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18th International Conference on Evaluation and Assessment in software engineering, EASE 2014

作者： Torre, Damiano Labiche, Yvan Genero, Marcela Carleton University Department of Systems and Computer Engineering Software Quality Engineering Laboratory 1125 Colonel By Drive Ottawa ON K1S5B6 Canada University of Castilla-La Mancha Department of Technologies and Information Systems ALARCOS Research Group Paseo de la Universidad 4 13071 Ciudad Real Spain

ISBN: (纸本)9781450324762

Context: The Unified Modeling Language (UML), with its 14 different diagram types, is the de-facto standard modeling language for object-oriented modeling and documentation. Since the various UML diagrams describe different aspects of one, and only one, software under development, they are not independent but strongly depend on each other in many ways. In other words, the UML diagrams describing a software product must be consistent. Inconsistencies between these diagrams may be a source of faults in software systems. It is therefore paramount that these inconsistencies be detected, analyzed and hopefully fixed. Objective: The aim of this article is to deliver a comprehensive summary of UML consistency rules as they are described in the literature to date to obtain an extensive and detailed overview of the current research in this area. Method: We performed a Systematic Mapping Study by following well-known guidelines. We selected 95 primary studies from a search with seven search engines performed in December 2012. Results: Different results are worth mentioning. First it appears that researchers tend to discuss very similar consistency rules, over and over again. Most rules are horizontal (98.10%) and syntactic (88.21%). The most used diagrams are the class diagram (71.58%), the sequence diagram (47.37%) and the state machine diagram (42.11%). Conclusion: The fact that many rules are duplicated in primary studies confirms the need for a well-accepted list of consistency rules. This paper is a first step in this direction. Results indicate that much more work is needed to develop consistency rules for all 14 UML diagrams, in all dimensions of consistency (e.g., semantic and syntactic on the one hand, horizontal, vertical and evolution on the other hand). Copyright 2014 ACM.

关键词： Unified Modeling Language

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On collecting and validating UML consistency rules: A research proposal 14

On collecting and validating UML consistency rules: A resear...

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18th International Conference on Evaluation and Assessment in software engineering, EASE 2014

作者： Torre, Damiano Carleton University Department of Systems and Computer Engineering Software Quality Engineering Laboratory 1125 Colonel By Drive Ottawa ON K1S5B6 Canada University of Castilla-La Mancha Department of Technologies and Information Systems ALARCOS Research Group Paseo de la Universidad 4 13071 Ciudad Real Spain

ISBN: (纸本)9781450324762

The main aim of my doctoral research is to create a comprehensive set of well-accepted consistency rules for UML diagrams that can be found in the literature, in reference textbooks or in the UML standard. Moreover, an important part of that aim will be the validation of the gathered UML consistency rules. This research will provide to the academic community and industrial organizations an extensive and detailed new base of knowledge about UML consistency rules which is a paramount topic in UML context. Therefore it will represent a sound starting point for all the researchers and designers involved in UML consistency. Copyright 2014 ACM.

关键词： Unified Modeling Language

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Minimal exposure path algorithms for directional sensor networks

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Wireless Communications and Mobile Computing 2014年第10期14卷 979-994页

作者： Liu, Liang Zhang, Xi Ma, Huadong Beijing Key Lab of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing 100876 China Networking and Information Systems Laboratory Department of Electrical and Computer Engineering Texas AandM University College Station TX 77843 United States

As a fundamental problem of wireless sensor networks, the minimal exposure path problem corresponding to the sensor network's worst-case coverage plays an important role in the applications for detecting intrusions. However, most existing works about minimal exposure path are based on omnidirectional sensors. In contrast, this paper studies the minimal exposure path problem for two different types of directional sensing models: the binary sector model and directional sensitivity model. For the binary sector model, we construct a special Voronoi diagram, called sector centroids-based Voronoi diagram, to transform the minimal exposure path problem from a continuous geometric problem into a discrete geometric problem. By using the sector centroids-based Voronoi diagram, we develop an approximate algorithm to find the minimal exposure path in the sensors deployment field. For the directional sensitive model, we formulate the minimal exposure path problem by using two sensing intensity functions: all-sensor intensity function and maximum-sensor intensity function and then generate two weighted grids to convert the minimal exposure path problem into two discrete geometric problems. On the basis of the aforementioned weighted grids, we also develop two approximation algorithms to find the minimal exposure path for the directional sensitive model. We conduct extensive experiment simulations to validate and evaluate our proposed models and algorithms. Copyright © 2012 John Wiley & Sons, Ltd. As a fundamental problem of wireless sensor networks, the minimal exposure path problem corresponding to the sensor network's worst-case coverage plays an important role in the applications for detecting intrusions. This paper studies the minimal exposure path problem for two different types of directional sensing models: the binary sector model and directional sensitivity *** construct a special Voronoi diagram and two weighted grids to transform the minimal exposure path problem f

