O atual sucesso da Internet vem inibindo a disseminação de novas arquiteturas e protocolos de rede. Especificamente, qualquer modificação no núcleo da rede requer comum acordo entre diversas partes. Face a isso, a Virtualização de Redes vem sendo proposta como um atributo diversificador para a Internet. Tal paradigma promove o desenvolvimento de novas arquiteturas e protocolos por meio da criação de múltiplas redes virtuais sobrepostas em um mesmo substrato físico. Adicionalmente, aplicações executando sobre uma mesma rede física podem ser isoladas mutuamente, propiciando a independência funcional entre as mesmas. Uma de suas mais promissoras vantagens é a capacidade de limitar o escopo de ataques, através da organização de uma infraestrutura em múltiplas redes virtuais, isolando o tráfego das mesmas e impedindo interferências. Contudo, roteadores e enlaces virtuais permanecem vulneráveis a ataques e falhas na rede física subjacente. Particularmente, caso determinado enlace do substrato seja comprometido, todos os enlaces virtuais sobrepostos (ou seja, alocados neste) serão afetados. Para lidar com esse problema, a literatura propõe dois tipos de estratégias: as que reservam recursos adicionais do substrato como sobressalentes, protegendo contra disrupções; e as que utilizam migração em tempo real para realocar recursos virtuais comprometidos. Ambas estratégias acarretam compromissos: o uso de recursos sobressalentes tende a tornar-se custoso ao provedor de infraestrutura, enquanto a migração de recursos demanda um período de convergência e pode deixar as redes virtuais inoperantes durante o mesmo. Esta dissertação apresenta ORE (Opportunistic Resilience Embedding – Mapeamento com Resiliência Oportunística), uma nova abordagem de mapeamento de redes para proteger enlaces virtuais contra disrupções no substrato físico. ORE é composto por duas estratégias: uma proativa, na qual enlaces virtuais são alocados em múltiplos caminhos para mitigar o impacto de uma disrupção; e uma reativa, a qual tenta recuperar, parcial ou integralmente, a capacidade perdida nos enlaces virtuais afetados. Ambas são modeladas como problemas de otimização. Ademais, como o mapeamento de redes virtuais é NP-Difícil, ORE faz uso de uma meta-heurística baseada em Simulated Annealing para resolver o problema de forma eficiente. Resultados numéricos mostram que ORE pode prover resiliência a disrupções por um custo mais baixo. / Recently, the Internet’s success has prevented the dissemination of novel networking architectures and protocols. Specifically, any modification to the core of the network requires agreement among many different parties. To address this situation, Network Virtualization has been proposed as a diversifying attribute for the Internet. This paradigm promotes the development of new architectures and protocols by enabling the creation of multiple virtual networks on top of a same physical substrate. In addition, applications running over the same physical network can be isolated from each other, thus allowing them to coexist independently. One of the main advantages of this paradigm is the use of isolation to limit the scope of attacks. This can be achieved by creating different, isolated virtual networks for each task, so traffic from one virtual network does not interfere with the others. However, routers and links are still vulnerable to attacks and failures on the underlying network. Particularly, should a physical link be compromised, all embedded virtual links will be affected. Previous work tackled this problem with two main strategies: using backup resources to protect against disruptions; or live migration to relocate a compromised virtual resource. Both strategies have drawbacks: backup resources tend to be expensive for the infrastructure provider, while live migration may leave virtual networks inoperable during the recovery period. This dissertation presents ORE (Opportunistic Resilience Embedding), a novel embedding approach for protecting virtual links against substrate network disruptions. ORE’s design is two-folded: while a proactive strategy embeds virtual links into multiple substrate paths in order to mitigate the initial impact of a disruption, a reactive one attempts to recover any capacity affected by an underlying disruption. Both strategies are modeled as optimization problems. Additionally, since the embedding problem is NP-Hard, ORE uses a Simulated Annealing-based meta-heuristic to solve it efficiently. Numerical results show that ORE can provide resilience to disruptions at a lower cost.

