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 Cluster Computing   [SJR: 0.605]   [H-I: 24]   [1 followers]  Follow         Hybrid journal (It can contain Open Access articles)    ISSN (Print) 1573-7543 - ISSN (Online) 1386-7857    Published by Springer-Verlag  [2345 journals]
• Urdu word sense disambiguation using machine learning approach
Abstract: This paper focuses on the word sense disambiguation (WSD) problem in the context of Urdu language. Word sense disambiguation (WSD) is a phenomena for disambiguating the text so that machine (computer) would be capable to deduce correct sense of individual given word(s). WSD is critical for solving natural language engineering (NLE) tasks such as machine translation and speech processing etc. It also increase the performance of other tasks such as text retrieval, document classification and document clustering etc. Research work in WSD has been conducted up to different extents in computationally developed languages of the world. In the context of Urdu language the NLE research in general and the WSD research in particular is still in the infancy stage due to the rich morphological structure of Urdu. In this paper, we use machine learning (ML) approaches such as Bayes net classifier (BN), support vector machine (SVM) and decision tree (DT) for WSD in native script Urdu text. The results shown that BN has better F-measure than SVM and DT. The maximum F-measure of 0.711 over 2.5 million words raw Urdu corpus was recorded for the Bayes net classifier.
PubDate: 2017-06-20
DOI: 10.1007/s10586-017-0918-0

• GALS implementation of randomly prioritized buffer-less routing
architecture for 3D NoC
• Authors: A. Karthikeyan; P. Senthil Kumar
Abstract: Recently, there has been enormous attention given to the network on chip (NoC) because it is scalable compared to the communication bus. Three dimensional (3D) NoC is getting more popular due to the reduction of wire length with that off two dimensional NoC. The router in the NoC is provides communication between the different computational units. In this paper, a two-phase bundled-data handshake latch is used with an asynchronous router. The Mousetrap latch controller forms the basis of this asynchronous router. The major part of the arbiter is to schedule the packet, then deliver to its destination without any loss of the packet. This paper proposes a novel asynchronous 3D lottery routing algorithm which is based on arbitral mechanism. The Lottery routing algorithm distinguishes the different priorities of the input port and makes sure that it responses to the higher priority port. The proposed hardware is implemented using Cadence 180 nm technology. The result shows that the power reduction is about 17% and a slight increase in area and delay of about 2% with respect to synchronous 3D NoC.
PubDate: 2017-06-20
DOI: 10.1007/s10586-017-0979-0

• ScHeduling of jobs and Adaptive Resource Provisioning (SHARP) approach in
cloud computing
• Authors: Dinesh Komarasamy; Vijayalakshmi Muthuswamy
Abstract: Affordability of appropriate computing resources for satisfying prerequisites of Service Level Agreement (SLA) of clients and optimal utilization of cloud service providers are limited in the present scenario of cloud computing. To overcome these limitations, researchers have exploited various scheduling algorithms to process the deadline based autonomous jobs. The scheduling algorithms however do not support multiprocessor demand and adaptive resource provisioning. This inference triggers to propose a new approach called ScHeduling of jobs and Adaptive Resource Provisioning (SHARP) in cloud computing to handle independent jobs that processes the jobs in a multilevel manner. The SHARP approach embeds multiple criteria decision analysis to preprocess the jobs, multiple attribute job scheduling to prioritize the jobs and adaptive resource provisioning to provide resources dynamically. These contributions alleviate SLA violations in terms of deadline, upgrade client satisfaction and enhance resource utilization. The empirical studies verify the proposed approach in a cloud environment and show the necessity of the proposed approach to support elastic resource provisioning and meet SLA requirements.
PubDate: 2017-06-20
DOI: 10.1007/s10586-017-0976-3

• The framework and algorithm for preserving user trajectory while using
location-based services in IoT-cloud systems
• Authors: Dan Liao; Gang Sun; Hui Li; Hongfang Yu; Victor Chang
Abstract: Internet of things (IoT) based location-based services (LBS) are playing an increasingly important role in our daily lives. However, since the LBS server may be hacked, malicious or not credible, there is a good chance that interacting with the LBS server may result in loss of privacy.As per journal instruction, author photo and biography are mandatory for this article. Please provide. Thus, protecting user privacy such as the privacy of user location and trajectory is an important issue to be addressed while using LBS. To address this problem, we first construct three kinds of attack models that may expose a user’s trajectory or path while the user is sending continuous queries to a LBS server. Then we construct a novel LBS system model for preserving privacy, and propose the k-anonymity trajectory (KAT) algorithm which is suitable for both single query and continuous queries. Different from existing works, the KAT algorithm selects $$k-1$$ dummy locations using the sliding window based k-anonymity mechanism when the user is making single query, and selects $$k-1$$ dummy trajectories using the trajectory select mechanism for continuous queries. We evaluate the effectiveness of our proposed algorithm by conducting simulations for the single-query and continuous-query scenarios. The simulation results show that our proposed algorithm can protect privacy of users better than existing approaches, while incurring a lower time complexity than those approaches.
PubDate: 2017-06-20
DOI: 10.1007/s10586-017-0986-1

