@article{ReinhardHelmerichBorasetal.2022, author = {Reinhard, Sebastian and Helmerich, Dominic A. and Boras, Dominik and Sauer, Markus and Kollmannsberger, Philip}, title = {ReCSAI: recursive compressed sensing artificial intelligence for confocal lifetime localization microscopy}, series = {BMC Bioinformatics}, volume = {23}, journal = {BMC Bioinformatics}, number = {1}, doi = {10.1186/s12859-022-05071-5}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-299768}, year = {2022}, abstract = {Background Localization-based super-resolution microscopy resolves macromolecular structures down to a few nanometers by computationally reconstructing fluorescent emitter coordinates from diffraction-limited spots. The most commonly used algorithms are based on fitting parametric models of the point spread function (PSF) to a measured photon distribution. These algorithms make assumptions about the symmetry of the PSF and thus, do not work well with irregular, non-linear PSFs that occur for example in confocal lifetime imaging, where a laser is scanned across the sample. An alternative method for reconstructing sparse emitter sets from noisy, diffraction-limited images is compressed sensing, but due to its high computational cost it has not yet been widely adopted. Deep neural network fitters have recently emerged as a new competitive method for localization microscopy. They can learn to fit arbitrary PSFs, but require extensive simulated training data and do not generalize well. A method to efficiently fit the irregular PSFs from confocal lifetime localization microscopy combining the advantages of deep learning and compressed sensing would greatly improve the acquisition speed and throughput of this method. Results Here we introduce ReCSAI, a compressed sensing neural network to reconstruct localizations for confocal dSTORM, together with a simulation tool to generate training data. We implemented and compared different artificial network architectures, aiming to combine the advantages of compressed sensing and deep learning. We found that a U-Net with a recursive structure inspired by iterative compressed sensing showed the best results on realistic simulated datasets with noise, as well as on real experimentally measured confocal lifetime scanning data. Adding a trainable wavelet denoising layer as prior step further improved the reconstruction quality. Conclusions Our deep learning approach can reach a similar reconstruction accuracy for confocal dSTORM as frame binning with traditional fitting without requiring the acquisition of multiple frames. In addition, our work offers generic insights on the reconstruction of sparse measurements from noisy experimental data by combining compressed sensing and deep learning. We provide the trained networks, the code for network training and inference as well as the simulation tool as python code and Jupyter notebooks for easy reproducibility.}, language = {en} } @article{WamserSeufertHalletal.2021, author = {Wamser, Florian and Seufert, Anika and Hall, Andrew and Wunderer, Stefan and Hoßfeld, Tobias}, title = {Valid statements by the crowd: statistical measures for precision in crowdsourced mobile measurements}, series = {Network}, volume = {1}, journal = {Network}, number = {2}, issn = {2673-8732}, doi = {10.3390/network1020013}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-284154}, pages = {215 -- 232}, year = {2021}, abstract = {Crowdsourced network measurements (CNMs) are becoming increasingly popular as they assess the performance of a mobile network from the end user's perspective on a large scale. Here, network measurements are performed directly on the end-users' devices, thus taking advantage of the real-world conditions end-users encounter. However, this type of uncontrolled measurement raises questions about its validity and reliability. The problem lies in the nature of this type of data collection. In CNMs, mobile network subscribers are involved to a large extent in the measurement process, and collect data themselves for the operator. The collection of data on user devices in arbitrary locations and at uncontrolled times requires means to ensure validity and reliability. To address this issue, our paper defines concepts and guidelines for analyzing the precision of CNMs; specifically, the number of measurements required to make valid statements. In addition to the formal definition of the aspect, we illustrate the problem and use an extensive sample data set to show possible assessment approaches. This data set consists of more than 20.4 million crowdsourced mobile measurements from across France, measured by a commercial data provider.