The Swedish software industry is fast-growing and in needof competent personnel, the education system is on the frontline of producing qualified graduates to meet the job marketdemand. Reports and studies show there exists a gapbetween industry needs and what is taught in highereducation, and that there is an undefined skills shortageleading to recruitment failures. This study explored theindustry-education gap with a focus on higher vocationaleducation (HVE) through the use of natural languageprocessing (NLP) to ascertain the demands of the industryand what is taught in HVE. Using the authors' custom-madetool Vocational Education and Labour Market Analyser(VELMA), job ads and HVE curricula were collected fromthe Internet. Then analysed through the topic modellingprocess latent Dirichlet allocation (LDA) to classify lowerlevel keywords into cohesive categories for documentfrequency analysis. Findings show that a large number ofHVE programmes collaborate with the industry via indirectfinancing and that job ads written in Swedish consist, inlarger part, of inconsequential words compared to adswritten in English. Moreover, An industry demand withincloud and embedded technologies, security engineers andsoftware architects can be observed. Whereas, the findingsfrom HVE curricula point to a focus on educating webdevelopers and general object-oriented programminglanguages. While there are limitations in the topic modellingprocess, the authors conclude that there is a mismatchbetween what is taught in HVE programmes and industrydemand. The skills identified to be lacking in HVE wereassociated with cloud-, embedded-, and security-relatedtechnologies together with architectural disciplines. Theauthors recommend future work with a focus on improvingthe topic modelling process and including curricula fromgeneral higher education.

<p>The goal of this thesis is to establish generalized isoperimetric inequalities on first exit time distributions as well as expectations of L\'evy processes.</p> <p>Firstly, we prove inequalities on first exit time distributions in the case that the L\'evy process is an $\alpha$-stable symmetric process $A_t$ on $\R^d$, $\alpha\in(0,2]$. Given $A_t$ and a bounded domain $D\subset\R^d$, we present a proof, based on the classical Brascamp-Lieb-Luttinger inequalities for multiple integrals, that the distribution of the first exit time of $A_t$ from $D$ increases under Steiner symmetrization. Further, it is shown that when a sequence of domains $\{D_m\}$ each contained in a ball $B\subset\R^d$ and satisfying the $\varepsilon$-cone property converges to a domain $D'$ with respect to the Hausdorff metric, the sequence of distributions of first exit times for Brownian motion from  $D_m$  converges to the distribution of the exit time of Brownian motion from $D'$. The second set of results in this thesis extends the theorems from \cite{BanMen} by proving generalized isoperimetric inequalities on expectations of L\'evy processes in the case of Steiner symmetrization.% using the Brascamp-Lieb-Luttinger inequalities used above. </p> <p>These results will then be used to establish inequalities involving distributions of first exit times of $\alpha$-stable symmetric processes $A_t$ from triangles and quadrilaterals. The primary application of these inequalities is verifying a conjecture from Ba\~nuelos for these planar domains. This extends a classical result of P\'olya and Szeg\"o to the fractional Laplacian with Dirichlet boundary conditions.</p>

No description available.

Understanding the effect of human-induced pollution on the environment is an important precursor to promoting public health and environmental stability. One aspect of understanding pollution is understanding pollution sources. Various methods have been used and developed to understand pollution sources and the amount of pollution those sources emit. Multivariate receptor modeling seeks to estimate pollution source profiles and pollution emissions from concentrations of pollutants such as particulate matter (PM) in the air. Previous approaches to multivariate receptor modeling make the following two key assumptions: (1) PM measurements are independent and (2) source profiles are constant through time. Notwithstanding these assumptions, the existence of temporal correlation among PM measurements and time-varying source profiles is commonly accepted. In this thesis an approach to multivariate receptor modeling is developed in which the temporal structure of PM measurements is accounted for by modeling source profiles as a time-dependent Dirichlet process. The Dirichlet process (DP) pollution model developed herein is evaluated using several simulated data sets. In the presence of time-varying source profiles, the DP model more accurately estimates source profiles and source contributions than other multivariate receptor model approaches. Additionally, when source profiles are constant through time, the DP model outperforms other pollution receptor models by more accurately estimating source profiles and source contributions.

This thesis aims to provide an introduction to the field of potential theory at an undergraduate level, by studying an important mathematical problem in the field, namely the Dirichlet problem. By examining the historical development of different methods for solving the problem in increasingly general contexts, and the mathematical concepts which were established to support these methods, the aim is to provide an overview of various basic techniques in the field of potential theory, as well as a summary of the fundamental results concerning the Dirichlet problem in Euclidean space. / Målet med detta arbete är att vara en introduktion på kandidatnivå till ämnesfältet potentialteori, genom att studera ett viktigt matematiskt problem inom potentialteori, Dirichletproblemet. Genom att undersöka den historiska utvecklingen av olika lösningsmetoder för problemet i mer och mer allmänna sammanhang, i kombination med de matematiska koncepten som utvecklades för att användas i dessa lösningsmetoder, ges en översikt av olika grundläggande tekniker inom potentialteori, samt en sammanfattaning av de olika matematiska resultaten som har att göra med Dirichletproblemet i det Euklidiska rummet.

