Presentation given by Rommel N. Carvalho at the 9th International Workshop on Uncertainty Reasoning for the Semantic Web at the 12th International Semantic Web Conference in October 21, 2013, Sydney, Australia. This was a joint work between the Research and Strategic Information Directorate from Brazil's Office of the Comptroller General and the Department of Computer Science from the University of Brasília.
Title: UMP-ST plug-in: a tool for documenting, maintaining, and evolving probabilistic ontologies.
Abstract: Although several languages have been proposed for dealing with uncertainty in the Semantic Web (SW), almost no support has been given to ontological engineers on how to create such probabilistic ontologies (PO). This task of modeling POs has proven to be extremely difficult and hard to replicate. This paper presents the first tool in the world to implement a process which guides users in modeling POs, the Uncertainty Modeling Process for Semantic Technologies (UMP-ST). The tool solves three main problems: the complexity in creating POs; the difficulty in maintaining and evolving existing POs; and the lack of a centralized tool for documenting POs. Besides presenting the tool, which is implemented as a plug-in for UnBBayes, this papers also presents how the UMP-ST plug-in could have been used to build the Probabilistic Ontology for Procurement Fraud Detection and Prevention in Brazil, a proof-of-concept use case created as part of a research project at the Brazilian Office of the Comptroller General (CGU).
The aim of "SP Theory of Intelligence" is Simplify and integrate concepts across artificial intelligence, mainstream computing and human perception and cognition, with information compression as a unifying theme
An Abstract Framework for Agent-Based Explanations in AIGiovanni Ciatto
We propose an abstract framework for XAI based on MAS encompassing the main definitions and results from the literature, focussing on the key notions of interpretation and explanation.
The aim of "SP Theory of Intelligence" is Simplify and integrate concepts across artificial intelligence, mainstream computing and human perception and cognition, with information compression as a unifying theme
An Abstract Framework for Agent-Based Explanations in AIGiovanni Ciatto
We propose an abstract framework for XAI based on MAS encompassing the main definitions and results from the literature, focussing on the key notions of interpretation and explanation.
Thought Leadership Session: Enterprise Semantics & Ontology, The Power of Und...Wim Laurier
Learn the 1-on-1 of Semantics & Ontology by international authorities. Explore how semantics and ontology is used as the underlying conceptual structure of an enterprise by transforming interoperability beyond existing boundaries. Understand the complex interdependencies of enterprise operations through semantics and ontology. Discover how the Global University Alliance researches, compares, analyzes and develops Best and LEADing Practices around Enterprise Semantics & Ontology.
Professor Simon Polovina
International authority and thought leader in Enterprise Semantics Sheffield Hallam University, United Kingdom
Head of Enterprise Semantics research and development at the Global University Alliance
Professor Wim Laurier
International authority and thought leader in Enterprise Ontology Université Saint-Louis, Bruxelles and Ghent University
Head of Enterprise Ontology research and development at the Global University Alliance
Thought Leadership Session: Enterprise Semantics & Ontology, The Power of Und...Wim Laurier
Learn the 1-on-1 of Semantics & Ontology by international authorities. Explore how semantics and ontology is used as the underlying conceptual structure of an enterprise by transforming interoperability beyond existing boundaries. Understand the complex interdependencies of enterprise operations through semantics and ontology. Discover how the Global University Alliance researches, compares, analyzes and develops Best and LEADing Practices around Enterprise Semantics & Ontology.
Professor Simon Polovina
International authority and thought leader in Enterprise Semantics Sheffield Hallam University, United Kingdom
Head of Enterprise Semantics research and development at the Global University Alliance
Professor Wim Laurier
International authority and thought leader in Enterprise Ontology Université Saint-Louis, Bruxelles and Ghent University
Head of Enterprise Ontology research and development at the Global University Alliance
Data Visualizations in Cyber Security: Still Home of the WOPR?Matthew Park
Visualization of security data has not advanced significantly since the days of the WOPR in War Games. Other tech industries have embraced the role of modern user interfaces to facilitate and expedite data search, analysis and discovery, which has significantly helped users in those industries gain insights from a big data environment. In contrast, the security industry prefers to relegate everyone into command line prompts and clunky interfaces with minimal functionality and an inability to scale to the volume, velocity, and variety of security data. I’ll address the core challenges and impact of the industry’s failure to take data visualization and user experience seriously, and provide recommendations on key areas that would most benefit from modern data visualization. Through the use of attack timelines, I’ll demonstrate how we, as an industry, must move beyond familiar visualization conventions (that tend to break at scale) and provide functional data visualization that is usable for analysts and operators across all levels of expertise.
This is the lecture delivered at Jadavpur University for the engineering students. The lecture was organised by the JU Entrepreneurship Cell and Alumni Association, Singapore Chapter.
GDG Cloud Southlake #17: Meg Dickey-Kurdziolek: Explainable AI is for EveryoneJames Anderson
If Artificial Intelligence (AI) is a black-box, how can a human comprehend and trust the results of Machine Learning (ML) alogrithms? Explainable AI (XAI) tries to shed light into that AI black-box so humans can trust what is going on. Our speaker Meg Dickey-Kurdziolek is currently a UX Researcher for Google Cloud AI and Industry Solutions, where she focuses her research on Explainable AI and Model Understanding. Recording of the presentation: https://youtu.be/6N2DNN_HDWU
Ouvidoria de Balcão vs Ouvidoria Digital: Desafios na Era Big DataRommel Carvalho
Apresentação realizada no dia 14/03/2017 por Rommel N. Carvalho na Semana de Ouvidoria e Acesso à Informação de 2017, organizada pela CGU.
YouTube: https://youtu.be/vNMtULu5X1c?t=3h20m24s
Como transformar servidores em cientistas de dados e diminuir a distância ent...Rommel Carvalho
Palestra ministrada pelo Dr. Rommel Novaes Carvalho, Coordenador-Geral do Observatório da Despesa Pública e Professor do Mestrado Profissional em Computação Aplicada da UnB.
Evento: Brasil 100% Digital: Integração e transparência a serviço da sociedade
Website: http://www.brasildigital.gov.br/
Data: 10/11/2016
Vídeo: https://www.youtube.com/watch?v=3WYQlPR-RLw&feature=youtu.be&t=2h4m44s
Thought Leadership Session: Enterprise Semantics & Ontology, The Power of Und...Wim Laurier
Learn the 1-on-1 of Semantics & Ontology by international authorities. Explore how semantics and ontology is used as the underlying conceptual structure of an enterprise by transforming interoperability beyond existing boundaries. Understand the complex interdependencies of enterprise operations through semantics and ontology. Discover how the Global University Alliance researches, compares, analyzes and develops Best and LEADing Practices around Enterprise Semantics & Ontology.
Professor Simon Polovina
International authority and thought leader in Enterprise Semantics Sheffield Hallam University, United Kingdom
Head of Enterprise Semantics research and development at the Global University Alliance
Professor Wim Laurier
International authority and thought leader in Enterprise Ontology Université Saint-Louis, Bruxelles and Ghent University
Head of Enterprise Ontology research and development at the Global University Alliance
Thought Leadership Session: Enterprise Semantics & Ontology, The Power of Und...Wim Laurier
Learn the 1-on-1 of Semantics & Ontology by international authorities. Explore how semantics and ontology is used as the underlying conceptual structure of an enterprise by transforming interoperability beyond existing boundaries. Understand the complex interdependencies of enterprise operations through semantics and ontology. Discover how the Global University Alliance researches, compares, analyzes and develops Best and LEADing Practices around Enterprise Semantics & Ontology.
