Sharif Digital Repository

The so-called “Fast Downward” is a successful heuristic planner. This planner extracts informative data structures during planning. A number of efforts have been made to detect some constraints for guiding search toward the goal, in the hope to speed up the planning process. These constraints usually result in a number of sub-goals and determining their proper ordering. These sub-goals are called landmarks, which must be true at some point in every valid solution plan. Landmarks can be used to decompose a given planning task into several smaller sub-tasks. In this dissertation a new method is proposed to extracts landmark and recognizes their ordering based on fast downward basic data... 

Machine translation has been an important subject in the field of natural language processing (NLP). In recent years, because of providing essential linguistic data resources, statistical approached have been deployed in machine translation. Although there have been several attempt to create English to Persian automatic translator, there has not been sufficient effort in the reverse direction. In this project, we reviewed previous works in machine translator for Persian and implemented a statistical machine translator from Persian to English. We needed a bilingual corpus for building the translator. For this purpose, we used a corpus of Phd and MSc abstracts in Persian and their translation... 

Event Extraction in Texts is one of the main challenges of Natural Language Processing. Event extraction is one of necessary components of question answering, summarization and information extraction systems. The purpose of this project has been the design and implementation of different statistical methods for event extraction in Persian and also correcting and expanding an existing corpus named PresTimeBank. The new system is composed of a preliminary rule based module that annotates events and find their features based on a predefined set of rules. The result of this stage is then revised in a subsequent manual annotation process. The output is a corpus that is compliant with the ISO... 

Sentiment analysis, is a subfield of natural language processing that aims at opinion mining to analyze thoughts, orientation and evaluation of users within some texts. Different organizations in multiple social domains, use this approach as a tool to asses their strengths and shortcomings. In sentiment analysis, the goal is to use machine learning techniques with the purpose of specifying users’ positive or negative orientation about a product or merchandise. The solution to this problem includes two main steps: extracting aspects and determining users’ positive or negative sentiments in respect to the aspects. Two main challenges of sentiment analysis in Farsi, are lack of comprehensive... 

Automated planning is a branch of artiticial intelligence that studies intelligent agents’ decision making process. The objective is to design agents that are able to decide on their own, about how to perform tasks that are assigned to them. In the past 20 years, a popular and appealing method for solving planning problems has been to use satisfiability (SAT) techniques. In this method, the planning ptoblem with a preset length would be encoded into a satisfiability problem, which is then solved by a general satisfiability solver. The solution to the planning problem is then extracted from the solution of the SAT problem. The length of the problem is proportional to the number of steps in... 

Considering numerical values is an important step toward real world problems in planning. Although planning community has been aware of this fact since many years ago, but the complication involved in reasoning with numerical values made this challenge too difficult, thus very little and occasional research has been done on this issue.This dissertation is an effort to find an efficient method for solving numerical planning problems; in this regard, we use the “planning as satisfiability” approach. Planning as satisfiability is one of the most important and successful approaches for solving planning problems. Furthermore, developing SAT solvers with the capability of considering numerical... 

To fully understanding a text written in a natural language, we need to comprehend the events within that text. Temporal relation extraction always have been one of the main challenges in natural language processing in semantic level. Temporal relation extraction makes the understanding and interpretation of text easier and the extracted information can be used in many natural language systems like question answering, summarization, and information retrieval systems. Early researches on temporal relation extraction was mainly on English and limited to rule based systems. However, with extending the English corpora and availability of temporal corpora in other languages, more attention has... 

Nowadays, because of growing web and consequently increasing data in different languages, the need for machine translation is inevitable. Machine translators are created to speed up the translation process. Machine translation methods are generally divided into three categories: rule-based, corpus-based, and hybrid. Rule-based machine translation uses grammar for translation, but it needs a complete grammar of language for correct translation. Corpus-based method has many variations. One of those variations is the statistical machine translation which uses probabilistic and statistical rules for translation and nowadays is frequently used. Hybrid machine translation benefits from the... 

Increasing relations between different cultures necessitates easier and more affordable methods of translation between different languages. Hence, using computers as Translators has been very attractive to many governmental and commercial organizations as well as scientific community since the very beginning of the computer era. So far, different approaches to MT have been proposed. Two of the main approaches to MT are Statistical MT and Rule-Based MT. Unlike Statistical MT, which uses statistical information for translation, Rule-Based MT utilizes precise linguistic information to understand the source and generate the target language. This inguisitic information is usually encoded as a... 

Grammar induction is one of the research topics of natural language processing. Grammar induction methods can be categorized into three main groups of supervised, semi-supervised, and unsupervised methods. Recently, developing Treebanks in different languages has motivated supervised methods. The main goal of this project has been extracting a dependency grammar based on a dependency Treebank. In a Treebank, the structure of every sentence represented as a dependency tree where the relation between words are specified. In this structure synonym sentences with free word order has the same dependency structure. Because of this property, dependency parsers accuracy does not decrease on Persian... 

