Sharif Digital Repository

Based on the importance of DNA/RNA binding proteins in different cellular processes, finding binding sites of them play crucial role in many applications, like designing drug/vaccine, designing protein, and cancer control. Many studies target this issue and try to improve the prediction accuracy with three strategies: complex neural-network structures, various types of inputs, and ML methods to extract input features. But due to the growing volume of sequences, these methods face serious processing challenges. So, this paper presents KDeep, based on CNN-LSTM and the primary form of DNA/RNA sequences as input. As the key feature improving the prediction accuracy, we propose a new encoding... 

Speech synthesis and its applications have been very attractive recently. The main purpose of this technique is to produce a speech signal with natural characteristics of human speech like prosody and emotion. Among all existing methods for speech synthesis, statistical parametric speech synthesis methods are more promising because ofhigher flexibility in comparison to other methods. One of the applications of speech synthesis is speech to speech translation. In these systems, the generated voice in target language should have the same characteristics as the input voice in source language. The main purpose of this research is to review and evaluate the cross lingual speaker adaptation... 

Surface piercing propellers are particular type of supercavitating propellers that are commonly used for High-speed vessels. Most studies on this type of propellers has been investigating the hydrodynamic forces. But in recent years with increase in SPPs diameter used in vessels, structural analysis of this type of propellers is considered. For this purpose, studies on the stresses exerted on the propeller structures under load is done with the help of Hydro elastic methods. In this type of analysis, structura of propeller is intended to be flexible and Displacements under pressure checked and Tensions resulting from it are studied.
The present Thesis using ANSYS software to analyze a... 

The increasing demand for autonomous vehicles and reliable communication protocols and hardware interfaces, such as CAN bus and USB, underscores the necessity for stable clock sources that maintain a low temperature coefficient (TC) over wide temperature ranges. This demand is particularly emphasized in applications such as wearables, network sensors, downhole devices, WSNs, and IoT, where long-lasting battery life and frequency-stable clock sources over a broad temperature range (e.g. -20 °C to 100 °C) are crucial. Traditionally, variations in frequency caused by temperature have been mitigated by employing off-chip components like crystals or ceramic based oscillators, but this approach... 

Pricing plays an important and essential role in the profit and income of companies. The importance of pricing is not only related to its role in the company's profitability, but it also changes the customer's understanding and loyalty towards the company and can create the company's reputation or destroy it. Determining the right price will increase product sales and increase customer loyalty and create a competitive advantage for the company. One of the most important and influential variables in product pricing is the amount of demand. The main challenge of companies for product pricing is the uncertainty in their demand. In order to deal with this problem, data-driven pricing is used.... 

In the current situation and considering the economic conditions, entrepreneurship and self-employment are important factors of business growth. To prepare people in this field, there is a need for effective training that can motivate people. Identifying the effective methods and different dimensions of the entrepreneurship education framework that can lead to setting up a successful program for training people and facilitate the way to achieve the goal of education is one of the important goals of this research. By studying the literature from 2010 onwards, this research systematically examined 96 articles and tried to improve the framework of entrepreneurship education that was stated in... 

The goal of this project is the development of a control algorithm for a flock of non-holonomic aerial agents. For this purpose,the swarm architecture having some unique features such as robustness, flexibility, and scalability is utilized. Swarm is defined as a group of simple agents having local interactions between themselves and the environmentwhich shows an unpredictable emergent behavior.Behavior based control which is inspired from the animal behaviors is employed to control the swarm of mobile agents. Accordingly, the necessary behaviors which are distance adjustment, velocity agreement, and virtual leader tracking together with a fuzzy coordinator are designed. In this study, in... 

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... 

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... 

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... 

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... 

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... 

The development of smart meters enables gathering and analysis of a large amount of data about electrical energy consumption in electric power distribution systems. This data and the obtained behavioral patterns of customers have a wide variety of applications. To name a few, classification of customers based on their consumption patterns, damaged smart meter identification, non-technical loss identification and measuring participation rate of customers in demand response programs are among the applications. So far, many studies have been done for consumption pattern identification. However, abnormality detection in electric energy consumption has captured growing attention due to the...