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Privacy-Aware Offloading Strategy via Self-Supervised Feature Mapping in the End-Edge-Cloud System

ACM Transactions on Sensor Networks 1000年

作者： Rui Zhang Xuemei Zhao Yajing Li Shipu Zheng Ruhui Ma Mengke Tian Youhua Xue Yong Wang Haibing Guan School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Equipment Project Management Center of Equipment Department of Aerospace System Department Shanghai China Aerospace System Engineering Shanghai Shanghai China China Electronics Corporation Hainan Joint Innovation Research Institute Co. Ltd Haikou China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Peking University Institute of Microelectronics Beijing China Beijing Microelectronic Technology Institute Beijing China

In the Internet of Things (IoT) era, the pervasive application of tremendous end devices puts forth an unprecedented demand for data processing. To address this challenge, the end-edge-cloud system has emerged as a solution, where task offloading plays a crucial role in efficiently allocating computing resources. Meanwhile, driven by the growing social awareness of privacy, privacy-aware task offloading methods have attracted significant attention. However, existing privacy-aware task offloading methods face various limitations, such as being applicable to specific scenarios, poor transfer ability of offloading strategies, etc. This paper studies the privacy-aware task offloading problem in the end-edge-cloud system and proposes PATO, a Privacy-Aware Task Offloading strategy. PATO consists of two core modules. Specifically, a novel self-supervised feature mapping module transforms sensitive information via complex unidirectional mapping. Subsequently, a DRL-based decision-making module is trained to utilize transformed information to make task offloading decisions. Subtly combining the self-supervised feature mapping module and the DRL-based decision-making module, the proposed PATO addresses both privacy protection and task offloading challenges. Furthermore, PATO is designed as a general solution for task offloading problems and exhibits good transfer ability.

关键词： task offloading strategy privacy-aware computing end-edge-cloud system

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Proceedings of the International Conference on Paradigms of Computing, Communication and data sciences 1

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丛书名： Algorithms for Intelligent Systems

1000年

作者： Rajendra Prasad Yadav Satyasai Jagannath Nanda Prashant Singh Rana Meng-Hiot Lim

ISBN: (数字)9789811987427

ISBN: (纸本)9789811987410;9789811987441

This book gathers selected high-quality research papers presented at International Conference on Paradigms of Communication, Computing and data sciences (PCCDS 2022), held at Malaviya National institute of Technology Jaipur, India, during 05 – 07 July 2022. It discusses high-quality and cutting-edge research in the areas of advanced computing, communications and data science techniques. The book is a collection of latest research articles in computation algorithm, communication and data sciences, intertwined with each other for efficiency.

关键词： Computational Intelligence Artificial Intelligence Cyber-physical systems, IoT Professional Computing Wireless and Mobile Communication data Structures and Information Theory

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Event-Triggered Active Disturbance Rejection Control 1

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丛书名： Studies in Systems, Decision and Control

1000年

作者： Dawei Shi Yuan Huang Junzheng Wang Ling Shi

ISBN: (数字)9789811602931

ISBN: (纸本)9789811602924;9789811602955

The past few years have seen the attention and rapid developments in event-triggered sampled-data systems, in which the effect of event-triggered sensor measurements and controller updates is explored in controller analysis and design.;This book offers the first systematic treatment of event-triggered sampled-data control system design using active disturbance rejection control (ADRC), an effective approach that is popular in both theoretic research and industrial applications. Extensive application examples with numerous illustrations are included to show how the event-triggered ADRC with theoretic performance guarantees can be implemented in engineering systems and how the performance can be actually achieved. For theoretic researchers and graduate students, the presented results provide new directions in theoretic research on event-triggered sampled-data systems; for control practitioners, the book offers an effective approach to achieving satisfactory performance with limited sampling rates.

