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arXiv 2018年

作者： Deng, Yang Li, Yaliang Shen, Ying Du, Nan Fan, Wei Yang, Min Lei, Kai School of Electronics and Computer Engineering Peking University PCL Research Center of Networks and Communications Peng Cheng Laboratory Chinese University of Hong Kong Alibaba Group Tencent Medical AI Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences

Knowledge Graph (KG) contains entities and the relations between entities. Due to its representation ability, KG has been successfully applied to support many medical/healthcare tasks. However, in the medical domain, knowledge holds under certain conditions. For example, symptom runny nose highly indicates the existence of disease whooping cough when the patient is a baby rather than the people at other ages. Such conditions for medical knowledge are crucial for decision-making in various medical applications, which is missing in existing medical KGs. In this paper, we aim to discovery medical knowledge conditions from texts to enrich KGs. Electronic Medical Records (EMRs) are systematized collection of clinical data and contain detailed information about patients, thus EMRs can be a good resource to discover medical knowledge conditions. Unfortunately, the amount of available EMRs is limited due to reasons such as regularization. Meanwhile, a large amount of medical question answering (QA) data is available, which can greatly help the studied task. However, the quality of medical QA data is quite diverse, which may degrade the quality of the discovered medical knowledge conditions. In the light of these challenges, we propose a new truth discovery method, MedTruth, for medical knowledge condition discovery, which incorporates prior source quality information into the source reliability estimation procedure, and also utilizes the knowledge triple information for trustworthy information computation. We conduct series of experiments on real-world medical datasets to demonstrate that the proposed method can discover meaningful and accurate conditions for medical knowledge by leveraging both EMR and QA data. Further, the proposed method is tested on synthetic datasets to validate its effectiveness under various scenarios. Copyright © 2018, The Authors. All rights reserved.

关键词： Medical computing

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Evaluation of Flexilink as Deterministic Unified Real-Time Protocol for Industrial networks

Evaluation of Flexilink as Deterministic Unified Real-Time P...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Tianao Ma Wei Hu Yonghao Wang Dalia El-Banna John Grant Hongjun Dai College of Computer Science and Technology Wuhan University of Science and Technology China DMT Lab Birmingham City University UK Nine Tiles Networks Ltd Cambridge UK School of Software Engineering Shandong University Jinan P.R. China

The QoS of real-time network based on the traditional TCP/IP architecture is always perceived as a dilemma. The current real-time network design is trade-off between different network performances such as delay, packet loss, and trans-mission speed. We believe the traditional network architecture becomes a barrier of deterministic network performance. Time deterministic networking is demanded by some applications such as industrial control network, aviation, and interactive multime-dia. In addition, it is important to guarantee the data integrity and timing accuracy even when the network is congested. This paper proposed a scheduling model for Flexilink which is a newly proposed dynamic TDM network protocol and architecture that is approved to be secure and stable. We compared proposed scheduling algorithm for Flexilink in comparison with classic best effort network and priority based network using simulation. The experiments results show that the proposed scheduling algorithm performs very stable even when the network is heavily loaded.

关键词： Real-time systems Task analysis Protocols Delays Packet loss Quality of service

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Block-Wise Index Modulation and Receiver Design for High-Mobility OTFS Communications

arXiv

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arXiv 2023年

作者： Qian, Mi Ji, Fei Ge, Yao Wen, Miaowen Cheng, Xiang Poor, H. Vincent The School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China The Continental-NTU Corporate Lab Nanyang Technological University Singapore The State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronics School of Electronics Engineering and Computer Science Peking University Beijing100871 China The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

