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With the development of technology allowing for a rapid expansion of data science and machine learning in our everyday lives, a significant gap is forming in the global job market where the demand for qualified workers in these fields cannot be properly satisfied. This worrying trend calls for an immediate action in education, where these skills must be taught to students at all levels in an efficient and up-to-date manner. This paper gives an overview of the current state of data science and machine learning education globally and both at the high school and university levels, while outlining some illustrative and positive examples. Special focus is given to vocational education and training (VET), where the teaching of these skills is at its very beginning. Also presented and analysed are survey results concerning VET students in Slovenia, Serbia, and North Macedonia, and their knowledge, interests, and prerequisites regarding data science and machine learning. These results confirm the need for development of efficient and accessible curricula and courses on these subjects in vocational schools. [ABSTRACT FROM AUTHOR]

Today's fast paced industrial production requires automation at multiple steps during its process. Involving humans during the quality control inspection provides high degree of confidence that the end products are with the best quality. Workers involved in the control process may have an impact on production capacity by lowering the throughput, depending on the complexity of the control process at the time the control is carried out, during the process which is a time-critical operation, or after the process is completed. Companies are striving to fully automate their quality control stages of production and it comes naturally to focus on using various machine learning methods to help build a quality control pipeline which will offer high throughput and high degree of quality. In this paper we give an overview of applying several machine learning approaches in order to achieve an autonomous quality control pipeline. The applications for these approaches were used to help improve the quality control pipeline of two of the biggest manufacturing companies in Slovenia. One of the most challenging part of the study was that the tests had to be performed only on a small number of defective products, as is in reality. The motivation was to test several methods to find the most promising one for later actual application. [ABSTRACT FROM AUTHOR]

Some landscape classifications officially determine financial obligations; thus, they must be objective and precise. We presume it is possible to quantitatively evaluate existing manually constructed classifications and correct them if necessary. One option for achieving this goal is a machine learning method. With (re)modeling of the landscape classification and an explanation of its structure, we can add quantitative proof to its original (qualitative) description. The main objectives of the paper are to evaluate the consistency of the existing manually constructed natural landscape classification with a machine learningbased approach and to test the newly developed general black-box explanation method in order to explain variable importance for the differentiation between natural landscape types. The approach consists of training a model of the existing classification and a general method for explaining variable importance. As an example, we evaluated the existing natural landscape classification of Slovenia from 1998, which is still officially used in the agricultural taxation process. Our results showed that the modeled classification confirms the original with a high rate of agreement--94%. The complementary map of classification uncertainty (entropy) gave us more information on the areas where the classification should be checked, and the analysis of the variable importance provided insight into the differentiation between types. Although the selection of the exclusively climatic variables seemed unusual at first, we were able to understand "the computer's logic" and support geographical explanations for the model. We conclude that the approach can enhance the explanation and evaluation of natural landscape classifications and can be transparently transferred to other areas. [ABSTRACT FROM AUTHOR]

Historically valuable canvas paintings are often exposed to conditions enabling microbial deterioration. Painting materials, mainly of organic origin, in combination with high humidity and other environmental conditions, favor microbial metabolism and growth. These preconditions are often present during exhibitions or storage in old buildings, such as churches and castles, and also in museum storage depositories. The accumulated dust serves as an inoculum for both indoor and outdoor fungi. In our study, we present the results on cultivable fungi isolated from 24 canvas paintings, mainly exhibited in Slovenian sacral buildings, dating from the 16th to 21st centuries. Fungi were isolated from the front and back of damaged and undamaged surfaces of the paintings using culture media with high- and low-water activity. A total of 465 isolates were identified using current taxonomic DNA markers and assigned to 37 genera and 98 species. The most abundant genus was Aspergillus, represented by 32 species, of which 9 xerophilic species are for the first time mentioned in contaminated paintings. In addition to the most abundant xerophilic A. vitricola, A. destruens, A. tardicrescens, and A. magnivesiculatus, xerophilic Wallemia muriae and W. canadensis, xerotolerant Penicillium chrysogenum, P. brevicompactum, P. corylophilum, and xerotolerant Cladosporium species were most frequent. When machine learning methods were used to predict the relationship between fungal contamination, damage to the painting, and the type of material present, proteins were identified as one of the most important factors and cracked paint was identified as a hotspot for fungal growth. Aspergillus species colonize paintings regardless of materials, while Wallemia spp. can be associated with animal fat. Culture media with low-water activity are suggested in such inventories to isolate and obtain an overview of fungi that are actively contaminating paintings stored indoors at low relative humidity. [ABSTRACT FROM AUTHOR]

