The internationalization of SMEs has been recognized as one of the important paths to growth in SMEs. However, internationalization is also a resource and competence-demanding process. This is especially true for smaller-sized SMEs – the small and micro-sized firms – which have a large resource constraint, making internationalization even more challenging. Although this group of small firms counts for an average of over 98% of the total population of enterprises in EU countries, extant research on the internationalization of this group is still limited. Therefore, the main purpose of this dissertation is to contribute to a better understanding of evolving patterns of internationalization in the smaller-sized SMEs. The study uses emerging market entry along the internationalization of small firms as a context to probe the dynamics of perceived risk (uncertainty) and perceived opportunity in different foreign markets which influence the important decisions of small firms during their internationalization. The main study takes a longitudinal approach and uses mixed methods to investigate the features in both the initial period and the continued period of internationalization. It mainly builds on a multiple-case study of 12 Swedish firms, which have/had emerging market entry experience and/or involvement. This study illustrates influences from the environmental, organizational and individual levels on evolving patterns of internationalization in the investigated firms. This dissertation concludes that critical decisions and actions taken in the internationalization process depend on interactions among the influence and resources from the three levels. Such interactions form a conditional preference on perceived risk (uncertainty) and perceived opportunity during the internationalization of small firms. The study further proposes that the dynamics in the internationalization process are caused by a prospect-guided change mechanism. This dissertation contributes to the literature by: differentiating patterns of internationalization; enriching the study of “born global” in the continued period of internationalization; introducing a new perspective on the interpretation of dynamics in the internationalization; and increasing the understanding on the interactions of resources from three levels on the internationalization of small firms.

There is an emerging demand on efficiently archiving and (temporal) querying different versions of evolving semantic Web data. As novel archiving systems are starting to address this challenge, foundations/standards for benchmarking RDF archives are needed to evaluate its storage space efficiency and the performance of different retrieval operations. To this end, we provide theoretical foundations on the design of data and queries to evaluate emerging RDF archiving systems. Then, we instantiate these foundations along a concrete set of queries on the basis of a real-world evolving dataset. Finally, we perform an empirical evaluation of various current archiving techniques and querying strategies on this data that is meant to serve as a baseline of future developments on querying archives of evolving RDF data.

No description available.

Data analysis is the process of inspecting, cleaning, extract, and modeling data with the intention of extracting useful information in order to support users in their decisions. With the advent of Big Data, data analysis was becoming more complicated due to the volume and variety of data. This process begins with the acquisition of the data and the selection of the data that is useful for the desiderata analysis. With such amount of data, also expert users are not able to inspect the data and understand if a dataset is suitable or not for their purposes. In this dissertation, we focus on five problems in the broad data analysis process to help users find insights from the data when they do not have enough knowledge about its data. First, we analyze the data description problem, where the user is looking for a description of the input dataset. We introduce data descriptions: a compact, readable and insightful formula of boolean predicates that represents a set of data records. Finding the best description for a dataset is computationally expensive and task-specific; we, therefore, introduce a set of metrics and heuristics for generating meaningful descriptions at an interactive performance. Secondly, we look at the problem of order dependency discovery, which discovers another kind of metadata that may help the user in the understanding of characteristics of a dataset. Our approach leverages the observation that discovering order dependencies can be guided by the discovery of a more specific form of dependencies called order compatibility dependencies. Thirdly, textual data encodes much hidden information. To allow this data to reach its full potential, there has been an increasing interest in extracting structural information from it. In this regard, we propose a novel approach for extracting events that are based on temporal co-reference among entities. We consider an event to be a set of entities that collectively experience relationships between them in a specific period of time. We developed a distributed strategy that is able to scale with the largest on-line encyclopedia available, Wikipedia. Then, we deal with the evolving nature of the data by focusing on the problem of finding synonymous attributes in evolving Wikipedia Infoboxes. Over time, several attributes have been used to indicate the same characteristic of an entity. This provides several issues when we are trying to analyze the content of different time periods. To solve it, we propose a clustering strategy that combines two contrasting distance metrics. We developed an approximate solution that we assess over 13 years of Wikipedia history by proving its flexibility and accuracy. Finally, we tackle the problem of identifying movements of attributes in evolving datasets. In an evolving environment, entities not only change their characteristics, but they sometimes exchange them over time. We proposed a strategy where we are able to discover those cases, and we also test our strategy on real datasets. We formally present the five problems that we validate both in terms of theoretical results and experimental evaluation, and we demonstrate that the proposed approaches efficiently scale with a large amount of data.

No description available.

