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Masayoshi Aritsugi
Faculty of Advanced Science and Technology, Kumamoto University, Kumamoto, Japan

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Journal article
Published: 23 June 2021 in IEEE Access
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Hyperparameters and architecture greatly influence the performance of convolutional neural networks (CNNs); therefore, their optimization is important to obtain the desired results. One of the state-of-the-art methods to achieve this is the use of neuroevolution that utilizes a genetic algorithm (GA) to optimize a CNN. However, the GA is often trapped into a local optimum resulting in premature convergence. In this study, we propose an approach called the “diversity-guided genetic algorithm-convolutional neural network (DGGA-CNN)” that uses adaptive parameter control and random injection to facilitate the search process by exploration and exploitation while preserving the population diversity. The alternation between exploration and exploitation is guided by using an average pairwise Hamming distance. Moreover, the DGGA fully handles the architecture of the CNN by using a novel finite state machine (FSM) combined with three novel mutation mechanisms that are specifically created for architecture chromosomes. Tests conducted on suggestion mining and twitter airline datasets reveal that the DGGA-CNN performs well with valid architectures and a comparison with other methods demonstrates its capability and efficiency.

ACS Style

Tirana Noor Fatyanosa; Masayoshi Aritsugi. An Automatic Convolutional Neural Network Optimization Using a Diversity-Guided Genetic Algorithm. IEEE Access 2021, 9, 1 -1.

AMA Style

Tirana Noor Fatyanosa, Masayoshi Aritsugi. An Automatic Convolutional Neural Network Optimization Using a Diversity-Guided Genetic Algorithm. IEEE Access. 2021; 9 ():1-1.

Chicago/Turabian Style

Tirana Noor Fatyanosa; Masayoshi Aritsugi. 2021. "An Automatic Convolutional Neural Network Optimization Using a Diversity-Guided Genetic Algorithm." IEEE Access 9, no. : 1-1.

Journal article
Published: 18 March 2021 in IEEE Access
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Review mining from app marketplaces has gained immense popularity from researchers in recent years. Most studies in this area, however, tend to focus on improving the performance of classification prediction. In this study, we consider review mining from a different perspective, that is, mining user actions/decisions along with their respective arguments/reasons. Our motivation is to obtain a deeper understanding of users’ decisions regarding applications and their underlying justifications, e.g., why users give ratings or recommendations. These information abstractions can benefit app developers, especially in planning app updates, by providing data-driven requirements from users’ points of view. We utilized a supervised learning approach and built a machine-based annotator to set the ground truth. Seven classifiers and different feature configurations were trained and evaluated on two app review datasets. We then extracted relations between user decisions and arguments based on functional and nonfunctional requirement attributes. The results show an improved performance over the results of the baselines and favorably acceptable performance compared to the results from a human assessment.

ACS Style

Anang Kunaefi; Masayoshi Aritsugi. Extracting Arguments Based on User Decisions in App Reviews. IEEE Access 2021, 9, 45078 -45094.

AMA Style

Anang Kunaefi, Masayoshi Aritsugi. Extracting Arguments Based on User Decisions in App Reviews. IEEE Access. 2021; 9 ():45078-45094.

Chicago/Turabian Style

Anang Kunaefi; Masayoshi Aritsugi. 2021. "Extracting Arguments Based on User Decisions in App Reviews." IEEE Access 9, no. : 45078-45094.

Journal article
Published: 20 February 2021 in Applied Sciences
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Phrase table combination in pivot approaches can be an effective method to deal with low-resource language pairs. The common practice to generate phrase tables in pivot approaches is to use standard symmetrization, i.e., grow-diag-final-and. Although some researchers found that the use of non-standard symmetrization could improve bilingual evaluation understudy (BLEU) scores, the use of non-standard symmetrization has not been commonly employed in pivot approaches. In this study, we propose a strategy that uses the non-standard symmetrization of word alignment in phrase table combination. The appropriate symmetrization is selected based on the highest BLEU scores in each direct translation of source–target, source–pivot, and pivot–target of Kazakh–English (Kk–En) and Japanese–Indonesian (Ja–Id). Our experiments show that our proposed strategy outperforms the direct translation in Kk–En with absolute improvements of 0.35 (a 11.3% relative improvement) and 0.22 (a 6.4% relative improvement) BLEU points for 3-gram and 5-gram, respectively. The proposed strategy shows an absolute gain of up to 0.11 (a 0.9% relative improvement) BLEU points compared to direct translation for 3-gram in Ja–Id. Our proposed strategy using a small phrase table obtains better BLEU scores than a strategy using a large phrase table. The size of the target monolingual and feature function weight of the language model (LM) could reduce perplexity scores.

