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 - Fathy, Moustafa A1 - Darwish, Mostafa A. A1 - Abdelhamid, Al-Shaimaa M. A1 - Alrashedy, Gehad M. A1 - Othman, Othman Ali A1 - Naseem, Muhammad A1 - Dandekar, Thomas A1 - Othman, Eman M. T1 - Kinetin ameliorates cisplatin-induced hepatotoxicity and lymphotoxicity via attenuating oxidative damage, cell apoptosis and inflammation in rats JF - Biomedicines N2 - Though several previous studies reported the in vitro and in vivo antioxidant effect of kinetin (Kn), details on its action in cisplatin-induced toxicity are still scarce. In this study we evaluated, for the first time, the effects of kinetin in cisplatin (cp)- induced liver and lymphocyte toxicity in rats. Wistar male albino rats were divided into nine groups: (i) the control (C), (ii) groups 2,3 and 4, which received 0.25, 0.5 and 1 mg/kg kinetin for 10 days; (iii) the cisplatin (cp) group, which received a single intraperitoneal injection of CP (7.0 mg/kg); and (iv) groups 6, 7, 8 and 9, which received, for 10 days, 0.25, 0.5 and 1 mg/kg kinetin or 200 mg/kg vitamin C, respectively, and Cp on the fourth day. CP-injected rats showed a significant impairment in biochemical, oxidative stress and inflammatory parameters in hepatic tissue and lymphocytes. PCR showed a profound increase in caspase-3, and a significant decline in AKT gene expression. Intriguingly, Kn treatment restored the biochemical, redox status and inflammatory parameters. Hepatic AKT and caspase-3 expression as well as CD95 levels in lymphocytes were also restored. In conclusion, Kn mitigated oxidative imbalance, inflammation and apoptosis in CP-induced liver and lymphocyte toxicity; therefore, it can be considered as a promising therapy. KW - cisplatin KW - hepatotoxicity KW - lymphotoxicity KW - oxidative stress KW - AKT KW - CD95 KW - caspase-3 Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-281686 SN - 2227-9059 VL - 10 IS - 7 ER - TY - THES A1 - Krenzer, Adrian T1 - Machine learning to support physicians in endoscopic examinations with a focus on automatic polyp detection in images and videos T1 - Maschinelles Lernen zur Unterstützung von Ärzten bei endoskopischen Untersuchungen mit Schwerpunkt auf der automatisierten Polypenerkennung in Bildern und Videos N2 - Deep learning enables enormous progress in many computer vision-related tasks. Artificial Intel- ligence (AI) steadily yields new state-of-the-art results in the field of detection and classification. Thereby AI performance equals or exceeds human performance. Those achievements impacted many domains, including medical applications. One particular field of medical applications is gastroenterology. In gastroenterology, machine learning algorithms are used to assist examiners during interventions. One of the most critical concerns for gastroenterologists is the development of Colorectal Cancer (CRC), which is one of the leading causes of cancer-related deaths worldwide. Detecting polyps in screening colonoscopies is the essential procedure to prevent CRC. Thereby, the gastroenterologist uses an endoscope to screen the whole colon to find polyps during a colonoscopy. Polyps are mucosal growths that can vary in severity. This thesis supports gastroenterologists in their examinations with automated detection and clas- sification systems for polyps. The main contribution is a real-time polyp detection system. This system is ready to be installed in any gastroenterology practice worldwide using open-source soft- ware. The system achieves state-of-the-art detection results and is currently evaluated in a clinical trial in four different centers in Germany. The thesis presents two additional key contributions: One is a polyp detection system with ex- tended vision tested in an animal trial. Polyps often hide behind folds or in uninvestigated areas. Therefore, the polyp detection system with extended vision uses an endoscope assisted by two additional cameras to see behind those folds. If a polyp is detected, the endoscopist