Telecare and especially Mobile Care Systems are getting more and more popular. They have two major benefits: first, they drastically improve the living standards and even health outcomes for patients. In addition, they allow significant cost savings for adult care by reducing the needs for medical staff. A common drawback of current Mobile Care Systems is that they are rather stationary in most cases and firmly installed in patients’ houses or flats, which makes them stay very near to or even in their homes. There is also an upcoming second category of Mobile Care Systems which are portable without restricting the moving space of the patients, but with the major drawback that they have either very limited computational abilities and only a rather low classification quality or, which is most frequently, they only have a very short runtime on battery and therefore indirectly restrict the freedom of moving of the patients once again. These drawbacks are inherently caused by the restricted computational resources and mainly the limitations of battery based power supply of mobile computer systems. This research investigates the application of novel Artificial Intelligence (AI) and Machine Learning (ML) techniques to improve the operation of 2 Mobile Care Systems. As a result, based on the Evolving Connectionist Systems (ECoS) paradigm, an innovative approach for a highly efficient and self-optimising distributed online machine learning algorithm called MECoS - Moving ECoS - is presented. It balances the conflicting needs of providing a highly responsive complex and distributed online learning classification algorithm by requiring only limited resources in the form of computational power and energy. This approach overcomes the drawbacks of current mobile systems and combines them with the advantages of powerful stationary approaches. The research concludes that the practical application of the presented MECoS algorithm offers substantial improvements to the problems as highlighted within this thesis.

A template tracker is a tracker based on matching a pre-initialised view of an object with the object's view in an image sequence. Using an error function, the intensity difference between the template view and the templated region in the image is measured. This error measure is used as the basis for a template alignment algorithm that will adjust the template's pose to more accurately register the template view with the view of the object in the image. Some significant problems present themselves with this simple tracker. Extraneous, or non-object, pixels within the template boundaries can cause bias in the registration of the template. Partial occlusions of the object's view in the image can also cause serious bias in the template's pose. Beyond simple occlusions there are transits of occlusions across an object. Occlusion transits are significant because over time they can occlude the entire object in an incremental fashion. If initially the template view is not completely known this kind of occlusion can easily cause a total tracking failure for an object. In this thesis three enhancements of the basic template tracker are proposed: Ownership Masks, Cooperative Templates, and Evolving Views. Ownership Masks are aimed at eliminating the extraneous pixels from the template view. Cooperative templates are used to separate the intensity probabilities when more than one template covers a pixel. Building upon both Ownership Masks and Cooperative Templates, Evolving Views update the template views when occlusion transits are a problem. With these enhancements we have been able to increase the accuracy of tracking objects where large portions of a template contain background pixels. Also occlusions and some types of unocclusions can be detected and discriminated. Finally, some failures in the basic tracker due to occlusion transits have been overcome.

Ecological modelling problems have characteristics both featured in other modelling fields and specific ones, hence, methods developed and tested in other research areas may not be suitable for modelling ecological problems or may perform poorly when used on ecological data. This thesis identifies issues associated with the techniques typically used for solving ecological problems and develops new generic methods for decision support, especially suitable for ecological data modelling, which are characterised by: (1) adaptive learning, (2) knowledge discovery and (3) accurate prediction. These new methods have been successfully applied to challenging real world ecological problems. Despite the fact that the number of possible applications of computational intelligence methods in ecology is vast, this thesis primarily concentrates on two problems: (1) species establishment prediction and (2) environmental monitoring. Our review of recent papers suggests that multi-layer perceptron networks trained using the backpropagation algorithm are most widely used of all artificial neural networks for forecasting pest insect invasions. While the multi-layer perceptron networks are appropriate for modelling complex nonlinear relationships, they have rather limited exploratory capabilities and are difficult to adapt to dynamically changing data. In this thesis an approach that addresses these limitations is proposed. We found that environmental monitoring applications could benefit from having an intelligent taste recognition system possibly embedded in an autonomous robot. Hence, this thesis reviews the current knowledge on taste recognition and proposes a biologically inspired artificial model of taste recognition based on biologically plausible spiking neurons. The model is dynamic and is capable of learning new tastants as they become available. Furthermore, the model builds a knowledge base that can be extracted during or after the learning process in form of IF-THEN fuzzy rules. It also comprises a layer that simulates the influence of taste receptor cells on the activity of their adjacent cells. These features increase the biological relevance of the model compared to other current taste recognition models. The proposed model was implemented in software on a single personal computer and in hardware on an Altera FPGA chip. Both implementations were applied to two real-world taste datasets.In addition, for the first time the applicability of transductive reasoning for forecasting the establishment potential of pest insects into new locations was investigated. For this purpose four types of predictive models, built using inductive and transductive reasoning, were used for predicting the distributions of three pest insects. The models were evaluated in terms of their predictive accuracy and their ability to discover patterns in the modelling data. The results obtained indicate that evolving connectionist systems can be successfully used for building predictive distribution models and environmental monitoring systems. The features available in the proposed dynamic systems, such as on-line learning and knowledge discovery, are needed to improve our knowledge of the species distributions. This work laid down the foundation for a number of interesting future projects in the field of ecological modelling, robotics, pervasive computing and pattern recognition that can be undertaken separately or in sequence.

