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4th international conference on Advanced computing, networking and Informatics, ICACNI 2016

4th International Conference on Advanced Computing, Networki...

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4th international conference on Advanced computing, networking and Informatics, ICACNI 2016

ISBN: (纸本)9789811033759

The proceedings contain 61 papers. The special focus in this conference is on Advanced computing, networking and Informatics. The topics include: A browser-based distributed framework for content sharing and student collaboration;seed point selection algorithm in clustering of image data;comparative analysis of AHP and its integrated techniques applied for stock index ranking;a proposed what-why-how (WWH) learning model for students and strengthening learning skills through computational thinking;band power tuning of primary motor cortex EEG for continuous bimanual movements;probabilistically generated ternary quasigroup based stream cipher;dynamic access control in a hierarchy with constant key derivation cost;an efficient LWE-based additively homomorphic encryption with shorter public keys;understanding perception of cache-based side-channel attack on cloud environment;an enhanced remote user authentication scheme for multi-server environment using smartcard;a proposed bucket based feature selection technique (BBFST) for phishing e-mail classification;a novel security mechanism in symmetric cryptography using MRGA;techniques for enhancing the security of fuzzy vault: A review;an efficient vector quantization based watermarking method for image integrity authentication;xSS attack prevention using DOM-based filter;friendship recommendation system using topological structure of social networks;personalized recommendation approach for academic literature using high-utility itemset mining technique;an estimation of user preferences for search engine results and its usage patterns;a comparative analysis of various spam classifications;a semantic approach to classifying Twitter users;review spam detection using opinion mining;review spam detection using semi-supervised technique;dimensionality reduction using decision-based framework for classification: Sky and ground;comparative analysis of adaptive beamforming techniques.

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Automatic Rumour Detection Model on Social Media

Automatic Rumour Detection Model on Social Media

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international conference on Parallel distributed and Grid computing (PDGC)

作者： Monika Bharti Himanshu Jindal Computer Science Engineering and Information Technology Jaypee University of Information Technology Waknaghat Solan

ISBN: (数字)9781728171326

ISBN: (纸本)9781728171333

Social networking site Twitter, in particular, has become a popular spot for gossip. Rumors or false news spread very easily through the Twitter network by re-tweeting users without understanding the real truth. These reports trigger popular confusion, threaten the authority of the government and pose a major threat to social order. It is also a very necessary job to dispel theories as quickly as possible. In this research, multiple descriptive and consumer-based features via tweets are retrieved and integrated these features with the TF-IDF system to develop a composite set of features. This composite set of features is then used by several machine learning techniques like Support Vector Machine (SVM), Linear regression, K-Nearest Neighbor (KNN), Naive Bayes, Decision Tree, Random Forest, and Gradient Boosting. Along with these machine learning classification models, a Convolutional Neural Network (CNN) algorithm is proposed to distinguish rumour and non-rumor tweets. The proposed model is evaluated with freely accessible twitter datasets. The existing machine-based learning models have acquired an Fl-score of 0.46 to 0.76 for rumour detection, while the CNN model attained an Fl-score of 0.77 for rumour class. Overall, the CNN model yields greater results with a weighted average Fl-score of 0.84 for both rumour and non-rumor categories. The potential mechanism will help to detect misinformation as quickly as possible to counteract the dissemination of rumours and build users' deep confidence in social media sites.

关键词： Social networking (online) Machine learning Blogs Feature extraction Support vector machines Information technology Handheld computers

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Unsplittable flow Edge Load factor Balancing in SDN using P4 Runtime 29

Unsplittable flow Edge Load factor Balancing in SDN using P4...

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29th international Telecommunication Networks and Applications conference (ITNAC)

作者： Kawaguchi, Eiichiro Kasuga, Hikaru Shinomiya, Norihiko Soka Univ Grad Sch Engn Tokyo Japan

ISBN: (数字)9781728136738

ISBN: (纸本)9781728136738

The spread of services and applications using cloud computing has led to an increase in the amount of network traffic. Therefore, traffic control methods using an SDN architecture for flexibly managing a network have been studied. Previous studies have proposed a traffic load balancing method by a single SDN controller. However, implementation with multiple SDN controllers has been considered to cope with scalability and redundancy. This paper model the distributed network as a flow network and defines the ratio of flow to capacity in an edge as a load factor. Besides, this study proposes a traffic load balancing method considering Unsplittable flow Edge Load factor Balancing problem and implements it to confirm its feasibility in a real network.

关键词： Software Defined networking (SDN) traffic engineering linear programming P4

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Load balancing and resource management in distributed B5G networks

Load balancing and resource management in distributed B5G ne...

