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L2OVX: An On-demand VPLS Service with Software-Defined Networks

L2OVX: An On-demand VPLS Service with Software-Defined Netwo...

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international Workshop on Bio and intelligent computing

作者： Jen-Wei Hu Chu-Sing Yang Te-Lung Liu National Center for High-performance Computing NARLabs Institute of Computer and Communication Engineering NCKU

ISBN: (纸本)9781509024629

Virtual Private LAN Service (VPLS) is a widely used network technology which connects geographically distributed customer sites as a local area network. However, the process of provisioning a new VPLS circuit is complicated because service provider has to check device configurations one by one. therefore, it is difficult to provide the service on demand. Open-Flow, the most notable protocol in Software Defined Networking (SDN), manage the network devices via a well-defined set of instructions and exposes the handling capability of flows to a centralized controller. By this logically centralized programmatic model, we have a consistent and convenient way to dynamically allocate an end-to-end path for a VPLS service. In this paper, we present a system L2OVX which leverages OpenVirteX, a network virtualization platform, to realize the VPLS service on-demand in SDN. this solution provides layer 2 translation to achieve virtualization process with line-rate performance, and enables the load balance function to improve the transfer bandwidth among tenants. the result of evaluation shows L2OVX is competitive with OpenVirteX according to the TCP throughput on software-based OpenFlow switches and moreover, supports most hardware OpenFlow-enabled switches to provide the line-rate throughput. Furthermore, for scalability, the number of flow entries for one tenant remains constant in L2OVX while grows exponentially in OpenVirteX.

关键词： Software-defined networking Network virtualization VPLS OpenFlow

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Path Planning Considering Time-Varying and Uncertain Movement Speed in Multi-Robot Automatic Warehouses: Problem Formulation and Algorithm

Path Planning Considering Time-Varying and Uncertain Movemen...

引用

IEEE international conference on Cloud computing and Intelligence Systems (CCIS)

作者： Jingchuan Chen Wei Chen Jing Li Xiguang Wei Wenzhe Tan Zuo-Jun Max Shen Hongbo Li Department of Industrial and Manufacturing Systems Engineering The University of Hong Kong Hong Kong China Geekplus Technology Co. Ltd. Beijing China Faculty of Engineering The University of Hong Kong Hong Kong China Faculty of Business and Economics The University of Hong Kong Hong Kong China College of Engineering University of California Berkeley Berkeley CA USA

ISBN: (纸本)9781665456579

Path planning in the multi-robot system refers to calculating a set of actions for each robot, which will move each robot to its goal without conflicting with other robots. Lately, the research topic has received significant attention for its extensive applications, such as airport ground, drone swarms, and automatic warehouses. Despite these available research results, most of the existing investigations are concerned with the cases of robots with a fixed movement speed without considering uncertainty. therefore, in this work, we study the problem of path-planning in the multi-robot automatic warehouse context, which considers the time-varying and uncertain robots’ movement speed. Specifically, the path-planning module searches a path with as few conflicts as possible for a single agent by calculating traffic cost based on customarily distributed conflict probability and combining it with the classic $\mathrm{A}^{*}$ algorithm. However, this probability-based method cannot eliminate all conflicts, and speed’s uncertainty will constantly cause new conflicts. As a supplement, we propose the other two modules. the conflict detection and re-planning module chooses objects requiring re-planning paths from the agents involved in different types of conflicts periodically by our designed rules. Also, at each step, the scheduling module fills up the agent’s preserved queue and decides who has a higher priority when the same element is assigned to two agents simultaneously. Finally, we compare the proposed algorithm with other algorithms from academia and industry, and the results show that the proposed method is validated as the best performance.

关键词： Industries Uncertainty Job shop scheduling Service robots Path planning Planning Multi-robot systems

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GameTE: A Game-theoretic distributed Traffic Engineering in Trustless Multi-Domain SDN

GameTE: A Game-Theoretic Distributed Traffic Engineering in ...