receives a vi- sual signal. While the detection system handles the additional two camera inputs, the endoscopist focuses on the main camera as usual. The second one are two polyp classification models, one for the classification based on shape (Paris) and the other on surface and texture (NBI International Colorectal Endoscopic (NICE) classification). Both classifications help the endoscopist with the treatment of and the decisions about the detected polyp. The key algorithms of the thesis achieve state-of-the-art performance. Outstandingly, the polyp detection system tested on a highly demanding video data set shows an F1 score of 90.25 % while working in real-time. The results exceed all real-time systems in the literature. Furthermore, the first preliminary results of the clinical trial of the polyp detection system suggest a high Adenoma Detection Rate (ADR). In the preliminary study, all polyps were detected by the polyp detection system, and the system achieved a high usability score of 96.3 (max 100). The Paris classification model achieved an F1 score of 89.35 % which is state-of-the-art. The NICE classification model achieved an F1 score of 81.13 %. Furthermore, a large data set for polyp detection and classification was created during this thesis. Therefore a fast and robust annotation system called Fast Colonoscopy Annotation Tool (FastCAT) was developed. The system simplifies the annotation process for gastroenterologists. Thereby the i gastroenterologists only annotate key parts of the endoscopic video. Afterward, those video parts are pre-labeled by a polyp detection AI to speed up the process. After the AI has pre-labeled the frames, non-experts correct and finish the annotation. This annotation process is fast and ensures high quality. FastCAT reduces the overall workload of the gastroenterologist on average by a factor of 20 compared to an open-source state-of-art annotation tool. N2 - Deep Learning ermöglicht enorme Fortschritte bei vielen Aufgaben im Bereich der Computer Vision. Künstliche Intelligenz (KI) liefert ständig neue Spitzenergebnisse im Bereich der Erkennung und Klassifizierung. Dabei erreicht oder übertrifft die Leistung von KI teilweise die menschliche Leistung. Diese Errungenschaften wirken sich auf viele Bereiche aus, darunter auch auf medizinische Anwendungen. Ein besonderer Bereich der medizinischen Anwendungen ist die Gastroenterologie. In der Gastroenterologie werden Algorithmen des maschinellen Lernens eingesetzt, um den Untersucher bei medizinischen Eingriffen zu unterstützen. Eines der größten Probleme für Gastroenterologen ist die Entwicklung von Darmkrebs, die weltweit eine der häufigsten krebsbedingten Todesursachen ist. Die Erkennung von Polypen bei Darmspiegelungen ist das wichtigste Verfahren zur Vorbeugung von Darmkrebs. Dabei untersucht der Gastroenterologe den Dickdarm im Rahmen einer Koloskopie, um z.B. Polypen zu finden. Polypen sind Schleimhautwucherungen, die unterschiedlich stark ausgeprägt sein können. Diese Arbeit unterstützt Gastroenterologen bei ihren Untersuchungen mit automatischen Erkennungssystemen und Klassifizierungssystemen für Polypen. Der Hauptbeitrag ist ein Echtzeitpolypenerkennungssystem. Dieses System kann in jeder gastroenterologischen Praxis weltweit mit Open- Source-Software installiert werden. Das System erzielt Erkennungsergebnisse auf dem neusten Stand der Technik und wird derzeit in einer klinischen Studie in vier verschiedenen Praxen in Deutschland evaluiert. In dieser Arbeit werden zwei weitere wichtige Beiträge vorgestellt: Zum einen ein Polypenerkennungssystem mit erweiterter Sicht, das in einem Tierversuch getestet wurde. Polypen verstecken sich oft hinter Falten oder in nicht untersuchten Bereichen. Daher verwendet das Polypenerkennungssystem mit erweiterter Sicht ein Endoskop, das von zwei zusätzlichen Kameras unterstützt wird, um hinter diese Falten zu sehen. Wenn ein Polyp entdeckt wird, erhält der Endoskopiker ein visuelles Signal. Während das Erkennungssystem die beiden zusätzlichen