Sharif Digital Repository

Background: Long reads provide valuable information regarding the sequence composition of genomes. Long reads are usually very noisy which renders their alignments on the reference genome a daunting task. It may take days to process datasets enough to sequence a human genome on a single node. Hence, it is of primary importance to have an aligner which can operate on distributed clusters of computers with high performance in accuracy and speed. Results: In this paper, we presented IMOS, an aligner for mapping noisy long reads to the reference genome. It can be used on a single node as well as on distributed nodes. In its single-node mode, IMOS is an Improved version of Meta-aligner (IM)... 

In real applications, there are situations where we need to model some problems based on uncertain data. This leads us to define an uncertain model for some classical geometric optimization problems and propose algorithms to solve them. The assigned version of the k-center problem for n uncertain points in a metric space is studied in this paper. The main approach is to replace each uncertain point with a clever choice of a certain point. We argue that the k-center solution for these certain replacements of our uncertain points, is a good constant approximation factor for the original uncertain k-center problem. This approach enables us to present fast and simple algorithms that give... 

There are some elements such as competition among companies and changes in demands which result in changes of customers' behaviors. Therefore, paying no attention to these changes may lead to a reduction in company benefits and loss of customers. Since data and their analyses determine the activities and decision makings of companies, data quality is of paramount in analyzing them because misinformation leads to wrong decision making. Since data mining has been designed to find out multi repetition patterns, it can be used to improve the product sales violations by sales people and increase the quality of data. Most of data mining models available try to find patterns in one table, but the... 

A total of 21 833 inhibitors of the central nervous system (CNS) were collected from Binding-database and analyzed using discriminant analysis (DA) techniques. A combination of genetic algorithm and quadratic discriminant analysis (GA-QDA) was proposed as a tool for the classification of molecules based on their therapeutic targets and activities. The results indicated that the one-against-one (OAO) QDA classifiers correctly separate the molecules based on their therapeutic targets and are comparable with support vector machines. These classifiers help in charting the chemical space of the CNS inhibitors and finding specific subspaces occupied by particular classes of molecules. As a next... 

A Comprehensive speech database is one of the important tools for developing speech recognition systems; these tools are necessary for telephony recognition, too. Although adequate databases for direct speech recognizers exist, there is not an appropriate database for telephony speech recognizers. Most methods suggested for solving this problem are based on building new databases which tends to consume much time and many resources; or they used a filter which simulates circuit switch behavior to transform direct databases to telephony ones, in this case resulted databases have many differences with real telephony databases. In this paper we introduce a framework for creating telephony speech... 

Fuzzy systems have demonstrated their ability to solve different kinds of problems in various applications domains. Currently, there is an increasing interest to augment fuzzy systems with learning and adaptation capabilities. Two of the most successful approaches to hybridize fuzzy systems with learning and adaptation methods have been made in the realm of soft computing. Neural fuzzy systems and genetic fuzzy systems hybridize the approximate reasoning method of fuzzy systems with the learning capabilities of neural networks and evolutionary algorithms. The objective of this paper is to describe a fuzzy genetics-based learning algorithm and discuss its usage to detect intrusion in a... 

The aim of the present study is to provide an Atlas of IRAN Offshore Renewable Energy Resources (hereafter called 'the Atlas') to map out wave and tidal resources at a national scale, extending over the area of the Persian Gulf and Sea of Oman. Such an Atlas can provide necessary tools to identify the areas with greatest resource potential and within reach of present technology development. To estimate available tidal energy resources at the site, a two-dimensional tidally driven hydrodynamic numerical model of Persian Gulf was developed using the hydrodynamic model in the MIKE 21 Flow Model (MIKE 21HD), with validation using tidal elevation measurements and tidal stream diamonds from... 

In recent years, IWR (Isolated Word Recognition) was one of the main concerns of speech processing. The challenging problems in this field appear when the database become so large or when we have a lot of word with similarly pronounce in the database. This paper introduces a general solution for a traditional problem in isolated similarly pronounced word recognition, especially in large databases. One the important problem of traditional IWR is referred to their segmentation algorithm, their methods were lacking in efficiency due to the following reasons: First, using equal segmentation is not at all intelligent at all and as a result, cannot produce accurate results; besides, utilizing... 

A novel distance measure for distance-based speaker segmentation is proposed. This distance measure is nonparametric, in contrast to common distance measures used in speaker segmentation systems, which often assume a Gaussian distribution when measuring the distance between two audio segments. This distance measure is essentially a k-nearest-neighbor distance measure. Non-vowel segment removal in preprocessing stage is also proposed. Speaker segmentation performance is tested on artificially created conversations from the TIMIT database and two AMI conversations. For short window lengths, Missed Detection Rated is decreased significantly. For moderate window lengths, a decrease in both... 

