Sequential pattern mining is an important data mining problem with broad applications. However, it is also a challenging problem since the mining may have to generate or examine a combinatorially explosive number of intermediate subsequences. Recent studies have developed two major classes of sequential pattern mining methods: (1) a candidate generation-and-test approach, represented by (I) GSP, a horizontal format-based sequential pattern mining method, and (ii) SPADE, a vertical format-based method; and (2) a pattern-growth method, represented by PrefixSpan and its further extensions, such as gSpan for mining structured patterns.In this study, we perform a systematic introduction and presentation of the pattern-growth methodology and study its principles and extensions. We first introduce two interesting pattern-growth algorithms, FreeSpan and PrefixSpan, for efficient sequential pattern mining. Then we introduce gSpan for mining structured patterns using the same methodology. Their relative performance in large databases is presented and analyzed. Several extensions of these methods are also discussed in the paper, including mining multi-level, multi-dimensional patterns and mining constraint-based patterns.

A reasonable transition rule is proposed for synchronized actions and some equational properties of bisimilarity and weak bisimilarity in the process algebra for reasoning about concurrent actions are presented.

The plane metrology using a single uncalibrated image is studied in the paper, and three novel approaches are proposed. The first approach, namely key-line-based method, is an improvement over the widely used key-point-based method, which uses line correspondences directly to compute homography between the world plane and its image so as to increase the computational accuracy. The second and third approaches are both based on a pair of vanishing points from two orthogonal sets of parallel lines in the space plane together with two unparallel referential distances, but the two methods deal with the problem in different ways. One is from the algebraic viewpoint which first maps the image points to an affine space via a transformation constructed from the vanishing points, and then computes the metric distance according to the relationship between the affine space and the Euclidean space, while the other is from the geometrical viewpoint based on the invariance of cross ratios. The second and third methods avoid the selection of control points and are widely applicable. In addition, a brief description on how to retrieve other geometrical entities on the space plane, such as distance from a point to a line, angle formed by two lines, etc., is also presented in the paper. Extensive experiments on simulated data as well as on real images show that the first and the second approaches are of better precision and stronger robustness than the key-point-based one and the third one, since these two approaches are fundamentally based on line information.

This paper presents a new technique of unified probabilistic models for face recognition from only one single example image per person. The unified models, trained on an obtained training set with multiple samples per person, are used to recognize facial images from another disjoint database with a single sample per person. Variations between facial images are modeled as two unified probabilistic models: within-class variations and between-class variations. Gaussian Mixture Models are used to approximate the distributions of the two variations and exploit a classifier combination method to improve the performance. Extensive experimental results on the ORL face database and the authors' database (the ICT-JDL database) including totally 1,750 facial images of 350 individuals demonstrate that the proposed technique, compared with traditional eigenface method and some well-known traditional algorithms, is a significantly more effective and robust approach for face recognition.

This paper proposes a virtual brush model based on droplet operation to simulate Chinese calligraphy and traditional Chinese painting in real time. Two ways of applying droplet model to virtual calligraphy and painting are discussed in detail. The second droplet model is more elaborated and can produce more vivid results while being slightly more time-consuming. The novel feature of the proposed droplet virtual brush model successfully enables the simulation painting system to overcome the poor expressional ability of virtual brush based on particle system and avoids the complex evaluation of physical brush with solid model. The model, derived from the actual calligraphy and painting experience, due to the simplicity of the droplet operation and its powerful expressive ability, considerably improves the performance of the simulation system and maintains painting effect comparable with real brush by supporting special Chinese brush effect such as dry brush, feng and stroke diffusion.

Super-resolution reconstruction algorithm produces a high-resolution image from a low-resolution image sequence. The accuracy and the stability of the motion estimation (ME) are essential for the whole restoration. In this paper, a new super-resolution reconstruction algorithm is developed using a robust ME method, which fuses multiple estimated motion vectors within the sequence. The new algorithm has two major improvements compared with the previous research. First, instead of only two frames, the whole sequence is used to obtain a more accurate and stable estimation of the motion vector of each frame; second, the reliability of the ME is quantitatively measured and introduced into the cost function of the reconstruction algorithm. The algorithm is applied to both synthetic and real sequences, and the results are presented in the paper.

In this paper, region features and relevance feedback are used to improve the performance of CBIR. Unlike existing region-based approaches where either individual regions are used or only simple spatial layout is modeled, the proposed approach simultaneously models both region properties and their spatial relationships in a probabilistic framework. Furthermore, the retrieval performance is improved by an adaptive filter based relevance feedback. To illustrate the performance of the proposed approach, extensive experiments have been carried out on a large heterogeneous image collection with 17,000 images, which render promising results on a wide variety of queries.