}, language = {en} } @article{BencurovaShityakovSchaacketal.2022, author = {Bencurova, Elena and Shityakov, Sergey and Schaack, Dominik and Kaltdorf, Martin and Sarukhanyan, Edita and Hilgarth, Alexander and Rath, Christin and Montenegro, Sergio and Roth, G{\"u}nter and Lopez, Daniel and Dandekar, Thomas}, title = {Nanocellulose composites as smart devices with chassis, light-directed DNA Storage, engineered electronic properties, and chip integration}, series = {Frontiers in Bioengineering and Biotechnology}, volume = {10}, journal = {Frontiers in Bioengineering and Biotechnology}, issn = {2296-4185}, doi = {10.3389/fbioe.2022.869111}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-283033}, year = {2022}, abstract = {The rapid development of green and sustainable materials opens up new possibilities in the field of applied research. Such materials include nanocellulose composites that can integrate many components into composites and provide a good chassis for smart devices. In our study, we evaluate four approaches for turning a nanocellulose composite into an information storage or processing device: 1) nanocellulose can be a suitable carrier material and protect information stored in DNA. 2) Nucleotide-processing enzymes (polymerase and exonuclease) can be controlled by light after fusing them with light-gating domains; nucleotide substrate specificity can be changed by mutation or pH change (read-in and read-out of the information). 3) Semiconductors and electronic capabilities can be achieved: we show that nanocellulose is rendered electronic by iodine treatment replacing silicon including microstructures. Nanocellulose semiconductor properties are measured, and the resulting potential including single-electron transistors (SET) and their properties are modeled. Electric current can also be transported by DNA through G-quadruplex DNA molecules; these as well as classical silicon semiconductors can easily be integrated into the nanocellulose composite. 4) To elaborate upon miniaturization and integration for a smart nanocellulose chip device, we demonstrate pH-sensitive dyes in nanocellulose, nanopore creation, and kinase micropatterning on bacterial membranes as well as digital PCR micro-wells. Future application potential includes nano-3D printing and fast molecular processors (e.g., SETs) integrated with DNA storage and conventional electronics. This would also lead to environment-friendly nanocellulose chips for information processing as well as smart nanocellulose composites for biomedical applications and nano-factories.}, language = {en} } @article{KrenzerMakowskiHekaloetal.2022, author = {Krenzer, Adrian and Makowski, Kevin and Hekalo, Amar and Fitting, Daniel and Troya, Joel and Zoller, Wolfram G. and Hann, Alexander and Puppe, Frank}, title = {Fast machine learning annotation in the medical domain: a semi-automated video annotation tool for gastroenterologists}, series = {BioMedical Engineering OnLine}, volume = {21}, journal = {BioMedical Engineering OnLine}, number = {1}, doi = {10.1186/s12938-022-01001-x}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-300231}, year = {2022}, abstract = {Background Machine learning, especially deep learning, is becoming more and more relevant in research and development in the medical domain. For all the supervised deep learning applications, data is the most critical factor in securing successful implementation and sustaining the progress of the machine learning model. Especially gastroenterological data, which often involves endoscopic videos, are cumbersome to annotate. Domain experts are needed to interpret and annotate the videos. To support those domain experts, we generated a framework. With this framework, instead of annotating every frame in the video sequence, experts are just performing key annotations at the beginning and the end of sequences with pathologies, e.g., visible polyps. Subsequently, non-expert annotators supported by machine learning add the missing annotations for the frames in-between. Methods In our framework, an expert reviews the video and annotates a few video frames to verify the object's annotations for the non-expert. In a second step, a non-expert has visual confirmation of the given object and can annotate all following and preceding frames with AI assistance. After the expert has finished, relevant frames will be selected and passed on to an AI model. This information allows the AI model to detect and mark the desired object on all following and preceding frames with an annotation. Therefore, the non-expert can adjust and modify the AI predictions and export the results, which can then be used to train the AI model. Results Using this framework, we were able to reduce workload of domain experts on average by a factor of 20 on our data. This is primarily due to the structure of the framework, which is designed to minimize the workload of the domain expert. Pairing this framework with a state-of-the-art semi-automated AI model enhances the annotation speed further. Through a prospective study with 10 participants, we show that semi-automated annotation using our tool doubles the annotation speed of non-expert annotators compared to a well-known state-of-the-art annotation tool. Conclusion In summary, we introduce a framework for fast expert annotation for gastroenterologists, which reduces the workload of the domain expert considerably while maintaining a very high annotation quality. The framework incorporates a semi-automated annotation system utilizing trained object detection models. The software and framework are open-source.