ACS Style

Sari Budiwati; Al Siagian; Tirana Fatyanosa; Masayoshi Aritsugi. Phrase Table Combination Based on Symmetrization of Word Alignment for Low-Resource Languages. Applied Sciences 2021, 11, 1868 .

AMA Style

Sari Budiwati, Al Siagian, Tirana Fatyanosa, Masayoshi Aritsugi. Phrase Table Combination Based on Symmetrization of Word Alignment for Low-Resource Languages. Applied Sciences. 2021; 11 (4):1868.

Chicago/Turabian Style

Sari Budiwati; Al Siagian; Tirana Fatyanosa; Masayoshi Aritsugi. 2021. "Phrase Table Combination Based on Symmetrization of Word Alignment for Low-Resource Languages." Applied Sciences 11, no. 4: 1868.

Journal article
Published: 26 May 2020 in Information and Software Technology
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There are many duplicate bug reports in the semi-structured software repository of various software bug triage systems. The duplicate bug report detection (DBRD) process is a significant problem in software triage systems. The DBRD problem has many issues, such as efficient feature extraction to calculate similarities between bug reports accurately, building a high-performance duplicate detector model, and handling continuous real-time queries. Feature extraction is a technique that converts unstructured data to structured data. The main objective of this study is to improve the validation performance of DBRD using a feature extraction model. This research focuses on feature extraction to build a new general model containing all types of features. Moreover, it introduces a new feature extractor method to describe a new viewpoint of similarity between texts. The proposed method introduces new textual features based on the aggregation of term frequency and inverse document frequency of text fields of bug reports in uni-gram and bi-gram forms. Further, a new hybrid measurement metric is proposed for detecting efficient features, whereby it is used to evaluate the efficiency of all features, including the proposed ones. The validation performance of DBRD was compared for the proposed features and state-of-the-art features. To show the effectiveness of our model, we applied it and other related studies to DBRD of the Android, Eclipse, Mozilla, and Open Office datasets and compared the results. The comparisons showed that our proposed model achieved (i) approximately 2% improvement for accuracy and precision and more than 4.5% and 5.9% improvement for recall and F1-measure, respectively, by applying the linear regression (LR) and decision tree (DT) classifiers and (ii) a performance of 91%−99% (average ~97%) for the four metrics, by applying the DT classifier as the best classifier. Our proposed features improved the validation performance of DBRD concerning runtime performance. The pre-processing methods (primarily stemming) could improve the validation performance of DBRD slightly (up to 0.3%), but rule-based machine learning algorithms are more useful for the DBRD problem. The results showed that our proposed model is more effective both for the datasets for which state-of-the-art approaches were effective (i.e., Mozilla Firefox) and those for which state-of-the-art approaches were less effective (i.e., Android). The results also showed that the combination of all types of features could improve the validation performance of DBRD even for the LR classifier with less validation performance, which can be implemented easily for software bug triage systems. Without using the longest common subsequence (LCS) feature, which is effective but time-consuming, our proposed features could cover the effectiveness of LCS with lower time-complexity and runtime overhead. In addition, a statistical analysis shows that the results are reliable and can be generalized to other datasets or similar classifiers.

ACS Style

Behzad Soleimani Neysiani; Seyed Morteza Babamir; Masayoshi Aritsugi. Efficient feature extraction model for validation performance improvement of duplicate bug report detection in software bug triage systems. Information and Software Technology 2020, 126, 106344 .

