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Tsinghua Science & Technology
Journal Prestige (SJR): 0.366  Citation Impact (citeScore): 2 Number of Followers: 1  Hybrid journal (It can contain Open Access articles)ISSN (Print) 1007-0214 Published by IEEE  [228 journals]
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- A High-Linear Radio Frequency Quadrature Modulator with Improved Sideband
Suppression and Carrier Leakage Performance-
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Authors: Yingdan Jiang;Zongguang Yu;Shutong Wu;Li Li;Xuelian Liu;
Pages: 635 - 643
Abstract: The quadrature modulator is a crucial block in transmitters that upconverts baseband signals to the Radio Frequency (RF) band of interest using local oscillator frequencies. In this paper, non-ideal factors that influence the performance of the quadrature modulator are considered, and solutions are accordingly taken in the quadrature modulator design. A high-linear RF quadrature modulator with improved sideband suppression and carrier leakage performance is presented in this work. The quadrature modulator implemented in the 0.18-µm SiGe process uses the current bleeding technique to improve the general performance of the double-balanced active Gilbert mixers. An on-chip prescaler followed by two cascaded limiting amplifiers is designed to provide accurate quadrature local oscillator signals. Predrivers at quadrature baseband signal input ports are proposed to eliminate DC offsets. The measured sideband suppression achieves a performance of better than -43 dBc and carrier leakage is less than -38 dBm over the output RF frequency range of 30 MHz to 2.15 GHz. The output 1 dB compression point equals 11.4 dBm at 800 MHz.
PubDate: MON, 04 DEC 2023 09:17:26 -04
Issue No: Vol. 29, No. 3 (2023)
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- Key-Part Attention Retrieval for Robotic Object Recognition
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Authors: Jierui Liu;Zhiqiang Cao;Yingbo Tang;
Pages: 644 - 655
Abstract: The ability to recognize novel objects with a few visual samples is critical in the robotic applications. Existing methods mainly concern the recognition of inter-category objects, however, the object recognition from different sub-classes within the same category remains challenging due to their similar appearances. In this paper, we propose a key-part attention retrieval solution to distinguish novel objects of different sub-classes according to a few samples without re-training. Especially, an object encoder, including convolutional neural network with attention and key-part aggregation, is designed to generate object attention map and extract the object-level embedding, where object attention map from the middle stage of the backbone is used to guide the key-part aggregation. Besides, to overcome the non-differentiability drawback of key-part attention, the object encoder is trained in a two-step scheme, and a more stable object-level embedding is obtained. On this basis, the potential objects are located from a scene image by mining connected domains of the attention map. By matching the embedding of each potential object and embeddings from support data, the recognition of the potential objects is achieved. The effectiveness of the proposed method is verified by experiments.
PubDate: MON, 04 DEC 2023 09:17:26 -04
Issue No: Vol. 29, No. 3 (2023)
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- Spatially Coupled Codes via Bidirectional Block Markov Superposition
Transmission-
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Authors: Gaoyan Li;Shancheng Zhao;Haiqiang Chen;Jinming Wen;
Pages: 656 - 670
Abstract: In this paper, we present a new class of spatially coupled codes obtained by using both non-recursive and recursive block-oriented superposition. The resulting codes are termed as bidirectional block Markov superposition transmission (BiBMST) codes. Firstly, we perform an iterative decoding threshold analysis according to protograph-based extrinsic information transfer (PEXIT) charts for the BiBMST codes over the binary erasure channels (BECs). Secondly, we derive the generator and parity-check matrices of the BiBMST codes. Thirdly, extensive numerical results are presented to show the advantages of the proposed BiBMST codes. Particularly, our numerical results show that, under the constraint of an equal decoding latency, the BiBMST codes perform better than the recursive BMST (rBMST) codes. However, the simulation results show that, in finite-length regime, negligible performance gain is obtained by increasing the encoding memory. We solve this limitation by introducing partial superposition, and the resulting codes are termed as partially-connected BiBMST (PC-BiBMST) code. Analytical results have confirmed the advantages of the PC-BiBMST codes over the original BiBMST codes. We also present extensive simulation results to show the performance advantages of the PC-BiBMST codes over the spatially coupled low-density parity-check (SC-LDPC) codes, spatially coupled generalized LDPC (SC-GLDPC) codes, and the original BiBMST codes in the finite-length regime.
