Traffic Risk Modelling and Analysis under Airport-Like Simulation Environment

Traffic safety plays a crucial role in the development of autonomous vehicles which attracts significant attention in the community. It is a challenge task to ensure autonomous vehicle safety under varied traffic environment interference, especially for airport-like closed-loop conditions. To that aim, we analyze autonomous vehicle safety at typical roadway conditions and traffic state constraints (e.g., car-following state at different speed distributions) by simulating the airport-like traffic conditions. The experimental results suggest that traffic collision risk is in a positive relationship with the speed difference and distance among adjacent vehicles. More specifically, the autonomous vehicle may collide with neighbors when the time to collision (TTC) indicator is lower than 4 s, and vice versa. The research findings can help both research community and practioners obtain additional information for improving traffic safety for autonomous vehicles.

Motivation-based Attacker Modelling for Cyber Risk Management: A Quantitative Content: Analysis and a Natural Experiment

Cyber-attacks have a tremendous impact on worldwide economic performance. Hence, it is vitally important to implement effective risk management for different cyber-attacks, which calls for profound attacker models. However, cyber risk modelling based on attacker models seems to be restricted to overly simplified models. This hinders the understanding of cyber risks and represents a heavy burden for efficient cyber risk management. This work aims to forward scientific research in this field by employing a multi-method approach based on a quantitative content analysis of scientific literature and a natural experiment. Our work gives evidence for the oversimplified modelling of attacker motivational patterns. The quantitative content analysis gives evidence for a broad and established misunderstanding of attackers as being illicitly malicious. The results of the natural experiment substantiate the findings of the content analysis. We thereby contribute to the improvement of attacker modelling, which can be considered a necessary prerequisite for effective cyber risk management.

Prediction of immediate and late renal functioning post-parathyroidectomy for primary hyperparathyroidism : Amalgamation of Ensemble approach and Automated Machine Learning

Prognostication is pursued with risk modelling for acute kidney injury postoperatively in such patients who have undergone parathyroidectomy for primary hyperparathyroidism. Novel composite variables notably contributing to close-to-perfect predictive competence of the proposed suite of prognosticative models are also unveiled.

Modelling Risk under Volatile Conditions: Tail Index Estimation and Validation

The subject of the research is to analyse and evaluate methods of investment risk modelling in dynamic, changing market circumstances, with a special focus on the assessment success of the expected effects of investment activities in ’extreme’ return points. In that sense, different Value at Risk models were used: the Historical Simulation (HS VaR), the Delta Normal VaR (D VaR) and the Extreme Value Theory model (EVT). The research objective is to test the performance of these models in specific, volatile, market circumstances, in terms of estimating the maximum possible losses from these activities. The basic hypothesis of the research is that it is possible to successfully anticipate the maximum possible losses from the investment activities in the extreme points of the return function by applying different methods of investment risk modelling in volatile market circumstances. The analysed financial data comprise daily stock returns of the BELEX15 (Serbia), BUX (Hungary), CROBEX (Croatia) and SBITOP (Slovenia) stock exchange indices in the period 2012-2019, which is relatively long time period suitable for the sound analyses. The main findings of the research point to the superior application adequacy of the Extreme Value Theory model (EVT) for successful risk modelling, i.e. for making optimal investment decisions. The research results represent innovated, concrete knowledge in the field of understanding the behaviour of the return function in its extremes, and consequently are of great importance to both the academic and professional public in the process of generating decisions on investment activities in volatile market conditions.

Point of Contact

COVID-19 exposed the need to identify newer tools to understand perception of information, behavioral conformance to instructions and model the effects of individual motivation and decisions on the success of measures being put in place. We approach this challenge through the lens of serious games. Serious games are designed to instruct and inform within the confines of their magic circle. We built a multiplayer serious game, Point of Contact (PoC), to investigate effects of a serious game on perception and behavior. We conducted a study with 23 participants to gauge perceptions of COVID-19 preventive measures and quantify the change after playing PoC. The results show a significant positive change to participants' perceptions towards COVID-19 preventive measures, shifting perceptions towards following guidelines more strictly due to a greater awareness of how the virus spreads. We discuss these implications and the value of a serious game like PoC towards pandemic risk modelling at a microcosm level.