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arXiv 2025年

作者： Li, Heng Li, Haojin Ou, Mingyang Yu, Xiangyang Zhang, Xiaoqing Niu, Ke Fu, Huazhu Liu, Jiang Research Institute of Trustworthy Autonomous Systems SUSTech Shenzhen China Department of Computer Science and Engineering SUSTech Shenzhen China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Computer School Beijing Information Science and Technology University Beijing China Singapore

As an affordable and convenient eye scan, fundus photography holds the potential for preventing vision impairment, especially in resource-limited regions. However, fundus image degradation is common under intricate imaging environments, impacting following diagnosis and treatment. Consequently, image quality assessment (IQA) and enhancement (IQE) are essential for ensuring the clinical value and reliability of fundus images. While existing reviews offer some overview of this field, a comprehensive analysis of the interplay between IQA and IQE, along with their clinical deployment challenges, is lacking. This paper addresses this gap by providing a thorough review of fundus IQA and IQE algorithms, research advancements, and practical applications. We outline the fundamentals of the fundus photography imaging system and the associated interferences, and then systematically summarize the paradigms in fundus IQA and IQE. Furthermore, we discuss the practical challenges and solutions in deploying IQA and IQE, as well as offer insights into potential future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Photography

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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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Beyond the Dialogue: Multi-chatbot Group Motivational Interviewing for Premenstrual Syndrome (PMS) Management 25

Beyond the Dialogue: Multi-chatbot Group Motivational Interv...

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Proceedings of the 2025 CHI Conference on Human Factors in computing systems

作者： Shixian Geng Remi Inayoshi Chi-Lan Yang Zefan Sramek Yuya Umeda Chiaki Kasahara Arissa J. Sato Simo Hosio Koji Yatani Interactive Intelligent Systems Laboratory The University of Tokyo Tokyo Japan Graduate School of Education (Division of Clinical Psychology) The University of Tokyo Tokyo Japan Graduate School of Interdisciplinary Information Studies The University of Tokyo Tokyo Japan Graduate School of Humanities and Sciences Ochanomizu University Tokyo Japan Department of Computer Sciences University of Wisconsin-Madison Madison Wisconsin USA Center for Ubiquitous Computing University of Oulu Oulu Finland

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Human-like conceptual representations emerge from language prediction

arXiv

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arXiv 2025年

作者： Xu, Ningyu Zhang, Qi Du, Chao Luo, Qiang Qiu, Xipeng Huang, Xuanjing Zhang, Menghan School of Computer Science Fudan University Shanghai China Institute of Modern Languages and Linguistics Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Shanghai China Research Institute of Intelligent Complex Systems Fudan University Shanghai China Shanghai Collaborative Innovation Center of Intelligent Visual Computing Shanghai China Ministry of Education Key Laboratory of Contemporary Anthropology Fudan University Shanghai China

People acquire concepts through rich physical and social experiences and use them to understand the world. In contrast, large language models (LLMs), trained exclusively through next-token prediction over language data, exhibit remarkably human-like behaviors. Are these models developing concepts akin to humans, and if so, how are such concepts represented and organized? To address these questions, we reframed the classic reverse dictionary task to simulate human concept inference in context and investigated the emergence of human-like conceptual representations within LLMs. Our results demonstrate that LLMs can flexibly derive concepts from linguistic descriptions in relation to contextual cues about other concepts. The derived representations converged towards a shared, context-independent structure that effectively predicted human behavior across key psychological phenomena, including computation of similarities, categories and semantic scales. Moreover, these representations aligned well with neural activity patterns in the human brain, even in response to visual rather than linguistic stimuli, providing evidence for biological plausibility. These findings establish that structured, human-like conceptual representations can naturally emerge from language prediction without real-world grounding. More broadly, our work positions LLMs as promising computational tools for understanding complex human cognition and paves the way for better alignment between artificial and human intelligence. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Privacy-Aware Joint DNN Model Deployment and Partition Optimization for Delay-Efficient Collaborative Edge Inference

arXiv

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arXiv 2025年

作者： Cheng, Zhipeng Xia, Xiaoyu Wang, Hong Liwang, Minghui Chen, Ning Fan, Xuwei Wang, Xianbin School of Future Science and Engineering Soochow University Suzhou215006 China School of Computing Technologies RMIT University MelbourneVIC Australia Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji University Shanghai201804 China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai201804 China Qingdao China College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou350002 China Department of Electrical and Computer Engineering Western University ON Canada