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international conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Lu Ma Weiling Chang Chao Li Shanjin Ni Jia Cui Ming Liu National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing P.R.China

ISBN: (数字)9781728199160

ISBN: (纸本)9781728192369

The traffic explosion and the rising of diverse requirements lead to many challenges for the mobile networks on flexibility, scalability, and deployability. To meet the challenges, end-to-end network slicing and mobile edge computing (MEC) are introduced into beyond fifth-generation (B5G) mobile communication networks. However, the edge servers are limited by computing and data processing capabilities, how to implement efficient load balancing and resource management in data center are still challenges for B5G network slices. In this paper, we first propose a model of load balancing for B5G network slices and formulate a total cost minimization problem, considering the energy cost, processing delay, backhaul bandwidth cost, and revenue loss associated with the backhaul delay. Then, a distributed algorithm based on Proximal Jacobian Alternating Direction Method of Multipliers (PJ-ADMM) is proposed to solve the formulated problem. Simulations are conducted to show that the proposed algorithm are effective in terms of reducing network operating cost.

关键词： Bandwidth Load management Minimization Delays Resource management distributed algorithms Load modeling

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Memory Abstraction and Optimization for distributed Executors

Memory Abstraction and Optimization for Distributed Executor...

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international conference on Collaborative computing: networking, Applications and Worksharing (CollaborateCom)

作者： Semih Sahin Ling Liu Wenqi Cao Qi Zhang Juhyun Bae Yanzhao Wu Georgia Institute of Technology

ISBN: (数字)9781728141466

ISBN: (纸本)9781728185422

This paper presents a suite of memory abstraction and optimization techniques for distributed executors, with the focus on showing the performance optimization opportunities for Spark executors, which are known to outperform Hadoop MapReduce executors by leveraging Resilient distributed Datasets (RDDs), a fundamental core of Spark. This paper makes three original contributions. First, we show that applications on Spark experience large performance deterioration, when RDD is too large to fit in memory, causing unbalanced memory utilizations and premature spilling. Second, we develop a suite of techniques to guide the configuration of RDDs in Spark executors, aiming to optimize the performance of iterative ML workloads on Spark executors when their allocated memory is sufficient for RDD caching. Third, we design DAHI, a light-weight RDD optimizer. DAHI provides three enhancements to Spark: (i) using elastic executors, instead of fixed size JVM executors; (ii) supporting coarser grained tasks and large size RDDs by enabling partial RDD caching; and (iii) automatically leveraging remote memory for secondary RDD caching in the shortage of primary RDD caching on a local node. Extensive experiments on machine learning and graph processing benchmarks show that with DAHI, the performance of ML workloads and applications on Spark improves by up to 12.4x.

关键词： Sparks Task analysis Memory management Optimization Proposals Writing Virtual machining

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Massive Parallelization for Finding Shortest Lattice Vectors Based on Ubiquity Generator Framework 20

Massive Parallelization for Finding Shortest Lattice Vectors...

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Supercomputing conference

作者： Nariaki Tateiwa Yuji Shinano Satoshi Nakamura Akihiro Yoshida Shizuo Kaji Masaya Yasuda Katsuki Fujisawa Graduate School of Mathematics Kyushu University Fukuoka Japan Mathematical Algorithmic Intelligence Applied Algorithmic Intelligence Methods (A-IM) Zuse Institute Berlin (ZIB) Berlin Germany NTT Secure Platform Laboratories Tokyo Japan Institute of Mathematics for Industry Kyushu University Fukuoka Japan Rikkyo University Tokyo Japan

ISBN: (数字)9781728199986

ISBN: (纸本)9781728199993

Lattice-based cryptography has received attention as a next-generation encryption technique, because it is believed to be secure against attacks by classical and quantum computers. Its essential security depends on the hardness of solving the shortest vector problem (SVP). In the cryptography, to determine security levels, it is becoming significantly more important to estimate the hardness of the SVP by high-performance computing. In this study, we develop the world's first distributed and asynchronous parallel SVP solver, the MAssively Parallel solver for SVP (MAP-SVP). It can parallelize algorithms for solving the SVP by applying the Ubiquity Generator framework, which is a generic framework for branch-and-bound algorithms. The MAP-SVP is suitable for massive-scale parallelization, owing to its small memory footprint, low communication overhead, and rapid checkpoint and restart mechanisms. We demonstrate its performance and scalability of the MAP-SVP by using up to 100,032 cores to solve instances of the Darmstadt SVP Challenge.

关键词： Quantum computing Scalability High performance computing Lattices Generators Encryption Next generation networking

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A Lightweight SOA-Based Network Slicing Creation System

A Lightweight SOA-Based Network Slicing Creation System

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IEEE international conference on Cloud computing, CLOUD

作者： Meng Wang Bo Cheng Junliang Chen State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728187808

ISBN: (纸本)9781728187815

The next-generation network system is envisioned to be a multi-service network supporting different applications with multiple requirements. In this vision, Network SLicing (NSL) is considered a key mechanism to create multiple virtual networks over the same physical infrastructure. However, it is a challenging problem to deploy NSL with great flexibility and full automation. In this paper, we propose a novel lightweight SOA-based NSL creation system, which includes the design domain, execution domain, and infrastructure domain. The design domain provides an easily-operating service design environment. Besides, we define a detailed description of NSL named Network SLicing Descriptor (NSLD), illustrating the resource and requirements for designing network services. The execution domain is a service execution environment with multi-tenancy support. This domain based on micro service architecture is distributed and self-organized. The infrastructure domain contains multiple network infrastructure resource domains that enable micro services. With these components, tenants can create NSL freely via an intuitive and easy-to-use UI on a web browser. We also implement an IoT NSL scenario to solve the multi-flow transmission problem. After adding sub-services and QoS policy into the design domain, the scenario can run automatically at the execution domain.

