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IEEE International Conference on Requirements engineering

作者： Nan Niu Amy Koshoffer Linda Newman Charu Khatwani Chatura Samarasinghe Juha Savolainen Department of Electrical Engineering and Computing Systems University of Cincinnati USA University of Cincinnati Libraries University of Cincinnati USA University of Cincinnati Cincinnati OH US Head of Software Architecture Roche Diagnostics Switzerland

ISBN: (纸本)9781509041220

Compared to building a single requirements view, modeling stakeholder viewpoints and then merging them is shown to improve the understanding of the problem domain, but also very time consuming. How has the situation changed? This paper reports our replication of a case study, where we take theoretical replication's advantage to mitigate the original study design's threat and to embrace an important evolving factor, namely automated tool support for producing i* models. Our replicate case study verifies the rich domain understanding gained through viewpoint-based modeling, and updates the prior results by showing the time saving enabled by the tool. Our work offers operational insights into independent, theoretical replications. These insights, we believe, can advance requirements engineering research toward an empirically backed body of knowledge.

关键词： Stakeholders Context modeling software engineering Merging Data models Analytical models Context

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Deep convolutional neural networks for detecting secondary structures in protein density maps from cryo-electron microscopy

Deep convolutional neural networks for detecting secondary s...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Rongjian Li Dong Si Tao Zeng Shuiwang Ji Jing He Department of Computer Science Old Dominion University Norfolk Virginia Division of Computing and Software Systems University of Washington Bothell Bothell WA School of Electrical Engineering and Computer Science Washington State University Pullman WA

ISBN: (纸本)9781509016129

The detection of secondary structure of proteins using three dimensional (3D) cryo-electron microscopy (cryo-EM) images is still a challenging task when the spatial resolution of cryo-EM images is at medium level (5-10Å). Prior researches focused on the usage of local features that may not capture the global information of image objects. In this study, we propose to use deep learning methods to extract high representative global features and then automatically detect secondary structures of proteins. In particular, we build a convolutional neural network (CNN) classifier that predicts the probability of label for every individual voxel in 3D cryo-EM image with respect to the secondary structure elements of proteins such as α-helix, β-sheet and background. To effectively incorporate the 3D spatial information in protein structures, we propose to perform 3D convolutions in the convolutional layers of CNNs. We show that the proposed CNN classifier can outperform existing SVM method on identifying the secondary structure elements of proteins from 3D cryo-EM medium resolution images.

关键词： Proteins Convolution Image resolution Three-dimensional displays Deconvolution Protein engineering Computational modeling

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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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A taxonomy for software testing projects 10

A taxonomy for software testing projects

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10th Iberian Conference on Information systems and Technologies, CISTI 2015

作者： Villalón, Jose Calvo-Manzano Agustin, Gonzalo Cuevas Gilabert, Tomás San Feliu De Jesús Jimenez Puello, José Department Languages and Informatics Systems and Software Engineering Polytechnic University of Madrid UPM Madrid Spain Department of Computer Science University of Panama UP Panama Panama

ISBN: (纸本)9789899843455

This article establishes taxonomy for software testing projects, allowing the development team or testing personnel to identify the tests to which the project must be subjected for validation. The taxonomy is focused on identifying software projects according to their technology. To establish the taxonomy, a development method comprised of 5 phases was applied. The developed taxonomy is comprised of 9 categories and 27 subcategories and was validated by a group of information technology (IT) managers and professionals in the field of IT through the use of a survey. The results obtained from the survey are subjected to the Mann-Whitney U test, which indicates that the taxonomy is validated. The taxonomy can be implemented in development organizations with or without a testing team that provides a classification for technology projects. © 2015 AISTI.

关键词： software testing

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Sparse Tensor Discriminative Locality Alignment for Gait Recognition

Sparse Tensor Discriminative Locality Alignment for Gait Rec...

