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Semantic path planning for indoor navigation and household tasks 20th

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Semantic path planning for indoor navigation and household t...

20th Towards Autonomous Robotic Systems Conference, TAROS 2019

作者： Sun, Nico Yang, Erfu Corney, Jonathan Chen, Yi Department of Design Manufacture and Engineering Management University of Strathclyde Glasgow United Kingdom Industry 4.0 Artificial Intelligence Laboratory School of Computer Science and Network Security Dongguan University of Technology Dongguan523808 China

ISBN: (纸本)9783030253318

Assisting people with daily living tasks in their own homes with a robot requires a navigation through a cluttered and varying environment. Sometimes the only possible path would be blocked by an obstacle which needs to be moved away but not into other obstructing regions like the space required for opening a door. This paper presents semantic assisted path planning in which a gridded semantic map is used to improve navigation among movable obstacles (NAMO) and partially plan simple household tasks like cleaning a carpet or moving objects to another location. Semantic planning allows the execution of tasks expressed in human-like form instead of mathematical concepts like coordinates. In our numerical experiments, spatial planning was completed well within a typical human-human dialogue response time, allowing for an immediate response by the robot. © Springer Nature Switzerland AG 2019.

关键词： Semantics

Evaluation Method of Node Importance in Temporal Satellite networks Based on Time Slot Correlation

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Research Square

Research Square 2021年

作者： Xu, Rui Di, Xiaoqiang He, Xiongwen Qi, Hui School of Computer Science and Technology Changchun University of Science and Technology Changchun130022 China School of Computer Science and Technology Changchun University of Science and Technology Changchun130022 China Jilin Province Key Laboratory of Network and Information Security Changchun University of Science and Technology Changchun130022 China Information Center Changchun University of Science and Technology Changchun130022 China Beijing Institute of Spacecraft System Engineering Beijing100094 China

Temporal satellite networks can accurately describe the dynamic process of satellite networks by considering the interaction relationship and interaction sequence between satellite nodes. In addition, the measurement of node importance in satellite networks plays a crucial role in understanding the structure and function of the network. The classical supra-adjacency matrix (SAM) temporal model identifies the key nodes in the temporal network to some extent, which ignores the differences of inter-layer connectivity relationships leading to the inability to reflect the dynamic variations of satellite nodes. Therefore, the evaluation method based on time slot correlation is proposed to measure the importance of satellite nodes in this paper. Firstly, the correlation coefficient of time slot nodes is defined to measure the coupling relationship of adjacent time slots. Secondly, the dynamic supra-adjacency matrix (DSAM) temporal network model is proposed considering the correlation between adjacent time slots and the characteristics of link time. Finally, the node importance ranking results in each time slot and a global perspective are obtained by utilizing the eigenvector centrality. Through experimental simulations of the Iridium and Orbcomm constellations, it is demonstrated that the DSAM method has a more accurate recognition rate and is more stable than the SAM method. © 2021, CC BY.

关键词： Satellites

Pocket diagnosis: Secure federated learning against poisoning attack in the cloud

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arXiv 2020年

作者： Ma, Zhuoran Ma, Jianfeng Miao, Yinbin Liu, Ximeng Choo, Kim-Kwang Raymond Deng, Robert H. School of Cyber Engineering Xidian University Xi’an710071 China Guangxi Key Laboratory of Cryptography and Information Security Guilin541004 China Shaanxi Key Laboratory of Network and System Security Xidian University Xi’an710071 China Key Laboratory of Information Security of Network Systems College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Department of Information Systems and Cyber Security University of Texas at San Antonio San AntonioTX78249 United States School of Information Systems Singapore Management University 188065 Singapore

Federated learning has become prevalent in medical diagnosis due to its effectiveness in training a federated model among multiple health institutions (i.e., Data Islands (DIs)). However, increasingly massive DI-level poisoning attacks have shed light on a vulnerability in federated learning, which inject poisoned data into certain DIs to corrupt the availability of the federated model. Previous works on federated learning have been inadequate in ensuring the privacy of DIs and the availability of the final federated model. In this paper, we design a secure federated learning mechanism with multiple keys to prevent DI-level poisoning attacks for medical diagnosis, called SFPA. Concretely, SFPA provides privacy-preserving random forest-based federated learning by using the multi-key secure computation, which guarantees the confidentiality of DI-related information. Meanwhile, a secure defense strategy over encrypted locally-submitted models is proposed to defense DI-level poisoning attacks. Finally, our formal security analysis and empirical tests on a public cloud platform demonstrate the security and efficiency of SFPA as well as its capability of resisting DI-level poisoning attacks. Copyright © 2020, The Authors. All rights reserved.

