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IEEE International Conference on Data Mining (ICDM)

作者： Hang Zhou Wentao Yu Sheng Wan Yongxin Tong Tianlong Gu Chen Gong School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China SKLSDE Beihang University China Engineering Research Center of Trustworthy AI (Ministry of Education) Jinan University China

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Accurate Traffic Flow Prediction (TFP) is crucial for enhancing the efficiency and safety of transportation systems, so it has attracted intensive researches by exploiting spatial-temporal dependencies within road networks. However, existing works only consider the case of centralized data collection with all traffic data observed, which may raise privacy concerns as each region of a city may have its own traffic administration department and the traffic data is not allowed to distribute. Therefore, this paper proposes to use Federated Learning (FL) to address this issue by allowing all clients (i.e., traffic administration departments in all regions in our problem) to collaboratively train TFP models without exchanging raw data, thereby offering a solution in maintaining data privacy. Nevertheless, most existing FL methods aim to learn a global model that performs well universally, so they cannot well handle the non-Independent and Identically Distributed (non-IID) traffic data naturally over different regions. To cope with this problem, this paper develops a new FL framework termed “personalized Federated learning with Traffic Pattern Sharing” (FedTPS) to solve federated TFP problem. Our FedTPS critically exploits the underlying common traffic patterns (e.g., morning and evening rush hours) shared across different city regions and meanwhile maintaining the region-specific data characteristics in a personalized FL manner. Specifically, to extract the common traffic patterns, we decompose the traffic data in each client via using discrete wavelet transform, where the low-frequency components uncover the stable traffic dynamics of different regions and thus can be considered as the common traffic patterns. These common patterns are then shared among different clients through traffic pattern repositories on the server side to aid the global collaborative traffic flow modeling. Moreover, the model components capturing spatial-temporal dependencies in traffic data are r

关键词： Training Data privacy Federated learning Urban areas Transportation Transforms Traffic control Data models Safety Servers

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Federated Distillation: A Survey

arXiv

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arXiv 2024年

作者： Li, Lin Gou, Jianping Yu, Baosheng Du, Lan Yi, Zhang Tao, Dacheng The School of Computer Science and Communication Engineering Jiangsu Key Laboratory of Security Tech. for Industrial Cyberspace Jiangsu University Zhenjiang212013 China The College of Computer and Information Science College of Software Southwest University Chongqing400715 China The School of Computer Science Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia The Faculty of Information Technology Monash University ClaytonVIC Australia The School of Computer Science Sichuan University Chengdu610065 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

Federated Learning (FL) seeks to train a model collaboratively without sharing private training data from individual clients. Despite its promise, FL encounters challenges such as high communication costs for large-scale models and the necessity for uniform model architectures across all clients and the server. These challenges severely restrict the practical applications of FL. To address these limitations, the integration of knowledge distillation (KD) into FL has been proposed, forming what is known as Federated Distillation (FD). FD enables more flexible knowledge transfer between clients and the server, surpassing the mere sharing of model parameters. By eliminating the need for identical model architectures across clients and the server, FD mitigates the communication costs associated with training large-scale models. This paper aims to offer a comprehensive overview of FD, highlighting its latest advancements. It delves into the fundamental principles underlying the design of FD frameworks, delineates FD approaches for tackling various challenges, and provides insights into the diverse applications of FD across different scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

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Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles

arXiv

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arXiv 2025年

作者： You, Jiahao Jia, Ziye Dong, Chao Wu, Qihui Han, Zhu The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing211106 China National Mobile Communications Research Laboratory Southeast University Jiangsu Nanjing211111 China Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

The increasing deployment of unmanned surface vehicles (USVs) require computational support and coverage in applications such as maritime search and rescue. Unmanned aerial vehicles (UAVs) can offer low-cost, flexible aerial services, and ground stations (GSs) can provide powerful supports, which can cooperate to help the USVs in complex scenarios. However, the collaboration between UAVs and GSs for USVs faces challenges of task uncertainties, USVs trajectory uncertainties, heterogeneities, and limited computational resources. To address these issues, we propose a cooperative UAV and GS based robust multi-access edge computing framework to assist USVs in completing computational tasks. Specifically, we formulate the optimization problem of joint task offloading and UAV trajectory to minimize the total execution time, which is in the form of mixed integer nonlinear programming and NP-hard to tackle. Therefore, we propose the algorithm of generative artificial intelligence-enhanced heterogeneous agent proximal policy optimization (GAI-HAPPO). The proposed algorithm integrates GAI models to enhance the actor network ability to model complex environments and extract high-level features, thereby allowing the algorithm to predict uncertainties and adapt to dynamic conditions. Additionally, GAI stabilizes the critic network, addressing the instability of multi-agent reinforcement learning approaches. Finally, extensive simulations demonstrate that the proposed algorithm outperforms the existing benchmark methods, thus highlighting the potentials in tackling intricate, cross-domain issues in the considered scenarios. © 2025, CC BY.

