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Congress on Evolutionary Computation

作者： Tianye Shu Yang Nan Ke Shang Hisao Ishibuchi Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

For evolutionary multi-objective optimization algorithms (EMOAs), an external archive can be utilized for saving good solutions found throughout the evolutionary process. Recent studies showed that a solution set selected from an external archive is usually superior to the final population. That is, the incorporation of an external archive improves the performance of EMOAs. However, the computation time for maintaining an external archive is long, especially when the archive size is large. To solve this issue, a simple archiving strategy is to save all solutions generated in the last several generations. In this paper, we examine this archiving strategy for three representative EMOAs on artificial test problems (Minus-DTLZ and WFG) and real-world problems (RE). Our results show that archiving the last several generations clearly improves the performance of EMOAs without severely increasing the computation time.

关键词： Costs Sociology Evolutionary computation Statistics Optimization Standards

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Concept Cognition for Knowledge Graphs: Mining Multi-Granularity Decision Rule

SSRN

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SSRN 2024年

作者： Duan, Jiangli Wang, Guoyin Hu, Xin Liu, Qun Jiang, Qin Zhu, Huamin Yangtze Normal University College of Big Data and Intelligent Engineering Chongqing408100 China Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Computational Intelligence Chongqing400065 China

As part of cognitive intelligence, concept cognition for knowledge graphs aims to clearly grasp the typical characteristics of the things referred to by the concept, which can provide prior knowledge for machine understanding and thinking. Different from concept learning and formal concept analysis that learns new concepts from data and general decision rule that comes from an independent information system, this paper cognizes an existing concept by decision rules that come from multiple granularities. Specifically, 1) concept cognition for knowledge graphs is realized from the perspective of mining multi-granularity decision rule. 2) Information systems corresponding to four granularities form a multi-granularity information system group, and then the result from coarser granularity can guide and help obtaining the result from finer granularity. 3) We propose a framework of mining multi-granularity decision rule, which involves going from a multi-granularity information system group to frequent maximal attribute pattern to decision rule to credible decision rule. Finally, we verified effectiveness of dividing positive and negative data, monotonicity of attribute patterns in a multi-granularity information system group, and downward monotonicity of credibility, and observed the impact of the parameter min_cov and min_conf on executing times. © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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Fast Intra Mode Prediction Algorithms for SCBS in VVC SCC

Fast Intra Mode Prediction Algorithms for SCBS in VVC SCC

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dayong Wang Yishen Deng Weisheng Li Xin Lu Frederic Dufaux Bo Hang Ce Zhu Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Faculty of Computing Engineering and Media (CEM) De Montfort University Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et systèmes The Computer School Hubei University of Arts and Science School of Information and Communication Engineering University of Electronic Science and Technology of China

Versatile Video Coding (VVC) now supports Screen Content Coding (SCC) by integrating two efficient coding modes: Intra Block Copy (IBC) and Palette (PLT). However, the numerous modes and the Quad-Tree Plus Multi-Type Tree (QTMT) structure inherent to VVC contribute to a very high coding complexity. To effectively reduce the computational complexity of VVC SCC, we propose a fast Intra mode prediction algorithm for VVC SCC. More specifically, we first use the difference of minimum Sum of Absolute Transformed Differences (SATD) value of four Directional Modes (DMs) of Intra and the SATD value of the IBC-merge mode to determine whether to early skip Intra checking. Subsequently, we use a decision tree to determine whether to early terminate the checking after block differential pulse coded modulation (BDPCM). Finally, we employ a decision tree to determine whether to early skip multiple transform selection (MTS) and low frequency non-separable transform (LFNST) checking. The results demonstrate that our algorithm achieves an average encoding time reduction of 34.34% with a negligible Bjøntegaard delta bitrate increase of 0.46%.

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On Performance of Cooperative V2X Communication with Vehicular Platoon Systems 23

On Performance of Cooperative V2X Communication with Vehicul...

