Recently Completed M.Sc. ThesesIn Reverse Chronological Order
Top-K Link Recommendation for Development of P2P Social Networks
Musical Instrument Source Separation aims to separate the individual instruments from a mixture. We work on mixtures where there are two instruments, playing the same constant pitch at the same time. When musical instruments play the same note, they overlap with each other and act as a single source. We use statistical source separation algorithms which perform separation by maximizing the statistical independence between the sources. A mixture can be recorded with more than one microphone; this enables us to extract spatial information of the instruments by comparing the recordings. However we work with the monaural case where there is one microphone. Some musical instruments have amplitude modulation and this modulation can be seen in the mixtures. We also aim to detect amplitude modulations to support the source separation success. We use NTF (Non-negative tensor factorization) to perform the separation. NTF separates the mixture into many components. These components should be clustered in order to synthesize the individual sources. We use k-means as well as manual clustering by comparing the SDR (Signal to Distortion Ratio) values of the components with the original sources.
Keywords:Source Separation, Statistical Source Separation, Monaural, Unison mixture, NMF, NTF
Top-K Link Recommendation for Development of P2P Social Networks
The common approach for implementing social networks has been using centralized infrastructures, which inherently include problems of privacy, censorship, scalability, and fault-tolerance. Although decentralized systems offer a natural solution, significant research is needed to build an end-to-end peer-to-peer social network where data is stored among trusted users. The centralized algorithms need to be revisited for a P2P setting, where the nodes have connectivity to only neighbors, have no information of global topology, and may go offine and churn resulting in changes of the graph structure. The social graph algorithms should be designed as robust to node failures and network changes. We model P2P social networks as uncertain graphs where each node can go ofine, and we introduce link recommendation algorithms that support the development of decentralized social networks. We propose methods to recommend top-k links to improve the underlying topology and efficiency of the overlay network, while preserving the locality of the social structure. Our approach aims to optimize the probabilistic reachability, improve the robustness of the local network and avoid loss from failures of the peers. We model the problem through discrete optimization and assign a score to each node to capture both the topological connectivity and the social centrality of the corresponding node. We evaluate the proposed methods with respect to performance and quality measures developed for P2P social networks.
Keywords: P2P Social Network, Link Recommendation
Representation, Editing and Real-time Visualization of Complex 3D Terrains
Terrain rendering is a crucial part of many real-time computer graphics applications such as video games and visual simulations. It provides the main frame-of-reference for the observer and constitutes the basis of an imaginary or simulated world that encases the observer. Storing and rendering terrain models in real-time applications usually require a specialized approach due to the sheer magnitude of data available and the level of detail demanded. The easiest way to process and visualize such large amounts of data in real-time is to constrain the terrain model in several ways. This process of regularization decreases the amount of data to be processed and also the amount of processing power needed at the cost of expressivity and the ability to create interesting terrains.
The most popular terrain representation, by far, used by modern real-time graphics applications is a regular 2D grid where the vertices are displaced in a third dimension by a displacement map, conventionally called a height map. It is the simplest and fastest possible terrain representation, but it is not possible to represent complex terrain models that include interesting terrain features such as caves, overhangs, cliffs and arches using a simple 2D grid and a height map. We propose a novel terrain representation combining the voxel and height map approaches that is expressive enough to allow creating complex terrains with caves, overhangs, cliffs and arches, and efficient enough to allow terrain editing, deformations and rendering in real-time. We also explore how to apply lighting, texturing, shadowing and level-of-detail to the proposed terrain representation.
Keywords: Terrain representation, terrain visualization, caves, overhangs, clis, voxel terrain, height map terrain.
An Actuated Flexible Spinal Mechanism for a Bounding Quadrupedal Robot
Evolution and experience based learning have given animals body structures and motion capabilities to survive in the nature by achieving many complicated tasks. Among these animals, legged vertebrates use their musculoskeletal bodies up to the limits to achieve actions involving high speeds and agile maneuvers. Moreover the flexible spine plays a very important role in providing auxiliary power and dexterity for such dynamic behaviors. Robotics research tries to imitate such dynamic abilities on mechanical platforms. However, most existing robots performing these dynamic motions does not include such a flexible spine architecture. In this thesis we investigate how quadrupedal bounding can be achieved with the help of an actuated flexible spine. Depending upon biological correspondences we first present a novel quadruped robot model with an actuated spine and relate it with our proposed new bounding gait controller model. By optimizing our model and a standard stiff backed model via repetitive parametric methods, we investigate the role of spinal actuation on the performance enhancements of the flexible model. By achieving higher ground speeds and hopping heights we discuss the relations between flexible body structure and stride properties of a dynamic bounding gait. Furthermore, we present an analytical model of the proposed robot structure along with the spinal architecture and analyze the dynamics and active forces on the overall system. By gathering simulation results we question how such a flexible spine system can be improved to achieve higher performances during dynamic gaits.
Keywords: Bio-inspired robotics, Legged robots, Dynamic locomotion, Quadrupedal bounding, Spinal actuation, Gait optimization
Software Design, Implementation, Application, and Refinement of a Bayesian Approach for the Assessment of Content and User Qualities
The internet provides unlimited access to vast amounts of information. Technical innovations and internet coverage allow more and more people to supply contents for the web. As a result, there is a great deal of material which is either inaccurate or out-of-date, making it increasingly difficult to find relevant and up-to-date content. In order to solve this problem, recommender systems based on collaborative filtering have been introduced. These systems cluster users based on their past preferences, and suggest relevant contents according to user similarities. Trustbased recommender systems consider the trust level of users in addition to their past preferences, since some users may not be trustworthy in certain categories even though they are trustworthy in others. Content quality levels are important in order to present the most current and relevant contents to users. The study presented here is based on a model which combines the concepts of content quality and user trust. According to this model, the quality level of contents cannot be properly determined without considering the quality levels of evaluators. The model uses a Bayesian approach, which allows the simultaneous co-evaluation of evaluators and contents. The Bayesian approach also allows the calculation of the updated quality values over time. In this thesis, the model is further refined and configurable software is implemented in order to assess the qualities of users and contents on the web. Experiments were performed on a movie data set and the results showed that the Bayesian co-evaluation approach performed more effectively than a classical approach which does not consider user qualities. The approach also succeeded in classifying users according to their expertise level.
Keywords: Information quality, Web 2.0, collaborative systems, recommender systems, collaborative filtering, Bayesian networks, co-evaluation, trust-based systems.
A Result Cache Invalidation Scheme for Web Search Engines
The result cache is a vital component for efficiency of large-scale web search engines, and maintaining the freshness of cached query results is a current research challenge. As a remedy to this problem, our work proposes a new mechanism to identify queries whose cached results are stale. The basic idea behind our mechanism is to maintain and compare generation time of query results with update times of posting lists and documents to decide on staleness of query results.
The proposed technique is evaluated using a Wikipedia document collection with real update information and a real-life query log. Throughout the experiments, we compare our approach with two baseline strategies from literature together with a detailed evaluation. We show that our technique has good prediction accuracy, relative to the baseline based on the time-to-live (TTL) mechanism. Moreover, it is easy to implement and incurs less processing overhead on the system relative to a recently proposed, more sophisticated invalidation mechanism.
Keywords: Web search, result cache, cache invalidation, time-to-live, freshness, adaptive.
Nearest-Neighbor Based Metric Functions for Indoor Scene Recognition
Indoor scene recognition is a challenging problem in the classical scene recognition domain due to the severe intra-class variations and inter-class similarities of man-made indoor structures. State-of-the-art scene recognition techniques such as capturing holistic representations of an image demonstrate low performance on indoor scenes. Other methods that introduce intermediate steps such as identifying objects and associating them with scenes have the handicap of successfully localizing and recognizing the objects in a highly cluttered and sophisticated environment.
We propose a classification method that can handle such difficulties of the problem domain by employing a metric function based on the nearest-neighbor classication procedure using the bag-of-visual words scheme, the so-called codebooks. Considering the codebook construction as a Voronoi tessellation of the feature space, we have observed that, given an image, a learned weighted distance of the extracted feature vectors to the center of the Voronoi cells gives a strong indication of the image's category. Our method outperforms state-of-the-art approaches on an indoor scene recognition benchmark and achieves competitive results on a general scene dataset, using a single type of descriptor.
In this study although our primary focus is indoor scene categorization, we also employ the proposed metric function to create a baseline implementation for the auto-annotation problem. With the growing amount of digital media, the problem of auto-annotating images with semantic labels has received signi cant interest from researches in the last decade. Traditional approaches where such content is manually tagged has been found to be too tedious and a time-consuming process. Hence, succesfully labeling images with keywords describing the semantics is a crucial task yet to be accomplished.
Keywords: scene classification, indoor scene recognition, nearest neighbor classifier, bag-of-visual words, image auto-annotation.
Efficiency and Effectiveness of XML Keyword Search Using a Full Element Index
In the last decade, both the academia and industry proposed several techniques to allow keyword search on XML databases and document collections. A common data structure employed in most of these approaches is an inverted index, which is the state-of-the-art for conducting keyword search over large volumes of textual data, such as world wide web. In particular, a full element-index considers (and indexes) each XML element as a separate document, which is formed of the text directly contained in it and the textual content of all of its descendants. A major criticism for a full element-index is the high degree of redundancy in the index (due to the nested structure of XML documents), which diminishes its usage for large-scale XML retrieval scenarios. As the first contribution of this thesis, we investigate the efficiency and effectiveness of using a full element-index for XML keyword search. First, we suggest that lossless index compression methods can significantly reduce the size of a full element-index so that query processing strategies, such as those employed in a typical search engine, can efficiently operate on it. We show that once the most essential problem of a full element-index, i.e., its size, is remedied, using such an index can improve both the result quality (effectiveness) and query execution performance (efficiency) in comparison to other recently proposed techniques in the literature. Moreover, using a full element-index also allows generating query results in different forms, such as a ranked list of documents (as expected by a search engine user) or a complete list of elements that include all of the query terms (as expected by a DBMS user), in a unified framework. As a second contribution of this thesis, we propose to use a lossy approach, static index pruning, to further reduce the size of a full element-index. In this way, we aim to eliminate the repetition of an element's terms at upper levels in an adaptive manner considering the element's textual content and search system's ranking function. That is, we attempt to remove the repetitions in the index only when we expect that removal of them would not reduce the result quality. We conduct a well-crafted set of experiments and show that pruned index files are comparable or even superior to the full element-index up to very high pruning levels for various ad hoc tasks in terms of retrieval effectiveness. As a final contribution of this thesis, we propose to apply index pruning strategies to reduce the size of the document vectors in an XML collection to improve the clustering performance of the collection. Our experiments show that for certain cases, it is possible to prune up to 70% of the collection (or, more specifically, underlying document vectors) and still generate a clustering structure that yields the same quality with that of the original collection, in terms of a set of evaluation metrics.
Keywords: Information Retrieval, XML Keyword Search, Full Element-Index, LCA, SLCA, Static Pruning, Clustering.
A Framework for the Use of Wireless Sensor Networks in Forest Fire Detection and Monitoring
Wireless sensor networks have a broad range of applications in the category of environmental monitoring. In this thesis, we consider the problem of forest fire detection and monitoring as a possible application area of wireless sensor networks. Forest fires are one of the main causes of environmental degradation nowadays. The current surveillance systems for forest fires lack in supporting real-time monitoring of every point of the region at all time and early detection of the fire threats. Solutions using wireless sensor networks, on the other hand, can gather temperature and humidity values from all points of field continuously, day and night, and, provide fresh and accurate data to the fire fighter center quickly. However, sensor networks and nodes face serious obstacles like limited energy resources and high vulnerability to harsh environmental conditions, that have to be considered carefully.
In our study, we propose a comprehensive framework for the use of wireless sensor networks for forest fire detection and monitoring. Our framework includes proposals for the wireless sensor network architecture, clustering and communication protocols, and environment/season-aware activity-rate selection schemes to detect the fire threat as early as possible and yet consider the energy consumption of the sensor nodes and the physical conditions that may hinder the activity of the network. We also implemented a simulator to validate and evaluate our proposed framework, which is using an external fire simulator library. We did extensive simulation experiments and observed that our framework can provide fast reaction to forest fires while also consuming energy efficiently.
Keywords: Wireless sensor networks, forest fire detection, fire forecast.
Naming Faces on the Web
In this study, we introduce a method to name less-frequently appearing people on the web via naming frequently appearing ones first. Current image search engines are widely used for querying a person, however; retrievals are based on textual content; therefore, the results are not satisfactory. On the other hand, although; face recognition is a long standing problem; it is tested for limited sizes and successful results are acquired just for face images captured under controlled environments. Faces on the web, contrarily are huge in amount and vary in pose, illumination, occlusion and facial attributes. Recent researches on the area, suggest not to use simply the visual or textual content alone, but to combine them both. With this approach, face recognition problem is simplified to a face-name association problem.
