Test case prioritization using test case diversification and fault-proneness estimations

, Article Automated Software Engineering ; Volume 29, Issue 2 , 2022 ; 09288910 (ISSN) Mahdieh, M ; Mirian Hosseinabadi, S. H ; Mahdieh, M ; Sharif University of Technology

Springer 2022

Abstract

Regression testing activities greatly reduce the risk of faulty software release. However, the size of the test suites grows throughout the development process, resulting in time-consuming execution of the test suite and delayed feedback to the software development team. This has urged the need for approaches such as test case prioritization (TCP) and test-suite reduction to reach better results in case of limited resources. In this regard, proposing approaches that use auxiliary sources of data such as bug history can be interesting. We aim to propose an approach for TCP that takes into account test case coverage data, bug history, and test case diversification. To evaluate this approach we...

Effects of Weighting and Root Matrices on LQG Compensators

, M.Sc. Thesis Sharif University of Technology Safa, Alireza (Author) ; Mobed, Mohammad (Supervisor)

Abstract

The LQG theory made its appearance in the fifties and sixties. It has now become one of the standard methods for compensator design. Despite the existing powerful tools in modern control, there seems to be few or no systematic approaches proposed for determining the weighting and root matrices which are the free parameters of the compensator. These matrices are often found by trial-and-error. Initially, this thesis presents an iterative algorithm for determining the compensator parameters. The algorithm is based upon making corrections to the singular values graphs in order to enhance closed-loop performance and robustness. This is how the traditional intuitive trial-and-error approach is...

Oily wastewater treatment using membrane bioreactor

, Article International Journal of Global Warming ; Vol. 6, issue. 2-3 , 2014 , pp. 295-302 ; ISSN: 17582083 Safa, M ; Alemzadeh, I ; Vossoughi, M ; Sharif University of Technology

2014

Abstract

A novel implementation of a membrane bioreactor (MBR) has been studied in this paper. It is utilised as combination of rotating biological contractor (RBC) and an external membrane for oily wastewater treatment. The performance of MBR was compared with the standalone RBC. The RBC used in this investigation is the same one combined with the membrane to form the MBR. Wastewater biodegradability has been evaluated by two factors: chemical oxygen demand (COD) and total petroleum hydrocarbon (TPH). They are both compared together for different hydraulic retention times (HRTs) and petroleum pollution concentrations in RBC and MBR. The ratio of TPH to COD of molasses has been varied between 0.2 to...

Functionalized carbon nanotubes in ZnO thin films for photoinactivation of bacteria

, Article Materials Chemistry and Physics ; Volume 130, Issue 1-2 , October , 2011 , Pages 598-602 ; 02540584 (ISSN) Akhavan, O ; Azimirad, R ; Safa, S ; Sharif University of Technology

2011

Abstract

Two types of unfunctionalized and functionalized multi-wall carbon nanotubes (MWCNTs) were prepared to be applied in fabrication of MWCNT-ZnO nanocomposite thin films with various MWCNT contents. X-ray photoelectron spectroscopy indicated formation of functional groups on surface of the functionalized MWCNTs in the MWCNT-ZnO nanocomposite. Formation of the effective carbonaceous bonds between the ZnO and the MWCNTs was also investigated through photoinactivation of Escherichia coli bacteria on surface of the both unfunctionalized and functionalized MWCNT-ZnO nanocomposites. The functionalized MWCNT-ZnO nanocomposites showed significantly stronger photoinactivation of the bacteria than the...

Multi-Modal Distance Metric Learning

, M.Sc. Thesis Sharif University of Technology Roostaiyan, Mahdi (Author) ; Soleymani, Mahdieh (Supervisor)

Abstract

In many real-world applications, data contain multiple input channels (e.g., web pages include text, images and etc). In these cases, supervisory information may also be available in the form of distance constraints such as similar and dissimilar pairs from user feedbacks. Distance metric learning in these environments can be used for different goals such as retrieval and recommendation. In this research, we used from dual-wing harmoniums to combining text and image modals to a unified latent space when similar-dissimilar pairs are available. Euclidean distance of data represented in this latent space used as a distance metric. In this thesis, we extend the dual-wing harmoniums for...