Professor Simon Polovina
International authority and thought leader in Enterprise Semantics Sheffield Hallam University, United Kingdom
Head of Enterprise Semantics research and development at the Global University Alliance
Professor Wim Laurier
International authority and thought leader in Enterprise Ontology Université Saint-Louis, Bruxelles and Ghent University
Head of Enterprise Ontology research and development at the Global University Alliance
Data Visualizations in Cyber Security: Still Home of the WOPR?Matthew Park
Visualization of security data has not advanced significantly since the days of the WOPR in War Games. Other tech industries have embraced the role of modern user interfaces to facilitate and expedite data search, analysis and discovery, which has significantly helped users in those industries gain insights from a big data environment. In contrast, the security industry prefers to relegate everyone into command line prompts and clunky interfaces with minimal functionality and an inability to scale to the volume, velocity, and variety of security data. I’ll address the core challenges and impact of the industry’s failure to take data visualization and user experience seriously, and provide recommendations on key areas that would most benefit from modern data visualization. Through the use of attack timelines, I’ll demonstrate how we, as an industry, must move beyond familiar visualization conventions (that tend to break at scale) and provide functional data visualization that is usable for analysts and operators across all levels of expertise.
This is the lecture delivered at Jadavpur University for the engineering students. The lecture was organised by the JU Entrepreneurship Cell and Alumni Association, Singapore Chapter.
GDG Cloud Southlake #17: Meg Dickey-Kurdziolek: Explainable AI is for EveryoneJames Anderson
If Artificial Intelligence (AI) is a black-box, how can a human comprehend and trust the results of Machine Learning (ML) alogrithms? Explainable AI (XAI) tries to shed light into that AI black-box so humans can trust what is going on. Our speaker Meg Dickey-Kurdziolek is currently a UX Researcher for Google Cloud AI and Industry Solutions, where she focuses her research on Explainable AI and Model Understanding. Recording of the presentation: https://youtu.be/6N2DNN_HDWU
Ouvidoria de Balcão vs Ouvidoria Digital: Desafios na Era Big DataRommel Carvalho
Apresentação realizada no dia 14/03/2017 por Rommel N. Carvalho na Semana de Ouvidoria e Acesso à Informação de 2017, organizada pela CGU.
YouTube: https://youtu.be/vNMtULu5X1c?t=3h20m24s
Como transformar servidores em cientistas de dados e diminuir a distância ent...Rommel Carvalho
Palestra ministrada pelo Dr. Rommel Novaes Carvalho, Coordenador-Geral do Observatório da Despesa Pública e Professor do Mestrado Profissional em Computação Aplicada da UnB.
Evento: Brasil 100% Digital: Integração e transparência a serviço da sociedade
Website: http://www.brasildigital.gov.br/
Data: 10/11/2016
Vídeo: https://www.youtube.com/watch?v=3WYQlPR-RLw&feature=youtu.be&t=2h4m44s
Proposta de Modelo de Classificação de Riscos de Contratos PúblicosRommel Carvalho
Palestra ministrada pelo Leonardo Jorge Sales no 2o Seminário sobre Análise de Dados na Administração Pública @ http://www.brasildigital.gov.br/
Resumo: Contratos públicos são o meio pelo qual os recursos do governo são aplicados e as políticas públicas são desenvolvidas. No Brasil, respondem por mais de 19% do PIB. Considerando o desafio das instituições de controle governamental brasileiras de garantir eficiência e regularidade nesses processos, propõe-se neste trabalho a utilização de modelos econométricos para seleção de casos para auditoria. São desenvolvidas três abordagens, sendo a primeira a estruturação de um modelo de classificação de risco do fornecedor (empresa contratada), baseado em características como sua capacidade operacional e histórico de contratações anteriores, e a segunda um modelo mais amplo de classificação do risco dos contratos, com base nas características do próprio fornecedor, do contrato e da licitação que o antecedeu. Esses dois modelos usarão a técnica de regressão logística para a estimação dos parâmetros. A terceira abordagem propõe um modelo de decisão multicritério para seleção final de contratos a serem auditados considerando os scores de risco criados juntamente com os aspectos logísticos mais relevantes para a execução das fiscalizações. O modelo multicritério utilizará a técnica de Analytic Hierarchy Process (AHP).
Palestrante: Leonardo Jorge Sales - Ministério da Transparência, Fiscalização e Controle
Currículo: Possui graduação em Administração pela Universidade Federal de Pernambuco e Pós-Graduação em Auditoria Governamental pelo Instituto Serzedello Correa - ISC, do Tribunal de Contas da União e Mestrado em Economia pela Universidade de Brasília (UNB). Tem experiência profissional nas áreas de controle governamental, auditoria interna e análise de dados aplicada à detecção de fraudes e ao desenvolvimento de indicadores de risco e qualidade. Trabalha atualmente no Ministério da Transparência, Fiscalização e Controle (MTFC), exercendo suas atividades na Diretoria de Pesquisas e Informações Estratégicas.
Categorização de achados em auditorias de TI com modelos supervisionados e nã...Rommel Carvalho
Palestra ministrada pela Patrícia Maia no 2o Seminário sobre Análise de Dados na Administração Pública @ http://www.brasildigital.gov.br/
Resumo: O trabalho consistiu na aplicação de técnicas de mineração de textos para identificação dos principais assuntos abordados nas auditorias dos últimos cinco anos. Foram utilizadas duas abordagens: a abordagem supervisionada aplicando classificação de textos com o algoritmo Random Forest e a abordagem não supervisionada através da técnica de modelagem de tópicos Latent Dirichlet Allocation (LDA). O projeto piloto foi validado com as constatações de TI e está agora sendo estendido a constatações relacionadas a outros temas. O objetivo é permitir catalogar o histórico de constatações emitidas e categorizar automaticamente novos registros. Com isso, os servidores poderão recuperar situações semelhantes para aplicação em novos trabalhos ou, ainda, tratar problemas recorrentes de forma estruturante. Além disso a mesma lógica pode ser usada para gerar conhecimento a partir de outros tipos de texto: pedidos com base na Lei de Acesso à Informações, manifestações do e-OUV, processos analisados pela CRG, notícias de interesse do órgão, etc.
Palestrante: Patrícia Maia - Ministério da Transparência, Fiscalização e Controle
Currículo: Possui mestrado em Computação Aplicada pela Universidade de Brasília (UNB), especialização em Modelagem de Processos e Engenharia de Requisitos pela Universidade Federal do Rio Grande do SUL (UFRGS) e graduação em Tecnologia da Informação. Tem experiência profissional nas áreas de mineração de textos, ETL, banco de dados e controle governamental. Trabalha atualmente no Ministério da Transparência, Fiscalização e Controle (MTFC), exercendo suas atividades na Diretoria de Pesquisas e Informações Estratégicas.
