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 Showing 1 - 200 of 872 Journals sorted alphabetically 3D Printing and Additive Manufacturing       (Followers: 14) Abakós       (Followers: 3) Academy of Information and Management Sciences Journal       (Followers: 68) ACM Computing Surveys       (Followers: 22) ACM Journal on Computing and Cultural Heritage       (Followers: 9) ACM Journal on Emerging Technologies in Computing Systems       (Followers: 13) ACM Transactions on Accessible Computing (TACCESS)       (Followers: 3) ACM Transactions on Algorithms (TALG)       (Followers: 16) ACM Transactions on Applied Perception (TAP)       (Followers: 6) ACM Transactions on Architecture and Code Optimization (TACO)       (Followers: 9) ACM Transactions on Autonomous and Adaptive Systems (TAAS)       (Followers: 7) ACM Transactions on Computation Theory (TOCT)       (Followers: 11) ACM Transactions on Computational Logic (TOCL)       (Followers: 4) ACM Transactions on Computer Systems (TOCS)       (Followers: 18) ACM Transactions on Computer-Human Interaction       (Followers: 12) ACM Transactions on Computing Education (TOCE)       (Followers: 3) ACM Transactions on Design Automation of Electronic Systems (TODAES)       (Followers: 1) ACM Transactions on Economics and Computation ACM Transactions on Embedded Computing Systems (TECS)       (Followers: 4) ACM Transactions on Information Systems (TOIS)       (Followers: 20) ACM Transactions on Intelligent Systems and Technology (TIST)       (Followers: 9) ACM Transactions on Interactive Intelligent Systems (TiiS)       (Followers: 4) ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)       (Followers: 10) ACM Transactions on Reconfigurable Technology and Systems (TRETS)       (Followers: 7) ACM Transactions on Sensor Networks (TOSN)       (Followers: 8) ACM Transactions on Speech and Language Processing (TSLP)       (Followers: 11) ACM Transactions on Storage ACS Applied Materials & Interfaces       (Followers: 21) Acta Automatica Sinica       (Followers: 3) Acta Universitatis Cibiniensis. 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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  [2353 journals]
• A novel fingerprinting using channel state information with
MIMO–OFDM
• Authors: Lei Zhang; Enjie Ding; Zhikai Zhao; Yanjun Hu; Xin Wang; Kai Zhang
Abstract: Abstract With the increasing demand of location-based services, indoor localization based on fingerprinting has become an increasingly important technique due to its high accuracy and low hardware requirement. The paper presents a novel fingerprinting system with a fine-grained information known as channel state information (CSI). The proposed fingerprint exploits multiple antennas and subcarriers of the IEEE 802.11n network using multiple input multiple output (MIMO)–orthogonal frequency division multiplexing system and takes account into the distance of features in each fingerprint. The experimental performance of the fingerprint is compared with RSSI-based Fingerprinting system and the CSI–MIMO Fingerprinting system. The experiment results show that the system achieves the accuracy of 0.61 and 0.9 m in static and dynamic scenarios respectively.
PubDate: 2017-08-01
DOI: 10.1007/s10586-017-1072-4

• CHOPPER: an intelligent QoS-aware autonomic resource management approach
for cloud computing
• Authors: Sukhpal Singh Gill; Inderveer Chana; Maninder Singh; Rajkumar Buyya
Abstract: Abstract Cloud computing is the future generation of computational services delivered over the Internet. As cloud infrastructure expands, resource management in such a large heterogeneous and distributed environment is a challenging task. In a cloud environment, uncertainty and dispersion of resources encounters problems of allocation of resources. Unfortunately, existing resource management techniques, frameworks and mechanisms are insufficient to handle these environments, applications and resource behaviors. To provide an efficient performance and to execute workloads, there is a need of quality of service (QoS) based autonomic resource management approach which manages resources automatically and provides reliable, secure and cost efficient cloud services. In this paper, we present an intelligent QoS-aware autonomic resource management approach named as CHOPPER (Configuring, Healing, Optimizing and Protecting Policy for Efficient Resource management). CHOPPER offers self-configuration of applications and resources, self-healing by handling sudden failures, self-protection against security attacks and self-optimization for maximum resource utilization. We have evaluated the performance of the proposed approach in a real cloud environment and the experimental results show that the proposed approach performs better in terms of cost, execution time, SLA violation, resource contention and also provides security against attacks.
PubDate: 2017-08-01
DOI: 10.1007/s10586-017-1040-z

