In this paper, we propose a novel similarity measure for document data processing and apply it to text classification and clustering. For two documents, the proposed measure takes three cases into account: (a) The feature considered appears in both documents, (b) the feature considered appears in only one document, and (c) the feature considered appears in none of the documents. For the first case, we give a lower bound and decrease the similarity according to the difference between the feature values of the two documents. For the second case, we give a fixed value disregarding the magnitude of the feature value. For the last case, we ignore its effectiveness. We apply it to the similarity based single-label classifier k-NN and multi-label classifier ML-KNN, and adopt these properties to measure the similarity between a document and a specific set for document clustering, i.e., k-means like algorithm, to compare the effectiveness with other measures. Experimental results show that our proposed method can work more effectively than others.

L'exploration de corpus à travers des requêtes fait aujourd'hui partie de la routine de nombreux chercheurs adoptant une approche empirique de la langue, mais aussi de non-spécialistes qui utilisent des moteurs de recherche ou des concordanciers dans le cadre de l'apprentissage d'une langue. Si les requêtes ainsi basées sur des mots-clés sont communes, les non-spécialistes semblent encore peu enclins à explorer des constructions syntaxiques. En effet, les requêtes syntaxiques requièrent souvent des connaissances spécifiques comme la maîtrise des expressions régulières, le langage de requête de l'outil utilisé, ou même simplement le jeu d'étiquettes morpho-syntaxiques du corpus étudié.Pour permettre aux apprenants de langue de se concentrer sur l'analyse des données langagières plutôt que sur la formulation de requêtes, nous proposons une méthodologie incluant un analyseur syntaxique et utilisant des mesures de similarité classiques pour comparer des séquences d'étiquettes syntaxiques ainsi obtenues de manière automatique. / Using queries to explore corpora is today part of the routine of not only researchers of various fields with an empirical approach to discourse, but also of non-specialists who use search engines or concordancers for language learning purposes. If keyword-based queries are quite common, non-specialists still seem to be less likely to explore syntactic constructions. Indeed, syntax-based queries usually require the use of regular expressions with grammatical words combined with morphosyntactic tags, which imply that users master both the query language of the tool and the tagset of the annotated corpus. However, non-specialists like language learners might want to focus on the output rather than spend time and efforts on mastering a query language.To address this shortcoming, we propose a methodology including a syntactic parser and using common similarity measures to compare sequences of morphosyntactic tags automatically provided.

[pt] Bases de conhecimento são ferramentas poderosas que fornecem suporte a um amplo espectro de aplicações como, por exemplo, busca exploratória, ranqueamento e recomendação. Bases de conhecimento podem ser vistas como grafos, onde os nós representam entidades e as arestas seus relacionamentos. Atualmente, motores de busca usam bases de conhecimento para melhorar suas recomendações. No entanto, motores de busca são orientados a uma única entidade e enfrentam dificuldades ao tentar explicar porque e como duas entidades estão relacionadas, um problema conhecido como relacionamento entre entidades. Esta tese explora o uso de bases de conhecimento em formato RDF para endereçar o problema de relacionamento entre entidades, em duas direções. Em uma direção, a tese define o conceito de perfis de conectividade para pares de entidades, que são explicações concisas sobre como as entidades se relacionam. A tese introduz uma estratégia para gerar um perfil de conectividade entre um par de entidades, que combina anotações semânticas e métricas de similaridade para resumir um conjunto de caminhos entre as duas entidades. Em seguida, introduz a ferramenta DBpedia profiler, que implementa a estratégia proposta, e cuja efetividade foi medida através de experimentos com usuários. Em outra direção, considerando os desafios para explorar grandes bases de conhecimento online, a tese apresenta uma estratégia genérica de busca baseada na heurística backward, a qual prioriza alguns caminhos sobre outros. A estratégia combina medidas de similaridade e de ranqueamento, criando diferentes alternativas. Por último, a tese avalia e compara as diferentes alternativas em dois domínios, música e filmes, adotando como ground truth rankings especializados de caminhos especialmente desenvolvidos para os experimentos. / [en] Knowledge bases are a powerful tool for supporting a large spectrum of applications such as exploratory search, ranking, and recommendation. Knowledge bases can be viewed as graphs whose nodes represent entities and whose edges represent relationships. Currently, search engines take advantage of knowledge bases to improve their recommendations. However, search engines are single entity-centric and face difficulties when trying to explain why and how two entities are related, a problem known as entity relatedness. This thesis explores the use of knowledge bases in RDF format to address the entity relatedness problem, in two directions. In one direction, it defines the concept of connectivity profiles for entity pairs, which are concise explanations about how the entities are related. The thesis introduces a strategy to generate a connectivity profile for an entity pair that combines semantic annotations and similarity metrics to summarize a set of relationship paths between the given entity pair. The thesis then describes the DBpedia profiler tool, which implements the strategy for DBpedia, and whose effectiveness was evaluated through user experiments. In another direction, motivated by the challenges of exploring large online knowledge bases, the thesis introduces a generic search strategy, based on the backward search heuristic, to prioritize certain paths over others. The strategy combines similarity and ranking measures to create different alternatives. Finally, the thesis evaluates and compares the different alternatives in two domains, music and movies, based on specialized path rankings taken as ground truth.

