Performance analysis of security aspects by weaving scenarios extracted from UML models

Aspect-oriented modeling (AOM) allows software designers to describe features that address pervasive concerns separately as aspects, and to systematically incorporate the features into a design model using model composition techniques. The goal of this paper is to analyze the performance effects of different security features that may be represented as aspect models. This is part of a larger research effort to integrate methodologies and tools for the analysis of security and performance properties early in the software development process. In this paper, we describe an extension to the AOM approach that provides support for performance analysis. We use the performance analysis techniques developed previously in the PUMA project, which take as input UML models annotated with the standard UML Profile for Schedulability, Performance and Time (SPT), and transform them first into Core Scenario Model (CSM), and then into different performance models. The composition of the aspects with the primary (base) model is performed at the CSM level. A new formal definition of CSM properties and operations is described as a foundation for scenario-based weaving. The proposed approach is illustrated with an example that utilizes two standards, TPC-W and SSL.     

An Optimizing Compiler for Parallel Chemistry Simulations

Well designed domain specific languages have three important benefits: (1) the easy expression of problems, (2) the application of domain specific optimizations (including parallelization), and (3) dramatic improvements in productivity for their users. In this paper we describe a compiler and parallel runtime system for modeling the complex kinetics of rubber vulcanization and olefin polymerization that achieves all of these goals. The compiler allows the development of a system of ordinary differential equations describing a complex vulcanization reaction or single-site olefin polymerization reaction—a task that used to require months—to be done in hours. A specialized common sub-expression elimination and other algebraic optimizations sufficiently simplify the complex machine generated code to allow it to be compiled—eliminating all but 8.0% of the operations in our largest program and enabling over 60 times faster execution on our largest benchmark codes. The parallel runtime and dynamic load balancing scheme enables fast simulations of the model.     

A modified particle swarm optimization via particle visual modeling analysis

A particle is treated as a whole individual in all researches on particle swarm optimization (PSO) currently, these are not concerned with the information of every particle’s dimensional vector. A visual modeling method describing particle’s dimensional vector behavior is presented in this paper. Based on the analysis of visual modeling, the reason for premature convergence and diversity loss in PSO is explained, and a new modified algorithm is proposed to ensure the rational flight of every particle’s dimensional component. Meanwhile, two parameters of particle-distribution-degree and particle-dimension-distance are introduced into the proposed algorithm in order to avoid premature convergence. Simulation results of the new PSO algorithm show that it has a better ability of finding the global optimum, and still keeps a rapid convergence as with the standard PSO.     

Empirically building and evaluating a probabilistic model of user affect

We present a probabilistic model of user affect designed to allow an intelligent agent to recognise multiple user emotions during the interaction with an educational computer game. Our model is based on a probabilistic framework that deals with the high level of uncertainty involved in recognizing a variety of user emotions by combining in a Dynamic Bayesian Network information on both the causes and effects of emotional reactions. The part of the framework that reasons from causes to emotions (diagnostic model) implements a theoretical model of affect, the OCC model, which accounts for how emotions are caused by one’s appraisal of the current context in terms of one’s goals and preferences. The advantage of using the OCC model is that it provides an affective agent with explicit information not only on which emotions a user feels but also why, thus increasing the agent’s capability to effectively respond to the users’ emotions. The challenge is that building the model requires having mechanisms to assess user goals and how the environment fits them, a form of plan recognition. In this paper, we illustrate how we built the predictive part of the affective model by combining general theories with empirical studies to adapt the theories to our target application domain. We then present results on the model’s accuracy, showing that the model achieves good accuracy on several of the target emotions. We also discuss the model’s limitations, to open the ground for the next stage of the work, i.e., complementing the model with diagnostic information.

Generating Segmented Quality Meshes from Images

Techniques devoted to generating triangular meshes from intensity images either take as input a segmented image or generate a mesh without distinguishing individual structures contained in the image. These facts may cause difficulties in using such techniques in some applications, such as numerical simulations. In this work we reformulate a previously developed technique for mesh generation from intensity images called Imesh. This reformulation makes Imesh more versatile due to an unified framework that allows an easy change of refinement metric, rendering it effective for constructing meshes for applications with varied requirements, such as numerical simulation and image modeling. Furthermore, a deeper study about the point insertion problem and the development of geometrical criterion for segmentation is also reported in this paper. Meshes with theoretical guarantee of quality can also be obtained for each individual image structure as a post-processing step, a characteristic not usually found in other methods. The tests demonstrate the flexibility and the effectiveness of the approach.

