Sharif Digital Repository

This paper presents a new Compressive Sensing (CS) scheme for detecting network congested links. We focus on decreasing the required number of measurements to detect all congested links in the context of network tomography. We have expanded the LASSO objective function by adding a new term corresponding to the prior knowledge based on the relationship between the congested links and the corresponding link Betweenness Centrality (BC). The accuracy of the proposed model is verified by simulations on two real datasets. The results demonstrate that our model outperformed the state-of-the-art CS based method with significant improvements in terms of F-Score  

Sensor networks usually generate mass of data, which if not structured for future applications, will require much effort on analytical processing and interpretations. Thus, storing sensor data in an effective and structured format is a key issue in the area of sensor networks. In the meantime, even a little improvement on data storing structure may lead to a significant effect on the lifetime and performance of the sensor network. This paper describes a new method for sensor storage that combines semantic web concepts, a data aggregation method along with aligning sensors in hierarchical form. This solution is able to reduce the amount of data stored at the sink nodes significantly. At the... 

We construct an efficient statistical zero-knowledge authentication protocol for smart cards based on general assumptions. We show how it can be instantiated using lattice-based primitives, which are conjectured to be secure against quantum attacks. We illustrate the practicality of our protocol on smart cards in terms of storage, computation, communication, and round complexities. Furthermore, we compare it to other lattice-based authentication protocols, which are either zero-knowledge or have a similar structure. The comparison shows that our protocol improves the best previous protocol in several aspects  

Recent evaluations of mapping systems show that lack of background knowledge, most often domain specific knowledge, is one of the key problems of mapping systems these days. In fact, at present, most state of the art systems, for the tasks of mapping large ontologies, perform not with such high values of recall (~ 30%), because they mainly rely on label and structure based similarity measures. Disregarding context knowledge in ontology mapping is another drawback that almost all current approaches suffer from. In this paper we use the semantic web as background knowledge and introduce a novel approach for capturing context knowledge from the ontology for improving mapping results. We have... 

Fuzzy logic is a suitable approach for linguistic knowledge representation and Artificial Life (A-Life) is a field of study dedicated to studying life and synthesis of creatures like those of real world. Thus, as our understanding about life is more in linguistic form than exact numbers, fuzzy logic seems to be a suitable logic for artificial creatures. In this paper, we have tried to apply fuzzy logic to an A-life environment and have studied the changes in capabilities of its creatures when using fuzzy minds. To do this, we have selected an A-life model called Zamin, which has creatures with case-based reasoner brains who must learn their environmental rules, search for food, avoid... 

This paper presents UOWD-Sharif team's approach for XML information retrieval. This approach is an extension of PLIR which is an experimental knowledge-based information retrieval system. This system like PLIR utilizes plausible inferences to first infer the relevance of sentences in XML documents and then propagates the relevance to the other textual units in the document tree. Two approaches have been used for propagation of confidence. The first approach labeled "propagate-DS" first propagates the confidence from sentences to upper elements and then combines these evidences by applying Dempster-Shafer theory of evidence to estimate the confidence in that element. The second approach... 

Business process reengineering (BPR) can help organisations to identify and improve their business processes. A major problem is the high volume of business process datasets with characteristics such as high dimensionality, noise, uncertainty in process datasets and complicated interactions among process variables. Data mining (DM) techniques facilitate the identification and analysis of business processes, and improve their performance by extracting the hidden knowledge in business process datasets. In this paper, we present the application of DM to BPR, based on two novel approaches. By a literature review, the first approach proposes DMbBPR model, mainly focuses on the applications of... 

In real applications of hub networks, the travel times may vary due to traffic, climate conditions, and land or road type. To facilitate this difficulty, in this paper, the travel times are assumed to be characterized by trapezoidal fuzzy variables to present a fuzzy capacitated single allocation p-hub centre transportation (FCSApHCP) with uncertain information. The proposed FCSApHCP is redefined into its equivalent parametric integer non-linear programming problem using credibility constraints. The aim is to determine the location of p capacitated hubs and the allocation of centre nodes to them in order to minimize the maximum travel time in a hub-and-centre network under uncertain... 

As Gregory Bateson, the British social scientists, once said "You can't live without an eraser", the process of learning is not a unidirectional way toward accumulation of knowledge, rather, organizations should critically evaluate the value of their knowledge and if necessary, actively forget no-longer-useful knowledge. A brief review of related literature shows that the concept of knowledge active forgetting (KAF) is a crucial aspect of organizational learning and knowledge management. However, many aspects of this topic deserve more attention by researchers. The main gap in the literature seems to be related to the linkage between peculiar aspects of knowledge (such as stickiness, context... 

Energy innovation is a key requirement to limit global warming and tackle climate change in the years to come. A better understanding of the public R&D mechanism is likely to improve allocation of resources for energy innovation. Thus, the present paper evaluates the impacts of public R&D and knowledge spillovers on the development of renewable energy sources. To achieve this goal, knowledge flow has been modeled as a function of public R&D expenditures, cumulative knowledge stocks and knowledge spillovers. To show the application of the model, the Nordic countries as one of the pioneers in renewable technologies have been chosen. Results show the cumulative knowledge stock will increase to... 

Twitter is among the most used microblogging and online social networking services. In Twitter, a name, phrase, or topic that is mentioned at a greater rate than others is called a «trending topic» or simply «trend». Twitter trends has shown their powerful ability in many public events, elections and market changes. Nevertheless, there has been very few works focusing on understanding the dynamics of these trending topics. In this article, we thoroughly examined the Twitter's trending topics of 2018. To this end, we accessed Twitter's trends API for the full year of 2018, and devised six criteria to evaluate our dataset. These six criteria are: lexical analysis, time to reach, trend... 

Steganography is a method for hidden exchange of information by hiding data in a cover media such as image or sound. Text Steganography is one of the most difficult methods because a text file is not a proper media to hide data in it. In this paper we propose a new text Steganography method. In this method, we hide data in TeX documents. This method hides the data in places where there is a ligature such as "fi". © 2008 IEEE  

Clustering the web documents is one of the most important approaches for mining and extracting knowledge from the web. Recently, one of the most attractive trends in clustering the high dimensional web pages has been tilt toward the learning and optimization approaches. In this paper, we propose novel hybrid harmony search (HS) based algorithms for clustering the web documents that finds a globally optimal partition of them into a specified number of clusters. By modeling clustering as an optimization problem, first, we propose a pure harmony search-based clustering algorithm that finds near global optimal clusters within a reasonable time. Then, we hybridize K-means and harmony clustering... 

Taxonomy learning is one of the major steps in ontology learning process. Manual construction of taxonomies is a time-consuming and cumbersome task. Recently many researchers have focused on automatic taxonomy learning, but still quality of generated taxonomies is not satisfactory. In this paper we have proposed a new compound similarity measure. This measure is based on both knowledge poor and knowledge rich approaches to find word similarity. We also used Neural Network model for combination of several similarity methods. We have compared our method with simple syntactic similarity measure. Our measure considerably improves the precision and recall of automatic generated taxonomies. ©...