基于VMware的计算机实验教学设计

本文指出了目前开展计算机实验教学存在的局限性,介绍了虚拟机技术的原理以及VMware软件,设计与实现了基于虚拟机技术的计算机组装和维护、操作系统、网络组建与维护、服务器配置与管理以及网络安全等多个实验,并对基于虚拟机技术的计算机实验作了评价。     

Discovery of Empirical Theories Based on the Measurement Theory

The purpose of this work is to analyse the cognitive process of the domain theories in terms of the measurement theory to develop a computational machine learning approach for implementing it. As a result, the relational data mining approach, the authors proposed in the preceding books, was improved. We present the approach as an implementation of the cognitive process as the measurement theory perceived. We analyse the cognitive process in the first part of the paper and present the theory and method of the logically most powerful empirical theory discovery in the second. The theory is based on the notion of law-like rules, which conform to all the properties of laws of nature, namely generality, simplicity, maximum refutability and minimum number of parameters. This notion is defined for deterministic and probabilistic cases. Based on the method, the discovery system is developed. The system was successfully applied to many practical tasks.     

学习层级说在教学设计中的应用

本文以笔者多年教学经验为基础,结合美国当代教育心理学家加涅的教学理论和学习层级说,探讨该教学理论在教学设计中的具体应用。     

Applying Learning by Examples for Digital Design Automation

This paper describes a new learning by example mechanism and its application for digital circuit design automation. This mechanism uses finite state machines to represent the inferred models or designs. The resultant models are easy to be implemented in hardware using current VLSI technologies. Our simulation results show that it is often possible to infer a well-defined deterministic model or design from just one sequence of examples. In addition this mechanism is able to handle sequential task involving long-term dependence. This new learning by example mechanism is used as a design by example system for automatic synthesis of digital circuits. Such systems have not previously been successfully developed mainly because of the lack of mechanism to implement them. From artificial neural network research, it seems possible to apply the knowledge gained from learning by example to form a design by example system. However, one of the problems with neural network approaches is that the resultant models are very difficult to be implemented in hardware using current VLSI technologies. By using the mechanism described in this paper, the resultant models are finite state machines that are well suited for digital designs. Several sequential circuit design examples are simulated and tested. Although our test results show that such a system is feasible for designing simple circuits or small-scale circuit modules, the feasibility of such a system for large-scale circuit design remains to be showed. Both the learning mechanism and the design method show potential and the future research directions are provided.     

Triple scheme of learning support design for scientific discovery learning based on computer simulation: experimental research

Abstract Learning support studies involving simulation‐based scientific discovery learning have tended to adopt an ad hoc strategies‐oriented approach in which the support strategies are typically pre‐specified according to learners' difficulties in particular activities. This article proposes a more integrated approach, a triple scheme for learning support design on the basis of the systematic analysis of the internal conditions of scientific discovery learning. The triple learning support scheme involves: (a) interpretative support that helps learners with knowledge access and the generation of meaningful and integrative understandings; (b) experimental support that scaffolds learners in systematic and valid experimental activities; and (c) reflective support that increases learners' self‐awareness of the discovery processes and prompts their reflective abstraction and integration. Two experiments were conducted with eighth graders (13‐year‐olds) to examine the effects of these learning supports embedded into a simulation program on floating and sinking. The overall results support the main hypotheses that learning supports in a simulation environment should be directed towards the three perspectives to invite meaningful, systematic, and reflective discovery learning.     