PubDate: MON, 04 DEC 2023 09:17:26 -04
Issue No: Vol. 29, No. 3 (2023)
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- Diminishing the Perception Bias in the Working Environment Using a Network
Generation-Based Framework-
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Authors: Jun Qian;Tongda Zhang;Xiao Sun;Yueting Chai;
Pages: 671 - 683
Abstract: Income inequality is widespread in human society and has important implications for human behavior. People's perception of the environment bridges income inequality and individual behavioral decisions. Existing research suggests that social income inequality is usually biased, either overestimated or underestimated. However, such phenomena have not been fully explored with quantitive prove, especially in the working environment based on performance data of actual production. In fact, the correct perception of people is the basis of a fair environment for their production decisions. Thus, the perception bias may weaken the adaptability and competitiveness of a company in the market. This paper first confirms the prevalence of individual perception bias of income inequality within the working environment based on actual production data. Our results show that people tend to underestimate income inequity around them, and this underestimation grows with the real unfairness of the working environment. Further, this paper proposes a network generation-based framework with a three-layer structure to correct perception bias using a cooperative network reengineering approach. Within the framework, a homophily-based generative network model is proposed as the key algorithm. Our simulation results show that our proposed framework effectively reduces individuals' perception bias of income inequality.
PubDate: MON, 04 DEC 2023 09:17:26 -04
Issue No: Vol. 29, No. 3 (2023)
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- Cost-Based Research on Energy Management Strategy of Electric Vehicles
Using Hybird Energy Storage System-
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Authors: Juanying Zhou;Jianyou Zhao;Lufeng Wang;
Pages: 684 - 697
Abstract: This paper uses the minimization and weighted sum of battery capacity loss and energy consumption under driving cycles as objective functions to improve the economy of Electric Vehicles (EVs) with an hybrid energy storage system composed of power batteries and ultracapacitors. Furthermore, Dynamic Programming (DP) is employed to determine the objective function values under different weight coefficients, the comprehensive cost consisting of battery aging and power consumption costs, and the relationship between the hybrid power distribution. We also evaluate the real-time fuzzy Energy Management Strategy (EMS), fuzzy control strategies, and a strategy based on DP using the World Light vehicle Test Procedure (WLTP) driving cycle and a synthesis driving cycle derived from New European Driving Cycle (NEDC), WLTP, and Urban Dynamometer Driving Schedule (UDDS) as examples. Then, the proposed strategy is compared with the fuzzy control strategy and the strategy based on DP. Compared with fuzzy energy management strategy (namely FZY-EMS), the proposed EMS reduces the battery capacity loss and system energy consumption. The results demonstrate the effectiveness of the proposed EMS in improving EV economy.
PubDate: MON, 04 DEC 2023 09:17:26 -04
Issue No: Vol. 29, No. 3 (2023)
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- Cooperative Spectrum Sensing Deployment for Cognitive Radio Networks for
Internet of Things 5G Wireless Communication-
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Authors: Thulasiraman Balachander;Kadiyala Ramana;Rasineni Madana Mohana;Gautam Srivastava;Thippa Reddy Gadekallu;
Pages: 698 - 720
Abstract: Recently, Cooperative Spectrum Sensing (CSS) for Cognitive Radio Networks (CRN) plays a significant role in efficient 5G wireless communication. Spectrum sensing is a significant technology in CRN to identify underutilized spectrums. The CSS technique is highly applicable due to its fast and efficient performance. 5G wireless communication is widely employed for the continuous development of efficient and accurate Internet of Things (IoT) networks. 5G wireless communication will potentially lead the way for next generation IoT communication. CSS has established significant consideration as a feasible resource to improve identification performance by developing spatial diversity in receiving signal strength in IoT. In this paper, an optimal CSS for CRN is performed using Offset Quadrature Amplitude Modulation Universal Filtered Multi-Carrier Non-Orthogonal Multiple Access (OQAM/UFMC/NOMA) methodologies. Availability of spectrum and bandwidth utilization is a key challenge in CRN for IoT 5G wireless communication. The optimal solution for CRN in IoT-based 5G communication should be able to provide optimal bandwidth and CSS, low latency, Signal Noise Ratio (SNR) improvement, maximum capacity, offset synchronization, and Peak Average Power Ratio (PAPR) reduction. The Energy Efficient All-Pass Filter (EEAPF) algorithm is used to eliminate PAPR. The deployment approach improves Quality of Service (QoS) in terms of system reliability, throughput, and energy efficiency. Our in-depth experimental results show that the proposed methodology provides an optimal solution when directly compares against current existing methodologies.
