Sharif Digital Repository

Background: Once aligned, long-reads can be a useful source of information to identify the type and position of structural variations. However, due to the high sequencing error of long reads, long-read structural variation detection methods are far from precise in low-coverage cases. To be accurate, they need to use high-coverage data, which in turn, results in an extremely time-consuming pipeline, especially in the alignment phase. Therefore, it is of utmost importance to have a structural variation calling pipeline which is both fast and precise for low-coverage data. Results: In this paper, we present SVNN, a fast yet accurate, structural variation calling pipeline for PacBio long-reads... 

Background: Once aligned, long-reads can be a useful source of information to identify the type and position of structural variations. However, due to the high sequencing error of long reads, long-read structural variation detection methods are far from precise in low-coverage cases. To be accurate, they need to use high-coverage data, which in turn, results in an extremely time-consuming pipeline, especially in the alignment phase. Therefore, it is of utmost importance to have a structural variation calling pipeline which is both fast and precise for low-coverage data. Results: In this paper, we present SVNN, a fast yet accurate, structural variation calling pipeline for PacBio long-reads... 

This paper presents a state of the art review of adsorption models for a new plant-based surfactant adsorption onto sandstone minerals. The adsorption data at both kinetic and equilibrium modes were obtained from batch experiments. Four adsorption kinetic models, five two-parameter, and six three-parameter equilibrium models were used for interpretation of the obtained data. Among the two and three-parameter isotherm models applied, the Jovanovic and the Khan isotherms showed the best fit, respectively. And the pseudo-second order model presented a better fit than other kinetic models. Finally, a computer-based modeling approach was developed and used for predicting the kinetics of... 

Many real-world systems and applications such as World Wide Web, and social interactions can be modeled as networks of interacting nodes. However, in many cases, one encounters the situation where the pattern of the node-to-node interactions (i.e., edges) or the structure of a network is unknown. We address this issue by studying the Network Reconstruction Problem: Given a network with missing edges, how is it possible to uncover the network structure based on certain observable quantities extracted from partial measurements? We propose a novel framework called CS-NetRec based on a newly emerged paradigm in sparse signal recovery called Compressive Sensing (CS). The results demonstrate that... 

The current multi-channel P2P video streaming architectures still suffer from several performance problems such as low Quality of Service (QoS) in unpopular channels. The P2P systems are inherently dynamic, and their performance problems could be categorized into four groups; peer churn, channel churn, uncooperative peers, and geographical distribution of peers. In this paper, for the first time, we develop a novel locality-incentive framework for multi-channel live video streaming. We propose a hierarchical overlay network architecture by utilizing a dual-mode locality-Awareness method (spatial and temporal). Moreover, an incentive mechanism for encouraging peers to dedicate their upload... 

Data streams are endless flow of data produced in high speed, large size and usually non-stationary environments. The main property of these streams is the occurrence of concept drifts. Using decision trees is shown to be a powerful approach for accurate and fast learning of data streams. In this paper, we present an incremental regression tree that can predict the target variable of newly incoming instances. The tree is updated in the case of occurring concept drifts either by altering its structure or updating its embedded models. Experimental results show the effectiveness of our algorithm in speed and accuracy aspects in comparison to the best state-of-the-art methods  

Human detection is one of the hard problems in object detection field. There are many challenges like variation in human pose, different clothes, non-uniform illumination, cluttered background and occlusion which make this problem much harder than other object detection problems. Defining good features, which can be robust to this wide range of variations, is still an open issue in this field. To overcome this challenge, in this paper we proposed a new set of hybrid features. We combined the Histogram of Oriented Gradient (HOG) with the new features called Histogram of Small Edges (HOSE) which is introduced in this paper. These two kinds of features have two different approaches for... 

In this paper, we propose an optimization framework to retrieve an optimal group of experts to perform a given multi-aspect task/project. Each task needs a diverse set of skills and the group of assigned experts should be able to collectively cover all required aspects of the task. We consider three types of multiaspect team formation problems and propose a unified framework to solve these problems accurately and efficiently. Our proposed framework is based on Facility Location Analysis (FLA) which is a well known branch of the Operation Research (OR). Our experiments on a real dataset show significant improvement in comparison with the state-of-the art approaches for the team formation... 

In this paper, we address the problem of data description using a Bayesian framework. The goal of data description is to draw a boundary around objects of a certain class of interest to discriminate that class from the rest of the feature space. Data description is also known as one-class learning and has a wide range of applications. The proposed approach uses a Bayesian framework to precisely compute the class boundary and therefore can utilize domain information in form of prior knowledge in the framework. It can also operate in the kernel space and therefore recognize arbitrary boundary shapes. Moreover, the proposed method can utilize unlabeled data in order to improve accuracy of... 

Process-centered Software Engineering Environments, or PSEEs, are intended for the definition, modification, and enactment of software process models, they thus bring software development processes into effect. Even though research efforts in process-centered software engineering abound, PSEE technology has not received the attention that it deserves. In order to create a concise but effective and practically applicable evaluation framework for PSEEs, this paper first presents a survey of PSEEs and highlights the current state of the art of the technology. The PSEEs which have been reviewed herein have been regarded as software systems, and as such, have been characterized in terms of their... 

