Sharif Digital Repository

Temporal relation classification is one of the contemporary demanding tasks in natural language processing. This task can be used in various applications such as question answering, summarization, and language specific information retrieval. In this paper, we propose an improved algorithm for classifying temporal relations between events and times, using support vector machines (SVM). Along with gold-standard corpus features, the proposed method aims at exploiting useful syntactic features, which are automatically generated, to improve accuracy of the classification. Accordingly, a number of novel kernel functions are introduced and evaluated for temporal relation classification. The result... 

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... 

One of the most challenging tasks in computer vision is human action recognition. The recent development of depth sensors has created new opportunities in this field of research. In this paper, a novel supervised spatio-temporal kernel descriptor (SSTKDes) is proposed from RGB-depth videos to establish a discriminative and compact feature representation of actions. To enhance the descriptive and discriminative ability of the descriptor, extracted primary kernel-based features are transformed into a new space by exploiting a supervised training strategy; i.e., large margin nearest neighbor (LMNN). The LMNN highly reduces the error of a nearest neighbor classifier by minimizing the intra-class... 

One of the most challenging tasks in computer vision is human action recognition. The recent development of depth sensors has created new opportunities in this field of research. In this paper, a novel supervised spatio-temporal kernel descriptor (SSTKDes) is proposed from RGB-depth videos to establish a discriminative and compact feature representation of actions. To enhance the descriptive and discriminative ability of the descriptor, extracted primary kernel-based features are transformed into a new space by exploiting a supervised training strategy; i.e., large margin nearest neighbor (LMNN). The LMNN highly reduces the error of a nearest neighbor classifier by minimizing the intra-class... 

Graph based methods are among the most active and applicable approaches studied in semi-supervised learning. The problem of neighborhood graph construction for these methods is addressed in this paper. Neighborhood graph construction plays a key role in the quality of the classification in graph based methods. Several unsupervised graph construction methods have been proposed that have addressed issues such as data noise, geometrical properties of the underlying manifold and graph hyper-parameters selection. In contrast, in order to adapt the graph construction to the given classification task, many of the recent graph construction methods take advantage of the data labels. However, these... 

Recently, super gas wet and gas wet surfaces have been extensively attended in petroleum industry, as supported by the increasing number of publications in the last decade related to wettability alteration in gas condensate reservoirs. In many cases, contact angle measurement has been employed to assess the wettability alteration. Even though contact angle measurement seems to be a straightforward approach, there exist many misuses of this technique and consequently misinterpretation of the corresponding results. In this regard, a critical inspection of the most recent updated concepts and the intervening parameters in the contact angle based wettability evaluation of liquid-solid-gas... 

In this paper, we aim to improve meeting summarization performance using discourse specific information. Since there are intrinsically different characteristics in utterances in different types of function segments, e.g., Monologue segments versus Discussion ones, we propose a new summarization framework where different summarizers are used for different segment types. For monologue segments, we adopt the integer linear programming-based summarization method; whereas for discussion segments, we use a graph-based method to incorporate speaker information. Performance of our proposed method is evaluated using the standard AMI meeting corpus. Results show a good improvement over previous... 

Cobalt is the most common binder in cemented carbides industry. However, there are some interests in use of alternatives. The similarity in properties has made nickel the first choice. In the present work, the effect of initial composition on modern hardmetals containing transition metal carbides/carbonitrides that are called "cemented carbonitrides" with nickel as binder was investigated. Change in quantity of additive carbides and tungsten to carbon (C/W) weight ratio through applying metallic tungsten powder in primary powder mixture had some effects on final hardness, transverse rupture strength, and microstructure of studied alloys. Addition of vanadium carbide not more than 0.2 wt.%,... 

Mixed-criticality systems are introduced due to industrial interest to integrate different types of functionalities with varying importance into a common and shared computing platform. Low-energy consumption is vital in mixed-criticality systems due to their ever-increasing computation requirements and the fact that they are mostly supplied with batteries. In case when high-criticality tasks overrun in such systems, low-criticality tasks can be whether ignored or degraded to assure high-criticality tasks timeliness. We propose a novel energy management method (called Stretch), which lowers the energy consumption of mixed-criticality systems with the cost of degrading service level of... 

