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Using Multivariate Statistics

Data Mining - Concepts and Techniques.

One of the key problems that should be solved is the collision between tags which lowers the efficiency of the RFID system. The existed popular anti-collision algorithms are ALOHA-type algorithms and QT. But these methods show good performance when the number of tags to read is small and not dynamic. However, when the number of tags to read is large and dynamic, the efficiency of recognition is very low. A novel approach to mapped correlation of ID for RFID anti-collision has been proposed to solve the problem in this paper. This method can increase the association between tags so that tags can send their own ID under certain trigger conditions, by mapped correlation of ID, querying on multi-tree becomes more efficient. In the case of not too big number of tags, by replacing the actual ID with the temporary ID, the method can greatly reduce the number of times that the reader reads and writes to tag's ID. In the case of dynamic ALOHA-type applications, the reader can determine the locations of the empty slots according to the position of the binary pulse, so it can avoid the decrease in efficiency which is caused by reading empty slots when reading slots. Experiments have shown this method can greatly improve the recognition efficiency of the system.

A Novel Approach to Mapped Correlation of ID for RFID Anti-Collision

Business process models play an important role for the management, design, and improvement of process organizations and process-aware information systems. Despite the extensive application of process modeling in practice, there are hardly empirical results available on quality aspects of process models. This paper aims to advance the understanding of this matter by analyzing the connection between formal errors (such as deadlocks) and a set of metrics that capture various structural and behavioral aspects of a process model. In particular, we discuss the theoretical connection between errors and metrics, and provide a comprehensive validation based on an extensive sample of EPC process models from practice. Furthermore, we investigate the capability of the metrics to predict errors in a second independent sample of models. The high explanatory power of the metrics has considerable consequences for the design of future modeling guidelines and modeling tools.

Understanding the Occurrence of Errors in Process Models Based on Metrics

Teens' concern for privacy when using social networking sites: An analysis of socialization agents and relationships with privacy-protecting behaviors

Software systems have become increasingly complex over the years. Complexity metrics measures software complexity using real numbers. It is, however, hard to gain insight into different complexities by looking at these numbers. In this paper, we present a software complexity metaphor that uses a jigsaw puzzle. In particular, each component of a software system is modelled as a piece of a jigsaw puzzle. The problem complexity is modelled as a pattern on the surface of the piece, and the interconnection complexity as connectors between puzzle pieces. We demonstrate the benefits of this approach using case studies of the complexity measures of a real software system.

Pattern puzzle: a metaphor for visualizing software complexity measures

UML Modeling for Regression Testing of Component Based Systems

Use-cases perform an important role in capturing and analys-ing software requirements in the IT industry. A number of guidelines have been proposed in the literature on how to write use-cases. Structural defects can occur when use-cases are written without following such guidelines. We develop a taxonomy of structural defects and analyse a sample of 360 industrial use-cases to understand the nature of defects in them. Our sample comes from both client-based projects and in-house projects. The results show that, compared to a sample of theoretical use-cases that follow Cockburn's guidelines, industrial use-cases on the average exhibit defects such as complex structures, lack of customer focus and missing actors. Given the shortage of analysis of real industry samples, our results make a significant contribution towards the understanding of the strengths and weaknesses in industrial use-cases in terms of structural defects. The results will be useful for industry practitioners in adopting use-case modelling standards to reduce the defects as well as for software engineering researchers to explore the reasons for such differences between the theory and the practice in use-case modelling.

An empirical study of structural defects in industrial use-cases

In the last decade, many computer-aided diagnosis (CAD) systems that utilize a broad range of diagnostic techniques have been proposed. Due to both the inherently complex structure of the breast tissues and the low intensity contrast found in most mammographic images, CAD systems that are based on conventional techniques have been shown to have missed malignant masses in mammographic images that would otherwise be treatable. On the other hand, systems based on fuzzy image processing techniques have been found to be able to detect masses in cases where conventional techniques would have failed. In the current chapter, recent advances in fuzzy image segmentation techniques as applied to mass detection in digital mammography are reviewed. Image segmentation is an important step in CAD systems since the quality of its outcome will significantly affect the processing downstream that can involve both detection and classification of benign versus malignant masses. DOI: 10.4018/978-1-4666-1830-5.ch021

Fuzzy Image Segmentation for Mass Detection in Digital Mammography: Recent Advances and Techniques

The node-node overlaps, which occur in drawing of labeled graphs, and more importantly, in the dynamic drawing, have been a problem in graph visualization. Several approaches have recently been proposed to solve such a problem. These approaches, however, lack the scalabilities of dealing with large number of overlapping nodes. Providing a formal description of the problem, this paper presents a scalable version of our previous approach called the Force-Transfer algorithm. Compared to other methods, our new algorithm is able to achieve the compact adjusted layout of a large graph.

Abudulkadir Sajeev

Papers

Pattern puzzle: a metaphor for visualizing software complexity measures

UML Modeling for Regression Testing of Component Based Systems

An empirical study of structural defects in industrial use-cases

Fuzzy Image Segmentation for Mass Detection in Digital Mammography: Recent Advances and Techniques

A Scalable Algorithm for Adjusting Node-Node Overlaps