Data mining using intelligent systems : an optimized weighted fuzzy decision tree approach

Data mining can be said to have the aim to analyze the observational datasets to find relationships and to present the data in ways that are both understandable and useful. In this thesis, some existing intelligent systems techniques such as Self-Organizing Map, Fuzzy C-means and decision tree are used to analyze several datasets. The techniques are used to provide flexible information processing capability for handling real-life situations. This thesis is concerned with the design, implementation, testing and application of these techniques to those datasets. The thesis also introduces a hybrid intelligent systems technique: Optimized Weighted Fuzzy Decision Tree (OWFDT) with the aim of improving Fuzzy Decision Trees (FDT) and solving practical problems. This thesis first proposes an optimized weighted fuzzy decision tree, incorporating the introduction of Fuzzy C-Means to fuzzify the input instances but keeping the expected labels crisp. This leads to a different output layer activation function and weight connection in the neural network (NN) structure obtained by mapping the FDT to the NN. A momentum term was also introduced into the learning process to train the weight connections to avoid oscillation or divergence. A new reasoning mechanism has been also proposed to combine the constructed tree with those weights which had been optimized in the learning process. This thesis also makes a comparison between the OWFDT and two benchmark algorithms, Fuzzy ID3 and weighted FDT. SIx datasets ranging from material science to medical and civil engineering were introduced as case study applications. These datasets involve classification of composite material failure mechanism, classification of electrocorticography (ECoG)/Electroencephalogram (EEG) signals, eye bacteria prediction and wave overtopping prediction. Different intelligent systems techniques were used to cluster the patterns and predict the classes although OWFDT was used to design classifiers for all the datasets. In the material dataset, Self-Organizing Map and Fuzzy C-Means were used to cluster the acoustic event signals and classify those events to different failure mechanism, after the classification, OWFDT was introduced to design a classifier in an attempt to classify acoustic event signals. For the eye bacteria dataset, we use the bagging technique to improve the classification accuracy of Multilayer Perceptrons and Decision Trees. Bootstrap aggregating (bagging) to Decision Tree also helped to select those most important sensors (features) so that the dimension of the data could be reduced. Those features which were most important were used to grow the OWFDT and the curse of dimensionality problem could be solved using this approach. The last dataset, which is concerned with wave overtopping, was used to benchmark OWFDT with some other Intelligent Systems techniques, such as Adaptive Neuro-Fuzzy Inference System (ANFIS), Evolving Fuzzy Neural Network (EFuNN), Genetic Neural Mathematical Method (GNMM) and Fuzzy ARTMAP. Through analyzing these datasets using these Intelligent Systems Techniques, it has been shown that patterns and classes can be found or can be classified through combining those techniques together. OWFDT has also demonstrated its efficiency and effectiveness as compared with a conventional fuzzy Decision Tree and weighted fuzzy Decision Tree

Nominal Unification of Higher Order Expressions with Recursive Let

A sound and complete algorithm for nominal unification of higher-order expressions with a recursive let is described, and shown to run in non-deterministic polynomial time. We also explore specializations like nominal letrec-matching for plain expressions and for DAGs and determine the complexity of corresponding unification problems.Comment: Pre-proceedings paper presented at the 26th International Symposium on Logic-Based Program Synthesis and Transformation (LOPSTR 2016), Edinburgh, Scotland UK, 6-8 September 2016 (arXiv:1608.02534

MacRuby: User Defined Macro Support for Ruby

Ruby does not have a way to create custom syntax outside what the language already offers. Macros allow custom syntax creation. They achieve this by code generation that transforms a small set of instructions into a larger set of instructions. This gives programmers the opportunity to extend the language based on their own custom needs. Macros are a form of meta-programming that helps programmers in writing clean and concise code. MacRuby is a hygienic macro system. It works by parsing the Abstract Syntax Tree(AST) and replacing macro references with expanded Ruby code. MacRuby offers an intuitive way to declare macro and expand the source code based on the expansion rules. We validated MacRuby by adding some features to the Ruby language that didn’t exist before or were removed as the user base for the feature was small. We fulfilled this by creating a library using the simple and easy syntax of MacRuby, thus demonstrating the library’s utility

