基于近邻方法的高维数据可视化聚类发现

提出了一种新颖的基于近邻方法的高维数据可经聚类方法,并实现了一个近邻可视化聚类发现系统ＶｉｓＮＮ。已有的解决高维数据可视化聚类方法主要是通过降维把维数据投影到二维或三维空间上,从而达到可视化目的。     

一种新型数据记录仪的设计与应用

提出了一种基于PDA的新型数据记录仪的设计方案。以 89c52单片机为核心设计了前端数据采集器 ,开发了基于PDA的数据记录应用程序。应用实验表明 ,该方案是可行的     

电网实时信息的二次开发

以实现电网调度系统SCADA系统实时信息共享为基础 ,提出调度MIS系统的设计思路 ,分析了应用调度MIS系统所需要的SCADA实时数据种类 ,介绍了在数据共享的基础上开发的以调度MIS系统实现的有关电网实时信息二次应用功能     

Implementing the Davis–Putnam Method

The method proposed by Davis, Putnam, Logemann, and Loveland for propositional reasoning, often referred to as the Davis–Putnam method, is one of the major practical methods for the satisfiability (SAT) problem of propositional logic. We show how to implement the Davis–Putnam method efficiently using the trie data structure for propositional clauses. A new technique of indexing only the first and last literals of clauses yields a unit propagation procedure whose complexity is sublinear to the number of occurrences of the variable in the input. We also show that the Davis–Putnam method can work better when unit subsumption is not used. We illustrate the performance of our programs on some quasigroup problems. The efficiency of our programs has enabled us to solve some open quasigroup problems.     

Merging potentially inconsistent items of structured text

Structured text is a general concept that is implicit in a variety of approaches to handling information. Syntactically, an item of structured text is a number of grammatically simple phrases together with a semantic label for each phrase. Items of structured text may be nested within larger items of structured text. The semantic labels in a structured text are meant to parameterize a stereotypical situation, and so a particular item of structured text is an instance of that stereotypical situation. Much information is potentially available as structured text including tagged text in XML, text in relational and object-oriented databases, and the output from information extraction systems in the form of instantiated templates. In this paper, we formalize the concept of structured text, and then focus on how we can identify inconsistency in the logical representation of items of structured text. We then present a new framework for merging logical theories that can be employed to merge inconsistent items of structured text. To illustrate, we consider the problem of merging reports such as weather reports.     

OBSERVER: An Approach for Query Processing in Global Information Systems Based on Interoperation Across Pre-Existing Ontologies

There has been an explosion in the types, availability and volume of data accessible in an information system, thanks to the World Wide Web (the Web) and related inter-networking technologies. In this environment, there is a critical need to replace or complement earlier database integration approaches and current browsing and keyword-based techniques with concept-based approaches. Ontologies are increasingly becoming accepted as an important part of any concept or semantics based solution, and there is increasing realization that any viable solution will need to support multiple ontologies that may be independently developed and managed. In particular, we consider the use of concepts from pre-existing real world domain ontologies for describing the content of the underlying data repositories. The most challenging issue in this approach is that of vocabulary sharing, which involves dealing with the use of different terms or concepts to describe similar information. In this paper, we describe the architecture, design and implementation of the OBSERVER system. Brokering across the domain ontologies is enabled by representing and utilizing interontology relationships such as (but not limited to) synonyms, hyponyms and hypernyms across terms in different ontologies. User queries are rewritten by using these relationships to obtain translations across ontologies. Well established metrics like precision and recall based on the extensions underlying the concepts are used to estimate the loss of information, if any.     

Identifying Objects in Procedural Programs Using Clustering Neural Networks

This paper presents a general approach for the identification of objects in procedural programs. The approach is based on neural architectures that perform an unsupervised learning of clusters. We describe two such neural architectures, explain how to use them in identifying objects in software systems and briefly describe a prototype tool, which implements the clustering algorithms. With the aid of several examples, we explain how our approach can identify abstract data types as well as groups of routines which reference a common set of data. The clustering results are compared to the results of many other object identification techniques. Finally, several case studies were performed on existing programs to evaluate the object identification approach. Results concerning two representative programs and their generated clusters are discussed.     

基于模运算的数字水印算法

提出了一种基于模运算的数字水印算法。该算法具有很好的性能：（1）水印隐藏的效果好,凭借人类的视觉系统无法看出与原图像的差别;（2）可以有效的抵抗JPEG等压缩编码的破坏;（3）可以领先部分图像检测水印,在图像受到损坏的情况下,仍可以检测到水印信息;（4）检测水印时受到口令的限制,不知道口令的人无法正确恢复水印;（5）检测水印时不需要原始图像。     

大幅面地图的快速自动地形晕渲

在提出一个建立三维规则数据场的新算法的基础上,提出了一个可用于大幅度地图的快速实用的地形自动晕渲算法,与传统数据网格化的方法不同,本算法采用了用网格点相邻等高线的高程值插值计算出网格点高程值的新思想,并且提出了相应的网格点相邻等高线的识别方法,该方法充分利用等高线所具有的先验知识,得到的网格点高程值质量很高,另外,该规则数据场的建立方法经改进后,其时间复杂性与数据点数（ｍ）和网格点数（ｎ）成线性关     

Customer Retention via Data Mining

``Customer Retention' is an increasingly pressing issue intoday's ever-competitive commercial arena. This is especially relevantand important for sales and services related industries. Motivated by areal-world problem faced by a large company, we proposed a solution thatintegrates various techniques of data mining, such as featureselection via induction, deviation analysis, and mining multipleconcept-level association rules to form an intuitive and novel approachto gauging customer loyalty and predicting their likelihood ofdefection. Immediate action triggered by these ``early-warnings'resulting from data mining is often the key to eventual customerretention.