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Q-Learning Based Energy Harvesting for Heterogeneous Statistical QoS Provisioning Over Multihop Big-Data Relay Networks 12

Q-Learning Based Energy Harvesting for Heterogeneous Statist...

引用

IEEE Int Congr on Cybermat / 12th IEEE Int Conf on Cyber, Phys and Social Comp (CPSCom) / 15th IEEE Int Conf on Green computing and Communications (GreenCom) / 12th IEEE Int Conf on Internet of Things (iThings) / 5th IEEE Int Conf on Smart Data

作者： Zhang, Xi Wang, Jingqing Zhu, Qixuan Texas A&M Univ Dept Elect & Comp Engn Networking & Informat Syst Lab College Stn TX 77843 USA

ISBN: (纸本)9781728129808

With the increasing demand for the data-intensive wireless multimedia services over the time-varying wireless channels, the big-data based wireless networks demand the 5G candidate framework to process such massive amount of multimedia data without causing extra burden to the backhaul links in supporting the heterogeneous statistical delay-bounded quality-of-service (QoS) provisionings. Due to the benefits of energy harvesting (EH) technologies, wireless devices are able to support the data intensive wireless multimedia services by harvesting energy from the environment. Energy harvesting has emerged as the promising technology to solve the energy supply problem while bringing new challenges due to the stochastic nature of the harvested energy in supporting the heterogeneous statistical quality-of-service (QoS) provisionings. However, due to the unknown dynamics of the harvested energy as well as the channel state information (CSI), it is challenging to design the efficient routing protocol for selecting the optimal routing and power allocation policies under the statistical delay-bounded QoS constraints. To overcome the aforementioned problems, in this paper we propose the Q-learning based optimal routing and power allocation policies through learning from the history of the energy harvesting process while satisfying the heterogeneous statistical delay-bounded QoS constraints over multihop big-data relay networks. In particular, under the heterogeneous statistical delay-bounded QoS requirements, we formulate the end-to-end effective-capacity optimization problem for the battery free energy harvesting based big-data multihop relay networks. Then, we apply the Markov decision process and Q-learning methods for deriving the optimal multihop routing algorithms over big-data multihop relay networks. Also conducted is a set of simulations which evaluate the system performances and show that our proposed Q-learning based multihop routing scheme outperforms the other exist

关键词： Q-learning energy harvesting heterogeneous statistical delay-bounded quality of service (QoS) effective capacity multihop big-data relay networks

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Analyzing Algorithms to Detect Disaster Events using Social Media

Analyzing Algorithms to Detect Disaster Events using Social ...

引用

International conference on Information Technology and multimedia (ICIM)

作者： Faris Azni Azlan Azhana Ahmad Salman Yussof Azimah Abdul Ghapar College of Graduate Studies Universiti Tenaga Nasional Kajang Malaysia College of Computing and Informatics Universiti Tenaga Nasional Kajang Malaysia

ISBN: (数字)9781728173108

ISBN: (纸本)9781728173115

Disasters are instabilities that occur on the interface between society and the environment. During disasters, people communicate to inform and request for support for themselves or their community. Social media is used as a medium for communication due to its wide reach and global audience. During disasters, people communicate via messages regarding similar or different types of emergencies in the same general location. Interpreting and validating these messages during the occurrence of a disaster costs a significant time and loss. Therefore, this study presents a comparison between three models, K-Nearest Neighbor (KNN), Naive Bayes (NB), and Support Vector Machine (SVM), to classify and label a message as a disaster event. In order to simulate the examining process further, a categorization system is introduced to categorize the severity of a disaster as described in each message in a disaster environment. performances are compared for each of the models using classification scores of supervised learning.

关键词： Support vector machines Social networking (online) Supervised learning Machine learning Predictive models Classification algorithms Task analysis

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Deep feature interaction embedding for pair matching prediction 1

Deep feature interaction embedding for pair matching predict...

引用

1st ACM International conference on multimedia in Asia, MMAsia 2019

作者： Zhang, Luwei Wang, Xueting Yamasaki, Toshihiko University of Tokyo Japan

ISBN: (纸本)9781450368414

Online dating services have become popular in modern society. Pair matching prediction between two users in these services can help efficiently increase the possibility of finding their life partners. Deep learning based methods with automatic feature interaction functions such as Factorization Machines (FM) and cross network of Deep & Cross Network (DCN) can model sparse categorical features, which are effective to many recommendation tasks of web applications. To solve the partner recommendation task, we improve these FM-based deep models and DCN by enhancing the representation of feature interaction embedding and proposing a novel design of interaction layer avoiding information loss. Through the experiments on two real-world datasets of two online dating companies, we demonstrate the superior performances of our proposed designs. © 2019 Association for computing Machinery.

关键词： Social networking (online)

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New cross-domain QoE guarantee method based on isomorphism flow 14th

New cross-domain QoE guarantee method based on isomorphism f...