AMA Style

Behzad Soleimani Neysiani, Seyed Morteza Babamir, Masayoshi Aritsugi. Efficient feature extraction model for validation performance improvement of duplicate bug report detection in software bug triage systems. Information and Software Technology. 2020; 126 ():106344.

Chicago/Turabian Style

Behzad Soleimani Neysiani; Seyed Morteza Babamir; Masayoshi Aritsugi. 2020. "Efficient feature extraction model for validation performance improvement of duplicate bug report detection in software bug triage systems." Information and Software Technology 126, no. : 106344.

Journal article
Published: 28 March 2019 in World Patent Information
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In this study, we proposed combined kernel-based methods to leverage patent citation graph performance for patent classification. The concept is to use the combined graph kernels of the citation graph to classify patent documents, as a hybrid approach. A multiple kernel framework was used for integrating multiple datasets of various kernels into a combined kernel. We employed seven graph kernels as the baselines and the combination of random walks and Weisfeiler–Lehman subtree kernels to achieve higher performance. We calculated the kernel values of each patent pairwise and employed an SVM classifier to carry out the classification task. The investigation results demonstrate that the combined graph kernel outperforms single kernels.

ACS Style

Budi Nugroho; Masayoshi Aritsugi; Yota Otachi; Yuki Manabe. Combined graph kernels for automatic patent classification: A hybrid approach. World Patent Information 2019, 57, 18 -24.

AMA Style

Budi Nugroho, Masayoshi Aritsugi, Yota Otachi, Yuki Manabe. Combined graph kernels for automatic patent classification: A hybrid approach. World Patent Information. 2019; 57 ():18-24.

Chicago/Turabian Style

Budi Nugroho; Masayoshi Aritsugi; Yota Otachi; Yuki Manabe. 2019. "Combined graph kernels for automatic patent classification: A hybrid approach." World Patent Information 57, no. : 18-24.

Conference paper
Published: 01 January 2019 in Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion - UCC '19 Companion
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ACS Style

Thanda Shwe; Masayoshi Aritsugi. Preventing Data Popularity Concentration in HDFS based Cloud Storage. Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion - UCC '19 Companion 2019, 65 -70.

AMA Style

Thanda Shwe, Masayoshi Aritsugi. Preventing Data Popularity Concentration in HDFS based Cloud Storage. Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion - UCC '19 Companion. 2019; ():65-70.

Chicago/Turabian Style

Thanda Shwe; Masayoshi Aritsugi. 2019. "Preventing Data Popularity Concentration in HDFS based Cloud Storage." Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion - UCC '19 Companion , no. : 65-70.

Journal article
Published: 01 December 2018 in IEICE Transactions on Information and Systems
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The Infrastructure as a Service (IaaS) Clouds are emerging as a promising platform for the execution of resource demanding and computation intensive workflow applications. Scheduling the execution of scientific applications expressed as workflows on IaaS Clouds involves many uncertainties due to the variable and unpredictable performance of Cloud resources. These uncertainties are modeled by probability distribution functions in past researches or totally ignored in some cases. In this paper, we propose a novel robust deadline constrained workflow scheduling algorithm which handles the uncertainties in scheduling workflows in the IaaS Cloud environment. Our proposal is a static scheduling algorithm aimed at addressing the uncertainties related to: the estimation of task execution times; and, the delay in provisioning computational Cloud resources. The workflow scheduling problem was considered as a cost-optimized, deadline-constrained optimization problem. Our uncertainty handling strategy was based on the consideration of knowledge of the interval of uncertainty, which we used to modeling the execution times rather than using a known probability distribution function or precise estimations which are known to be very sensitive to variations. Experimental evaluations using CloudSim with synthetic workflows of various sizes show that our proposal is robust to fluctuations in estimates of task runtimes and is able to produce high quality schedules that have deadline guarantees with minimal penalty cost trade-off depending on the length of the interval of uncertainty. Scheduling solutions for varying degrees of uncertainty resisted against deadline violations at runtime as against the static IC-PCP algorithm which could not guarantee deadline constraints in the face of uncertainty.

ACS Style

Bilkisu Larai Muhammad-Bello; Masayoshi Aritsugi. A Robust Algorithm for Deadline Constrained Scheduling in IaaS Cloud Environment. IEICE Transactions on Information and Systems 2018, E101.D, 2942 -2957.