Explanation-based interpretation of open-textured concepts in logical models of legislation

In this paper we discuss a view of the Machine Learning technique called Explanation-Based Learning (EBL) or Explanation-Based Generalization (EBG) as a process for the interpretation of vague concepts in logic-based models of law.The open-textured nature of legal terms is a well-known open problem in the building of knowledge-based legal systems. EBG is a technique which creates generalizations of given examples on the basis of background domain knowledge. We relate these two topics by considering EBG's domain knowledge as corresponding to statute law rules, and EBG's training example as corresponding to a precedent case.By making the interpretation of vague predicates as guided by precedent cases, we use EBG as an effective process capable of creating a link between predicates appearing as open-textured concepts in law rules, and predicates appearing as ordinary language wording for stating the facts of a case.Standard EBG algorithms do not change the deductive closure of the domain theory. In the legal context, this is only adequate when concepts vaguely defined in some law rules can be reformulated in terms of other concepts more precisely defined in other rules. We call theory reformulation the process adopted in this situation of complete knowledge.In many cases, however, statutory law leaves some concepts completely undefined. We then propose extensions to the EBG standard that deal with this situation of incomplete knowledge, and call theory revision the extended process. In order to fill in knowledge gaps we consider precedent cases supplemented by additional heuristic information. The extensions proposed treat heuristics represented by abstraction hierarchies with constraints and exceptions.In the paper we also precisely characterize the distinction between theory reformulation and theory revision by stating formal definitions and results, in the context of the Logic Programming theory.We offer this proposal as a possible contribution to cross fertilization between machine learning and legal reasoning methods.     

自主学习型多媒体课件的设计

本文针对《几何画扳制作课件培训软件》多媒体课件的设计开发,提出利用合适的教与学理论指导教学设计,并通过恰当的技术将学习内容展示给学习者,使得学习者能够主动参与到学习过程中,同时还可以对相关的技能及时进行实践加以掌握。经过这样的学习过程学习者不但学到了知识,还能够运用其解决实际问题。     

SDH光传输技术教学实验设计

根据华为技术有限公司OptiX155／622HSDH光传输通信设备的性能与特点,结合普通高等院校通信与信息工程专业相关课程要求。设计了一系列SDH光传输教学实验。能够充分发挥实验设备的作用,加深学生对设备特点、知识原理的理解,有利于提高学生的实际应用与扩展能力。     

A Distribution Design Methodology for Object DBMS

The design of distributed databases involves making decisions on the fragmentation and placement of data and programs across the sites of a computer network. The first phase of the distribution design in a top-down approach is the fragmentation phase, which clusters in fragments the information accessed simultaneously by applications. Most distribution design algorithms propose a horizontal or vertical class fragmentation. However, the user has no assistance in the choice between these techniques. In this work we present a detailed methodology for the design of distributed object databases that includes: (i) an analysis phase, to indicate the most adequate fragmentation technique to be applied in each class of the database schema; (ii) a horizontal class fragmentation algorithm, and (iii) a vertical class fragmentation algorithm. Basically, the analysis phase is responsible for driving the choice between the horizontal and the vertical partitioning techniques, or even the combination of both, in order to assist distribution designers in the fragmentation phase of object databases. Experiments using our methodology have resulted in fragmentation schemas offering a high degree of parallelism together with an important reduction of irrelevant data.     

Plausible Justification Trees: A Framework for Deep and Dynamic Integration of Learning Strategies

This article describes a framework for the deep and dynamic integration of learning strategies. The framework is based on the idea that each single-strategy learning method is ultimately the result of certain elementary inferences (like deduction, analogy, abduction, generalization, specialization, abstraction, concretion, etc.). Consequently, instead of integrating learning strategies at a macro level, we propose to integrate the different inference types that generate individual learning strategies. The article presents a concept-learning and theory-revision method that was developed in this framework. It allows the system to learn from one or from several (positive and/or negative) examples, and to both generalize and specialize its knowledge base. The method integrates deeply and dynamically different learning strategies, depending on the relationship between the input information and the knowledge base. It also behaves as a single-strategy learning method whenever the applicability conditions of such a method are satisfied.     

