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7th IEEE International Conference on big data Security on Cloud, 7th IEEE International Conference on High Performance and Smart Computing, and 6th IEEE International Conference on intelligent data and Security, bigdataSecurity/HPSC/IDS 2021

作者： Chen, Shuyu Xie, Mingyue Liu, Jun Zhang, Yunan School of Big Data and Software Engineering Chongqing University Chongqing China College of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China

ISBN: (纸本)9781665439275

Contemporarily, DPoS is the fastest, most effective, decentralized and flexible consensus mechanism among all consensus mechanisms. Similar to the vote of the board, the holders cast a certain number of delegates to perform verification and accounting on their behalf in DPoS. In order to select the most appropriate delegates to implement the operations in blockchain, the DPoS consensus mechanism needs to be improved. Therefore, an improved DPoS consensus mechanism based on Probabilistic Linguistic Term Set (PLTS) is proposed in this paper. Just like human elections, this method allows each node to vote 'support' and 'oppose' for the delegate node. The efficient and flexible method reduces the probability of malicious nodes becoming delegates and guarantees the security of block generation in the blockchain. The effectiveness of the improved DPoS was verified by examples. © 2021 IEEE.

关键词： Blockchain

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A Fraud Detection Approach Based on Combined Feature Weighting 10th

A Fraud Detection Approach Based on Combined Feature Weighti...

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10th EAI International Conference on Cloud Computing, CloudComp 2020

作者： Liu, Xiaoqian Yu, Chenfei Xia, Bin Gu, Haiyan Wang, Zhenli Department of Computer Information and Cyber Security Jiangsu Police Institute Nanjing210031 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Electronic Data Forensics and Analysis Engineering Research Center Nanjing China Jiangsu Provincial Public Security Department Key Laboratory of Digital Forensics Nanjing China

ISBN: (纸本)9783030699918

data mining technology has yielded fruitful results in the area of crime discovery and intelligent decision making. Credit card is one of the most popular payment methods, providing great convenience and efficiency. However, due to the vulnerabilities of credit card transactions, criminals are able to commit fraud to infringe on the interests of the state and citizens. How to discover potential fraudsters while guaranteeing high efficiency becomes an extremely valuable problem to solve. In this work, we talk about the advantages and disadvantages of different models to detect credit card fraud. We first introduce the data preprocessing measures for handling imbalanced fraud detection dataset. Then we compare related models to implement fraudster recognition. We also propose a feature selection approach based on combined feature weights. Some future research interests are also envisioned. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Crime

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Robust Semi-Supervised Community Detection Based on Symmetric Nonnegative Matrix Factorization

SSRN

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SSRN 2024年

作者： Peng, Siyuan Xie, Wenyun Yang, Zhijing Lei, Fangyuan Nie, Feiping Lin, Zhiping School of Information Engineering Guangdong University of Technology 510006 China Guangdong Provincial Key Laboratory of Intellectual Property & Big Data Guangdong Polytechnic Normal University Guangzhou510665 China Northwestern Polytechnical University China School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore

The symmetric nonnegative matrix factorization (SNMF) model, renowned for its interpretability and efficiency, has been extensively adopted for community detection in the domain of complex networks. However, existing SNMF-based approaches exhibit limitations, particularly in capitalizing on limited supervisory information for enhanced detection performance and in their vulnerability to data distortion due to non-Gaussian noise and outliers. To overcome these limitations, a novel SNMF-based community detection method, namely correntropy-based semi-supervised SNMF (CSSNMF), is proposed in this paper for boosting the performance of SNMF-based methods in community detection tasks. More specifically, CSSNMF is characterized by two principal innovations: 1) adopting the pairwise constraints propagation (PCP) algorithm to fully utilize a small amount of supervisory information for reconstructing a more informative adjacency matrix;2) using the correntropy instead of the traditional squared Frobenius norm as the similarity measure in SNMF for enhancing the robustness against noise and outliers. Notably, the learned adjacency matrix is not only utilized as the decomposed matrix within the SNMF model, but also integrated into the graph regularization for deriving a discriminative community indicator ***, the proposed method is delved into a thorough analysis, considering aspects of convergence, the influence of supervisory information, and computational complexity. Through extensive experimental evaluations, the superiority of CSSNMF in terms of effectiveness and robustness has been demonstrated in community detection applications, particularly when benchmarked against several leading SNMF-based methods on real-world networks with and without significant noise interference. © 2024, The Authors. All rights reserved.

