The impact of requirements analysis upon user satisfaction with packaged software

This article explores the relationship between carrying out requirements analysis activities and ultimate satisfaction with the software acquired. The requirements analysis activities performed by a sample of twelve organizations which acquired a particular general accounting package are evaluated. The satisfaction with the package is determined and analysis performed to establish the relationships between requirements analysis and user satisfaction. It is determined that for the particular population sampled there is little relationship. Possible reasons for this counter-intuitive finding are advanced.     

Analysis of Interconnected Oscillators by Dissipativity Theory

This paper employs dissipativity theory for the global analysis of limit cycles in particular dynamical systems of possibly high dimension. Oscillators are regarded as open systems that satisfy a particular dissipation inequality. It is shown that this characterization has implications for the global stability analysis of limit cycle oscillations: i) in isolated oscillators, ii) in interconnections of oscillators, and iii) for the global synchrony analysis in interconnections of identical oscillators     

基于系统思维的软件安全性需求开发框架

糟糕的软件需求是导致安全性关键系统发生灾难性事故的最主要原因。为解决需求开发问题,建构了一个系统建模与系统分析相结合、基于系统思维的软件安全性需求开发框架。针对系统模型的特定等级特定领域,提出了集成安全性分析的需求开发方法。该方法既能最大限度地约束安全性需求缺陷,防止其向同一分析等级内的其它领域或下一分析等级传播,并尽早重新生成安全性需求,又能够不断生成证据,支持安全性论据的构建。     

Time complexity of a path formulated optimal routing algorithm(second printing)

A detailed analysis of convergence rate is presented for an iterative path formulated optimal routing algorithm. In particular, it is quantified, analytically, how the convergence rate changes as the number of nodes in the underlying graph increases. The analysis is motivated by a particular path formulated gradient projection algorithm that has demonstrated excellent convergence rate properties through extensive numerical studies. The analytical result proven in this note is that the number of iterations for convergence depends on the number of nodes only through the network diameter     

Time complexity of a path formulated optimal routing algorithm

A detailed analysis of convergence rate is presented for an iterative path formulated optimal routing algorithm. In particular, it is quantified, analytically, how the convergence rate changes as the number of nodes in the underlying graph increases. The analysis is motivated by a particular path formulated gradient projection algorithm that has demonstrated excellent convergence rate properties through extensive numerical studies. The analytical result proven in this note is that the number of iterations for convergence depends on the number of nodes only through the network diameter     

Asymptotic behavior and synchronizability characteristics of a class of recurrent neural networks

We propose an approach to the analysis of the influence of the topology of a neural network on its synchronizability in the sense of equal output activity rates given by a particular neural network model. The model we introduce is a variation of the Zhang model. We investigate the time-asymptotic behavior of the corresponding dynamical system (in particular, the conditions for the existence of an invariant compact asymptotic set) and apply the results of the synchronizability analysis on a class of random scale free networks and to the classical random networks with Poisson connectivity distribution.     

On the use of a modified Newton method for nonlinear finite element analysis

In this paper, the usefulness of modified Newton methods for solving certain minimization problems arising in nonlinear finite element analysis is investigated. The application considered is nonlinear elasticity, in particular geometrically nonlinear shells. On a test problem, it is demonstrated that a particular implementation of a modified Newton method using both descent directions and directions of negative curvature is able to identify a minimizer, whereas an unmodified Newton method and modified Newton methods using only descent directions fail to converge to the minimizer. The use of modified Newton methods is suggested as a useful complement to the present continuation methods used for nonlinear finite element analysis.     

ISS properties of nonholonomic vehicles

The paper presents the first result on nonholonomic systems enjoying input to state stability (ISS) properties. Although it is known that smooth stabilizability implies ISS, the converse is not generally true. This leaves the possibility of non-smoothly stabilizable systems being ISS with respect to a particular input, after an appropriate feedback transformation. This is shown to be true for the case of the unicycle with a dynamic extension, in a particular topology induced by a metric appropriate for this type of systems. A feedback control law renders the closed-loop system locally ISS in the particular topology. Potential applications include stability and robustness analysis of formations of mobile robots.     

