This thesis investigates how pre-trained contextualized language models can be adapted for multi-label text classification of Swedish news articles. Various classifiers are built on pre-trained BERT and ELECTRA models, exploring global and local classifier approaches. Furthermore, the effects of domain specialization, using additional metadata features and model compression are investigated. Several hundred thousand news articles are gathered to create unlabeled and labeled datasets for pre-training and fine-tuning, respectively. The findings show that a local classifier approach is superior to a global classifier approach and that BERT outperforms ELECTRA significantly. Notably, a baseline classifier built on SVMs yields competitive performance. The effect of further in-domain pre-training varies; ELECTRA’s performance improves while BERT’s is largely unaffected. It is found that utilizing metadata features in combination with text representations improves performance. Both BERT and ELECTRA exhibit robustness to quantization and pruning, allowing model sizes to be cut in half without any performance loss.

In recent years, there has been an increase in environmental awareness in the United States leading to steady growth in environmentally conscious consumerism. These changes have come in response to issues such as the energy crisis, climate change, exponential population growth, and rapid urbanization. This fact is further supported by environmental campaigns and the green movement. Looking to the future of green home marketing, understanding the green consumer behavior of Generation Z (GenZ) is important for environmental and business reasons. The purpose of this research is to better understand the perception of GenZ on Green Homes (GHs). The study uses the lenses of dual inheritance and normative motivation theory to explain the influence of benefits and norms related to environmentalism and sustainability on GenZ consumers’ green behavior. This study seeks to evaluate 1) GenZ’s preferences related to Green Home Features (GHFs), 3) the extent of the influence of certain barriers on the adoption of GHFs, and 3) the types of motivation (intrinsic, instrumental and non-normative) influencing GenZ towards green home consumerism. Data was collected using an online survey questionnaire exclusively at Purdue University during March – April of 2021 (IRB 2020-1414). One hundred sixteen GenZ participants responded to the survey.The findings show that these GenZ consumers prefer a certain type of GHFs over others. Additionally, based on descriptive tests of GHFs, energy-related features were the most prized features, while the least preferred was water-efficient features. Descriptive tests on barriers suggest that GenZ consumers perceive the lack of choice in selecting GHFs in their homes to be a top barrier, followed by a lack of information and the perceived effort to analyze GHFs. Inferential tests for the same indicated that GenZ consumers perceive these barriers differently. Lastly, for GenZ consumers, intrinsic and non-normative motivations significantly affect their willingness to buy GHs. The findings concur with previous studies on green consumer behavior, yet they provide a new benchmark for understanding GenZ consumer behavior on GHs and an updated view of what GHFs they prefer. This research can be used by home marketers and policy makers to study future home trends, attract more potential homeowners to GHs, and help create a sustainable environment for future generations.

This article deals with deviation in Euphrase Kezilahabi´s novel Gamba la Nyoka (1979). We analyse four different types of deviation, namely grammatical, lexical, phonological, and semantic deviation. The objective of this study is to combine linguistic analysis with literary riticism, in order to show how these different types of deviation correspond with the overall message the author conveys in this novel, which is a political novel dealing with the era of establishing Ujamaa policies in rural Tanzania.