• The talent planning model and empirical research to the key disciplines in
science and technology
• Authors: Hao Xu; Dongrui Wu; Lining Xing; Lan Huang
Abstract: Abstract With to the impact of economic globalization, the talent construction of key disciplines in science and technology should be administrated with humanism. An analysis of existing articles shows that the research of talent development mainly relates to the following aspects: cultivating objectives, cultivator, cultivation way, and evaluation criteria. In recent years, with the continuous improvement of education system in China and the increased awareness of talents, the talent construction of key discipline in science and technology has been greatly improved. With the actuality and circumstance analysis of the talent construction of key disciplines, a talent planning model is proposed to the key disciplines in science and technology. The proposed model is III-level tree structure, of which there are 2 I-level indexes, 8 II-level indexes and 23 III-level indexes. The Analytic Hierarchy Process is employed to determine the weights of talent planning indexes. This research will make the more scientific, systematic, strategic talent planning, and adapt to the development needs of key disciplines.
PubDate: 2017-08-01
DOI: 10.1007/s10586-017-1060-8

• Anomaly detection model based on data stream clustering
• Authors: Chunyong Yin; Sun Zhang; Zhichao Yin; Jin Wang
Abstract: Abstract Intrusion detection provides important protection for network security and anomaly detection as a type of intrusion detection, which can recognize the pattern of normal behaviors and label the behaviors which departure from normal pattern as anomaly behaviors. The updating of network equipment and broadband speed makes the data mining object change from static data sets to dynamic data streams. We think that the traditional methods based on data set do not satisfy the needs of dynamic network environment. The network data stream is temporal and cannot be treated as static data set. The concept and distribution of data objects is variety in different time stamps and the changing is unpredictable. Therefore, we propose an improved data stream clustering algorithm and design the anomaly detection model according to the improved algorithm. The established model can be modified with the changing of data stream and detect anomaly behaviors in time.
PubDate: 2017-08-01
DOI: 10.1007/s10586-017-1066-2

• Identify and analyze key industries and basic economic structures using
interregional industry network
• Authors: Wen-wen Xiao; Li-li Wang; Zhi-ying Zhang; Cheng-wei Wang
Abstract: Abstract This paper extends the use of graph theory and network science to the study of interregional industry network, trying to identify key industries and basic economic structures of five northwestern provinces in “Silk Road Economic Belt”, which is an area that hitherto eluded empirical analysis. From the perspective of industry network, “Silk Road Economic Belt” is regarded as an interregional industry network formed by a set of industries and the relationships between them. In this paper, we study the structure of inter-industry relationships by creating and analyzing an interregional industry network using input–output table. We develop a measure to find the basic substructure of the network and analyze the relationships between industries so as to identify key industries and basic economic structures of some region. We apply this new procedure to the I–O table of five northwestern provinces in “Silk Road Economic Belt”. The empirical results reveal that (1) Agricultural and resource-based industries are the key industries for five northwestern provinces; (2) Shaanxi and Xinjiang play a more important role in constructing “silk road economic belt”; (3) It’s suggested that the key industries are identified and basic economic structures are revealed by using the method proposed in this paper, which would be difficult to find by only using the original input–output analysis.
PubDate: 2017-07-29
DOI: 10.1007/s10586-017-1067-1

