Sharif Digital Repository

Internet traffic classification is important in many aspects of network management such as data exploitation detection, malicious user identification, and restricting application traffic. Previously, features such as port and protocol numbers have been used to classify traffic, but these features can now be changed easily, making their use in traffic classification inadequate. Consequently, traffic classification based on machine learning (ML) is now employed. The number of features used in an ML algorithm has a significant impact on performance, in particular accuracy. In this paper, a minimum best feature set is chosen using a supervised method to obtain uncorrelated features. Outlier... 

The gut-brain axis is believed to constitute a bidirectional communication mechanism that affects both mental and digestive processes. Recently, the role of the gut microbiota in cognitive performance has been the focus of much research. In this paper, we discuss the effects of gut microbiota and nutrition on spatial memory and learning. Studies have shown the influence of diet on cognitive capabilities such as spatial learning and memory. It has been reported that a high-fat diet can alter gut microbiota which subsequently leads to changes in spatial learning and memory. Some microorganisms in the gut that can significantly affect spatial learning and memory are Akkermansia muciniphila,... 

This paper presents the effect of robotics assisted language learning (RALL) on the vocabulary learning and retention of Iranian English as foreign language (EFL) junior high school students in Tehran, Iran. After taking a vocabulary pre-test, 46 beginner level female students at the age of 12, studying in their first year of junior-high participated in two groups of RALL (30 students) and non-RALL (16 students) in this study. The textbook used was the English book (Prospect-1) devised by the Iranian Ministry of Education for 7th graders, and the vocabulary taught and tested (pre-test and post-test) were taken from this book. Moreover, the treatment given by a teacher accompanied by a... 

This study aimed to examine the effect of robot assisted language learning (RALL) on the anxiety level and attitude in English vocabulary acquisition amongst Iranian EFL junior high school students. Forty-six female students, who were beginners at the age of 12, participated in this study and were randomly assigned into two groups of RALL (30 students) and non-RALL (16 students). The textbook, the materials, as well as the teacher were the same in the two groups. However in the RALL group, the treatment was given by a teacher accompanied by a humanoid robot assistant. Two questionnaires of anxiety and attitude were utilized to measure the students’ anxiety and attitude (Horwitz et al. 1986;... 

In this paper we aimed to explore young Iranian EFL learners’ attitude towards Robot Assisted Language Learning (RALL) in an English language classroom. To this end, 19 preschool children ranging from 3 to 6 years old were randomly assigned to a RALL group which had a robot as an assistant to the teacher. Their attitude towards the robot was video recorded for one month, during ten sessions of their English classroom course. Their overall attitude was examined focusing on the three factors of anxiety, motivation and interaction based on human-robot interaction (HRI) theory. The result of this study showed that children’s motivation increased as a result of interacting with the robot and... 

This study explores the effect of RALL (Robot Assisted Language Learning) on instructing greeting to young EFL (English as Foreign Language) students in Tehran, Iran. To this end, 38 preschool EFL children ranging from 3 to 6 years old were randomly assigned to the RALL (19 students) and game-based (19 students) groups, and the greeting sentences were extracted from the Functional Communication in English book [1] and ESL library website scenarios. In addition, the robot for the RALL group was used as an assistant to the teacher while for the game-based group the instruction was based on gaming methods such as command, mystery bag, and pass the ball. The instructional duration for both... 

Calibrating the inverse kinematics of complex robots is often a challenging task. Finding analytical solutions is not always possible and the convergence of numerical methods is not guaranteed. The model-free approaches, based on machine learning and artificial intelligence, are fast and easy to work, however, they need a huge amount of experimental training data to provide acceptable results. In this article, we proposed a hybrid method to benefit the advantage of both model-based and model-free approaches. The forward kinematics of the robot is calibrated using a model-based approach, and its inverse kinematics using a neural network. Hence, while there is no need to solve the nonlinear... 

Traditional AI planners often suffer from their poor efficiency. There are many choice points in the planning process, but lack of information precludes proper decision. In this paper, we introduce a new method for adding automatic learning capability to a forward planning system. Our idea is based on a dynamic voting algorithm to choose the best action to proceed to the next state. In every planning cycle, applicable actions (i.e. those actions whose preconditions are satisfied in the current world state) vote to, and compete with each other. As a result of this voting, gradually more useful actions are chosen. This idea has been applied to the blocks world domain, and the preliminary... 

Sentiment analysis, is a subfield of natural language processing that aims at opinion mining to analyze thoughts, orientation and, evaluation of users within some texts. The solution to this problem includes two main steps: extracting aspects and determining users' positive or negative sentiments with respect to the aspects. Two main challenges of sentiment analysis in the Persian language are lack of comprehensive tagged data sets and use of colloquial language in texts. In this paper we propose, a system to specify and extract sentiment words using unsupervised methods in the Persian language that also support colloquial words. Additionally, we also proposed and implemented a state-of-art... 

Extraction of fetal electrocardiogram (FECG) from the abdominal region of the mother’s skin is challenge task due to the high overlapping of maternal and fetal signals in this area. To overcome the problem, this paper proposes the utilization of extreme learning model (ELM) as the prediction algorithm to train on the FECG signal extracted by least mean square approach from the input abdominal and thoracic signals. The trained ELM model is used to model the FECG signal for the testing samples. Also, this paper investigates the firefly algorithm (FA) to tune the parameters of ELM and improve its performance. Due to the high complexity and too many parameters of FA, this paper embeds the... 

