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Line of Approach to a Problem of Water Resistance of Anhydrite Cements

Procedia engineering 2017年 172卷 982-990页

作者： Stanislav Sychugov Yuriy Tokarev Tatiana Plekhanova Olesya Mikhailova Igor Pudov Rinat Faizullin Albert Gaifullin Ruslan Sagdiev Department of Construction and Road-Building Machinery Faculty of Civil Engineering Izhevsk State Technical University named after M.T. Kalashnikov Studencheskaya str. 7 426069 Izhevsk Russia Department of Geo-Engineering and Building Materials Faculty of Civil Engineering Izhevsk State Technical University named after M.T. Kalashnikov Studencheskaya str. 7 426069 Izhevsk Russia Department of Computer Software Faculty of Computer Science Izhevsk State Technical University named after M.T. Kalashnikov Studencheskaya str. 7 426069 Izhevsk Russia Department of Building Materials University of Building Technologies Engineering and Environmental Systems Kazan State University of Architecture and Engineering Zelenaya str. 1420043 Kazan Russia

In the present paper the experimental research dedicated to derivation of anhydrite cements with upgraded mechanical, physical properties and water resistance. The mechanism of the influence of technogenic fine-dispersed additives such as burnt galvanic sludges to the structure and properties of anhydrite matrix is discovered. The anhydrite cements were selected and the rational spheres for its application in the building industry were offered.

关键词： finely-dispersed additives galvanic sludges thermal treatment anhydrite cements.

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Exploiting Correlation Subspace to Predict Heterogeneous Cross-Project Defects

Exploiting Correlation Subspace to Predict Heterogeneous Cro...

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作者： Cheng, Ming Wu, Guoqing Wan, Hongyan You, Guoan Yuan, Mengting Jiang, Min State Key Laboratory of Software Engineering School of Computer Wuhan University Wuhan Hubei China Department of Cognitive Science and Technology School of Information Science and Engineering Xiamen University Xiamen Fujian China Fujian Key Laboratory of Brain-Like Intelligent Systems School of Information Science and Engineering Xiamen University Xiamen Fujian China

Cross-project defect prediction trains a prediction model using historical data from source projects and applies the model to target projects. Most previous efforts assumed the cross-project data have the same metrics set, which means the metrics used and the size of metrics set are the same. However, this assumption may not hold in practical scenarios. In addition, software defect datasets have the class-imbalance problem which increases the difficulty for the learner to predict defects. In this paper, we advance canonical correlation analysis by deriving a joint feature space for associating cross-project data. We also propose a novel support vector machine algorithm which incorporates the correlation transfer information into classifier design for cross-project prediction. Moreover, we take different misclassification costs into consideration to make the classification inclining to classify a module as a defective one, alleviating the impact of imbalanced data. The experimental results show that our method is more effective compared to state-of-the-art methods. © 2016 World Scientific Publishing Company.

关键词： Support vector machines

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Heterogeneous defect prediction via exploiting correlation subspace 28

Heterogeneous defect prediction via exploiting correlation s...

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28th International Conference on software engineering and Knowledge engineering, SEKE 2016

作者： Cheng, Ming Wu, Guoqing Jiang, Min Wan, Hongyan You, Guoan Yuan, Mengting State Key Laboratory of Software Engineering School of Computer Wuhan University Wuhan430072 China Department of Cognitive Science and Technology Fujian Key Laboratory of Brain-Like Intelligent Systems Xiamen University Xiamen361005 China

ISBN: (纸本)189170639X

software defect prediction generally builds models from intra-project data. Lack of training data at the early stage of software testing limits the efficiency of prediction in practice. Thereby researchers proposed cross-project defect prediction using the data from other projects. Most previous efforts assumed the cross-project defect data have the same metrics set which means the metrics used and size of metrics set are same in the data of projects. However, in real scenarios, this assumption may not hold. In addition, software defect datasets have the class imbalance problem increasing the difficulty for the learner to predict defects. In this paper, we advance canonical correlation analysis for deriving a joint feature space for associating crossproject data and propose a novel support vector machine algorithm which incorporates the correlation transfer information into classifier design for cross-project prediction. Moreover, we take different misclassification costs into consideration to make the classification inclining to classify a module as a defective one, alleviating the impact of imbalanced data. Experiments on public heterogeneous datasets from different projects show that our method is more effective, compared to state-of-the-art methods. Copyright © 2016 by KSI Research Inc. and Knowledge systems Institute Graduate School.

