Penyederhanaan Pemilihan Umum di Indonesia melalui (Re)-desain Surat Suara

Penelitian ini mengamati pemilihan umum tahun 2019 untuk mengidentifikasi salah satu masalah yang paling disorot yaitu kompleksitas penyelenggaraannya, yang kemudian menyebabkan masalah turunan lainnya: banyaknya jumlah suara tidak sah, lamanya durasi teknis pemilihan dan kelebihan beban kerja bagi pekerja kepemiluan yang menyebabkan kematian. Dari kompleksitas itu, penelitian menjawab pertanyaan: apa yang menjadi faktor kompleksitas pemilu di Indonesia? dan bagaimana dari faktor tersebut, pemilu dapat disederhanakan? Tulisan ini menggunakan metode kualitatif, dengan mempelajari berbagai dokumen sebagai teknis analisis. Temuan dari tulisan ini menunjukkan banyaknya jenis surat suara menjadi titik sentral dari kompleksnya pemilu di Indonesia. Untuk itu, proses penyederhanaan pemilu berikutnya sangat penting diarahkan untuk memikirkan desain ulang terhadap surat suara. Menggunakan teori kesederhanaan Ockham, dan konsep pembuatan surat suara yang diatur dalam UU Nomor 7 tahun 2017 tentang Pemilihan Umum, penelitian ini berupaya memberikan gambaran diskursus penyesuasian surat suara, dan sekaligus memberikan alternatif surat suara yang mengusung konsep penyederhanaan

A Methodology for Fitting and Validating Metamodels in Simulation

This expository paper discusses the relationships among metamodels, simulation models, and problem entities. A metamodel or response surface is an approximation of the input/output function implied by the underlying simulation model. There are several types of metamodel: linear regression, splines, neural networks, etc. This paper distinguishes between fitting and validating a metamodel. Metamodels may have different goals: (i) understanding, (ii) prediction, (iii) optimization, and (iv) verification and validation. For this metamodeling, a process with thirteen steps is proposed. Classic design of experiments (DOE) is summarized, including standard measures of fit such as the R-square coefficient and cross-validation measures. This DOE is extended to sequential or stagewise DOE. Several validation criteria, measures, and estimators are discussed. Metamodels in general are covered, along with a procedure for developing linear regression (including polynomial) metamodels.

Evaluation of pesticide toxicity: a hierarchical QSAR approach to model the acute aquatic toxicity and avian oral toxicity of pesticides

The thesis aimed to extract information relevant to the hazard and risk assessment of pesticides. In particular, quantitative structure-activity relationship (QSAR) approaches have been used to build up a mathematical model able to predict the aquatic acute toxicity, LC50, and the avian oral toxicity, LD50, for pesticides. Ecotoxicological values were collected from several databases, and screened according to quality criteria. A hierarchical QSAR approach was applied for the prediction of acute aquatic toxicity. Chemical structures were encoded into molecular descriptors by an automated, seamless procedure available within the OpenMolGRID system. Different linear and non-linear regression techniques were used to obtain reliable and thoroughly validated QSARs. The final model was developed by a counter-propagation neural network coupled with genetic algorithms for variable selection. The proposed QSAR is consistent with McFarland's principle for biological activity and makes use of seven molecular descriptors. The model was assessed thoroughly in test (R2 = 0.8) and validation sets (R2 = 0.72), the y-scrambling test and a sensitivity/stability test. The second endpoint considered in this thesis was avian oral toxicity. As previously, the chemical description of chemicals was generated automatically by the OpenMolGRID system. The best classification model was chosen on the basis of the performances on a validation set of 19 data points, and was obtained from a support vector machine using 94 data points and nine variables selected by genetic algorithms (Error Ratetraining = 0.021, Error Ratevalidation = 0.158). The model allowed for a mechanistic estimation of the toxicological action. In fact, several descriptors selected for the final classification model encode for the interaction of the pesticides with other molecules. The presence of hetero-atoms, e.g. sulphur atoms, is correlated with the toxicity, and the pool of descriptor selected is generally dependent from the 3D conformation of the structures. These suggest that, in the case of avian oral toxicity, pesticides probably exert their toxic action through the interaction with some macromolecule and/or protein of the biological system