Following these approaches, in our method textual and visual information is combined to name faces. We divide the problem into two sub problems, first the more frequently appearing faces, then the less-frequently appearing faces on the web images are named. A supervised algorithm is used for naming a specified number of categories belonging to more frequently appearing faces. The faces that are not matched with any category are then considered to be the less-frequently appearing faces and labeled using the textual content. We extracted all the names from textual contents, and then eliminate the ones used to label frequentlyappearing faces before. The remaining names are the candidate categories for lessfrequently appearing faces. Each detected less-frequently appearing face finally matched to the names extracted from their corresponding textual content. In order to prune the irrelevant face images, finally, the most similar faces among this collection are found to be matched with their corresponding category.
In our experiments, the method is applied on two different datasets. Both datasets are constructed from the images captured in realistic environments, varying in pose, illumination, facial expressions, occlusions and etc. The results of the experiments proved that the combination of textual and visual contents on realistic face images outperforms the methods that use either one of them. Besides, handling the face recognition problem as a face-name association, improves the results for the face images collected from uncontrolled environments.
Keywords: Face recognition, face detection, face retrieval, face naming, naming faces, SVM, SIFT.
Noun Phrase Chunker for Turkish Using Dependency Parser
Noun phrase chunking is a sub-category of shallow parsing that can be used for many natural language processing tasks. In this thesis, we propose a noun phrase chunker system for Turkish texts. We use a weighted constraint dependency parser to represent the relationship between sentence components and to determine noun phrases.
The dependency parser uses a set of hand-crafted rules which can combine morphological and semantic information for constraints. The rules are suitable for handling complex noun phrase structures because of their flexibility. The developed dependency parser can be easily used for shallow parsing of all phrase types by changing the employed rule set.
The lack of reliable human tagged datasets is a significant problem for natural language studies about Turkish. Therefore, we constructed the first noun phrase dataset for Turkish. According to our evaluation results, our noun phrase chunker gives promising results on this dataset.
The correct morphological disambiguation of words is required for the correctness of the dependency parser. Therefore, in this thesis, we propose a hybrid morphological disambiguation technique which combines statistical information, hand-crafted grammar rules, and transformation based learning rules. We have also constructed a dataset for testing the performance of our disambiguation system. According to tests, the disambiguation system is highly effective.
Keywords: Natural Language Processing, Noun Phrase Chunker, Turkish, Shallow Parsing, Morphological Disambiguation.
Novelty Detection in Topic Tracking
News portals provide many services to the news consumers such as information retrieval, personalized information filtering, summarization and news clustering. Additionally, many news portals using multiple sources enable their users to evaluate developments from different perspectives by richening the content. However, increasing number of sources and incoming news makes it difficult for news consumers to find news of their interest in news portals. Different types of organizational operations are applied to ease browsing over the news for this reason. New event detection and tracking (NEDT) is one of these operations which aims to organize news with respect to the events that they report. NEDT may not also be enough by itself to satisfy the news consumers' needs because of the repetitions of information that may occur in the tracking news of a topic due to usage of multiple sources. In this thesis, we investigate usage of novelty detection (ND) in tracking news of a topic. For this aim, we built a Turkish ND experimental collection, BilNov, consisting of 59 topics with an average of 51 tracking news. We propose usage of three methods; cosine similarity-based ND method, language model-based ND method and cover coefficient-based ND method. Additionally, we experiment on category-based threshold learning which has not been worked on previously in ND literature. We also provide some experimental pointers for ND in Turkish such as restriction of document vector lengths and smoothing methods. Finally, we experiment on TREC Novelty Track 2004 dataset. Experiments conducted by using BilNov show that language model-based ND method outperforms other two methods significantly and category-based threshold learning has promising results when compared to general threshold learning.
Keywords: Novelty detection, Topic tracking
Real-Time Crowd Simulation in Virtual Urban Environments using Adaptive Grids
Crowd simulation is a relatively new research area, attracting increasing attention from both academia and industry. This thesis proposes Adaptive Grids, a novel hybrid approach for controlling the behavior of agents in a virtual crowd. In this approach, the motion of each agent within the crowd is planned considering both global and local path planning strategies. For global path planning, a cellular adaptive grid is constructed from a regular navigation map that represents the 2-D topology of the simulation terrain. A navigation graph with efficient size is then pre-computed from the adaptive grid for each possible agent goal. Finally, the navigation graph is used to generate a potential field on the adaptive grid by using the connectivity information of the irregular cells. Global path planning per agent has constant time complexity. For local path planning, Helbing Traffic-Flow model is used to avoid obstacles and agents. Potential forces are then applied on each agent considering the local and global decisions of the agent, while providing each agent the freedom to act independently.
Keywords: Crowd Simulation, Real-Time Animation, Motion Planning, Urban Visualization.
Risk Estimation by Maximizing Area Under Receiver Operating Characteristics Curve with Application to Cardiovascular Surgery
Risks exist in many different domains; medical diagnoses, financial markets, fraud detection and insurance policies are some examples. Various risk measures and risk estimation systems have hitherto been proposed and this thesis suggests a new risk estimation method. Risk estimation by maximizing the area under a Receiver Operating Characteristics (ROC) curve (REMARC) defines risk estimation as a ranking problem. Since the area under ROC curve (AUC) is related to measuring the quality of ranking, REMARC aims to maximize the AUC value on a single feature basis to obtain the best ranking possible on each feature. For a given categorical feature, we prove a sufficient condition that any function must satisfy to achieve the maximum AUC. Continuous features are also discretized by a method that uses AUC as a metric. Then, a heuristic is used to extend this maximization to all features of a dataset. REMARC can handle missing data, binary classes and continuous and nominal feature values. The REMARC method does not only estimate a single risk value, but also analyzes each feature and provides valuable information to domain experts for decision making. The performance of REMARC is evaluated with many datasets in the UCI repository by using different state-of-the-art algorithms such as Support Vector Machines, nave Bayes, decision trees and boosting methods. Evaluations of the AUC metric show REMARC achieves predictive performance significantly better compared with other machine learning classification methods and is also faster than most of them.
In order to develop new risk estimation framework by using the REMARC method cardiovascular surgery domain is selected. The TurkoSCORE project is used to collect data for training phase of the REMARC algorithm. The predictive performance of REMARC is compared with one of the most popular cardiovascular surgical risk evaluation method, called EuroSCORE. EuroSCORE is evaluated on Turkish patients and it is shown that EuroSCORE model is insufficient for Turkish population. Then, the predictive performances of EuroSCORE and TurkoSCORE that uses REMARC for prediction are compared. Empirical evaluations show that REMARC achieves better prediction than EuroSCORE on Turkish patient population.
Keywords: Risk Estimation, AUC Maximization, AUC, Ranking, Cardiovascular Operation Risk Evaluation
Color Graph Representation for Structural Analysis of Tissue Images
Computer aided image analysis tools are becoming increasingly important in automated cancer diagnosis and grading. They have the potential of assisting pathologists in histopathological examination of tissues, which may lead to a considerable amount of subjectivity. These analysis tools help reduce the subjectivity, providing quantitative information about tissues. In literature, it has been proposed to implement such computational tools using different methods that represent a tissue with different set of image features. One of the most commonly used methods is the structural method that represents a tissue quantifying the spatial relationship of its components. Although previous structural methods lead to promising results for different tissue types, they only use the spatial relations of nuclear tissue components without considering the existence of different components in a tissue. However, additional information that could be obtained from other components of the tissue has an importance in better representing the tissue, and thus, in making more reliable decisions.
This thesis introduces a novel structural method to quantify histopathological images for automated cancer diagnosis and grading. Unlike the previous structural methods, it proposes to represent a tissue considering the spatial distribution of different tissue components. To this end, it constructs a graph on multiple tissue components and colors its edges depending on the component types of their end points. Subsequently, a new set of structural features is extracted from these "color graphs" and used in the classification of tissues. Experiments conducted on 3236 photomicrographs of colon tissues that are taken from 258 different patients demonstrate that the color graph approach leads to 94.89 percent training accuracy and 88.63 percent test accuracy. Our experiments also show that the introduction of color edges to represent the spatial relationship of different tissue components and the use of graph features defined on these color edges significantly improve the classification accuracy of the previous structural methods.
A Fuzzy Logic-based Approach for Enhancing Depth Perception in Computer Graphics
Rapid progress in 3D rendering and display technologies brings the problem of better visualization of 3D content. Providing correct depth information and enabling the user to perceive the spatial relationship between the objects is one of the main concerns during the visualization of 3D content.
In this thesis, we introduce a solution that can either be used for automatically enhancing the depth perception of a given scene, or as a component that suggests suitable rendering methods to application developers.
In this novel solution, we propose a framework that decides on the suitable depth cues for a given 3D scene and the rendering methods which provide these cues. First, the system calculates the importance of each depth cue using a fuzzy logic based algorithm which considers the user's tasks in the application and the spatial layout of the scene. Then, a knapsack model is constructed to keep the balance between the rendering costs of the graphical methods that provide these cues and their contribution to depth perception. This cost-prot analysis step selects the proper rendering methods for the given scene.
In this work, we also present several objective and subjective experiments which show that our automated depth perception enhancement system is statistically (p < 0.05) better than the other method selection techniques that are tested.
Keywords: Depth perception, depth cues, cue combination, computer graphics, perceptually-aware rendering
Data Dissemination Strategies for Mobile Peer-To-Peer Information Systems with Applications to Healthcare
Peer-to-peer (P2P) architecture is becoming increasingly popular for various applications, replacing the classical Client-Server architecture. With the enhanced capabilities of mobile devices (PDAs, mobile phones, etc.) wireless networks started to take advantage of P2P paradigm along with its properties like infrastructure-free operation, scalability, balanced and distributed workload. Mobile peer-to-peer (MP2P) networks refer to the application of P2P architecture over wireless networks. Problems about dissemination of data in both P2P and MP2P networks are widely studied, and there are many proposed solutions.
Healthcare information systems are helping clinicians to hold the information belonging to patients and diseases, and to communicate with each other since early 1950s. Today, they are widely used in hospitals, being constructed using Client-Server network architecture. Wireless technologies are also applied to medical domain, especially for monitoring purposes.
In this thesis, we present and evaluate various data dissemination strategies to work on a mobile peer-to-peer (MP2P) network designed for a medical healthcare environment. First, the designed network system is presented along with the network topology. Then, the proposed data dissemination strategies are described. And finally, these strategies are evaluated according to the properties and needs of a medical system.
Keywords: Peer-to-peer architecture, Mobile Peer-to-peer networks, Data dissemination, Healthcare information systems.
Content-Based Video Copy Detection Using Multimodal Analysis
Huge and increasing amount of videos broadcast through networks has raised the need of automatic video copy detection for copyright protection. Recent developments in multimedia technology introduced content-based copy detection (CBCD) as a new research field alternative to the watermarking approach for identification of video sequences.
This thesis presents a multimodal framework for matching video sequences using a three-step approach: First, a high-level face detector identifies facial frames/shots in a video clip. Matching faces with extended body regions gives the flexibility to discriminate the same person (e.g., an anchor man or a political leader) in different events or scenes. In the second step, a spatiotemporal sequence matching technique is employed to match video clips/segments that are similar in terms of activity. Finally the non-facial shots are matched using low-level visual features. In addition, we utilize fuzzy logic approach for extracting color histogram to detect shot boundaries of heavily manipulated video clips. Methods for detecting noise, frame-droppings, picture-in-picture transformation windows, and extracting mask for still regions are also proposed and evaluated.
The proposed method was tested on the query and reference dataset of CBCD task of TRECVID 2008. Our results were compared with the results of top-8 most successful techniques submitted to this task. Experimental results show that the proposed method performs better than most of the state-of-the-art techniques, in terms of both effectiveness and efficiency.
Keywords: copy detection, video processing, shot-boundary detection, video segmentation, subsequence matching, face detection.
Qualitative Test-Cost Sensitive ClassificationDecision making is a procedure for selecting the best action among several alternatives. In many real-world problems, decision has to be taken under the circumstances in which one has to pay to acquire information. In this thesis, we propose a new framework for test-cost sensitive classification that considers the misclassification cost together with the cost of feature extraction, which arises from the effort of acquiring features. This proposed framework introduces two new concepts to test-cost sensitive learning for better modeling the real-world problems: qualitativeness and consistency.
First, this framework introduces the incorporation of qualitative costs into the problem formulation. This incorporation becomes important for many real world problems, from finance to medical diagnosis, since the relation between the misclassification cost and the cost of feature extraction could be expressed only roughly and typically in terms of ordinal relations for these problems. For example, in cancer diagnosis, it could be expressed that the cost of misdiagnosis is larger than the cost of a medical test. However, in the test-cost sensitive classification literature, the misclassification cost and the cost of feature extraction are combined quantitatively to obtain a single loss/utility value, which requires expressing the relation between these costs as a precise quantitative number.
Second, it considers the consistency between the current information and the information after feature extraction to decide which features to extract. For example, it does not extract a new feature if it brings no new information but just confirms the current one; in other words, if the new feature is totally consistent with the current information. By doing so, the proposed framework could significantly decrease the cost of feature extraction, and hence, the overall cost without decreasing the classification accuracy. Such consistency behavior has not been considered in the previous test-cost sensitive literature.
