Sharif Digital Repository

Web search engines play a significant role in answering users’ information needs based on the huge amount of data available on the internet. Although evaluating the performance of these systems is very important for their improvement, there is no comprehensive, unbiased, low-cost, and reusable method for this purpose. Previous works used a small and limited set of queries for their evaluation process that restricts the assessment domain. Moreover, these methods mainly rely on human evaluators for manual assessment of search engines which makes the results of the evaluation subjective to the opinion of human evaluators and also prone to error. In addition, repeating the evaluation would be a... 

The number of digital images uploaded in the virtual world is rapidly growing every day. Therefore, an automatic image annotation system that can retrieve information from these images seems to be in high demand. One of the challenges in this field is the imbalanced data sets and the difficulty of successfully learning tags from them. Even if a nearly balanced data set exists for image annotation, it is unlikely to find a single learner, which could learn all tags with the same accuracy. In this paper, we suggest a novel integration system that selects an elite group of models from all existing annotation models and then combines them to take the best advantage of each model’s learning... 

We consider the problem of private information retrieval (PIR), where a single user with private side information (PSI) aims to retrieve multiple files from a library stored at some servers. We assume that the side information (SI) at the user includes a subset of files stored privately. Moreover, the identity of requests and side information at the user are not revealed to any of the servers. The problem involves finding the minimum load transmitted from the servers to the user such that the requested files can be decoded with the help of received data and side information. By providing matching lower and upper bounds for certain regimes, we characterize the minimum load imposed on all the... 

The real-time availability of the Internet has engaged millions of users around the world. The usage of regional languages is being preferred for effective and ease of communication that is causing multilingual data on social networks and news channels. People share ideas, opinions, and events that are happening globally i.e., sports, inflation, protest, explosion, and sexual assault, etc. in regional (local) languages on social media. Extraction and classification of events from multilingual data have become bottlenecks because of resource lacking. In this research paper, we presented the event classification task for the Urdu language text existing on social media and the news channels by... 

We investigate various types of fuzzy linear programming problems based on models and solution methods. First, we review fuzzy linear programming problems with fuzzy decision variables and fuzzy linear programming problems with fuzzy parameters (fuzzy numbers in the definition of the objective function or constraints) along with the associated duality results. Then, we review the fully fuzzy linear programming problems with all variables and parameters being allowed to be fuzzy. Most methods used for solving such problems are based on ranking functions, α-cuts, using duality results or penalty functions. In these methods, authors deal with crisp formulations of the fuzzy problems. Recently,... 

In this paper, we study the problem of private function computation, where a user wants to compute a function of some inputs, using N in mathbb{N} servers, where the function is a private combination/composition of some K in mathbb{N} public basic functions {f 1 , f 2 ,., f K }. More precisely, for some inputs W m , m [1 : M], the user's goal is to calculate hleft( {{W-m}} ight) = sumolimits-{j = 1}^J {{alpha -j}} {h-j}left( {{W-m}} ight), for some J in mathbb{N}, some scalers α j , j [1 : J], and some functions h j (.), j [1 : J], where each is an arbitrary compositions of the basic functions {f 1 , f 2 ,., f K }. The computation is done through a sequence of queries to N servers. In each... 

Recently, Ghanbari and Mahdavi-Amiri (Appl Math Model 34:3363–3375, 2010) gave the general compromised solution of an LR fuzzy linear system using ABS algorithm. Here, using this general solution, we solve quadratic programming problems with fuzzy LR variables. We convert fuzzy quadratic programming problem to a crisp quadratic problem by using general solution of fuzzy linear system. By using this method, the crisp optimization problem has fewer variables in comparison with other methods, specially when rank of the coefficient matrix is full. Thus, solving the fuzzy quadratic programming problem by using our proposed method is computationally easier than the solving fuzzy quadratic... 

In this paper, we argue that in many basic algorithms for machine learning, including support vector machine (SVM) for classification, principal component analysis (PCA) for dimensionality reduction, and regression for dependency estimation, we need the inner products of the data samples, rather than the data samples themselves.Motivated by the above observation, we introduce the problem of private inner product retrieval for distributed machine learning, where we have a system including a database of some files, duplicated across some non-colluding servers. A user intends to retrieve a subset of specific size of the set of the inner product of every pair of data items in the database with... 

In some applicable scenarios, such as community patrolling, mobile nodes are restricted to move only in their own communities. Exploiting the meetings of the nodes within the same community and the nodes within the neighboring communities, a delay tolerant network (DTN) can provide communication between any two nodes. In this paper, two analytical models based on stochastic reward nets (SRNs) are proposed to evaluate the performance of the epidemic content retrieval in such multi-community DTNs. Performance measures computed by the proposed models are the average retrieval delay and the average number of transmissions. The monolithic SRN model proposed in the first step is not scalable, in... 

We consider the problem of private information retrieval (PIR) where a single user with private side information aims to retrieve multiple files from a library stored (uncoded) at a number of servers. We assume the side information at the user includes a subset of files stored privately (i.e., the server does not know the indices of these files). In addition, we require that the identity of requests and side information at the user are not revealed to any of the servers. The problem involves finding the minimum load to be transmitted from the servers to the user such that the requested files can be decoded with the help of received and side information. By providing matching lower and upper... 

