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chaichi--mahdieh
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Test case prioritization using test case diversification and fault-proneness estimations
, Article Automated Software Engineering ; Volume 29, Issue 2 , 2022 ; 09288910 (ISSN) ; 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...
Synthesis and Characterization of Ni-Pd-0.1CNT/PU Nanocatalyst, Applicable for Methane Reforming
, M.Sc. Thesis Sharif University of Technology ; Sadrnezhad, Khatiboleslam (Supervisor)
Abstract
Influence of attachment of Multi Walled Carbon Nanotubes (MWCNT) on various nickel nanocatalysts produced by the electroless plating method was investigated via Transmission Electron Microscopy (TEM), Field Emission Scanning Electron Microscopy (FESEM), Energy Dispersive Spectroscopy (EDS), Atomic Absorption Spectroscopy (AAS), X-ray Diffraction (XRD), Brunauer-Emmett-Teller (BET) measurements, Temperature Programmed Reduction (TPR) and Temperature Programmed Oxidation (TPO) tests. Application of TPR/TPO data to Kissinger (Redhead) Equation yielded the activation energies of both nickel reduction and nickel oxidation reactions. Addition of MWCNT improved the catalytic effects by easing of...
Synthesis and characterization of supportless Ni-Pd-CNT nanocatalyst for hydrogen production via steam reforming of methane
, Article International Journal of Hydrogen Energy ; Volume 43, Issue 3 , 2018 , Pages 1319-1336 ; 03603199 (ISSN) ; Sadrnezhaad, S. K ; Malekjafarian, M ; Sharif University of Technology
Elsevier Ltd
2018
Abstract
Supportless Ni-Pd-0.1CNT foamy nanocatalyst with specific surface area of 611.3 m2/g was produced by electroless deposition of nickel, palladium and multiwall carbon nanotube (MWCNT) on interim polyurethane substrate. Application of temperature programmed reduction (TPR) and temperature programmed oxidation (TPO) data into Kissinger (Redhead) kinetic model showed lessening of their activation energies due to Pd and CNT addition. Presence of foamy Ni/SiC caused 8% higher steam reforming of methane; while Ni-Pd-0.1CNT presence resulted in 22% higher methane conversion. The catalytic behavior of the samples was described by morphological and compositional studies which were carried out by...
Trainable Loss Weights for Image Super-Resolution
, M.Sc. Thesis Sharif University of Technology ; Kasaei, Shohreh (Supervisor)
Abstract
Image super-resolution is the process of estimating a high-quality image from a low-quality image. With the growth of remote sensing images, computer games, and the development of artificial intelligence applications in medical image analysis, research in this area of machine vision has seen significant growth. In recent years, research on super-resolution has primarily focused on the development of unsupervised models, blind networks, and the use of optimization methods in non-blind models. However, limited research has discussed the loss function in the super-resolution process. The majority of those studies have only used perceptual similarity in a conventional way. This is while the...
Multi-Modal Distance Metric Learning
, M.Sc. Thesis Sharif University of Technology ; 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...
Fabrication and Characterization of Thermoplastic Starch Based Nanocomposite for Bone Scaffold
, M.Sc. Thesis Sharif University of Technology ; 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...
Unsupervised Domain Adaptation via Representation Learning
, M.Sc. Thesis Sharif University of Technology ; 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...
Deep Learning for Multimodal Data
, M.Sc. Thesis Sharif University of Technology ; 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 Learning For Recommender Systems
, M.Sc. Thesis Sharif University of Technology ; 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 ; 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 ; 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...
Deep Zero-shot Learning
, M.Sc. Thesis Sharif University of Technology ; 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...
Improving Graph Construction for Semi-supervised Learning in Computer Vision Applications
, M.Sc. Thesis Sharif University of Technology ; 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...