As a promising technique for high-mobility wireless communications, orthogonal time frequency space (OTFS) has been proved to enjoy excellent advantages with respect to traditional orthogonal frequency division multiplexing (OFDM). Although multiple studies have considered index modulation (IM) based OTFS (IM-OTFS) schemes to further improve system performance, a challenging and open problem is the development of effective IM schemes and efficient receivers for practical OTFS systems that must operate in the presence of channel delays and Doppler shifts. In this paper, we propose two novel block-wise IM schemes for OTFS systems, named delay-IM with OTFS (DeIM-OTFS) and Doppler-IM with OTFS (DoIM-OTFS), where a block of delay/Doppler resource bins are activated simultaneously. Based on a maximum likelihood (ML) detector, we analyze upper bounds on the average bit error rates for the proposed DeIM-OTFS and DoIM-OTFS schemes, and verify their performance advantages over the existing IM-OTFS systems. We also develop a multi-layer joint symbol and activation pattern detection (MLJSAPD) algorithm and a customized message passing detection (CMPD) algorithm for our proposed DeIM-OTFS and DoIM-OTFS systems with low complexity. Simulation results demonstrate that our proposed MLJSAPD and CMPD algorithms can achieve desired performance with robustness to the imperfect channel state information (CSI). © 2023, CC BY.

关键词： Message passing

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Data analytics for fog computing by distributed online learning with asynchronous update

arXiv

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arXiv 2019年

作者： Li, Guangxia Zhao, Peilin Lu, Xiao Liu, Jia Shen, Yulong Shaanxi Key Laboratory of Network and System Security Xidian University China State Key Laboratory of Integrated Service Networks Xidian University China School of Computer Science and Technology Xidian University China Tencent AI Lab Tencent Inc. China Department of Electrical and Computer Engineering University of Alberta Canada

—Fog computing extends the cloud computing paradigm by allocating substantial portions of computations and services towards the edge of a network, and is, therefore, particularly suitable for large-scale, geo-distributed, and data-intensive applications. As the popularity of fog applications increases, there is a demand for the development of smart data analytic tools, which can process massive data streams in an efficient manner. To satisfy such requirements, we propose a system in which data streams generated from distributed sources are digested almost locally, whereas a relatively small amount of distilled information is converged to a center. The center extracts knowledge from the collected information, and shares it across all subordinates to boost their performances. Upon the proposed system, we devise a distributed machine learning algorithm using the online learning approach, which is well known for its high efficiency and innate ability to cope with streaming data. An asynchronous update strategy with rigorous theoretical support is applied to enhance the system robustness. Experimental results demonstrate that the proposed method is comparable with a model trained over a centralized platform in terms of the classification accuracy, whereas the efficiency and scalability of the overall system are improved. Copyright © 2019, The Authors. All rights reserved.

关键词： Fog computing

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Covert Transmission Assisted by Intelligent Reflecting Surface

arXiv

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arXiv 2020年

作者： Si, Jiangbo Li, Zan Cheng, Julian Guan, Lei Shi, Jia Al-Dhahir, Naofal Integrated Service Networks Lab of Xidian University Xi’an710071 China School of Engineering University of British Columbia KelownaBCV1V 1V7 Canada Department of Electrical and Computer Engineering University of Texas at Dallas RichardsonTX75080 United States

Covert transmission is studied for an intelligent reflecting surface (IRS) aided communication system, where Alice aims to transmit messages to Bob without being detected by the warden Willie. Specifically, an IRS is used to increase the data rate at Bob under a covert constraint. For the considered model, when Alice is equipped with a single antenna, the transmission power at Alice and phase shifts at the IRS are jointly optimized to maximize the covert transmission rate with either instantaneous or partial channel state information (CSI) of Willie’s link. In addition, when multiple antennas are deployed at Alice, we formulate a joint transmit beamforming and IRS phase shift optimization problem to maximize the covert transmission rate. One optimal algorithm and two low-complexity suboptimal algorithms are proposed to solve the problem. Furthermore, for the case of imperfect CSI of Willie’s link, the optimization problem is reformulated by using the triangle and the Cauchy-Schwarz inequalities. The reformulated optimization problems are solved using an alterative algorithm, semidefinite relaxation (SDR) and Gaussian randomization techniques. Finally, simulations are performed to verify our analysis. The simulation results show that an IRS can degrade the covert transmission rate when Willie is closer to the IRS than Bob. Copyright © 2020, The Authors. All rights reserved.

