The SASSYS LMFBR systems analysis code

The main purpose of the SASSYS code is to analyze the consequences of failures in the shutdown heat removal system of a liquid metal cooled fast breeder reactor. The code is also capable of analyzing a wide range of transients, from mild operational transients through severe hypothetical core disruptive accidents. Some important features of the code include a detailed multi-channel core thermal hydraulics treatment with transient flow redistribution between assemblies, a model for boiling of sodium in the fuel assemblies, a general thermal hydraulics treatment for an arbitrary arrangement of components in the primary and intermidiate heat transport loops, and a fast-running steam generator model. The numerical methods mainly use semi-implicit or fully implicit time differencing to provide large time step sizes and rapid calculations.     

Flocking shape analysis of multi-agent systems

In this paper,we consider the shape control in flocking behavior of a multi-agent system with a virtual leader.Besides the traditional flocking control terms,which include a gradient-based term,a velocity consensus term and a navigational feed-back in general,a new piecewise smooth neighbor-based local controller is added to regulate the configuration to the desired flocking shape.All agent velocities approach the desired velocity asymptotically,while collisions among agents can be avoided.Furthermore,based...     

Practical code inspection techniques for object-oriented systems: an experimental comparison

Although inspection is established as an effective mechanism for detecting defects in procedural systems, object-oriented systems have different structural and execution models. This article describes the development and empirical investigation of three different techniques for reading OO code during inspection.     

Facial feature localization based on an improved active shape model

The original active shape model (ASM) has already been applied to the areas such as image segmentation, feature points localization, and contour extraction. However, the original ASM suffers from the loss of accuracy and low speed in real time applications. Due to this, a new scheme of active shape model for facial feature extraction is proposed in this paper. In this scheme, the improvement of the performance of the original ASM concerns the following three aspects. Firstly, the profile of the original ASM is extended from 1D to 2D. Secondly, each profile related to different features are constructed separately. Thirdly, the length of the profile varies with different levels. The simulations are carried out using the SJTU dataset, which contains 2273 face images. Experimental results demonstrate that the proposed scheme exhibits better performance than the original ASM.     

Early failure prediction in feature request management systems: an extended study

Online feature request management systems are popular tools for gathering stakeholders?? change requests during system evolution. Deciding which feature requests require attention and how much upfront analysis to perform on them is an important problem in this context: too little upfront analysis may result in inadequate functionalities being developed, costly changes, and wasted development effort; too much upfront analysis is a waste of time and resources. Early predictions about which feature requests are most likely to fail due to insufficient or inadequate upfront analysis could facilitate such decisions. Our objective is to study whether it is possible to make such predictions automatically from the characteristics of the online discussions on feature requests. This paper presents a study of feature request failures in seven large projects, an automated tool-implemented framework for constructing failure prediction models, and a comparison of the performance of the different prediction techniques for these projects. The comparison relies on a cost-benefit model for assessing the value of additional upfront analysis. In this model, the value of additional upfront analysis depends on its probability of success in preventing failures and on the relative cost of the failures it prevents compared to its own cost. We show that for reasonable estimations of these two parameters, automated prediction models provide more value than a set of baselines for many failure types and projects. This suggests automated failure prediction during requirements elicitation to be a promising approach for guiding requirements engineering efforts in online settings.     

Prominent feature extraction for review analysis: an empirical study

Sentiment analysis (SA) research has increased tremendously in recent times. SA aims to determine the sentiment orientation of a given text into positive or negative polarity. Motivation for SA research is the need for the industry to know the opinion of the users about their product from online portals, blogs, discussion boards and reviews and so on. Efficient features need to be extracted for machine-learning algorithm for better sentiment classification. In this paper, initially various features are extracted such as unigrams, bi-grams and dependency features from the text. In addition, new bi-tagged features are also extracted that conform to predefined part-of-speech patterns. Furthermore, various composite features are created using these features. Information gain (IG) and minimum redundancy maximum relevancy (mRMR) feature selection methods are used to eliminate the noisy and irrelevant features from the feature vector. Finally, machine-learning algorithms are used for classifying the review document into positive or negative class. Effects of different categories of features are investigated on four standard data-sets, namely, movie review and product (book, DVD and electronics) review data-sets. Experimental results show that composite features created from prominent features of unigram and bi-tagged features perform better than other features for sentiment classification. mRMR is a better feature selection method as compared with IG for sentiment classification. Boolean Multinomial Naïve Bayes) algorithm performs better than support vector machine classifier for SA in terms of accuracy and execution time.     

