With tracking systems becoming more widespread in sports research and regular training and competitions, more data are available for sports analytics and performance prediction. We analyzed 2523 ski ...jumps from 205 athletes on five venues. For every jump, the dataset includes the 3D trajectory, 3D velocity, skis’ orientation, and metadata such as wind, starting gate, and ski jumping hill data. Using this dataset, we aimed to predict the expected jump length (xLength) inspired by the expected goals metric in soccer (xG). We evaluate the performance of a fully connected neural network, a convolutional neural network (CNN), a long short-term memory (LSTM), and a ResNet architecture to estimate the xLength. For the prediction of the jump length one second after take-off, we achieve a mean absolute error (MAE) of 5.3 m for the generalization to new athletes and an MAE of 5.9 m for the generalization to new ski jumping hills using ResNet architectures. Additionally, we investigated the influence of the input time after the take-off on the predictions’ accuracy. As expected, the MAE becomes smaller with longer inputs. Due to the real-time transmission of the sensor’s data, xLength can be updated during the flight phase and used in live TV broadcasting. xLength could also be used as an analysis tool for experts to quantify the quality of the take-off and flight phases.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
The most important goal of customer service is to keep the customer satisfied. However, service resources are always limited and must prioritize specific customers. Therefore, it is essential to ...identify customers who potentially become unsatisfied and might lead to escalations. Data science on IoT data (especially log data) for machine health monitoring and analytics on enterprise data for customer relationship management (CRM) have mainly been researched and applied independently. This paper presents a data-driven decision support system framework that combines IoT and enterprise data to model customer sentiment and predicts escalations. The proposed framework includes a fully automated and interpretable machine learning pipeline using state-of-the-art methods. The framework is applied in a real-world case study with a major medical device manufacturer providing data from a fleet of thousands of high-end medical devices. An anonymized version of this industrial benchmark is released for the research community based on the presented case study, which has interesting and challenging properties. In our extensive experiments, we achieve a Recall@50 of 50.0% for the task of predicting customer escalations. In addition, we show that combining IoT and enterprise data can improve prediction results and ease troubleshooting. Additionally, we propose a practical workflow for end-users when applying the proposed framework.
Unravelling the interplay between a human’s microbiome and physiology is a relevant task for understanding the principles underlying human health and disease. With regard to human chemical ...communication, it is of interest to elucidate the role of the microbiome in shaping or generating volatiles emitted from the human body. In this study, we characterized the microbiome and volatile organic compounds (VOCs) sampled from the neck and axilla of ten participants (five male, five female) on two sampling days, by applying different methodological approaches. Volatiles emitted from the respective skin site were collected for 20 min using textile sampling material and analyzed on two analytical columns with varying polarity of the stationary phase. Microbiome samples were analyzed by a culture approach coupled with MALDI-TOF-MS analysis and a 16S ribosomal RNA gene (16S RNA) sequencing approach. Statistical and advanced data analysis methods revealed that classification of body sites was possible by using VOC and microbiome data sets. Higher classification accuracy was achieved by combination of both data pools.
Cutibacterium
,
Staphylococcus
,
Micrococcus
,
Streptococcus
,
Lawsonella
,
Anaerococcus
, and
Corynebacterium
species were found to contribute to classification of the body sites by the microbiome. Alkanes, esters, ethers, ketones, aldehydes and cyclic structures were used by the classifier when VOC data were considered. The interdisciplinary methodological platform developed here will enable further investigations of skin microbiome and skin VOCs alterations in physiological and pathological conditions.
Progress in making neural networks more robust against adversarial attacks is mostly marginal, despite the great efforts of the research community. Moreover, the robustness evaluation is often ...imprecise, making it challenging to identify promising approaches. We do an observational study on the classification decisions of 19 different state-of-the-art neural networks trained to be robust against adversarial attacks. This analysis gives a new indication of the limits of the robustness of current models on a common benchmark. In addition, our findings suggest that current untargeted adversarial attacks induce misclassification toward only a limited amount of different classes. Similarly, we find that previous attacks under-explore the perturbation space during optimization. This leads to unsuccessful attacks for samples where the initial gradient direction is not a good approximation of the final adversarial perturbation direction. Additionally, we observe that both over- and under-confidence in model predictions result in an inaccurate assessment of model robustness. Based on these observations, we propose a novel loss function for adversarial attacks that consistently improves their efficiency and success rate compared to prior attacks for all 30 analyzed models.
