned, dedicated separation of AU-relevant and domain-relevant factors, and it enables intuitive, scale-specific control of the cross-domain facial image synthesis. Extensive experiments demonstrate the efficacy of D2CA in successfully decoupling AU and domain factors, yielding visually pleasing cross-domain synthesized facial images. Meanwhile, D2CA consistently outperforms state-of-the-art cross-domain AU detection approaches, achieving an average F1 score improvement of 6%-14% across various cross-domain scenarios. © 2025, CC BY-NC-SA.

关键词： Contrastive learning

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Big Data Solutions for Predictive Healthcare Analytics: Enhancing Patient Outcomes and Operational Efficiency

Big Data Solutions for Predictive Healthcare Analytics: Enha...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Kumar, Nishi Kant Nandhini, J. Vinitha, S. Madhubala, P. Trimukhe, Dipmala Bhagwat Salunkhe, Kalpana Birla Institute of Technology Department of Computer Science and Engineering Mesra Off Campus Jharkhand Deoghar India Jai Shriram Engineering College Department of Ece Tirupur India Jerusalem College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India Bharathiyar Institute of Engineering for Women Department of Computer Science and Engineering Salem India Government College of Engineering & Research Department of Instrumentation Avasari Khurd India School of Computational Sciences Jspm University Wagholi Pune India

ISBN: (纸本)9798331542375

The embracing of Big Data solutions in healthcare comes with widely prospect to enhance patient satisfaction and increase organizational effectiveness. The research aims to use Predictive techniques and models coupled with modern data integration systems to improve the health care services. The components of the suggested methodology are deep neural networks for patient monitoring and prognosis, wearable devices, and operational prognosis models for resources use. From the practice of this approach, we have noticed marked increase in the patient monitoring frequency, efficiency of timely intervention, and generalization of health care organization productivity factors such as wait time and other resource utilization. In addition, the Onshape plan carried measurable benefits for the patient results such as satisfaction, reduced mortality, improved chronic disease, and the lesser hospital readmission. The results emphasize that the opportunities that Big Data and predictive analytics offer for healthcare can be indeed immense - offering improved care and more effective outcomes at a lower total cost. This research focuses on how Big Data solutions can help create a better future of healthcare improving the clinical and operational activities in this sphere and, thus, the population's health. © 2025 IEEE.

关键词： Patient treatment

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Introducing QuantConn: Overcoming Challenging Diffusion Acquisitions with Harmonization 15th

Introducing QuantConn: Overcoming Challenging Diffusion Acqu...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Newlin, Nancy Schilling, Kurt Koudoro, Serge Chandio, Bramsh Qamar Kanakaraj, Praitayini Moyer, Daniel Kelly, Claire E. Genc, Sila Yang, Joseph Yuan-Mou Wu, Ye Adluru, Nagesh Nath, Vishwesh Pathak, Sudhir Schneider, Walter Gade, Anurag Consagra, William Rathi, Yogesh Hendriks, Tom Vilanova, Anna Chamberland, Maxime Pieciak, Tomasz Ciupek, Dominika Vega, Antonio Tristán Aja-Fernández, Santiago Malawski, Maciej Ouedraogo, Gani Machnio, Julia Thompson, Paul M. Jahanshad, Neda Garyfallidis, Eleftherios Landman, Bennett Department of Computer Science Vanderbilt University NashvilleTN United States Department of Radiology and Radiological Sciences Vanderbilt University Medical Center Nashville United States Indiana University Bloomington BloomingtonIN United States Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of USC Los AngelesCA United States Developmental Imaging Murdoch Children’ Research Institute Melbourne Australia Murdoch Children’s Research Institute Melbourne Australia Turner Institute for Brain and Mental Health School of Psychological Sciences Monash University Melbourne Australia Department of Neurosurgery Royal Children’s Hospital Melbourne Australia Neuroscience Research Murdoch Children’s Research Institute Melbourne Australia Department of Paediatrics University of Melbourne Melbourne Australia School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China Waisman Center Department of Radiology University of Wisconsin Madison United States NVIDIA Santa Clara United States Learning Research and Development Center University of Pittsburgh Pittsburgh United States Brigham and Women’s Hospital Boston United States Brigham and Women’s Hospital Harvard Medical School Boston United States Department of Computer Science and Mathmatics Eindhoven University of Technology Eindhoven Netherlands LPI ETSI Telecomunicación Universidad de Valladolid Castilla y León Spain Sano Centre for Computational Medicine Kraków30-054 Poland Aix-Marseille Université Marseille France Vanderbilt University Institute of Imaging Science Vanderbilt University NashvilleTN United States Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States