AMA Style

Bilkisu Larai Muhammad-Bello, Masayoshi Aritsugi. A Robust Algorithm for Deadline Constrained Scheduling in IaaS Cloud Environment. IEICE Transactions on Information and Systems. 2018; E101.D (12):2942-2957.

Chicago/Turabian Style

Bilkisu Larai Muhammad-Bello; Masayoshi Aritsugi. 2018. "A Robust Algorithm for Deadline Constrained Scheduling in IaaS Cloud Environment." IEICE Transactions on Information and Systems E101.D, no. 12: 2942-2957.

Journal article
Published: 02 November 2018 in Sensors
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Efficient matching of incoming events of data streams to persistent queries is fundamental to event stream processing systems in wireless sensor networks. These applications require dealing with high volume and continuous data streams with fast processing time on distributed complex event processing (CEP) systems. Therefore, a well-managed parallel processing technique is needed for improving the performance of the system. However, the specific properties of pattern operators in the CEP systems increase the difficulties of the parallel processing problem. To address these issues, a parallelization model and an adaptive parallel processing strategy are proposed for the complex event processing by introducing a histogram and utilizing the probability and queue theory. The proposed strategy can estimate the optimal event splitting policy, which can suit the most recent workload conditions such that the selected policy has the least expected waiting time for further processing of the arriving events. The proposed strategy can keep the CEP system running fast under the variation of the time window sizes of operators and the input rates of streams. Finally, the utility of our work is demonstrated through the experiments on the StreamBase system.

ACS Style

Fuyuan Xiao; Masayoshi Aritsugi. An Adaptive Parallel Processing Strategy for Complex Event Processing Systems over Data Streams in Wireless Sensor Networks. Sensors 2018, 18, 3732 .

AMA Style

Fuyuan Xiao, Masayoshi Aritsugi. An Adaptive Parallel Processing Strategy for Complex Event Processing Systems over Data Streams in Wireless Sensor Networks. Sensors. 2018; 18 (11):3732.

Chicago/Turabian Style

Fuyuan Xiao; Masayoshi Aritsugi. 2018. "An Adaptive Parallel Processing Strategy for Complex Event Processing Systems over Data Streams in Wireless Sensor Networks." Sensors 18, no. 11: 3732.

Conference paper
Published: 29 September 2018 in Advances in Intelligent Systems and Computing
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Since many people are often confused by rumors diffused in Twitter, it is required to detect them effectively. In this paper, we attempt to make good use of social graph structures in the detection. We study features derived from the structure which has been studied in other application fields and consider if they could be useful for rumor detection. We empirically investigate if they are effective for the detection under a support vector machine classifier. Experimental results show that the structures could be useful for improving detection performance. Our observations with J48 decision tree indicate that users’ purposes in using Twitter inferred from social graph structures could relate to information credibility of tweets.

ACS Style

Zen Yoshida; Masayoshi Aritsugi. Rumor Detection in Twitter with Social Graph Structures. Advances in Intelligent Systems and Computing 2018, 589 -598.

AMA Style

Zen Yoshida, Masayoshi Aritsugi. Rumor Detection in Twitter with Social Graph Structures. Advances in Intelligent Systems and Computing. 2018; ():589-598.

Chicago/Turabian Style

Zen Yoshida; Masayoshi Aritsugi. 2018. "Rumor Detection in Twitter with Social Graph Structures." Advances in Intelligent Systems and Computing , no. : 589-598.