关键词： Matrix algebra

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Salix-Leaf: Find Main Veins of Signal Clusters for Practical Parallel Decoding

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IEEE Transactions on Mobile Computing 2025年

作者： Li, Yajun Zhao, Jumin Li, Dengao Wu, Hejun Xu, Shuang Bai, Ruiqin Taiyuan University of Technology College of Electronic Information and Optical Engineering Taiyuan China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan China Taiyuan University of Technology College of Computer Science and Technology College of Data Science Taiyuan China Sun Yat-sen University Department of Computer Science Guangzhou China

Parallel decoding of backscatter improves communication throughput by enabling concurrent transmission of backscatter tags. In practical applications of parallel decoding, it is extremely difficult to distinguish collided signals in superclusters where multiple signal clusters overlap. Existing methods are usually effective for superclusters with uniformly distributed signals. Nevertheless, there are many more scenarios in which signals in superclusters tend to gather unevenly, and existing methods cannot work. Such uneven clustering of signals occurs due to the following two possible causes: (1) signal-strengthdifferences (SSDs) among tags;or (2) cluster drifting (CD) driven by interferences from other objects within communication environments. This paper proposes a novel scheme called SalixLeaf, which aims to identify the main veins of signal clusters to address this problem of superclusters with unevenly distributed signals. Salix-Leaf identifies the main vein of each signal cluster for fine-grained clustering so that the direction of the main veins can be used to verify the accuracy of clustering. In addition, SalixLeaf employs a supercluster decomposer that divides signals into different segments for clustering analysis, enhancing robustness and practicability. Experimental results show that Salix-Leaf achieves a 1.2-fold increase in throughput and a 25% reduction in bit error rate (BER) compared to the state-of-the-art. © 2002-2012 IEEE.

关键词： Backscattering

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Research on Multi-AGVs dynamic scheduling based on deep reinforcement learning

Research on Multi-AGVs dynamic scheduling based on deep rein...

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IEEE International Conference on e-Business engineering (ICEBE)

作者： Zhuorui Qin Xiaoqian Wu Yimin Zheng Qingyao Wu School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot Ministry of Education Shenzhen Bangqi Technology Intelligent Development Co. Ltd Shenzhen China Pazhou Lab Guangzhou China

Driven by the latest advances in industrial artificial intelligence, automatic guided vehicles (AGVs) have been widely used for material handling in flexible workshops. In the pursuit of agile and flexible production transportation planning, the ability to handle unpredictable and dynamic events is becoming increasingly important. In order to achieve this real-time dynamic control, this study proposes a distributed multi-agent architecture to solve the dynamic Multi-AGVs scheduling problem. Double Deep Q-Network algorithm is used to train the scheduling agent, capture the relationship between production transportation operation information and scheduling goals, and make real-time scheduling decisions for flexible jobs with constant arrival intervals. Specialized state, action, and reward representations are proposed to handle variable specifications of dynamic scheduling problems. The performance of this method under different scene scales was verified through simulation studies. Numerical results show that compared with existing traditional heuristic scheduling strategies, this method not only has superior performance, but also its advantages persist even if the manufacturing system job scale changes.

