Distance Coluring Graphs

, M.Sc. Thesis Sharif University of Technology Malekian, Mahdieh (Author) ; Mahmoodian, Ebadollah (Supervisor)

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...

Modeling of Cancer Progression by Using Evolutionary Game Theory

, M.Sc. Thesis Sharif University of Technology Malekian Boroujeni, Negin (Author) ; Habibi, Jafar (Supervisor)

Abstract

Mathematical modeling and computer simulation are powerful tools to help cancer research because they provide a good insight of cancer progression and an efficient framework to test biological hypothesis. Most of the previous studies ignored many intracellular communication between tumor cells. Gap junction is one of the interaction ways between tumor cells which plays a crucial role in cancer progression. In the first phase of this thesis, a model of intracellular communication through gap junction using evolutionary game theory scoring is proposed. In the second phase of this thesis, cancer is modeled by Markov decision process (MDP). Given that considering nutrient level constant is one...

Monitoring of Ion Implanter Radiation

, M.Sc. Thesis Sharif University of Technology Malekian Sourki, Mehrdad (Author) ; Rashidian, Bizhan (Supervisor)

Abstract

X-ray radiation is an ionizing radiation that can be produced under some conditions in an ion implanter and can have significant effects on equipment and personnel health. In this dissertation, we first examine the properties of ionizing radiations and their effects of electronic devices and discuss the general properties of common detectors. Next, Using Silvaco TCAD and GEANT4 we will simulate the interaction of ionzing radiation with energies up to 300 keV with electronic devices including a commercial off the shelf photo diode and a specialized x-ray detector and show that these devices can efficiently reveal informations of a radiation source to the user. Also, it has been shown that...

On The Existence of Arithmetic Progressions In Subsets of Integers

, M.Sc. Thesis Sharif University of Technology Malekian, Reihaneh (Author) ; Alishahi, Kasra (Supervisor) ; Hatami, Omid (Supervisor)

Abstract

Suppose that A is a large subset of N. It is interesting to think about the arithmetic progressions in A.In 1936, Erdos and Turan conjectured that for > 0 and k 2 N, there exists N = N(k; ) that for all subsets A {1; 2; : : : ;N}, if lAl N, A has a nontrivial arithmetic progression of length k. Roth proved the conjecture for k = 3 in 1953. In 1969, Szemeredi proved the case k = 4 and in 1975, he gave a combinatorial proof for the general case. In 1977, using ergodic theory, Furstenberg gave a different proof for the Erdos-Turan conjecture (or Szemeredi Theorem!) and finally Gowers found another proof for the Szemeredi theorem, which was an elegant generalization of the Roth’s proof for k =...

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...

Experimental and Numerical Study of a Shape Memory Alloy Wire Rope Behavior in Release Mechanism

, M.Sc. Thesis Sharif University of Technology Malekian, Ali (Author) ; Naghdabadi, Reza (Supervisor) ; Arghavani Hadi, Jamal (Supervisor)

Abstract

Smart materials are able to change their physical properties under various environmental conditions. Shape memory alloys (SMA) are relatively new smart materials that can respond to environmental stimuli e.g., heat, electricity, etc. Unique behaviors of SMAs, called shape memory effect and superelasticity, have motivated many applications in various fields of study (aerospace, medical, civil engineering, etc.). Although the behavior of SMAs is complicated, modeling and utilizing these materials have been receiving much attention in the past 20 years. Since a cable tolerate more tension than a wire or rod, the shape memory alloy cables could have a broad range of potential applications....

Integrating evolutionary game theory into an agent-based model of ductal carcinoma in situ: Role of gap junctions in cancer progression

, Article Computer Methods and Programs in Biomedicine ; Volume 136 , 2016 , Pages 107-117 ; 01692607 (ISSN) Malekian, N ; Habibi, J ; Zangooei, M. H ; Aghakhani, H ; Sharif University of Technology

Elsevier Ireland Ltd 2016

Abstract

Background and objective There are many cells with various phenotypic behaviors in cancer interacting with each other. For example, an apoptotic cell may induce apoptosis in adjacent cells. A living cell can also protect cells from undergoing apoptosis and necrosis. These survival and death signals are propagated through interaction pathways between adjacent cells called gap junctions. The function of these signals depends on the cellular context of the cell receiving them. For instance, a receiver cell experiencing a low level of oxygen may interpret a received survival signal as an apoptosis signal. In this study, we examine the effect of these signals on tumor growth. Methods We make an...

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...

Optimising multi-item economic production quantity model with trapezoidal fuzzy demand and backordering: Two tuned meta-heuristics

, Article European Journal of Industrial Engineering ; Volume 10, Issue 2 , 2016 , Pages 170-195 ; 17515254 (ISSN) Sadeghi, J ; Niaki, S. T. A ; Malekian, M. R ; Sadeghi, S ; Sharif University of Technology

Inderscience Enterprises Ltd 2016

Abstract

In this paper, a multi-item economic production quantity model with fuzzy demand is developed in which shortages are backordered and the warehouse space is limited. While the demand is assumed to be a trapezoidal fuzzy number, the centroid defuzzification method is used to defuzzify fuzzy output functions. The Lagrangian relaxation procedure is first employed to solve the problem. Then, the model is extended to a constrained fuzzy integer nonlinear programming, in order to suit real-world situations. As the extended model cannot be solved in a reasonable time using exact methods, two meta-heuristic algorithms, named the genetic algorithm (GA) and the particle swarm optimisation (PSO) each...

A Lagrangian relaxation for a fuzzy random EPQ Problem with Shortages and Redundancy Allocation: Two Tuned Meta-heuristics

, Article International Journal of Fuzzy Systems ; Volume 20, Issue 2 , 2018 , Pages 515-533 ; 15622479 (ISSN) Sadeghi, J ; Niaki, S. T. A ; Malekian, M. R ; Wang, Y ; Sharif University of Technology

Springer Berlin Heidelberg 2018

Abstract

This paper develops an economic production quantity model for a multi-product multi-objective inventory control problem with fuzzy-stochastic demand and backorders. In this model, the annual demand is represented by trapezoidal fuzzy random numbers. The centroid defuzzification and the expected value methods are applied to defuzzify and make decisions in a random environment. In the case where the warehouse space is limited, the Lagrangian relaxation procedure is first employed to determine the optimal order and the maximum backorder quantities of the products such that the total inventory cost is minimized. The optimal solution obtained by the proposed approach is compared with that...

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...