MevaL: A Visual Machine Learning Model Evaluation Tool for Financial Crime Detection

Data Science and Machine Learning are two valuable allies to fight financial crime,the domain where Feedzai seeks to leverage its value proposition in support of its mission:to make banking and commerce safe. Data is at the core of both fields and this domain, sostructuring instances for visual consumption provides an effective way of understandingthe data and communicating insights.The development of a solution for each project and use case requires a careful andeffective Machine Learning Model Evaluation stage, as it is the major source of feedbackbefore deployment. The tooling for this stage available at Feedzai can be improved,accelerated, visually supported, and diversified to enable data scientists to boost theirdaily work and the quality of the models.In this work, I propose to collect and compile internal and external input, in terms ofworkflow and Model Evaluation, in a proposal hierarchically segmented by well-definedobjectives and tasks, to instantiate the proposal in a Python package, and to iteratively val-idate the package with Feedzai’s data scientists. Therefore, the first contribution is MevaL,a Python package for Model Evaluation with visual support, integrated into Feedzai’s DataScience environment by design. In fact, MevaL is already being leveraged as a visualization package on two internal reporting projects that are serving some of Feedzai’s majorclients.In addition to MevaL, the second contribution of this work is the Model EvaluationTopology developed to ensure clear communication and design of features.A Ciência de Dados e a Aprendizagem Automática [277] são duas valiosas aliadas no combate à criminalidade económico-financeira, o domínio em que a Feedzai procura potenciar a sua proposta de valor em prol da sua missão: tornar o sistema bancário e o comércio seguros. Além disso, os dados estão no centro das duas áreas e deste domínio.Assim, a estruturação visual dos mesmos fornece uma maneira eficaz de os entender e transmitir informação.O desenvolvimento de uma solução para cada projeto e caso de uso requer um estágiocuidadoso e eficaz de Avaliação de Modelos de Aprendizagem Automática, pois esteestágio coincide com a principal fonte de retorno (feedback) antes da implementaçãoda solução. As ferramentas de Avaliação de Modelos disponíveis na Feedzai podem seraprimoradas, aceleradas, suportadas visualmente e diversificadas para permitir que oscientistas de dados impulsionem o seu trabalho diário e a qualidade destes modelos.Neste trabalho, proponho a recolha e compilação de informação interna e externa, em termos de fluxo de trabalho e Avaliação de Modelos, numa proposta hierarquicamente segmentada por objetivos e tarefas bem definidas, a instanciação desta proposta num pacote Python e a validação iterativa deste pacote em colaboração com os cientistas de dados da Feedzai. Posto isto, a primeira contribuição deste trabalho é o MevaL, um pacote Python para Avaliação de Modelos com suporte visual, integrado no ambiente de Ciência de Dados da Feedzai. Na verdade, o MevaL já está a ser utilizado como um pacote de visualização em dois projetos internos de preparação de relatórios automáticos para alguns dos principais clientes da Feedzai.Além do MevaL, a segunda contribuição deste trabalho é a Topologia de Avaliação de Modelos desenvolvida para garantir uma comunicação clara e o design enquadrado das diferentes funcionalidades