• Research from the perspective of resource orchestration on digital
ecosystem
• Authors: Zhengyan Cui; Taohua-Ouyang
Abstract: Since digital technologies have brought opportunities for marginal enterprises to rise, it deserves attentions from the academia about how marginal enterprises reconfigure ecosystems. This thesis has a case study of Duch Group (Duch) in 3D printing from the perspective of resource orchestration. It deeply investigates the dynamic process of how marginal enterprises reconfigure the ecosystem by digital technologies and attain the core position. It is found that the marginal enterprises, adhering to the “point-line-surface” principle, successively reconfigure the manufacturing process, industry chain and ecosystem by implementing actions of structuring, bundling and leveraging on the key resources such as digital technologies to attain the core position. This research benefits both academics and practitioners by contributing to cumulative theoretical developments and by offering practical insights.
PubDate: 2017-06-19
DOI: 10.1007/s10586-017-0906-4

• Predictive current control of a switched reluctance machine in the
direct-drive manipulator of cloud robotics
• Authors: Bingchu Li; Xiao Ling; Yixiang Huang; Liang Gong; Chengliang Liu
Abstract: Cloud robotics has undergone rapid development. As an important candidate for direct-drive manipulator, switched reluctance machines (SRMs) face significant challenge in terms of control used in cloud robotics because of latency and package losses in network communication. In this paper, predictive current control of SRMs is extended to use in controller upon cloud in the face of latency and package losses. The starting point of predictive model is modified to eliminate errors caused by latency in sensor-controller communication, and the execution of control command sequence is dynamically regulated according to the arrival time of the following sequence to adapt for latency and package losses in controller-actuator communication. The proposed control method is evaluated in a 1.5 kW SRM test platform and comparison with a conventional control method is performed; the results show that the proposed control method has better tracking performance in face of time delay and package losses in transmission.
PubDate: 2017-06-17
DOI: 10.1007/s10586-017-0983-4

• Hybrid spatial air index for processing queries in road networks
• Authors: M. Veeresha; M. Sugumaran
Abstract: In case of road networks, for the clients to disseminate data, wireless broadcast is a scalable and secure strategy. As far as network partition index (NPI) is concerned, it doesn’t address certain problems; as to how to handle if there has been a large number of data objects in broadcast system, in the same way how to handle link errors and cache management. Hence, to address such problems, hybrid spatial air index (HSAI) an NPI based air index has been proposed wherein hybrid broadcast scheduling, adaptive XOR-based network coding and adaptive cooperative caching are effectively used for spatial queries in road networks. Experiments are conducted for evaluating query performance and HSAI is compared with state-of-the-art NPI. The experimental results show the outperformance of HSAI.
PubDate: 2017-06-17
DOI: 10.1007/s10586-017-0975-4

• The model and algorithm of distributing cooperation profits among
operators of urban rail transit under PPP pattern
• Authors: Li Jia; Dingyou Lei; Yinggui Zhang; Qiongfang Zeng; Juan Wang
Abstract: The financing channels, investors and operators of urban rail transit are becoming more and more diversified, and public private partnership pattern has been increasingly suggested in financing and investment field of urban rail transit in China. The diversification of investors of urban rail transit will no doubt lead to the diversification of operators of urban rail transit network. To legitimately distribute the cooperation profits among operators, a model is developed based on passenger’s path choice behavior by considering travel period, travel time, transfer convenience and the comprehensive proportion of different service types provided by operators. In accordance with the features of urban rail transit network and origin-destination (OD) pairs of transferring among lines of different operators, a scheme of improved rail transit network is proposed. On the basis of the algorithm of breadth-first search and depth-first search, an algorithm of searching effective paths based on backtracking and traversing along the shortest path is established by considering the factor of transfer. Taking the example of Shenzhen’s rail transit network, three typical OD pairs are selected to measure and calculate, compare and analyze by six different conditions. The result shows that travel period, travel time, transfer convenience, and service types provided by operators exert great influence on the distribution of cooperation profits. Therefore, it is advisable to comprehensively consider all of these factors to improve the accuracy of cooperation profits distribution. Moreover, the proposed algorithm can search effective paths efficiently.
PubDate: 2017-06-17
DOI: 10.1007/s10586-017-0973-6

