Matching Three-Dimensional Objects Using a Relational Paradigm

A relational model for describing three-dimensional objects has been designed and implemented as part of a database system. The models which provide rough descriptions to be used at the top level of a hierarchy for describing objects, were designed for initial matching attempts on an unknown object. The descriptions are in terms of the set of simple parts of the objects. Simple parts can be sticks (long, thin parts), plates (flat, wide parts) , and blobs (parts that have three significant dimensions). The relations include an attribute-value table for global properties of the object, the properties of the simple parts, binary connection and support relationships, ternary connection relationships, parallel relationships, perpendicular relationships, and binary constraints. An important use of the system is to characterize the similarity and differences between three-dimensional objects. Toward this end, we have defined a measure of relational similarity between three-dimensional object models and a measure of feature similarity, based only on Euclidean distance between attribute-value tables. In a series of experiments, we compare the results of using the two different similarity measures and conclude that the relational similarity is much more powerful than the feature similarity and should be used when grouping the objects in the database for fast access

Judgement of conceptual identity in monkeys

Baboons (Papio anubis) were tested on categorization tasks at two different conceptual levels. The monkeys showed their ability (1) to judge as identical or different the objects belonging to two categories, on a perceptual basis, and (2) to perform a judgment of conceptual identity—that is, to use the same/different relation between two previously learned categories. This latter experiment represents the first demonstration of judgment of conceptual identity in a monkey specie

Modeling views in the layered view model for XML using UML

In data engineering, view formalisms are used to provide flexibility to users and user applications by allowing them to extract and elaborate data from the stored data sources. Conversely, since the introduction of Extensible Markup Language (XML), it is fast emerging as the dominant standard for storing, describing, and interchanging data among various web and heterogeneous data sources. In combination with XML Schema, XML provides rich facilities for defining and constraining user-defined data semantics and properties, a feature that is unique to XML. In this context, it is interesting to investigate traditional database features, such as view models and view design techniques for XML. However, traditional view formalisms are strongly coupled to the data language and its syntax, thus it proves to be a difficult task to support views in the case of semi-structured data models. Therefore, in this paper we propose a Layered View Model (LVM) for XML with conceptual and schemata extensions. Here our work is three-fold; first we propose an approach to separate the implementation and conceptual aspects of the views that provides a clear separation of concerns, thus, allowing analysis and design of views to be separated from their implementation. Secondly, we define representations to express and construct these views at the conceptual level. Thirdly, we define a view transformation methodology for XML views in the LVM, which carries out automated transformation to a view schema and a view query expression in an appropriate query language. Also, to validate and apply the LVM concepts, methods and transformations developed, we propose a view-driven application development framework with the flexibility to develop web and database applications for XML, at varying levels of abstraction

Sensor Search Techniques for Sensing as a Service Architecture for The Internet of Things

The Internet of Things (IoT) is part of the Internet of the future and will comprise billions of intelligent communicating "things" or Internet Connected Objects (ICO) which will have sensing, actuating, and data processing capabilities. Each ICO will have one or more embedded sensors that will capture potentially enormous amounts of data. The sensors and related data streams can be clustered physically or virtually, which raises the challenge of searching and selecting the right sensors for a query in an efficient and effective way. This paper proposes a context-aware sensor search, selection and ranking model, called CASSARAM, to address the challenge of efficiently selecting a subset of relevant sensors out of a large set of sensors with similar functionality and capabilities. CASSARAM takes into account user preferences and considers a broad range of sensor characteristics, such as reliability, accuracy, location, battery life, and many more. The paper highlights the importance of sensor search, selection and ranking for the IoT, identifies important characteristics of both sensors and data capture processes, and discusses how semantic and quantitative reasoning can be combined together. This work also addresses challenges such as efficient distributed sensor search and relational-expression based filtering. CASSARAM testing and performance evaluation results are presented and discussed.Comment: IEEE sensors Journal, 2013. arXiv admin note: text overlap with arXiv:1303.244

An Analogical Paradox for Nonhuman Primates: Bridging the Perceptual-Conceptual Gap

Over the past few decades, the dominant view by comparative psychologists of analogical reasoning in nonhuman primates was one of dichotomy between apes, including humans, and monkeys: the distinction between the analogical ape and paleological monkey (Thompson & Oden, 2000). Whereas evidence for analogy proper by representation reinterpretation in monkeys is sparse and debated, the gap between that which is analogic and paleologic has been narrowed by the studies presented here. Representation of relational concepts important for analogy proves difficult for rhesus and capuchin monkeys without the ability to rely on a greater amount of perceptual variability, implicating a perceptually-bound predisposition in problem-solving (Chapters 2-3). A shift in attention from perceptual features to abstract concepts for employment in relational matching is again difficult, but not impossible given cognitive incentive in the form of differential outcomes to refocus attention on conceptual properties (Chapter 4). Finally, chimpanzees unlike monkeys appear more apt to reason by analogy, perhaps due to a more default conceptual focus (Chapter 5). Taken together, these studies provide an account for the emergence of analogical reasoning skills throughout the primate lineage in contrast to views regarding analogy a hallmark of human intelligence