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23rd IEEE International Conference on High Performance computing and Communications, 7th IEEE International Conference on data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Zhou, Siyuan Wu, Qianqian Tan, Guoping Yang, Dengsong Ni, Baili College of Computer and Information Hohai University Nanjing China Jiangsu Intelligent Transportation and Intelligent Driving Research Institute Nanjing China Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing China Jiangsu Expressway Company Limited China

ISBN: (纸本)9781665494571

With the development of intelligent traffic system, it is critical to use vehicle platooning to improve the safety and efficiency of vehicles. Vehicle-to-everything (V2X) communication technology is the key to guarantee the stability of platooning and cooperative communication technique can be leveraged to improve the communication coverage. In this paper, we first constructed an Amplify-and-forward (AF) relay communication network for vehicular platooning where the base station (BS) or the road side unit (RSU) can be selected as the relay node. Then, we carried out the performance analysis of the communication success probability by using stochastic geometry. The distribution of the locations of vehicles and RSUs is modeled by Poisson line process (PLP) and the spatial layout of BSs is modeled by a 2D Poisson point process (PPP). The BS or RSU is selected as the relay node according to their location. By deriving the association probability to BS or RSU, we derive the analytical solution of the success probability of the system. Simulation results verify our analysis and reveal the impact of the system parameters on the relay network performance. © 2021 IEEE.

关键词： Smart cities Simulation Roads Vehicle driving Stochastic processes Stability analysis Topology

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Padding Adversarial Training: Enhancing Robustness with Adversarial Space Hypothesis

SSRN

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SSRN 2022年

作者： Sun, Jiaze Wen, Sulei School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an710121 China Xi 'an Key Laboratory of Big Data and Intelligent Computing Xi’an710127 China

Adversarial training, represented by the projected gradient descent(PGD) adversarial training, effectively improves adversarial robustness within the upper bound of perturbation to the deep neural network(DNN) by applying adversarial examples to model retraining. When the perturbation is beyond the pale, though, the defense it provides to the model is invalid. Furthermore, it is also vulnerable to adaptive attacks and novel attacks uninvolved in adversarial training. To solve these problems, we propose the adversarial space hypothesis based on the manifold theory to explain the existence of adversarial examples from a feature space perspective. We further introduce padding adversarial training, a technique enhancing adversarial training with the adversarial space hypothesis. Padding adversarial training improves the generalization of the standard adversarial training’s robustness by 20% and provides an additional detection defense for adversarial examples with high perturbation. Moreover, we propose trap smoothing, which optimizes the target manifold’s distribution and establishes the inducing relationship between the target manifold and the feature space labelled by the padding classes. On MNIST and CIFAR-10, padding adversarial training is robust to white, black and adaptive attacks in low perturbation conditions and provides the 80%~98% detection rate of adversarial examples in high perturbation conditions. The code is available at:https://***/Ccsk-Xian/Padding-adversarial-training. © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Adaptive Federated Learning via Mean Field Approach 15

Adaptive Federated Learning via Mean Field Approach

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2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social computing, CPSCom 2022 and 8th IEEE International Conference on Smart data, Smartdata 2022

作者： Tu, Kaifei Zheng, Shensheng Wang, Xuehe Hu, Xiping School of Artificial Intelligence Sun Yat-sen University Zhuhai China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

In order to solve the problem of "data island"and preserve individual's privacy, federated learning, as a distributed machine learning technology, has emerged recently. In federated learning, the model training is distributed over edge clients and coordinated by a central server. Each client only needs to send the updated model parameter to the central server for aggregation without sharing its private data. However, due to the data divergence of heterogeneous clients, the convergence rate of the global model training may be very slow, especially for non-IID data case. To deal with this issue and achieve fast convergence, we propose an adaptive learning rate strategy for each client by considering the deviation of the local model parameter from the global model parameter at each global training iteration. To enable decentralized learning rate design for each client, a mean-field scheme is introduced to estimate the global model parameters over time, which does not even require many clients to communicate frequently. Finally, we run numerical experiments to validate our results. © 2022 IEEE.

关键词： Learning algorithms

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Network Traffic Prediction with Attention-based Spatial-Temporal Graph Network

Network Traffic Prediction with Attention-based Spatial-Temp...

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IEEE Workshop on High Performance Switching and Routing

作者： Yufei Peng Yingya Guo Run Hao Junda Lin Department of Computer and Data Science Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University

Network traffic prediction plays a significant role in network management. Previous network traffic prediction methods mainly focus on the temporal relationship between network traffic, and used time series models to predict network traffic, ignoring the spatial information contained in traffic data. Therefore, the prediction accuracy is limited, especially in long-term prediction. To improve the prediction accuracy of the dynamic network traffic in the long term, we propose an Attention-based Spatial-Temporal Graph Network (ASTGN) model for network traffic prediction to better capture both the temporal and spatial relations between the network traffic. Specifically, in ASTGN, we exploit an encoder-decoder architecture, where the encoder encodes the input network traffic and the decoder outputs the predicted network traffic sequences, integrating the temporal and spatial information of the network traffic data through the Spatio-Temporal Embedding module. The experimental results demonstrate the superiority of our proposed method ASTGN in long-term prediction.