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CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Machine learning (ML) has made enormous progress in the last two decades. Specifically, Deep Neural Networks (DNNs) have led to several breakthroughs. The applications range from synthesizing ...high-resolution images that are indistinguishable from real photos to large-scale language models that achieve human-level performance on various tasks.Yet, while humans can apply learned knowledge to new situations with only a few examples, neural networks often fail at this task. As a result, real-world distribution shifts in the environment, demographics, or data collection process pose severe safety risks to humans. For instance, autonomous cars may fail to adapt to unknown road conditions, and medical systems may provide incorrect diagnoses for minorities not included in the training data. Another security threat is the vulnerability of neural networks to small adversarially crafted perturbations. As such, even imperceptible changes in the data can lead to erroneous model behavior. In this cumulative dissertation, I demonstrate a literature gap regarding methods that simultaneously address real-world and adversarial distribution shifts. Therefore, I propose three objectives to increase the robustness of neural networks against both threats.The first objective consists of the detection of potentially harmful model decisions caused by distribution shifts in the data. Here, we showed that the input-gradient geometry of neural networks can be used to detect both real-world and adversarial distribution shifts P1. Unlike prior work, we demonstrated the flexibility of our method by showing its effectiveness on both image and time series classification tasks.The second objective considers the accurate quantification of network robustness against adversarial distribution shifts (attacks), which is essential to assess the worst-case risk in safety-critical applications. Toward this objective, we propose to improve two critical components of gradient-based adversarial attacks. In one contribution, we improved the convergence of gradient-descent-based optimization by including past gradient information in the optimization history P2. In another contribution, we introduced a novel optimization objective that leads to an increased attack success rate while simultaneously reducing the perturbation magnitude of adversarial attacks P3.Third, we present a novel approach to mitigate vulnerabilities against real-world and adversarial distribution shifts P4. To this end, we theoretically motivate how properties of local extrema in the loss landscape can be used to identify spurious predictions. Based on these findings, we propose the Decision Region Quantification (DRQ) algorithm that analyzes the robustness of local decision regions in the vicinity of a given data point to find the most robust prediction for a given sample.
In mobile robotics, perception in uncontrolled environments like autonomous driving is a central hurdle. Existing active learning frameworks can help enhance perception by efficiently selecting data ...samples for labeling, but they are often constrained by the necessity of full data availability in data centers, hindering real-time, on-field adaptations. To address this, our work unveils a novel active learning formulation optimized for multi-robot settings. It harnesses the collaborative power of several robotic agents, considerably enhancing the data acquisition and synchronization processes. Experimental evidence indicates that our approach markedly surpasses traditional active learning frameworks by up to 2.5 percent points and 90% less data uploads, delivering new possibilities for advancements in the realms of mobile robotics and autonomous systems.
It has been reported that a distinct ‘old person smell’ can develop with advancing age, however, this odour has not yet been sufficiently described in previous research. Sensory evaluation by a ...trained panel might be useful to describe alterations with age in body odour (BO). To evaluate the alterations and achieve first insights into the ‘old person smell’, this pilot study determined the odour profiles of BO samples from both a younger and an older age group with a trained panel. In addition, we aimed to assess whether the panellists can recognize the age group based on the smell of the BO samples. Eight younger (20–28 years) and eight older (80–83 years) participants sampled their BO by wearing a cotton T‐shirt for one night. The samples were sensorially evaluated by a trained panel, including ratings of total intensity and pleasantness. Additionally, an age labelling task was performed as a forced‐choice decision. Results revealed that the odour profiles of the BO samples were very similar for both age groups. Nevertheless, trained panellists were able to predict the age group with significantly higher accuracy (p = .042) than expected by chance (61% mean accuracy over all panellists). Furthermore, a linear support vector machine (SVM) classifier achieved an average accuracy of 69%. This finding indicates that the age of a person affects the BO, though it is not reflected in significantly distinct odour profiles.