Kameraeingaben verarbeitet, konzentriert sich der Endoskopiker wie gewohnt auf die Hauptkamera. Das zweite sind zwei Polypenklassifizierungsmodelle, eines für die Klassifizierung anhand der Form (Paris) und das andere anhand der Oberfläche und Textur (NICE-Klassifizierung). Beide Klassifizierungen helfen dem Endoskopiker bei der Behandlung und Entscheidung über den erkannten Polypen. Die Schlüsselalgorithmen der Dissertation erreichen eine Leistung, die dem neuesten Stand der Technik entspricht. Herausragend ist, dass das auf einem anspruchsvollen Videodatensatz getestete Polypenerkennungssystem einen F1-Wert von 90,25 % aufweist, während es in Echtzeit arbeitet. Die Ergebnisse übertreffen alle Echtzeitsysteme für Polypenerkennung in der Literatur. Darüber hinaus deuten die ersten vorläufigen Ergebnisse einer klinischen Studie des Polypenerkennungssystems auf eine hohe Adenomdetektionsrate ADR hin. In dieser Studie wurden alle Polypen durch das Polypenerkennungssystem erkannt, und das System erreichte einen hohe Nutzerfreundlichkeit von 96,3 (maximal 100). Bei der automatischen Klassifikation von Polypen basierend auf der Paris Klassifikations erreichte das in dieser Arbeit entwickelte System einen F1-Wert von 89,35 %, was dem neuesten Stand der Technik entspricht. Das NICE-Klassifikationsmodell erreichte eine F1- Wert von 81,13 %. Darüber hinaus wurde im Rahmen dieser Arbeit ein großer Datensatz zur Polypenerkennung und -klassifizierung erstellt. Dafür wurde ein schnelles und robustes Annotationssystem namens FastCAT entwickelt. Das System vereinfacht den Annotationsprozess für Gastroenterologen. Die Gastroenterologen annotieren dabei nur die wichtigsten Teile des endoskopischen Videos. Anschließend werden diese Videoteile von einer Polypenerkennungs-KI vorverarbeitet, um den Prozess zu beschleunigen. Nachdem die KI die Bilder vorbeschriftet hat, korrigieren und vervollständigen Nicht-Experten die Annotationen. Dieser Annotationsprozess ist schnell und gewährleistet eine hohe Qualität. FastCAT reduziert die Gesamtarbeitsbelastung des Gastroenterologen im Durchschnitt um den Faktor 20 im Vergleich zu einem Open-Source-Annotationstool auf dem neuesten Stand der Technik. KW - Deep Learning KW - Maschinelles Lernen KW - Maschinelles Sehen KW - Machine Learning KW - Object Detection KW - Medical Image Analysis KW - Computer Vision KW - Gastroenterologische Endoskopie KW - Polypektomie Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-319119 ER - TY - RPRT ED - Hoßfeld, Tobias ED - Wunderer, Stefan T1 - White Paper on Crowdsourced Network and QoE Measurements – Definitions, Use Cases and Challenges N2 - The goal of the white paper at hand is as follows. The definitions of the terms build a framework for discussions around the hype topic ‘crowdsourcing’. This serves as a basis for differentiation and a consistent view from different perspectives on crowdsourced network measurements, with the goal to provide a commonly accepted definition in the community. The focus is on the context of mobile and fixed network operators, but also on measurements of different layers (network, application, user layer). In addition, the white paper shows the value of crowdsourcing for selected use cases, e.g., to improve QoE or regulatory issues. Finally, the major challenges and issues for researchers and practitioners are highlighted. This white paper is the outcome of the Würzburg seminar on “Crowdsourced Network and QoE Measurements” which took place from 25-26 September 2019 in Würzburg, Germany. International experts were invited from industry and academia. They are well known in their communities, having different backgrounds in crowdsourcing, mobile networks, network measurements, network performance, Quality of Service (QoS), and Quality of Experience (QoE). The discussions in the seminar focused on how crowdsourcing will support vendors, operators, and regulators to determine the Quality of Experience in new 5G networks that enable various new applications and network