The authors deal with the robust design of multiple-input-multiple-output (MIMO) space-time transmit code (STTC) and space-time receive filter (STRF) for a point-like target embedded in signal-dependent interference. Specifically, they assume that the radar exploits knowledge provided by dynamic environmental database, to roughly predict the actual scattering scenario. Then, they devise an iterative method to optimise the (constrained) STTC and the (constrained) STRF which sequentially improves the worst-case (over interfering scatterers statistics) signal-to-interference-plus-noise ratio (SINR). Each iteration of the algorithm is handled via solving two (hidden) convex optimisation... 

This paper presents a new interpretable fuzzy classification system. Simulated annealing heuristic is employed to effectively investigate the large search space usually associated with classification problem. Here, two criteria are used to evaluate the proposed method. The first criterion is accuracy of extracted fuzzy if-then rules, and the other is comprehensibility of obtained rules. Experiments are performed with some data sets from UCI machine learning repository. Results are compared with several well-known classification algorithms, and show that the proposed approach provides more accurate and interpretable classification system. © 2009 IEEE  

Automatic image annotation is a challenging problem in the field of image retrieval. Dealing with large databases makes the annotation problem more difficult and therefore an effective approach is needed to manage such databases. In this work, an annotation system has been developed which considers images in separate categories and constructs a profiling model for each category. To describe an image, we propose a new feature extraction method based on color and texture information that describes image content using discrete distribution signatures. Image signatures of one category are partitioned using spectral clustering and a prototype is determined for each cluster by solving an... 

It is of fundamental importance to find algorithms obtaining optimal performance for learning of statistical models in distributed and communication limited systems. Aiming at characterizing the optimal strategies, we consider learning of Gaussian Processes (GP) in distributed systems as a pivotal example. We first address a very basic problem: how many bits are required to estimate the inner-products of some Gaussian vectors across distributed machines? Using information theoretic bounds, we obtain an optimal solution for the problem which is based on vector quantization. Two suboptimal and more practical schemes are also presented as substitutes for the vector quantization scheme. In... 

In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed. In our model, samples are stored in a set of distributed machines where each machine has access to only a subset of features. A central machine is then responsible for learning the structure based on received messages from the other nodes. We present a set of communication-efficient strategies, which are theoretically proved to convey sufficient information for reliable learning of the structure. In particular, our analyses show that even if each machine sends only the signs of its local data samples to the central node, the tree structure can still be recovered with high accuracy. Our... 

In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed. In our model, samples are stored in a set of distributed machines where each machine has access to only a subset of features. A central machine is then responsible for learning the structure based on received messages from the other nodes. We present a set of communication-efficient strategies, which are theoretically proved to convey sufficient information for reliable learning of the structure. In particular, our analyses show that even if each machine sends only the signs of its local data samples to the central node, the tree structure can still be recovered with high accuracy. Our... 

In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed. In our model, samples are stored in a set of distributed machines where each machine has access to only a subset of features. A central machine is then responsible for learning the structure based on received messages from the other nodes. We present a set of communication-efficient strategies, which are theoretically proved to convey sufficient information for reliable learning of the structure. In particular, our analyses show that even if each machine sends only the signs of its local data samples to the central node, the tree structure can still be recovered with high accuracy. Our... 

Since their introduction, Cognitive Radio Networks (CRN), as a new solution to the problem of spectrum scarcity, have received great attention from the research society. An important field in database-driven CRN studies is pivoted on their security issues. A critical issue in this context is user’s location privacy, which is potentially under serious threat. The query process by secondary users (SU) from the database is one of the points where the problem rises. In this paper, we propose a Privacy-Preserving Query Process (PPQP), accordingly. This method lets SUs deal in the process of spectrum query without sacrificing their location information. Analytical assessment of PPQP’s privacy... 

Hi-C is a genome-wide chromosome conformation capture technology that detects interactions between pairs of genomic regions and exploits higher order chromatin structures. Conceptually Hi-C data counts interaction frequencies between every position in the genome and every other position. Biologically functional interactions are expected to occur more frequently than transient background and artefactual interactions. To identify biologically relevant interactions, several background models that take biases such as distance, GC content and mappability into account have been proposed. Here we introduce MaxHiC, a background correction tool that deals with these complex biases and robustly... 

This paper presents the results of the shared task with the aim of arousal level recognition for the competition held in conjunction with the 27th Iranian Conference on Electrical Engineering (ICEE 2019). A database annotated with arousal level labels released by Research Center for Development of Advanced Technologies. The contest was held on arousal database according to a defined protocol. Three teams were able to enter into the final stage of the competition according to compare their performance measure with the baseline method. The baseline method is proposed by the data owner. The aim of this paper is outlining the database, protocol design, and providing an overview of top-ranked... 

Today, communication science is advancing very rapidly; one of these advances is mobile location determination which means finding position of a mobile station. There are some methods to location determination; one of these methods is Signal Strength (Power of Arrivals). In this research, Mobile Location Determination using enhanced signature database method in a Location grid will be introduced; several ways for positioning error reduction will also be described. © 2009 IEEE