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A Variance Minimization Approach to Temporal-Difference Learning

arXiv

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arXiv 2024年

作者： Chen, Xingguo Gong, Yu Yang, Shangdong Wang, Wenhao Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China College of Electronic Engineering National University of Defense Technology China

Fast-converging algorithms are a contemporary requirement in reinforcement learning. In the context of linear function approximation, the magnitude of the smallest eigenvalue of the key matrix is a major factor reflecting the convergence speed. Traditional value-based RL algorithms focus on minimizing errors. This paper introduces a variance minimization (VM) approach for value-based RL instead of error minimization. Based on this approach, we proposed two objectives, the Variance of Bellman Error (VBE) and the Variance of Projected Bellman Error (VPBE), and derived the VMTD, VMTDC, and VMETD algorithms. We provided proofs of their convergence and optimal policy invariance of the variance minimization. Experimental studies validate the effectiveness of the proposed algorithms. © 2024, CC BY-SA.

关键词： Contrastive Learning

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A Spatiotemporal Analysis of Age of Information at V2X-enabled Intersection 23

A Spatiotemporal Analysis of Age of Information at V2X-enabl...

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作者： Wang, Yaying Tan, Guoping Zhou, Siyuan Yang, Dengsong Ni, Baili Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing China College of Computer and Information Hohai University Nanjing China Jiangsu Intelligent Transportation and Intelligent Driving Research Institute Nanjing China Jiangsu Expressway Company Limited China

ISBN: (纸本)9781665494571

Ensuring the freshness of information is of importance to autonomous vehicles at vehicle-to-everything (V2X)-enabled intersection. In this paper we theoretically investigate the Age of information (AoI) at V2X-enabled suburban intersections and urban intersections. Specifically, we model the locations of connected vehicles by using stochastic geometry and consider a last-come first-serve discipline with preemption. Then we analyze the moments and distribution of the conditional successful probability and the conditional average AoI. Finally, we obtain the moments and the distribution of conditional average AoI. The accuracy of theoretical derivation is verified via simulations. Our numerical results show the influence of different parameters on the average AoI and the theoretical analysis provides a useful guideline for further system design. © 2021 IEEE.

关键词： Transmitters Smart cities Roads Stochastic processes Receivers Information age Spatiotemporal phenomena

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Lyapunov-guided Deep Reinforcement Learning for Semantic-aware AoI Minimization in UAV-assisted Wireless Networks

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IEEE Transactions on Wireless Communications 2025年

作者： Long, Yusi Gong, Shimin Sun, Sumei Lee, Gary C.F Li, Lanhua Niyato, Dusit Shenzhen Campus of Sun Yat-sen University School of Intelligent Systems Engineering Shenzhen518000 China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology China A*STAR Singapore Nanyang Technological University College of Computing and Data Science Singapore

This paper investigates an unmanned aerial vehicle (UAV)-assisted semantic network where the ground users (GUs) periodically capture and upload the sensing information to a base station (BS) via UAVs’ relaying. Both the GUs and the UAVs can extract semantic information from large-size raw data and transmit it to the BS for recovery. Smaller-size semantic information reduces latency and improves information freshness, while larger-size semantic information enables more accurate data reconstruction at the BS, preserving the value of original information. We introduce a novel semantic-aware age-of-information (SAoI) metric to capture both information freshness and semantic importance, and then formulate a time-averaged SAoI minimization problem by jointly optimizing the UAV-GU association, the semantic extraction, and the UAVs’ trajectories. We decouple the original problem into a series of subproblems via the Lyapunov framework and then use hierarchical deep reinforcement learning (DRL) to solve each subproblem. Specifically, the UAV-GU association is determined by DRL, followed by the optimization module updating the semantic extraction strategy and UAVs’ deployment. Simulation results show that the hierarchical structure improves learning efficiency. Moreover, it achieves low AoI through semantic extraction while ensuring minimal loss of original information, outperforming the existing baselines. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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