}, language = {en} } @article{KaltdorfSchulzeHelmprobstetal.2017, author = {Kaltdorf, Kristin Verena and Schulze, Katja and Helmprobst, Frederik and Kollmannsberger, Philip and Dandekar, Thomas and Stigloher, Christian}, title = {Fiji macro 3D ART VeSElecT: 3D automated reconstruction tool for vesicle structures of electron tomograms}, series = {PLoS Computational Biology}, volume = {13}, journal = {PLoS Computational Biology}, number = {1}, doi = {10.1371/journal.pcbi.1005317}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-172112}, year = {2017}, abstract = {Automatic image reconstruction is critical to cope with steadily increasing data from advanced microscopy. We describe here the Fiji macro 3D ART VeSElecT which we developed to study synaptic vesicles in electron tomograms. We apply this tool to quantify vesicle properties (i) in embryonic Danio rerio 4 and 8 days past fertilization (dpf) and (ii) to compare Caenorhabditis elegans N2 neuromuscular junctions (NMJ) wild-type and its septin mutant (unc-59(e261)). We demonstrate development-specific and mutant-specific changes in synaptic vesicle pools in both models. We confirm the functionality of our macro by applying our 3D ART VeSElecT on zebrafish NMJ showing smaller vesicles in 8 dpf embryos then 4 dpf, which was validated by manual reconstruction of the vesicle pool. Furthermore, we analyze the impact of C. elegans septin mutant unc-59(e261) on vesicle pool formation and vesicle size. Automated vesicle registration and characterization was implemented in Fiji as two macros (registration and measurement). This flexible arrangement allows in particular reducing false positives by an optional manual revision step. Preprocessing and contrast enhancement work on image-stacks of 1nm/pixel in x and y direction. Semi-automated cell selection was integrated. 3D ART VeSElecT removes interfering components, detects vesicles by 3D segmentation and calculates vesicle volume and diameter (spherical approximation, inner/outer diameter). Results are collected in color using the RoiManager plugin including the possibility of manual removal of non-matching confounder vesicles. Detailed evaluation considered performance (detected vesicles) and specificity (true vesicles) as well as precision and recall. We furthermore show gain in segmentation and morphological filtering compared to learning based methods and a large time gain compared to manual segmentation. 3D ART VeSElecT shows small error rates and its speed gain can be up to 68 times faster in comparison to manual annotation. Both automatic and semi-automatic modes are explained including a tutorial.}, language = {en} } @article{DoellingerWienrichLatoschik2021, author = {D{\"o}llinger, Nina and Wienrich, Carolin and Latoschik, Marc Erich}, title = {Challenges and opportunities of immersive technologies for mindfulness meditation: a systematic review}, series = {Frontiers in Virtual Reality}, volume = {2}, journal = {Frontiers in Virtual Reality}, doi = {10.3389/frvir.2021.644683}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-259047}, pages = {644683}, year = {2021}, abstract = {Mindfulness is considered an important factor of an individual's subjective well-being. Consequently, Human-Computer Interaction (HCI) has investigated approaches that strengthen mindfulness, i.e., by inventing multimedia technologies to support mindfulness meditation. These approaches often use smartphones, tablets, or consumer-grade desktop systems to allow everyday usage in users' private lives or in the scope of organized therapies. Virtual, Augmented, and Mixed Reality (VR, AR, MR; in short: XR) significantly extend the design space for such approaches. XR covers a wide range of potential sensory stimulation, perceptive and cognitive manipulations, content presentation, interaction, and agency. These facilities are linked to typical XR-specific perceptions that are conceptually closely related to mindfulness research, such as (virtual) presence and (virtual) embodiment. However, a successful exploitation of XR that strengthens mindfulness requires a systematic analysis of the potential interrelation and influencing mechanisms between XR technology, its properties, factors, and phenomena and existing models and theories of the construct of mindfulness. This article reports such a systematic analysis of XR-related research from HCI and life sciences to determine the extent to which existing research frameworks on HCI and mindfulness can be applied to XR technologies, the potential of XR technologies to support mindfulness, and open research gaps. Fifty papers