TY - JOUR A1 - Steininger, Michael A1 - Kobs, Konstantin A1 - Davidson, Padraig A1 - Krause, Anna A1 - Hotho, Andreas T1 - Density-based weighting for imbalanced regression JF - Machine Learning N2 - In many real world settings, imbalanced data impedes model performance of learning algorithms, like neural networks, mostly for rare cases. This is especially problematic for tasks focusing on these rare occurrences. For example, when estimating precipitation, extreme rainfall events are scarce but important considering their potential consequences. While there are numerous well studied solutions for classification settings, most of them cannot be applied to regression easily. Of the few solutions for regression tasks, barely any have explored cost-sensitive learning which is known to have advantages compared to sampling-based methods in classification tasks. In this work, we propose a sample weighting approach for imbalanced regression datasets called DenseWeight and a cost-sensitive learning approach for neural network regression with imbalanced data called DenseLoss based on our weighting scheme. DenseWeight weights data points according to their target value rarities through kernel density estimation (KDE). DenseLoss adjusts each data point’s influence on the loss according to DenseWeight, giving rare data points more influence on model training compared to common data points. We show on multiple differently distributed datasets that DenseLoss significantly improves model performance for rare data points through its density-based weighting scheme. Additionally, we compare DenseLoss to the state-of-the-art method SMOGN, finding that our method mostly yields better performance. Our approach provides more control over model training as it enables us to actively decide on the trade-off between focusing on common or rare cases through a single hyperparameter, allowing the training of better models for rare data points. KW - supervised learning KW - imbalanced regression KW - cost-sensitive learning KW - sample weighting KW - Kerneldensity estimation Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-269177 SN - 1573-0565 VL - 110 IS - 8 ER - TY - JOUR A1 - Davidson, Padraig A1 - Düking, Peter A1 - Zinner, Christoph A1 - Sperlich, Billy A1 - Hotho, Andreas T1 - Smartwatch-Derived Data and Machine Learning Algorithms Estimate Classes of Ratings of Perceived Exertion in Runners: A Pilot Study JF - Sensors N2 - The rating of perceived exertion (RPE) is a subjective load marker and may assist in individualizing training prescription, particularly by adjusting running intensity. Unfortunately, RPE has shortcomings (e.g., underreporting) and cannot be monitored continuously and automatically throughout a training sessions. In this pilot study, we aimed to predict two classes of RPE (≤15 “Somewhat hard to hard” on Borg’s 6–20 scale vs. RPE >15 in runners by analyzing data recorded by a commercially-available smartwatch with machine learning algorithms. Twelve trained and untrained runners performed long-continuous runs at a constant self-selected pace to volitional exhaustion. Untrained runners reported their RPE each kilometer, whereas trained runners reported every five kilometers. The kinetics of heart rate, step cadence, and running velocity were recorded continuously ( 1 Hz ) with a commercially-available smartwatch (Polar V800). We trained different machine learning algorithms to estimate the two classes of RPE based on the time series sensor data derived from the smartwatch. Predictions were analyzed in different settings: accuracy overall and per runner type; i.e., accuracy for trained and untrained runners independently. We achieved top accuracies of 84.8 % for the whole dataset, 81.8 % for the trained runners, and 86.1 % for the untrained runners. We predict two classes of RPE with high accuracy using machine learning and smartwatch data. This approach might aid in individualizing training prescriptions. KW - artificial intelligence KW - endurance KW - exercise intensity KW - precision training KW - prediction KW - wearable Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-205686 SN - 1424-8220 VL - 20 IS - 9 ER - TY - JOUR A1 - Koopmann, Tobias A1 - Stubbemann, Maximilian A1 - Kapa, Matthias A1 - Paris, Michael A1 - Buenstorf, Guido A1 - Hanika, Tom A1 - Hotho, Andreas A1 - Jäschke, Robert A1 - Stumme, Gerd T1 - Proximity dimensions and the emergence of collaboration: a HypTrails study on German AI research JF - Scientometrics N2 - Creation and exchange of knowledge depends on collaboration. Recent work has suggested that the emergence of collaboration frequently relies on geographic proximity. However, being co-located tends to be associated with other dimensions