关键词： Diagnosis

How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View

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arXiv 2021年

作者： Li, Ren Cao, Yanan Zhu, Qiannan Bi, Guanqun Fang, Fang Liu, Yi Li, Qian Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China Gaoling School of Artificial Intelligence Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods China National Computer Network Emergency Response Technical Team Coordination Center of China China University of Technology Sydney Australia

Knowledge Graph Embedding (KGE) aims to learn representations for entities and relations. Most KGE models have gained great success, especially on extrapolation scenarios. Specifically, given an unseen triple (h, r, t), a trained model can still correctly predict t from (h, r,?), or h from (?, r, t), such extrapolation ability is impressive. However, most existing KGE works focus on the design of delicate triple modeling function, which mainly tells us how to measure the plausibility of observed triples, but offers limited explanation of why the methods can extrapolate to unseen data, and what are the important factors to help KGE extrapolate. Therefore in this work, we attempt to study the KGE extrapolation of two problems: 1. How does KGE extrapolate to unseen data? 2. How to design the KGE model with better extrapolation ability? For the problem 1, we first discuss the impact factors for extrapolation and from relation, entity and triple level respectively, propose three Semantic Evidences (SEs), which can be observed from train set and provide important semantic information for extrapolation. Then we verify the effectiveness of SEs through extensive experiments on several typical KGE methods. For the problem 2, to make better use of the three levels of SE, we propose a novel GNN-based KGE model, called Semantic Evidence aware Graph Neural network (SE-GNN). In SE-GNN, each level of SE is modeled explicitly by the corresponding neighbor pattern, and merged sufficiently by the multi-layer aggregation, which contributes to obtaining more extrapolative knowledge representation. Finally, through extensive experiments on FB15k-237 and WN18RR datasets, we show that SE-GNN achieves state-of-the-art performance on Knowledge Graph Completion task and performs a better extrapolation ability. Our code is available at https://***/renli1024/SE-GNN. Copyright © 2021, The Authors. All rights reserved.

关键词： Knowledge graph

Unsupervised Domain Adaptive Person Re-Identification via Human Learning Imitation

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arXiv 2021年

作者： Peng, Yang Liu, Ping Luo, Yawei Zhou, Pan Xu, Zichuan Liu, Jingen Center for Frontier AI Research Agency for Science Technology and Research Singapore Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan China College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Liaoning Dalian China

Unsupervised domain adaptive person re-identification has received significant attention due to its high practical value. In past years, by following the clustering and finetuning paradigm, researchers propose to utilize the teacher-student framework in their methods to decrease the domain gap between different person re-identification datasets. Inspired by recent teacher-student framework based methods, which try to mimic the human learning process either by making the student directly copy behavior from the teacher or selecting reliable learning materials, we propose to conduct further exploration to imitate the human learning process from different aspects, i.e., adaptively updating learning materials, selectively imitating teacher behaviors, and analyzing learning materials structures. The explored three components, collaborate together to constitute a new method for unsupervised domain adaptive person re-identification, which is called Human Learning Imitation framework. The experimental results on three benchmark datasets demonstrate the efficacy of our proposed method. Copyright © 2021, The Authors. All rights reserved.

关键词： Students

VerifyTL: Secure and verifiable collaborative transfer learning

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arXiv 2020年

作者： Ma, Zhuoran Ma, Jianfeng Miao, Yinbin Liu, Ximeng Zheng, Wei Choo, Kim-Kwang Raymond Deng, Robert H. School of Cyber Engineering Xidian University Xi'an710071 China Shaanxi Key Laboratory of Network and System Security Xidian University Xi'an710071 China Key Laboratory of Information Security of Network Systems College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Department of Information Systems and Cyber Security University of Texas at San Antonio San AntonioTX78249 United States School of Information Systems Singapore Management University Singapore188065 Singapore

Getting access to labelled datasets in certain sensitive application domains can be challenging. Hence, one often resorts to transfer learning to transfer knowledge learned from a source domain with sufficient labelled data to a target domain with limited labelled data. However, most existing transfer learning techniques only focus on one-way transfer which brings no benefit to the source domain. In addition, there is the risk of a covert adversary corrupting a number of domains, which can consequently result in inaccurate prediction or privacy leakage. In this paper we construct a secure and Verifiable collaborative Transfer Learning scheme, VerifyTL, to support two-way transfer learning over potentially untrusted datasets by improving knowledge transfer from a target domain to a source domain. Further, we equip VerifyTL with a cross transfer unit and a weave transfer unit employing SPDZ computation to provide privacy guarantee and verification in the two-domain setting and the multi-domain setting, respectively. Thus, VerifyTL is secure against covert adversary that can compromise up to n − 1 out of n data domains. We analyze the security of VerifyTL and evaluate its performance over two real-world datasets. Experimental results show that VerifyTL achieves significant performance gains over existing secure learning schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Knowledge management

Incentive mechanism for data uploading in mobile crowdsensing

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Incentive mechanism for data uploading in mobile crowdsensin...

