Efficient virtual machine support of runtime structural reflection

Increasing trends towards adaptive, distributed, generative and pervasive software have made object-oriented dynamically typed languages become increasingly popular. These languages offer dynamic software evolution by means of reflection, facilitating the development of dynamic systems. Unfortunately, this dynamism commonly imposes a runtime performance penalty. In this paper, we describe how to extend a production JIT-compiler virtual machine to support runtime object-oriented structural reflection offered by many dynamic languages. Our approach improves runtime performance of dynamic languages running on statically typed virtual machines. At the same time, existing statically typed languages are still supported by the virtual machine.We have extended the .Net platform with runtime structural reflection adding prototype-based object-oriented semantics to the statically typed class-based model of .Net, supporting both kinds of programming languages. The assessment of runtime performance and memory consumption has revealed that a direct support of structural reflection in a production JIT-based virtual machine designed for statically typed languages provides a significant performance improvement for dynamically typed languages.     

A database model for object dynamics

To effectively model complex applications in which constantly changing situations can be represented, a database system must be able to support the runtime specification of structural and behavioral nuances for objects on an individual or group basis. This paper introduces the role mechanism as an extension of object-oriented databases to support unanticipated behavioral oscillations for objects that may attain many types and share a single object identity. A role refers to the ability to represent object dynamics by seamlessly integrating idiosyncratic behavior, possibly in response to external events, with pre-existing object behavior specified at instance creation time. In this manner, the same object can simultaneously be an instance of different classes which symbolize the different roles that this object assumes. The role concept and its underlying linguistic scheme simplify the design requirements of complex applications that need to create and manipulate dynamic objects. Edited by D. McLeod / Received March 1994 / Accepted January 1996     

ADO对象模型和用VB实现其应用的两种方式

介绍了ADO对象模型和采用ADO技术访问数据的模型，通过控件绑定和ADO类编程的两个实例，用对比的方法说明在解决简单应用问题和复杂应用问题时对ADO对象模型的使用及其技术的实现。     

基于Snake模型的视频对象分割和跟踪算法

视频对象的分割是基于内容的视频处理中重要的组成部分。提出并实现了一种半自动视频对象分割和跟踪算法。算法主要基于Williams活动轮廓模型，通过求取轮廓点的局部能量最小值对轮廓线进行更新。轮廓扩张技术用来追踪变形的轮廓边缘。通过对轮廓中心点运动的统计，预测对象的运动方向和大小。实验仿真结果表明，这种改进的Snake算法能够收缩到图像的凹陷部分，而且能较好地跟踪视频对象的运动。     

融合多尺度对比与贝叶斯模型的显著目标检测

针对传统显著目标检测方法中目标不能均匀高亮,背景噪声难以抑制的问题,提出了一种融合多尺度对比与贝叶斯模型的显著目标检测方法。将图像分割为一系列紧凑且颜色相同的超像素,并通过K-means算法对所得超像素重聚类得到多尺度分割图;引入背景先验及凸包中心先验计算不同尺度下的显著图,并加权融合成粗略显著图;将粗略显著图二值化得到的区域假定为前景目标,再计算观测似然概率,使用贝叶斯模型进一步抑制图像的背景并凸出显著区域。在公开数据集MSRA-1000上与6种主流算法进行对比,实验表明提出的算法相比其他算法能更均匀地高亮显著目标,有更高的查准率和更低的平均绝对误差。     

A new object model of batch equipment and procedural control for better recipe reuse

Application of the ISA-88 standard in industrial batch-process control often leads to repetition of information in recipes and to a low level of their reuse. This problem stems from the deficiencies of the standard batch-process control object model. A solution to the problem is proposed that is based on a more sophisticated object model of equipment and procedural control, with dynamically defined and potentially overlapping unit classes. The new concept, together with its elements, is described, and its use is illustrated and validated by means of a real batch control project. The validation is carried out as a comparison of the number of master recipes and unit procedures created under the new object model with the number of master recipes and unit procedures needed to perform the same functionality using the standard object model. The comparison demonstrates that the proposed approach has a significant advantage.     