Bayesian analysis of finite mixtures of multinomial and negative-multinomial distributions

The Bayesian implementation of finite mixtures of distributions has been an area of considerable interest within the literature. Computational advances on approximation techniques such as Markov chain Monte Carlo (MCMC) methods have been a keystone to Bayesian analysis of mixture models. This paper deals with the Bayesian analysis of finite mixtures of two particular types of multidimensional distributions: the multinomial and the negative-multinomial ones. A unified framework addressing the main topics in a Bayesian analysis is developed for the case with a known number of component distributions. In particular, theoretical results and algorithms to solve the label-switching problem are provided. An illustrative example is presented to show that the proposed techniques are easily applied in practice.     

MARKET AREA ANALYSIS AND INTELLIGENCE THROUGH GEOWEB MAP SEGMENTATION

Segmentation is of particular interest in market area analysis and intelligence. A generalized Voronoi influence model provides a flexible framework for segmenting diverse business cases and market tessellations. In this article, we propose a Voronoi influence model–based computational framework for mining various user-supplied business and market data sets from a GeoWeb model, in particular from Yahoo Local. The market area analysis and intelligence provided by the generalized Voronoi model can be effectively used for various market intelligence and strategy decision support. The advantages of using this computational model from the GeoWeb framework is the access to enormous amounts of participation-driven information on businesses and services available on the Internet. The proposed model supports weighted analysis, order-k analysis (k-nearest neighbor analysis), various metrics modeling different scenarios, complex modeling data types representing complex real-world situations, and obstacles modeling real-world barriers through a series of generalized Voronoi diagrams. We provide a series of cases studies that demonstrate the usefulness and practicability of the proposed model.     

Curvature analysis of frequency modulated manifolds in dimensionality reduction

Recent advances in the analysis of high-dimensional signal data have triggered an increasing interest in geometry-based methods for nonlinear dimensionality reduction (NDR). In many applications, high-dimensional datasets typically contain redundant information, and NDR methods are important for an efficient analysis of their properties. During the last few years, concepts from differential geometry were used to create a new range of NDR methods. In the construction of such geometry-based strategies, a natural question is to understand their interaction with classical and modern signal processing tools (convolution transforms, Fourier analysis, wavelet functions). In particular, an important task is the analysis of the incurred geometrical deformation when applying signal transforms to the elements of a dataset. In this paper, we propose the concepts of modulation maps and modulation manifolds for the construction of particular datasets relevant in signal processing and NDR. We consider numerical methods for analyzing geometrical properties of the modulation manifolds, with a particular focus on their scalar and mean curvature. In our numerical examples, we apply the resulting geometry-based analysis to simple test cases, where we present geometrical and topological effects of relevance in manifold learning.     

Tools for secure systems development with UML

For model-based development to be a success in practice, it needs to have a convincing added-value associated with its use. Our goal is to provide such added-value by developing tool-support for the analysis of UML models against difficult system requirements. Towards this goal, we describe a UML verification framework supporting the construction of automated requirements analysis tools for UML diagrams. The framework is connected to industrial CASE tools using XMI and allows convenient access to this data and to the human user. As a particular example, we present plugins for verifying models defined using the security extension UMLsec of UML. The verification framework allows advanced users of the UMLsec approach to themselves implement verification routines for the constraints of self-defined stereotypes. In particular, we focus on an analysis plug-in that utilizes the model-checker Spin to verify security properties of cryptography-based systems.     