of ACM Digital Library and National Institutes of Health's National Library of Medicine (PubMed) with and without empirical efficacy evaluation were included in our analysis. The results reveal that at the current time, empirical research on XR-based mindfulness support mainly focuses on therapy and therapeutic outcomes. Furthermore, most of the currently investigated XR-supported mindfulness interactions are limited to vocally guided meditations within nature-inspired virtual environments. While an analysis of empirical research on those systems did not reveal differences in mindfulness compared to non-mediated mindfulness practices, various design proposals illustrate that XR has the potential to provide interactive and body-based innovations for mindfulness practice. We propose a structured approach for future work to specify and further explore the potential of XR as mindfulness-support. The resulting framework provides design guidelines for XR-based mindfulness support based on the elements and psychological mechanisms of XR interactions.}, language = {en} } @article{PrakashUnnikrishnanPryssetal.2021, author = {Prakash, Subash and Unnikrishnan, Vishnu and Pryss, R{\"u}diger and Kraft, Robin and Schobel, Johannes and Hannemann, Ronny and Langguth, Berthold and Schlee, Winfried and Spiliopoulou, Myra}, title = {Interactive system for similarity-based inspection and assessment of the well-being of mHealth users}, series = {Entropy}, volume = {23}, journal = {Entropy}, number = {12}, issn = {1099-4300}, doi = {10.3390/e23121695}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-252333}, year = {2021}, abstract = {Recent digitization technologies empower mHealth users to conveniently record their Ecological Momentary Assessments (EMA) through web applications, smartphones, and wearable devices. These recordings can help clinicians understand how the users' condition changes, but appropriate learning and visualization mechanisms are required for this purpose. We propose a web-based visual analytics tool, which processes clinical data as well as EMAs that were recorded through a mHealth application. The goals we pursue are (1) to predict the condition of the user in the near and the far future, while also identifying the clinical data that mostly contribute to EMA predictions, (2) to identify users with outlier EMA, and (3) to show to what extent the EMAs of a user are in line with or diverge from those users similar to him/her. We report our findings based on a pilot study on patient empowerment, involving tinnitus patients who recorded EMAs with the mHealth app TinnitusTips. To validate our method, we also derived synthetic data from the same pilot study. Based on this setting, results for different use cases are reported.}, language = {en} } @article{KraftBirkReichertetal.2020, author = {Kraft, Robin and Birk, Ferdinand and Reichert, Manfred and Deshpande, Aniruddha and Schlee, Winfried and Langguth, Berthold and Baumeister, Harald and Probst, Thomas and Spiliopoulou, Myra and Pryss, R{\"u}diger}, title = {Efficient processing of geospatial mHealth data using a scalable crowdsensing platform}, series = {Sensors}, volume = {20}, journal = {Sensors}, number = {12}, issn = {1424-8220}, doi = {10.3390/s20123456}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-207826}, year = {2020}, abstract = {Smart sensors and smartphones are becoming increasingly prevalent. Both can be used to gather environmental data (e.g., noise). Importantly, these devices can be connected to each other as well as to the Internet to collect large amounts of sensor data, which leads to many new opportunities. In particular, mobile crowdsensing techniques can be used to capture phenomena of common interest. Especially valuable insights can be gained if the collected data are additionally related to the time and place of the measurements. However, many technical solutions still use monolithic backends that are not capable of processing crowdsensing data in a flexible, efficient, and scalable manner. In this work, an architectural design was conceived with the goal to manage geospatial data in challenging crowdsensing healthcare scenarios. It will be shown how the proposed approach can be used to provide users with an interactive map of environmental noise, allowing tinnitus patients and other health-conscious people to avoid locations with harmful sound levels. Technically, the shown approach combines cloud-native applications with Big Data and stream processing concepts. In general, the presented architectural design shall serve as a foundation to implement practical and scalable crowdsensing platforms for various healthcare scenarios beyond the addressed use case.