Internet offre une énorme quantité d’informations à portée de main et dans une telle variété de sujets, que tout le monde est en mesure d’accéder à une énorme variété de connaissances. Une telle grande quantité d’information pourrait apporter un saut en avant dans de nombreux domaines (moteurs de recherche, réponses aux questions, tâches NLP liées) si elle est bien utilisée. De cette façon, un enjeu crucial de la communauté d’intelligence artificielle a été de recueillir, d’organiser et de faire un usage intelligent de cette quantité croissante de connaissances disponibles. Heureusement, depuis un certain temps déjà des efforts importants ont été faits dans la collecte et l’organisation des connaissances, et beaucoup d’informations structurées peuvent être trouvées dans des dépôts appelés Bases des Connaissances (BCs). Freebase, Entity Graph Facebook ou Knowledge Graph de Google sont de bons exemples de BCs. Un grand problème des BCs c’est qu’ils sont loin d’êtres complets. Par exemple, dans Freebase seulement environ 30% des gens ont des informations sur leur nationalité. Cette thèse présente plusieurs méthodes pour ajouter de nouveaux liens entre les entités existantes de la BC basée sur l’apprentissage des représentations qui optimisent une fonction d’énergie définie. Ces modèles peuvent également être utilisés pour attribuer des probabilités à triples extraites du Web. On propose également une nouvelle application pour faire usage de cette information structurée pour générer des informations non structurées (spécifiquement des questions en langage naturel). On pense par rapport à ce problème comme un modèle de traduction automatique, où on n’a pas de langage correct comme entrée, mais un langage structuré. Nous adaptons le RNN codeur-décodeur à ces paramètres pour rendre possible cette traduction. / Internet provides a huge amount of information at hand in such a variety of topics, that now everyone is able to access to any kind of knowledge. Such a big quantity of information could bring a leap forward in many areas if used properly. This way, a crucial challenge of the Artificial Intelligence community has been to gather, organize and make intelligent use of this growing amount of available knowledge. Fortunately, important efforts have been made in gathering and organizing knowledge for some time now, and a lot of structured information can be found in repositories called Knowledge Bases (KBs). A main issue with KBs is that they are far from being complete. This thesis proposes several methods to add new links between the existing entities of the KB based on the learning of representations that optimize some defined energy function. We also propose a novel application to make use of this structured information to generate questions in natural language.

Submitted by Alison Vanceto (alison-vanceto@hotmail.com) on 2017-03-17T12:24:22Z No. of bitstreams: 1 DissCOSF.pdf: 3751345 bytes, checksum: 50050f093a497de77a404a0a957ad02c (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-04-24T13:10:09Z (GMT) No. of bitstreams: 1 DissCOSF.pdf: 3751345 bytes, checksum: 50050f093a497de77a404a0a957ad02c (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-04-24T13:10:17Z (GMT) No. of bitstreams: 1 DissCOSF.pdf: 3751345 bytes, checksum: 50050f093a497de77a404a0a957ad02c (MD5) / Made available in DSpace on 2017-04-24T13:13:58Z (GMT). No. of bitstreams: 1 DissCOSF.pdf: 3751345 bytes, checksum: 50050f093a497de77a404a0a957ad02c (MD5) Previous issue date: 2016-11-22 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / The expansion of different areas of knowledge through many types of information brought the necessity to support complex data (images, sounds, videos, strings, DNA chains, etc.), that do not have a Total Order Relationship and need other management mechanisms, like the contentbased retrieval. In general, they are represented in metric space domains, where we have only the elements and the distances between them. Through the characteristics extracted from them, we perform the similarity search. Considering the necessity to associate temporal information on these data in many applications, this work aims to analyze the temporal evolve of metric data. One alternative for this is embedding them into a multidimensional space to allow trajectories estimates. We studied different methods of embedding and analyzed how this affected the data’s distribution and, consequently, the estimates. Two new methods were purposed to estimate an element’s status on a different time