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Opis: A foremost problem in network science is how to reconstruct (infer) the topology of a real network from signals measured from its internal units. Grasping the architecture of complex networks is key, not only to understand their functioning, but also to predict and control their behaviour. Currently available methods largely focus on the detection of links of undirected networks and often require strong assumptions about the system. However, many of these methods cannot be applied to networks with directional connections. To address this problem, in this doctoral work we focus at the inference of directed networks. Specifically, we develop a model-based network reconstruction method that combines statistics of derivative-variable correlations with simulated annealing. We furthermore develop a data-driven reconstruction method based on a nonlinear interdependence measure. This method allows one to infer the topology of directed networks of chaotic Lorenz oscillators for a subrange of the coupling strength and link density. Finally, we apply the data-driven method to multichannel electroencephalographic recordings from an epilepsy patient. The functional brain networks obtained from this approach are consistent with the available medical information.
Ključne besede: network reconstruction, simulated annealing, dynamical systems, nonlinear interdependence measure, EEG
Objavljeno: 19.04.2019; Ogledov: 2445; Prenosov: 144
Celotno besedilo (6,96 MB)

Opis: Avtor ne dovoljuje objave na spletni strani oz. v elektronski knjižnici FIŠ zaradi prepovedi organizacije, v sklopu katere je bil pripravljen empirični del naloge.
Ključne besede: sledljivost, spletna aplikacija, HTML, CSS, JavaScript, PHP, MySQL
Objavljeno: 15.01.2019; Ogledov: 2838; Prenosov: 0

Opis: Complex systems are omnipresent in nature, society as well as in human culture. Last few decades saw an increase of interest for their study, particularly by using graph-theoretic methodologies. By identifying systems' units as nodes and modelling interactions between the units as links, the study of complex networks spread to a number of disciplines including sociology, biology and linguistics, to just mention a few. The research done in this doctoral dissertation falls in this context. The core of this doctoral work is the data-driven multilevel analysis of major human languages, which was done in two stages. First, we looked at the speed of growth of Wikipedias in 26 different languages over the span of 15 years. This involved creating and analysing a dataset with 14962 articles, each of which exists in all 26 languages. We found six well-defined clusters of Wikipedias that share common growth patterns, with their make-up robust against the method used for their determination. Interestingly, the identified clusters were found to have little correlation with the respective language families. Rather, our results suggest that growth of Wikipedias is primarily governed by an intricate set of other factors, from culture to information literacy. Second, to approach human languages at another independent level, we gathered a dataset comprising a list of syllables and a list of syllables words in 10 different languages, specifically: English, Dutch, German, Russian, Slovenian, Croatian, French, Spanish, Latin and Basque. These datasets were obtained from recognized repositories for each language and benchmarked in the same way. Syllable networks were created by looking at pairs of syllables that jointly compose at least one word. We then carried out a systematic network analysis, relying on both standard network analysis methods and more recent techniques, such as K-core analysis and graphlet statistics. Research revealed striking similarities between the architectures of syllable networks that belong to the same language family, along with expected differences between the families. Indeed, structures of syllable networks were found to well quantify the linguistic similarities among these 10 languages, exactly as known from classical linguistics. Most interestingly, we found that Basque language, whose classification is as of today still unknown, bares a strong resemblance to Latin, at least when syllable network representation is concerned. Earlier stages of this doctoral work involved comparing the performance of network alignment algorithms, used in bioinformatics for studying protein networks. Several alignment algorithms were compared by scoring their performance on standard protein datasets. It was found that three algorithms, HUBALIGN, L-GRAAL and NATALIE, regularly produce the most topologically and biologically coherent alignments. Due to the change of doctoral adviser, this research topic was abandoned in favour of language/syllable networks. In sum, this doctoral work involved two distinct directions of research in network science, one related to developing the methodology of network analysis (alignment algorithms), and the other devoted to extracting new information from specifically designed datasets (syllable networks). Therefore, the original contribution of this work to science includes both theory and methodology. Future research avenues include advancement along both directions, most interesting being the application of network alignment methods to syllable datasets, which could reveal more precise quantification of structural differences among syllable networks.
Ključne besede: computational statistics, biostatistics, bioinformatics, machine learning, computational linguistics
Objavljeno: 21.12.2018; Ogledov: 3110; Prenosov: 138
Celotno besedilo (17,51 MB)

Opis: Magistrska naloga predstavlja razvoj aplikacije za spremljanje prevozov v podjetju. To je spletna aplikacija, ki je namenjena podjetju Transport Bruna d.o.o. Cilj spletne aplikacije je vozniku olajšati beleženje prevozov in obenem podjetju nuditi sproten nadzor nad prevozi in stroški, ki zadevajo prevoze ter njihova vozila. V nalogi so opisana uporabljena razvojna orodja in tehnologije za razvoj spletnih aplikacij, kot tudi sama predstavitev in delovanje spletne aplikacije. Rezultat je izdelana spletna aplikacija, ki omogoča beleženje prevozov, strank in stroškov.
Ključne besede: spletna aplikacija, MVC, ASP.NET, prevozi, podatkovna baza
Objavljeno: 30.11.2018; Ogledov: 3959; Prenosov: 204
Celotno besedilo (2,74 MB)

Opis: Ljudje pozabljamo dogodke, ki so se že zgodili, ker so nenehno dogajajo novi dogodki. Prav tako pa dogodke skozi čas ponovno podoživljamo zaradi različnih sprožilcev, kot so mediji, tako dogodek ponovno obudimo, ampak le za kratek čas. V tem projektu uporabljam spletne podatke z analizo statistike obiskovalcev člankov iz Wikipedije o terorističnih napadih, letalskih nesrečah, naravnih nesrečah, športnih dogodkih in glasbenih koncertih po letu 2015. Najprej predstavim kritičen pregled literature o kolektivnem spominu, vplivu medijev in opis posameznih dogodkov. V metodologiji predstavim cilje in namen naloge, s katerimi orodji sem podatke vzorčil, zbiral in analiziral. Nato prikažem ugotovitve posameznih grafov s pomočjo linearne regresijske premice, s katero pokažem, kako hiter je upad zanimanja za posamezen dogodek. Na koncu o tem tudi prediskutiram in potrdim oziroma zavrnem hipoteze ter predstavim razmišljanja o nadaljnji raziskavi.
Ključne besede: podatki, analiza podatkov, RStudio, kolektivni spomin, digitalni mediji, teroristični napadi, naravne nesreče, letalske nesreče, kulturni dogodki, športni dogodki, pozabljanje
Objavljeno: 30.11.2018; Ogledov: 3351; Prenosov: 145
Celotno besedilo (2,08 MB)