Edge inference (EI) is a key solution to address the growing challenges of delayed response times, limited scalability, and privacy concerns in cloud-based Deep Neural Network (DNN) inference. However, deploying DNN models on resource-constrained edge devices faces more severe challenges, such as model storage limitations, dynamic service requests, and privacy risks. This paper proposes a novel framework for privacy-aware joint DNN model deployment and partition optimization to minimize long-term average inference delay under resource and privacy constraints. Specifically, the problem is formulated as a complex optimization problem considering model deployment, user-server association, and model partition strategies. To handle the NP-hardness and future uncertainties, a Lyapunov-based approach is introduced to transform the long-term optimization into a single-time-slot problem, ensuring system performance. Additionally, a coalition formation game model is proposed for edge server association, and a greedy-based algorithm is developed for model deployment within each coalition to efficiently solve the problem. Extensive simulations show that the proposed algorithms effectively reduce inference delay while satisfying privacy constraints, outperforming baseline approaches in various scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Differential privacy

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Compound graph based hybrid data center topologies

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Frontiers of Computer Science 2015年第6期9卷 860-874页

作者： Lailong LUO Deke GUO Wenxin LI Tian ZHANG Junjie XIE Xiaolei ZHOU Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha 410073 China Network and Cloud Computing Laboratory Dalian University of Technology Dalian 116024 China School of Information Management Wuhan University Wuhan 430070 China

In large-scale data centers, many servers are in- terconnected via a dedicated networking structure, so as to satisfy specific design goals, such as the low equipment cost, the high network capacity, and the incremental expansion. The topological properties of a networking structure are criti- cal factors that dominate the performance of the entire data center. The existing networking structures are either fully random or completely structured. Although such networking structures exhibit advantages on given aspects, they suffer ob- vious shortcomings in other essential fields. In this paper, we aim to design a hybrid topology, called R3, which is the com- pound graph of structured and random topology. It employs random regular graph as a unit duster and connects many such clusters by means of a structured topology, i.e., the gen- eralized hypercube. Consequently, the hybrid topology com- bines the advantages of structured as well as random topolo- gies seamlessly. Meanwhile, a coloring-based algorithm is proposed for R3 to enable fast and accurate routing. R3 pos- sesses many attractive characteristics, such as the modularity and expansibility at the cost of only increasing the degree of any node by one. Comprehensive evaluation results show that our hybrid topology possesses excellent topology properties and network performance.

关键词： data center networking compound graph hy-brid topology routing design

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Towards AI-Powered Edge Intelligence for Object Detection in Self-Driving Cars: Enhancing IoV Efficiency and Safety

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IEEE Internet of Things Journal 2025年

作者： Ahmed, Imran Ahmad, Misbah Siddiqi, Muftooh Ur Rehman Chehri, Abdellah Jeon, Gwangill Anglia Ruskin University School of Computing and Information Science Cambridge United Kingdom University of West of England Centre for Machine Vision Bristol Robotics Laboratory Bristol United Kingdom Hartpury University Department of Animal and Agriculture Gloucester United Kingdom Aston University College of Engineering and Physical Sciences Birmingham United Kingdom Department of Mathematics and Computer Science Canada Incheon National University Department of Embedded Systems Engineering Incheon Korea Republic of

In the rapidly advancing field of intelligent transportation systems, integrating Artificial Intelligence (AI) with edge computing presents a promising way to enhance the safety and efficiency of the Internet of Vehicles (IoV). This study explores and presents a deep learning based object detection model within an edge computing framework which aims to facilitate real time object detection in self driving cars. Using an urban traffic scenarios based dataset, our research shows the ability of the model to accurately detect and classify various objects important for autonomous driving. The YOLOv8 model is used in this work due to its optimal balance between accuracy and computational efficiency. This model has also demonstrated its worth by achieving good performance results, including an average precision of 0.79, a recall of 0.62, and an F1-score of 0.69. The results are demonstrated by a detailed confusion matrix, highlighting the model's effectiveness in complex driving environments and underscoring its reliability for in-vehicle deployment. By implementing AI directly on edge devices within vehicles, our approach might be helpful in significantly reducing latency, boosting decision-making speed, and enhancing data privacy by minimising dependence on cloud processing. The findings not only support the model's capabilities but also illustrate the practical benefits of edge intelligence in autonomous vehicles. These benefits, such as faster decision-making and improved data privacy, contribute effectively to the IoV infrastructure. This study marks a substantial step towards recognising the possibility of AI-enhanced edge computing in driving the next generation of autonomous vehicle technology. © 2025 IEEE.

关键词： Artificial Intelligence Autonomous Vehicles Deep leaning Edge computing Internet of Vehicles

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Fault-tolerant Workflow Scheduling using Spot Instances on clouds

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Procedia Computer Science 2014年 29卷 523-533页

作者： Deepak Poola Kotagiri Ramamohanarao Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory Department of Computing and Information Systems The University of Melbourne Australia

Scientific workflows are used to model applications of high throughput computation and complex large scale data analysis. In recent years, cloud computing is fast evolving as the target platform for such applications among researchers. Furthermore, new pricing models have been pioneered by cloud providers that allow users to provision resources and to use them in an efficient manner with significant cost reductions. In this paper, we propose a scheduling algorithm that schedules tasks on cloud resources using two different pricing models (spot and on-demand instances) to reduce the cost of execution whilst meeting the workflow deadline. The proposed algorithm is fault tolerant against the premature termination of spot instances and also robust against performance variations of cloud resources. Experimental results demonstrate that our heuristic reduces up to 70% execution cost as against using only on-demand instances.

