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arXiv 2017年

作者： Lu, Jianfeng Xin, Yun Zhang, Zhao Tang, Shaojie Yan, Songyuan Tang, Changbing Department of Computer Science and Engineering Zhejiang Normal University Jinhua Zhejiang China State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu China Department of Information Systems University of Texas at Dallas RichardsonTX75080 United States

Crowdsourcing has emerged as a paradigm for leveraging human intelligence and activity to solve a wide range of tasks. However, strategic workers will find enticement in their self-interest to free-ride and attack in a crowdsourcing contest dilemma game. Hence, incentive mechanisms are of great importance to overcome the inefficiency of the socially undesirable equilibrium. Existing incentive mechanisms are not effective in providing incentives for cooperation in crowdsourcing competitions due to the following features: heterogeneous workers compete against each other in a crowdsourcing platform with imperfect monitoring. In this paper, we take these features into consideration, and develop a novel game-theoretic design of rating protocols, which integrates binary rating labels with differential pricing to maximize the requester's utility, by extorting selfish workers and enforcing cooperation among them. By quantifying necessary and sufficient conditions for the sustainable social norm, we formulate the problem of maximizing the revenue of the requester among all sustainable rating protocols, provide design guidelines for optimal rating protocols, and design a low-complexity algorithm to select optimal design parameters which are related to differential punishments and pricing schemes. Simulation results demonstrate how intrinsic parameters impact on design parameters, as well as the performance gain of the proposed rating protocol. Copyright © 2017, The Authors. All rights reserved.

关键词： Crowdsourcing

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Ductal carcinoma in situ detection in breast thermography by extreme learning machine and combination of statistical measure and fractal dimension

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Journal of Ambient Intelligence and Humanized Computing 2017年 1-11页

作者： Wang, Shui-Hua Muhammad, Khan Phillips, Preetha Dong, Zhengchao Zhang, Yu-Dong Jiangsu Key Laboratory of Advanced Manufacturing Technology HuaiyinJiangsu223003 China School of Computer Science and Technology Henan Polytechnic University JiaozuoHenan454000 China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Nanjing China College of Software Convergence Sejong University Seoul Korea Republic of Department of Informatics University of Leicester LeicesterLE1 7RH United Kingdom West Virginia School of Osteopathic Medicine 400 N Lee St LewisburgWV24901 United States Translational Imaging Division and MRI Unit Columbia University and New York State Psychiatric Institute New YorkNY10032 United States

Ductal carcinoma in situ (DCIS) is a severe breast disease. It generates little symptom and may be neglected in prodromal stage. In this study, we developed a novel DCIS detection method based on breast thermography, which can provide earlier alert than other exams. We created a 40 breast-thermogram dataset. We used six statistical measures, and we used fractal dimension to describe the texture measure. The extreme learning machine was used as the classifier. Our developed system yielded a sensitivity of 93.0 ± 2.6%, a specificity of 92.5 ± 2.6%, and an accuracy of 92.8 ± 1.8%. The extreme learning machine was better than support vector machine, artificial neural network, decision tree, and weighted k-nearest neighbors. Besides, our developed system was superior to six state-of-the-art approaches. © 2017 Springer-Verlag GmbH Germany, part of Springer Nature

关键词： Fractal dimension

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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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Combined Cloud： A Mixture of Voluntary Cloud and Reserved Instance Marketplace

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Journal of computer science & Technology 2016年第6期31卷 1110-1123页

作者： Wei Shen Wan-Chun Dou Fan Wu Shaojie Tang Qiang Ni State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210046 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China Department of Information Systems University of Texas at Dallas Texas TX 75080 U.S.A. School of Computing and Communications Lancaster University Lancaster LA1 4WA U.K.

Voluntary cloud is a new paradigm of cloud computing. It provides an alternative selection along with some well-provisioned clouds. However, for the uncertain time span that participants share their computing resources in voluntary cloud, there are some challenging issues, i.e., fluctuation, under-capacity and low-benefit. In this paper, an architecture is first proposed based on Bittorrent protocol. In this architecture, resources could be reserved or requested from Reserved Instance Marketplace and could be accessed with a lower price in a short circle. Actually, these resources could replenish the inadequate resource pool and relieve the fluctuation and under-capacity issue in voluntary cloud. Then, the fault rate of each node is used to evaluate the uncertainty of its sharing time. By leveraging a linear prediction model, it is enabled by a distribution function which is used for evaluating the computing capacity of the system. Moreover, the cost optimization problem is investigated and a computational method is presented to solve the low-benefit issue in voluntary cloud. At last, the system performance is validated by two sets of simulations. And the experimental results show the effectiveness of our computational method for resource reservation optimization.