Mapeamento de risco de corrupção na administração pública federalRommel Carvalho
Palestra ministrada pelo Dr. Rommel Novaes Carvalho no 2o Seminário sobre Análise de Dados na Administração Pública @ http://www.brasildigital.gov.br/
Resumo: O Ministério da Transparência (CGU) tem, entre suas atribuições, investigar possíveis irregularidades cometidas por servidores públicos federais. Nesse contexto, nasceu o projeto MARA, que aplica mineração de dados para gerar modelos preditivos com a finalidade de avaliar risco de corrupção de servidores públicos federais a partir de diversas bases de dados governamentais. Esse trabalho possibilitou o uso mais eficiente e eficaz de recursos e pessoal da CGU; com impacto nacional; incremento na metodologia de priorização de trabalhos; e o fortalecimento do controle prévio.
Palestrante: Rommel Novaes Carvalho - Ministério da Transparência, Fiscalização e Controle
Currículo: Coordenador-Geral do Observatório da Despesa Pública (ODP), lidera equipe de aproximadamente 15 Cientistas de Dados responsáveis por monitorar gastos públicos, identificar fraudes e combater a corrupção. Além disso, é Professor do Mestrado Profissional em Computação Aplicada da UnB.
Palestra realizada pelo Coordenador-Geral do Observatório da Despesa Pública (ODP) do Ministério da Transparência, Fiscalização e Controle (MTFC) no evento Ciência de Dados e Sociedade organizado pelo Instituto Brasileiro de Pesquisa e Análise de Dados. (IBPAD).
Evento: http://www.eventbrite.com.br/e/seminarios-ibpad-ciencia-de-dados-e-sociedade-tickets-25481490825
Aplicação de técnicas de mineração de textos para classificação automática de...Rommel Carvalho
O uso de classificação automática de textos tem se tornado cada vez mais comum nos últimos anos. Contudo, ao se trabalhar com classificação em larga escala, a complexidade aumenta consideravelmente. Foi realizado um estudo de caso, aplicado à triagem de denúncias na Controladoria Geral da União, utilizando uma grande quantidade de categorias a serem classificadas. A solução proposta empregou aprendizagem de máquina e classificação multilabel. Essas técnicas tiveram como objetivo a construção de um modelo capaz de solucionar adversidades inerentes a este contexto, apresentando ganhos significativos
Patrícia Helena Maia Alves de Andrade - Controladoria-Geral da União
Analista de Finanças e Controle da CGU, atuando na área de mineração de textos e análise de dados, na Diretoria de Pesquisa e Informações Estratégicas. Atualmente está finalizando o Mestrado Profissional em Computação Aplicada na Universidade de Brasília
Filiação partidária e risco de corrupção de servidores públicos federaisRommel Carvalho
Esta palestra apresenta o trabalho referente a um estudo de caso de aprendizagem de máquina aplicada para mensurar o risco de corrupção de servidores públicos federais usando dados de filiação partidária. Inicialmente, um teste de hipótese verifica a dependência entre corrupção e filiação partidária. Em seguida, são preparados três conjuntos de dados com normalização e três técnicas diferentes de discretização. Usando o ambiente Weka, este trabalho mostra a aplicação de quatro algoritmos de classificação para construir modelos de previsão de risco de corrupção: Redes Bayesianas, Support Vector Machines, Random Forest e Redes Neurais Artificiais com backpropagation.
Para avaliar os modelos, são usadas métricas como precisão, sensibilidade, kappa e acurácia. Por último, o estudo de caso compara o modelo de melhor desempenho construído com um modelo dos especialistas em combate à corrupção. A comparação não apenas confirma afirmações dos especialistas, como também fornece novas visões sobre a relação filiação-corrupção.
Ricardo Silva Carvalho - Controladoria-Geral da União
Graduado em Engenharia da Computação pelo Instituto Tecnológico de Aeronáutica (ITA). Atualmente está finalizando Mestrado em Computação Aplicada na Universidade de Brasília (UnB) trabalhando com projeto na área de Aprendizagem de Máquina. Ocupa cargo de Analista de Finanças e Controle na Controladoria-Geral da União (CGU) com foco na construção de modelos preditivos para mapeamento de risco de corrupção usando mineração de dados. Tem experiência na área de Ciência da Computação, com ênfase em Inteligência Artificial, Aprendizagem de Máquina, Mineração de Dados, Análise de Algoritmos e Engenharia de Software
Uso de mineração de dados e textos para cálculo de preços de referência em co...Rommel Carvalho
Uma das grandes responsabilidades da CGU é identificar as compras do governo com valores diferentes dos praticados pelo mercado. Dessa forma, é possível mensurar o grau de eficiência das compras realizadas pelos órgãos governamentais. Essa informação é útil tanto para o auditor, que é responsável por fiscalizar o uso dos recursos públicos, como para o gestor, que pode melhorar seus processos observando as melhores práticas de outras unidades do governo. Dada a enorme quantidade e a diversidade das compras realizadas pelo Governo, essa análise se torna praticamente inviável sem a ajuda de algum mecanismo automatizado. No entanto, para que essa análise automatizada seja possível, é preciso ter antes de tudo, uma base de dados com os preços médios, ou de referência, para cada produto que se deseja analisar. Apesar de todas as compras do Governo Federal serem inseridas em um sistema único e centralizado, as informações armazenadas não são detalhadas e estruturadas o suficiente para se calcular esses preços de referência.
Essa palestra apresenta a metodologia desenvolvida na CGU, baseada em técnicas de mineração de dados, para extrair as informações necessárias desse sistema centralizado de forma a possibilitar o cálculo de preços de referência para produtos comprados pelo Governo Federal. Além disso, são apresentadas também algumas análises feitas com base no banco de preços criado a partir dessa metodologia de forma a enfatizar sua importância para a melhoria da gestão dos recursos públicos.
Rommel Novaes Carvalho - Controladoria-Geral da União
Coordenador-Geral do Observatório da Despesa Pública da CGU (http://www.cgu.gov.br/assuntos/informacoes-estrategicas/observatorio-da-despesa-publica), realizou seu PhD e Pós-Doc na George Mason University, EUA, na área de Inteligência Artificial, Web Semântica e Mineração de Dados e também é professor do Mestrado Profissional em Computação Aplicada da UnB
Detecção preventiva de fracionamento de comprasRommel Carvalho
Essa palestra apresenta o uso de Mineração de Dados para identificar fracionamentos de forma proativa, ou seja, antes mesmo do fracionamento se concretizar. Dessa forma, é possível alertar o usuário e evitar que a irregularidade aconteça. Nesse trabalho, foram utilizados diversos algoritmos diferentes de classificação, todos baseados em redes bayesianas. Foram analisadas mais de 50 mil compras na área de TI e o modelo final foi capaz de classificar corretamente todos os casos de fracionamento de forma proativa e obteve uma acurácia geral de 99,197%
Rommel Novaes Carvalho - Controladoria-Geral da União
Coordenador-Geral do Observatório da Despesa Pública da CGU (http://www.cgu.gov.br/assuntos/informacoes-estrategicas/observatorio-da-despesa-publica), realizou seu PhD e Pós-Doc na George Mason University, EUA, na área de Inteligência Artificial, Web Semântica e Mineração de Dados e também é professor do Mestrado Profissional em Computação Aplicada da UnB
Identificação automática de tipos de pedidos mais frequentes da LAIRommel Carvalho
Essa palestra apresenta o uso de Mineração de Textos para identificar os principais tipos de pedidos diferentes que são feitos no sistema e-SIC através da LAI. Foram analisados mais de
300 mil pedidos. O processamento desses dados na forma tradicional leva mais de 240 horas, ou 10 dias corridos. Já com o uso de técnicas de Big Data, foi possível diminuir esse processamento para menos de 8 horas
Rommel Novaes Carvalho - Controladoria-Geral da União
Coordenador-Geral do Observatório da Despesa Pública da CGU (http://www.cgu.gov.br/assuntos/informacoes-estrategicas/observatorio-da-despesa-publica), realizou seu PhD e Pós-Doc na George Mason University, EUA, na área de Inteligência Artificial, Web Semântica e Mineração de Dados e também é professor do Mestrado Profissional em Computação Aplicada da UnB
BMAW 2014 - Using Bayesian Networks to Identify and Prevent Split Purchases i...Rommel Carvalho
Presentation given by Rommel N. Carvalho at the 11th Bayesian Modeling Applications Workshop (BMAW 2014) at the 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014) in July 27, 2014, Quebec City, Quebec, Canada. This was a joint work between the Research and Strategic Information Directorate from Brazil's Office of the Comptroller General and the Department of Computer Science from the University of Brasília.
