En önskan om en enad internationell uppsättning av redovisningsstandarder har uppkommit på senare tid på grund av globaliseringen. FASB har i ett försök att närma sig IASB regelverk implementerat mer principer i US GAAP medan IASB har implementerat fler regler i IFRS. Trenden pekar mot harmoniseringen för att lyckas med en enad internationell uppsättning redovisningsstandarder. Globaliseringen påverkar inte enbart börsnoterade företag utan även små och medelstora företag (SME). Arbetet för harmonisering av små och medelstora företags (SME) regelverk har dock inte berörts i lika stor grad. Studiens syfte är att undersöka till vilken grad regelverken IFRS for SME, FRS 102 och FRF for SME är princip- eller regelbaserade gentemot varandra samt att utreda till vilken grad dessa är harmoniserade med varandra.För att uppfylla syftet har en kvalitativ studie utförs med hjälp av en innehållsanalys. Kvalitativ data används i uppbyggnad av den teoretiska referensramen med skriftliga källor i form av vetenskapliga artiklar. Empirin består av delar ur lagtexter av regelverken IFRS for SME, FRS 102 och FRF for SME som analyseras med utgångspunkt i den teoretiska referensramen.Det vi kom fram till var att IFRS for SME och FRS 102 är principbaserade regelverk medan FRF for SME är ett regelbaserat regelverk. När de sedan ställdes emot varandra betraktas FRS 102 precis som IFRS for SME som ett principbaserat regelverk men till en större grad. IFRS for SME och FRS 102 bedöms således som harmoniserade. FRF for SME anser vi å andra sidan vara ett regelbaserat regelverk och när det ställs emot IFRS for SME och FRS 102 anses det därmed svagt regelbaserade. Vi kan följaktligen inte betrakta FRS for SME som harmoniserade med varken IFRS for SME eller FRS 102.Vår studie bidrar därmed till litteratur om harmoniseringen av SME företag samt till IFRS for SME, FRS 102 och FRF for SME är huvudsakligen princip- eller regelbaserad. Dessutom görs en reflektion över om den enda aspekten med redovisningen är jämförbarhet då är det optimala läget för harmonisering när regelverken är stark regelbaserade. / AbstractA desire for a united international set of accounting standards has arisen in recent times due to globalization. FASB has in an attempt to approach the IASB regulations implemented more principles in US GAAP, while the IASB has implemented more rules in IFRS. The trend is pointing towards harmonization to succeed in the desire for an united international set of accounting standards. Globalisation does not only affect listed companies but also small and medium-sized enterprises (SMEs). Harmonisation of these small and medium-sized enterprises (SMEs) framework have not been written about to the same degree as the listed companies. The study aims to examine to which extent regulations IFRS for SME, FRS 102 and FRF for SME's are principle or rule-based to one another. The second purpose of the study is to investigate the degree to which these are harmonized with each other.A qualitative study with a content analyse have been conducted to fulfill the purpose of the study. Qualitative data is used in the construction of the theoretical framework in form of written sources such as scientific articles. The empirical data is also of qualitative character and consists of parts of the legal texts of the regulations IFRS for SME, FRS 102 and FRF for SMEs. The empirical data is analyzed on the basis of the theoretical framework.The conclusion was that IFRS for SME and FRS 102 are principle-based frameworks while FRF for SMEs is a rules-based framework. When they were facing each other FRS 102 was considered as a principle-based regulations but to a greater degree then IFRS for SME. IFRS for SME and FRS 102 is consequently considered harmonized. FRF for SMEs on the other hand is a rules-based framework and when it is set against the IFRS for SME and FRS 102 it is considered weak rule-based. We cannot therefore regard FRS for SMEs harmonized with either IFRS for SMEs or FRS 102.Our study contributes to the literature on the harmonization of SME companies, and whether the IFRS for SME, FRS 102 and FRF for SMEs are mainly principle- or rulebased. In addition, a reflection on if the only aspect of the financial report is comparability then optimal framework for harmonization are one that is strong rulebased.

