With more and more images being uploaded to social networks each day, the resources for identifying a large portion of the world are available. However the tools to harness and utilize this information are not sufficient. This thesis presents a system, called PhacePhinder, which can build a face database from a social network and have it accessible from mobile devices. Through combining existing technologies, this is made possible. It also makes use of a fusion probabilistic latent semantic analysis to determine strong connections between users and content. Using this information we can determine the most meaningful social connection to a recognized person, allowing us to inform the user of how they know the person being recognized. We conduct a series of offline and user tests to verify our results and compare them to existing algorithms. We show, that through combining a user’s friendship information as well as picture occurrence information, we can make stronger recommendations than based on friendship alone. We demonstrate a working prototype that can identify a face from a picture taken from a mobile phone, using a database derived from images gathered directly from a social network, and return a meaningful social connection to the recognized face.

Nowadays, a multitude of functionally equal web services are available. By thisbroad offer, the need of a service recommendation based on non-functional characteristics(e.g. price, response time, availability) is increasing. The static ServiceLevel Agreements (SLAs) of service providers cannot suffice this need. SLAs arenot reliable enough, due to the fact that they do not cover the dynamic performanceand quality changes of services during their lifetime. This bachelor’s thesis waswritten within a research project of the Linnaeus University in Sweden and the KarlsruheUniversity of Applied Science in Germany. The goal of this research projectis to eliminate the issues as described above. For this reason, a framework for anoptimized service selection was developed. Instead of using the static SLAs, measurementsof each service call are taken. On the basis of the measurements and therequirements of the consumer, the framework then provides an automated best-fitservice selection. The purpose of this thesis is to involve the geographic location of each serviceconsumer in the automated service selection. Therefore, a mobile app was developedto get a sufficient amount of real world test data. This app measures service calls andadditionally records the geographic location of the user. Based on the geographiclocation, the collected measurement data then were grouped into regions. Thereby,it could be shown that the geographic location of the user can be used to improve theoptimal service selection. / Service-Oriented Computing

abstract: Responding to the allegedly biased research reports issued by large investment banks, the Global Research Analyst Settlement and related regulations went to great lengths to weaken the conflicts of interest faced by investment bank analysts. In this paper, I investigate the effects of these changes on small and large investor confidence and on trading profitability. Specifically, I examine abnormal trading volumes generated by small and large investors in response to security analyst recommendations and the resulting abnormal market returns generated. I find an overall increase in investor confidence in the post-regulation period relative to the pre-regulation period consistent with a reduction in existing conflicts of interest. The change in confidence observed is particularly striking for small traders. I also find that small trader profitability has increased in the post-regulation period relative to the pre-regulation period whereas that for large traders has decreased. These results are consistent with the Securities and Exchange Commission's primary mission to protect small investors and maintain the integrity of the securities markets. / Dissertation/Thesis / Ph.D. Accountancy 2011

Recommendation systems serve to users of e-commerce applications for individual recommendations to certain products or services based on their preferences. The aim of this thesis is to create a module of recommender system. The work includes analysis of recommendation systems and the methods used in these systems, including a description of the calculations. This work also solves the cold start problem, which is a problem when generation of some good recommendations for the new user is needed, but the recommendation system has no or little information about this user. Based on analysis is in this thesis designed module for recommender system, which is applicable e.g. internet for e-commerce or other internet-based application. Part of this module is the realization of a platform Apache Mahout, which some parts are built on a distributed computing platform Apache Hadoop project. Furthermore, in this work, on the aforementioned platform Mahout, selected methods of calculating the similarity using selected criteria (e.g. the average time for a recommendation, and the number of users for who have not been able to generate recommendations) are tested.

