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IEEE international conference on Engineering of Complex Computer Systems

作者： Muhammad Ashad Kabir Jun Han Alan Colman Naif R. Aljohani Mohammed Basheri Zhenchang Xing Shang-Wei Lin School of Computing and Mathematics Charles Sturt University NSW Australia School of Software and Elect Engg Swinburne University of Technology Australia Faculty of Computing and IT King Abdulaziz University Jeddah KSA School of Computer Science and Engg Nanyang Technological University Singapore

ISBN: (纸本)9781509055289

With the convergence of pervasive mobile computing and social networking, interest has grown significantly in software systems and applications that are aware of users' social context to make pervasive applications more intelligent and accessible. Thus, socially-aware systems have further advanced context-aware systems taking account of human social context such as social relationships to enable the attainment of users' tasks in different domains. However, social context-awareness introduces a variety of software engineering challenges. In this paper, we address these challenges by proposing a software engineering process that provides a methodological framework for developing various types of socially-aware applications from requirements elicitation through to concrete implementation. We provide context models and software infrastructure to assist developers in rapid prototyping. We also present two case studies to demonstrate the feasibility and applicability of our software engineering process by presenting how this process can be used to develop two different types of socially-aware applications utilizing our model and infrastructure. Finally, we evaluate our software engineering approach with respect to a set of software quality metrics.

关键词： Context Vehicles Runtime Software Adaptation models Context modeling

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Grey wolf optimizer and case-based reasoning model for water quality assessment 1st

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1st international conference on Advanced intelligent System and Informatics, AISI 2015

作者： Sweidan, Asmaa Hashem Bendary, Nashwa El Hassanien, Aboul Ella Hegazy, Osman Mohammed Mohamed, A. E.-K. Fayoum University Fayoum Egypt Arab Academy for Science Technologyand Maritime Transport Cairo Egypt Cairo University Cairo Egypt Beni-Suef University Beni-suef Egypt Cairo Egypt Department of Zoology Fayoum University Fayoum Egypt

ISBN: (纸本)9783319266886

This paper presents a bio-inspired optimized classification model for assessing water quality. As fish gills histopathology is a good biomarker for indicating water pollution, the proposed classification model uses fish gills microscopic images in order to asses water pollution and determine water quality. The proposed model comprises five phases;namely, case representation for defining case attributes via pre-processing and feature extraction steps, retrieve, reuse/adapt, revise, and retain phases. Wavelet transform and edge detection algorithms have been utilized for feature extraction stage. Case-based reasoning (CBR) has been employed, along with the bio-inspired Gray Wolf Optimization (GWO) algorithm, for optimizing feature selection and the k case retrieval parameters in order to asses water pollution. The datasets used for conducted experiments in this research contain real sample microscopic images for fish gills exposed to copper and water pH in different histopathlogical stages. Experimental results showed that the average accuracy achieved by the proposed GWO-CBR classification model exceeded 97.2 % considering variety of water pollutants. © Springer international Publishing Switzerland 2016.

关键词： Feature extraction

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Euro-Par 2015: Parallel processing: 21st international conference on Parallel and distributed computing Vienna, Austria, August 24–28, 2015 proceedings 21st

Euro-Par 2015: Parallel processing: 21st International Confe...

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21st international conference on Parallel and distributed computing, Euro-Par 2015

作者： Träff, Jesper Larsson Hunold, Sascha Versaci, Francesco Vienna University of Technology Vienna Austria

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Interference Management for distributed Parallel Applications in Consolidated Clusters

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ACM SIGPLAN NOTICES 2016年第4期51卷 443-456页

作者： Han, Jaeung Jeon, Seungheun Choi, Young-ri Huh, Jaehyuk Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea UNIST Sch Elect & Comp Engn Ulsan South Korea

Consolidating multiple applications on a system can improve the overall resource utilization of data center systems. However, such consolidation can adversely affect the performance of some applications due to interference caused by resource contention. Despite many prior studies on the interference effects in single-node systems, the interference behaviors of distributed parallel applications have not been investigated thoroughly. With distributed applications, a local interference in a node can affect the whole execution of an application spanning many nodes. This paper studies an interference modeling methodology for distributed applications to predict their performance under interference effects in consolidated clusters. This study first characterizes the effects of interference for various distributed applications over different interference settings, and analyzes how diverse interference intensities on multiple nodes affect the overall performance. Based on the characterization, this study proposes a static profiling-based model for interference propagation and heterogeneity behaviors. In addition, this paper presents use case studies of the modeling method, two interference-aware placement techniques for consolidated virtual clusters, which attempt to maximize the overall throughput or to guarantee the quality-of-service.

关键词： Resource contention consolidated system cloud computing distributed parallel application interference model placement algorithm

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An extended model of real-time distributed systems for cloud computing 21

An extended model of real-time distributed systems for cloud...

