The integration of Vision Transformers (ViTs) using Masked Autoencoder pre-training (ViTMAE) into real estate valuation is investigated in this Master’s thesis, addressing the challenge of effectively analyzing visual information from real estate images. This integration aims to enhance the accuracy and efficiency of valuation, a task traditionally dependent on realtor expertise. The research involved developing a model that combines ViTMAE-extracted visual features from real estate images with traditional property data. Focusing on residential properties in Sweden, the study utilized a dataset of images and metadata from online real estate listings. An adapted ViTMAE model, accessed via the Hugging Face library, was trained on the dataset for feature extraction, which was then integrated with metadata to create a comprehensive multimodal valuation model. Results indicate that including ViTMAE-extracted image features improves prediction accuracy in real estate valuation models. The multimodal approach, merging visual and traditional metadata, improved accuracy over metadata-only models. This thesis contributes to real estate valuation by showcasing the potential of advanced image processing techniques in enhancing valuation models. It lays the groundwork for future research in more refined holistic valuation models, incorporating a wider range of factors beyond visual data. / Detta examensarbete undersöker integrationen av Vision Transformers (ViTs) med Masked Autoencoder pre-training (ViTMAE) i bostadsvärdering, genom att addressera utmaningen att effektivt analysera visuell information från bostadsannonser. Denna integration syftar till att förbättra noggrannheten och effektiviteten i fastighetsvärdering, en uppgift som traditionellt är beroende av en fysisk besiktning av mäklare. Arbetet innefattade utvecklingen av en modell som kombinerar bildinformation extraherad med ViTMAE från fastighetsbilder med traditionella fastighetsdata. Med fokus på bostadsfastigheter i Sverige använde studien en databas med bilder och metadata från bostadsannonser. Den anpassade ViTMAE-modellen, tillgänglig via Hugging Face-biblioteket, tränades på denna databas för extraktion av bildinformation, som sedan integrerades med metadata för att skapa en omfattande värderingsmodell. Resultaten indikerar att inklusion av ViTMAE-extraherad bildinformation förbättrar noggranheten av bostadssvärderingsmodeller. Den multimodala metoden, som kombinerar visuell och traditionell metadata, visade en förbättring i noggrannhet jämfört med modeller som endast använder metadata. Denna uppsats bidrar till bostadsvärdering genom att visa på potentialen hos avancerade bildanalys för att förbättra värderingsmodeller. Den lägger grunden för framtida forskning i mer raffinerade holistiska värderingsmodeller som inkluderar ett bredare spektrum av faktorer utöver visuell data.

<p dir="ltr">In this work, first, unsupervised machine learning is proposed as a study for detecting and classifying jamming attacks targeting unmanned aerial vehicles (UAV) operating at a 2.4 GHz band. Three scenarios are developed with a dataset of samples extracted from meticulous experimental routines using various unsupervised learning algorithms, namely K-means, density-based spatial clustering of applications with noise (DBSCAN), agglomerative clustering (AGG) and Gaussian mixture model (GMM). These routines characterize attack scenarios entailing barrage (BA), single- tone (ST), successive-pulse (SP), and protocol-aware (PA) jamming in three different settings. In the first setting, all extracted features from the original dataset are used (i.e., nine in total). In the second setting, Spearman correlation is implemented to reduce the number of these features. In the third setting, principal component analysis (PCA) is utilized to reduce the dimensionality of the dataset to minimize complexity. The metrics used to compare the algorithms are homogeneity, completeness, v-measure, adjusted mutual information (AMI) and adjusted rank index (ARI). The optimum model scored 1.00, 0.949, 0.791, 0.722, and 0.791, respectively, allowing the detection and classification of these four jamming types with an acceptable degree of confidence.</p><p dir="ltr">Second, following a different study, supervised learning (i.e., random forest modeling) is developed to achieve a binary classification to ensure accurate clustering of samples into two distinct classes: clean and jamming. Following this supervised-based classification, two-class and three-class unsupervised learning is implemented considering three of the four jamming types: BA, ST, and SP. In this initial step, the four aforementioned algorithms are used. This newly developed study is intended to facilitate the visualization of the performance of each algorithm, for example, AGG performs a homogeneity of 1.0, a completeness of 0.950, a V-measure of 0.713, an ARI of 0.557 and an AMI of 0.713, and GMM generates 1, 0.771, 0.645, 0.536 and 0.644, respectively. Lastly, to improve the classification of this study, semi-supervised learning is adopted instead of unsupervised learning considering the same algorithms and dataset. In this case, GMM achieves results of 1, 0.688, 0.688, 0.786 and 0.688 whereas DBSCAN achieves 0, 0.036, 0.028, 0.018, 0.028 for homogeneity, completeness, V-measure, ARI and AMI respectively. Overall, this unsupervised learning is approached as a method for jamming classification, addressing the challenge of identifying newly introduced samples.</p>