Drawing an accurate relationship between settling time and the power consumption of the amplifier is a challenging problem in Switch Capacitor circuits especially when it includes non-linear effects. In this paper, a new method for the estimation of this relationship including both non-linear settling as a result of slew-rate and small signal settling in the 3 rd order amplifier is proposed. The results show that the proposed settling time estimation is more accurate than other conventional methods when it is compared with the circuit level simulations. The proposed method has error smaller than 10% for the third order OpAmp in estimating settling error. This is about two times more accurate... 

Appearances of specific transition patterns during data transfer in bus lines of modern high-performance computing systems, such as communicating structures of accelerators for deep convolutional neural networks, commercial Network on Chips, and memories, can lead to crosstalk faults. With the shrinkage of technology size, crosstalk faults occurrence boosts and leads to degradation of reliability and performance, as well as the increasing power consumption of lines. One effective way to alleviate crosstalk faults is to avoid the appearance of these specific transition patterns by using numerical-based crosstalk avoidance codes (CACs). However, a serious problem with numerical-based CACs is... 

The trend in biomaterials development has now headed for tailoring the properties and making hybrid materials to achieve the optimal performance metrics in a product. Modern manufacturing processes along with advanced computational techniques enable systematical fabrication of new biomaterials by design strategy. Functionally graded materials as a recent group of hybrid materials have found numerous applications in biomedical area, particularly for making orthopedic prostheses. This article, therefore, seeks to address the following research questions: (RQ1) What is the desired structure of orthopedic hybrid materials? (RQ2) What is the contribution of the literature in the development of... 

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... 

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... 

In this paper, we combine Hidden Markov Models HMMs with i-vector extractors to address the problem of text-dependent speaker recognition with random digit strings. We employ digit-specific HMMs to segment the utterances into digits, to perform frame alignment to HMM states and to extract Baum-Welch statistics. By making use of the natural partition of input features into digits, we train digit-specific i-vector extractors on top of each HMM and we extract well-localized i-vectors, each modelling merely the phonetic content corresponding to a single digit. We then examine ways to perform channel and uncertainty compensation, and we propose a novel method for using the uncertainty in the... 

In this article a new scheme is proposed to use mean supervector in text-prompted speaker verification system. In this scheme, for each month name a subsystem is constructed and a final score based on passphrase is computed by the combination of the scores of these subsystems. Results from the telephony dataset of Persian month names show that the proposed method significantly reduces EER in comparison with the-State-of-the-art State-GMM-MAP method. Furthermore, it is shown that based on training set and testing set we can use 12 models per speaker instead of 220. Therefore, this scheme reduces EER and computational burden. In addition, the use of HMM instead of GMM as words' model improves... 

Classification of video events based on frame-level descriptors is a common approach to video recognition. In the meanwhile, proper encoding of the frame-level descriptors is vital to the whole event classification procedure. While there are some pretty efficient video descriptor encoding methods, temporal ordering of the descriptors is often ignored in these encoding algorithms. In this paper, we show that by taking into account the temporal inter-frame dependencies and tracking the chronological order of video sub-events, accuracy of event recognition is further improved. First, the frame-level descriptors are extracted using convolutional neural networks (CNNs) pre-trained on ImageNet,... 

Human action recognition from skeletal data is one of the most popular topics in computer vision which has been widely studied in the literature, occasionally with some very promising results. However, being supervised, most of the existing methods suffer from two major drawbacks; (1) too much reliance on massive labeled data and (2) high sensitivity to outliers, which in turn hinder their applications in such real-world scenarios as recognizing long-term and complex movements. In this paper, we propose a novel unsupervised 3D action recognition method called Sparseness Embedding in which the spatiotemporal representation of action sequences is nonlinearly projected into an unwarped feature... 

In this paper, we propose an algorithm for recovery of sparse and low-rank components of matrices using an iterative method with adaptive thresholding. In each iteration of the algorithm, the low-rank and sparse components are obtained using a thresholding operator. The proposed algorithm is fast and can be implemented easily. We compare it with the state-of-the-art algorithms. We also apply it to some applications such as background modeling in video sequences, removing shadows and specularities from face images, and image restoration. The simulation results show that the proposed algorithm has a suitable performance with low run-time. © 2017 Elsevier Inc