Mapping Social Network to Software Architecture to Detect Structure Clashes in Agile Software Development

Software development is rarely an individual effort and generally involves teams of developers collaborating together in order to generate reliable code. Such collaborations require proper communication and regular coordination among the team members. In addition, coordination is required to sort out problems due to technical dependencies that exist when components of one part of the architecture requires services or data input from components of another part of the architecture. The dynamic allocation of the different tasks to people results in various socio-technical structure clashes (STSCs). These STSCs become more pronounced in an Agile Software Development environment and managerial intervention is constantly required to alleviate problems due to STSCs. In this paper we provide a method to detect these STSCs in a longitudinal fashion with the help of a tool that we are developing. We test this method in a case study of a software company and show how such structure clashes can be detected by analyzing the social network (along with the betweenness centrality index) in relation to the task dependencies due to the software architec

Hubble Space Telescope Combined Strong and Weak Lensing Analysis of the CLASH Sample: Mass and Magnification Models and Systematic Uncertainties

We present results from a comprehensive lensing analysis in HST data, of the complete CLASH cluster sample. We identify new multiple-images previously undiscovered allowing improved or first constraints on the cluster inner mass distributions and profiles. We combine these strong-lensing constraints with weak-lensing shape measurements within the HST FOV to jointly constrain the mass distributions. The analysis is performed in two different common parameterizations (one adopts light-traces-mass for both galaxies and dark matter while the other adopts an analytical, elliptical NFW form for the dark matter), to provide a better assessment of the underlying systematics - which is most important for deep, cluster-lensing surveys, especially when studying magnified high-redshift objects. We find that the typical (median), relative systematic differences throughout the central FOV are $\sim40\%$ in the (dimensionless) mass density, $\kappa$, and $\sim20\%$ in the magnification, $\mu$. We show maps of these differences for each cluster, as well as the mass distributions, critical curves, and 2D integrated mass profiles. For the Einstein radii ($z_{s}=2$) we find that all typically agree within $10\%$ between the two models, and Einstein masses agree, typically, within $\sim15\%$. At larger radii, the total projected, 2D integrated mass profiles of the two models, within r\sim2\arcmin, differ by $\sim30\%$. Stacking the surface-density profiles of the sample from the two methods together, we obtain an average slope of $d\log (\Sigma)/d\log(r)\sim-0.64\pm0.1$, in the radial range [5,350] kpc. Lastly, we also characterize the behavior of the average magnification, surface density, and shear differences between the two models, as a function of both the radius from the center, and the best-fit values of these quantities.Comment: 35 pages (20 main text pages, plus 15 pages for additional figures and tables); 2 Tables, 17 Figures. V3: accepted version; some minor corrections and additions made. V4: corrected several entries in Table 2. All mass models and magnification maps are made publicly available for the communit

