Aim: The overall aim of the thesis was to explore and describe the concepts of guilt and shame and gain a greater understanding of the next-of-kin’s experiences of guilt and shame in end-of-life care. Methods: Study I was a qualitative secondary analysis of 47 interviews with next-of-kin searching for experiences of guilt and shame. In study II a semantic concept analysis of the two concepts guilt and shame was performed. In studies III and IV a hermeneutic approach inspired by Gadamer was used to analyze next-of-kin’s experiences of guilt (Study III), and shame (Study IV) in end-of-life care. Main findings: The concept of guilt focus on behaviour and the concept of shame on the influence on the self.  The situation of being next-of-kin in end-of-life care involves a commitment to make the remaining time for the loved one as good as possible. When, for some reason, the commitment cannot be accomplished there is a risk that the next-of-kin experience guilt such as not having done enough, not having been together during important events, not having talked enough to each other, or not having done the right things. Aspects such as not having fulfilled a commitment, omission, and being the cause of can be present in these experiences. The guilt experience has a focus on what the next-of-kin has, or has not done. The experiences of shame are also linked to a perception that the remaining time for the loved one should be as good as possible. Shame can occur when the next-of-kin is involved and actually causes harm to the loved one as well as in situations that are beyond their control. Shame that the next-of-kin experience can also emanate from being put in situations by other people. Feelings of inferiority and powerlessness, second order shame, and family conflicts that are brought into the open are experiences of shame found in the studies as well as ignominy, humiliation, and disgrace. The shame experience has a focus on the next-of-kin’s self. Conclusion: The situation of being next-of-kin in end-of-life care is complex and demanding, something that health professionals should be aware of. Acknowledgement of experiences of guilt and shame can help the next-of-kin in their adaptation to the end-of-life situation as a whole and maybe also give useful tools to support next-of-kin during bereavement.

Made available in DSpace on 2016-06-02T20:13:10Z (GMT). No. of bitstreams: 1 2561.pdf: 1772597 bytes, checksum: b436e70ace58d964350583051111b8b2 (MD5) Previous issue date: 2009-08-24 / Financiadora de Estudos e Projetos / The objective of this dissertation is analyze the problem of truth as presented by Alfred Tarski in his essay The Semantic Conception of Truth . Other theories of truth are considered, explained and classified in the first chapter. The second chapter attempts provide a general characterization of correspondence theories of truth. Tarski s essay is discussed in the third, and, finally, in the fourth chapter, the semantic and correspondence theories are compared, and the philosophical importance of the former is evaluated. / Esta dissertação tem por finalidade analisar o problema da verdade no trabalho apresentado por Alfred Tarski, chamado de Concepção Semântica da Verdade . Mas esta discussão não será apresentada isolada das pesquisas sobre as diferentes concepções da verdade, mas inserida em um contexto mais amplo das teorias da verdade. Assim, no primeiro capítulo, serão abordadas as diversas teorias e suas classificações. O segundo capítulo descreve as características principais de uma teoria da verdade-como-correspondência, visando à possível comparação com a concepção tarskiana. O terceiro capítulo discute filosoficamente a Concepção Semântica da Verdade apresentada por Tarski. E, finalmente, o quarto capítulo compara a concepção semântica da verdade com a concepção da verdadecomo- correspondência e busca descrever seu valor filosófico.

Abstract Growth in storage capacity has led to large digital video repositories and complicated the discovery of specific information without the laborious manual annotation of data. The research focuses on creating a retrieval system that is ultimately independent of manual work. To retrieve relevant content, the semantic gap between the searcher's information need and the content data has to be overcome using content-based technology. Semantic gap constitutes of two distinct elements: the ambiguity of the true information need and the equivocalness of digital video data. The research problem of this thesis is: what computational content-based models for retrieval increase the effectiveness of the semantic retrieval of digital video? The hypothesis is that semantic search performance can be improved using pattern recognition, data abstraction and clustering techniques jointly with human interaction through manually created queries and visual browsing. The results of this thesis are composed of: an evaluation of two perceptually oriented colour spaces with details on the applicability of the HSV and CIE Lab spaces for low-level feature extraction; the development and evaluation of low-level visual features in example-based retrieval for image and video databases; the development and evaluation of a generic model for simple and efficient concept detection from video sequences with good detection performance on large video corpuses; the development of combination techniques for multi-modal visual, concept and lexical retrieval; the development of a cluster-temporal browsing model as a data navigation tool and its evaluation in several large and heterogeneous collections containing an assortment of video from educational and historical recordings to contemporary broadcast news, commercials and a multilingual television broadcast. The methods introduced here have been found to facilitate semantic queries for novice users without laborious manual annotation. Cluster-temporal browsing was found to outperform the conventional approach, which constitutes of sequential queries and relevance feedback, in semantic video retrieval by a statistically significant proportion.