The research presented in this paper is based on the hypothesis that the machine learning approach improves the accuracy of soil properties prediction. The correlations obtained in this research are important for understanding the overall strategy for soil properties prediction using optical spectroscopy sensors. Several research results have been stated and investigated. A comparison is made between six commonly used techniques: Random Forest, Decision Tree, Naïve Bayes, Support Vector Machine, Least-Square Support Vector Machine and Artificial Neural Network, showing that the best prediction accuracy cannot always be achieved by the most common and complicated method. The influence of the chosen category for nutrient characterization was investigated, indicating better prediction when a multi-component strategy was used. In contrast, the prediction of single-component soil properties was less accurate. In addition, the influence of category levels was not as significant as expected when choosing between 3-level, 5-level or 13-level nutrient characterization for some nutrients, which can be used for a more precise nutrient characterization strategy. A comparative analysis was performed between soil from a local farm with similar texture and soils collected from different locations in Slovenia, which gave a better prediction for a local farm. Finally, the influence of principal component analysis was validated using 5, 10, 20 and 50 first principal components, indicating the better performance of machine learning when using the 50 principal components. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) plants typically suffer from a significant degradation in performance over time due to multiple factors. Operation and maintenance systems aim at increasing the efficiency and profitability of PV plants by analyzing the monitoring data and by applying data-driven methods for assessing the causes of such performance degradation. Two main classes of degradation exist, being it either gradual or a sudden anomaly in the PV system. This has motivated our work to develop and implement statistical methods that can reliably and accurately detect the performance issues in a cost-effective manner. In this paper, we introduce different approaches for both gradual degradation assessment and anomaly detection. Depending on the data available in the PV plant monitoring system, the appropriate method for each degradation class can be selected. The performance of the introduced methods is demonstrated on data from three different PV plants located in Slovenia and Italy monitored for several years. Our work has led us to conclude that the introduced approaches can contribute to the prompt and accurate identification of both gradual degradation and sudden anomalies in PV plants. [ABSTRACT FROM AUTHOR]

In this paper, a novel feature selection algorithm for inference from high-dimensional data (FASTENER) is presented. With its multi-objective approach, the algorithm tries to maximize the accuracy of a machine learning algorithm with as few features as possible. The algorithm exploits entropy-based measures, such as mutual information in the crossover phase of the iterative genetic approach. FASTENER converges to a (near) optimal subset of features faster than other multi-objective wrapper methods, such as POSS, DT-forward and FS-SDS, and achieves better classification accuracy than similarity and information theory-based methods currently utilized in earth observation scenarios. The approach was primarily evaluated using the earth observation data set for land-cover classification from ESA's Sentinel-2 mission, the digital elevation model and the ground truth data of the Land Parcel Identification System from Slovenia. For land cover classification, the algorithm gives state-of-the-art results. Additionally, FASTENER was tested on open feature selection data sets and compared to the state-of-the-art methods. With fewer model evaluations, the algorithm yields comparable results to DT-forward and is superior to FS-SDS. FASTENER can be used in any supervised machine learning scenario. [ABSTRACT FROM AUTHOR]

Part-of-speech (PoS) or, better, morphosyntactic tagging is the process of assigning morphosyntactic categories to words in a text, an important pre-processing step for most human language technology applications. PoS-tagging of Slovene texts is a challenging task since the size of the tagset is over one thousand tags (as opposed to English, where the size is typically around sixty) and the state-of-the-art tagging accuracy is still below levels desired. The paper describes an experiment aimed at improving tagging accuracy for Slovene, by combining the outputs of two taggers -- a proprietary rule-based tagger developed by the Amebis HLT company, and TnT, a tri-gram HMM tagger, trained on a hand-annotated corpus of Slovene. The two taggers have comparable accuracy, but there are many cases where, if the predictions of the two taggers differ, one of the two does assign the correct tag. We investigate training a classifier on top of the outputs of both taggers that predicts which of the two taggers is correct. We experiment with selecting different classification algorithms and constructing different feature sets for training and show that some cases yield a meta-tagger with a significant increase in accuracy compared to that of either tagger in isolation. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we analyze the phenology of spruce bark beetles (Ips typographus and Pityogenes chalcographus) in the Dinaric mountain forests of southwestern Slovenia. The study of I. typographus took place from 1986 to 2000, while that of P. chalcographus took place from 1993 to 2000 in an area characterized as a Dinaric Fir-Beech forest community (Abieti-Fagetum dinaricum) on the Karst plateau (447–751m above sea level). On the studied area Norway spruce (Picea abies) has been planted between 60 and 90 years ago on approximately 1000ha. Frequent catastrophic weather conditions are characteristic for this area, followed by an increased trophic capacity of the forest for the various bark beetle species. The population density of spruce bark beetles was monitored at five locations at varying exposures using commercial pheromones (Pheroprax® and Chalcoprax®) in traps under the trade name Theysohn. Both species studied (I. typographus and P. chalcographus) have a relatively high abundance and have two main generations per year; both species may also produce two sister generations. Data on some environmental factors as well as data on bark beetle catches have been collected and then analyzed to model the dependence of spruce bark beetle catches on the environmental factors. The machine learning methodology of liner regression as well as model tree induction was used for this purpose. The following attributes were used to analyze the occurrence of both species of bark beetle: position (NW, NE, W, E, N and S), age of pheromone, number of days since last monitoring, average monthly temperature, monthly precipitation, month and previous number of bark beetles. There was a strong correlation between a high population density of I. typograpus and Northeast (NE) and position and a high density of populations of P. chalcographus and West (W) and North (N) positions. [Copyright &y& Elsevier]

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