Nondestructive RAM Testing by Analyzing the Output Data for Symmetry

For the nondestructive march memory testing based on the symmetry analysis of the read out flows of output data, consideration was given to the conditions for manifestation of the bit-stuck, transition, and coupling faults and to construction of algorithms providing their hundred-percent covering by a minimum-complexity test and its minimum-complexity hardware realization.     

Self-Adaptive Output Tracking with Applications to Active Binocular Tracking

In this article we present a neurally-inspired self-adaptive active binocular tracking scheme and an efficient mathematical model for online computation of desired binocular-head trajectories. The self-adaptive neural network (NN) model is general and can be adopted in output tracking schemes of any partly known robotic systems. The tracking scheme ingeniously combines the conventional Resolved Velocity Control (RVC) technique and an adaptive compensating NN model constructed using SoftMax basis functions as nonlinear activation function. Desired trajectories to the servo controller are computed online by the use of a suitable linear kinematics mathematical model of the system. Online weight tuning algorithm guarantees tracking with small errors and error rates as well as bounded NN weights.     

数据表(.dbf文件)的几种数据输出方法

在日常工作中我们经常要接触各种各样的数据库文件,其中最常见的要数用ＦｏｘＢＡＳＥ＋或ＦｏｘＰｒｏ创建的数据表（．ｄｂｆ文件）了。初学者不难发现数据表中的记录在直接使用ｌｉｓｔ ｔｏ ｐｒｉｎｔ命令打印输出时,字体、行间距偏小,且无法分页,实在是面目可憎。下面介绍两种解决数据输出不够美观大方的办法。 ｌ文件转换法 １．将．ｄｂｆ文件转换为．ｘｌｓ文件。 在Ｅｘｃｅｌ软件中单击"打开"按钮,接着在"打开"对话框中的"文件类型"下拉式选项中选择"ｄＢａｓｅ文件"（或"所有文件"）,然后在"查找范围"框中找到想要输出的…     

The Neutrality of Money Revisited with a Bottom-Up Approach: Decentralisation,Limited Information and Bounded Rationality

In the mainstream top-down approach, money is neutral except with special assumptions. Intending to make money “essential”, random-matching models introduced decentralisation by considering pair-wise transactions. Nevertheless, in both cases top-level equilibrium constrains agents’ behaviour. Instead, here we use a bottom-up approach. In a competitive market, decentralised autonomous agents meet and exchange a commodity for money. Their decisions use minimal information. They are triggered by simple rules founded on a “satisficing” procedure and on a random decision process that simulates bounded rationality. The conclusions are, first, that non-monetary costs are essential to avoid collapse of the economy. Second, mainly “price setters” who are adequately satisfied achieve equilibrium protecting themselves by evolving advantages to avoid competition that is too tough. Third, and the most important conclusion is that money ceases to be neutral as soon as competition arises between individual firms.     

Bounded-Collision Memory-Mapping Schemes for Data Structures with Applications to Parallel Memories

Techniques are developed for mapping structured data to an ensemble of parallel memory modules in a way that limits the number of conflicts, i.e., simultaneous accesses by distinct processors to the same memory module. The techniques determine, for any given conflict tolerance c, the smallest ensemble that allows one to store any n-node data structure "of type X" in such a way that no more than c nodes of a structure are stored on the same module. This goal is achieved by determining the smallest c-perfect universal graphs for data structures "of type X." Such a graph is the smallest graph that contains a homomorphic image of each n-node structure "of type X" with each node of the image holding < c nodes of the structure. In the current paper, "type X" refers to rooted binary trees and three array-like structures: chaotic arrays, ragged arrays, and rectangular arrays. For each of these families of data structures, the number of memory modules needed to achieve conflict tolerance c is determined to within constant factors.     

基于模型的Web应用测试

提出了基于模型的Web应用测试方法,包括建模、测试用例生成、测试用例的执行、模型以及测试用例的可视化等关键技术.设计并实现一个基于模型的Web应用测试系统.以FSM作为被测Web应用的形式测试模型,集成了模型转换器、测试目标分析器、测试序列生成器、FSM和测试序列可视化以及Web应用测试执行引擎等工具.除支持状态覆盖、...     

Specification-based Testing for Gui-based Applications

The development of GUI-based applications has raised a lot of new issues, one of them being how to automate effective testing for applications with complicated graphical user interactions. In this paper, we discuss the architectural issues and the implementation concerns of our approach to an automated specification-based testing technique for GUI-based applications. This approach is carried out by enriching existing architecture for automated specification-based testing. An essential part of our work is a visual environment to obtain test specifications. This environment pre-runs the Application Under Test (AUT) under its own control, with two prominent characteristics: First, testers can edit test specifications within the true GUI environment of the AUT. Second, the recorded input and output contain the same references as those in the AUT, so that the test cases generated from the edited specification can be used directly by test oracles during the automated testing procedure.We present our running prototype of a visual specification editor that allows users to graphically manipulate test specifications when these specifications are given in term of Finite State Machines (FSM) and the implementations of the AUT are GUI-based Java applications.     