The rapid advances in technologies make the explosive growth of multimedia data possible and available to the public. Multimedia data can be defined as data collection, which is composed of various data types and different representations. Due to the fact that multimedia data carries knowledgeable information, it has been widely adopted to different genera, like surveillance event detection, medical abnormality detection, and many others. To fulfil various requirements for different applications, it is important to effectively classify multimedia data into semantic concepts across multiple domains. In this dissertation, a correlation-based multimedia semantic concept detection framework is seamlessly integrated with the deep learning technique. The framework aims to explore implicit and explicit correlations among features and concepts while adopting different Convolutional Neural Network (CNN) architectures accordingly. First, the Feature Correlation Maximum Spanning Tree (FC-MST) is proposed to remove the redundant and irrelevant features based on the correlations between the features and positive concepts. FC-MST identifies the effective features and decides the initial layer's dimension in CNNs. Second, the Negative-based Sampling method is proposed to alleviate the data imbalance issue by keeping only the representative negative instances in the training process. To adjust dierent sizes of training data, the number of iterations for the CNN is determined adaptively and automatically. Finally, an Indirect Association Rule Mining (IARM) approach and a correlation-based re-ranking method are proposed to reveal the implicit relationships from the correlations among concepts, which are further utilized together with the classification scores to enhance the re-ranking process. The framework is evaluated using two benchmark multimedia data sets, TRECVID and NUS-WIDE, which contain large amounts of multimedia data and various semantic concepts.

Cette thèse s'inscrit dans le cadre de l'émergence de l'apprentissage profond et aborde la compréhension de la parole assimilée à l'extraction et à la représentation automatique du sens contenu dans les mots d'une phrase parlée. Nous étudions une tâche d'étiquetage en concepts sémantiques dans un contexte de dialogue oral évaluée sur le corpus français MEDIA. Depuis une dizaine d'années, les modèles neuronaux prennent l'ascendant dans de nombreuses tâches de traitement du langage naturel grâce à des avancées algorithmiques ou à la mise à disposition d'outils de calcul puissants comme les processeurs graphiques. De nombreux obstacles rendent la compréhension complexe, comme l'interprétation difficile des transcriptions automatiques de la parole étant donné que de nombreuses erreurs sont introduites par le processus de reconnaissance automatique en amont du module de compréhension. Nous présentons un état de l'art décrivant la compréhension de la parole puis les méthodes d'apprentissage automatique supervisé pour la résoudre en commençant par des systèmes classiques pour finir avec des techniques d'apprentissage profond. Les contributions sont ensuite exposées suivant trois axes. Premièrement, nous développons une architecture neuronale efficace consistant en un réseau récurent bidirectionnel encodeur-décodeur avec mécanisme d’attention. Puis nous abordons la gestion des erreurs de reconnaissance automatique et des solutions pour limiter leur impact sur nos performances. Enfin, nous envisageons une désambiguïsation de la tâche de compréhension permettant de rendre notre système plus performant. / This thesis is a part of the emergence of deep learning and focuses on spoken language understanding assimilated to the automatic extraction and representation of the meaning supported by the words in a spoken utterance. We study a semantic concept tagging task used in a spoken dialogue system and evaluated with the French corpus MEDIA. For the past decade, neural models have emerged in many natural language processing tasks through algorithmic advances or powerful computing tools such as graphics processors. Many obstacles make the understanding task complex, such as the difficult interpretation of automatic speech transcriptions, as many errors are introduced by the automatic recognition process upstream of the comprehension module. We present a state of the art describing spoken language understanding and then supervised automatic learning methods to solve it, starting with classical systems and finishing with deep learning techniques. The contributions are then presented along three axes. First, we develop an efficient neural architecture consisting of a bidirectional recurrent network encoder-decoder with attention mechanism. Then we study the management of automatic recognition errors and solutions to limit their impact on our performances. Finally, we envisage a disambiguation of the comprehension task making the systems more efficient.