关键词： Unmanned surface vehicles

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Research on Self-Attention Image Description technology Based on Object Detection

Research on Self-Attention Image Description Technology Base...

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International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)

作者： Kun Ma Tao Xu Yingying Zhang Baocheng Sha Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University School of Computer Science Jiangsu University of Science and Technology Zhenjiang China

Image description is transform the image into a text description that tells about the image, but the characters in the image and their attributes and relationships will affect the one-to-one correspondence between the text description and the image. In response to the above problems, we design an encode-decoder structure based on a self-aware target detector to extract distinct feature and role information, and design a role integrity check module to supplement the description sentences with rich object information, which improves the accuracy of image description for role in the image. We have conducted many experiments on Flickr8k and Flickr30k and on the MS COCO dataset, this method can make the generated image description and images have better semantic consistency, and compared with traditional deep learning methods and the semantic diversity has a good effect.

关键词： Deep learning Semantics Transforms Object detection Detectors Feature extraction Manufacturing

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Identity Based Encryption and Biometric Authentication Scheme for Secure Data Access in Cloud Computing

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Chinese Journal of Electronics 2023年第2期21卷 254-259页

作者： Hongbing Cheng Chunming Rong Zhenghua Tan Qingkai Zeng State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Information Engineering Jiangsu City Vocation College Nanjing China Department of Electronic Engineering and Computer Science University of Stavanger Stavanger Norway Department of Electronic Systems Aalborg University Aalborg Denmark

Cloud computing will be a main information infrastructure in the future; it consists of many large datacenters which are usually geographically distributed and heterogeneous. How to design a secure data access for cloud computing platform is a big challenge. In this paper, we propose a secure data access scheme based on identity-based encryption and biometric authentication for cloud computing. Firstly, we describe the security concern of cloud computing and then propose an integrated data access scheme for cloud computing, the procedure of the proposed scheme include parameter setup, key distribution, feature template creation, cloud data processing and secure data access control. Finally, we compare the proposed scheme with other schemes through comprehensive analysis and simulation. The results show that the proposed data access scheme is feasible and secure for cloud computing.

关键词： Access control Cloud computing Identity-based encryption Biometrics (access control) Authentication process control Memory

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Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

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computer Systems Science & Engineering 2021年第12期39卷 373-390页

作者： Jiaming Mao Mingming Zhang Mu Chen Lu Chen Fei Xia Lei Fan ZiXuan Wang Wenbing Zhao State Grid Jiangsu Electric Power Co. Ltd.Information and Telecommunication BranchNanJing210003China Institute of Information and Communication Global Energy Interconnection Research InstituteState Grid Key Laboratory of Information and Network SecurityNanjing210003China Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Research and Development Center of Post Industry Technology of the State Posts Bureau(Internet of Things Technology)Broadband Wireless Communication Technology Engineering Research Center of the Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing210003China Department of Electrical Engineering and Computer Science Cleveland State UniversityCleveland44115USA

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased net-work traffic *** the past few decades,network traffic identification has been a research hotspot in the field of network management and security ***,as more network services use encryption technology,network traffic identification faces many *** classic machine learning methods can solve many problems that cannot be solved by port-and payload-based methods,manually extract features that are frequently updated is time-consuming and *** learning has good automatic feature learning capabilities and is an ideal method for network traffic identification,particularly encrypted traffic identification;Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled ***,in real scenarios,labeled samples are often difficult to *** paper adjusts the structure of the auxiliary classification generation adversarial network(ACGAN)so that it can use unlabeled samples for training,and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised *** results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network(CNN)based classifier.

关键词： Encrypted traffic recognition deep learning generative adversarial network traffic classification semisupervised learning

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Outlier Detection for Sensor Data Streams Based on Maximum Frequent and Minimum Rare Patterns 15th

Outlier Detection for Sensor Data Streams Based on Maximum F...

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15th CCF Conference on computer Supported Cooperative Work and Social Computing, Chinese CSCW 2020

作者： Shi, Xiaochen Cai, Saihua Sun, Ruizhi College of Information and Electrical Engineering China Agricultural University Beijing100083 China School of Computer Science and Communication Engineering Jiangsu University Zhenjiang212013 China Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace Jiangsu University Zhenjiang Jiangsu212013 China The Ministry of Agriculture Beijing100083 China

ISBN: (纸本)9789811625398

Identifying outliers in data is an essential assignment in data mining. It aims to find the data that is significantly different from other data in the data streams, and scholars have proposed many outlier detection methods in recent years. Because pattern-based outlier recognition technology clearly illustrates the causes of anomalies, they have received extensive attention. However, the existing pattern-based outlier identification techniques main use frequent patterns or rare patterns with perform the outlier identification operations, which will affect the precision from outlier identification. To improve the exactness of outlier detection on data streams, in this paper, the maximum frequent patterns and minimum rare patterns are combined together and regarded as a whole as the pattern used in the process of outlier detection, and then it redefines the deviation index, so as to perfectly measure the abnormal degrees of outlier in the data streams. Dependent upon those search patterns and characterized deviation index, Novel maximum frequent and minimum rare pattern-based outlier detection method are put forward, that is, specific MFaMRP-OD, which can faultlessly recognize those possibility outliers. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Anomaly detection

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Multi-attribute Graph Based Mixed Integer Linear Programming Model for a Job Shop Scheduling Problem Considering Intelligent Vehicle Transport

Multi-attribute Graph Based Mixed Integer Linear Programming...

