2341522
PZ nets a formal method integrating Petri nets with Z
Abstract In this paper, a formal method (called PZ nets) for specifying concurrent and distributed systems is presented. PZ nets integrate two well-known existing formal methods Petri nets and Z such that Petri nets are used to specify the overall structure, control flows, causal relation, and dynamic behavior of a system; and Z is used to define tokens, labels and constrains of the system. The essence, benefits, and problems of the integration are discussed. A set of heuristics and transformations to develop PZ nets and a technique to analyze PZ nets are proposed and demonstrated through a well-known example.
28749706
An Improved Algorithm for Concurrency Control in Distributed Database Systems
Abstract Deadlock and livelock prevention is critical in concurrency control of distributed database systems. This paper presents an improved algorithm for concurrency control, which not only prevents deadlock and livelock problems but also improves system efficiency by lowering restart ratio and communication cost. The proposed algorithm is effective in environments where most transactions have one-way request (either forward waiting or backward waiting).
12973064
Integrating predicate transition nets with first order temporal logic in the specification and verification of concurrent systems
This paper presents some results of integrating predicate transition nets with first order temporal logic in the specification and verification of concurrent systems. The intention of this research is to use predicate transition nets as a specification method and to use first order temporal logic as a verification method so that their strengths — the easy comprehension of predicate transition nets and the reasoning power of first order temporal logic can be combined. In this paper, a theoretical relationship between the computation models of these two formalisms is presented; an algorithm for systematically translating a predicate transition net into a corresponding temporal logic system is outlined; and a special temporal refutation proof technique is proposed and illustrated in verifying various concurrent properties of the predicate transition net specification of the five dining philosophers problem.
46303239
Object Orientation in Hierarchical Predicate Transition Nets
In this paper, an approach of using hierarchical predicate transition nets (HPrTNs in the sequel) for object-oriented specification is proposed. The realization of various object-oriented features (including encapsulation, inheritance, and polymorphism) in HPrTNs is presented and is demonstrated with examples. We believe that the approach can achieve the benefits of the object-oriented methods while maintaining the analyzability of HPrTNs.
20452185
A methodology for test selection
Software creation requires not only testing during the development cycle by the development staff, but also independent testing following the completion of the implementation. However in the latter case, the amount of testing that can be carried out is often limited by time and resources. At the very most, independent testing can be expected to provide 100% test coverage of the test requirements (or specifications) associated with the software element with the minimum of effort. This paper describes a methodology employing integer programming by which the amount of testing required to provide the maximum possible test coverage of the test requirements (for the given test set) is assured while at the same time minimizing the total number of tests to be included in a test suite. A collateral procedure provides recommendations on which tests might be eliminated if less than 100% test coverage of the test requirements is permitted. This latter procedure will be useful in determining the risk of not running the minimum set of tests for 100% test coverage. A third process selects from the test matrix the set of tests to be applied to the system following maintenance modification of any test requirements-- that is, to provide a submatrix for regression testing. The potential benefits for applying the integer programming technique in test data selection is also discussed.
17445618
A Formal Definition of Hierarchical Predicate Transition Nets
Hierarchical predicate transition nets have recently been introduced as a visual formalism for specifying complex reactive systems. They extend predicate transition nets with hierarchical structures so that large systems can be specified and understood stepwisely, and thus are more suitable for real-world applications. In this paper, we provide a formal syntax and an algebraic semantics for hierarchical predicate transition nets, which establish the theory of hierarchical predicate transition nets for precise specification and formal reasoning.
32419724
Grid Embedding of 4-Connected Plane Graphs
A straight line grid embedding of a plane graph G is a drawing of G such that the vertices are drawn at grid points and the edges are drawn as nonintersecting straight line segments. In this paper we show that if a 4-connected plane graph G has at least four vertices on its external face, then G can be embedded on a grid of size W × H such that W + H ≤n, W ≤ (n + 3)/2 and H ≤ 2(n - 1)/3, where n is the number of vertices of G. Such an embedding can be computed in linear time.
