Kantonsschule Schaffhausen

Structured quantitative data, such as survey responses in human resource management research, are often analysed using machine learning methods, including logistic regression. Although these methods provide accurate statistical predictions, their results are frequently abstract and difficult for non-specialists to comprehend. This limits their usefulness in practice, particularly in contexts where eXplainable Artificial Intelligence (XAI) is essential. This study proposes a domain-independent approach for the autonomous classification and interpretation of quantitative data using visual processing. This method transforms individual responses based on rating scales into visual representations, which are subsequently processed by Convolutional Neural Networks (CNNs). In combination with Class Activation Maps (CAMs), image-based CNN models enable not only accurate and reproducible classification but also visual interpretability of the underlying decision-making process. Our evaluation found that CNN models with bar chart coding achieved an accuracy of between 93.05% and 93.16%, comparable to the 93.19% achieved by logistic regression. Compared with conventional numerical approaches, exemplified by logistic regression in this study, the approach achieves comparable classification accuracy while providing additional comprehensibility and transparency through graphical representations. Robustness is demonstrated by consistent results across different visualisations generated from the same underlying data. By converting abstract numerical information into visual explanations, this approach addresses a core challenge: bridging the gap between model performance and human understanding. Its transparency, domain-agnostic design, and straightforward interpretability make it particularly suitable for XAI-driven applications across diverse disciplines that use quantitative response data.

Creativity is the primary driver of our cultural evolution. The astonishing potential of artificial intelligence (AI) and its possible application in the creative process poses an urgent and dramatic challenge for humanity; how can we maximize the benefits of AI while minimizing the associated risks? In this article, we identify all forms of human-AI collaboration in this realm as cyber-creativity. We introduce the following two forward-looking scenarios: a utopian vision for cyber-creativity, in which AI serves to enhance and not replace human creativity, and a dystopian view associated with the pre-emption of all human creative agency caused by the rise of AI. In our view, the scientific community is called to bring its contribution, however small, to help humanity make steps towards the utopian scenario, while avoiding the dystopian one. Here, we present a decalogue of research challenges identified for this purpose, touching upon the following dimensions: (1) the theoretical framework for cyber-creativity; (2) sociocultural perspectives; (3) the cyber-creative process; (4) the creative agent; (5) the co-creative team; (6) cyber-creative products; (7) cyber-creative domains; (8) cyber-creative education; (9) ethical aspects; and (10) the dark side of cyber-creativity. For each dimension, a brief review of the state-of-the-art is provided, followed by the identification of a main research challenge, then specified into a list of research questions. Whereas there is no claim that this decalogue of research challenges represents an exhaustive classification, which would be an impossible objective, it still should serve as a valid starting point for future (but urgent) research endeavors, with the ambition to provide a significant contribution to the understanding, development, and alignment of AI to human values the realm of creativity.

Predicting failures in production environments allows service providers to activate countermeasures that prevent harming the users of the applications. The most successful approaches predict failures from error states that the current approaches identify from anomalies in time series of fixed sets of KPI values collected at runtime. They cannot handle time series of KPI sets with size that varies over time. Thus these approaches work with applications that run on statically configured sets of components and computational nodes, and do not scale up to the many popular cloud applications that exploit autoscaling. This paper proposes Preface, a novel approach to predict failures in cloud applications that exploit autoscaling. Preface originally augments the neural-network-based failure predictors successfully exploited to predict failures in statically configured applications, with a Rectifier layer that handles KPI sets of highly variable size as the ones collected in cloud autoscaling applications, and reduces those KPIs to a set of rectified-KPIs of fixed size that can be fed to the neural-network predictor. The Preface Rectifier computes the rectified-KPIs as descriptive statistics of the original KPIs, for each logical component of the target application. The descriptive statistics shrink the highly variable sets of KPIs collected at different timestamps to a fixed set of values compatible with the input nodes of the neural-network failure predictor. The neural network can then reveal anomalies that correspond to error states, before they propagate to failures that harm the users of the applications. The experiments on both a commercial application and a widely used academic exemplar confirm that Preface can indeed predict many harmful failures early enough to activate proper countermeasures.

Quantum computing is a growing field with significant potential applications. Learning how to code quantum programs means understanding how qubits work and learning to use quantum gates. This is analogous to creating classical algorithms using logic gates and bits. Even after learning all concepts, it is difficult to create new algorithms, which hinders the acceptance of quantum programming by most developers.

Ist ein Fet durch ein subpartales Ereignis irreversibel von der Sauerstoffzufuhr abgeschnitten, ist ab 10 min mit Hirnschäden zu rechnen. Eine solche Schädigung kann nur dann verhindert oder vermindert werden, wenn sowohl die Notsituation sehr schnell erkannt als auch sofort die Entbindung per Notsectio durchgeführt werden. Bei einer verzögerten Reaktionszeit nützt eine kurze Entscheidung-Entbindung-Zeit (EE-Zeit) wenig und umgekehrt ist ein sofortiger Entscheid nutzlos, wenn infrastrukturell bedingt die Zeit bis zur Entbindung viel zu lange dauert. Die fast 20-jährigen Erfahrungen an der Klinik für Geburtshilfe am Universitätsspital Zürich zeigen, dass mit geeigneten Methoden konstant die EE-Zeit um 10 min gehalten werden kann. Damit ist es möglich, die Pathologie-Entbindung-Zeit nahe an die im Extremfall kritischen 10 min zu bringen. Die Erfahrungen an dieser Klinik können als Blaupause für andere Krankenhäuser dienen.

The COVID-19 pandemic has brought about numerous changes in the education sector, including the transition to online learning. This literature review explored the impact of the pandemic on higher education and the challenges and opportunities associated with online learning. Results indicate that students and teachers in developing nations faced significant difficulties due to the lack of proper digital infrastructure. Additionally, a dearth of information was found regarding the impact of COVID-19 on academic performance. Nevertheless, the pandemic also brought about opportunities for innovation and capacity building in the education sector. The study concluded that neither teachers nor students were fully prepared for the shift to online learning during the pandemic. This literature review highlights the need for future research to investigate the effect of COVID-19 on academic performance and to develop strategies to improve online learning experiences for all students.