Semantic graphs have been used for optimizing various natural language processing tasks as well as augmenting search and information retrieval tasks. In most cases these semantic graphs have been constructed through supervised machine learning methodologies that depend on manually curated ontologies such as Wikipedia or similar. In this thesis, which consists of two parts, we explore in the first part the possibility to automatically populate a semantic graph from an ad hoc data set of 50 000 newspaper articles in a completely unsupervised manner. The utility of the visual representation of the resulting graph is tested on 14 human subjects performing basic information retrieval tasks on a subset of the articles. Our study shows that, for entity finding and document similarity our feature engineering is viable and the visual map produced by our artifact is visually useful. In the second part, we explore the possibility to identify entity relationships in an unsupervised fashion by employing abstractive deep learning methods for sentence reformulation. The reformulated sentence structures are qualitatively assessed with respect to grammatical correctness and meaningfulness as perceived by 14 test subjects. We negatively evaluate the outcomes of this second part as they have not been good enough to acquire any definitive conclusion but have instead opened new doors to explore. / Semantiska grafer har använts för att optimera olika processer för naturlig språkbehandling samt för att förbättra sökoch informationsinhämtningsuppgifter. I de flesta fall har sådana semantiska grafer konstruerats genom övervakade maskininlärningsmetoder som förutsätter manuellt kurerade ontologier såsom Wikipedia eller liknande. I denna uppsats, som består av två delar, undersöker vi i första delen möjligheten att automatiskt generera en semantisk graf från ett ad hoc dataset bestående av 50 000 tidningsartiklar på ett helt oövervakat sätt. Användbarheten hos den visuella representationen av den resulterande grafen testas på 14 försökspersoner som utför grundläggande informationshämtningsuppgifter på en delmängd av artiklarna. Vår studie visar att vår funktionalitet är lönsam för att hitta och dokumentera likhet med varandra, och den visuella kartan som produceras av vår artefakt är visuellt användbar. I den andra delen utforskar vi möjligheten att identifiera entitetsrelationer på ett oövervakat sätt genom att använda abstraktiva djupa inlärningsmetoder för meningsomformulering. De omformulerade meningarna utvärderas kvalitativt med avseende på grammatisk korrekthet och meningsfullhet såsom detta uppfattas av 14 testpersoner. Vi utvärderar negativt resultaten av denna andra del, eftersom de inte har varit tillräckligt bra för att få någon definitiv slutsats, men har istället öppnat nya dörrar för att utforska.

Over the last couple of years, Machine Learning has seen a very high increase in usage. Many previously manual tasks are becoming automated and it stands to reason that this development will continue in an incredible pace. This paper builds on the work in Topic Classification and attempts to provide a baseline on how to analyse the Swedish Central Bank Minutes and gather information using both Latent Dirichlet Allocation and a simple Neural Networks. Topic Classification is done on Monetary Policy Minutes from 2004 to 2018 to find how the distributions of topics change over time. The results are compared to empirical evidence that would confirm trends. Finally a business perspective of the work is analysed to reveal what the benefits of implementing this type of technique could be. The results of these methods are compared and they differ. Specifically the Neural Network shows larger changes in topic distributions than the Latent Dirichlet Allocation. The neural network also proved to yield more trends that correlated with other observations such as the start of bond purchasing by the Swedish Central Bank. Thus, our results indicate that a Neural Network would perform better than the Latent Dirichlet Allocation when analyzing Swedish Monetary Policy Minutes. / Under de senaste åren har artificiell intelligens och maskininlärning fått mycket uppmärksamhet och växt otroligt. Tidigare manuella arbeten blir nu automatiserade och mycket tyder på att utvecklingen kommer att fortsätta i en hög takt. Detta arbete bygger vidare på arbeten inom topic modeling (ämnesklassifikation) och applicera detta i ett tidigare outforskat område, riksbanksprotokoll. Latent Dirichlet Allocation och Neural Network används för att undersöka huruvida fördelningen av diskussionspunkter (topics) förändras över tid. Slutligen presenteras en teoretisk diskussion av det potentiella affärsvärdet i att implementera en liknande metod. Resultaten för de olika modellerna uppvisar stora skillnader över tid. Medan Latent Dirichlet Allocation inte finner några större trender i diskussionspunkter visar Neural Network på större förändringar över tid. De senare stämmer dessutom väl överens med andra observationer såsom påbörjandet av obligationsköp. Därav indikerar resultaten att Neural Network är en mer lämplig metod för analys av riksbankens mötesprotokoll.

