Sharif Digital Repository

Peer-to-peer networks are beginning to form the infrastructure of future applications. The use of semantic information to organize nodes in a P2P network is a prosperous approach. In this paper we present a novel method, called Semantic Partition Tree, which organizes nodes in a semantic space, using an upper ontology. In this approach each node can find a target in a time proportioned to the depth of the upper ontology, which is significantly less than the number of nodes in the network. Also we simulated our method with several configurations and measured its performance. The results of the simulation show that our scheme is highly scalable and can be used by real P2P networks. © 2007 IEEE... 

Motivated by the problem of automatically composing network accessible services, such as those on the World Wide Web, this paper proposes an approach to exploit all semantic information available for semantic web services to complete this task. For semantic web services, we propose a prioritized and limited list of other web services, with composability measure value based on feasibility of being succeeding service in a composition to fulfill the request. Due to the fact that in semantic web services, semantic information- all description and signatures- is defined by ontology language, we use some of the mapping rules to estimate a correlation between services for composability. Then we... 

We present an attempt to improve the performance of multi-class image segmentation systems based on a multilevel description of segments. The multi-class image segmentation system used in this paper marks the segments in an image, describes the segments via multilevel feature vectors and passes the vectors to a multi-class object classifier. The focus of this paper is on the segment description section. We first propose a robust, scale-invariant texture feature set, named directional differences (DDs). This feature is designed by investigating the flaws of conventional texture features. The advantages of DDs are justified both analytically and experimentally. We have conducted several... 

The semantic sensor web adds semantic web technologies such as ontology to sensor network. Semantic technologies can help the better management of query and data aggregation of the sensor network. So far, several ontologies have been presented for the semantic presentation of sensor networks concepts. However, applications and end-users require physical entities information rather than technical details and information regarding sensors and sensor network. This paper semantically models physical entities whose information is collected by sensor networks at a level higher than sensors and their observations. Hence, first, an ontology is presented for semantically modelling physical entities... 

The semantic sensor web adds semantic web technologies such as ontology to sensor network. Semantic technologies can help the better management of query and data aggregation of the sensor network. So far, several ontologies have been presented for the semantic presentation of sensor networks concepts. However, applications and end-users require physical entities information rather than technical details and information regarding sensors and sensor network. This paper semantically models physical entities whose information is collected by sensor networks at a level higher than sensors and their observations. Hence, first, an ontology is presented for semantically modelling physical entities... 

Grid computing is a type of parallel and distributed system that provides the possibility of sharing, choosing and collecting the autonomous resources (such as computer, software, databases, and equipments) that are distributed geographically. Resource discovery is one of the key subjects in distributed systems and especially Grids. We should consider that Grid consists of different applications that each of them consist of different hardware and software resources, therefore recognizing these resources is important in a Grid system. Classical approaches to Grid resource discovery are either centralized or hierarchical [1] (and will maybe prove inefficient as the scale of Grid systems... 

Pronoun resolution is one of the challenges of natural language processing (NLP). The proposed solutions range from heuristic rule-based to machine learning data driven approaches. In this article, we follow a previous machine learning approach on Persian pronoun anaphora resolution. The primary goal of this paper is to improve the results, mainly by extracting more balanced data through using heuristic rules in instance sampling, and utilizing more relevant features in classification. Using PCAC2008 dataset, we consider noun phrase structure as a way to extract more suitable training data. Incorporated features include syntactic and semantic information. Finally, we train and test different...