2019 International Conference on networking and network Applications, NaNA 2019

作者： Liu, Xiaolan Wang, Wu Yang, Bin Fan, Yuanyuan School of Computer and Information Engineering Chuzhou University Chuzhou Anhui239000 China Shaanxi Key Laboratory of Network and System Security Xidian University Xi'an Shaanxi710071 China School of Mathematics and Computer Science Yunnan Minzu University Kunming Yunnan650000 China

ISBN: (纸本)9781728126296

Mobile crowdsensing is a promising service paradigm utilizing the pervasive mobile devices to cooperatively perform large-scale sensing tasks. Data uploading which aims to deliver the sensed data to the control center, is an important problem in mobile crowdsensing. Existing works assume that the devices participate in device-to-device (D2D) cooperation voluntarily to assist other devices for data uploading. In reality, to assist other devices to upload data, the devices are requirement of consuming their own resources (e.g., memory, channel and time), which affects their willingness to participate in D2D cooperation. To this limitation, we propose an incentive mechanism to stimulate efficient D2D cooperation among devices. With this incentive mechanism, the reward of each device is determined by the reduced data uploading latency. According to the incentive mechanism, we design a chain formation algorithm, where the nearby devices can cooperate to construct a D2D chain by employing bottom to top mode. Finally, we corroborate the effectiveness of our incentive mechanism in reducing the average data uploading latency by extensive simulations. © 2019 IEEE.

关键词： D2D communication Data uploading Formation algorithm Incentive mechanism Mobile crowdsensing

Trading Based Service-Oriented Spectrum-Aware RAN-Slicing Under Spectrum Sharing

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Trading Based Service-Oriented Spectrum-Aware RAN-Slicing Un...

IEEE Conference on Wireless Communications and networking

作者： Kajia Jiao Xuanheng Li Miao Pan Fan Jiang Ming Li School of Information and Communication Engineering Dalian University of Technology Dalian China Department of Electrical and Computer Engineering University of Houston Houston TX USA Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts and Telecommunications Xian Shaanxi China

ISBN: (数字)9781728131061

ISBN: (纸本)9781728131078

The fast development on emerging services makes our telecommunications networks witness two key problems. One is the flexibility to fulfill the diverse service requests and the other is the shortage on spectrum. network slicing and spectrum sharing have been regarded as two prominent solutions, which, however, are barely jointly studied. When taking the shared spectrum into account, its unique feature of heterogeneity and uncertainty will bring new challenges for the slicing. In this paper, we propose a service-oriented spectrum-aware RAN-slicing trading (SSRT) scheme with a comprehensive consideration on both aspects. For the SSRT scheme, we jointly slice three kinds of resources, namely, time, spectrum (including both licensed one and shared one), and network facilities, according to the diverse traffic requests, which are classified into delay-tolerant (DT) ones and delay-sensitive (DS) ones, as well as the willing payments from different service providers (SPs). To achieve both inter-slice and intra-slice isolation, we construct a three-dimensional (3D) conflict graph and formulate the SSRT scheme into a mixed-integer nonlinear programming (MINLP) problem with a cross-layer spectrum-aware resource allocation and a hybrid transmission mode (including both single-hop and multi-hop). Since finding all the maximum independent sets (MIS) for the 3D conflict graph is an NP-hard problem, we further develop an iterative heuristic algorithm for the MIS determination.

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An adaptive system for detecting malicious queries in web attacks

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Science China(Information Sciences) 2018年第3期61卷 175-190页

作者： Ying DONG Yuqing ZHANG Hua MA Qianru WU Qixu LIU Kai WANG Wenjie WANG National Computer Network Intrusion Protection Center University of Chinese Academy of Sciences State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of Sciences School of Mathematics and Statistics Xidian University Security Department Alibaba Group Zhanlu Laboratory Tencent Incorporation

Web request query strings(queries), which pass parameters to a referenced resource, are always manipulated by attackers to retrieve sensitive data and even take full control of victim web servers and web applications. However, existing malicious query detection approaches in the literature cannot cope with changing web attacks. In this paper, we introduce a novel adaptive system(AMOD) that can adaptively detect web-based code injection attacks, which are the majority of web attacks, by analyzing queries. We also present a new adaptive learning strategy, called SVM HYBRID, leveraged by our system to minimize manual work. In the evaluation, an up-to-date detection model is trained on a ten-day query dataset collected from an academic institute's web server logs. The evaluation shows our approach overwhelms existing approaches in two respects. Firstly, AMOD outperforms existing web attack detection methods with an F-value of 99.50%and FP rate of 0.001%. Secondly, the total number of malicious queries obtained by SVM HYBRID is3.07 times that by the popular support vector machine adaptive learning(SVM AL) method. The malicious queries obtained can be used to update the web application firewall(WAF) signature library.

关键词： web attacks adaptive learning intrusion detection anomaly detection SVM