Consumer loans are part of loan products provided by bank institutions. This diploma thesis is focused on possibility of identifying risk clients with the consumer loans, using available data set. In the first part of the work was briefly mentioned the credit process and also theoretical basis of statistic methods used in empirical part of the work. In the second part were investigated dependencies, and was described the clients structure. Then the discriminant analysis was applied, with the aim to identify the sorting criteria, which could recognize the risk and unproblematic clients. Subsequently the results of the analysis were evaluated and described the identified connections.

Genre and domain influence an author\''s style of writing and therefore a text\''s characteristics. Natural language processing is prone to such variations in textual characteristics: it is said to be genre and domain dependent. This thesis investigates genre and domain dependencies in sentiment analysis. Its goal is to support the development of robust sentiment analysis approaches that work well and in a predictable manner under different conditions, i.e. for different genres and domains. Initially, we show that a prototypical approach to sentiment analysis -- viz. a supervised machine learning model based on word n-gram features -- performs differently on gold standards that originate from differing genres and domains, but performs similarly on gold standards that originate from resembling genres and domains. We show that these gold standards differ in certain textual characteristics, viz. their domain complexity. We find a strong linear relation between our approach\''s accuracy on a particular gold standard and its domain complexity, which we then use to estimate our approach\''s accuracy. Subsequently, we use certain textual characteristics -- viz. domain complexity, domain similarity, and readability -- in a variety of applications. Domain complexity and domain similarity measures are used to determine parameter settings in two tasks. Domain complexity guides us in model selection for in-domain polarity classification, viz. in decisions regarding word n-gram model order and word n-gram feature selection. Domain complexity and domain similarity guide us in domain adaptation. We propose a novel domain adaptation scheme and apply it to cross-domain polarity classification in semi- and unsupervised domain adaptation scenarios. Readability is used for feature engineering. We propose to adopt readability gradings, readability indicators as well as word and syntax distributions as features for subjectivity classification. Moreover, we generalize a framework for modeling and representing negation in machine learning-based sentiment analysis. This framework is applied to in-domain and cross-domain polarity classification. We investigate the relation between implicit and explicit negation modeling, the influence of negation scope detection methods, and the efficiency of the framework in different domains. Finally, we carry out a case study in which we transfer the core methods of our thesis -- viz. domain complexity-based accuracy estimation, domain complexity-based model selection, and negation modeling -- to a gold standard that originates from a genre and domain hitherto not used in this thesis.

Spotify is a music streaming service that allows users to listen to their favourite music. Due to the rapid growth in the number of users, the amount of processing that must be provided by the company’s data centers is also growing. This growth in the data centers is necessary, despite the fact that much of the music content is actually sourced by other users based on a peer-to-peer model. Spotify’s backend (the infrastructure that Spotify operates to provide their music streaming service) consists of a number of different services, such as track search, storage, and others. As this infrastructure grows, some service may behave not as expected. Therefore it is important not only for Spotify’s operations (footnote: Also known as the Service Reliability Engineers Team (SRE)) team, but also for developers, to understand exactly how the various services are actually communicating. The problem is challenging because of the scale of the backend network and its rate of growth. In addition, the company aims to grow and expects to expand both the number of users and the amount of content that is available. A steadily increasing feature-set and support of additional platforms adds to the complexity. Another major challenge is to create tools which are useful to the operations team by providing information in a readily comprehensible way and hopefully integrating these tools into their daily routine. The ultimate goal is to design, develop, implement, and evaluate a tool which would help the operations team (and developers) to understand the behavior of the services that are deployed on Spotify’s backend network. The most critical information is to alert the operations staff when services are not operating as expected. Because different services are deployed on different servers the communication between these services is reflected in the network communication between these servers. In order to understand how the services are behaving when there are potentially many thousands of servers we will look for the patterns in the topology of this communication, rather than looking at the individual servers. This thesis describes the tools that successfully extract these patterns in the topology and compares them to the expected behavior. / Spotify är en växande musikströmningstjänst som möjliggör för dess användare att lyssna på sin favoritmusik. Med ett snabbt växande användartal, följer en tillväxt i kapacitet som måste tillhandahållas genom deras datacenter. Denna växande kapacitet är nödvändig trots det faktum att mycket av deras innehåll hämtas från andra användare via en peer-to-peer modell. Spotifys backend (den infrastruktur som kör Spotifys tjänster) består av ett antal distinkta typer som tillhandahåller bl.a. sökning och lagring. I takt med att deras backend växer, ökar risken att tjänster missköter sig. Därför är det inte bara viktigt för Spotifys driftgrupp, utan även för deras utvecklare, att förstå hur dessa kommunicerar. Detta problem är en utmaning p.g.a. deras storskaliga infrastruktur, och blir större i takt med att den växer. Företaget strävar efter tillväxt och förväntar detta i både antalet användare och tillgängligt innehåll. Stadigt ökande funktioner och antalet distinkta plattformar bidrar till komplexitet. Ytterligare en utmaning är att bidra med verktyg som kan användas av driftgrupp för att tillhandahålla information i ett tillgängligt och överskådligt format, och att förhoppningsvis integrera dessa i en daglig arbetsrutin. Det slutgiltiga målet är att designa, utveckla, implementera och utvärdera ett verktyg som låter deras driftgrupp (och utvecklare) förstå beteenden i olika tjänster som finns i Spotifys infrastruktur. Då dessa tjänster är utplacerade på olika servrar, reflekteras kommunikationen mellan dem i deras nätverketskommunikation. För att förstå tjänsternas beteende när det potentiellt kan finnas tusentals servrar bör vi leta efter mönster i topologin, istället för beteenden på individuella servrar.

