Automatic Inference of Graph Models for Complex Networks with Genetic Programming

Complex networks can arise naturally and spontaneously from all things that act as a part of a larger system. From the patterns of socialization between people to the way biological systems organize themselves, complex networks are ubiquitous, but are currently poorly understood. A number of algorithms, designed by humans, have been proposed to describe the organizational behaviour of real-world networks. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications and the social sciences have recently resulted. The algorithms, called graph models, represent significant human effort. Deriving accurate graph models is non-trivial, time-intensive, challenging and may only yield useful results for very specific phenomena. An automated approach can greatly reduce the human effort required and if effective, provide a valuable tool for understanding the large decentralized systems of interrelated things around us. To the best of the author's knowledge this thesis proposes the first method for the automatic inference of graph models for complex networks with varied properties, with and without community structure. Furthermore, to the best of the author's knowledge it is the first application of genetic programming for the automatic inference of graph models. The system and methodology was tested against benchmark data, and was shown to be capable of reproducing close approximations to well-known algorithms designed by humans. Furthermore, when used to infer a model for real biological data the resulting model was more representative than models currently used in the literature.

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.

The Role of Experience in the Organization and Refinement of Face Space

Adults code faces in reference to category-specific norms that represent the different face categories encountered in the environment (e.g., race, age). Reliance on such norm-based coding appears to aid recognition, but few studies have examined the development of separable prototypes and the way in which experience influences the refinement of the coding dimensions associated with different face categories. The present dissertation was thus designed to investigate the organization and refinement of face space and the role of experience in shaping sensitivity to its underlying dimensions. In Study 1, I demonstrated that face space is organized with regard to norms that reflect face categories that are both visually and socially distinct. These results provide an indication of the types of category-specific prototypes that can conceivably exist in face space. Study 2 was designed to investigate whether children rely on category-specific prototypes and the extent to which experience facilitates the development of separable norms. I demonstrated that unlike adults and older children, 5-year-olds rely on a relatively undifferentiated face space, even for categories with which they receive ample experience. These results suggest that the dimensions of face space undergo significant refinement throughout childhood; 5 years of experience with a face category is not sufficient to facilitate the development of separable norms. In Studies 3 through 5, I examined how early and continuous exposure to young adult faces may optimize the face processing system for the dimensions of young relative to older adult faces. In Study 3, I found evidence for a young adult bias in attentional allocation among young and older adults. However, whereas young adults showed an own-age recognition advantage, older adults exhibited comparable recognition for young and older faces. These results suggest that despite the significant experience that older adults have with older faces, the early and continuous exposure they received with young faces continues to influence their recognition, perhaps because face space is optimized for young faces. In Studies 4 and 5, I examined whether sensitivity to deviations from the norm is superior for young relative to older adult faces. I used normality/attractiveness judgments as a measure of this sensitivity; to examine whether biases were specific to norm-based coding, I asked participants to discriminate between the same faces. Both young and older adults were more accurate when tested with young relative to older faces—but only when judging normality. Like adults, 3- and 7-year-olds were more accurate in judging the attractiveness of young faces; however, unlike adults, this bias extended to the discrimination task. Thus by 3 years of age children are more sensitive to differences among young relative to older faces, suggesting that young children's perceptual system is more finely tuned for young than older adult faces. Collectively, the results of this dissertation help elucidate the development of category-specific norms and clarify the role of experience in shaping sensitivity to the dimensions of face space.

Automatic Preparation of ETD Material from the Internet Archive for the DSpace Repository Platform

A big challenge associated with getting an institutional repository off the ground is getting content into it. This article will look at how to use digitization services at the Internet Archive alongside software utilities that the author developed to automate the harvesting of scanned dissertations and associated Dublin Core XML files to create an ETD Portal using the DSpace platform. The end result is a metadata-rich, full-text collection of theses that can be constructed for little out of pocket cost.

Object-Oriented Genetic Programming for the Automatic Inference of Graph Models for Complex Networks

Complex networks are systems of entities that are interconnected through meaningful relationships. The result of the relations between entities forms a structure that has a statistical complexity that is not formed by random chance. In the study of complex networks, many graph models have been proposed to model the behaviours observed. However, constructing graph models manually is tedious and problematic. Many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. However, recently, an approach that automates the inference of graph models was proposed by Bailey [10] The proposed methodology employs genetic programming (GP) to produce graph models that approximate various properties of an exemplary graph of a targeted complex network. However, there is a great deal already known about complex networks, in general, and often specific knowledge is held about the network being modelled. The knowledge, albeit incomplete, is important in constructing a graph model. However it is difficult to incorporate such knowledge using existing GP techniques. Thus, this thesis proposes a novel GP system which can incorporate incomplete expert knowledge that assists in the evolution of a graph model. Inspired by existing graph models, an abstract graph model was developed to serve as an embryo for inferring graph models of some complex networks. The GP system and abstract model were used to reproduce well-known graph models. The results indicated that the system was able to evolve models that produced networks that had structural similarities to the networks generated by the respective target models.