We conduct our experiments on three medical data sets and the results demonstrate that the proposed framework significantly decreases the feature extraction cost without decreasing the classification accuracy.
Keywords: Cost-sensitive learning, qualitative decision theory, feature extraction cost, feature selection, decision theory.
Integrated Segmentation and Recognition of Connected Ottoman Script
In this thesis, a novel context-sensitive segmentation and recognition method for connected letters in Ottoman script is proposed. This method first extracts a set of possible segments from a connected script and determines the candidate letters to which extracted segments are most similar. Next, a function is defined for scoring each different syntactically correct sequence of these candidate letters. To find the candidate letter sequence that maximizes the score function, a directed acyclic graph is constructed. The letters are finally recognized by computing the longest path in this graph. Experiments using a collection of printed Ottoman documents reveal that the proposed method provides very high precision and recall figures in terms of character recognition. In a further set of experiments we also demonstrate that the framework can be used as a building block for an information retrieval system for digital Ottoman archives.
Keywords: Optical character recognition (OCR), segmentation and recognition of connected scripts, connected scripts, information retrieval (IR).
Predicting Risk of Mortality in Patients Undergoing Cardiovascular Surgery
It is very important to inform the patients and their relatives about the risk of mortality before a cardiovascular operation. For this respect, a model called EuroSCORE (The European System for Cardiac Operative Risk Evaluation) has been developed by European cardiovascular surgeons. This system gives the risk of mortality during or 30 days after the operation, based on the values of some parameters measured before the operation. The model used by EuroSCORE has been developed by statistical data gathered from large number of operations performed in Europe.
Even though due to the surgical techniques that have been developed recently and the risk of mortality has been reduced in a large extent, predicting that risk as accurately as possible is still primary concern for the patients and their relatives in cardiovascular operations. The risk of operation also essentially tells the surgeon how a patient with similar comorbidity would be expected to fare based on a standard care. The risk of patient is also important for the health insurance companies, both public or private. In the context of this project, a model that can be used for mortality is developed.
In this research project, a database system for storing data about cardiovascular operations performed in Turkish hospitals, a web application for gathering data, and a machine learning system on this database to learn a risk model, similar to EuroSCORE, are developed. This thesis proposes a risk estimation system for predicting the risk of mortality in patients undergoing cardiovascular operations by maximizing the Area under the Receiver Operating Characteristic (ROC) Curve (AUC).
When the genetic characteristics and life styles of Turkish patients are taken into consideration, it is highly probable that the mortality risks of Turkish patients may be dierent than European patients. This thesis also intends to investigate this issue.
Keywords: Machine learning, ROC, AUC, risk estimation, cardiovascular operation, data mining.
Query Processing for an MPEG-7 Compliant Video Database
Based on the recent advancements in multimedia, communication, and storage technologies, the amount of audio-visual content stored is increased dramatically. The need to organize and access the growing multimedia content led researchers to develop multimedia database management systems. However, each system has its own way of describing the multimedia content that disables interoperability among other systems. To overcome this problem and to be able to standardize the description of audio-visual content stored in those databases, MPEG-7 standard has been developed by MPEG (Moving Picture Experts Group).
In this thesis, a query language and a query processor for an MPEG-7 compliant video database system is proposed. The query processor consists of three main modules: query parsing module, query execution module, and result fusion module. The query parsing module parses the XML based query and divides it into subqueries. Each sub-query is then executed with related query execution module and the final result is obtained by fusing the results of the sub-queries according to user defined weights. The prototype video database system BilVideo v2.0, which is formed as a result of this thesis work, supports spatio-temporal and low level feature queries that contain any weighted combination of keyword, temporal, spatial, trajectory, and low level visual feature (color, shape and texture) queries. Compatibility with MPEG-7, low-level visual query support, and weighted result fusion feature are the major factors that highly differentiate between BilVideo v2.0 and its predecessor, BilVideo.
Keywords: MPEG-7, XML databases, video databases, multimedia databases, query processing, content-based retrieval, video query languages.
Modeling and Populating Virtual Cities: Automatic Production of Building Models and Emergency Crowd Simulation
In this thesis, we present an automatic building generation method based on procedural modeling approach, and a crowd animation system that simulates a crowd of pedestrians inside a city. While modeling the buildings, to achieve complex and consistent geometries we use shape grammars. The derivation process incorporates randomness so the produced models have the desired variation. The end shapes of the building models could be defined in a certain extent by the derivation rules. The behavior of human crowds inside a city is affected by the simulation scenario. In this thesis, we specifically intend to simulate the virtual crowds in emergency situations caused by an incident, such as a fire, an explosion, or a terrorist attack. We prefer to use a continuum dynamics-based approach to simulate the escaping crowd, which produces more efficient simulations than the agent-based approaches. Only the close proximity of the incident region, which includes the crowd affected by the incident, is simulated. In order to speed up the animation and visualization of the resulting simulation, we employ an offline occlusion culling technique. During runtime, we animate and render a pedestrian model only if it is visible to the user. In the pre-processing stage, the navigable area of the scene is decomposed into a grid of cells and the from-region visibility of these cells is computed with the help of hardware occlusion queries.
Keywords: Procedural modeling, emergency, crowd simulation, crowd animation, occlusion culling, from-region visibility
Real-Time Parameterized Locomotion Generation
Reuse and blending of captured motions for creating realistic motions of human body is considered as one of the challenging problems in animation and computer graphics. Locomotion (walking, running and jogging) is one of the most common types of daily human motion. Based on blending of multiple motions, we propose a two-stage approach for generating locomotion according to user-specified parameters, such as linear and angular velocities. Starting from a large dataset of various motions, we construct a motion graph of similar short motion segments. This process includes the selection of motions according to a set of predefined criteria, the correction of errors on foot positioning, pre-adjustments, motion synchronization, and transition partitioning. In the second stage, we generate an animation according to the specified parameters by following a path on the graph during run-time, which can be performed in real-time. Two different blending techniques are used at this step depending on the number of the input motions: blending based on scattered data interpolation and blending based on linear interpolation. Our approach provides an expandable and efficient motion generation system, which can be used for real time applications.
Keywords: Animation, data scattered interpolation, blending, locomotion
Animated Mesh Simplification Based on Saliency Metrics
Mesh saliency identifies the visually important parts of a mesh. Mesh simplification algorithms using mesh saliency as simplification criterion preserve the salient features of a static 3D model. In this thesis, we propose a saliency measure that will be used to simplify animated 3D models. This saliency measure uses the acceleration and deceleration information about a dynamic 3D mesh in addition to the saliency information for static meshes. This provides the preservation of sharp features and visually important cues during animation. Since oscillating motions are also important in determining saliency, we propose a technique to detect oscillating motions and incorporate it into the saliency based animated model simplification algorithm. The proposed technique is experimented on animated models making oscillating motions and promising visual results are obtained.
Keywords: Simplification, animation, mesh saliency, deformation.
Combined Filtering and Keyframe Reduction For Motion Capture Data
Two new methods for combined filtering and key-frame reduction of motion capture data are proposed. Filtering of motion capture data is necessary to eliminate any jitter introduced by a motion capture system. Although jitter removal is needed to obtain a more realistic animation, it may result in an over-smoothed motion data if it is not done properly. Key-frame reduction, on the other hand, allows animators to easily edit motion data by representing animation curves with a significantly smaller number of key frames. One of the proposed techniques achieves key frame reduction and jitter removal simultaneously by fitting a Hermite curve to motion capture data using dynamic programming. Another method is to use curve simplification algorithms on the motion capture data until the desired reduction is reached. In this research, the results of these algorithms are evaluated and compared. Both subjective and objective results are presented.
Keywords: Motion capture, keyframe reduction, curve fitting, curve simplification, noise filtering.
Kronecker Representation and Decompositional Analysis of Closed Queueing Networks with Phase-type Service Distributions and Arbitrary Buffer Sizes
This thesis extends two approximative fixed-point iterative methods based on decomposition for closed queueing networks (QNs) with Coxian service distributions and arbitrary buffer sizes from the literature to include phase-type service distributions. It shows how the irreducible Markov chain associated with each subnetwork in the decomposition can be represented hierarchically using Kronecker products. The proposed methods are implemented in a software tool, which is capable of computing the steadyby a multilevel method at each fixedcompared with others, one being the multilevel method for the closed QN itself, for accuracy and efficiency on a number of examples using the tool, and their convergence properties are discussed. Numerical results indicate that there is a niche among the problems considered which is filled by the two approximative fixed point iterative methods.
Keywords: Closed queueing networks, Phase-type service distributions, Kronecker representations, Network decomposition, Fixed-point iteration, Multilevel methods.
A Hybrid Hair Model Using Three Dimensional Fuzzy TexturesHuman hair modeling and rendering have always been a challenging topic in computer graphics. The techniques for human hair modeling consist of explicit geometric models as well as volume density models. Recently, hybrid cluster models have also been successful in this subject. In this study, we present a novel three dimensional texture model called 3D Fuzzy Textures and algorithms to generate them. Then, we use the developed model along with a cluster model to give human hair complex hairstyles such as curly and wavy styles. Our model requires little user effort to model curly and wavy hair styles. With this study, we aim at eliminating the drawbacks of the volume density model and the cluster hair model with 3D fuzzy textures. A three dimensional cylindrical texture mapping function is introduced for mapping purposes. Current generation graphics hardware is utilized in the design of rendering system enabling high performance rendering.
Keywords: Hair modeling, hair rendering, curly hair, wavy hair, cluster hair model, volume density model, 3D texture, 3D texture mapping.
Ghostware and Rootkit Detection Techniques for Windows
Spyware is a significant problem for most computer users. In public, the term spyware is used with the same meaning as adware, a kind of malicious software used for showing advertisements to the user against his will. Spyware programs are also known for their tendency to hide their presence, but advanced stealth techniques used to be either nonexistent or relatively primitive in terms of effectiveness. This made spyware easily detectable with simple file-scanning and registry-scanning techniques. New spyware programs have merged with rootkits and gained stealth abilities, forming spyware with advanced stealth techniques. In this work we focus on this important subclass of spyware, namely ghostware. Ghostware programs hide their resources from the Operating System Application Programming Interfaces that were designed to query and enumerate them. We enumerated these hiding techniques and studied the stealth detection methodologies and investigated the effectiveness of the hiding techniques against popular anti-virus programs and anti-spyware programs together with publicly available ghostware detection and rootkit detection tools. The results show that, anti-virus programs or anti-spyware programs are not effective for detecting or removing ghostware applications. Hidden object detection or rootkit detection tools can be useful, however, these tools can only work after the computer is infected and they do not provide any means for removing the ghostware. As a result, our work shows the need for understanding the potential dangers and applications of ghostware and implementing new detection and prevention tools.
Keywords: spyware, ghostware, rootkit, stealth, detection.
Motion Capture from Single Video Sequence
3D human pose reconstruction is a popular research area since it can be used in various applications. Currently most of the methods work for constrained environments, where multi camera views are available and camera calibration is known, or a single camera view is available, which requires intensive user effort. However most of the currently available data do not satisfy these constraints, thus they cannot be processed by these algorithms. In this thesis a framework is proposed to reconstruct 3D pose of a human for animation from a sequence of single view video frames. The framework for pose construction starts with background estimation. Once the image background is estimated, the body silhouette is extracted by using image subtraction for each frame. Then the body silhouettes are automatically labeled by using a model-based approach. Finally, the 3D pose is constructed from the labeled human silhouette by assuming orthographic projection. The proposed approach does not require camera calibration. The proposed framework assumes that the input video has a static background and it has no significant perspective effects and the performer is in upright position.
Keywords: Motion capture, framework, single camera, uncalibrated camera, vision-based, animation.
Automatic Detection of Salient Objects for a Video Database System
Recently, the increase in the amount of multimedia data has unleashed the development of storage techniques. Multimedia databases is one of the most popular of these techniques because of its scalability and ability to be queried by the media features. One downside of these databases is the necessity for processing of the media for feature extraction prior to storage and querying. Ever growing pile of media makes this processing harder to be completed manually. This is the case with BilVideo Video Database System, as well. Improvements on computer vision techniques for object detection and tracking have made automation of this tedious manual task possible. In this thesis, we propose a tool for the automatic detection of objects of interest and deriving spatio-temporal relations between them in video frames. The proposed framework covers the scalable architecture for video processing and the stages for cut detection, object detection and tracking. We use color histograms for cut detection. Based on detected shots, the system detects salient objects in the scene, by making use of color regions and camera focus estimation. Then, the detected objects are tracked based on their location, shape and estimated speed.
Keywords: Video Databases, Video Object Detection, Object Tracking, Camera Focus Estimation.
Pattern Information Extraction From Crystal Structures
Determining crystal structure parameters of a material is a quite important issue in crystallography. Knowing the crystal structure parameters helps to understand physical behavior of material. For complex structures, particularly for materials which also contain local symmetry as well as global symmetry, obtaining crystal parameters can be quite hard. This work provides a tool that will extract crystal parameters such as primitive vectors, basis vectors and space group from atomic coordinates of crystal structures. A visualization tool for examining crystals is also provided. Accordingly, this work presents a useful tool that help crystallographers, chemists and material scientists to analyze crystal structures efficiently.
