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[pt] A Extração de Informação é uma tarefa importante no domínio jurídico. Embora a presença de dados estruturados seja escassa, dados não estruturados na forma de documentos jurídicos, como sentenças, estão amplamente disponíveis. Se processados adequadamente, tais documentos podem fornecer informações valiosas sobre processos judiciais anteriores, permitindo uma melhor avaliação por profissionais do direito e apoiando aplicativos baseados em dados. Este estudo aborda a Extração de Informação no domínio jurídico, extraindo valor de sentenças relacionados a reclamações de consumidores. Mais especificamente, a extração de cláusulas categóricas é abordada através de classificação, onde seis modelos baseados em diferentes estruturas são analisados. Complementarmente, a extração de valores monetários relacionados a indenizações por danos morais é abordada por um modelo de Reconhecimento de Entidade Nomeada. Para avaliação, um conjunto de dados foi criado, contendo 964 sentenças anotados manualmente (escritas em português) emitidas por juízes de primeira instância. Os resultados mostram uma média de aproximadamente 97 por cento de acurácia na extração de cláusulas categóricas, e 98,9 por cento na aplicação de NER para a extração de indenizações por danos morais. / [en] Information Extraction is an important task in the legal domain. While the presence of structured and machine-processable data is scarce, unstructured data in the form of legal documents, such as legal opinions, is largely available. If properly processed, such documents can provide valuable information with regards to past lawsuits, allowing better assessment by legal professionals and supporting data-driven applications. This study addresses Information Extraction in the legal domain by extracting value from legal opinions related to consumer complaints. More specifically, the extraction of categorical provisions is addressed by classification, where six models based on different frameworks are analyzed. Moreover, the extraction of monetary values related to moral damage compensations is addressed by a Named Entity Recognition (NER) model. For evaluation, a dataset was constructed, containing 964 manually annotated legal opinions (written in Brazilian Portuguese) enacted by lower court judges. The results show an average of approximately 97 percent of accuracy when extracting categorical provisions, and 98.9 percent when applying NER for the extraction of moral damage compensations.

<p dir="ltr">In the ever-changing landscape of game development, the choice of game engine plays a critical role in deciding the efficiency and performance of a game. This research paper presents a comparative analysis of the performance benchmarks of large general purpose game engines, specifically Unreal Engine 5, Unity, and Godot, versus small genre-specific engines in the context of a simple 2D projectile dodging game. The study focuses on two-dimensional games, which are particularly popular with small studios and indie developers. All three general purpose engines evaluated claim to support building both 2D and 3D applications, however since 2D game logic tends to be smaller scoped and more compact such games are impacted greater by any overhead and bloat the engine introduces, which this research paper intends to evaluate. A series of controlled experiments are conducted to assess each engine's performance in processor utilization, power consumption, memory usage and storage space requirements.</p>

As part of our work on the NSF funded Integrated Digital Event Archiving and Library (IDEAL) project and the Global Event and Trend Archive Research (GETAR) project, we collected over 1.4 billion tweets using over 1,000 keywords, key phrases, mentions, or hashtags, starting from 2009. Since many tweets talk about events (with useful location information), such as natural disasters, emergencies, and accidents, it is important to geo-locate those tweets whenever possible. Due to possible location ambiguity, finding a tweet's location often is challenging. Many distinct places have the same geoname, e.g., "Greenville" matches 50 different locations in the U.S.A. Frequently, in tweets, explicit location information, like geonames mentioned, is insufficient, because tweets are often brief and incomplete. They have a small fraction of the full location information of an event due to the 140 character limitation. Location indicative words (LIWs) may include latent location information, for example, "Water main break near White House" does not have any geonames but it is related to a location "1600 Pennsylvania Ave NW, Washington, DC 20500 USA" indicated by the key phrase 'White House'. To disambiguate tweet locations, we first extracted geospatial named entities (geonames) and predicted implicit state (e.g., Virginia or California) information from entities using machine learning algorithms including Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF). Implicit state information helps reduce ambiguity. We also studied how location information of events is expressed in tweets and how latent location indicative information can help to geo-locate tweets. We then used a machine learning (ML) approach to predict the implicit state using geonames and LIWs. We conducted experiments with tweets (e.g., about potholes), and found significant improvement in disambiguating tweet locations using a ML algorithm along with the Stanford NER. Adding state information predicted by our classifiers increased the possibility to find the state-level geo-location unambiguously by up to 80%. We also studied over 6 million tweets (3 mid-size and 2 big-size collections about water main breaks, sinkholes, potholes, car crashes, and car accidents), covering 17 months. We found that up to 91.1% of tweets have at least one type of location information (geo-coordinates or geonames), or LIWs. We also demonstrated that in most cases adding LIWs helps geo-locate tweets with less ambiguity using a geo-coding API. Finally, we conducted additional experiments with the five different tweet collections, and found significant improvement in disambiguating tweet locations using a ML approach with geonames and all LIWs that are present in tweet texts as features. / Ph. D.