• HHSRP: a cluster based hybrid hierarchical secure routing protocol for
wireless sensor networks
• Authors: C. Deepa; B. Latha
Abstract: Abstract Wireless sensor networks happen to be fundamentally networks with energy restrictions. This is limited over longer period of time proves to be the challenge in clustering algorithms. We suggested a new method related to co-ordinator head (CNH) selection by using a mixed hierarchical cluster based algorithm that includes selecting the CNH happens to be the greatest value of the co-ordinator node (CN) and fitness value. This analysis put forward two different algorithms; mixed hierarchical cluster oriented routing program and hybrid hierarchical secure algorithm. A mixed hierarchal cluster based algorithm has been suggested for generating CNH and CN, for identifying the harmful node and packet is securely delivered. The source node transmits details about packet to the CN and then it chooses the pathway which is the shortest, depending upon the trust value among intermediate node and sensor node. The behavior activities of all the nodes are analyzed by the CNH, if anyone of the node’s behavior found to be incorrect, the organizer who finds the harmful node and CNH will drop those particular node behaviors. The CNH then changes to other path way immediately and transmits the delivery packet securely to the target destination under short time.
PubDate: 2017-07-29
DOI: 10.1007/s10586-017-1065-3

•
• Authors: Shuwei Jing; Rui Li; Junai Yan; Fudong Yang; Cheng-Chung Chen
Abstract: Abstract Given that there are more non-value-added parts of product development process for technological innovation in enterprises. This research puts forward and applies the product development value stream technology on the basis of the theory for traditional value stream. Then the paper takes a case about product development of a manufacturing enterprise to analyze the whole process of product development from product planning, preliminary design, detailed design, and prototype test to put in production. According to analyze, we find the waste stages in the development and optimizing the development process. According to the contrast between two figures of development value stream in current and future situation, we find that the optimizing method can help recognize effectively the waste in the process of product development, cut down the period of development, reduce R&D costs, improve product development efficiency, and satisfy clients’ needs.
PubDate: 2017-07-28
DOI: 10.1007/s10586-017-1068-0

• Dragonfly optimization and constraint measure-based load balancing in
cloud computing
• Authors: Vijayakumar Polepally; K. Shahu Chatrapati
Abstract: Abstract Load balancing is the significant task in the cloud computing because the cloud servers need to store avast amount of information which increases the load on the servers. The objective of the load balancing technique is that it maintains a trade-off on servers by distributing equal load with less power. Accordingly, this paper presents the load balancing technique based on the constraint measure. Initially, the capacity and load of each virtual machine are calculated. If the load of the virtual machine is greater than the balanced threshold value then,the load balancing algorithm is used for allocating the tasks. The load balancing algorithm calculates the deciding factor of each virtual machine and checks the load of the virtual machine. Then, it calculates the selection factor of each task. Then, the task which has better selection factor is allocated to the virtual machine. The performance of the proposed load balancing method is evaluated with the existing load balancing methods, such as HBB-LB, DLB, and HDLB for the evaluation metrics load and capacity. The experimental results show that the proposed method migrate only three tasks while the existing method HDLB migrates seven tasks.
PubDate: 2017-07-28
DOI: 10.1007/s10586-017-1056-4

• Identifying key influential parameters of high profile criminals through
statistical correlation
• Authors: Danish Wadood; Azhar Rauf; Shah Khusro; Shaukat Ali
Abstract: Abstract Identification of influential parameters using data mining and statistical tools is an important subject of criminology. These parameters are further used in criminal prediction and crime reduction which is a well-known problem in law enforcement agencies. In this research, we have analyzed the 5 years data of the Prison Department of the Government of KP. More than thirty parameters related to criminals were cleaned, preprocessed and analyzed. Correlation of these parameters with high act crimes was identified. There are five parameters which have strong correlation with high act crimes that includes total number of group members, recovery of assets, total number of hearings, crime frequency, and total number of non-blood visitors. On the other hand, there are three parameters that have negative correlation with high act crimes which includes education level of a criminal, total number of dependents, and prison duration. The overall objective of this research is to identify influential parameters related to high category crimes and support the law enforcement agencies of the KP province in reducing the crime ratio.
PubDate: 2017-07-28
DOI: 10.1007/s10586-017-1059-1

