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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Sam Skalicky Sonia Lopez Marcin Lukowiak Andrew G. Schmidt Department of Computer Engineering Rochester Institute of Technology Information Sciences Institute University of Southern California

Compute-intensive applications incorporate ever increasing data processing requirements on hardware systems. Many of these applications have only recently become feasible thanks to the increasing computing power of modern processors. the Matlab language is uniquely situated to support the description of these compute-intensive scientific applications, and consequently has been continuously improved to provide increasing computational support in the form of multithreading for CPUs and utilizing accelerators such as GPUs and FPGAs. Moreover, to take advantage of the computational support in these heterogeneous systems from the problem domain to the computer architecture necessitates a wide breadth of knowledge and understanding. In this work, we present a framework for the development of compute-intensive scientific applications in Matlab using heterogeneous processor systems. We investigate systems containing CPUs, GPUs, and FPGAs. We leverage the capabilities of Matlab and supplement them by automating the mapping, scheduling, and parallel code generation. Our experimental results on a set of benchmarks achieved from 20x to 60x speedups compared to the standard Matlab CPU environment with minimal effort required on the part of the user.

关键词： MATLAB Kernel Processor scheduling Field programmable gate arrays Data transfer Scheduling Message systems

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Availability-Aware Virtual Network Embedding for Multi-tier Applications in Cloud Networks

Availability-Aware Virtual Network Embedding for Multi-tier ...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Meng Shen Xu Ke Fuliang Li Fan Li Liehuang Zhu Lei Guan Ministry of Education Key Laboratory of Computer Network and Information Integration (Southeast University) P. R. China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications School of Computer Science Beijing Institute of Technology P. R. China Tsinghua University Beijing Beijing CN College of Information Science and Engineering Northeastern University P. R. China DNSLAB China Internet Network Iniormation Center Beijing P. R. China School of Management & Economics Beijing Institute of Technology P. R. China

ISBN: (纸本)9781479989386

there is a growing trend for service providers (SPs) to migrate their multi-tier applications from local clusters to public cloud networks. In the cloud environment, it is a great concern of SPs to improve the availability of their virtual networks, in addition to guarantees of their virtual resource requirements, such as VMs and bandwidth. However, meeting these requirements results in an inefficient utilization of physical resources, which goes against the operational goal of cloud providers. To address this challenge, in this paper, we propose an availability-aware virtual network embedding framework that simultaneously improves the availability of virtual networks and the resource efficiency. We first propose a new metric to quantify the availability cost of a multi-tier virtual network, and then formulate the embedding problem as a joint optimization of the aggregated bandwidth and availability costs. Due to the NP-hardness of the embedding problem, we devise a heuristic algorithm that can solve the problem in polynomial time. Extensive simulation results show that the proposed algorithm enables CP to achieve various trade-offs between resource efficiency and availability, and to gain more revenue (e.g., 16.1% under a datacenter load of 80%) than the availability-agonistic algorithm.

关键词： Bandwidth Cloud computing Topology Network topology Servers Optimization Algorithm design and analysis

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Performance evaluation of similarity measures on similar and dissimilar text retrieval

Performance evaluation of similarity measures on similar and...

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international Joint conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K)

作者： Victor U. thompson Christo Panchev Michael Oakes Department of Computing Engineering and Technology University of Sunderland Sunderland U.K.

ISBN: (纸本)9781509019670

Many information Retrieval (IR) and Natural language processing (NLP) systems require textual similarity measurement in order to function, and do so with the help of similarity measures. Similarity measures function differently, some measures which work better on highly similar texts do not always do so well on highly dissimilar texts. In this paper, we evaluated the performances of eight popular similarity measures on four levels (degree) of textual similarity using a corpus of plagiarised texts. the evaluation was carried out in the context of candidate selection for plagiarism detection. Performance was measured in terms of recall, and the best performed similarity measure(s) for each degree of textual similarity was identified. Results from our Experiments show that the performances of most of the measures were equal on highly similar texts, with the exception of Euclidean distance and Jensen-Shannon divergence which had poorer performances. Cosine similarity and Bhattacharryan coefficient performed best on lightly reviewed text, and on heavily reviewed texts, Cosine similarity and Pearson Correlation performed best and next best respectively. Pearson Correlation had the best performance on highly dissimilar texts. the results also show term weighing methods and n-gram document representations that best optimises the performance of each of the similarity measures on a particular level of intertextual similarity.