ISBN: (纸本)9783031869198

White matter alterations are increasingly implicated in neurological diseases and their progression. Diffusion-weighted magnetic resonance imaging (DW-MRI) has been included in many international-scale studies to identify alterations in white matter microstructure and connectivity. Yet, quantitative investigation of DW-MRI data is hindered by a lack of consistency due to variations in acquisition protocols, sites, and scanners. Specifically, there is a need to harmonize the preprocessing of DW-MRI datasets to ensure that compatible and reproducible quantitative metrics are derived from each site, including (1) bundle-wise microstructure measures, (2) features of white matter fiber bundles, and (3) connectomics measures. In the MICCAI CDMRI 2023 QuantConn challenge, participants are provided raw data from the same individuals taken with two different acquisition protocols on a single 4 tesla scanner in the same scanning session and asked to preprocess the data in order to minimize acquisition differences while retaining biological variation. Here, we outline the testing framework, provide baseline pre-harmonized results, and discuss the learning implications of this challenge. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Photointerpretation

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Collaborative and Integrated Platform to Support Distributed Manufacturing System Using a Service-Oriented Approach Based On Cloud Computing Paradigm

Collaborative and Integrated Platform to Support Distributed...

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Ankita Nainwal Vishal Sharma Neeraj Varshney Nandini Shirish Boob NV Uma Reddy K. Jyothsna Reddy Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Lovely Professional University Phagwara India Department of Computer Engineering and Applications GLA University Mathura Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu Department of Artificial Intelligence & Machine Learning New Horizon College of Engineering Bangalore Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

Increasing interest in distributed manufacturing systems is a result of the dynamic nature of today's business environment and with that comes the popularity of their use. This has given rise in the interest in distributed manufacturing systems. The indirect consequence of this has led to much higher use of distributed manufacturing systems. A large number of production processes, dispersed across a multitude of locations geographically separate, must be controlled and integrated in order to achieve production agility, flexibility and efficiency of costs. In our most recent project, we developed an integrated, collaborative manufacturing platform, the centerpiece of which was LAYMOD. To address the issues posed by such systems, this research presents a collateral and integrated platform based on a service oriented approach that can operate within the cloud computing paradigm. These systems present problems which the study is conducted in order to solve. Utilising the features of cloud computing, it aims to serve as a platform in solving process coordination and hence improving efficiency and scalability of distant manufacturing processes and potential real time communications among various stake holders. Furthermore, statistical research on the rates of platform module adoption in different settings provides specifics of (a) use patterns of some platform modules and (b) what capabilities are provided by some platform modules. When extrapolated to a wider size, our work is applied towards the creation of collaborative and integrated platforms for a number of the various manufacturing processes which are currently in use.

关键词： Cloud computing Manufacturing processes Scalability Collaboration Production Companies Real-time systems Service-oriented architecture Standards Business

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Deep learning-Powered Edge Analytics for IoT-based Sensor Networks

Deep Learning-Powered Edge Analytics for IoT-based Sensor Ne...

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Machine learning and Autonomous Systems (ICMLAS), International Conference on

作者： M. Saravana Karthikeyan Ponnusamy Subramani M. Panneer Selvam M. L. Sworna Kokila R. Vijayalakshmi P. Sundaravadivel Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Physics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electrical and Electronics Engineering Sona College of Technology Salem India Department of Computing Technologies Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The rapid expansion of loT-based sensor networks has necessitated the development of efficient edge analytics frameworks to process vast amounts of data in real time while minimizing computational overhead. Deep learning-powered edge computing has emerged as a transformative approach, enhancing data processing capabilities at the network edge while addressing constraints related to latency, bandwidth, and energy consumption. This study introduces a novel algorithm, Lightweight AI Model Compression for Energy-Efficient IoT Edge Devices (LiteEdgeAI), designed to optimize deep learning models for resource-constrained edge environments. The proposed method employs quantization, pruning, and knowledge distillation techniques to achieve significant reductions in model size and computational complexity while maintaining high inference accuracy. To validate the efficiency of LiteEdgeAI, a comparative simulation analysis was conducted against existing state-of-the-art algorithms using key performance metrics such prediction accuracy, energy efficiency and computational complexity. Experimental results demonstrated that LiteEdgeAI outperformed baseline models. The findings highlight the potential of LiteEdgeAI in enabling scalable, low-power, and real-time edge intelligence for IoT-based sensor networks.