Conference paper
Published: 01 July 2018 in 2018 IEEE International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD)
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Social network services (SNS) are more and more popular. People are increasingly accustomed to express their opinions on SNS in two ways: (1) product reviews in online shopping sites and (2) posts, comments, tweets, and chats in social network sites. SNS text classification is challenging due to various natural language phenomena, such as spelling mistakes and variations, polysemy, contextual ambiguity and semantic variations. In this paper, we propose a novel deep learning architecture called Hybrid two Convolutional Neural Networks and Bidirectional LSTM (H2CBi) which combines the strength of both Convolutional Neural Networks (CNNs) and Bidirectional LSTM (BLSTM). We use two CNNs for extracting different positive/negative or bullied/no bullied features from SNS data. We use BLSTM to produce a sentence-level representation by maintaining the order of words and get the ability to learn sequential correlations for the sentence which is the negative sentence without having any negative word. We used two kinds of SNS data in this paper: product review data (Amazon, Movie Review and Yelp) and (2) social network sites data (Twitter I, Twitter II, Facebook and FormSpring.me). Some of our H2CBi models, namely 2WH2CBi, WFH2CBi and WGH2CBi have better performance than baseline models in six out of the seven SNS datasets in terms of accuracy and F measure.

ACS Style

Zar Zar Wint; Yuki Manabe; Masayoshi Aritsugi. Deep Learning Based Sentiment Classification in Social Network Services Datasets. 2018 IEEE International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD) 2018, 91 -96.

AMA Style

Zar Zar Wint, Yuki Manabe, Masayoshi Aritsugi. Deep Learning Based Sentiment Classification in Social Network Services Datasets. 2018 IEEE International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD). 2018; ():91-96.

Chicago/Turabian Style

Zar Zar Wint; Yuki Manabe; Masayoshi Aritsugi. 2018. "Deep Learning Based Sentiment Classification in Social Network Services Datasets." 2018 IEEE International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD) , no. : 91-96.

Journal article
Published: 01 July 2018 in Computers & Electrical Engineering
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This study proposes a transparent approach to performance analysis and comparison of Infrastructure as a Service (IaaS) providers. Using established benchmarks, we focused on obtaining deeper insights into the performance of the public cloud infrastructure as reflected by the CPU, memory and disk I/O subsystems. We conducted experiments on a real public cloud environment and demonstrated how prospective cloud users can use our transparent methodology to discover how well virtualized public cloud resources meet their application requirements. Our transparent approach is unique in the sense that it helps prospective cloud users to decipher public cloud benchmarking data and appraise the performance of public cloud services relative to their application's performance objectives. Furthermore, we show workload correlations to the benchmarks using three real-life analytics applications. Our empirical results show significant performance differences for comparable instances on the public cloud, underscoring the need for thoughtful and transparent IaaS provider selection.

ACS Style

Bilkisu Larai Muhammad-Bello; Masayoshi Aritsugi. A transparent approach to performance analysis and comparison of infrastructure as a service providers. Computers & Electrical Engineering 2018, 69, 317 -333.

AMA Style

Bilkisu Larai Muhammad-Bello, Masayoshi Aritsugi. A transparent approach to performance analysis and comparison of infrastructure as a service providers. Computers & Electrical Engineering. 2018; 69 ():317-333.

Chicago/Turabian Style

Bilkisu Larai Muhammad-Bello; Masayoshi Aritsugi. 2018. "A transparent approach to performance analysis and comparison of infrastructure as a service providers." Computers & Electrical Engineering 69, no. : 317-333.

Conference paper
Published: 05 December 2017 in Companion Proceedings of the10th International Conference on Utility and Cloud Computing
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ACS Style

Bilkisu L. Muhammad-Bello; Masayoshi Aritsugi. Robust Deadline-Constrained Resource Provisioning and Workflow Scheduling Algorithm for Handling Performance Uncertainty in IaaS Clouds. Companion Proceedings of the10th International Conference on Utility and Cloud Computing 2017, 29 -34.

AMA Style

Bilkisu L. Muhammad-Bello, Masayoshi Aritsugi. Robust Deadline-Constrained Resource Provisioning and Workflow Scheduling Algorithm for Handling Performance Uncertainty in IaaS Clouds. Companion Proceedings of the10th International Conference on Utility and Cloud Computing. 2017; ():29-34.

Chicago/Turabian Style

Bilkisu L. Muhammad-Bello; Masayoshi Aritsugi. 2017. "Robust Deadline-Constrained Resource Provisioning and Workflow Scheduling Algorithm for Handling Performance Uncertainty in IaaS Clouds." Companion Proceedings of the10th International Conference on Utility and Cloud Computing , no. : 29-34.