Since that heuristic state space planners have been very successful in classical planning, this approach is currently the most popular strategy in dealing with temporal planning, too. However, all current state-space temporal planners use a search method known as decision epoch planning, which is not complete for problems with required concurrency. In theory, this flaw can be overcome by employing another search method, called temporally lifted progression planning. In this paper, we show that there are two major problems which, if not tackled properly, can cause the latter method to be very inefficient in practice. The first problem is dealing with the remarkably large state space of... 

The spread of information cascades over social networks forms the diffusion networks. The latent structure of diffusion networks makes the problem of extracting diffusion links difficult. As observing the sources of information is not usually possible, the only available prior knowledge is the infection times of individuals. We confront these challenges by proposing a new method called DNE to extract the diffusion networks by using the time-series data. We model the diffusion process on information networks as a Markov random walk process and develop an algorithm to discover the most probable diffusion links. We validate our model on both synthetic and real data and show the low dependency... 

In this letter, we introduce a semi-supervised manifold regularization framework for human pose estimation. We utilize the unlabeled data to compensate for the complexities in the input space and model the underlying manifold by a nearest neighbor graph. We argue that the optimal graph is a subgraph of the k nearest neighbors (k-NN) graph. Then, we estimate distances in the output space to approximate this subgraph. In addition, we use the underlying manifold of the points in the output space to introduce a novel regularization term which captures the correlation among the output dimensions. The modified graph and the proposed regularization term are utilized for a smooth regression over... 

This paper proposes an efficient mechanism that mitigates crosstalk faults in Network-on-Chips (NoCs). This is done by using a Packet Manipulating mechanism called PAM for reliable data transfer of NoCs. PAM investigates the transitions of a packet to minimize the forbidden transition patterns appearing during the flit traversal in NoCs. To do this, the content of a packet is manipulated using three different manipulating mechanisms. In other words, PAM manipulates the content of packet in three manipulating modes including: vertical, horizontal and diagonal modes. Then, comparing the transitions of these manipulating mechanisms, a packet with minimum numbers of transitions is selected to be... 

In this paper, we introduce a novel approach based on Tucker Decomposition and quasi-periodic nature of ECG signal for fetal ECG extraction from abdominal ECG mixture. We adapt variable periodicity constraint of the ECG components to main objective function of the Tucker Decomposition and shape it to matrix form in order to simply optimize the objective function. We form a 3rd order tensor by stacking the mixed multichannel ECG and reconstructed fetal and maternal subspaces using BSS methods in order to have the benefit of further artificial observations, and apply our proposed penalized decomposition on it. The proposed method is evaluated on synthetic and real datasets using the criteria... 

Nowadays, the number of digital images has increased so that the management of this volume of data needs an efficient system for browsing, categorising and searching. Automatic image annotation is designed for assigning tags to images for more accurate retrieval. Non-negative matrix factorisation (NMF) is a traditional machine learning technique for decomposing a matrix into a set of basis and coefficients under the non-negative constraints. In this study, the authors propose a two-step algorithm for designing an automatic image annotation system that employs the NMF framework for its first step and a variant of K-nearest neighbourhood as its second step. In the first step, a new multimodal... 

This paper presents a state of the art review of adsorption models for a new plant-based surfactant adsorption onto sandstone minerals. The adsorption data at both kinetic and equilibrium modes were obtained from batch experiments. Four adsorption kinetic models, five two-parameter, and six three-parameter equilibrium models were used for interpretation of the obtained data. Among the two and three-parameter isotherm models applied, the Jovanovic and the Khan isotherms showed the best fit, respectively. And the pseudo-second order model presented a better fit than other kinetic models. Finally, a computer-based modeling approach was developed and used for predicting the kinetics of... 

With rapid proliferation of affordable video capturing devices and state-of-the-art video editing software tools, it is now easier than ever to manipulate video contents. In this paper a passive method for copy/move video forgery detection in MPEG videos is proposed. The method first divides each video frame into suspicious and apparently innocent parts. Subsequently, an optical flow coefficient is computed from each part. Forgeries are located when an unusual trend in the optical flow coefficient of the suspicious object is detected. Experiments on a set of forged and original sequences validate the justifications made by the proposed method  

Aggregation services play an important role in the domain of Wireless Sensor Networks (WSNs) because they significantly reduce the number of required data transmissions, and improve energy efficiency on those networks. In most of the existing aggregation methods that have been developed based on the mathematical models or functions, the user of the WSN has not access to the original observations. In this paper, we propose an algorithm which let the base station access the observations by introducing a distributed method for computing the Principal Component Analysis (PCA). The proposed algorithm is based on transmission workload of the intermediate nodes. By using PCA, we aggregate the... 

In this article, we look at early, recent, and state-of-the-art methods for registration of medical images using a range of high-performance computing (HPC) architectures including symmetric multiprocessing (SMP), massively multiprocessing (MMP), and architectures with distributed memory (DM), and nonuniform memory access (NUMA). The article is designed to be self-sufficient. We will take the time to define and describe concepts of interest, albeit briefly, in the context of image registration and HPC. We provide an overview of the registration problem and its main components in the section "Registration." Our main focus will be HPC-related aspects, and we will highlight relevant issues as...