关键词： Wireless sensor networks

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Supervised hashing with latent factor models 14

Supervised hashing with latent factor models

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37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014

作者： Zhang, Peichao Zhang, Wei Li, Wu-Jun Guo, Minyi Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University China National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China

ISBN: (纸本)9781450322591

Due to its low storage cost and fast query speed, hashing has been widely adopted for approximate nearest neighbor search in large-scale datasets. Traditional hashing methods try to learn the hash codes in an unsupervised way where the metric (Euclidean) structure of the training data is preserved. Very recently, supervised hashing methods, which try to preserve the semantic structure constructed from the semantic labels of the training points, have exhibited higher accuracy than unsupervised methods. In this paper, we propose a novel supervised hashing method, called latent factor hashing (LFH), to learn similarity-preserving binary codes based on latent factor models. An algorithm with convergence guarantee is proposed to learn the parameters of LFH. Furthermore, a linear-time variant with stochastic learning optimization is proposed for training LFH on large-scale datasets. Experimental results on two large datasets with semantic labels show that LFH can achieve superior accuracy than state-of-the-art methods with comparable training time. Copyright 2014 ACM.

关键词： Big data

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Large-scale supervised multimodal hashing with semantic correlation maximization 28

Large-scale supervised multimodal hashing with semantic corr...

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28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014

作者： Zhang, Dongqing Li, Wu-Jun Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University China National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China

ISBN: (纸本)9781577356790

Due to its low storage cost and fast query speed, hashing has been widely adopted for similarity search in multimedia data. In particular, more and more attentions have been payed to multimodal hashing for search in multimedia data with multiple modalities, such as images with tags. Typically, supervised information of semantic labels is also available for the data points in many real applications. Hence, many supervised multimodal hashing (SMH) methods have been proposed to utilize such semantic labels to further improve the search accuracy. However, the training time complexity of most existing SMH methods is too high, which makes them unscalable to large-scale datasets. In this paper, a novel SMH method, called semantic correlation maximization (SCM), is proposed to seamlessly integrate semantic labels into the hashing learning procedure for large-scale data modeling. Experimental results on two real-world datasets show that SCM can significantly outperform the state-of-the-art SMH methods, in terms of both accuracy and scalability. Copyright © 2014, Association for the Advancement of Artificial Intelligence.

关键词： Semantics

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Distributed stochastic ADMM for matrix factorization 14

Distributed stochastic ADMM for matrix factorization

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23rd ACM International Conference on Information and Knowledge Management, CIKM 2014

作者： Yu, Zhi-Qin Shi, Xing-Jian Yan, Ling Li, Wu-Jun Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China

ISBN: (纸本)9781450325981

Matrix factorization (MF) has become the most popular technique for recommender systems due to its promising performance. Recently, distributed (parallel) MF models have received much attention from researchers of big data community. In this paper, we propose a novel model, called distributed stochastic alternating direction methods of multipliers (DS-ADMM), for large-scale MF problems. DS-ADMM is a distributed stochastic variant of ADMM. In particular, we first devise a new data split strategy to make the distributed MF problem fit for the ADMM framework. Then, a stochastic ADMM scheme is designed for learning. Finally, we implement DS-ADMM based on message passing interface (MPI), which can run on clusters with multiple machines (nodes). Experiments on several data sets from recommendation applications show that our DS-ADMM model can outperform other state-of-the-art distributed MF models in terms of both efficiency and accuracy. Copyright 2014 ACM.

关键词： Recommender systems

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A Search-based Approach for Cost-Effective software Test Automation Decision Support and an Industrial Case Study

A Search-based Approach for Cost-Effective Software Test Aut...

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IEEE International Conference on software Testing, Verification and Validation Workshops

作者： Yasaman Amannejad Vahid Garousi Rob Irving Zahra Sahaf Software Quality Engineering Research Group (SoftQual) Department of Electrical and Computer Engineering University of Calgary Pason Systems Corporation

ISBN: (纸本)9781479957910

Test automation is a widely-used approach to reduce the cost of manual software testing. However, if it is not planned or conducted properly, automated testing would not necessarily be more cost effective than manual testing. Deciding what parts of a given System Under Test (SUT) should be tested in an automated fashion and what parts should remain manual is a frequently-asked and challenging question for practitioner testers. In this study, we propose a search-based approach for deciding what parts of a given SUT should be tested automatically to gain the highest Return On Investment (ROI). This work is the first systematic approach for this problem, and significance of our approach is that it considers automation in the entire testing process (i.e., from test-case design, to test scripting, to test execution, and test-result evaluation). The proposed approach has been applied in an industrial setting in the context of a software product used in the oil and gas industry in Canada. Among the results of the case study is that, when planned and conducted properly using our decision-support approach, test automation provides the highest ROI. In this study, we show that if automation decision is taken effectively, test-case design, test execution, and test evaluation can result in about 307%, 675%, and 41% ROI in 10 rounds of using automated test suites.