PubDate: MON, 04 DEC 2023 09:17:28 -04
Issue No: Vol. 29, No. 3 (2023)
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- Cyber-Syndrome: Concept, Theoretical Characterization, and Control
Mechanism-
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Authors: Feifei Shi;Huansheng Ning;Liming Chen;Sahraoui Dhelim;
Pages: 721 - 735
Abstract: The prevalence of social media and mobile computing has led to intensive user engagement in the emergent Cyber-Physical-Social-Thinking (CPST) space. However, the easy access, the lack of governance, and excessive use has generated a raft of new behaviors within CPST, which affects users' physical, social, and mental states. In this paper, we conceive the Cyber-Syndrome concept to denote the collection of cyber disorders due to excessive or problematic Cyberspace interactions based on CPST theories. Then we characterize the Cyber-Syndrome concept in terms of Maslow's theory of Needs, from which we establish an in-depth theoretical understanding of Cyber-Syndrome from its etiology, formation, symptoms, and manifestations. Finally, we propose an entropy-based Cyber-Syndrome control mechanism for its computation and management. The goal of this study is to give new insights into this rising phenomenon and offer guidance for further research and development.
PubDate: MON, 04 DEC 2023 09:17:26 -04
Issue No: Vol. 29, No. 3 (2023)
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- Effective Identity Authentication Based on Multiattribute Centers for
Secure Government Data Sharing-
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Authors: Meiquan Wang;Junhua Wu;Tongdui Zhang;Junhao Wu;Guangshun Li;
Pages: 736 - 752
Abstract: As one of the essential steps to secure government data sharing, Identity Authentication (IA) plays a vital role in the processing of large data. However, the centralized IA scheme based on a trusted third party presents problems of information leakage and single point of failure, and those related to key escrow. Therefore, herein, an effective IA model based on multiattribute centers is designed. First, a private key of each attribute of a data requester is generated by the attribute authorization center. After obtaining the private key of attribute, the data requester generates a personal private key. Second, a dynamic key generation algorithm is proposed, which combines blockchain and smart contracts to periodically update the key of a data requester to prevent theft by external attackers, ensure the traceability of IA, and reduce the risk of privacy leakage. Third, the combination of blockchain and interplanetary file systems is used to store attribute field information of the data requester to further reduce the cost of blockchain information storage and improve the effectiveness of information storage. Experimental results show that the proposed model ensures the privacy and security of identity information and outperforms similar authentication models in terms of computational and communication costs.
