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5th ACM International Conference on Collaboration Across Boundaries, CABS 2014

作者： Pan, Mei-Hua Wang, Hao-Chuan College of Electrical Engineering and Computer Science National Tsing Hua University Taiwan Department of Computer Science Institute of Information Systems and Applications National Tsing Hua University Taiwan

ISBN: (纸本)9781450325578

Machine translation (MT) has the potential to bridge the language barrier in multilingual community, but its utility to support cross-lingual communication is often limited by its quality. Recent studies have shown the supporting effect of keyword highlighting on translation comprehension. What's missing is a way to identify and highlight keywords in translations efficiently and reliably. In this paper, we investigate crowdsourcing strategies for keyword highlighting. We compare three methods that involve human workers to highlight keywords in English-to-Chinese translations: English-only for adding keyword highlights on the original English sentences, Chinese-only for adding keyword highlights on the translated sentences, and Bilingual that allows people to choose keywords in either the original or the translated sentences. Results show that adding highlights to translated sentences (Chinese-only) is the fastest but doesn't improve the comprehensibility of translation. Highlighting both sentences (Bilingual) helps improve the understandability of hard-to-understand translations. © 2014 Authors.

关键词： Crowdsourcing

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A STT-RAM-based low-power hybrid register file for GPGPUs

A STT-RAM-based low-power hybrid register file for GPGPUs

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Design Automation Conference

作者： Gushu Li Xiaoming Chen Guangyu Sun Henry Hoffmann Yongpan Liu Yu Wang Huazhong Yang Deparrnent of E.E. Tsinghua University Beiiing China Electrical and Computer Engineering Department Carnegie Mellon University Pittsburgh PA USA Center for Energy-efficient Computing and Applications Peking University Beijing China Deparment of Computer Science University of Chicago Chicago IL USA Tsinghua University Beijing Beijing CN

ISBN: (纸本)9781479980536

Recently, general-purpose graphics processing units (GPGPUs) have been widely used to accelerate computing in various applications. To store the contexts of thousands of concurrent threads on a GPU, a large static random-access memory (SRAM)-based register file is employed. Due to high leakage power of SRAM, the register file consumes 20% to 40% of the total GPU power consumption. Thus, hybrid memory system, which combines SRAM and the emerging non-volatile memory (NVM), has been employed for register file design on GPUs. Although it has shown strong potential to alleviate the power issue of GPUs, existing hybrid memory solutions might not exploit the intrinsic feature of GPU register file. By leveraging the warp schedule on GPU, this paper proposes a hybrid register architecture which consists of a NVM-based register file and mixed SRAM-based write buffers with a warp-aware write back strategy. Simulation results show that our design can eliminate 64% of write accesses to NVM and reduce power of register file by 66% on average, with only 4.2% performance degradation. After we apply the power gating technique, the register power is further reduced to 25% of SRAM counterpart on average.

关键词： Registers Random access memory Graphics processing units Radio frequency Nonvolatile memory Power demand Benchmark testing

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Q-learning-based data replication for highly dynamic distributed hash tables

Q-learning-based data replication for highly dynamic distrib...

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2014 International Conference on the Network of the Future, NOF 2014

作者： Feki, Souhir Louati, Wassef Masmoudi, Nadia Jmaiel, Mohamed Research Laboratory on Development and Control of Distributed Applications Department of Computer Science and Applied Mathematics National School of Engineering of Sfax University of Sfax Tunisia

ISBN: (纸本)9781479975310

This paper focuses on data replication in structured peer-to-peer systems over highly dynamic networks. A Q-learning-based replication approach is proposed. Data availability is periodically computed using the Q-learning function. The reward/penalty property of this function attenuates the impact of the network dynamism on the replication overhead. Hence, the departure of a node does not necessarily lead to the addition of a replica in the network. The replication process is triggered according to the overall data availability. Simulation results proved that the proposed approach ensures data availability in dynamic environments with minimum data transfer costs. © 2014 IEEE.

