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arXiv 2018年

作者： Xu, Jiamiao Yu, Shujian You, Xinge Leng, Mengjun Jing, Xiao-Yuan Chen, C. L. Philip School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Department of Computer Science University of Houston HoustonTX77204 United States State Key Laboratory of Software Engineering School of Computer Wuhan University China Department of Computer and Information Science Faculty of Science and Technology University of Macau Macau99999 China Dalian Maritime University Dalian116026 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

We present a novel cross-view classification algorithm where the gallery and probe data come from different views. A popular approach to tackle this problem is the multiview subspace learning (MvSL) that aims to learn a latent subspace shared by multi-view data. Despite promising results obtained on some applications, the performance of existing methods deteriorates dramatically when the multi-view data is sampled from nonlinear manifolds or suffers from heavy outliers. To circumvent this drawback, motivated by the Divideand-Conquer strategy, we propose Multi-view Hybrid Embedding (MvHE), a unique method of dividing the problem of cross-view classification into three subproblems and building one model for each subproblem. Specifically, the first model is designed to remove view discrepancy, whereas the second and third models attempt to discover the intrinsic nonlinear structure and to increase discriminability in intra-view and interview samples respectively. The kernel extension is conducted to further boost the representation power of MvHE. Extensive experiments are conducted on four benchmark datasets. Our methods demonstrate overwhelming advantages against the stateof-the-art MvSL based cross-view classification approaches in terms of classification accuracy and robustness. Copyright © 2018, The Authors. All rights reserved.

关键词： Embeddings

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Using measurement and simulation for understanding distributed development processes in the cloud 17

Using measurement and simulation for understanding distribut...

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27th International Workshop on software Measurement and 12th International Conference on software Process and Product Measurement, IWSM Mensura 2017

作者： Lunesu, Ilaria Marchesi, Michele Münch, Jürgen Kuhrmann, Marco University of Cagliari Department of Electrical and Electronic Engineering Italy University of Cagliari Department of Mathematics and Computer Science Italy Reutlingen University Business Informatics and Herman Hollerith Center Germany Clausthal University of Technology Institute for Applied Software Systems Engineering Germany

ISBN: (纸本)9781450348539

Organizations increasingly develop software in a distributed manner. The Cloud provides an environment to create and maintain software-based products and services. Currently, it is widely unknown which software processes are suited for Cloud-based development and what their effects in specific contexts are. This paper presents a process simulation to study distributed development in the Cloud. We contribute a simulation model, which helps analyzing different project parameters and their impact on projects carried out in the Cloud. The simulator helps reproducing activities, developers, issues and events in the project, and it generates statistics, e.g., on throughput, total time, and lead and cycle time. The aim of this simulation model is thus to analyze the tradeoffs regarding throughput, total time, project size, and team size. Furthermore, the modified simulation model aims to help project managers select the most suitable planning alternative. Based on observed projects in Finland and Spain, we simulated a distributed project using artificial and real data. Particularly, we studied the variables project size, team size, throughput, and total project duration. A comparison of the real project data with the results obtained from the simulation shows the simulation producing results close to the real data, and we could successfully replicate a distributed software project. By improving the understanding of distributed development processes, our simulation model thus supports project managers in their decision-making. © 2017 Association for Computing Machinery.

关键词： Throughput

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Node verification to join the cloud environment using third party verification server

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2017年第4期11卷 55-65页

作者： Aljammal, Ashraf H. Bani-Salameh, Hani Alsarhan, Ayoub Kharabsheh, Mohammad Obiedat, Mamoon Department of Computer Science and its Applications Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology The Hashemite University Zarqa Jordan Department of Software Engineering Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology The Hashemite University Zarqa Jordan Department of Computer Information Systems Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology The Hashemite University Zarqa Jordan

Currently, cloud computing is facing different types of threats whether from inside or outside its environment. This may cause cloud to be crashed or at least unable to provide services to the requests made by clients. In this paper, a new technique is proposed to make sure that the new node which asks to join the cloud is not composing a threat on the cloud environment. Our new technique checks the node before it will be guaranteed to join the cloud whether it runs malwares or software that could be used to launch an attack. In this way the cloud will allow only the clean node to join it, eliminating the risk of some types of threats that could be caused by infected nodes.