• W-Scheduler: whale optimization for task scheduling in cloud computing
• Authors: Karnam Sreenu; M. Sreelatha
Abstract: Abstract One of the important steps in cloud computing is the task scheduling. The task scheduling process needs to schedule the tasks to the virtual machines while reducing the makespan and the cost. Number of scheduling algorithms are proposed by various researchers for scheduling the tasks in cloud computing environments. This paper proposes the task scheduling algorithm called W-Scheduler based on the multi-objective model and the whale optimization algorithm (WOA). Initially, the multi-objective model calculates the fitness value by calculating the cost function of the central processing unit (CPU) and the memory. The fitness value is calculated by adding the makespan and the budget cost function. The proposed task scheduling algorithm with the whale optimization algorithm can optimally schedule the tasks to the virtual machines while maintaining the minimum makespan and cost. Finally, we analyze the performance of the proposed W-Scheduler with the existing methods, such as PBACO, SLPSO-SA, and SPSO-SA for the evaluation metrics makespan and cost. From the experimental results, we conclude that the proposed W-Scheduler can optimally schedule the tasks to the virtual machines while having the minimum makespan of 7 and minimum average cost of 5.8.
PubDate: 2017-07-28
DOI: 10.1007/s10586-017-1055-5

• Authentication of outsourced linear function query with efficient updates
• Authors: Gang Sheng; Chunming Tang; Hongyan Han; Wei Gao; Xing Hu
Abstract: Abstract Storing the large-scale data on the cloud server side becomes nowadays an alternative for the data owner with the popularity and maturity of the cloud computing technique, where the data owner can manage the data with limited resources, and the user issues the query request to the cloud server instead of the data owner. As the server is not completely trusted, it is necessary for the user to perform results authentication to check whether or not the returned results from the cloud server are correct. We investigate in this paper how to perform efficient data update for the result authentication of the outsourced univariate linear function query. We seek to outsource almost all the data and computing to the server, and as few data and computations as possible are stored and performed on the data owner side, respectively. We present a novel scheme to achieve the security goal, which is divided into two parts. The first part is a verification algorithm for the outsourced computing of line intersections, which enables the data owner to store most of the data on the server side, and to execute less of the computing of the line intersections. The second part is an authentication data structure Two Level Merkle B Tree for the outsourced univariate linear function query, where the top level is used to index the user input and authenticate the query results, and the bottom level is used to index the query condition and authenticate the query results. The authentication data structure enables the data owner to update the data efficiently, and to implement the query on the server side. The theoretic analysis shows that our proposed scheme works with higher efficiency.
PubDate: 2017-07-27
DOI: 10.1007/s10586-017-1064-4

• Railway passengers travel behavior based on bounded rationality by rough
set weight
• Authors: Hai-jun Li; Hong-chang Zhou; Jian-rong Feng; Xiao-hong Chen; Wei Zhang
Abstract: Abstract Pointing at the bounded rationality problem of passenger travel choice in the railway corridor, this paper proposes a travel choice theory considering passengers psychological expectation, and establishes the travel mode decision model based on prospect theory. With the comprehensive prospect value combined by analyzing the travel time, travel expenses and the comfort degree of the psychological reference point, it obtains the passengers travel mode choice results. Taking railway transport corridor from Baoji to Lanzhou as an example, by calculating the comprehensive prospect value, and analyzing the punctuality, the scheduled arrival time, price and the sensitivity from speed on travel choice, it finds out that the passengers tend to choose high-speed rail travel with a higher punctuality rate and tend to a mode with relatively lower fares and faster speed. When the predetermined arrival time is abundant, the travel proportion of choosing the general rail travel with longer travel time and better economy is gradually increasing.
PubDate: 2017-07-26
DOI: 10.1007/s10586-017-1061-7

• On the use of chaotic iterations to design keyed hash function
• Authors: Zhuosheng Lin; Christophe Guyeux; Simin Yu; Qianxue Wang; Shuting Cai
Abstract: Abstract Due to the complex dynamical properties of chaos, designing chaos-based hash functions emerged as a new research direction to reinforce information security of data sent through the Internet. This paper aims at developing a novel methodology to construct keyed hash functions based on chaotic iterations, which can avoid dynamic degradation caused by finite precision. The chaotic iterations are used first in the design of strategies thanks to particular pseudorandom number generators. They are also used in hash value computation, by iterating on state-of-the-art hash functions. Security investigations related to sensitiveness, diffusion and confusion, and collision analysis validate the proposed systematic methodology, showing that such post-processing on standard hash functions will preserve their security properties.
PubDate: 2017-07-26
DOI: 10.1007/s10586-017-1062-6