引用

international conference on distributed computing Systems

作者： Yangyang Liu Jingyu Hua Yuan Zhang Sheng Zhong The State Key Laboratory of Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

With growing network service demands, rational and efficient multi-domain resource allocation is paramount. Research aims to develop intelligent Traffic Engineering (TE) algorithms that can dynamically allocate resources, adapt to changing conditions, and meet user needs. TE algorithms based on Software-Defined Networking (SDN) have proven effective for this goal by leveraging the centralized control plane and programmable data plane of SDN. this enables flexible and dynamic optimization of routing and resource allocation across multiple domains to meet traffic demands. However, domains operated by different service providers may exhibit non-cooperative behavior due to conflicts of interest and competition. Some domains may act selfishly by hiding bandwidth or exaggerating inter-domain requests to reserve more resources for themselves. this complicates TE design as algorithms can no longer assume universal cooperation in multi-domain networks. Game theory provides a framework to model competition between domains through behaviors like request forwarding, dropping and study cooperation strategies. this paper presents GameTE, a game-theoretic distributed TE algorithm for multi-domain SDN environments without trusted relationships between domains. By incorporating incentives and punishments, our algorithm suppresses selfish behaviors and promotes efficient resource utilization. Evaluation results demonstrate that GameTE is effective in curbing deception, enhancing resource sharing between domains, and improving overall network performance compared to baseline schemes.

关键词： Heuristic algorithms Routing Dynamic scheduling Trustless services Resource management Software defined networking Game theory

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Measuring Network-Aware Worm Spreading Ability

Measuring Network-Aware Worm Spreading Ability

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IEEE Annual Joint conference: INFOCOM, IEEE Computer and Communications Societies

作者： Z. Chen C. Ji School of Electrical & Computer Engineering Georgia Institute of Technology Atlanta GA USA

this work investigates three aspects: (a) a network vulnerability as the non-uniform vulnerable-host distribution, (b) threats, i.e., intelligent worms that exploit such a vulnerability, and (c) defense, i.e., challenges for fighting the threats. We first study five data sets and observe consistent clustered vulnerable-host distributions. We then present a new metric, referred to as the non-uniformity factor, which quantifies the unevenness of a vulnerable-host distribution. this metric is essentially the Renyi information entropy and better characterizes the non-uniformity of a distribution than the Shannon entropy. We then analytically and empirically measure the infection rate and the propagation speed of network-aware worms. We show that a representative network-aware worm can increase the spreading speed by exactly or nearly a non-uniformity factor when compared to a random-scanning worm at the early stage of worm propagation. this implies that when a worm exploits an uneven vulnerable-host distribution as a network-wide vulnerability, the Internet can be infected much more rapidly. Furthermore, we analyze the effectiveness of defense strategies on the spread of network-aware worms. Our results demonstrate that counteracting network-aware worms is a significant challenge for the strategies that include host-based defense and IPv6.

关键词： Computer worms Internet Communications Society Electric variables measurement Computer networks distributed computing intelligent networks Information entropy Velocity measurement IP networks

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AN ANDROID MALWARE DETECTION MEthOD BASED ON ANDROIDMANIFEST FILE

AN ANDROID MALWARE DETECTION METHOD BASED ON ANDROIDMANIFEST...

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2016 4th IEEE international conference on Cloud computing and Intelligence Systems（IEEE CCIS2016）

作者： Xiang Li Jianyi Liu Yanyu Huo Ru Zhang Yuangang Yao Beijing University of Posts and Telecommunications China Information Technology Security Evaluation Center

ISBN: (纸本)9781509012572

As one of the most developed intelligent operating systems on mobile devices, Android has taken the most part of the cell phone market. A rapid increase in the number of mobile applications make them more and more relevant to people's daily lives than ever before. Due to Android's security mechanism and the validation lack of publishing Android apps, Android malware detection still remains to be a critical issue. To solve this problem, this paper found that the statistical information of Android components(mainly activity)from the Manifest file cannot be ignored, based on the traditional method of Android permission detection. In this paper, a new feature vector is extracted from the Android Manifest file, which combines the permission information and the component information of the Android application. We combine the naive Bias classification algorithm, and propose a malicious application detection method based on Android Manifest file information. the experimental results show that the new method performance better than that of the traditional permission detection.