Topological relationships between two spatial features represent important knowledge in Geographical Information Systems (GIS). In the last few years, many models that represent topological relationships have been proposed. But these models cannot represent the topological relationships between heterogeneous geometrycollection features, which are composed of different dimensional geometries. In this paper, the formal definition of regular heterogeneous geometrycollection and regularization rules are given. Based on the spatial model, two methods for representing topological relationships between these complex features are proposed. The first method is Dimensionally Extended Nine-Intersection Model Based on Components (DE-9IMBC) that extends Dimensionally Extended Nine-Intersection Model (DE-9IM) and takes into account the topological relationships between components of these complex features. The advantage of DE-9IMBC is that a large number of different topological relationships can be checked. The second method extends the definitions of topological relationships in Open Geodata Interoperability Specification (OpenGIS), and redefines the seven named topological relationships: Disjoint, Touches, Within, Crosses, Overlaps, Contains and Equal, to represent the topological relationships between heterogeneous geometrycollection features. It is proven that the seven extended topological relationships are complete and mutually exclusive, and they are suitable for being embedded in spatial query languages.

Inherent heterogeneity and distribution of knowledge strongly prevent knowledge from sharing and reusing among different agents and software entities, and a formal ontology has been viewed as a promising means to tackle this problem. In this paper, a domain-specific formal ontology of archaeology is presented. The ontology mainly consists of three parts: archaeological categories, their relationships and axioms. The ontology not only captures the semantics of archaeological knowledge, but also provides archaeology with an explicit and formal specification of a shared conceptualization, thus making archaeological knowledge shareable and reusable across humans and machines in a structured fashion. Further, we propose a method to verify ontology correctness based on the individuals of categories. As applications of the ontology, we have developed an ontology-driven approach to knowledge acquisition from archaeological text and a question answering system for archaeological knowledge.

Data cube computation is a well-known expensive operation and has been studied extensively. It is often not feasible to compute a complete data cube due to the huge storage requirement. Recently proposed quotient cube addressed this fundamental issue through a partitioning method that groups cube cells into equivalent partitions. The effectiveness and efficiency of the quotient cube for cube compression and computation have been proved. However, as changes are made to the data sources, to maintain such a quotient cube is non-trivial since the equivalent classes in it must be split or merged. In this paper, incremental algorithms are designed to update existing quotient cube efficiently based on Galois lattice. Performance study shows that these algorithms are efficient and scalable for large databases.

With the development of Internet, frequent pattern mining has been extended to more complex patterns like tree mining and graph mining. Such applications arise in complex domains like bioinformatics, web mining, etc. In this paper, we present a novel algorithm, named Chopper, to discover frequent subtrees from ordered labeled trees. An extensive performance study shows that the newly developed algorithm outperforms TreeMinerV, one of the fastest methods proposed previously, in mining large databases. At the end of this paper, the potential improvement of Chopper is mentioned.

With the growing popularity of the World Wide Web, large volume of user access data has been gathered automatically by Web servers and stored in Web logs. Discovering and understanding user behavior patterns from log files can provide Web personalized recommendation services. In this paper, a novel clustering method is presented for log files called Clustering large Weblog based on Key Path Model (CWKPM), which is based on user browsing key path model, to get user behavior profiles. Compared with the previous Boolean model, key path model considers the major features of users' accessing to the Web: ordinal, contiguous and duplicate. Moreover, for clustering, it has fewer dimensions. The analysis and experiments show that CWKPM is an efficient and effective approach for clustering large and high-dimension Web logs.

In this paper, the class of regular disjunction-free default theories is introduced and investigated. A transformation from regular default theories to normal default theories is established. The initial theory and the transformed theory have the same extensions when restricted to old variables. Hence, regular default theories enjoy some similar properties (e.g., existence of extensions, semi-monotonicity) as normal default theories. Then, a new algorithm for credulous reasoning of regular theories is developed. This algorithm runs in a time not more than O(1.45^n), where n is the number of defaults. In case of regular prerequisite-free or semi-2CNF default theories, the credulous reasoning can be solved in polynomial time. However, credulous reasoning for semi-Horn default theories is shown to be NP-complete although it is tractable for Horn default theories. Moreover, skeptical reasoning for regular unary default theories is co-NP-complete.

We discuss the projection temporal logic (PTL), based on a primitive projection operator, prj. A framing technique is also presented, using which a synchronization operator, await, is defined within the underlying logic. A framed temporal logic programming language (FTLL) is presented. To illustrate how to use both the language and framing technique, some examples are given.

In this paper, a labelled transition semantics for higher-order process calculi is studied. The labelled transition semantics is relatively clean and simple, and corresponding bisimulation equivalence can be easily formulated based on it. And the congruence properties of the bisimulation equivalence can be proved easily. To show the correspondence between the proposed semantics and the well-established ones, the bisimulation is characterized as a version of barbed equivalence and a version of context bisimulation.

Orientation update message filtering is an important issue in collaborative virtual environments (CVEs). Dead-reckoning (DR) is a known effective mechanism for update message filtering. Yet, previous dead-reckoning techniques mainly focus on the update message filtering for positions. The existing orientation dead-reckoning algorithms are based on fixed threshold values. The drawbacks of fixed thresholding for orientations (FTO) are discussed in this paper. We propose a variable thresholding for orientations (VTO) based on average recent angular velocity. The main advantage of the proposed VTO is the ability of balancing the number of state update messages and shift frequency of direction and speed of rotation.