• Parallel high-dimensional multi-objective feature selection for EEG

• Authors: Juan José Escobar; Julio Ortega; Jesús González; Miguel Damas; Antonio F. Díaz
Abstract: Many bioinformatics applications that analyse large volumes of high-dimensional data comprise complex problems requiring metaheuristics approaches with different types of implicit parallelism. For example, although functional parallelism would be used to accelerate evolutionary algorithms, the fitness evaluation of the population could imply the computation of cost functions with data parallelism. This way, heterogeneous parallel architectures, including central processing unit (CPU) microprocessors with multiple superscalar cores and accelerators such as graphics processing units (GPUs) could be very useful. This paper aims to take advantage of such CPU–GPU heterogeneous architectures to accelerate electroencephalogram classification and feature selection problems by evolutionary multi-objective optimization, in the context of brain computing interface tasks. In this paper, we have used the OpenCL framework to develop parallel master-worker codes implementing an evolutionary multi-objective feature selection procedure in which the individuals of the population are dynamically distributed among the available CPU and GPU cores.
PubDate: 2017-06-17
DOI: 10.1007/s10586-017-0980-7

• Android malware detection method based on naive Bayes and permission
correlation algorithm
• Authors: Fengjun Shang; Yalin Li; Xiaolin Deng; Dexiang He
Abstract: In order to detect Android malware more effectively, an Android malware detection model was proposed based on improved naive Bayes classification. Firstly, considering the unknown permission that may be malicious in detection samples, and in order to improve the Android detection rate, the algorithm of malware detection is proposed based on improved naive Bayes. Considering the limited training samples, limited permissions, and the new malicious permissions in the test samples, we used the impact of the new malware permissions and training permissions as the weight. The weighted naive Bayesian algorithm improves the Android malware detection efficiency. Secondly, taking into account the detection model, we proposed a detection model of permissions and information theory based on the improved naive Bayes algorithm. We analyzed the correlation of the permission. By calculating the Pearson correlation coefficient, we determined the value of Pearson correlation coefficient r, and delete the permissions whose value r is less than the threshold $$\rho$$ and get the new permission set. So, we got the improved detection model by clustering based on information theory. Finally, we detected the 1725 Android malware and 945 non malicious application of multiple data sets in the same simulation environment. The detection rate of the improved the naive Bayes algorithm is 86.54%, and the detection rate of the non-malicious application is increased to 97.59%. Based on the improved naive Bayes algorithm, the false detection rate of the improved detection model is reduced by 8.25%.
PubDate: 2017-06-17
DOI: 10.1007/s10586-017-0981-6

• A strategy for scheduling reduce task based on intermediate data locality
of the MapReduce
• Authors: Fengjun Shang; Xuanling Chen; Chenyun Yan
Abstract: In this paper, researching on task scheduling is a way from the perspective of resource allocation and management to improve performance of Hadoop system. In order to save the network bandwidth resources in Hadoop cluster environment and improve the performance of Hadoop system, a ReduceTask scheduling strategy that based on data-locality is improved. In MapReduce stage, there are two main data streams in cluster network, they are slow task migration and remote copies of data. The two overlapping burst data transfer can easily become bottlenecks of the cluster network. To reduce the amount of remote copies of data, combining with data-locality, we establish a minimum network resource consumption model (MNRC). MNRC is used to calculate the network resources consumption of ReduceTask. Based on this model, we design a delay priority scheduling policy for the ReduceTask which is based on the cost of network resource consumption. Finally, MNRC is verified by simulation experiments. Evaluation results show that MNRC outperforms the saving cluster network resource by an average of 7.5% in heterogeneous.
PubDate: 2017-06-17
DOI: 10.1007/s10586-017-0972-7

• Zombies Arena: fusion of reinforcement learning with augmented reality on
NPC
Abstract: Augmented reality (AR) is a discipline having less cognizance but it is the door to new advance technologies. Accustomed games doesn’t facilitate user to physically interact with the surroundings which resulted into reduced learning capabilities. Our objective is to develop AR based first person shooter game empowering reinforcement learning. This act as a building block to capacitate users to interact with the physical environment. Non-player characters will be able to learn and adopt strategy more wisely after each move to capacitate players. Game is played by hundred users at different stages. Reported results are summarized in experiment section.
PubDate: 2017-06-13
DOI: 10.1007/s10586-017-0969-2