关键词： Network security

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Fuzzy adaptive tuning of a particle swarm optimization algorithm for variable-strength combinatorial test suite generation

arXiv

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arXiv 2018年

作者： Zamli, Kamal Z. Ahmed, Bestoun S. Mahmoud, Thair Afzal, Wasif Faculty of Computer Systems and Software Engineering University Malaysia Pahang Gambang Malaysia Department of Computer Science Faculty of Electrical Engineering Czech Technical University Karlovo ńam. 13 Praha 2121 35 Czech Republic School of Engineering Edith Cowan University 270 Joondalup Dr JoondalupWA6027 Australia School of Innovation Design and Engineering Mälardalen University Sweden

Combinatorial interaction testing is an important software testing technique that has seen lots of recent interest. It can reduce the number of test cases needed by considering interactions between combinations of input parameters. Empirical evidence shows that it effectively detects faults, in particular, for highly configurable software systems. In real-world software testing, the input variables may vary in how strongly they interact;variable strength combinatorial interaction testing (VS-CIT) can exploit this for higher effectiveness. The generation of variable strength test suites is a non-deterministic polynomial-time (NP) hard computational problem [1]. Research has shown that stochastic population-based algorithms such as particle swarm optimization (PSO) can be efficient compared to alternatives for VS-CIT problems. Nevertheless, they require detailed control for the exploitation and exploration trade-off to avoid premature convergence (i.e. being trapped in local optima) as well as to enhance the solution diversity. Here, we present a new variant of PSO based on Mamdani fuzzy inference system [2, 3, 4], to permit adaptive selection of its global and local search operations. We detail the design of this combined algorithm and evaluate it through experiments on multiple synthetic and benchmark problems. We conclude that fuzzy adaptive selection of global and local search operations is, at least, feasible as it performs only second-best to a discrete variant of PSO, called DPSO. Concerning obtaining the best mean test suite size, the fuzzy adaptation even outperforms DPSO occasionally. We discuss the reasons behind this performance and outline relevant areas of future work. Copyright © 2018, The Authors. All rights reserved.

关键词： Particle swarm optimization (PSO)

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Content-centric event-insensitive big data reduction in internet of things

Content-centric event-insensitive big data reduction in inte...

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2017 IEEE Global Communications Conference, GLOBECOM 2017

作者： Bhuiyan, Zakirul Alam Wang, Guojun Wang, Tian Rahman, Arafatur Wu, Jie Department of Computer and Information Sciences Fordham University New YorkNY10458 United States School of Computer Science and Educational Software Guangzhou University Guangzhou510006 China College of Computer Science and Technology Huaqiao University Xiamen361021 China Faculty of Computer Systems and Software Engineering University Malaysia Pahang26300 Malaysia Center for Networked Computing Temple University PhiladelphiaPA19122 United States

ISBN: (纸本)9781509050208;9781509050192

As more knowledge discovery functions or sensing units for event detection are added to sensor devices in the Internet of Things (IoT), devices acquire big data that is bigger than they are able to deliver using their radios in a given time window. As a result, energy consumption for big data acquisition and transmission and real-time data processing are great challenges. In this paper, we introduce BigReduce, a low- cost IoT framework for event detection that reduces a big amount of data at the time of data acquisition and before the data transmission across the network. BigReduce works on the analysis of the frequency content of signals as they are acquired and efficiently adapts the frequency rate based on the sensitivity to a respective event, such as fire event. Instead of transmitting the entire set of acquired data, BigReduce transmits only the signals that have a high event-sensitivity. We provide a detailed algorithm for fire event sensitivity indication based on the frequency consents. Results achieved through a lab testbed show that BigReduce is able to reduce energy consumption by at least 78% and data volume by 82% in comparison to other frameworks. © 2017 IEEE.