Conference paper
Published: 03 November 2017 in Computer Vision
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In this study, we focus on utilizing the patent citation graph structure. We investigate the effect of using only one document feature which is patent class along citation graph for the classification task. We collect advantages of a kernel-based method and build kernel function to represent feature and citation associated information. We use k-step random walk paths algorithm to calculate kernel values of each patent pairwise and SVM classifier to do the classification task. We employ sub graph technique for a large patent graph to represent citation graph information. The method is based on the property of neighborhood in a graph. The evaluation of the k-step random walk paths kernel metrics on three datasets from the United States Patent and Trademark Office (USPTO) database shows that using patent citation graph structure with only one feature achieved better performance than previous studies.

ACS Style

Budi Nugroho; Masayoshi Aritsugi. Application of k-Step Random Walk Paths to Graph Kernel for Automatic Patent Classification. Computer Vision 2017, 14 -29.

AMA Style

Budi Nugroho, Masayoshi Aritsugi. Application of k-Step Random Walk Paths to Graph Kernel for Automatic Patent Classification. Computer Vision. 2017; ():14-29.

Chicago/Turabian Style

Budi Nugroho; Masayoshi Aritsugi. 2017. "Application of k-Step Random Walk Paths to Graph Kernel for Automatic Patent Classification." Computer Vision , no. : 14-29.

Conference paper
Published: 01 November 2017 in TENCON 2017 - 2017 IEEE Region 10 Conference
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This paper presents a strategy to improve positioning estimation from low-cost Inertia Measurement Unit (IMU) sensor and Global Positioning System (GPS) for apron vehicle localization. IMU sensor provides raw acceleration values and its attitude, while GPS provides geodetic position, velocity, and heading course values. Fusion result from both sensors believed could comply Advanced-Surface Movement Guidance and Control System (A-SMGCS) standard with less economical cost. Within this paper, we propose graded Kalman filter method with several fusion steps. Our method consists of certain process filtering and process update which was associated one to each other. We also introduce a technique to handle the time synchronization and how to determine the low-cost sensor's error tolerance. Our preliminary experiment shows that proposed fusion strategy is able to accommodate both IMU and GPS sensors to provide better position estimation with lesser RMSE value in compared to the ground truth.

ACS Style

Bondan Suwandi; Teruaki Kitasuka; Masayoshi Aritsugi. Low-cost IMU and GPS fusion strategy for apron vehicle positioning. TENCON 2017 - 2017 IEEE Region 10 Conference 2017, 449 -454.

AMA Style

Bondan Suwandi, Teruaki Kitasuka, Masayoshi Aritsugi. Low-cost IMU and GPS fusion strategy for apron vehicle positioning. TENCON 2017 - 2017 IEEE Region 10 Conference. 2017; ():449-454.

Chicago/Turabian Style

Bondan Suwandi; Teruaki Kitasuka; Masayoshi Aritsugi. 2017. "Low-cost IMU and GPS fusion strategy for apron vehicle positioning." TENCON 2017 - 2017 IEEE Region 10 Conference , no. : 449-454.

Journal article
Published: 01 April 2017 in Journal of Systems and Software
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Highlights•Algorithms for exploiting ground truths in crowdsourcing are developed.•Quite general workers are assumed in the development.•The algorithms can be of benefit to general EM algorithm-based approaches.•Evaluation demonstrates that our algorithms can work well in various situations. AbstractIt is expected that ground truths can result in many good labels in the crowdsourcing of labeling tasks. However, the use of ground truths has so far not been adequately addressed. In this paper, we develop algorithms that determine the number of ground truths that are necessary. We determine this number by iteratively calculating the expected quality of labels for tasks with various sets of ground truths, and then comparing the quality with the limit of the estimated label quality expected to be obtained by crowdsourcing. We assume that each worker has a different unknown labeling ability and performs a different number of tasks. Under this assumption, we develop assignment strategies for ground truths based on the estimated confidence intervals of the workers. Our algorithms can utilize different approaches based on the expectation maximization to estimate good-quality consensus labels. An experimental evaluation demonstrates that our algorithms work well in various situations.