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Machine Learning, Control, and Robotics (MLCR), International Conference on

作者： Hui Zhang Min Huang School of Economics and Management Jiangsu University of Science and Technology Zhenjiang China Stat Key Laboratory of Synthetical Automation for Process Industries College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9781665454605

Nowadays, intelligent vehicles have be used for transporting jobs in many manufacturing plants; thus, routing vehicles properly is very important for processing jobs. This paper studied a job shop scheduling problem considering intelligent vehicle transport. The problem is difficult to be handled because it needs to optimize the route of a vehicle and the processing sequences of jobs on machines simultaneously. To address this problem, a multi-attribute directed graph is proposed, in which a determined vehicle path associated with the attributes of job and machine is defined as a node and an undetermined vehicle path is defined as an arc; thereby, a mixed integer linear programming model is established. Numerical experiments show the effectiveness of the proposed model.

关键词： Job shop scheduling Dairy products Intelligent vehicles Computational modeling Machine learning Routing Numerical models

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Time-Aware Missing Traffic Flow Prediction for Sensors with Privacy-Preservation 11th

Time-Aware Missing Traffic Flow Prediction for Sensors with ...

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11th International Conference on computer Engineering and Networks, CENet2021

作者： Qi, Lianyong Wang, Fan Xu, Xiaolong Dou, Wanchun Zhang, Xuyun Khosravi, Mohammad R. Zhou, Xiaokang School of Computer Science Qufu Normal University Qufu China School of Computer and Software Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China Department of Computing Macquarie University SydneyNSW2109 Australia Department of Computer Engineering Persian Gulf University Bushehr Iran Department of Electrical and Electronic Engineering Shiraz University of Technology Shiraz Iran Faculty of Data Science Shiga University Hikone Japan

ISBN: (纸本)9789811665530

With the continuous development of IoT, a number of sensors establish on the roadside to monitor traffic conditions in real time. The continuously traffic data generated by these sensors makes traffic management feasible. However, loss of data may occur due to inevitable sensor failure, impeding traffic managers to understand traffic dynamics clearly. In this situation, it is becoming a necessity to predict missing traffic flow accurately for effective traffic management. Furthermore, the traffic sensor data are often distributed and stored by different agencies, which inhibits the multi-party sensor data sharing significantly due to privacy concerns. Therefore, it has become a major obstacle to balance the tradeoff between data sharing and vehicle privacy. In light of these challenges, we propose a privacy-aware and accurate missing traffic flow prediction approach based on time-aware Locality-Sensitive Hashing technique. At last, we deploy a set of experiments based on a real traffic dataset. Experimental reports demonstrate the feasibility of our proposal in terms of traffic flow prediction accuracy and efficiency while guaranteeing sensor data privacy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data privacy

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Research on Vegetable Commodity Pricing and Replenishment Strategy Based on Simulated Annealing Algorithm

Research on Vegetable Commodity Pricing and Replenishment St...

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Signal processing, Communications and Engineering Management Conference (ISPCEM), International

作者： Jinfeng Shi Rui Zhang Siqi Feng Qiang Zhang School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Systems Science in Metallurgical Process College of Science Wuhan University of Science and Technology Wuhan University of Science and Technology Wuhan China

In fresh food superstores, the freshness period of most vegetable products is relatively short, and the quality of vegetables will deteriorate due to the increase in storage time and cannot be sold on the next day, so it is very important to determine the total daily replenishment for superstores. This paper discusses the optimization problem of daily replenishment total and pricing decisions. In this paper, firstly, the bivariate correlation coefficient analysis is carried out using SPSS to calculate the correlation coefficients between the total sales volume of each category and the unit price of sales; secondly, the regression model is established to derive the fitting equations and the R2 the test is carried out. For the superstore revenue optimization problem, firstly, the total daily replenishment strategy model of each vegetable category was established, and secondly, the pricing strategy model of each vegetable category was established through the wholesale price and sales unit price. Considering the transportation loss and deterioration of vegetables, the goods will be sold at a discount, this paper establishes the pricing strategy model of undiscounted and discounted sales. Finally, the optimal model is obtained based on the simulated annealing algorithm to ensure revenue maximization. The final maximum returns (RMB) from July 1–7, 2023 are 8091.56, 8102.48, 8097.47, 8114.66, 8166.51, 8115.96, and 8126.11, respectively.

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