L-Fuzzy Structured Query Language

Lattice valued fuzziness is more general than crispness or fuzziness based on the unit interval. In this work, we present a query language for a lattice based fuzzy database. We define a Lattice Fuzzy Structured Query Language (LFSQL) taking its membership values from an arbitrary lattice L. LFSQL can handle, manage and represent crisp values, linear ordered membership degrees and also allows membership degrees from lattices with non-comparable values. This gives richer membership degrees, and hence makes LFSQL more flexible than FSQL or SQL. In order to handle vagueness or imprecise information, every entry into an L-fuzzy database is an L-fuzzy set instead of crisp values. All of this makes LFSQL an ideal query language to handle imprecise data where some factors are non-comparable. After defining the syntax of the language formally, we provide its semantics using L-fuzzy sets and relations. The semantics can be used in future work to investigate concepts such as functional dependencies. Last but not least, we present a parser for LFSQL implemented in Haskell.

AutoFACT: An Automatic Functional Annotation and Classification Tool

Affiliation: Centre Robert-Cedergren de l'Université de Montréal en bio-informatique et génomique & Département de biochimie, Université de Montréal

Una aproximación evolucionista para la generación automática de sentencias SQL a partir de ejemplos

En la actualidad, el uso de las tecnologías ha sido primordial para el avance de las sociedades, estas han permitido que personas sin conocimientos informáticos o usuarios llamados “no expertos” se interesen en su uso, razón por la cual los investigadores científicos se han visto en la necesidad de producir estudios que permitan la adaptación de sistemas, a la problemática existente dentro del ámbito informático. Una necesidad recurrente de todo usuario de un sistema es la gestión de la información, la cual se puede administrar por medio de una base de datos y lenguaje específico, como lo es el SQL (Structured Query Language), pero esto obliga al usuario sin conocimientos a acudir a un especialista para su diseño y construcción, lo cual se ve reflejado en costos y métodos complejos, entonces se plantea una pregunta ¿qué hacer cuando los proyectos son pequeñas y los recursos y procesos son limitados? Teniendo como base la investigación realizada por la universidad de Washington[39], donde sintetizan sentencias SQL a partir de ejemplos de entrada y salida, se pretende con esta memoria automatizar el proceso y aplicar una técnica diferente de aprendizaje, para lo cual utiliza una aproximación evolucionista, donde la aplicación de un algoritmo genético adaptado origina sentencias SQL válidas que responden a las condiciones establecidas por los ejemplos de entrada y salida dados por el usuario. Se obtuvo como resultado de la aproximación, una herramienta denominada EvoSQL que fue validada en este estudio. Sobre los 28 ejercicios empleados por la investigación [39], 23 de los cuales se obtuvieron resultados perfectos y 5 ejercicios sin éxito, esto representa un 82.1% de efectividad. Esta efectividad es superior en un 10.7% al establecido por la herramienta desarrollada en [39] SQLSynthesizer y 75% más alto que la herramienta siguiente más próxima Query by Output QBO[31]. El promedio obtenido en la ejecución de cada ejercicio fue de 3 minutos y 11 segundos, este tiempo es superior al establecido por SQLSynthesizer; sin embargo, en la medida un algoritmo genético supone la existencia de fases que amplían los rangos de tiempos, por lo cual el tiempo obtenido es aceptable con relación a las aplicaciones de este tipo. En conclusión y según lo anteriormente expuesto, se obtuvo una herramienta automática con una aproximación evolucionista, con buenos resultados y un proceso simple para el usuario “no experto”.