PubDate: MON, 04 DEC 2023 09:17:26 -04
Issue No: Vol. 29, No. 3 (2023)
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- Increasing the Maximum Capacity Path in a Network and Its Application for
Improving the Connection Between Two Routers-
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Authors: Adrian M. Deaconu;Javad Tayyebi;
Pages: 753 - 765
Abstract: This paper addresses the problem of improving the optimal value of the Maximum Capacity Path (MCP) through expansion in a flexible network, and minimizing the involved costs. The only condition applied to the cost functions is to be non-decreasing monotone. This is a non-restrictive condition, reflecting the reality in practice, and is considered for the first time in the literature. Moreover, the total cost of expansion is a combination of max-type cost (e.g., for supervision) and sum-type cost (e.g. for building infrastructures, price of materials, price of labor, etc.). For this purpose, two types of strategies are combined: (I) increasing the capacity of the existing arcs, and (II) adding potential new arcs. Two different problems are introduced and solved. Both the problems have immediate applications in Internet routing infrastructure. The first one is to extend the network, so that the capacity of an MCP in the modified network becomes equal to a prescribed value, therefore the cost of modifications is minimized. A strongly polynomial-time algorithm is deduced to solve this problem. The second problem is a network expansion under a budget constraint, so that the capacity of an MCP is maximized. A weakly polynomial-time algorithm is presented to deal with it. In the special case when all the costs are linear, a Meggido's parametric search technique is used to develop an algorithm for solving the problem in strongly polynomial time. This new approach has a time complexity of $O(n^{4})$, which is better than the time complexity of $O(n^{4}\log^{2}(n))$ of the previously known method from literature.
PubDate: MON, 04 DEC 2023 09:17:26 -04
Issue No: Vol. 29, No. 3 (2023)
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- SmartEagleEye: A Cloud-Oriented Webshell Detection System Based on Dynamic
Gray-Box and Deep Learning-
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Authors: Xin Liu;Yingli Zhang;Qingchen Yu;Jiajun Min;Jun Shen;Rui Zhou;Qingguo Zhou;
Pages: 766 - 783
Abstract: Compared with traditional environments, the cloud environment exposes online services to additional vulnerabilities and threats of cyber attacks, and the cyber security of cloud platforms is becoming increasingly prominent. A piece of code, known as a Webshell, is usually uploaded to the target servers to achieve multiple attacks. Preventing Webshell attacks has become a hot spot in current research. Moreover, the traditional Webshell detectors are not built for the cloud, making it highly difficult to play a defensive role in the cloud environment. SmartEagleEye, a Webshell detection system based on deep learning that is successfully applied in various scenarios, is proposed in this paper. This system contains two important components: gray-box and neural network analyzers. The gray-box analyzer defines a series of rules and algorithms for extracting static and dynamic behaviors from the code to make the decision jointly. The neural network analyzer transforms suspicious code into Operation Code (OPCODE) sequences, turning the detection task into a classification problem. Comprehensive experiment results show that SmartEagleEye achieves an encouraging high detection rate and an acceptable false-positive rate, which indicate its capability to provide good protection for the cloud environment.
PubDate: MON, 04 DEC 2023 09:17:26 -04
Issue No: Vol. 29, No. 3 (2023)
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- DeepSI: A Sensitive-Driven Testing Samples Generation Method of Whitebox
CNN Model for Edge Computing-
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Authors: Zhichao Lian;Fengjun Tian;
Pages: 784 - 794
Abstract: In recent years, Deep Learning (DL) technique has been widely used in Internet of Things (IoT) and Industrial Internet of Things (IIoT) for edge computing, and achieved good performances. But more and more studies have shown the vulnerability of neural networks. So, it is important to test the robustness and vulnerability of neural networks. More specifically, inspired by layer-wise relevance propagation and neural network verification, we propose a novel measurement of sensitive neurons and important neurons, and propose a novel neuron coverage criterion for robustness testing. Based on the novel criterion, we design a novel testing sample generation method, named DeepSI, which involves definitions of sensitive neurons and important neurons. Furthermore, we construct sensitive-decision paths of the neural network through selecting sensitive neurons and important neurons. Finally, we verify our idea by setting up several experiments, then results show our proposed method achieves superior performances.
