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Abstract: Corruption is a pervasive societal issue, entailing the misuse of public authority for personal benefits. Traditionally, corruption was estimated via perception surveys, which rely on probing the individuals about their views on corruption rather than directly measuring it. Such assessments encounter challenges in accurately capturing corruption and often diverge from actual corruption levels. Recent advancements in data collection, spurred by calls for transparency in public institutions and fueled by enhanced computational and storage capabilities, opened unprecedented opportunities for a far more precise analysis of corruptive processes. By quantitatively analyzing concrete datasets, such as transactions between public sector and private companies, contractual documents, public procurement records, bid outcomes, and healthcare product prices, novel avenues emerged for both addressing and predicting corruption. These scientific endeavors aim to discover the best policies to mitigate corruption and rebuild trust in public institutions. This doctoral dissertation pioneers this novel approach, forging a collaborative partnership with the Commission for the Prevention of Corruption in Slovenia (CPC). Harnessing state-of-the-art data mining, statistical analysis, and machine learning, we analyze a large CPC’s datasets detailing 17 years of public spending on private companies and reported receiving of gifts to public officials. We uncover an array of findings along three research directions: 1. We reveal the presence of self-organizing principles that govern Slovenian public expenditure. Such mechanisms are usually observed in more orderly (e.g. physical) systems and come across as surprising in this context, where interactions are dominated by human factors. 2. We construct an interactive framework tailored for CPC's use. It enables quick identification of suspicious private companies whose revenues from public sources exhibit visible disparities that correlate with changes of the government. 3. Finally, employing natural language processing, we uncover how seemingly innocent ceremonial gifts can foster favoritism and enable misuse of public positions for personal gains. We illustrate the disparities between the laws regulating gift reporting and the actual practices. In conclusion, this research contributed: (i) new computational methods for data-driven analysis of corruption, and (ii) better understanding of societal processes that govern public spending in Slovenia. Our work delivers valuable recommendations to governmental, public, and administrative bodies. We hope these insights will bolster the use of transparent public data as the key tool in the fight against corruption.
Keywords: corruption, public spending, gift reporting, transparency, data mining, time series, unsupervised learning
Published in ReVIS: 17.02.2025; Views: 301; Downloads: 11
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Abstract: Magistrska naloga vsebuje statistično analizo pogostosti potresov v svetovnem merilu. V nalogi predstavimo zbiranje podatkov o potresnih dejavnostih, ki se nahajajo na spletni strani ameriške znanstvene agencije. Z uporabo programskega jezika Python zbrane podatke očistimo, jih analiziramo in predstavimo grafično. Z uporabo knjižnice GeoPandas posamezne lokacije potresov prestavimo tudi na zemljevidu. Za vodilo analize uporabimo zastavljene hipoteze s katerimi skušamo preveriti korelacijo med magnitudo, globino, posamezno lokacijo in časovno komponento potresov. Pri tem se osredotočimo na potresno zanimive lokacije, obravnavamo tudi Slovenijo.
Keywords: potresi, seizmologija, analiza velikih količin podatkov, magnituda, globina, gručenje
Published in ReVIS: 19.07.2023; Views: 1331; Downloads: 92
Full text (3,83 MB)

Abstract: The thesis touches on important concepts of innovation and regional development. Their empirical relevance for economic and social growth is a subject of active debate. Studies revealed that interactions have a major impact, de facto, on the capacity to innovate. Diversifying knowledge sources, joint ventures and co-creational practices can explain much of the locational factors and innovation in a-spatial networks. With the rise of globalisation, scholars found that Multinational Corporations and their global production chains are integrating more and more into regions worldwide. They share practices and engage local stakeholders in their innovation projects. This research aims to explore the importance of locational and global factors in this process. The goal is to see how these two forces contribute to innovation separately and when intertwined regionally, closing the gap between regional and global innovation frameworks. The thesis contributes to science by developing a new synthesis model based on mainstream Innovation theories. It includes the endogenous and exogenous forces that create environmental pressures and opportunities, pushing economic agents to innovate. The central aspect of this model is the acknowledgement of the multiscalar nature of innovation. Local institutions, mindsets and influence from the production chains create conditions and pressures in the network, which motivates companies to innovate. Depending on the local competencies and the needs of production networks, it creates various environments for companies. It is up to enterprises to use created opportunities, which can be local, national or global. Data collected in eight (distinct) regions in Central and Eastern Europe (active in automotive and electronics industries) was used in qualitative and logical analysis to observe patterns in regional behaviour. The results revealed that endogenous and exogenous forces act circumstantially in each region, partially proving all the hypotheses on endogenous and exogenous factors and validating the model. Each region displays different rates of local and regional engagement and, thus, different patterns of innovation, determining their competitive potential. The research also revealed additional factors (e.g., the importance of trust, particularities of information diffusion, etc.) relevant to innovation in Central and Eastern Europe.
Keywords: innovation, regional development, transnational value chains, networks, institutions, cognitive mindset, regional-global co-operation
Published in ReVIS: 25.10.2022; Views: 1209; Downloads: 83
Full text (5,96 MB)

