检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Sun, Xiaobing Zhang, Jiabao Zhao, Qiming Liu, Shenghua Chen, Jinglei Zhuang, Ruoyu Shen, Huawei Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Insititute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences Beijing China China Construction Bank Fintech China

Money laundering (ML) is the behavior to conceal the source of money achieved by illegitimate activities, and always be a fast process involving frequent and chained transactions. How can we detect ML and fraudulent activity in large scale attributed transaction data (i.e. tensors)? Most existing methods detect dense blocks in a graph or a tensor, which do not consider the fact that money are frequently transferred through middle accounts. CubeFlow proposed in this paper is a scalable, flow-based approach to spot fraud from a mass of transactions by modeling them as two coupled tensors and applying a novel multi-attribute metric which can reveal the transfer chains accurately. Extensive experiments show CubeFlow outperforms state-of-the-art baselines in ML behavior detection in both synthetic and real data. Copyright © 2021, The Authors. All rights reserved.

关键词： Tensors

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OpenKN: An open knowledge computational engine for network big data

OpenKN: An open knowledge computational engine for network b...

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International Conference on Advances in Social network Analysis and Mining, ASONAM

作者： Yantao Jia Yuanzhuo Wang Xueqi Cheng Xiaolong Jin Jiafeng Guo CAS Key Laboratory of Network Data Science and Technology Chinese Academy of Sciences Beijing P. R. China

With the coming of the era of big data, it is most urgent to establish the knowledge computational engine for the purpose of discovering implicit and valuable knowledge from the huge, rapidly dynamic, and complex network data. In this paper, we first survey the mainstream knowledge computational engines from four aspects and point out their deficiency. To cover these shortages, we propose the open knowledge network (OpenKN), which is a self-adaptive and evolutionable knowledge computational engine for network big data. To the best of our knowledge, this is the first work of designing the end-to-end and holistic knowledge processing pipeline in regard with the network big data. Moreover, to capture the evolutionable computing capability of OpenKN, we present the evolutionable knowledge network for knowledge representation. A case study demonstrates the effectiveness of the evolutionable computing of OpenKN.

关键词： Knowledge based systems Knowledge engineering Big data Engines Syntactics Semantics data mining

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Conditional GANs with Auxiliary Discriminative Classifier

arXiv

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arXiv 2021年

作者： Hou, Liang Cao, Qi Shen, Huawei Pan, Siyuan Li, Xiaoshuang Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Shanghai Jiao Tong University Shanghai China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

Conditional generative models aim to learn the underlying joint distribution of data and labels to achieve conditional data generation. Among them, the auxiliary classifier generative adversarial network (AC-GAN) has been widely used, but suffers from the problem of low intra-class diversity of the generated samples. The fundamental reason pointed out in this paper is that the classifier of AC-GAN is generator-agnostic, which therefore cannot provide informative guidance for the generator to approach the joint distribution, resulting in a minimization of the conditional entropy that decreases the intra-class diversity. Motivated by this understanding, we propose a novel conditional GAN with an auxiliary discriminative classifier (ADC-GAN) to resolve the above problem. Specifically, the proposed auxiliary discriminative classifier becomes generator-aware by recognizing the class-labels of the real data and the generated data discriminatively. Our theoretical analysis reveals that the generator can faithfully learn the joint distribution even without the original discriminator, making the proposed ADC-GAN robust to the value of the coefficient hyperparameter and the selection of the GAN loss, and stable during training. Extensive experimental results on synthetic and real-world datasets demonstrate the superiority of ADC-GAN in conditional generative modeling compared to state-of-the-art classifier-based and projection-based conditional GANs. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Ε-weakened Robustness of deep neural networks

arXiv

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arXiv 2021年

作者： Huang, Pei Jia, Fuqi Yang, Yuting Ma, Feifei Liu, Minghao Zhang, Jian State Key Laboratory of Computer Science Institution of Software CAS Beijing China Key Laboratory of Intelligent Information Institute of Computing Technology CAS Beijing China

This paper introduces a notation of Ε-weakened robustness for analyzing the reliability and stability of deep neural networks (DNNs). Unlike the conventional robustness, which focuses on the "perfect" safe region in the absence of adversarial examples, Ε-weakened robustness focuses on the region where the proportion of adversarial examples is bounded by user-specified Ε. Smaller Ε means a smaller chance of failure. Under such robustness definition, we can give conclusive results for the regions where conventional robustness ignores. We prove that the Ε-weakened robustness decision problem is PP-complete and give a statistical decision algorithm with user-controllable error bound. Furthermore, we derive an algorithm to find the maximum Ε-weakened robustness radius. The time complexity of our algorithms is polynomial in the dimension and size of the network. So, they are scalable to large real-world networks. Besides, We also show its potential application in analyzing quality issues. © 2021, CC BY.