<p>Next generation sequencing technologies have provided us with a wealth of data profiling a diverse range of biological processes. In an effort to better understand the process of gene regulation, two predictive machine learning models specifically tailored for analyzing gene transcription and polyadenylation are presented.</p><p>Transcriptional enhancers are specific DNA sequences that act as ``information integration hubs" to confer regulatory requirements on a given cell. These non-coding DNA sequences can regulate genes from long distances, or across chromosomes, and their relationships with their target genes are not limited to one-to-one. With thousands of putative enhancers and less than 14,000 protein-coding genes, detecting enhancer-gene pairs becomes a very complex machine learning and data analysis challenge. </p><p>In order to predict these specific-sequences and link them to genes they regulate, we developed McEnhancer. Using DNAseI sensitivity data and annotated in-situ hybridization gene expression clusters, McEnhancer builds interpolated Markov models to learn enriched sequence content of known enhancer-gene pairs and predicts unknown interactions in a semi-supervised learning algorithm. Classification of predicted relationships were 73-98% accurate for gene sets with varying levels of initial known examples. Predicted interactions showed a great overlap when compared to Hi-C identified interactions. Enrichment of known functionally related TF binding motifs, enhancer-associated histone modification marks, along with corresponding developmental time point was highly evident.</p><p>On the other hand, pre-mRNA cleavage and polyadenylation is an essential step for 3'-end maturation and subsequent stability and degradation of mRNAs. This process is highly controlled by cis-regulatory elements surrounding the cleavage site (polyA site), which are frequently constrained by sequence content and position. More than 50\% of human transcripts have multiple functional polyA sites, and the specific use of alternative polyA sites (APA) results in isoforms with variable 3'-UTRs, thus potentially affecting gene regulation. Elucidating the regulatory mechanisms underlying differential polyA preferences in multiple cell types has been hindered by the lack of appropriate tests for determining APAs with significant differences across multiple libraries. </p><p>We specified a linear effects regression model to identify tissue-specific biases indicating regulated APA; the significance of differences between tissue types was assessed by an appropriately designed permutation test. This combination allowed us to identify highly specific subsets of APA events in the individual tissue types. Predictive kernel-based SVM models successfully classified constitutive polyA sites from a biologically relevant background (auROC = 99.6%), as well as tissue-specific regulated sets from each other. The main cis-regulatory elements described for polyadenylation were found to be a strong, and highly informative, hallmark for constitutive sites only. Tissue-specific regulated sites were found to contain other regulatory motifs, with the canonical PAS signal being nearly absent at brain-specific sites. We applied this model on SRp20 data, an RNA binding protein that might be involved in oncogene activation and obtained interesting insights. </p><p>Together, these two models contribute to the understanding of enhancers and the key role they play in regulating tissue-specific expression patterns during development, as well as provide a better understanding of the diversity of post-transcriptional gene regulation in multiple tissue types.</p> / Dissertation

