Object Feature Extraction by Using the Searching Temporal and Feature Salient Technique

This research uses the boundary cutting, spatial-temporal segmentation, block based searching, and gray scale histogram technique to extract the eyes, nose, and mouth images. In this research, gray scale histograms are used to salient the eyes, nose, and mouth features. A spatial-temporal template is designed to slide the eyes, nose, and mouth images to extract the eyes, nose, and mouth image. In order to obtain the best result, the generic algorithm and spatial region partition techniques need to be used to remove noise and to precisely bind the object region to obtain a more accurate result.     

Searching in Cooperative Patent Classification: Comparison between keyword and concept-based search

International patent corpus is a gigantic source containing today about 80 million of documents. Every patent is manually analyzed by patent officers and then classified by a specific code called Patent Class (PC). Cooperative Patent Classification CPC is the new classification system introduced since January 2013 in order to standardize the classification systems of all major patent offices. Like keywords for papers, PCs point to the core of the invention, describing concisely what they contain inside. Most of patents strategies are based on PC as filter for results therefore the selection of relevant PCs is often a primary and crucial activity. This task is considered particularly challenging and only few tools have been specially developed for this purpose. The most efficient tools are provided by patent offices of EPO and WIPO.This paper analyzes their PCs search strategy (mainly based on keyword-based engines) in order to identify main limitations in terms of missing relevant PCs (recall) and non-relevant results (precision). Patents have been processed by KOM, a semantic patent search tool developed by the authors. Unlike all other PC search tools, KOM uses semantic parser and many knowledge bases for carrying out a conceptual patent search. Its functioning is described step by step through a detailed analysis pointing out the benefits of a concept-based search vis-à-vis a keyword-based search. An exemplary case is proposed dealing with CPCs describing the sterilization of contact lenses. Comparison could be likewise conducted on other PCs such as International (IPC), European (ECLA) or United States (USPC) patent classification codes.     

应急系统中时态数据的本体表示与检索研究

时间是信息的重要属性,时态数据在信息管理系统中起着关键性作用。双时态数据库有很多优点,但是双时态数据库需要较高的储存空间,另外,有效时间和事务时间的二元时间结构,给本体表示和时态数据的查询带来了很大的困难。针对这些问题提出了一种时态数据的本体表示方法,并用本体语言表示出了应急系统中的具体时态数据。最后,通过语义检索验证了时态数据本体表示的正确性。     

分形技术图像比对搜索模型

传统的搜索引擎只能搜索文字型的资料,显然无法满足用户想通过搜索引擎来取得与图像相关的信息的需求．提出一种图像比对搜索引擎,利用分形图像处理和索引技术来建立图像特征数据库．当用户输入查询图像时,系统对于用户输入的图像也采用与分形图像处理相同的方式取得特征值,然后再与图像特征数据库的特征矢量作比对,达到数据搜索的目的．实验表明,图像比对搜索引擎除了可以找出用户输入的相似图像外,对于查询图像的旋转、模糊或噪声,图像比对搜索引擎也能够找出正确的图像,证明文中方法对图像的容错性和适应性好。     

Temporal Kohonen Map and the Recurrent Self-Organizing Map: Analytical and Experimental Comparison

This paper compares two Self-Organizing Map (SOM) based models for temporal sequence processing (TSP) both analytically and experimentally. These models, Temporal Kohonen Map (TKM) and Recurrent Self-Organizing Map (RSOM), incorporate leaky integrator memory to preserve the temporal context of the input signals. The learning and the convergence properties of the TKM and RSOM are studied and we show analytically that the RSOM is a significant improvement over the TKM, because the RSOM allows simple derivation of a consistent learning rule. The results of the analysis are demonstrated with experiments.     

Data Stream Management: Aggregation, Classification, Modeling, and Operator Placement

Data stream management is concerned with managing large streams of data arriving from data communications or sensor networks. Interest in this area is increasing, and it will grow rapidly over the coming decade, driven by rapid growth in the pervasiveness and bandwidth of digital-communications networks and an impending explosion in sensor networks. This special issue provides a snapshot of ongoing work in this area.     

Aggregation functions: Construction methods, conjunctive, disjunctive and mixed classes

In this second part of our state-of-the-art overview on aggregation theory, based again on our recent monograph on aggregation functions, we focus on several construction methods for aggregation functions and on special classes of aggregation functions, covering the well-known conjunctive, disjunctive, and mixed aggregation functions. Some fields of applications are included.     