16416421
Bayesian shape localization for face recognition using global and local textures
We present a fully automatic system for face recognition in databases, with only a small number of samples (even a single sample) for each individual. The shape localization problem is formulated in the Bayesian framework. In the learning stage, the RankBoost approach is introduced to model the likelihood of local features associated with the fiducial point, while preserving the prior ranking order between the ground truth position and its neighbors; in the inferring stage, a simple efficient iterative algorithm is proposed to uncover the MAP shape by locally modeling the likelihood distribution around each fiducial point. Based on the accurately located fiducial points, two popular mutual enhancing texture features for human face representation are automatically extracted and integrated: global texture features, which are the normalized shape-free gray-level values enclosed in the mean shape: local texture features, which are represented by Gabor wavelets extracted at the fiducial points (eye corners, mouth, etc.). Global texture mainly encodes the low-frequency information of a face, while local texture encodes the local high-frequency components. Extensive experiments illustrate that our proposed shape localization approach significantly improves the shape location accuracy, robustness, and face recognition rate; moreover, experiments conducted on the FERET and Yale databases show that our algorithm outperforms the classical eigenfaces and fisherfaces, as well as other approaches utilizing shape and global and local textures.
14585493
Learning a locality preserving subspace for visual recognition
We have demonstrated that the face recognition performance can be improved significantly in low dimensional linear subspaces. Conventionally, principal component analysis (PCA) and linear discriminant analysis (LDA) are considered effective in deriving such a face subspace. However, both of them effectively see only the Euclidean structure of face space. We propose a new approach to mapping face images into a subspace obtained by locality preserving projections (LPP) for face analysis. We call this Laplacianface approach. Different from PCA and LDA, LPP finds an embedding that preserves local information, and obtains a face space that best detects the essential manifold structure. In this way, the unwanted variations resulting from changes in lighting, facial expression, and pose may be eliminated or reduced. We compare the proposed Laplacianface approach with eigenface and fisherface methods on three test datasets. Experimental results show that the proposed Laplacianface approach provides a better representation and achieves lower error rates in face recognition.
5723803
Incremental semi-supervised subspace learning for image retrieval
Subspace learning techniques are widespread in pattern recognition research. They include Principal Component Analysis (PCA), Locality Preserving Projection (LPP), etc. These techniques are generally unsupervised which allows them to model data in the absence of labels or categories. In relevance feedback driven image retrieval system, the user provided information can be used to better describe the intrinsic semantic relationships between images. In this paper, we propose a semi-supervised subspace learning algorithm which incrementally learns an adaptive subspace by preserving the semantic structure of the image space, based on user interactions in a relevance feedback driven query-by-example system. Our algorithm is capable of accumulating knowledge from users, which could result in new feature representations for images in the database so that the system's future retrieval performance can be enhanced. Experiments on a large collection of images have shown the effectiveness and efficiency of our proposed algorithm.
1473802
Locality preserving clustering for image database
It is important and challenging to make the growing image repositories easy to search and browse. Image clustering is a technique that helps in several ways, including image data preprocessing, user interface designing, and search result representation. Spectral clustering method has been one of the most promising clustering methods in the last few years, because it can cluster data with complex structure, and the (near) global optimum is guaranteed. However, existing spectral clustering algorithms, like Normalized Cut, are difficult to handle data points out of training set. In this paper, we propose a clustering algorithm named Locality Preserving Clustering (LPC), which shares many of the data representation properties of nonlinear spectral method. Yet LPC provides an explicit mapping function which is defined everywhere, both on training data points and testing points. Experimental results show that LPC is more accurate than both "direct Kmeans" and "PCA + Kmeans". We also show that LPC produces in general comparable results with Normalized Cut, yet is more efficient than Normalized Cut.