Diversified query expansion

La diversification des résultats de recherche (DRR) vise à sélectionner divers documents à partir des résultats de recherche afin de couvrir autant d’intentions que possible. Dans les approches existantes, on suppose que les résultats initiaux sont suffisamment diversifiés et couvrent bien les aspects de la requête. Or, on observe souvent que les résultats initiaux n’arrivent pas à couvrir certains aspects. Dans cette thèse, nous proposons une nouvelle approche de DRR qui consiste à diversifier l’expansion de requête (DER) afin d’avoir une meilleure couverture des aspects. Les termes d’expansion sont sélectionnés à partir d’une ou de plusieurs ressource(s) suivant le principe de pertinence marginale maximale. Dans notre première contribution, nous proposons une méthode pour DER au niveau des termes où la similarité entre les termes est mesurée superficiellement à l’aide des ressources. Quand plusieurs ressources sont utilisées pour DER, elles ont été uniformément combinées dans la littérature, ce qui permet d’ignorer la contribution individuelle de chaque ressource par rapport à la requête. Dans la seconde contribution de cette thèse, nous proposons une nouvelle méthode de pondération de ressources selon la requête. Notre méthode utilise un ensemble de caractéristiques qui sont intégrées à un modèle de régression linéaire, et génère à partir de chaque ressource un nombre de termes d’expansion proportionnellement au poids de cette ressource. Les méthodes proposées pour DER se concentrent sur l’élimination de la redondance entre les termes d’expansion sans se soucier si les termes sélectionnés couvrent effectivement les différents aspects de la requête. Pour pallier à cet inconvénient, nous introduisons dans la troisième contribution de cette thèse une nouvelle méthode pour DER au niveau des aspects. Notre méthode est entraînée de façon supervisée selon le principe que les termes reliés doivent correspondre au même aspect. Cette méthode permet de sélectionner des termes d’expansion à un niveau sémantique latent afin de couvrir autant que possible différents aspects de la requête. De plus, cette méthode autorise l’intégration de plusieurs ressources afin de suggérer des termes d’expansion, et supporte l’intégration de plusieurs contraintes telles que la contrainte de dispersion. Nous évaluons nos méthodes à l’aide des données de ClueWeb09B et de trois collections de requêtes de TRECWeb track et montrons l’utilité de nos approches par rapport aux méthodes existantes.

Polyaniline as an Automatic Beam Steering Material

This 'study' deals with a preliminary study of automatic beam steering properly in conducting polyaniline . Polyaniline in its undoped and doped .state was prepared from aniline by the chemical oxidative polymerization method. Dielectric properties of the samples were studied at S-band microwave frequencies using cavity perturbation technique. It is found that undoped po/vanihne is having greater dielectric loss and conductivity contpared with the doped samples. The beam steering property is studied using a perspex rod antenna and HP 85/OC vector network analyzer. The shift in the radiated beam is studied for different do voltages. The results show that polyaniline is a good nutterial far beam steering applications.

A Multilingual Query Processing System using Software Agents

The goal of this work was developing a query processing system using software agents. Open Agent Architecture framework is used for system development. The system supports queries in both Hindi and Malayalam; two prominent regional languages of India. Natural language processing techniques are used for meaning extraction from the plain query and information from database is given back to the user in his native language. The system architecture is designed in a structured way that it can be adapted to other regional languages of India. . This system can be effectively used in application areas like e-governance, agriculture, rural health, education, national resource planning, disaster management, information kiosks etc where people from all walks of life are involved.

Studies on grain refinement and alloying additions on the microstructure and mechanical properties of Mg-8Zn-4Al alloy

MAGNESIUM ALLOYS have strong potential for weight reduction in a wide range of technical applications because of their low density compared to other structural metallic materials. Therefore, an extensive growth of magnesium alloys usage in the automobile sector is expected in the coming years to enhance the fuel efficiency through mass reduction. The drawback associated with the use of commercially cheaper Mg-Al based alloys, such as AZ91, AM60 and AM50 are their inferior creep properties above 100ºC due to the presence of discontinuous Mg17A112 phases at the grain boundaries. Although rare earth-based magnesium alloys show better mechanical properties, it is not economically viable to use these alloys in auto industries. Recently, many new Mg-Al based alloy systems have been developed for high temperature applications, which do not contain the Mg17Al12 phase. It has been proved that the addition of a high percentage of zinc (which depends upon the percentage of Al) to binary Mg-Al alloys also ensures the complete removal of the Mg17Al12 phase and hence exhibits superior high temperature properties.ZA84 alloy is one such system, which has 8%Zn in it (Mg-8Zn-4Al-0.2Mn, all are in wt %) and shows superior creep resistance compared to AZ and AM series alloys. These alloys are mostly used in die casting industries. However, there are certain large and heavy components, made up of this alloy by sand castings that show lower mechanical properties because of their coarse microstructure. Moreover, further improvement in their high temperature behaviour through microstructural modification is also an essential task to make this alloy suitable for the replacement of high strength aluminium alloys used in automobile industry. Grain refinement is an effective way to improve the tensile behaviour of engineering alloys. In fact, grain refinement of Mg-Al based alloys is well documented in literature. However, there is no grain refiner commercially available in the market for Mg-Al alloys. It is also reported in the literature that the microstructure of AZ91 alloy is modified through the minor elemental additions such as Sb, Si, Sr, Ca, etc., which enhance its high temperature properties because of the formation of new stable intermetallics. The same strategy can be used with the ZA84 alloy system to improve its high temperature properties further without sacrificing the other properties. The primary objective of the present research work, “Studies on grain refinement and alloying additions on the microstructure and mechanical properties of Mg-8Zn-4Al alloy” is twofold: 1. To investigate the role of individual and combined additions of Sb and Ca on the microstructure and mechanical properties of ZA84 alloy. 2. To synthesis a novel Mg-1wt%Al4C3 master alloy for grain refinement of ZA84 alloy and investigate its effects on mechanical properties.

