Extracting FSMs from Object-Z specifications with history invariants
Object-Z with history invariants can present precise and abstract models for complex systems. The system behavior patterns are often implicitly embedded within various state/operational constraints and history invariants. Without explicit system behavior representations, it is difficult to implement those abstract models. In this paper, we present a sound and systematic approach to automatically extract explicit system behaviors (as FSMs) from the abstract Object-Z specifications. Safety and liveness and additional crucial requirements for open systems are ensured.

title: XML-Based Static Type Checking and Dynamic Visualization for TCOZ
abstract: Timed Communicating Object Z(TCOZ) combines Object-Z's strengths in modelling complex data and state with TCSP's strengths in modeling real-time concurrency. Based on our previous work on the XML environment for TCOZ, this paper firstly demonstrates the development of a type checker for detecting static semantic errors of the TCOZ specification, then illustrates a transformation tool to automatically project TCOZ models into UML statechart diagrams for visualising the dynamic system behaviour.

title: Validating Semistructured Data Using OWL
abstract: Semistructured data has become prevalent in both web applications and database systems. This rapid growth in use makes the design of good semistructured data essential. Formal semantics and automated reasoning tools enable us to reveal the inconsistencies in a semistructured data model and its instances. The Object Relationship Attribute model for Semistructured data (ORA-SS) is a graphical notation for designing and representing semistructured data. This paper presents a methodology of encoding the semantics of ORA-SS in the Web Ontology Language (OWL) and automatically validating the semistructured data design using the OWL reasoning tool – RACER. Our methodology provides automated consistency checking of an ORA-SS data model at both the schema and instance levels.

title: Z Approach to Semantic Web
abstract: The Semantic Web (SW) service is a web application using Semantic Web techniques which usually involve cooperation between several intelligent agents. The design of SW systems requires precise modelling techniques to capture ontology domain properties and application functionalities. We believe that Z as a specification technique can contribute to the Semantic Web-based system development in many ways. In this paper, we firstly conduct a case study of applying Z to the design of a SW service system (online talk discovery), and then present translation techniques and tools which can extract the SW ontology from the Z model automatically. Furthermore, we discuss how existing Z tools, i.e. Z/EVES, can be used to improve the quality of SW ontology design.

title: SVG Web Environment for Z Specification Language
abstract: This paper presents a web environment for the Z formal specification language using the Scalable Vector Graphics (SVG) technology. The Z Specification Web Editor (ZSWE) is the first prototype of a web based graphical editor for the Z specification language. It not only supports graphical editing and global accessibility for the Z formal specifications, but also provides model comprehension facilities such as schema expansion, specification navigation and model querying. This paper outlines the requirement, design and implementation of the tool and its future improvements.

title: Object-Z web environment and projections to UML
abstract: This paper presents the XML/XSL approach to the development of a web environment for the formal speci cation language Object-Z. The projection techniques and tools from Object-Z (in XML) to UML (in XMI) are developed using XSL Transformations (XSLT). Furthermore, Object-Z (itself) is used to specify and design the essential functionalities of the web environment and the projection tools to UML. In a sense, the paper also demonstrates a formal approach to modeling web applications.

title: Stereo Matching Using Belief Propagation
abstract: In this paper, we formulate the stereo matching problem as a Markov network consisting of three coupled Markov random fields (MRF's). These three MRF's model a smooth field for depth/disparity, a line process for depth discontinuity and a binary process for occlusion, respectively. After eliminating the line process and the binary process by introducing two robust functions, we obtain the maximum a posteriori (MAP) estimation in the Markov network by applying a Bayesian belief propagation (BP) algorithm. Furthermore, we extend our basic stereo model to incorporate other visual cues (e.g., image segmentation) that are not modeled in the three MRF's, and again obtain the MAP solution. Experimental results demonstrate that our method outperforms the state-of-art stereo algorithms for most test cases.

title: Hybrid Control of Automotive Powertrain Systems: A Case Study
abstract: In this paper, a hybrid control design approach is used to develop control strategies for coordination of automotive engine and transmission systems. The main goal is to improve system performance, fuel economy, robustness and other performance specifications. The method employs performance indices in guiding both analog and discrete control actions such that robust stability and performance of closed-loop hybrid systems are maintained, in the presence of modeling errors, disturbances and structural uncertainties.

title: Flash matting
abstract: In this paper, we propose a novel approach to extract mattes using a pair of flash/no-flash images. Our approach, which we call flash matting, was inspired by the simple observation that the most noticeable difference between the flash and no-flash images is the foreground object if the background scene is sufficiently distant. We apply a new matting algorithm called joint Bayesian flash matting to robustly recover the matte from flash/no-flash images, even for scenes in which the foreground and the background are similar or the background is complex. Experimental results involving a variety of complex indoors and outdoors scenes show that it is easy to extract high-quality mattes using an off-the-shelf, flash-equipped camera. We also describe extensions to flash matting for handling more general scenes.

title: A Data Mining Based Intrusion Detection Model
abstract: Intrusion Detection Systems (IDSs) have become a critical part of security systems. The goal of an intrusion detection system is to block intrusion effectively and accurately. However, the performance of IDS is not satisfying. In this paper, we study the issue of building a data mining based intrusion detection model to raise the detection performance. The key ideas are to use data mining techniques to discover consistent and useful patterns for intrusion and use the set of patterns to recognize intrusion. By applying statistics inference theory to this model, the patterns mined from a set of test data are effective to detect the attacks in the same category, and therefore can detect most novel attacks that are variants of known attacks.

title: A comparison of implicit and explicit links for web page classification
abstract: It is well known that Web-page classification can be enhanced by using hyperlinks that provide linkages between Web pages. However, in the Web space, hyperlinks are usually sparse, noisy and thus in many situations can only provide limited help in classification. In this paper, we extend the concept of linkages from explicit hyperlinks to implicit links built between Web pages. By observing that people who search the Web with the same queries often click on different, but related documents together, we draw implicit links between Web pages that are clicked after the same queries. Those pages are implicitly linked. We provide an approach for automatically building the implicit links between Web pages using Web query logs, together with a thorough comparison between the uses of implicit and explicit links in Web page classification. Our experimental results on a large dataset confirm that the use of the implicit links is better than using explicit links in classification performance, with an increase of more than 10.5% in terms of the Macro-F1 measurement.