This study examines the efficacy of Sentinel-1 Radial Velocity (RVL) imagery at determining the position of ocean current front features, using the Gulf Stream (GS) and Agulhas Current (AC) as case studies. Fronts derived from RVL imagery are compared to fronts derived from Sea Surface Temperature (SST) imagery, specifically Multi-scale Ultra-high Resolution Sea Surface Temperature Analysis (MURSST) data. In the case of the GS, front locations from the Naval Oceanographic Office (NAVOCEANO) were also used for comparison. Only the northern walls of ocean current features are considered in this study, which is broken into three main steps: Preprocessing, front extraction, and front comparison. First, RVL imagery is selected from Sentinel-1 ocean products, preprocessed to remove antenna mispointing artifacts, and all products from the same orbit are combined into a single swath. Second, front features are extracted from both the RVL and MURSST imagery using a ridge detection algorithm, the main ocean current is chosen from all ridge features using a ranking algorithm, and the northern wall of this current is extracted. Third, the RVL, SST, and in the case of the GS, NAVOCEANO GS locations, features are compared using a symmetric Hausdorff Distance (HD) measure, and Mean Hausdorff Distance (MHD). In some cases, the automatic front extraction failed by either misclassifying an eddy or similar ocean feature as the ocean current in either the RVL or SST image or failed to extract the entire length of the front visible within the image. All the SST and RVL fronts were classified manually to determine the success rate of the automatic front extraction and to exclude failed front extractions from the analysis, as they are not accurate representations of the SST and RVL data’s ability to detect fronts. In special cases, the RVL image itself does not detect the entire ocean current, such that there are noticeable gaps in the ocean current. Similarly, in special cases the MURSST does not detect the entire ocean current. The automatic front extraction succeeded 65% of the time, including the special cases. The results demonstrated that RVL products were effective at determining the location of ocean fronts where the angle of the front's normal vector is within approximately 40° of the sensor’s azimuthal heading. A mean HD of 31.9 km and a mean MHD of 13.2 km was calculated for all front pairs over all study areas. The RVL fronts appeared consistently to the north of the SST fronts, with an average offset of 25.4 km between the centroids of the SST and RVL fronts. Positive correlations were noted between cloud coverage and MURSST error in both study regions. Several RVL images detected ocean currents in regions of high MURSST error where the MURSST did not detect the ocean currents, suggesting that RVL may provide more accuracy than SST-based products in clouded regions where there is no auxiliary data.

