19528385
A semi-formal approach to assist software design with reuse
Design with reuse has been accepted as a cost-effective way to software development. Software reuse covers the process of identification, representation, retrieval, adaptation, and integration of reusable software components. In this paper, we propose a semi-formal approach to software reuse. The approach consists of the following major steps: (1) software components are annotated with formal information, (2) the software components are then translated into predicate transition nets, and (3) consistency checking of the reusable and new components is carried out using the reachability analysis technique of predicate transition (PrT) nets. The approach is demonstrated through an 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).
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.
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.
20141160
A method for analyzing properties of hierarchical predicate transition nets
Hierarchical high level Petri nets have been proposed in recent years as a powerful formal method for modeling large concurrent and distributed systems but they are even more difficult to analyze than flat high level Petri nets, which are also lacking effective analysis methods themselves. A method for analyzing properties of hierarchical predicate transition Petri nets is proposed. The method employs two temporal induction techniques, one for safety properties and the other for liveness properties, without using temporal logic formalism. Within each induction technique, a hybrid reasoning technique combining net structural and behavioral reasoning, and ordinary first order logic reasoning is used.
28830107
Introducing software architecture specification and analysis in SAM through an example
Abstract Software architecture study has become one of the most active research areas in software engineering in the recent years. Although there have been many published results on specification and analysis method of software architectures, information on sound systematic methodology for modeling and analyzing software architectures is lacking. In this article, we present a formal systematic software architecture specification and analysis methodology called SAM and show how to apply SAM to specify a command control (C2) system and to analyze its real-time constraints.
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.
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.
14516443
Learning a semantic space from user's relevance feedback for image retrieval
As current methods for content-based retrieval are incapable of capturing the semantics of images, we experiment with using spectral methods to infer a semantic space from user's relevance feedback, so that our system will gradually improve its retrieval performance through accumulated user interactions. In addition to the long-term learning process, we also model the traditional approaches to query refinement using relevance feedback as a short-term learning process. The proposed short- and long-term learning frameworks have been integrated into an image retrieval system. Experimental results on a large collection of images have shown the effectiveness and robustness of our proposed algorithms.
1887353
An Application of Well-Orderly Trees in Graph Drawing
Well-orderly trees seem to have the potential of becoming a powerful technique capable of deriving new results in graph encoding, graph enumeration and graph generation [3, 4]. In this paper, we reduce the height of the visibility representation of plane graphs from 5n/6 to (4n–1)/5, by using well-orderly trees.
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.