architectures. As a result of the discussions, the need for a white paper manifested, with the goal of providing a scientific discussion of the terms “crowdsourced network measurements” and “crowdsourced QoE measurements”, describing relevant use cases for such crowdsourced data, and its underlying challenges. During the seminar, those main topics were identified, intensively discussed in break-out groups, and brought back into the plenum several times. The outcome of the seminar is this white paper at hand which is – to our knowledge – the first one covering the topic of crowdsourced network and QoE measurements. KW - Crowdsourcing KW - Network Measurements KW - Quality of Service (QoS) KW - Quality of Experience (QoE) KW - crowdsourced network measurements KW - crowdsourced QoE measurements Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-202327 ER - TY - JOUR A1 - Pawellek, Ruben A1 - Krmar, Jovana A1 - Leistner, Adrian A1 - Djajić, Nevena A1 - Otašević, Biljana A1 - Protić, Ana A1 - Holzgrabe, Ulrike T1 - Charged aerosol detector response modeling for fatty acids based on experimental settings and molecular features: a machine learning approach JF - Journal of Cheminformatics N2 - 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. KW - High-performance liquid chromatography (HPLC) KW - Charged aerosol detector (CAD) KW - Gradient boosted trees (GBT) KW - Quantitative structure-property relationship modeling (QSPR) KW - Fatty acids Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-261618 VL - 13 IS - 1 ER - TY - JOUR A1 - Unruh, Fabian A1 - Landeck, Maximilian A1 - Oberdörfer, Sebastian A1 - Lugrin, Jean-Luc A1 - Latoschik, Marc Erich T1 - The Influence of Avatar Embodiment on Time Perception - Towards VR for Time-Based Therapy JF - Frontiers in Virtual Reality N2 - 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. KW - virtual reality KW - time perception KW - avatar embodiment KW - immersion KW - human computer interaction (HCI) Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-259076 VL - 2 ER - TY - THES A1 - Huber, Stephan T1 - Proxemo: Documenting Observed Emotions in HCI T1 - Proxemo: Die Dokumentation Beobachteter Emotionen in der Mensch-Computer-Interaktion N2 - For formative evaluations of user experience (UX) a variety of methods have been developed over the years. However, most techniques require the users to interact with the study as a secondary task. This active involvement in the evaluation is not inclusive of all users and potentially biases the experience currently being studied. Yet there is a lack of methods for situations in which the user has no spare cognitive resources. This condition occurs when 1) users' cognitive abilities are impaired (e.g., people with dementia) or 2) users are confronted with very demanding tasks (e.g., air traffic controllers). In this work we focus on emotions as a key component of UX and propose the new structured observation method Proxemo for formative UX evaluations. Proxemo allows qualified observers to document users' emotions by proxy in real time and then directly link them to triggers. Technically this is achieved by synchronising the timestamps of emotions documented by observers with a video recording of the interaction. In order to facilitate the documentation of observed emotions in highly diverse contexts we conceptualise and implement two separate versions of a documentation aid named Proxemo App. For formative UX evaluations of technology-supported reminiscence sessions with people with dementia, we create a smartwatch app to discreetly document emotions from the categories anger, general alertness, pleasure, wistfulness and pride. For formative UX evaluations of prototypical user interfaces with air traffic controllers we create a smartphone app to efficiently document emotions from the categories anger, boredom, surprise, stress and pride. Descriptive case studies in both application domains indicate the feasibility and utility of the method Proxemo and the appropriateness of the