Sharif Digital Repository

A complte set of characteristic parameters of the sandpile models is still unknown. We have studied the existence of ”weak chaos” critical exponent in different sandpile models and we have shown that it is a characteristic exponent of deterministic models. We have shown that BTW and Zhang models do not belong to the same universality class (contrary to Zhang’s previous conjecture and contrary to Ben-Hur & Biham’s results.) Also we have shown that directed models, specificly Ramaswamy-Dhar’s directed model form a different universality class. ”Weak chaos” exponent in also studied in massive models and we have shown that by increase of dissipation, the exponent decreases rapidly to an... 

Epilepsy is one of the most significant neurological disorders that about one percent of people suffer from it. Epilepsy can only be controlled, and so far no cure for it has been provided. Despite the many advances in the treatment of diseases, for a quarter of patients there is no medical treatment solution for controlling epileptic seizures. In the studies of medical groups on the epilepsy, one approach is employment of some models for each type of epilepsy. These types may be created in the animals to allow studying of the mechanism of epilepsy and also finding drugs of treatment or controlling seizures for each type of epilepsy. There is a type of epilepsy that is called absence... 

Traditional methods for data classification only make use of the labeled data. However, in most of the applications, labeling the unlabeled data is expensive, time consuming and requires expert knowledge. To overcome these problems, Semi-supervised Learning (SSL) methods have become an area of recent research that aim to effectively addressing the problem of limited labeled data.One of the recently introduced SSL methods is the classification based on geometric structure of the data, namely the data manifold. In this approach unlabeled data is utilized to recover the underlying structure of the data. The common assumption is that despite of being represented in a high dimensional space, data... 

Deontic logic is used to formalize legal reasoning. To apply this logic in law, we describe tersely some efforts to improve this logic by relativizing its operations with respect to different people and groups of society. Until now, this logic was restricted to formalize “what must be”. We extend this logic to dyadic logic to formalize “what must be done”.
In practice, legal reasoning leads to non-monotonic logics, the most applicable one in law is defeasible logic. So it is necessary to combine deontic and defeasible logics to formalize legal reasoning in a more appropriate way. To do that, we must adjust possible worlds of these two logics. In this way, we find a method for... 

Semantic classification of words based on unsupervised learning methods is a challenging issue in computational lexical semantics. The goal of this field of study is to recognize the words that are in the same semantic classes; i.e., can have the same set of arguments. Among all word categories, verb is known as one the most important and is assumed as the central part of the sentence in certain linguistic theories such as case grammar and dependency grammar. Based on Levin’s idea, diathesis alternations and the similarity between these alternations are the clues for the semantic classification of verbs. This idea is verified in languages such as English and German with promising results.... 

Nowadays, with the increasing size of data,it’s impossible to collect data and fast classification by human, and needs for an automated classification and data analysis, is more interested. Data classification is a process of giving the training data along with their class labels to the learning agent, which learns the relation between the instances and the labels. Then make a prediction to the label of the training data.In this thesis we will observe the classification of the multi-label data. Multi-label data have more than one label. In other words, each instance appears with a vector of labels.In this thesis, a method based on nearest neighbor is proposed to classify the multi-label... 

Temporal relation classification is one of the contemporary demanding tasks in natural language processing. This task can be used in various applications such as question answering, summarization, and language specific information retrieval. Temporal relation classification methods can be categorized into three main groups of supervised, semi-supervised, and unsupervised (based on the type of the training data that they need). In this thesis, we have two main goals: first, improving accuracy of temporal relation learning, and second, decreasing supervision of algorithm as much as possible. For achieving these goals, three main steps are proposed. In the first step, we propose an improved... 

Event extraction is one of the important tasks in Natural Language Processing (NLP). Many NLP applications like question answering, information extraction and summarization need to have some knowledge about events of input documents. There are several definitions for events in NLP domains. In this dissertation, the event is viewed as an element in a network of temporal information. Therefore, the project has been based on the ISO-TimeML specification language, which is the standard scheme for temporal information processing in natural texts. Event extraction based on ISO-TimeML has been performed for a number of languages including English, French, Spanish, and Korean. However, for Persian... 

Classification of hyperspectral remote sensing images is a challenging problem, because of the small number of labeled pixels, high dimensionality of the data and large number of pixels. In this context, semisupervised learning can improve the classification accuracy by extracting information form the distribution of all the labeled and unlabeled data. Among semi-supervised methods, manifold-based algorithms have been frequently used in recent years. In most of the previous works, manifolds are constructed according to spectral representation of data, while spatial dependency of pixel labels is an important property of the images in remote sensing applications. In this thesis, after... 