Influence of polyaniline synthesis conditions on its capability for removal and recovery of chromium from aqueous solution
, Article Iranian Journal of Chemistry and Chemical Engineering ; Volume 30, Issue 3 , 2011 , Pages 97-100 ; 10219986 (ISSN) ; Borghei, S. M ; Olad, A ; Chaichi, M. J ; Sharif University of Technology
2011
Abstract
Absorptive characteristics of polyaniline synthesized in different solvents were studied. Water and mixture of water with other solvents were implemented for polyaniline synthesis. Synthesized polyanilines in powder shape is used as an adsorbent to remove toxic hexavalent chromium from aqueous solutions. Experiments were conducted in batch mode. Removal mechanism involving polyaniline is the combination of surface adsorption and reduction. The kind of solvent used at synthesizing stage can affect the capacity of produced polyanilines for removal of heavy metals including chromium. Synthesized polyaniline in water had the maximum chromium removal efficiency. The morphology study of...
Removal of chromium from aqueous solution using two kinds of polyaniline
, Article Journal of Environmental Studies ; Volume 36, Issue 55 , 2010 , Pages 91-98 ; 10258620 (ISSN) ; Borghei, S. M ; Olad, A ; Chaichi, M. J ; Sharif University of Technology
2010
Removal of chromium from aqueous solution using polyaniline - Poly ethylene glycol composite
, Article Journal of Hazardous Materials ; Volume 184, Issue 1-3 , December , 2010 , Pages 248-254 ; 03043894 (ISSN) ; Borghei, S. M ; Olad, A ; Chaichi, M. J ; Sharif University of Technology
2010
Abstract
The adsorption of chromium compounds from solutions by a composite of polyaniline/poly ethylene glycol (PANi/PEG) was investigated in this study. Experiments were conducted in batch mode under various operational conditions including agitation time, solution pH, PANi/PEG dose and initial concentration of chromium salts. Results showed that concentration of PEG at synthesizing stage has a significant effect on the capacity of produced composite for removal of chromium. Morphologically, PANi/PEG composite is closely dependent on the concentration of PEG. Maximum removal of hexavalent chromium was experienced when 2. g/L of PEG was used in synthesis of PANi/PEG. Removal of hexavalent chromium...
Mechanical and microstructure properties of deformed Al-Al2O3 nanocomposite at elevated temperature
, Article Journal of Materials Research ; Volume 32, Issue 6 , 2017 , Pages 1118-1128 ; 08842914 (ISSN) ; Sajjadi, S. A ; Chaichi, A ; Ebrahimi, G. R ; Sharif University of Technology
2017
Abstract
Hot isotherm compression tests were performed in temperature range of 350-500 °C and at strain rates of 0.0005 to 0.5 s-1 for Al6061 alloy reinforced with alumina nanoparticles. Effect of deformation parameters and optimal conditions for hot working this nanocomposite were comprehended thoroughly via hot working data analyses, electron microscopy images, and X-ray diffractograms. The results indicated the severity of hot deformation process and an increase in the activation energy to 320 kJ/mol due to the addition of nanoparticles. Dynamic recovery (DRV) was considered as the individual determinative softening mechanism during hot deformation of this nanocomposite, and no sign of dynamic...
Multi-label Classification by Considering Label Dependencies
, M.Sc. Thesis Sharif University of Technology ; 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...
Adaptation for Evolving Domains
, M.Sc. Thesis Sharif University of Technology ; 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...
Cancer Prediction Using cfDNA Methylation Patterns With Deep Learning Approach
, M.Sc. Thesis Sharif University of Technology ; Soleymani Baghshah, Mahdieh (Supervisor)
Abstract
Liquid biopsy includes information about the progress of the tumor, the effectiveness of the treatment and the possibility of tumor metastasis. This type of biopsy obtains this information by doing diagnosis and enumerating genetic variations in cells and cell-free DNA (cfDNA). Only a small fraction of cfDNA which might be free circulation tumor DNA (ctDNA) fragments, has mutations and is usually identified by epigenetic variations. On the other hand, the use of liquid biopsy has decreased, and tumors in the final stages are often untreatable due to the low accuracy in prediction of cancer. In this research, the aim is to predict cancer using cfDNA methylation patterns. We obtain these...