Abstract: The modern computer resources and the data analysis methods allow for a biomedical data to be examined in a more detail than ever. The heart rate variability (HRV) is an easily accessible vital signal that offers a range of useful information about the person under a study. One such application regards an automatical determining whether a person is awake or asleep from the HRV data only. This is of an importance not just for medical but also for practical applications, such as a traffic safety or smart homes. In this doctoral work we study the HRV data of 75 healthy individuals of a varying age and sex, recorded with a microsecond precision. We employ the empirical fact that heart and respiration cycles couples differently during a sleep and awake period. Namely, a respiratory modulation of a heart rhythm or a respiratory sinus arrhythmia (RSA) is more pronounced while asleep, as both sleep and RSA are connected to a strong vagal activity. Therefore, the onset of sleep can be recognized or perhaps even predicted by a carefully examining the cardio-respiratory coupling. We show that the above can indeed be used, at least in principle, to design an algorithmic method to automatically determine the state of a person's consciousness from the HRV data only. On the methodological front we rely on quantifying the (self)similarity among the shapelets, the short chunks of the HRV time series, that allow for a consistent comparison among and within the time series. To establish a better benchmark, we also carry out a comprehensive analysis of the overall HRV dynamics depending on age and sex. Our results include: (i) that a distinctive patterns of the HRV dynamics are consistent across age and sex, (ii) that they are not only an indicative of sleep and awake, but allow to pinpoint the change from awake to sleep and vice versa almost immediately, (iii) that the shapelet analysis is a viable tool to examine these data with a great precision. We conclude that a more systematic analysis involving more subjects could lead to a practical method for the prediction of the onset of sleep.
Keywords: algoritem, EKG, Holter, vzorčna entropija, razmerje signal-šum, klasifikacija, doktorska disertacija
Published in ReVIS: 18.09.2020; Views: 3240; Downloads: 233
Full text (5,09 MB)

Abstract: This PhD thesis is mainly devoted to the study of different problems in nonequilibrium systems that undergo certain phase transitions. The first part of this thesis constitutes a brief review of fundamental concepts in statistical physics. Many of them, of course, will be useful in the remaining of the thesis. My research is thus articulated in three main chapters. The second chapter focuses on a numerical and experimental evidence of an absorbing phase transition, so far associated with spatiotemporal dynamics provided in a purely temporal optical system. We provide a numerical and experimental study of an effective model for a bistable semiconductor laser, with long-delayed opto-electronic feedback and multiplicative noise showing the peculiar features of a critical phenomenon belonging to the directed percolation universality class. In the third part of the thesis we present a random network of heterogeneous phase oscillators in which the links mediating the interactions are constantly rearranged with a characteristic timescale and an extremely low instantaneous connectivity. We will show that, provided strong coupling and fast enough rewiring are considered, the network is able to reach partial synchronisation even in the vanishing connectivity limit. The last part is dedicated to a systematic test of an effective thermodynamics approach proposed for the identification of critical phase transitions in nonequilibrium systems by making a formal analogy with equilibrium systems. When the authors apply it to experimental data of neurons, this method seems to bring a signature of a "special critical point". However, the approach has never been tested on synthetic data and for this reason we will test it on out of equilibrium toy models that display critical transition in a known range of parameters. In the concluding section I summarise and briefly comment my main results and sketch some directions for further research.
Keywords: statistična fizika, ravnotežna statistična fizika, neenakomerna statistična fizika, termodinamika, kritični pojavi, kritična točka, Kuramoto model, skupina za renormalizacijo, samoorganizirana kritičnost omrežja, Zipfov zakon, efektivni termodinamični pristop, doktorska disertacija
Published in ReVIS: 28.05.2019; Views: 3451; Downloads: 180
Full text (14,36 MB)