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21st ISSAT international conference on Reliability and Quality in Design

作者： Kimura, Mitsutaka Imaizumi, Mitsuhiro Nakagawa, Toshio Department of Cross Cultural Studies Gifu City Women's College 7-1 Hitoichibakita-machi Gifu501-0192 Japan College of Contemporary Management Aichi Gakusen University 1 Shirotori Oike-cho Toyota471-8532 Japan Department of Business Administration Aichi Institute of Technology 1247 Yachigusa Yakusa-cho Toyota470-0392 Japan

ISBN: (纸本)9780991057627

Recently, cloud computing has been widely used for the purpose of protecting client data on the Internet [1, 2]. But cloud computing has a character of slow response time because the data center is located in a remote place. In order to solve the problem, real-time distributed systems for cloud computing system has been proposed [3, 5, 6]. The cloud system consists of some intelligent nodes as well as a data center. The data center manages all of client data. The intelligent node provides client service near clients. It enables to provide client service at short response time[3]. We considered the reliability model of distributed information processing for cloud computing, derived cost effectiveness and discussed the optimal replication interval to minimize it [9]. Authors had deal with the server system with one failure mode. In this paper, we consider the reliability model of realtime distributed system with n intelligent nodes and formulate a stochastic model of the server system with n failure modes. We derive the expected numbers of the replication and of application processing. Further, we derive the expected cost and discuss an optimal replication interval to minimize it.

关键词： Cloud computing

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Scaling up Dynamic Topic Models 16

Scaling up Dynamic Topic Models

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25th international conference on World Wide Web (WWW)

作者： Bhadury, Arnab Chen, Jianfei Zhu, Jun Liu, Shixia Tsinghua Univ Dept Comp Sci & Technol State Key Lab Intelligent Technol & Syst Tsinghua Natl Lab Informat Sci & TechnolCtr Bioi Beijing 100084 Peoples R China Tsinghua Univ Sch Software Beijing 100084 Peoples R China Flipboard Inc 210-128 W Hastings St Vancouver BC V6B 1G8 Canada

ISBN: (纸本)9781450341431

Dynamic topic models (DTMs) are very effective in discovering topics and capturing their evolution trends in time series data. To do posterior inference of DTMs, existing methods are all batch algorithms that scan the full dataset before each update of the model and make inexact variational approximations with mean-field assumptions. Due to a lack of a more scalable inference algorithm, despite the usefulness, DTMs have not captured large topic dynamics. This paper fills this research void, and presents a fast and parallelizable inference algorithm using Gibbs Sampling with stochastic Gradient Langevin Dynamics that does not make any unwarranted assumptions. We also present a Metropolis-Hastings based O(1) sampler for topic assignments for each word token. In a distributed environment, our algorithm requires very little communication between workers during sampling (almost embarrassingly parallel) and scales up to large-scale applications. We are able to learn the largest Dynamic Topic Model to our knowledge, and learned the dynamics of 1,000 topics from 2.6 million documents in less than half an hour, and our empirical results show that our algorithm is not only orders of magnitude faster than the baselines but also achieves lower perplexity.

关键词： Topic Model Dynamic Topic Model Large Scale Machine Learning Parallel computing MCMC MPI

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Unobtrusive Posture Recognition via Online Learning of Multi-Dimensional RFID Received Signal strength 21

Unobtrusive Posture Recognition via Online Learning of Multi...

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21st IEEE international conference on Parallel and distributed Systems ICPADS

作者： Yao, Lina Sheng, Quan Z. Ruan, Wenjie Li, Xue Wang, Sen Yang, Zhi Univ Adelaide Sch Comp Sci Adelaide SA 5005 Australia Univ Queensland Sch ITEE Brisbane Qld 4072 Australia

ISBN: (纸本)9780769557854

Activity recognition is a core component of ubiquitous computing applications (e.g., fall detection of elder people) since many of such applications require an intelligent environment to infer what a person is doing or attempting to do. Unfortunately, the success of existing approaches on activity recognition relies heavily on people's involvement such as wearing battery-powered sensors, which might not be practical in real-world situations (e.g., people may forget to wear sensors). In this paper, we propose a device-free, real-time posture recognition technique using an array of pure passive RFID tags. In particular, posture recognition is treated as a machine learning problem where a series of probabilistic model is built via learning how the Received Signal strength Indicator (RSSI) from the tag array is distributed when a person performs different postures. We also design a segmentation algorithm to divide the continuous, multidimensional RSSI data stream into a set of individual segments by analyzing the shape of the RSSI data. Our approach for posture recognition eliminates the need for the monitored subjects to wear any devices. To the best of our knowledge, this work is the first on device-free posture recognition using low cost, unobtrusive RFID technology. Our experimental studies demonstrate the feasibility of the proposed approach for posture recognition.