模型驱动的移动应用测试方法

移动互联网时代,移动智能终端和移动应用已经成为各领域竞相采用的祖尧解决方案。与此同时,移动终端硬件和平台的多样性和异构性,造成了移动应用开发和测试中存在大量工作冗余以及难以复用的情况,带来了跨平台问题,成为了学术界和工业界研究和实践的热点。文中提出使用模型驱动的移动应用测试方法。首先,使用UML状态机模型刻画移动应用的行为;然后,基于移动应用的行为模型,自动生成平台无关的测试用例;最后,将与平台无关的测试用例映射到多个移动平台,产生可执行的自动化测试用例。在此基础上,选择形如掌上电力的应用作为案例,分别实现IOS和Android平台上的自动化测试,验证了模型驱动移动应用测试方法在解决跨平台问题时的有效性。     

数据融合关键技术在靶场实验系统中的应用

通过比较数据融合各算法的优缺点,选择了适用于本系统的数据算法以及应用于一般非线性系统的推广Kalman滤波算法,并分别进行了详尽的描述;结合靶场实验系统的环境以及对于数据融合的要求,基于所选用的数据融合关键技术,提出了解决靶场数据融合问题的可靠性方案;最后通过对靶场数据的计算机仿真充分说明了这种方案的有效性、可行性.     

面向季节性时空数据的预测式循环网络及其在城市计算中的应用

实际生活中有很多带有季节特征的时空数据,在城市计算领域分布尤广,例如交通流量数据便具有较为明显的以天或周为周期的统计学特征.如何有效利用这种季节特征,如何捕捉历史观测与待预测数据之间的相关性,成为了预测此类时空数据未来变化趋势的关键.传统时序建模方法将时序数据分解为多个信号分量,并使用线性模型来进行预测.此类方法具有较强的理论基础,但对于数据的平稳性要求过于严格,难以预测趋势信息复杂的数据,更不适用于高维的时空数据.然而在真实场景下,季节性时空数据的周期长短可变,且不同周期的对应关系往往并不固定,存在时间、空间上的模式变化与偏移,很难作为理想的周期信号以传统时序方法建模.相比之下,深度神经网络建模能力更强,可拟合更为复杂的数据.近几年有许多工作研究了如何利用卷积神经网络和循环神经网络来处理时空数据,也有一些工作讨论了如何有效利用周期性信息提升预测的准确性.但深度神经网络受困于梯度消失和误差累积,难以捕捉时序数据中的长时间依赖,且少有方法讨论如何在深度神经网络中有效建模上述具有弹性周期对应关系的时空信号.本文针对真实场景下季节性时空数据的上述问题,给出具有弹性周期对应关系的时空数据预测问题的形式化定义,并提出了一种新的季节性时空数据预测模型.该模型包含季节网络、趋势网络、时空注意力模块三个部分,可捕捉短期数据中的临近变化趋势和长期数据中隐含的季节性趋势,并广泛考虑历史周期中的每个时空元素对未来预测值的影响.为了解决深度循环网络难以捕捉时序数据中的长时间依赖的问题,本文提出一种新的循环卷积记忆单元,该单元将上述模块融合于一个可端到端训练的神经网络中,一方面实现了时间和空间信息统一建模,另一方面实现了短期趋势特征与历史周期特征的统一建模.进一步地,为了解决季节性数据中的各周期时空元素对应关系不固定的问题,本文探讨了多种基于注意力模块的时空数据融合方式,创新性地提出一种级联式的时空注意力模块,并将其嵌入于上述循环卷积记忆单元内.该模块建模记忆单元的隐藏状态在不同周期内的弹性时空对应关系,自适应地选取相关度高的季节性特征辅助预测.实验部分,我们选取了两个时空数据预测在城市计算中最为典型的应用:交通流量预测和气象数据预报.本文所提出的时空周期性循环神经网络在北京、纽约的交通流量数据集、美国气象数据集上均取得了目前最高的预测准确性.     

Robust Distance-Based Clustering with Applications to Spatial Data Mining

In this paper we present a method for clustering geo-referenced data suitable for applications in spatial data mining, based on the medoid method. The medoid method is related to k -MEANS, with the restriction that cluster representatives be chosen from among the data elements. Although the medoid method in general produces clusters of high quality, especially in the presence of noise, it is often criticized for the Ω(n ² ) time that it requires. Our method incorporates both proximity and density information to achieve high-quality clusters in subquadratic time; it does not require that the user specify the number of clusters in advance. The time bound is achieved by means of a fast approximation to the medoid objective function, using Delaunay triangulations to store proximity information. Received December 21, 1998; revised August 25, 1999, and October 25, 1999.     