A framework for digital model checking

Dissertação de mestrado em European Master in Building Information ModellingDigital model checking (DMC) is a solution that has the power to become a primary key player for the AEC industry concerns. Despite the research achievements on DMC, there are still gaps to make it practical to solve real-world problems. DMC, as an emerging research discipline, is still an area of development and not yet completely formalized. This means that there is still a need for enhanced system capabilities, updated processes, and adjustments to the current project delivery documents and proper standardization of DMC aspects. The work of this dissertation proposes a diagnostic approach based on using pre-defined principles to analyse digital model checking (DMC) and a formal framework and implementation plan. These principles are the Digital Information model (DIM), Rule-set, and checking platform. To set up a formal framework a modularization approach was used focused on “what things are”, “what is the logic behind extending the pre-existing concepts” and “how it assists the DMC process”. These modules play a fundamental role and they must be captured, tracked, and interconnected during the development of the framework. Throughout the expansion of principles, modules were built on a basis that 1) DIMs are the wholeness of information that should include existing physical systems not only buildings, 2) verification rules are not only sourced from regulatory codes and standards, and there are other sources of rules that should be taken into consideration, 3) the role of involved stakeholders, native system and project phases has not been ignored, 4) evaluate the effectiveness of DIMs to integrate, exchange, identify, and verify its content and 5) highlight on the existent classifications that could aid the DMC process. Moreover, DMC is a dependent activity that has cause and effect on former and subsequent activities. Thus, this dissertation also proposes a DMC implementation plan that could fit within the other project activities.A verificação de modelo digital (DMC) é uma solução que tem o poder de se tornar um ator principal para as preocupações da indústria de AEC. Apesar dos resultados da investigação sobre DMC, ainda existem lacunas para torná-lo prático para resolver problemas do mundo real. DMC, como uma área de investigação emergente, é ainda uma área em desenvolvimento e não completamente formalizada. Isso significa que existe ainda necessidade de aprimorato das capacidades dos sistemas, atualização de processos, ajustes aos atuais documentos de entrega do projeto e padronização adequada dos aspectos de DMC. O trabalho desta dissertação visa propor uma abordagem de diagnóstico baseada no uso de princípios pré-definidos para analisar o processo de verificação de modelo digital (DMC), um framework formal e um plano de implementação. Esses princípios são o modelo digital de informação (DIM), o conjunto de regras e a plataforma de verificação. Para configurar uma metodologia formal, uma abordagem de modularização foi usada com foco em “o que as coisas são”, “qual é a lógica por trás da extensão dos conceitos pré-existentes” e “como isso auxilia o processo DMC”. Esses módulos desempenham um papel fundamental e devem ser capturados, verificados e interconectados durante o desenvolvimento da metodologia. Ao longo da expansão dos princípios, os módulos foram construídos com base em: 1) os DIMs representam a totalidade da informação os quais devem incluir todos sistemas físicos existentes, não apenas os edifícios, 2) as regras de verificação não são apenas originárias de códigos e padrões regulatórios, existindo outras fontes de regras que devem ser levadas em consideração, 3) o papel das partes interessadas envolvidas, sistemas nativos e as fases do projeto não foram ignorados, 4) avaliar a eficácia dos DIMs para integrar, trocar, identificar e verificar seu conteúdo e 5) destacar a existencia de systemas de classificação que poderiam auxiliar no processo de DMC. Além disso, o DMC é uma atividade dependente que tem causa e efeito nas atividades anteriores e subsequentes. Assim, esta dissertação também propoe um plano de implementação do DMC para se enquadrar nas outras atividades do projeto

AUTOMATED SEMANTIC AND SYNTACTIC BIM DATA VALIDATION USING VISUAL PROGRAMMING LANGUAGE

Building Information Modeling (BIM) is part of a digitalization process that, in recent years, has been revolutionizing the way buildings and infrastructures are designed, built, and maintained. Compared to traditional processes, BIM enhances the production and the management of data related to buildings and infrastructures throughout their life cycle. It is founded on a three-dimensional graphical model based on the specificity of project goals following the “level of information need” defined in BIM procurement documents. In this framework, an automated process for checking information within a BIM model plays a role of fundamental importance. Although this increases the model’s reliability, on the other hand, it decreases the time of working. Therefore, this research aims to develop a working methodology based on Visual Programming Language (VPL) for an automated BIM Data Validation process. This workflow aims to meet the growing need of owners to centralize data relating to their real estate assets to always have the appropriate one at the operational level. This methodology has been tested in different case studies to evaluate the strengths and weaknesses of using a standardization protocol in a large portfolio and complex buildings. This allows the huge amount of data from BIM models to be checked and summary reports to be produced, sharing with the various stakeholders involved in the knowledge process