PubDate: MON, 04 DEC 2023 09:17:28 -04
Issue No: Vol. 29, No. 3 (2023)
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- Edge-Enabled Metaverse: The Convergence of Metaverse and Mobile Edge
Computing-
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Authors: Nyothiri Aung;Sahraoui Dhelim;Liming Chen;Huansheng Ning;Luigi Atzori;Tahar Kechadi;
Pages: 795 - 805
Abstract: Metaverse is a virtual environment where users are represented by their avatars to navigate a virtual world having strong links with its physical counterpart. The state-of-the-art Metaverse architectures rely on a cloud-based approach for avatar physics emulation and graphics rendering computation. The current centralized architecture of such systems is unfavorable as it suffers from several drawbacks caused by the long latency of cloud access, such as low-quality visualization. To this end, we propose a Fog-Edge hybrid computing architecture for Metaverse applications that leverage an edge-enabled distributed computing paradigm. Metaverse applications leverage edge devices' computing power to perform the required computations for heavy tasks, such as collision detection in the virtual universe and high-computational 3D physics in virtual simulations. The computational costs of a Metaverse entity, such as collision detection or physics emulation, are performed at the device of the associated physical entity. To validate the effectiveness of the proposed architecture, we simulate a distributed social Metaverse application. The simulation results show that the proposed architecture can reduce the latency by 50% when compared with cloud-based Metaverse applications.
PubDate: MON, 04 DEC 2023 09:17:28 -04
Issue No: Vol. 29, No. 3 (2023)
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- Improved Double Deep Q Network-Based Task Scheduling Algorithm in Edge
Computing for Makespan Optimization-
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Authors: Lei Zeng;Qi Liu;Shigen Shen;Xiaodong Liu;
Pages: 806 - 817
Abstract: Edge computing nodes undertake an increasing number of tasks with the rise of business density. Therefore, how to efficiently allocate large-scale and dynamic workloads to edge computing resources has become a critical challenge. This study proposes an edge task scheduling approach based on an improved Double Deep Q Network (DQN), which is adopted to separate the calculations of target Q values and the selection of the action in two networks. A new reward function is designed, and a control unit is added to the experience replay unit of the agent. The management of experience data are also modified to fully utilize its value and improve learning efficiency. Reinforcement learning agents usually learn from an ignorant state, which is inefficient. As such, this study proposes a novel particle swarm optimization algorithm with an improved fitness function, which can generate optimal solutions for task scheduling. These optimized solutions are provided for the agent to pre-train network parameters to obtain a better cognition level. The proposed algorithm is compared with six other methods in simulation experiments. Results show that the proposed algorithm outperforms other benchmark methods regarding makespan.
PubDate: MON, 04 DEC 2023 09:17:26 -04
Issue No: Vol. 29, No. 3 (2023)
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- A Survey of Edge Caching: Key Issues and Challenges
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Authors: Hanwen Li;Mingtao Sun;Fan Xia;Xiaolong Xu;Muhammad Bilal;
Pages: 818 - 842
Abstract: With the rapid development of mobile communication technology and intelligent applications, the quantity of mobile devices and data traffic in networks have been growing exponentially, which poses a great burden to networks and brings huge challenge to servicing user demand. Edge caching, which utilizes the storage and computation resources of the edge to bring resources closer to end users, is a promising way to relieve network burden and enhance user experience. In this paper, we aim to survey the edge caching techniques from a comprehensive and systematic perspective. We first present an overview of edge caching, summarizing the three key issues regarding edge caching, i.e., where, what, and how to cache, and then introducing several significant caching metrics. We then carry out a detailed and in-depth elaboration on these three issues, which correspond to caching locations, caching objects, and caching strategies, respectively. In particular, we innovate on the issue “what to cache”, interpreting it as the classification of the “caching objects”, which can be further classified into content cache, data cache, and service cache. Finally, we discuss several open issues and challenges of edge caching to inspire future investigations in this research area.