关键词： Plagiarism Euclidean distance Correlation Weight measurement Semantics Natural language processing Length measurement

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Risk Management Using Big Real Time Data

Risk Management Using Big Real Time Data

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IEEE international conference on Cloud Computing technology and Science (CloudCom)

作者： Jie Cheng Chunming Rong Huijuan Ye Xianghan Zheng Department of Electrical Engineering and Computer Science Risk Management Using Big Real Time Data Department of Computer Science and Electronic Engineering University of Stavanger Stavanger Norway College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China

Adding to societal changes today, are the miscellaneous big data produced in different fields. Coupled with these data is the appearance of risk management. Admittedly, to predict future trend by using these data is conducive to make everything more efficient and easy. Now, no matter companies or individuals, they increasingly focus on identifying risks and managing them before risks. Effective risk management will lead them to deal with potential problems. this thesis focuses on risk management of flight delay area using big real time data. It proposes two different prediction models, one is called General Long Term Departure Prediction Model and the other is named as Improved Real Time Arrival Prediction Model. By studying the main factors lead to flight delay, this thesis takes weather, carrier, National Aviation System, security and previous late aircraft as analysis factors. By utilizing our models can do not only long time but also short term flight delay predictions. the results demonstrate goodness of fit. Besides the theory part, it also presents a practical and beautiful web application for real time flight arrival prediction based on our second model.

关键词： Delays Biological system modeling Meteorology Atmospheric modeling Predictive models Airports Splines (mathematics)

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Analysis and evaluation the websites of agridata base on link analysis

IFIP Advances in Information and Communication Technology

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IFIP Advances in information and Communication technology 2014年 419卷 145-151页

作者： Jian, Wang Ding-Feng, Wu Agricultural Information Institute of CAAS Beijing100081 China

ISBN: (纸本)9783642543432

through data collection tools, the Pagerank, inlink and the total number of retrieved pages of Agridata are computed. By considering website traffic as the flag of network influence levels, the paper analyze correlations between website traffic and Pagerank, inlink and the number of retrieved pages. Finally, the feasubily to regard the web link analysis as a factor to measure network influence of agricultural websites is discussed. © IFIP international Federation for information processing 2014.

关键词： websites

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Using common-sense knowledge-base for detecting word obfuscation in adversarial communication

Using common-sense knowledge-base for detecting word obfusca...

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international Communication Systems and Networks and Workshops, COMSNETS

作者： Swati Agarwal Ashish Sureka IIIT-D Indraprasth Institute of Information and Technology Delhi India

Word obfuscation or substitution means replacing one word with another word in a sentence to conceal the textual content or communication. Word obfuscation is used in adversarial communication by terrorist or criminals for conveying their messages without getting red-flagged by security and intelligence agencies intercepting or scanning messages (such as emails and telephone conversations). ConceptNet is a freely available semantic network represented as a directed graph consisting of nodes as concepts and edges as assertions of common sense about these concepts. We present a solution approach exploiting vast amount of semantic knowledge in ConceptNet for addressing the technically challenging problem of word substitution in adversarial communication. We frame the given problem as a textual reasoning and context inference task and utilize ConceptNet's natural-language-processing tool-kit for determining word substitution. We use ConceptNet to compute the conceptual similarity between any two given terms and define a Mean Average Conceptual Similarity (MACS) metric to identify out-of-context terms. the test-bed to evaluate our proposed approach consists of Enron email dataset (having over 600000 emails generated by 158 employees of Enron Corporation) and Brown corpus (totaling about a million words drawn from a wide variety of sources). We implement word substitution techniques used by previous researches to generate a test *** conduct a series of experiments consisting of word substitution methods used in the past to evaluate our approach. Experimental results reveal that the proposed approach is effective.

关键词： Bismuth Postal services

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Crawling Open-source Data for Indicators of Human Trafficking 14

Crawling Open-source Data for Indicators of Human Traffickin...

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IEEE/ACM 7th international conference on Utility and Cloud Computing (UCC)

作者： Brewster, Ben Ingle, Timothy Rankin, Glynn Sheffield Hallam Univ CENTRIC Ctr Excellence Terrorism Resilience Intel Sheffield S Yorkshire England Off Police & Crime Commissioner West Yorkshire Wakefield England Rankin Associates Ltd Birkenhead Merseyside England

ISBN: (纸本)9781479978816

information available from open-source mediums such as the web and social media are increasingly being used to aid the response to emergent crimes and reinforce existing Law Enforcement Agency intelligence capability. In this paper we discuss the rationale for, and development of, a framework for crawling open-source data for information regarding, and indicators of, Human Trafficking. the three-Level model presented aims to add value to the modelling, and increase the understanding of Human Trafficking in order to facilitate the detection and analysis of indicators to ultimately increase the value and range of information available to decision makers.