关键词： Control, Robotics, Mechatronics Systems Theory, Control

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data Management, analytics and Innovation 1

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丛书名： Lecture Notes in Networks and Systems

1000年

作者： Neha Sharma Amol C. Goje Amlan Chakrabarti Alfred M. Bruckstein

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Feature Disentanglement Based Heterogeneous Defect Prediction

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ACM Transactions on Software engineering and Methodology 1000年

作者： Xu Yu Jiaqi Yan Qinqin Gao Qinglong Peng Bin Yu Junwei Du Ying Xing Dunwei Gong Qingdao Institute of Software China University of Petroleum East China) Qingdao China College of Computer Science and Technology China University of Petroleum East China) Qingdao China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software China University of Petroleum East China) Qingdao China School of Data Science Qingdao University of Science and Technology Qingdao China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Changchun China School of Data Science Qingdao University of Science and Technology Qingdao China School of Computer and Information Technology Beijing Jiaotong University Beijing China School of Data Science Qingdao University of Science and TechnologyQingdao China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Yunnan Key Laboratory of Software Engineering Kunming China College of Automation and Electronic Engineering Qingdao University of Science and Technology Qingdao China

Cross-project defect prediction (CPDP) utilizes the existing labeled data in the source project to assist with the prediction of unlabeled projects in the target dataset, which effectively improves the prediction performance and has become a research hotspot in software engineering. At present, CPDP can be categorized into homogeneous cross-project defect prediction and heterogeneous cross-project defect prediction (HDP), in which HDP doesn’t require that the source project and the target project have the same feature space, thus, it is more widely used in the actual CPDP. Most of current HDP methods map the original features to the latent feature space and reduce the inter-project variation by transferring domain-independent features, but the transferring process ignores the use of domain-related features, which affects the prediction performance of the model. Moreover, the mapped latent features are not conducive to the model’s interpretability. Based on these, this paper proposes a heterogeneous defect prediction method based on feature disentanglement (FD-HDP). We disentangle the features using domain-related and domain-independent feature extractors, respectively, to improve the interpretability of the model by maximizing the domain adversarial loss during training and guiding the feature extractors to produce accurate domain-related and domain-independent features. The weighted sum of the prediction results from domain-related and domain-independent predictors is used as the final prediction result of the project during the prediction process, which realizes the combination of domain-independent and domain-related features and effectively improves the prediction performance. In this paper, we conducted experiments using four publicly available defect datasets to construct heterogeneous scenarios. The results demonstrate that the FD-HDP model shows significant advantages over state-of-the-art methods in six metrics.

关键词： Heterogeneous Defect Prediction Feature Disentanglement Domain-related Features Domain-independent Features

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AGC 2023 1

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丛书名： Communications in computer and Information science

1000年

作者： Suparna Dhar Sanjay Goswami U. Dinesh Kumar Indranil Bose Rameshwar Dubey Chandan Mazumdar

ISBN: (数字)9783031508158

ISBN: (纸本)9783031508141

This book constitutes the refereed proceedings of the 1st analytics Global Conference, AGC 2023, held in Kolkata, India, in April 2023. The AGC conference sought to facilitate industry-academia interfacing in the domain of machine learning and artificial intelligence.;The 11 full papers presented in these proceedings were carefully reviewed and selected from 36 submissions. The papers are organized in the two topical sections: Applications of analytics in Business and Machine Learning & Deep Learning and Text analytics.

关键词： Artificial Intelligence data Structures and Information Theory Machine Learning

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Neurocomputation in Remote Sensing data Analysis 1

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1000年

作者： Ioannis Kanellopoulos Graeme G. Wilkinson Fabio Roli James Austin

ISBN: (数字)9783642590412

ISBN: (纸本)9783540633167;9783642638282

Since 1994 the European Commission has been supporting activities under the Environment and Climate programme of research and technological de velopment, with the aim of developing cost-effective applications of satellite Earth observation (EO) for both environmental monitoring and research. This action has included support to methodological research, aimed at the development and evaluation of new techniques forming part ofthe chain of processing needed to transform data into useful information. Wherever appropriate, the Commission has emphasised the coordination of ongoing research funded at the national level, through the mechanism of concerted actions. Concerted actions are flexible and efficient means to marshal efforts at the European level for a certain period. They are proposed by groups of researchers active in a given field who have identified the added value to be gained by European cooperation, whilst continuing to pursue their own individual projects. In view of the rapid developments in the field of neural network over the last 10 years, together with the growing interest ofthe Earth observation community in this approach as a tool for data interpretation, the Commission decided in 1995 to support the concerted action COMPARES, following a proposal from a group of acknowledged European experts.

