Formal semantics and verification for feature modeling
Research on features has received much attention in the domain engineering community. Feature modeling plays an important role in the design and implementation of complex software systems. However, the presentation and analysis of feature models are still largely informal. There is also an increasing need for methods and tools that can support automated feature model analysis. This paper presents a formal engineering approach to the specification and verification of feature models. A formal semantics for the feature modeling language is defined using first-order logic. It provides a precise and rigorous formal interpretation for the graphical notation. In addition, further validation of the semantics using the Z/EVES theorem prover is presented. Finally, we demonstrate that the consistency of a feature model and its configurations can be automatically verified by encoding the semantics into the Alloy Analyzer. A case study of the Key Word in Context (KWIC) index systems feature model is presented to illustrate the verification process.

title: A tools environment for developing and reasoning about ontologies
abstract: Started in the beginning of 2001, the semantic Web is regarded by many as the next generation of the Web. Ontology languages are the building blocks of semantic Web as they provide basic vocabularies for data markups: the ontologies. The correctness of shared ontologies is crucial to the proper functioning of agents. Hence ensuring the consistency of ontologies is a central issue in both the design and deployment phases of any semantic Web-aware application. Our experiences show that semantic Web is a novel and fruitful application domain for formal languages and their mature reasoning tool support. In order to ease the application of formal methods and tools to the semantic Web, we developed an integrated tools environment to support systematic development of OWL ontologies and then transformation, reasoning assistance and querying of them.

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: 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: 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: A Z Approach in Validating ORA-SS Data Models
abstract: The rapid growth of the World Wide Web has resulted in more data being accessed over the Internet. In turn there is an increase in the use of semistructured data, which plays a crucial role in many web applications particularly with the introduction of XML and its related technologies. This increase in use makes the design of good semistructured data structures essential. The Object Relationship Attribute model for Semistructured data (ORA-SS) is a graphical notation for designing and representing semistructured data. In this paper, we demonstrate an approach to formally validate the ORA-SS data models in order to enhance the correctness of semistructured data design. A mathematical semantics for the ORA-SS notation is defined using the Z formal language, and further validation processes are carried out to check the correctness of the semistructured data models at both the schema and instance levels.

title: Document Summarization Using Conditional Random Fields
abstract: Many methods, including supervised and unsupervised algorithms, have been developed for extractive document summarization. Most supervised methods consider the summarization task as a two-class classification problem and classify each sentence individually without leveraging the relationship among sentences. The unsupervised methods use heuristic rules to select the most informative sentences into a summary directly, which are hard to generalize. In this paper, we present a Conditional Random Fields (CRF) based framework to keep the merits of the above two kinds of approaches while avoiding their disadvantages. What is more, the proposed framework can take the outcomes of previous methods as features and seamlessly integrate them. The key idea of our approach is to treat the summarization task as a sequence labeling problem. In this view, each document is a sequence of sentences and the summarization procedure labels the sentences by 1 and 0. The label of a sentence depends on the assignment of labels of others. We compared our proposed approach with eight existing methods on an open benchmark data set. The results show that our approach can improve the performance by more than 7.1% and 12.1% over the best supervised baseline and unsupervised baseline respectively in terms of two popular metrics F1 and ROUGE-2. Detailed analysis of the improvement is presented as well.

title: A semi-local paradigm for wavelet denoising
abstract: Wavelet denoising methods have been proven useful for many one- and two-dimensional problems. Most existing methods can in principle be carried over to three-dimensional problems, such as the denoising of volumetric positron emission tomography (PET) images, but they may not be sufficiently flexible in allowing some regions of an image to be denoised more aggressively than others. In this paper, we propose a semi-local paradigm for wavelet denoising. The semi-local paradigm involves the division of an image into suitable blocks, which are then individually denoised. To denoise the blocks, we use our modification of the generalized cross validation (GCV) technique of Jansen and Bultheel to choose thresholding parameters; we also present risk estimators to guide some of the other choices involved in the implementation. Experiments with phantom PET images show that the semi-local paradigm provides superior denoising compared to standard application of the GCV technique. An asymptotic analysis demonstrates that, under some regularity conditions, semi-local denoising is asymptotically consistent on the logarithmic scale. The paper concludes with a discussion on the nature of semi-local denoising and some topics for future research.

title: Efficient Immunization Algorithm for Peer-to-Peer Networks
abstract: In this paper, we present a detail study about the immunization of viruses in Peer-to-Peer networks with power-law degree distributions. By comparing two different immunization strategies, we conclude that it is efficient to immunize the highly connected nodes in order to eradicate viruses from the network. Furthermore, we propose an efficient updating algorithm of global virus database according to the degree-based immunization strategy.

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: Text classification improved through multigram models
abstract: Classification algorithms and document representation approaches are two key elements for a successful document classification system. In the past, much work has been conducted to find better ways to represent documents. However, most of the attempts rely on certain extra resources such as WordNet, or they face the problem of extremely high dimension. In this paper, we propose a new document representation approach based on n-multigram language models. This approach can automatically discover the hidden semantic sequences in the documents under each category. Based on n-multigram language models and n-gram language models, we put forward two text classification algorithms. The experiments on RCV1 show that our proposed algorithm based on n-multigram models alone can achieve the similar or even better classification performance compared with the classifier based on n-gram models but the model size of our algorithm is much smaller than that of the latter. Another proposed algorithm based on the combination of n-multigram models and n-gram models improves the micro-F1 and macro-F1 values from 89.5% to 92.6% and 87.2% to 91.1% respectively. All these observations support the validity of our proposed document representation approach.