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SARA: Controllable Makeup Transfer with Spatial Alignment and Region-Adaptive Normalization

arXiv

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arXiv 2023年

作者： Zhong, Xiaojing Huang, Xinyi Wu, Zhonghua Lin, Guosheng Wu, Qingyao School of Software Engineering South China University of Technology China School of Computer Science and Engineering Nanyang Technological University Singapore Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Peng Cheng Laboratory China SenseTime Research China

Makeup transfer is a process of transferring the makeup style from a reference image to the source image, while preserving the source image’s identity. In addition to achieving fine-grained control over the makeup transfer, incorporating semantic alignment into the transformation is crucial, as the poses of the reference and source images are often inconsistent. We propose a novel Semantic-guided Alignment and Region-Adaptive normalization framework (SARA) to effectively transfer makeup styles under misaligned poses, offering flexible control to meet the demands of real-world applications, such as partial transfer, intensity adjustment, and makeup removal. Specifically, SARA comprises three modules: Firstly, we propose a semantic-guided alignment module to explicitly construct dense correspondence between the reference image and the target semantic map, employing unbalanced optimal transport matching to handle semantic region mismatches. Secondly, a region-adaptive normalization module is responsible for dynamically combining the warped style features with shape-independent style codes obtained by region-wise average pooling, mitigating feature loss during the alignment. Lastly, a makeup fusion module progressively fuses the identity features with the makeup styles to render the final output image. Furthermore, we combine optimal transport with histogram matching to generate the pseudo ground truth, which is used to facilitate the transfer in terms of both spatial alignment and color distribution. Experimental results show that our proposed SARA outperforms existing methods on two public datasets. © 2023, CC BY.

关键词： Semantics

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The dynamics of tea plantation encroachment into forests and effect on forest landscape pattern during 1991–2021 through time series Landsat images

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Ecological Indicators 2022年 141卷

作者： Xiong, Yuan Kang, Qianwen Xu, Weiheng Huang, Shaodong Dai, Fei Wang, Leiguang Lu, Ning Kou, Weili College of Big Data and Intelligent Engineering Southwest Forestry University Kunming650233 China Institute of Big Data and Artificial Intelligence Southwest Forestry University Yunnan Kunming650233 China Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data Southwest Forestry University Yunnan Kunming650233 China

Tea plantations encroachment into forests has occurred in the major tea planting area of Yunnan province, China in the past decades. However, the dynamics of tea plantation encroachment into forests and the effect on forest landscape pattern is not clear. In this study, we proposed a method to evaluate the dynamic effects of tea plantation encroachment on forest landscape pattern change by using time series Landsat 5/7/8 data from 1991 to 2021 on the Google Earth Engine (GEE) platform. First, a pixel- and phenology-based algorithm was applied to generate tea plantations encroachment into forests maps for seven historical periods. Second, three indices including the tea plantations encroachment index (TPEI), the growth area of tea plantations encroachment into forests (GATEF), and the growth index of tea plantations encroachment into forests (GITEF) were used to quantify the tea plantations encroachment into forests of seven historical periods. Third, the effects of forests landscape pattern resulting from tea plantations encroachment were evaluated in class metrics and landscape metrics respectively. Our results show a total of 13,721 ha of GATEF from the early 1990s to the early 2020s. The GITEF increased from the early 1990s to the early 2010s and decreased from the early 2010s to the early 2020s in our study area. And the TPEI indicates that the edge encroachment mode dominated in our study period. Correlation analysis indicates that tea plantations encroachment level had significant correlation coefficients with landscape pattern indices (such as CONTAG (r = −0.91), ED (r = 0.95), DIVISION (r = 0.85), and SHEI (r = 0.86)), which indicates that the level of tea plantations encroachment affects significantly the forest landscape pattern. This study provides a promising approach to quantifying the effect of forest landscape pattern results from tea plantations encroachment. © 2022 The Authors

关键词： Deforestation

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Road Crack Detection Algorithm Based on Improved YOLOv8

Road Crack Detection Algorithm Based on Improved YOLOv8

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IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)

作者： Peng Yanfei Ji Yue Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications School of Electronic and Information Engineering Liaoning Technical University Huludao Liaoning China School of Electronic and Information Engineering Liaoning Technical University Huludao Liaoning China