The main and fundamental goal of this work is the taxation of the various types of the companies. Of course, the height of taxation is one of the general questions. A lot of people are also interested in the way of guarantee. In the Czech law there are these types of companies: limited company, join stock company, special limited partnership and co partnership. In every company the economic result is to tax another system. The economic result is the difference between revenues and costs. But it cannot be considered a tax base. The economic result is increased by non-taxable costs. And this is called the tax base. In the Czech law there are two types of taxation. The first one is the legal entity income tax and its rate is 21 % and the other one is the natural person income and its rate is 15 % for both (in 2008).

Master`s thesis named „Evaluation of Building Company“ is focused on the explanation of the concept of value and his various forms and use, the definition of procedure of evaluation of company, some parts of the procedure are broken down, but the main part of this thesis is a description of used methods of evaluation of company. In the practical part particular company is evaluated by selected methods based on statements of accounts and other obtained information.

This master's thesis describes Semantic Enrichment Component (SEC), that searches entities (e.g., persons or places) in the input text document and returns information about them. The goals of this component are to create a single interface for named entity recognition tools, to enable parallel document processing, to save memory while using the knowledge base, and to speed up access to its content. To achieve these goals, the output of the named entity recognition tools in the text was specified, the tool for storing the preprocessed knowledge base into the shared memory was implemented, and the client-server scheme was used to create the component.

Master´s thesis deals with possibility of choice different legal forms of business in the economic environment of Czech republic and choice of the enterprise which is the most suitable from the sight of income tax. I deal with choice of an appropriate type of partnership and comparison of different legal forms, which are based on the analysis of individual types of partnership by important criteria. Especially I focused on the criterion of the tax burden. Besides of the criterion of the tax burden it´s necessary also mention and judge the proper place - seat of an individual/a legal entity which is connected with taxation to the individual country. And take possibility of international tax planning into consideration. The aim of the thesis is to find and choose the most appropriate legal form of the enterprise to the potential entrepreneur.

現今各國之稅攻，類皆以推行法人所得稅為要務，期能建立直接稅為主幹的稅制。法 人所得稅的發展，其歷史至今不過六、七十年，但在各國歲入上已占有重要的地位。 因之，對法人所得稅的諸項問題加以研討，似有其必要。 法人所得稅與個人所得稅之並行，各國不乏其例。法人所得在課征法人所得稅之後， 分配於其構成分子時，尚須課征個人所得稅，遂使同一所得被課雙重所得稅，此種情 形是否為重複課稅，不無問題。若其為重複課稅，又應如何補救﹖其補救方法雖甚繁 多，但在實行時則應視經濟環境與需要，以及稅務行政效率如何作為選擇之依據。 對於法人所得稅的性質，一般均認為是直接稅。直接稅是不能轉嫁其稅負於他人的。 然而法人所得稅是否不能轉嫁，則頗有疑問。傳統的理論認為絕對不會轉嫁，但新的 理論及企業界的統計，則認為法人所得稅在相當期間將會轉嫁，祇其轉嫁的程度如何 不易測定而已。 法人所得稅係屬所得稅之一種，是否可以與個人所得稅同樣採用高度的累進稅率，不 無研究餘地。就平均社會財富的觀點來看，法人所得稅採用累進稅率，對平均財富確 具有相當的功效。因為根據固德氏（Richard Goode ）的統計，法人所得確是造成財富集中的一項重要原因，尤其在經濟發達的國家更是 如此。但就鼓勵儲蓄，增加投資的觀點來說，因為累進稅足以妨害儲蓄、減低投資的 誘因，對於經濟發展發生不利的影響，所以法人所稅之採用累進稅率很不適宜。另就 各國法人所得稅稅的演變來看，法人所得稅的稅率亦有趨於比例稅的傾向。此乃經濟 政策重於社會政策的應有措施。 本稿在研究以上三個理論問題以後，再提出兩個技術性的問題。一為固定資產的折舊 問題；一為存貨估價的問題。前者是固定資產估價的主要問題，後者是流動資產評價 的重要項目。折舊的高低、存貨的多寡，對法人所得稅都有直接性的影響。本稿對折 舊及存貨皂的計算，就各種可能採用的方法加以討論，以明其對法人所得稅究有如何 的影響。