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IEEE International Conference on Requirements engineering

作者： Richard Berntsson Svensson Tony Gorschek Björn Regnell Richard Torkar Ali Shahrokni Robert Feldt Aybuke Aurum Department of Computer Science Lund University Lund Sweden School of Computing Blekinge Institute of Technology Karlskrona Sweden Software Engineering and Technology Chalmers University of Technology Goteborg Sweden School of Information Systems Technology and Management University of New South Wales Sydney Australia

Requirements prioritization is recognized as an important but challenging activity in software product development. For a product to be successful, it is crucial to find the right balance among competing quality requirements. Although literature offers many methods for requirements prioritization, the research on prioritization of quality requirements is limited. This study identifies how quality requirements are prioritized in practice at 11 successful companies developing software intensive systems. We found that ad-hoc prioritization and priority grouping of requirements are the dominant methods for prioritizing quality requirements. The results also show that it is common to use customer input as criteria for prioritization but absence of any criteria was also common. The results suggests that quality requirements by default have a lower priority than functional requirements, and that they only get attention in the prioritizing process if decision-makers are dedicated to invest specific time and resources on QR prioritization. The results of this study may help future research on quality requirements to focus investigations on industry-relevant issues.

关键词： Companies Interviews Industries Ad hoc networks Instruments Planning Telecommunications

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Face recognition based on local uncorrelated and weighted global uncorrelated discriminant transforms

Face recognition based on local uncorrelated and weighted gl...

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IEEE International Conference on Image Processing

作者： Xiaoyuan Jing Sheng Li David Zhang Jingyu Yang State Key Laboratory for Software Engineering Wuhan University of China Wuhan China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China College of Automation Nanjing University of Posts and Telecommunications China Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong China College of Computer Science Nanjing University of Science and Technology of PLA China

Feature extraction is one of the most important problems in image recognition tasks. In many applications such as face recognition, it is desirable to eliminate the redundancy among the extracted discriminant features. In this paper, we propose two novel feature extraction approaches named local uncorrelated discriminant transform (LUDT) and weighted global uncorrelated discriminant transform (WGUDT) for face recognition, respectively. LUDT and WGUDT separately construct the local uncorrelated constraints and the weighted global uncorrelated constraints. Then they iteratively calculate the optimal discriminant vectors that maximize the Fisher criterion under the corresponding statistical uncorrelated constraints, respectively. The proposed LUDT and WGUDT approaches are evaluated on the public AR and FERET face databases. Experimental results demonstrate that the proposed approaches outperform several representative feature extraction methods.

关键词： Feature extraction Vectors Databases Face Face recognition Transforms

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Class-imbalance learning based discriminant analysis

Class-imbalance learning based discriminant analysis

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Asian Conference on Pattern Recognition (ACPR)

作者： Xiaoyuan Jing Chao Lan Min Li Yongfang Yao David Zhang Jingyu Yang State Key Laboratory of Software Engineering Wuhan University of China Wuhan China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China College of Automation Nanjing University of Posts and Telecommunications Nanjing China Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong China College of Computer Science Nanjing University of Science and Technology of PLA Nanjing China

Feature extraction is an important research topic in the field of pattern recognition. The class-specific idea tends to recast a traditional multi-class feature extraction and recognition task into several binary class problems, and therefore inevitably class imbalance problem, where the minority class is the specific class, and the majority class consists of all the other classes. However, discriminative information from binary class problems is usually limited, and imbalanced data may have negative effect on the recognition performance. For solving these problems, in this paper, we propose two novel approaches to learn discriminant features from imbalanced data, named class-balanced discrimination (CBD) and orthogonal CBD (OCBD). For a specific class, we select a reduced counterpart class whose data are nearest to the data of specific class, and further divide them into smaller subsets, each of which has the same size as the specific class, to achieve balance. Then, each subset is combined with the minority class, and linear discriminant analysis (LDA) is performed on them to extract discriminative vectors. To further remove redundant information, we impose orthogonal constraint on the extracted discriminant vectors among correlated classes. Experimental results on three public image databases demonstrate that the proposed approaches outperform several related image feature extraction and recognition methods.