Keywords: crystal, crystallography, chemistry, material science, pattern recognition, primitive vectors, basis vectors, space group, symmetry.
Reducing Query Overhead Through Route Learning In Unstructured Peer-to-Peer Networks
In unstructured peer-to-peer networks, such as Gnutella, peers propagate query messages towards the resource holders by flooding them through the network. This is, however, a costly operation since it consumes node and link resources excessively and most of the time unnecessarily. There is no reason, for example, for a peer to receive a query message if the peer does not own any matching resource or if the peer is not on the path reaching to a peer holding a matching resource.
Semantic routing is a technique that tries to forward the queries only to those nodes where replies are likely to come from. In this thesis, we present ``Route Learning", a semantic routing scheme, which aims to reduce query traffic in unstructured peer-to-peer networks by utilizing a well-known estimation technique called ``Parzen Windows". In Route Learning, peers try to find the most likely neighbors through which replies can be obtained for submitted queries. In this way, a query is forwarded only to a subset of the neighbors of a peer, or is dropped if no neighbor, which is likely to return a reply, is found. The scheme has also mechanisms to cope with variations in user submitted queries, like changes in the keywords. This way the scheme can also evaluate the route for a query for which it is not trained. The proposed scheme consists of three phases: training, evaluation, and recursive learning. The last phase enables the scheme to adapt itself to changes in a dynamic peer-to-peer network. Our simulation results show that our scheme reduces the bandwidth overhead significantly without scarifying user satisfaction compared to a pure flooding based querying approach.
GUAP - A Strong User Authentication Protocol for GSM
Traditionally, the authentication protocols for cellular phone networks have been designed for device authentication rather than user authentication, which brings limitations and restrictions on the functionality of the system. In this thesis we propose a user authentication protocol for Global Standards for Mobile (GSM). Our protocol permits the use of weak secrets (e.g. passwords or PINs) for authentication and provides certain flexibilities for GSM users. The simulation results on currently established user authentication protocols and GUAP are presented. The protocol also has a capture resilience extension to disable captured cellular phones securely.
Keywords: Wireless network security, user authentication, GSM, strong password protocols, capture resilient.
Moving Object Detection, Tracking and Classification for Smart Video Surveillance
Video surveillance has long been in use to monitor security sensitive areas such as banks, department stores, highways, crowded public places and borders. The advance in computing power, availability of large-capacity storage devices and high speed network infrastructure paved the way for cheaper, multi sensor video surveillance systems. Traditionally, the video outputs are processed online by human operators and are usually saved to tapes for later use only after a forensic event. The increase in the number of cameras in ordinary surveillance systems overloaded both the human operators and the storage devices with high volumes of data and made it infeasible to ensure proper monitoring of sensitive areas for long times. In order to filter out redundant information generated by an array of cameras, and increase the response time to forensic events, assisting the human operators with identification of important events in video by the use of "smart" video surveillance systems has become a critical requirement. The making of video surveillance systems "smart" requires fast, reliable and robust algorithms for moving object detection, classification, tracking and activity analysis.
In this thesis, a smart visual surveillance system with real-time moving object detection, classification and tracking capabilities is presented. The system operates on both color and gray scale video imagery from a stationary camera. It can handle object detection in indoor and outdoor environments and under changing illumination conditions. The classification algorithm makes use of the shape of the detected objects and temporal tracking results to successfully categorize objects into pre-defined classes like human, human group and vehicle. The system is also able to detect the natural phenomenon fire in various scenes reliably. The proposed tracking algorithm successfully tracks video objects even in full occlusion cases. In addition to these, some important needs of a robust smart video surveillance system such as removing shadows, detecting sudden illumination changes and distinguishing left/removed objects are met.
Keywords: Video-Based Smart Surveillance, Moving Object Detection, Background Subtraction, Object Tracking, Silhouette-Based Object Classification, Fire Detection.
3D Garment Design and Simulation System
In this thesis study, a 3D graphics environment for virtual garment design and simulation is presented. The proposed system enables the three dimensional construction of a garment from its two dimensional cloth panels, for which the underlying structure is a mass-spring model. Construction of the garment is performed through cutting, boundary smoothing , seaming and scaling. Afterwards, it is possible to do fitting on virtual mannequins like in the real life as if in a tailor's workshop. The behavior of cloth under different environmental conditions is implemented applying a physically-based approach. As well as the simulation of the draping of garments, efficient and realistic visualization of garments is an important issue in cloth modelling. There are various material types and reflectance properties for fabrics. We have implemented a number of material and rendering options such as knitwear, woven cloth and standard shading methods such as Gouraud shading. Performance results of the system are presented at the end.
Keywords: Garment design, Garment simulation, Fabric rendering, Physically-based modeling.
Aspect-Oriented Evolution of Legacy Information Systems
A legacy information system is an old system that typically has been developed several years ago, and remains in operation within an organization. Since the software requirements change, legacy systems must be evolved accordingly. Various approaches such as wrapping, migration and redevelopment have been proposed to maintain legacy information systems. Unfortunately, these approaches have not explicitly considered the concerns that are dicult to capture in single components, and tend to crosscut many components. Examples of such crosscutting concerns include distribution, synchronization, persistence, security, logging and real-time behavior. The crosscutting property of concerns seriously complicates the maintenance of legacy systems because the code of the system needs to be changed at multiple places, and conventional maintenance techniques fall short to do this effectively.
Aspect-Oriented Software Development (AOSD) provides explicit mechanisms for coping with these crosscutting concerns. However, current AOSD approaches have primarily focused on coping with crosscutting concerns in software systems that are developed from scratch. Hereby, the crosscutting concerns are implemented as aspects at the beginning, hence localized in single modules. In this way the implementation and maintenance of crosscutting concerns can be prepared to a large extent so that the maintenance of these systems will be easier later on. Unfortunately, legacy systems impose harsher requirements, because crosscutting concerns in legacy systems are neither explicitly identifyinged nor have been prepared before.
We provide a systematic process for analyzing the impact of crosscutting concerns on legacy systems. The process, which is called Aspectual Legacy Analysis Process (ALAP), consists of three sub-processes, Feasibility Analysis, Aspectual Analysis and Maintenance Analysis. All the three sub-processes consist of a set of heuristic rules and the corresponding control. Feasibility Analysis, which consists of two phases, describes rules for categorizing legacy systems, in the correspondingrst phase; and describes the rules for evaluating legacy systems with respect to the ability to implement static crosscutting and ability to implement dynamic crosscutting, in the second phase. The rules of the secondrst phase are based on the categories of legacy systems that we have describesned after a thorough study to legacy information systems, and the rules of the second phase are based on our discussion of these categories with respect to crosscutting implementation. Once the legacy system has been categorized and evaluated with respect to crosscutting implementation, the Aspectual Analysis sub-process describes rules for identifying and specifying aspects in legacy systems. Based on the results of the Feasibility Analysis and Aspectual Analysis sub-processes, the Maintenance Analysis describes the rules for the selection of the appropriate legacy maintenance approach.
ALAP has been implemented in the Aspectual Legacy Analysis Tool (ALAT), which implements the rules of the three sub-processes and as such helps to support the legacy maintainer in analyzing the legacy system and identifying the appropriate maintenance approach.
Keywords: Legacy Information Systems, Aspect-Oriented Software Development, Heuristic Rule Modelling.
Using a Data Mining approach for Prediction of User Movements in Mobile Environments
Mobility prediction is one of the most essential issues that need to be explored for mobility management in mobile computing systems. In this thesis, we propose a new algorithm for predicting the next inter-cell movement of a mobile user in a Personal Communication Systems network. In the first phase of our three-phase algorithm, user mobility patterns are mined from the history of mobile user trajectories. In the second phase, mobility rules are extracted from these patterns, and in the last phase, mobility predictions are accomplished by using these rules. The performance of the proposed algorithm is evaluated through simulation as compared to two other prediction methods. The performance results obtained in terms of Precision and Recall indicate that our method can make more accurate predictions than the other methods.
Keywords: Location prediction, data mining, mobile computing, mobility patterns, mobility prediction.
Vision Based Handwritten Character Recognition
Onine automatic recognition of handwritten text has been an onoing research problem for four decades. It is used in automated postal address and ZIP code and form reading, data acquisition in bank checks, processing of archived institutional records, automatic validation of passports, etc. It has been gaining more interest lately due to the increasing popularity of handeld computers, digital notebooks and advanced cellular phones. Traditionally, human-machine communication has been based on keyboard and pointing devices. Onine handwriting recognition promises to provide a dynamic means of communication with computers through a pen like stylus, not just an ordinary keyboard. This seems to be a more natural way of entering data into computers.
In this thesis, we develop a character recognition system that combines the advantage of both on-line and off-line systems. Using an USB CCD Camera, positions of the pen-tip between frames are detected as they are written on a sheet of regular paper. Then, these positions are used for calculation of directional information. Finally, handwritten character is characterized by a sequence of writing directions between consecutive frames. The directional information of the pen movement points is used for character pre-classification and positional information is used for fine classification. After characters are recognized they are passed to LaTeX code generation subroutine. Supported LaTeX environments are array construction, citation, section, itemization, equation, verbatim and normal text environments. During experiments a recognition rate of 90% was achieved. The main recognition errors were due to the abnormal writing and ambiguity among similar shaped characters.
Keywords: pattern recognition, character Recognition, on-line recognition systems, LaTeX.
Human Motion Control Using Inverse Kinematics
Articulated figure animation receives particular attention of the computer graphics society. The techniques for animation of articulated figures range from simple interpolation between keyframes methods to motion-capture techniques. One of these techniques, inverse kinematics, which is adopted from robotics, provides the animator the ability to specify a large quantity of motion parameters that results with realistic animations. This study presents an interactive hierarchical motion control system used for the animation of human figure locomotion. We aimed to develop an articulated figure animation system that creates movements using motion control techniques at different levels, like goal-directed motion and walking. Inverse Kinematics using Analytical Methods (IKAN) software, which was developed at the University of Pennsylvania, is utilized for controlling the motion of the articulated body using inverse kinematics.
Keywords: kinematics, inverse kinematics, articulated figure, motion control, spline, gait.
Realistic Rendering of a Multi-Layered Human Body Model
In this thesis study, a framework is proposed and implemented for the realistic rendering of a multi-layered human body model while it is moving. The proposed human body model is composed of three layers: a skeleton layer, a muscle layer, and a skin layer. The skeleton layer, represented by a set of joints and bones, controls the animation of the human body model using inverse kinematics. Muscles are represented by action lines, which are defined by a set of control points. The action line expresses the force produced by a muscle on the bones and on the skin mesh. The skin layer is modeled in a 3D modeler and deformed during animation by binding the skin layer to both the skeleton layer and the muscle layer. The skin is deformed by a two-step algorithm according to the current state of the skeleton and muscle layers. In the first step, the skin is deformed by a variant of the skinning algorithm, which deforms the skin based on the motion of the skeleton. In the second step, the skin is deformed by the underlying muscular layer. Visual results produced by the implementation is also presented. Performance experiments show that it is possible to obtain real-time frame rates for a moderately complex human model containing approximately 33,000 triangles on the skin layer.
Keywords: Human body modeling and animation, multi-layered modeling, articulated figure, kinematics, inverse kinematics, action line, skinning, deformation.
On-Line New Event Detection and Event Clustering Using the Concepts of the Cover Coefficient Based Clustering Methodology
In this study, we use the concepts of the cover coefficient-based clustering methodology (C3M) for on-line new event detection and event clustering. The main idea of the study is to use the seed selection process of the C3M algorithm for the purpose of detecting new events. Since C3M works in a retrospective manner, we modify the algorithm to work in an on-line environment. Furthermore, in order to prevent producing oversized event clusters, and to give equal chance to all documents to be the seed of a new event, we employ the window size concept. Since we desire to control the number of seed documents, we introduce a threshold concept to the event clustering algorithm. We also use the threshold concept, with a little modification, in the on-line event detection. In the experiments we use TDT1 corpus, which is also used in the original topic detection and tracking study. In event clustering, we use both binary and weighted versions of TDT1 corpus. With the binary implementation, we obtain better results. When we compare our on-line event detection results to the results of UMASS approach, we obtain better performance in terms of false alarm rates.
Keywords: Clustering, on-line event clustering, on-line event detection.
Comparison of Four Approximating Subdivision Surface Schemes
The idea of subdivision surfaces was first introduced in 1978, and there are many methods proposed till now. A subdivision surface is defined as the limit of repeated recursive refinements. In this thesis, we studied the properties of approximating subdivision surface schemes. We started by modeling a complex surface with splines that typically requires a number of spline patches, which must be smoothly joined, making splines burdensome to use. Unlike traditional spline surfaces, subdivision surfaces are defined algorithmically. Subdivision schemes generalize splines to domains of arbitrary topology. Thus, subdivision functions can be used to model complex surfaces without the need to deal with patches. We studied four well-known schemes Catmull-Clark, Doo-Sabin, Loop and the sqrt(3)-subdivision. The first two of these schemes are quadrilateral and the other two are triangular surface subdivision schemes. Modeling sharp features, such as creases, corners or darts, using subdivision schemes requires some modifications in subdivision procedures and sometimes special tagging in the mesh. We developed the rules of sqrt(3)-subdivision to model such features and compared the results with the extended Loop scheme. We have implemented exact normals of Loop and p3-subdivision since using interpolated normals causes creases and other sharp features to appear smooth.