<p>Дисертација &quot;Географске основе пољопривредне производње у Лесковачкој котлини&quot; обухвата валоризацију географских основа пољопривредне производње једног од најинтересантнијих производних подручја брдско-планинске Србије. Лесковачка котлина простире се у оквиру Родопског система, у средишњем делу тока Јужне Мораве. Просторни размештај пољопривредне производње детерминисан је природним и друштвеним условима. Последњих деценија у структури коришћења пољопривредног земљишта дошло је до промене у правцу смањивања површина под ораницама, виноградима и пашњацима и благог повећања површина под воћњацима и ливадама. Истовремено, у структури ораничног земљишта смањена је површина под житарицама, а повећана површина под повртарским културама и крмним биљем. У укупној пољопривреди котлине и економским ефектима посебан значај има повртарска производња. Већи део производње поврћа који се гаји на отвореном и затвореном простору намењен је тржишту. Воћарство, виноградарство и сточарство су традиционалне пољопривредне гране у Лесковачкој котлини. Перспективе пољопривреде Лесковачке котлине леже у развоју интензивне пољопривредне производње прилагођене природним условима, аграрној структури, потребама тржишта и изграђеним складишним и прерадним капацитетима. У том смислу посебан значај има повећање производње индустријског биља, поврћа, воћа, грожђа и пољопривредних култура намењених сточарству.</p> / <p>Disertacija &quot;Geografske osnove poljoprivredne proizvodnje u Leskovačkoj kotlini&quot; obuhvata valorizaciju geografskih osnova poljoprivredne proizvodnje jednog od najinteresantnijih proizvodnih područja brdsko-planinske Srbije. Leskovačka kotlina prostire se u okviru Rodopskog sistema, u središnjem delu toka Južne Morave. Prostorni razmeštaj poljoprivredne proizvodnje determinisan je prirodnim i društvenim uslovima. Poslednjih decenija u strukturi korišćenja poljoprivrednog zemljišta došlo je do promene u pravcu smanjivanja površina pod oranicama, vinogradima i pašnjacima i blagog povećanja površina pod voćnjacima i livadama. Istovremeno, u strukturi oraničnog zemljišta smanjena je površina pod žitaricama, a povećana površina pod povrtarskim kulturama i krmnim biljem. U ukupnoj poljoprivredi kotline i ekonomskim efektima poseban značaj ima povrtarska proizvodnja. Veći deo proizvodnje povrća koji se gaji na otvorenom i zatvorenom prostoru namenjen je tržištu. Voćarstvo, vinogradarstvo i stočarstvo su tradicionalne poljoprivredne grane u Leskovačkoj kotlini. Perspektive poljoprivrede Leskovačke kotline leže u razvoju intenzivne poljoprivredne proizvodnje prilagođene prirodnim uslovima, agrarnoj strukturi, potrebama tržišta i izgrađenim skladišnim i preradnim kapacitetima. U tom smislu poseban značaj ima povećanje proizvodnje industrijskog bilja, povrća, voća, grožđa i poljoprivrednih kultura namenjenih stočarstvu.</p> / <p>Dissertation &quot;Geographical Bases of Agricultural output in Leskovac Valley&quot; encompasses valorization of geographical bases of agricultural output in one of most interesting production locations of hill-and-mountain regions of Serbia. Leskovac valley extends as part of the Rhodope System, in the central part of the South Morava course. On the area of 1, 929 square kilometres there are about 49.000 population that are engaged in agriculture. Area arrangement of agricultural output is determined owing to natural and social conditions. From the agricultural output point of view when natural conditions are concerned, then special significance is given to relief, agroclimatic conditions, land, and so on. Owing to degree of natural conditions, favourable for agricultural output, three relatively homogeneous regions are selected. Social conditions in which agriculture is formed, have significant role in arrangement and structure of agriculture output, such as quantit and quality of labour force, agrarian structure, agrotechnical measures, and the like. Unfavourable age structure and composition of labour force of agricultural population effect negatively volume of production and use of contemporary agrotechnical measures. Agrarian structure is very bad becausev small holdigns with a great number of lots prevail. Leskovac valley agriculture perspectives are in developing intensive agricultural output adjusted to national conditions, agrarian structure, market needs and established storage space and production facilities. In that sense it is of special significance to expand output of industrial crops, vegetables, fruit, grapes, and agricultural cultures for cattle raising.</p>

Hyperspectral imaging has become one of the most popular technologies in plant phenotyping because it can efficiently and accurately predict numerous plant physiological features such as plant biomass, leaf moisture content, and chlorophyll content. Various hyperspectral imaging systems have been deployed in both greenhouse and field phenotyping activities. However, the hyperspectral imaging quality is severely affected by the continuously changing environmental conditions such as cloud cover, temperature and wind speed that induce noise in plant spectral data. Eliminating these environmental effects to improve imaging quality is critically important. In this thesis, two approaches were taken to address the imaging noise issue in greenhouse and field separately. First, a computational simulation model was built to simulate the greenhouse microclimate changes (such as the temperature and radiation distributions) through a 24-hour cycle in a research greenhouse. The simulated results were used to optimize the movement of an automated conveyor in the greenhouse: the plants were shuffled with the conveyor system with optimized frequency and distance to provide uniform growing conditions such as temperature and lighting intensity for each individual plant. The results showed the variance of the plants’ phenotyping feature measurements decreased significantly (i.e., by up to 83% in plant canopy size) in this conveyor greenhouse. Secondly, the environmental effects (i.e., sun radiation) on <a>aerial </a>hyperspectral images in field plant phenotyping were investigated and modeled. <a>An artificial neural network (ANN) method was proposed to model the relationship between the image variation and environmental changes. Before the 2019 field test, a gantry system was designed and constructed to repeatedly collect time-series hyperspectral images with 2.5 minutes intervals of the corn plants under varying environmental conditions, which included sun radiation, solar zenith angle, diurnal time, humidity, temperature and wind speed. Over 8,000 hyperspectral images of </a>corn (<i>Zea mays </i>L.) were collected with synchronized environmental data throughout the 2019 growing season. The models trained with the proposed ANN method were able to accurately predict the variations in imaging results (i.e., 82.3% for NDVI) caused by the changing environments. Thus, the ANN method can be used by remote sensing professionals to adjust or correct raw imaging data for changing environments to improve plant characterization.