Unsupervised Domain Adaptation via Representation Learning

, M.Sc. Thesis Sharif University of Technology Gheisary, Marzieh (Author) ; Soleymani, Mahdieh (Supervisor)

Abstract

The existing learning methods usually assume that training and test data follow the same distribution, while this is not always true. Thus, in many cases the performance of these learning methods on the test data will be severely degraded. We often have sufficient labeled training data from a source domain but wish to learn a classifier which performs well on a target domain with a different distribution and no labeled training data. In this thesis, we study the problem of unsupervised domain adaptation, where no labeled data in the target domain is available. We propose a framework which finds a new representation for both the source and the target domain in which the distance between these...

Fabrication and Characterization of Thermoplastic Starch Based Nanocomposite for Bone Scaffold

, M.Sc. Thesis Sharif University of Technology Mahdieh, Zahra (Author) ; Bagheri, Reza (Supervisor)

Abstract

This project aimed to fabricate the bone scaffolds with applying thermoplastic starch-based nano-biocomposites. The starting materials for this scaffold are as follows: thermoplastic starch, ethylene vinyl alcohol as the polymer matrix and nanoforsterite as the ceramic reinforcing phase. Furthermore, vitamin E was used as antioxidant for preserving starch against thermo-mechanical degradations. Likewise, 3D pore structure was developed using azo-dicarbonamide and water in injection moulding process. With blending thermoplastic starch and ethylene vinyl alcohol, some thermoplastic starch’s properties including degradation rate and water absorption were modified. In addition, having...

Deep Learning for Multimodal Data

, M.Sc. Thesis Sharif University of Technology Rastegar, Sarah (Author) ; Soleymani, Mahdieh (Supervisor)

Abstract

Recent advances in data recording has lead to different modalities like text, image, audio and video. Images are annotated and audio accompanies video. Because of distinct modality statistical properties, shallow methods have been unsuccessful in finding a shared representation which maintains the most information about different modalities. Recently, deep networks have been used for extracting high-level representations for multimodal data. In previous methods, for each modality, one modality-specific network was learned. Thus, high-level representations for different modalities were extracted. Since these high-level representations have less difference than raw modalities, a shared...

Deep Zero-shot Learning

, M.Sc. Thesis Sharif University of Technology Shojaee, Mohsen (Author) ; Soleymani, Mahdieh (Supervisor)

Abstract

In some of object recognition problems, labeled data may not be available for all categories. Zero-shot learning utilizes auxiliary information (also called signatures) describing each category in order to find a classifier that can recognize samples from categories with no labeled instance. On the other hand, with recent advances made by deep neural networks in computer vision, a rich representation can be obtained from images that discriminates different categorizes and therefore obtaining a unsupervised information from images is made possible. However, in the previous works, little attention has been paid to using such unsupervised information for the task of zero-shot learning. In this...

Deep Learning For Recommender Systems

, M.Sc. Thesis Sharif University of Technology Abbasi, Omid (Author) ; Soleimani, Mahdieh (Supervisor)

Abstract

Collaborative fltering (CF) is one of the best and widely employed approaches in Recommender systems (RS). This approach tries to fnd some latent features for users and items so it would predict user rates with these features. Early CF methods used matrix factorization to learn users and items latent features. But these methods face cold start as well as sparsity problem. Recent years methods employ side information along with rating matrix to learn users and items latent features. On the other hand, deep learning models show great potential for learning effective representations especially when auxiliary information is sparse. Due to this feature of deep learning, we use deep learning to...

