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This diploma thesis analyzes mortality from external causes in the region of CZ between 2014 and 2018. The main focus is on external causes of death as a group, suicide rates, and mortality from traffic accidents. Another aim of this thesis is, besides the description of mortality from external causes, to identify regional correlation and differences in mortality from external causes and selected causes of death (suicides and traffic accidents). This study uses correlation, factor, and cluster analysis. Because of the low numbers of deaths in the case of women (in all dependent variables), the study analyzes mortality from both sexes together. Based on created factors clusters of regions are developed. Results show the difference between regions in CZ in mortality external causes. Regions also vary in suicide mortality rates and traffic accident mortality rates. Independent variables that correlate with mortality from external causes vary for external causes as a group, for suicides, and for mortality from traffic accidents. Keywords: external causes of death, suicides, traffic accidents, factor analysis, cluster analysis

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Credit institutions constitute a central part of life as it is today and has been doing so for a long time. A fault within the banking system can cause a tremendous amount of damage to individuals as well as countries. A recent and memorable fault is the global financial crisis 2007-2009. It has affected millions of people in different ways ever since it struck. What caused it is a complex issue which cannot be answered easily. But what has been done to prevent something similar to occur once again? How has the business models of the credit institutions changed since the crisis? Cluster analysis is used in this thesis to address these questions. Banking-data were processed with Calinski-Harabasz Criterion and Ward's method and this resulted in two clusters being found. A cluster is a collection of observations that have similar characteristics or business model in this case. The business models that the clusters represents are universal banking with a retail focus and universal banking with a wholesale focus. These business models have been analyzed over time (2007-2016), which revealed that the credit institutions have developed in a healthy direction. Thus, credit institutions were more financially reliable in 2016 compared to 2007. According to trends in the data this development is likely to continue. / Kreditinstituten utgör en central del av livet som det ser ut idag och har gjort det under en lång tid. Ett fel inom banksystemet kan orsaka enorma skador för individer likväl som länder. Ett nutida och minnesvärt fel är den globala finanskrisen 2007-2009. Den har påverkat millioner människor på olika vis ända sedan den slog till. Vad som orsakade den är en komplex fråga som inte kan besvaras med lätthet. Men vad har gjorts för att förebygga att något liknande händer igen? Hur har affärsmodellerna för kreditinstituten ändrats sedan krisen? Klusteranalys används i denna rapport för att adressera dessa frågor. Bankdata processerades med Calinski-Harabasz Kriteriet and Wards metod och detta resulterade i att två kluster hittades. Ett kluster är en samling observationer med liknande karakteristik eller affärsmodell i detta fall. De affärsmodeller som klustrena representerar är universella banker med retail fokus samt universella banker med wholessale fokus. Dessa affärsmodeller har analyserats över tid, vilket har avslöjat att kreditinstituten har utvecklats i en hälsosam riktning. Kreditinstituten var mer finansiellt pålitliga 2016 jämfört med 2007. Enligt trender i datan så är det troligt att denna utveckling forsätter.

As one of the most essential data mining tasks, clustering analysis has been widely discussed and employed since its invention. Czekanowski’s diagram, which has been around for over a century as a visualization tool for exploring cluster distributions, is being improved continually. RMaCzek is a package of R, which is used to implement Czekanowski’s diagram. By using this package, users can plot a symmetric or asymmetric Czekanowski’s diagram. However, the user still has to manually judge the clustering result through the diagram, which will inevitably lead to the deviation of the subjective judgement and increase the user’s workload. In order to keep the advantages of Czekanowski’s diagram and exploit its potential, Czekanowski’s clustering algorithm is proposed in this thesis. A new clustering algorithm based on Czekanowski’s diagram that allows it to label the clustering results directly and mark the findings on the Czekanowski’s diagram. Czekanowski’s clustering supports two clustering methods, namely exact Czekanowski’s clustering and fuzzy Czekanowski’s clustering, so that users can choose different methods according to the characteristics of the analysis object. Besides, this thesis will also cover the upgraded RMaCzek R package’s application method, including how to use it for Czekanowski’s clustering, how to express the clustering outcomes by Czekanowski’s diagram and the improvement of plotting function. On the other hand, the performance of the new clustering algorithm will be evaluated in this thesis by comparing it with the other five commonly used clustering algorithms. Also, through some experiments, we were able to determine the impact of various algorithm parameters on clustering performance.

Shared mobility is considered a more sustainable alternative to private modes. Nonetheless, its sudden and sometimes “out of control” emergence poses issues that need to be addressed. Lack of regulations and public space mismanagement cause sidewalks and city roads to be overcrowded with shared vehicles (especially in the case of micromobility). This causes nuisance and safety concerns and hinders the societal benefits shared mobility may provide. Shared mobility hubs have the potential to address these issues. The research was carried out within the context of the SmartHubs project, an EIT Urban Mobility project initiated in 2021 by a diverse consortium of 7 cities, companies, and universities to develop and validate effective and economically viable mobility hub solutions. This degree project aims to improve the Decision-Support-Tool (DST) developed by SmartHubs to identify locations of shared-mobility hubs having high potential in driving sustainable travel usage. To achieve that, the thesis proposes a methodology for determining smart hub locations and their corresponding utilities based on the combination of GIS cluster analysis of free-floating shared mobility parking patterns and a stated-preference study. The potential hub locations were determined from the cluster analysis of free-floating trip characteristics. Using the stated preference survey data, the thesis develops a model to estimate the probability of parking at the hub as a function of explanatory variables, including walking distance, reward policies and the parking situation. The model testing results showed that the proposed methodology can well predict the hub (usage) demand and improve the current DST originally developed in the SmartHubs project.

