Nash equilibrium based semantic cache in mobile sensor grid database systems

Mobile applications are being increasingly deployed on a massive scale in various mobile sensor grid database systems. With limited resources from the mobile devices, how to process the huge number of queries from mobile users with distributed sensor grid databases becomes a critical problem for such mobile systems. While the fundamental semantic cache technique has been investigated for query optimization in sensor grid database systems, the problem is still difficult due to the fact that more realistic multi-dimensional constraints have not been considered in existing methods. To solve the problem, a new semantic cache scheme is presented in this paper for location-dependent data queries in distributed sensor grid database systems. It considers multi-dimensional constraints or factors in a unified cost model architecture, determines the parameters of the cost model in the scheme by using the concept of Nash equilibrium from game theory, and makes semantic cache decisions from the established cost model. The scenarios of three factors of semantic, time and locations are investigated as special cases, which improve existing methods. Experiments are conducted to demonstrate the semantic cache scheme presented in this paper for distributed sensor grid database systems.

Place categorization and semantic mapping on a mobile robot

In this paper we focus on the challenging problem of place categorization and semantic mapping on a robot with-out environment-specific training. Motivated by their ongoing success in various visual recognition tasks, we build our system upon a state-of-the-art convolutional network. We overcome its closed-set limitations by complementing the network with a series of one-vs-all classifiers that can learn to recognize new semantic classes online. Prior domain knowledge is incorporated by embedding the classification system into a Bayesian filter framework that also ensures temporal coherence. We evaluate the classification accuracy of the system on a robot that maps a variety of places on our campus in real-time. We show how semantic information can boost robotic object detection performance and how the semantic map can be used to modulate the robot’s behaviour during navigation tasks. The system is made available to the community as a ROS module.

Explorations in the distributional and semantic similarity of words

A straightforward computation of the list of the words (the `tail words' of the list) that are distributionally most similar to a given word (the `head word' of the list) leads to the question: How semantically similar to the head word are the tail words; that is: how similar are their meanings to its meaning? And can we do better? The experiment was done on nearly 18,000 most frequent nouns in a Finnish newsgroup corpus. These nouns are considered to be distributionally similar to the extent that they occur in the same direct dependency relations with the same nouns, adjectives and verbs. The extent of the similarity of their computational representations is quantified with the information radius. The semantic classification of head-tail pairs is intuitive; some tail words seem to be semantically similar to the head word, some do not. Each such pair is also associated with a number of further distributional variables. Individually, their overlap for the semantic classes is large, but the trained classification-tree models have some success in using combinations to predict the semantic class. The training data consists of a random sample of 400 head-tail pairs with the tail word ranked among the 20 distributionally most similar to the head word, excluding names. The models are then tested on a random sample of another 100 such pairs. The best success rates range from 70% to 92% of the test pairs, where a success means that the model predicted my intuitive semantic class of the pair. This seems somewhat promising when distributional similarity is used to capture semantically similar words. This analysis also includes a general discussion of several different similarity formulas, arranged in three groups: those that apply to sets with graded membership, those that apply to the members of a vector space, and those that apply to probability mass functions.

Three-year-olds are sensitive to semantic prominence during online language comprehension

Recent evidence from adult pronoun comprehension suggests that semantic factors such as verb transitivity affect referent salience and thereby anap- hora resolution. We tested whether the same semantic factors influence pronoun comprehension in young children. In a visual world study, 3-year- olds heard stories that began with a sentence containing either a high or a low transitivity verb. Looking behaviour to pictures depicting the subject and object of this sentence was recorded as children listened to a subsequent sentence containing a pronoun. Children showed a stronger preference to look to the subject as opposed to the object antecedent in the low transitivity condition. In addition there were general preferences (1) to look to the subject in both conditions and (2) to look more at both potential antecedents in the high transitivity condition. This suggests that children, like adults, are affected by semantic factors, specifically semantic prominence, when interpreting anaphoric pronouns.

Structural aspects of semantic-directed clusters

Knowledge-based clusters are studied from the structural point of view. Generalized descriptions for such clusters are stated and illustrated. Peculiarities of certain knowledge-based cluster configurations are highlighted. The adequacy of the connectives logical and (“and”) logical or (“exclusive-or”) in describing such clusters is justified. The definition of “concept” is elaborated from the clustering point of view and used to establish the equivalence between, descriptions of clusters and concepts. The order-independence of semantic-directed clustering approach is established formally based on axiomatic considerations.

Fuzzy Clustering Based Ad Recommendation for TV Programs

Advertisements(Ads) are the main revenue earner for Television (TV) broadcasters. As TV reaches a large audience, it acts as the best media for advertisements of products and services. With the emergence of digital TV, it is important for the broadcasters to provide an intelligent service according to the various dimensions like program features, ad features, viewers’ interest and sponsors’ preference. We present an automatic ad recommendation algorithm that selects a set of ads by considering these dimensions and semantically match them with programs. Features of the ad video are captured interms of annotations and they are grouped into number of predefined semantic categories by using a categorization technique. Fuzzy categorical data clustering technique is applied on categorized data for selecting better suited ads for a particular program. Since the same ad can be recommended for more than one program depending upon multiple parameters, fuzzy clustering acts as the best suited method for ad recommendation. The relative fuzzy score called “degree of membership” calculated for each ad indicates the membership of a particular ad to different program clusters. Subjective evaluation of the algorithm is done by 10 different people and rated with a high success score.

Shape-function-relationship (SFR) framework for semantic interoperability of product model

The problem of semantic interoperability arises while integrating applications in different task domains across the product life cycle. A new shape-function-relationship (SFR) framework is proposed as a taxonomy based on which an ontology is developed. Ontology based on the SFR framework, that captures explicit definition of terminology and knowledge relationships in terms of shape, function and relationship descriptors, offers an attractive approach for solving semantic interoperability issue. Since all instances of terms are based on single taxonomy with a formal classification, mapping of terms requires a simple check on the attributes used in the classification. As a preliminary study, the framework is used to develop ontology of terms used in the aero-engine domain and the ontology is used to resolve the semantic interoperability problem in the integration of design and maintenance. Since the framework allows a single term to have multiple classifications, handling context dependent usage of terms becomes possible. Automating the classification of terms and establishing the completeness of the classification scheme are being addressed presently.