Talk: https://www.youtube.com/watch?v=UVOsztdSQ3A
Paper: http://seor.gmu.edu/~klaskey/BMAW2014/BMAW2014_papers/bmaw2014_paper_6.pdf
Title: Using Bayesian Networks to Identify and Prevent Split Purchases in Brazil.
Abstract: To cope with society's demand for transparency and corruption prevention, the Brazilian Office of the Comptroller General (CGU) has carried out a number of actions, including: awareness campaigns aimed at the private sector; campaigns to educate the public; research initiatives; and regular inspections and audits of municipalities and states. Although CGU has collected information from various different sources - Revenue Agency, Federal Police, and others -, going through all the data in order to find suspicious transactions has proven to be really challenging. In this paper, we present a Data Mining study applied on real data - government purchases - for finding transactions that might become irregular before they are considered as such in order to act proactively. Moreover, we compare the performance of various Bayesian Network (BN) learning algorithms with different parameters in order to fine tune the learned models and improve their performance. The best result was obtained using the Tree Augmented Network (TAN) algorithm and oversampling the minority class in order to balance the data set. Using a 10-fold cross-validation, the model correctly classified all split purchases, it obtained a ROC area of .999, and its accuracy was 99.197%.
Presentation given by Rommel N. Carvalho at the 9th International Workshop on Uncertainty Reasoning for the Semantic Web at the 12th International Semantic Web Conference in October 21, 2013, Sydney, Australia. This was a joint work between the Research and Strategic Information Directorate from Brazil's Office of the Comptroller General and the Department of Computer Science from the University of Brasília.
Title: A GUI for MLN.
Abstract: This paper focuses on the incorporation of the Markov Logic Network (MLN) formalism as a plug-in for UnBBayes, a Java framework for probabilistic reasoning based on graphical models. MLN is a formalism for probabilistic reasoning which combines the capacity of dealing with uncertainty tolerating imperfections and contradictory knowledge based a Markov Network (MN) with the expressiveness of First Order Logic. A MLN provides a compact language for specifying very large MNs and the ability to incorporate, in modular form, large domain of knowledge (expressed in First Order Logic sentences) inside itself. A Graphical User Interface for the software Tuffy was implemented into UnBBayes to facilitate the creation, and inference of MLN models. Tuffy is a Java open source MLN engine.
Integração do Portal da Copa @ Comissão CMA do Senado FederalRommel Carvalho
Apresentação preparada por Rommel N. Carvalho e apresentada pela Diretora de Sistemas e Informações da Controladoria-Geral da União (CGU), Tatiana Z. Panisset, na reunião da Comissão de Meio Ambiente, Defesa do Consumidor e Fiscalização e Controle (CMA) do Senado Federal (SF). A reunião teve como foco o debate da unificação da entrada de dados dos Portais de Transparência da Copa de 2014 do SF (www.copatransparente.gov.br) e da CGU (http://transparencia.gov.br/copa2014). Mais informações sobre a reunião em http://goo.gl/KCBD6.
As alternativas apresentadas foram discutidas e deliberadas pela CMA com aprovação da colaboração oficial entre o poder Legislativo e o poder Executivo para executar a integração da entrada de dados dos respectivos portais da copa do mundo. Notícias sobre essa colaboração podem ser encontradas em goo.gl/N8cbr, goo.gl/RVMGd, goo.gl/Ze3uJ, goo.gl/6o7BZ e goo.gl/C1CFv.
Título:
O que é e como usar dados abertos governamentais
Resumo:
A Web Semântica visa associar os dados disponibilizados na Web aos seus significados de forma a possibilitar que esses dados sejam compreensíveis tanto por humanos quanto por máquinas. Isso permitirá que tarefas, antes realizadas apenas por humanos, possam agora ser delegadas a máquinas. Técnicas de Web Semântica têm se difundido com o significativo aumento no número de aplicações que fazem uso de ontologias e semântica através de tecnologias como RDF, OWL, dentre outras, e as várias iniciativas espalhadas pelo mundo referente à disponibilização de dados abertos, em especial, de dados abertos governamentais. Dados abertos governamentais são definidos pela W3C – Consórcio da Web, como “a publicação e disseminação na Web de dados gerados pelo Setor Público, compartilhados em formato bruto e aberto, compreensíveis logicamente, de modo a permitir sua reutilização em aplicações digitais desenvolvidas pela sociedade”. O objetivo dessa palestra é apresentar os principais conceitos que norteiam as diversas iniciativas de dados abertos governamentais, a situação atual dessa iniciativa no Brasil, os benefícios que essa iniciativa traz para a sociedade como o uso desses dados abertos para contribuir com a melhoria e transparência da gestão pública.
Palestrante:
Dr. Rommel Novaes Carvalho, Ph.D
Postdoctoral Research Associate – C4I Center @ GMU
Analista de Finanças e Controle – CGU
http://mason.gmu.edu/~rcarvalh
CV resumido:
Rommel Novaes Carvalho é bacharel em Ciência da Computação e Mestre em Informática pela Universidade de Brasília, e doutor em Engenharia de Sistemas e Pesquisa Operacional pela Universidade George Mason, Estados Unidos. Pesquisador em Inteligência Artificial (IA) e membro do Grupo de Pesquisa em Inteligência Artificial da Universidade de Brasília (GIA). Suas áreas de interesse abrangem representação e raciocínio com incerteza na Web Semântica usando inferência bayesiana, mineração de dados, e engenharia de software. Desenvolvedor Java certificado, com experiência em implementação de sistemas de redes probabilísticas, sendo o arquiteto principal do projeto UnBBayes – Framework para raciocino probabilístico, em desenvolvimento pelo GIA desde 2000. Em seu doutorado propôs e implementou a versão 2 para o PR-OWL – Probabilistic OWL, para permitir o reuso de ontologias determinísticas existentes, sua interoperabilidade com ontologias probabilísticas representadas em PR-OWL, e raciocínio misto ontológico e probabilístico. Desde 2005 trabalha na Controladoria-Geral da União como especialista em Tecnologia da Informação. Em 2011, tornou-se pesquisador associado de Pós-Doutorado na George Mason University.