Information discriminant analysis: feature extraction with an information-theoretic objective

Using elementary information-theoretic tools, we develop a novel technique for linear transformation from the space of observations into a low-dimensional (feature) subspace for the purpose of classification. The technique is based on a numerical optimization of an information-theoretic objective function, which can be computed analytically. The advantages of the proposed method over several other techniques are discussed and the conditions under which the method reduces to linear discriminant analysis are given. We show that the novel objective function enjoys many of the properties of the mutual information and the Bayes error and we give sufficient conditions for the method to be Bayes-optimal. Since the objective function is maximized numerically, we show how the calculations can be accelerated to yield feasible solutions. The performance of the method compares favorably to other linear discriminant-based feature extraction methods on a number of simulated and real-world data sets.     

Optimal shape design of an electrostatic comb drive inmicroelectromechanical systems

Polynomial driving-force comb drives are synthesized using numerical simulation. The electrode shapes are obtained using the indirect boundary element method. Variable-gap comb drives that produce combinations of linear, quadratic, and cubic driving-force profiles are synthesized. This inverse problem is solved by an optimization procedure. Sensitivity analysis is carried out by the direct differentiation approach (DDA) in order to compute design sensitivity coefficients (DSCs) of force profiles with respect to parameters that define the shapes of the fingers of a comb drive. The DSCs are then used to drive iterative optimization procedures. Designs of variable-gap comb drives with linear, quadratic, and cubic driving force profiles are presented in this paper     

基于数字图像处理的油菜种子形状特征参数提取及模糊聚类分析

基于数字图像处理与分析技术,利用采集的油菜种子图像提取7个种子形状特征参数,选用圆形度和短长轴比作为油菜种子分类参数,应用模糊C-均值聚类分析方法把全体样本分为3类并得到油菜种子形状量化参数,在此基础上对样本油菜种子形状进行量的规定．进一步讨论基于种子形状的油菜种子分级,为油菜种子分类与鉴定提供可靠依据．     

ANN modeling of DNA sequences: new strategies using DNA shape code

Two new encoding strategies, namely, wedge and twist codes, which are based on the DNA helical parameters, are introduced to represent DNA sequences in artificial neural network (ANN)-based modeling of biological systems. The performance of the new coding strategies has been evaluated by conducting three case studies involving mapping (modeling) and classification applications of ANNs. The proposed coding schemes have been compared rigorously and shown to outperform the existing coding strategies especially in situations wherein limited data are available for building the ANN models.     

The feasibility study in information systems: an analysis of criteria and contents

The feasibility study has been prescribed and described as an important step in information system development. Yet there has been little research on issues pertaining to its contents and criteria. This paper addresses these issues and reports research from a quasi-experimental investigation using actual system development projects. The empirical results show that the two most important factors in a feasibility study are: convincing the users that the system they get will actually meet their functional requirements, and also that the system will work effectively within the organizational environment. A surprising result was the low importance attached by respondents to meeting the needs of the clerical personnel providing inputs to and handling the outputs from the system. Also, economic factors, which have been the focus of attention for many researchers, were ranked in the middle of all factors considered.     

