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arXiv 2021年

作者： Jiang, Shanqing Yang, Lin Cheng, Guang Gao, Xianming Feng, Tao Zhou, Yuyang School of Cyber Science and Engineering Southeast University Nanjing China National Key Laboratory of Science and Technology on Information System Security Beijing China Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing China

Measuring and evaluating network resilience has become an important aspect since the network is vulnerable to both uncertain disturbances and malicious attacks. networked systems are often composed of many dynamic components and change over time, which makes it difficult for existing methods to access the changeable situation of network resilience. This paper establishes a novel quantitative framework for evaluating network resilience using the Dynamic Bayesian network. The proposed framework can be used to evaluate the network's multistage resilience processes when suffering various attacks and recoveries. First, we define the dynamic capacities of network components and establish the network's five core resilience capabilities to describe the resilient networking stages including preparation, resistance, adaptation, recovery, and evolution;the five core resilience capabilities consist of rapid response capability, sustained resistance capability, continuous running capability, rapid convergence capability, and dynamic evolution capability. Then, we employ a two-time slices approach based on the Dynamic Bayesian network to quantify five crucial performances of network resilience based on core capabilities proposed above. The proposed approach can ensure the time continuity of resilience evaluation in time-varying networks. Finally, our proposed evaluation framework is applied to different attacks and recovery conditions in typical simulations and real-world network topology. Results and comparisons with extant studies indicate that the proposed method can achieve more accurate and comprehensive evaluation and can be applied to network scenarios under various attack and recovery intensity. Copyright © 2021, The Authors. All rights reserved.

关键词： Recovery

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Disentangled High Quality Salient Object Detection

Disentangled High Quality Salient Object Detection

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International Conference on computer Vision (ICCV)

作者： Lv Tang Bo Li Yijie Zhong Shouhong Ding Mofei Song Youtu Lab Tencent Shanghai China The School of Computer Science and Engineering Southeast University Nanjing China The Key Lab of Computer Network and Information Integration (Ministry of Education) Southeast University Nanjing China

ISBN: (纸本)9781665428132

Aiming at discovering and locating most distinctive objects from visual scenes, salient object detection (SOD) plays an essential role in various computer vision systems. Coming to the era of high resolution, SOD methods are facing new challenges. The major limitation of previous methods is that they try to identify the salient regions and estimate the accurate objects boundaries simultaneously with a single regression task at low-resolution. This practice ignores the inherent difference between the two difficult problems, resulting in poor detection quality. In this paper, we propose a novel deep learning framework for high-resolution SOD task, which disentangles the task into a low-resolution saliency classification network (LRSCN) and a high-resolution refinement network (HRRN). As a pixel-wise classification task, LRSCN is designed to capture sufficient semantics at low-resolution to identify the definite salient, background and uncertain image regions. HRRN is a regression task, which aims at accurately refining the saliency value of pixels in the uncertain region to preserve a clear object boundary at high-resolution with limited GPU memory. It is worth noting that by introducing uncertainty into the training process, our HRRN can well address the high-resolution refinement task without using any high-resolution training data. Extensive experiments on high-resolution saliency datasets as well as some widely used saliency benchmarks show that the proposed method achieves superior performance compared to the state-of-the-art methods.

关键词： Training Deep learning computer vision Uncertainty Semantics Refining Graphics processing units

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DimSum: Disentangling representation with automatically generated multi-category summary templates for fine-grained opinion summarization

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Expert Systems with Applications 2025年 290卷

作者： Yanyue Zhang Yilong Lai Zhenglin Wang Deyu Zhou School of Computer Science and Engineering Ministry of Education Key Laboratory of Computer Network and Information Integration Southeast University No. 2 Southeast University Road Jiangning District Nanjing Jiangsu Province 210000 China

Supervised opinion summarization often employs a select-then-generate framework, while noticeable templated phenomena are observed in the generated summaries, indicating the inability to produce a fine-grained summary with the corresponding aspect information. Therefore, we propose DimSum , an end-to-end supervised summarization method based on disentangling summary representation with multi-category summary templates. Considering the product category the input describes, different summary templates are used to disentangle the summary representations into content and pattern parts. Only the content part is utilized to generate the final summary. Moreover, summary templates are automatically generated for each product category by training a template generator with contrastive learning and then clustering the outputs. Automatic and human evaluation results on the summarization dataset AmaSum show that the proposed approach outperforms several strong baselines, indicating its effectiveness in opinion summarization.