The growing globalization within the business world and of capital market are contributing to a need of further harmonize accounting practices. This is because the differences in the accounting rules created differences in the financial outcomes. In 2005, all members within EU decided to move from a rule-based framework to a principle-based framework. A transition to the international regulations, International Financial Reporting Standards (IFRS), created better conditions for companies and stakeholders. With the aim to minimize the discrepancies in accounting practices, the European standard setting body, IASB aligned with the American standard setting body, FASB in a harmonization project to create a common and harmonized framework. The project started in 2002, and IASB and FASB corporation towards a common goal of an improved comparability of the financial statements by a uniform set of accounting standards. The purpose of this dissertation is to analyze the similarities and differences between IASB:s and FASB:s accounting for intangible assets and their attitude to the developments in the international harmonization process. We have conducted a study to what extent the different standards are harmonized in relation to each other. To fulfill this purpose the study has been performed from an abductive approach with a comparative design where we have classified different quots from each framework. The character of the analysis is qualitative and we chose to study four areas within each framework fair value, useful life, depreciation and impairments. Based on the information from the economic literature and scientific articles we used the dimensional theory to demonstrate how harmonized the standards are in relation to each other. The results show that there has been a harmonization between the IASB:s and FASB:s rules, however, disparity remain in terms of cultural and institutional differences. The analysis shows that IFRS:s are more rule-based than principle-based compared to US GAAP in terms of intangible assets. We think that a standard should include both rules and principles. / Det sker en ständig globalisering inom företagsvärlden, inte minst inom kapitalmarknadsområdet. I denna process har skillnader mellan regler kring olika redovisningssystem inte följt med i samma takt. Skillnader mellan olika redovisningssystem har påvisats skapa skillnader i finansiella utfall. Denna företeelse har i sin tur framkallat ett behov av att skapa ett harmoniserat redovisningssystem. År 2005 beslutade sig därför samtliga medlemsländer inom EU att en övergång från ett regelbaserat regelverk till ett alltmer principbaserat regelverk skulle äga rum. Övergången till det internationella regelverket, International Financial Reporting Standards (IFRS), skulle hjälpa till att skapa de förutsättningar som krävdes för en samstämmighet inom området. Konceptet riktade sig främst mot företag och intressenter som var verksamma på den internationella marknaden. För att praktiskt kunna minimera redovisningsskillnader mellan olika redovisningssystem ingick den internationella normgivaren IASB, tillsammans med den amerikanska normgivaren FASB, i ett konvergeringsprojekt. Syftet var att skapa ett gemensamt och harmoniserat regelverk. Projektet mellan IASB och FASB inleddes år 2002 för att arbeta mot ett gemensamt mål om en förbättrad jämförbarhet mellan de finansiella rapporterna. Syftet med studien är att undersöka och analysera skillnaderna mellan IASB:s och FASB:s hantering av immateriella tillgångar, samt hur de ställer sig till utvecklingen inom den internationella harmoniseringsprocessen. Utifrån detta syfte har vi valt att genomföra en kvalitativ studie då vi anser att denna forskningsform ger oss möjligheten till att besvara våra forskningsfrågor. I vår undersökning har vi studerat i vilken grad de olika standarderna harmoniserar med varandra. För att uppfylla syftet har studien genomförts utifrån en abduktiv ansats, med en komparativ design, där vi har klassificerat utdrag från vardera regelverk. Vi har utgått från en kvalitativ undersökningsansats där fyra områden valts ut och analyserats. Områdena består i redovisning av verkligt värde, nedskrivningar, avskrivningar och nyttjandeperiod. Utifrån ekonomisk litteratur och vetenskapliga artiklar, har vi med hjälp av dimensionsteorin, kunnat visa i vilken mån regelverken är harmoniserade. Resultatet visar att det har skett en harmonisering mellan IASB:s och FASB:s regelverk, dock kvarstår det kulturella och institutionella skillnader. Analysen visar att IFRS är mer regelbaserad än principbaserad i förhållande till US GAAP vad det gäller immateriella tillgångar. Slutsatsen motsäger alltså den generella bilden av att US GAAP är regelbaserad och IFRS är principbaserad. Slutligen går utvecklingen inom harmoniseringen mot att utövaren får användning av sitt professionella omdöme och att regleringarna blir mer klara och flexibla. Enligt vår mening är det mest effektivt att en standard innehåller både regler och principer.