关键词： Data mining

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Brain morphometry analysis with surface foliation theory

arXiv

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arXiv 2018年

作者： Wen, Chengfeng Lei, Na Ma, Ming Qi, Xin Zhang, Wen Wang, Yalin Gu, David Xianfeng Department of Computer Science Stony Brook University Stony BrookNY11794 United States School of Software and Technology Dalian University of Technology Dalian116620 China School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States

Brain morphometry study plays a fundamental role in neuroimaging research. In this work, we propose a novel method for brain surface morphometry analysis based on surface foliation theory. Given brain cortical surfaces with automatically extracted landmark curves, we first construct finite foliations on surfaces. A set of admissible curves and a height parameter for each loop are provided by users. The admissible curves cut the surface into a set of pairs of pants. A pants decomposition graph is then constructed. Strebel differential is obtained by computing a unique harmonic map from surface to pants decomposition graph. The critical trajectories of Strebel differential decompose the surface into topological cylinders. After conformally mapping those topological cylinders to standard cylinders, parameters of standard cylinders (height, circumference) are intrinsic geometric features of the original cortical surfaces and thus can be used for morphometry analysis purpose. In this work, we propose a set of novel surface features rooted in surface foliation theory. To the best of our knowledge, this is the first work to make use of surface foliation theory for brain morphometry analysis. The features we computed are intrinsic and informative. The proposed method is rigorous, geometric, and automatic. Experimental results on classifying brain cortical surfaces between patients with Alzheimer's disease and healthy control subjects demonstrate the efficiency and efficacy of our method. Copyright © 2018, The Authors. All rights reserved.

关键词： Brain

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Cross-domain sentiment classification via Constructing semantic correlations

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2017年第2期44卷 172-179页

作者： Wei, Xiaocong Lin, Hongfei Yang, Liang Department of Computer Science and Technology Dalian University of Technology Dalian116024 China Department of Software Engineering Dalian University of Foreign Language Dalian116044 China

Cross-domain sentiment classification trains robust classifiers across domains with the help of source domain labeled data. Sentiment is expressed differently in different domains. Sentiment terms that occur in a source domain may not appear in a different target domain. Such feature mismatches hinder cross-domain sentiment classification. Previous studies have addressed this problem by constructing a common feature representation or subspace;however, they have not considered semantic correlations between features. In this paper, we propose a cross-domain semantic correlation auto-correspondence method (CSCW) by capturing similar semantic features from different domains. First, our method uses sentiment-invariance words as features by considering their properties as strong sentiment indicators and their invariance across domains. Second, we extracted the top-N pivot features using a common frequency among source and target domains. These pivot features can then be employed to find semantically similar sentiment features from both domains. Third, for each pivot feature, by calculating the semantic similarity between non-pivot features and pivot features from either domain with the help of Word2Vec, we construct similar-pivot feature pairs that express similar sentiments but in different representations in either domain. Finally, we transform these pairs to align similar sentiment feature representations. This process avoids feature mismatches and reduces sentiment discrepancies between domains. The experimental results from testing on 12 source-target domain pairs of Amazon product reviews demonstrate that our method significantly outperforms previous approaches in sentiment classification.

关键词： Semantics

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Cloud scalable multi-objective task scheduling algorithm for cloud computing using cat swarm optimization and simulated annealing 8

Cloud scalable multi-objective task scheduling algorithm for...

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8th International Conference on Information Technology, ICIT 2017

作者： Gabi, Danlami Ismail, Abdul Samad Zainal, Anazida Zakaria, Zalmiyah Al-Khasawneh, Ahmad Department of Computer Science Faculty of Computing Universiti Teknologi Malaysia Skudai Johor81310 Malaysia Department of Computer Science Kebbi State University of Science and Technology Aliero Nigeria Department of Software Engineering Faculty of Computing Universiti Teknologi Malaysia Skudai Johor81310 Malaysia Computer and Information Systems Faculty of Prince Al-Hussein Bin Abdullah II of Information Technology Hashemite University Zarqa Jordan

ISBN: (纸本)9781509063321

In cloud computing, customers-desired Quality of Service (QoS) expectations are quite superficial due to lack of scalable task scheduling solutions that can adjust to long-time changes. Researchers in the literature have put forward several task scheduling algorithms to account for customers' QoS expectations. Unfortunately, most of these algorithms need improvements to ensure the provisioning of better consumers' QoS expectation. In this study, a Multi-Objective QoS model to address customers' expectation based on execution time and execution cost criteria is presented. A Cloud Scalable Multi-Objective Cat Swarm Optimization (CSO) based Simulated Annealing (SA) (CSM-CSOSA) algorithm is then proposed to solve the model. In this method, the Taguchi Orthogonal approach is used to enhanced the SA and incorporated into the local search of the proposed algorithm for enhancing it exploration capability. Implementation of the algorithm is carried out on CloudSim tool and evaluated using one dataset (Normal distributed) and one Parallel Workload (High-Performance Computing Center North(HPC2N)). Quantitative analysis of the algorithm performance is taken based on metrics of execution time, execution cost, QoS and percentage improvement. Result obtained is compared with that of Multi-Objective Genetic Algorithm (MOGA), Multi-Objective Ant Colony (MOSACO) and Multi-Objective Particle Swarm Optimization (MOPSO), where proposed method is able to return substantial performance with improved QoS. © 2017 IEEE.

