List of Articles partitioning

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  1 - Concept Detection in Images Using SVD Features and Multi-Granularity Partitioning and Classification
  Kamran  Farajzadeh Esmail  Zarezadeh Jafar Mansouri
  10.7508/jist.2017.19.004
  New visual and static features, namely, right singular feature vector, left singular feature vector and singular value feature vector are proposed for the semantic concept detection in images. These features are derived by applying singular value decomposition (SVD) " More

  New visual and static features, namely, right singular feature vector, left singular feature vector and singular value feature vector are proposed for the semantic concept detection in images. These features are derived by applying singular value decomposition (SVD) "directly" to the "raw" images. In SVD features edge, color and texture information is integrated simultaneously and is sorted based on their importance for the concept detection. Feature extraction is performed in a multi-granularity partitioning manner. In contrast to the existing systems, classification is carried out for each grid partition of each granularity separately. This separates the effect of classifications on partitions with and without the target concept on each other. Since SVD features have high dimensionality, classification is carried out with K-nearest neighbor (K-NN) algorithm that utilizes a new and "stable" distance function, namely, multiplicative distance. Experimental results on PASCAL VOC and TRECVID datasets show the effectiveness of the proposed SVD features and multi-granularity partitioning and classification method Manuscript profile
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  2 - Using Static Information of Programs to Partition the Input Domain in Search-based Test Data Generation
  Atieh Monemi Bidgoli Hassan haghighi
  10.29252/jist.8.32.219
  The quality of test data has an important effect on the fault-revealing ability of software testing. Search-based test data generation reformulates testing goals as fitness functions, thus, test data generation can be automated by meta-heuristic algorithms. Meta-heurist More

  The quality of test data has an important effect on the fault-revealing ability of software testing. Search-based test data generation reformulates testing goals as fitness functions, thus, test data generation can be automated by meta-heuristic algorithms. Meta-heuristic algorithms search the domain of input variables in order to find input data that cover the targets. The domain of input variables is very large, even for simple programs, while this size has a major influence on the efficiency and effectiveness of all search-based methods. Despite the large volume of works on search-based test data generation, the literature contains few approaches that concern the impact of search space reduction. In order to partition the input domain, this study defines a relationship between the structure of the program and the input domain. Based on this relationship, we propose a method for partitioning the input domain. Then, to search in the partitioned search space, we select ant colony optimization as one of the important and prosperous meta-heuristic algorithms. To evaluate the performance of the proposed approach in comparison with the previous work, we selected a number of different benchmark programs. The experimental results show that our approach has 14.40% better average coverage versus the competitive approach Manuscript profile
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  3 - A Novel Approach for Establishing Connectivity in Partitioned Mobile Sensor Networks using Beamforming Techniques
  Abbas Mirzaei Shahram Zandian
  10.52547/jist.16302.10.40.300
  Network connectivity is one of the major design issues in the context of mobile sensor networks. Due to diverse communication patterns, some nodes lying in high-traffic zones may consume more energy and eventually die out resulting in network partitioning. This phenomen More

  Network connectivity is one of the major design issues in the context of mobile sensor networks. Due to diverse communication patterns, some nodes lying in high-traffic zones may consume more energy and eventually die out resulting in network partitioning. This phenomenon may deprive a large number of alive nodes of sending their important time critical data to the sink. The application of data caching in mobile sensor networks is exponentially increasing as a high-speed data storage layer. This paper presents a deep learning-based beamforming approach to find the optimal transmission strategies for cache-enabled backhaul networks. In the proposed scheme, the sensor nodes in isolated partitions work together to form a directional beam which significantly increases their overall communication range to reach out a distant relay node connected to the main part of the network. The proposed methodology of cooperative beamforming-based partition connectivity works efficiently if an isolated cluster gets partitioned with a favorably large number of nodes. We also present a new cross-layer method for link cost that makes a balance between the energy used by the relay. By directly adding the accessible auxiliary nodes to the set of routing links, the algorithm chooses paths which provide maximum dynamic beamforming usage for the intermediate nodes. The proposed approach is then evaluated through simulation results. The simulation results show that the proposed mechanism achieves up to 30% energy consumption reduction through beamforming as partition healing in addition to guarantee user throughput. Manuscript profile
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  4 - Efficient Document Partitioning for Load Balancing between Servers Using Term Frequency of Past Queries
  Reyhaneh Torab Sajjad Zarifzadeh
  The main goal of web search engines is to find the most relevant results with respect to the user query in a shortest possible time. To do so, the crawled documents have to be partitioned between several servers in order to use their aggregate retrieval and processing p More

  The main goal of web search engines is to find the most relevant results with respect to the user query in a shortest possible time. To do so, the crawled documents have to be partitioned between several servers in order to use their aggregate retrieval and processing power. The search engines use different policies for efficient partitioning of documents. In this paper, we propose a new document partitioning method that intends to balance the load between servers to reduce the response time of queries. The idea is to weigh each term based on its daily frequency in log of past queries. We then assign a weight to each document via summing the weight of its substituent terms. The weight of a document approximates the likelihood of its presence in future search results. Finally, the documents are partitioned between servers in a way that the sum of document weights in each server becomes roughly equal. Our evaluation results show that the proposed method is able to balance the load by about 20% better than former algorithms, especially in the peak of search engine traffic. Manuscript profile
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  5 - A Two-Level Method Based on Dynamic Programming for Partitioning and Optimization of the Communication Cost in Distributed Quantum Circuits
  zohreh davarzani maryam zomorodi-moghadam M. Houshmand
  Nowadays, quantum computing has played a significant role in increasing the speed of algorithms. Due to the limitations in the manufacturing technologies of quantum computers, the design of a large-scale quantum computer faces many challenges. One solution to overcome t More

  Nowadays, quantum computing has played a significant role in increasing the speed of algorithms. Due to the limitations in the manufacturing technologies of quantum computers, the design of a large-scale quantum computer faces many challenges. One solution to overcome these challenges is the design of distributed quantum systems. In these systems, quantum computers are connected to each other through the teleportation protocol to transfer quantum information. Since quantum teleportation requires quantum resources, it is necessary to reduce the number of that. The purpose of this paper is to present a distributed quantum system considering the two goals of balanced distribution of qubits and minimizing the number of teleportation protocols in two levels. In the first level, by presenting a dynamic programming algorithm, an attempt has been made to distribute qubits in a balanced manner and reduce the number of connections between subsystems. According to the output partitioning obtained from the first level, in the second level and in the stage of implementation of global gates, when one of the qubits of this gate is teleported from the home to the desired destination, this qubit may be able to be used by a number of global gates, observing the precedence restrictions and as a result it reduces the number of teleportations. The obtained results show the better performance of the proposed algorithm. Manuscript profile