关键词： Workflows cloud Fault-Tolerance Scheduling Spot Instances

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SCRM: Secure and Controllable Similarity Retrieval in Multi-user Settings

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IEEE Internet of Things Journal 2025年

作者： Li, Yingying Li, Feng Hou, Gaopan Guan, Yu Liu, Zhiquan Xie, Qi Hangzhou Normal University School of Mathematics Hangzhou311121 China Singapore Management University School of Computing and Information Systems Singapore178902 Singapore Xidian University Hangzhou Institute of Technology Zhejiang Hangzhou311231 China Jinan University College of Cyber Security Guangzhou510632 China Hangzhou Normal University Key Laboratory of Cryptography of Zhejiang Province Hangzhou311121 China

cloud computing has become an essential paradigm for facilitating large-scale and privacy-preserving encrypted image retrieval in Internet of Things (IoT) environments. However, existing encrypted image retrieval schemes face challenges in balancing retrieval efficiency and data security, which hinders their practical adoption. On one hand, retrieval-efficient schemes based on secure k-Nearest Neighbor (kNN) are prone to known-plaintext attacks;on the other hand, highly secure schemes based on homomorphic encryption often suffer from excessive computational and storage overhead. Furthermore, supporting multi-user environments and enforcing fine-grained access control over query users are critical challenges in IoT-based retrieval systems. To tackle these issues, we propose a Secure and Controllable similarity Retrieval scheme in Multi-user settings (SCRM), which achieves a practical trade-off between efficiency and security while enabling multi-user management. First, we design an efficient and privacy-preserving similarity computation method that is resilient against known-plaintext attacks. Second, we introduce a key conversion protocol that enables similarity retrieval in multi-user settings without requiring key sharing. Third, we integrate attribute-based encryption to enforce fine-grained access control and trace query users who may leak decryption keys. A correctness analysis confirms that SCRM ensures accurate similarity retrieval while supporting access control. Furthermore, a formal security analysis demonstrates that SCRM effectively protects data privacy against known-plaintext attacks. Finally, extensive experiments on real-world image dataset validate the efficiency and effectiveness of SCRM. © 2014 IEEE.

关键词： Authentication

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Toward AI-Powered Edge Intelligence for Object Detection in Self-Driving Cars: Enhancing IoV Efficiency and Safety

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IEEE Internet of Things Journal 2025年第11期12卷 16990-16997页

作者： Imran Ahmed Misbah Ahmad Muftooh Ur Rehman Siddiqi Abdellah Chehri Gwangill Jeon School of Computing and Information Science Anglia Ruskin University Cambridge U.K. Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol U.K. Department of Animal and Agriculture Hartpury University Gloucester U.K. College of Engineering and Physical Sciences Aston University Birmingham U.K. Department of Mathematics and Computer Science Royal Military College of Canada Kingston ON Canada Department of Embedded Systems Engineering Incheon National University Incheon South Korea

In the rapidly advancing field of intelligent transportation systems, integrating artificial intelligence (AI) with edge computing presents a promising way to enhance the safety and efficiency of the Internet of Vehicles (IoV). This study explores and presents a deep learning-based object detection model within an edge computing framework which aims to facilitate real time object detection in self driving cars. Using an urban traffic scenarios-based dataset, our research shows the ability of the model to accurately detect and classify various objects important for autonomous driving. The YOLOv8 model is used in this work due to its optimal balance between accuracy and computational efficiency. This model has also demonstrated its worth by achieving good performance results, including an average precision of 0.79, a recall of 0.62, and an F1-score of 0.69. The results are demonstrated by a detailed confusion matrix, highlighting the model’s effectiveness in complex driving environments and underscoring its reliability for in-vehicle deployment. By implementing AI directly on edge devices within vehicles, our approach might be helpful in significantly reducing latency, boosting decision-making speed, and enhancing data privacy by minimising dependence on cloud processing. The findings not only support the model’s capabilities but also illustrate the practical benefits of edge intelligence in autonomous vehicles. These benefits, such as faster decision making and improved data privacy, contribute effectively to the IoV infrastructure. This study marks a substantial step toward recognizing the possibility of AI-enhanced edge computing in driving the next generation of autonomous vehicle technology.

关键词： Computational modeling Object detection Edge computing Image edge detection Artificial intelligence Computer architecture Data models Accuracy Servers Safety

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