关键词： Support vector machines

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On the feasibility of detecting cross-platform code clones via identifier similarity 5

On the feasibility of detecting cross-platform code clones v...

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5th International Workshop on software Mining, softwareMining 2016 - co-located with ASE 2016

作者： Cheng, Xiao Jiang, Lingxiao Zhong, Hao Yu, Haibo Zhao, Jianjun Department of Computer Science and Engineering Shanghai Jiao Tong University China School of Information Systems Singapore Management University Singapore Singapore School of Software Shanghai Jiao Tong University China Department of Advanced Information Technology Kyushu University Japan

ISBN: (纸本)9781450345118

More and more mobile applications run on multiple mobile operating systems to attract more users of different platforms. Although versions on different platforms are implemented in different programming languages (e.g., Java and Objective-C), there must be many code snippets that implement the similar business logic on different platforms. Such code snippets are called cross-platform clones. It is challenging but essential to detect such clones for software maintenance. Due to the practice that developers usually use some common identifiers when implementing the same business logic on different platforms, in this paper, we investigate the identifier similarity of the same mobile application on different platforms and provide insights about the feasibility of cross-platform clone detection via identifier similarity. In our experiment, we have analyzed the source code of 18 open-source cross-platform applications which are implemented on Android, iOS and Windows Phone, and find that the smaller KL-Divergence the application has, the more accurate the clones detected by identifiers will be. © 2016 ACM.

关键词： Open source software

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Querying Medical Datasets While Preserving Privacy

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Procedia computer science 2016年第0期98卷 324-331页

作者： Nafees Qamar Yilong Yang Andras Nadas Zhiming Liu College of Computer Science and Engineering University of Hafr Al Batin Department of Computer and Information Science Faculty of Science and Technology University of Macau Institute for Software Integrated Systems Vanderbilt University Southwest University China

This paper addresses the challenge of identifying clinically-relevant patterns in medical datasets without endangering patient privacy. To this end, we treat medical datasets as black box for both internal and external users of the data enabling a remote query mechanism to construct and execute database queries. The novelty of the approach lies in avoiding the complex data de-identification process which is often used to preserve patient privacy. The implemented toolkit combines software engineering technologies such as Java EE and RESTful web services, to allow exchanging medical data in an unidentifiable XML format along with restricting users to the need-to-know privacy principle. Consequently, the technique inhibits retrospective processing of data, such as attacks by an adversary on a medical dataset using advanced computational methods to reveal Protected Health Information (PHI). The approach is validated on an endoscopic reporting application based on openEHR and MST standards. The proposed approach is largely motivated by the issues related to querying datasets by clinical researchers, governmental or non-governmental organizations in monitoring health care services to improve quality of care.

关键词： Data disclosure Data de-identification Web Services Data Privacy Automated software engineering

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Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles

arXiv

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arXiv 2018年

作者： Binder, Alexander Bockmayr, Michael Hägele, Miriam Wienert, Stephan Heim, Daniel Hellweg, Katharina Stenzinger, Albrecht Parlow, Laura Budczies, Jan Goeppert, Benjamin Treue, Denise Kotani, Manato Ishii, Masaru Dietel, Manfred Hocke, Andreas Denkert, Carsten Müller, Klaus-Robert Klauschen, Frederick Machine-Learning Group Department of Software Engineering and Theoretical Computer Science Technical University of Berlin Marchstr. 23 Berlin10587 Germany Systems Pathology Lab Institute of Pathology Charité University Hospital Charitéplatz 1 Berlin10117 Germany Department of Internal Medicine Infectious Diseases and Pulmology Charitéplatz 1 Berlin10117 Germany Institute of Pathology University of Heidelberg Im Neuenheimer Feld 224 Heidelberg Germany Laboratory of Cellular Dynamics Immunology Frontier Research Center Osaka University 2-2 Yamada-oka Suita Osaka565-0871 Japan ISTD Pillar Singapore University of Technology and Design 8 Somapah Road Singapore487372 Singapore Charité Berlin Charitéplatz 1 Berlin10117 Germany Department of Brain and Cognitive Engineering Korea University 145 Anam-ro Seongbukgu Seoul136-713 Korea Republic of Max-Planck-Institute for Informatics Saarbrücken Germany Department of Pediatric Hematology and Oncology University Medical Center Hamburg-Eppendorf Hamburg Germany