Road crack detection is of great significance for maintaining road quality, improving road safety, optimizing resource allocation and protecting environment. Aiming at the problems of low recognition accuracy, high false detection rate and false detection rate of existing road crack detection, an improved road crack detection algorithm YOLOv8-YP based on YOLOv8 is proposed. Firstly, the attention mechanism GAM is introduced into the backbone network and neck network to enhance the ability of extracting features of different scales from fracture images. Secondly, SPDConv is used to replace Conv in the backbone network to improve the recognition rate of slender and tiny cracks in the backbone network. Finally, DIoU function is used to optimize the bounding box loss of the network, so that the network can converge quickly and provide finer detection results. The experimental results show that the improved algorithm model YOLOv8-YP and the original algorithm YOLOv8n are improved by 2.2% and 2.3% in mAP @ 0.5 and mAP @ 0.5: 0.95, respectively. The improved algorithm YOLOv8-YP has better detection effect on road cracks, and can identify and locate road cracks quickly and accurately.

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Semi-supervised Learning with Nearest-Neighbor Label and Consistency Regularization 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Zheng, Guolin Li, Zuoyong Hu, Wenkai Fan, Haoyi Ching, Fum Yew Yu, Zhaochai Chen, Kaizhi College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China School of Computer Sciences Universiti Sains Malaysia Penang11800 Malaysia Minjiang University Fuzhou350121 China

ISBN: (数字)9783031200991

ISBN: (纸本)9783031200984

Semi-supervised learning, a system dedicated to making networks less dependent on labeled data, has become a popular paradigm due to its strong performance. A common approach is to use pseudo-labels with unlabeled data for training, however, pseudo-labels cannot correct their own errors. In this paper, we propose a semi-supervised method that uses nearest neighbor samples to obtain pseudo-labels and combines consistency regularization for image classification. Our method obtains pseudo-labels by computing the similarity of the data distribution between the weakly-augmented version of the unlabeled data and the labeled data stored in the support set and combines the consistency of the strongly-augmented version and the weakly-augmented version of the unlabeled data. We compared with several standard semi-supervised learning benchmarks and achieved a competitive performance. For example, we achieved an accuracy of 94.02 % on CIFAR-10 with 250 labels and 97.50 % on SVNH with 250 labels. It even achieved 91.59 % accuracy with only 40 labels data in the CIFAR-10. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Benchmarking

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Multi-agent Collaboration for Vehicular Task Offloading Using Federated Deep Reinforcement Learning

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IEEE Transactions on Mobile Computing 2025年

作者： Chen, Xing Xiao, Bohuai Lin, Xinyu Chen, Zheyi Min, Geyong Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science ExeterEX4 4QF United Kingdom

Mobile Edge Computing (MEC) distributes resources such as computing, storage, and bandwidth to the side close to users, which can provide low-latency services to in-vehicle users, thus promising a more efficient and safer driving environment. However, due to the dynamic scale of vehicle and the variability of resource requirements, it is a significant challenge to quickly obtain effective task offloading in large-scale vehicle scenarios. The existing studies generally adopt the centralized decision-making method, with long decision-making time and high computational overhead, which cannot effectively achieve good offloading decisions in large-scale scenarios. To address these problems, we propose a Multi-agent Collaborative Method for vehicular task offloading using Federated Deep Reinforcement Learning called MCM-FDRL. First, each vehicle as an agent, independently makes offloading decisions based on local information. Next, the offloading decision model of each vehicle is obtained through federated reinforcement learning training. At runtime, an effective vehicle offloading plan can be gradually developed through multi-agent collaboration. Using two real-world datasets, experiments show that the MCM-FDRL has good adaptability and scalability. Moreover, compared to the state-of-the-art methods, the task's average response time of the MCM-FDRL is reduced by 9.75%-64.90%, respectively. © 2002-2012 IEEE.

关键词： Federated learning

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