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.
28965788
A useful approach to developing reverse engineering metrics
If software metrics are useful in a forward software engineering environment, they are vital in a reverse engineering environment. We are endeavouring to discover approaches to developing reverse engineering metrics for software engineers who need them for reverse engineering legacy systems. The major contribution of the paper is the presentation of a systematic research base and a hierarchical approach to the development of software metrics for reverse engineering. Measurement is fundamental to the software engineering discipline as a whole. A software metric is a quantitative measure of the degree to which a system, component, or process possesses a given attribute (N.E. Fenton and S.L. Pfleeger, 1996). Software reverse engineering is the process of analysing a subject system to: identify the system's components and their interrelationships; and create representations of the system at a higher level of abstraction (E.J. Chikofsky and J.H. Cross II, 1990). The goal of developing reverse engineering metrics is to identify measures that are needed for assessing the status of reverse engineering projects, products, processes and resources, and helping engineers to understand both what is happening and what will happen during reverse engineering procedures. Consequently, these concrete reverse engineering measures make more visible to us aspects of process and product in reverse engineering, in particular, the product of specifications and the process of abstractions and transformations. This makes it possible to control reverse engineering projects. Aspects of measurement in reverse engineering are outlined.
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.
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.
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.
30190247
A Fast General Methodology for Information-Theoretically Optimal Encodings of Graphs
We propose a fast methodology for encoding graphs with information-theoretically minimum numbers of bits. Specifically, a graph with property $\pi$ is called a {\em $\pi$-graph}. If $\pi$ satisfies certain properties, then an n-node m-edge $\pi$-graph G can be encoded by a binary string X such that (1) G and X can be obtained from each other in O(n log n) time, and (2) X has at most $\beta(n)+o(\beta(n))$ bits for any continuous superadditive function $\beta(n)$ so that there are at most $2^{\beta(n)+o(\beta(n))}$ distinct $n$-node $\pi$-graphs. The methodology is applicable to general classes of graphs; this paper focuses on planar graphs. Examples of such $\pi$ include all conjunctions over the following groups of properties: (1) G is a planar graph or a plane graph; (2) $G$ is directed or undirected; (3) $G$ is triangulated, triconnected, biconnected, merely connected, or not required to be connected; (4) the nodes of G are labeled with labels from $\{1,\ldots, \ell_1\}$ for $\ell_1\leq n$; (5) the edges of G are labeled with labels from $\{1,\ldots, \ell_2\}$ for $\ell_2\leq m$; and (6) each node (respectively, edge) of G has at most $\ell_3=O(1)$ self-loops (respectively, $\ell_4=O(1)$ multiple edges). Moreover, $\ell_3$ and $\ell_4$ are not required to be O(1) for the cases of $\pi$ being a plane triangulation. These examples are novel applications of small cycle separators of planar graphs and are the only nontrivial classes of graphs, other than rooted trees, with known polynomial-time information-theoretically optimal coding schemes.
14270830
Laplacian Score for Feature Selection
In supervised learning scenarios, feature selection has been studied widely in the literature. Selecting features in unsupervised learning scenarios is a much harder problem, due to the absence of class labels that would guide the search for relevant information. And, almost all of previous unsupervised feature selection methods are "wrapper" techniques that require a learning algorithm to evaluate the candidate feature subsets. In this paper, we propose a "filter" method for feature selection which is independent of any learning algorithm. Our method can be performed in either supervised or unsupervised fashion. The proposed method is based on the observation that, in many real world classification problems, data from the same class are often close to each other. The importance of a feature is evaluated by its power of locality preserving, or, Laplacian Score. We compare our method with data variance (unsupervised) and Fisher score (supervised) on two data sets. Experimental results demonstrate the effectiveness and efficiency of our 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.
6692390
Learning and inferring 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 the 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.
13365148
Orthogonal Laplacianfaces for Face Recognition
Following the intuition that the naturally occurring face data may be generated by sampling a probability distribution that has support on or near a submanifold of ambient space, we propose an appearance-based face recognition method, called orthogonal Laplacianface. Our algorithm is based on the locality preserving projection (LPP) algorithm, which aims at finding a linear approximation to the eigenfunctions of the Laplace Beltrami operator on the face manifold. However, LPP is nonorthogonal, and this makes it difficult to reconstruct the data. The orthogonal locality preserving projection (OLPP) method produces orthogonal basis functions and can have more locality preserving power than LPP. Since the locality preserving power is potentially related to the discriminating power, the OLPP is expected to have more discriminating power than LPP. Experimental results on three face databases demonstrate the effectiveness of our proposed algorithm