from that available in database, in order to reduce the number of non-relevant elements on search results. These methods are based on radius search reduction (range) and evaluation of retrieved element’s proximity by using an approximation of reverse k- NN. We performed experiments which showed that purposed methods could improve the estimate’s result, that used to be performed only using k-NN searches. / A expansão de diferentes áreas do conhecimento com os diversos tipos de informação tornou necessário o suporte a dados complexos (imagens, sons, vídeos, cadeias de DNA, entre outros), que por não possuírem uma Relação de Ordem Total (ROT), necessitam de outros mecanismos de gerenciamento, como a recuperação por conteúdo. Em geral, esses dados são representados em domínios de espaços métricos, onde apenas se tem os elementos e as distâncias entre eles. Através das características extraídas dos mesmos, realiza-se consultas por similaridade. Considerando a necessidade de associar a informação temporal a esses dados em muitas aplicações, este trabalho visa analisar a evolução temporal dos dados métricos. Para isso, uma alternativa é mapeá-los para um espaço multidimensional, a fim possibilitar a estimativa de trajetórias. Neste trabalho, foram estudados diferentes métodos de mapeamento, sendo também analisado como o mapeamento afetou a distribuição dos mesmos e, por conseguinte, a realização das estimativas. Foram propostos dois novos métodos para estimar o estado de um elemento em um tempo diferente daqueles disponíveis na base de dados, com o objetivo de reduzir no conjunto resposta a quantidade de elementos não relevantes. Os métodos propostos são baseados na redução do raio de consulta na região estimada pela delimitação do raio de consulta (range) e a avaliação da proximidade dos elementos retornados utilizando verificação (aproximação) do k-NN reverso. Foram realizados experimentos que mostraram que os métodos propostos melhoraram o resultado final das estimativas, que anteriormente eram realizadas apenas com consultas aos vizinhos mais próximos.

Submitted by Diego Sa de Lima null (diegos.lima88@hotmail.com) on 2018-03-03T04:54:32Z No. of bitstreams: 1 Dissertacao_Teorias_duais_massivas_spin32.pdf: 569707 bytes, checksum: 2062a40c0ffdddb4c5801a33f1f13f9d (MD5) / Approved for entry into archive by Pamella Benevides Gonçalves null (pamella@feg.unesp.br) on 2018-03-05T18:32:28Z (GMT) No. of bitstreams: 1 lima_ds_me_guara.pdf: 569707 bytes, checksum: 2062a40c0ffdddb4c5801a33f1f13f9d (MD5) / Made available in DSpace on 2018-03-05T18:32:28Z (GMT). No. of bitstreams: 1 lima_ds_me_guara.pdf: 569707 bytes, checksum: 2062a40c0ffdddb4c5801a33f1f13f9d (MD5) Previous issue date: 2018-02-05 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Nesta dissertação serão analisados os dois modelos conhecidos na literatura que descrevem partículas massivas de spin 3/2 em D=2+1 dimensões. Essa análise será feita, assim como nos trabalhos relacionados aos bósons (spin 1, spin 2 e spin 3), via procedimento de Imersão de Calibre de Noether (ICN), Solda generalizada, análise de vínculos e condições de Fierz-Pauli. Através de argumentos de simetria, via ICN, apresentaremos uma forma de relacionar os dois modelos e mostraremos que é possível construir um novo modelo de terceira ordem em derivadas. Apresentaremos um modelo de dubleto de segunda ordem em derivadas de onde é possível obter os demais modelos auto-duais da teoria. A partir da aplicação da ICN no modelo de dubleto construiremos um novo modelo, de quarta ordem em derivadas, análogo a versão linearizada da chamada " New Massive Gravity". / In this master's thesis we will analyze the two known models in the literature wich describe massive spin 3/2 particles in D = 2 + 1 dimensions. This analysis will be done, as was previously done on works related to the bosons (spin- 1 , spin- 2 and spin- 3 ), via Noether gauge embedment (NGE) procedure, generalized soldering, hamiltonian constraints analysis and Fierz-Pauli conditions. Through symmetry arguments, by NGE, we will present a way of relating the two models and show that it is possible to construct a new model in third order derivatives. We will show a second order derivative doublet-model whence it is possible to obtain the other self-dual models of the theory. From the application of NGE in the dublet model we will construct a new model, wich has a fourth-order derivative term, analogue to the linearized version of the so-called "New Massive Gravity"