We explore the dictionary learning problem for sparse representation when the signals are dependent. In this paper, a first-order Markovian model is considered for dependency of the signals, that has many applications especially in medical signals. It is shown that the considered dependency among the signals can degrade the performance of the existing dictionary learning algorithms. Hence, we propose a method using the Maximum Log-likelihood Estimator (MLE) and the Expectation Minimization (EM) algorithm to learn the dictionary from the signals generated under the first-order Markovian model. Simulation results show the efficiency of the proposed method in comparison with the... 

Increasing access to very large and non-stationary datasets in many real problems has made the classical data mining algorithms impractical and made it necessary to design new online classification algorithms. Online learning of data streams has some important features, such as sequential access to the data, limitation on time and space complexity and the occurrence of concept drift. The infinite nature of data streams makes it hard to label all observed instances. It seems that using the semi-supervised approaches have much more compatibility with the problem. So in this paper we present a new semi-supervised ensemble learning algorithm for data streams. This algorithm uses the majority... 

Meet in the middle (MITM) attack is one of the most important and applicable methods for cryptanalysis of block ciphers. In this paper, a more generalized method for MITM attack is considered. For this purpose, a notion, namely cut-set, is utilized by which several numbers of MITM attacks can be performed. However, manual investigation on these cases is time-consuming and sometimes not error-free. Therefore, a new search algorithm is also provided to obtain proper attacks in a timely manner. For examination, this new search algorithm, which could make an automated attack along with some certain ideas, is applied on HIGHT, Piccolo-128, CRAFT and AES-128 block ciphers. The least time... 

The novel coronavirus pandemic has rapidly spread around the world since December 2019. Various techniques have been applied in identification of SARS-CoV-2 or COVID-19 infection including computed tomography imaging, whole genome sequencing, and molecular methods such as reverse transcription polymerase chain reaction (RT-PCR). This review article discusses the diagnostic methods currently being deployed for the SARS-CoV-2 identification including optical biosensors and point-of-care diagnostics that are on the horizon. These innovative technologies may provide a more accurate, sensitive and rapid diagnosis of SARS-CoV-2 to manage the present novel coronavirus outbreak, and could be... 

The novel coronavirus pandemic has rapidly spread around the world since December 2019. Various techniques have been applied in identification of SARS-CoV-2 or COVID-19 infection including computed tomography imaging, whole genome sequencing, and molecular methods such as reverse transcription polymerase chain reaction (RT-PCR). This review article discusses the diagnostic methods currently being deployed for the SARS-CoV-2 identification including optical biosensors and point-of-care diagnostics that are on the horizon. These innovative technologies may provide a more accurate, sensitive and rapid diagnosis of SARS-CoV-2 to manage the present novel coronavirus outbreak, and could be... 

Functional magnetic resonance imaging (fMRI) is a non-invasive method that helps to analyze brain function based on BOLD signal fluctuations. Functional Connectivity (FC) catches the transient relationship between various brain regions usually measured by correlation analysis. The elements of the correlation matrix are between -1 to 1. Some of them are very small values usually related to weak and spurious correlations due to noises and artifacts. They can not be concluded as real strong correlations between brain regions and their existence could make a misconception and leads to fake results. It is crucial to make a conclusion based on reliable and informative correlations. In order to... 

Functional magnetic resonance imaging (fMRI) is a non-invasive method that helps to analyze brain function based on BOLD signal fluctuations. Functional Connectivity (FC) catches the transient relationship between various brain regions usually measured by correlation analysis. The elements of the correlation matrix are between -1 to 1. Some of them are very small values usually related to weak and spurious correlations due to noises and artifacts. They can not be concluded as real strong correlations between brain regions and their existence could make a misconception and leads to fake results. It is crucial to make a conclusion based on reliable and informative correlations. In order to... 

There is a great deal of interest in the manufacturing industry for quantitative measures of process performance with multiple quality characteristics. Unfortunately, multivariate process capability indices that are currently employed, except for a handful of cases, depend intrinsically on the underlying data being normally distributed. In this paper, we propose a general multivariate capability index based on the Mahanalobis distance, which is very easy to use. We also approximate the distribution of these distances by the Burr XII distribution and then estimate its parameters using a simulated annealing search algorithm. Finally, we give an example, based on real manufacturing process... 

In this paper, we present a novel speech steganography method using discrete wavelet transform and sparse decomposition to address the undetectability concern in speech steganography. The proposed speech steganography method exploits the sparse representation to embed secret messages into higher semantic levels of the cover signal, resulting in increased undetectability. The proposed method also yields improvements on both stego signal quality and embedding capacity, which are the two major requirements of a steganography algorithm. Our experimental results illustrate that the stego signals generated by the proposed method are perceptually indistinguishable from the original cover signals,... 

Sparse decomposition of wavelet coefficients of cover image blocks for data hiding is addressed in this paper. By using the proposed algorithm, the embedded secret message can be reliably extracted without resorting to the original image. We use all four sub-images (LL, LH, HL and HH) of the 2D wavelet transform for data embedding without losing the image imperceptibility. An over-complete dictionary matrix is estimated by using the KSVD dictionary learning algorithm, and then the secret message bits are inserted in the sparse representation of the wavelet coefficients over the estimated dictionary. This is believed to be one of the first approaches to the image data hiding that uses the...