关键词： Simulated annealing

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Multi-classification of Parkinson's Disease via Sparse Low-Rank Learning

Multi-classification of Parkinson's Disease via Sparse Low-R...

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International Conference on Pattern Recognition

作者： Haijun Lei Yujia Zhao Zhongwei Huang Feng Zhou Limin Huang Baiying Lei College of Computer Science & Software Engineering Guangdong Province Key Laboratory of Popular High-Performance Computers Shenzhen China Department of Industrial and Manufacturing Systems Engineering University of Michigan Dearborn Michigan USA The Operations Center of Shenzhen People's Hospital Shenzhen China School of Biomedical Engineering Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging Shenzhen China

Neuroimaging techniques have been widely applied to various neurodegenerative disease analysis to reveal the intricate brain structure. The high dimensional neuroimaging features and limited sample size are the main challenges for the diagnosis task due to the unbalanced input data. To handle it, a sparse low-rank learning framework is proposed, which unveils the underlying relationships between input data and output targets by building a matrix-regularized feature network. Then we obtain the feature weight from the network based on local clustering coefficients. By discarding the irrelevant features and preserving the discriminative structured features, our proposed method can select the most relevant features and identify different stages of Parkinson's disease (PD) from normal controls. Extensive experimental results evaluated on the Parkinson's progression markers initiative (PPMI) dataset demonstrate that the proposed method achieves promising classification performance and outperforms the conventional algorithms. Furthermore, it can detect potential brain regions related to PD for future medical analysis.

关键词： Sparse matrices Feature extraction Diseases Neuroimaging Diffusion tensor imaging Support vector machines

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Architecture-aware design and implementation of CNN algorithms for embedded inference: the ALOHA project

Architecture-aware design and implementation of CNN algorith...

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International Conference on Microelectronics, ICM

作者： Paolo Meloni Daniela Loi Gianfranco Deriu Andy D. Pimentel Dolly Saprat Maura Pintort Battista Biggio Oscar Ripolles David Solans Francesco Conti Luca Benini Todor Stefanov Svetlana Minakova Bernhard Moser Natalia Shepeleva Michael Masin Francesca Palumbo Nikos Fragoulis Ilias Theodorakopoulos Department of Electrical and Electronic Engineering University of Cagliari Italy Institute of Informatics University of Amsterdam The Netherlands Pluribus One Italy CA Technologies Spain Integrated Systems Laboratory ETH Zurich Switzerland Institute of Advanced Computer Science Leiden University The Netherlands Software Competence Center Hagenberg Austria IBM Research - Haifa Israel IDEA Lab University of Sassari Italy IRIDA Labs Computer Vision Systems Greece

ISBN: (纸本)9781538681688;9781538681671

The use of Deep Learning (DL) algorithms is increasingly evolving in many application domains. Despite the rapid growing of algorithm size and complexity, performing DL inference at the edge is becoming a clear trend to cope with low latency, privacy and bandwidth constraints. Nevertheless, traditional implementation on low-energy computing nodes often requires experience-based manual intervention and trial-and-error iterations to get to a functional and effective solution. This work presents a computer-aided design (CAD) support for effective implementation of DL algorithms on embedded systems, aiming at automating different design steps and reducing cost. The proposed tool flow comprises capabilities to consider architecture-and hardware-related variables at very early stages of the development process, from pre-training hyperparameter optimization and algorithm configuration to deployment, and to adequately address security, power efficiency and adaptivity requirements. This paper also presents some preliminary results obtained by the first implementation of the optimization techniques supported by the tool flow.

关键词： Tools Inference algorithms Engines computer architecture Computational modeling Microelectronics Training

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