Title: Processing of Czech court decisions Author: Bohdan Maslowski Department: Institute of Formal and Applied Linguistics Supervisor: Mgr. Barbora Vidová Hladká, Ph.D. Abstract: The objective of this thesis is a comparison of various language processing methods of Czech case-law documents. In particular, the tasks of extraction of information about parties (names, roles, addresses, etc.) and document classification by two criteria, subject and result have been solved. Machine learning methods are evaluated and compared to rule-based approach. For the purpose of training and evaluation of classifiers, a corpus of 400 Czech case-law documents has been created and manually annotated. The thesis includes a web application used for demonstration of the results of different approaches and a tool for running and evaluation of testing scenarios. Keywords: natural language processing, information extraction, legislative domain, machine learning, rule-based systems

In recent years many companies have investigated the use of hybrid technology due to the potential of increasing the driveline’s efficiency and thus reducing fuel consumption. Previous studies show that hydraulic hybrid technology can be favourable to use in construction machinery such as wheel loaders, which often operate in repetitive drive cycles and have high transient power demands. Parallel as well as Series hybrid configurations are both found suitable for wheel loader applications as the hybrid configurations can decrease the dependency on the torque converter. This project has investigated a novel hydraulic hybrid concept which utilizes the wheel loaders auxiliary pump as a supplement to enable both Series and Parallel hybrid operation. Impact of accumulator sizes has also been investigated, for which smaller accumulator sizes resembles a hydrostatic transmission. The hybrid concept has been evaluated by developing a wheel loader simulation model and a control system based on a rule-based energy management strategy. Simulation results indicate improved energy efficiency of up to 18.80 % for the Combined hybrid. Moreover, the accumulator sizes prove to have less impact on the energy efficiency. A hybrid system with decreased accumulator sizes shows improved energy efficiency of up to 16.40 %.

Cette thèse porte sur le Web Sémantique des Objets (WSdO), un domaine de recherche à l'interface de l'Internet des Objets (IdO) et du Web Sémantique (WS). L’intégration des approche du WS à l'IdO permettent de traiter l'importante hétérogénéité des ressources, des technologies et des applications de l'IdO, laquelle est une source de problèmes d'interopérabilité freinant le déploiement de systèmes IdO. Un premier verrou scientifique est lié à la consommation en ressource des technologies du WS, là où l'IdO s’appuie sur des objets aux capacités de calcul et de communication limitées. De plus, les réseaux IdO sont déployés à grande échelle, quand la montée en charge est difficile pour les technologies du WS. Cette thèse a pour objectif de traiter ce double défi, et comporte deux contributions. La première porte sur l'identification de critères de qualité pour les ontologies de l'IdO, et l’élaboration de IoT-O, une ontologie modulaire pour l'IdO. IoT-O a été implantée pour enrichir les données d'un bâtiment instrumenté, et pour être moteur de semIoTics, notre application de gestion autonomique. La seconde contribution est EDR (Emergent Distributed Reasoning), une approche générique pour distribuer dynamiquement le raisonnement à base de règles. Les règles sont propagées de proche en proche en s'appuyant sur les descriptions échangées entre noeuds. EDR est évaluée dans deux scénario concrets, s'appuyant sur un serveur et des noeuds contraints pour simuler le déploiement. / This thesis is situated in the Semantic Web of things (SWoT) domain, at the interface between the Internet of Things (IoT) and the Semantic Web (SW). The integration of SW approaches into the IoT aim at tackling the important heterogeneity of resources, technologies and applications in the IoT, which creates interoperability issues impeding the deployment of IoT systems. A first scientific challenge is risen by the resource consumption of the SW technologies, inadequated to the limites computation and communication capabilities of IoT devices. Moreover, IoT networks are deployed at a large scale, when SW technologies have scalability issues. This thesis addresses this double challenge by two contributions. The first one is the identification of quality criteria for IoT ontologies, leading to the proposition of IoT-O, a modular IoT ontology. IoT-O is deployed to enrich data from a smart building, and drive semIoTics, our autonomic computing application. The second contribution is EDR (Emergent Distributed Reasoning), a generic approach to dynamically distributed rule-based reasoning. Rules are propagated peer-to-peer, guided by descriptions exchanged among nodes. EDR is evaluated in two use-cases, using both a server and some constrained nodes to simulate the deployment.