关键词： information security malware detection Android permission Android component Bayesian-classification

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Smart devices for the early detection and interpretation of cardiological syndromes

Smart devices for the early detection and interpretation of ...

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IEEE EMBS international conference on Information technology Applications in Biomedicine (ITAB)

作者： F. Gouaux L. Simon-Chautemps S. Adami M. Arzi D. Assanelli J. Fayn M.C. Forlini C. Malossi A. Martinez J. Placide G.L. Ziliani P. Rubel INSERM ERM 107 Lyon France INSA-LISI Lyon France Elettronica Trentina Cavareno Italy University of Brescia Brescia Italy Soft Computing group STMicroelectronics Crema Italy

In western countries, heart disease is the main cause of premature death. Most of victims do not survive long enough to benefit from in-hospital treatments. To reduce the time before treatment, the only useful diagnostic tool to assess the presence of a cardiac event in the pre-hospital care situations is the electrocardiogram (ECG). Event and transtelephonic ECG recorders are being used to improve decision-making but require setting up new infrastructures. the pervasive solution proposed by the European EPI-MEDICS project is a wearable, intelligent Personal ECG Monitor for the early detection of cardiac events. It records pseudo-orthogonal 3-lead ECGs from an easy-to-wear 4-electrode sub-system embedding professional recording and processing capabilities, includes part of the patient electronic health record (EHR), embeds a web server and powerful soft computing decision-making techniques, generates different alarm levels and forwards alarm messages to the relevant care providers by means of new generation wireless communication. It is cost saving, involving care providers only if necessary, without requiring to set-up specific infrastructures. Healthcare becomes personalized, wearable and ubiquitous.

关键词： Cardiology Electrocardiography Decision making Biomedical monitoring distributed power generation Power generation Cardiac disease Patient monitoring Event detection Web server

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Dynamic-Static-based Spatiotemporal Multi-Graph Neural Networks for Passenger Flow Prediction

Dynamic-Static-based Spatiotemporal Multi-Graph Neural Netwo...

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international conference on Parallel and distributed Systems (ICPADS)

作者： Jingyan Ma Jingjing Gu Qiang Zhou Qiuhong Wang Ming Sun College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

Various sensing and computing technologies have gradually outlined the future of the intelligent city. Passenger flow prediction of public transports has become an important task in intelligent Transportation System (ITS), which is the prerequisite for traffic management and urban planning. there exist many methods based on deep learning for learning the spatiotemporal features from high non-linearity and complexity of traffic flows. However, they only utilize temporal correlation and static spatial correlation, such as geographical distance, which is insufficient in the mining of dynamic spatial correlation. In this paper, we propose the Dynamic-Static-based Spatiotemporal Multi-Graph Neural Networks model (DSSTMG) for predicting traffic passenger flows, which can concurrently incorporate the temporal and multiple static and dynamic spatial correlations. Firstly, we exploit the multiple static spatial correlations by multi-graph fusion convolution operator, including adjacent relation, station functional zone similarity and geographical distance. Secondly, we exploit the spatial dynamic correlations by calculating the similarity between the flow pattern of stations over a period of time, and build the dynamic spatial attention. Moreover, we use time attention and encoder-decoder architecture to capture temporal correlation. the experimental results on two realworld datasets show that the proposed DSSTMG outperforms state-of-the-art methods.

关键词： Correlation Convolution Neural networks Urban planning Spatiotemporal phenomena Sensors Task analysis

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Measuring the impact of cognitive distractions on driving performance using time series analysis

Measuring the impact of cognitive distractions on driving pe...

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17th international IEEE conference on intelligent Transportation Systems & the Asia-Pacific Council on Systems Engineering conference 2014

作者： Matias Garcia-Constantino Paolo Missier Phil Blythe Amy Weihong Guo the Computing Science Department Newcastle UniversityUnited Kingdom. the Transport Operations Research Group Newcastle UniversityUnited Kingdom.

