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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2139-2150页

作者： Liu, Yanyu Zeng, Yifeng Ma, Biyang Pan, Yinghui Gao, Huifan Zhang, Yuting Xiamen University Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Department of Automation Xiamen361102 China Northumbria University Department of Computer and Information Sciences NewcastleNE1 8ST United Kingdom Minnan Normal University Department of Computer Science Zhangzhou363000 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Reinforcement learning (RL) has made great success in recent years. Generally, the learning process requires a huge amount of interaction with the environment before an agent can achieve acceptable performance. This motivates many techniques, such as incorporating prior knowledge which is usually presented as experts' demonstration, and using a probability distribution to represent state-and-action values, to accelerate the learning process. The methods perform well when the prior knowledge is genuinely correct and no much change occurs to the learning environment. However, the requirement is not perfectly realistic in many complex applications. The demonstration knowledge may not reflect the true environment and even be full of noise. In this article, we introduce a dynamic distribution merging method to improve knowledge utilization in a general RL algorithm, namely Q-learning. The new method adapts a normal distribution to represent state-action values and merges the prior and learned knowledge in a discriminative way. We theoretically analyze the new learning method and demonstrate its empirical performance over multiple problem domains. © 2020 IEEE.

关键词： Merging

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Customizable Graphite-On-Paper-Based Keypads: Toward Disposable and Recyclable Wireless Human-Machine Interfaces

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2023年第1期2卷 25-33页

作者： Zulfiqar, Muhammad Hamza Hassan, Mahmood Ul Maqbool, Khawaja Qasim Zubair, Muhammad Mehmood, Muhammad Qasim Riaz, Kashif Massoud, Yehia MicroNano Laboratory Department of Electrical Engineering Lahore54600 Pakistan University of Engineering and Technology Lahore Department of Biomedical Engineering Lahore54890 Pakistan Bahria University Department of Computer Science Lahore Campus Islamabad44000 Pakistan Thuwal23955 Saudi Arabia

There is a rapid increase in the use of affordable electronic devices and human-machine interfaces (HMIs) with short serviceable life in almost every aspect of our lives. It is estimated that the waste electrical and electronic equipment (WEEE) and electronic waste (e-waste) generated in year 2021 was 57.5 million metric tons (Mt) and it is expected that the production of e-waste will increase to 110 Mt by the end of 2050. To mitigate these wastes, green HMIs are required which can be customized for multiple applications and can be recycled or disposed of with minimal environmental impact. This work presents customizable graphite-on-paper (GOP)-based keypad consists of interdigitated capacitive (IDC) touch sensors is demonstrated as HMIs to interact with different electronic and media applications wirelessly. The GOP keypads are fabricated through facile and green fabrication process by direct writing of graphite on flexible paper substrate. The GOP keypads can be fabricated in a home setting as the required materials are readily available, i.e., paper, pencils, and Arduino. The GOP keypads can be easily disposed of or recycled at the end of its life or requirement due to the employment of biodegradable materials like paper and graphite. The IDC touch sensors are optimized by analyzing the number of electrode fingers, finger's width, finger's overlap length, and spacing between the electrode fingers. The same GOP keypad is customized to interact with different electronic and media applications wirelessly, i.e., laptop cursor navigation, calculator app on mobile, numeric keypad, etc. The customizable GOP keypads have potential to be used as green wireless HMIs to enforce a circular economy by mitigating electronic and plastic waste, which leads to the vision of a sustainable and green world. © 2022 IEEE.

关键词： Substrates

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A family of resonance-based tunable frequency oscillators and application to chaos generation

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International Journal of Electronics Letters 2024年第4期12卷 495-505页

作者： Elwakil, A.S. Maundy, B.J. Psychalinos, C. Department of Electrical and Computer Engineering University of Sharjah Sharjah United Arab Emirates Department of Electrical and Software Engineering University of Calgary Alberta Canada Department of Physics Electronics Laboratory University of Patras Rio Patras Greece

We propose a family of second-order resonance-based sinusoidal oscillators with electronically tunable frequencies. Each oscillator is comprised of two amplifiers, surrounded by four impedances which must be a single (Formula presented.), (Formula presented.) or (Formula presented.) and we show that in this configuration eight resonance-based oscillators are possible. We also show the conditions for each oscillator to oscillate along with the frequency of oscillation and then design a non-autonomous chaotic oscillator based on one of these circuits. Circuit simulations using LTspice and experimental results are presented to verify the theory. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： frequency tuning LC oscillators phase tuning Second-order oscillators

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Protecting machine learning systems using blockchain: solutions, challenges and future prospects

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Multimedia Tools and Applications 2024年 1-28页

作者： Hajlaoui, Rejab Dhahri, Salah Mahfoudhi, Sami Moulahi, Tarek Alotibi, Gaseb Department of Computer and Informtion Sciences College of Computer Science and Engineering University of Hail Hail City Saudi Arabia Laboratory of Advanced Multifunctional Materials and Technological Applications Faculty of Science and Technology of Sidi Bouzid University of Kairouan Sidi Bouzid Tunisia Department of Information and Technology Management Mediterranean School of Business South Mediterranean University Tunis Tunisia Department of Information Technology College of Computer Qassim University Buraidah Saudi Arabia Department of Computers and Information Technology Alwajah College University of Tabuk Tabuk Saudi Arabia