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International Joint Conference on Neural Networks

作者： Nengwen Zhao Lefei Zhang Bo Du Liangpei Zhang Dacheng Tao Jane You State Key Laboratory of Software Engineering School of Computer Wuhan University Department of Computing The Hong Kong Polytechnic University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Centre for Quantum Computation & Intelligent Systems and the Faculty of Engineering and Information Technology University of Technology

ISBN: (纸本)9781509006212

Gait recognition is a rising biometric technology which aims to distinguish people purely through the analysis of the way they walk, while the problem is that the dimensionality of the gait data is too high, so it is necessary to carry on dimensionality reduction task. Up to date, in the area of computer vision and pattern recognition, various dimensionality reduction algorithms have been employed for gait data, including the conventional vector representation based methods principal components analysis (PCA) and, locality preserving projection (LPP), and the recently proposed multi-linear subspace learning based approaches such as multilinear principal component analysis (MPCA). In this paper, inspired by the advantages of the tensor representation and manifold learning, we propose a novel sparse tensor discriminative locality alignment for human gait feature representation and dimensionality reduction algorithm, and sub-sequently apply the refined feature for gait recognition by a lazy classifier of the KNN. The proposed method adopts sparse multi-way projection based on the high-order version of discriminative locality alignment, by which the class separability is enhanced and the potential model overfitting is simultaneously avoided. Extensive experiments on the University of South Florida (USF) HumanID Gait Database show that the proposed method achieves better recognition rate compared with some existing classical dimensionality reduction algorithms.

关键词： Gait recognition dimensionality reduction images computer vision Tensor Locomotion Gait principal components analysis vector representation Alignment

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Editorial A Broader Coverage of the IEEE Education Society IEEE-RITA Electronic Journal: Latin American and Caribbean Consortium of engineering Institutions (LACCEI) Collaboration

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Revista Iberoamericana de Tecnologias del Aprendizaje 2015年第3期10卷 100-101页

作者： Castro, Manuel Tovar, Edmundo Esparragorza, Ivan E. Petrie, Maria M. Larrondo Department of Electrical and Computer Engineering Spanish University for Distance Education Madrid Spain Department of Languages and Computer Systems and Software Engineering Technical University of Madrid Madrid Spain Penn State University MediaPA United States Department of Computer Engineering Florida Atlantic University Boca RatonFL United States

This editorial introduces the first selection of papers in IEEE-RITA from the LACCEI annual conference held in 2014. It publishes a view of the LACCEI coverage of issues and topics inside their engineering coverage of... 详细信息

关键词： Special issues and sections Meetings IEEE Education Society engineering education

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Using decision rules for solving conflicts in extended feature models 8

Using decision rules for solving conflicts in extended featu...

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8th ACM SIGPLAN International Conference on software Language engineering, SLE 2015

作者： Ochoa, Lina González-Rojas, Oscar Thüm, Thomas Systems and Computing Engineering Department School of Engineering Universidad de Los Andes Bogotá Colombia Institute of Software Engineering and Automotive Informatics Technische Universität Braunschweig Brunswick Germany

ISBN: (纸本)9781450336864

software Product Line engineering has introduced feature modeling as a domain analysis technique used to represent the variability of software products and decision-making scenarios. We present a model-based transformation approach to solve conflicts among configurations performed by different stakeholders on feature models. We propose the usage of a domain-specific language named CoCo to specify attributes as non-functional properties of features, and to describe busineb-related decision rules in terms of costs, time, and human resources. These specifications along with the stakeholders' configurations and the feature model are transformed into a constraint programming problem, on which decision rules are executed to find a non-conflicting set of solution configurations that are aligned to busineb objectives. We evaluate CoCo's compositionality and model complexity simplification while using a set of motivating decision scenarios. © 2015 ACM.

关键词： Constraint satisfaction problems

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Mobile application testing in industrial contexts: An exploratory multiple case-study 14th

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14th International Conference on New Trends in Intelligent software Methodology, Tools, and Techniques, SoMeT 2015

作者： Zein, Samer Salleh, Norsaremah Grundy, John Department of Computer Science International Islamic University Kuala Lumpur Malaysia Centre for Computing and Engineering Software Systems Swinburne University of Technology Melbourne Australia

ISBN: (纸本)9783319226880

Recent empirical studies in the area of mobile application testing indicate the need for specific testing techniques and methods for mobile applications. This is due to mobile applications being significantly different than traditional web and desktop applications, particularly in terms of the physical constraints of mobile devices and the very different features of their operating systems. In this paper, we presented a multiple case-study involving four software development companies in the area of mobile and smartphones application. We aimed to identify testing techniques currently being applied by developers and challenges that they are facing. Our principle results are that many industrial teams seem to lack sufficient knowledge on how to test mobile applications, particularly in the areas of mobile application life-cycle conformance, context-awareness, and integration testing. We also found that there is no formal testing approach or methodology that can facilitate a development team to systematically test a critical mobile application. © Springer International Publishing Switzerland 2015.

