SEP spectra derived from neutron monitor data and from EPHIN space detector data during recent GLEs and sub-GLEs

The Electron Proton Helium Instrument (EPHIN) aboard the Solar Heliospheric Observatory (SOHO) observed several SEP events with protons accelerated to energies E>500 MeV, whereas no neutron monitor (NM) of the worldwide network showed a significant increase in their counting rate. For instance, the SEP event on 8 November 2000 with maximum proton intensity at 500 MeV of >0.1 (cm2 s sr MeV)-1 is outstanding, as this maximum pro-ton flux is comparable with the GLEs on 14 July 2000 and on 15 April 2001 (max. count rate increase in 5-min data of 225% at South Pole NM). In a first step we applied a forward modelling approach of the SEP event on 8 November 2000, i.e. we computed the expected NM count rate increases for selected NM stations, utilizing as input para-meters the SEP spectra determined from EPHIN data as well as anticipated pitch angle distribution and apparent source direction. The simulated count rate increases for selected NM stations showed that this SEP event should have be seen as a clear GLE. To further understand this situation, we investigated in a next step recent GLEs and sub-GLEs. Consequently, a total of four SEP events were selected, two clearly identified GLEs and two sub-GLEs. We performed a “GLE analysis” based on the data of the worldwide network of NMs for each of the four SEP events and then compared the derived SEP spectra with the proton spectra as determined from EPHIN measurements

Utilizing Active Machine Learning for Quality Assurance: A Case Study of Virtual Car Renderings in the Automotive Industry

Computer-generated imagery of car models has become an indispensable part of car manufacturers' advertisement concepts. They are for instance used in car configurators to offer customers the possibility to configure their car online according to their personal preferences. However, human-led quality assurance faces the challenge to keep up with high-volume visual inspections due to the car models’ increasing complexity. Even though the application of machine learning to many visual inspection tasks has demonstrated great success, its need for large labeled data sets remains a central barrier to using such systems in practice. In this paper, we propose an active machine learning-based quality assurance system that requires significantly fewer labeled instances to identify defective virtual car renderings without compromising performance. By employing our system at a German automotive manufacturer, start-up difficulties can be overcome, the inspection process efficiency can be increased, and thus economic advantages can be realized

Energiespektren der nahezu relativistischen galaktischen kosmischen Strahlung und von solaren energiereichen Teilchen: Erweiterung der Messfähigkeiten von EPHIN

Measurements of the energy spectra of particles in the heliosphere are of major importance in order to understand basic physical acceleration and transport processes. Furthermore, understanding their temporal variations is crucial with regard to their impact in dosimetry for future manned space missions. The two main components of particles in the heliosphere with energies above 100 MeV are the sporadic Solar Energetic Particle (SEP) events and Galactic Cosmic Rays (GCR), which have their intensity and energy spectrum modulated by the Sun on time scales of up to several years. In this work, the observable energy range of the Electron Proton Helium Instrument (EPHIN) onboard the Solar and Heliospheric Observatory (SOHO) was increased with detailed simulations of the instrument and its response to energetic particles. Since the developed method can be applied to data already taken by the instrument, energy spectra for protons between 100 MeV and above 1 GeV were derived for the entire SOHO mission and therefore significantly extend the available data sets for SEP events and GCRs in terms of energy and time coverage. The annual proton spectra from 1995 to 2014 have been derived in an energy range from 250 MeV up to 1.6 GeV and discussed with a focus on drift effects. Furthermore, the possibility to apply the new method to Helium and heavy ions has been examined. SEP events have been investigated by statistical means utilizing the continous measurements from EPHIN . For this purpose, the SEP events with an increase in the intensity of protons above energies of 500 MeV which occured between 1995 and 2015 have been identified. The spectral properties of these events have been calculated and analysed with special emphasis on whether or not the event has been detected by ground-based instruments, i.e. Neutron Monitors.Messungen der Energiespektren von Teilchen in der Heliosphäre sind wichtig um sowohl deren Beschleunigungs- und Transportprozesse zu untersuchen als auch den Beitrag dieser Teilchen zur Strahlungsdosis zukünftiger bemannter Raumfahrtmissionen abschätzen zu können. Die beiden Hauptkomponenten von Teilchen in der Heliosphäre mit Energien über 100 MeV sind die unregelmäßig auftretenden solaren energiereichen Teilchen (SEP) Ereignisse und die Galaktische Kosmische Strahlung (GCR), deren Intensität und Energiespektrum durch die solare Modulation auf Zeitskalen von bis zu mehreren Jahren beinflusst wird. In dieser Arbeit wurde der Energiebereich des Electron Proton Helium Instruments (EPHIN) auf dem Solar and Heliospheric Observatory (SOHO) mittels Simulationen des Instrumentes und dessen Ansprechverhalten auf energiereiche Teilchen signifikant erweitert. Mittels der entwickelten Methode können Energiespektren von Protonen von 100 MeV bis über 1 GeV für die gesamte SOHO Mission berechnet werden. Auf Basis dieser erweiterten Messfähigkeiten wurden energiereiche SEP Ereignisse und die solare Modulation der GCR untersucht. Für beide Teilchenpopulationen wurden die systematischen Unsicherheiten mittels Simulationen und Vergleichen mit anderen Missionen untersucht. In den GCR Studien wurden die jährlichen Proton Spektren vom 1995 bis 2014 in einem Energiebereich von 250 MeV bis 1.6 GeV berechnet und Drifteffekte an Hand dieser Daten untersucht. Außerdem wurde untersucht, in wie weit sich die entwickelte Methode auf Helium und schwere Ionen erweitern lässt. SEP Ereignisse wurden in einer statistischen Analyse untersucht. Hierfür wurden alle Ereignisse mit einem Intensitätsanstieg von Protonen über 500 MeV zwischen 1995 und 2015 identifiziert. Die spektralen Eigenschaften dieser Ereignisse wurden dann berechnet und analysiert, insbesondere im Hinblick darauf, ob ein Ereigniss auch von Instrumenten auf dem Erdboden, im speziellen Neutronen Monitoren, gemessen wurde