关键词： Deep learning Adaptation models Accuracy Computational modeling Predictive models Prediction algorithms Energy efficiency Real-time systems Internet of Things Computational complexity

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A new nano-scale authentication architecture for improving the security of human-computer interaction systems based on quantum computing

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Analog Integrated Circuits and Signal Processing 2025年第1期124卷 1-15页

作者： Ahmadpour, Seyed-Sajad Zohaib, Muhammad Navimipour, Nima Jafari Misra, Neeraj Kumar Rasmi, Hadi Salahov, Huseyn Avval, Danial Bakhshayeshi Navin, Ahmad Habibizad Hosseinzadeh, Mehdi Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Turkiye Istanbul Turkey Department of Electrical and Electronics Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravathi522237 India Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Information Systems Engineering Sakarya University Türkiye Sakarya Turkey Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz*** Iran Pattern Recognition and Machine Learning Laboratory School of Computing Gachon University Seongnam Korea Republic of

Human-computer Interaction (HCI) is an interdisciplinary area of study focusing on the interaction of users and computers by scheming interactive computer interfaces. In addition, HCI systems need security to confirm user authentication, which is a crucial issue in these systems. Hence, user authentication is vital, allowing only authorized users to access data. Authentication is critical to the digital world since it provides security and safety for digital data. Moreover, a digital signature is an authentication method to confirm the accuracy and reliability of digital documents or communications. In addition, designing the circuit based on the complementary metal-oxide semiconductor (CMOS) technology can affect the security and safety of digital data due to the excessive heat dissipation of circuits. On the other hand, quantum-dot cellular automata (QCA) and reversible logic as alternative technologies to CMOS address these problems. Since QCA and reversible logic circuits have minimal energy dissipation, which is considered nearly zero, approaching these technologies proves extremely difficult for any hacker. This work presents an effective structure for the authenticator and human-computer interaction using QCA and IBM quantum computing with Qiskit simulations. The proposed structure has outperformed current circuits in terms of area, cell count, and latency. The paper demonstrates the QCA reversible logic layout of the proposed HCI authenticator and integrates IBM quantum computing simulations using Qiskit for validation. The implementation and testing of results are performed utilizing QCADesigner-2.0.3 and Qiskit simulation tools. The accuracy and efficiency of the proposed design are validated through simulation-derived comparison values, and energy dissipation simulations prove that the suggested circuit dissipates minimal energy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Qubits

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A Domain-Specific Multilingual Speech Translation Corpus via Simultaneous Interpretation

A Domain-Specific Multilingual Speech Translation Corpus via...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Seunghee Han Gary Geunbae Lee Hung Soon Kim Sunhee Kim Minhwa Chung Learning Sciences Research Institute Seoul National University Seoul Republic of Korea Department of Computer Science and Engineering Postech Pohang Republic of Korea Department of Electronics Engineering Pusan National University Busan Republic of Korea Department of French Language Education Seoul National University Seoul Republic of Korea Department of Linguistics Seoul National University Seoul Republic of Korea

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper presents a novel multilingual speech translation corpus for complex, domain-specific content in Korean, English, Spanish, and Japanese. The corpus contains 4,000 hours of parallel speech, including 1,000 hours of Korean audio with simultaneous sight interpretations in the other three languages by 294 professionals (242 interpreters and 52 Korean voice actors). It also includes transcriptions, translations, and annotations for all languages. The Dewey Decimal Classification was adapted to balance knowledge representation, and speech tasks were conducted in a controlled studio environment to ensure data consistency. Translation, transcription, and annotation workflows were managed through a custom-built platform. The corpus captures nuanced contexts, cultural sensitivities, and domain-specific terminology, addressing linguistic challenges like structural differences between SOV (Korean, Japanese) and SVO languages (English, Spanish). Preliminary evaluations indicate its potential to enhance end-to-end speech translation models, support cross-lingual transfer learning, and tackle real-time translation issues.