基于马尔可夫随机场与活动轮廓的运动目标分割

针对视频序列图像中的运动目标分割,提出了将马尔可夫随机场模型和活动轮廓模型相结合的运动目标分割算法。该算法首先利用马尔可夫随机场模型的运动检测算法,得到运动目标的初始模板。在此基础上提取出活动轮廓模型的初始轮廓点,然后构造活动轮廓模型的能量函数。用改进的贪婪算法求得能量函数最小值,提取出运动目标的精确轮廓,从而得到具有精确边缘的运动目标。实验结果表明该算法能有效地分割和提取出视频序列中的运动目标。     

自然语言合理句子的生成系统

基于语法规则的使用Chomsky上下文无关文法产生的句子虽合乎语法规则但并不一定合乎语义常理.文中提出一种可以产生合理的自然语言句子的产生系统{TI,N,BI,CI,S,FI,PI},其中,I是指定的自然语言.该系统建立在语义语言理论上,使用Chomsky短语文法.系统的关键技术是引入了语义单元技术、类型参量,类型参量的作用不仅体现在语义单元中而且还体现在句树、事物树和函数集中.语义单元和类型参量共同作用能有效的对产生的句子进行语义约束使得生成的句子合理.     

The factors affecting acceptance and use of interactive whiteboard within the scope of FATIH project: A structural equation model based on the Unified Theory of acceptance and use of technology

Purpose of this research is to explain the factors affecting high school teachers' acceptance and use of interactive whiteboard within the scope of FATIH project according to the Unified theory of acceptance and use of technology (UTAUT, Venkatesh et al., 2003). The study was designed using causal research design in order to examine the cause-effect relationship between the variables. Sample of the study consists of 158 teachers, determined via criteria sampling, who have participated in the study voluntarily. Data has been collected using a 7 points Likert scale, developed for this study, which covers UTAUS's factors and variables featuring interactive whiteboard acceptance and use of the teachers. The collected data has been analyzed with simple and multiple linear regressions and structural equation model. The findings have shown that performance expectancy, effort expectancy and social influence had positive effects on behavioral intention; behavioral intention and facilitating conditions had positive effects on the usage time of interactive whiteboard.     

A data model and algebra for probabilistic complex values

We present a probabilistic data model for complex values. More precisely, we introduce probabilistic complex value relations, which combine the concept of probabilistic relations with the idea of complex values in a uniform framework. We elaborate a model-theoretic definition of probabilistic combination strategies, which has a rigorous foundation on probability theory. We then define an algebra for querying database instances, which comprises the operations of selection, projection, renaming, join, Cartesian product, union, intersection, and difference. We prove that our data model and algebra for probabilistic complex values generalizes the classical relational data model and algebra. Moreover, we show that under certain assumptions, all our algebraic operations are tractable. We finally show that most of the query equivalences of classical relational algebra carry over to our algebra on probabilistic complex value relations. Hence, query optimization techniques for classical relational algebra can easily be applied to optimize queries on probabilistic complex value relations.     

A strategic analysis for successful open source software utilization based on a structural equation model

Commercial software companies face many challenges when competing in today’s fast moving and competitive industry environment. Recently, the use of open source software (OSS) has been proposed as a possible way to address those challenges. OSS provides many benefits, including high-quality software and substantial profits. Nevertheless, OSS has not been effectively utilized in real business. The purpose of this paper is to find what affects the utilization of OSS. For this study, we propose a structural equation model (SEM) to analyze the relationships between the quality factors based on ISO/IEC 9126 and OSS utilization. In addition, we suggest an open source software utilization index (OSSUI) based on the proposed SEM. The results provide us with the controllable feedback information to improve user (programmer) satisfaction during OSS utilization.     