Trained panellists established odour profiles for axillary body odour samples donated by younger and older persons and were able to predict the age group with significantly higher accuracy than expected by chance. By training a linear SVM classifier based on the trained panellists' ratings, higher accuracy was achieved. This finding indicates that hints towards the age of the body odour donor were present in the body odour samples.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
•Stress chemosignals partly improve emotional perspective taking in depressed individuals.•Stress chemosignals partly enhance affective responsiveness in depressed individuals.•Salivary cortisol ...levels remain unaffected by stress chemosignals.•Friendly-TSST chemosignals appear to have calming effects.
Human body odors contain chemical signals that play a key role in our non-verbal communication regarding health, genetic identity, immune system, fitness, and emotional state. Studies on human chemosignaling in individuals with psychiatric diseases are scarce but indicate altered smell perception and emotion recognition in depressed individuals. In the present project, we aimed to investigate the influence of chemosensory substances in social stress sweat on emotion recognition, perspective taking, affective responsiveness as well as stress level in healthy and depressed individuals. Therefore, chemosensory stimuli (sweat samples from Trier social stress test (TSST) and friendly-TSST (fTSST)) were obtained from 39 healthy participants (19 females). In a next step, chemosensory stimuli and an odor-free blank (cotton pad) were used to stimulate another group of 40 healthy participants (20 females) and 37 individuals with depression (24 females). Those stimuli were examined regarding their influence on subjective feelings of stress, emotion perception and empathic reactions using an empathy test. Furthermore, physiological data (breathing, heart rate, skin conductance response, stress hormones) of the participants were collected during chemosensory stimulation. Depressed individuals improved their ability of perspective taking and affective responsiveness for the emotion grief when presented with stress chemosignals compared to no chemosignals. Healthy individuals remained unaffected regarding perspective taking and affective responsiveness. Both depressed and healthy individuals showed no increased stress hormone cortisol and α-amylase values during the social stress chemosignals condition, but reduced values for fTSST condition compared to no chemosignals respectively. The results imply that stress chemosignals do not trigger a stress reaction, but for depressed individuals they lead to a better emotion assessment for grief.
This research contributes to a deeper understanding of the effects of social stress chemosignals on healthy and depressed individuals. Knowing the impact of human chemosignals on emotional processing is crucial for a better understanding of non-verbal human interaction.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Over the past few decades, the topic of artificial intelligence (AI) has gained considerable attention in both research and industry. In particular, the healthcare sector has witnessed a surge in the ...use of AI applications, as the maturity of these methods increased. However, as the use of machine learning (ML) in healthcare continues to grow, we believe it will become increasingly important to examine public perceptions of this trend to identify potential impediments and future directions. Current work focuses mainly on academic data sources and industrial applications of AI. However, to gain a comprehensive understanding of the increased societal interest in AI, digital media such as podcasts should be consulted, as they are accessible to a broader audience. In order to examine this hypothesis, we investigate the AI trend development in healthcare from 2015 until 2021. In this study, we propose a web mining approach to collect a novel data set consisting of 29 healthcare podcasts with 3449 episodes. We identify 102 AI-related buzzwords that were extracted from various glossaries and hype cycles. These buzzwords were used to conduct an extensive trend detection and analysis study on the collected data using machine learning-based approaches. We successfully detect an AI trend and follow its evolution in healthcare podcasts over several years. Besides the focus area of AI, we are able to detect 14 topic clusters and visualize the trending or decreasing dominant topics over the whole period under consideration. In addition, we analyze the sentiments in podcasts towards the identified topics and deliver further insights for trend detection in healthcare. Finally, the collected data set can be used for trend detection besides AI-related topics using topic clustering.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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