Keywords: Computational geometry and object modeling, subdivision surfaces, Loop, Catmull-Clark, Doo-Sabin, sqrt(3)-subdivision, modeling sharp features.
Simplification of Tetrahedral Meshes by Scalar Value Assignment
A new approach to simplification of volumetric data over an unstructured tetrahedral mesh is presented. The data consist of sample values of a scalar field defined over a spatial domain, which is subdivided with a tetrahedral mesh. Simplification is performed by means of contraction of the tetrahedra and also of the edges. The simplification algorithm can provide a continuum of aproximate models of the given dataset with any desired degree of accuracy. Hence, the simplification method is suitable for multi-resolution modeling.
The novelty of the approach comes from the arbitrariness in the selection of the point to which a tetrahedron or an edge is contracted. Unlike most of the existing methods, the final vertex of the contraction need not be a vertex of the original mesh. The scalar value to be assigned to the final vertex of contraction is determined by an extremely simple method (both conceptually and computationally), which also provides an estimate of the error of simplification.
The proposed method is applied to two volumetric grids to illustrate its effectiveness in simplification of volumetric data.
Keywords: Volumetric dataset, tetrahedral mesh, tetrahedron contraction, edge contraction, scalar value assignment.
Modeling and Animating Personalized Faces
A very important and challenging problem in computer graphics is modeling and animation of individualized face models. In this thesis, we describe a facial modeling and animation system attempting to address this problem. The system uses muscle-based generic face model and deforms it using deformation techniques to model individualized faces. Two orthogonal photos of the real faces are used for this purpose. Image processing techniques are employed to extract certain features on the photographs, which are then refined manually by the user through the facilities of the user interface of the system. The feature points located on the frontal and side views of a real face are used to deform the generic model. Then, the muscle vectors in the individualized face model are arranged accordingly. Individualized face models produced in this manner are animated using parametric interpolation techniques.
Keywords: Facial animation, rendering, texture mapping, deformation, feature extraction.
A Semantic Data Model and Query Language for Video Data
Advances in compression techniques, decreasing cost of storage, and high-speed transmission have facilitated the way video is created, stored and distributed. As a consequence, video is now being used in many application areas. The increase in the amount of video data deployed and used in today's applications not only caused video to draw more attention as a multimedia data type, but also led to the requirement of efficient management of video data. Management of video data paved the way for new research areas, such as indexing and retrieval of videos with respect to their spatio-temporal, visual and semantic contents. In this thesis, semantic content of video is studied, where video metadata, activities, actions and objects of interest are considered within the context of video semantic content. A data model is proposed to model video semantic content, which is extracted from video data by a video annotation tool. A video query language is also provided to support semantic queries on video data.
The outcome of this thesis work will be used in a project, which proposes a video database system architecture with spatio-temporal, object-trajectory and object-apperance query facilities so as to build a complete video search system to query video data by its spatio-temporal, visual and semantic features.
Keywords: Video databases, semantic video modeling, annotation of video data, semantic querying of video data.
An Efficient Query Optimization Strategy for Spatio-Temporal Queries in Video Databases
The interest for multimedia database management systems has grown rapidly due to the need for the storage of huge volumes of multimedia data in computer systems. An important building block of a multimedia database system is the query processor, and a query optimizer embedded to the query processor is needed to answer user queries efficiently. Query optimization problem has been widely studied for conventional database systems, however it is a new research area for multimedia database systems. Due to the differences in query processing strategies, query optimization techniques used in multimedia database systems are different from those used in traditional databases. In this paper, a query optimization strategy is proposed for processing spatio-temporal queries in video database systems. The proposed strategy includes reordering algorithms to be applied on query execution tree. The performance results obtained by testing the reordering algorithms on different query sets are also presented.
Keywords: Video databases, query optimization, query tree, querying of video data.
A Content-Based Image Retrieval System for Texture and Color Queries
In recent years, very large collections of images and videos have grown rapidly. In parallel with this growth, content-based retrieval and querying the indexed collections are required to access visual information. Two of the main components of the visual information are texture and color. In this thesis, a content-based image retrieval system is presented that computes texture and color similarity among images. The underlying technique is based on the adaptation of a statistical approach to texture analysis. An optimal set of five second-order texture statistics are extracted from the Spatial Grey Level Dependency Matrix of each image, so as to render the feature vector for each image maximally informative, and yet to obtain a low vector dimensionality for efficiency in computation. The method for color analysis is the color histograms, and the information captured within histograms is extracted after a pre-processing phase that performs color transformation, quantization, and filtering. The features thus extracted and stored within feature vectors are later compared with an intersection-based method. The system is also extended for pre-processing images to segment regions with different textural quality, rather than operating globally over the whole image. The system also includes a framework for object-based color and texture querying, which might be useful for reducing the similarity error while comparing rectangular regions as objects. It is shown through experimental results and precision-recall analysis that the content-based retrieval system is effective in terms of retrieval and scalability.
Keywords: Texture Analysis, Color Histograms, Texture Similarity Measurement, Content-Based Image Retrieval, Image Databases
Benefit Maximizing Classification Using Feature Intervals
For a long time, classification algorithms have focused on minimizing the quantity of prediction errors by assuming that each possible error has identical consequences. However, in many real-world situations, this assumption is not convenient. For instance, in a medical diagnosis domain, misdiagnosing a sick patient as healthy is much more serious than its opposite. For this reason, there is a great need for new classification methods that can handle asymmetric cost and benefit constraints of classifications. In this thesis, we discuss cost-sensitive classification concepts and propose a new classification algorithm called Benefit Maximization with Feature Intervals (BMFI) that uses the feature projection based knowledge representation. In the framework of BMFI, we introduce five different voting methods that are shown to be effective over different domains. A number of generalization and pruning methodologies based on benefits of classification are implemented and experimented. Empirical evaluation of the methods has shown that BMFI exhibits promising performance results compared to recent wrapper cost-sensitive algorithms, despite the fact that classifier performance is highly dependent on the benefit constraints and class distributions in the domain. In order to evaluate cost-sensitive classification techniques, we describe a new metric, namely benefit accuracy which computes the relative accuracy of the total benefit obtained with respect to the maximum possible benefit achievable in the domain.
Keywords: Machine learning, classification, cost-sensitivity, benefit maximization, feature intervals, voting, pruning.
Predicting Next Page Access by Time Length Reference in the Scope of Effective Use of Resources
Access log file is like a box of treasure waiting to be exploited containing valuable information for the web usage mining system. We can convert this information hidden in the access log files into knowledge by analyzing them. Analysis of web server access data can help understand the user behavior and provide information on how to restructure a web site for increased effectiveness, thereby improving the design of this collection of resources. We designed and developed a new system in this thesis to make dynamic recommendation according to the interest of the visitors by recognizing them through the web. The system keeps all user information and uses this information to recognize the other user visiting the web site. After the visitor is recognized, the system checks whether she/he has visited the web site before or not. If the visitor has visited the web site before, it makes recommendation according to his/her past actions. Otherwise, it makes recommendation according to the visitors coming from the parent domain. Here, "parent domain" identifies the domain in which the identity belongs to. For instance, "bilkent.edu.tr" is the parent domain of the "cs.bilkent.edu.tr". The importance of the pages that the visitors are really interested in and the identity information forms the skeleton of the system. The assumption that the amount of time a user spends on page correlates to whether the page should be classified as a navigation or content page for that user. The other criterion, the identity information, is another important point of the thesis. In case of having no recommendation according to the past experiences of the visitor, the identity information is located into appropriate parent domain or class to get other recommendation according to the interests of the visitors coming from its parent domain or class because we assume that the visitors from the same domain will have similar interests. Besides, the system is designed in such a way that it uses the resources of the system efficiently. "Memory Management", "Disk Capacity" and "Time Factor" options have been used in our system in the scope of "Efficient Use of the Resources" concept. We have tested the system on the web site of CS Department of Bilkent University. The results of the experiments have shown the efficiency and applicability of the system.
Keywords: Access log file, personalization, identity information, recommendation
Efficient Parallel Frequency Mining Based On a Novel Top-Down Partitioning Scheme for Transactional Data
In recent years, large quantities of data have been amassed with advances in data acquisition capabilities. Automated detection of useful information is required for vast data obtained from scientific and business domains. Data Mining is the application of efficient algorithmic solutions on a variety of immense data for such knowledge discovery. Frequency mining discovers all frequent patterns in a transaction or relational database and it comprises the core of several data mining algorithms such as association rule mining and sequence mining. Frequency pattern discovery has become a challenge for parallel programming since it is a highly complex operation on huge datasets demanding efficient and scalable algorithms. In this thesis, we propose a new family of parallel frequency mining algorithms. We introduce a novel transaction set partitioning scheme that can be used to divide the frequency mining task in a top-down fashion. The method operates on the graph of frequent patterns with length two (GF2) from which a graph partitioning by vertex separator (GPVS) is mapped to a two-way partitioning on the transaction set. The two parts obtained can be mined independently and therefore can be utilized for concurrency. In order for this property to hold, there is an amount of replication dictated by the separator in GF2 which is minimized by the GPVS algorithm. A k-way partitioning is derived from recursive application of 2-way partitioning scheme which is used in the design of a generic parallel frequency mining algorithm. First we compute GF2 in parallel, succeeding that we designate a k-way partitioning of the database for k processors with a parallel recursive procedure. The database is redistributed such that each processor is assigned one part. Subsequent mining proceeds simultaneously and independently at each processor with a given serial mining algorithm. A complete implementation in which we employ FP-Growth as the sequential algorithm has been achieved. The performance study of the algorithm on a Beowulf system demonstrates favorable performance for synthetic databases. For hard instances of the problem, we have gained approximately twice the speedup of a state-of-the-art algorithm. We also present a correction and optimization to FP-Growth algorithm.
Keywords: Parallel Data Mining, Frequency Mining
Web-based User Interface for Query Specification in a Video Database System
The enhancements in multimedia technology have accelerated the development of new techniques for querying and retrieval in video databases. Based on these new techniques, effective user interfaces are designed especially for query specification. Since the Internet is a suitable multiple client environment, most of the video database systems are designed to work over the Internet. In this thesis, a web-based user interface for a video database system is presented. The interface is composed of a hierarchical windows-based structure where logically separable sub-queries are handled in separate windows. For example, spatial and motion queries are handled separately. The separation of the interface aims to facilitate the query specification process. Furthermore, the user is allowed to get partial results for the sub-queries. The retrieval and result presentation process displays the query results in the form of video frame sequences on a separate window with the capability of playing each result separately. As another contribution of this thesis, considering the fact that most of the systems include querying by object features, a novel approach for color and shape querying and retrieval is proposed. The proposed approach is histogram-based and it is shown through the performance experiments that it overcomes the deficiencies of the existing methods. This new approach is supported by an auxiliary object extraction tool in the query-by-feature sub-system of the video database system. The object extraction tool is semi-automatic, hence it can successfully capture salient object regions for most of the images and/or video frames.
Keywords: Web-based query specification interface, object extraction, flood fill for extraction, distance histogram, color histogram, kinematics model for polygon simplification.
Realistic Rendering of Human Models
Realistic rendering of human models has an increasing importance in computer graphics. Simulation of muscle bulging and proper deformations of skin at joints, makes human animation more realistic. In this thesis, we describe a layered human animation system in which muscles move with respect to the bones and the skin deforms according to the muscles. Muscles are modelled as ellipsoids and their shapes are deformed with the movements of the bones represented by sticks in the skeleton. The skin is anchored to the muscles andit changes its shape as the muscles bulge. Every muscle may have different user defined bulging ratios according to the joint movements.
Keywords: Human animation, skin and muscle deformation, 3D modelling, rendering.
Animation of Human Motion with Inverse Kinematics using Nonlinear Programming
Animation of articulated figures has always been an interesting subject of computer graphics due to a wide range of applications, like military, ergonomic design etc. An articulated figure is usually modelled as a set of segments linked with joints. Changing the joint angles brings the articulated figure to a new posture. An animator can define the joint angles for a new posture (forward kinematics). However, it is difficult to estimate the exact joint angles needed to place the articulated figure to a predefined position. Instead of this, an animator can specify the desired position for an end-effector, and then an algorithm computes the joint angles needed (inverse kinematics). In this thesis, we present the implementation of an inverse kinematics algorithm using nonlinear optimization methods. This algorithm computes a potential function value between the end-effector and the desired posture of the end-effector called goal. Then, it tries to minimize the value of the function. If the goal cannot be reached due to constraints then an optimum solution is found and applied by the algorithm. The user may assign priority to the joint angles by scaling initial values estimated by the algorithm. In this way, the joint angles change according to the animator?s priority.
Keywords: Animation, human motion, inverse kinematics, nonlinear programming, optimization.