关键词： Artificial Intelligence Economic Geology Regional/Spatial science Pattern Recognition Geography, general Earth sciences, general

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6G Enabled Fog Computing in IoT 1

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1000年

作者： Mohit Kumar Jitendra Kumar Samriya Sukhpal Singh Gill Steve Uhlig

ISBN: (数字)9783031301018

ISBN: (纸本)9783031301001;9783031301032

Over the past few years, the demand for data traffic has experienced explosive growth thanks to the increasing need to stay online. New applications of communications, such as wearable devices, autonomous systems, drones, and the Internet of Things (IoT), continue to emerge and generate even more data traffic with vastly different performance requirements. With the COVID-19 pandemic, the need to stay online has become even more crucial, as most of the fields, would they be industrial, educational, economic, or service-oriented, had to go online as best as they can. As the data traffic is expected to continuously strain the capacity of future communication networks, these networks need to evolve consistently in order to keep up with the growth of data traffic. Thus, more intelligent processing, operation, and optimization will be needed for tomorrow’s communication networks. The Sixth Generation (6G) technology is latest approach for mobile systems or edge devices in terms of reduce traffic congestions, energy consumption blending with IoT devices applications. The 6G network works beyond the 5G (B5G), where we can use various platforms as an application e.g. fog computing enabled IoT networks, Intelligent techniques for SDN network, 6G enabled healthcare industry, energy aware location management. Still this technology must resolve few challenges like security, IoT enabled trust network.;This book will focus on the use of AI/ML-based techniques to solve issues related to 6G enabled networks, their layers, as well as their applications. It will be a collection of original contributions regarding state-of-the-art AI/ML-based solutions for signal detection, channel modeling, resource optimization, routing protocol design, transport layer optimization, user/application behavior prediction 6G enabled software-defined networking, congestion control, communication network optimization, security, and anomaly detection. The proposed editedbook emphasis on the 6G network ble

关键词： computer Communication Networks computer Applications Wireless and Mobile Communication

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Simulated Evolution and Learning 1

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丛书名： Lecture Notes in computer science

1000年

作者： Grant Dick Will N. Browne Peter Whigham Mengjie Zhang Lam Thu Bui Hisao Ishibuchi Yaochu Jin Xiaodong Li Yuhui Shi Pramod Singh Kay Chen Tan Ke Tang

ISBN: (数字)9783319135632

ISBN: (纸本)9783319135625

This volume constitutes the proceedings of the 10th International Conference on Simulated Evolution and Learning, SEAL 2012, held in Dunedin, New Zealand, in December 2014. The 42 full papers and 29 short papers presented were carefully reviewed and selected from 109 submissions. The papers are organized in topical sections on evolutionary optimization; evolutionary multi-objective optimization; evolutionary machine learning; theoretical developments; evolutionary feature reduction; evolutionary scheduling and combinatorial optimization; real world applications and evolutionary image analysis.

关键词： Computation by Abstract Devices Artificial Intelligence data Mining and Knowledge Discovery Simulation and Modeling Discrete Mathematics in computer science Information Systems Applications (incl. Internet)

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Advances in Swarm Intelligence 1

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丛书名： Lecture Notes in computer science

1000年

作者： Ying Tan Yuhui Shi Yi Chai Guoyin Wang

ISBN: (数字)9783642215155

ISBN: (纸本)9783642215148

The two-volume set (LNCS 6728 and 6729) constitutes the refereed proceedings of the International Conference on Swarm Intelligence, ICSI 2011, held in Chongqing, China, in June 2011. The 143 revised full papers presented were carefully reviewed and selected from 298 submissions. The papers are organized in topical sections on theoretical analysis of swarm intelligence algorithms, particle swarm optimization, applications of pso algorithms, ant colony optimization algorithms, bee colony algorithms, novel swarm-based optimization algorithms, artificial immune system, differential evolution, neural networks, genetic algorithms, evolutionary computation, fuzzy methods, and hybrid algorithms - for part I. Topics addressed in part II are such as multi-objective optimization algorithms, multi-robot, swarm-robot, and multi-agent systems, data mining methods, machine learning methods, feature selection algorithms, pattern recognition methods, intelligent control, other optimization algorithms and applications, data fusion and swarm intelligence, as well as fish school search - foundations and applications.

关键词： Algorithm Analysis and Problem Complexity Information Systems Applications (incl. Internet) Artificial Intelligence Computation by Abstract Devices computer Communication Networks Information Storage and Retrieval

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