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18th Working Conference on Requirements Engineering: Foundation for Software Quality, REFSQ 2012

作者： Daramola, Olawande Moser, Thomas Sindre, Guttorm Biffl, Stefan Department of Computer and Information Science Norwegian University of Science and Technology Trondheim Norway Christian Doppler Laboratory for Software Engineering Integration for Flexible Automation Systems Vienna University of Technology Austria

ISBN: (纸本)9783642287138

[Context and motivation] Implicit requirements (ImRs) are defined as requirements of a system which are not explicitly expressed during requirements elicitation, often because they are considered so basic that developers should already know them. Many products have been rejected or users made unhappy because implicit requirements were not sufficiently addressed. [Question/Problem] Requirement management tools have not addressed the issue of managing ImRs, also despite the challenges of managing ImRs that exist in practice the issue has not received sufficient attention in the literature. [Principal Idea/results] This planned research will investigate how automated support can be provided for managing ImRs within an organizational context, which is currently lacking in practice. This work proposed an approach that is based on semantic case-based reasoning for managing ImRs. [Contribution] We present the concept of a tool which enables managing of ImRs through the analogy-based requirements reuse of previously known ImRs. This ensures the discovery, structured documentation, proper prioritization, and evolution of ImRs, which improves the overall success of software development processes. © 2012 Springer-Verlag.

关键词： Case based reasoning

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Link Prediction in a Modified Heterogeneous Bibliographic Network

Link Prediction in a Modified Heterogeneous Bibliographic Ne...

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International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： John Boaz Lee Henry Adorna Department of Information Systems and Computer Science Ateneo de Manila University Philippines Algorithms and Complexity Laboratory Department of Computer Science University of the Philippines Diliman Philippines

Researchers have discovered, in recent years, the advantages of modeling complex systems using heterogeneous information networks. These networks are comprised of heterogeneous sets of nodes and edges that better represent the different entities and relationships often found in the real world. Although heterogeneous networks provide a richer semantic view of the data, the added complexity makes it difficult to directly apply existing techniques that work well on homogeneous networks. In this paper, we propose a graph modification process that alters an existing heterogeneous bibliographic network into another network, with the purpose of highlighting the important relations in the bibliographic network. Several importance scores, some adopted from existing work and others defined in this work, are then used to measure the importance of links in the modified network. The link prediction problem is studied on the modified network by implementing a random walk-based algorithm on the network. The importance scores and the structure of the modified graph are used to guide a random walker towards relevant parts of the graph, i.e. towards nodes to which new links will be created in the future. The different properties of the proposed algorithm are evaluated experimentally on a real world bibliographic network, the DBLP. Results show that the proposed method outperforms the state-of-the-art supervised technique as well as various approaches based on topology and node attributes.

关键词： Prediction algorithms Frequency measurement Weight measurement Joining processes computer science Buildings Social network services

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Multi-stage multi-task feature learning

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2013年第1期14卷

作者： Kevin Murphy Bernhard Schölkopf Pinghua Gong Jieping Ye Changshui Zhang Google MPI for Intelligent Systems State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Automation Tsinghua University Beijing China Computer Science and Engineering Center for Evolutionary Medicine and Informatics The Biodesign Institute Arizona State University Tempe AZ

Multi-task sparse feature learning aims to improve the generalization performance by exploiting the shared features among tasks. It has been successfully applied to many applications including computer vision and biomedical informatics. Most of the existing multi-task sparse feature learning algorithms are formulated as a convex sparse regularization problem, which is usually suboptimal, due to its looseness for approximating an l0-type regularizer. In this paper, we propose a non-convex formulation for multi-task sparse feature learning based on a novel nonconvex regularizer. To solve the non-convex optimization problem, we propose a Multi-Stage Multi-Task Feature Learning (MSMTFL) algorithm; we also provide intuitive interpretations, detailed convergence and reproducibility analysis for the proposed algorithm. Moreover, we present a detailed theoretical analysis showing that MSMTFL achieves a better parameter estimation error bound than the convex formulation. Empirical studies on both synthetic and real-world data sets demonstrate the effectiveness of MSMTFL in comparison with the state of the art multi-task sparse feature learning algorithms.