关键词： Integration testing

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Regularizing Deep Convolutional Neural Networks with a Structured Decorrelation Constraint

Regularizing Deep Convolutional Neural Networks with a Struc...

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IEEE International Conference on Data Mining (ICDM)

作者： Wei Xiong Bo Du Lefei Zhang Ruimin Hu Dacheng Tao Computer School of Wuhan University National Engineering Research Center for Multimedia Software Wuhan China Collaborative Innovation Center of Geospatial Technology China Department of Computing The Hong Kong Polytechnic University Hong Kong China National Engineering Research Center for Multimedia Software Computer School of Wuhan University China Centre for Quantum Computation & Intelligent Systems University of Technology Sydney NSW Australia

ISBN: (纸本)9781509054749

Deep convolutional networks have achieved successful performance in data mining field. However, training large networks still remains a challenge, as the training data may be insufficient and the model can easily get overfitted. Hence the training process is usually combined with a model regularization. Typical regularizers include weight decay, Dropout, etc. In this paper, we propose a novel regularizer, named Structured Decorrelation Constraint (SDC), which is applied to the activations of the hidden layers to prevent overfitting and achieve better generalization. SDC impels the network to learn structured representations by grouping the hidden units and encouraging the units within the same group to have strong connections during the training procedure. Meanwhile, it forces the units in different groups to learn non-redundant representations by minimizing the cross-covariance between them. Compared with Dropout, SDC reduces the co-adaptions between the hidden units in an explicit way. Besides, we propose a novel approach called Reg-Conv that can help SDC to regularize the complex convolutional layers. Experiments on extensive datasets show that SDC significantly reduces overfitting and yields very meaningful improvements on classification performance (on CIFAR-10 6.22% accuracy promotion and on CIFAR-100 9.63% promotion).

关键词： Neurons Correlation Decorrelation Training Data models Electronic mail Biological neural networks

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Quantum state and process tomography via adaptive measurements

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Science China(Physics,Mechanics & Astronomy) 2016年第10期59卷 25-32页

作者： HengYan Wang WenQiang Zheng NengKun Yu KeRen Li DaWei Lu Tao Xin Carson Li ZhengFeng Ji David Kribs Bei Zeng XinHua Peng JiangFeng Du Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology of ChinaHefei 230026China Department of Applied Physics Zhejiang University of TechnologyHangzhou 310023China Institute for Quantum Computing University of WaterlooWaterloo N2L 3G1Canada 4Department of Mathematics&Statistics University of GuelphGuelph N1G 2W1Canada Centre for Quantum Computation&Intelligent Systems Faculty of Engineering and Information TechnologyUniversity of Technology SydneySydney NSW 2007Australia State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics Tsinghua UniversityBeijing 100084China Department of Physics University of GuelphGuelph N1G 2W1Canada State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China Canadian Institute for Advanced Research Toronto M5G 1Z8Canada

We investigate quantum state tomography(QST) for pure states and quantum process tomography(QPT) for unitary channels via adaptive measurements. For a quantum system with a d-dimensional Hilbert space, we first propose an adaptive protocol where only 2d. 1 measurement outcomes are used to accomplish the QST for all pure states. This idea is then extended to study QPT for unitary channels, where an adaptive unitary process tomography(AUPT) protocol of d2+d.1measurement outcomes is constructed for any unitary channel. We experimentally implement the AUPT protocol in a 2-qubit nuclear magnetic resonance system. We examine the performance of the AUPT protocol when applied to Hadamard gate, T gate(/8 phase gate), and controlled-NOT gate,respectively, as these gates form the universal gate set for quantum information processing purpose. As a comparison, standard QPT is also implemented for each gate. Our experimental results show that the AUPT protocol that reconstructing unitary channels via adaptive measurements significantly reduce the number of experiments required by standard QPT without considerable loss of fidelity.

关键词： quantum tomography state reconstruction nuclear magnetic resonance

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