[pt] O problema do relacionamento de entidades refere-se à questão de explorar uma base de conhecimento, representada como um grafo RDF, para descobrir e entender como duas entidades estão conectadas. Esta questão pode ser resolvida implementando-se uma estratégia de busca de caminhos que combina uma medida de similaridade de entidades, um limite para o grau das entidades, e um limite de expansão para reduzir o espaço de busca de caminhos, e uma medida de ranqueamento de caminhos para ordenar os caminhos relevantes entre um determinado par de entidades no grafo RDF. Esta tese inicialmente apresenta um framework, chamado CoEPinKB, juntamente com uma implementação, para experimentar estratégias de busca de caminhos. O framework apresenta como pontos de flexibilização a medida de similaridade entre entidades, o limite máximo do grau das entidades, o limite de expansão, a medida de classificação de caminhos, e a base de conhecimento. Em seguida, a tese apresenta uma avaliação de desempenho de nove estratégias de busca de caminhos usando um benchmark envolvendo dois domínios de entretenimento sobre o OpenLink Virtuoso SPARQL protocol endpoint da DBpedia. Por fim, a tese apresenta o DCoEPinKB, uma versão distribuída do framework baseado em Apache Spark, que suporta a avaliação empírica de estratégias de busca de caminhos, e apresenta uma avaliação de seis estratégias de busca de caminhos em dois domínios de entretenimento sobre dados reais coletados da DBpedia. Os resultados fornecem intuições sobre o desempenho das estratégias de busca de caminhos e sugerem que a implementação do framework, instanciado com o par de medidas de melhor desempenho, pode ser usado, por exemplo, para expandir os resultados dos motores de busca em bases de conhecimento para incluir entidades relacionadas. / [en] The entity relatedness problem refers to the question of exploring a knowledge base, represented as an RDF graph, to discover and understand how two entities are connected. This question can be addressed by implementing a path search strategy that combines an entity similarity measure with an entity degree limit and an expansion limit to reduce the path search space and a path ranking measure to order the relevant paths between a given pair of entities in the RDF graph. This thesis first introduces a framework, called CoEPinKB, together with an implementation, to experiment with path search strategies. The framework features as hot spots the entity similarity measure, the entity degree limit, the expansion limit, the path ranking measure, and the knowledge base. The thesis moves on to present a performance evaluation of nine path search strategies using a benchmark from two entertainment domains over the OpenLink Virtuoso SPARQL protocol endpoint of the DBpedia. The thesis then introduces DCoEPinKB, a distributed version of the framework based on Apache Spark, that supports the empirical evaluation of path search strategies, and presents an evaluation of six path search strategies over two entertainment domains over real-data collected from DBpedia. The results provide insights about the performance of the path search strategies and suggest that the framework implementation, instantiated with the best performing pair of measures, can be used, for example, to expand the results of search engines over knowledge bases to include related entities.

No description available.

Cette thèse traite de la reconnaissance d'entités dans les documents océrisés guidée par une base de données. Une entité peut être, par exemple, une entreprise décrite par son nom, son adresse, son numéro de téléphone, son numéro TVA, etc. ou des méta-données d'un article scientifique tels que son titre, ses auteurs et leurs affiliations, le nom de son journal, etc. Disposant d'un ensemble d'entités structurées sous forme d'enregistrements dans une base de données et d'un document contenant une ou plusieurs de ces entités, nous cherchons à identifier les entités contenues dans le document en utilisant la base de données. Ce travail est motivé par une application industrielle qui vise l'automatisation du traitement des images de documents administratifs arrivant en flux continu. Nous avons abordé ce problème comme un problème de rapprochement entre le contenu du document et celui de la base de données. Les difficultés de cette tâche sont dues à la variabilité de la représentation d'attributs d'entités dans la base et le document et à la présence d'attributs similaires dans des entités différentes. À cela s'ajoutent les redondances d'enregistrements et les erreurs de saisie dans la base de données et l'altération de la structure et du contenu du document, causée par l'OCR. Devant ces problèmes, nous avons opté pour une démarche en deux étapes : la résolution d'entités et la reconnaissance d'entités. La première étape consiste à coupler les enregistrements se référant à une même entité et à les synthétiser dans un modèle entité. Pour ce faire, nous avons proposé une approche supervisée basée sur la combinaison de plusieurs mesures de similarité entre attributs. Ces mesures permettent de tolérer quelques erreurs sur les caractères et de tenir compte des permutations entre termes. La deuxième étape vise à rapprocher les entités mentionnées dans un document avec le modèle entité obtenu. Nous avons procédé par deux manières différentes, l'une utilise le rapprochement par le contenu