}, language = {en} } @article{KlemzRote2022, author = {Klemz, Boris and Rote, G{\"u}nter}, title = {Linear-Time Algorithms for Maximum-Weight Induced Matchings and Minimum Chain Covers in Convex Bipartite Graphs}, series = {Algorithmica}, volume = {84}, journal = {Algorithmica}, number = {4}, issn = {1432-0541}, doi = {10.1007/s00453-021-00904-w}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-267876}, pages = {1064-1080}, year = {2022}, abstract = {A bipartite graph G=(U,V,E) is convex if the vertices in V can be linearly ordered such that for each vertex u∈U, the neighbors of u are consecutive in the ordering of V. An induced matching H of G is a matching for which no edge of E connects endpoints of two different edges of H. We show that in a convex bipartite graph with n vertices and m weighted edges, an induced matching of maximum total weight can be computed in O(n+m) time. An unweighted convex bipartite graph has a representation of size O(n) that records for each vertex u∈U the first and last neighbor in the ordering of V. Given such a compact representation, we compute an induced matching of maximum cardinality in O(n) time. In convex bipartite graphs, maximum-cardinality induced matchings are dual to minimum chain covers. A chain cover is a covering of the edge set by chain subgraphs, that is, subgraphs that do not contain induced matchings of more than one edge. Given a compact representation, we compute a representation of a minimum chain cover in O(n) time. If no compact representation is given, the cover can be computed in O(n+m) time. All of our algorithms achieve optimal linear running time for the respective problem and model, and they improve and generalize the previous results in several ways: The best algorithms for the unweighted problem versions had a running time of O(n\(^{2}\)) (Brandst{\"a}dt et al. in Theor. Comput. Sci. 381(1-3):260-265, 2007. https://doi.org/10.1016/j.tcs.2007.04.006). The weighted case has not been considered before.}, language = {en} } @article{DavidsonDuekingZinneretal.2020, author = {Davidson, Padraig and D{\"u}king, Peter and Zinner, Christoph and Sperlich, Billy and Hotho, Andreas}, title = {Smartwatch-Derived Data and Machine Learning Algorithms Estimate Classes of Ratings of Perceived Exertion in Runners: A Pilot Study}, series = {Sensors}, volume = {20}, journal = {Sensors}, number = {9}, issn = {1424-8220}, doi = {10.3390/s20092637}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-205686}, year = {2020}, abstract = {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.}, language = {en} } @article{PawellekKrmarLeistneretal.2021, author = {Pawellek, Ruben and Krmar, Jovana and Leistner, Adrian and Djajić, Nevena and Otašević, Biljana and Protić, Ana and Holzgrabe, Ulrike}, title = {Charged aerosol detector response modeling for fatty acids based on experimental settings and molecular features: a machine learning approach}, series = {Journal of Cheminformatics}, volume = {13}, journal = {Journal of Cheminformatics}, number = {1}, doi = {10.1186/s13321-021-00532-0}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-261618}, year = {2021}, abstract = {The charged aerosol detector (CAD) is the latest representative of aerosol-based detectors that generate a response independent of the analytes' chemical structure. This study was aimed at accurately predicting the CAD response of homologous fatty acids under varying experimental conditions. Fatty acids from C12 to C18 were used as model substances due to semivolatile characterics that caused non-uniform CAD behaviour. Considering both experimental conditions and molecular descriptors, a mixed quantitative structure-property relationship (QSPR) modeling was performed using Gradient Boosted Trees (GBT). The ensemble of 10 decisions trees (learning rate set at 0.55, the maximal depth set at 5, and the sample rate set at 1.0) was able to explain approximately 99\% (Q\(^2\): 0.987, RMSE: 0.051) of the observed variance in CAD responses. Validation using an external test compound confirmed the high predictive ability of the model established (R-2: 0.990, RMSEP: 0.050). With respect to the intrinsic attribute selection strategy, GBT used almost all independent variables during model building. Finally, it attributed the highest importance to the power function value, the flow rate of the mobile phase, evaporation temperature, the content of the organic solvent in the mobile phase and the molecular descriptors such as molecular weight (MW), Radial Distribution Function-080/weighted by mass (RDF080m) and average coefficient of the last eigenvector from distance/detour matrix (Ve2_D/Dt). The identification of the factors most relevant to the CAD responsiveness has contributed to a better understanding of the underlying mechanisms of signal generation. An increased CAD response that was obtained for acetone as organic modifier demonstrated its potential to replace the more expensive and environmentally harmful acetonitrile.