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The Journal of Machine Learning Research 2023年第1期24卷 8447-8521页

作者： Wenhao Li Bo Jin Xiangfeng Wang Junchi Yan Hongyuan Zha School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Software Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China School of Computer Science and Technology Key Laboratory of Mathematics and Engineering Applications Ministry of Education East China Normal University Shanghai China Department of Computer Science and Engineering Key Laboratory of Artificial Intelligence Ministry of Education Shanghai Jiao Tong University Shanghai China

Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications due to non-interactivity between agents, the curse of dimensionality, and computation complexity. Hence, several decentralized MARL algorithms are motivated. However, existing decentralized methods only handle the fully cooperative setting where massive information needs to be transmitted in training. The block coordinate gradient descent scheme they used for successive independent actor and critic steps can simplify the calculation, but it causes serious bias. This paper proposes a exible fully decentralized actor-critic MARL framework, which can combine most of the actor-critic methods and handle large-scale general cooperative multi-agent settings. A primal-dual hybrid gradient descent type algorithm framework is designed to learn individual agents separately for decentralization. From the perspective of each agent, policy improvement and value evaluation are jointly optimized, which can stabilize multi-agent policy learning. Furthermore, the proposed framework can achieve scalability and stability for the large-scale environment. This framework also reduces information transmission by the parameter sharing mechanism and novel modeling-other-agents methods based on theory-of-mind and online supervised learning. Sufficient experiments in cooperative Multi-agent Particle Environment and StarCraft II show that the proposed decentralized MARL instantiation algorithms perform competitively against conventional centralized and decentralized methods.

关键词： cooperative MARL decentralized actor-critic primal-dual method

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Correction: Agile meets quantum: a novel genetic algorithm model for predicting the success of quantum software development project

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Automated Software engineering 2024年第1期32卷 1-11页

作者： Khan, Arif Ali Akbar, Muhammad Azeem Lahtinen, Valtteri Paavola, Marko Niazi, Mahmood Alatawi, Mohammed Naif Alotaibi, Shoayee Dlaim M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta Finland Quanscient Oy Tampere Finland VTT Technical Research Ctr of Finland Ltd Oulu Finland Department of Information and Computer Science King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Interdisciplinary Research Centre for Intelligent Secure Systems King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Information Technology Department Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Department of Artificial Intelligence and Data Science College of Computer Science and Engineering University of Hail Hail Saudi Arabia

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A new block diagonal preconditioner for a class of 3 × 3 block saddle point problems

arXiv

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arXiv 2021年

作者： Abdolmaleki, Maryam Karimi, Saeed Salkuyeh, Davod Khojasteh Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr Iran Faculty of Mathematical Sciences University of Guilan Rasht Iran University of Guilan Rasht Iran

We study the performance of a new block preconditioner for a class of 3 × 3 block saddle point problems which arise from finite element methods for solving time-dependent Maxwell equations and some other practical problems. We also estimate the lower and upper bounds of eigenvalues of the preconditioned matrix. Finally, we examine our new preconditioner to accelerate the convergence speed of the GMRES method which shows the effectiveness of the *** Codes 65F10, 65F50, 65F08 © 2021, CC BY.

关键词： Eigenvalues and eigenfunctions

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Computational Evaluation of Coating Materials for Humidity Sensing using Fiber Bragg Grating

Computational Evaluation of Coating Materials for Humidity S...

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OCEANS

作者： Bharathi Raju Kumar R Samiappan Dhanalakshmi Gerard Dooly Dineshbabu Duraibabu Department of ECE College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur Chengalpatu TN India Centre for Robotics and Intelligent Systems University of Limerick Ireland Department of Mechatronic Engineering Atlantic Technological University ATU Sligo Sligo Ireland

Humidity is a prominent pollutant that must be regulated as well as a process variable that must be assessed in industrial applications. Humidity can degrade service quality, impair manufacturing equipment, and hikes the operating expenses considerably. Currently, demand for accurate humidity sensing significantly increases in various fields. Optical based sensors seek attention towards humidity sensing due to their inherent properties like multiplexing, accuracy and durability. For transducing humidity into a measurable parameter, coating materials contribute significantly. The performance of coating materials used for humidity monitoring was analyzed in terms of their thickness, response time, sensitivity and their compatibility in harsh environments. Four different types of coating materials such as PVAc, Agarose, PMMA and Polyimide were analyzed in terms of sensitivity, thickness, response time, resolution limit of detection and application.