Machine learning-based systems have emerged as the primary means for achieving the highest levels of productivity and efficiency. They have become the most influential competitive factor for many technologies and business companies such as Cloud AI Companies (Google, Facebook…) and Health Care AI Companies (Tempus, Nanox …). However, privacy and security issues have become the biggest challenge facing all ML applications. These threats are known as Adversarial Machine Learning (AML). Their main goal is to maliciously manipulate training data and model parameters to obtain misleading results. To overcome these challenges, several research studies have proven that Blockchain, which relies on cryptographic technologies, constitutes a promising solution for securing ML applications. It provides high guarantees to make the system scalable, reliable and more secure. In this survey, we provide an overview of ML applications and the most hostile attacks they face. Then, we define the blockchain technique and its distinctive features. After that, we conduct a comprehensive and detailed survey of the latest and best blockchain-based research to address security issues in ML applications (integrity, confidentiality and availability). We analyze, compare and comment on all the proposed solutions. In the end, we discuss in detail the various challenges that hinder the adoption of blockchain technology and we extract the most important scientific trends that will serve as guidance and support for researchers in their future works. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Edge-Driven Industrial Computing Power Networks: Digital Twin-Empowered Service Provisioning by Hybrid Soft Actor-Critic

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8095-8109页

作者： Zhang, Long Song, Deng-Ao Zhang, Hongliang Tian, Ni Zhuang, Zirui Niyato, Dusit Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

With the proliferation of data-intensive industrial applications, the collaboration of computing powers among standalone edge servers is vital to provision such services for smart devices. In this paper, we propose an edge-driven industrial computing power network (CPN) by orchestrating the computing and network resources of edge servers through the centralized resource scheduling and decentralized task computing. However, efficient task offloading and collaborative processing is challenging, which requires higher degrees of network automation and intelligence. Therefore, we incorporate digital twins (DTs) into the edge-driven CPN architecture, where the DTs are created as the digital replicas to assist both the computation offloading and collaborative processing. A joint optimization problem of the computing power assignment, service association, task partition, and transmit power control is formulated for maximizing the system average weighted utility. Due to the temporal-spatial variability of tasks and the resulted dynamic environment, we transform the original problem as a Markov decision process aiming at maximizing the long-term average weighted utility. To efficiently handle the high-dimensional discrete-continuous action space, a hybrid soft actor-critic based deep reinforcement learning algorithm is developed for optimizing the joint design. Simulation results validate the superiority of our proposed algorithm over the benchmarks, showing the significant gains obtained by integrating DTs into the edge-driven CPN. © 1967-2012 IEEE.

关键词： Markov processes

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Dual-level Mixup for Graph Few-shot Learning with Fewer Tasks 25

Dual-level Mixup for Graph Few-shot Learning with Fewer Task...

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34th ACM Web Conference, WWW 2025

作者： Liu, Yonghao Li, Mengyu Giunchiglia, Fausto Huang, Lan Li, Ximing Feng, Xiaoyue Guan, Renchu College of Computer Science and Technology Jilin University Changchun China Department of Information Engineering and Computer Science University of Trento Trento Italy Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education China

ISBN: (纸本)9798400712746

Graph neural networks have been demonstrated as a powerful paradigm for effectively learning graph-structured data on the web and mining content from it. Current leading graph models require a large number of labeled samples for training, which unavoidably leads to overfitting in few-shot scenarios. Recent research has sought to alleviate this issue by simultaneously leveraging graph learning and meta-learning paradigms. However, these graph meta-learning models assume the availability of numerous meta-training tasks to learn transferable meta-knowledge. Such assumption may not be feasible in the real world due to the difficulty of constructing tasks and the substantial costs involved. Therefore, we propose a SiMple yet effectIve approach for graph few-shot Learning with fEwer tasks, named SMILE. We introduce a dual-level mixup strategy, encompassing both within-task and across-task mixup, to simultaneously enrich the available nodes and tasks in meta-learning. Moreover, we explicitly leverage the prior information provided by the node degrees in the graph to encode expressive node representations. Theoretically, we demonstrate that SMILE can enhance the model generalization ability. Empirically, SMILE consistently outperforms other competitive models by a large margin across all evaluated datasets with in-domain and cross-domain settings. Our anonymous code can be found here. © 2025 Copyright held by the owner/author(s).