The rapid development of Internet technologies has resulted in a sharp increase in the number of Internet users who create content online. User-generated content often represents people's opinions, thoughts, speculations and sentiments and is a valuable source of information for companies, organisations and individual users. This has led to the emergence of the field of sentiment analysis, which deals with the automatic extraction and classification of sentiments expressed in texts. Sentiment analysis has been intensively researched over the last ten years, but there are still many issues to be addressed. One of the main problems is the lack of labelled data necessary to carry out precise supervised sentiment classification. In response, research has moved towards developing semi-supervised and cross-domain techniques. Semi-supervised approaches still need some labelled data and their effectiveness is largely determined by the amount of these data, whereas cross-domain approaches usually perform poorly if training data are very different from test data. The majority of research on sentiment classification deals with the binary classification problem, although for many practical applications this rather coarse sentiment scale is not sufficient. Therefore, it is crucial to design methods which are able to perform accurate multiclass sentiment classification. The aims of this thesis are to address the problem of limited availability of data in sentiment analysis and to advance research in semi-supervised and cross-domain approaches for sentiment classification, considering both binary and multiclass sentiment scales. We adopt graph-based learning as our main method and explore the most popular and widely used graph-based algorithm, label propagation. We investigate various ways of designing sentiment graphs and propose a new similarity measure which is unsupervised, easy to compute, does not require deep linguistic analysis and, most importantly, provides a good estimate for sentiment similarity as proved by intrinsic and extrinsic evaluations. The main contribution of this thesis is the development and evaluation of a graph-based sentiment analysis system that a) can cope with the challenges of limited data availability by using semi-supervised and cross-domain approaches b) is able to perform multiclass classification and c) achieves highly accurate results which are superior to those of most state-of-the-art semi-supervised and cross-domain systems. We systematically analyse and compare semi-supervised and cross-domain approaches in the graph-based framework and propose recommendations for selecting the most pertinent learning approach given the data available. Our recommendations are based on two domain characteristics, domain similarity and domain complexity, which were shown to have a significant impact on semi-supervised and cross-domain performance.

An SVM-based clustering algorithm is introduced that clusters data with no a priori knowledge of input classes. The algorithm initializes by first running a binary SVM classifier against a data set with each vector in the set randomly labeled. Once this initialization step is complete, the SVM confidence parameters for classification on each of the training instances can be accessed. The lowest confidence data (e.g., the worst of the mislabeled data) then has its labels switched to the other class label. The SVM is then re-run on the data set (with partly re-labeled data). The repetition of the above process improves the separability until there is no misclassification. Variations on this type of clustering approach are shown.

This thesis is mostly about supervised visual recognition problems. Based on a general definition of categories, the contents are divided into two parts: one which models categories and one which is not category based. We are interested in data driven solutions for both kinds of problems. In the category-free part, we study novelty detection in temporal and spatial domains as a category-free recognition problem. Using data driven models, we demonstrate that based on a few reference exemplars, our methods are able to detect novelties in ego-motions of people, and changes in the static environments surrounding them. In the category level part, we study object recognition. We consider both object category classification and localization, and propose scalable data driven approaches for both problems. A mixture of parametric classifiers, initialized with a sophisticated clustering of the training data, is demonstrated to adapt to the data better than various baselines such as the same model initialized with less subtly designed procedures. A nonparametric large margin classifier is introduced and demonstrated to have a multitude of advantages in comparison to its competitors: better training and testing time costs, the ability to make use of indefinite/invariant and deformable similarity measures, and adaptive complexity are the main features of the proposed model. We also propose a rather realistic model of recognition problems, which quantifies the interplay between representations, classifiers, and recognition performances. Based on data-describing measures which are aggregates of pairwise similarities of the training data, our model characterizes and describes the distributions of training exemplars. The measures are shown to capture many aspects of the difficulty of categorization problems and correlate significantly to the observed recognition performances. Utilizing these measures, the model predicts the performance of particular classifiers on distributions similar to the training data. These predictions, when compared to the test performance of the classifiers on the test sets, are reasonably accurate. We discuss various aspects of visual recognition problems: what is the interplay between representations and classification tasks, how can different models better adapt to the training data, etc. We describe and analyze the aforementioned methods that are designed to tackle different visual recognition problems, but share one common characteristic: being data driven. / <p>QC 20140604</p>

In forestry, natural forests are forest areas with high biodiversity, in need of preservation. The current mapping of natural forests is a tedious task that requires manual labor that could possibly be automated. In this paper we explore the main features used by a random forest algorithm to classify natural forest and managed forest in northern Sweden. The goal was to create a model with a substantial strength of agreement, meaning a Kappa value of 0.61 or higher, placing the model in the same range as models produced in previous research. We used raster data gathered from airborne LiDAR, combined with labeled sample areas, both supplied by the Swedish Forest Agency. Two experiments were performed with different features. Experiment 1 used features extracted using methods inspired from previous research while Experiment 2 further added upon those features. From the total number of used sample areas (n=2882), 70% was used to train the models and 30% was used for evaluation. The result was a Kappa value of 0.26 for Experiment 1 and 0.32 for Experiment 2. Features shown to be prominent are features derived from canopy height, where the supplied data also had the highest resolution. Percentiles, kurtosis and canopy crown areas derived from the canopy height were shown to be the most important for classification. The results fell short of our goal, possibly indicating a range of flaws in the data used. The size of the sample areas and resolution of raster data are likely important factors when extracting features, playing a large role in the produced model’s performance.