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A Secure Authentication Framework for Consumer Mobile Crowdsourcing Networks

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IEEE Transactions on Consumer Electronics 2024年

作者： Aldosary, Abdallah Tanveer, Muhammad Ahmad, Musheer Maghrabi, Louai A. Ahmed, Emad A. Hussain, Amir El-Latif, Ahmed A. Abd Department of Computer Engineering Prince Sattam bin Abdulaziz University Riyadh Wadi Addwasir11991 Saudi Arabia University of Management and Technology Department of Computer Science Lahore54770 Pakistan Jamia Millia Islamia Department of Computer Engineering New Delhi110025 India University of Business and Technology College of Engineering Department of Software Engineering Jeddah Saudi Arabia South Valley University Faculty of Computers and Information Department of Computer Science Qena Egypt Edinburgh Napier University United Kingdom Prince Sultan University EIAS Data Science Lab College of Computer and Information Sciences Center of Excellence in Quantum and Intelligent Computing Riyadh11586 Saudi Arabia Menoufia University Faculty of Science Department of Mathematics and Computer Science Shebin El-Koom32511 Egypt

The Mobile crowdsourcing network (MCN) leverages collaborative intelligence to solve complex tasks through group cooperation. It comprises three main components: the end-user, the service provider, and the mobile user. The end-user requests crowd-sensing and computing services from the service provider, who handles task decomposition, distribution, re-composition, and recommendation. Mobile users perform the crowd sensing and computing tasks. Despite its benefits, MCN faces significant security and privacy challenges. Communications between the service provider and the end-user involve sensitive data, and mobile users and service providers communicate over open channels, making them vulnerable to security threats. To address these concerns, we propose a secure authentication framework for MCN, called SAF-MCN. This framework combines the lightweight cryptographic primitive ' ASCON' with elliptic curve cryptography. SAF-MCN allows the service provider to authenticate mobile users before they join the MCN. Once authenticated, a secure channel is established for encrypted communication over the public Internet. The security of SAFMCN is validated using both formal and informal methods, demonstrating its resilience against potential attacks. The use of Scyther further ensures the security of SAF-MCN. Comparative analysis shows that SAF-MCN is superior in terms of security features, computational efficiency, and communication cost. © 1975-2011 IEEE.

关键词： ASCON Authenticated Encryption Authentication Mobile Crowdsourcing Network Scyther Security

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A Rollout Heuristic-Reinforcement Learning Hybrid Algorithm for Disassembly Sequence Planning with Uncertain Depreciation Condition and Diversified Recovering Strategies

SSRN

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SSRN 2024年

作者： Ren, Yaping Xu, Zhehao Guo, Hongfei Zhang, Yanzi Liu, Jiayi Meng, Leilei Lin, Wenwen Guangdong International Cooperation Base of Science and Technology for GBA Smart Logistics Zhuhai China School of Intelligent Systems Science and Engineering Institute of Physical Internet Jinan University Zhuhai Campus Zhuhai China School of Mechanical Engineering Beijing Institute of Technology Beijing China School of Information Engineering Wuhan University of Technology Wuhan China School of Computer Science Liaocheng University Liaocheng China Faculty of Mechanical Engineering and Mechanics Ningbo University Zhejiang Ningbo China

Disassembly is one of the crucial aspects of green manufacturing. For the end-of-life products, an effective disassembly sequence planning method can enhance recovery value and mitigate the negative consequences of resource depletion and waste generation. However, both the uncertainty of product depreciation condition and the NP-hard characteristics (including the determination of disassembly sequences and the selection of recovering strategies of subassemblies) of the disassembly sequence planning results in difficulties to determine the optimal/near-optimal disassembly solutions. To address these challenges, this work establishes an extended petri net that considers diversified recovering strategies of each subassembly caused by uncertain product depreciation condition. Then, a rollout heuristic-reinforcement learning hybrid algorithm that integrates a rollout decision rule into the reinforcement learning procedure is proposed to rapidly find the high-quality disassembly solutions based on the extended petri net, in which the uncertainty of disassembly information is tackled by training disassembly samples and the global exploration capability of the learning procedure is significantly improved by using the rollout decision rule. Finally, a hybrid Li-ion battery pack of Audi A3 Sportback e-tron is selected as the case study to verify the performance of the proposed algorithm, and the experimental results indicate that our rollout decision rule-based reinforcement learning algorithm are significantly easier to find the best disassembly solutions comparing with other existing methods. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Decentralized and Lightweight Cross-Chain Transaction Scheme Based on Proxy Re-signature