respectively adapted design of the Proxemo App. The third part of this work is a series of meta-evaluation studies to determine quality criteria of Proxemo. We evaluate Proxemo regarding its reliability, validity, thoroughness and effectiveness, and compare Proxemo's efficiency and the observers' experience to documentation with pen and paper. Proxemo is reliable, as well as more efficient, thorough and effective than handwritten notes and provides a better UX to observers. Proxemo compares well with existing methods where benchmarks are available. With Proxemo we contribute a validated structured observation method that has shown to meet requirements formative UX evaluations in the extreme contexts of users with cognitive impairments or high task demands. Proxemo is agnostic regarding researchers' theoretical approaches and unites reductionist and holistic perspectives within one method. Future work should explore the applicability of Proxemo for further domains and extend the list of audited quality criteria to include, for instance, downstream utility. With respect to basic research we strive to better understand the sources leading observers to empathic judgments and propose reminisce and older adults as model environment for investigating mixed emotions. N2 - Für formative Evaluationen der User Experience (UX) wurden im Laufe der Jahre zahlreiche Methoden entwickelt. Die meisten Methoden erfordern jedoch, dass die Benutzer als Nebenaufgabe mit der Studie interagieren. Diese aktive Beteiligung an der Evaluation kann das untersuchte Erlebnis verfälschen und schließt Benutzer komplett aus, die keine kognitiven Ressourcen zur Verfügung haben. Dies ist der Fall, wenn 1) die kognitiven Fähigkeiten der Benutzer beeinträchtigt sind (z. B. Menschen mit Demenz) oder 2) Benutzer mit sehr anspruchsvollen Aufgaben konfrontiert sind (z. B. Fluglotsen). In dieser Arbeit konzentrieren wir uns auf Emotionen als eine Schlüsselkomponente von UX und schlagen die neue strukturierte Beobachtungsmethode Proxemo für formative UX-Evaluationen vor. Proxemo ermöglicht es qualifizierten Beobachtern, die Emotionen der Nutzer in Echtzeit zu dokumentieren und sie direkt mit Auslösern zu verknüpfen. Technisch wird dies erreicht, indem die Zeitstempel der von den Beobachtern dokumentierten Emotionen mit einer Videoaufzeichnung der Interaktion synchronisiert werden. Um die Dokumentation von beobachteten Emotionen in sehr unterschiedlichen Kontexten zu erleichtern, konzipieren und implementieren wir zwei verschiedene Versionen einer Dokumentationshilfe namens Proxemo App. Für formative UX-Evaluationen von technologiegestützten Erinnerungssitzungen mit Menschen mit Demenz erstellen wir eine Smartwatch-App zur unauffälligen Dokumentation von Emotionen aus den Kategorien Ärger, allgemeine Wachsamkeit, Freude, Wehmut und Stolz. Für formative UX-Evaluationen prototypischer Nutzerschnittstellen mit Fluglotsen erstellen wir eine Smartphone-App zur effizienten Dokumentation von Emotionen aus den Kategorien Ärger, Langeweile, Überraschung, Stress und Stolz. Deskriptive Fallstudien in beiden Anwendungsfeldern zeigen die Machbarkeit und den Nutzen der Methode Proxemo und die Angemessenheit des jeweiligen Designs der Proxemo App. Der dritte Teil dieser Arbeit besteht aus einer Reihe von Meta-Evaluationsstudien zu den Gütekriterien von Proxemo. Wir evaluieren Proxemo hinsichtlich der Reliabilität, Validität, Gründlichkeit und Effektivität, und vergleichen die Effizienz von Proxemo und die UX der Beobachter mit der Dokumentation mit Stift und Papier. Proxemo ist reliabel, sowie effizienter, gründlicher und effektiver als handschriftliche Notizen und bietet den Beobachtern eine bessere UX. Proxemo schneidet gut ab im Vergleich zu bestehenden Methoden, für die Benchmarks verfügbar sind. Mit Proxemo stellen wir eine validierte, strukturierte Beobachtungsmethode vor, die nachweislich den Anforderungen formativer UX Evaluationen in den extremen Kontexten von Benutzern mit kognitiven Beeinträchtigungen oder