• Grid-enabled evolution strategies for large-scale home care crew
scheduling
• Authors: Francisco Luna; Alejandro Cervantes; Pedro Isasi; Juan F. Valenzuela-Valdés
Abstract: Abstract The home care crew scheduling (HCCS) problem is a planning task whose goal is to allocate a set of professional caregivers in the most efficient way to perform a number of assistencial and health care visits to the customers private homes. This is part of an important trend in advanced health care systems, to promote “independent living” specially in situations of dependency on long-term care. This not only ensures a higher quality of life but also a lower cost for society. Real instances of the HCCS problem are large and highly constrained due to both caregivers’ contract limitations and customers’ needs. This paper presents an advanced parallel model that solves HCCS problems using a grid-based asynchronous evolutionary algorithm (EA). Our approach has been tested using a grid computing facility of up to 300 nodes. The algorithm is a modified $$(1 + \lambda )$$ EA, parallelized using a master/worker model that minimizes communication requirements and processor bottlenecks by distributing both the execution of the EA operators and the evaluation of solutions. We have used three large real-world instances provided by a private company to perform experimentation with different configurations of the EA and number of workers. Results show that our algorithm achieves solutions that clearly outperform the solution provided by the company and the grid-based algorithm is able to handle real world HCCS problems
PubDate: 2017-07-26
DOI: 10.1007/s10586-017-1058-2

• Analyzing students’ performance using multi-criteria classification
• Authors: Feras Al-Obeidat; Abdallah Tubaishat; Anna Dillon; Babar Shah
Abstract: Abstract Education is a key factor for achieving long-term economic progress. During the last decades, higher standards in education have become easier to attain due to the availability of knowledge and resources worldwide. With the emergence of new technology enhanced by using data mining it has become easier to dig into data and extract useful knowledge from data. In this research, we use data analytic techniques applied to real case studies to predict students’ performance using their past academic experience. We introduce a new hybrid classification technique which utilize decision tree and fuzzy multi-criteria classification. The technique is used to predict students’ performance based on several criteria such as age, school, address, family size, evaluation in previous grades, and activities. To check the accuracy of the model, our proposed method is compared with other well-known classifiers. This study on existing student data showed that this method is a promising classification tool.
PubDate: 2017-07-25
DOI: 10.1007/s10586-017-0967-4

• The mechanical arm control based on harmony search genetic algorithm
• Authors: Zhaolan He; Bo Pan; Zongze Liu; Xianxian Tang
Abstract: Abstract Considering the low efficiency and instability of traditional genetic algorithm optimized PID controller, an improved algorithm named harmony search genetic algorithm to optimize PID controller’s parameters of mechanical arm is proposed in this paper. Using harmony search algorithm in the initial population generation process of genetic algorithm improved the algorithm’s performance. Harmony search genetic algorithm is more suitable to optimize PID controller’s parameters than traditional genetic algorithm in six degrees of freedom mechanical arm system. Compared to the traditional control optimization method, as shown in the simulation results, the new kind of optimization method is better in both validity and stability.
PubDate: 2017-07-24
DOI: 10.1007/s10586-017-1053-7

• AEGEUS++: an energy-aware online partition skew mitigation algorithm for
mapreduce in cloud
• Authors: Vimalkumar Kumaresan; R. Baskaran; P. Dhavachelvan
Abstract: Abstract This paper investigates the partition skew problem at reduce phase in the mapreduce jobs. Our study summarize the skew problem in both offline and online manner. Offline is a heuristics based approach waits for the completion of map tasks and it involves computation overhead to estimate the partition size. In online approach, the overloaded tasks are distributed across other nodes that needs extra split and merge operations. These extra operations and ineffective utilization of resources in turn hamper the performance of the entire system. In this paper, we propose Aegeus++, to address the skew mitigation and adaptive data sampling problems for mapreduce jobs which enables to build an online prediction model with improved accuracy in minimal waiting time. In addition, we propose near linear skew detection and fine-grained Resource Allocation algorithms for identifying the skewed partition and allocating appropriate resources to reducers based on the partition size. Finally, our energy-aware opportunistic frequency tuning algorithm improves the performance of the reducer container on-fly, that can process the skewed data faster with minimal energy consumption. We evaluated Aegeus++ in the cloud setup by using benchmark datasets, compared its performance with native Hadoop and its other approaches. Based on our observation, Aegeus++ outperforms native Hadoop by 44% by maximizing its overall performance of the application and decreases the energy consumption by 37.67% when compared with existing approaches.
PubDate: 2017-07-24
DOI: 10.1007/s10586-017-1044-8