et l'autre intègre le rapprochement par la structure. Pour le rapprochement par le contenu, nous avons proposé deux méthodes : M-EROCS et ERBL. M-EROCS, une amélioration/adaptation d'une méthode de l'état de l'art, consiste à faire correspondre les blocs de l'OCR avec le modèle entité en se basant sur un score qui tolère les erreurs d'OCR et les variabilités d'attributs. ERBL consiste à étiqueter le document par les attributs d'entités et à regrouper ces labels en entités. Pour le rapprochement par les structures, il s'agit d'exploiter les relations structurelles entre les labels d'une entité pour corriger les erreurs d'étiquetage. La méthode proposée, nommée G-ELSE, consiste à utiliser le rapprochement inexact de graphes attribués modélisant des structures locales, avec un modèle structurel appris pour cet objectif. Cette thèse étant effectuée en collaboration avec la société ITESOFT-Yooz, nous avons expérimenté toutes les étapes proposées sur deux corpus administratifs et un troisième corpus extrait du Web / This thesis focuses on entity recognition in documents recognized by OCR, driven by a database. An entity is a homogeneous group of attributes such as an enterprise in a business form described by the name, the address, the contact numbers, etc. or meta-data of a scientific paper representing the title, the authors and their affiliation, etc. Given a database which describes entities by its records and a document which contains one or more entities from this database, we are looking to identify entities in the document using the database. This work is motivated by an industrial application which aims to automate the image document processing, arriving in a continuous stream. We addressed this problem as a matching issue between the document and the database contents. The difficulties of this task are due to the variability of the entity attributes representation in the database and in the document and to the presence of similar attributes in different entities. Added to this are the record redundancy and typing errors in the database, and the alteration of the structure and the content of the document, caused by OCR. To deal with these problems, we opted for a two-step approach: entity resolution and entity recognition. The first step is to link the records referring to the same entity and to synthesize them in an entity model. For this purpose, we proposed a supervised approach based on a combination of several similarity measures between attributes. These measures tolerate character mistakes and take into account the word permutation. The second step aims to match the entities mentioned in documents with the resulting entity model. We proceeded by two different ways, one uses the content matching and the other integrates the structure matching. For the content matching, we proposed two methods: M-EROCS and ERBL. M-EROCS, an improvement / adaptation of a state of the art method, is to match OCR blocks with the entity model based on a score that tolerates the OCR errors and the attribute variability. ERBL is to label the document with the entity attributes and to group these labels into entities. The structure matching is to exploit the structural relationships between the entity labels to correct the mislabeling. The proposed method, called G-ELSE, is based on local structure graph matching with a structural model which is learned for this purpose. This thesis being carried out in collaboration with the ITESOFT-Yooz society, we have experimented all the proposed steps on two administrative corpuses and a third one extracted from the web

Les données manquantes constituent un challenge commun en reconnaissance de forme et traitement de signal. Une grande partie des techniques actuelles de ces domaines ne gère pas l'absence de données et devient inutilisable face à des jeux incomplets. L'absence de données conduit aussi à une perte d'information, des difficultés à interpréter correctement le reste des données présentes et des résultats biaisés notamment avec de larges sous-séquences absentes. Ainsi, ce travail de thèse se focalise sur la complétion de larges séquences manquantes dans les séries monovariées puis multivariées peu ou faiblement corrélées. Un premier axe de travail a été une recherche d'une requête similaire à la fenêtre englobant (avant/après) le trou. Cette approche est basée sur une comparaison de signaux à partir d'un algorithme d'extraction de caractéristiques géométriques (formes) et d'une mesure d'appariement élastique (DTW - Dynamic Time Warping). Un package R CRAN a été développé, DTWBI pour la complétion de série monovariée et DTWUMI pour des séries multidimensionnelles dont les signaux sont non ou faiblement corrélés. Ces deux approches ont été comparées aux approches classiques et récentes de la littérature et ont montré leur faculté de respecter la forme et la dynamique du signal. Concernant les signaux peu ou pas corrélés, un package DTWUMI a aussi été développé. Le second axe a été de construire une similarité floue capable de prender en compte les incertitudes de formes et d'amplitude du signal. Le système FSMUMI proposé est basé sur une combinaison floue de similarités classiques et un ensemble de règles floues. Ces approches ont été appliquées à des données marines et météorologiques dans plusieurs contextes : classification supervisée de cytogrammes phytoplanctoniques, segmentation non