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3rd International Conference on Smart Manufacturing, Industrial and Logistics Engineering, SMILE 2023 and the 7th Asian Conference of Management science and Applications, ACMSA 2023

作者： Wang, Hao Wang, Zongshui Beijing Information Science and Technology University Beijing China Beijing Key Laboratory of Green Development and Decision Making Based on Big Data Beijing China

ISBN: (纸本)9789819701933

The existing methods of operating express/local mode require additional railway lines. However, renovation projects of existing lines are often limited by engineering technology and funding. Meanwhile, tidal passenger flow in rush hour is obvious. To sum up, this paper proposes a method of operating express/local mode under existing railway lines and unidirectional tidal passenger flow. This method sets that all trains in the same direction of unidirectional tidal passenger flow are express trains, and all trains in the opposite direction of unidirectional tidal passenger flow are local trains. After simulation, this method has been proven to effectively reduce total passenger travel time in specific scenarios, providing references for subsequent research and engineering applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Travel time

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Cooperative Classification and Rationalization for Graph Generalization 24

Cooperative Classification and Rationalization for Graph Gen...

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33rd ACM Web Conference, WWW 2024

作者： Yue, Linan Liu, Qi Liu, Ye Gao, Weibo Yao, Fangzhou Li, Wenfeng Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence University of Science and Technology of China Hefei China Douyin Vision Co. Ltd Beijing China

ISBN: (纸本)9798400701719

Graph Neural Networks (GNNs) have achieved impressive results in graph classification tasks, but they struggle to generalize effectively when faced with out-of-distribution (OOD) data. Several approaches have been proposed to address this problem. Among them, one solution is to diversify training distributions in vanilla classification by modifying the data environment, yet accessing the environment information is complex. Besides, another promising approach involves rationalization, extracting invariant rationales for predictions. However, extracting rationales is difficult due to limited learning signals, resulting in less accurate rationales and diminished predictions. To address these challenges, in this paper, we propose a Cooperative Classification and Rationalization (C2R) method, consisting of theclassification and therationalization module. Specifically, we first assume that multiple environments are available in theclassification module. Then, we introduce diverse training distributions using an environment-conditional generative network, enabling robust graph representations. Meanwhile, therationalization module employs a separator to identify relevant rationale subgraphs while the remaining non-rationale subgraphs are de-correlated with labels. Next, we align graph representations from theclassification module with rationale subgraph representations using the knowledge distillation methods, enhancing the learning signal for rationales. Finally, we infer multiple environments by gathering non-rationale representations and incorporate them into theclassification module for cooperative learning. Extensive experimental results on both benchmarks and synthetic datasets demonstrate the effectiveness of C2R. Code is available at https://***/yuelinan/Codes-of-C2R. © 2024 ACM.

关键词： Classification (of information)

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Representation learning via an integrated autoencoder for unsupervised domain adaptation

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Frontiers of Computer science 2023年第5期17卷 75-87页

作者： Yi ZHU Xindong WU Jipeng QIANG Yunhao YUAN Yun LI School of Information Engineering Yangzhou UniversityYangzhou 225127China Key Laboratory of Knowledge Engineering with Big Data(Ministry of Education of China) Hefei University of TechnologyHefei 230009China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230601China

The purpose of unsupervised domain adaptation is to use the knowledge of the source domain whose data distribution is different from that of the target domain for promoting the learning task in the target *** key bottleneck in unsupervised domain adaptation is how to obtain higher-level and more abstract feature representations between source and target domains which can bridge the chasm of domain ***,deep learning methods based on autoencoder have achieved sound performance in representation learning,and many dual or serial autoencoderbased methods take different characteristics of data into consideration for improving the effectiveness of unsupervised domain ***,most existing methods of autoencoders just serially connect the features generated by different autoencoders,which pose challenges for the discriminative representation learning and fail to find the real cross-domain *** address this problem,we propose a novel representation learning method based on an integrated autoencoders for unsupervised domain adaptation,called *** capture the inter-and inner-domain features of the raw data,two different autoencoders,which are the marginalized autoencoder with maximum mean discrepancy(mAE)and convolutional autoencoder(CAE)respectively,are proposed to learn different feature *** higher-level features are obtained by these two different autoencoders,a sparse autoencoder is introduced to compact these inter-and inner-domain *** addition,a whitening layer is embedded for features processed before the mAE to reduce redundant features inside a local *** results demonstrate the effectiveness of our proposed method compared with several state-of-the-art baseline methods.

