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40th IEEE International Conference on data engineering, ICDE 2024

作者： Chen, Dong Zhao, Xiang Xiao, Weidong National University of Defense Technology National Key Laboratory of Information Systems Engineering China National University of Defense Technology Laboratory for Big Data and Decision China

ISBN: (纸本)9798350317152

Dynamic graphs are ubiquitous in our lives, yet they are also susceptible to the risks imposed by malicious activities. However, identifying anomalies in these dynamic graphs presents a challenging task due to the complex graph structures. Existing methods for dynamic anomaly detection primarily focus on learning representations for each timestamp and using sequence modeling techniques to capture and model the temporal information. Despite extensive research, dynamic anomaly detection still faces two key challenges. First, existing methods are limited in effectively using fine-grained temporal information. Second, they have limited generalization capabilities under unsupervised settings. Overcoming these challenges is crucial for advances in dynamic anomaly detection. In this paper, we propose a novel unsupervised anomaly detection method for dynamic graphs. Our approach leverages complex temporal information through fine-grained sampling and embedding modules. Additionally, we introduce an attention alignment strategy to minimize discrepancies in contextual attention between source and target nodes. Through a comprehensive evaluation, we demonstrate that our strategy effectively mitigates overfitting and improves generalization. Experiments on ten dynamic graph datasets validate the effectiveness of our proposed method in detecting anomalies. © 2024 IEEE.

关键词： Anomaly detection

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SMEC:Scene Mining for E-Commerce

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Journal of Computer Science & Technology 2024年第1期39卷 192-210页

作者：王罡李翔郭子义殷大伟马帅 State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Data Science and Engineering East China Normal UniversityShanghai 200062China *** Inc.Beijing 100176China Baidu Inc.Beijing 100085China

Scene-based recommendation has proven its usefulness in E-commerce,by recommending commodities based on a given ***,scenes are typically unknown in advance,which necessitates scene discovery for *** this article,we study scene discovery for E-commerce *** first formalize a scene as a set of commodity cate-gories that occur simultaneously and frequently in real-world situations,and model an E-commerce platform as a heteroge-neous information network(HIN),whose nodes and links represent different types of objects and different types of rela-tionships between objects,*** then formulate the scene mining problem for E-commerce as an unsupervised learning problem that finds the overlapping clusters of commodity categories in the *** solve the problem,we pro-pose a non-negative matrix factorization based method SMEC(Scene Mining for E-Commerce),and theoretically prove its *** six real-world E-commerce datasets,we finally conduct an extensive experimental study to evaluate SMEC against 13 other methods,and show that SMEC consistently outperforms its competitors with regard to various evaluation measures.

关键词： graph clustering E-commerce heterogeneous information network(HIN) scene mining

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Dual Prompt-Based Few-Shot Learning for Automated Vulnerability Patch Localization 31

Dual Prompt-Based Few-Shot Learning for Automated Vulnerabil...

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31st IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2024

作者： Zhang, Junwei Hu, Xing Bao, Lingfeng Xia, Xin Li, Shanping Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China Software Engineering Application Technology Lab Huawei China

ISBN: (纸本)9798350330663

Vulnerabilities are disclosed with corresponding patches so that users can remediate them in time. However, there are instances where patches are not released with the disclosed vulnerabilities, causing hidden dangers, especially if dependent software remains uninformed about the affected code repository. Hence, it is crucial to automatically locate security patches for disclosed vulnerabilities among a multitude of commits. Despite the promising performance of existing learning-based localization approaches, they still suffer from the following limitations: (1) They cannot perform well in data scarcity scenarios. Most neural models require extensive datasets to capture the semantic correlations between the vulnerability description and code commits, while the number of disclosed vulnerabilities with patches is limited. (2) They struggle to capture the deep semantic correlations between the vulnerability description and code commits due to inherent differences in semantics and characters between code changes and commit messages. It is difficult to use one model to capture the semantic correlations between vulnerability descriptions and code commits. To mitigate these two limitations, in this paper, we propose a novel security patch localization approach named Prom VPat, which utilizes the dual prompt tuning channel to capture the semantic correlation between vulnerability descriptions and commits, especially in data scarcity (i.e., few-shot) scenarios. We first input the commit message and code changes with the vulnerability description into the prompt generator to generate two new inputs with prompt templates. Then, we adopt a pre-trained language model (i.e., PLM) as the encoder, utilize the prompt tuning method to fine-tune the encoder, and generate two correlation probabilities as the semantic features. In addition, we extract 26 handcrafted features from the vulnerability descriptions and the code commits. Finally, we utilize the attention mechanism to fuse the

关键词： Semantics

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Order Forecasting Method for Large-Scale Personalized Customization Model 16th

Order Forecasting Method for Large-Scale Personalized Custom...