O diagnóstico de eventos que afetam a qualidade da energia elétrica tem se tornado preocupação de magnitude mundial, em especial em dois temas importantes que são: a localização relativa da origem do evento (LROE) e a classificação automática da causa fundamental de eventos (CACFE). O primeiro está relacionado com a identificação da fonte do evento, isto é, a montante ou a jusante do medidor de qualidade de energia (MQE). O segundo pode ser dividido em dois grupos: a classificação das causas internas e das causas externas. As causas internas estão relacionadas a eventos produzidos pela operação do sistema elétrico (energização ou desenergização do sistema, energização de transformador, chaveamento de capacitores dentre outros), e as causas externas estão vinculadas a eventos produzidos por faltas externas ao sistema elétrico (contato com galhos de árvore, animais, descargas atmosféricas, dentre outros). Ambos os temas, LROE e CACFE, são abordados nesta tese de doutorado. Para classificar eventos por causas internas ou externas é necessário antes definir se realmente trata-se ou não de um evento, para o qual é imprescindível conhecer a LROE. Este último necessita de um processo de segmentação das formas de onda de tensão e corrente para funcionar de forma correta. A segmentação identifica segmentos transitórios e não transitórios nas formas de onda e contribui também na extração de características para os diferentes algoritmos de classificação. Neste sentido, neste trabalho de pesquisa é proposta uma metodologia de diagnóstico da qualidade de eventos, focada em LROE e CACFE. Para isto foram desenvolvidos diferentes algoritmos de segmentação, extração de características e classificação, sendo criada uma ferramenta computacional em MatLab® que inclui pré-processamento de sinais de tensão e corrente de um banco de dados real fornecido por uma concessionária do Estado de São Paulo. Além disto, foram propostos novos algoritmos de LROE com resultados satisfatórios quando comparados com outros dois disponíveis na literatura científica. Para as causas internas, dois novos índices são propostos para separar eventos produzidos por faltas e energização de transformadores. Finalmente, são propostos novos algoritmos de extração de características baseados na energia dos coeficientes de decomposição da transformada wavelet bem como o algoritmo à trous modificado. São propostos dois novos vetores de descritores de energia (VDE) baseados no primeiro segmento transitório do evento. Para a classificação destes eventos foi utilizado um algoritmo de indução de regras de decisão (CN2), que gera regras de simples implementação. Todos os métodos de classificação utilizados nesta tese estão baseados em regras, sendo seu desempenho avaliado por meio da matriz de confusão. / Diagnosing events that affect power quality have become a worldwide concern, especially with respect to two important issues related to the relative location of the event origin (RLEO) and automatic cause classification of events (ACCE). The first one is related to the identification of the event source, i.e. either upstream or downstream in relation to the power quality meter (PQM). The second one can be subdivided into two groups, namely the classification of internal causes and of external causes. Internal causes are related to events produced by power system operation (connection or disconnection of feeders, power transformer inrush, capacitor switching, amongst others) and external causes that are related to events produced by external faults to the power system (network contacts to tree branches, animals contact, atmospheric discharges, amongst others). Both topics, RLEO and ACCE, are herein considered. In order to classify events due to internal or external causes, one should first define whether it is an actual event, what demands the RLEO. This makes use of a segmentation process applied to the voltage and current waveforms. The segmentation identifies the transient and stationary segments within the waveforms, contributing also to the feature extraction for different classification algorithms. Based on the aforementioned, this research proposes a methodology to diagnose power quality events, focusing on RLEO and ACCE. Different algorithms of segmentation, feature extraction and classification were then developed by the use of a computational tool implemented in MatLab®, that considers also the preprocessing of voltage and current signals in a real data base which was made available by a distribution company in Sao Paulo State. Besides that, new RLEO algorithms have shown satisfactory results when compared to algorithms published in the scientific literature. As for the internal causes, two new indices were proposed in order to separate events produced by faults or by the connection of power transformers. New algorithms for feature extraction are proposed, which are based on the energy of decomposition coefficients of the wavelet transform as well as the modified à trous algorithm. Two vectors of energy descriptors are proposed, which are based on the first transient segment of the event. The classification of such events was carried out by an induction algorithm of decision rules (CN2), that generates easily implementable rules. All classification methods utilized in this thesis are based on rules and their performances are assessed by the confusion matrix.