Recent advances in cancer research largely rely on new developments in microscopic or molecular profiling techniques offering high level of detail with respect to either spatial or molecular features, but usually not both. Here, we present a novel machine learning-based computational approach that allows for the identification of morphological tissue features and the prediction of molecular properties from breast cancer imaging data. This integration of microanatomic information of tumors with complex molecular profiling data, including protein or gene expression, copy number variation, gene methylation and somatic mutations, provides a novel means to computationally score molecular markers with respect to their relevance to cancer and their spatial associations within the tumor microenvironment. Copyright © 2018, The Authors. All rights reserved.

关键词： Fluorescence microscopy

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From product architectures to a managed automotive software product line architecture 16

From product architectures to a managed automotive software ...

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31st Annual ACM Symposium on Applied Computing, SAC 2016

作者： Cool, Benjamin Knieke, Christoph Rausch, Andreas Schindler, Mirco Strasser, Arthur Vogel, Martin Brox, Oliver Jauns-Seyfried, Stefanie TU Clausthal Department of Computer Science Software Systems Engineering Julius-Albert-Straße 4 Clausthal-ZellerfeldD-38678 Germany Volkswagen AG Powertrain Electronics P.O. 16870 WolfsburgD-38436 Germany

ISBN: (纸本)9781450337397

To keep the software development for vehicles cost efficient, software components are reused for different variants as well as for succeeding generations. Furthermore, cost reductions are achieved by software sharing between the Original Equipment Manufacturer (OEM) and the suppliers. However, as a consequence of the blackboxed view caused by software sharing, no common detailed software product line architecture specification for the Electronic Control Unit (ECU) software exists, as it would be required for analyzing the quality of the product line architecture, planning changes on the product line architecture, checking the compliance between the product architecture and the product line architecture, and therefore, avoiding architecture erosion. Thus, after several product generations, software erosion is growing steadily, resulting in an increasing effort of reusing software components, and planning of further development. Here, we propose an approach for repairing an eroded software consisting of a set of product architectures by applying strategies for recovery and discovery of the product line architecture. Furthermore, we give a methodology for a long-term manageable, plannable, and reuseable software product line architecture for automotive software systems.

关键词： Erosion

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Author Correction: Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

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Scientific data 2021年第1期8卷 242页

作者： Julien Cohen-Adad Eva Alonso-Ortiz Mihael Abramovic Carina Arneitz Nicole Atcheson Laura Barlow Robert L Barry Markus Barth Marco Battiston Christian Büchel Matthew Budde Virginie Callot Anna J E Combes Benjamin De Leener Maxime Descoteaux Paulo Loureiro de Sousa Marek Dostál Julien Doyon Adam Dvorak Falk Eippert Karla R Epperson Kevin S Epperson Patrick Freund Jürgen Finsterbusch Alexandru Foias Michela Fratini Issei Fukunaga Claudia A M Gandini Wheeler-Kingshott Giancarlo Germani Guillaume Gilbert Federico Giove Charley Gros Francesco Grussu Akifumi Hagiwara Pierre-Gilles Henry Tomáš Horák Masaaki Hori James Joers Kouhei Kamiya Haleh Karbasforoushan Miloš Keřkovský Ali Khatibi Joo-Won Kim Nawal Kinany Hagen H Kitzler Shannon Kolind Yazhuo Kong Petr Kudlička Paul Kuntke Nyoman D Kurniawan Slawomir Kusmia René Labounek Maria Marcella Laganà Cornelia Laule Christine S Law Christophe Lenglet Tobias Leutritz Yaou Liu Sara Llufriu Sean Mackey Eloy Martinez-Heras Loan Mattera Igor Nestrasil Kristin P O'Grady Nico Papinutto Daniel Papp Deborah Pareto Todd B Parrish Anna Pichiecchio Ferran Prados Àlex Rovira Marc J Ruitenberg Rebecca S Samson Giovanni Savini Maryam Seif Alan C Seifert Alex K Smith Seth A Smith Zachary A Smith Elisabeth Solana Y Suzuki George Tackley Alexandra Tinnermann Jan Valošek Dimitri Van De Ville Marios C Yiannakas Kenneth A Weber Ii Nikolaus Weiskopf Richard G Wise Patrik O Wyss Junqian Xu NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. jcohen@polymtl.ca. Functional Neuroimaging Unit CRIUGM Université de Montréal Montreal QC Canada. jcohen@polymtl.ca. Mila - Quebec AI Institute Montreal QC Canada. jcohen@polymtl.ca. NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. Department of Radiology Swiss Paraplegic Centre Nottwil Switzerland. Centre for Advanced Imaging The University of Queensland Brisbane Australia. Department of Radiology University of British Columbia Vancouver BC Canada. Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Charlestown MA USA. Department of Radiology Harvard Medical School Boston MA USA. Harvard-Massachusetts Institute of Technology Health Sciences & Technology Cambridge MA USA. School of Information Technology and Electrical Engineering The University of Queensland Brisbane Australia. NMR Research Unit Queen Square MS Centre UCL Queen Square Institute of Neurology Faculty of Brain Sciences University College London London UK. Department of Systems Neuroscience University Medical Center Hamburg-Eppendorf Hamburg Germany. Department of Neurosurgery Medical College of Wisconsin Milwaukee WI USA. Aix-Marseille Univ CNRS CRMBM Marseille France. APHM Hopital Universitaire Timone CEMEREM Marseille France. Vanderbilt University Institute of Imaging Science Vanderbilt University Medical Center Nashville TN USA. Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada. CHU Sainte-Justine Research Centre Montreal QC Canada. Centre de Recherche CHUS CIMS Sherbrooke Canada. Sherbrooke Connectivity Imaging Lab (SCIL) Computer Science department Université de Sherbrooke Sherbrooke Canada. Université de Strasbourg CNRS ICube Strasbourg France. UHB - University Hospital Brno and Masaryk University Department of Radiology and Nuclear