• Clustering based on words distances
• Authors: Hongtao Liu; Hongwei Guan; Jie Jian; Xueyan Liu; Ying Pei
Abstract: In order to find the relevance of the key words in the hot topics effectively, we proposed a clustering method based on words-distances. We calculated the distances between the words firstly, then calculated the sectional density of each words. We regarded the words which have higher sectional density and far away from sectional density point as the center point in the clustering. After find the center point, we start to clustering. This method through decision diagram on estimating the number of clusters. At last, we can find the results on the evaluating indicator of accuracy rate and recall rate.
PubDate: 2017-06-09
DOI: 10.1007/s10586-017-0963-8

• Image 1D OMP sparse decomposition with modified fruit-fly optimization
algorithm
• Authors: Ming Yang; Ning-bo Liu; Wei Liu
Abstract: The Fruit-fly optimization algorithm (FOA) is good at parallel search ability in the evolution process, but it traps in local optimum sometimes. Simulated Annealing (SA) algorithm accepts the second-optimum solution with Mrtropolis criterion so as to jump out of the local optimum. So, combined the advantages of two algorithms, modified FOA (FOA-SA) algorithm is presented in this paper. In FOA-SA, the smell concentration function is improved as well, so as to get the whole searching directions for fruit-fly. At the same time, in order to solve the problem of the computational complexity in image 2D sparse decomposition, image 1D orthogonal matching pursuit (OMP) algorithm with FOA-SA algorithm is implemented. Experimental results show that the convergence of FOA-SA is better than that in FOA, and the speed of image 1D sparse algorithm is 2.41 times faster than 2D for the 512  $$\times$$  512 image under the same conditions.
PubDate: 2017-06-09
DOI: 10.1007/s10586-017-0966-5

• Development of a medical big-data mining process using topic modeling
• Authors: Chang-Woo Song; Hoill Jung; Kyungyong Chung
Abstract: With the development of convergence information technology, all of the spaces and objects of human living have become digitized. In the health- and medical-service areas, IT supports Internet of things (IoT)-based medical services and health-care systems for patients. Medical facilities have been advanced on the basis of such IoT devices, and the digitized information on human behaviors and health makes the delivery of efficient and convenient health care possible. Under the given circumstances, health and medical care have been researched. For some of this research, the patient-health data were collected using IoT-based medical devices, and they served as a tool for medical diagnosis and treatment. This study proposes the development of a medical big-data mining process for which topic modeling is employed. The proposed method uses the big data that are offered by the open system of the health- and medical-services big data from the Health Insurance Review and Assessment Service, and their application follows the guidelines of the knowledge discovery in big-data process for data mining and topic modeling. For the medical data regarding the topic modeling, the public structured health- and medical-services big data, Open API, and patient datasets were used. For the document classification in the semantic situation of a topic, the Bag of Words technique and the latent Dirichlet allocation method were applied to find the document association for the development of the medical big-data mining process. In addition, this study conducted a performance evaluation of the topic-modeling accuracy based on the medical big-data mining process and the topic-modeling efficiency, and the effectiveness of the proposed method was examined.
PubDate: 2017-06-09
DOI: 10.1007/s10586-017-0942-0

• Corporate feed based metamaterial antenna for wireless applications
• Authors: M. Sugadev; E. Logashanmugam
Abstract: In recent years, metamaterial based planar antenna design is gaining popularity because of enhanced radiation. Also, coplanar waveguide (CPW) fed antennas provide inherent impedance matching for broader frequency range. In this paper, analysis of high efficiency dual band antenna for advanced LTE and other wireless applications is discussed. This proposed design uses two annular rings for the radiating layer with CPW feed. It uses split ring resonator elements for achieving metamaterial property. Resonance is achieved at 2.8 and 4.6 GHz with a maximum gain of 5.53 dB. The impedance bandwidth of the proposed antenna is 450 MHz.
PubDate: 2017-06-09
DOI: 10.1007/s10586-017-0965-6

