BAM: Benchmarking Argument Mining on Scientific Documents

In this paper, we present BAM, a unified Benchmark for Argument Mining (AM). We propose a method to homogenize both the evaluation process and the data to provide a common view in order to ultimately produce comparable results. Built as a four stage and end-to-end pipeline, the benchmark allows for the direct inclusion of additional argument miners to be evaluated. First, our system pre-processes a ground truth set used both for training and testing. Then, the benchmark calculates a total of four measures to assess different aspects of the mining process. To showcase an initial implementation of our approach, we apply our procedure and evaluate a set of systems on a corpus of scientific publications. With the obtained comparable results we can homogeneously assess the current state of AM in this domain

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

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

Challenges and opportunities of democracy in the digital society: report from Dagstuhl Seminar 22361

Digital technologies amplify and change societal processes. So far, society and intellectuals have painted two extremes of viewing the effects of the digital transformation on democratic life. While the early 2000s to mid-2010s declared the "liberating" aspects of digital technology, the post-Brexit events and the 2016 US elections have emphasized the "dark side" of the digital revolution. Now, explicit effort is needed to go beyond tech saviorism or doom scenarios. To this end, we organized the Dagstuhl Seminar 22361 "Challenges and Opportunities of Democracy in the Digital Society" to discuss the future of digital democracy. This report presents a summary of the seminar, which took place in Dagstuhl in September 2022. The seminar attracted scientific scholars from various disciplines, including political science, computer science, jurisprudence, and communication science, as well as civic technology practitioners

Implementations in Machine Ethics: A Survey

Increasingly complex and autonomous systems require machine ethics to maximize the benefits and minimize the risks to society arising from the new technology. It is challenging to decide which type of ethical theory to employ and how to implement it effectively. This survey provides a threefold contribution. First, it introduces a trimorphic taxonomy to analyze machine ethics implementations with respect to their object (ethical theories), as well as their nontechnical and technical aspects. Second, an exhaustive selection and description of relevant works is presented. Third, applying the new taxonomy to the selected works, dominant research patterns, and lessons for the field are identified, and future directions for research are suggested

Implementations in Machine Ethics: A Survey

Increasingly complex and autonomous systems require machine ethics to maximize the benefits and minimize the risks to society arising from the new technology. It is challenging to decide which type of ethical theory to employ and how to implement it effectively. This survey provides a threefold contribution. First, it introduces a trimorphic taxonomy to analyze machine ethics implementations with respect to their object (ethical theories), as well as their nontechnical and technical aspects. Second, an exhaustive selection and description of relevant works is presented. Third, applying the new taxonomy to the selected works, dominant research patterns, and lessons for the field are identified, and future directions for research are suggested.Comment: published version, journal paper, ACM Computing Surveys, 38 pages, 7 tables, 4 figure

Central relaxin-3 receptor (RXFP3) activation impairs social recognition and modulates ERK-phosphorylation in specific GABAergic amygdala neurons

This is a pre-print of an article published in Brain Structure and Function. The final authenticated version is available online at: https://doi.org/10.1007/s00429-018-1763-5In mammals, the extended amygdala is a neural hub for social and emotional information processing. In the rat, the extended amygdala receives inhibitory GABAergic projections from the nucleus incertus (NI) in the pontine tegmentum. NI neurons produce the neuropeptide relaxin-3, which acts via the Gi/o-protein-coupled receptor, RXFP3. A putative role for RXFP3 signalling in regulating social interaction was investigated by assessing the effect of intracerebroventricular infusion of the RXFP3 agonist, RXFP3-A2, on performance in the 3-chamber social interaction paradigm. Central RXFP3-A2, but not vehicle, infusion, disrupted the capacity to discriminate between a familiar and novel conspecific subject, but did not alter differentiation between a conspecific and an inanimate object. Subsequent studies revealed that agonist-infused rats displayed increased phosphoERK(pERK)-immunoreactivity in specific amygdaloid nuclei at 20 min post-infusion, with levels similar to control again after 90 min. In parallel, we used immunoblotting to profile ERK phosphorylation dynamics in whole amygdala after RXFP3-A2 treatment; and multiplex histochemical labelling techniques to reveal that after RXFP3-A2 infusion and social interaction, pERK-immunopositive neurons in amygdala expressed vesicular GABA-transporter mRNA and displayed differential profiles of RXFP3 and oxytocin receptor mRNA. Overall, these findings demonstrate that central relaxin-3/RXFP3 signalling can modulate social recognition in rats via effects within the amygdala and likely interactions with GABA and oxytocin signalling

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

Implementations in Machine Ethics: A Survey

Increasingly complex and autonomous systems require machine ethics to maximize the benefits and minimize the risks to society arising from the new technology. It is challenging to decide which type of ethical theory to employ and how to implement it effectively. This survey provides a threefold contribution. First, it introduces a trimorphic taxonomy to analyze machine ethics implementations with respect to their object (ethical theories), as well as their nontechnical and technical aspects. Second, an exhaustive selection and description of relevant works is presented. Third, applying the new taxonomy to the selected works, dominant research patterns, and lessons for the field are identified, and future directions for research are suggested