An integrative approach to requirements analysis: How task models support requirements reuse in a user-centric design framework

Many software systems fail to address their intended purpose because of a lack of user involvement and requirements deficiencies. This paper discusses the elaboration of a requirements-analysis process that integrates a critical-parameter-based approach to task modeling within a user-centric design framework. On one hand, adapting task models to capture requirements bridges the gap between scenarios and critical parameters which benefits design from the standpoint of user involvement and accurate requirements. On the other hand, using task models as a reusable component leverages requirements reuse which benefits design by increasing quality while simultaneously reducing development costs and time-to-market. First, we present the establishment of both a user-centric and reuse-centric requirements process along with its implementation within an integrated design tool suite. Secondly, we report the design, procedures, and findings of two user studies aimed at assessing the feasibility for novice designers to conduct the process as well as evaluating the resulting benefits upon requirements-analysis deliverables, requirements quality, and requirements reuse.     

A fuzzy modeling method via Enhanced Objective Cluster Analysis for designing TSK model

This paper proposes a fuzzy modeling method via Enhanced Objective Cluster Analysis to obtain the compact and robust approximate TSK fuzzy model. In our approach, the Objective Cluster Analysis algorithm is introduced. In order to obtain more compact and more robust fuzzy rule prototypes, this algorithm is enhanced by introducing the Relative Dissimilarity Measure and the new consistency criterion to represent the similarity degree between the clusters. By these additional criteria, the redundant clusters caused by iterations are avoided; the subjective influence from human judgment for clustering is weakened. Moreover the clustering results including the number of clusters and the cluster centers are considered as the initial condition of the premise parameters identification. Thus the traditional iteration modeling procedure for determining the number of rules and identifying parameters is changed into one-off modeling, which significantly reduces the burden of computation. Furthermore the decomposition errors and the approximation errors resulted from premise parameters identification by Fuzzy c-Means clustering are decreased. For the consequence parameters identification, the Stable Kalman Filter algorithm is adopted. The performance of the proposed modeling method is evaluated by the example of Box–Jenkins gas furnace. The simulation results demonstrate the power of our model.     

基于OWL的软件工程数据建模

网络本体语言(Web ontology language,OWL)是语义网技术的一个重要组成部分,适合于对复杂的数据进行语义描述和建模.在软件系统的开发过程中通常会产生大量结构复杂、语义丰富的数据,而建立一个灵活的语义模型是对各类软件工程数据进行统一管理的基础.从设计和实现海量软件工程数据管理平台的需求出发,提出了一种基于OWL的软件工程数据描述模型.该模型不仅能够对源代码、需求、测试、版本和缺陷数据进行描述,同时还能对这些数据之问的语义关联进行描述.通过案例分析对模型的有效性进行了讨论.     

复杂适应系统的多层级建模研究

针对复杂系统仿真普遍缺乏层级思想,局限于广度模拟的情况,对复杂适应系统（CAS）层级建模进行研究,协同进化理论,从主体平均适应度的角度,分析中间子系统的生成、过渡和演化,得到层级结构产生的内在动因,提出层级CAS演化的数学模型和层级框架。该模型应用于多人重复囚徒博弈,结果证明其能在高N值时形成稳定高合作率。     

基于超螺旋模型的头发动态模拟方法

针对现有头发模型缺乏柔性的问题,提出一种改进的基于超螺旋模型的头发建模与仿真方法,将导引发丝构造成具有有限自由度的分段螺旋体,运用拉格朗日力学对发丝运动进行计算。通过改进卷发曲率和捻度的计算方法,并采用自适应控制结构对头发进行重组和细分,提高模拟过程的准确性和稳定性。实验结果证明,该方法可以获得较为逼真的动态模拟头发的效果。