关键词： Deep neural networks

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Traffic Labeller： Collecting Internet Traffic Samples with Accurate Application Information

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China Communications 2014年第1期11卷 69-78页

作者： PENG Lizhi ZHANG Hongli YANG Bo CHEN Yuehui WU Tong School of Computer Science and Technology Harbin Institute of Technology Harbin 150002 China Provincial Key Laboratory for Network Based Intelligent Computing University of Jinan Jinan 250022 China School of Computer and Information Technology Beijing Jiaotong University Beijing 100044 China

Traffic classification research has been suffering from a trouble of collecting accurate samples with ground truth.A model named Traffic Labeller(TL) is proposed to solve this *** system captures all user socket calls and their corresponding application process information in the user mode on a Windows *** a sending data call has been captured,its 5-tuple {source IP,destination IP,source port,destination port and transport layer protocol},associated with its application information,is sent to an intermediate NDIS driver in the kernel *** the intermediate driver writes application type information on TOS field of the IP packets which match the *** this way,each IP packet sent from the Windows host carries their application ***,traffic samples collected on the network have been labelled with the accurate application information and can be used for training effective traffic classification models.

关键词： network measurement traffic cla- ssification data collection ground truth

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network Security Situational Awareness Model Based on Threat Intelligence 14th

Network Security Situational Awareness Model Based on Threat...

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14th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2021

作者： Zhang, Hongbin Yin, Yan Zhao, Dongmei Liu, Bin Gao, Hongbin School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050018 China Hebei Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang050024 China School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China Research Center of Big Data and Social Computing Hebei University of Science Shijiazhuang05000 China

ISBN: (纸本)9783030898137

In order to deal with the problems that the increasing scale of the network in the real environment leads to the continuous high incidence of network attacks, the threat intelligence was applied to situational awareness, and the situational awareness model based on random game was constructed. Threat perception of the target system was performed by comparing the similarity between the exogenous threat intelligence and the internal security events of the system. At the same time, internal threat intelligence was generated based on the threat information inside the system. In this process, game theory was used to quantify the current network security situation of the system, evaluate the security status of the network. Finally, the prediction of the network security situation was realized. The experimental results show that the network security situation awareness method based on threat intelligence can reflect the changes in the network security situation and predict attack behaviors accurately. © 2021, ICST institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Game theory

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Attacking the Dialogue System at Smart Home 16th

Attacking the Dialogue System at Smart Home

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16th EAI International Conference on Collaborative computing: networking, Applications, and Worksharing, CollaborateCom 2020

作者： Deng, Erqiang Qin, Zhen Li, Meng Ding, Yi Qin, Zhiguang School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu China Institute of Electronic and Information Engineering of UESTC Chengdu China Institute of Electronic and Information Engineering of UESTC in Guangdong Guangdong523808 China

ISBN: (纸本)9783030675363

Intelligent dialogue systems are widely applied in smart home systems, and the security of such systems deserves concern [1, 2]. In this paper, we design a threatening scenario of dialogue systems at a smart home. A trojan robot is disguised as one part of the whole system but generates dialogue adversarial examples to attack the normal robots according to the information of users. To achieve the goal in such a scenario, the responding speed, the correctness of the grammar, and the consistency of semantic is necessary. Based on these requirements, we propose a novel method named Attention weight Probability Estimation Attack (APE) to allocate the keys words in dialogue and substitute these words with synonyms in real-time. We perform our experiments on popular classification datasets in the DNN model, and the result shows that APE effectively attacks the system with low responding time and a high success rate. © 2021, ICST institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Semantics

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Research on unified recognition model and algorithm for multi-modal gestures

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The Journal of China Universities of Posts and Telecommunications 2019年第2期26卷 30-42页

作者： Guo Xiaopei Feng Zhiquan Sun Kaiyun Liu Hong Xie Wei Bi Jianping School of Information Science and Engineering University of JinanJinan 250022China Shandong Provincial Key Laboratory of Network-based Intelligent Computing Jinan 250022China School of Information Science and Engineering Shandong Normal UniversityJinan 250014China School of Information and Electrical Engineering Harbin Institute of Technology at WeihaiWeihai 264209China Affiliated Hospital of Shandong University of Traditional Chinese Medicine Jinan 250022China