关键词： unsupervised domain adaptation representation learning marginalized autoencoder convolutional autoen-coder sparse autoencoder

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Multicomponent Similarity Graphs for Cross-Network Node Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1411-1424页

作者： Zhang, Yuhong Shi, Congmei Li, Xinzheng Zhang, Zan Hu, Xuegang School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei230009 China

Cross-network node classification aims to train a classifier for an unlabeled target network using a source network with rich labels. In applications, the degree of nodes mostly conforms to the long-tail distribution, i.e., most nodes in the network are tail nodes with sparse neighborhoods. The established methods focus on either the discrepancy cross network or the long tail in a single network. As for the cross-network node classification under long tail, the coexistence of sparsity of tail nodes and the discrepancy cross-network challenges existing methods for long tail or methods for the cross-network node classification. To this end, a multicomponent similarity graphs for cross-network node classification (MS-CNC) is proposed in this article. Specifically, in order to address the sparsity of the tail nodes, multiple component similarity graphs, including attribute and structure similarity graphs, are constructed for each network to enrich the neighborhoods of the tail nodes and alleviate the long-tail phenomenon. Then, multiple representations are learned from the multiple similarity graphs separately. Based on the multicomponent representations, a two-level adversarial model is designed to address the distribution difference across networks. One level is used to learn the invariant representations cross network in view of structure and attribute components separately, and the other level is used to learn the invariant representations in view of the fused structure and attribute graphs. Extensive experimental results show that the MS-CNC outperforms the state-of-the-art methods. Impact Statement-Node classification is an important task in graph mining. With the unavailability of labels, some researchers propose cross-network node classification, using one labeled network to assist the node classification of another unlabeled network. However, the long-tail of nodes leads to unsatisfactory performance and challenges the recent cross-network node classification m

关键词： Graphic methods

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A DL Intrusion Detection Method for Low-Rate Denial of Service Attacks

A DL Intrusion Detection Method for Low-Rate Denial of Servi...

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2023 Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2023

作者： Wu, Yuhong Lv, Yunshan Hu, Xiangdong School of Big Data & Computer Science Engineering Chongqing College of Mobile Communication China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Key Laboratory of Public Big Data Security Technology Chongqing Chongqing China School of Big Data & Computer Science Engineering Chongqing College of Mobile Communication Key Laboratory of Public Big Data Security Technology Chongqing China School of Automation Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781665457033

Denial of service (DoS) attacks is among the most harmful network threats. Low-rate DoS (LDoS) attacks feature good concealment and are likely to evade detection by conventional signal analysis-based methods. A LDoS attack detection method that combines the convolutional neural network and the gated recurrent unit is proposed in the present work. To evaluate the proposed method, we collect real user traffic from a university's website, label the traffic with LDoS attacks as normal traffic. In order to get the real attack traffic, this paper has collected some new LDoS attack programs, such as slowloris, slowhttptest, pwnlloris, torhammer and httplog, which can be obtained from public channels, and constructed a LAN environment that conformed to the working scenario of the program. By running the attack program, the attack traffic could be obtained. In a large sample scenario, the average detection rate of our proposed method can reach 98.68%. This article focuses on the research: the average detection rate of the detection method proposed in this article in the case that the security agency can only intercept a small number of attack samples. That is, in the small sample scenario, the detection of the collected traffic shows that the average detection rate of the proposed detection method can reach 89.54% in a small sample scenario. Its effective detection rate is obviously superior to the existing similar detection methods. © 2023 IEEE.

