Trolleys, crashes, and perception-a survey on how current autonomous vehicles debates invoke problematic expectations.

Ongoing debates about ethical guidelines for autonomous vehicles mostly focus on variations of the 'Trolley Problem'. Using variations of this ethical dilemma in preference surveys, possible implications for autonomous vehicles policy are discussed. In this work, we argue that the lack of realism in such scenarios leads to limited practical insights. We run an ethical preference survey for autonomous vehicles by including more realistic features, such as time pressure and a non-binary decision option. Our results indicate that such changes lead to different outcomes, calling into question how the current outcomes can be generalized. Additionally, we investigate the framing effects of the capabilities of autonomous vehicles and indicate that ongoing debates need to set realistic expectations on autonomous vehicle challenges. Based on our results, we call upon the field to re-frame the current debate towards more realistic discussions beyond the Trolley Problem and focus on which autonomous vehicle behavior is considered not to be acceptable, since a consensus on what the right solution is, is not reachable.

Active querying approach to epidemic source detection on contact networks.

The problem of identifying the source of an epidemic (also called patient zero) given a network of contacts and a set of infected individuals has attracted interest from a broad range of research communities. The successful and timely identification of the source can prevent a lot of harm as the number of possible infection routes can be narrowed down and potentially infected individuals can be isolated. Previous research on this topic often assumes that it is possible to observe the state of a substantial fraction of individuals in the network before attempting to identify the source. We, on the contrary, assume that observing the state of individuals in the network is costly or difficult and, hence, only the state of one or few individuals is initially observed. Moreover, we presume that not only the source is unknown, but also the duration for which the epidemic has evolved. From this more general problem setting a need to query the state of other (so far unobserved) individuals arises. In analogy with active learning, this leads us to formulate the active querying problem. In the active querying problem, we alternate between a source inference step and a querying step. For the source inference step, we rely on existing work but take a Bayesian perspective by putting a prior on the duration of the epidemic. In the querying step, we aim to query the states of individuals that provide the most information about the source of the epidemic, and to this end, we propose strategies inspired by the active learning literature. Our results are strongly in favor of a querying strategy that selects individuals for whom the disagreement between individual predictions, made by all possible sources separately, and a consensus prediction is maximal. Our approach is flexible and, in particular, can be applied to static as well as temporal networks. To demonstrate our approach's practical importance, we experiment with three empirical (temporal) contact networks: a network of pig movements, a network of sexual contacts, and a network of face-to-face contacts between residents of a village in Malawi. The results show that active querying strategies can lead to substantially improved source inference results as compared to baseline heuristics. In fact, querying only a small fraction of nodes in a network is often enough to achieve a source inference performance comparable to a situation where the infection states of all nodes are known.

Visualising data science workflows to support third-party notebook comprehension: an empirical study.

Data science is an exploratory and iterative process that often leads to complex and unstructured code. This code is usually poorly documented and, consequently, hard to understand by a third party. In this paper, we first collect empirical evidence for the non-linearity of data science code from real-world Jupyter notebooks, confirming the need for new approaches that aid in data science code interaction and comprehension. Second, we propose a visualisation method that elucidates implicit workflow information in data science code and assists data scientists in navigating the so-called garden of forking paths in non-linear code. The visualisation also provides information such as the rationale and the identification of the data science pipeline step based on cell annotations. We conducted a user experiment with data scientists to evaluate the proposed method, assessing the influence of (i) different workflow visualisations and (ii) cell annotations on code comprehension. Our results show that visualising the exploration helps the users obtain an overview of the notebook, significantly improving code comprehension. Furthermore, our qualitative analysis provides more insights into the difficulties faced during data science code comprehension.

Workflow analysis of data science code in public GitHub repositories.

Despite the ubiquity of data science, we are far from rigorously understanding how coding in data science is performed. Even though the scientific literature has hinted at the iterative and explorative nature of data science coding, we need further empirical evidence to understand this practice and its workflows in detail. Such understanding is critical to recognise the needs of data scientists and, for instance, inform tooling support. To obtain a deeper understanding of the iterative and explorative nature of data science coding, we analysed 470 Jupyter notebooks publicly available in GitHub repositories. We focused on the extent to which data scientists transition between different types of data science activities, or steps (such as data preprocessing and modelling), as well as the frequency and co-occurrence of such transitions. For our analysis, we developed a dataset with the help of five data science experts, who manually annotated the data science steps for each code cell within the aforementioned 470 notebooks. Using the first-order Markov chain model, we extracted the transitions and analysed the transition probabilities between the different steps. In addition to providing deeper insights into the implementation practices of data science coding, our results provide evidence that the steps in a data science workflow are indeed iterative and reveal specific patterns. We also evaluated the use of the annotated dataset to train machine-learning classifiers to predict the data science step(s) of a given code cell. We investigate the representativeness of the classification by comparing the workflow analysis applied to (a) the predicted data set and (b) the data set labelled by experts, finding an F1-score of about 71% for the 10-class data science step prediction problem.

Outbreak detection for temporal contact data.