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16th International Conference on Modelling, Identification and Control, ICMIC 2024

作者： Wang, Lijun Wu, Chaohang Chen, Xianzhong Li, Qing Yao, Qijia School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Key Laboratory of Knowledge Automation for Industrial Processes of Science and Technology Beijing Beijing China

ISBN: (纸本)9789819617760

Based on the personalized customization model in bearing steel enterprises, it is studied in this paper how historical data can be fully utilized to predict orders in the face of multi-variety and small-batch order models.. In order to overcome the bottleneck of information exchange that cannot be obtained from pure historical data based on diverse small batch customer needs. A demand fusion mechanism based on local rules is proposed, taking fuzzy customer demand preferences as "individuals" and introducing historical data such as order area and external environmental factors. First, based on the Hegselmann-Krause (HK) model, a personalized customized customer demand preference prediction model is proposed to simulate and predict the evolution process of large-scale customer demand preferences. Secondly, according to the fuzzy rules, the customer's demand preference in the historical data is used as the center point to introduce the fuzzy individual, and the standard deviation is determined based on the order area. Finally, the effectiveness of the proposed method was verified through simulation results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Channel assignment and power allocation for throughput improvement with PPO in B5G heterogeneous edge networks

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Digital Communications and Networks 2024年第1期10卷 109-116页

作者： Xiaoming He Yingchi Mao Yinqiu Liu Ping Ping Yan Hong Han Hu The College of Computer and Information Hohai UniversityNanjingChina The Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai UniversityNanjing China The School of Computer Science and Engineering Nanyang Technological UniversitySingapore The College of Textile and Clothing Engineering Soochow UniversitySuzhouChina The College of Internet of Things Nanjing University of Posts and TelecomurunicationsNanjingChina

In Beyond the Fifth Generation(B5G)heterogeneous edge networks,numerous users are multiplexed on a channel or served on the same frequency resource block,in which case the transmitter applies coding and the receiver uses interference ***,uncoordinated radio resource allocation can reduce system throughput and lead to user inequity,for this reason,in this paper,channel allocation and power allocation problems are formulated to maximize the system sum rate and minimum user achievable *** the construction model is non-convex and the response variables are high-dimensional,a distributed Deep Reinforcement Learning(DRL)framework called distributed Proximal Policy Optimization(PPO)is proposed to allocate or assign ***,several simulated agents are trained in a heterogeneous environment to find robust behaviors that perform well in channel assignment and power ***,agents in the collection stage slow down,which hinders the learning of other ***,a preemption strategy is further proposed in this paper to optimize the distributed PPO,form DP-PPO and successfully mitigate the straggler *** experimental results show that our mechanism named DP-PPO improves the performance over other DRL methods.

关键词： B5G Heterogeneous edge networks PPO Channel assignment Power allocation Throughput

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A Vehicle Arrival Residual Model to Detect Stop-and-go Waves from Low-resolution Traffic data

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第2期9卷 1134-1155页

作者： Liao, Lyuchao Zheng, Qi Li, Zhengrong Lai, Shukun Zhao, Zhaolin Xiong, Rong Fujian Provincial Universities Key Laboratory of Industrial Control and Data Analysis Fujian Provincial Universities Engineering Research Center for Intelligent Driving Technology School of Transportation Fujian University of Technology Fuzhou350118 China School of Electronic and Control Engineering Chang’an University Xi’an710061 China

A traffic stop-and-go wave is a common phenomenon when the traffic state changes from free flow to congestion. A stop-and-go wave can reduce the maximum flow of a road by up to 30%;therefore, how to detect the formation and propagation of stop-and-go waves is essential. Existing studies focus on high-resolution data to detect stop-and-go waves by monitoring the change in traffic state through density and speed. To improve the efficiency of stop-and-go wave detection, we proposed a framework based on abnormal detection by monitoring vehicle counts. Firstly, the Bayesian Dynamic Linear Model (BDLM) is used to extract the typical day. Then, we define a variable called Vehicle arrival residual (VAR) to extract the abnormal situation by vehicle counts. The VAR is obtained by calculating the difference between the parameters of actual days’ vehicle arrival distribution and the typical day’s vehicle arrival distribution. Finally, we use the wavelet transform to get the energy of VAR. Then the energy peak could detect the formation and propagation of stop-and-go waves. The result shows that our framework could detect the stop-and-go wave accurately under low-resolution data. In addition, abnormal detection could extract the change in traffic state by a single traffic flow parameter. Generally, based on vehicle counts, this study provides another way to detect the stop-and-go wave in low-resolution conditions. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Vehicles

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YOLOvis: a yolo style object detection network for drone vision 3

YOLOvis: a yolo style object detection network for drone vis...