Robotic Intelligence for Industrial Automation: Object Flaw Auto Detection and Pattern Recognition by Object Location Searching, Object Alignment, and Geometry Comparison

This research investigates the techniques using image subtraction to find flaws in the cosmetic products. The technique developed in this research moves the perfect image to overlap with the flawed image. Then, the perfect and the flawed images are aligned in the same orientation. After the perfect image has overlapped with the flawed image, the flawed image is subtracted from the perfect image. If there are flaws in the flawed image, after the image subtraction, the flaws will remain in the subtracted result. From beginning to end the inspection is done by machine automatically. There is no further human effort involved. The technique developed in this research can find the flaws in two-dimensional images very accurately. This paper explains the method using the second moment to find the orientations of cosmetic products. By the orientations of the cosmetic products, the perfect cosmetic product and the flawed cosmetic product can be aligned in the same orientation. A detailed process of image rotation is addressed in this paper.     

Supporting Scientific Collaboration: Methods, Tools and Concepts

This paper discusses the interrelationship between e-Science and CSCW in terms of key substantive, methodological and conceptual innovations made in both fields. In so doing, we hope to draw out the existing relationship between CSCW and e-Science research, and to map out some key future challenges where the two areas of research may become more closely aligned. In considering what may be required to draw the two more closely together, the paper focuses primarily on investigations that have been undertaken in two dedicated initiatives into e-Science, along with the key issues emerging from these studies.     

Temporal XML: modeling, indexing, and query processing

In this paper we address the problem of modeling and implementing temporal data in XML. We propose a data model for tracking historical information in an XML document and for recovering the state of the document as of any given time. We study the temporal constraints imposed by the data model, and present algorithms for validating a temporal XML document against these constraints, along with methods for fixing inconsistent documents. In addition, we discuss different ways of mapping the abstract representation into a temporal XML document, and introduce TXPath, a temporal XML query language that extends XPath 2.0. In the second part of the paper, we present our approach for summarizing and indexing temporal XML documents. In particular we show that by indexing continuous paths, i.e., paths that are valid continuously during a certain interval in a temporal XML graph, we can dramatically increase query performance. To achieve this, we introduce a new class of summaries, denoted TSummary, that adds the time dimension to the well-known path summarization schemes. Within this framework, we present two new summaries: LCP and Interval summaries. The indexing scheme, denoted TempIndex, integrates these summaries with additional data structures. We give a query processing strategy based on TempIndex and a type of ancestor-descendant encoding, denoted temporal interval encoding. We present a persistent implementation of TempIndex, and a comparison against a system based on a non-temporal path index, and one based on DOM. Finally, we sketch a language for updates, and show that the cost of updating the index is compatible with real-world requirements.     

Panel Summaries: Real-Time Volume Diagnostics--Requirements and Challenges

A report on the IEEE VLSI Test Symposium (VTS 06) panel on "Real-time volume diagnostics: requirements and challenges.     

AdaptWID: An Adaptive, Memory-Efficient Window Aggregation Implementation

Memory efficiency is important for processing high-volume data streams. Previous stream-aggregation methods can exhibit excessive memory overhead in the presence of skewed data distributions. Further, data skew is a common feature of massive data streams. The authors introduce the AdaptWID algorithm, which uses adaptive processing to cope with time-varying data skew. AdaptWID models the memory usage of alternative aggregation algorithms and selects between them at runtime on a group-by-group basis. The authors' experimental study using the NiagaraST stream system verifies that the adaptive algorithm improves memory usage while maintaining execution cost and latency comparable to existing implementations.     

SpectralCAT: Categorical spectral clustering of numerical and nominal data

Data clustering is a common technique for data analysis, which is used in many fields, including machine learning, data mining, customer segmentation, trend analysis, pattern recognition and image analysis. Although many clustering algorithms have been proposed, most of them deal with clustering of one data type (numerical or nominal) or with mix data type (numerical and nominal) and only few of them provide a generic method that clusters all types of data. It is required for most real-world applications data to handle both feature types and their mix. In this paper, we propose an automated technique, called SpectralCAT, for unsupervised clustering of high-dimensional data that contains numerical or nominal or mix of attributes. We suggest to automatically transform the high-dimensional input data into categorical values. This is done by discovering the optimal transformation according to the Calinski–Harabasz index for each feature and attribute in the dataset. Then, a method for spectral clustering via dimensionality reduction of the transformed data is applied. This is achieved by automatic non-linear transformations, which identify geometric patterns in the data, and find the connections among them while projecting them onto low-dimensional spaces. We compare our method to several clustering algorithms using 16 public datasets from different domains and types. The experiments demonstrate that our method outperforms in most cases these algorithms.     