关键词： voluntary cloud system architecture resource reservation

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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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GB-RC4: Effective brute force attacks on RC4 algorithm using GPU 7

GB-RC4: Effective brute force attacks on RC4 algorithm using...

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7th International Green and Sustainable Computing Conference, IGSC 2016

作者： Xue, Pei Li, Tao Dong, Han Liu, Chunbo Ma, Wenjing Pei, Songwen College of Computer and Control Engineering Nankai University Tianjin300071 China Information Security Evaluation Center Civil Aviation University of China Tianjin300300 China Laboratory of Parallel Software and Computational Science State Key Laboratory of Computing Science Institute of Software CAS Beijing China Shanghai Key Lab of Modern Optical Systems Department of Computer Science and Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9781509051175

Encryption algorithms are applied to a variety of fields and the security of encryption algorithms depends heavily on the computational infeasibility of exhaustive key-space search. RC4 algorithm has an extensive application for stream encryption, however, the disadvantages of traditional RC4 serial algorithm are large computational quantity and slow computation speed, which means a great challenge for throughput of computing system. Graphics processors recently have been widely used as accelerators for high throughput computing. In this paper, GPU is used to improve the efficiency and performance of attacking RC4 algorithm. First, we revised RC4 algorithm to make it easier to parallelize on a GPU. Then we take advantage of constant memory and shared memory in GPU to search the key space of RC4 algorithm efficiently. Besides, we solved the bank conflict problem in shared memory to further improve the performance. Our approach was evaluated in the whole key space of 40-bit, and the results show it can complete searching of the whole key space within 12.8 hours. © 2016 IEEE.

关键词： Graphics processing unit

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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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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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Learning with only multiple instance positive bags

Learning with only multiple instance positive bags

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2016 International Joint Conference on Neural Networks, IJCNN 2016

作者： Wang, Zhigang Zhao, Zengshun Zhang, Changshui Computer and Communication Engineering School Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology China Key Laboratory of Computer Vision and System Tianjin University of Technology Ministry of Education China College of Electronics Communication and Physics Shandong University of Science and Technology China Automation Department Tsinghua University State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology China

ISBN: (纸本)9781509006199

In traditional multiple instance learning (MIL), both positive and negative bags are required to learn a prediction function. However, a high human cost is needed to know the label of each bag - positive or negative. Only positive bags contain our focus (positive instances) while negative bags consist of noise or background (negative instances). So we do not expect to spend too much to label the negative bags. Contrary to our expectation, nearly all existing MIL methods require enough negative bags besides positive ones. In this paper we propose an algorithm called 'Positive Multiple Instance' (PMI), which learns a classifier given only a set of positive bags. So the annotation of negative bags becomes unnecessary in our method. PMI is constructed based on the assumption that the unknown positive instances in positive bags be similar each other and constitute one compact cluster in feature space and the negative instances locate outside this cluster. The experimental results demonstrate that PMI achieves the performances close to or a little worse than those of the traditional MIL algorithms on benchmark and real data sets. However, the number of training bags in PMI is reduced significantly compared with traditional MIL algorithms. © 2016 IEEE.

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Data collection in open ended learning environment for learning analytics

Data collection in open ended learning environment for learn...

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24th International Conference on computers in Education - Workshop, ICCE 2016

作者： Tscholl, Michael Rajendran, Ramkumar Biswas, Gautam Goldberg, Benjamin S. Sottilare, Robert A. Department of Electrical Engineering and Computer Science Institute for Software Integrated Systems Vanderbilt University 1025 16th Avenue South NashvilleTN37212 United States U.S. Army Research Laboratory - Human Research and Engineering Simulation and Training Technology Center OrlandoFL32826 United States

ISBN: (纸本)9789869401210

As the development and use of Open-Ended Learning Environments (OELEs) continues to increase, supporting students' learning in these environments with Intelligent Tutoring is rapidly becoming an important area of research. Many existing learning environments guide students in step-by-step processes to reach their learning goal;consequently, data preprocessing is well defined. In OELEs, in contrast, students may achieve task goals through multiple pathways, and there exist multiple ways to assess performance. We present a simulation OELE designed to teach students decision-making in a complex problem solving task. To provide Intelligent Tutoring Support, we are required to track performance along several dimensions. We present our approach to extract data for performance assessments that can be leveraged to provide Intelligent Tutoring Support. We generalize our approach and present guidelines applicable for similar OELEs. Copyright 2016 Asia-Pacific Society for computers in Education. All rights reserved.

关键词： Students

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