hohen Aufgabenanforderungen gerecht wird. Proxemo ist agnostisch bezüglich der theoretischen Ansätze von Forschenden und vereint reduktionistische und ganzheitliche Perspektiven in einer Methode. Zukünftige Arbeiten sollten die Anwendbarkeit von Proxemo für weitere Domänen erkunden und die Liste der geprüften Gütekriterien erweitern, zum Beispiel um das Kriterium Downstream Utility. In Bezug auf die Grundlagenforschung werden wir versuchen, die Quellen besser zu verstehen, auf denen die empathischen Urteile der Beobachter fußen und schlagen Erinnerungen und ältere Erwachsene als Modellumgebung für die künftige Erforschung gemischter Emotionen vor. KW - Gefühl KW - Wissenschaftliche Beobachtung KW - Methode KW - Benutzererlebnis KW - Benutzerforschung KW - Emotionserkennung KW - Emotion inference KW - Emotionsinterpretation Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-305730 ER - TY - THES A1 - Geißler, Stefan T1 - Performance Evaluation of Next-Generation Data Plane Architectures and their Components T1 - Leistungsbewertung von Data Plane Architekturen der Nächsten Generation sowie ihrer Einzelkomponenten N2 - In this doctoral thesis we cover the performance evaluation of next generation data plane architectures, comprised of complex software as well as programmable hardware components that allow fine granular configuration. In the scope of the thesis we propose mechanisms to monitor the performance of singular components and model key performance indicators of software based packet processing solutions. We present novel approaches towards network abstraction that allow the integration of heterogeneous data plane technologies into a singular network while maintaining total transparency between control and data plane. Finally, we investigate a full, complex system consisting of multiple software-based solutions and perform a detailed performance analysis. We employ simulative approaches to investigate overload control mechanisms that allow efficient operation under adversary conditions. The contributions of this work build the foundation for future research in the areas of network softwarization and network function virtualization. N2 - Diese Doktorarbeit behandelt die Leistungsbewertung von Data Plane Architekturen der nächsten Generation, die aus komplexen Softwarelösungen sowie programmierbaren Hardwarekomponenten bestehen. Hierbei werden Mechanismen entwickelt, die es ermöglichen, die Leistungsfähigkeit einzelner Komponenten zu messen und zentrale Leistungsindikatoren softwarebasierter Systeme zur Verarbeitung von Datenpaketen zu modellieren. Es werden neuartige Ansätze zur Netzabstraktion entworfen, die eine vollständig transparente Integration heterogener Technologien im selben Netz ermöglichen. Schließlich wird eine umfassende Leistungsbewertung eines komplexen Systems, das aus einer Vielzahl softwarebasierter Netzfunktionen besteht, durchgeführt. Anhand simulativer Modelle werden Überlastkontrollmechanismen entwickelt, die es dem System erlauben auch unter Überlast effizient zu arbeiten. Die Beiträge dieser Arbeit bilden die Grundlage weiterer Forschungen im Bereich der Softwarisierung von Netzen sowie der Virtualisierung von Netzfunktionen. T3 - Würzburger Beiträge zur Leistungsbewertung Verteilter Systeme - 02/21 KW - Leistungsbewertung KW - Simulation KW - Zeitdiskretes System KW - Implementierung KW - performance evaluation KW - simulation KW - discrete-time analysis KW - network softwarization KW - mobile networks Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-260157 SN - 1432-8801 ER - TY - JOUR A1 - Kammerer, Klaus A1 - Pryss, Rüdiger A1 - Hoppenstedt, Burkhard A1 - Sommer, Kevin A1 - Reichert, Manfred T1 - Process-driven and flow-based processing of industrial sensor data JF - Sensors N2 - 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. KW - data stream processing KW - cyber-physical systems KW - processing pipeline KW - sensor networks Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-213089 SN - 1424-8220 VL - 20 IS - 18 ER -