关键词： Optimization

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ZSTAD: Zero-shot temporal activity detection

arXiv

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arXiv 2020年

作者： Zhang, Lingling Chang, Xiaojun Liu, Jun Luo, Minnan Wang, Sen Ge, Zongyuan Hauptmann, Alexander School of Computer Science and Technology Xian Jiaotong University Xian China Ministry of Education Key Lab For Intelligent Networks and Network Security Xian China Faculty of Information Technology Monash University Australia National Engineering Lab for Big Data Analytics Xian Jiaotong University Xian China School of Information Technology and Electrical Engineering University of Queensland Australia School of Computer Science Carnegie Mellon University United States

An integral part of video analysis and surveillance is temporal activity detection, which means to simultaneously recognize and localize activities in long untrimmed videos. Currently, the most effective methods of temporal activity detection are based on deep learning, and they typically perform very well with large scale annotated videos for training. However, these methods are limited in real applications due to the unavailable videos about certain activity classes and the time-consuming data annotation. To solve this challenging problem, we propose a novel task setting called zero-shot temporal activity detection (ZSTAD), where activities that have never been seen in training can still be detected. We design an end-to-end deep network based on R-C3D as the architecture for this solution. The proposed network is optimized with an innovative loss function that considers the embeddings of activity labels and their super-classes while learning the common semantics of seen and unseen activities. Experiments on both the THUMOS14 and the Charades datasets show promising performance in terms of detecting unseen activities. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Secure Wireless Communication via Movable-Antenna Array

arXiv

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arXiv 2023年

作者： Hu, Guojie Wu, Qingqing Xu, Kui Si, Jiangbo Al-Dhahir, Naofal The College of Communication Engineering Rocket Force University of Engineering Xi’an710025 China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China The College of Communications Engineering The Army of Engineering University Nanjing210007 China The Integrated Service Networks Lab of Xidian University Xi’an710100 China The Department of Electrical and Computer Engineering The University of Texas at Dallas RichardsonTX75080 United States

Movable antenna (MA) array is a novel technology recently developed where positions of transmit/receive antennas can be flexibly adjusted in the specified region to reconfigure the wireless channel and achieve a higher capacity. In this letter, we, for the first time, investigate the MA array-assisted physical-layer security where the confidential information is transmitted from a MA array-enabled Alice to a single-antenna Bob, in the presence of multiple single-antenna and colluding eavesdroppers. We aim to maximize the achievable secrecy rate by jointly designing the transmit beamforming and positions of all antennas at Alice subject to the transmit power budget and specified regions for positions of all transmit antennas. The resulting problem is highly non-convex, for which the projected gradient ascent (PGA) and the alternating optimization methods are utilized to obtain a high-quality suboptimal solution. Simulation results demonstrate that since the additional spatial degree of freedom (DoF) can be fully exploited, the MA array significantly enhances the secrecy rate compared to the conventional fixed-position antenna (FPA) array. Copyright © 2023, The Authors. All rights reserved.

关键词： Beamforming

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Joint Task Scheduling and Container Image Caching in Edge Computing

arXiv

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arXiv 2023年

作者： Mou, Fangyi Tang, Zhiqing Lou, Jiong Guo, Jianxiong Wang, Wenhua Wang, Tian Institute of Artificial Intelligence and Future Networks Beijing Normal University China Department of Computer Science and Engineering Shanghai Jiao Tong University China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College China

In Edge Computing (EC), containers have been increasingly used to deploy applications to provide mobile users services. Each container must run based on a container image file that exists locally. However, it has been conspicuously neglected by existing work that effective task scheduling combined with dynamic container image caching is a promising way to reduce the container image download time with the limited bandwidth resource of edge nodes. To fill in such gaps, in this paper, we propose novel joint Task Scheduling and Image Caching (TSIC) algorithms, specifically: 1) We consider the joint task scheduling and image caching problem and formulate it as a Markov Decision Process (MDP), taking the communication delay, waiting delay, and computation delay into consideration;2) To solve the MDP problem, a TSIC algorithm based on deep reinforcement learning is proposed with the customized state and action spaces and combined with an adaptive caching update algorithm. 3) A real container system is implemented to validate our algorithms. The experiments show that our strategy outperforms the existing baseline approaches by 23% and 35% on average in terms of total delay and waiting delay, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Edge computing

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Distributed Machine Learning for Resilient Operation of Electric Systems

Distributed Machine Learning for Resilient Operation of Elec...

