Caracterização da investigação em fraude fiscal

O principal objetivo deste estudo consiste na caracterização da investigação ao nível do tema fraude fiscal até outubro de 2020, tendo-se definido com objetivos específicos, por um lado, a caracterização da investigação ao nível do seu conteúdo e por outro, a caracterização dos autores. Tendo como base a metodologia Prisma, os dados utilizados na análise foram recolhidos a partir da base Scopus, através da pesquisa de todos os documentos que apresentassem a palavra chave "tax fraud". Da análise aos resultados, verificou-se que existe uma predominância do tipo de investigação descritiva, sendo que o número de publicações e apresentações tem vindo a aumentar de forma sustentada ao longo dos últimos anos, verificando-se esta tendência também ao nível das citações. O tipo de documento mais frequente foi o artigo científico, verificado em cerca de 73% dos documentos selecionados. Foram identificadas 47 fontes, sendo que apenas 3 contribuíram com mais do que um documento. Verificou-se ainda que as temáticas mais associadas à fraude fiscal foram a evasão fiscal, a deteção da fraude fiscal, a conformidade fiscal e o branqueamento de capitais. Ao nível da caracterização dos autores, o tipo de autoria mais frequente foi a coletiva, presente em aproximadamente ¾ dos documentos, sendo que o número de autores mais frequente foi de dois. No que concerne à afiliação geográfica, o continente mais representado foi o europeu sendo que todos os continentes apresentaram pelo menos um autor filiado.The main purpose of this study consists in the characterization of the tax fraud investigation, until October of 2020, having been defined as specific objectives, on the one hand, the characterization of the investigation according to the subject presented and in the other hand, the authors characterization. Based on the Prisma methodology, the data used on this analysis was collected from the Scopus database, by searching all the documents that presented the keyword tax fraud. From the analysis of the results, it was found that there is a predominance of the descriptive investigation typology, and the number of publications and presentations has been increasing steadily over the last few years, with this trend also being seen in terms of citations. The most frequent type of document was the article, shown in approximately 73% of the selected documents. 47 sources were identified, which only 3 contributed with more than one document. It was also found that the concepts most associated with tax fraud were the tax evasion, the tax fraud detection, the tax compliance and the money laundering. In terms of the characterization of the authors, the most frequent type was the collective one, present in approximately ¾ of the documents, with the most frequent number of authors being the duos. In matter of the geographic affiliation, the most represented continent was the European, and all the continents were represented with at least one researcher

Leveraging feature selection to detect potential tax fraudsters

Tax evasion is any act that knowingly or unknowingly, legally or unlawfully, leads to non-payment or underpayment of tax due. Enforcing the correct payment of taxes by taxpayers is fundamental in maintaining investments that are necessary and benefits a society as a whole. Indeed, without taxes it is not possible to guarantee basic services such as health-care, education, sanitation, transportation, infrastructure, among other services essential to the population. This issue is especially relevant in developing countries such as Brazil. In this work we consider a real-world case study involving the Treasury Office of the State of Ceará (SEFAZ-CE, Brazil), the agency in charge of supervising more than 300,000 active taxpayers companies. SEFAZ-CE maintains a very large database containing vast amounts of information concerning such companies. Its enforcement team struggles to perform thorough inspections on taxpayers accounts as the underlying traditional human-based inspection processes involve the evaluation of countless fraud indicators (i.e., binary features), thus requiring burdensome amounts of time and being potentially prone to human errors. On the other hand, the vast amount of taxpayer information collected by fiscal agencies opens up the possibility of devising novel techniques able to tackle fiscal evasion much more effectively than traditional approaches. In this work we address the problem of using feature selection to select the most relevant binary features to improve the classification of potential tax fraudsters. Finding out possible fraudsters from taxpayer data with binary features presents several challenges. First, taxpayer data typically have features with low linear correlation between themselves. Also, tax frauds may originate from intricate illicit tactics, which in turn requires to uncover non-linear relationships between multiple features. Finally, few features may be correlated with the targeted class. In this work we propose ALICIA, a new feature selection method based on association rules and propositional logic with a carefully crafted graph centrality measure that attempts to tackle the above challenges while, at the same time, being agnostic to specific classification techniques. ALICIA is structured in three phases: first, it generates a set of relevant association rules from a set of fraud indicators (features). Subsequently, from such association rules ALICIA builds a graph, which structure is then used to determine the most relevant features. To achieve this ALICIA applies a novel centrality measure we call the Feature Topological Importance. We perform an extensive experimental evaluation to assess the validity of our proposal on four different real-world datasets, where we compare our solution with eight other feature selection methods. The results show that ALICIA achieves F-measure scores up to 76.88%, and consistently outperforms its competitors

Methods of human activity classification in buildings

The number of smart homes is rapidly increasing. Smart homes typically feature functions such as voice-activated functions, automation, monitoring, and tracking events. Besides comfort and convenience, the integration of smart home functionality with data processing methods can provide valuable information about the well-being of the smart home residence. This study is aimed at taking the data analysis within smart homes beyond occupancy monitoring and detection of falling events of people. Two different approaches are proposed to integrate human activity recognition within smart homes. The first approach utilizes KNX standard-based devices to obtain room air quality data (humidity, CO2, temperature) and combine the obtained data with two wearable devices that provide movement-related data. The second approach simplifies, improves, and addresses a few of the shortcomings of the first approach, it utilizes different measuring devices with higher sampling rates. It examines multiple statistical methods and ultimately chooses a simpler multi-layer perceptron neural network model. Resulting in a less computationally intensive solution with higher accuracy levels. The study achieved cross-validation accuracy levels above 98 %.Chytrých domácností rychle přibývá. Inteligentní domy obvykle obsahují funkce, jako jsou hlasově aktivované funkce, automatizace, monitorování a sledování událostí. Kromě komfortu a pohodlí může integrace funkcí chytré domácnosti s metodami zpracování dat poskytnout cenné informace o pohodě rezidence chytré domácnosti. Tato studie je zaměřena na analýzu dat v inteligentních domácnostech nad rámec monitorování obsazenosti a detekce pádu osob. Jsou navrženy dva různé přístupy k integraci rozpoznávání lidské činnosti do inteligentních domácností. První přístup využívá zařízení založená na standardu KNX k získávání dat o kvalitě vzduchu v místnosti (vlhkost, CO2, teplota) a kombinování získaných dat se dvěma nositelnými zařízeními, které poskytují údaje související s pohybem. Druhý přístup zjednodušuje, zlepšuje a řeší několik nedostatků prvního přístupu, využívá různá měřicí zařízení s vyšší vzorkovací frekvencí. Zkoumá více statistických metod a nakonec volí jednodušší vícevrstvý model perceptronové neuronové sítě. Výsledkem je méně výpočetně náročné řešení s vyšší úrovní přesnosti. Studie dosáhla úrovně přesnosti křížové validace nad 98 %.450 - Katedra kybernetiky a biomedicínského inženýrstvívyhově