关键词： multi-stage multi-task learning non-convex sparse learning

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Modified Iterative-Learning-Control-Based Ramp Metering Strategies for Freeway Traffic Control With Iteration-Dependent Factors

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IEEE Transactions on Intelligent Transportation systems 2012年第2期13卷 606-618页

作者： Zhongsheng Hou Jingwen Yan Jian-Xin Xu Zhenjiang Li Advanced Control Systems Laboratory of School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering National University of Singapore Singapore Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences Beijing China

For a freeway traffic system with strict repeatable pattern, iterative learning control (ILC) has been successfully applied to local ramp metering for a macroscopic freeway environment by formulating the original ramp metering problem as an output tracking, disturbance rejection, and error compensation problem. In this paper, we address the freeway traffic ramp-metering system under a nonstrict repeatable pattern. ILC-based ramp metering and ILC add-on to ALINEA strategies are modified to deal with the presence of iteration-dependent parameters, iteration-dependent desired trajectory, and input constraints. Theoretical analysis and extensive simulations are used to verify the effectiveness of the proposed approaches.

关键词： Traffic control Trajectory Mathematical model Vehicles Equations Feedback control

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Shared control teleoperation for targets acquisition

Shared control teleoperation for targets acquisition

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Chinese Control and Decision Conference, CCDC

作者： Dongli Wang Huaping Liu Fuchun Sun Yonggui Du Automation college of information engineering Taiyuan university of technology 030024 china Tsinghua national laboratory for information science and technology state key laboratory of intelligent technology and systems department of computer science and technology tsinghua university Beijing 100084 china

It is very difficult to capture the target in the microgravity environment, simply by relying on ground operators. Based on shared control theory, this paper effectively combines the decision-making ability of ground operators and the independent ability of space robot, through making full use of independent control ability of the space robot, and reducing operators' workload and working time, to achieve more valid capture for targets. Shared control is more accurate capture of the target compared with teleoperation in experimental.

关键词： Aerospace electronics Robot kinematics Grippers Control systems Solid modeling Heuristic algorithms

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IMAGE SCALING METHOD BASED ON PROTECTED REGION

IMAGE SCALING METHOD BASED ON PROTECTED REGION

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2012 IEEE 2nd International Conference on Cloud Computing and Intelligence systems

作者： Lisha Hou Junping Du Lingling Zi Min Zuo Katsunori Shimohara Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer ScienceBeijing University of Posts and Telecommunications School of Computer and Information Engineering Beijing Technology and Business University Department of Information Systems Design Doshisha University

To solve the problem of seriously distortion while enlarging images via seam carving,we proposed an improved method in this ***,we enlarged the image with hierarchical interpolation model to the size of power of two times of original,which is closest to target size,while applying iterative curvature-based interpolation algorithm to protected areas and nearest interpolation to non-protected *** then,some seams to the target size were avoided via seam carving *** the subjective analysis and objective evaluation,the experiment on both standard library images and spatial images showed that the proposed method gave satisfactory results.

关键词： Image scaling ICBI Seam carving Protect region

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STUDY AND IMPLEMENTATION OF PERSONALIZED SEARCHING SYSTEM BASED ON USER'S INTEREST MODEL

STUDY AND IMPLEMENTATION OF PERSONALIZED SEARCHING SYSTEM BA...

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2012 IEEE 2nd International Conference on Cloud Computing and Intelligence systems

作者： Xue Li Junping Du Lingling Zi Katsunori Shimohara Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer ScienceBeijing University of Posts and Telecommunications School of Electronic and Information Engineering Liaoning Technical University Department of Information Systems Design Doshisha University

The common personalized searching technology can neither make full use of features of domain knowledge,nor understand the user's special needs in this *** at solving this issue,this paper studies personalized searching technology combining the characteristics of *** tourism fields,this paper designs and realizes a user-based personalized searching system,and provides with its general framework and implementation of system *** tourism field knowledge database is firstly *** to the user's browsing history and personal information,this paper builds the user-interest model based on tourism areas,and then realizes the personalized information recommendation technology and information expanding *** results show that the information searching based on domain knowledge and tourism information achieves better results in recall rate,accuracy and recommend function than traditional searching engine.