Decentralized and Lightweight Cross-Chain Transaction Scheme...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Huiying Zou Jia Duan Xi Liu Wei Ren Tao Li Xianghan Zheng Kim-Kwang Raymond Choo School of Computer Science China University of Geosciences Wuhan China The Third Surveying and Mapping Institute of Hunan Province Hunan Engineering Research Center of Geographic Information Security and Application Hunan China Key Laboratory of Data Protection and Intelligent Management Sichuan University Ministry of Education China State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR CASM Beijing China College of Computer and Big Data Fuzhou University School of Information and Intelligent Engineering Sanya College China Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio USA

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

With the widespread application of digital assets and the rapid development of blockchain technology, achieving secure and efficient transactions between different blockchain networks has become an urgent challenge. Existing cross-chain methods impose limitations, e.g., hash lock technology exhibits low scalability, side-chain technology is overly complex, and notary schemes pose centralization risks. In order to address these issues, we propose a cross-chain transaction solution based on proxy re-signature technology. To tackle the centralization concerns, we employ proxy re-signature technology to decentralize the authority of notaries among transaction participants, minimizing the potential for centralization at a low cost. We further present an extended scheme that can guarantee more requirements such as higher transaction amount and shorter transaction time. Furthermore, we analyze and compare the signature technologies used in the proposed solution and provide a systematic proof of our approach’s security.

关键词： Privacy Systematics Costs Scalability Blockchains Security

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NurViD: a large expert-level video database for nursing procedure activity understanding 23

NurViD: a large expert-level video database for nursing proc...

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Proceedings of the 37th International Conference on Neural Information Processing systems

作者： Ming Hu Lin Wang Siyuan Yan Don Ma Qingli Ren Peng Xia Wei Feng Peibo Duan Lie Ju Zongyuan Ge AIM Lab Faculty of IT Monash University Australia and Airdoc-Monash Research Airdoc China and Faculty of Engineering Monash University Australia AIM Lab Faculty of IT Monash University Australia and Airdoc-Monash Research Airdoc China and College of Intelligent Systems Science and Engineering Harbin Engineering University China AIM Lab Faculty of IT Monash University Australia and Faculty of Engineering Monash University Australia AIM Lab Faculty of IT Monash University Australia Nursing College Shanxi Medical University China Faculty of Engineering Monash University Australia

The application of deep learning to nursing procedure activity understanding has the potential to greatly enhance the quality and safety of nurse-patient interactions. By utilizing the technique, we can facilitate training and education, improve quality control, and enable operational compliance monitoring. However, the development of automatic recognition systems in this field is currently hindered by the scarcity of appropriately labeled datasets. The existing video datasets pose several limitations: 1) these datasets are small-scale in size to support comprehensive investigations of nursing activity; 2) they primarily focus on single procedures, lacking expertlevel annotations for various nursing procedures and action steps; and 3) they lack temporally localized annotations, which prevents the effective localization of targeted actions within longer video sequences. To mitigate these limitations, we propose NurViD, a large video dataset with expert-level annotation for nursing procedure activity understanding. NurViD consists of over 1.5k videos totaling 144 hours, making it approximately four times longer than the existing largest nursing activity datasets. Notably, it encompasses 51 distinct nursing procedures and 177 action steps, providing a much more comprehensive coverage compared to existing datasets that primarily focus on limited procedures. To evaluate the efficacy of current deep learning methods on nursing activity understanding, we establish three benchmarks on NurViD: procedure recognition on untrimmed videos, procedure and action recognition on trimmed videos, and action detection. Our benchmark and code will be available at https://***/minghu0830/NurViD-benchmark.