Probabilistic Ontology: Representation and Modeling MethodologyRommel Carvalho
Oral Defense of Doctoral Dissertation
Volgenau School of Engineering, George Mason University
Rommel Novaes Carvalho
Bachelor of Science, University of Brasília, Brazil, 2003
Master of Science, University of Brasília, Brazil, 2008
Probabilistic Ontology: Representation and Modeling Methodology
Tuesday, June 28, 2011, 2:00pm -- 4:00pm
Nguyen Engineering Building, Room 4705
Committee
Kathryn Laskey, Chair
Paulo Costa
Kuo-Chu Chang
David Schum
Larry Kerschberg
Fabio Cozman
Abstract
The past few years have witnessed an increasingly mature body of research on the Semantic Web (SW), with new standards being developed and more complex problems being addressed. As complexity increases in SW applications, so does the need for principled means to cope with uncertainty in SW applications. Several approaches addressing uncertainty representation and reasoning in the SW have emerged. Among these is Probabilistic Web Ontology Language (PR-OWL), which provides Web Ontology Language (OWL) constructs for representing Multi-Entity Bayesian Network (MEBN) theories. However, there are several important ways in which the initial version PR-OWL 1.0 fails to achieve full compatibility with OWL. Furthermore, although there is an emerging literature on ontology engineering, little guidance is available on the construction of probabilistic ontologies.
This research proposes a new syntax and semantics, defined as PR-OWL 2.0, which improves compatibility between PR-OWL and OWL in two important respects. First, PR-OWL 2.0 follows the approach suggested by Poole et al. to formalizing the association between random variables from probabilistic theories with the individuals, classes and properties from ontological languages such as OWL. Second, PR-OWL 2.0 allows values of random variables to range over OWL datatypes.
To address the lack of support for probabilistic ontology engineering, this research describes a new methodology for modeling probabilistic ontologies called Uncertainty Modeling Process for Semantic Technologies (UMP-ST). To better explain the methodology and to verify that it can be applied to different scenarios, this dissertation presents step-by-step constructions of two different probabilistic ontologies. One is used for identifying frauds in public procurements in Brazil and the other is used for identifying terrorist threats in the maritime domain. Both use cases demonstrate the advantages of PR-OWL 2.0 over its predecessor.
SWRL-F - A Fuzzy Logic Extension of the Semantic Web Rule LanguageRommel Carvalho
Presentation given by Tomasz Wlodarczyk at the 6th Uncertainty Reasoning for the Semantic Web Workshop at the 9th International Semantic Web Conference in 2010.
Paper: SWRL-F - A Fuzzy Logic Extension of the Semantic Web Rule Language
Abstract: Enhancing Semantic Web technologies with an ability to express uncertainty and imprecision is widely discussed topic. While SWRL can provide additional expressivity to OWL-based ontologies, it does not provide any way to handle uncertainty or imprecision. We introduce an extension of SWRL called SWRL-F that is based on SWRL rule language and uses SWRL's strong semantic foundation as its formal underpinning. We extend it with a SWRL-F ontology to enable fuzzy reasoning in the rule base. The resulting language provides small but powerful set of fuzzy operations that do not introduce inconsistencies in the host ontology.
Default Logics for Plausible Reasoning with Controversial AxiomsRommel Carvalho
Presentation given by Thomas Scharrenbach at the 6th Uncertainty Reasoning for the Semantic Web Workshop at the 9th International Semantic Web Conference in 2010.
Paper: Default Logics for Plausible Reasoning with Controversial Axioms
Abstract: Using a variant of Lehmann's Default Logics and Probabilistic Description Logics we recently presented a framework that invalidates those unwanted inferences that cause concept unsatisfiability without the need to remove explicitly stated axioms. The solutions of this methods were shown to outperform classical ontology repair w.r.t. the number of inferences invalidated. However, conflicts may still exist in the knowledge base and can make reasoning ambiguous. Furthermore, solutions with a minimal number of inferences invalidated do not necessarily minimize the number of conflicts. In this paper we provide an overview over finding solutions that have a minimal number of conflicts while invalidating as few inferences as possible. Specifically, we propose to evaluate solutions w.r.t. the quantity of information they convey by recurring to the notion of entropy and discuss a possible approach towards computing the entropy w.r.t. an ABox.
Tractability of the Crisp Representations of Tractable Fuzzy Description LogicsRommel Carvalho
Presentation given by Fernando Bobillo at the 6th Uncertainty Reasoning for the Semantic Web Workshop at the 9th International Semantic Web Conference in 2010.
Paper: Tractability of the Crisp Representations of Tractable Fuzzy Description Logics
Abstract: An important line of research within the field of fuzzy DLs is the computation of an equivalent crisp representation of a fuzzy ontology. In this short paper, we discuss the relation between tractable fuzzy DLs and tractable crisp representations. This relation heavily depends on the family of fuzzy operators considered.
PrOntoLearn: Unsupervised Lexico-Semantic Ontology Generation using Probabili...Rommel Carvalho
Presentation given by Saminda Abeyruwan at the 6th Uncertainty Reasoning for the Semantic Web Workshop at the 9th International Semantic Web Conference in November 7, 2010.
Paper: PrOntoLearn: Unsupervised Lexico-Semantic Ontology Generation using Probabilistic Methods
Abstract: Formalizing an ontology for a domain manually is well-known as a tedious and cumbersome process. It is constrained by the knowledge acquisition bottleneck. Therefore, researchers developed algorithms and systems that can help to automatize the process. Among them are systems that include text corpora for the acquisition. Our idea is also based on vast amount of text corpora. Here, we provide a novel unsupervised bottom-up ontology generation method. It is based on lexico-semantic structures and Bayesian reasoning to expedite the ontology generation process. We provide a quantitative and two qualitative results illustrating our approach using a high throughput screening assay corpus and two custom text corpora. This process could also provide evidence for domain experts to build ontologies based on top-down approaches.
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
The Metaverse and AI: how can decision-makers harness the Metaverse for their...Jen Stirrup
The Metaverse is popularized in science fiction, and now it is becoming closer to being a part of our daily lives through the use of social media and shopping companies. How can businesses survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and how does the Metaverse fit into business strategy when futurist ideas are developing into reality at accelerated rates? How do we do this when our data isn't up to scratch? How can we move towards success with our data so we are set up for the Metaverse when it arrives?
How can you help your company evolve, adapt, and succeed using Artificial Intelligence and the Metaverse to stay ahead of the competition? What are the potential issues, complications, and benefits that these technologies could bring to us and our organizations? In this session, Jen Stirrup will explain how to start thinking about these technologies as an organisation.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Secstrike : Reverse Engineering & Pwnable tools for CTF.pptx
URSW 2013 - UMP-ST plug-in
1. UMP-ST plug-in: a tool for documenting,
maintaining, and evolving probabilistic
ontologies
Rommel N. Carvalho, Henrique A. da Rocha, and Gilson L. Mendes
Brazilian Office of the Comptroller General
Marcelo Ladeira, Rafael M. de Souza, and Shou Matsumoto
Universidade de Brasília
!
Paper - Uncertainty Reasoning for the Semantic Web
URSW - ISWC
10/21/2013 - Sydney, Australia
9. Logic + Uncertainty Big Bang
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
4
10. Logic + Uncertainty Big Bang
In the last decade there has been a significant
increase in formalisms that integrate uncertainty
representation into ontology languages:
PR-OWL [5–7],
PR-OWL 2 [4, 3],
OntoBayes [20],
BayesOWL [8],
and probabilistic extensions of SHIF(D) and SHOIN(D)
[15]
among others.