}, language = {en} } @article{UnruhLandeckOberdoerferetal.2021, author = {Unruh, Fabian and Landeck, Maximilian and Oberd{\"o}rfer, Sebastian and Lugrin, Jean-Luc and Latoschik, Marc Erich}, title = {The Influence of Avatar Embodiment on Time Perception - Towards VR for Time-Based Therapy}, series = {Frontiers in Virtual Reality}, volume = {2}, journal = {Frontiers in Virtual Reality}, doi = {10.3389/frvir.2021.658509}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-259076}, pages = {658509}, year = {2021}, abstract = {Psycho-pathological conditions, such as depression or schizophrenia, are often accompanied by a distorted perception of time. People suffering from this conditions often report that the passage of time slows down considerably and that they are "stuck in time." Virtual Reality (VR) could potentially help to diagnose and maybe treat such mental conditions. However, the conditions in which a VR simulation could correctly diagnose a time perception deviation are still unknown. In this paper, we present an experiment investigating the difference in time experience with and without a virtual body in VR, also known as avatar. The process of substituting a person's body with a virtual body is called avatar embodiment. Numerous studies demonstrated interesting perceptual, emotional, behavioral, and psychological effects caused by avatar embodiment. However, the relations between time perception and avatar embodiment are still unclear. Whether or not the presence or absence of an avatar is already influencing time perception is still open to question. Therefore, we conducted a between-subjects design with and without avatar embodiment as well as a real condition (avatar vs. no-avatar vs. real). A group of 105 healthy subjects had to wait for seven and a half minutes in a room without any distractors (e.g., no window, magazine, people, decoration) or time indicators (e.g., clocks, sunlight). The virtual environment replicates the real physical environment. Participants were unaware that they will be asked to estimate their waiting time duration as well as describing their experience of the passage of time at a later stage. Our main finding shows that the presence of an avatar is leading to a significantly faster perceived passage of time. It seems to be promising to integrate avatar embodiment in future VR time-based therapy applications as they potentially could modulate a user's perception of the passage of time. We also found no significant difference in time perception between the real and the VR conditions (avatar, no-avatar), but further research is needed to better understand this outcome.}, language = {en} } @article{KammererPryssHoppenstedtetal.2020, author = {Kammerer, Klaus and Pryss, R{\"u}diger and Hoppenstedt, Burkhard and Sommer, Kevin and Reichert, Manfred}, title = {Process-driven and flow-based processing of industrial sensor data}, series = {Sensors}, volume = {20}, journal = {Sensors}, number = {18}, issn = {1424-8220}, doi = {10.3390/s20185245}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-213089}, year = {2020}, abstract = {For machine manufacturing companies, besides the production of high quality and reliable machines, requirements have emerged to maintain machine-related aspects through digital services. The development of such services in the field of the Industrial Internet of Things (IIoT) is dealing with solutions such as effective condition monitoring and predictive maintenance. However, appropriate data sources are needed on which digital services can be technically based. As many powerful and cheap sensors have been introduced over the last years, their integration into complex machines is promising for developing digital services for various scenarios. It is apparent that for components handling recorded data of these sensors they must usually deal with large amounts of data. In particular, the labeling of raw sensor data must be furthered by a technical solution. To deal with these data handling challenges in a generic way, a sensor processing pipeline (SPP) was developed, which provides effective methods to capture, process, store, and visualize raw sensor data based on a processing chain. Based on the example of a machine manufacturing company, the SPP approach is presented in this work. For the company involved, the approach has revealed promising results.}, language = {en} } @article{WickHarteltPuppe2019, author = {Wick, Christoph and Hartelt, Alexander and Puppe, Frank}, title = {Staff, symbol and melody detection of Medieval manuscripts written in square notation using deep Fully Convolutional Networks}, series = {Applied Sciences}, volume = {9}, journal = {Applied Sciences}, number = {13}, issn = {2076-3417}, doi = {10.3390/app9132646}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197248}, year = {2019}, abstract = {Even today, the automatic digitisation of scanned documents in general, but especially the automatic optical music recognition (OMR) of historical manuscripts, still remains an enormous challenge, since both handwritten musical symbols and text have to be identified. This paper focuses on the Medieval so-called square