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The Gaussian Q-function is of great importance in the field of communications, where the noise is often characterized by the Gaussian distribution. However, no simple exact closed form of the Q-function is known. Consequently, a number of approximations have been proposed over the past several decades. In this paper, we use Genetic Programming with semantic based crossover to approximate the Q-function...
This paper investigates basic semantic aspects of evaluation logic. We propose a verbalized approach to the design and use of the Generalized Conjunction/disjunction (GCD) aggregators. The main goals of verbalized approach are to help in specifying semantic components of GCD, and to facilitate the use of soft computing evaluation logic and corresponding evaluation methods (such as LSP), making those...
What do people care about in an image? To drive computational visual recognition toward more human-centric outputs, we need a better understanding of how people perceive and judge the importance of content in images. In this paper, we explore how a number of factors relate to human perception of importance. Proposed factors fall into 3 broad types: 1) factors related to composition, e.g. size, location,...
Since high-level events in images (e.g. “dinner”, “motorcycle stunt”, etc.) may not be directly correlated with their visual appearance, low-level visual features do not carry enough semantics to classify such events satisfactorily. This paper explores a fully compositional approach for event based image retrieval which is able to overcome this shortcoming. Furthermore, the approach is fully scalable...
The paper makes a case for expanding the range of words that Computing With Words typically considers to, eventually, all the words in a natural language, thus accounting accurately for the inherent vagueness of natural language meaning and creating an overlap with computational semantics. The claim is illustrated with examples of a few English nouns and verbs rather than the usual adjectives and...
Curve fragments, as opposed to unorganized edge elements, are of interest and use in a large number of applications such as multiview reconstructions, tracking, motion-based segmentation, and object recognition. A large number of contour grouping algorithms have been developed, but progress in this area has been hampered by the fact that current evaluation methodologies are mainly edge-based, thus...
This paper presents a probabilistic framework combining heterogeneous, uncertain, information such as object observations, shape, size, appearance of rooms and human input for semantic mapping. It abstracts multi-modal sensory information and integrates it with conceptual common-sense knowledge in a fully probabilistic fashion. It relies on the concept of spatial properties which make the semantic...
Cognitive information processing at higher conceptual levels requires a computational approach to knowledge representation and analysis. Semantic network analysis bridges the gap between probabilistic pattern recognition techniques and symbolic representations by replacing cumbersome and computationally complex forms of logic-based semantic inference common in symbolic approaches with mathematical...
Semantic information can help both humans and robots to understand their environments better. In order to obtain semantic information efficiently and link it to a metric map, we present a semantic mapping approach through human activity recognition in an indoor human-robot coexisting environment. An intelligent mobile robot platform can create a 2D metric map, while human activity can be recognized...
The tools for situation description and decision making are considered. We demonstrate how to relate the typical collection of similar systems and situations with algebraic systems. The procedures for transforming the statements in the so-called compressed or constraint language into corresponding algebraic system theory formula are described. The inferring of formulas for decision making is also...
For the first time natural language processing approaches are applied on a large scale to psychometric methods. Psychometric methods have been applied in hundreds of thousands of published studies. This study examines automated approach to discovering behavioral knowledge that are encoded as constructs in social and behavioral science disciplines. To date, constructs relationships are ordinarily revealed...
This paper presents a novel approach to accessing information stored in legacy relational databases (RDB), based on Semantic Web (SW) and multiagent systems (MAS) technologies. Its purpose is to provide the users of enterprise decision-support systems with direct, flexible, and customized access to information, through high-level semantic queries, without the need to modify the underlying legacy databases...
This paper describes the structure and function of conceptual dictionary placing in our narrative generation system architecture to provide knowledge of verb concepts and noun concepts associated with an event concept that is a basic constitutional unit in narrative. Currently, verb concept dictionary has 5337 case frames and modified 1158 constraints, and noun concept dictionary contains 142168 noun...
Events occurring in the real world are covered by news reports from different sources. Each report generally contains information that is found in others, but may also contain unique information. To learn all the information about a particular event, a user will need to read all the different reports. This is a duplication of effort since most information will be repeated in the different reports...
This paper proposes a general learning framework for robots to learn behaviors through imitation and interaction. A modified codebook based method is used for robots to segment and recognize new objects in the environment. Task related semantic information is learned by robots through the speech communication with humans. Dynamic Movement Primitive method is used to generate similar behaviors to complete...
Canonical views are referred to the classical three-quarter views of a 3D object, always preferred by human beings, because they are stable and able to produce more meaningful and understandable images for the viewer. Unlike existing methods to measure features in the 3D space for view selection, this paper proposes to measure features on the viewing plane, taking into account the influence of feature...
Processing short texts is becoming a trend in information retrieval. Since the text has rarely external information, it is more challenging than document. In this paper, keyword clustering is studied for automatic categorization. To obtain semantic similarity of the keywords, a broad-coverage lexical resource WordNet is employed. We introduce a semantic hierarchical clustering. For automatic keyword...
We propose an approach to recognize group activities which involve several persons based on modeling the interactions between human bodies. Benefitted from the recent progress in pose estimation [1], we model the activities as the interactions between the parts belong to the same person (intra-person) and those between the parts of different persons (inter-person). Then a unified, discriminative model...
Saliency in 2D imagery has been receiving increasing attention over the last few years owing to the need to minimize computation requirements through visual search space reduction, especially in the field of domestic robotics. Saliency and pre-attention mechanisms such as the Itti-Koch model have largely been focused on multi-scale local features mimicking low level attention processes in visual system,...
As behavioral research has expanded in Information Systems and other scientific fields, researchers are recognizing that construct proliferation increases the difficulty in identifying the nomological networks of constructs pertaining to any given research question. An Inter-Nomological Network uses semantic analysis to systematically identify, categorize, and predict relationships among the constructs...
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