Adversarial Networks for Sequence Generation

, M.Sc. Thesis Sharif University of Technology Montahaei, Ehsan (Author) ; Soleymani, Mahdieh (Supervisor)

Abstract

Lots of essential structures can be modeled as sequences and sequences can be utilized to model the structures like molecules, graphs and music notes. On the other hand, generating meaningful and new sequences is an important and practical problem in different applications. Natural language translation and drug discovery are examples of sequence generation problem. However, there are substantial challenges in sequence generation problem. Discrete spaces of the sequence and challenge of the proper objective function can be pointed out.On the other, the baseline methods suffer from issues like exposure bias between training and test time, and the ill-defined objective function. So, the...

Improving Sampling Efficiency of Probabilistic Graphical Models

, M.Sc. Thesis Sharif University of Technology Mahdieh, Mohsen (Author) ; Beigy, Hamid (Supervisor)

Abstract

Deep learning methods have become more popular in the past years. These methods use complex network architectures to model rich, hierarchical datasets. Although most of the research has been centered around Discriminative models, however, recently a lot of research is focused on Deep Generative Models. Two of the pioneering models in this field are Generative Adversarial Networks and Variational Auto-Encoders. In addition, knowing the structure of data helps models to search in a narrower hypothesis space. Most of the structure in datasets are models using Probabilistic Graphical Models. Using this structural information, one can achieve better parameter estimations. In the case of...

A different switching surface stabilizing an existing unstable periodic gait: an analysis based on perturbation theory

, Article Nonlinear Dynamics ; Volume 81, Issue 4 , 2015 , Pages 2127-2140 ; 0924090X (ISSN) Safa, A. T ; Alasty, A ; Naraghi, M ; Sharif University of Technology

Kluwer Academic Publishers 2015

Abstract

Limit cycle walkers are known as a class of walking robots capable of presenting periodic repetitive gaits without having local controllability at all times during motion. A well-known subclass of these robots is McGeer’s passive dynamic walkers solely activated by the gravity field. The mathematics governing this style of walking is hybrid and described by a set of nonlinear differential equations along with impulses. In this paper, by applying perturbation method to a simple model of these machines, we analytically prove that for this type of nonlinear impulsive system, there exist different switching surfaces, leading to the same equilibrium points (periodic solutions) with different...

Stable handspring maneuvers with passive flight phases: Results from an inverted pendulum-like template

, Article International Journal of Non-Linear Mechanics ; Volume 128 , 2021 ; 00207462 (ISSN) Tehrani Safa, A ; Nouriani, A ; Alasty, A ; Sharif University of Technology

Elsevier Ltd 2021

Abstract

Inverted pendulum (IP) has been broadly used to model locomotor systems. In this paper, we demonstrate that an IP-like model could simulate stable periodic handspring maneuvers with passive flight phases. The model is a 2-D symmetric rigid body which is merely controlled during the contact phase. To benefit from an open-loop sensorless strategy, the control policy is implemented only by an unvaried torque input. The system's dynamics is an example of nonlinear impulsive systems studied and analyzed by the Poincaré section method. The numerical results reveal that the stable periodic solutions are sufficiently robust for a broad range of the parameter space. © 2020 Elsevier Ltd

Improving Graph Construction for Semi-supervised Learning in Computer Vision Applications

, M.Sc. Thesis Sharif University of Technology Mahdieh, Mostafa (Author) ; Rabiee, Hamid Reza (Supervisor)

Abstract

Semi-supervised Learning (SSL) is an extremely useful approach in many applications where unlabeled data can be easily obtained. Graph based methods are among the most studied branches in SSL. Since neighborhood graph is a key component in these methods, we focus on methods of graph construction in this project. Graph construction methods based on Euclidean distance have the common problem of creating shortcut edges. Shortcut edges refer to the edges which connect two nearby points that are far apart on the manifold. Specifically, we show both in theory and practice that using geodesic distance for selecting and weighting edges results in more appropriate neighborhood graphs. We propose an...