notation developed in the 11th-12th century, which is already composed of staff lines, staves, clefs, accidentals, and neumes that are roughly spoken connected single notes. The aim is to develop an algorithm that captures both the neumes, and in particular its melody, which can be used to reconstruct the original writing. Our pipeline is similar to the standard OMR approach and comprises a novel staff line and symbol detection algorithm based on deep Fully Convolutional Networks (FCN), which perform pixel-based predictions for either staff lines or symbols and their respective types. Then, the staff line detection combines the extracted lines to staves and yields an F\(_1\) -score of over 99\% for both detecting lines and complete staves. For the music symbol detection, we choose a novel approach that skips the step to identify neumes and instead directly predicts note components (NCs) and their respective affiliation to a neume. Furthermore, the algorithm detects clefs and accidentals. Our algorithm predicts the symbol sequence of a staff with a diplomatic symbol accuracy rate (dSAR) of about 87\%, which includes symbol type and location. If only the NCs without their respective connection to a neume, all clefs and accidentals are of interest, the algorithm reaches an harmonic symbol accuracy rate (hSAR) of approximately 90\%. In general, the algorithm recognises a symbol in the manuscript with an F\(_1\) -score of over 96\%.}, language = {en} } @article{SeufertSchroederSeufert2021, author = {Seufert, Anika and Schr{\"o}der, Svenja and Seufert, Michael}, title = {Delivering User Experience over Networks: Towards a Quality of Experience Centered Design Cycle for Improved Design of Networked Applications}, series = {SN Computer Science}, volume = {2}, journal = {SN Computer Science}, number = {6}, issn = {2661-8907}, doi = {10.1007/s42979-021-00851-x}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-271762}, year = {2021}, abstract = {To deliver the best user experience (UX), the human-centered design cycle (HCDC) serves as a well-established guideline to application developers. However, it does not yet cover network-specific requirements, which become increasingly crucial, as most applications deliver experience over the Internet. The missing network-centric view is provided by Quality of Experience (QoE), which could team up with UX towards an improved overall experience. By considering QoE aspects during the development process, it can be achieved that applications become network-aware by design. In this paper, the Quality of Experience Centered Design Cycle (QoE-CDC) is proposed, which provides guidelines on how to design applications with respect to network-specific requirements and QoE. Its practical value is showcased for popular application types and validated by outlining the design of a new smartphone application. We show that combining HCDC and QoE-CDC will result in an application design, which reaches a high UX and avoids QoE degradation.}, language = {en} } @article{KrupitzerEberhardingerGerostathopoulosetal.2020, author = {Krupitzer, Christian and Eberhardinger, Benedikt and Gerostathopoulos, Ilias and Raibulet, Claudia}, title = {Introduction to the special issue "Applications in Self-Aware Computing Systems and their Evaluation"}, series = {Computers}, volume = {9}, journal = {Computers}, number = {1}, issn = {2073-431X}, doi = {10.3390/computers9010022}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-203439}, year = {2020}, abstract = {The joint 1st Workshop on Evaluations and Measurements in Self-Aware Computing Systems (EMSAC 2019) and Workshop on Self-Aware Computing (SeAC) was held as part of the FAS* conference alliance in conjunction with the 16th IEEE International Conference on Autonomic Computing (ICAC) and the 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO) in Ume{\aa}, Sweden on 20 June 2019. The goal of this one-day workshop was to bring together researchers and practitioners from academic environments and from the industry to share their solutions, ideas, visions, and doubts in self-aware computing systems in general and in the evaluation and measurements of such systems in particular. The workshop aimed to enable discussions, partnerships, and collaborations among the participants. This special issue follows the theme of the workshop. It contains extended versions of workshop presentations as well as additional contributions.