O atual sucesso da Internet vem inibindo a disseminação de novas arquiteturas e protocolos de rede. Especificamente, qualquer modificação no núcleo da rede requer comum acordo entre diversas partes. Face a isso, a Virtualização de Redes vem sendo proposta como um atributo diversificador para a Internet. Tal paradigma promove o desenvolvimento de novas arquiteturas e protocolos por meio da criação de múltiplas redes virtuais sobrepostas em um mesmo substrato físico. Adicionalmente, aplicações executando sobre uma mesma rede física podem ser isoladas mutuamente, propiciando a independência funcional entre as mesmas. Uma de suas mais promissoras vantagens é a capacidade de limitar o escopo de ataques, através da organização de uma infraestrutura em múltiplas redes virtuais, isolando o tráfego das mesmas e impedindo interferências. Contudo, roteadores e enlaces virtuais permanecem vulneráveis a ataques e falhas na rede física subjacente. Particularmente, caso determinado enlace do substrato seja comprometido, todos os enlaces virtuais sobrepostos (ou seja, alocados neste) serão afetados. Para lidar com esse problema, a literatura propõe dois tipos de estratégias: as que reservam recursos adicionais do substrato como sobressalentes, protegendo contra disrupções; e as que utilizam migração em tempo real para realocar recursos virtuais comprometidos. Ambas estratégias acarretam compromissos: o uso de recursos sobressalentes tende a tornar-se custoso ao provedor de infraestrutura, enquanto a migração de recursos demanda um período de convergência e pode deixar as redes virtuais inoperantes durante o mesmo. Esta dissertação apresenta ORE (Opportunistic Resilience Embedding – Mapeamento com Resiliência Oportunística), uma nova abordagem de mapeamento de redes para proteger enlaces virtuais contra disrupções no substrato físico. ORE é composto por duas estratégias: uma proativa, na qual enlaces virtuais são alocados em múltiplos caminhos para mitigar o impacto de uma disrupção; e uma reativa, a qual tenta recuperar, parcial ou integralmente, a capacidade perdida nos enlaces virtuais afetados. Ambas são modeladas como problemas de otimização. Ademais, como o mapeamento de redes virtuais é NP-Difícil, ORE faz uso de uma meta-heurística baseada em Simulated Annealing para resolver o problema de forma eficiente. Resultados numéricos mostram que ORE pode prover resiliência a disrupções por um custo mais baixo. / Recently, the Internet’s success has prevented the dissemination of novel networking architectures and protocols. Specifically, any modification to the core of the network requires agreement among many different parties. To address this situation, Network Virtualization has been proposed as a diversifying attribute for the Internet. This paradigm promotes the development of new architectures and protocols by enabling the creation of multiple virtual networks on top of a same physical substrate. In addition, applications running over the same physical network can be isolated from each other, thus allowing them to coexist independently. One of the main advantages of this paradigm is the use of isolation to limit the scope of attacks. This can be achieved by creating different, isolated virtual networks for each task, so traffic from one virtual network does not interfere with the others. However, routers and links are still vulnerable to attacks and failures on the underlying network. Particularly, should a physical link be compromised, all embedded virtual links will be affected. Previous work tackled this problem with two main strategies: using backup resources to protect against disruptions; or live migration to relocate a compromised virtual resource. Both strategies have drawbacks: backup resources tend to be expensive for the infrastructure provider, while live migration may leave virtual networks inoperable during the recovery period. This dissertation presents ORE (Opportunistic Resilience Embedding), a novel embedding approach for protecting virtual links against substrate network disruptions. ORE’s design is two-folded: while a proactive strategy embeds virtual links into multiple substrate paths in order to mitigate the initial impact of a disruption, a reactive one attempts to recover any capacity affected by an underlying disruption. Both strategies are modeled as optimization problems. Additionally, since the embedding problem is NP-Hard, ORE uses a Simulated Annealing-based meta-heuristic to solve it efficiently. Numerical results show that ORE can provide resilience to disruptions at a lower cost.