School teachers spend approximately 30 percent of their time grading exams and other assessments. With an increasingly digitized education, a research field have been initiated that aims to reduce the time spent on grading by automating it. This is an easy task for multiple-choice questions but much harder for open-ended questions requiring free-text answers, where the latter have shown to be superior for knowledge assessment and learning consolidation. While results in previous work have presented promising results of up to 90 percent grading accuracy, it is still problematic using a system that gives the wrong grade in 10 percent of the cases. This has given rise to a research field focusing on assisting teachers in the grading process, instead of fully replacing them. Cluster analysis has been the most popular tool for this, grouping similar answers together and letting teachers process groups of answers at once, instead of evaluating each question one-at-a-time. This approach has shown evidence to decrease the time spent on grading substantially, however, the methods for performing the clustering vary widely between studies, leaving no apparent methodology choice for real-use implementation. Using several techniques for pre-processing, text representation and choice of clustering algorithm, this work compared various methods for clustering free-text answers by evaluating them on a dataset containing almost 400 000 student answers. The results showed that using all of the tested pre-processing techniques led to the best performance, although the difference to using minimum pre-processing were small. Sentence embeddings were the text representation approach that performed the best, however, it remains to be answered how it should be used when spelling and grammar is part of the assessment, as it lacks the ability to identify such errors. A suitable choice of clustering algorithm is one where the number of clusters can be specified, as determining this automatically proved to be difficult. Teachers can then easily adjust the number of clusters based on their judgement. / Skollärare spenderar ungefär 30 procent av sin tid på rättning av prov och andra bedömningar. I takt med att mer utbildning digitaliseras, försöker forskare hitta sätt att automatisera rättning för att minska den administrativa bördan för lärare. Flervalsfrågor har fördelen att de enkelt kan rättas automatiskt, medan öppet ställda frågor som kräver ett fritt formulerat svar har visat sig vara ett bättre verktyg för att mäta elevers förståelse. Dessa typer av frågor är däremot betydligt svårare att rätta automatiskt, vilket lett till forskning inom automatisk rättning av dessa. Även om tidigare forskning har lyckats uppnå resultat med upp till 90 procents träffsäkerhet, är det fortfarande problematiskt att det blir fel i de resterande 10 procenten av fallen. Detta har lett till forskning som fokuserar på underlätta för lärare i rättningen, istället för att ersätta dem. Klusteranalys har varit det mest populära tillvägagångssättet för att åstadkomma detta, där liknande svar grupperas tillsammans, vilket möjliggör rättning av flera svar samtidigt. Denna metod har visat sig minska rättningstiden signifikant, däremot har metoderna för att göra klusteranalysen varierat brett, vilket gör det svårt att veta hur en implementering i ett verkligt scenario bör se ut. Genom att använda olika tekniker för textbearbetning, textrepresentation och val av klusteralgoritm, jämför detta arbete olika metoder för att klustra fritext-svar, genom att utvärdera dessa på nästan 400 000 riktiga elevsvar. Resultatet visar att mer textbearbetning generellt är bättre, även om skillnaderna är små. Användning av så kallade sentence embeddings ledde till bäst resultat när olika tekniker för textrepresentation jämfördes. Däremot har denna teknik svårare att identifiera grammatik- och stavningsfel, hur detta ska hanteras är en fråga för framtida forskning. Ett lämpligt val av klustringsalgoritm är en där antalet kluster kan bestämmas av användaren, då det visat sig svårt att bestämma det automatiskt. Lärare kan då justera antalet kluster ifall det skulle vara för få eller för många.

This work is about how the multivariate analysis tool cluster analysis can be appliedto find meaningfull groups of players based on player statistics. The aim of the work isan attempt to find good clusters among players within the Spanish top football divisionLa Liga for the 2022-2023 season. A comparison between agglomerative hierarchical and k-means has been applied as a method to answer the purpose. The result of the workshowed that no good clusters could be identified among the players based on playerstatistics from La Liga season 22-23.