引用

14th EAI International conference on Collaborative computing: networking, Applications and Worksharing, CollaborateCom 2018

作者： Wang, Zaijian Chen, Chen Wang, Xinheng Yang, Lingyun Tang, Pingping Anhui Normal University WuhuAnhui China University of West London LondonW5 5RF United Kingdom

ISBN: (纸本)9783030129804

This paper investigates the issue of Quality of Experience (QoE) for multimedia services over heterogeneous networks. A new concept of "Isomorphism Flow" (iFlow) was introduced for analyzing multimedia traffics, which is inspired by the abstract algebra based on experimental research. By using iFlow, the multimedia traffics with similar QoE requirements for different users are aggregated. A QoE evaluation method was also proposed for the aggregated traffics. Then a new cross-domain QoE guarantee method based on the iFlow QoE is proposed in this paper to adjust the network resource from the perspective of user perception. The proposed scheme is validated through simulations. Simulation results show that the proposed scheme achieves an enhancement in QoE performance and outperforms the existing schemes. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： multimedia services

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Architecture of a Cloud-based Fault-Tolerant Control Platform for improving the QoS of Social multimedia Applications on SD-WAN

Architecture of a Cloud-based Fault-Tolerant Control Platfor...

引用

International conference on Communications (COMM)

作者： Kashinath Basu Aws Hamdullah Frank Ball School of Engineering Computing and Mathematics Oxford Brookes University Oxford UK Frank Ball Consulting Oxford UK

ISBN: (数字)9781728156118

ISBN: (纸本)9781728156125

Social media application are becoming multimedia centric with live and stored video, audio, augmented reality, haptic, etc. emerging as the main categories of traffic. Their QoS requirements are more stringent than their legacy counterparts. At the carrier level, Software Defined - Wide Area Network (SD-WAN) is one of the promising technologies for transporting these multimedia traffic. A SD-WAN will typically have a mesh of centralized controllers managing the networking infrastructure. Reliable operations of these controllers are a key requirement for the successful operation of the WAN. Controller failure will prevent the forwarding switches from communicating with the controller. This will prevent the switches from forwarding any new traffic, as well as flow entries from existing traffic will also time out after a period bringing the network to a standstill. Rebooting a controller or starting a new one will introduce delays degrading the QoS. This research presents an architecture for handling controller failure via transparent migration of the controller load in a semi-meshed controller environment. The architecture includes a real time cloud-based centralized storage of the flow states north of the controllers and a virtualized connection management unit at the south. The results demonstrate that the proposed model can transparently handle controller failure without affecting the QoS.

关键词： Control systems Fault tolerance Fault tolerant systems Delays Quality of service Computer architecture

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Chaining of hardware accelerated Virtual Network Functions in PCIe Environments 19

Chaining of hardware accelerated Virtual Network Functions i...

引用

20th International Middleware conference Demos and Posters, Middleware 2019

作者： Kundel, Ralf Burkert, Tim Griwodz, Carsten Koldehofe, Boris Multimedia Communications Lab Technische Universität Darmstadt Germany Zoi TechCon GmbH Stuttgart Germany Department of Informatics University of Oslo Norway

ISBN: (纸本)9781450370424

The growth of compute-intensive applications causes an increasing demand for computing resources in both data centers and underlying networks. To satisfy these computing and networking demands, hardware-accelerated computing with GPUs and FPGAs becomes more and more prevalent. However, current approaches of accelerated Virtual Network Functions (VNF), typically based on PCIe accelerator cards, suffer from many superfluous data transfers between the memory of hosts and accelerator devices. In this work we propose "host bypassing" for chained hardware-accelerated network functions, which reduces memory copying to a minimum, which increases throughput and reduces latency. © 2019 Copyright held by the owner/author(s).

关键词： Field programmable gate arrays (FPGA)

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Twitter Scrapping for Profiling Education Staff

Twitter Scrapping for Profiling Education Staff

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International conference on Informatics and computing (ICIC)

作者： Herlawati Herlawati Rahmadya Trias Handayanto Inna Ekawati Kardinah Indrianna Meutia Jelita Asian Umar Aditiawarman Informatics Engineering Universitas Bhayangkara Jakarta Raya Bekasi Indonesia Computer Engineering Universitas Islam 45 Bekasi Indonesia Management Universitas Bhayangkara Jakarta Raya Bekasi Indonesia School of Computer Science Nusa Putra University Sukabumi Indonesia

ISBN: (数字)9781728180472

ISBN: (纸本)9781728180489

Social media (Facebook, Instagram, Twitter, etc.) have been widely used. They have many advantages, especially for business. However, such media sometimes invite negative effects, e.g. decreasing employee performance, conflict in a relationship, crime, etc. Therefore, this study proposes a method to scrap one of the social media, i.e. Twitter for profiling. Gephi application is used for network analysis after scrapping the network using Twecoll, a Python-based scrapping application. A web-based application is also created including the Apache-based server and Python-based script. The result shows that the scrapped account has several groups/communities including the weight of each connection. In addition, the result can be used for group profiling and additional analysis to complete the sentiment analysis based on tweets.