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Retrieve, program, repeat: Complex knowledge base question answering via alternate meta-learning 29

Retrieve, program, repeat: Complex knowledge base question a...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Hua, Yuncheng Li, Yuan-Fang Haffari, Gholamreza Qi, Guilin Wu, Wei School of Computer Science and Engineering Southeast University Nanjing China Faculty of Information Technology Monash University Melbourne Australia Southeast University Monash University Joint Research Institute Suzhou China Key Laboratory of Computer Network and Information Integration Southeast University China

ISBN: (纸本)9780999241165

A compelling approach to complex question answering is to convert the question to a sequence of actions, which can then be executed on the knowledge base to yield the answer, aka the programmer-interpreter approach. Use similar training questions to the test question, meta-learning enables the programmer to adapt to unseen questions to tackle potential distributional biases quickly. However, this comes at the cost of manually labeling similar questions to learn a retrieval model, which is tedious and expensive. In this paper, we present a novel method that automatically learns a retrieval model alternately with the programmer from weak supervision, i.e., the system's performance with respect to the produced answers. To the best of our knowledge, this is the first attempt to train the retrieval model with the programmer jointly. Our system leads to state-of-the-art performance on a large-scale task for complex question answering over knowledge bases. We have released our code at https://***/DevinJake/MARL. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Knowledge based systems

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A Semi-Supervised Deep network Embedding Approach Based on the Neighborhood Structure

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Big Data Mining and Analytics 2019年第3期2卷 205-216页

作者： Wenmao Wu Zhizhou Yu Jieyue He School of Computer Science and Engineering Southeast UniversityNanjing 211100China MOE Key Laboratory of Computer Network and Information Integration Southeast UniversityNanjing 211100China

network embedding is a very important task to represent the high-dimensional network in a lowdimensional vector space,which aims to capture and preserve the network *** existing network embedding methods are based on shallow ***,actual network structures are complicated which means shallow models cannot obtain the high-dimensional nonlinear features of the network *** recently proposed unsupervised deep learning models ignore the labels *** address these challenges,in this paper,we propose an effective network embedding method of Structural Labeled Locally Deep Nonlinear Embedding(SLLDNE).SLLDNE is designed to obtain highly nonlinear features through utilizing deep neural network while preserving the label information of the nodes by using a semi-supervised classifier component to improve the ability of ***,we exploit linear reconstruction of neighborhood nodes to enable the model to get more structural *** experimental results of vertex classification on two real-world network datasets demonstrate that SLLDNE outperforms the other state-of-the-art methods.

关键词： network embedding deep learning network analysis

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Enhanced Practical Byzantine Fault Tolerance for Service Function Chain Deployment:Advancing Big Data Intelligence in Control Systems

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computers, Materials & Continua 2025年第6期83卷 4393-4409页

作者： Peiying Zhang Yihong Yu Jing Liu ChongLv Lizhuang Tan Yulin Zhang Qingdao Institute of Software College of Computer Science and TechnologyChina University of Petroleum(East China)Qingdao266580China Shandong Key Laboratory of Intelligent Oil&Gas Industrial Software Qingdao266580China Library of Shanghai Lixin University of Accounting and Finance Shanghai201209China Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan250014China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer ScienceJinan250014China Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE Minzu University of ChinaBeijing100081China Key Laboratory of Intelligent Game Yangtze River Delta Research Institute of NPUTaicang215400China Key Laboratory of Education Informatization for Nationalities(Yunnan Normal University) Ministry of EducationKunming650092China

As Internet ofThings(IoT)technologies continue to evolve at an unprecedented pace,intelligent big data control and information systems have become critical enablers for organizational digital transformation,facilitating data-driven decision making,fostering innovation ecosystems,and maintaining operational *** this study,we propose an advanced deployment algorithm for Service Function Chaining(SFC)that leverages an enhanced Practical Byzantine Fault Tolerance(PBFT)*** main goal is to tackle the issues of security and resource efficiency in SFC implementation across diverse network *** integrating blockchain technology and Deep Reinforcement Learning(DRL),our algorithm not only optimizes resource utilization and quality of service but also ensures robust security during SFC ***,the enhanced PBFT consensus mechanism(VRPBFT)significantly reduces consensus latency and improves Byzantine node detection through the introduction of a Verifiable Random Function(VRF)and a node reputation grading *** results demonstrate that compared to traditional PBFT,the proposed VRPBFT algorithm reduces consensus latency by approximately 30%and decreases the proportion of Byzantine nodes by 40%after 100 rounds of ***,the DRL-based SFC deployment algorithm(SDRL)exhibits rapid convergence during training,with improvements in long-term average revenue,request acceptance rate,and revenue/cost ratio of 17%,14.49%,and 20.35%,respectively,over existing ***,the CPU resource utilization of the SDRL algorithmreaches up to 42%,which is 27.96%higher than other *** findings indicate that the proposed algorithm substantially enhances resource utilization efficiency,service quality,and security in SFC deployment.

关键词： Big data intelligent transformation heterogeneous networks service function chain blockchain deep reinforcement learning trusted deployment

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Finding the most reliable maximum flow in transport network 2nd

Finding the most reliable maximum flow in transport network

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2nd International Cognitive Cities Conference, IC3 2019

作者： Wang, Jie Cai, Wei Zhou, Sihai Liu, Yundi Liao, Weicheng Zhang, Baili School of Computer Science and Engineering Southeast University Nanjing211189 China Key Laboratory of Computer Network and Information Integration of Ministry of Education Nanjing China Research Center for Judicial Big Data Supreme Count of China Nanjing211189 China

ISBN: (纸本)9789811561122

This paper intends to solve the most reliable maximum flow problem (MRMF) on transport network. A subgraph path division algorithm (SPDA) is proposed to get the most reliable maximum flow distribution, which avoid the negative impact of the number of simple paths and its bottleneck capacity. SPDA divides the sub-graph space of a transport network into a set of disjoint closed intervals, which satisfies the maximum flow constraints. Among the lower bounds of all the intervals, the one with discovered probability has proven to be the most reliable maximum. Finally, experimental results reveal the effectiveness and efficiency of the proposed algorithm. © Springer Nature Singapore Pte Ltd 2020.