supervisée en états environnementaux d'un jeu de 19 capteurs issus d'une station marine MAREL CARNOT en France et la prédiction météorologique de données collectées au Vietnam. / Missing data are a prevalent problem in many domains of pattern recognition and signal processing. Most of the existing techniques in the literature suffer from one major drawback, which is their inability to process incomplete datasets. Missing data produce a loss of information and thus yield inaccurate data interpretation, biased results or unreliable analysis, especially for large missing sub-sequence(s). So, this thesis focuses on dealing with large consecutive missing values in univariate and low/un-correlated multivariate time series. We begin by investigating an imputation method to overcome these issues in univariate time series. This approach is based on the combination of shape-feature extraction algorithm and Dynamic Time Warping method. A new R-package, namely DTWBI, is then developed. In the following work, the DTWBI approach is extended to complete large successive missing data in low/un-correlated multivariate time series (called DTWUMI) and a DTWUMI R-package is also established. The key of these two proposed methods is that using the elastic matching to retrieving similar values in the series before and/or after the missing values. This optimizes as much as possible the dynamics and shape of knowledge data, and while applying the shape-feature extraction algorithm allows to reduce the computing time. Successively, we introduce a new method for filling large successive missing values in low/un-correlated multivariate time series, namely FSMUMI, which enables to manage a high level of uncertainty. In this way, we propose to use a novel fuzzy grades of basic similarity measures and fuzzy logic rules. Finally, we employ the DTWBI to (i) complete the MAREL Carnot dataset and then we perform a detection of rare/extreme events in this database (ii) forecast various meteorological univariate time series collected in Vietnam

Le traitement d’images est un axe de recherche très actif depuis des années. L’interprétation des images constitue une de ses branches les plus importantes de par ses applications socio-économiques et scientifiques. Cependant cette interprétation, comme la plupart des processus de traitements d’images, nécessite une phase de segmentation pour délimiter les régions à analyser. En fait l’interprétation est un traitement qui permet de donner un sens aux régions détectées par la phase de segmentation. Ainsi, la phase d’interprétation ne pourra analyser que les régions détectées lors de la segmentation. Bien que l’objectif de l’interprétation automatique soit d’avoir le même résultat qu’une interprétation humaine, la logique des techniques classiques de ce domaine ne marie pas celle de l’interprétation humaine. La majorité des approches classiques d’interprétation d’images séparent la phase de segmentation et celle de l’interprétation. Les images sont d’abord segmentées puis les régions détectées sont interprétées. En plus, au niveau de la segmentation les techniques classiques parcourent les images de manière séquentielle, dans l’ordre de stockage des pixels. Ce parcours ne reflète pas nécessairement le parcours de l’expert humain lors de son exploration de l’image. En effet ce dernier commence le plus souvent par balayer l’image à la recherche d’éventuelles zones d’intérêts. Dans le cas échéant, il analyse les zones potentielles sous trois niveaux de vue pour essayer de reconnaitre de quel objet s’agit-il. Premièrement, il analyse la zone en se basant sur ses caractéristiques physiques. Ensuite il considère les zones avoisinantes de celle-ci et enfin il zoome sur toute l’image afin d’avoir une vue complète tout en considérant les informations locales à la zone et celles de ses voisines. Pendant son exploration, l’expert, en plus des informations directement obtenues sur les caractéristiques physiques de l’image, fait appel à plusieurs sources d’informations qu’il fusionne pour interpréter l’image. Ces sources peuvent inclure les connaissent acquises grâce à son expérience professionnelle, les contraintes existantes entre les objets de ce type d’images, etc. L’idée de l’approche présentée ici est que simuler l’activité visuelle de l’expert permettrait une meilleure compatibilité entre les résultats de l’interprétation et ceux de l’expert. Ainsi nous retenons de cette analyse trois aspects importants du processus d’interprétation d’image que nous allons modéliser dans l’approche proposée dans ce travail : 1. Le processus de segmentation n’est pas nécessairement séquentiel comme la plus part des techniques de segmentations qu’on rencontre, mais plutôt une suite de décisions pouvant remettre en cause leurs prédécesseurs. L’essentiel étant à la fin d’avoir la meilleure classification des régions. L’interprétation ne doit pas être limitée par la segmentation. 2. Le processus de caractérisation d’une zone d’intérêt n’est pas strictement monotone i.e. que l’expert peut aller d’une vue centrée sur la zone à vue plus large incluant ses voisines pour ensuite retourner vers la vue contenant uniquement la zone et vice-versa. 