Sharif Digital Repository

Quality Assurance is one of the crucial steps in software production and should be repeated. These days, software companies are using continuous integration (CI) pipelines for building software and controlling software quality. Regression test, is a test which is performed after each change in software, to assess the quality of the software system and its ability to meet functional and non-functional needs. Each execution of this test, leads to infrastructure costs and a waste of time for the software development team. The reason is that the development team has to wait until the test is completed to ensure that the changes are correct. To reduce this cost, methods such as prioritizing and... 

Helium is a valuable substance that is widely used in industry and medicine because of its unique properties. Due to the increasing demand for helium in the global market, several helium extraction units have been launched in the world in recent years; Nevertheless, there is still a significant gap between projected global helium production capacity and demand. Today, natural gas is known as the main source of helium on the earth. The method of industrial extraction of helium from natural gas is cryogenic. Due to the low temperature of this process, relatively high power consumption is used to extract helium from natural gas. Therefore, the optimization of this process is of great... 

In the Ion Implanter system a controled current through a tungesten filament, supplies the heat which causes the thermoionic emmition to ionize the gas molecules. Then the negativly charged electrones that are boiled off the filament, and also the ones that are removed from the outer ring of the gas molecules during the ionization process, are attracted by an adjustable voltage source. In this thesis, two resonant converter topologies are proposed to designe and to implement the adjustable current and voltage sources for ion implantation system. Unlike the PWM converters soft switching techniques, used auxiliary circuits to reduce the switching losses and EMI noises, in the proposed resonant... 

Low salinity/smart waterflooding is simple to apply and a promising enhanced oil recovery method in which ion-tuned saltwater is injected into subsurface oil reservoirs. Many aspects of this operation, in particular those pertaining to nano-scale, are not yet fully understood. The present dissertation is an attempt to shed light on the microscopic properties and behavior of rock/brine/oil interfaces throughout low salinity/smart waterflooding. Several simulations were carried out for oil/brine and calcite/brine categories each containing various ions and hydrocarbons compounds. The surface contribution of non-functional oil compounds (aromatics and aliphatics) near brine medium was... 

In the method treatment of thermobrachytherapy, the method of this project, simultaneously use of two processes of thermotherapy and brachytherapy is considered, which can be a more effective treatment for the destruction of tumor tissue. In thermotherapy, the temperature of the tissue is artificially raised to a temperature that leads to cell dysfunction resulting in cell death. In brachytherapy, the destruction of defective tissue is done by placing a source in the tissue. In the present project, we supposed to consider both mechanisms simultaneously for treatment at the same time. In this project, radioactive material 125I is used as a source of radiation emission for brachytherapy. In... 

This research aims to explore the effectiveness of past tariff evolutions in shaping firms' response to current input prices. Additionally, it investigates how asymmetric fuel pricing across different industries influences overall industry efficiency and prosperity. Furthermore, the study examines the impact of fuel subsidies on the equilibrium distribution of companies independent of welfare decline. In Chapter 1, utilizing microdata from Iranian industrial workshops between 1386-1396 (2007-2017) A key finding is that new firms exhibit a significant and stronger reaction to energy price variations compared to other firms when it comes to technology choices.Chapter 2 presents a structural... 

Assuring households’ access to essential goods at economy’s hard times has always been of policymakers’ main concerns. Using Household’s Income and Expenditure Survey data during Iran-Iraq war during 80’s, we have calculated welfare and distribution effects of rationing by coupons of three essential goods in these years. We found that rationed quantities were inefficiently excessive and due to their significant low prices, they were overconsumed. Also, without any extra financial burden, government could have improved lowest income quantile’s aggregate utility by 5.2% or 5.6%, if cash subsidy equal to each household’s previous subsidy value or an equal per-capita subsidy were distributed... 

The price elasticity of gasoline demand is a key parameter in evaluation of various policy options. However, most of the literature uses aggregate data to identify this elasticity. Temporal and spatial aggregation make elasticity estimates unreliable. We employ a unique dataset of all gasoline transactions in Iran during a four-month period around an unexpected exogenous price change to identify price elasticity. After controlling for date and individual fixed effects we estimate a robust significant price elasticity of -0.127. Aggregation of the same data by week, month, and city yields an estimate of -0.3 indicating a significant bias in earlier studies. We also identify a significant... 

In this work, the dynamic competition between firms providing internet services is studied. The framework is Markov equilibrium whereby structural parameters are obtained using two-step estimations, allowing for analyzing the situation in case of subsidies for service upgrade. The results show that such subsidy has little effect on the number of firms while increasing the number of fast firms  

The most popular system for quantification of the facial behaviors and expressions is the Facial Action Coding System (FACS). FACS provides a description of all possible and visually detectable facial variations in terms of 33 Action Units (AUs). The activation of each AU leads to a slight variation in the facial appearance, and any facial expression can be modeled by a single AU or a combination of AUs. Definition of AUs is such that they are sparse in multiple domains. The goal of this dissertation is utilizing these sparsity properties to develop an effective algorithm for automatic intensity estimation of AUs. One of the sparsity domains of AUs is the spatial domain that means the... 