关键词： Translation Sensitivity Annotations Terminology Transfer learning Speech recognition Speech enhancement Real-time systems Multilingual Recording

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Deep learning-based detection of incisal translucency patterns

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Journal of Prosthetic Dentistry 2025年

作者： Shetty, Sthithika Gali, Sivaranjani R, Venkatesh MS, Jeswin MN, Thippeswamy Postgraduate student Department of Prosthodontics and Crown & Bridge Faculty of Dental Sciences M.S. Ramaiah University of Applied Sciences Bangalore India Head Department of Prosthodontics and Crown & Bridge Faculty of Dental Sciences M.S. Ramaiah University of Applied Sciences Bangalore India Graduate student Department of Computer Science & Engineering (Artificial Intelligence & Machine Learning) Ramaiah Institute of Technology Bangalore India Principal Dr. Ambedkar Institute of Technology Bangalore India

Statement of problem: The evaluation of incisal translucency in anterior teeth greatly influences esthetic treatment outcomes. This evaluation is mostly subjective and often overlooked among dental professionals. The application of artificial intelligence-based models to detect the incisal translucency of anterior teeth may be of value to dentists in their restorative dental practice, but studies are lacking. Purpose: The purpose of this study was to assess the accuracy of deep learning models in predicting the translucency patterns of anterior teeth. Material and methods: Approximately 240 Joint Photographic Experts Group (JPEG) images of anterior teeth from participants over 18 years were collected using a smartphone. These images were resized to 224×224 pixels and classified by the presence or absence of translucency. Augmentation techniques enhanced the training dataset, and a 3-model deep learning approach was used: YOLOv5 detected central incisors, Vision Transformers (ViT) identified translucency, and U-Net segmented the translucent areas. The images were split 80 to 20 for training and testing, with performance evaluated using accuracy, precision, recall, F1 score, confusion matrix, and dice scores. Results: YOLOv5 achieved a precision of 1.00 at a confidence threshold of 0.910. The ViT system showed an accuracy of 91.66%, with 58 of 64 images predicting correctly with an F1 score of 94.83%. U-Net segmentation after training with annotated images achieved an accuracy of 91% with a dice score of 0.948. Conclusions: The integration of YOLOv5 for detection, ViT for classification, and U-Net for segmentation demonstrates a comprehensive approach to addressing the classification of incisal translucencies. By leveraging the strengths of deep learning models, high accuracy and precision can be achieved in detecting the incisal translucency patterns of anterior teeth. © 2025 Editorial Council for The Journal of Prosthetic Dentistry

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'Anti-blackness is no glitch': The need for critical conversations within computer science education

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XRDS: Crossroads 2020年第2期27卷 42-46页

作者： Jones, Stephanie T. Melo, Natalie Computer Science and Learning Sciences Northwestern University United States

The conversation around and application of computer science often reinforces neoliberal ideals of what pathways students should take. computer science education is said to be the great equalizer for marginalized youth... 详细信息

关键词： Education computing

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A Hybrid learning-Based Q-Commerce Framework Evolving from E-Commerce

A Hybrid Learning-Based Q-Commerce Framework Evolving from E...

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Machine learning and Autonomous Systems (ICMLAS), International Conference on

作者： X. Arogya Presskila J. Relin Francis Raj P. Selva Kumar G. Ram Sankar G. Vinoth Rajkumar P. Sundaravadivel Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Management Studies Francis Xavier Engineering College Tirunelveli India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Department of Electronics and Communication Engineering J. P. College of Engineering Tenkasi India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The rapid evolution of E-Commerce has led to the emergence of Quick Commerce (Q-Commerce), a model that prioritizes ultra-fast deliveries and real-time decision-making. This paper proposes a Hybrid learning-based framework that enhances Q-Commerce operations by integrating Self-Supervised, Supervised, Reinforcement, and Unsupervised learning techniques. The model leverages BERT for self-supervised pretraining, enabling context-aware customer personalization and improved search relevance. LSTMs and CNNs are employed for demand forecasting and product classification, ensuring optimized inventory management. GNNs enhance personalized recommendations by analyzing user-product relationships. For logistics optimization, DQN and PPO are implemented to dynamically adjust last-mile delivery routes, reducing delivery times and operational costs. To ensure fraud prevention, AEs and GANs detect anomalous transaction behaviors, improving security while minimizing false positives. The proposed hybrid deployment strategy, combining cloud-based AI inference and Edge AI at micro-fulfillment centers (MFCs), enhances system responsiveness and scalability. The framework is evaluated using MAE, RMSE, HR, Fl-Score, and ROC curves, ensuring accurate demand prediction, optimized logistics, and effective fraud detection. Future advancements in Autonomous Deliveries, AI-Powered Warehousing, and Blockchain Integration will further revolutionize Q-Commerce, making it an indispensable extension of E-Commerce.

关键词： Costs Scalability Demand forecasting Edge AI Real-time systems Fraud Electronic commerce Security Optimization Logistics

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