关键词： Action research Cost-benefit analysis Industrial case study Search-based software engineering software test automation

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Coupled group lasso for web-scale CTR prediction in display advertising 31

Coupled group lasso for web-scale CTR prediction in display ...

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31st International Conference on Machine Learning, ICML 2014

作者： Yan, Ling Li, Wu-Jun Xue, Gui-Rong Han, Dingyi Department of Computer Science and Engineering Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University China Department of Computer Science and Technology National Key Laboratory for Novel Software Technology Nanjing University China Alibaba Group China

ISBN: (纸本)9781634393973

In display advertising, click through rate (CTR) prediction is the problem of estimating the probability that an advertisement (ad) is clicked when displayed to a user in a specific context. Due to its easy implementation and promising performance, logistic regression (LR) model has been widely used for CTR prediction, especially in industrial systems. However, it is not easy for LR to capture the nonlinear information, such as the conjunction information, from user features and ad features. In this paper, we propose a novel model, called coupled group lasso (CGL), for CTR prediction in display advertising. CGL can seamlessly integrate the conjunction information from user features and ad features for modeling. Furthermore, CGL can automatically eliminate useless features for both users and ads, which may facilitate fast online prediction. Scalability of CGL is ensured through feature hashing and distributed implementation. Experimental results on real-world data sets show that our CGL model can achieve state-of-the-art performance on web- scale CTR prediction tasks. Copyright © (2014) by the International Machine Learning Society (IMLS) All rights reserved.

关键词： Forecasting

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MOPED: A Mobile Open Platform for Experimental Design of Cyber-Physical systems

MOPED: A Mobile Open Platform for Experimental Design of Cyb...

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Euromicro Conference on software engineering and Advanced Applications (SEAA)

作者： Jakob Axelsson Avenir Kobetski Ze Ni Shuzhou Zhang Eilert Johansson Software and Systems Engineering Laboratory Swedish Institute of Computer Science (SICS) Kista Sweden

Due to the increasing importance of cyber-physical and embedded systems in industry, there is a strong demand for engineers with an updated knowledge on contemporary technology and methods in the area. This is a challenge for educators, in particular when it comes to creating hands-on experiences of real systems, due to their complexity and the fact that they are usually proprietary. Therefore, a laboratory environment that is representative of the industrial solutions is needed, with a focus on software and systems engineering issues. This paper describes such an environment, called the Mobile Open Platform for Experimental Design (MOPED). It consists of a model car chassis, equipped with a network of three control units based on standard hardware, and running the automotive software standard AUTOSAR, which consists of operating system, middleware, and application software structures. It is equipped with various sensors and actuators, and is open to extensions both in hardware and software. It also contains elements of future systems, since it allows connectivity to cloud services, development of federated embedded systems, and continuous deployment of new functionality. In this way, the platform provides a very relevant learning environment for cyber-physical systems, today and in the future.

关键词： Sensors Hardware Automotive engineering Motorcycles Embedded systems

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Distributed Adaptive Two-stage Kalman Filter for Target Tracking in the Presence of Unknown Dynamic Bias 33

Distributed Adaptive Two-stage Kalman Filter for Target Trac...

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第三十三届中国控制会议

作者： ZHANG Cui JIA Yingmin DU Junping ZHANG Jun The Seventh Research Division and the Department of Systems and Control Beihang University (BUAA) Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and TechnologyBeijing University of Posts and Telecommunications School of Electronic and Information Engineering Beihang University (BUAA)

ISBN: (纸本)9781479947249

This paper is concerned with the problem of tracking target with multiple sensors in the presence of unknown dynamic bias. A suboptimal adaptive two-stage Kalman filter(ATKF) is designed with two reduce-order filters to estimate the target state and the dynamic bias in parallel when the bias model information is incomplete. Moreover, a distributed adaptive two-stage Kalman filter(DATKF) is developed for multi-sensor system based on the ATKF. The effectiveness of the ATKF and the DATKF are illustrated by the Monte Carlo simulation results.

关键词： Dynamic Bias,Adaptive Two-stage Kalman Filter,Distributed Fusion,Multi-sensor System

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