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An implementation of Scalable, Vegas, Veno, and YeAH congestion control algorithms in ns-3 16

An implementation of Scalable, Vegas, Veno, and YeAH congest...

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8th Workshop on Network Simulator (ns-3), WNS3 2016

作者： Nguyen, Truc Anh N. Gangadhar, Siddharth Rahman, Md Moshfequr Sterbenz, James P.G. Information and Telecommunication Technology Center Department of Electrical Engineering and Computer Science The University of Kansas LawrenceKS66045 United States InfoLab21 Lancaster University LA1 4WA United Kingdom Department of Computing Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong

ISBN: (纸本)9781450342162

Despite the modern advancements in networking, TCP congestion control is still one of the key mechanisms that ensure the stability of the Internet. Given its principal role, it is a popular research topic revisited every time TCP and its variants are studied. Open-source network simulators such as ns-3 are important tools used by the research community to gain valuable insight into existing TCP congestion control algorithms and to develop new variants. However, the current TCP infrastructure in ns-3 supports only a few congestion control algorithms. As part of the ongoing effort to extend TCP functionalities in the simulator, we implement Scalable, Vegas, Veno, and YeAH based on the original literature and their implementations in the Linux kernel;this paper presents our implementation details. the paper also discusses our validation of the added models against the theories to demonstrate their correctness. through our evaluation, we highlight the key features of each algorithm that we study. © 2016 ACM.

关键词： Simulators

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Detecting rumors through modeling information propagation networks in a social media environment 8

Detecting rumors through modeling information propagation ne...

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8th international conference on Social computing, Behavioral-Cultural Modeling, and Prediction, SBP 2015

作者： Liu, Yang Xu, Songhua Tourassi, Georgia New Jersey Insititute of Technology University Heights NewarkNJ07102 United States Health Data Sciences Institute Oak Ridge National Laboratory Biomedical Science and Engineering Center 1 Bethel Valley Road Oak RidgeTN37830 United States

ISBN: (纸本)9783319162676

In the midst of today’s pervasive influence of social media content and activities, information credibility has increasingly become a major issue. Accordingly, identifying false information, e.g. rumors circulated in social media environments, attracts expanding research attention and growing interests. Many previous studies have exploited user-independent features for rumor detection. these prior investigations uniformly treat all users relevant to the propagation of a social media message as instances of a generic entity. Such a modeling approach usually adopts a homogeneous network to represent all users, the practice of which ignores the variety across an entire user population in a social media environment. Recognizing this limitation of modeling methodologies, this study explores user-specific features in a social media environment for rumor detection. the new approach hypothesizes that whether a user tends to spread a rumor is dependent upon specific attributes of the user in addition to content characteristics of the message itself. Under this hypothesis, information propagation patterns of rumors versus those of credible messages in a social media environment are systematically differentiable. To explore and exploit this hypothesis, we develop a new information propagation model based on a heterogeneous user representation for rumor recognition. the new approach is capable of differentiating rumors from credible messages through observing distinctions in their respective propagation patterns in social media. Experimental results show that the new information propagation model based on heterogeneous user representation can effectively distinguish rumors from credible social media content. © Springer international Publishing Switzerland 2015.

关键词： Social networking (online)

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Creating private network overlays for high performance scientific computing 2007

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Proceedings of the 8th ACM/IFIP/USENIX international conference on Middleware

作者： Edward Walker Texas Advanced Computing Center The University of Texas at Austin Austin Texas

ISBN: (纸本)9783540767770

In this paper we describe an approach in creating private network overlays in user-space to support the dynamic creation of personal clusters on-demand. these personal clusters are created by submitting job proxies to High Performance computing (HPC) clusters. Job proxies contribute CPU resources back to the personal cluster when they eventually run, allowing application jobs to execute on them in a system call virtualized run-time environment. the virtualized run-time environment enables additional personal cluster-wide services to be interposed, including a private network overlay instantiated for each personal cluster created. the interposed private network overlay allows the personal clusters to tunnel IP traffic thorough gateway nodes at each contributing HPC cluster site in order to provision resources across private networks, survive transient network outages, support critical services like distributed filesystems, and in some cases, improve network transfer throughput across the wide-area network. this paper describes our design and implementation strategy, and concludes with some general guiding principles to aid other projects of a similar nature.