Les flux de contenus audiovisuels peuvent être représentés sous forme de séquences d’événements (par exemple, des suites d’émissions, de scènes, etc.). Ces données séquentielles se caractérisent par des relations chronologiques pouvant exister entre les événements successifs. Dans le contexte d’une chaîne TV, la programmation des émissions suit une cohérence définie par cette même chaîne, mais peut également être influencée par les programmations des chaînes concurrentes. Dans de telles conditions,les séquences d’événements des flux parallèles pourraient ainsi fournir des connaissances supplémentaires sur les événements d’un flux considéré.La modélisation de séquences est un sujet classique qui a été largement étudié, notamment dans le domaine de l’apprentissage automatique. Les réseaux de neurones récurrents de type Long Short-Term Memory (LSTM) ont notamment fait leur preuve dans de nombreuses applications incluant le traitement de ce type de données. Néanmoins,ces approches sont conçues pour traiter uniquement une seule séquence d’entrée à la fois. Notre contribution dans le cadre de cette thèse consiste à élaborer des approches capables d’intégrer conjointement des données séquentielles provenant de plusieurs flux parallèles.Le contexte applicatif de ce travail de thèse, réalisé en collaboration avec le Laboratoire Informatique d’Avignon et l’entreprise EDD, consiste en une tâche de prédiction du genre d’une émission télévisée. Cette prédiction peut s’appuyer sur les historiques de genres des émissions précédentes de la même chaîne mais également sur les historiques appartenant à des chaînes parallèles. Nous proposons une taxonomie de genres adaptée à de tels traitements automatiques ainsi qu’un corpus de données contenant les historiques parallèles pour 4 chaînes françaises.Deux méthodes originales sont proposées dans ce manuscrit, permettant d’intégrer les séquences des flux parallèles. La première, à savoir, l’architecture des LSTM parallèles(PLSTM) consiste en une extension du modèle LSTM. Les PLSTM traitent simultanément chaque séquence dans une couche récurrente indépendante et somment les sorties de chacune de ces couches pour produire la sortie finale. Pour ce qui est de la seconde proposition, dénommée MSE-SVM, elle permet de tirer profit des avantages des méthodes LSTM et SVM. D’abord, des vecteurs de caractéristiques latentes sont générés indépendamment, pour chaque flux en entrée, en prenant en sortie l’événement à prédire dans le flux principal. Ces nouvelles représentations sont ensuite fusionnées et données en entrée à un algorithme SVM. Les approches PLSTM et MSE-SVM ont prouvé leur efficacité dans l’intégration des séquences parallèles en surpassant respectivement les modèles LSTM et SVM prenant uniquement en compte les séquences du flux principal. Les deux approches proposées parviennent bien à tirer profit des informations contenues dans les longues séquences. En revanche, elles ont des difficultés à traiter des séquences courtes.L’approche MSE-SVM atteint globalement de meilleures performances que celles obtenues par l’approche PLSTM. Cependant, le problème rencontré avec les séquences courtes est plus prononcé pour le cas de l’approche MSE-SVM. Nous proposons enfin d’étendre cette approche en permettant d’intégrer des informations supplémentaires sur les événements des séquences en entrée (par exemple, le jour de la semaine des émissions de l’historique). Cette extension, dénommée AMSE-SVM améliore remarquablement la performance pour les séquences courtes sans les baisser lorsque des séquences longues sont présentées. / In the same way as TV channels, data streams are represented as a sequence of successive events that can exhibit chronological relations (e.g. a series of programs, scenes, etc.). For a targeted channel, broadcast programming follows the rules defined by the channel itself, but can also be affected by the programming of competing ones. In such conditions, event sequences of parallel streams could provide additional knowledge about the events of a particular stream. In the sphere of machine learning, various methods that are suited for processing sequential data have been proposed. Long Short-Term Memory (LSTM) Recurrent Neural Networks have proven its worth in many applications dealing with this type of data. Nevertheless, these approaches are designed to handle only a single input sequence at a time. The main contribution of this thesis is about developing approaches that jointly process sequential data derived from multiple parallel streams. The application task of our work, carried out in collaboration with the computer science laboratory of Avignon (LIA) and the EDD company, seeks to predict the genre of a telecast. This prediction can be based on the histories of previous telecast genres in the same channel but also on those belonging to other parallel channels. We propose a telecast genre taxonomy adapted to such automatic processes as well as a dataset containing the parallel history sequences of 4 French TV channels. Two original methods are proposed in this work in order to take into account parallel stream sequences. The first one, namely the Parallel LSTM (PLSTM) architecture, is an extension of the LSTM model. PLSTM simultaneously processes each sequence in a separate recurrent layer and sums the outputs of each of these layers to produce the final output. The second approach, called MSE-SVM, takes advantage of both LSTM and Support Vector Machines (SVM) methods. Firstly, latent feature vectors are independently generated for each input stream, using the output event of the main one. These new representations are then merged and fed to an SVM algorithm. The PLSTM and MSE-SVM approaches proved their ability to integrate parallel sequences by outperforming, respectively, the LSTM and SVM models that only take into account the sequences of the main stream. The two proposed approaches take profit of the information contained in long sequences. However, they have difficulties to deal with short ones. Though MSE-SVM generally outperforms the PLSTM approach, the problem experienced with short sequences is more pronounced for MSE-SVM. Finally, we propose to extend this approach by feeding additional information related to each event in the input sequences (e.g. the weekday of a telecast). This extension, named AMSE-SVM, has a remarkably better behavior with short sequences without affecting the performance when processing long ones.