Epidemic spreading is a widely studied process due to its importance and possibly grave consequences for society. While the classical context of epidemic spreading refers to pathogens transmitted among humans or animals, it is straightforward to apply similar ideas to the spread of information (e.g., a rumor) or the spread of computer viruses. This paper addresses the question of how to optimally select nodes for monitoring in a network of timestamped contact events between individuals. We consider three optimization objectives: the detection likelihood, the time until detection, and the population that is affected by an outbreak. The optimization approach we use is based on a simple greedy approach and has been proposed in a seminal paper focusing on information spreading and water contamination. We extend this work to the setting of disease spreading and present its application with two example networks: a timestamped network of sexual contacts and a network of animal transports between farms. We apply the optimization procedure to a large set of outbreak scenarios that we generate with a susceptible-infectious-recovered model. We find that simple heuristic methods that select nodes with high degree or many contacts compare well in terms of outbreak detection performance with the (greedily) optimal set of nodes. Furthermore, we observe that nodes optimized on past periods may not be optimal for outbreak detection in future periods. However, seasonal effects may help in determining which past period generalizes well to some future period. Finally, we demonstrate that the detection performance depends on the simulation settings. In general, if we force the simulator to generate larger outbreaks, the detection performance will improve, as larger outbreaks tend to occur in the more connected part of the network where the top monitoring nodes are typically located. A natural progression of this work is to analyze how a representative set of outbreak scenarios can be generated, possibly taking into account more realistic propagation models.

Real-Time Assessment of Stress and Stress Response Using Digital Phenotyping: A Study Protocol.

Background: Stress is a complex phenomenon that may have a negative influence on health and well-being; consequently, it plays a pivotal role in mental health. Although the incidence of mental disorders has been continuously rising, development of prevention and treatment methods has been rather slow. Through the ubiquitous presence of smartphones and wearable devices, people can monitor stress parameters in everyday life. However, the reliability and validity of such monitoring are still unsatisfactory. Methods: The aim of this trial is to find a relationship between psychological stress and saliva cortisol levels on the one hand and physiological parameters measured by smartphones in combination with a commercially available wearable device on the other. Participants include cohorts of individuals with and without a psychiatric disorder. The study is conducted in two settings: one naturalistic and one a controlled laboratory environment, combining ecological momentary assessment (EMA) and digital phenotyping (DP). EMA is used for the assessment of challenging and stressful situations coincidentally happening during a whole observation week. DP is used during a controlled stress situation with the Trier Social Stress Test (TSST) as a standardized psychobiological paradigm. Initially, participants undergo a complete psychological screening and profiling using a standardized psychometric test battery. EMA uses a smartphone application, and the participants keep a diary about their daily routine, activities, well-being, sleep, and difficult and stressful situations they may encounter. DP is conducted through wearable devices able to continuously monitor physiological parameters (i.e., heart rate, heart rate variability, skin conductivity, temperature, movement and acceleration). Additionally, saliva cortisol samples are repeatedly taken. The TSST is conducted with continuous measurement of the same parameters measured during the EMA. Discussion: We aim to identify valid and reliable digital biomarkers for stress and stress reactions. Furthermore, we expect to find a way of early detection of psychological stress in order to evolve new opportunities for interventions reducing stress. That may allow us to find new ways of treating and preventing mental disorders. Trial Registration: The competing ethics committee of the Canton of Zurich, Switzerland, approved the study protocol V05.1 May 28, 2019 [BASEC: 2019-00814]; the trial was registered at ClinicalTrials.gov [NCT04100213] on September 19, 2019.

A Transdisciplinary Approach Supporting the Implementation of a Big Data Project in Livestock Production: An Example From the Swiss Pig Production Industry.

Big Data approaches offer potential benefits for improving animal health, but they have not been broadly implemented in livestock production systems. Privacy issues, the large number of stakeholders, and the competitive environment all make data sharing, and integration a challenge in livestock production systems. The Swiss pig production industry illustrates these and other Big Data issues. It is a highly decentralized and fragmented complex network made up of a large number of small independent actors collecting a large amount of heterogeneous data. Transdisciplinary approaches hold promise for overcoming some of the barriers to implementing Big Data approaches in livestock production systems. The purpose of our paper is to describe the use of a transdisciplinary approach in a Big Data research project in the Swiss pig industry. We provide a brief overview of the research project named "Pig Data," describing the structure of the project, the tools developed for collaboration and knowledge transfer, the data received, and some of the challenges. Our experience provides insight and direction for researchers looking to use similar approaches in livestock production system research.

The pig transport network in Switzerland: Structure, patterns, and implications for the transmission of infectious diseases between animal holdings.

The topology of animal transport networks contributes substantially to how fast and to what extent a disease can transmit between animal holdings. Therefore, public authorities in many countries mandate livestock holdings to report all movements of animals. However, the reported data often does not contain information about the exact sequence of transports, making it impossible to assess the effect of truck sharing and truck contamination on disease transmission. The aim of this study was to analyze the topology of the Swiss pig transport network by means of social network analysis and to assess the implications for disease transmission between animal holdings. In particular, we studied how additional information about transport sequences changes the topology of the contact network. The study is based on the official animal movement database in Switzerland and a sample of transport data from one transport company. The results show that the Swiss pig transport network is highly fragmented, which mitigates the risk of a large-scale disease outbreak. By considering the time sequence of transports, we found that even in the worst case, only 0.34% of all farm-pairs were connected within one month. However, both network connectivity and individual connectedness of farms increased if truck sharing and especially truck contamination were considered. Therefore, the extent to which a disease may be transmitted between animal holdings may be underestimated if we only consider data from the official animal movement database. Our results highlight the need for a comprehensive analysis of contacts between farms that includes indirect contacts due to truck sharing and contamination. As the nature of animal transport networks is inherently temporal, we strongly suggest the use of temporal network measures in order to evaluate individual and overall risk of disease transmission through animal transportation.