Coreference resolution is an essential step toward understanding discourses, and it is needed by many NLP tasks such as summarization, machine translation, question answering, etc. Pronoun resolution is a major and challenging subpart of coreference resolution, in which only the resolution of pronouns is considered. The existing coreference resolution approaches can be classified into two broad categories: linguistic and machine learning approaches. Linguistic approaches need a lot of linguistic information for the resolution process. Acquisition of such information is an error- prone and time-consuming process. In contrast, learning approaches need less linguistic information and provide... 

One of the subfields of natural language processing is sentiment analysis. Generally, sentiment analysis, analyzes the positivity, or negativity of an opinion expressed in a sentence or document. Because each person's opinions have a huge impact on the decisions of other people and businesses, automatic analysis of texts has a particular importance; on which, extensive researches have been conducted in recent years. One of the common problems in sentiment analysis of some languages, including Persian, is the lack of proper resources in them. Cross-lingual sentiment analysis is one solution to this problem. In these methods, the goal is, through using the rich resources available in a source... 

Movement-Related Cortical Potentials (MRCPs) are a subset of Event Related Potentials (ERPs). The event that MRCPs are related to is the endogenous event of self-paced voluntary movement. Like many other ERPs, MRCPs have small amplitudes relative to the background EEG activity, making it difficult to detect them on a single-trial basis. Nevertheless, detection of MRCPs with good accuracy can be vastly benecial to automated rehabilitation systems and to Brain-Computer Interfaces. In this project, a new experimental protocol for MRCP is introduced and signals recorded using this protocol are analyzed. The protocol has been designed and recordings have been made by the author during the summer... 

With the increase of internet usage and the volume of available data, the need for data mining and text processing is felt. One of the common obstacles for using these methods is usage of colloquial and non-standard language in writings. Due to this fact, combined with the fact that NLP tasks in Persian language had always faced data shortage issues, in this thesis, we first collect and construct a parallel data set, consisting of colloquial texts used in social media. Then after examining various methods used in other languages for text normalization, we propose a combination of new hybrid methods, involving Statistical Machine Translation methodology with some modification, to normalize... 

Keywords or keyphrases are of importance as the smallest unit of representing the meaning of a text. Automated Keyword Extraction (AKE), as one of the natural language processing tasks is used in various applications such as searching, indexing and information retrieval. Keywords of scientific articles are basically specified manually by their authors, whereas most of the information available on the internet lack such keywords. In this research, we endeavor to automatically extract keywords of a set of Persian paper abstracts using an unsupervised machine learning method. The method used is to extract a set of candidate phrases from the text, and to cluster the document words to find a set... 

In recent years, the use of embedded processors has grown increasingly in wide range of computer systems; so that most of manufactured CPUs are used in emebedded systems many of which are safty-critical systems such as medical devices, aircraft flight control, space systems, nuclear systems, etc. The incidence of failure in these systems can cause irreversible damages on human life, financial or environmental matters. Silicon process technology trends, such as reducing the threshold voltage, increasing the frequency and decreasing the size of transistors, not only caused increase in single-bit fault rate but also caused occurance of multi-bit faults. Due to importance ofcorrect operation of... 

Nowadays, one of the most important challenges in the design of digital circuits is their susceptibility to the strike of high energy particles which leads to the Single Event Transient (SET) and Multiple Event Transients (MET). In fact, technology scaling which results in lower supply voltage, higher operating frequency, and lower nodal capacitance, makes today’s digital circuits more susceptible not only to high energy particles but also to low energy particles. Moreover emerging deep sub-micron technologies and the integration of more cells in today’s chips have caused higher probability of MET occurrences. A lot of research has tried to reduce the soft error rate due to high energy... 

Wireless Sensor Networks (WSNs) are prone to faults due to battery depletion of nodes, where a node failure can disturb routing, as it plays a key role in transferring sensed data to the end users. A WSN may be partitioned into groups of nodes called clusters; for every cluster, there is a node, called Cluster Head (CH) that is responsible for collecting and transferring data from all nodes in that cluster. This means that the CH is a single point of failure; i.e., once a CH fails, the whole cluster will fail, which leads to inability of its members for transferring data outside the cluster. This thesis presents a Fault-Tolerant and Energy-Aware algorithm (FTEA), which prolongs the lifetime... 

The increasing size of valuable data emphasizes the importance of applying reliability in storage systems. One way to protect storage system failures is using erasure codes. The advantages of using erasure codes are their low overheads and high reliability. Soft errors caused by high-energetic particles do not only corrupt data in the SSD-based storage systems, but also in the erasure codes. In this regards, it is important to protect erasure code implementations against soft errors. This thesis proposes a fault-tolerant implementation of erasure codes. The proposed method is based on the structure of each erasure code. This method is analytically evaluated on four erasure codes, i.e.... 

With continuous device down-scaling and increase in transistor counts on a chip, complementary metal-oxide-semiconductor (CMOS) technology has become extremely sensitive to soft errors. Soft errors are transient errors caused by energetic particles such as neutrons and alpha particles. An essential step to design a soft error tolerant digital system with minimal performance and power overheads is Soft Error Rate (SER) estimation of system components. Until recently, Single Event Upsets (SEUs) in latches and Filp-Flops (FFs) and Single Event Transients (SETs) in combinational logic parts of digital circuits were regarded as the main effects of particle strikes. However, with the emerging...