Due to cars' negative impact on our cities and the strain cars put on the environment, more and more cities strive towards sustainable mobility and a reduction of car dependencies. This thesis will examine how two new contemporary planning projects view the car and what implications follow an eventual decrease of car dependent planning. The two projects are Brunnshög in Lund and Stationsstaden in Kävlinge, located in the South-West of Sweden, in the southern county of Skåne. Our study is based on research questions aiming to find out if these two projects are planning to reduce cars and how they will do it, as well as looking at the impacts that follow this kind of planning and consequences it can have on less mobile groups in society. The study is conducted by first gathering research surrounding the history of the car, its impacts on cities as well as current measures to reduce automobile dependencies. Following this, official planning documents and policies for both projects were analysed and interviews were conducted with relevant actors to gain a deeper understanding of the projects and work as a complement to the planning documents. With five theories acting as our framework the material was analysed and discussed from different concepts point of view, to gain a deeper understanding of the complex question that reducing cars in cities constitutes.  Through the study we concluded that the two projects work with reducing car dependencies to different degrees, Brunnshög more so than Stationsstaden. Identified reasons for their differences are due to challenges which include, political support, customer demand, time frames and surrounding infrastructure among other things. We also concluded that more consideration for the effects on certain groups in society is needed, as a reduction of cars might come with great effects that can cause injustices towards certain vulnerable groups.

The use of dependencies have been increasing in popularity over the past decade, especially as package managers such as JavaScript's npm has made getting these packages a simple command to run. However, while incidents such as the left-pad incident has increased awareness of how vulnerable relying on these packages are, there is still some work to be done when it comes to getting developers to take the extra research step to determine if a package is up to standards. Finding metrics of different packages and comparing them is always a difficult and time consuming task, especially since potential vulnerabilities are not the only metric to consider. For example, considering how popular and how actively maintained the package is also just as important. Therefore, we propose a visualization tool called DependencyVis that is specific to JavaScript projects and npm packages as a solution by analyzing a project's dependencies in order to help developers by looking up the many basic metrics that can address a dependency's popularity, activeness, and vulnerabilities such as the number of GitHub stars, forks, and issues as well as security advisory information from npm audit. This thesis then proposes many use cases for DependencyVis to help users compare dependencies by displaying the dependencies in a graph with metrics represented by aspects such as node color or node size.

Non-projective dependencies remain an at large issue in the field of dependency parsing. Regardless of what parsing algorithm is used, researchers run into the issue of computational speed and lower parsing performance on non-projective dependencies than on projective dependencies. Through a better understanding of non-projectivity, we may be able to address both issues. This thesis is aimed to discover what types of non-projective dependencies are prevalent in the three languages English, German, and Czech. Moreover, this thesis is aimed to define and create a linguistically informed projectivization scheme and to find out the extent to which the scheme improves upon the performance of the baseline parser. In order to achieve these aims, the eight most frequently occurring non-projective dependencies in English, German, and Czech were surveyed. This means that the causes of their non-projectivity were discovered, the structures of the non-projective dependencies were analyzed, and generalizations and comparisons between non-projective dependencies were made. After the survey, an attempt to define and create a linguistically informed projectivization scheme was made. The goals were not only to projectivize the non-projective relations but to do so by assigning the closest possible new parent in the sentence to the non-projective child and to minimize the number of projectivization transformations that needed to be made. Although the survey of the non-projective dependencies yielded good results, as we were able to identify that the causes of the more frequently occurring non-projective dependencies in German and Czech were the same and the structures of them the same as well, we reached no solid conclusion on how a linguistically informed projectivization scheme could be defined, as further research is needed. However, the novel projectivization scheme we did come up with managed to marginally outperform the baseline parser in English and German, and moderately outperform the baseline parser in Czech which is the language with the most non-projective dependencies of the group.