关键词： Personalized searching Tourism area Regional weight Attractions priority

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{\cal U}Boost: Boosting with the Universum

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2012年第4期34卷 825-832页

作者： Chunhua Shen Peng Wang Fumin Shen Hanzi Wang Australian Center for Visual Technologies and School of Computer Science University of Adelaide North Terrace SA Australia School of Automation Science and Electrical Engineering Beihang University Beijing China School of Computer Science and Technology Nanjing University of Science and Technology of PLA Nanjing China Cognitive Science Department School of Information Science and Technology and Fujian Key Laboratory of the Brain-like Intelligent Systems Xiamen University Xiamen Fujian China School of Computer Science University of Adelaide SA Australia

It has been shown that the Universum data, which do not belong to either class of the classification problem of interest, may contain useful prior domain knowledge for training a classifier [1], [2]. In this work, we design a novel boosting algorithm that takes advantage of the available Universum data, hence the name UBoost. UBoost is a boosting implementation of Vapnik's alternative capacity concept to the large margin approach. In addition to the standard regularization term, UBoost also controls the learned model's capacity by maximizing the number of observed contradictions. Our experiments demonstrate that UBoost can deliver improved classification accuracy over standard boosting algorithms that use labeled data alone.

关键词： Boosting Training Educational institutions Optimization Algorithm design and analysis Histograms Accuracy

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Multi-Stage Multi-Task Feature Learning* 12

Multi-Stage Multi-Task Feature Learning*

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Annual Conference on Neural information Processing systems

作者： Pinghua Gong Jieping Ye Changshui Zhang State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology (TNList) Department of Automation Tsinghua University Beijing 100084 China Computer Science and Engineering Center for Evolutionary Medicine and Informatics The Biodesign Institute Arizona State University Tempe AZ 85287 USA

ISBN: (纸本)9781627480031

Multi-task sparse feature learning aims to improve the generalization performance by exploiting the shared features among tasks. It has been successfully applied to many applications including computer vision and biomedical informatics. Most of the existing multi-task sparse feature learning algorithms are formulated as a convex sparse regularization problem, which is usually suboptimal, due to its looseness for approximating an ?_0o-type regularizer. In this paper, we propose a non-convex formulation for multi-task sparse feature learning based on a novel regularizer. To solve the non-convex optimization problem, we propose a Multistage Multi-Task Feature Learning (MSMTFL) algorithm. Moreover, we present a detailed theoretical analysis showing that MSMTFL achieves a better parameter estimation error bound than the convex formulation. Empirical studies on both synthetic and real-world data sets demonstrate the effectiveness of MSMTFL in comparison with the state of the art multi-task sparse feature learning algorithms.

关键词： Learning learning algorithms Non-Convex Biomedical informatics Theoretical analysis Parameter estimation images computer vision state of the art Dataset Convexity Religious Missions SYNTHETICS

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Doubly selective channel estimation for amplify-and-forward relay networks

Doubly selective channel estimation for amplify-and-forward ...

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IEEE Conference on Wireless Communications and Networking

作者： Gongpu Wang Feifei Gao Jiaru Lin Chintha Tellambura School of Computer and Information Technology Beijing Jiaotong University China National Mobile Communications Research Laboratory South East University Nanjing China State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Automation Tsinghua University Beijing China Key Laboratory of Universal Wireless Communications Ministry of Education China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

In this paper, the estimation of doubly selective channel is considered for amplify-and-forward (AF) relay networks. The complex exponential basis expansion model (CE-BEM) is chosen to describe the time-varying channel, from which the infinite channel parameters are mapped onto finite ones. Since direct estimation of these coefficients encounters high computational complexity and large spectral cost, we develop an efficient estimator targeting at some specially defined channel parameters. The training sequence design that can minimize the channel estimation mean-square error is also proposed.

关键词： Training Channel estimation Relays Wireless communication Educational institutions Estimation Vectors

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