The purpose of this research was to apply machine learning techniques to automate rule generation in the construction of Intelligent Tutoring Systems. By using a pair of somewhat intelligent iterative-deepening, depth-first searches, we were able to generate production rules from a set of marked examples and domain background knowledge. Such production rules required independent searches for both the“if" and“then" portion of the rule. This automated rule generation allows generalized rules with a small number of sub-operations to be generated in a reasonable amount of time, and provides non-programmer domain experts with a tool for developing Intelligent Tutoring Systems.

This thesis focuses on the design, statistical operating characteristics and interpretation of early phase oncology clinical trials. Anti-cancer drugs are generally highly toxic and it is imperative to deliver a dose to the patient that is low enough to be safe but high enough to produce a clinically meaningful response. Thus, a study of dose limiting toxicities (DLTs) and a determination of the maximum tolerated dose (MTD) of a drug that can be used in later phase trials is the focus of most Phase I oncology trials. We first comprehensively compare the statistical operating characteristics of various early phase oncology designs, finding that all the designs examined select the MTD more accurately when there is a clear separation between the true DLT rate at the MTD and the rates at the dose levels immediately above and below. Among the rule-based designs studied, we found that the 3+3 design under-doses a large percentage of patients and is not accurate in selecting the MTD for all the cases considered. The 5+5 a design picks the MTD as accurately as the model based designs for the true DLT rates generated using the chosen log-logistic and linear dose-toxicity curves, but requires enrolling a larger number of patients. The model based designs examined, mTPI, TEQR, BOIN, CRM and EWOC designs, perform well on the whole, assign the maximum percentage of patients to the MTD, and pick the MTD fairly accurately. However, the limited sample size of these Phase I oncology trials makes it difficult to accurately predict the MTD. Hence, we next study the effect of sample size and cohort size on the accuracy of dose selection in early phase oncology designs, finding that an adequate sample size is crucial. We then propose some integrated Phase 1/2 oncology designs, namely the 20+20 accelerated titration design and extensions of the mTPI and TEQR designs, that consider both toxicity and efficacy in dose selection, utilizing a larger sample size. We demonstrate that these designs provide an improvement over the existing early phase designs. / 2019-12-01T00:00:00Z

O diagnóstico de eventos que afetam a qualidade da energia elétrica tem se tornado preocupação de magnitude mundial, em especial em dois temas importantes que são: a localização relativa da origem do evento (LROE) e a classificação automática da causa fundamental de eventos (CACFE). O primeiro está relacionado com a identificação da fonte do evento, isto é, a montante ou a jusante do medidor de qualidade de energia (MQE). O segundo pode ser dividido em dois grupos: a classificação das causas internas e das causas externas. As causas internas estão relacionadas a eventos produzidos pela operação do sistema elétrico (energização ou desenergização do sistema, energização de transformador, chaveamento de capacitores dentre outros), e as causas externas estão vinculadas a eventos produzidos por faltas externas ao sistema elétrico (contato com galhos de árvore, animais, descargas atmosféricas, dentre outros). Ambos os temas, LROE e CACFE, são abordados nesta tese de doutorado. Para classificar eventos por causas internas ou externas é necessário antes definir se realmente trata-se ou não de um evento, para o qual é imprescindível conhecer a LROE. Este último necessita de um processo de segmentação das formas de onda de tensão e corrente para funcionar de forma correta. A segmentação identifica segmentos transitórios e não transitórios nas formas de onda e contribui também na extração de características para os diferentes algoritmos de classificação. Neste sentido, neste trabalho de pesquisa é proposta uma metodologia de diagnóstico da qualidade de eventos, focada em LROE e CACFE. Para isto foram desenvolvidos diferentes algoritmos de segmentação, extração de características e classificação, sendo criada uma ferramenta computacional em MatLab® que inclui pré-processamento de sinais de tensão e corrente de um banco de dados real fornecido por uma concessionária do Estado de São Paulo. Além disto, foram propostos novos algoritmos de LROE com resultados satisfatórios quando comparados com outros dois disponíveis na literatura científica. Para as causas internas, dois novos índices são propostos para separar eventos produzidos por faltas e energização de transformadores. Finalmente, são propostos novos algoritmos de extração de características baseados na energia dos coeficientes de decomposição da transformada wavelet bem como o algoritmo à trous