Submitted by Márcia Silva (marcialbuquerq@yahoo.com.br) on 2017-11-21T15:13:35Z No. of bitstreams: 1 Dissertação_DFS_v26_final.pdf: 1805000 bytes, checksum: 4d76d6be8271bc5cada9495ca570805d (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2017-11-21T15:37:01Z (GMT) No. of bitstreams: 1 Dissertação_DFS_v26_final.pdf: 1805000 bytes, checksum: 4d76d6be8271bc5cada9495ca570805d (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2017-11-21T15:39:27Z (GMT) No. of bitstreams: 1 Dissertação_DFS_v26_final.pdf: 1805000 bytes, checksum: 4d76d6be8271bc5cada9495ca570805d (MD5) / Made available in DSpace on 2017-11-21T15:39:27Z (GMT). No. of bitstreams: 1 Dissertação_DFS_v26_final.pdf: 1805000 bytes, checksum: 4d76d6be8271bc5cada9495ca570805d (MD5) Previous issue date: 2017-07-10 / FAPEAM - Fundação de Amparo à Pesquisa do Estado do Amazonas / Among several textual representation techniques in the literature, the distributed representation of words is standing out recently in many tasks of Natural Language Processing through its representations based on dense vectors of 𝑑 dimensions that can capture syntactic and semantic information of the words. Therefore, it’s expected that similar words regarding to syntactic and sematic are closer of each other in the vector space. However, while this representation is becoming effective to isolated words, there isn’t a consensus in the literature regarding to the best way to represent more complex structures, such as phrases and sentences. The trend of recent years is the use of compositional models that represents these complex structures through the composition of the representations of its constituent structures using some combination function. However, it’s known that the obtained results by this technique depends directly of the domain in which they are applied. In this work, we analyzed several compositional models applied to the domain of stock price prediction in order to identify which of these models better represent the financial news title for various machine learning methods to predict the index polarity of the S & P 500 stock exchange. / Dentre as várias técnicas de representação textual existentes na literatura, a representação distribuída de palavras (word embedding) vem se destacando ultimamente em várias tarefas de processamento de linguagem natural através de suas representações baseadas em vetores densos de 𝑑 dimensões que são capazes de capturar informações semânticas e sintáticas das palavras. Desta forma, espera-se que as palavras com semelhanças sintáticas e semânticas estejam mais próximas umas das outras no espaço vetorial. No entanto, enquanto essa representação tem se mostrado eficaz para palavras isoladas, não há um consenso na literatura em relação à melhor forma de representar estruturas mais complexas, como frases e orações. A tendência dos últimos anos é a utilização dos modelos composicionais que representam essas estruturas complexas através da composição das representações de suas estruturas constituintes utilizando alguma função de combinação. Entretanto, sabe-se que os resultados obtidos pelos modelos composicionais dependem diretamente do domínio em que são aplicados. Nesse trabalho, nós analisamos diversos modelos de composição aplicados ao domínio de previsão de preços no mercado de ações com o objetivo de identificar qual desses modelos melhor representa os títulos de notícias financeiras para diversos métodos de aprendizado de máquina com o intuito de prever a polaridade do índice da bolsa de valore S & P 500.

