Search

This paper introduces a lossless data compression competition that benchmarks solutions (computer programs) by the compressed size of the 44,981 concatenated SARS-CoV-2 sequences, with a total uncompressed size of 1,339,868,341 bytes. The data, downloaded on 13 December 2020, from the severe acute respiratory syndrome coronavirus 2 data hub of ncbi.nlm.nih.gov is presented in FASTA and 2Bit format. The aim of this competition is to encourage multidisciplinary research to find the shortest lossless description for the sequences and to demonstrate that data compression can serve as an objective and repeatable measure to align scientific breakthroughs across disciplines. The shortest description of the data is the best model; therefore, further reducing the size of this description requires a fundamental understanding of the underlying context and data. This paper presents preliminary results with multiple well-known compression algorithms for baseline measurements, and insights regarding promising research avenues. The competition's progress will be reported at \url{https://coronavirus.innar.com}, and the benchmark is open for all to participate and contribute., Comment: 6 pages, 2 tables

The focus of this paper is using a convolutional machine learning model with a modified U-Net structure for creating land cover classification mapping based on satellite imagery. The aim of the research is to train and test convolutional models for automatic land cover mapping and to assess their usability in increasing land cover mapping accuracy and change detection. To solve these tasks, authors prepared a dataset and trained machine learning models for land cover classification and semantic segmentation from satellite images. The results were analysed on three different land classification levels. BigEarthNet satellite image archive was selected for the research as one of two main datasets. This novel and recent dataset was published in 2019 and includes Sentinel-2 satellite photos from 10 European countries made in 2017 and 2018. As a second dataset the authors composed an original set containing a Sentinel-2 image and a CORINE land cover map of Estonia. The developed classification model shows a high overall F\textsubscript{1} score of 0.749 on multiclass land cover classification with 43 possible image labels. The model also highlights noisy data in the BigEarthNet dataset, where images seem to have incorrect labels. The segmentation models offer a solution for generating automatic land cover mappings based on Sentinel-2 satellite images and show a high IoU score for land cover classes such as forests, inland waters and arable land. The models show a capability of increasing the accuracy of existing land classification maps and in land cover change detection., Comment: Submitted to IEEE Access; 11 pages, 6 figures

The aim of this study is to demostrate that mobile phone usage data can be used to make predictions and find the best classification method for credit scoring even if the dataset is small (2,503 customers). We use different classification algorithms to split customers into paying and non-paying ones using mobile data, and then compare the predicted results with actual results. There are several related works publicly accessible in which mobile data has been used for credit scoring, but they are all based on a large dataset. Small companies are unable to use datasets as large as those used by these related papers, therefore these studies are of little use for them. In this paper we try to argue that there is value in mobile phone usage data for credit scoring even if the dataset is small. We found that with a dataset that consists of mobile data based only on 2,503 customers, we can predict credit risk. The best classification method gave us the result 0.62 AUC (area under the curve)., Comment: 14 pages, submitted to DB&IS 2020

This paper presents results of a European Big Data Hackathon entry to facilitate data analysis and visualization of patterns in provided and external datasets about European jobs and skills mismatch and labour market in general. The main contributions of this work are: the development of a method to represent the complex labour market internal structure from the perspective of occupations sharing skills; developing and presenting the prototype, together with an extended description of constructing a graph and related necessary data processing. Since labour market is not an isolated phenomenon and is constantly impacted with external trends and interventions, the presented tool is designed to enable adding extra layers of external information: what is the impact of a megatrend or an intervention to the labour market? Which parts of labour market of what country is most vulnerable to approaching megatrend or planned intervention? A case of analysing the labour market together with the megatrend of automation and computerization of jobs is presented. The source code of the prototype is released as open source for better repeat-ability.

The goal of the paper is to quantify the simultaneous competition and cooperation that takes place in organizations. As the concepts seem to be dichotomous opposites at first, the term internal coopetition duality is put forth. Parallels are drawn between coopetitive processes in big multinational corporations (MNCs) and these taking place in universities, also the structural solutions used in both are analyzed. Data mining is used while looking at how specializations inside the university are in competition for better students. We look at the profiles that students have and find natural divisions between the specializations, by applying graph theory and modularity algorithms for community detection. The competitive position of the specializations is evident in the average grades of the detected communities. The ratio of intercommunity ties to intracommunity ties (conductance) quantifies the cooperative stance, though, as students with similar profiles express linkages in the curricula; and the choices regarding career development undertaken become evident. Managerial implications discussed include the imperative for actively managing and financially rewarding the outcomes of the coopetitive duality.