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6th Annual 2018 International Conference on Geo-Spatial Knowledge and Intelligence, GSKI 2018

作者： Liu, Yang He, Qian Li, Xiongying Zhou, Shuiming Guangxi Transportation Research and Consulting Co. Ltd Nanning530007 China Guangxi Key Laboratory of Cryptograph and Information Security Guangxi University Key Laboratory of Cloud Computing and Complex System Guilin University of Electronic Technology Guilin541004 China

Tradition Software Defined Network (SDN) suffers from single point failure and uneven load distribution problems. Multiple SDN controllers work together to construct a distributed dynamic adaptive cluster with dynamic load balancing for large scale SDN switch stream requesting. Introducing such features as CPU, memory and network load, a Dynamic Adaptive and Fast algorithm (DAF) can select source, target and destination SDN controllers to balance load dynamically. The results of experiments show that DAF works well and improve the cloud service system throughput. © Published under licence by IOP Publishing Ltd.

关键词： Controllers

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Network Risk Assessment Based on Improved MulVAL Framework and HMM 2nd

Network Risk Assessment Based on Improved MulVAL Framework a...

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2nd EAI International Conference on Security and Privacy in New computing Environments, SPNCE 2019

作者： Wang, Chundong Li, Kongbo Tian, Yunkun He, Xiaonan Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Ministry of Education Tianjin University of Technology Tianjin300384 China Tianjin E-Hualu Information Technology Co. Ltd. Tianjin China

ISBN: (纸本)9783030213725

With the increasingly extensive applications of the network, the security of internal network of enterprises is facing more and more threats from the outside world, which implies the importance to master the network risk assessment skills. In the big data era, there are various security protection techniques and different types of group data. Meanwhile, Online Social Networks (OSNs) and Social Internet of Things (SIoT) are becoming popular patterns of meeting people and keeping in touch with friends [2, 5]. However, risk assessment, as a bridge between security experts and network administrators, to some extent, whose accuracy can influence the judgment of administrators to the entire network state. In order to solve this problem, this essay proposes the improved MulVAL framework to optimize the risk assessment process by establishing the HMM model and the Bayesian model, which can improve the accuracy of the evaluation value. Firstly, behavior of the attacker is described in-depth by the attack graph generated through MulVAL. Then, with the quantitative evaluation conducted by the Common Vulnerability Scoring system, the nodes on the attack path can will be evaluated and the value will be further evaluated by the Bayesian model. Finally, by establishing the hidden Markov model, the corresponding parameters can be defined and the most likely probabilistic state transition sequence can be calculated by using the Viterbi algorithm to deduce the attack intent with the highest possibility. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Network security

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Two-Level Feature Selection Method for Low Detection Rate Attacks in Intrusion Detection 2nd

Two-Level Feature Selection Method for Low Detection Rate At...

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2nd EAI International Conference on Security and Privacy in New computing Environments, SPNCE 2019

作者： Wang, Chundong Ye, Xin He, Xiaonan Tian, Yunkun Gong, Liangyi Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Ministry of Education Tianjin University of Technology Tianjin300384 China Tianjin E-Hualu Information Technology Co. Ltd. Tianjin300350 China

ISBN: (纸本)9783030213725

In view of the fact that some attacks have low detection rates in intrusion detection dataset, a two-level feature selection method based on minimal-redundancy-maximal-relevance (mRMR) and information gain (IG) was proposed. In this method, irrelevant and redundant features were filtered preliminarily to reduce data dimension by using mRMR algorithm, and highly correlated features to low detection rate attacks were obtained based on the calculation of information gain, and finally these features were integrated together to get final feature subset. The experimental results showed that the classification result of the feature subset filtered by this method had a better classification performance than the current filtering methods and improved the testing results of some attacks with low detection rates effectively. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Intrusion detection

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Large-scale video compression： recent advances and challenges

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Frontiers of Computer Science 2018年第5期12卷 825-839页

作者： Tao TIAN Hanli WANG Department of Computer Science and Technology Tongji University Shanghai 201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China Shanghai Engineering Research Center of Industrial Vision Perception & Intelligent Computing Shanghai 200092 China

The evolution of social network and multimedia technologies encourage more and more people to generate and upload visual information, which leads to the generation of large-scale video data. Therefore, preeminent compression technologies are highly desired to facilitate the storage and transmission of these tremendous video data for a wide variety of applications. In this paper, a systematic review of the recent advances for large-scale video compression （LSVC） is presented. Specifically, fast video coding algorithms and effective models to improve video compression efficiency are introduced in detail, since coding complexity and compression efficiency are two important factors to evaluate video coding approaches. Finally, the challenges and fu- ture research trends for LSVC are discussed.