PubDate: MON, 04 DEC 2023 09:17:28 -04
Issue No: Vol. 29, No. 3 (2023)
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- A Fast Insertion Tabu Search with Conflict-Avoidance Heuristic for the
Multisatellite Multimode Crosslink Scheduling Problem-
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Authors: Weiyi Yang;Lei He;Xiaolu Liu;Weican Meng;Yingwu Chen;
Pages: 843 - 862
Abstract: An agile earth-observing satellite equipped with multimode cameras capable of transmitting observation data to other satellites is developed to rapidly respond to requests with multiple observation modes. This gives rise to the Multisatellite Multimode Crosslink Scheduling (MMCS) problem, which involves allocating observation requests to agile satellites, selecting appropriate timing and observation modes for the requests, and transmitting the data to the ground station via the satellite communication system. Herein, a mixed integer programming model is introduced to include all complex time and operation constraints. To solve the MMCS problem, a two-stage heuristic method, called Fast insertion Tabu Search with Conflict-avoidance (FTS-C) heuristic, is developed. In the first stage, a conflict-avoidance insertion algorithm is designed to generate a high-quality initial solution by considering the requests transmission and download. Further, the tabu search-based second stage optimizes the initial solution. Finally, an extensive empirical study based on a real-world situation demonstrates that FTS-C can generate a solution with higher quality in less time than other state-of-the-art algorithms and the CPLEX solver.
PubDate: MON, 04 DEC 2023 09:17:26 -04
Issue No: Vol. 29, No. 3 (2023)
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- Architecture of Graphics System with 3D Acceleration Support for Embedded
Operating Systems-
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Authors: Alexander Giatsintov;Kirill Mamrosenko;Pavel Bazhenov;
Pages: 863 - 873
Abstract: An increasing number of tasks now require the use of hardware accelerators to reduce the time required for computation and display the computational results. This paper presents a new graphics system architecture for operating systems (OSs) with microkernel architecture, including real-time OSs. The proposed system architecture provides capabilities for displaying graphical images on various information display devices and for accelerating graphical operations on GPU. The architecture of the graphics system uses a concept of allocators to manage system and video memory, provides an abstraction of memory operations with a single interface for video memory management, and simplifies memory handling where incorrect operation is the cause of many failures. A comparison between the performance of a real-time OS and Linux OS implementing the graphics system using the example of a glmark2 benchmark is presented, thereby the superiority of the proposed architecture in several scenarios is demonstrated.
PubDate: MON, 04 DEC 2023 09:17:26 -04
Issue No: Vol. 29, No. 3 (2023)
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- Deciphering a Million-Plus RSA Integer with Ultralow Local Field
Coefficient h and Coupling Coefficient J of the Ising Model by D-Wave
2000Q-
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Authors: Chao Wang;Qiaoyun Hu;Haonan Yao;Sumin Wang;Zhi Pei;
Pages: 874 - 882
Abstract: This work is the first to determine that a real quantum computer (including generalized and specialized) can decipher million-scale RSA relying solely on quantum algorithms, showing the real attack potential of D-Wave machines. The influence of different column widths on RSA factorization results is studied on the basis of a multiplication table, and the optimal column method is determined by traversal experiments. The traversal experiment of integer factorization within 10 000 shows that the local field and coupling coefficients are 75%-93% lower than the research of Shanghai University in 2020 and more than 85% lower than that of Purdue University in 2018. Extremely low Ising model parameters are crucial to reducing the hardware requirements, prompting factoring 1 245 407 on the D-Wave 2000Q real machine. D-Wave advantage already has more than 5000 qubits and will be expanded to 7000 qubits during 2023–2024, with remarkable improvements in decoherence and topology. This machine is expected to promote the solution of large-scale combinatorial optimization problems. One of the contributions of this paper is the discussion of the long-term impact of D-Wave on the development of post-quantum cryptography standards.
PubDate: MON, 04 DEC 2023 09:17:28 -04
Issue No: Vol. 29, No. 3 (2023)
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- An Electrical Impedance Imaging System Towards Edge Intelligence for
Non-Destructive Testing of Concrete-
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Authors: Abolfazl Roshanpanah;Mahdi Abbasi;Hani Attar;Ayman Amer;Mohammad R. Khosravi;Ahmed A. Solyman;
Pages: 883 - 896
Abstract: In the construction industry, to prevent accidents, non-destructive tests are necessary and cost-effective. Electrical impedance tomography is a new technology in non-invasive imaging in which the image of the inner part of conductive bodies is reconstructed by the arrays of external electrodes that are connected on the periphery of the object. The equipment is cheap, fast, and edge compatible. In this imaging method, the image of electrical conductivity distribution (or its opposite; electrical impedance) of the internal parts of the target object is reconstructed. The image reconstruction process is performed by injecting a precise electric current to the peripheral boundaries of the object, measuring the peripheral voltages induced from it and processing the collected data. In an electrical impedance tomography system, the voltages measured in the peripheral boundaries have a non-linear equation with the electrical conductivity distribution. This paper presents a cheap Electrical Impedance Tomography (EIT) instrument for detecting impurities in the concrete. A voltage-controlled current source, a micro-controller, a set of multiplexers, a set of electrodes, and a personal computer constitute the structure of the system. The conducted tests on concrete with impurities show that the designed EIT system can reveal impurities with a good accuracy in a reasonable time.