Designing Resilient AI-based Robo-Advisors: A Prototype for Real Estate Appraisal

For most people, buying a home is a life-changing decision that involves financial obligations for many years into the future. Therefore, it is crucial to realistically assess the value of a property before making a purchase decision. Recent research has shown that artificial intelligence (AI) has the potential to predict property prices accurately. As a result, more and more AI-based robo-advisors offer real estate estimation advice. However, a recent scandal has shown that automated algorithms are not always reliable. Triggered by the Covid-19 pandemic, one of the largest robo-advisors (Zillow) bought houses overvalued, eventually resulting in the dismissal of 2,000 employees. This demonstrates the current weaknesses of AI-based algorithms in real estate appraisal and highlights the need for troubleshooting AI advice. Therefore, we propose to leverage techniques from the explainable AI (XAI) knowledge base to help humans question AI consultations. We derive design principles based on the literature and implement them in a configurable real estate valuation artifact. We then evaluate it in two focus groups to confirm the validity of our approach. We contribute to research and practice by deriving design knowledge in accordance with a unique artifact

Should I Follow AI-based Advice? Measuring Appropriate Reliance in Human-AI Decision-Making

Many important decisions in daily life are made with the help of advisors, e.g., decisions about medical treatments or financial investments. Whereas in the past, advice has often been received from human experts, friends, or family, advisors based on artificial intelligence (AI) have become more and more present nowadays. Typically, the advice generated by AI is judged by a human and either deemed reliable or rejected. However, recent work has shown that AI advice is not always beneficial, as humans have shown to be unable to ignore incorrect AI advice, essentially representing an over-reliance on AI. Therefore, the aspired goal should be to enable humans not to rely on AI advice blindly but rather to distinguish its quality and act upon it to make better decisions. Specifically, that means that humans should rely on the AI in the presence of correct advice and self-rely when confronted with incorrect advice, i.e., establish appropriate reliance (AR) on AI advice on a case-by-case basis. Current research lacks a metric for AR. This prevents a rigorous evaluation of factors impacting AR and hinders further development of human-AI decision-making. Therefore, based on the literature, we derive a measurement concept of AR. We propose to view AR as a two-dimensional construct that measures the ability to discriminate advice quality and behave accordingly. In this article, we derive the measurement concept, illustrate its application and outline potential future research

A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making

Research in artificial intelligence (AI)-assisted decision-making is experiencing tremendous growth with a constantly rising number of studies evaluating the effect of AI with and without techniques from the field of explainable AI (XAI) on human decision-making performance. However, as tasks and experimental setups vary due to different objectives, some studies report improved user decision-making performance through XAI, while others report only negligible effects. Therefore, in this article, we present an initial synthesis of existing research on XAI studies using a statistical meta-analysis to derive implications across existing research. We observe a statistically positive impact of XAI on users\u27 performance. Additionally, the first results indicate that human-AI decision-making tends to yield better task performance on text data. However, we find no effect of explanations on users\u27 performance compared to sole AI predictions. Our initial synthesis gives rise to future research investigating the underlying causes and contributes to further developing algorithms that effectively benefit human decision-makers by providing meaningful explanations

Factors that Influence the Adoption of Human-AI Collaboration in Clinical Decision-Making

Recent developments in Artificial Intelligence (AI) have fueled the emergence of human-AI collaboration, a setting where AI is a coequal partner. Especially in clinical decision-making, it has the potential to improve treatment quality by assisting overworked medical professionals. Even though research has started to investigate the utilization of AI for clinical decision-making, its potential benefits do not imply its adoption by medical professionals. While several studies have started to analyze adoption criteria from a technical perspective, research providing a human-centered perspective with a focus on AI\u27s potential for becoming a coequal team member in the decision-making process remains limited. Therefore, in this work, we identify factors for the adoption of human-AI collaboration by conducting a series of semi-structured interviews with experts in the healthcare domain. We identify six relevant adoption factors and highlight existing tensions between them and effective human-AI collaboration