CuO/Cu(OH)2 hierarchical nanostructures as bactericidal photocatalysts

, Article Journal of Materials Chemistry ; Volume 21, Issue 26 , May , 2011 , Pages 9634-9640 ; 09599428 (ISSN) Akhavan, O ; Azimirad, R ; Safa, S ; Hasani, E ; Sharif University of Technology

2011

Abstract

Various morphologies of CuO/Cu(OH)2 nanostructures with different adsorbed -OH contents were synthesized on an acid-treated Cu foil through variation of NaOH concentration from 0 to 50 mM with an in situ oxidation method. X-ray diffractometry and X-ray photoelectron spectroscopy (XPS) indicated formation of CuO on the Cu(OH)2 crystalline phase at a growth temperature of 60°C for 20 h. Antibacterial activity of the nanostructures against Escherichia coli bacteria was studied in the dark and under light irradiation. The nanostructures grown at a NaOH concentration of 30 mM showed the highest surface area and the strongest antibacterial activity among the samples. After elimination of the...

Adaptation for Evolving Domains

, M.Sc. Thesis Sharif University of Technology Bitarafan, Adeleh (Author) ; Soleymani Baghshah, Mahdieh (Supervisor)

Abstract

Until now many domain adaptation methods have been proposed. A major limitation of almost all of these methods is their assumption that all test data belong to a single stationary target distribution and a large amount of unlabeled data is available for modeling this target distribution. In fact, in many real world applications, such as classifying scene image with gradually changing lighting and spam email identification, data arrives sequentially and the data distribution is continuously evolving. In this thesis, we tackle the problem of adaptation to a continuously evolving target domain that has been recently introduced and propose the Evolving Domain Adaptation (EDA) method to classify...

Multi-label Classification by Considering Label Dependencies

, M.Sc. Thesis Sharif University of Technology Farahnak Ghazani, Fatemeh (Author) ; Soleymani, Mahdieh (Supervisor)

Abstract

In multi-label classification problems each instance can simultaneously have multiple labels. In these problems, in addition to the complexities of the input feature space we encounter the complexities of output label space. In the multi-label classification problems, there are dependencies between different labels that need to be considered. Since the dimensionality of the label space in real-world applications can be (very) high, most methods which explicitly model these dependencies are ineffective in practice and recently those methods that transform the label space into a latent space have received attention. A class of these methods which uses output space dimension reduction, first...

Answering Questions about Image Contents by Deep Networks

, M.Sc. Thesis Sharif University of Technology Chavoshian, Mohammad (Author) ; Soleymani Baghshah, Mahdieh (Supervisor)

Abstract

Due to the recent advances in the learning of multimodal data, humans tend to use computer systems in order to solve more complex problems. One of them is Visual Question Answering (VQA), where the goal is finding the answer of a question asked about the visual contents of a given image. This is an interdisciplinary problem between the areas of Computer Vision, Natural Language Processing and Reasoning. Because of recent achievements of Deep Neural Networks in these areas, recent works used them to address the VQA task. In this thesis, three different methods have been proposed which adding each of them to existing solutions to the VQA problem can improve their results. First method tries to...

Adversarial Robustness of Deep Neural Networks in Text Domain

, M.Sc. Thesis Sharif University of Technology Behjati, Melika (Author) ; Soleymani Baghshah, Mahdieh (Supervisor)

Abstract

In recent years, neural networks have been widely used in most machine learning domains. However, it has been shown that these networks are vulnerable to adversarial examples. adversarial examples are small and imperceptible perturbations applied to the input which lead to producing wrong output and thus, fooling the network. This will become an important issue in security related applications of deep neural networks, such as self-driving cars and medical diagnostics. Since, in the wort-case scenario, even human lives could be threatened. Although, many works have focused on crafting adversarial examples for image data, only a few studies have been done on textual data due to the existing...