A hybrid approach to integrate genetic algorithm into dual scoring model in enhancing the performance of credit scoring model

Credit scoring model is an important tool for assessing risks in financial industry, consequently the majority of financial institutions actively develops credit scoring model on the credit approval assessment of new customers and the credit risk management of existing customers. Nonetheless, most past researches used the one-dimensional credit scoring model to measure customer risk. In this study, we select important variables by genetic algorithm (GA) to combine the bank’s internal behavioral scoring model with the external credit bureau scoring model to construct the dual scoring model for credit risk management of mortgage accounts. It undergoes more accurate risk judgment and segmentation to further discover the parts which are required to be enhanced in management or control from mortgage portfolio. The results show that the predictive ability of the dual scoring model outperforms both one-dimensional behavioral scoring model and credit bureau scoring model. Moreover, this study proposes credit strategies such as on-lending retaining and collection actions for corresponding customers in order to contribute benefits to the practice of banking credit.     

A hybrid decision support system based on rough set and extreme learning machine for diagnosis of hepatitis disease

Hepatitis is a disease which is seen at all levels of age. Hepatitis disease solely does not have a lethal effect, but the early diagnosis and treatment of hepatitis is crucial as it triggers other diseases. In this study, a new hybrid medical decision support system based on rough set (RS) and extreme learning machine (ELM) has been proposed for the diagnosis of hepatitis disease. RS-ELM consists of two stages. In the first one, redundant features have been removed from the data set through RS approach. In the second one, classification process has been implemented through ELM by using remaining features. Hepatitis data set, taken from UCI machine learning repository has been used to test the proposed hybrid model. A major part of the data set (48.3%) includes missing values. As removal of missing values from the data set leads to data loss, feature selection has been done in the first stage without deleting missing values. In the second stage, the classification process has been performed through ELM after the removal of missing values from sub-featured data sets that were reduced in different dimensions. The results showed that the highest 100.00% classification accuracy has been achieved through RS-ELM and it has been observed that RS-ELM model has been considerably successful compared to the other methods in the literature. Furthermore in this study, the most significant features have been determined for the diagnosis of the hepatitis. It is considered that proposed method is to be useful in similar medical applications.     

Scenario modelling to support industry strategic planning and decision making

The Pastoral Properties Futures Simulator (PPFS) is a dynamic systems model, developed within a participatory action research partnership with the pastoral industry of Australia's Northern Territory. The model was purpose-built to support the industry's strategic planning capacity in the face of environmental, market and institutional uncertainty. The mediated modelling process sought to maximise social learning of industry stakeholders. Simulations were conducted using scenarios representing combinations of climatic, market, institutional and technological assumptions. Stochastic parameters included rainfall and product prices. Economic and environmental performance of model farms, including greenhouse gas emissions, were estimated. A critical evaluation of the tool finds the PPFS fit for purpose. However, limitations include lack of output validation, small number of scenarios and simplistic treatment of environmental impact dimensions. With further development, the PPFS can provide a platform (a) to assist with industry planning across the whole of Northern Australia and beyond, and (b) for policy analysis and development in the context of the Australian pastoral industry.     

A hybrid reality environment and its application to the study of earthquake engineering

Visualization can provide the much needed computer-assisted design and analysis environment to foster problem-based learning, while Virtual Reality (VR) can provide the environment for hands-on manipulation, stimulating interactive learning in engineering and the sciences. In this paper, an interactive 2D and 3D (hybrid) environment is described, which facilitates collaborative learning and research and utilizes techniques in visualization and VR, therefore enhancing the interpretation of physical problems within these fields. The environment described, termed VizClass, incorporates a specially designed lecture room and laboratory integrating both 2-D and 3-D spatial activities by coupling a series of interactive projection display boards (touch-sensitive whiteboards) and a semi-immersive 3D wall display. The environment is particularly appealing for studying critical, complex engineering problems, for example, where time-varying feature modifications and coupling between multiple modes of movement are occurring. This paper describes the hardware architecture designed for this new hybrid environment as well as an initial application within the environment to the study of a real case history building subjected to a variety of earthquakes. The example simulation uses field measured seismic data sources, and illustrations of simple visual paradigms to provide an enhanced understanding of the physical model, the damage accumulated by the model, and the association between the measured and observed data. A detailed evaluation survey was also conducted to determine the merits of the presented environment and the techniques implemented. Results substantiate the plausibility of using these techniques for more general, everyday users. Over 70% of the survey participants believed that the techniques implemented were valuable for engineers.