关键词： overlay networks high performance computing resource management cluster computing

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BIGS: A framework for large-scale image processing and analysis over distributed and heterogeneous computing resources

BIGS: A framework for large-scale image processing and analy...

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IEEE international conference on e-Science and Grid computing

作者： Raúl Ramos-Pollán Fabio A. González Juan C. Caicedo Angel Cruz-Roa Jorge E. Camargo Jorge A. Vanegas Santiago A. Pérez Jose David Bermeo Juan Sebastián Otálora Paola K. Rozo John E. Arévalo Bioingenium Research Group at Universidad Nacional de Colombia Bogotá Colombia Universidad Nacional de Colombia Bogota CO

this paper presents BIGS the Big Image Data Analysis Toolkit, a software framework for large scale image processing and analysis over heterogeneous computing resources, such as those available in clouds, grids, computer clusters or throughout scattered computer resources (desktops, labs) in an opportunistic manner. through BIGS, eScience for image processing and analysis is conceived to exploit coarse grained parallelism based on data partitioning and parameter sweeps, avoiding the need of inter-process communication and, therefore, enabling loosely coupled computing nodes (BIGS workers). It adopts an uncommitted resource allocation model where (1) experimenters define their image processing pipelines in a simple configuration file, (2) a schedule of jobs is generated and (3) workers, as they become available, take over pending jobs as long as their dependency on other jobs is fulfilled. BIGS workers act autonomously, querying the job schedule to determine which one to take over. this removes the need for a central scheduling node, requiring only access by all workers to a shared information source. Furthermore, BIGS workers are encapsulated within different technologies to enable their agile deployment over the available computing resources. Currently they can be launched through the Amazon EC2 service over their cloud resources, through Java Web Start from any desktop computer and through regular scripting or SSH commands. this suits well different kinds of research environments, both when accessing dedicated computing clusters or clouds with committed computing capacity or when using opportunistic computing resources whose access is seldom or cannot be provisioned in advance. We also adopt a NoSQL storage model to ensure the scalability of the shared information sources required by all workers, including within BIGS support for HBase and Amazon's DynamoDB service. Overall, BIGS now enables researchers to run large scale image processing pipelines in an easy, affo

关键词： Pipelines Schedules Databases Feature extraction Biomedical imaging Computers

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Research of Cooperative Product Design Based on a Rapid Inducing Solid Model

Research of Cooperative Product Design Based on a Rapid Indu...

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the 8th international conference on Computer Supported Cooperative Work in Design(第八届计算机支持的协同工作设计国际会议)(CSCWD2004)

作者： Hongming Cai Yuanjun He Yong Wu Computer Science Dept. Shanghai Jiaotong Univ.200030ShanghaiChina

ISBN: (纸本)0780379411

Web-based interactive design always brings outmass of data transferring and makes it difficult todisplay real-time model. In order to decreasetransferring data, a model called RISM (RapidInducing Solid Model) is provided to build the shaperepresentation and operation structure. Based on thestructure, an operation history tree is build byextending traditional CSG model. then traditionalproduct CAD model is divided into two parts in RISM,one is displaying model contained surface attributes forproduct design and run in client, the other isrepresented model contained geometry attributes fortransforming to CAM model and running in *** to the idea, RISM structure is built toconstruct CSG-based operations history tree. thenbased on RISM common operations set and RISMmessage mechanism, a CSCD framework is proposedtherefore, displaying model and representation modelcould work individually and integrate in one unitedbody. At last, a cooperative design system based onACIS is implemented for testing. It is shown that theRISM enhance design system effectively by decreasingtransferring data largely, and the model provides a newframework to realize distributing web application.

关键词： product design solid modelling groupware CAD/CAM Internet client-server systems virtual prototyping production engineering computing cooperative product design rapid inducing solid model Web-based interactive design data transfer real-time model shape representation product CAD model surface attributes geometry attributes CAM model RISM structure CSG-based operation history tree RISM message mechanism display model representation model ACIS distributed Web application

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Enhancing Product Review System: Leveraging CUDA-BB Algorithm for Sentiment Classification

Enhancing Product Review System: Leveraging CUDA-BB Algorith...