• A bi-level optimization for an HVAC system
• Authors: Luping Zhuang; Xi Chen; Xiaohong Guan
Abstract: Abstract Improving the control strategy of an heating, ventilation, and air-conditioning (HVAC) system can result in substantial energy saving. In this paper, we formulate the whole HVAC system to a bi-level optimization problem to minimize the energy consumption of the HVAC system and maximize the satisfaction of indoor human comfort. The hierarchical evolutionary algorithm with preliminary feasibility conditions and crude energy index is proposed to find the good-quality control strategy of the HVAC system. Numerical results demonstrate the efficiency and effectiveness of the proposed method and show the performance of the obtained control strategy.
PubDate: 2017-07-24
DOI: 10.1007/s10586-017-1050-x

• Computationally efficient generic adaptive filter (CEGAF)
• Authors: Muqaddas Abid; Muhammad Ishtiaq; Farman Ali Khan; Salabat Khan; Rashid Ahmad; Peer Azmat Shah
Abstract: Abstract Enhancement to clean speech from noisy speech has always been a challenging issue for the researcher’s community. Various researchers have used different techniques to resolve this problem. These techniques can be classified into the unsupervised and supervised approaches. Amongst the unsupervised approaches, Spectral Subtraction and Wiener Filter are commonly exploited. However, such approaches do not yield significant enhancement in the speech quality as well as intelligibility. As compared to unsupervised, supervised approaches such as Hidden Markov Model produces enhanced speech signals with better quality. However, supervised approaches need prior knowledge about the type of noise which is considered their major drawback. Moreover, for each noise type, separate models need to be trained. In this paper, a novel hybrid approach for the enhancement of speech is presented to overcome the limitations of both supervised and unsupervised approaches. The filter weights adjustment on the basis of Delta Learning Rule makes it a supervised approach. To address the issue of construction of new model for each noise type, the filter adjusts its weights automatically through minimum mean square error. It is unsupervised as there is no need of estimation of noise power spectral density. Various experiments are performed to test the performance of proposed filter with respect to different parameters. Moreover, the performance of the proposed filter is compared with state-of-the-art approaches using objective and subjective measures. The results indicate that CEGAF outperforms the algorithms such as Wiener Filter, supervised NMF and online NMF.
PubDate: 2017-07-24
DOI: 10.1007/s10586-017-1046-6

• Enhanced speaker verification using an adaptive multiple low-rank
representation based on the modified adaptive Gaussian mixture model
framework
• Authors: Tan Dat Trinh; Xinjie Ma; Jin Young Kim; Hyoung Gook Kim
Abstract: Abstract In this paper, a new method for the calculation of the observation-confidence value that is applied in the modified adaptive Gaussian mixture model framework is proposed for speaker verification. First, an adaptive version of the multiple low-rank representation method, for which a weighted decomposition that incorporates the prior information regarding the speech/non-speech content is considered, is proposed to find the enhanced speech and for the estimation of the frame signal-to-noise ratio (SNR) values. Then, a simple sigmoid function is applied to convert the frame SNR values into the observation-confidence values. To verify the accuracy of the system, we use utterances from the Korean movie You Came From The Stars. The experiment results show that our proposed approach achieves a greater accuracy compared with the other well-known baseline methods, such as the GMM-based universal background model, the GMM supervector-based support vector machine (SVM), the i-vector-based SVM, and the sparse representation, under the noisy environment.
PubDate: 2017-07-20
DOI: 10.1007/s10586-017-1051-9

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