关键词： Ubiquitous computing

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Machine learning based classification approach for predicting students performance in blended learning 1st

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1st international conference on Advanced intelligent System and Informatics, AISI 2015

作者： Nespereira, Celia González Elhariri, Esraa El-Bendary, Nashwa Vilas, Ana Fernández Redondo, Rebeca P. Díaz I and C Laboratory AtlantTIC Research Center University of Vigo Vigo36310 Spain Fayoum University Fayoum Egypt Arab Academy for Science Technology and Maritime Transport Cairo Egypt Cairo Egypt

ISBN: (纸本)9783319266886

Nowadays, recognizing and predicting students learning achievement introduces a significant challenge, especially in blended learning environments, where online (web-based electronic interaction) and offline (direct face-to-face interaction in classrooms) learning are combined. This paper presents a Machine Learning (ML) based classification approach for students learning achievement behavior in Higher Education. In the proposed approach, Random Forests (RF) and Support Vector Machines (SVM) classification algorithms are being applied for developing prediction models in order to discover the underlying relationship between students past course interactions with Learning Management Systems (LMS) and their tendency to pass/fail. In this paper, we considered daily students interaction events, based on time series, with a number of Moodle LMS modules as the leading characteristics to observe students learning performance. The dataset used for experiments is constructed based on anonymized real data samples traced from web-log files of students access behavior concerning different modules in a Moodle online LMS throughout two academic years. Experimental results showed that the proposed RF classification system has outperformed the typical SVMs classification algorithm. © Springer international Publishing Switzerland 2016.

关键词： Support vector machines

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RDMA-based Cooperative Caching for a distributed File System 21

RDMA-based Cooperative Caching for a Distributed File System

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21st IEEE international conference on Parallel and distributed Systems ICPADS

作者： Sasaki, Shin Matsumiya, Ryo Takahashi, Kazushi Oyama, Yoshihiro Tatebe, Osamu Univ Electrocommun Dept Informat Tokyo Japan Univ Tsukuba Dept Comp Sci Tsukuba Ibaraki 305 Japan

ISBN: (纸本)9780769557854

Efficient caching of file data is critical in order to achieve high performance in data-intensive applications. However, only a limited amount of memory is usually available to cache files in client nodes even on high-performance computing platforms. Cooperative caching is an approach that enables client nodes to share memory for file caching and thereby provide a large amount of memory for the file cache in the aggregate. Many studies have confirmed the efficacy of applying cooperative caching to distributed file systems. However, to the best of our knowledge, no study has evaluated an implementation of cooperative caching integrated into a modern distributed file system running on a high-speed network. In this paper, we propose a method that improves the performance of a distributed file system oriented to high-performance computing by integrating cooperative caching into it. In the proposed method, the metadata server of the distributed file system maintains information about the cache in all client nodes, and provides clients with the predicted cache location of any requested file. Further, InfiniBand RDMA is utilized to achieve fast cache transfer between the page caches of client nodes. Implementation of the proposed method in the Gfarm distributed file system and measurement of the performance of three real-world data-intensive applications indicate that the proposed method achieves a maximum speedup of 5.8%.

关键词： distributed file systems file caching cooperative caching InfiniBand RDMA data-intensive applications high-performance computing

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Detection of dead stained microscopic cells based on color intensity and contrast 1st

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1st international conference on Advanced intelligent System and Informatics, AISI 2015

作者： Kotyk, Taras Dey, Nilanjan Ashour, Amira S. Drugarin, Cornelia Victoria Anghel Gaber, Tarek Hassanien, Aboul Ella Snasel, Vaclav Ivano-Frankivsk National Medical University Ivano-Frankivsk Ukraine Department of CSE Bengal College of Engineering and Technology Durgapur India Department of Electronics and Electrical Communications Engineering Tanta University Tanta Egypt CIT College Taif University Al Huwaya Saudi Arabia Department of Electrical and Informatics Engineering Eftimie Murgu University of Resita Resita Romania IT4Innovation VSB-TU of Ostrava Ostrava Czech Republic Suez Canal University Ismailia Egypt Cairo University Cairo Egypt FEECS Department of Computer Science VSB-TU of Ostrava Ostrava Czech Republic Cairo Egypt

ISBN: (纸本)9783319266886

Apoptosis is an imperative constituent of various processes including proper progression and functioning of the immune system, embryonic development as well as chemical-induced cell death. Improper apoptosis is a reason in numerous human/animal’s conditions involving ischemic damage, neurodegenerative diseases, autoimmune disorders and various types of cancer. An outstanding feature of neurodegenerative diseases is the loss of specific neuronal populations. Thus, the detection of the dead cells is a necessity. This paper proposes a novel algorithm to achieve the dead cells detection based on color intensity and contrast changes and aims for fully automatic apoptosis detection based on image analysis method. A stained cultures images using Caspase stain of albino rats hippocampus specimens using light microscope (total 21 images) were used to evaluate the system performance. The results proved that the proposed system is efficient as it achieved high accuracy (98.89 ± 0.76 %) and specificity (99.36 ± 0.63 %) and good mean sensitivity level of (72.34 ± 19.85 %). © Springer international Publishing Switzerland 2016.

关键词： Cell death

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