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international conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： S Ramanjaneyulu Bhvs Ramakrishnam Raju D Rajya Lakshmi Department of CSE Geethanjali College of Engineering and Technology Keesara(M) Medchal Dist. Telangana INDIA Dept of IT SRKR Engineering College Bhimavaram AP India Department of CSE JNTU Vizianagaram Vizianagaram AP India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Sentiment analysis is an important method in natural language processing (NLP) for detecting emotions and categorizing text as positive, negative, or neutral. It is invaluable for evaluating customer feedback, which is becoming increasingly accessible due to the massive amounts of data published on sites like as Facebook, Twitter, Amazon, and Flipkart. Businesses rely heavily on feedback to improve product quality and maintain a competitive advantage. To improve sentiment analysis, the CUDABB technique is proposed, which uses GPU-based parallel computing to forecast overall smart-phones star ratings. this strategy reduces bias and variation, which improves forecast accuracy and operating efficiency. the approach excels in forecasting smart-phones ratings and demonstrates the value of GPU computing (parallel processing) in sentiment analysis. With a $95.5 \%$ accuracy rate, the proposed classifier outperformed other base models and provided insightful customer feedback to support corporate decision-making.

关键词： Sentiment analysis Accuracy Social networking (online) Reviews Stars Graphics processing units Parallel processing Product design Quality assessment Forecasting

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Domestic Violence Crisis Recognition Method based on Bi-LSTM+Attention

Domestic Violence Crisis Recognition Method based on Bi-LSTM...

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international conference on Network and Information Systems for Computers (ICNISC)

作者： Zhixiao Wang Wenyao Yan Zhuochun Li Min Huang Qinyuan Fan Xin Wang Xi'an University of Technology Xi'an China Shaanxi Key Laboratory of Network Computing and Security Xi'an China College of Xi'an Innovation Yan'an University Xi'an China University of Pittsburgh Pittsburgh PA USA Art and Design Xi'an University of Technology Xi'an China Fakultät Verkehrs- und Maschinensysteme Technische Universität Berlin Berlin Germany Computer Science and Engineering Xi'an University of Technology Xi'an China

ISBN: (纸本)9781665453523

Domestic violence (DV) is getting more and more attention due to the critical trouble and danger that pose great challenge to human rights and health. the popularity of various social medias can help these victims to share their experiences and obtain more possible support from numerous open communities. However, it is not only inefficient but also laborious and time-consuming to manually access and browse through a great deal of available posts on the Internet and social media space. therefore, considering the advantages of Deep Learning (DL) technology in Natural Language Processing, adopting DL as an efficient strategy for automatic recognition and evaluation of DV victims is in the urgent requirements. this paper takes social media Facebook posts about domestic violence as the research object and uses Word2Vec to build word vector model. the paper uses Bi-LSTM+self-Attention deep learning models to accomplish DV crisis recognition task and compares it with CNN, RNN and LSTM. the assessment of experimental results show that the accuracy of CNN and LSTM are both above 90% which is better than RNN; And the accuracy of Bi-LSTM is the highest after using the Attention mechanism. the research results of this article will provide reference and help for public DVCS to rescue DV victims.

关键词： Deep learning Social networking (online) Computational modeling Blogs Manuals Predictive models Data models

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the Detection of Fake News through the use of a Hierarchical Convolutional Attention Network

The Detection of Fake News through the use of a Hierarchical...

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international conference for Convergence of Technology (I2CT)

作者： Chameera De Silva thilina Halloluwa School of Computing University of Colombo Colombo Sri Lanka

In the current digital era, the popularity of social media platforms has expanded tremendously. Every one of us, no matter what age or gender or class we belong to, are active on social media, sharing so much of our personal and professional life with the world. We are connected with people, countries and borders apart via our phone now. News companies are also recognizing this change in the world and are now making business through this by publishing information online. the rapid spread of an article on the internet by the normal public without checking for its authenticity is resulting in a surge in the amount of incorrect information available online. No end-user finds it simple to confirm the veracity of a given news article. these fake articles are increasing rapidly and making it far more difficult for the real information to reach the public. the purpose of this work is to develop an efficient model for assessing the credibility of a news article. this work addresses the classification problem of Fake News, given just the news article's content and the author's name. the system proposed in this paper works with Hierarchical Convolutional-Attention Network (HCAN) and Convolutional Neural Network to classify the fake news articles based on the words and phrases used in the published piece. Hierarchical Convolutional-Attention Network (HCAN) is comprised of attention-enhanced word-level and sentence-level encoders as well as a CNN to capture sequential correlation. We have also used Recurrent Neural Network (RNN) for word encoder and sentence encoder in our system. We carried out the experiment on our system using a dataset and compared the results with various baseline models like CNN, RCNN, BERT or LR. Extensive trials demonstrate that HCAN outperforms the most advanced baseline models on a collected dataset. Results of our experiment shows that our model is achieving the F1 score of 0.98.