In gesture recognition,static gestures,dynamic gestures and trajectory gestures are collectively known as multi-modal *** solve the existing problem in different recognition methods for different modal gestures,a unified recognition algorithm is *** angle change data of the finger joints and the movement of the centroid of the hand were acquired respectively by data glove and *** the preprocessing of the multi-source heterogeneous data,all hand gestures were considered as curves while solving hand shaking,and a uniform hand gesture recognition algorithm was established to calculate the Pearson correlation coefficient between hand gestures for gesture *** this way,complex gesture recognition was transformed into the problem of a simple comparison of curves *** main innovations:1) Aiming at solving the problem of multi-modal gesture recognition,an unified recognition model and a new algorithm is proposed;2) The Pearson correlation coefficient for the first time to construct the gesture similarity operator is *** testing 50 kinds of gestures,the experimental results showed that the method presented could cope with intricate gesture interaction with the 97.7% recognition rate.

关键词： Kinect data glove multi-modal gesture gesture interaction

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Personalized Federated Learning with Collaborative Aggregation networks for Multi-Site Brain Disorder Diagnosis 4

Personalized Federated Learning with Collaborative Aggregati...

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4th International Conference on Industrial Automation, Robotics and Control Engineering, IARCE 2024

作者： Si, Qian Li, Yang School of Cyber Science and Technology Beihang University Beijing China Department of Automation Science and Electrical Engineering The Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Virtual Reality Technology and Systems Beijing China Advanced Institute of Information Technology Peking University Beijing China Beihang University Beijing China

ISBN: (纸本)9798350380323

In multi-site brain disease diagnosis studies, traditional centralized training methods necessitate sharing medical data, posing significant privacy risks. Federated learning (FL) offers a privacy-preserving solution by enabling global model training through aggregating locally trained models from multiple data centers without sharing raw data. However, current FL approaches rely on a server-based network topology, where central server failure disrupts training. Additionally, data heterogeneity across sites often slows convergence and reduces accuracy. To overcome these issues, we introduce a decentralized personalized federated learning collaborative aggregation network (pFedCAN). This framework has two core components: (1) separating local models into shared and personalized layers, and (2) forming a collaborative aggregation network via similarity detection in the shared layers. Specifically, each center trains its local model, then separates it into shared and personalized layers. The shared layer is exchanged with other centers, while the personalized layer remains local. data centers analyze similarities in received shared layers to build a collaborative network, where shared layers from similar centers are aggregated to refine the model. This approach flexibly adapts to varying levels of data heterogeneity, enhancing model training efficiency. Validation on public datasets, ABIDE I and ADHD, shows that the proposed method outperforms current leading techniques. © 2024 IEEE.

关键词： Federated learning

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ProtoEM: A Prototype-Enhanced Matching Framework for Event Relation Extraction

arXiv

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arXiv 2023年

作者： Hu, Zhilei Li, Zixuan Xu, Daozhu Bai, Long Jin, Cheng Jin, Xiaolong Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China State Key Laboratory of Geo-Information Engineering Xi’an710054 China Xi’an Research Institute of Surveying and Mapping Xi’an710054 China

Event Relation Extraction (ERE) aims to extract multiple kinds of relations among events in texts. However, existing methods singly categorize event relations as different classes, which are inadequately capturing the intrinsic semantics of these relations. To comprehensively understand their intrinsic semantics, in this paper, we obtain prototype representations for each type of event relation and propose a Prototype-Enhanced Matching (ProtoEM) framework for the joint extraction of multiple kinds of event relations. Specifically, ProtoEM extracts event relations in a two-step manner, i.e., prototype representing and prototype matching. In the first step, to capture the connotations of different event relations, ProtoEM utilizes examples to represent the prototypes corresponding to these relations. Subsequently, to capture the interdependence among event relations, it constructs a dependency graph for the prototypes corresponding to these relations and utilized a Graph Neural network (GNN)-based module for modeling. In the second step, it obtains the representations of new event pairs and calculates their similarity with those prototypes obtained in the first step to evaluate which types of event relations they belong to. Experimental results on the MAVEN-ERE dataset demonstrate that the proposed ProtoEM framework can effectively represent the prototypes of event relations and further obtain a significant improvement over baseline models. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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