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
4
11. Ontology
A probabilistic ontology is an explicit, formal knowledge representation
that expresses knowledge about a domain of application. This includes:
Types of entities that exist in the domain;
Properties of those entities;
firstName,
Relationships among entities;
Person, Procurement, Enterprise, ...
lastName, procurementNumber, ...
motherOf, ownerOf, isFrontFor ...
Processes and events that happen with those entities;
Statistical regularities that characterize the domain;
analyzing if requirements
are met,
choosing better proposal, ...
Inconclusive, ambiguous, incomplete, unreliable, and dissonant knowledge related to entities
of the domain;
Uncertainty about all the above forms of knowledge;
where the term entity refers to any concept (real or fictitious, concrete or abstract) that
can be described and reasoned about within the domain of application [5].
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
5
12. Probabilistic Ontology
A probabilistic ontology is an explicit, formal knowledge representation
that expresses knowledge about a domain of application. This includes:
Types of entities that exist in the domain;
Properties of those entities;
firstName,
Relationships among entities;
Person, Procurement, Enterprise, ...
lastName, procurementNumber, ...
motherOf, ownerOf, isFrontFor ...
Processes and events that happen with those entities;
Statistical regularities that characterize the domain;
analyzing if requirements
are met,
choosing better proposal, ...
Inconclusive, ambiguous, incomplete, unreliable, and dissonant knowledge related to entities
of the domain;
Uncertainty about all the above forms of knowledge;
where the term entity refers to any concept (real or fictitious, concrete or abstract) that
can be described and reasoned about within the domain of application [5].
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
5
13. Probabilistic Ontology
A probabilistic ontology is an explicit, formal knowledge representation
that expresses knowledge about a domain of application. This includes:
Types of entities that exist in the domain;
Properties of those entities;
firstName,
Relationships among entities;
Person, Procurement, Enterprise, ...
lastName, procurementNumber, ...
motherOf, ownerOf, isFrontFor ...
Processes and events that happen with those entities;
Statistical regularities that characterize the domain;
analyzing if requirements
are met,
choosing better proposal, ...
Inconclusive, ambiguous, incomplete, unreliable, and dissonant knowledge related to entities
of the domain;
Uncertainty about all the above forms of knowledge;
where the term entity refers to any concept (real or fictitious, concrete or abstract) that
can be described and reasoned about within the domain of application [5].
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
5
14. Probabilistic Ontology
A probabilistic ontology is an explicit, formal knowledge representation
that expresses knowledge about a domain of application. This includes:
Types of entities that exist in the domain;
Properties of those entities;
firstName,
Relationships among entities;
Person, Procurement, Enterprise, ...
lastName, procurementNumber, ...
motherOf, ownerOf, isFrontFor ...
Processes and events that happen with those entities;
Statistical regularities that characterize the domain;
analyzing if requirements
are met,
choosing better proposal, ...
Inconclusive, ambiguous, incomplete, unreliable, and dissonant knowledge related to entities
of the domain;
Uncertainty about all the above forms of knowledge;
where the term entity refers to any concept (real or fictitious, concrete or abstract) that
can be described and reasoned about within the domain of application [5].
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
5
15. Probabilistic Ontology
A probabilistic ontology is an explicit, formal knowledge representation
that expresses knowledge about a domain of application. This includes:
Types of entities that exist in the domain;
Properties of those entities;
firstName,
Relationships among entities;
Person, Procurement, Enterprise, ...
lastName, procurementNumber, ...
motherOf, ownerOf, isFrontFor ...
Processes and events that happen with those entities;
Statistical regularities that characterize the domain;
analyzing if requirements
are met,
choosing better proposal, ...
Inconclusive, ambiguous, incomplete, unreliable, and dissonant knowledge related to entities
of the domain;
P(isFrontFor|
Uncertainty about all the above forms of knowledge;
valueOfProcurement = 1M,
annualIncome = 10k) = 90%
where the term entity refers to any concept (real or fictitious, concrete or abstract) that
can be described and reasoned about within the domain of application [5].
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
5
16. Probabilistic Ontology
My objective is to define and
represent a context model for
the interoperability of Sensor is an explicit, formal knowledge representation
A probabilistic ontology
Networks. As my background is about a domain of application. This includes:
that expresses knowledge
not computer science, it's
Types of entities that
being a little hard to exist in the domain;
Person, Procurement, Enterprise, ...
understand how to put in
Properties of those entities;
firstName, lastName, procurementNumber, ...
practice a probabilistic
ontology.
Relationships among entities;
motherOf, ownerOf, isFrontFor ...
PhD student, Wageningen University, The Netherlands
Processes and events that happen with those entities;
Statistical regularities that characterize the domain;
analyzing if requirements
are met,
choosing better proposal, ...
Inconclusive, ambiguous, incomplete, unreliable, and dissonant knowledge related to entities
of the domain;
P(isFrontFor|
Uncertainty about all the above forms of knowledge;
valueOfProcurement = 1M,
annualIncome = 10k) = 90%
where the term entity refers to any concept (real or fictitious, concrete or abstract) that
can be described and reasoned about within the domain of application [5].
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
5
17. Probabilistic Ontology
This seems a very promising
My objective is to define and
tool, but we need to learn how
represent a context model for
to best make use of it. When
the interoperability of Sensor is an explicit, formal knowledge representation
we try to design using
A probabilistic ontology
Networks. As my background is about a domain of application. This includes:
UnBBayes, the questions we
that expresses knowledge
not computer science, it's
are trying to answer is how do
Types of entities that
being a little hard to exist in the domain;
Person, Procurement, Enterprise, ...
you identify which entities
understand how to put in
are relevant to the problem
Properties of those entities;
firstName, lastName, procurementNumber, ...
practice a probabilistic
and how translate them as
ontology. among entities;
motherOf, ownerOf, in your system.
variables isFrontFor ...
Relationships The Netherlands
PhD student, Wageningen University,
Fusion Engineer, EADS Innovation Works, UK
Processes and events that happen with those entities;
Statistical regularities that characterize the domain;
analyzing if requirements
are met,
choosing better proposal, ...
Inconclusive, ambiguous, incomplete, unreliable, and dissonant knowledge related to entities
of the domain;
P(isFrontFor|
Uncertainty about all the above forms of knowledge;
valueOfProcurement = 1M,
annualIncome = 10k) = 90%
where the term entity refers to any concept (real or fictitious, concrete or abstract) that
can be described and reasoned about within the domain of application [5].
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
5
18. Probabilistic Ontology
This seems a very promising
My objective is to define and
tool, but we need to learn how
represent a context model for
to best make use of it. When
the interoperability of Sensor is an explicit, formal knowledge representation
we try to design using
A probabilistic ontology
Networks. As my background is about a domain of application. This includes:
UnBBayes, the questions we
that expresses knowledge
not computer science, it's
are trying to answer is how do
Types of entities that
being a little hard to exist in the domain;
Person, Procurement, Enterprise, ...
you identify which entities
understand how to put in
are relevant to the problem
Properties of those entities;
firstName, lastName, procurementNumber, ...
practice a probabilistic
and how translate them as
ontology. among entities;
motherOf, ownerOf, in your system.
variables isFrontFor ...
Relationships The Netherlands
PhD student, Wageningen University,
Fusion Engineer, EADS Innovation Works, UK
analyzing if requirements
are met,
choosing better proposal, ...