Modeling Connectionist Network Structures: Some Geometric and Categorical Aspects

This contribution deals with an approach for mathematical modeling of the network structures of a certain connectionist network paradigm. Analysis of the structure of an artificial neural network (ANN) in that class of networks shows a possibility to introduce geometric and categorical modeling methods. This can be described briefly as follows. A (noncommutative) geometric space can be interpreted as a so-called geometric net. To a given ANN a corresponding geometric net can be associated. Geometric spaces form a category. Consequently, one obtains a category of geometric nets with a suitable notion of morphism. Then it is natural to interpret a learning step of an ANN as a morphism, thus learning corresponds to a finite sequence of morphisms (the associated networks are the objects). An associated (local) geometric net is less complex than the original ANN, but it contains all necessary information about the network structure. The association process together with learning (expressed by morphisms) leads to a commutative diagram corresponding to a suitable natural transformation, in terms of category theory. Commutativity of the diagram can be exploited to make learning cheaper. The simplified mathematical network model was used in ANN simulation applied in an industrial project on quality control. The economy of the model could be observed in a considerable increase of performance and decrease of production costs. Some prospects on the role of group operations that are induced by the regular structure of the underlying networks conclude the article.     

Supporting concurrent ontology development: Framework, algorithms and tool

We propose a novel approach to facilitate the concurrent development of ontologies by different groups of experts. Our approach adapts Concurrent Versioning, a successful paradigm in software development, to allow several developers to make changes concurrently to an ontology. Conflict detection and resolution are based on novel techniques that take into account the structure and semantics of the ontology versions to be reconciled by using precisely-defined notions of structural and semantic differences between ontologies and by extending state-of-the-art ontology debugging and repair techniques. We also present ContentCVS, a system that implements our approach, and a preliminary empirical evaluation which suggests that our approach is both computationally feasible and useful in practice.     

Flexecution, Part 2: Understanding and Supporting Flexible Execution

For pt.1 see ibid G. Klein, vol.22, no.5, p.79-83 (2007). This article is about planning and execution with ill-defined and conflicting goals. These essays aim to describe the insights of researchers in AI and cognitive systems engineering to a wider audience and to broaden efforts in supporting flexible execution with ill-defined goals.     

Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals

Data analysis applications typically aggregate data across manydimensions looking for anomalies or unusual patterns. The SQL aggregatefunctions and the GROUP BY operator produce zero-dimensional orone-dimensional aggregates. Applications need the N-dimensionalgeneralization of these operators. This paper defines that operator, calledthe data cube or simply cube. The cube operator generalizes the histogram,cross-tabulation, roll-up,drill-down, and sub-total constructs found in most report writers.The novelty is that cubes are relations. Consequently, the cubeoperator can be imbedded in more complex non-procedural dataanalysis programs. The cube operator treats each of the Naggregation attributes as a dimension of N-space. The aggregate ofa particular set of attribute values is a point in this space. Theset of points forms an N-dimensional cube. Super-aggregates arecomputed by aggregating the N-cube to lower dimensional spaces.This paper (1) explains the cube and roll-up operators, (2) showshow they fit in SQL, (3) explains how users can define new aggregatefunctions for cubes, and (4) discusses efficient techniques tocompute the cube. Many of these features are being added to the SQLStandard.     

Matlab环境下素数筛选算法的分析及比较

利用Matlab对矩阵科学运算的支持,在Matlab环境下对埃拉托斯特尼筛法、Dirichlet定理衍生的素数筛法、辛答拉姆筛法和基于奇合数分解式的素数筛法进行算法实现和初步优化,并测试其性能,研究发现在计算大数范围的素数表时,算法之间的性能差异明显.通过对这些算法的比较和评价,分析各个算法的优缺点.研究结果表明,对于不同环境要求和不同的待解问题需要选取合适的素数筛选算法,因此,文中结论具有一定的指导意义和实际参考价值.