PubDate: MON, 04 DEC 2023 09:17:26 -04
Issue No: Vol. 29, No. 3 (2023)
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- A Deep Neural Collaborative Filtering Based Service Recommendation Method
with Multi-Source Data for Smart Cloud-Edge Collaboration Applications-
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Authors: Wenmin Lin;Min Zhu;Xinyi Zhou;Ruowei Zhang;Xiaoran Zhao;Shigen Shen;Lu Sun;
Pages: 897 - 910
Abstract: Service recommendation provides an effective solution to extract valuable information from the huge and ever-increasing volume of big data generated by the large cardinality of user devices. However, the distributed and rich multi-source big data resources raise challenges to the centralized cloud-based data storage and value mining approaches in terms of economic cost and effective service recommendation methods. In view of these challenges, we propose a deep neural collaborative filtering based service recommendation method with multi-source data (i.e., NCF-MS) in this paper, which adopts the cloud-edge collaboration computing paradigm to build recommendation model. More specifically, the Stacked Denoising Auto Encoder (SDAE) module is adopted to extract user/service features from auxiliary user profiles and service attributes. The Multiple Layer Perceptron (MLP) module is adopted to integrate the auxiliary user/service features to train the recommendation model. Finally, we evaluate the effectiveness of the NCF-MS method on three public datasets. The experimental results show that our proposed method achieves better performance than existing methods.
PubDate: MON, 04 DEC 2023 09:17:28 -04
Issue No: Vol. 29, No. 3 (2023)
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- Federated Meta Reinforcement Learning for Personalized Tasks
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Authors: Wentao Liu;Xiaolong Xu;Jintao Wu;Jielin Jiang;
Pages: 911 - 926
Abstract: As an emerging privacy-preservation machine learning framework, Federated Learning (FL) facilitates different clients to train a shared model collaboratively through exchanging and aggregating model parameters while raw data are kept local and private. When this learning framework is applied to Deep Reinforcement Learning (DRL), the resultant Federated Reinforcement Learning (FRL) can circumvent the heavy data sampling required in conventional DRL and benefit from diversified training data, besides privacy preservation offered by FL. Existing FRL implementations presuppose that clients have compatible tasks which a single global model can cover. In practice, however, clients usually have incompatible (different but still similar) personalized tasks, which we called task shift. It may severely hinder the implementation of FRL for practical applications. In this paper, we propose a Federated Meta Reinforcement Learning (FMRL) framework by integrating Model-Agnostic Meta-Learning (MAML) and FRL. Specifically, we innovatively utilize Proximal Policy Optimization (PPO) to fulfil multi-step local training with a single round of sampling. Moreover, considering the sensitivity of learning rate selection in FRL, we reconstruct the aggregation optimizer with the Federated version of Adam (Fed-Adam) on the server side. The experiments demonstrate that, in different environments, FMRL outperforms other FL methods with high training efficiency brought by Fed-Adam.
PubDate: MON, 04 DEC 2023 09:17:28 -04
Issue No: Vol. 29, No. 3 (2023)
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- Call for Papers: Special Issue on Cross-Layer and Collaborative
Optimization Techniques in Space-Air-Ground-Sea Integrated Networks-
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Pages: 927 - 927
Abstract: null
PubDate: MON, 04 DEC 2023 09:17:26 -04
Issue No: Vol. 29, No. 3 (2023)
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