Le raisonnement par analogie est reconnu comme une des principales caractéristiques de l'intelligence humaine. En tant que tel, il a pendant longtemps été étudié par les philosophes et les psychologues, mais de récents travaux s'intéressent aussi à sa modélisation d'un point de vue formel à l'aide de proportions analogiques, permettant l'implémentation de programmes informatiques. Nous nous intéressons ici à l'utilisation des proportions analogiques à des fins prédictives, dans un contexte d'apprentissage artificiel. Dans de récents travaux, les classifieurs analogiques ont montré qu'ils sont capables d'obtenir d'excellentes performances sur certains problèmes artificiels, là où d'autres techniques traditionnelles d'apprentissage se montrent beaucoup moins efficaces. Partant de cette observation empirique, cette thèse s'intéresse à deux axes principaux de recherche. Le premier sera de confronter le raisonnement par proportion analogique à des applications pratiques, afin d'étudier la viabilité de l'approche analogique sur des problèmes concrets. Le second axe de recherche sera d'étudier les classifieurs analogiques d'un point de vue théorique, car jusqu'à présent ceux-ci n'étaient connus que grâce à leurs définitions algorithmiques. Les propriétés théoriques qui découleront nous permettront de comprendre plus précisément leurs forces, ainsi que leurs faiblesses. Comme domaine d'application, nous avons choisi celui des systèmes de recommandation. On reproche souvent à ces derniers de manquer de nouveauté ou de surprise dans les recommandations qui sont adressées aux utilisateurs. Le raisonnement par analogie, capable de mettre en relation des objets en apparence différents, nous est apparu comme un outil potentiel pour répondre à ce problème. Nos expériences montreront que les systèmes analogiques ont tendance à produire des recommandations d'une qualité comparable à celle des méthodes existantes, mais que leur complexité algorithmique cubique les pénalise trop fortement pour prétendre à des applications pratiques où le temps de calcul est une des contraintes principales. Du côté théorique, une contribution majeure de cette thèse est de proposer une définition fonctionnelle des classifieurs analogiques, qui a la particularité d'unifier les approches préexistantes. Cette définition fonctionnelle nous permettra de clairement identifier les liens sous-jacents entre l'approche analogique et l'approche par k plus-proches-voisins, tant au plan algorithmique de haut niveau qu'au plan des propriétés théoriques (taux d'erreur notamment). De plus, nous avons pu identifier un critère qui rend l'application de notre principe d'inférence analogique parfaitement certaine (c'est-à-dire sans erreur), exhibant ainsi les propriétés linéaires du raisonnement par analogie. / Analogical reasoning is recognized as a core component of human intelligence. It has been extensively studied from philosophical and psychological viewpoints, but recent works also address the modeling of analogical reasoning for computational purposes, particularly focused on analogical proportions. We are interested here in the use of analogical proportions for making predictions, in a machine learning context. In recent works, analogy-based classifiers have achieved noteworthy performances, in particular by performing well on some artificial problems where other traditional methods tend to fail. Starting from this empirical observation, the goal of this thesis is twofold. The first topic of research is to assess the relevance of analogical learners on real-world, practical application problems. The second topic is to exhibit meaningful theoretical properties of analogical classifiers, which were yet only empirically studied. The field of application that was chosen for assessing the suitability of analogical classifiers in real-world setting is the topic of recommender systems. A common reproach addressed towards recommender systems is that they often lack of novelty and diversity in their recommendations. As a way of establishing links between seemingly unrelated objects, analogy was thought as a way to overcome this issue. Experiments here show that while offering sometimes similar accuracy performances to those of basic classical approaches, analogical classifiers still suffer from their algorithmic complexity. On the theoretical side, a key contribution of this thesis is to provide a functional definition of analogical classifiers, that unifies the various pre-existing approaches. So far, only algorithmic definitions were known, making it difficult to lead a thorough theoretical study. From this functional definition, we clearly identified the links between our approach and that of the nearest neighbors classifiers, in terms of process and in terms of accuracy. We were also able to identify a criterion that ensures a safe application of our analogical inference principle, which allows us to characterize analogical reasoning as some sort of linear process.

Made available in DSpace on 2014-06-12T15:52:44Z (GMT). No. of bitstreams: 2 arquivo1844_1.pdf: 1599198 bytes, checksum: 84a19c5d7769a76fba813a0cac740509 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2009 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / As organizações, com o intuito de aumentarem o seu grau de competitividade no mercado, vêm a cada instante buscando novas formas de evoluir a produtividade e a qualidade dos produtos desenvolvidos, além da diminuição de custos que está diretamente relacionada ao aumento do faturamento líquido. Para que tais objetivos possam ser alcançados é primordial explorar ao máximo o potencial de seus colaboradores e os possíveis relacionamentos que esses colaboradores têm uns com os outros, ou seja, encontrar e partilhar conhecimento tácito. Como o conhecimento tático está na mente das pessoas, é difícil de ser formalizado e documentado, por isso, o ideal seria identificar e recomendar a pessoa que detém o conhecimento. Diante disso, a presente dissertação apresenta o Sistema de Recomendação de Especialistas SWEETS e a sua implantação no ambiente a.m.i.g.o.s., uma plataforma de gestão de conhecimento baseada em conceitos voltados às redes sociais. O SWEETS foi desenvolvido em duas versões, 1.0 e 2.0. A versão 1.0, de forma pró-ativa, aproxima pessoas com especialidades em comum, ora pelos seus conhecimentos (perfil de escrita), ora pelos seus interesses (perfil de leitura). Já a versão 2.0 do SWEETS não atua de forma pró-ativa, ou seja, é necessário que haja a requisição de um usuário especialista em determinada área, e é baseada em folksonomia para extração de uma ontologia, fundamental para identificar as especialidades das pessoas de forma mais eficaz. Esta ontologia é refletida pela co-ocorrência das tags (conceitos) em relação aos itens (instâncias) e é independente de domínio principal contribuição dessa dissertação. A implantação do SWEETS no a.m.i.g.o.s. visa trazer benefícios como: minimizar o problema de comunicação na corporação, prover um incentivo ao conhecimento social e partilhar conhecimento; proporcionando, assim, à empresa, a utilização mais eficaz dos conhecimentos de seus colaboradores