关键词： Graph neural networks

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Guest Editorial Introduction to the Special Section on Video and Language

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IEEE Transactions on Circuits and Systems for Video Technology 2022年第1期32卷 1-4页

作者： Mei, Tao Corso, Jason J. Kim, Gunhee Luo, Jiebo Shen, Chunhua Zhang, Hanwang Computer Vision and Multimedia Laboratory Jd Explore Academy *** Beijing100101 China Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Department of Computer Science and Engineering Seoul National University Gwanak-gu Seoul08826 Korea Republic of Department of Computer Science University of Rochester RochesterNY14627 United States College of Computer Science and Technology Zhejiang University Hangzhou310058 China School of Computer Science and Engineering Nanyang Technological University 639798 Singapore

computer vision (CV) and Natural Language Processing (NLP) are two most fundamental disciplines under a broad area of artificial intelligence (AI). CV is regarded as a field of research that explores the techniques to teach computers to see and understand digital content such as images and videos. NLP is a branch of linguistics that enables computers to process, interpret, and even generate human language. With the rise and development of deep learning over the past decade, there has been a steady momentum of innovation and breakthroughs that convincingly push the limits and improve the state-of-the-art of both vision and language modeling. An interesting observation is that the research in the two areas starts to interact, with a significant growth in both the volume of publications and extensive applications. Meanwhile, many previous experiences have shown that this can naturally build up the circle of human intelligence.

关键词： Special issues and sections computer vision Question answering (information retrieval) Natural language processing Visual captioning

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PLAME: Piecewise-Linear Approximate Measure for Additive Kernel SVM (Extended abstract) 40

PLAME: Piecewise-Linear Approximate Measure for Additive Ker...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Chan, Tsz Nam Li, Zhe Leong Hou, U. Cheng, Reynold Shenzhen University Department of Computer Science and Software Engineering China Alibaba Cloud University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science China The University of Hong Kong Department of Computer Science and Musketeers Foundation Institute of Data Science Hong Kong

ISBN: (纸本)9798350317152

Additive Kernel SVM has been extensively used in many applications, including human activity detection and pedestrian detection. Since training an additive kernel SVM model is very time-consuming, which is not scalable to largescale datasets, many efficient solutions have been developed in the past few years. However, most of the existing methods normally fail to achieve one of these three important conditions which are (1) low classification error, (2) low memory space, and (3) low training time. In order to simultaneously fulfill these three conditions, we develop the new piecewise-linear approximate measure (PLAME) for training additive kernel SVM models. Experimental results verify that this approach can achieve the best trade-off between accuracy, memory space, and training time compared with different types of state-of-the-art methods. © 2024 IEEE.

关键词： Additives

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Marine water quality index classification and prediction using machine learning framework

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International Journal of Water 2022年第1期15卷 21-38页

作者： Karuppanan, Komathy Funded Research Laboratory Department of Computer Science and Engineering Academy of Maritime Education and Training Chennai600097 India

Quality of marine water has a direct impact on the evolution of the ocean ecosystem, which aggravates the survival of marine organisms and animals. Additionally, the ballast water, carried in ships drawn from the open sea/ocean, may lead to bio-invasion if the quality of the water is unchecked and released into another sea/ocean. The ballast water treatment methods may sometime alter the physicochemical properties, which might be harmful to receiving water region. The objective of this paper is, therefore, to propose a new method for the estimation of marine water quality from the physiochemical and biological properties of the marine water and then a suitable computational architecture supporting machine learning techniques to assist in classifying and predicting the marine water quality. The proposed framework has evaluated various classification models to select the best-fit algorithm for this application through model training and optimising. The finalised model called stacking classifier was then recommended for ballast water quality prediction with 100% accuracy, which could be deployed prior to the ballast water exchange. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Water quality

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P3DC:Reducing DRAM Cache Hit Latency by Hybrid Mappings

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Journal of computer Science & Technology 2024年第6期39卷 1341-1360页

作者： Ye Chi Ren-Tong Guo Xiao-Fei Liao Hai-Kun Liu Jianhui Yue National Engineering Research Center for Big Data Technology and System Wuhan 430074China Services Computing Technology and System Laboratory Wuhan 430074China Cluster and Grid Computing Laboratory Wuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China School of Big Data and Internet Shenzhen Technology UniversityShenzhen 518118China Department of Computer Science Michigan Technological UniversityHoughton 49931-1295U.S.A.

Die-stacked dynamic random access memory(DRAM)caches are increasingly advocated to bridge the performance gap between the on-chip cache and the main *** fully realize their potential,it is essential to improve DRAM cache hit rate and lower its cache hit *** order to take advantage of the high hit-rate of set-association and the low hit latency of direct-mapping at the same time,we propose a partial direct-mapped die-stacked DRAM cache called *** design is motivated by a key observation,i.e.,applying a unified mapping policy to different types of blocks cannot achieve a high cache hit rate and low hit latency *** address this problem,P3DC classifies data blocks into leading blocks and following blocks,and places them at static positions and dynamic positions,respectively,in a unified set-associative *** also propose a replacement policy to balance the miss penalty and the temporal locality of different *** addition,P3DC provides a policy to mitigate cache thrashing due to block type *** results demonstrate that P3DC can reduce the cache hit latency by 20.5%while achieving a similar cache hit rate compared with typical set-associative caches.P3DC improves the instructions per cycle(IPC)by up to 66%(12%on average)compared with the state-of-the-art direct-mapped cache—BEAR,and by up to 19%(6%on average)compared with the tag-data decoupled set-associative cache—DEC-A8.

关键词： die-stacked dynamic random access memory(DRAM) cache set-associative direct-mapped hit latency

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