Ebrahim Ansari

Institute for Advanced Studies in Basic Sciences | IASBS · Department of Computer Science and Information Technology

This paper presents an automatic speech translation system aimed at live subtitling of conference presentations. We describe the overall architecture and key processing components. More importantly, we explain our strategy for building a complex system for end-users from numerous individual components, each of which has been tested only in laborato...

The rapid growth of malicious software (malware) production in recent decades and the increasing number of threats posed by malware to network environments, such as the Internet and intelligent environments, emphasize the need for more research on the security of computer networks in information security and digital forensics. The method presented...

Language recognition has been significantly advanced in recent years by means of modern machine learning methods such as deep learning and benchmarks with rich annotations. However, research is still limited in low-resource formal languages. This consists of a significant gap in describing the colloquial language especially for low-resourced ones s...

This paper considers the task of matching images and sentences by learning a visual-textual embedding space for cross-modal retrieval. Finding such a space is a challenging task since the features and representations of text and image are not comparable. In this work, we introduce an end-to-end deep multimodal convolutional-recurrent network for le...

LSCP is hierarchically organized in asemantic taxonomy that focuses on multi-task informal Persian language understanding as a comprehensive problem. This encompasses the recognition of multiple semantic aspects in the human-level sentences, which naturally captures from the real-world sentences. The LSCP has 120M sentences from 27M casual Persian...

Internet continues to evolve and touches every aspect of our daily life thus communications through internet is becoming inevitable. Computer security has been hence becoming one of the important concerns of internet users. Malware, a malicious software, is a harmful code that poses security thread for infected machines, thus malware detection has...

Nowadays, automobile manufacturers make efforts to develop ways to make cars fully safe. Monitoring driver’s actions by computer vision techniques to detect driving mistakes in real-time and then planning for autonomous driving to avoid vehicle collisions is one of the most important issues that has been investigated in the machine vision and Intel...

Learn more about this project at https://iasbs.ac.ir/~ansari/faraz/

Today, third-party JavaScript resources are an indispensable part of the web platform. More than 88% of the world’s top websites include at least one JavaScript resource from a remote host. However, there is a great security risk behind using a third-party JavaScript resource, if an attacker can infect one of these remote JavaScript resources all w...

This book presents outstanding theoretical and practical findings in data science and associated interdisciplinary areas. Its main goal is to explore how data science research can revolutionize society and industries in a positive way, drawing on pure research to do so. The topics covered range from pure data science to fake news detection, as well...

Word sense disambiguation is the task of assigning the correct sense of a polysemous word in the context in which it appears. In recent years, word embeddings have been applied successfully to many NLP tasks. Thanks to their ability to capture distributional semantics, more recent attention have been focused on utilizing word embeddings to disambig...

In this work, we introduce a new large hand-annotated morpheme-segmentation lexicon of Persian words and present an algorithm that builds a morphological network using this segmented lexicon. The resulting network captures both derivational and inflectional relations. The algorithm for inducing the network approximates the distinction between root m...

Bilingual dictionaries are very important in various fields of natural language processing. In recent years, research on extracting new bilingual lexicons from non-parallel (comparable) corpora have been proposed. Almost all use a small existing dictionary or other resources to make an initial list called the "seed dictionary". In this paper, we di...

Morphological segmentation of words is the process of dividing a word into smaller units called morphemes; it is tricky especially when a morphologically rich or polysynthetic language is under question. In this work, we designed and evaluated several Recurrent Neural Network (RNN) based models as well as various other machine learning-based approa...

Supervised Word Sense Disambiguation (WSD) systems use features of the target word and its context to learn about all possible samples in an annotated dataset. Recently, word embeddings have emerged as a powerful feature in many NLP tasks. In supervised WSD, word embeddings can be used as a high-quality feature representing the context of an ambigu...

The rapid growth of malicious software (malware) production in recent decades and the increasing number of threats posed by malware to network environments, such as the Internet and intelligent environments, emphasize the need for more research on the security of computer networks in information security and digital forensics. The method presented...

This dataset includes 45300 Persian word forms which are manually segmented into sequences of morphemes.