关键词： large-scale video compression fast video coding compression efficiency

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A Deep Concept-aware Model for predicting and explaining restaurant future status

A Deep Concept-aware Model for predicting and explaining res...

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IEEE International Conference on Web services (ICWS)

作者： Hao Liao Xiaojie Zhang Xin Li Mingyang Zhou Alexandre Vidmer Rui Mao Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Guangdong Province Engineering Center of China-made High Performance Data Computing System Shenzhen City Key Laboratory of Service Computing and Application Shenzhen China Institute of Big Data Intelligent Management and Decision Shenzhen University Shenzhen China

ISBN: (数字)9781728187860

ISBN: (纸本)9781728187877

Nowadays, with the development of web services, the survival of online service has captured public attention significantly. Future prediction of business is crucial to the shopkeepers. Predicting how the business will develop and explaining what key factors are leading to it are two most important tasks. Existing literatures usually tackle only one of these two tasks, ignoring that they are two closely related tasks and complement each other. In this paper, we propose a review-based neural model, named Deep Concept-aware Model (DCA), to predict restaurants' future status and provide explainable sentences simultaneously in an end2end framework. Specifically, we use co-attention to select concepts and implement prediction and explanation tasks through Factorization Machine and Gated Recurrent Unit, respectively. We conduct extensive experiments on three Chinese cities' datasets. The proposed joint model outperforms the state-of-the-art baseline methods for both prediction (average 40.96% improvement in AUC) and explanation (average 9.72% improvement in BLEU and 86.05% in Precision metric of ROUGE).

关键词： Web services Urban areas Predictive models Prediction algorithms Data models Task analysis Business

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Learning How to Avoiding Obstacles for End-to-End Driving with Conditional Imitation Learning 19

Learning How to Avoiding Obstacles for End-to-End Driving wi...

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Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning

作者： Enwei Zhang Hongtu Zhou Yongchao Ding Junqiao Zhao Chen Ye The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University School of Electronics and Information Engineering Shanghai P. R. China Tongji University School of Electronics and Information Engineering Shanghai P. R. China Tongji University School of Electronics and Information Engineering P. R. China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University School of Electronics and Information Engineering P. R. China

ISBN: (纸本)9781450372213

Obstacle avoiding is one of the most complex tasks for autonomous driving systems, which was also ignored by many cutting-edge end-to-end learning-based methods. The difficulties stem from the integrated process of detection and interpretation of environment and obstacles and generation of proper behaviors. We make the use of CARLA, a simulator for autonomous driving research, and collect massive human drivers' reactions to obstacles on road subjecting to given driving commands, i.e. follow, go straight, turn left and turn right for about 6 hours. A behavior-Cloning neural network architecture is proposed with the modified loss that enlarge the effects of errors for steer, which indicates the benefit to high an accuracy. We found the data augmentation of the image is crucial to the training of the proposed network. And a reasonable limit allows avoiding unexpected stop. The experiments demonstrate 3 obstacle avoidance cases: for the same type as the training dataset, other automobile and two-wheeled vehicles. Finally, the CARLA benchmark is also tested.

关键词： imitation learning obstacle avoidance end-to-end autonomous vehicles

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Random Occlusion Recovery with Noise Channel for Person Re-identification 16th

Random Occlusion Recovery with Noise Channel for Person Re-i...

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16th International Conference on Intelligent computing, ICIC 2020

作者： Zhang, Kun Wu, Di Yuan, Changan Qin, Xiao Wu, Hongjie Zhao, Xingming Zhang, Lijun Du, Yuchuan Wang, Hanli Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai China Guangxi Academy of Science Nanning530025 China School of Computer and Information Engineering Nanning Normal University Nanning530299 China School of Computer Science and Technology Soochow University Suzhou215006 China School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou215009 China Fudan University Shanghai200433 China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Ministry of Education Shanghai China Collaborative Innovation Center of Intelligent New Energy Vehicle and School of Automotive Studies Tongji University Shanghai201804 China The Key Laboratory of Road and Traffic Engineering of the Ministry of Education Department of Transportation Engineering Tongji University Shanghai201804 China Department of Computer Science and Technology the Key Laboratory of Embedded System and Service Computing and Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China

ISBN: (纸本)9783030607982

Person re-identification, as the basic task of a multi-camera surveillance system, plays an important role in a variety of surveillance applications. However, the current mainstream person re-identification model based on deep learning requires a lot of labeled data, which takes a lot of time and manpower. In this study, we proposed a person re-identification method based on random occlusion recovery with noise channel. We add random occlusion blocks to the original image, use the GAN model for repair, and use the repaired image to expand the original training set. After that, the generated image is adjusted through the noise channel. Finally, we use the enhanced data set to train the baseline model. Our model achieves the state-of-the-art on Market-1501 dataset, proving that the method is effective. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Age-Upon-Decisions Minimizing Scheduling in Internet of Things: To Be Random or to Be Deterministic?