关键词： Recurrent neural networks Correlation Social networking (online) Publishing Bit error rate Companies Convolutional neural networks

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Predictive Modelling for Lung Cancer Detection using Machine Learning Techniques

Predictive Modelling for Lung Cancer Detection using Machine...

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international conference on Communication and Electronics Systems (ICCES)

作者： B. Yamini K. Sudha M. Nalini G. Kavitha R. Siva Subramanian R. Sugumar Dept of Networking and Communications School of Computing College of Engg and Tech SRM Institute of Science and Tech Dept of CSBS R.M.D Engineering College Dept of CSE S.A.Engineering College Dept of CSE Rajalakshmi Insitute of Technology Dept of CSE R.M.K.College of Engg and Tech Institute of CSE Saveetha School of Engineering Saveetha Institute of Medical & Technical Sciences Chennai

Lung cancer (LC), generally called lung carcinoma, is a fatal and debilitating illness that affects the lungs. Lung cancer is caused by the genetic damage that occurs to the DNA of the cell as a result of numerous environmental factors. there are no common signs that can be used to determine if a man or a woman has lung cancer unless the illness has progressed to an advanced stage or a lung screening method has been utilized. Early detection and intervention will help save lives from the lung cancer epidemic. Based on this research problem, lung cancer prediction is investigated and tested. Several ML approaches are considered to the study of lung cancer. ML is an area of AI that seeks to generate predictions based on historical and past data. the experimental technique utilizes lung cancer data collected from the public respiratory system. Lung cancer datasets are analyzed using LR, DT, RF, SVM, XGB classifier, Gradient Boosting, and KNN models. Four distinct measures (accuracy, sensitivity, precision, and F1-score) are used to project and compare the experimental outcomes acquired by ML models: In relation Compared to other ML models such as LR, DT, RF, SVM, Gradient Boosting, & KNN, the experimental findings demonstrate that the XGB classifier provides accurate predictions. Finally from all parameter aspects, it clearly shows XGB classifier good prediction results compare to other ML models like LR, DT, RF, SVM, Gradient Boosting, and KNN.

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RSU-driven Cloud Construction and Management Mechanism in VANETs

RSU-driven Cloud Construction and Management Mechanism in VA...

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IEEE international conference on Cloud networking (CLOUDNET)

作者： Yoonhyeong Lee Hyunseok Choi Youngju Nam Sungjin Park Euisin Lee School of Information & Communication Engineering Chungbuk National University Cheongju Republic of Korea

ISBN: (数字)9781728148328

ISBN: (纸本)9781728148335

Recently, Vehicular Ad hoc Networks (VANETs) introduce a Vehicular Cloud (VC) model for collaborating to share and use resources of vehicles to create value-added services. To construct a VC, a vehicle should search vehicles that intend to provide their own resource. the single-hop search cannot search enough provider vehicles due to a small coverage and out-of-sight-zone of communications. On the other hand, the multi-hop search causes very high traffics for large coverage searching and frequent connection breakages. Recently, many Roadside Units (RSUs) have been deployed on roads to collect the information of vehicles in their own coverages and connect them to Internet. thus, we propose a RSU-aided vehicular resource search and cloud construction mechanism in VANETS. In the proposed mechanism, a RSU selects provider vehicles enabled to provide resources needed for constructing a VC based on the information of location and mobility of vehicles. In the proposed mechanism, the criteria for determining provider vehicles are the connection duration between each candidate vehicle and the requester vehicle, the resource size of each candidate vehicle, and its connection starting time to the requester vehicle. Simulation results verify that the proposed mechanism achieves better performance than the existing mechanism.

关键词： Cloud computing Trajectory Vehicular ad hoc networks Roads Simulation Spread spectrum communication Reliability

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