关键词： Data transfer

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Using Bipartite Graphs for 3D Cardiac Model Retrieval

Using Bipartite Graphs for 3D Cardiac Model Retrieval

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Annual IEEE Symposium on computer-Based Medical Systems

作者： Leila C.C. Bergamasco Hellyan Oliveira Helton Bíscaro Harry Wechsler Fátima L.S. Nunes Polytechnic School - Graduate Program in Electrical Engineering University of Sao Paulo São Paulo SP Brasil Laboratory of Health Informatics Applications University of Sao Paulo São Paulo SP Brasil Department of Computer Science George Mason University Fairfax VA USA

Three-dimensional models have been used to aid medical diagnoses, using images generated by modalities like Magnetic Resonance Imaging. They can provide a more complete vision of objects since their depth is taken into account. Content-based Image Retrieval (CBIR) has also been used to aid the diagnosis. One important step in Three-dimensional CBIR (Model Retrieva) systems is the comparison between two models by using a set of features extracted and stored in a database. In this paper we present a novel method to compare two models, using the Bipartite graphs technique, with the aim to improve the retrieval precision. This technique retrieves 3D medical models of the left ventricle in order to aid the diagnosis of Congestive Heart Failure. Results showed that the novel method improved the precision by 10% when compared to the Similarity Function of Euclidean and Manhattan distance. These results confirmed that bipartite graph techniques can be used to improve the accuracy of Model Retrieval systems.

关键词： Solid modeling Three-dimensional displays Computational modeling Medical diagnostic imaging Feature extraction Bipartite graph

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Event-Triggered Observers and Observer-Based Controllers for a Class of Nonlinear Systems

Event-Triggered Observers and Observer-Based Controllers for...

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American Control Conference

作者： L. Etienne S. Di Gennaro J.-P. Barbot Department of Information Engineering Computer Science and Mathematics University of L'Aquila Via Vetoio Loc. Coppito 67100 L'Aquila Italy Ecole Nationale Superieure d'Electronique et de Ses Applications - ENSEA 6 Avenue du Ponceau 95014 Cergy Pontoise Cedex

ISBN: (纸本)9781479917730

In this paper, we investigate the stabilization of a nonlinear plant subject to network constraints, under the assumption of partial knowledge of the plant state. The event triggered paradigm is used for the observation and the control of the system. Necessary conditions, making use of the ISS property, are given to guarantee the existence of a triggering mechanism, leading to asymptotic convergence of the observer and system states. The proposed triggering mechanism is illustrated in the stabilization of a robot with a flexible link robot.

关键词： Event trigger Trigger mechanisms Robot Observers Controllers control systems flexible link

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Gene expression data classification using Support Vector Machine and mutual information-based gene selection 1

Gene expression data classification using Support Vector Mac...

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IUTAM Symposium on the Dynamics of Extreme Events Influenced by Climate Change (2013)

作者： Devi Arockia Vanitha, C. Devaraj, D. Venkatesulu, M. Department of Computer Science S.F.R College for Women Sivakasi Tamil Nadu626123 India Department of Computer Science and Engineering Kalasahngam University KrishnankoilTamil Nadu626126 India Department of Computer Applications Kalasalingam University KrishnankoilTamil Nadu626126 India

DNA microarray technology can monitor the expression levels of thousands of genes simultaneously during important biological processes and across collections of related samples. Knowledge gained through microarray data analysis is increasingly important as they are useful for phenotype classification of diseases. This paper presents an effective method for gene classification using Support Vector Machine (SVM). SVM is a supervised learning algorithm capable of solving complex classification problems. Mutual information (MI) between the genes and the class label is used for identifying the informative genes. The selected genes are utilized for training the SVM classifier and the testing ability is evaluated using Leave-one-Out Cross Validation (LOOCV) method. The performance of the proposed approach is evaluated using two cancer microarray datasets. From the simulation study it is observed that the proposed approach reduces the dimension of the input features by identifying the most informative gene subset and improve classification accuracy when compared to other approaches. © 2015 The Authors. Published by Elsevier B.V.

关键词： Support vector machines

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Elastic and Efficient Virtual Network Provisioning for Cloud-Based Multi-tier applications

Elastic and Efficient Virtual Network Provisioning for Cloud...

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International Conference on Parallel Processing (ICPP)

作者： Meng Shen Ke Xu Fan Li Kun Yang Liehuang Zhu Lei Guan Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology P. R. China Department of Computer Science Tsinghua University P. R. China School of Computer Science & Electronic Engineering University of Essex UK The ISN National Key Lab XiDian University P. R. China School of Management & Economics Beijing Institute of Technology P. R. China