Distributional uncertainty analysis using power series and polynomial chaos expansions

This paper provides an overview of computationally efficient approaches for quantifying the influence of parameter uncertainties on the states and outputs of nonlinear dynamical systems with finite-time control trajectories, focusing primarily on computing probability distributions. The advantages and disadvantages of various uncertainty analysis approaches, which use approximate representations of the full nonlinear model using power series or polynomial chaos expansions, are discussed in terms of computational cost and accuracy in computing the shape and tails of the state and output distributions. Application of the uncertainty analysis methods to a simulation study is used to provide advice as to which uncertainty analysis methods to select for a particular application. In particular, the results indicate that first-order series analysis can be accurate enough for the design of real-time robust feedback controllers for batch processes, although it is cautioned that the accuracy of such analysis should be confirmed a posteriori using a more accurate uncertainty analysis method. The polynomial chaos expansion is well suited to robust design and control when the objectives are strongly dependent on the shape or tails of the distributions of product quality or economic objectives.     

Invariant kernel functions for pattern analysis and machine learning

In many learning problems prior knowledge about pattern variations can be formalized and beneficially incorporated into the analysis system. The corresponding notion of invariance is commonly used in conceptionally different ways. We propose a more distinguishing treatment in particular in the active field of kernel methods for machine learning and pattern analysis. Additionally, the fundamental relation of invariant kernels and traditional invariant pattern analysis by means of invariant representations will be clarified. After addressing these conceptional questions, we focus on practical aspects and present two generic approaches for constructing invariant kernels. The first approach is based on a technique called invariant integration. The second approach builds on invariant distances. In principle, our approaches support general transformations in particular covering discrete and non-group or even an infinite number of pattern-transformations. Additionally, both enable a smooth interpolation between invariant and non-invariant pattern analysis, i.e. they are a covering general framework. The wide applicability and various possible benefits of invariant kernels are demonstrated in different kernel methods. Editor: Phil Long.     

Biometrics from brain electrical activity: a machine learning approach

The potential of brain electrical activity generated as a response to a visual stimulus is examined in the context of the identification of individuals. Specifically, a framework for the visual evoked potential (VEP)-based biometrics is established, whereby energy features of the gamma band within VEP signals were of particular interest. A rigorous analysis is conducted which unifies and extends results from our previous studies, in particular, with respect to 1) increased bandwidth, 2) spatial averaging, 3) more robust power spectrum features, and 4) improved classification accuracy. Simulation results on a large group of subject support the analysis     

基于特征和弹性网格模型的快速人脸建模

为解决特定人脸的建模问题提供了一个简单而行之有效的方法。给定特定人脸的正面侧面照片,以及内嵌具有人脸特征信息的弹性人脸网格模型,采用基于小波分析的方法进行人脸特征识别,基于特定人脸的特征线相对于一般人脸模型上的特征线的位移,根据弹性系数求解所有点的位移变化,适配特定人脸几何。纹理映射之后生成能以任意视线方向观察的高度真实感特定人脸。该方法能在廉价的PC平台上快速而方便地得到实现。     

State and input observability for structured linear systems: A graph-theoretic approach

This paper deals with the state and input observability analysis for structured linear systems with unknown inputs. The proposed method is based on a graph-theoretic approach and assumes only the knowledge of the system's structure. Using a particular decomposition of the systems into two subsystems, we express, in simple graphic terms, necessary and sufficient conditions for the generic state and input observability. These conditions are easy to check because they are based on comparison of integers and on finding particular subgraphs in a digraph. Therefore, our approach is suited to study large-scale systems.     

Simple algorithm page layout analysis

An algorithm for page layout analysis (segmentation) is suggested in the paper. It allows whitespace between text blocks to be detected on a document page. The algorithm could be used in document analysis and recognition problems. In particular, it can be used for column recognition in multicolumn text and tables. The suggested algorithm is quite simple for implementation.     

An Agent-Based Hybrid System for Microarray Data Analysis

This article reports our experience in agent-based hybrid construction for microarray data analysis. The contributions are twofold: We demonstrate that agent-based approaches are suitable for building hybrid systems in general, and that a genetic ensemble system is appropriate for microarray data analysis in particular. Created using an agent-based framework, this genetic ensemble system for microarray data analysis excels in both sample classification accuracy and gene selection reproducibility.