• An architecture for the autonomic curation of crowdsourced knowledge
• Authors: Alina Patelli; Peter R. Lewis; Aniko Ekart; Hai Wang; Ian Nabney; David Bennett; Ralph Lucas; Alex Cole
Abstract: Human knowledge curators are intrinsically better than their digital counterparts at providing relevant answers to queries. That is mainly due to the fact that an experienced biological brain will account for relevant community expertise as well as exploit the underlying connections between knowledge pieces when offering suggestions pertinent to a specific question, whereas most automated database managers will not. We address this problem by proposing an architecture for the autonomic curation of crowdsourced knowledge, that is underpinned by semantic technologies. The architecture is instantiated in the career data domain, thus yielding Aviator, a collaborative platform capable of producing complete, intuitive and relevant answers to career related queries, in a time effective manner. In addition to providing numeric and use case based evidence to support these research claims, this extended work also contains a detailed architectural analysis of Aviator to outline its suitability for automatically curating knowledge to a high standard of quality.
PubDate: 2017-06-09
DOI: 10.1007/s10586-017-0908-2

• A case study of HMS using CIPA
• Authors: S. Angeline Julia; Paul Rodrigues
Abstract: In recent years, research into software architecture (SA) has become an important topic within the domain of software engineering. Since architecture plays a dominant role, analysis of SA is more important. Aim of analysing SA is to predict the quality of that system before it has been built. Among many SA analysis methods available, architecture trade-off analysis method is the most desirable one. Pattern’s which has impact on quality attributes is also used in this paper. This paper analyses hospital management system case study as a major work and uses new patterns named creative innovative patterns for architecture analysis to analyse its architecture.
PubDate: 2017-06-08
DOI: 10.1007/s10586-017-0956-7

• A novel pattern recognition technique based on group clustering computing
and convex optimization for dimensionality reduction
• Authors: Shiqi Li; Mingming Wang; Shiping Liu; Yan Fu
Abstract: In the field of pattern recognition and data learning process the dimensionality reduction is a necessary method for providing a personalized data in perfect numbers. The increase in research proclaims the importance of data reduction in current trends. The old traditional reduction techniques use ranking systems. But this differs from others in reduction of data without lagging its performance in terms of increased efficiency and less percentage of error occurrences. The area of dimensionality reduction not only ends in pattern recognition and also in many high dimensional data processing elements such as text categorization, indexing of documents and mainly in gene expression data. The feature extraction and feature selection are the two steps of process in reduction. The process proposed is statistical pattern recognition process and a paradigm for this approach to the problem is summarized herein. The four data process includes is (1) evaluation (2) acquisition (3) feature selection, and (4) statistical model of feature selection. We show how to reduce the dimensionality by utilizing group and convexity model. This paper concluded by an integrated approach to the implementation dimensionality reduction process with an effort to maximize the efficiency and accuracy.
PubDate: 2017-06-08
DOI: 10.1007/s10586-017-0952-y

• Arrhythmia and ischemia classification and clustering using QRS-ST-T (QT)
analysis of electrocardiogram
• Authors: Akash Kumar Bhoi; Karma Sonam Sherpa; Bidita Khandelwal
Abstract: The objective is to propose a collective analytical model for QRS complex, ST segment and T wave (i.e., QT complex) of electrocardiogram to evaluate the onset or occurrence of cardiovascular abnormalities. The proposed methodologies also classify healthy subjects, arrhythmic and ischemic patients. The idea is to extract the QRS-ST-T features; where, QT interval and 99% occupied bandwidth (Hz) features are extracted from QT complex and QRS versus ST-T interval ratio (%) is also formulated after segmenting the QT complex into QRS complex and ST-T segment by localizing the inflection points. The evaluation of this proposed approach has been carried out using the selected 36 recordings (true positive (TP) beats) from each standard databases i.e., MIT-BIH arrhythmia database, FANTASIA and European ST-T database. The method is initiated with the preprocessing stage and then the inflection points (i.e., $$Q,S,T_{\mathrm{offset}})$$ are detected using Pan-Tompkins method and curve analysis techniques. Then the time-frequency domain features (e.g. QT interval (s) and 99% occupied bandwidth (Hz)) are extracted from the segmented mean QT complex and the QRS versus ST-T interval (%) ratio is extracted from the segmented mean QRS versus ST-T segments simultaneously. These features are introduced to the classifier like decision tree, support vector machine and K-means for clustering operation. The classification success rate is 97.03% and resubstitution error rate is 2.97% among the arrhythmia, ischemia and healthy classes using QT interval and QRS versus ST-T interval ratio (%) features. The evaluations of other features are also analyzed along with graphical classification results. Allied evaluation of segments belonging to ventricular depolarization (QRS complex) and repolarization (ST segment and T wave) i.e., QT complex, will certainly improve the detection probability of ischemia and arrhythmia with further correlative parametric features. This also leads to automatic detection and classification of arrhythmia and ischemia by avoiding visual inspection and error free decison making.
PubDate: 2017-06-08
DOI: 10.1007/s10586-017-0957-6

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