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As many automotive companies have moved towards a higher degree of variability in the product lines they offer their customers, a necessary need has emerged for so called feature dependency structures that are used to describe product feature dependencies and verify order validity. In this study, the possibility of using a node-link graph representation to visualize such a feature dependency structure and the associated affordances and limitations were investigated by the implementation of a case study at the Swedish automotive company Scania CV AB. Qualitative data gathering methods such as contextual inquiry and semi-structured interviews with employees were used to identify key tasks and issues involved in maintenance and analysis of Scania’s in-house feature dependency structure. These findings were used together with user-supported iterative prototyping to create a few visualization prototypes intended to provide support with performance of some of the identified tasks. User evaluation of the prototypes showed that a node-link graph representation was a viable solution to support users with structure maintenance, exhibiting the following affordances: structure exploration, overview and context. Furthermore, the major limitations of the tested representation were found to be lookup of specific information and access to detail. The findings of this study are expected to be of use for other automotive companies that employ a high degree of feature variability in their product lines through the use of complex feature dependency structures. / I samband med att flera fordonstillverkare gått över till att erbjuda en allt större grad av varians i de produktlinjer man erbjuder sina kunder så har ett nödvändigt behov uppstått av att ha regelverk som beskriver de beroenden som finns mellan produktegenskaper och verifierar att inkomna ordrar är giltiga. I den här studien så har möjligheten att visualisera den typen av regelverk med en så kallad ”node-link”-graf samt de styrkor och svagheter som följer med en sådan representation undersökts genom en fallstudie på den svenska fordonstillverkaren Scania CV AB. Med hjälp av kvalitativa datainsamlingsmetoder som så kallad ”Contextual inquiry” och semistrukturerade intervjuer med anställda specialiserade på underhåll av Scanias egna egenskapsregelverk så kunde nyckeluppgifter och svårigheter relaterade till regelverket identifieras. Dessa upptäckter användes sedan tillsammans med användarcentrerat iterativt prototypande för att skapa ett antal visualiseringsprototyper avsedda att underlätta utförandet av några av de tidigare identifierade uppgifterna. Användarutvärdering av prototyperna visade att en visualisering baserad på en ”node-link”-representation var en gångbar lösning som kunde underlätta för användarna. Dess styrkor var att stödja utforskande av strukturen med bra överblick av innehållet och bibehållet sammanhang. Representation var dock svag när det gällde att stödja användaren i att leta upp specifik information och att tillhandahålla mer ingående detaljer. Dessa resultat förväntas vara användbara för andra fordonstillverkare som bygger sina produktlinjer på en hög grad av varians med hjälp av komplexa beroenderegelverk för produktegenskaper.

Degradação do design é um problema central investigado na área de evolução de software. A densa rede de interdependências que emerge entre classes e módulos ao longo do tempo resulta em código difícil de mudar, não reutilizável e que não comunica por si só sua intenção. Dentre outros motivos, designs degradam porque requisitos mudam de maneiras não antecipadas pelo design inicial, ou seja, as modificações no código introduzem dependências novas e não planejadas entre classes e módulos do sistema. A gerência de dependências visa reduzir a degradação do design por meio de uma série de mecanismos que auxiliam na administração da complexidade estrutural inerente de sistemas orientados a objetos. Neste trabalho, investigamos as técnicas de identificação de dependências estruturais e lógicas. Em particular, por meio de um estudo de larga escala, comparamos os conjuntos desses dois tipos de dependências. Em seguida, conduzimos um estudo de caso a fim de identificar as origens de dependências lógicas. Por fim, fazemos um levantamento das técnicas de visualização de dependências e mostramos a ferramenta XFlow. / Design degradation is a central problem investigated in the area of software evolution. The dense web of interdependencies that emerges among classes and modules over time results in code that is hard to change, not reusable and that does not communicate its intention. Among other reasons, designs degrade because requirements changes in ways that were not anticipated by the initial design, i.e. the changes in code introduce new and unplanned dependencies among classes and modules of the system. Dependency management aims to reduce design degradation by means of a series of mechanisms that helps in the management of the inherent structural complexity of object oriented systems. In this work, we investigate structural and logical dependencies identification techniques. In particular, by means of a large scale study, we compare the sets of these two kinds of dependencies. Afterwards, we conduct a case study in order to uncover the origins of logical dependencies. Finally, we survey dependency visualization techniques and present the XFlow tool.