关键词： Denial-of-service attack

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Lethal pulmonary thromboembolism in mice induced by intravenous human umbilical cord mesenchymal stem cell-derived large extracellular vesicles in a dose-and tissue factor-dependent manner

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中国药理学报（英文版） 2024年第11期45卷 2300-2312页

作者： Bian-lei Yang Yao-ying Long Qian Lei Fei Gao Wen-xiang Ren Yu-lin Cao Di Wu Liu-yue Xu Jiao Qu He Li Ya-li Yu An-yuan Zhang Shan Wang Hong-xiang Wang Zhi-chao Chen Qiu-bai Li Department of Rheumatology and Immunology Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430022China Department of Hematology Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430022China West China Biomedical Big Data Center West China HospitalSichuan UniversityChengdu 610041China Department of Hematology Key Laboratory for Molecular Diagnosis of Hubei ProvinceThe Central Hospital of WuhanTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430014China Department of Rheumatology and Immunology Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430022China Hubei Engineering Research Center for Application of Extracellular Vesicles Hubei University of Science and TechnologyXianning 437100China

Mesenchymal stem cell-derived extracellular vesicles(MSC-EVs)have obvious advantages over MSC *** the strong procoagulant properties of MSC-EVs pose a potential risk of thromboembolism,an issue that remains insufficiently *** this study,we systematically investigated the procoagulant activity of large EVs derived from human umbilical cord MSCs(UC-EVs)both in vitro and in ***-EVs were isolated from cell culture *** were injected with UC-EVs(0.125,0.25,0.5,1,2,4 μg/g body weight)in 100μL PBS via the tail *** and mortality were monitored for 30 min after *** showed that these UC-EVs activated coagulation in a dose-and tissue factor-dependent ***-EVs-induced coagulation in vitro could be inhibited by addition of tissue factor pathway ***,intravenous administration of high doses of the UC-EVs(1 μg/g body weight or higher)led to rapid mortality due to multiple thrombus formations in lung tissue,platelets,and fibrinogen depletion,and prolonged prothrombin and activated partial thromboplastin ***,we demonstrated that pulmonary thromboembolism induced by the UC-EVs could be prevented by either reducing the infusion rate or by pre-injection of heparin,a known *** conclusion,this study elucidates the procoagulant characteristics and mechanisms of large UC-EVs,details the associated coagulation risk during intravenous delivery,sets a safe upper limit for intravenous dose,and offers effective strategies to prevent such mortal risks when high doses of large UC-EVs are needed for optimal therapeutic effects,with implications for the development and application of large UC-EV-based as well as other MSC-EV-based therapies.

关键词： mesenchymal stem cells extracellular vesicles tissue factor coagulation pulmonary thromboembolism heparin

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Legal Risks and Governance Paths for Generative AI - A Case Study of ChatGPT 13

Legal Risks and Governance Paths for Generative AI - A Case ...

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13th International Conference on Information technology in Medicine and Education, ITME 2023

作者： Zhenfeng, Wang Quanwang, Lin Huihui, Gao Xiehua, Yu University of Fujian Province Key Laboratory of Big Data and Artificial Intelligence China MinNan Science and Technology University Fujian China School of Computer and Information MinNan Science and Technology University Quanzhou China University of Fujian Key Laboratory of Big Data and Artificial Intelligence Quanzhou China

ISBN: (纸本)9798350319156

The release of OpenAI's ChatGPT chatbot has sparked widespread concern around the world about the threats and legal issues of generative AI. Although this technological innovation has opened a new chapter in the field of artificial intelligence, it has also caused the public to worry about its possible negative effects. In order to solve the above problems and reasonably regulate the legal risks of generative artificial intelligence, this paper mainly adopts the literature, quantitative and qualitative analysis methods, etc. The first part mainly introduces the development process and current situation of generative artificial intelligence, the second part mainly discusses the impact of generative artificial intelligence on tort law, the third part mainly discusses the legal risks of generative artificial intelligence, and the fourth part mainly discusses the governance path of generative artificial intelligence legal risks. The last part is the conclusion of the research. Among them, the fourth part is the focus of this paper. By taking these issues into account, we can provide legal guarantees for the sustainable development of the generative AI industry and ensure that the widespread application of AI benefits human society[1]. © 2023 IEEE.

关键词： Artificial intelligence

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Construction and Analysis of Scientific Research Knowledge Graph in the Field of Hydrogen Energy technology 4th

Construction and Analysis of Scientific Research Knowledge G...