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International Conference on Smart Energy Systems and Technologies (SEST)

作者： M. Hadi Amini Ahmed Imteaj Javad Mohammadi School of Computing and Information Sciences Florida International University Miami FL USA Sustainability Optimization and Learning for InterDependent networks laboratory (solid lab) FIU Miami FL USA Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9781728147017

ISBN: (纸本)9781728147024

Power system resilience is crucial to ensure secure energy delivery to electricity consumers. Power system outages lead to economical and societal burdens for the society and industries. To mitigate the socio-economical impacts of a power outage, we need to develop efficient algorithms to ensure resilient operation of the power system. In this paper, we first explain the notion of data-driven resilience. Then, we present a pathway of leveraging edge intelligence to improve resilience. To this end, we propose a novel distributed machine learning paradigm. Our proposed structure relies on local Resilience Management Systems (RMS) that serve as intelligent decision-making entities in each area, e.g. an autonomous micro-grid or a smart home can act as RMS. The RMS agents, which are available in different areas, can share their local data (i.e., a microgrid's operational data) with their neighboring RMS to coordinate their decisions in a distributed fashion. This will provide two major advantages: 1) distributed intelligence replaces centralized decision-making leading to robust decision-making and enhanced resilience; 2) since local data are locally shared among all entities within an RMS, if one of the RMS agents fails to communicate with the rest of network, we still can maintain a feasible solution (which is not necessarily optimal). Finally, we presents different scenarios in the simulation results section that showcases the system performance for two buildings under various outage scenarios.

关键词： Resilience Power system reliability Decision making Optimization Load management Collaborative work

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Traffic-Aware Fast Reroute Mechanism Exploiting Disjoint Subpaths for Named Data networks

Traffic-Aware Fast Reroute Mechanism Exploiting Disjoint Sub...

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IEEE International Conference on Communications (ICC)

作者： Boyang Zhou Chunming Wu Qiang Yang Zhen Zhu Yinghui Nie Wenjie Yu Zhejiang Lab Intelligent Networks Institute Hangzhou China College of Computer Science Zhejiang University Hangzhou China College of Electrical Engineering Zhejiang University Hangzhou China School of Intelligent Science and Technology Hangzhou Institute for Advanced Study UCAS Hangzhou China

The popularized named data network (NDN) needs a fast reroute (FRR) to improve resilience in dealing with the norm of link faults. The resilience of the existing disjoint arborescences and paths based approaches is restricted in reroute options, due to the limited number of disjoint end-to-end routes available. Such restrictions can be weakened by exploiting of the disjoint subpaths (DSP) between two intermediate nodes in the primary path (PP), as what DSP can link together is not only the two ends. In this paper, we propose a novel NDN based traffic-aware fast reroute (TA-FRR) mechanism that exploits a new resilient subgraph (RSG) route involving multiple DSPs. TA-FRR proactively constructs an RSG with scalable computational complexity to maximize the resilience and to avoid a too high reroute stretch. In the RSG, TA-FRR efficiently computes the shortest detour path for the disconnected components actively identified according to packet arrival timeouts. We evaluated the performance of TA-FRR in the ndnSIM simulator using synthsized Internet topologies with different degrees of link faults. The results show that TA-FRR can efficiently recover serious link faults in PP with a scalable RSG construction and outperforms the existing approaches with a significantly higher resilience and the maximum stretch similar to the arborescences approach.

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