Mining User Access Patterns and Identity Information from Web Logs for Effective Personalization
Web is a huge source of data in terms of its usage as a result of being visited by millions of people on each day. Its usage data is stored in web server logs which contain a detailed description of every single hit taken by the corresponding web server. Recently, it has been discovered that analysis of this data for understanding the user behavioral patterns may have critical implications. Understanding the behavioral patterns of visitors is especially important for e-commerce companies which try to gain customers and sell products through the web. Interactive sites that recognize their customer and customize themselves accordingly may save lots of money to the companies. Usage Based Personalization is a study on designing such personalized sites. In this thesis, we present a new usage based personalization system. The system we designed and implemented is capable of guessing the web pages that may be requested by the on-line visitors during the rest of their visits. The system shows the subset of these pages with highest scores as recommendations to the visitors as being attached to the original pages. The system has two major modules. The off-line module mines the log files off-line for determining the behavioral patterns of the previous visitors of the web site considered. The information obtained by the off-line module is utilized by the on-line module of the system for recognizing new visitors and producing on-line recommendations. The first criterion for identifying on-line visitors is the paths followed by them. A path of a particular visitor consists of pages retrieved by him throughout his visit to the web site. Another criterion considered by the system is the identity information (IP address or domain name) of the visitors. By using identity information, it is possible to learn old preferences of the visitor himself or visitors from similar domains. The similarity between domains is determined by the help of the domain name hierarchy which is represented by a hierarchical tree structure. The leaves of the tree representing domain name hierarchy contain the domain names of the machines connecting to the Internet while the inner nodes contain the general domains such as com, edu.tr, etc. In domain name hierarchy, the similarity between two domains increases as the number of common parents of them increases. In the light of these observations, for guessing the navigational behavior of a particular visitor, our system makes use of the common behavioral trends of the visitors whose machines belong to the parent domains of the domain of that visitor. We have tested the system on the web site of CS department of Bilkent University. The results of the experiments show the efficiency and applicability of the system.
Keywords: Web log mining, access pattern, personalization.
On-Line New Event Detection and Tracking in a Multi-Resource Environment
As the amount of electronically available information resources increase, the need for information also increases. Today, it is almost impossible for a person to keep track all the information resources and find new events as soon as possible. In this thesis, we present an on-line new event detection and tracking system, which automatically detects new events from multiple news resources and immediately start tracking events as they evolve. Since we implemented the on-line version of event detection approach, the novelty decision about a news story is done before processing the next one. We also present a new threshold, called support threshold, used in detection process to decrease the number of new event alarms that are caused by informative and one-time-only news. The support threshold can be used to tune the weights of news resources. We implemented the tracking phase as an unsupervised learning process, that is, detected events are automatically tracked by training the system using the first news story of an event. Since events evolve over time, an unsupervised adaptation is used to retrain the tracking system in order to increase the tracking system performance. Adaptation is achieved by adding predicted documents to the training process. From the corpus observations, we conclude that one news story can be associated to more than one event. For this reason, the tracking system can relate a news story to more than one event. The on-line new event detection and tracking system has been tested on the Reuters news feed, available on the Internet. The Reuters news feed, that we used, comprises four independent news resources. The news stories are in Turkish.
Keywords: Event detection, event tracking, information retrieval.
Fast Stereoscopic View-Dependent Visualization of Terrain Height Fields
Visualization of large geometric environments has always been an important problem of computer graphics. In this thesis, we present a framework for the stereoscopic view-dependent visualization of large scale terrain models. We use a quadtree based multiresolution representation for the terrain data. This structure is queried to obtain the view-dependent approximations of the terrain model at different levels of detail. In order not to loose depth information, which is crucial for the stereoscopic visualization, we make use of a different simplification criterion, namely distance-based angular error threshold. We also present an algorithm for the construction of stereo pairs in order to speed up the view-dependent stereoscopic visualization. The approach we use is the simultaneous generation of the triangles for two stereo images using a single draw-list so that the view frustum culling and vertex activation is done only once for each frame. The cracking problem is solved using the dependency information stored for each vertex. We eliminate the popping artifacts that can occur while switching between different resolutions of the data using morphing. We implemented the proposed algorithms on personal computers and graphics workstations. Performance experiments show that the second eye image can be produced approximately 45 % faster than drawing the two images separately and a smooth stereoscopic visualization can be achieved at interactive frame rates using continuous multi-resolution representation of height fields.
Keywords: Stereoscopic visualization, terrain height fields, multiresolution rendering, quadtrees.
Application of K-NN and FPTC Based Text Categorization Algorithms to Turkish News Reports
New technological developments, such as easy access to Internet, optical character readers, high-speed networks and inexpensive massive storage facilities, have resulted in a dramatic increase in the availability of on-line text-newspaper articles, incoming (electronic) mail, technical reports, etc. The enormous growth of on-line information has led to a comparable growth in the need for methods that help users organize such information. Text Categorization may be the remedy of increased need for advanced techniques. Text Categorization is the classification of units of natural language texts with respect to a set of pre-existing categories. Categorization of documents is challenging, as the number of discriminating words can be very large. This thesis presents compilation of a Turkish dataset, called Anadolu Agency Newsgroup in order to study in Text Categorization. Turkish is an agglutinative language in which words contain no direct indication where the morpheme boundaries are, furthermore, morphemes take a shape dependent on the morphological and phonological context. In Turkish, the process of adding one suffix to another can result in a relatively long word, furthermore, a single Turkish word can give rise to a very large number of variants. Due to this complex morphological structure, Turkish requires text processing techniques different than English and similar languages. Therefore, besides converting all words to lower case and removing punctuation marks, some preliminary work is required such as stemming, removal of stop words and formation of a keyword list.
Keywords: Text categorization, classification, feature projections, stemming, wild card matching, stopword.
Categorization in a Hierarchically Structured Text Database
Over the past two decades there has been a huge increase in the amount of data being stored in databases and the on-line flow of data by the effects of improvements in Internet. This huge increase brought out the needs for intelligent tools to manage that size of data and its flow. Hierarchical approach is the best way to satisfy these needs and it is so widespread among people dealing with databases and Internet. Usenet newsgroups system is one of the on-line databases that have built-in hierarchical structures. Our point of departure is this hierarchical structure which makes categorization tasks easier and faster. In fact most of the search engines in Internet also exploit inherent hierarchy of Internet. Growing size of data makes most of the traditional categorization algorithms obsolete. Thus we developed a brand-new categorization learning algorithm which constructs an index tree out of Usenet news database and then decides the related newsgroups of a new news by categorizing it over the index tree. In learning phase it has an agglomerative and bottom-up hierarchical approach. In categorization phase it does an overlapping and supervised categorization. K-Nearest Neighbor categorization algorithm is used to compare the complexity measure and accuracy of our algorithm. This comparison does not only mean comparing two different algorithms but also means comparing hierarchical approach vs. flat approach, similarity measure vs. distance measure and importance of accuracy vs. importance of speed. Our algorithm prefers hierarchical approach and similarity measure, and greatly outperforms k-Nearest Neighbor categorization algorithm in speed with minimal loss of accuracy.
Keywords: Learning, categorization, clustering, hierarchy, Usenet, newsgroup, top-level, header-line, posting, frequency, norm-scaling, similarity measure, distance measure, agglomerative, bottom-up, stemming, stopword, index.
Hypergraph Based Declustering for Multi-Disk Databases
In very large distributed database systems, the data is declustered in order to exploit parallelism while processing a query. Declustering refers to allocating the data into multiple disks in such a way that the tuples belonging to a relation are distributed evenly across disks. There are many declustering strategies proposed in the literature, however these strategies are domain specific or have deficiencies. We propose a model that exactly fits the problem and show that iterative improvement schemes can capture detailed per-relation basis declustering objective. We provide a two phase iterative improvement based algorithm and appropriate gain functions for these algorithms. The experimental results show that the proposed algorithm provides a significant performance improvement compared to the state-of-the-art graph-partitioning based declustering strategy.
Keywords: Distributed Databases, Declustering, Hypergraph Partitioning, Max-cut Graph Partitioning
An Efficient Broadcast Scheduling Algorithm for Pull-Based Mobile Environments
Thanks to the advances in telecommunications and computers, today mobile computing becomes a significant means in every pace of life. Many people are now carrying portable devices such as laptop computers, Personal Digital Assistants (PDAs), and cellular phones. These mobile computing devices are supported by rapidly expanding telecommunication technology. Cellular communication, wireless LAN and WAN, and satellite services are available for daily life applications, and portable devices make use of these wireless connections to contact with the information providers. Thus, a user does not need to maintain a fixed connection in the network and may enjoy almost unrestricted user mobility.
As the new and various mobile infrastructures emerge, users demand a new class of applications running in this environment. However, the narrow bandwidth of the wireless communication channels, the relatively short active life of the power supplies of mobile units, and the mobility of clients make the problem of data retrieval more difficult than that in wired networks. Therefore, mechanisms to efficiently transmit information to vast numbers of mobile users are of significant interest. Data broadcasting has been considered one of the most promising ways of data dissemination in mobile environments. There are two basic data broadcasting approaches available: push and pull. In push-based broadcasting approach, data is broadcast to mobile users according to users' profiles or subscriptions, whereas in pull-based broadcasting approach, transmission of data is initiated by the explicit request of users.
In this thesis, we have focused on the problem of scheduling data items to broadcast in a pull-based environment. We have developed an efficient broadcast scheduling algorithm, and comparing its performance against several well-known algorithms, we have observed that our algorithm appears to be one of the best algorithms proposed so far.
Regression by Selecting Best Feature(s)
Two new machine learning methods, Regression by Selecting Best Feature Projections (RSBFP) and Regression by Selecting Best Features (RSBF), are presented for regression problems. These methods heavily make use of least squares regression to induce eager, parametric and context-sensitive models. Famous regression approaches of machine learning and statistics literature such as DART, MARS, RULE and kNN can not construct models that are both predictive and having reasonable training and/or querying time durations. We developed RSBFP and RSBF to fill the gap in the literature for a regression method having higher predictive accuracy and faster training and querying time durations. RSBFP constructs a decision list consisting of simple linear regression lines belonging to linear features and/or categorical feature segments. RSBF is the extended version of RSBFP such that the decision list consists of both simple, belonging to categorical feature segments, and/or multiple, belonging to linear features, linear regression lines. A relevancy heuristic has been developed to determine the features involving in the multiple regression lines. It is shown that the proposed methods are robust to irrelevant features, missing feature values and target feature noise, which make them suitable prediction tools for real-world databases. In terms of robustness, RSBFP and RSBF give better results when compared to other famous regression methods.
Stochastic Comparison on Nearly Completely Decomposable Markov Chains
This thesis presents an improved version of a componentwise bounding algorithm for the steady state probability vector of nearly completely decomposable Markov chains. The given two-level algorithm uses aggregation and stochastic comparison with the strong stochastic (st) order. In order to improve accuracy, it employs reordering of states and a better componentwise probability bounding algorithm given st upper- and lower-bounding probability vectors. A thorough analysis of the algorithm from the point of view of irreducibility is provided. The bounding algorithm is implemented in sparse storage and its implementation details are given. Numerical results on an application of wireless Asynchronous Transfer Mode network show that there are cases in which the given algorithm proves to be useful in computing bounds on the performance measures of the system. An improvement in the algorithm that must be considered to obtain better bounds on performance measures is also presented at the end.
Instance-Based Regression by Partitioning Feature Projections
A new instance-based learning method is presented for regression problems with high-dimensional data. As an instance-based approach, the conventional K-Nearest Neighbor (KNN) method has been applied to both classification and regression problems. Although KNN performs well for classification tasks, it does not perform similarly for regression problems. We have developed a new instance-based method, called Regression by Partitioning Feature Projections (RPFP), to fill the gap in the literature for a lazy method that achieves a higher accuracy for regression problems. We also present some additional properties and even better performance when compared to famous eager approaches of machine learning and statistics literature such as MARS, rule-based regression, and regression tree induction systems. The most important property of RPFP is that it performs much better than all other eager or lazy approaches on many domains that have missing values. If we consider databases today, where there are generally large number of attributes, such sparse domains are very frequent. RPFP handles such missing values in a very natural way, since it does not require all the attribute values to be present in the data set.
Updating Large Itemsets With Early Pruning
With the computerization of many business and government transactions, huge amounts of data have been stored in computers. The existing database systems do not provide the users with the necessary tools and functionalities to capture all stored information easily. Therefore, automatic knowledge discovery techniques have been developed to capture and use the voluminous information hidden in large databases. Discovery of association rules is an important class of data mining, which is the process of extracting interesting and frequent patterns from the data. Association rules aim to capture the co-occurrences of items, and have wide applicability in many areas. Discovering association rules is based on the computation of large itemsets (set of items that occur frequently in the database) efficiently, and is a computationally expensive operation in large databases. Thus, maintenance of them in large dynamic databases is an important issue. In this thesis, we propose an efficient algorithm, to update large itemsets by considering the set of previously discovered itemsets. The main idea is to prune an itemset as soon as it is understood to be small in the updated database, and to keep the set of candidate large itemsets as small as possible. The proposed algorithm outperforms the existing update algorithms in terms of the number of scans over the databases, and the number of candidate large itemsets generated and counted. Moreover, it can be applied to other data mining tasks that are based on large itemset framework easily.