关键词： Cloud computing Network slicing conferences Computer architecture Quality of service Virtualization Next generation networking

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Election Result Prediction Using Twitter Sentiments Analysis

Election Result Prediction Using Twitter Sentiments Analysis

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international conference on Parallel distributed and Grid computing (PDGC)

作者： Payal Khurana Batra Aditi Saxena Shruti Chaitanya Goel Jaypee Institute of Information Technology Noida India

ISBN: (数字)9781728171326

ISBN: (纸本)9781728171333

Democracy is the form of government where people have the right to choose their leaders. In our country, India, elections are held periodically to choose their political leader. Nowadays, people like to know beforehand that who is going to win the elections. They predict results based on news, their discussions and online platforms. Online sites such as Facebook, Twitter and WhatsApp have become a trend these days. Anyone with internet connection has access to these social sites. The views, news and discussions available on these platforms help in predicting the results beforehand. These are the platforms to share ones views' on an online junction. Twitter is one such platform which becomes popular during any event in our country. Anything that is news becomes a trend on twitter. People share their views, hatred, campaigns towards any political party, figure or leader. It is very useful to predict electoral results before the exit poll results. Therefore, this work analyzes tweets collected from twitter and predicts election results by performing sentimental analysis on them.

关键词： Voting Social networking (online) Blogs Decision trees Machine learning Feature extraction Predictive models

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Towards Content-Centric Control Plane Supporting Efficient Anomaly Detection Functions 15

Towards Content-Centric Control Plane Supporting Efficient A...

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15th Int Conf on Network and Serv Management (CNSM) / 1st Int Workshop on Analyt for Serv and Application Management (AnServApp) / Int Workshop on High-Precision Networks Operat and Control, Segment Routing and Serv Function Chaining (HiPNet+SR/SFC)

作者： Mai, Hoang Long Doyen, Guillaume Mallouli, Wissam de Oca, Edgardo Montes Festor, Olivier Univ Technol Troyes CNRS UMR 6281 ICD F-10004 Troyes France Montimage 39 Rue Bobillot F-75013 Paris France Univ Lorraine TELECOM Nancy CNRS LORIAUMR 7503 F-54506 Vandoeuvre Les Nancy France

ISBN: (纸本)9783903176249

Anomaly detection remains a challenging task due to both the ever more complex functions that need to be executed and the evolution of current networking devices which induces limitation of computational resources such as the Internet of Things (IoT). Furthermore, results of anomaly function computations can be repeated gradually over time or executed in neighboring nodes, thus leading to a waste of such limited computing resources in constrained nodes. To tackle these issues, the content-centric paradigm enhanced with computing features offers a promising solution to reduce the computation resources and finally improve the scalability of anomaly detection functions. In this paper, we propose a first step toward a content-oriented control plane which enables the distribution of the processing and the sharing of results of anomaly detection functions in the network. We present the way we leverage NFN to support Bayesian Network inference to detect anomalies in network traffic. The relevance and performance of our proposed approach are demonstrated by considering the Content Poisoning Attack (CPA) through numerous experiment data.

关键词： distributed anomaly detection Bayesian Network Named Function networking

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Novel Machine Learning Approach for Sentiment Analysis of Real Time Twitter Data with Apache Flume

Novel Machine Learning Approach for Sentiment Analysis of Re...

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international conference on Parallel distributed and Grid computing (PDGC)

作者： Mamoon Rashid Aamir Hamid Nazir Ahmad Mohammed Burhanur Rehman Mir Mohammad Yousuf School of Computer Science & Engineering Lovely Professional University Jalandhar India Swami Vivekanand Institute of Engineering & Technology Chandigarh India Community College King Khalid University Muhayel Saudi Arabia

ISBN: (数字)9781728171326

ISBN: (纸本)9781728171333

A lot of data is generated from multiple sources. This data contains many hidden patterns and information. Many researchers are trying to get meaningful insights out of these patterns. Data from these sources mostly contains opinions. Opinions can be mined to lead various extractions from organizational point of view. One approach is to use Sentiment Analysis. In this paper, the authors are storing the Twitter Streaming Data into HDFS of Hadoop by using Flume and then extracting with Apache Hive. Later, Machine Learning classification algorithms are applied to decode the sentiment in this data using Apache Mahout. A novel approach based on hybrid Naïve Bayes and Decision Tree Algorithms are used to enhance the performance of sentiment analysis of streaming twitter data. The implemented research approach achieved an accuracy of 86.44% in comparison to 81.11% for Naïve Bayes Classifier.

关键词： Social networking (online) Blogs Classification algorithms Machine learning algorithms Sentiment analysis Machine learning Support vector machines

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