Age-Upon-Decisions Minimizing Scheduling in Internet of Thin...

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作者： Dong, Yunquan Chen, Zhengchuan Liu, Shanyun Fan, Pingyi Letaief, Khaled Ben School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing210044 China Key Laboratory of Dependable Service Computing in Cyber-Physical Society Ministry of Education Chongqing China Department of Electronic Engineering Tsinghua University Beijing China Department of Electrical and Computer Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

In this article, we consider an Internet of Things (IoT) system in which a sensor delivers updates to a monitor with exponential service time and first-come-first-served (FCFS) discipline. We investigate the freshness of the received updates and propose a new metric termed as age upon decisions (AuD), which is defined as the time elapsed from the generation of each update to the epoch it is used to make decisions (e.g., estimations, inferences, and controls). Within this framework, we aim at improving the freshness of updates at decision epochs by scheduling the update arrival process and the decision-making process. The theoretical results show that: 1) when the decisions are made according to a Poisson process, the average AuD is independent of decision rate and will be minimized if the arrival process is periodic (i.e., deterministic);2) when both the decision process and the arrival process are periodic, the average AuD is larger, but decreases with decision rate to, the average AuD of the corresponding system with the Poisson decisions (i.e., random);and 3) when both the decision process and the arrival process are periodic, the average AuD can be further decreased by optimally controlling the offset between the two processes. For practical IoT systems, therefore, it is suggested to employ periodic arrival processes and random decision processes. Nevertheless, making the periodical updates and decisions with properly controlled offset is also a promising solution, if the timing information of the two processes can be accessed by the monitor. © 2014 IEEE.

关键词： Scheduling

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Finite time anti-synchronization of complex-valued neural networks with bounded asynchronous time-varying delays

arXiv

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arXiv 2019年

作者： Liu, Xiwei Li, Zihan Department of Computer Science and Technology Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China

In this paper, we studied the finite time anti-synchronization of master-slave coupled complex-valued neural networks (CVNNs) with bounded asynchronous time-varying delays. With the decomposing technique and the generalized {ξ, ∞}-norm, several criteria for ensuring the finite-time anti-synchronization are obtained. The whole anti-synchronization process can be divided into two parts: first, the norm of each error state component will change from initial values to 1 in finite time, then from 1 to 0 in fixed time. Therefore, the whole time is finite. Finally, one typical numerical example is presented to demonstrate the correctness of our obtained results. key words: Anti-synchronization, asynchronous, complex-valued neural network, finite time, time-varying delay Copyright © 2019, The Authors. All rights reserved.

关键词： Time varying control systems

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Improving read performance of lsm-tree based KV stores via dual grained caches 21

Improving read performance of lsm-tree based KV stores via d...

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21st IEEE International Conference on High Performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and systems, HPCC/SmartCity/DSS 2019

作者： Li, Xiang Xu, Guangping Fan, Hao Lu, Hongli Tang, Bo Xue, Yanbing Zong, Ziliang School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China Department of Computer Science Texas State University San MarcosTX United States

ISBN: (纸本)9781728120584

key-value (KV) stores based on the Log-Structure Merge tree (LSM-tree) have been widely used in modern storage systems (e.g. LevelDB and RocksDB) to achieve high write throughput. However, conventional LSM-tree design has high latency for random read operations, especially in the workloads with concurrent read and write operations. This paper proposes a dual-grained cache scheme to improve the performance of LSM-tree based KV stores, in which a coarse-grained cache improves read performance by leveraging spatial locality and a fine-grained cache tracks the frequently accessed KV pairs. We implement the dual-grained caching on a LSM-tree simulator and evaluate its performance using various Yahoo! Cloud service Benchmark (YCSB) workloads. Our experimental results show that the proposed way of dual-grained caches can significantly improve the read performance of KV stores without sacrificing the advantages of the LSM-tree for write-intensive workloads. © 2019 IEEE.

关键词： Compaction Throughput Conferences Indexes Computer science Benchmark testing Distributed databases

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