A specification language and service‐oriented architecture to support distributed data management

In some research analysis organizations, effort is duplicated when disseminating raw information to multiple research groups. Although the various groups work on different problems, many times, the initial raw information used is the same. A solution to this problem is creating a centralized process for storing and disseminating common information using database technologies. In this paper, the requirements that must be met to create this distributed database management architecture are described. To support these requirements, a specification language, Specialized Format Markup Language (SFML), and supporting service‐oriented architecture, Specialized Format Generation (SFG), are introduced. These implementations and technologies support the translation of data from relational database formats into user‐specified legacy formats. In addition, this implementation supports the presentation of the resulting data across a Web‐based, distributed medium. SFML and SFG incorporate current technologies such as the eXtensible Markup Language (XML), Java Servlets, the eXtensible Stylesheet Language (XSL), and other relational database approaches. Copyright © 2004 John Wiley & Sons, Ltd.     

A support vector machine classifier with automatic confidence and its application to gender classification

In this paper, we propose a support vector machine with automatic confidence (SVMAC) for pattern classification. The main contributions of this work to learning machines are twofold. One is that we develop an algorithm for calculating the label confidence value of each training sample. Thus, the label confidence values of all of the training samples can be considered in training support vector machines. The other one is that we propose a method for incorporating the label confidence value of each training sample into learning and derive the corresponding quadratic programming problems. To demonstrate the effectiveness of the proposed SVMACs, a series of experiments are performed on three benchmarking pattern classification problems and a challenging gender classification problem. Experimental results show that the generalization performance of our SVMACs is superior to that of traditional SVMs.     

Internal model based tracking and disturbance rejection for stable well-posed systems

In this paper we solve the tracking and disturbance rejection problem for infinite-dimensional linear systems, with reference and disturbance signals that are finite superpositions of sinusoids. We explore two approaches, both based on the internal model principle. In the first approach, we use a low gain controller, and here our results are a partial extension of results by Hämäläinen and Pohjolainen. In their papers, the plant is required to have an exponentially stable transfer function in the Callier-Desoer algebra, while in this paper we only require the plant to be well-posed and exponentially stable. These conditions are sufficiently unrestrictive to be verifiable for many partial differential equations in more than one space variable. Our second approach concerns the case when the second component of the plant transfer function (from control input to tracking error) is positive. In this case, we identify a very simple stabilizing controller which is again an internal model, but which does not require low gain. We apply our results to two problems involving systems modeled by partial differential equations: the problem of rejecting external noise in a model for structure/acoustics interactions, and a similar problem for two coupled beams.     

A novel hesitant fuzzy linguistic hybrid cloud model and extended best-worst method for multicriteria decision making

Developing effective and accurate model to handle complex uncertainties of linguistic assessments in multicriteria decision making (MCDM) has important theoretical significance and practical value of engineering. This paper proposes a novel hesitant fuzzy linguistic hybrid cloud (HFLHC) model that integrates hesitant fuzzy linguistic term set and cloud model to handle the hesitancy, fuzziness, and randomness of linguistic expression. The normal cloud and trapezium cloud are integrated to represent hybrid-length linguistic variables of HFLHC model, which can effectively avoid evaluation information loss and distortion. Aiming at applying HFLHC model to MCDM, some hybrid operations for normal cloud and trapezium cloud are developed. Moreover, an improved method for aggregating multiple linguistic concepts into an integrated trapezium cloud in HFLHC model is proposed, with consideration of the different representation region of each linguistic concept. Furthermore, a novel HFLHC-based best-worst method is proposed to obtain optimal criteria weights with developing a HFLHC optimization programming model and a modified consistency ratio. Finally, an illustrative example of sustainable supplier selection is presented. Several comparative analyses demonstrate that our method can provide more consistency and greater reliability.