Using current sensing technology,a wealth of data on driving sessions is potentially available through a combination of vehicle sensors and drivers' physiology sensors(heart rate,breathing rate,skin temperature,etc.).Our hypothesis is that it should be possible to exploit the combination of time series produced by such multiple sensors during a driving session,in order to(i) learn models of normal driving behaviour,and(ii) use such models to detect important and potentially dangerous deviations from the norm in real-time,and thus enable the generation of appropriate ***,we believe that such models and interventions should and can be personalised and tailor-made for each individual *** an initial step towards this goal,in this paper we present techniques for assessing the impact of cognitive distraction on drivers,based on simple time series *** have tested our method on a rich dataset of driving sessions,carried out in a professional simulator,involving a panel of volunteer *** session included a different type of cognitive distraction,and resulted in multiple time series from a variety of on-board sensors as well as sensors worn by the ***,each driver also recorded an initial session with no *** our model,such initial session provides the baseline times series that make it possible to quantitatively assess driver performance under distraction conditions.

关键词： Measuring the impact of cognitive distractions on driving performance using time series analysis

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Penta-Encoder with Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation

Penta-Encoder with Medical Transformer for Incomplete Multim...

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international conference on Signal Processing Proceedings (ICSP)

作者： Guohui Yu Lihui Wang Qijian Chen Li Wang Kun Tang Xinyu Cheng Yuemin Zhu State Key Laboratory of Public Big Data College of Computer Science and Technology Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Guizhou University Guiyang China INSA Lyon CNRS Inserm CREATIS UMR 5220 U1206 Univ Lyon Lyon France

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Accurate brain tumor segmentation of magnetic resonance imaging (MRI) images requires the integration of multimodal data in effective collaborative learning. However, clinical scenarios often face missing modalities, significantly impacting segmentation performance. In this paper, we propose the penta-encoder with medical transformer (Penta-MedTrans) for incomplete multimodal learning, which consists of four main components: a modality-specific encoder (MSE), a modality-dynamic encoder (MDE), a multimodal fusion module (MMFM) and a convolutional decoder. the MSE extracts unique features for each modality, while the MDE captures inter-modal semantics to address missing modalities. the MMFM establishes and aligns long-range correlations between modal-invariant features and global semantics of tumor regions. the convolutional decoder generates robust segmentation results by progressively upsampling and fusing modality-invariant features. We extensively validated our method on the BraTS 2020 and BraTS 2021 datasets for brain tumor segmentation. Results showed that our method outperforms SOTA for incomplete multimodal brain tumor segmentation across most subsets.

关键词： Image segmentation Correlation Convolution Magnetic resonance imaging Semantics Brain tumors Transformers Feature extraction Decoding Biomedical imaging

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Device Management Model based on Internet of things 22

Device Management Model based on Internet of Things

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Proceedings of the 2022 6th international conference on Electronic Information technology and Computer Engineering

作者： Yizhuo Yang Hongjin Zeng Tingping Chen Northwest Institute of Nuclear Technology China

ISBN: (纸本)9781450397148

Under the rapid development of Internet of things (IoT), it is possible to connect more intelligent devices by network. Recent research shows that IoT has great application potential in information-intensive industries. However, the transmission protocols of devices produced by different manufacturers are diverse, which makes it difficult for terminal devices to access the network. In the application layer, different devices adopt different parsing protocols, which also makes it difficult to parse data. Firstly, this paper proposes a set of IoT access model, which describes the attributes and behaviors of access network devices by ternary form Internet of things Data (IoTD) represented by Device Object (DO), Link Object (LO) and State Object (SO). Secondly, based on the IoT access model, a set of device access system is realized combined with cloud computing and edge computing technology, so that devices with different protocols can quickly access the network. Finally, the device access system is verified by experiments from three aspects: heterogeneous connectivity, delay characteristics and throughput characteristics. the results show that in the distributed heterogeneous protocol device environment, the device access system based on the IoT access model supports the fast access of multiple protocol devices, and has the transmission performance that meets the field requirements.

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