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Tennis, as one of the most popular individual sports in the world, holds an important role in the betting world. There are two main categories of bets: pre-match betting, which is conducted before the match starts, and live betting, which allows placing bets during the sporting event. Betting systems rely on setting sports odds, something historically done by domain experts. Setting odds for live betting represents a challenge due to the need to follow events in real-time and react accordingly. In tennis, hierarchical models often stand out as a popular choice when trying to predict the outcome of the match. These models commonly leverage a recursive approach that aims to predict the winner or the final score starting at any point in the match. However, recursive expressions inherently contain computational complexity which hinders the efficiency of methods relying on them. This paper proposes a more resource-effective alternative in the form of a combinatorial approach based on a binomial distribution. The resulting accuracy of the combinatorial approach is identical to that of the recursive approach while being vastly more efficient when considering the execution time, making it a superior choice for live betting in this domain.

Global networking, growing computer infrastructure complexity and the ongoing migration of many private and business aspects to the electronic domain commonly mandate using cutting-edge technologies based on data analysis, machine learning, and artificial intelligence to ensure high levels of network and information system security. Transparency is a major barrier to the deployment of black box intelligent systems in high-risk domains, such as the cybersecurity domain, with the problem getting worse as machine learning models increase in complexity. In this research, explainable machine learning is used to extract information from the CIC-IDS2017 dataset and to critically contrast the knowledge attained by analyzing if–then decision tree rules with the knowledge attained by the SHAP approach. The paper compares the challenges of the knowledge extraction using the SHAP method and the if–then decision tree rules, providing guidelines regarding different approaches suited to specific situations. [ABSTRACT FROM AUTHOR]

The prediction of sport event results has always drawn attention from a vast variety of different groups of people, such as club managers, coaches, betting companies, and the general population. The specific nature of each sport has an important role in the adaption of various predictive techniques founded on different mathematical and statistical models. In this paper, a common approach of modeling sports with a strongly defined structure and a rigid scoring system that relies on an assumption of independent and identical point distributions is challenged. It is demonstrated that such models can be improved by introducing dynamics into the match models in the form of sport momentums. Formal mathematical models for implementing these momentums based on conditional probability and empirical Bayes estimation are proposed, which are ultimately combined through a unifying hybrid approach based on the Monte Carlo simulation. Finally, the method is applied to real-life volleyball data demonstrating noticeable improvements over the previous approaches when it comes to predicting match outcomes. The method can be implemented into an expert system to obtain insight into the performance of players at different stages of the match or to study field scenarios that may arise under different circumstances. [ABSTRACT FROM AUTHOR]

One of the biggest challenges in the process of establishing modern e-learning systems is figuring out ways to leverage legacy course materials and integrating them in the new information systems. Existing exam query repositories in particular are a very valuable data source, but one which usually lacks enough metadata to help establish relationships between exam questions and corresponding learning concepts whose adoption is being evaluated. In this paper we present the continuation of our research regarding the usage of educational data mining methods able to automatically annotate pre-existing exam queries with information about learning concepts they relate to. In our novel approach we leverage both textual and visual information contained in the queries. By combining the power of natural language processing which focuses on the text of the question, and annotated data extracted from figures accompanying the questions, we are able to further refine our classification methods and achieve noticeably improved results. By identifying learning concepts more accurately we further facilitate automatic creation of exams as well as even better insight into learning concept adoption. Our approach is again applied on data gathered from a large scale university course, and the results were validated in consultation with educational domain experts. [ABSTRACT FROM AUTHOR]

In the last few decades sociologists were trying to explain human behaviour by analysing social networks, which requires access to data about interpersonal relationships. This represented a big obstacle in this research field until the emergence of online social networks (OSNs), which vastly facilitated the process of collecting such data. Nowadays, by crawling public profiles on OSNs, it is possible to build a social graph where “friends” on OSN become represented as connected nodes. OSN connection does not necessarily indicate a close real-life relationship, but using OSN interaction records may reveal real-life relationship intensities, a topic which inspired a number of recent researches. Still, published research currently lacks an extensive exploratory analysis of OSN interaction records, i.e. a comprehensive overview of users' interaction via different ways of OSN interaction. In this paper, we provide such an overview by leveraging results of conducted extensive social experiment which managed to collect records for over 3200 Facebook users interacting with over 1,400,000 of their friends. Our exploratory analysis focuses on extracting population distributions and correlation parameters for 13 interaction parameters, providing valuable insight into OSN interaction for future researches aimed at this field of study. [ABSTRACT FROM AUTHOR]

Common goal of descriptive data mining techniques is presenting new information in concise, easily interpretable and understandable ways. Hierarchical clustering technique for example enables simple visualization of distances between analyzed objects or attributes. However, common distance measures used by existing data mining tools are usually not well suited for analyzing transactional data using this particular technique. Including new types of measures specifically aimed at transactional data can make hierarchical clustering a much more feasible choice for transactional data analysis. This paper presents and analyzes convenient measure types, providing methods of transforming them to represent distances between transaction elements more appropriately. Developed measures are implemented, verified and compared in hierarchical clustering analysis on both artificial data as well as referent transactional datasets. [ABSTRACT FROM AUTHOR]

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