《网页设计与制作》专题学习网站的设计

信息技术的发展促进了教育信息化,而网络环境下教育资源的建设是教育信息化的重要内容,专题学习网站则是教育资源建设的重要表现方式。该文以茂名学院高州师范分院《网页设计与制作》学科专题为例,介绍专题学习网站的设计,主要阐述了专题学习网站的需求分析、系统设计和功能设计三大方面。     

多Agent信念修正研究

Belief Revision is a theory that studies how to integrate new information into original belief set.Classical BR theory uses AGM frame,but it only resolves problems in single agent BR system.Multi-agent BR faces problems such as the collision of many information sources and how to maximize the logic consistence of multi-agent system.On the basis of game theory model,we form profit matrix under different BR strategies in muhi-agent system and try to get the best strategy that satisfies logic consistence of the system through negotiation.     

A Theory of Historical Discovery: The Construction of Componential Models

One of the major goals of 18th century chemistry was to determine the components of substances. In this paper we describe STAHL, a system that models significant portions of 18th century reasoning about compositional models. The system includes a number of heuristics for generating componential models from reactions, as well as error recovery mechanisms for dealing with inconsistent results. STAHL processes chemical reactions incrementally, and is therefore capable of reconstructing extended historic episodes, such as the century-long development of the phlogiston theory. We evaluate STAHL’s heuristics in the light of historical data, and conclude that the same reasoning mechanisms account for a variety of historical achievements, including Black’s models of mild alkali and Lavoisier’s oxygen theory. STAHL explains the generation of competing accounts of the same reactions, since the system’s reasoning chain depends on knowledge it has accumulated at earlier stages.     

虚拟仪器用于核物理常用实验仪器的开发

介绍了利用具有强大虚拟仪器功能和先进图形化编程环境的LabVIEW平台与NI公司的数据采集卡进行核物理常用实验仪器的开发设计 ,拓展了虚拟仪器应用的新领域。     

基于资源信息细分的学习评价系统的设计与实现

在网络化学习系统中,如何准确地发现学习者的缺陷是学习评价系统的主要任务,也是一个非常困难的问题。该系统通过对学习资源标注知识点、能力点、方法点、错误现象和错误归因等信息,将学习资源所包含的信息进一步细分和深化,再以测试结果为基础进行评价,能够更准确地发现学习者的缺陷。     

虚拟实验指导系统的交互设计研究

本文通过对虚拟实验的概念、特点及虚拟实验学习者、虚拟实验指导系统进行分析,在产品界面交互设计基本原则的基础上,提出了虚拟实验指导系统应当遵循的两个原则。在此基础上以开放式实验教学与实验室管理系统的虚拟实验指导系统为例,分析了该指导系统的优缺点及改进方案。     

DEXTER: A System that Experiments with Choices of Training Data Using Expert Knowledge in the Domain of DNA Hydration

In this paper, we describe a system, DEXTER, that uses knowledge to suggest inductive learning experiments in the domain of DNA hydration pattern prediction. These experiments vary the training data presented to a classifier learner. Such experiments are necessary in this domain, since, as in many other scientific domains, data are noisy, the relevance of particular attributes is not well established, and the number of training cases is limited. In each experiment, DEXTER chooses a set of training cases, attributes and classes to learn. To generate an experiment, it examines the results of previous experiments, and uses domain knowledge and domain independent heuristics to select and modify a previous experiment. For the domain expert interested in using the induced rules to understand data, DEXTER's explicit use of knowledge provides several advantages that other data selection techniques do not. In particular, the variation of classifiers induced in different experiments yields insights into the roles and interactions of particular attributes in determining hydration. In addition, many of the classifiers induced from DEXTER's choices of data are of accuracy greater than or equal to those induced using the entire set of available data or data chosen by several other techniques. This work is of theoretical and pragmatic importance to molecular biophysicists. The learned hydration predictors provide insights about factors influencing DNA hydration. Also, the hydration predictors could lead to a tool for automatically predicting water positions around DNA molecules for which crystallographic data are not available.     

Taguchi方法在WSMR虚拟样机实验设计中的应用

运用虚拟样机技术研究WSMR转向半径与各影响因素关系时，采用Taguchi方法构造实验方案表进行仿真试验，分析表明用Taguchi方法得到的数据信息丰富且分布均匀，适于构造训练样本进行网络训练。