Este trabalho apresenta o projeto de um bloco LNA-Misturador dentro de um mesmo circuito integrado para aplicações em um receptor Bluetooth 2;45GHz. Uma estratégia de projeto bem clara, concisa e com uma boa base física e matemática foi desenvolvida para auxiliar o processo de projeto de um bloco LNA-Misturador, composto por um LNA cascode em cascata com um misturador de chaveamento de corrente com entradas simples e degeneração indutiva nas fontes dos estágios de transcondutância. Esta estratégia foi adaptada de trabalhos apresentados na literatura. A estratégia de projeto proposta considera o compromisso entre ruído, linearidade, ganho, dissipação de potência, casamento de impedâncias e isolamento de portas, usando as dimensões dos dispositivos e condições de polarização como variáveis de projeto. Com base nesta estratégia se obteve um bloco LNA-Misturador que atinge algumas especificações propostas. Um bloco LNA-Misturador foi projetado e fabricado em uma tecnologia CMOS 0;35µm para validar a estratégia de projeto proposta. Além disso, para atingir os objetivos, durante o desenvolvimento deste trabalho foi dada atenção especial no projeto dos indutores. Foi projetado, fabricado e medido um chip de teste. Para tal fim foram aplicadas técnicas e estruturas de de-embedding nas medidas para conseguir resultados mais confiáveis. Os resultados experimentais obtidos para os indutores e os resultados preliminares do bloco LNA-Misturador s~ao satisfatórios de acordo com as especificações e os esperados das simulações. No entanto, os indutores integrados degradam significativamente o desempenho do bloco LNA-Misturador. Se forem usados processos de fabricação nos quais os indutores apresentem melhor desempenho, os resultados do bloco LNA-Misturador aplicando a estratégia de projeto desenvolvida neste trabalho podem ser melhorados. Finalmente, é importante ressaltar que a estratégia de projeto proposta neste trabalho já está sendo usada e adaptada em outros projetos com o propósito de melhorar os resultados obtidos, e conseguir auxiliar o processo de projeto deste tipo de blocos. / This work presents a fully integrated LNA-Mixer design for a Bluetooth receiver application at 2:45GHz. A concise design strategy with good physics and mathematics basis was developed to assist the design process of a LNA-Mixer block, formed by a cascode LNA in cascade to a single balanced current commutation Mixer with inductive degeneration. This strategy was adapted from literature and considers the trade-offs between noise, linearity, gain, power dissipation, impedance matching and ports isolation, using the device dimensions and bias conditions as design variables. Based on this strategy, the proposed LNA-Mixer design specifications were achieved. To validate the proposed design strategy, the LNA-Mixer were fabricated in a 0:35µm CMOS process. Furthermore, to achieve the specifications, during the development of this work a special attention to the RF CMOS inductors was given. A test chip was designed, fabricated and measured applying de-embedding structures to obtain more reliable results. The experimental results obtained for the inductors and the preliminary results for the LNA-Mixer are satisfactory compared to the specifications and as expected from simulations. However, the integrated inductors degrade the performance of the block significantly and if a manufacturing process in which the inductor has better performance is used, the resulting LNA-Mixer design applying the strategy developed in this work can be improved. Finally, it is important to highlight that the design strategy proposed in this work is already being used and adapted in other designs in order to improve the results, and to assist the design process of such blocks.