With more and more images being uploaded to social networks each day, the resources for identifying a large portion of the world are available. However the tools to harness and utilize this information are not sufficient. This thesis presents a system, called PhacePhinder, which can build a face database from a social network and have it accessible from mobile devices. Through combining existing technologies, this is made possible. It also makes use of a fusion probabilistic latent semantic analysis to determine strong connections between users and content. Using this information we can determine the most meaningful social connection to a recognized person, allowing us to inform the user of how they know the person being recognized. We conduct a series of offline and user tests to verify our results and compare them to existing algorithms. We show, that through combining a user’s friendship information as well as picture occurrence information, we can make stronger recommendations than based on friendship alone. We demonstrate a working prototype that can identify a face from a picture taken from a mobile phone, using a database derived from images gathered directly from a social network, and return a meaningful social connection to the recognized face.

Due to the rapid increase of social network resources and services, Internet users are now overwhelmed by the vast quantity of social media available. By utilizing the user’s context while consuming diverse multimedia contents, we can identify different personal preferences and settings. However, there is still a need to reinforce the recommendation process in a systematic way, with context-adaptive information. This thesis proposes a recommendation model, called HPEM, that establishes a bridge between the multimedia resources, user collaborative preferences, and the detected contextual information, including physiological parameters. The collection of contextual information and the delivery of the resulted recommendation is made possible by adapting the user’s environment using Ambient Intelligent (AmI) interfaces. Additionally, this thesis presents the potential of including a user’s biological signal and leveraging it within an adapted collaborative filtering algorithm in the recommendation process. First, the different versions of the proposed HPEM model utilize existing online social networks by incorporating social tags and rating information in ways that personalize the search for content in a particular detected context. By leveraging the social tagging, our proposed model computes the hidden preferences of users in certain contexts from other similar contexts, as well as the hidden assignment of contexts for items from other similar items. Second, we demonstrate the use of an optimization function to maximize the Mean Average Prevision (MAP) measure of the resulted recommendations. We demonstrate the feasibility of HPEM with two prototype applications that use contextual information for recommendations. Offline and online experiments have been conducted to measure the accuracy of delivering personalized recommendations, based on the user’s context; two real-world and one collected semi-synthetic datasets were used. Our evaluation results show a potential improvement to the quality of the recommendation when compared to state-of-the-art recommendation algorithms that consider contextual information. We also compare the proposed method to other algorithms, where user’s context is not used to personalize the recommendation results. Additionally, the results obtained demonstrate certain improvements on cold start situations, where relatively little information is known about a user or an item.

In this thesis focus is on developing an online social network application with a Peer-to-Peer infrastructure motivated by BestPeer++ architecture and BATON overlay structure. BestPeer++ is a data processing platform which enables data sharing between enterprise systems. BATON is an open-sourced project which implements a peer-to-peer with a topology of a balanced tree. We designed and developed the components for users to manage their accounts, maintain friend relationships, and publish their contents with privacy control and newsfeed, notification requests in this social network- ing application. We also developed a Hashtag Recommendation system for this social net- working application. A user may invoke a recommendation procedure while writing a content. After being invoked, the recommendation pro- cedure returns a list of candidate hashtags, and the user may select one hashtag from the list and embed it into the content. The proposed ap- proach uses Latent Dirichlet Allocation (LDA) topic model to derive the latent or hidden topics of different content. LDA topic model is a well developed data mining algorithm and generally effective in analyzing text documents with different lengths. The topic model is further used to identify the candidate hashtags that are associated with the texts in the published content through their association with the derived hidden top- ics. We considered different methods of recommendation approach for the pro- cedure to select candidate hashtags from different content. Some methods consider the hashtags contained in the contents of the whole social net- work or of the user self. These are content-based recommendation tech- niques which matching user’s own profile with the profiles of items.. Some methods consider the hashtags contained in contents of the friends or of the similar users. These are collaborative filtering based recommendation techniques which considers the profiles of other users in the system. At the end of the recommendation procedure, the candidate hashtags are or- dered by their probabilities of appearance in the content and returned to the user. We also conducted experiments to evaluate the effectiveness of the hashtag recommendation approach. These experiments were fed with the tweets published in Twitter. The hit-rate of recommendation is measured in these experiments. Hit-rate is the percentage of the selected or relevant hashtags contained in candidate hashtags. Our experiment results show that the hit-rate above 50% is observed when we use a method of recommendation approach independently. Also, for the case that both similar user and user preferences are considered at the same time, the hit-rate improved to 87% and 92% for top-5 and top-10 candidate recommendations respectively.

The diploma thesis is focused on the employee appraisal and its using in company personnel management. The aim is to analyse the current appraisal which is used in particular company and to offer possible recommendation of its improvement.