La symétrie est une propriété géométrique importante en perception visuelle qui traduit notre perception des correspondances entre les différents objets ou formes présents dans une scène. Elle est utilisée comme élément caractéristique dans de nombreuses applications de la vision par ordinateur (comme par exemple la détection, la segmentation ou la reconnaissance d'objets) mais également comme une caractéristique formelle en sciences de l'art (ou en analyse esthétique). D’importants progrès ont été réalisés ces dernières décennies pour la détection de la symétrie dans les images mais il reste encore de nombreux verrous à lever. Dans cette thèse, nous nous intéressons à la détection des symétries de réflexion, dans des images réelles, à l'échelle globale. Nos principales contributions concernent les étapes d'extraction de caractéristiques et de représentation globale des axes de symétrie. Nous proposons d'abord une nouvelle méthode d'extraction de segments de contours à l'aide de bancs de filtres de Gabor logarithmiques et une mesure de symétrie intersegments basée sur des caractéristiques locales de forme, de texture et de couleur. Cette méthode a remporté la première place à la dernière compétition internationale de symétrie pour la détection mono- et multi-axes. Notre deuxième contribution concerne une nouvelle méthode de représentation des axes de symétrie dans un espace linéaire-directionnel. Les propriétés de symétrie sont représentées sous la forme d'une densité de probabilité qui peut être estimée, de manière non-paramétrique, par une méthode à noyauxbasée sur la distribution de Von Mises-Fisher. Nous montrons que la détection des axes dominants peut ensuite être réalisée à partir d'un algorithme de type "mean-shift” associé à une distance adaptée. Nous introduisons également une nouvelle base d'images pour la détection de symétrie mono-axe dans des photographies professionnelles issue de la base à grande échelle AVA (Aestetic Visual Analysis). Nos différentes contributions obtiennent des résultats meilleurs que les algorithmes de l'état de l'art, évalués sur toutes les bases disponibles publiquement, spécialement dans le cas multi-axes. Nous concluons que les propriétés de symétrie peuvent être utilisées comme des caractéristiques visuelles de niveau sémantique intermédiaire pour l'analyse et la compréhension de photographies. / Symmetry is a fundamental principle of the visual perception to feel the equally distributed weights within foreground objects inside an image. It is used as a significant visual feature through various computer vision applications (i.e. object detection and segmentation), plus as an important composition measure in art domain (i.e. aesthetic analysis). The development of symmetry detection has been improved rapidly since last century. In this thesis, we mainly aim to propose new approaches to detect reflection symmetry inside real-world images in a global scale. In particular, our main contributions concern feature extraction and globalrepresentation of symmetry axes. First, we propose a novel approach that detects global salient edges inside an image using Log-Gabor filter banks, and defines symmetry oriented similarity through textural and color around these edges. This method wins a recent symmetry competition worldwide in single and multiple cases.Second, we introduce a weighted kernel density estimator to represent linear and directional symmetrical candidates in a continuous way, then propose a joint Gaussian-vonMises distance inside the mean-shift algorithm, to select the relevant symmetry axis candidates along side with their symmetrical densities. In addition, we introduce a new challenging dataset of single symmetry axes inside artistic photographies extracted from the large-scale Aesthetic Visual Analysis (AVA) dataset. The proposed contributions obtain superior results against state-of-art algorithms among all public datasets, especially multiple cases in a global scale. We conclude that the spatial and context information of each candidate axis inside an image can be used as a local or global symmetry measure for further image analysis and scene understanding purposes.

This master's thesis deals with knowledge discovery in databases, especially basic methods of classification and prediction used for data mining are described here. The next chapter contains introduction to multimedia databases and knowledge discovery in multimedia databases. The main goal of this chapter was to focus on extraction of low level features from video data and images. In the next parts of this work, there is described data set and results of experiments in applications RapidMiner, LibSVM and own developed application. The last chapter summarises results of used methods for high level feature extraction from low level description of data.

In this master thesis, object's detection in image and tracking these objects in temporal area will be presented. First, theoretical background of the image's preprocessing, image filtration, the foreground extraction, and many others various image's features will be described. Next, design and implementation of detector will be processed. This part of my master thesis containes mainly information about detection of objects on belt conveyer Finally,results, conclusion and many supplementary data such as a photography camera's location will be shown.

This master's thesis is dedicated to theme of knowledge discovery in Multimedia Databases, especially basic methods of classification and prediction used for data mining. The other part described about extraction of low level features from video data and images and summarizes information about content-based search in multimedia content and indexing this type of data. Final part is dedicated to implementation Gaussian mixtures model for classification and compare the final result with other method SVM.

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