Processes and events that happenawith those entities;
I am evaluating PR-OWL as
knowledge representation as
Statistical regularities that characterize the domain;
well as reasoning formalism.
I'd like to explore if/how it can
Inconclusive, ambiguous, incomplete, unreliable, and dissonant knowledge related to entities
be used
of the domain;
for applications
P(isFrontFor|
using resource devices.
valueOfProcurement = 1M,
PhD student, University of the Arlington, USA
Uncertainty about allTexas atabove forms of knowledge;
annualIncome = 10k) = 90%
where the term entity refers to any concept (real or fictitious, concrete or abstract) that
can be described and reasoned about within the domain of application [5].
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
5
19. Probabilistic Ontology
This seems a very promising
My objective is to define and
tool, but we need to learn how
represent a context model for
to best make use of it. When
the interoperability of Sensor is an explicit, formal knowledge representation
we try to design using
A probabilistic ontology
Networks. As my background is about a domain of application. This includes:
UnBBayes, the questions we
that expresses knowledge
not computer science, it's
are trying to answer is how do
Types of entities that
being a little hard to exist in the domain;
Person, Procurement, Enterprise, ...
you identify which entities
understand how to put in
are relevant to the problem
Properties of those entities;
firstName, lastName, procurementNumber, ...
practice a probabilistic
and how translate them as
ontology. among entities;
motherOf, ownerOf, in your system.
variables isFrontFor ...
Relationships The Netherlands
PhD student, Wageningen University,
Fusion Engineer, EADS Innovation Works, UK
analyzing if requirements
entities;
are met,
choosing better proposal,
Why use these variables?...
Processes and events that happenawith those
I am evaluating PR-OWL as
knowledge representation as
Statistical regularities that characterize the domain;
Why they are connected in
well as reasoning formalism.
such a way? How do you
I'd like to ambiguous, incomplete, unreliable, and dissonant knowledge related to entities
explore if/how it can
Inconclusive,
choose what type of
be used
of the domain;
for applications
P(isFrontFor|
variable it is?
using resource devices.
valueOfProcurement = Works, UK
Fusion Engineer, EADS Innovation 1M,
PhD student, University of the Arlington, USA
Uncertainty about allTexas atabove forms of knowledge;
annualIncome = 10k) = 90%
where the term entity refers to any concept (real or fictitious, concrete or abstract) that
can be described and reasoned about within the domain of application [5].
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
5
20. Probabilistic Ontology
This seems a very promising
My objective is to define and
tool, but we need to learn how
represent a context model for
to best make use of it. When
the interoperability of Sensor is an explicit, formal knowledge representation
we try to design using
A probabilistic ontology
Networks. As my background is about a domain of application. This includes:
UnBBayes, the questions we
that expresses knowledge
not computer science, it's
are trying to answer is how do
One thing which might be beyond the Person,of this tutorial is a
scope
Types of entities to exist in the domain;
identifyProcurement, Enterprise, ...
that
being a little hard
you
which entities
write-up about Art of Modeling with MEBN. Both narration and
understand how to put in
are relevant to the problem
the resultant MEBN help in understanding the problem, but
Properties of those entities;
firstName, lastName, procurementNumber, ...
practice a probabilistic
and how translate them as
how one reach from a problem description to a MEBN at
ontology. among entities;
variables isFrontFor
your system.
ownerOf,
Relationships not very clear. motherOf, Fusion Engineer, in toInnovation Works, UK
times is The Netherlands
... So when it comes MEBN,...
how
PhD student, Wageningen University,
EADS
one decides aboutthat happenawith those entities;
analyzing if requirements
are met,
Processes and events the context nodes, input nodes and resident
I am evaluating PR-OWL as
nodes? Most of the times
choosing better proposal,
Why use these variables?...
knowledge representation as it might be pretty obvious but
sometimes it is that characterize
Statistical regularities not very clear why domain;
nodes arethey are connected in
Why modeled
well as reasoning formalism. the certain
as input explore if/how it can
fragment when they could also be a way? How do you
such modeled
I'd like to nodes in aincomplete, unreliable, and dissonant knowledge related to entities
Inconclusive, ambiguous, etc. Should we follow an object-oriented type of
as contextfor applications
choose what
be used nodes,
of the domain;
P(isFrontFor|
approach when identifying important entities or should we it is?
variable
using resource devices.
valueOfProcurement = Works, UK
Fusion Engineer, EADS
think inabout allTexas predicate logic, etc.? As a annualIncome = Innovation = 90%
terms of atabove forms of knowledge;
modeler what 10k) 1M,
PhD student,
Uncertainty University of the Arlington, USA
drives our thinking process?
Professor, Institute of Business (real or fictitious,
where the term entity refers to any concept Administration, Pakistan concrete or abstract) that
can be described and reasoned about within the domain of application [5].
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
5
21. Our Goal
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
6
22. Our Goal
Uncertainty Modeling Process for Semantic Technologies
(UMP-ST)
Describes the main tasks involved in creating probabilistic
ontologies.
But it is only a guideline for ontology designers.
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
6
23. Our Goal
Uncertainty Modeling Process for Semantic Technologies
(UMP-ST)
Describes the main tasks involved in creating probabilistic
ontologies.
But it is only a guideline for ontology designers.
UMP-ST plug-in overcomes three main problems:
the complexity in creating probabilistic ontologies;
the difficulty in maintaining and evolving existing probabilistic
ontologies; and
the lack of a centralized tool for documenting probabilistic
ontologies.
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
6
29. Modeling Cycle - Procurement Fraud
Goal: Find suspicious procurements
Query: Is there any relation between the committee and
the enterprises that participated in the procurement?
Evidence: They are siblings
They live at the same address
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
9
30. Modeling Cycle - Procurement Fraud
Goal: Find suspicious procurements
Query: Is there any relation between the committee and
the enterprises that participated in the procurement?
Evidence: They are siblings
They live at the same address
Person
Procurement
Enterprise
ownerOf
participatesIn
livesAt
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
9
31. Modeling Cycle - Procurement Fraud
Goal: Find suspicious procurements
Query: Is there any relation between the committee and
the enterprises that participated in the procurement?
Evidence: They are siblings
They live at the same address
Person
Procurement
Enterprise
ownerOf
participatesIn
livesAt
If a member of the committee
lives at the same address as a
person responsible for a bidder in
the procurement, a relationship is
more likely to exist between the
committee and the enterprises,
which lowers competition.
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
9
32. Modeling Cycle - Procurement Fraud
Goal: Find suspicious procurements
Query: Is there any relation between the committee and
the enterprises that participated in the procurement?
Evidence: They are siblings
They live at the same address
Person
Procurement
Enterprise
ownerOf
participatesIn
livesAt
If a member of the committee
lives at the same address as a
person responsible for a bidder in
the procurement, a relationship is
more likely to exist between the
committee and the enterprises,
which lowers competition.
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
9
33. Modeling Cycle - Procurement Fraud
Goal: Find suspicious procurements
Query: Is there any relation between the committee and
the enterprises that participated in the procurement?
Evidence: They are siblings
They live at the same address
Person
Procurement
Enterprise
ownerOf
participatesIn
livesAt
If a member of the committee
lives at the same address as a
person responsible for a bidder in
the procurement, a relationship is
more likely to exist between the
committee and the enterprises,
which lowers competition.