Integrative Windowing

In this paper we re-investigate windowing for rule learning algorithms. We show that, contrary to previous results for decision tree learning, windowing can in fact achieve significant run-time gains in noise-free domains and explain the different behavior of rule learning algorithms by the fact that they learn each rule independently. The main contribution of this paper is integrative windowing, a new type of algorithm that further exploits this property by integrating good rules into the final theory right after they have been discovered. Thus it avoids re-learning these rules in subsequent iterations of the windowing process. Experimental evidence in a variety of noise-free domains shows that integrative windowing can in fact achieve substantial run-time gains. Furthermore, we discuss the problem of noise in windowing and present an algorithm that is able to achieve run-time gains in a set of experiments in a simple domain with artificial noise.Comment: See http://www.jair.org/ for any accompanying file

Combining classification algorithms

Dissertação de Doutoramento em Ciência de Computadores apresentada à Faculdade de Ciências da Universidade do PortoA capacidade de um algoritmo de aprendizagem induzir, para um determinado problema, uma boa generalização depende da linguagem de representação usada para generalizar os exemplos. Como diferentes algoritmos usam diferentes linguagens de representação e estratégias de procura, são explorados espaços diferentes e são obtidos resultados diferentes. O problema de encontrar a representação mais adequada para o problema em causa, é uma área de investigação bastante activa. Nesta dissertação, em vez de procurar métodos que fazem o ajuste aos dados usando uma única linguagem de representação, apresentamos uma família de algoritmos, sob a designação genérica de Generalização em Cascata, onde o espaço de procura contem modelos que utilizam diferentes linguagens de representação. A ideia básica do método consiste em utilizar os algoritmos de aprendizagem em sequência. Em cada iteração ocorre um processo com dois passos. No primeiro passo, um classificador constrói um modelo. No segundo passo, o espaço definido pelos atributos é estendido pela inserção de novos atributos gerados utilizando este modelo. Este processo de construção de novos atributos constrói atributos na linguagem de representação do classificador usado para construir o modelo. Se posteriormente na sequência, um classificador utiliza um destes novos atributos para construir o seu modelo, a sua capacidade de representação foi estendida. Desta forma as restrições da linguagem de representação dosclassificadores utilizados a mais alto nível na sequência, são relaxadas pela incorporação de termos da linguagem derepresentação dos classificadores de base. Esta é a metodologia base subjacente ao sistema Ltree e à arquitecturada Generalização em Cascata.O método é apresentado segundo duas perspectivas. Numa primeira parte, é apresentado como uma estratégia paraconstruir árvores de decisão multivariadas. É apresentado o sistema Ltree que utiliza como operador para a construção de atributos um discriminante linear. ..

Applications of Markov Chain Monte Carlo methods to continuous gravitational wave data analysis

A new algorithm for the analysis of gravitational wave data from rapidly rotating neutron stars has been developed. The work is based on the Markov Chain Monte Carlo algorithm and features enhancements specifically targeted to this problem. The algorithm is tested on both synthetic data and hardware injections in the LIGO Hanford interferometer during its third science run ("S3''). By utilising the features of this probabilistic algorithm a search is performed for a rotating neutron star in the remnant of SN1987A within in frequency window of 4 Hz and a spindown window of 2E-10 Hz/s. A method for setting upper limits is described and used on this data in the absence of a detection setting an upper limit on strain of 7.3E-23. A further application of MCMC methods is made in the area of data analysis for the proposed LISA mission. An algorithm is developed to simultaneously estimate the number of sources and their parameters in a noisy data stream using reversible jump MCMC. An extension is made to estimate the position in the sky of a source and this is further improved by the implementation of a fast approximate calculation of the covariance matrix to enhance acceptance rates. This new algorithm is also tested upon synthetic data and the results are presented here. Conclusions are drawn from the results of this work, and comments are made on the development of MCMC algorithms within the field of gravitational wave data analysis, with a view to their increasing usage

Learning qualitative models from physiological signals

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.Includes bibliographical references (leaves 165-169).by David Tak-Wai Hau.M.S

The Guide-Dog approach : a methodology for ecology

In ecology the central problem is not the lack of theory or the lack of data but the lack of research able to link them systematically and critically. The description and analysis of such an integrated research process is the focus of the present study. Our approach is called the Guide-Dog approach, because we hope that it is able to guide all those who are blinded or perplexed by the increasing technical sophistication and fragmentation of modern science