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4th International Conference on Artificial Intelligence in China, AIC 2022

作者： Zhang, Min Yang, Rui Xu, Jingling Wuhan Library of Chinese Academy of Sciences Wuhan430071 China Hubei Key Laboratory of Big Data in Science and Technology Wuhan Library of Chinese Academy of Sciences Wuhan430071 China

ISBN: (纸本)9789819912551

In order to effectively reveal the various scientific research entities and semantic relationships among them in the field of hydrogen energy technology, the top-down construction method was used to explore the construction process of the scientific research knowledge graph in the field of hydrogen energy technology. Ontology was used to construct the schema layer of the knowledge graph, after knowledge extraction and knowledge fusion in the data layer, the knowledge was stored in the Neo4j graph database. The knowledge graph constructed included 345300 entities of 8 types and 2167484 entity relationships of 12 types. Through the construction of the knowledge graph, the complex knowledge system visual analysis of various scientific research entities and their relationships can be effectively realized, it can provide support for researchers to grasp the whole research situation in the field of hydrogen energy technology. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Knowledge graph

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Electricity Carbon Quota Trading Scheme based on Certificateless Signature and Blockchain

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Computer Modeling in Engineering & sciences 2024年第2期138卷 1695-1712页

作者： Xiaodong Yang Runze Diao Tao Liu Haoqi Wen Caifen Wang College of Computer Science and Engineering Northwest Normal UniversityLanzhou730070China Institute of China TelecomWanwei China TelecomWanwei Information Technology Co.Ltd.Lanzhou730030China College of Big Data and Internet Shenzhen Technology UniversityShenzhen518118China Key Laboratory of Cryptography and Data Analytics Northwest Normal UniversityLanzhou730070China

The carbon tradingmarket can promote“carbon peaking”and“carbon neutrality”at low cost,but carbon emission quotas face attacks such as data forgery,tampering,counterfeiting,and replay in the electricity trading *** signatures are a new cryptographic technology that can address traditional cryptography’s general essential certificate requirements and avoid the problem of crucial escrowbased on identity ***,most certificateless signatures still suffer fromvarious security *** present a secure and efficient certificateless signing scheme by examining the security of existing certificateless signature *** ensure the integrity and verifiability of electricity carbon quota trading,we propose an electricity carbon quota trading scheme based on a certificateless signature and *** scheme utilizes certificateless signatures to ensure the validity and nonrepudiation of transactions and adopts blockchain technology to achieve immutability and traceability in electricity carbon quota *** addition,validating electricity carbon quota transactions does not require time-consuming bilinear pairing *** results of the analysis indicate that our scheme meets existential unforgeability under adaptive selective message attacks,offers conditional identity privacy protection,resists replay attacks,and demonstrates high computing and communication performance.

关键词： Electricity carbon trading certificateless signature blockchain forgery attack carbon quota

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An Efficient Pairing-Free Certificateless Signcryption Scheme Under the Standard Model for VANET

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IEEE Internet of Things Journal 2025年第12期12卷 22142-22154页

作者： Liu, Xianxiang Zhou, Yanwei Yang, Bo Zhu, Tianqing Zhang, Mingwu Shaanxi Normal University School of Computer Science Xi'an710119 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China City University of Macau Faculty of Data Science Macau999078 China Hubei University of Technology School of Computer Wuhan430068 China

With the rapid advancement of the Internet of Things (IoT), its applications are becoming increasingly essential in actual application. Specifically, the recent surge in electric vehicles has spurred significant advancements in vehicle ad hoc networks (VANET). Therefore, efficient data transmission is a critical challenge in IoT, especially VANET. The certificateless signcryption (CLS) scheme has high authentication efficiency, which has become increasingly important in IoT. However, most existing schemes rely on bilinear pairing, resulting in high calculation costs or failure to achieve their claimed security. Furthermore, the security of some CLS schemes is proved in the random oracle, which limits the usefulness of the CLS scheme. To further address these issues, we propose an efficient and secure CLS scheme in the standard model for VANET, which enhances its practical applicability in VANET. Our construction employs formal security proofs and cost simulations to ensure the scheme's security and low calculation costs, making it suitable for VANET. Finally, the security and efficiency analyses prove that our scheme offers enhanced security features and superior performance compared to existing schemes. © 2014 IEEE.

关键词： Cryptography

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