In this paper, we introduce a document clustering method based on Sparse Topical Coding, called Cluster-based Sparse Topical Coding. Topic modeling is capable of improving textual document clustering by describing documents via bag-of-words models and projecting them into a topic space. The latent semantic descriptions derived by the topic model can be utilized as features in a clustering process. In our proposed method, document clustering and topic modeling are integrated in a unified framework in order to achieve the highest performance. This framework includes Sparse Topical Coding, which is responsible for topic mining, and K-means that discovers the latent clusters in documents... 

Cyber-physical systems contain networked embedded systems. Such systems may implement cryptographic algorithms for processing and/or communication. Therefore, they can be prone to side-channel attacks. Differential power analysis is one of such attacks, which is considered among the most serious threats against cryptographic devices. Various metrics have been proposed to evaluate the resistance of different implementations against these attacks. Some of these metrics need side-channel attacks to be conducted and depend on the considered power model. Due to the vast variety of proposed side-channel attacks and power models, comprehensively evaluating a design under these metrics is commonly... 

We discuss how to solve bipolar fuzzy quadratic programming problems, where the parameters are bipolar triangular fuzzy numbers, making use of linear ranking functions. Also, we explore some duality properties of bipolar triangular fuzzy number quadratic programming problem (BTFNQPP). © 2017 IEEE  

The widespread use of cloud computing services raises the question of how one can delegate the processing tasks to the untrusted distributed parties without breaching the privacy of its data and algorithms. Motivated by the algorithm privacy concerns in a distributed computing system, in this paper, we introduce the private function retrieval (PFR) problem, where a user wishes to efficiently retrieve a linear function of K messages from N non-communicating replicated servers while keeping the function hidden from each individual server. The goal is to find a scheme with minimum communication cost. To characterize the fundamental limits of the communication cost, we define the capacity of PFR... 

Users in social networks whose posts stay at the top of their followers' feeds the longest time are more likely to be noticed. Can we design an online algorithm to help them decide when to post to stay at the top? In this paper, we address this question as a novel optimal control problem for jump stochastic differential equations. For a wide variety of feed dynamics, we show that the optimal broadcasting intensity for any user is surprisingly simple - it is given by the position of her most recent post on each of her follower's feeds. As a consequence, we are able to develop a simple and highly efficient online algorithm, RedQueen, to sample the optimal times for the user to post.... 

With the extensive growth in information, text classification as one of the text mining methods, plays a vital role in organizing and management information. Most text classification methods represent a documents collection as a Bag of Words (BOW) model and then use the histogram of words as the classification features. But in this way, the number of features is very large; therefore performing text classification faces serious computational cost problems. Moreover, the BOW representation is unable to recognize semantic relations between words. Recently, topic-model approaches have been successfully applied for text classification to overcome the problems of BOW. Our main goal in this paper... 

Rank aggregation, that is merging multiple ranked lists, is a pivotal challenge in many information retrieval (IR) systems, especially in distributed IR and multilingual IR. From the evaluation point of view, being able to calculate the upper-bound of performance of the final aggregated list lays the ground for evaluating different aggregation strategies, independently. In this paper, we propose an algorithm based on dynamic programming which, using relevancy information, obtains the aggregated list with the maximum performance that could be possibly achieved by any aggregation strategy. We also provide a detailed proof for the optimality of the result of the algorithm. Furthermore, we... 

In this paper, we address the problem of scientific-social network integration to find a matching relationship between members of these networks. Utilizing several name similarity patterns and contextual properties of these networks, we design a focused crawler to find high probable matching pairs, then the problem of name disambiguation is reduced to predict the label of each candidate pair as either true or false matching. By defining matching dependency graph, we propose a joint label prediction model to determine the label of all candidate pairs simultaneously. An extensive set of experiments have been conducted on six test collections obtained from the DBLP and the Twitter networks to... 

Clustering has become an increasingly important and highly complicated research area for targeting useful and relevant information in modern application domains such as the World Wide Web. Recent studies have shown that the most commonly used partitioning-based clustering algorithm, the K-means algorithm, is more suitable for large datasets. However, the K-means algorithm may generate a local optimal clustering. In this paper, we present novel document clustering algorithms based on the Harmony Search (HS) optimization method. By modeling clustering as an optimization problem, we first propose a pure HS based clustering algorithm that finds near-optimal clusters within a reasonable time.... 

Recognizing TimeML events and identifying their attributes, are important tasks in natural language processing (NLP). Several NLP applications like question answering, information retrieval, summarization, and temporal information extraction need to have some knowledge about events of the input documents. Existing methods developed for this task are restricted to limited number of languages, and for many other languages including Persian, there has not been any effort yet. In this paper, we introduce two different approaches for automatic event recognition and classification in Persian. For this purpose, a corpus of events has been built based on a specific version of ISO-TimeML for Persian....