La photosynthèse est un processus biologique naturel qui convertit l'énergie lumineuse en énergie chimique par l'action de centres réactionnels photosynthétiques. L'énergie convertie est stockée sous forme de produits de haute énergie synthétisés par la branche réductive du processus photosynthétique. Les électrons nécessaires à ces réactions sont fournis par des molécules d'eau lors de leur oxydation par le centre de dégagement de l'oxygène (Oxygen Evolving Complex: OEC) pour le système de photosynthèse II (PSII). La photosynthèse artificielle cherche à reproduire les réactions qui se produisent dans les organismes naturels afin de i) de mieux comprendre les processus chimiques qui se déroulent dans les systèmes naturels, et ii) de parvenir à exploiter l'énergie solaire pour le développement de carburants propres et renouvelables. Chaque étape qui survient dans le processus de photosynthèse naturelle, telle que la capture de lumière, le transfert d'énergie, le transfert d'électron, la séparation de charge, l'activation du catalyseur et la réaction catalytique doit se produire au sein du système artificiel. La photosynthèse artificielle cherche à reproduire les réactions qui se produisent dans les organismes naturels afin de i) de mieux comprendre les processus chimiques qui se déroulent dans les systèmes naturels, et ii) de parvenir à exploiter l'énergie solaire pour le développement de carburants propres et renouvelables. Chaque étape qui survient dans le processus de photosynthèse naturelle, telle que la capture de lumière, le transfert d'énergie, le transfert d'électron, la séparation de charge, l'activation du catalyseur et la réaction catalytique doit se produire au sein du système artificiel. Avec ces concepts en vue, nous avons conçu, synthétisé et caractérisé des molécules qui imitent les réactions réalisées par les antennes et les centres réactionnels présents dans le photosystème II. Ces molécules sont capables de reproduire la séparation de charges induite par la lumière, le transfert d'électrons et l'accumulation d'équivalents oxydo-réducteurs observés pendant la photosynthèse naturelle. Les antennes artificielles se constituent de caroténoïdes et phthalocyanines. Ces molécules présentent des profiles d'absorption large avec des coefficients d'extinction élevés, et sont capables de supporter des transferts d'énergie ultra rapides qui permettent l'état de séparation de charges. En faisant varier la longueur de la chaine conjuguée des caroténoïdes de neuf à onze liaisons doubles, nous avons pu mettre en évidence comment ces molécules peuvent agir aussi bien comme donneurs que comme agents dissipateurs d'énergie, effet caractéristique qui s'apparente au processus de trempe non-photochimique (Non Photochemical Quenching: NPQ) qui se produit dans le cycle de la zéaxanthine. Les mimiques des agents donneurs du photosystème II ont aussi été étudiées. Ces systèmes supramoléculaires contiennent une partie photoactive liée de façon covalente par un intermédiaire à une cavité contenant un ion ou un agrégat d'ions métalliques. La photosensibilisateur utilisé est un complexe du ruthénium [Ru(bipy)3]2+ (bpy = 2,20-bipyridine), homologue du P680, qui absorbe la lumière dans le spectre visible et déclenche le transfert d'électron. Les espèces RuIII résultantes ont un potentiel d'oxydation réversible de 1.3 V vs SCE, comparables à celui de P680 (1.25 V vs NHE) et présentent donc la possibilité d'oxyder à la fois un complexe manganèse ainsi qu'une source d'électron. Concernant les molécules imitant le coté donneur du PSII, nous avons synthétisé des paires ruthénium-phénol, ainsi que des systèmes ruthénium-manganèse bimétalliques. Parmi ces dernières, nous avons étudié celles présentant des cavités de coordination constituées de terpyridines, vu qu'il a déjà été montré que les dimères Mn-di-μ-oxo-Mn de ce type peuvent catalyser l'oxydation de l'eau en oxygène moléculaire. Des salènes et salophènes ont aussi été examinés étant donné que de tels groupes peuvent accomplir l'oxydation à deux électrons de substrats organique. Dans la littérature, ces réactions sont toutes conduites par l'action d'oxydants chimiques externes, tandis que nous avons pour but d'utiliser des espèces oxydantes induites par l'action de la lumière.

A template tracker is a tracker based on matching a pre-initialised view of an object with the object's view in an image sequence. Using an error function, the intensity difference between the template view and the templated region in the image is measured. This error measure is used as the basis for a template alignment algorithm that will adjust the template's pose to more accurately register the template view with the view of the object in the image. Some significant problems present themselves with this simple tracker. Extraneous, or non-object, pixels within the template boundaries can cause bias in the registration of the template. Partial occlusions of the object's view in the image can also cause serious bias in the template's pose. Beyond simple occlusions there are transits of occlusions across an object. Occlusion transits are significant because over time they can occlude the entire object in an incremental fashion. If initially the template view is not completely known this kind of occlusion can easily cause a total tracking failure for an object. In this thesis three enhancements of the basic template tracker are proposed: Ownership Masks, Cooperative Templates, and Evolving Views. Ownership Masks are aimed at eliminating the extraneous pixels from the template view. Cooperative templates are used to separate the intensity probabilities when more than one template covers a pixel. Building upon both Ownership Masks and Cooperative Templates, Evolving Views update the template views when occlusion transits are a problem. With these enhancements we have been able to increase the accuracy of tracking objects where large portions of a template contain background pixels. Also occlusions and some types of unocclusions can be detected and discriminated. Finally, some failures in the basic tracker due to occlusion transits have been overcome.