关键词： Databases Feature extraction Vectors Correlation Handwriting recognition Training Educational institutions

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IT 2008: The history of a new computing discipline

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Communications of the ACM 2010年第12期53卷 133-141页

作者： Lunt, Barry Ekstrom, J. Reichgelt, Han Bailey, Michael Leblanc, Richard Department of IT Brigham Young University Provo UT United States Computing and Software Engineering Southern Polytechnic State University Marietta GA United States Adaptive Computing Provo UT United States Department of Computer Science and Software Engineering Seattle University Seattle WA United States

The IT model curriculum represents an excellent starting point toward understanding more about IT as an academic discipline. The early 1990s saw the emergence of the Internet from the environs of the technical cognoscenti into the dot-com world with an interface for the masses. Additionally, the personal computer had reached the point that essentially everyone in all enterprises had one, and used it heavily. The increased complexity and importance of computing technologies for the success of organizations and individuals led to a growing need for professionals to select, create, apply, integrate, and administer an organizational IT infrastructure. Organizations typically filled these positions using individuals with widely varying backgrounds whose educational experiences often provided poor preparation for the demands of the position. The skill sets needed for the new breed of network and system administrators were not provided by the more algorithmically and analytically oriented computer science programs of the time. Moreover, information systems programs, with the business education requirements of their accreditation bodies, were equally unwilling or unable to include the technical depth required. In response to this new educational need, programs arose such as those from Purdue University and Pennsylvania College of Technology, which were called Information Systems (IS) and Computer Science (CS) respectively, but were something else entirely. These programs, and others like them, had sprung up independently and spontaneously to satisfy the needs of employers for workers with skills in networks, distributed systems, and beginning in the mid-1990s, the Web. By the peak of the dot-com boom in 2000, there were at least 17 institutions around the U.S. that had or were forming programs with similar characteristics, and which were most commonly called "Information Technology." The largest of them was at Rochester Institute of Technology (RIT) in Rochester, NY, with o

关键词： Students

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Towards Progressive and Load Balancing Distributed Computation:A Case Study on Skyline Analysis

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Journal of Computer Science & Technology 2010年第3期25卷 431-443页

作者：黄晋赵丰陈健裴健印鉴 School of Information Science and Technology Sun Yat-Sen University School of Software Engineering South China University of Technology School of Computing Science Simon Fraser University

Many latest high performance distributed computational environments come with high bandwidth in commu- nication. Such high bandwidth distributed systems provide unprecedented opportunities for analyzing huge datasets, but simultaneously posts new technical challenges. For users, progressive query answering is important. For utility of systems, load balancing is critical. How we can achieve progressive and load balancing distributed computation is an interesting and promising research direction. As skyline analysis has been shown very useful in many multi-criteria decision making applications, in this paper, we study the problem of progressive and load balancing distributed skyline analysis. We propose a simple yet scalable approach which comes with several nice properties for progressive and load balancing query answering. We conduct extensive experiments which demonstrate the feasibility and effectiveness of the proposed method.

关键词： skyline progressive query answering load balancing distributed computing

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Color features integrated with line detection for object extraction and recognition in traffic images retrieval

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International Journal of Computer Science Issues 2011年第6 6-3期8卷 192-198页

作者： Wang, Hui Hui Mohamad, Dzulkifli Ismail, N.A. Department Of Software Engineering and Multimedia Computing Faculty of Computer Science and Information Technology Universiti Malaysia Sarawak 94300 Kota Samarahan Sarawak Malaysia Department of Computer Graphics and Multimedia Faculty of Computer Science and Information Technology Universiti Teknologi Malaysia 81300 Skudai Johor Bahru Malaysia

This paper proposes a simple object extraction and recognition method with efficient searching for identifying and extracting the objects in a complex scene based on the color features. The background of the images is needed to be extracted and recognized in order to get the object of the interested in the images first. This can be achieved by getting the best separation line between building and road, followed by the interested objects (vehicles) on the road. The vehicle objects are represented by using Minimum Bound Rectangle (MBR) and the vehicle object representative points will be the left bottom coordinate of the MBR. The color of the vehicles will be used as the attributes of the objects. Experiments have been conducted to demonstrate that single and multiple known objects in complex scenes can be extracted by using this approach.

关键词： Object recognition

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A Decomposition-Based Approach to OWL DL Ontology Diagnosis

A Decomposition-Based Approach to OWL DL Ontology Diagnosis

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Jianfeng Du Guilin Qi Jeff Z. Pan Yi-Dong Shen Guangdong University of Foreign Studies (GDUFS) Guangzhou China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China School of Computer Science and Engineering South East University NanJing China Department of Computing Science University of Aberdeen Aberdeen UK

computing all diagnoses of an inconsistent ontology is important in ontology-based applications. However, the number of diagnoses can be very large. It is impractical to enumerate all diagnoses before identifying the target one to render the ontology consistent. Hence, we propose to represent all diagnoses by multiple sets of partial diagnoses, where the total number of partial diagnoses can be small and the target diagnosis can be directly retrieved from these partial diagnoses. We also propose methods for computing the new representation of all diagnoses in an OWL DL ontology. Experimental results show that computing the new representation of all diagnoses is much easier than directly computing all diagnoses.