Grain refinement studies on Mg and Mg-Al based alloys

Demand on magnesium and its alloys is increased significantly in the automotive industry because of their great potential in reducing the weight of components, thus resulting in improvement in fuel efficiency of the vehicle. To date, most of Mg products have been fabricated by casting, especially, by die-casting because of its high productivity, suitable strength, acceptable quality & dimensional accuracy and the components produced through sand, gravity and low pressure die casting are small extent. In fact, higher solidification rate is possible only in high pressure die casting, which results in finer grain size. However, achieving high cooling rate in gravity casting using sand and permanent moulds is a difficult task, which ends with a coarser grain nature and exhibit poor mechanical properties, which is an important aspect of the performance in industrial applications. Grain refinement is technologically attractive because it generally does not adversely affect ductility and toughness, contrary to most other strengthening methods. Therefore formation of fine grain structure in these castings is crucial, in order to improve the mechanical properties of these cast components. Therefore, the present investigation is “GRAIN REFINEMENT STUDIES ON Mg AND Mg-Al BASED ALLOYS”. The primary objective of this present investigation is to study the effect of various grain refining inoculants (Al-4B, Al- 5TiB2 master alloys, Al4C3, Charcoal particles) on Pure Mg and Mg-Al alloys such as AZ31, AZ91 and study their grain refining mechanisms. The second objective of this work is to study the effect of superheating process on the grain size of AZ31, AZ91 Mg alloys with and without inoculants addition. In addition, to study the effect of grain refinement on the mechanical properties of Mg and Mg-Al alloys. The thesis is well organized with seven chapters and the details of the studies are given below in detail.

Development of Techniques for the Automatic Extraction and Grade Detection of Glioma Tumors from Conventional Brain Magnetic Resonant Images

Cerebral glioma is the most prevalent primary brain tumor, which are classified broadly into low and high grades according to the degree of malignancy. High grade gliomas are highly malignant which possess a poor prognosis, and the patients survive less than eighteen months after diagnosis. Low grade gliomas are slow growing, least malignant and has better response to therapy. To date, histological grading is used as the standard technique for diagnosis, treatment planning and survival prediction. The main objective of this thesis is to propose novel methods for automatic extraction of low and high grade glioma and other brain tissues, grade detection techniques for glioma using conventional magnetic resonance imaging (MRI) modalities and 3D modelling of glioma from segmented tumor slices in order to assess the growth rate of tumors. Two new methods are developed for extracting tumor regions, of which the second method, named as Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA) can also extract white matter and grey matter from T1 FLAIR an T2 weighted images. The methods were validated with manual Ground truth images, which showed promising results. The developed methods were compared with widely used Fuzzy c-means clustering technique and the robustness of the algorithm with respect to noise is also checked for different noise levels. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and detection. Based on the thresholds of discriminant first order and gray level cooccurrence matrix based second order statistical features three feature sets were formulated and a decision system was developed for grade detection of glioma from conventional T2 weighted MRI modality.The quantitative performance analysis using ROC curve showed 99.03% accuracy for distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant glioma. The developed brain texture analysis techniques can improve the physician’s ability to detect and analyse pathologies leading to a more reliable diagnosis and treatment of disease. The segmented tumors were also used for volumetric modelling of tumors which can provide an idea of the growth rate of tumor; this can be used for assessing response to therapy and patient prognosis.