Metagenomics allows studying genetic potentials of microorganisms without prior cultivation. Since metagenome assembly results in fragmented genomes, a key challenge is to cluster the genome fragments (contigs) into more or less complete genomes. The goal of this project was to investigate how well CONCOCT bins assembled contigs into taxonomically relevant clusters using the abundance profiles of the contigs over multiple samples. This was done by studying the effects of different parameter settings for CONCOCT on the clustering results when clustering metagenome contigs from in silico model communities generated by mixing data from isolate genomes. These parameters control how the model that CONCOCT trains is tuned and then how the model fits contigs to their cluster. Each parameter was tested in isolation while others were kept at their default values. For each of the data set used, the number of clusters was kept constant at the known number of species and strains in their respective data set. The resulting configuration was to use a tied covariance model, using principal components explaining 90% of the variance, and filtering out contigs shorter than 3000 bp. It also suggested that all available samples should be used for the abundance profiles. Using these parameters for CONCOCT, it was executed to have it estimate the number of clusters automatically. This gave poor results which lead to the conclusion that the process for selecting the number of clusters that was implemented in CONCOCT, “Bayesian Information Criterion”, was not good enough. That led to the testing of another similar mathematical model, “Dirichlet Process Gaussian Mixture Model”, that uses a different algorithm to estimate number of clusters. This new model gave much better results and CONCOCT has adapted a similar model in later versions. / Metagenomik möjliggör analys av arvsmassor i mikrobiella floror utan att först behöva odla mikroorgansimerna. Metoden innebär att man läser korta DNA-snuttar som sedan pusslas ihop till längre genomfragment (kontiger). Genom att gruppera kontiger som härstammar från samma organism kan man sedan återskapa mer eller mindre fullständiga genom, men detta är en svår bioinformatisk utmaning. Målsättningen med det här projektet var att utvärdera precisionen med vilken mjukvaran CONCOCT, som vi nyligen utvecklat, grupperar kontiger som härstammar från samma organism baserat på information om kontigernas sekvenskomposition och abundansprofil över olika prover. Vi testade hur olika parametrar påverkade klustringen av kontiger i artificiella metagenomdataset av olika komplexitet som vi skapade in silico genom att blanda data från tidigare sekvenserade genom. Parametrarna som testades rörde indata såväl som den statistiska modell som CONCOCT använder för att utföra klustringen. Parametrarna varierades en i taget medan de andra parametrarna hölls konstanta. Antalet kluster hölls också konstant och motsvarade antalet olika organismer i flororna. Bäst resultat erhölls då vi använde en låst kovariansmodell och använde principalkomponenter som förklarade 90% av variansen, samt filtrerade bort kontiger som var kortare än 3000 baspar. Vi fick också bäst resultat då vi använde alla tillgängliga prover. Därefter använde vi dessa parameterinställningar och lät CONCOCT själv bestämma lämpligt antal kluster i dataseten med “Bayesian Information Criterion” - metoden som då var implementerad i CONCOCT. Detta gav otillfredsställande resultat med i regel för få och för stora kluster. Därför testade vi en alternativ metod, “Dirichlet Process Gaussian Mixture Model”, för att uppskatta antal kluster. Denna metod gav avsevärt bättre resultat och i senare versioner av CONCOCT har en liknande metod implementerats.

This thesis addresses the new item problem in recommender systems, which pertains to the challenges of providing personalized recommendations for items which have limited user interaction history. The study proposes and evaluates a method for generating personalized recommendations for movies, shows, and series on one of Sweden’s largest streaming platforms. By treating these items as documents of the attributes which characterize them and utilizing item similarity through the k-nearest neighbor algorithm, user preferences for new items are predicted based on users’ past preferences for similar items. Two models for feature representation, namely the Vector Space Model (VSM) and a Latent Dirichlet Allocation (LDA) topic model, are considered and compared. The k-nearest neighbor algorithm is utilized to identify similar items for each type of representation, with cosine distance for VSM and Kullback-Leibler divergence for LDA. Furthermore, three different ways of predicting user preferences based on the preferences for the neighbors are presented and compared. The performances of the models in terms of predicting preferences for new items are evaluated with historical streaming data. The results indicate the potential of leveraging item similarity and previous streaming history to predict preferences of new items. The VSM representation proved more successful; using this representation, 77 percent of actual positive instances were correctly classified as positive. For both types of representations, giving higher weight to preferences for more similar items when predicting preferences yielded higher F2 scores, and optimizing for the F2 score implied that recommendations should be made when there is the slightest indication of preference for the neighboring items. The results indicate that the neighbors identified through the VSM representation were more representative of user preferences for new items, compared to those identified through the LDA representation.

No description available.