关键词： Ontologies OWL Educational institutions Semantics Computational modeling Grounding Protons

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RASS: A real-time, accurate and scalable system for tracking transceiver-free objects

RASS: A real-time, accurate and scalable system for tracking...

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IEEE International Conference on Pervasive computing and Communications (PerCom)

作者： Dian Zhang Yunhuai Liu Lionel M. Ni College of Software Shenzhen University China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China Shenzhen Institutes of Advanced Technology Chinese Academy and Sciences China Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiaotong University China

Transceiver-free object tracking is to trace a moving object without carrying any communication device in an environment where the environment is pre-deployed with some monitoring nodes. Among all the tracking technologies, RF-based technology is an emerging research field facing many challenges. Although we proposed the original idea, until now there is no method achieving scalability without sacrificing latency and accuracy. In this paper, we put forward a real-time tracking system RASS, which can achieve this goal and is promising in the applications like the safeguard system. Our basic idea is to divide the tracking field into different areas, with adjacent areas using different communication channels. So the interference among different areas can be prevented. For each area, three communicating nodes are deployed on the ceiling as a regular triangle to monitor this area. In each triangle area, we use a Support Vector Regression (SVR) model to locate the object. This model simulates the relationship between the signal dynamics caused by the object and the object position. It not only considers the ideal case of signal dynamics caused by the object, but also utilizes their irregular information. As a result it can reach the tracking accuracy to around 1m by just using three nodes in a triangle area with 4m in each side. The experiments show that the tracking latency of the proposed RASS system is bounded by only about 0.26s. Our system scales well to a large deployment field without sacrificing the latency and accuracy.

关键词： Accuracy Wireless communication Wireless sensor networks Interference Lead Synchronization Predictive models

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SKRWM based descriptor for pedestrian detection in thermal images

SKRWM based descriptor for pedestrian detection in thermal i...

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IEEE Workshop on Multimedia Signal Processing

作者： Zelin Li Qiang Wu Jian Zhang Glenn Geers School of Computer Science and Engineering University of New South Wales Australia National ICT Australia Limited Australia School of Computing and Communications University of Technology Sydney Germany Advanced Analytics Institute School of Software University of Technology Sydney Germany National ICT Australia Limited Sydney Australia

Pedestrian detection in a thermal image is a difficult task due to intrinsic challenges:1) low image resolution, 2) thermal noising, 3) polarity changes, 4) lack of color, texture or depth information. To address these challenges, we propose a novel mid-level feature descriptor for pedestrian detection in thermal domain, which combines pixel-level Steering Kernel Regression Weights Matrix (SKRWM) with their corresponding covariances. SKRWM can properly capture the local structure of pixels, while the covariance computation can further provide the correlation of low level feature. This mid-level feature descriptor not only captures the pixel-level data difference and spatial differences of local structure, but also explores the correlations among low-level features. In the case of human detection, the proposed mid-level feature descriptor can discriminatively distinguish pedestrian from complexity. For testing the performance of proposed feature descriptor, a popular classifier framework based on Principal Component Analysis (PCA) and Support Vector Machine (SVM) is also built. Overall, our experimental results show that proposed approach has overcome the problems caused by background subtraction in [1] while attains comparable detection accuracy compared to the state-of-the-arts.

关键词： Feature extraction Kernel Covariance matrix Principal component analysis Coordinate measuring machines Humans Support vector machines

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Vulnerability categorization using Bayesian networks 10

Vulnerability categorization using Bayesian networks

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Proceedings of the Sixth Annual Workshop on Cyber Security and Information Intelligence Research

作者： Wang, Ju An Guo, Minzhe Southern Polytechnic State University School of Computing and Software Engineering United States

ISBN: (纸本)9781450300179

This paper proposes a novel model and methodology to classify and categorize vulnerabilities according to their security types. We use Bayesian networks to automate the process. An example is provided to demonstrate the process of categorization. The automatically generated result is compared to the CVE type in NVD [6], and it proved the correctness of our method. © 2010 ACM.

关键词： Bayesian networks

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