modificado. São propostos dois novos vetores de descritores de energia (VDE) baseados no primeiro segmento transitório do evento. Para a classificação destes eventos foi utilizado um algoritmo de indução de regras de decisão (CN2), que gera regras de simples implementação. Todos os métodos de classificação utilizados nesta tese estão baseados em regras, sendo seu desempenho avaliado por meio da matriz de confusão. / Diagnosing events that affect power quality have become a worldwide concern, especially with respect to two important issues related to the relative location of the event origin (RLEO) and automatic cause classification of events (ACCE). The first one is related to the identification of the event source, i.e. either upstream or downstream in relation to the power quality meter (PQM). The second one can be subdivided into two groups, namely the classification of internal causes and of external causes. Internal causes are related to events produced by power system operation (connection or disconnection of feeders, power transformer inrush, capacitor switching, amongst others) and external causes that are related to events produced by external faults to the power system (network contacts to tree branches, animals contact, atmospheric discharges, amongst others). Both topics, RLEO and ACCE, are herein considered. In order to classify events due to internal or external causes, one should first define whether it is an actual event, what demands the RLEO. This makes use of a segmentation process applied to the voltage and current waveforms. The segmentation identifies the transient and stationary segments within the waveforms, contributing also to the feature extraction for different classification algorithms. Based on the aforementioned, this research proposes a methodology to diagnose power quality events, focusing on RLEO and ACCE. Different algorithms of segmentation, feature extraction and classification were then developed by the use of a computational tool implemented in MatLab®, that considers also the preprocessing of voltage and current signals in a real data base which was made available by a distribution company in Sao Paulo State. Besides that, new RLEO algorithms have shown satisfactory results when compared to algorithms published in the scientific literature. As for the internal causes, two new indices were proposed in order to separate events produced by faults or by the connection of power transformers. New algorithms for feature extraction are proposed, which are based on the energy of decomposition coefficients of the wavelet transform as well as the modified à trous algorithm. Two vectors of energy descriptors are proposed, which are based on the first transient segment of the event. The classification of such events was carried out by an induction algorithm of decision rules (CN2), that generates easily implementable rules. All classification methods utilized in this thesis are based on rules and their performances are assessed by the confusion matrix.

<p>A difficult issue in stress management is to use biomedical sensor signal in the diagnosis and treatment of stress. Clinicians often make their diagnosis and decision based on manual inspection of physiological signals such as, ECG, heart rate, finger temperature etc. However, the complexity associated with manual analysis and interpretation of the signals makes it difficult even for experienced clinicians. Today the diagnosis and decision is largely dependent on how experienced the clinician is interpreting the measurements.  A computer-aided decision support system for diagnosis and treatment of stress would enable a more objective and consistent diagnosis and decisions.</p><p>A challenge in the field of medicine is the accuracy of the system, it is essential that the clinician is able to judge the accuracy of the suggested solutions. Case-based reasoning systems for medical applications are increasingly multi-purpose and multi-modal, using a variety of different methods and techniques to meet the challenges of the medical domain. This research work covers the development of an intelligent clinical decision support system for diagnosis, classification and treatment in stress management. The system uses a finger temperature sensor and the variation in the finger temperature is one of the key features in the system. Several artificial intelligence techniques have been investigated to enable a more reliable and efficient diagnosis and treatment of stress such as case-based reasoning, textual information retrieval, rule-based reasoning, and fuzzy logic. Functionalities and the performance of the system have been validated by implementing a research prototype based on close collaboration with an expert in stress. The case base of the implemented system has been initiated with 53 reference cases classified by an experienced clinician. A case study also shows that the system provides results close to a human expert. The experimental results suggest that such a system is valuable both for less experienced clinicians and for experts where the system may function as a second option.</p> / IPOS, PROEK