A Compositional Framework for Developing Parallel Programs on Two-Dimensional Arrays

Computations on two-dimensional arrays such as matrices and images are one of the most fundamental and ubiquitous things in computational science and its vast application areas, but development of efficient parallel programs on two-dimensional arrays is known to be hard. In this paper, we propose a compositional framework that supports users, even with little knowledge about parallel machines, to develop both correct and efficient parallel programs on dense two-dimensional arrays systematically. The key feature of our framework is a novel use of the abide-tree representation of two-dimensional arrays. The presentation not only inherits the advantages of tree representations of matrices where recursive blocked algorithms can be defined to achieve better performance, but also supports transformational development of parallel programs and architecture-independent implementation owing to its solid theoretical foundation – the theory of constructive algorithmics.     

基于Matlab的元胞自动机的仿真设计

元胞自动机不仅是理论计算机科学领域的一个重要的计算模型 ,而且可视为一无限维的离散动力系统 ,被广泛应用于自然科学和社会科学各领域的复杂性的研究中 ,是一个重要的研究方法和工具。该文以Matlab为开发工具 ,开发了元胞自动机的可视模型 ,为其在各个领域的复杂性研究提供了一个简易、可行的计算机仿真工具。     

Methods of computational analysis of semantic models for quality assessment of scientific texts

The aim of this work is to construct computer methods for objective quality assessment of scientific documents (science and technical papers, dissertations, reports on R&D and design projects, application documents to hold them, patent documentation, etc.). Unlike computer programs, databases, handbooks of physical constants and other documents written in a specially structured formal language, such documents are unstructured. To objectify procedures for quality assessment of scientific texts written in natural languages, an approach is proposed that leverages computational analysis of semantic models of individual documents and collections of documents.     

视觉认知计算模型综述

视觉认知计算模型作为联系视觉认知和信息计算的有效手段,其研究涉及到认知科学、信息科学等多个交叉学科,具有复杂性和多样性等特点。为能更好地把握其发展规律,文中从视觉计算角度系统总结视觉认知计算模型,以其两个主要来源为主线分别从生物视觉机制和视觉计算理论回顾视觉认知计算模型的发展。根据其研究的特点,对视觉认知计算模型的发展做出一定评述,并指出视觉认知计算模型的发展必将对计算视觉理论和生物视觉机制产生深远影响。     

Inductive Learning with a Computational Network

This paper introduces a computational network which combines heterogeneous rule-extraction algorithms for intelligent data analysis. Combining induction programs may alleviate the possible negative effects of data set representation and individual program's influences, such as inductive bias. The application of the computational network to a diabetes data set shows that, when combining the various programs, an increase in rule set accuracy and comprehensibility are obtained.     

Automated Synthesis of Numerical Programs for Simulation of Rigid Mechanical Systems in Physics-Based Animation

Physics-based animation programs are important in a variety of contexts, including science, engineering, education and entertainment among others. Manual construction of such programs is expensive, time-consuming and prone to error. We have developed a system for automatically synthesizing physics-based animation programs for a significant class of problems: constrained systems of rigid bodies, subject to driving and dissipative forces, under the control of an interactive user. Our system includes a graphical interface for specifying a physical scenario, including objects, geometry and coordinate systems, along with a symbolic interface for specifying dynamical variables, forces and constraints operating in the scenario. The entities defined in the graphical interface serve as the underlying vocabulary for specifications entered in the symbolic interface. Our system partitions the constraints and dynamical variables into classes and assigns each class to be implemented in a different component of a general simulation program scheme. It generates a numerical C⁺⁺ simulation program that drives a real-time animation of the specified scenario. Our system is implemented as a collection of rewrite rules in the Mathematica programming language. Our approach provides some of the benefits of formal deductive program synthesis, while keeping the computational costs of program synthesis more in line with conventional program generator technology. We have successfully tested our system on numerous examples.     

An improved distributed concolic testing approach

Distributed concolic testing (DCT) for complex programs takes a remarkable computational time. Also, the achieved modified condition/decision coverage (MC/DC) for such programs is often inadequate. We propose an improved DCT approach that reduces the computational time and simultaneously enhanced the MC/DC. We have named our approach SMCDCT (scalable MC/DC percentage calculator using DCT). Our experimental study on forty‐five C programs indicates 6.62% of average increase in MC/DC coverage. Copyright © 2016 John Wiley & Sons, Ltd.     

Storytelling by computer

Abstract

In common with designing programs to pass the Turing Test, story generation is an aspect of artificial intelligence that has popular appeal but has never been regarded as a core research activity. But storytelling is an activity that characterises human intelligence, and computational story writing offers new perspectives on human and digital creativity. This paper reviews work at the University of Sussex and elsewhere on story generation. It describes the main approaches, based on story grammars and character plans, and discusses their limitations. Although the results so far are unimpressive, the process of designing such programs is worthwhile. The modest successes indicate facets of creativity that can be described in computational terms. The failures reveal aspects of creative writing, such as tellability, that have previously received little attention, in a form that can be examined and systematically manipulated. The computational techniques developed for story generation could form the basis of learning environments for children to simulate and explore the process of storytelling, and of a new generation of storytelling software agents.     

Floating point arithmetic teaching for computational science

Computational science is based upon numerical computing and, consequently, requires excellent knowledge of floating point computer arithmetic. In general, the average computational science student has a relatively limited understanding of the implications of floating point computation. This paper presents an initiative to teach floating point number representation and arithmetic in undergraduate courses in computational science. The approach is based on carefully designed practical exercises which highlight the main properties and computational issues of finite length number representation and arithmetic. In conjunction to the exercises, an auxiliary educational tool constitutes a valuable support for students to learn and understand the concepts involved. Simpler formats are used as an introduction to the IEEE 754 standard, with the aim of presenting the fundamentals of the floating point computation and emphasizing its limitations. This approach could be included in courses related to computer organization, programming, discrete mathematics, numerical methods or scientific computing in computational science curricula.