No abstract
The democratization/decentralization of both the production and consumption of information has resulted in a subjective and often misleading depiction of facts known as Fake News -a phenomenon that is effectively shaping the perception of reality for many individuals. Manual fact-checking is time-consuming and cannot scale and although automatic factchecking, vis a vis machine learning holds promise, it is significantly hindered by a deficit of suitable training data. We present both a novel dataset, VERITAS(VERIfying Textual Aspects), a collection of fact-checked claims, containing their original documents and LUX(Language Under eXamination), a text classifier that makes use of an extensive linguistic analysis to infer the likelihood of the input being a piece of fake-news.8 https://en.wikipedia.org/wiki/ Krippendorff%27s_alpha 9 https://www.businessinsider.com/mostbiased-news-outlets-in-america-cnn-foxnytimes-2018-810 businessinsider.com/most-and-leasttrusted-news-outlets-in-america-cnn-foxnews-new-york-times-2019-4
In order to provide content-based search on image media, including images and video, they are typically accessed based on manual or automatically assigned concepts or tags, or sometimes based on image-image similarity depending on the use case. While great progress has been made in very recent years in automatic concept detection using machine learning, we are still left with a mis-match between the semantics of the concepts we can automatically detect, and the semantics of the words used in a user's query, for example. In this paper we report on a large collection of images from wearable cameras gathered as part of the Kids'Cam project, which have been both manually annotated from a vocabulary of 83 concepts, and automatically annotated from a vocabulary of 1,000 concepts. This collection allows us to explore issues around how language, in the form of two distinct concept vocabularies or spaces, one manually assigned and thus forming a ground-truth, is used to represent images, in our case taken using wearable cameras. It also allows us to discuss, in general terms, issues around mis-match of concepts in visual media, which derive from language mismatches. We report the data processing we have completed on this collection and some of our initial experimentation in mapping across the two language vocabularies.
Defined as the intentional or unintentional spread of false information (K et al., 2019) through context and/or content manipulation, fake news has become one of the most serious problems associated with online information (Waldrop, 2017). Consequently, it comes as no surprise that Fake News Detection has become one of the major foci of various fields of machine learning and while machine learning models have allowed individuals and companies to automate decision-based processes that were once thought to be only doable by humans, it is no secret that the real-life applications of such models are not viable without the existence of an adequate training dataset. In this paper we describe the Veritas Annotator, a web application for manually identifying the origin of a rumour. These rumours, often referred as claims, were previously checked for validity by Fact-Checking Agencies.
Objetivo: É indiscutível a importância do transporte marítimo no comércio global. Entretanto, por ser um dos modais mais utilizados para o transporte de mercadorias, é evidente que os impactos ambientais gerados sejam tão relevantes quanto, uma vez que além das emissões de gases tóxicos e material particulado na atmosfera, ainda existe a poluição sonora gerada pela queima do combustível fóssil nos motores de combustão das embarcações. O Shore Power surge como um recurso que visa reduzir estes impactos, uma vez que ao substituir a fonte primária de energia do navio atracado em uma instalação portuária pela energia elétrica ongrid, pode-se desligar os motores movidos a combustível fóssil, reduzindo significativamente a poluição aérea e sonora gerada. Entretanto, apesar desta tecnologia ser extremamente promissora, ainda não é utilizada nacionalmente. Desta forma, através da aplicação em uma das instalações portuárias mais significativas da região Sudeste brasileira, visa-se estimular o investimento nesta tecnologia. Métodos: Para realizar esta a simulação desta aplicação, optou-se por utilizar a Calculadora de Viabilidade de Instalação do Sistema Shore Power através da Simulação de Monte Carlo, desenvolvido por Vidal (VIDAL, 2022), coautor deste estudo, que fornece uma análise financeira que considera também a imprevisibilidade do mercado financeiro, trazendo assim uma maior confiabilidade aos resultados obtidos. Resultados: Através da simulação do realizada, foi possível obter uma Taxa Interna de Retorno de cerca de 23%, sendo mais que o dobro da Taxa Mínima de Atratividade em consideração, de 9,25%. Conclusão: Portanto, além dos benefícios ambientais gerados pela implantação da tecnologia Shore Power na instalação portuária de Itaguaí, o sistema ainda traria ganhos financeiros consideráveis, mostrando, assim, que o grande potencial de instalação no território brasileiro.
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