音樂歌單是由一組多首不同元素、風格的音樂所組成的，它包含了編輯者的個人品味以及因應主題、目的性產生而成。我們可以透過樂曲的律動、節奏、歌曲的主題精神，進而編輯一個相應契合的系列歌曲。當今的音樂收聽市場主要是在網路串流平台上進行隨時、隨地的聆聽，主要的平台有Spotify、Apple Music 以及KKBOX。各家業者不單只是提供使用者歌曲的搜索、單曲的聆聽，更提供訂閱專業歌單編輯者的歌單訂閱服務，甚至是讓一般的使用者參與歌單自訂編輯的過程。然而如何在有限的時間內針對使用者的聆聽習慣去介紹平台上豐富的音樂資源是個很大的挑戰。上述的過程我們稱之為推薦，而當前的音樂推薦研究大多是在對使用者進行相關歌曲的推薦，鮮少能進一步在更抽象層次上的歌單上進行推薦。這邊我們就此一推薦應用提供嵌入式向量表示法學習模型，在有著使用者、歌曲、歌單的異質性社交網路上，對使用者進行歌單的推薦。為了能有效的學習出歌單推薦的模型，我們更將使用者、歌單和歌曲的異質性圖形重組成二分圖（bipartite graph）， 並在此圖形的邊上賦予不等的權重，此一權重是基於使用者隱式反饋獲得的。接著再透過隨機漫步（random walk），根據邊上的權值進行路徑的抽樣選取，最後再將路徑上經過的節點進行嵌入式向量表示法的學習。我們使用歐幾里德距離計算各節點表示法的鄰近關係，再將與使用者較為相關的歌單推薦給使用者。實驗驗證的部分，我們蒐集KKBOX 兩年份的資料進行模型訓練並進行推薦，並將推薦的結果與使用者所喜愛的歌單進行準確度（Precision）評估， 結果證實所得到的推薦效果較一般熱門歌單的推薦來的好，且為更具個人化的歌單推薦。 / Music playlist is crafted with a series of songs, in which the playlist creator has controlled over the vibe, tempo, theme, and all the ebbs and flows that come within the playlist. To provide a personalization service to users and discover suitable playlists among lots of data, we need an effective way to achieve this goal. In this paper, we modify a representation learning method for learning the representation of a playlist of songs, and then use the representation for recommending playlists to users. While there have been some well-known methods that can model the preference between users and songs, little has been done in the literature to recommend music playlists. In light of this, we apply DeepWalk, LINE and HPE to a user-song-playlist network. To better encode the network structure, we separate user, song, and playlist nodes into two different sets, which are grouped by the user and playlist set and song as the other one. In the bipartite graph, the user and playlist node are connected to their joint songs. By adopting random walks on the constructed graph, we can embed users and playlists via the common information between each other. Therefore, users can discover their favorite playlists through the learned representations. After the embedding process, we then use the learned representations to perform playlist recommendation task. Experiments conducted on a real-world dataset showed that these embedding methods have a better performance than the popularity baseline. In addition, the embedding method learns the informative representations and brings out the personal recommendation results.

Image and video quality evaluation is very important. In applications involving signal transmission, the Reduced- or No-Reference quality metrics are generally more practical than the Full-Reference metrics. Digital watermarking based quality evaluation emerges as a potential Reduced- or No-Reference quality metric, which estimates signal quality by assessing the degradation of the embedded watermark. Since the watermark contains a small amount of information compared to the cover signal, performing accurate signal quality evaluation is a challenging task. Meanwhile, the watermarking process causes signal quality loss. To address these problems, in this thesis, a framework for image and video quality evaluation is proposed based on semi-fragile and adaptive watermarking. In this framework, adaptive watermark embedding strength is assigned by examining the signal quality degradation characteristics. The "Ideal Mapping Curve" is experimentally generated to relate watermark degradation to signal degradation so that the watermark degradation can be used to estimate the quality of distorted signals. With the proposed framework, a quantization based scheme is first implemented in DWT domain. In this scheme, the adaptive watermark embedding strengths are optimized by iteratively testing the image degradation characteristics under JPEG compression. This iterative process provides high accuracy for quality evaluation. However, it results in relatively high computational complexity. As an improvement, a tree structure based scheme is proposed to assign adaptive watermark embedding strengths by pre-estimating the signal degradation characteristics, which greatly improves the computational efficiency. The SPIHT tree structure and HVS masking are used to guide the watermark embedding, which greatly reduces the signal quality loss caused by watermark embedding. Experimental results show that the tree structure based scheme can evaluate image and video quality with high accuracy in terms of PSNR, wPSNR, JND, SSIM and VIF under JPEG compression, JPEG2000 compression, Gaussian low-pass filtering, Gaussian noise distortion, H.264 compression and packet loss related distortion.