Turing Test and Conversation
The Turing Test is one of the most disputed topics in Artificial Intelligence, Philosophy of Mind and Cognitive Science. It has been proposed 50 years ago, as a method to determine whether machines can think or not. It embodies important philosophical issues, as well as computational ones. Moreover, because of its characteristics, it requires interdisciplinary attention. The Turing Test posits that, to be granted intelligence, a computer should imitate human conversational behavior so well that it should be indistinguishable from a real human being. From this, it follows that conversation is a crucial concept in its study. Surprisingly, focusing on conversation in relation to the Turing Test has not been a prevailing approach in previous research. This thesis first provides a thorough and deep review of the 50 years of the Turing Test. Philosophical arguments, computational concerns, and repercussions in other disciplines are all discussed. Furthermore, this thesis studies the Turing Test as a special kind of conversation. In doing so, the relationship between existing theories of conversation and human-computer communication is explored. In particular, Grice's cooperative principle and conversational maxims are concentrated on. Viewing the Turing Test as conversation and computers as language users have significant effects on the way we look at Artificial Intelligence, and on communication in general.
Processing of Continuous Queries from Moving Objects in Mobile Computing Systems
Recent advances in computer hardware technology and wireless communication networks have led to the emergence of mobile computing systems. In a mobile computing environment, a user with a wireless connection to the information network can access data via submitting queries to the data server. Since the mobility is the most distinguishing feature of the mobile computing paradigm, location becomes an important piece of information for the so called "location-dependent queries" where the answer to a query depends on the current location of the user who issued the query. A location-dependent query submitted by a mobile user can become more difficult to process when it is submitted as a "continuous query" for which the answer changes as the user moves. The answer to a location-dependent continuous query is a set that consists of tuples
indicating that object S is the answer of the query from time begin to time end. Once the tuples in the answer set are determined, the next step is to determine when to send these tuples to the user. The transmission time of the tuples is critical in the sense that it can affect the communication overhead imposed on the wireless network and the availability of tuples in case of disconnections. In this thesis, we propose three tuple transmission approaches that determine the transmission time of a tuple in the answer set of a location-dependent continuous query. We also design and implement a simulation model to compare the performance of the proposed tuple transmission approaches under different settings of environmental parameters.
Parallelization of Hierarchical Radiosity Algorithms on Distributed Memory Computers
Computing distribution of light in a given environment is an important problem in computer-aided photo-realistic image generation. Radiosity method has been proposed to address this problem which requires an enormous amount of calculation and memory. Hierarchical radiosity method is a recent approach that reduces these computational requirements by careful error analysis. It has its idea from the solution methods of N-body problems. Although hierarchical approach has greatly reduced the amount of calculations, satisfactory results still cannot be obtained in terms of processing time. Exploiting parallelism is a practical way to reduce the computation time further. In this thesis, we have designed and implemented a parallel hierarchical radiosity algorithm for distributed memory computers. Due to its highly irregular computational structure, hierarchical radiosity algorithms do not yield easily to parallelization on distributed memory machines. Dynamically changing computational patterns of the algorithm cause severe load imbalances. Therefore, we have developed a dynamic load balancing technique for the parallel hierarchical radiosity calculation.
Multilevel Heuristics for Task Assignment in Distributed Systems
Task assignment problem deals with assigning tasks to processors in order to minimize the sum of execution and communication costs in a distributed system. In this work, we propose a novel task clustering scheme which considers the differences between the execution times of tasks to be clustered as well as the communication costs between them. We use this clustering approach with proper assignment schemes to implement two-phase assignment algorithms which can be used to find suboptimal solutions to any task assignment problem. In addition, we adapt the multilevel scheme used in graph/hypergraph partitioning to the task assignment. Multilevel assignment algorithms reduce the size of the original problem by collapsing tasks, find an initial assignment on the smaller problem, and then projects it towards the original problem by successively refining the assignment at each level. We propose several clustering schemes for multilevel assignment algorithms. The performance of all proposed algorithms are evaluated through an experim ental study where the assignment qualities are compared with two up-to-date heuristics. Experimental results show that our algorithms substantially outperform both of the existing heuristics.
Inductive Synthesis of Recursive Logic Programs
The learning of recursive logic programs (i.e. the class of logic programs where at least one clause is recursive) from incomplete information, such as input/output examples, is a challenging subfield both of ILP (Inductive Logic Programming) and of the synthesis (in general) of logic programs from formal specifications. This is an extremely important class of logic programs, as the recent work on constructive induction shows that necessarily invented predicates have recursive programs, and it even turns out that their induction is much harder than the one of non-recursive programs. We call this inductive program synthesis. We introduce a system called DIALOGS-II (Dialogue-based Inductive and Abductive LOGic Program Synthesizer-II) whose ancestor is DIALOGS. It is a schema-guided, interactive, and non-incremental synthesizer of recursive logic programs that takes the initiative and queries a (possibly naive) specifier for evidence in her/his conceptual language. It can be used by any learner (including itself) that detects, or merely conjectures, the necessity of invention of a new predicate. Moreover, due to its powerful codification of "recursion-theory" into program schemata and schematic constraints, it needs very little evidence and is very fast.
Non-Incremental Classification Learning Algorithms Based on Voting Feature Intervals
Learning is one of the necessary abilities of an intelligent agent. This thesis proposes several learning algorithms for multi-concept descriptions in the form of feature intervals, called Voting Feature Intervals (VFI) algorithms. These algorithms are non-incremental classification learning algorithms, and use feature projection based knowledge representation for the classification knowledge induced from a set of preclassified examples. The concept description learned is a set of intervals constructed separately for each feature. Each interval carries classification information for all classes. The classification of an unseen instance is based on a voting scheme, where each feature distributes its vote among all classes. Empirical evaluation of the VFI algorithms have shown that they are the best performing algorithms among other previously developed feature projection based methods in terms of classification accuracy. In order to further improve the accuracy, genetic algorithms are developed to learn the optimum feature weights for any given classifier. Also a new crossover operator, called continuous uniform crossover, to be used in this weight learning genetic algorithm is proposed and developed during this thesis. Since the explanation ability of a learning system is as much important as its accuracy, VFI classifiers are supplemented with a facility to convey what they have learned in a comprehensible way to humans.
Schema-Based Logic Program Transformation
In traditional programming methodology, developing a correct and efficient program is divided into two phases: in the first phase, called the synthesis phase, a correct, but maybe inefficient program is constructed, and in the second phase, called the transformation phase, the constructed program is transformed into a more efficient equivalent program. If the synthesis phase is guided by a schema that embodies the algorithm design knowledge abstracting the construction of a particular family of programs, then the transformation phase can also be done in a schema-guided fashion using transformation schemas, which encode the transformation techniques from input program schemas to output program schemas by defining the conditions that have to be verified to have a more efficient equivalent program.
Seven program schemas are proposed, which capture sub-families of divide-and-conquer programs and the programs that are constructed using some generalization methods. The proposed transformation schemas either automate transformation strategies, such as accumulator introduction and tupling generalization, which is a special case of structural generalization, or simulate and extend a basic theorem in functional programming (the first duality law of the fold operators) for logic programs. A prototype transformation system is presented that can transform programs, using the proposed transformation schemas.
Iterative Methods Based on Splittings for Stochastic Automata Networks
This thesis presents iterative methods based on splittings (Jacobi, Gauss--Seidel, Successive Over Relaxation) and their block versions for Stochastic Automata Networks (SANs). These methods prove to be better than the power method that has been used to solve SANs until recently. Through the help of three examples we show that the time it takes to solve a system modeled as a SAN is still substantial and it does not seem to be possible to solve systems with tens of millions of states on standard desktop workstations with the current state of technology. However, the SAN methodology enables one to solve much larger models than those could be solved by explicitly storing the global generator in the core of a target architecture especially if the generator is reasonably dense.
Experiments with Two-Stage Iterative Solvers and Preconditioned Krylov Subspace Methods on Nearly Completely Decomposable Markov Chains
Preconditioned Krylov subspace methods are state-of-the-art iterative solvers developed mostly in the last fifteen years that may be used, among other things, to solve for the stationary distribution of Markov chains. Assuming Markov chains of interest are irreducible, the problem amounts to computing a positive solution vector to a homogeneous system of linear algebraic equations with a singular coefficient matrix under a normalization constraint. That is, the (n x 1) unknown stationary vector x in
Ax=0, ||x||1=1 (1)
is sought. Hence A=I-PT, an n x n singular M-matrix, and P is the one-step stochastic transition probability matrix.
Albeit the recent advances, practicing performance analysts still widely prefer iterative methods based on splittings when they want to compare the performance of newly devised algorithms against existing ones, or when they need candidate solvers to evaluate the performance of a system model at hand. In fact, experimental results with Krylov subspace methods on Markov chains, especially the ill-conditioned nearly completely decomposable (NCD) ones, are few. We believe there is room for research in this area specifically to help us understand the effect of the degree of coupling of NCD Markov chains and their nonzero structure on the convergence characteristics and space requirements of preconditioned Krylov subspace methods.
The work of several researchers have raised important and interesting questions that led to research in another, yet related direction. These questions are the following: "How must one go about partitioning the global coefficient matrix A in equation (1) into blocks if the system is NCD and a two-stage iterative solver (such as block successive overrelaxation---SOR) is to be employed? Are block partitionings dictated by the NCD normal form of P necessarily superior to others? Is it worth investing alternative partitionings? Better yet, for a fixed labeling and partitioning of the states, how does the performance of block SOR (or even that of point SOR) compare to the performance of the iterative aggregation-disaggregation (IAD) algorithm? Finally, is there any merit in using two-stage iterative solvers when preconditioned Krylov subspace methods are available?"
Experimental results show that in most of the test cases two-stage iterative solvers are superior to Krylov subspace methods with the chosen preconditioners, on NCD Markov chains. For two-stage iterative solvers, there are cases in which a straightforward partitioning of the coefficient matrix gives a faster solution than can be obtained using the NCD normal form.
Design and Performance Evaluation of Indexing Metods for Dynamic Attributes in Mobile Database Management Systems
In traditional databases, we deal with static attributes which change very infrequently over time and their change is handled with an explicit update operation. In temporal databases, the time of change of attributes is also important and every update creates a new version. Attributes, typically change more frequently over time. A more agitated category of attributes are the so-called dynamic attributes whose value changes continuously over time, thus making it impractical to explicitly update them as they change. In this thesis, we conduct a performance evaluation study of two indexing methods for dynamic attributes. These are based on the key idea of using a linear function that describes the way the attribute changes over time and allows us to predict its value in the future based on any value of it in the past. The problem is rooted in the context of mobile data management and draws upon the fields of spatial and temporal indexing. We contribute various experimental results, mathematical analyses, and improvement and optimization algorithms. Finally, inspired by a few of the observed shortcomings of both of the studied techniques, we propose a novel indexing method which we call the FP-Index which is shown analytically to have promising prospects and to beat both methods over most performance parameters.
Design and Implementation of a Computational Lexicon for Turkish
All natural language processing systems (such as parsers, generators, taggers) need to have access to a lexicon about the words in the language. This thesis presents a lexicon architecture for natural language processing in Turkish. Given a query form consisting of a surface form and other features acting as restrictions, the lexicon produces feature structures containing morphosyntactic, syntactic, and semantic information for all possible interpretations of the surface form satisfying those restrictions. The lexicon is based on contemporary approaches like feature-based representation, inheritance, and unification. It makes use of two information sources: a morphological processor and a lexical database containing all the open and closed-class words of Turkish. The system has been implemented in SICStus Prolog as a standalone module for use in natural language processing applications.
Batch Learning of Disjoint Feature Intervals
This thesis presents several learning algorithms for multi-concept descriptions in the form of disjoint feature intervals, called Feature Interval Learning algorithms (FIL). These algorithms are batch supervised inductive learning algorithms, and use feature projections of the training instances for the representation of the classification knowledge induced. These projections can be generalized into disjoint feature intervals. Therefore, the concept description learned is a set of disjoint intervals separately for each feature. The classification of an unseen instance is based on the weighted majority voting among the local predictions of features. In order to handle noisy instances, several extensions are developed by placing weights to intervals rather than features. Empirical evaluation of the FIL algorithms is presented and compared with some other similar classification algorithms. Although the FIL algorithms achieve comparable accuracies with other algorithms, their average running times are much more less than the others.
This thesis also presents a new adaptation of the well-known k-NN classification algorithm to the feature projections approach, called k-NNFP for k-Nearest Neighbor on Feature Projections, based on a majority voting on individual classifications made by the projections of the training set on each feature and compares with the k-NN algorithm on some real-world and artificial datasets.