关键词： web Crawling Natural language processing (NLP) Intelligence-Led Policing (ILP) Open-Source Intelligence (OSINT) Human Trafficking Big Data

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A Hadoop based web log Analysis System 7

A Hadoop based Web log Analysis System

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7th IEEE international conference on Ubi-Media Computing and Workshops (U-MEDIA)

作者： Wang, Chen-Hau Tsai, Ching-Tsorng Fan, Chia-Chen Yuan, Shyan-Ming Natl Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan Tunghai Univ Informat Engn Taichung 40704 Taiwan

ISBN: (纸本)9781479942664

In recent years, cloud computing has been an important issue in the Field of research. Cloud computing employs distributed storage and distributed computing technology to achieve a large number of stored data, as well as fast data analysis and processing. As the rapid development of Internet technology, digital data showing explosive growth, the face of massive data processing, the traditional text software and relational database technology has been facing a bottleneck, presented the results are not very satisfactory. For this problem, the concept of cloud computing is a more appropriate choice. In this paper, based on the architecture of Hadoop with HDFS (Hadoop Distributed File System) and Hadoop NlapRecluce software framework and Pig Latin language, we design and implement an enterprise web log analysis system. Experimental results, by analyzing daily web log records, we get Application Server traffic trends, performance of program statistical reports, and performance reports of different intervals and different actions of program by user request. the main purpose of this system is to assist system administrators to quickly capture and analyze data hidden in the massive potential value, thus providing an important basis for business decisions.

关键词： Cloud Computing Distributed File System Pig programming language

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Proceedings - ALPIT 2008, 7th international conference on advanced language processing and web information technology

Proceedings - ALPIT 2008, 7th International Conference on Ad...

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ALPIT 2008, 7th international conference on advanced language processing and web information technology

ISBN: (纸本)9780769532738

the proceedings contain 101 papers. the topics discussed include: exploring various features in semantic role labeling;transformation rule learning without rule templates: a case study in part of speech tagging;word sense disambiguation based on relation structure;Chinese sentence similarity measure based on words and structure information;email classification using semantic feature space;a clustering approach of conceptual sentence groups;deriving a semantic classification tree of Korean verbs based on semantic features;Korean syntactic analysis using dependency rules and segmentation;tree kernel-based semantic relation extraction using unified dynamic relation tree;near-duplicates detection for Vietnamese documents in large database;latent semantic kernels for wordnet: transforming a tree-like structure into a matrix;and a new feature-fusion sentence selecting strategy for query-focused multi-document summarization.

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Chinese-English Mixed Text Normalization 14

Chinese-English Mixed Text Normalization

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7th ACM international conference on web Search and Data Mining (WSDM)

作者： Zhang, Qi Chen, Huan Huang, Xuanjing Fudan Univ Sch Comp Sci Shanghai Key Lab Intelligent Informat Proc 825 Zhangheng Rd Shanghai Peoples R China

ISBN: (纸本)9781450323512

Along with the expansion of globalization, multilingualism has become a popular social phenomenon. More than one language may occur in the context of a single conversation. this phenomenon is also prevalent in China. A huge variety of informal Chinese texts contain English words, especially in emails, social media, and other user generated informal contents. Since most of the existing natural language processing algorithms were designed for processing monolingual information, mixed multilingual texts cannot be well analyzed by them. Hence, it is of critical importance to preprocess the mixed texts before applying other tasks. In this paper, we firstly analyze the phenomena of mixed usage of Chinese and English in Chinese microblogs. then, we detail the proposed two-stage method for normalizing mixed texts. We propose to use a noisy channel approach to translate in-vocabulary words into Chinese. For better incorporating the historical information of users, we introduce a novel user aware neural network language model. For the out-of-vocabulary words (such as pronunciations, informal expressions and et al.), we propose to use a graph-based unsupervised method to categorize them. Experimental results on a manually annotated microblog dataset demonstrate the effectiveness of the proposed method. We also evaluate three natural language parsers with and without using the proposed method as the preprocessing step. From the results, we can see that the proposed method can significantly benefit other NLP tasks in processing mixed text.

关键词： Words Normalization Chinese-English Mixed Text User Aware Neural Network language Model

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