Web应用测试技术进展

Web应用的特殊性使得其测试具有挑战性,但目前针对其测试的研究同其设计开发的研究相比相对薄弱.从Web应用的体系结构、实现技术、组成成分、运行机制、运行环境、开发设计等方面分析了影响测试的因素.归纳了现有Web应用设计阶段、开发阶段、运行阶段、维护阶段所涉及的主要测试方面及使用的动态、静态测试技术.并从测试的几个关键方面,如链接测试、组件黑盒测试、组件白盒测试、WS及WSC测试及性能测试等方面综述当前研究热点难点,包括面向对象的建模、统计建模、控制流测试、数据流测试、数据组合测试、WS测试、WS组测试、WSC测试、协议测试、性能测试的负载模型与用户会话数据集的优化等问题.指出了有关研究的局限性.从模型驱动测试、基于Agent测试、WS及SOA测试、性能测试等方面探讨了Web应用测试未来的研究方向.     

Applications of Symbolic Execution to Program Testing

The advanced method of symbolic evaluation can be applied to program testing situations with results close to those of formal correctness proofs–but without the high cost.     

An Approach to the Modeling of Software Testing with Some Applications

In this paper, an approach to the modeling of software testing is described. A major aim of this approach is to allow the assessment of the effects of different testing (and debugging) strategies in different situations. It is shown how the techniques developed can be used to estimate, prior to the commencement of testing, the optimum allocation of test effort for software which is to be nonuniformly executed in its operational phase. In addition, the question of application of statistical models in cases where the data environment undergoes changes is discussed. Finally, two models are presented for the assessment of the effects of imperfections in the debugging process.     

Data Generation for Path Testing

We present two stochastic search algorithms for generating test cases that execute specified paths in a program. The two algorithms are: a simulated annealing algorithm (SA), and a genetic algorithm (GA). These algorithms are based on an optimization formulation of the path testing problem which include both integer- and real-value test cases. We empirically compare the SA and GA algorithms with each other and with a hill-climbing algorithm, Korel's algorithm (KA), for integer-value-input subject programs and compare SA and GA with each other on real-value subject programs. Our empirical work uses several subject programs with a number of paths. The results show that: (a) SA and GA are superior to KA in the number of executed paths, (b) SA tends to perform slightly better than GA in terms of the number of executed paths, and (c) GA is faster than SA; however, KA, when it succeeds in finding the solution, is the fastest.     

An Approach to Automatic Performance Prediction for Cloud-Enhanced Mobile Applications with Sparse Data

In mobile cloud computing (MCC), offloading compute-intensive parts of a mobile application onto the cloud is an attractive method to enhance application performance. To make good offloading decisions, history-based machinelearning techniques are proposed to predict application performance under various offloading schemes. However, the data sparsity problem is common in a realistic MCC scenario but is rarely the concern of existing work. In this paper, we employ a two-phase hybrid framework to predict performance for cloud-enhanced mobile applications, which is designed to be robust to the data sparsity. By training several multi-layer neural networks with historical execution records, the first phase automatically predicts some intermediate parameters for each execution of an application. The models learned by these neural networks can be shared among different applications, thus alleviating the data sparsity. Based on these predicted intermediate parameters and the application topology, the second phase deterministically calculates the estimated values of the performance metrics. The deterministic algorithm can partially guarantee the prediction accuracy of newly published applications even with no execution records. We evaluate our approach with a cloud-enhanced object recognition application and show that our approach can precisely predict the application performance and is robust to data sparsity.     

财务数据仓库和数据挖掘技术应用简介

本文以研究数据仓库和数据挖掘技术为基础,通过对财务数据仓库与数据挖掘技术特点的分析,提出了企业财务数据仓库,综合利用现有业务信息,提供了决策系统支持框架。通过数据仓库技术进行数据的集成,运用数据挖掘技术实现信息价值的提升,使得企业财务管理得到深度应用。     

大数据在商业银行反洗钱的应用

随着电子商务和移动互联网的发展,数据流量的持续增长和以双十一为代表的多种数据洪峰的出现,给商业银行传统的反洗钱手段带来了巨大的压力。海量交易数据下隐藏着各种洗钱行为,传统的反洗钱方式在应对持续增长的数据时越来越捉襟见肘。当前,大数据技术的发展为海量数据数据的收集、存储、处理等提供了技术支撑。本文分析了商业银行的反洗钱业务需求,从业务的角度对比研究当前大数据领域众多新技术,提出了一套实用、可扩展的反洗钱处理架构,并且提出了的大数据反洗钱的演进方向。     

输入输出均为时变函数的过程神经网络及应用

为了解决实际系统中输入、输出经常是时变连续函数的问题,提出了一类基于基函数展开的过程神经元网络模型.该模型利用过程神经元网络所具有的对时间变量的非线性映射能力,实现系统的输入、输出之间的连续映射关系.另外,还给出了一种学习算法.为了简化计算,选择正交函数作为基函数,并以油藏开发仿真为例,验证了模型和算法的有效性.