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Support Vector Regression for Predicting the Enhancement Duration of software Projects

Support Vector Regression for Predicting the Enhancement Dur...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Cuauhtemoc Lopez-Martin Shadi Banitaan Andres Garcia-Floriano Cornelio Yanez-Marquez Department of Information Systems Universidad de Guadalajara México Department of Mathematics Computer Science and Software Engineering University of Detroit Mercy Detroit MI USA Unidad de Estudios Superiores Tepotzotlan Universidad Mexiquense del Bicentenario México State México Laboratorio de Computo Inteligente Centro de Investigacion en Computacion México City México

software engineering (SE) has been defined as the application of a systematic, disciplined, quantifiable approach to the development, operation, and maintenance of software. Enhancement is a type of software maintenance. SE involves software planning (SP), and SP includes prediction. In this study, we propose the application of two types of support vector regression (SVR) termed ε-SVR and ν-SVR to predict the duration of the software enhancement. A SVR is a type of support vector machine, which is a machine learning technique. Two data sets of software projects were used for training and testing the ε-SVR and ν-SVR. The prediction accuracy of the SVRs was compared to that of a statistical regression. Based on statistical tests, results showed that a ε-SVR with linear kernel was statistically better than that of a statistical regression model when software projects were enhanced on Mid Range platform and coded in programming languages of third generation.

关键词： software Support vector machines Data models Predictive models Maintenance engineering Training Testing

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Data mining in intelligent SSD: Simulation-based evaluation

Data mining in intelligent SSD: Simulation-based evaluation

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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Yong-Yeon Jo Sang-Wook Kim Moonjun Chung Hyunok Oh Department of Computer and Software Hanyang University Korea Division of Computer Science and Engineering Hanyang University Korea Department of Information Systems Hanyang University Korea

Due to an explosive growth of Internet applications, the amount of data has increased enormously. In order to store and process this big data more efficiently, a solid-state device (SSD) has replaced a hard disk drive (HDD) as a primary storage media. In spite of high internal bandwidth, SSD has its performance bottleneck on the host interface whose bandwidth is relatively low. To overcome the problem of performance bottleneck in big data processing, the notion of intelligent SSD (iSSD) was proposed to give computing power to SSD. However, its real implementation has not been provided to the public yet. In this paper, we are going to verify the potential of iSSD in handling data-intensive algorithms. To the end, we first develop an iSSD simulator and then evaluate the performance of data mining algorithms inside iSSD on the top of it in comparison with that by the host CPU. The results reveal that data mining with iSSD outperforms that with host CPUs up to around 300%.

关键词： Data mining Random access memory Itemsets Instruction sets Electronic mail Bandwidth Performance evaluation

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