In this thesis, we examine the performance of different forecasting methods. We use dataof monthly house prices from the larger Stockholm area and the municipality of Uppsalabetween 2005 and early 2019 as the time series to be forecast. Firstly, we compare theperformance of two machine learning methods, the Long Short-Term Memory, and theSupport Vector Machine methods. The two methods forecasts are compared, and themodel with the lowest forecasting error measured by three metrics is chosen to be comparedwith a classic seasonal ARIMA model. We find that the Long Short-Term Memorymethod is the better performing machine learning method for a twelve-month forecast,but that it still does not forecast as well as the ARIMA model for the same forecast period.

O grande volume de dados que temos disponíveis em ambientes virtuais pode ser excelente fonte de novos recursos para estudos em diversas tarefas de Processamento de Linguagem Natural, como a Análise de Sentimentos. Infelizmente é elevado o custo de anotação de novos córpus, que envolve desde investimentos financeiros até demorados processos de revisão. Nossa pesquisa propõe uma abordagem de anotação semissupervisionada, ou seja, anotação automática de um grande córpus não anotado partindo de um conjunto de dados anotados manualmente. Para tal, introduzimos o TweetSentBR, um córpus de tweets no domínio de programas televisivos que possui anotação em três classes e revisões parciais feitas por até sete anotadores. O córpus representa um importante recurso linguístico de português brasileiro, e fica entre os maiores córpus anotados na literatura para classificação de polaridades. Além da anotação manual do córpus, realizamos a implementação de um framework de aprendizado semissupervisionado que faz uso de dados anotados e, de maneira iterativa, expande o mesmo usando dados não anotados. O TweetSentBR, que possui 15:000 tweets anotados é assim expandido cerca de oito vezes. Para a expansão, foram treinados modelos de classificação usando seis classificadores de polaridades, assim como foram avaliados diferentes parâmetros e representações a fim de obter um córpus confiável. Realizamos experimentos gerando córpus expandidos por cada classificador, tanto para a classificação em três polaridades (positiva, neutra e negativa) quanto para classificação binária. Avaliamos os córpus gerados usando um conjunto de held-out e comparamos a FMeasure da classificação usando como treinamento os córpus anotados manualmente e semiautomaticamente. O córpus semissupervisionado que obteve os melhores resultados para a classificação em três polaridades atingiu 62;14% de F-Measure média, superando a média obtida com as avaliações no córpus anotado manualmente (61;02%). Na classificação binária, o melhor córpus expandido obteve 83;11% de F1-Measure média, superando a média obtida na avaliação do córpus anotado manualmente (79;80%). Além disso, simulamos nossa expansão em córpus anotados da literatura, medindo o quão corretas são as etiquetas anotadas semi-automaticamente. Nosso melhor resultado foi na expansão de um córpus de reviews de produtos que obteve FMeasure de 93;15% com dados binários. Por fim, comparamos um córpus da literatura obtido por meio de supervisão distante e nosso framework semissupervisionado superou o primeiro na classificação de polaridades binária em cross-domain. / The high volume of data available in the Internet can be a good resource for studies of several tasks in Natural Language Processing as in Sentiment Analysis. Unfortunately there is a high cost for the annotation of new corpora, involving financial support and long revision processes. Our work proposes an approach for semi-supervised labeling, an automatic annotation of a large unlabeled set of documents starting from a manually annotated corpus. In order to achieve that, we introduced TweetSentBR, a tweet corpora on TV show programs domain with annotation for 3-point (positive, neutral and negative) sentiment classification partially reviewed by up to seven annotators. The corpus is an important linguistic resource for Brazilian Portuguese language and it stands between the biggest annotated corpora for polarity classification. Beyond the manual annotation, we implemented a semi-supervised learning based framework that uses this labeled data and extends it using unlabeled data. TweetSentBR corpus, containing 15:000 documents, had its size augmented in eight times. For the extending process, we trained classification models using six polarity classifiers, evaluated different parameters and representation schemes in order to obtain the most reliable corpora. We ran experiments generating extended corpora for each classifier, both for 3-point and binary classification. We evaluated the generated corpora using a held-out subset and compared the obtained F-Measure values with the manually and the semi-supervised annotated corpora. The semi-supervised corpus that obtained the best values for 3-point classification achieved 62;14% on average F-Measure, overcoming the results obtained by the same classification with the manually annotated corpus (61;02%). On binary classification, the best extended corpus achieved 83;11% on average F-Measure, overcoming the results on the manually corpora (79;80%). Furthermore, we simulated the extension of labeled corpora in literature, measuring how well the semi-supervised annotation works. Our best results were in the extension of a product review corpora, achieving 93;15% on F1-Measure. Finally, we compared a literature corpus which was labeled by using distant supervision with our semi-supervised corpus, and this overcame the first in binary polarity classification on cross-domain data.