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3rd International Conference on Machine Vision, Automatic Identification, and Detection, MVAID 2024

作者： Peng, Yanfei Li, Jincheng Guo, Xinyue School of Electronic and Information Engineering Liaoning University of Technology and Engineering Huludao125105 China Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Huludao125105 China

ISBN: (纸本)9781510681880

In recent years, with the rapid development of drone photography technology, drones have emerged as a new method for photography and observation. We have applied object detection technology to drone photography and proposed a YOLO-style 2D object detection network called YOLOvis based on the YOLOv8 architecture. We introduced the PatchFPN structure to improve the network's robustness in detecting small objects and proposed the TranConcat structure to scale features for enhanced usability. YOLOvis utilizes the challenging VisDrone data set as the model's task data set. With a slight increase in computational cost, the model achieves an accuracy of mAP50 33.7%, which is a 3.7% improvement compared to YOLOv8 of the same size and a 17.0% improvement compared to YOLOv5 of the same size. Additionally, to better tailor the model to specific needs, we have proposed YOLOvis models of different sizes. The code and pretrained models are in https://***/BarryGUN/bgyolo_v8_***. © 2024 SPIE.

关键词： Drones

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A Hybrid Max-Min Ant System Algorithm for Electric Capacitated Vehicle Routing Problem

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2024年第3期51卷 195-203页

作者： Hou, Yan-e Wang, Congran Zhang, Chunyang Dang, Lanxue Xiao, Chunjing Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China College of Computer and Information Engineering Henan University Kaifeng475004 China College of Computer and Information Engineering Henan University Kaifeng475004 China College of Computer and Information Engineering Henan University Kaifeng475004 China School of Computer and Information Engineering Henan University Kaifeng475004 China

Electric vehicles have become the first choice for products delivery in logistics, due to their environmental friendliness and low cost compared to the traditional fuel vehicles. This paper deals with an electric capacitated vehicle routing problem with variable energy consumption rate (E-CVRP), which aims to minimize the total cost including vehicle fixed cost, traveled distance cost and energy cost. First, a mixed integer linear programming model is constructed based on the fact that the energy consumption is affected by the vehicle load. Second, a hybrid Max-Min Ant System (HMMAS) algorithm is proposed to solve this problem. Three neighborhood structures are introduced and executed in a Variable Neighborhood Descent (VND) algorithm to improve the current solution. In addition, an effective charging station adjustment strategy is adopted to further improve the solution obtained by the local search process. Finally, some experiments have been conducted on public instances, and the results demonstrate the effectiveness and stability of the algorithm. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Energy utilization

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P-TECS: An Energy Balance Algorithm for Opportunistic Networks Integrating Multiple Node Attributes 5th

P-TECS: An Energy Balance Algorithm for Opportunistic Netwo...

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5th International Conference on Security and Privacy in New Computing Environments, SPNCE 2022

作者： Xu, Gang Yang, Xiaoying Chen, Jingjian Huang, Baoqi College of Computer Science Inner Mongolia University Hohhot010021 China Inner Mongolia A.R. Key Laboratory of Data Mining and Knowledge Engineering Inner Mongolia University Hohhot010021 China

ISBN: (纸本)9783031306228

This paper proposes an energy balance opportunistic networks routing algorithm P-TECS. The P-TECS solves the problems of energy consumption of key nodes in existing opportunistic routing algorithms. This paper defines the relay degree of communication between nodes, and designs a P-TECS opportunistic routing algorithm integrating dynamic attributes of multiple nodes. Simulation results show that in resource-constrained opportunistic networks, P-TECS significantly improves the message delivery rate and average remaining energy of nodes, and significantly reduces the routing overhead rate. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Routing algorithms

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Phase equilibria in Co-Ti-Re ternary system

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Rare Metals 2023年第8期42卷 2736-2746页

作者： Ling-Ling Li Cui-Ping Wang Shui-Yuan Yang Jia-Jia Han Yong Lu Jin-Bin Zhang Xing-Jun Liu College of Materials and Fujian Provincial Key Laboratory of Materials Genome Xiamen UniversityXiamen 361005China College of Materials Science and Engineering Guangdong Ocean UniversityYangjiang 529500China Xiamen Key Laboratory of High-Performance Metals and Materials Xiamen UniversityXiamen 361005China School of Materials Science and Engineering Institute of Materials Genome and Big DataHarbin Institute of TechnologyShenzhen 518055China

The phase equilibria in the Co-Ti-Re ternary system at 800,1000,1100 and 12000C were studied combined with electron probe microanalyzer(EPMA),X-ray diffraction(XRD),scanning electron microscope(SEM)and transmission electron microscope(TEM).The new phaseτwith a large solubility was discovered at all four isothermal sections,and its structure still needs a further study.A large solubility of Re in the CoTi phase was *** solubility of Re in the Co_(3)Ti phase is6.1%at 800℃,8.7%at 1000℃and 7.7%at 1100℃.The present work could be significant for alloy design of Co-based alloys and thermodynamic analysis of the Co-TiRe ternary system.

关键词： Isothermal section Phase equilibria Co-Ti-Re Co-based supperalloys

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