ACS Style

Takuya Kubota; Masayoshi Aritsugi. Assignment strategies for ground truths in the crowdsourcing of labeling tasks. Journal of Systems and Software 2017, 126, 113 -126.

AMA Style

Takuya Kubota, Masayoshi Aritsugi. Assignment strategies for ground truths in the crowdsourcing of labeling tasks. Journal of Systems and Software. 2017; 126 ():113-126.

Chicago/Turabian Style

Takuya Kubota; Masayoshi Aritsugi. 2017. "Assignment strategies for ground truths in the crowdsourcing of labeling tasks." Journal of Systems and Software 126, no. : 113-126.

Conference paper
Published: 06 December 2016 in Proceedings of the 9th International Conference on E-Education, E-Business, E-Management and E-Learning - IC4E '18
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ACS Style

Bilkisu Larai Muhammad-Bello; Masayoshi Aritsugi. TCloud. Proceedings of the 9th International Conference on E-Education, E-Business, E-Management and E-Learning - IC4E '18 2016, 228 -233.

AMA Style

Bilkisu Larai Muhammad-Bello, Masayoshi Aritsugi. TCloud. Proceedings of the 9th International Conference on E-Education, E-Business, E-Management and E-Learning - IC4E '18. 2016; ():228-233.

Chicago/Turabian Style

Bilkisu Larai Muhammad-Bello; Masayoshi Aritsugi. 2016. "TCloud." Proceedings of the 9th International Conference on E-Education, E-Business, E-Management and E-Learning - IC4E '18 , no. : 228-233.

Conference paper
Published: 28 November 2016 in Proceedings of the 18th International Conference on Supporting Group Work
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While a lot of useful information can be found in SNS, false information also diffuses through it, thereby confusing many people sometimes. In this paper, we predict a tendency of tweets to be well retweeted and consider applying the tendency to false information detection. The tendency prediction can be implemented with simple features of tweets. We examine the effect of the tendency when it is used in false information detection empirically. Our experimental results indicate that it would be valuable to take the tendency into account for the detection. We also discuss findings when applying them to tweets in Japanese.

ACS Style

Zen Yoshida; Masayoshi Aritsugi. Applying a tendency to be well retweeted to false information detection. Proceedings of the 18th International Conference on Supporting Group Work 2016, 154 -159.

AMA Style

Zen Yoshida, Masayoshi Aritsugi. Applying a tendency to be well retweeted to false information detection. Proceedings of the 18th International Conference on Supporting Group Work. 2016; ():154-159.

Chicago/Turabian Style

Zen Yoshida; Masayoshi Aritsugi. 2016. "Applying a tendency to be well retweeted to false information detection." Proceedings of the 18th International Conference on Supporting Group Work , no. : 154-159.

Journal article
Published: 01 September 2016 in Artificial Intelligence in Medicine
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For efficient and sophisticated analysis of complex event patterns that appear in streams of big data from health care information systems and support for decision-making, a triaxial hierarchical model is proposed in this paper. Our triaxial hierarchical model is developed by focusing on hierarchies among nested event pattern queries with an event concept hierarchy, thereby allowing us to identify the relationships among the expressions and sub-expressions of the queries extensively. We devise a cost-based heuristic by means of the triaxial hierarchical model to find an optimised query execution plan in terms of the costs of both the operators and the communications between them. According to the triaxial hierarchical model, we can also calculate how to reuse the results of the common sub-expressions in multiple queries. By integrating the optimised query execution plan with the reuse schemes, a multi-query optimisation strategy is developed to accomplish efficient processing of multiple nested event pattern queries. We present empirical studies in which the performance of multi-query optimisation strategy was examined under various stream input rates and workloads. Specifically, the workloads of pattern queries can be used for supporting monitoring patients' conditions. On the other hand, experiments with varying input rates of streams can correspond to changes of the numbers of patients that a system should manage, whereas burst input rates can correspond to changes of rushes of patients to be taken care of. The experimental results have shown that, in Workload 1, our proposal can improve about 4 and 2 times throughput comparing with the relative works, respectively; in Workload 2, our proposal can improve about 3 and 2 times throughput comparing with the relative works, respectively; in Workload 3, our proposal can improve about 6 times throughput comparing with the relative work. The experimental results demonstrated that our proposal was able to process complex queries efficiently which can support health information systems and further decision-making.