关键词： Social networking (online) Terrorism Education Blogs multimedia Web sites Organizations Servers

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Towards H-SDN Traffic Analytic Through Visual Analytics and Machine Learning 1

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12th International conference on Security, Privacy, and Anonymity in Computation, Communication, and Storage, SpaCCS 2019

作者： Tze Chiang, Tin Saw Chin, Tan Ching Kwang, Lee Zulfadzli, Y. Rizaludin, K. Faculty of Informatics and Computing Multimedia University CyberjayaSelangor63100 Malaysia Faculty of Engineering Multimedia University CyberjayaSelangor63100 Malaysia Telekom Malaysia R&D Jalan Persiaran Mulitmedia CyberjayaSelangor63100 Malaysia

ISBN: (数字)9783030249076

ISBN: (纸本)9783030249069

With new networking paradigm emerged through Software-Defined networking (SDN) offering various networking advantages over the traditional paradigm, organizations are attracted to migration their legacy networks to SDN networks. However, it is both challenging and impractical for organizations to migrate from traditional network architecture to full SDN architecture overnight. Therefore, the migration plan is performed in stages, resulting in a new type of network termed hybrid SDN (H-SDN). Effective migration and traffic scheduling in H-SDN environment are the two areas of challenges organizations face. Various solutions have been proposed in the literatures to address these two challenges. Differing from the approaches taken in the literatures, this work utilizes visual analytic and machine learning to address the two challenges. In both full SDN and H-SDN environment, literatures showed that data analytics applications have been successfully developed for various purposes as network security, traffic monitoring and traffic engineering. The success of data analytic applications is highly dependent on prior data analysis from both automated processing and human analysis. However, with the increasing volume of traffic data and the complex networking environment in both SDN and H-SDN networks, the need for both visual analytic and machine learning in inevitable for effective data analysis of network problems. Hence, the objectives of this article are three-folds: Firstly, to identify the limitations of the existing migration plan and traffic scheduling in H-SDN, followed by highlighting the challenges of the existing research works on SDN analytics in various network applications, and lastly, to propose the future research directions of SDN migration and H-SDN traffic scheduling through visual analytics and machine learning. Finally, this article presents the proposed framework termed VA-hSDN, a framework that utilizes visual analytics with machine learning to meet t

关键词： Scheduling

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A social link based private storage cloud

A social link based private storage cloud

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International conference on Parallel and Distributed Systems (ICPADS)

作者： Michalis Konstantopoulos Nikos Chondros Mema Roussopoulos University of Athens Greece

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

In this paper, we present O 3 , a social link based private storage cloud for decentralized collaboration. O 3 allows users to share and collaborate on collections of files (shared folders) with others, in a decentralized manner, without the need for intermediaries (such as public cloud storage servers) to intervene. Thus, users can keep their working relationships (who they work with) and what they work on private from third parties. Using benchmarks and traces from real workloads, we experimentally evaluate O 3 and demonstrate that the system scales linearly when synchronizing increasing numbers of concurrent users, while performing on-par with the ext4 non-version-tracking filesystem.

关键词： Cloud computing Protocols Social networking (online) Web and internet services Collaboration Servers multimedia communication

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Glad: Global And Local Anomaly Detection

Glad: Global And Local Anomaly Detection

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IEEE International conference on multimedia and Expo (ICME)

作者： Lihai Nie Laiping Zhao Keqiu Li Tianjin Key Lab. of Advanced Networking (TANKLab) College of Intelligence and Computing (CIC) Tianjin University

ISBN: (数字)9781728113319

ISBN: (纸本)9781728113326

Detecting anomaly in images is challenging due to the high dimension nature of image data. While the previous learning-based anomaly detection approaches can detect a particular type of anomaly precisely, they often fail in detecting multiple types of abnormal samples *** identify the two specific types of anomalies that can be precisely detected by either compress-based or reconstruction-based anomaly detection approaches, named global anomaly and local anomaly. We then propose Glad, an anomaly detector that can precisely detect both of them at the same time. Glad adopts a joint approach combining the density estimation and auto-encoder. Firstly, it designs a multimodal density estimation model to derive the latent representation probability for identifying the global anomaly. Then, it uses structural similarity to measure the reconstruction loss for characterizing local anomaly. Finally, both anomalies can be diagnosed according to the joint density of latent representation and reconstruction loss. Experimental results on public benchmark datasets demonstrate that Glad outperforms the state-of-the-art methods significantly.

关键词： Image reconstruction Anomaly detection Estimation Decoding Training Neural networks Convolution

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