of proximity, such as social ties or a shared organizational environment. To account for such factors, multiple dimensions of proximity have been proposed, including cognitive, institutional, organizational, social and geographical proximity. Since they strongly interrelate, disentangling these dimensions and their respective impact on collaboration is challenging. To address this issue, we propose various methods for measuring different dimensions of proximity. We then present an approach to compare and rank them with respect to the extent to which they indicate co-publications and co-inventions. We adapt the HypTrails approach, which was originally developed to explain human navigation, to co-author and co-inventor graphs. We evaluate this approach on a subset of the German research community, specifically academic authors and inventors active in research on artificial intelligence (AI). We find that social proximity and cognitive proximity are more important for the emergence of collaboration than geographic proximity. KW - collaboration KW - dimensions of proximity KW - co-authorships KW - co-inventorships KW - embedding techniques Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-269831 SN - 1588-2861 VL - 126 IS - 12 ER - TY - JOUR A1 - Sîrbu, Alina A1 - Becker, Martin A1 - Caminiti, Saverio A1 - De Baets, Bernard A1 - Elen, Bart A1 - Francis, Louise A1 - Gravino, Pietro A1 - Hotho, Andreas A1 - Ingarra, Stefano A1 - Loreto, Vittorio A1 - Molino, Andrea A1 - Mueller, Juergen A1 - Peters, Jan A1 - Ricchiuti, Ferdinando A1 - Saracino, Fabio A1 - Servedio, Vito D.P. A1 - Stumme, Gerd A1 - Theunis, Jan A1 - Tria, Francesca A1 - Van den Bossche, Joris T1 - Participatory Patterns in an International Air Quality Monitoring Initiative JF - PLoS ONE N2 - The issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment both locally (e.g., local air/water quality, noise disturbance) and globally (e.g., climate change, resource use). Urban environments represent a crucial example, with an increasing realization that the most effective way of producing a change is involving the citizens themselves in monitoring campaigns (a citizen science bottom-up approach). This is possible by developing novel technologies and IT infrastructures enabling large citizen participation. Here, in the wider framework of one of the first such projects, we show results from an international competition where citizens were involved in mobile air pollution monitoring using low cost sensing devices, combined with a web-based game to monitor perceived levels of pollution. Measures of shift in perceptions over the course of the campaign are provided, together with insights into participatory patterns emerging from this study. Interesting effects related to inertia and to direct involvement in measurement activities rather than indirect information exposure are also highlighted, indicating that direct involvement can enhance learning and environmental awareness. In the future, this could result in better adoption of policies towards decreasing pollution. KW - transport microenvironments KW - exposure KW - pollution KW - carbon Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-151379 VL - 10 IS - 8 ER - TY - JOUR A1 - Düking, Peter A1 - Hotho, Andreas A1 - Holmberg, Hans-Christer A1 - Fuss, Franz Konstantin A1 - Sperlich, Billy T1 - Comparison of Non-Invasive Individual Monitoring of the Training and Health of Athletes with Commercially Available Wearable Technologies JF - Frontiers in Physiology N2 - Athletes adapt their training daily to optimize performance, as well as avoid fatigue, overtraining and other undesirable effects on their health. To optimize training load, each athlete must take his/her own personal objective and subjective characteristics into consideration and an increasing number of wearable technologies (wearables) provide convenient monitoring of various parameters. Accordingly, it is important to help athletes decide which parameters are of primary interest and which wearables can monitor these parameters most effectively. Here, we discuss the wearable technologies available for non-invasive monitoring of various parameters concerning an athlete's training and health. On the basis of these considerations, we suggest directions for future development. Furthermore, we propose that a combination of several wearables is most effective for accessing all relevant parameters, disturbing the athlete as little as possible, and optimizing performance and promoting health. KW - sports technology KW - wearable technologies KW - performance parameters KW - health monitoring KW - performance monitoring Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-165516 VL - 7 IS - 71 ER -