}, language = {en} } @article{PfitznerMayNuechter2018, author = {Pfitzner, Christian and May, Stefan and N{\"u}chter, Andreas}, title = {Body weight estimation for dose-finding and health monitoring of lying, standing and walking patients based on RGB-D data}, series = {Sensors}, volume = {18}, journal = {Sensors}, number = {5}, doi = {10.3390/s18051311}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-176642}, pages = {1311}, year = {2018}, abstract = {This paper describes the estimation of the body weight of a person in front of an RGB-D camera. A survey of different methods for body weight estimation based on depth sensors is given. First, an estimation of people standing in front of a camera is presented. Second, an approach based on a stream of depth images is used to obtain the body weight of a person walking towards a sensor. The algorithm first extracts features from a point cloud and forwards them to an artificial neural network (ANN) to obtain an estimation of body weight. Besides the algorithm for the estimation, this paper further presents an open-access dataset based on measurements from a trauma room in a hospital as well as data from visitors of a public event. In total, the dataset contains 439 measurements. The article illustrates the efficiency of the approach with experiments with persons lying down in a hospital, standing persons, and walking persons. Applicable scenarios for the presented algorithm are body weight-related dosing of emergency patients.}, language = {en} } @article{SirbuBeckerCaminitietal.2015, author = {S{\^i}rbu, Alina and Becker, Martin and Caminiti, Saverio and De Baets, Bernard and Elen, Bart and Francis, Louise and Gravino, Pietro and Hotho, Andreas and Ingarra, Stefano and Loreto, Vittorio and Molino, Andrea and Mueller, Juergen and Peters, Jan and Ricchiuti, Ferdinando and Saracino, Fabio and Servedio, Vito D.P. and Stumme, Gerd and Theunis, Jan and Tria, Francesca and Van den Bossche, Joris}, title = {Participatory Patterns in an International Air Quality Monitoring Initiative}, series = {PLoS ONE}, volume = {10}, journal = {PLoS ONE}, number = {8}, doi = {10.1371/journal. pone.0136763}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-151379}, pages = {e0136763}, year = {2015}, abstract = {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.}, language = {en} } @article{KaiserLeschRotheetal.2020, author = {Kaiser, Dennis and Lesch, Veronika and Rothe, Julian and Strohmeier, Michael and Spieß, Florian and Krupitzer, Christian and Montenegro, Sergio and Kounev, Samuel}, title = {Towards Self-Aware Multirotor Formations}, series = {Computers}, volume = {9}, journal = {Computers}, number = {1}, issn = {2073-431X}, doi = {10.3390/computers9010007}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-200572}, pages = {7}, year = {2020}, abstract = {In the present day, unmanned aerial vehicles become seemingly more popular every year, but, without regulation of the increasing number of these vehicles, the air space could become chaotic and uncontrollable. In this work, a framework is proposed to combine self-aware computing with multirotor formations to address this problem. The self-awareness is envisioned to improve the dynamic behavior of multirotors. The formation scheme that is implemented is called platooning, which arranges vehicles in a string behind the lead vehicle and is proposed to bring order into chaotic air space. Since multirotors define a general category of unmanned aerial vehicles, the focus of this thesis are quadcopters, platforms with four rotors. A modification for the LRA-M self-awareness loop is proposed and named Platooning Awareness. The implemented framework is able to offer two flight modes that enable waypoint following and the self-awareness module to find a path through scenarios, where obstacles are present on the way, onto a goal position. The evaluation of this work shows that the proposed framework is able to use self-awareness to learn about its environment, avoid obstacles, and can successfully move a platoon of drones through multiple scenarios.}, language = {en} } @article{GrohmannHerbstChalbanietal.2020, author = {Grohmann, Johannes and Herbst, Nikolas and Chalbani, Avi and Arian, Yair and Peretz, Noam and Kounev, Samuel}, title = {A Taxonomy of Techniques for SLO Failure Prediction in Software Systems}, series = {Computers}, volume = {9}, journal = {Computers}, number = {1}, issn = {2073-431X}, doi = {10.3390/computers9010010}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-200594}, pages = {10}, year = {2020}, abstract = {Failure prediction is an important aspect of self-aware computing systems. Therefore, a multitude of different approaches has been proposed in the literature over the past few years. In this work, we propose a taxonomy for organizing works focusing on the prediction of Service Level Objective (SLO) failures. Our taxonomy classifies related work along the dimensions of the prediction target (e.g., anomaly detection, performance prediction, or failure prediction), the time horizon (e.g., detection or prediction, online or offline application), and the applied modeling type (e.g., time series forecasting, machine learning, or queueing theory). The classification is derived based on a systematic mapping of relevant papers in the area. Additionally, we give an overview of different techniques in each sub-group and address remaining challenges in order to guide future research.}, language = {en} }