Animating Facial Images with Drawings
The work presented here describes the power of 2D animation with texture mapping controlled by line drawings. Animation is specifically intended for facial animation and not restricted by the human face. We initially have a sequence of facial images which are taken from a video sequence of the same face and an image of another face to be animated. The aim is to animate the face image with the same expressions as those of the given video sequence. To realize the animation, a set of frames are taken from a video sequence. Key features of the first frame are rotoscoped and the other frames are automatically rotoscoped using the first frame. Similarly,the corresponding features of the image which will be animated are rotoscoped. The key features of the first frame of the sequence and the image to be animated are mapped and using cross-synthesis procedure, other drawings for the given image are produced. Using these animated line drawings and the original image, the corresponding frame sequence is produced by image warping. The resulting sequence has the same expressions as those of the video sequence. This work encourages the reuse of animated motion by gathering facial motion sequences into a database. Furthermore, by using motion sequences of a human face, non-human characters can be animated realistically or complex characters can be animated by the help of motion sequences of simpler characters.
Turkish Text Generation with Systemic-Functional Grammar
Natural Language Generation (NLG) is roughly decomposed into two stages: text planning, and text generation. In the text planning stage, the semantic description of the text is produced from the conceptual inputs. Then, the text generation system transforms this semantic description into an actual text. This thesis focuses on the design and implementation of a Turkish text generation system rather than text planning. To develop a text generator, we need a linguistic theory that describes the resources of the desired natural language, and also a software tool that represents and performs these linguistic resources in a computational environment. In this thesis, in order to carry out the mentioned requirements, we have used a functional linguistic theory called Systemic--Functional Grammar (SFG), and the FUF text generation system as a software tool. The ultimate text generation system takes the semantic description of the text sentence by sentence, and then produces a morphological description for each lexical constituent of the sentence. The morphological descriptions are worded by a Turkish morphological generator. Because of our concentration on the text generation, we have not considered the details of the text planning. Hence, we assume that the semantic description of the text is produced and lexicalized by an application (currently given by hand).
Using Multiple Sources of Information for Constraint-Based Morphological Disambiguation
This thesis presents a constraint-based morphological disambiguation approach that is applicable to languages with complex morphology--specifically agglutinative languages with productive inflectional and derivational morphological phenomena. For morphologically complex languages like Turkish, automatic morphological disambiguation involves selecting for each token morphological parse(s), with the right set of inflectional and derivational markers. Our system combines corpus independent hand-crafted constraint rules, constraint rules that are learned via unsupervised learning from a training corpus, and additional statistical information obtained from the corpus to be morphologically disambiguated. The hand-crafted rules are linguistically motivated and tuned to improve precision without sacrificing recall. In certain respects, our approach has been motivated by Brill's recent work, but with the observation that his transformational approach is not directly applicable to languages like Turkish. Our approach also uses a novel approach to unknown word processing by employing a secondary morphological processor which recovers any relevant inflectional and derivational information from a lexical item whose root is unknown. With this approach, well below 1% of the tokens remains as unknown in the texts we have experimented with. Our results indicate that by combining these hand-crafted, statistical and learned information sources, we can attain a recall of 96 to 97% with a corresponding precision of 93 to 94%, and ambiguity of 1.02 to 1.03 parses per token.
Design and Implementation of a Tactical Generator for Turkish, a Free Constituent Order Language
This thesis describes a tactical generator for Turkish, a free constituent order language, in which the order of the constituents may change according to the information structure of the sentences to be generated. In the absence of any information regarding the information structure of a sentence (i.e., topic, focus, background, etc.), the constituents of the sentence obey a default order, but the order is almost freely changeable, depending on the constraints of the text flow or discourse. We have used a recursively structured finite state machine for handling the changes in constituent order, implemented as a right-linear grammar backbone. Our implementation environment is the GenKit system, developed at Carnegie Mellon University-Center for Machine Translation. Morphological realization has been implemented using an external morphological analysis/generation component which performs concrete morpheme selection and handles morphographemic processes.
Objective: A Benchmark for Object-Oriented Active Database Systems
Although much work in the area of Active Database Management Systems (ADBMSs) has been done, there have been only a few attempts to evaluate the performance of these systems, and it is not yet clear how the performance of an active DBMS can be evaluated systematically. In this thesis, we describe the OBJECTIVE Benchmark for object-oriented ADBMSs, and present experimental results from its implementation in an active database system prototype. OBJECTIVE can be used to identify performance bottlenecks and active functionalities of an ADBMS, and compare the performance of multiple ADBMSs. The philosophy of OBJECTIVE is to isolate components providing active functionalities, and concentrate only on the performance of these components while attempting to minimize the effects of other factors.
Adaptive Source Routing and Route Generation for Multicomputers
Scalable multicomputers are based upon interconnection networks that typically provide multiple communication routes between any given pair of processor nodes. In such networks, the selection of the routes is an important problem because of its impact on the communication performance. We propose the adaptive source routing (ASR) scheme which combines adaptive routing and source routing into one which has the advantages of both schemes. In ASR, the degree of adaptivity of each packet is determined at the source processor. Every packet can be routed in a fully adaptive or partially adaptive or non-adaptive manner, all within the same network at the same time. The ASR scheme permits any network topology to be used provided that deadlock constraints are satisfied. We evaluate and compare performance of the adaptive source routing and non-adaptive randomized routing by simulations. Also we propose an algorithm to generate adaptive routes for all pairs of processors in any multistage interconnection network. Adaptive routes are stored in a route table in each processor's memory and provide high bandwidth and reliable interprocessor communication. We evaluate the performance of the algorithm on IBM SP2 networks in terms of obtained bandwidth, time to fill in the route tables, and efficiency exploited by the parallel execution of the algorithm.
Classification with Overlapping Feature Intervals
This thesis presents a new form of exemplar-based learning method, based on overlapping feature intervals. Classification with Overlapping Feature Intervals (COFI) is a particular implementation of this technique. In this incremental, inductive and supervised learning method, the basic unit of the representation is an interval. The COFI algorithm learns the projections of the intervals in each class dimension for each feature. An interval is initially a point on a class dimension, then it can be expanded through generalization. No specialization of intervals is done on class dimensions by this algorithm. Classification in the COFI algorithm is based on a majority voting among the local predictions that are made individually by each feature.
Design and Implementation of a Verb Lexicon and a Verb Sense Disambiguator for Turkish
The lexicon has a crucial role in all natural language processing systems and has special importance in machine translation systems. This thesis presents the design and implementation of a verb lexicon and a verb sense disambiguator for Turkish. The lexicon contains only verbs because verbs encode events in sentences and play the most important role in natural language processing systems, especially in parsing (syntactic analyzing) and machine translation. The verb sense disambiguator uses the information stored in the verb lexicon that we developed. The main purpose of this tool is to disambiguate senses of verbs having several meanings, some of which are idiomatic. We also present a tool implemented in Lucid Common Lisp under X-Windows for adding, accessing, modifying, and removing entries of the lexicon, and a semantic concept ontology containing semantic features of commonly used Turkish nouns.
Mapping and FPGA Global Routing Using Mean Field Annealing
Mean Field Annealing algorithm which was proposed for solving combinatorial optimization problems combines the properties of neural networks and Simulated Annealing. In this thesis, MFA is formulated for mapping problem in parallel processing and global routing problem in physical design automation of Field Programmable Gate Array (FPGAs) A new Mean Field Annealing (MFA) formulation is proposed for the mapping problem for mesh-connected and hypercube architectures. The proposed MFA heuristic exploits the conventional routing scheme used in mesh and hypercube interconnection topologies to introduce an efficient encoding scheme. An efficient implementation scheme which decreases the complexity of the proposed algorithm by asymptotical factors is also developed. Experimental results also show that the proposed MFA heuristic approaches the speed performance of the fast Kernighan-Lin heuristic while approaching the solution quality of the powerful simulated annealing heuristic. Also, we propose an order-independent global routing algorithm for SRAM type FPGAs based on Mean Field Annealing. The performance of the proposed global routing algorithm is evaluated in comparison with LocusRoute global router on ACM/SIGDA Design Automation benchmarks. Experimental results indicate that the proposed MFA heuristic performs better than the LocusRoute.
Contexts and Situations
The issue of context arises in assorted areas of Artificial Intelligence, Mathematical Logic, and Natural Language Semantics. Although its importance is realized by various researchers, there is not much work towards a useful formalization. In this report, we will try to identify the problem, and decide what we need for an acceptable (formal) account of the notion of context. We will present a preliminary model (based on Situation Theory) and give examples to show the use of context in various fields, and the advantages gained by the acceptance of our proposal.
Tagging and Morphological Disambiguation of Turkish Text
A part-of-speech (POS) tagger is a system that uses various sources information to assign possibly unique POS to words. Automatic text tagging is an important component in higher level analysis of text corpora. Its output can also be used in many natural language processing applications. In languages like Turkish or Finnish, with agglutinative morphology, morphological disambiguation is a very crucial process in tagging as the structures of many lexical forms are morphologically ambiguous. This thesis presents a POS tagger for Turkish text based on a full-scale two-level specification of Turkish morphology. The tagger is augmented with a multi-word and idiomatic construct recognizer, and most importantly morphological disambiguator based on local lexical neighborhood constraints, heuristics and limited amount of statistical information. The tagger also has additional functionality for statistics compilation and fine tuning of the morphological analyzer, such as logging erroneous morphological parses, commonly used roots, etc. Test results indicate that the tagger can tag about 97% to 99% of the texts accurately with very minimal user intervention. Furthermore for sentences morphologically disambiguated with the tagger, an LFG parser developed for Turkish, on the average, generates 50% less ambiguous parses and parses almost 2.5 times faster.
Situated Modeling of Epistemic Puzzles
Situation theory is a mathematical theory of meaning introduced by Jon Barwise and John Perry. It has evoked great theoretical and practical interest and motivated the framework of a few `computational' systems. PROSIT is the pioneering work in this direction. Unfortunately, there is a lack of real-life applications on these systems and this study is a preliminary attempt to remedy this deficiency. Here, we examine how much PROSIT reflects situation-theoretic concepts and solve a group of epistemic puzzles using the constructs provided by this programming language.
Focusing for Pronoun Resolution in English Discourse: An Implementation
Anaphora resolution is one of the most active research areas in natural language processing. This study examines focusing as a tool for the resolution of pronouns which are a kind of anaphora. Focusing is a discourse phenomenon like anaphora. Candy Sidner formalized focusing in her 1979 MIT PhD thesis and devised several algorithms to resolve definite anaphora including pronouns. She presented her theory in a computational framework but did not generally implement the algorithms. Her algorithms related to focusing and pronoun resolution are implemented in this thesis. This implementation provides a better comprehension of the theory both from a conceptual and a computational point of view. The resulting program is tested on different discourse segments, and evaluation and analysis of the experiments are presented together with the statistical results.
Parallelization of an Interior Point Algorithm for Linear Programming
In this study, we present the parallelization of Mehrotra's predictor-corrector interior point algorithm, which is a Karmarkar-type optimization method for linear programming. Computation types needed by the algorithm are identified and parallel algorithms for each type are presented. The repeated solution of large symmetric sets of linear equations, which constitutes the major computational effort in Karmarkar-type algorithms, is studied in detail. Several forward and backward solution algorithms are tested, and buffered backward solution algorithm is developed. Heuristic bin packing algorithms are used to schedule sparse matrix-vector product and factorization operations. Algorithms having the best performance results are used to implement a system to solve linear programs in parallel on multicomputers. Design considerations and implementation details of the system are discussed, and some performance results are presented from a number of real problems.
Learning with Feature Partitioning
This report presents a new methodology of learning from examples, based on feature partitioning. Classification by Feature Partitioning (CFP) is a particular implementation of this technique, which is an inductive, incremental, and supervised learning method. Learning in CFP is accomplished by storing the objects separately in each feature dimension as disjoint partitions of values. A partition, a basic unit of representation which is initially a point in the feature dimension, is expanded through generalization. The CFP algorithm specializes a partition by subdividing it into two subpartitions. Theoretical (with respect to PAC-model) and empirical evaluation of the CFP is presented and compared with some other similar techniques.
Keywords: Machine Learning, inductive learning, incremental learning, supervised learning, feature partitioning.
An ATN Grammar For Turkish
Syntactic parsing is an important step in any natural language processing system. Augmented Transition Networks (ATNs) are procedural mechanisms which have been one of the earliest and most common paradigms for parsing natural language. ATNs have the generative power of a Turing machine and were first popularized by Woods in 1970. This thesis presents our efforts in developing an ATN grammar for a subset of Turkish including simple and complex sentences. There are five networks in our grammar: the sentence (S) network, which includes the sentence structures that falls in our scope, the noun phrase (NP) network, the adverbial phrase (ADVP) network and finally the clause (CLAUSE) and gerund (GERUND) networks for handling complex sentences. We present results from parsing a large number of Turkish sentences.
A Lexical-Functional Grammar for Turkish
Natural language processing is a research area which is becoming increasingly popular each day for both academic and commercial reasons. Syntactic parsing underlies most of the applications in natural language processing. Although there have been comprehensive studies of Turkish syntax from a linguistic perspective, this is one of the first attempts for investigating it extensively from a computational point of view. In this thesis, a lexical-functional grammar for Turkish syntax is presented. Our current work deals with regular Turkish sentences that are structurally simple or complex.