关键词： Flow graphs

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Differential Privacy Based on Data Provenance Publishing Method

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Jisuanji Xuebao/Chinese Journal of computers 2020年第3期43卷 573-586页

作者： Ni, Wei-Wei Shen, Tao Yan, Dong Department of Computer Science and Engineering Southeast University Nanjing211189 China Key Laboratory of Computer Network and Information Integration in Southeast University Ministry of Education Nanjing211189 China

Data provenance describes the mechanism and process of data generation and evolution, which records information about the node module executions used to produce concrete data items, as well as those intermediate data items acting as parameters passed between nodes' executions. Data provenance plays an important role in research and applications of data quality assessment, data recovery and data analysis. With increasingly deepening of data sharing, the need for publishing and sharing workflow of provenance, which is the main representation structure of data provenance, becomes increasingly urgent. However, the provenance workflow often contains private or confidential data. For instance, the node modules included in the provenance workflow, as well as the temporal relations among those nodes, may involve the privacy of the data owner. Direct release of provenance workflow will inevitably bring privacy protection issues. Privacy-preserving data provenance publication becomes an urgent problem to be solved. That is to say, the utility of published provenance workflow needs well maintaining under the premise that data privacy should not be disclosed. The existing research mainly focuses on the maintenance of the local mapping relationship of provenance workflow. For example, privacy protection process is implemented to the provenance workflow, which ensures that the degree of a single node would not be leaked, or sensitive mapping relationship would not be leaked in parallel with effective maintaining of provenance workflow's overall input and output mapping relations. For another important manifestation of provenance workflow's utility, i.e. temporal dependence among those front and corresponding back task nodes of the provenance workflow, the maintenance effect is relatively poor. As for the privacy of adjacent nodes distribution in the provenance workflow, the protection ability of existing methods is also far insufficient. To solve the above problems, the definitio

关键词： Mapping

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Accurate and Fast Detection of DDoS Attacks in High-Speed network with Asymmetric Routing

Accurate and Fast Detection of DDoS Attacks in High-Speed Ne...

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2021 IEEE Global Communications Conference (GLOBECOM)

作者： Hua Wu Tingzheng Chen Ziling Shao Guang Cheng Xiaoyan Hu School of Cyber Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China

ISBN: (纸本)9781728181059

The existing DDoS attack detection methods based on a single monitoring point only consider symmetric routing scenarios, which may not be practical. Such schemes will produce high false positives when facing the asymmetric routing scenarios. Besides, few of them are applicable in high-speed networks. The paper designs a DDoS detection scheme customized for high-speed networks and takes asymmetric routing scenarios into account. Systematic sampling is applied to high-speed incoming traffic, and a proposed Double Composite Structure Sketch (DCSS) is utilized for fast recording and extraction of features based on the characteristics of DDoS attacks in both symmetric and asymmetric routing scenarios. Then classifiers are trained for online DDoS detection. Our experimental results using the public dataset show that in a 10Gbps network with asymmetric routing, our approach can accurately detect UDP Flood and SYN Flood attacks within 20 seconds when the sampling rate is set to 1/2048.

关键词： Systematics High-speed networks Conferences Routing Denial-of-service attack Feature extraction Global communication

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Variational Gaussian Topic Model with Invertible Neural Projections

arXiv

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arXiv 2021年

作者： Wang, Rui Zhou, Deyu Xiong, Yuxuan Huang, Haiping School of Computer Science Nanjing University of Posts and Telecommunications China Key Laboratory of Computer Network and Information Integration School of Computer Science and Engineering Ministry of Education Southeast University China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing China

Neural topic models have triggered a surge of interest in extracting topics from text automatically since they avoid the sophisticated derivations in conventional topic models. However, scarce neural topic models incorporate the word relatedness information captured in word embedding into the modeling process. To address this issue, we propose a novel topic modeling approach, called Variational Gaussian Topic Model (VaGTM). Based on the variational auto-encoder, the proposed VaGTM models each topic with a multivariate Gaussian in decoder to incorporate word relatedness. Furthermore, to address the limitation that pre-trained word embeddings of topicassociated words do not follow a multivariate Gaussian, Variational Gaussian Topic Model with Invertible neural Projections (VaGTM-IP) is extended from VaGTM. Three benchmark text corpora are used in experiments to verify the effectiveness of VaGTM and VaGTMIP. The experimental results show that VaGTM and VaGTM-IP outperform several competitive baselines and obtain more coherent topics. Copyright © 2021, The Authors. All rights reserved.

关键词： Gaussian distribution

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