With the rise of artificial intelligence, applications for machine learning can be found in nearly everyaspect of modern life, from healthcare and transportation to software services like recommendationsystems. Consequently, there are now more developers engaged in the field than ever - with the numberof implementations rapidly increasing by the day. In order to meet the new demands, it would be usefulto provide services that allow for an easy orchestration of a large number of repositories. Enabling usersto easily share, access and search for source code would be beneficial for both research and industryalike. A first step towards this is to find methods for clustering source code by functionality. The problem of clustering source code has previously been studied in the literature. However, theproposed methods have so far not leveraged the capabilities of deep neural networks (DNN). In thiswork, we investigate the possibility of using DNNs to learn embeddings of source code for the purpose ofclustering by functionality. In particular, we evaluate embeddings from Code2Vec and cuBERT modelsfor this specific purpose. From the results of our work we conclude that both Code2Vec and cuBERT are capable of learningsuch embeddings. Among the different frameworks that we used to fine-tune cuBERT, we found thebest performance for this task when fine-tuning the model under the triplet loss criterion. With thisframework, the model was capable of learning embeddings that yielded the most compact and well-separated clusters. We found that a majority of the cluster assignments were semantically coherent withrespect to the functionalities implemented by the methods. With these results, we have found evidenceindicating that it is possible to learn embeddings of source code that encode the functional similaritiesamong the methods. Future research could therefore aim to further investigate the possible applicationsof the embeddings learned by the different frameworks. / Med den avsevärda ökningen av användandet av artificiell intelligens går det att finna tillämpningar förmaskininlärningsalgoritmer i nästan alla aspekter av det moderna livet, från sjukvård och transport tillmjukvarutjänster som rekommendationssystem. Till följd av detta så är det fler utvecklare än någonsinengagerade inom området, där antalet nya implementationer ökar för var dag. För att möta de nyakraven skulle det vara användbart att kunna tillhandahålla tjänster som möjliggör en enkel hantering avett stort antal kodförråd. Att göra det möjligt för användare att enkelt dela, komma åt och söka efterkällkod skulle vara till nytta inom både forskning och industri. Ett första steg mot detta är att hittametoder som gör det möjligt att klustra källkod med avseende på funktionalitet. Problemet med klustring av källkod är något som har tidigare studerats. De föreslagna metoderna hardock hittils inte utnyttjat kapaciteten hos djupa neurala nätverk (DNN). I detta arbete undersöker vimöjligheten att använda DNN för inlärning av inbäddningar av källkod i syfte att klustra med avseendepå funktionalitet. I synnerhet så utvärderar vi inbäddningar från Code2Vec- och cuBERT-modeller fördetta specifika ändamål. Från resultatet av vårt arbete drar vi slutsatsen att både Code2Vec och cuBERT har kapacitet för attlära sig sådana inbäddningar. Bland de olika ramverken som vi undersökte för att finjustera cuBERT,fann vi att modellen som finjusterades under triplet-förlustkriteriet var bäst lämpad för denna uppgift.Med detta ramverk kunde modellen lära sig inbäddningar som resulterade i de mest kompakta och välseparerade klusterna, där en majoritet av klustertilldelningarna var semantiskt sammanhängande medavseende på funktionaliteten som metoderna implementerade. Med dessa resultat har vi funnit beläggsom tyder på att det är möjligt att lära sig inbäddning av källkod som bevarar och åtger funktionellalikheter mellan metoder. Framtida forskning kan därför syfta till att ytterligare undersöka de olikamöjliga användningsområdena för de inbäddningar som lärts in inom de olika ramverken.

This thesis comprises two manuscripts prepared for scholarly journals. Chapter 2 comprises an article entitled “Exploring Historic Changes in Musical Communication: Deconstructing Emotional Cues in Preludes by Bach and Chopin.”, which examines emotion perception in historic prelude sets by J.S. Bach and F. Chopin. This work connects psychological research on perceived musical emotion to musicological research describing changes in music structure. Using a technique called commonality analysis to deconstruct cues’ individual and joint roles in predicting participants’ perceived emotions, the chapter clarifies how music’s conveyed emotion can differ in compositions from different eras. Chapter 3 comprises an article entitled “Parsing Musical Patterns in Prelude Sets: Bridging Qualitative and Quantitative Epistemologies in Historical Music Research”. This chapter bridges gaps between qualitative and quantitative research on music history through an analytical approach engaging with both fields. Specifically, cluster analyses of Bach and Chopin’s preludes reveal notable differences in the composers’ expressive toolkits, consistent with work from historical and empirical music research. Through a novel analytical framework, the chapter illustrates a method for detecting groups of pieces demarcated by salient musical differences, assessing cues’ importance within these groups, and determining the most influential cue values for each group. Together, these articles provide new insight into the subtle sonic relationships influencing musical meaning and emotion perception. / Thesis / Master of Science (MSc) / Music’s capacity to express emotion has received considerable attention in psychological and musicological research. Whereas efforts from psychology clarify the musical cues for emotion through perceptual experiments, efforts from musicology track changes in compositional practice over time—finding changing relationships between music’s cues for emotion in historically diverse compositions. To date, the implications of these changing musical relationships for emotion perception remain unclear. This thesis analyzes musical scores and listeners’ emotion ratings to gain insight into music’s structural changes throughout history and their implications for perceived emotion. By applying statistical techniques to (i) detect musical patterns in prelude sets by J.S. Bach and F. Chopin and (ii) clarify how cue relationships influence emotion perception, this thesis sheds light on the relationship between music’s historic context and its emotional meaning.