Evaluation of voice disorders in patients with active laryngeal tuberculosis

Submitted by Repositório Arca ([email protected]) on 2019-04-24T17:38:43Z No. of bitstreams: 1 license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5)Approved for entry into archive by Janaína Nascimento ([email protected]) on 2019-08-22T15:09:35Z (GMT) No. of bitstreams: 2 ve_Lucena_Marcia_etal_INI_2015.pdf: 1816542 bytes, checksum: afb20b237aae5286b1926311a2fe598d (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5)Made available in DSpace on 2019-08-22T15:09:35Z (GMT). No. of bitstreams: 2 ve_Lucena_Marcia_etal_INI_2015.pdf: 1816542 bytes, checksum: afb20b237aae5286b1926311a2fe598d (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2015Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Laboratório de Vigilância em Leishmanioses. Rio de Janeiro, RJ, Brasil / Federal University of Rio de Janeiro. Department of Speech Pathology. Rio de Janeiro, RJ, Brazil.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Laboratório de Vigilância em Leishmanioses. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Laboratório de Vigilância em Leishmanioses. Rio de Janeiro, RJ, Brasil.Federal University of Rio de Janeiro. Rio de Janeiro, RJ, Brazil.Federal University of Rio de Janeiro. Department of Speech Pathology. Rio de Janeiro, RJ, Brazil.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Laboratório de Vigilância em Leishmanioses Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Laboratório de Vigilância em Leishmanioses. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Laboratório de Vigilância em Leishmanioses. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Laboratório de Vigilância em Leishmanioses. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Laboratório de Vigilância em Leishmanioses. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Laboratório de Pesquisa Clínica em Micobacterioses. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Laboratório de Vigilância em Leishmanioses. Rio de Janeiro, RJ, Brasil / Federal University of Rio de Janeiro. Department of Otorhinolaryngology and Ophthalmology. Rio de Janeiro, RJ, Brazil.Introduction: Laryngeal tuberculosis (LTB) is the most frequent larynx granulomatous disease. In general there is lung involvement, but in an important proportion of cases you can find LTB without pulmonary disease. The lesions observed in LTB, such as ulceration and fibrosis, can interfere in the process of voice production. The involvement of the mucous lining of the vocal folds can change their flexibility and, consequently, change voice quality, and the main symptom is dysphonia present in almost 90% of cases. Objective: To describe the anatomical characteristics and voice quality in LTB patients. Material and Method: A descriptive cross-sectional study was conducted with 24 patients. Result: The most frequently affected sites were vocal folds in 87.5% patients, vestibular folds in 66.7%, epiglottis in 41.7%, arytenoid in 50%, aryepiglottic folds in 33.3%, and interarytenoid region in 33.3% patients. We found 95.8% cases of dysphonia. The voice acoustic analysis showed 58.3% cases of Jitter alterations, 83.3% of Shimmer and 70.8% of GNE. Conclusion: Voice disorders found in active laryngeal tuberculosis are similar to those reported after clinical healing of the disease, suggesting that sequelae and vocal adjustments may install during the active phase of the disease, negatively impacting the process of vocal quality reestablishment

Degree of voice perturbation in the GRBASI^* scale in 24 patients with laryngeal tuberculosis, Evandro Chagas National Institute of Infectious Diseases, Oswaldo Cruz Foundation, Rio de Janeiro, 2014.

<p>n- number.</p><p>*GRBASI scale.</p><p>G—hoarseness, R—roughness B—breathiness; A—asthenia; S—strain</p><p>Degree of voice perturbation in the GRBASI^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0126876#t002fn002" target="_blank">*</a>} scale in 24 patients with laryngeal tuberculosis, Evandro Chagas National Institute of Infectious Diseases, Oswaldo Cruz Foundation, Rio de Janeiro, 2014.</p

Clinical and vocal characteristics of 24 patients with active laryngeal tuberculosis, Evandro Chagas National Institute of Infectious Diseases, Oswaldo Cruz Foundation, 2014.

<p>^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0126876#t001fn001" target="_blank">*</a>}M- male; F- female</p><p>Clinical and vocal characteristics of 24 patients with active laryngeal tuberculosis, Evandro Chagas National Institute of Infectious Diseases, Oswaldo Cruz Foundation, 2014.</p

Videolaryngoscopy images and diagrams of phonation deviation in 3 patients with laryngeal tuberculosis, Evandro Chagas, National Institute of Infectious Diseases, Brazil, 2014.

<p>*(A) epiglottis with granular appearance with amputation of the edges with uninjured vocal folds (B) phonatory deviation diagram from the normal range (C) granulous infiltrate of the vallecula, necronis area of the epiglottis, infiltration of aryepiglottic folds and arytenoid, precluding visualization of the vocal folds (D) phonatory deviation diagram from the normal range (E) presence of infiltration and hyperemia of the arytenoid, left vocal fold and left vestibular fold (F) phonatory deviation diagram from the normal range.</p