The main purpose of this dissertation is to investigate and improve the efficiency of Sparse Reconstruction method for structural health monitoring application in order to solve the challenges including; finding multiple simultaneous damages, damage detection and location in anisotropic structures and real engineering complex structures. The main approach of this research is based on presenting theoretical concepts and developing practical methods based on the presented concepts, as well as evaluating and implementing the developed methods using numerical simulations and experimental tests. In this work, the finite element method is used for numerical simulation. In the experimental test,... 

Spoken language understanding unit is one of the most important parts of a spoken dialogue system. The input of this system is the output of speech recognition unit. The main function of this unit is to extract the semantic information from the input utterances. There are two main types of approaches to do this task: rule-based approaches, and data-driven approaches. Today data-driven approaches are of more interest because they are more flexible and robust compared to the rule-based approaches. The main drawback of these methods is that they need a large amount of fully annotated or in some cases Treebank data. Preparing such data is time consuming and expensive. The goal of this thesis is... 

Large data sources labeled by experts at cost are essential for deep learning success in various domains. But, when labeling is expensive and labeled data is scarce, deep learning generally does not perform well. The goal of semi-supervised learning is to leverage abundant unlabeled data that one can easily collect. New semi-supervised algorithms based on data augmentation techniques have reached new advances in this field. In this work, by studying different textual augmentation techniques, a new approach is proposed that can obtain effective information signals from unlabeled data. The method encourages the model to generate the same representation vectors for different augmented versions... 

Many Text-to-Speech (TTS) systems, particularly in low-resource environments, struggle to produce natural and intelligible speech from grapheme sequences. One solution to this problem is to use Grapheme-to-Phoneme (G2P) conversion to increase the information in the input sequence and improve the TTS output. However, current G2P systems are not accurate or efficient enough for Persian texts due to the language’s complexity and the lack of short vowels in Persian grapheme sequences. In our study, we aimed to improve resources for the Persian language. To achieve this, we introduced two new G2P training datasets, one manually-labeled and the other machine-generated, containing over five million... 

Our study is mainly related to speaker verification systems. Given a speech segment and a claimed identity, these systems must decide whether the claimant is admissible or a fraud. Our main focus in on making the speaker verification system robust in case of limited training data. When there is limited training data, the accuracy of speaker verification systems reduces drastically. Our main purpose is to review this problem deeply and to represent methods in order to encounter this challenge. Recently, some methods such as PLDA and distance metric learning have been applied in text-independent speaker verification in order to encounter limited data crisis. One of the important cases in which... 

Nowadays sharing data between users and organizations can be difficult due to privacy and legal reasons. Therefore, real-world data is not fully exploited by machine learning methods. A novel model training method called Federated Learning was proposed to alleviate the aforementioned problem by enabling users to jointly train learning models without sharing data. Federated learning utilization faces many challenges among which high communication cost, statistical heterogeneity and system heterogeneity are most important. This research deals with these challenges by proposing two methods of Reduced Clustering Federated Learning (RCFL) and Weighted Federated Distillation (WFD). Moreover, we... 

Membership inference attacks are one of the most important privacy-violating attacks in machine learning, as well as infrastructure of more serious attacks such as data extraction attacks. Since membership inference attack is used as a measure to evaluate the level of privacy protection of machine learning models, different researches have investigated and provided new methods for this attack. However, the accuracy of these attacks has not been investigated on models trained with the latest techniques such as data augmentation and regularization techniques. In this research, we see that the Lira attack, the latest membership inference attack, which has much more power compared to previous... 

The human heart is a complex system that contains many clues about its function in its electrocardiogram (ECG) signal. Due to the high mortality rate of heart diseases, detection and recognition of ECG arrhythmias is essential. The most difficult problem faced by ECG analysis is the vast variations among morphologies of ECG signals. In this study, we propose an approach for y detection of abnormal beats and data visualization with respect to ECG morphologies by using manifold learning. In order to do so, a nonlinear dimensionality reduction method based on the Laplacian Eigenmaps is used to reduce the high dimensions of the ECG signals, followed by the application of Bayesian and FLDA method... 

Protocol identification and reverse engineering have recently received much attention due to their importance in many communication and security applications. In this field, the main challenges are: protocol identification, clustering unknown protocols, extracting protocol fields, and finding the protocol format based on these fields and their relations. Most of the proposed methods for the first two parts (protocol identification and clustering) use machine learning and AI techniques. For the last part, some bioinformatics techniques like sequence alignment algorithms are used. In this thesis, after reviewing different methods for protocol identification and reverse engineering, some...