Role-Playing Simulation Games using ChatGPT

Since the COVID-19 pandemic, educational institutions have embarked on digital transformation projects. The success of these projects depends on integrating new technologies and understanding the needs of digitally literate students. The “learning by doing” approach suggests that real success in learning new skills is achieved when students can try out and practise these skills. In this article, we demonstrate how Large Language Models (LLMs) can enhance the quality of teaching by using ChatGPT in a role-playing simulation game scenario to promote active learning. Moreover, we discuss how LLMs can boost students’ interest in learning by allowing them to practice real-life scenarios using ChatGPT

Function-as-a-Service Benchmarking Framework

Cloud Service Providers deliver their products in form of ”as-a-Service”, which are typically categorized by the level of abstraction. This approach hides the implementation details and shows only functionality to the user. However, the problem is that it is hard to measure the performance of Cloud services, because they behave like black boxes. Especially with Function-as-a-Service it is even more difficult because it completely hides server and infrastructure management from users by design. Cloud Service Prodivers usually restrict the maximum size of code, memory and runtime of Cloud Functions. Nevertheless, users need clarification if more ressources are needed to deliver services in high quality. In this regard, we present the architectural design of a new Function-as-a-Service benchmarking tool, which allows users to evaluate the performance of Cloud Functions. Furthermore, the capabilities of the framework are tested on an isolated platform with a specific workload. The results show that users are able to get insights into Function-as-a-Service environments. This, in turn, allows users to identify factors which may slow down or speed up the performance of Cloud Functions

Analysing Design Approaches for the Power Consumption in Cyber-Physical Systems

The importance of Cyber Physical Systems (CPS) and Internet of Things (IoT) applications is constantly increasing, especially in the context of Industry 4.0. Architectural decisions are crucial not just for performance, security and resilience reasons but also regarding costs and resource usage. In this paper we analyse two of the fundamental approaches to design control loops (i.e. time-driven and event-driven), show how they can be realised and evaluate their power requirements. Through this the design criteria can be extended also considering the optimization of energy related aspects

A Security Cost Modelling Framework for Cyber-Physical Systems

Cyber-Physical Systems (CPS) are formed through interconnected components capable of computation, communication, sensing and changing the physical world. The development of these systems poses a significant challenge since they have to be designed in a way to ensure cyber-security without impacting their performance. This article presents the Security Cost Modelling Framework (SCMF) and shows supported by an experimental study how it can be used to measure, normalise and aggregate the overall performance of a CPS. Unlike previous studies, our approach uses different metrics to measure the overall performance of a CPS and provides a methodology for normalising the measurement results of different units to a common Cost Unit. Moreover, we show how the Security Costs can be extracted from the overall performance measurements which allows to quantify the overhead imposed by performing security-related tasks. Furthermore, we describe the architecture of our experimental testbed and demonstrate the applicability of SCMF in an experimental study. Our results show that measuring the overall performance and extracting the security costs using SCMF can serve as basis to redesign interactions to achieve the same overall goal at less costs

Towards a security-aware benchmarking framework for function-as-a-service

In a world, where complexity increases on a daily basis the Function-as-a-Service (FaaS) cloud model seams to take countermeasures. In comparison to other cloud models, the fast evolving FaaS increasingly abstracts the underlying infrastructure and refocuses on the application logic. This trend brings huge benefits in application and performance but comes with difficulties for benchmarking cloud applications. In this position paper, we present an initial investigation of benchmarking FaaS in close to reality production systems. Furthermore, we outline the architectural design including the necessary benchmarking metrics. We also discuss the possibility of using the proposed framework for identifying security vulnerabilities

Towards a Security Cost Model for Cyber-Physical Systems

In times of Industry 4.0 and cyber-physical systems (CPS) providing security is one of the biggest challenges. A cyber attack launched at a CPS poses a huge threat, since a security incident may affect both the cyber and the physical world. Since CPS are very flexible systems, which are capable of adapting to environmental changes, it is important to keep an overview of the resulting costs of providing security. However, research regarding CPS currently focuses more on engineering secure systems and does not satisfactorily provide approaches for evaluating the resulting costs. This paper presents an interaction-based model for evaluating security costs in a CPS. Furthermore, the paper demonstrates in a use case driven study, how this approach could be used to model the resulting costs for guaranteeing security

Temperature Monitoring of Agricultural Areas in a Secure Data Room

Agricultural production is highly dependent on naturally occurring environmental conditions like change of seasons and the weather. Especially in fruit and wine growing, late frosts occurring shortly after the crops have sprouted have the potential to cause massive damage to plants [L1,L2] [1]. In this article we present a cost-efficient temperature monitoring system for detecting and reacting to late frosts to prevent crop failures. The proposed solution includes a data space where Internet of Things (IoT) devices can form a cyber-physical system (CPS) to interact with their nearby environment and securely exchange data. Based on this data, more accurate predictions can be made in the future using machine learning (ML), which will further contribute to minimising economic damage caused by crop failures

Towards Comparing Programming Paradigms

Rapid technological progress in computer sciences finds solutions and at the same time creates ever more complex requirements. Due to an evolving complexity today's programming languages provide powerful frameworks which offer standard solutions for recurring tasks to assist the programmer and to avoid the re-invention of the wheel with so-called “out-of-the-box-features”. In this paper, we propose a way of comparing different programming paradigms on a theoretical, technical and practical level. Furthermore, the paper presents the results of an initial comparison of two representative programming approaches, both in the closed SAP environment