Software testing processes as a linear dynamic system

Software testing is essential for software reliability improvement and assurance, and the processes of software testing are intrinsically dynamic. However they are seldom investigated in a mathematically rigorous manner. In this paper a theoretical study is presented to examine the dynamic behavior of software testing. More specifically, a set of simplifying assumptions is adopted to formulate and quantify the software testing processes. The mathematical formulae for the expected number of observed software failures are rigorously derived, the bounds and trends of the expected number of observed software failures are analyzed, and the variance of the number of observed software failures is examined. On the other hand, it is demonstrated that under the simplifying assumptions, the software testing processes can be treated as a linear dynamic system. This suggests that the software testing processes could be classified as linear or non-linear, and there be intrinsic link between software testing and system dynamics.     

Safety, domain independence and translation of complex value database queries

This paper considers the theory of database queries on the complex value data model with external functions. Motivated by concerns regarding query evaluation, we first identify recursive sets of formulas, called embedded allowed, which is a class with desirable properties of “reasonable” queries.We then show that all embedded allowed calculus (or fix-point) queries are domain independent and continuous. An algorithm for translating embedded allowed queries into equivalent algebraic expressions as a basis for evaluating safe queries in all calculus-based query classes has been developed.Finally we discuss the topic of “domain independent query programs”, compare the expressive power of the various complex value query languages and their embedded allowed versions, and discuss the relationship between safety, embedded allowed, and domain independence in the various calculus-based queries.     

Optimal models for single-period supply chain problems with fuzzy demand

The aim of this paper is to develop an optimal technique for dealing with the fuzziness aspect of demand uncertainties. Triangular fuzzy numbers are used to model external demand, and decision models in both non-coordination and coordination situations are constructed. It is shown that in the decision models there exists a unique solution that can be expressed analytically. Based on the closed form solutions for both models, the behaviors and relationships of both the manufacturer and the retailer are quantitatively analyzed, and a cooperative policy for the optimization of the whole supply chain is put forward.     

Non-negative matrix factorization for semi-supervised data clustering

Traditional clustering algorithms are inapplicable to many real-world problems where limited knowledge from domain experts is available. Incorporating the domain knowledge can guide a clustering algorithm, consequently improving the quality of clustering. In this paper, we propose SS-NMF: a semi-supervised non-negative matrix factorization framework for data clustering. In SS-NMF, users are able to provide supervision for clustering in terms of pairwise constraints on a few data objects specifying whether they “must” or “cannot” be clustered together. Through an iterative algorithm, we perform symmetric tri-factorization of the data similarity matrix to infer the clusters. Theoretically, we show the correctness and convergence of SS-NMF. Moveover, we show that SS-NMF provides a general framework for semi-supervised clustering. Existing approaches can be considered as special cases of it. Through extensive experiments conducted on publicly available datasets, we demonstrate the superior performance of SS-NMF for clustering.

Caustic spot light for rendering caustics

It is difficult to render caustic patterns at interactive frame rates. This paper introduces new rendering techniques that relax current constraints, allowing scenes with moving, non-rigid scene objects, rigid caustic objects, and rotating directional light sources to be rendered in real-time with GPU hardware acceleration. Because our algorithm estimates the intensity and the direction of caustic light, rendering of non-Lambertian surfaces is supported. Previous caustics algorithms have separated the problem into pre-rendering and rendering phases, storing intermediate results in data structures such as photon maps or radiance transfer functions. Our central idea is to use specially parameterized spot lights, called caustic spot lights (CSLs), as the intermediate representation of a two-phase algorithm. CSLs are flexible enough that a small number can approximate the light leaving a caustic object, yet simple enough that they can be efficiently evaluated by a pixel shader program during accelerated rendering.We extend our approach to support changing lighting direction by further dividing the pre-rendering phase into per-scene and per-frame components: the per-frame phase computes frame-specific CSLs by interpolating between CSLs that were pre-computed with differing light directions.     

Global dynamics for non-autonomous reaction-diffusion neural networks with time-varying delays

In this paper, a class of non-autonomous reaction-diffusion neural networks with time-varying delays is considered. Novel methods to study the global dynamical behavior of these systems are proposed. Employing the properties of diffusion operator and the method of delayed inequalities analysis, we investigate global exponential stability, positive invariant sets and global attracting sets of the neural networks under consideration. Furthermore, conditions sufficient for the existence and uniqueness of periodic attractors for periodic neural networks are derived and the existence range of the attractors is estimated. Finally two examples are given to demonstrate the effectiveness of these results.     

An improved NEH-based heuristic for the permutation flowshop problem

NEH is an effective heuristic for solving the permutation flowshop problem with the objective of makespan. It includes two phases: generate an initial sequence and then construct a solution. The initial sequence is studied and a strategy is proposed to solve job insertion ties which may arise in the construct process. The initial sequence which is generated by combining the average processing time of jobs and their standard deviations shows better performance. The proposed strategy is based on the idea of balancing the utilization among all machines. Experiments show that using this strategy can improve the performance of NEH significantly. Based on the above ideas, a heuristic NEH-D (NEH based on Deviation) is proposed, whose time complexity is O(mn²), the same as that of NEH. Computational results on benchmarks show that the NEH-D is significantly better than the original NEH.     

Strategic advantages of interoperability for global manufacturing using CNC technology

In the domain of manufacturing, computer numerically controllers (CNC) technology is a major contributor to the production capacity of the enterprises. The advances in CNC technology coupled with enhancements in computing systems have provided the basis to re-examine the way in which computer-aided systems (CAx) can be used to enable global manufacturing. Interoperability of the various components of the CAx chain is therefore a major prerequisite for manufacturing enterprises for becoming strategically agile and consequently globally competitive. Being interoperable, resources can be utilized interchangeably in a plug-and-produce manner. Over the last 8 years the eminence of a STEP standard for machining entitled STEP-NC (numerical control) has become a well-known vehicle for research to improve the level of information availability at the CNC machine tool. In this paper, the authors introduce the background to the evolution of CNC manufacturing over the last 50 years and the current standards available for programming. A review of the literature in interoperable CNC manufacturing is then provided relating to milling, turn–mill and other NC processes. The major part of the paper provides a strategic view of how interoperability can be implemented across the CAx chain with a range of standards used to regulate the flow of information. Finally, the paper outlines the advantages and major issues for future developments in interoperability, identifying future key requirements and limiting factors.     

Knowledge discovery in corporate events by neural network rule extraction

Over the last two decades, artificial neural networks (ANN) have been applied to solve a variety of problems such as pattern classification and function approximation. In many applications, it is desirable to extract knowledge from trained neural networks for the users to gain a better understanding of the network’s solution. In this paper, we use a neural network rule extraction method to extract knowledge from 2222 dividend initiation and resumption events. We find that the positive relation between the short-term price reaction and the ratio of annualized dividend amount to stock price is primarily limited to 96 small firms with high dividend ratios. The results suggest that the degree of short-term stock price underreaction to dividend events may not be as dramatic as previously believed. The results also show that the relations between the stock price response and firm size is different across different types of firms. Thus, drawing the conclusions from the whole dividend event data may leave some important information unexamined. This study shows that neural network rule extraction method can reveal more knowledge from the data.