O processo de Mineração de Dados (MD) consiste na extração automática de padrões que representam o conhecimento implícito em grandes bases de dados. Em geral, a MD pode ser classificada em duas categorias: preditiva e descritiva. Tarefas da primeira categoria, tal como a classificação, realizam inferências preditivas sobre os dados enquanto que tarefas da segunda categoria, tal como o clustering, exploram o conjunto de dados em busca de propriedades que o descrevem. Diferentemente da classificação, que analisa exemplos rotulados, o clustering utiliza exemplos para os quais o rótulo da classe não é previamente conhecido. Nessa tarefa, agrupamentos são formados de modo que exemplos de um mesmo cluster apresentam alta similaridade, ao passo que exemplos em clusters diferentes apresentam baixa similaridade. O clustering pode ainda facilitar a organização de clusters em uma hierarquia de agrupamentos, na qual são agrupados eventos similares, criando uma taxonomia que pode simplificar a interpretação de clusters. Neste trabalho, é proposto e desenvolvido um módulo de aprendizado não-supervisionado, que agrega algoritmos de clustering hierárquico e ferramentas de análise de clusters para auxiliar o especialista de domínio na interpretação dos resultados do clustering. Uma vez que o clustering hierárquico agrupa exemplos de acordo com medidas de similaridade e organiza os clusters em uma hierarquia, o usuário/especialista pode analisar e explorar essa hierarquia de agrupamentos em diferentes níveis para descobrir conceitos descritos por essa estrutura. O módulo proposto está integrado em um sistema maior, em desenvolvimento no Laboratório de Inteligência Computacional ? LABIC ?, que contempla todas as etapas do processo de MD, desde o pré-processamento de dados ao pós-processamento de conhecimento. Para avaliar o módulo proposto e seu uso para descoberta de conceitos a partir da estrutura hierárquica de clusters, foram realizados diversos experimentos sobre conjuntos de dados naturais, assim como um estudo de caso utilizando um conjunto de dados real. Os resultados mostram a viabilidade da metodologia proposta para interpretação dos clusters, apesar da complexidade do processo ser dependente das características do conjunto de dados. / The Data Mining (DM) process consists of the automated extraction of patterns representing knowledge implicitly stored in large databases. In general, DM tasks can be classified into two categories: predictive and descriptive. Tasks in the first category, such as classification and prediction, perform inference on the data in order to make predictions, while tasks in the second category, such as clustering, characterize the general properties of the data. Unlike classification and prediction, which analyze class-labeled data objects, clustering analyses data objects without a known class-label. Clusters of objects are formed so that objects that are in the same cluster have a close similarity among them, but are very dissimilar to objects in other clusters. Clustering can also facilitate the organization of clusters into a hierarchy of clusters that group similar events together. This taxonomy formation can facilitate interpretation of clusters. In this work, we propose and develop tools to deal with this task by implementing a module which comprises hierarchical clustering algorithms and several cluster analysis tools, aiming to help the domain specialist to interpret the clustering results. Once clusters group objects based on similarity measures which are organized into a hierarchy, the user/specialist is able to carry out an analysis and exploration of the agglomeration hierarchy at different levels of the hierarchy in order to discover concepts described by this structure. The proposed module is integrated into a large system under development by researchers from the Computational Intelligence Laboratory ? LABIC ?- which contemplates all the DM process steps, from data pre-processing to knowledge post-processing. To evaluate the implemented module and its use to discover concepts from the hierarchical structure of clusters, several experiments on natural databases were carried out as well as a case study using a real database. Results show the viability of the proposed methodology although the process could be complex depending on the characteristics of the database.