The main focus of this research is to resolve the semantic ambiguity in Persian. In this study, a semi-supervised machine learning method is proposed to choose the most proper meaning of a target word in the context. Several statistical methods are compared, and the most accurate one is chosen for developing a sense tagger. An initial seed data is built by searching collocation lists for each sense. After developing the sense tagger and initial seed set, a bootstrapping method is used to sense tag all occurences of a target word in corpus with 90% accuracy  

Nowadays, living in information era has forced us to face with a great deal of problems of which the input data is received like a nonstop endless stream. Intrusion detection in networks or filtering spam emails out of legal ones are instances of such problems. In such areas, traditional classification algorithms show function improperly, thus it is necessary to make use of novel algorithms that can tackle these problems. Among classification algorithms, decision trees have significant advantages such as being independent of any parameter and acting robust against outliers or unrelated attributes. Moreover, results of a decision tree are quite easy to interpret and extract rules from.... 

Text classification accompanied by high precision has become a challenging issue in computational linguistics and natural language processing science. Proper data set accessibility, utilizing the best method and prominent linguistics features has been always regarded as the basic concern of this process. The following study relying on Bijan Khan Corpus is tried to represent keywords vectors of different documents using tf_idf. These vectors are regarded as an input for latent topic models algorithms including probabilistic latent semantic analysis. The output of this algorithm will be the documents feature vectors which will be later used in order to train different classifiers like K... 

The emergence of applications such as spam detection and online advertising coupled with the dramatic growth of user-generated content has attracted more and more attention to stream classification. The data stream in such applications is large or even unbounded; moreover, the system is often required to respond in an online manner. Furthermore, one of the main challenges of stream classification is that often the process that generates the data is non-stationary. This phenomenon, known as concept drift, poses different challenges to the classification problem.Therefore, an adaptive approach is required that can manage concept drift in an online fashion. This thesis presents a probabilistic... 

In the recent decades, the estimation of direction of arrival has been an active area and some high resolution direction finding methods like MUSIC and ESPIRIT have been developed. These methods suffer from factors like mutual coupling. Direction of arrival estimation in presence of mutual coupling is a widely studied problem. In order to resolve this problem, many array calibration algorithms have been developed. However it may be impossible or difficult to set some calibration source in some situation. In this work we show that the effect of mutual coupling can be dynamically compensated by the inherent mechanism. First we show that how mutual coupling can degrades the performance of... 

This thesis investigates the expertise ranking problem. Recently, the expertise ranking problem has attracted lots of attention in Information Retrieval community. The broad usages of expert ranking algorithms in commercial search engines indicate its importance and usability. Expertise ranking problem is concerned with finding people who are knowledgeable in a given topic. The main research questions in this thesis are related to three important questions related to expert ranking problem. The first question is what the sources of evidences are and how we can infer expertise of a person on a given topic. The second question is concerned with the modeling of information related to each... 

The rapid increase in the size of digital multimedia data collections has resulted in wide availability of multimedia contents to the general users. Effective and efficient management of these collections is an important task that has become a focus in the research of multimedia signal processing and pattern recognition. In this thesis, we address the problem of automatic classification of music, as one of the main multimedia signals. In this context, music genres are crucial descriptors that are widely used to organize the large music collections. The two main components of automatic music genre classification systems are feature extraction and classification. While features are a compact... 

Expert retrieval when the number of experts are limited is an open problem. Undoubtedly, becoming an expert in a field is a time consuming and expensive task; thus finding the best candidates is a crucial task. In addition, passage of time and growth of knowledge could change the view of a person towards life and his work, which may lead to the change of his or her field of work. When considering the changes each person makes in his or her life, it becomes obvious that they are not far from the original status. Therefore, recommending all possible options around a person could really help the task of decision making. This research is addressing two similar issues of finding experts, in a... 

Spoken Language Understanding (SLU) is aimed at extracting meaning from natural spoken language. Meaning extraction ranges from "extracting specific phrases" to "extracting users' intentions from their speech" and goes as far as "extracting the entities and details of their intentions". Extracting the exact intended meaning of the user is a sophisticated process. In this research, considering the lack of standard data in Persian, an SLU system for this language has been implemented using Markov Logic Networks (MLNs), in order to reduce the need for extra datasets. MLNs combine the explanatory power and orderliness of First-Order Logic with the uncertainty of probabilities. Therefore, these... 

The traditional machine learning methods assume that the training data and the test data are drawn from the same distribution (or drawn from the same domain). In practice, in many computer vision applications, this assumption may not hold. Unfortunately, the performance of these methods degrades on dataset drawn from a different domain. Domain adaptation attempts to minimize this degradation caused by distribution mismatch between the training and test data. Domain adaptation tries to adapt a model trainded from one domain to another domain. We focus on supervised domain adaptation method in which limited labeled data is available from the target domain. We propose a new domain adaptation... 

Make a connection between brain and computer, or Brain Computer Interface (BCI) for broad applications in areas such as medical and gamming has caused the subject to one of the most important and attractive issues in recent decades. From the perspective of pattern recognition, BCI is a classification issue that should receive signals that relate to the certain decisions of the brain and then after processing, it is concluded that the person has thought to what decision. Decisions that taken by individual, is sent from the brain to the body by signals, which is called Electroencephalogram (EEG). The number of these decisions is further, classified it also becomes more difficult. That is why...