3. Lors de la décision plusieurs sources d’informations sont sollicitées et fusionnées pour une meilleure certitude. La modélisation proposée de ces trois niveaux met particulièrement l’accent sur les connaissances utilisées et le raisonnement qui mène à la segmentation des images. / Image processing has been a very active area of research for years. The interpretation of images is one of its most important branches because of its socio-economic and scientific applications. However, the interpretation, like most image processing processes, requires a segmentation phase to delimit the regions to be analyzed. In fact, interpretation is a process that gives meaning to the regions detected by the segmentation phase. Thus, the interpretation phase can only analyze the regions detected during the segmentation. Although the ultimate objective of automatic interpretation is to produce the same result as a human, the logic of classical techniques in this field does not marry that of human interpretation. Most conventional approaches to this task separate the segmentation phase from the interpretation phase. The images are first segmented and then the detected regions are interpreted. In addition, conventional techniques of segmentation scan images sequentially, in the order of pixels appearance. This way does not necessarily reflect the way of the expert during the image exploration. Indeed, a human usually starts by scanning the image for possible region of interest. When he finds a potential area, he analyzes it under three view points trying to recognize what object it is. First, he analyzes the area based on its physical characteristics. Then he considers the region's surrounding areas and finally he zooms in on the whole image in order to have a wider view while considering the information local to the region and those of its neighbors. In addition to information directly gathered from the physical characteristics of the image, the expert uses several sources of information that he merges to interpret the image. These sources include knowledge acquired through professional experience, existing constraints between objects from the images, and so on.The idea of the proposed approach, in this manuscript, is that simulating the visual activity of the expert would allow a better compatibility between the results of the interpretation and those ofthe expert. We retain from the analysis of the expert's behavior three important aspects of the image interpretation process that we will model in this work: 1. Unlike what most of the segmentation techniques suggest, the segmentation process is not necessarily sequential, but rather a series of decisions that each one may question the results of its predecessors. The main objective is to produce the best possible regions classification. 2. The process of characterizing an area of interest is not a one way process i.e. the expert can go from a local view restricted to the region of interest to a wider view of the area, including its neighbors and vice versa. 3. Several information sources are gathered and merged for a better certainty, during the decision of region characterisation. The proposed model of these three levels places particular emphasis on the knowledge used and the reasoning behind image segmentation.

This work makes significant contributions in the field of sufficient schedulability tests for rate-monotonic scheduling (RMS) and their application to partitioned RMS. Goal is the maximization of possible utilization in worst or average case under a given number of processors. This scenario is more realistic than the dual case of minimizing the number of necessary processors for a given task set since the hardware is normally fixed. Sufficient schedulability tests are useful for quick estimates of task set schedulability in automatic system-synthesis tools and in online scheduling where exact schedulability tests are too slow. Especially, the approach of Accelerated Simply Periodic Task Sets (ASPTSs) and the concept of circular period similarity are cornerstones of improvements in the success ratio of such schedulability tests. To the best of the author's knowledge, this is the first application of circular statistics in real-time scheduling. Finally, the thesis discusses the use of sharp total utilization thresholds for partitioned EDF. A constant-time admission control is enabled with a controlled residual risk. / Diese Arbeit liefert entscheidende Beiträge im Bereich der hinreichenden Planbarkeitstests für ratenmonotones Scheduling (RMS) und deren Anwendung auf partitioniertes RMS. Ziel ist die Maximierung der möglichen Last im Worst Case und im Average Case bei einer gegebenen Zahl von Prozessoren. Dieses Szenario ist realistischer als der duale Fall der Minimierung der Anzahl der notwendigen Prozessoren für eine gegebene Taskmenge, da die Hardware normalerweise fixiert ist. Hinreichende Planbarkeitstests sind für schnelle Schätzungen der Planbarkeit von Taskmengen in automatischen Werkzeugen zur Systemsynthese und im Online-Scheduling sinnvoll, wo exakte Einplanungstests zu langsam sind. Insbesondere der Ansatz der beschleunigten einfach-periodischen Taskmengen und das Konzept der zirkulären Periodenähnlichkeit sind Eckpfeiler für Verbesserungen in der Erfolgsrate solcher Einplanungstests. Nach bestem Wissen ist das die erste Anwendung zirkulärer Statistik im Echtzeit-Scheduling. Schließlich diskutiert die Arbeit plötzliche Phasenübergänge der Gesamtlast für partitioniertes EDF. Eine Zugangskontrolle konstanter Zeitkomplexität mit einem kontrollierten Restrisiko wird ermöglicht.