ISBN: (纸本)9781467375887

The multi-tier architecture is prevalently adopted by cloud applications, such as the three-tier web application. It is highly desirable for both tenants and providers to provide virtual networks in an efficient and elastic way, where tenant applications can automatically scale in or out with varying workloads and providers can accommodate as many requests as possible in the underlying network. However, due to potential conflicts between efficiency and elasticity, it is challenging to achieve these two goals simultaneously in abstracting tenant requirements and designing corresponding provisioning algorithms. In this paper, we propose an efficient and elastic virtual network provisioning solution called Easy Alloc, which is comprised of an elasticity-aware abstraction model and a virtual network provisioning algorithm. To accurately capture the tenant requirement and maintain the provisioning simplicity for providers, the elasticity-aware model enables two types of decoupling, i.e., Always-on VMs for normal load and on-demand VMs for dynamic scaling, and the bandwidth requirement of each VM for intra- and inter-tier communications. Then we formulate the virtual network provisioning as an overhead minimization problem, where the objective simultaneously considers the bandwidth and elasticity overhead. Due to the NP-completeness of this problem, we leverage two heuristics, slot reservation and tier iteration, to obtain an efficient algorithm. Extensive simulation results show that compared with a typical elasticity-agnostic method under a heavy load, Easy Alloc enables a 9% increase of request acceptance rate and a 16.8% improvement of the successful extension rate. To the best of our knowledge, this is the first work targeting at the elastic virtual network provisioning.

关键词： Bandwidth Elasticity Cloud computing Algorithm design and analysis Load modeling Resource management

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Adaptive spectrum detecting algorithm in cognitive radio

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and Computing 2014年 215卷 461-475页

作者： Liu, Yun Peng, Qicong Sun, Fuchun Shao, Huaizong Chen, Xingfeng Wang, Ling School of Communication and Technology Engineering University of Electronic Science and Technology of China Chengdu 610054 China State Key Lab of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing 100084 China State Key Laboratory of Remote Sensing Science Institute of Remote Sensing Applications CAS Beijing 100101 China

Cognitive radio (CR) network can make an opportunistic access of spectrum licensed to a primary user (PU). The CR must perform spectrum sensing to detect active PU, thereby avoiding interfering with it. This chapter focuses on adaptively spectrum sensing, so that negative impacts to the performance of the CR network are minimized when CR users experience both stochastic data arrival and time-varying channel. Since the frequency of the spectrum sensing has a directly impact on system throughput and the probability of collision between the PU and CR user, the PU activity is modeled based on the characteristic analysis of the PU spectrum utilization. Based on that, an efficient adaptive sensing algorithm that takes into account the system stability, collision, and throughput is proposed. The CR users can make a balance between them by utilizing a "periodic control factor" which controls the adaptive adjustment of the spectrum sensing frequency. The simulation results indicate that the proposed algorithm has excellent performance on collision probability and throughput compared with conventional periodic spectrum sensing scheme. Meanwhile, it is shown that the proposed algorithm has low implementation complexity for practical applications. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Cognitive radio

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A novel spectrum sensing algorithm in cognitive radio system based on OFDM

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and Computing 2014年 215卷 131-138页

作者： Yun, Liu Peng, Qicong Sun, Fuchun Shao, Huaizong Chen, Xingfeng Wang, Ling School of Communication and Technology Engineering University of Electronic Science and Technology of China Chengdu 610054 China State Key Lab of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing 100084 China State Key Laboratory of Remote Sensing Science Institute of Remote Sensing Applications CAS Beijing 100101 China

In cognitive radio (CR) networks, spectrum sensing which attracts a lot of interest is a significant task. Aiming at the problem that conventional spectrum sensing technique is usually focused on signal band. This paper introduces a multiband joint spectrum detection based on multiple signal classification (MUSIC) algorithm for orthogonal frequency division multiplexing (OFDM) cognitive ratio system. The proposed eigenvalue-construct method only uses signal autocorrelation of OFDM symbols and simple sorting to achieve the spectrum detection. The computer simulations show that the proposed approach has a good performance compared with the conventional energy sensing method which uses the same threshold over multiple frequency bands. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Cognitive radio

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Indian Language Text Representation and Categorization Using Supervised Learning Algorithm

Indian Language Text Representation and Categorization Using...

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International Conference on Intelligent Computing applications (ICICA)

作者： M. Narayana Swamy M. Hanumanthappa N.M. Jyothi Department of Computer Applications Presidency College Bangalore India Department of Computer Science & Applications Bangalore University Karnataka India Department of Computer Science & Applications Bapuji Institute of Engineering and Technology Davangere Karnataka India

In the Constitution of India, a provision is made for each of the Indian states to choose their own official language for communicating at the state level for official purpose. The availability of constantly increasing amount of textual data of various Indian regional languages in electronic form has accelerated. So the Classification of text documents based on languages is essential. The objective of the work is the representation and categorization of Indian language text documents using text mining techniques. Several text mining techniques such as naive Bayes classifier, k-Nearest-Neighbor classifier and decision tree for text categorization have been used.

关键词： Text categorization Classification algorithms Educational institutions Decision trees Text mining Vectors Support vector machine classification

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