Today, third-party JavaScript resources are indispensable part of the web platform. More than 88\% of world's top websites include at least one JavaScript resource from a remote host. However, there is a great security risk behind using a third-party JavaScript resource, if an attacker can infect one of these remote JavaScript resources all website...

Nowadays, automobile manufacturers make efforts to develop ways to make cars fully safe. Monitoring driver's actions by computer vision techniques to detect driving mistakes in real-time and then planning for autonomous driving to avoid vehicle collisions is one of the most important issues that has been investigated in the machine vision and Intel...

Information hiding methods are currently deployed by many security developers for various applications, hence the remarkable value of proposing an efficient and feasible information hiding method is essential. In this paper, two new implementations and algorithms are presented and compared with classic and related ones. First, a new implementation...

A different molecular mechanism underlying human papilloma virus (HPV)‐negative and HPV‐active pathogenesis is responsible for better response to therapies in HPV‐associated oropharyngeal squamous cell carcinoma (OPSCC). In this study, we aim to provide an insight into molecular basis underlying this distinction and introduce possible targeted ther...

In this paper we propose a new optimization for Apriori-based association rule mining algorithms where the frequency of items can be encoded and treated in a special manner drastically increasing the efficiency of the frequent itemset mining process. An efficient algorithm, called TFI-Apriori, is developed for mining the complete set of frequent it...

Image editing is an important part of the computational photography. The growing number of digital images in recent years has increased the need to develop powerful editing tools. Every implemented algorithm should be fast enough for users to see the results instantly (real-time manipulation) and control the whole process on the fly. PatchMatch is...

Parallel data are an important part of a reliable Statistical Machine Translation (SMT) system. The more of these data are available, the better the quality of the SMT system. However, for some language pairs such as Persian-English, parallel sources of this kind are scarce. In this paper, a bidirectional method is proposed to extract parallel sent...

The effectiveness of a statistical machine translation system (SMT) is very dependent upon the amount of parallel corpus used in the training phase. For low-resource language pairs there are not enough parallel corpora to build an accurate SMT. In this paper, a novel approach is presented to extract bilingual Persian-Italian parallel sentences from...

Bilingual dictionaries are very important in various fields of natural language processing. In recent years, research on extracting new bilingual lexicons from non-parallel (comparable) corpora have been proposed. Almost all use a small existing dictionary or other resource to make an initial list called the "seed dictionary". In this paper we disc...

In recent years, many studies on extracting new bilingual lexicons from non-parallel (comparable) corpora have been proposed. Nearly all apply an existing small dictionary or other resource to make an initial list named seed dictionary. In this paper we discuss on using different types of dictionaries and their combinations as the initial starting...

Presenting an efficient general feature selection method for the problem of the curse of dimensionality is still an open problem in pattern recognition, and, considering the cooperation among features through search processes, it is the most important challenge. In this paper, a combinatorial approach has been proposed, which consists of three feat...

Many problems in sciences and industry such as signal optimization, traffic assignment, economic market,… have been modeled, or their mathematical models can be approximated, by linear bilevel programming (LBLP) problems, where in each level one must optimize some objective functions. In this paper, we use fuzzy set theory and fuzzy programming to...

Finding association rules is one of the most investigated fields of data mining. Computation and communication are two important factors in distributed association rule mining. In this problem Association rules are generated by first mining of frequent itemsets in distributed data. In this paper we proposed a new distributed trie-based algorithm (D...

The curse of dimensionality is still a big problem in the pattern recognition field. Feature extraction and feature selection have been presented as two general solutions for this problem. In this paper, a new approach based on combination of these methods has been proposed to classify different classes in large dimensional problems. Among the vast...

Finding association rules is one of the most investigated fields of data mining. Computation and communication are two important factors in distributed association rule mining. In this problem Association rules are generated by first mining of frequent itemsets in distributed data. In this paper we proposed a new distributed trie-based algorithm (D...

Finding association rules is one of the most investigated fields of data mining. Computation and communication are two important factors in distributed association rule mining. In this problem Association rules are generated by first mining of frequent itemsets in distributed data. In this paper we proposed a new distributed trie-based algorithm (D...