ACS Style

Fuyuan Xiao; Masayoshi Aritsugi; Qing Wang; Rong Zhang. Efficient processing of multiple nested event pattern queries over multi-dimensional event streams based on a triaxial hierarchical model. Artificial Intelligence in Medicine 2016, 72, 56 -71.

AMA Style

Fuyuan Xiao, Masayoshi Aritsugi, Qing Wang, Rong Zhang. Efficient processing of multiple nested event pattern queries over multi-dimensional event streams based on a triaxial hierarchical model. Artificial Intelligence in Medicine. 2016; 72 ():56-71.

Chicago/Turabian Style

Fuyuan Xiao; Masayoshi Aritsugi; Qing Wang; Rong Zhang. 2016. "Efficient processing of multiple nested event pattern queries over multi-dimensional event streams based on a triaxial hierarchical model." Artificial Intelligence in Medicine 72, no. : 56-71.

Journal article
Published: 24 August 2016 in SpringerPlus
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Backoff-free fragment retransmission (BFFR) scheme enhances the performance of legacy MAC layer fragmentation by eliminating contention overhead. The eliminated overhead is the result of backoff executed before a retransmission attempt is made when fragment transmission failure occurs within a fragment burst. This paper provides a mathematical analysis of BFFR energy efficiency and further assesses, by means of simulations, the energy efficiency, throughput and delay obtained when BFFR is used. The validity of the new scheme is evaluated in different scenarios namely, constant bit rate traffic, realistic bursty internet traffic, node mobility, rigid and elastic flows and their combinations at different traffic loads. We also evaluate and discuss the impact of BFFR on MAC fairness when the number of nodes is varied from 4 to 10. It is shown that BFFR has advantages over legacy MAC fragmentation scheme in all the scenarios.

ACS Style

Prosper Mafole; Masayoshi Aritsugi. Analysis and performance assessment of a fragment retransmission scheme for energy efficient IEEE 802.11 WLANs. SpringerPlus 2016, 5, 1403 .

AMA Style

Prosper Mafole, Masayoshi Aritsugi. Analysis and performance assessment of a fragment retransmission scheme for energy efficient IEEE 802.11 WLANs. SpringerPlus. 2016; 5 (1):1403.

Chicago/Turabian Style

Prosper Mafole; Masayoshi Aritsugi. 2016. "Analysis and performance assessment of a fragment retransmission scheme for energy efficient IEEE 802.11 WLANs." SpringerPlus 5, no. 1: 1403.

Proceedings article
Published: 01 March 2016 in 2016 Wireless Days (WD)
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In IEEE 802.11 based wireless local area networks (WLANs), channel induced errors and collisions cause transmission failures which waste bandwidth and energy. To improve the energy efficiency of battery powered mobile devices we propose a scheme which mitigates channel induced errors and collisions by using two ideas namely, backoff-free fragment retransmission and collision-free transmission schedules, respectively. Performance evaluation using ns-3 showed that the proposed scheme outperforms existing schemes in energy efficiency, throughput and delay.

ACS Style

Prosper Mafole; Mussa Kissaka; Masayoshi Aritsugi. Fragment retransmission scheme with enhanced collision avoidance for energy-efficient IEEE 802.11 WLANs. 2016 Wireless Days (WD) 2016, 1 -4.

AMA Style

Prosper Mafole, Mussa Kissaka, Masayoshi Aritsugi. Fragment retransmission scheme with enhanced collision avoidance for energy-efficient IEEE 802.11 WLANs. 2016 Wireless Days (WD). 2016; ():1-4.

Chicago/Turabian Style

Prosper Mafole; Mussa Kissaka; Masayoshi Aritsugi. 2016. "Fragment retransmission scheme with enhanced collision avoidance for energy-efficient IEEE 802.11 WLANs." 2016 Wireless Days (WD) , no. : 1-4.