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
9
34. Modeling Cycle - Procurement Fraud
Goal: Find suspicious procurements
Query: Is there any relation between the committee and
the enterprises that participated in the procurement?
Evidence: They are siblings
They live at the same address
Person
Procurement
Enterprise
ownerOf
participatesIn
livesAt
If a member of the committee
lives at the same address as a
person responsible for a bidder in
the procurement, a relationship is
more likely to exist between the
committee and the enterprises,
which lowers competition.
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
9
35. Modeling Cycle - Procurement Fraud
Goal: Find suspicious procurements
Query: Is there any relation between the committee and
the enterprises that participated in the procurement?
Evidence: They are siblings
They live at the same address
Person
Procurement
Enterprise
ownerOf
participatesIn
livesAt
If a member of the committee
lives at the same address as a
person responsible for a bidder in
the procurement, a relationship is
more likely to exist between the
committee and the enterprises,
which lowers competition.
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
9
36. UMP-ST Plug-in
Goal: Find suspicious procurements
Query: Is there any relation between the committee and
the enterprises that participated in the procurement?
Evidence: They are siblings
They live at the same address
Person
Procurement
Enterprise
ownerOf
participatesIn
livesAt
If a member of the committee
lives at the same address as a
person responsible for a bidder in
the procurement, a relationship is
more likely to exist between the
committee and the enterprises,
which lowers competition.
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
10
37. UMP-ST Plug-in
Goal: Find suspicious procurements
Query: Is there any relation between the committee and
the enterprises that participated in the procurement?
Evidence: They are siblings
They live at the same address
“Requirements traceability refers to
the ability to describe and follow the
life of a requirement, in both
forward and backward
directions.” [11]
Person
Procurement
Enterprise
ownerOf
participatesIn
livesAt
If a member of the committee
lives at the same address as a
person responsible for a bidder in
the procurement, a relationship is
more likely to exist between the
committee and the enterprises,
which lowers competition.
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
10
42. ⇤ ii) Procure por um membro da comissão e um resp
participante da licitação que vivam no mesmo endere
Requirements
As figuras 5.1 e 5.2 trazem uma parte da GUI do UMP-ST plugin
Goal: Find suspicious procurements
Query: Is there any relation between the committee andrelacionadas ao dois objetivos em ques
de visualização das hipóteses
the enterprises that participated in the procurement?
Evidence: They are siblings
They live at the same address
Figura 5.1: Painel de hipóteses do primeiro objeti
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
15
43. ⇤ ii) Procure por um membro da comissão e um resp
participante da licitação que vivam no mesmo endere
Requirements
As figuras 5.1 e 5.2 trazem uma parte da GUI do UMP-ST plugin
Goal: Find suspicious procurements
Query: Is there any relation between the committee andrelacionadas ao dois objetivos em ques
de visualização das hipóteses
the enterprises that participated in the procurement?
Evidence: They are siblings
They live at the same address
Figura 5.1: Painel de hipóteses do primeiro objeti
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
15
44. , o relatórioJudicialCriminal, que tem informações sobre o veredicto.
Analysis figuras. Foram criadas 4 atributos:
as entidades não aparecem nas Design - Entities
pessoa), 2.Valor (referente ao contrato), 3. estaSuspenso (relativo a
Person
Procurement
ntoAnual (relativo a impostoDeRenda). Como citado na abertura
Enterprise
as algumas telas serão apresentadas nesta monografia. Caso o leitor
ownerOf
r em detalhes todas as outras entidades, relacionamentos e atributos
participatesIn
livesAt
m CD contendo todas as telas.
gura 5.3: Painel de
Figura 5.4: Painel de relacionamentos do UMP-ST plugin
podem ser determinísticas ou não determinísticas (que envolvem probabilidade). A
darei apenas as regras não determinísticas, uma vez que as regras determinísticas d
entidadesestão resumidas a relações de cardinalidade e unicidade.
ontologia do UMP-ST plugin
1. Se - UMP-ST UnBBayes Plug-in Architecture Introductionum membro- do comitê tiver um parente (pai, mãe, irmão ou irmã) respons
16
por descrever os requisitos - Conclusion
UMP-ST Plug-in Use Case da licitação, então há mais chances de haver uma rela
entre comitê e empresa, o que inibe a concorrência.
45. , o relatórioJudicialCriminal, que tem informações sobre o veredicto.
Analysis figuras. Foram criadas 4 atributos:
as entidades não aparecem nas Design - Entities
pessoa), 2.Valor (referente ao contrato), 3. estaSuspenso (relativo a
Person
Procurement
ntoAnual (relativo a impostoDeRenda). Como citado na abertura
Enterprise
as algumas telas serão apresentadas nesta monografia. Caso o leitor
ownerOf
r em detalhes todas as outras entidades, relacionamentos e atributos
participatesIn
livesAt
m CD contendo todas as telas.
gura 5.3: Painel de
Figura 5.4: Painel de relacionamentos do UMP-ST plugin
podem ser determinísticas ou não determinísticas (que envolvem probabilidade). A
darei apenas as regras não determinísticas, uma vez que as regras determinísticas d
entidadesestão resumidas a relações de cardinalidade e unicidade.
ontologia do UMP-ST plugin
1. Se - UMP-ST UnBBayes Plug-in Architecture Introductionum membro- do comitê tiver um parente (pai, mãe, irmão ou irmã) respons
16
por descrever os requisitos - Conclusion
UMP-ST Plug-in Use Case da licitação, então há mais chances de haver uma rela
entre comitê e empresa, o que inibe a concorrência.
46. Analysis Design - Rules
If a member of the committee
lives at the same address as a
person responsible for a bidder in
the procurement, a relationship is
more likely to exist between the
committee and the enterprises,
which lowers competition.
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
17
47. Analysis Design - Rules
If a member of the committee
lives at the same address as a
person responsible for a bidder in
the procurement, a relationship is
more likely to exist between the
committee and the enterprises,
which lowers competition.
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
17
48. Analysis Design - Groups
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
18
49. Analysis Design - Groups
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
18
50. Analysis Design - Traceability
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
19
53. Conclusion
First tool in the world to implement UMP-ST
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
21
54. Conclusion
First tool in the world to implement UMP-ST
Also the first in the world to support the design of
POs
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
21
55. Conclusion
First tool in the world to implement UMP-ST
Also the first in the world to support the design of
POs
A GUI tool for designing, maintaining, and evolving POs
Overcomes the complexity in creating POs by providing a
step by step guidance
Provides a centralized tool for documenting POs
Provides a constant attention to where and what your
changes might impact through the implementation of
requirements traceability
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
21
56. Future Work
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
22
57. Future Work
More tests (still a beta tool)
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
22
58. Future Work
More tests (still a beta tool)
Exporting all documentation to a single PDF of
HTML file
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
22
59. Future Work
More tests (still a beta tool)
Exporting all documentation to a single PDF of
HTML file
Generating MFrags automatically based on the
groups defined in the last step of the Analysis
Design discipline, in order to facilitate the creation
of a MEBN model (i.e., PR-OWL PO) during the
Implementation discipline
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
22
60. Future Work
More tests (still a beta tool)
Exporting all documentation to a single PDF of
HTML file
Generating MFrags automatically based on the
groups defined in the last step of the Analysis
Design discipline, in order to facilitate the creation
of a MEBN model (i.e., PR-OWL PO) during the
Implementation discipline
Apply same methodology to different PO languages
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
22