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Dadm: Enhancing Eeg Artifact Removal by Fusing Artifact Representation into the Time-Frequency Domain

SSRN

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SSRN 2024年

作者： Li, Haoran Feng, Fan Kang, Jiarong Zhang, Jin Gong, Xiaoli Lu, Tingjuan Sun, Zhe Solé-Casals, Jordi College of Computer Science Nankai University Tianjin China Department of Biomedical Engineering School of Medicine Tsinghua University Beijing China Tianjin Key Laboratory of Brain Science and Intelligent Rehabilitation Nankai University Tianjin China State Key Laboratory of High-end Server and Storage Technology China 903rd Hospital of PLA China Faculty of Medicine Faculty of Health Data Science Juntendo University Japan Data and Signal Processing Group University of Vic Central University of Catalonia Catalonia Vic08500 Spain

The electroencephalogram (EEG) provides essential data for analyzing brain activities. However, artifacts such as electrooculography (EOG) and electromyography (EMG) often interleave with the EEG signals, significantly affecting the quality of EEG signal analysis. The heterogeneous distribution of these artifacts in the time-frequency domain makes it challenging to remove multiple artifacts using a unified model. In this paper, we propose an Artifact-Aware EEG Denoising Model, referred to as DADM, to effectively remove various types of artifacts in a unified manner. We first obtain an artifact representation that indicates the type of artifact from a pre-trained artifact classification model. This artifact representation is then used as prior knowledge, which is fused into the denoising model in the frequency-time domain. This enables the model to become aware of the artifact type and precisely remove artifacts based on their type. Due to the heterogeneous distributions of artifacts in the frequency domain, we introduce a frequency enhancement module that can identify specific types of artifacts based on their representation and remove them using a hard attention mechanism. Additionally, we design a time-domain compensation module to enhance the denoising capability of DADM by compensating for potential losses of global information. Comprehensive experiments demonstrate that DADM significantly outperforms the Novel CNN in denoising EEG signals, showing a notable 7.77% improvement in correlation coefficient (CC) metrics. © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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Microscale Channels Produced by Micro Friction Stir Channeling (Μfsc)

SSRN

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SSRN 2024年

作者： Sabor, Wagner de Campos Damásio, Daniel F.B. Tasnicenco, Rúben S. Sorger, Gonçalo Santos, Telmo G. Machado, Miguel A. Vidal, Catarina I.S. UNIDEMI Department of Mechanical and Industrial Engineering Faculty of Science and Technology NOVA University of Lisbon Caparica2829-516 Portugal Federal Institute of Education Science and Technology Campus SP São Paulo01109-010 Brazil Department of Mechanical Engineering School of Engineering Aalto University Espoo02150 Finland Associated Laboratory of Intelligent Systems LASI Guimarães4800-058 Portugal

The existing literature lacks research on reaching the processing limits of the Friction Stir Channeling process for the smallest continuous and integral channel, using tools with threaded probes 2 mm in diameter or smaller. For the first time, we explore the extreme limits of the microscale FSC process, and its possible applications, such as super compact heat exchangers, seeking to develop and optimize more efficient and sustainable systems. Customized tools were manufactured, with predefined dimensions and geometries and tested. A set of parameters capable of consistently producing continuous microchannels was established, producing the largest possible hydraulic diameter, based on the specifications and geometry of each tool. Continuity, tightness, microcomputed tomography, neutron computed tomography, microhardness, and thermal measurement tests were carried out to ensure continuity, watertight channels, and mechanical resistance for applications requiring super-compact heating or cooling systems. Internal channels in 5 mm thick AW1050-H111 aluminum alloy plates were obtained using tools with threaded probes measuring 2.0, 1.0, and 0.5 mm in diameter. The smallest channel produced had a hydraulic diameter of 191 µm, using a threaded probe with a diameter of 0.5 mm, characterizing it as a microchannel. The thermal performance of a compact heat exchanger model was tested. Although the cost of producing tools is high, depending on the production processes used, the process has consistently demonstrated that it is viable, reliable, and repeatable in the production of mini-channels and micro-channels. © 2024, The Authors. All rights reserved.

关键词： Probes

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