检测迷惑恶意代码的层次化特征选择方法

各种迷惑恶意代码能够轻易躲避传统静态检测,而动态检测方式虽有较好的检测率,却消耗大量系统资源。为提高低系统开销下迷惑恶意代码的检测率,提出一种层次化特征选择方法,依次在引导层、个体层、家族层和全局层上生成并选择特征。层次方法以逐层精化特征的方式寻求特征冗余和信息漏选之间的平衡。实际数据集上的实验结果表明所提方法的迷惑恶意代码检测率较高,与传统特征选择方法相比,具有所需训练样本集小、泛化能力强的优点。     

QRev: migrating quantum code towards hybrid information systems

Software Quality Journal - Quantum computing is now a reality, and its incomparable computational power has led companies to show a great interest in being able to work with quantum software in...     

Robustness analysis of nonlinear feedback systems: an input-outputapproach

This paper presents an approach to robustness analysis for nonlinear feedback systems. We pursue a notion of model uncertainty based on the closeness of input-output trajectories which is not tied to a particular uncertainty representation, such as additive, parametric, structured, etc. The basic viewpoint is to regard systems as operators on signal spaces. We present two versions of a global theory where stability is captured by induced norms or by gain functions. We also develop local approaches (over bounded signal sets) and give a treatment for systems with potential for finite-time escape. We compute the relevant stability margin for several examples and demonstrate robustness of stability for some specific perturbations, e.g., small-time delays. We also present examples of nonlinear control systems which have zero robustness margin and are destabilized by arbitrarily small gap perturbations. The paper considers the case where uncertainty is present in the controller as well as the plant and the generalization of the approach to the case where uncertainty occurs in several subsystems in an arbitrary interconnection     

Design of efficient shape feature for object-based watermarking technology

Multimedia Tools and Applications - The accelerated development of IT technologies on the Internet for fast provision of commercial multimedia services has stimulated an urgent demand for reliable...     

The consideration of organizational issues during the systems development process: an empirical analysis

The lack of consideration of organizational issues in systems development can lead to project failure. A review of the literature and a pre-test survey suggested classifying organizational issues into five categories and examining how these are considered by IT managers. A postal survey with responses from 64 senior IT specialists over a cross section of industry and commerce showed that there is a general awareness of the importance of organizational issues but there was little consensus on how they should be addressed in the development process. These IT managers were consistent in spending most effort on the issues perceived as most important from the list of 14 issue provided, but there was considerable variation in which specific issues they rated most important. In general those organizational issues with a 'technical' aspect were given more prominence than those which are less tangible, but which may be more critical to a system's success.     

Collecting unused processing capacity: an analysis of transientdistributed systems

It is suggested that if the large numbers of idle computers and workstations in distributed systems could be used then considerable computing power could be harnessed at low cost. Such systems are analyzed using Brownian motion with drift to model the execution of a program distributed over the idle computers in a network of idle and busy processors. The ways in which the use of these transient processors affects a program's execution time is determined. The probability density of a program's finishing time on both single and multiple transient processors is found. These results are explored for qualitative insight. Some approximations for the finishing time probability density are suggested     

Evaluating shape correspondence for statistical shape analysis: a benchmark study

This paper introduces a new benchmark study to evaluate the performance of landmark-based shape correspondence used for statistical shape analysis. Different from previous shape-correspondence evaluation methods, the proposed benchmark first generates a large set of synthetic shape instances by randomly sampling a given statistical shape model that defines a ground-truth shape space. We then run a test shape-correspondence algorithm on these synthetic shape instances to identify a set of corresponded landmarks. According to the identified corresponded landmarks, we construct a new statistical shape model, which defines a new shape space. We finally compare this new shape space against the ground-truth shape space to determine the performance of the test shape-correspondence algorithm. In this paper, we introduce three new performance measures that are landmark independent to quantify the difference between the ground-truth and the newly derived shape spaces. By introducing a ground-truth shape space that is defined by a statistical shape model and three new landmark-independent performance measures, we believe the proposed benchmark allows for a more objective evaluation of shape correspondence than previous methods. In this paper, we focus on developing the proposed benchmark for $2$D shape correspondence. However it can be easily extended to $3$D cases.