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This work addresses the challenge of calibrating multiple solid-state LIDAR systems. The study focuses on three different solid-state LIDAR sensors that implement different hardware designs, leading to distinct scanning patterns for each system. Consequently, detecting corresponding points between the point clouds generated by these LIDAR systems—as required for calibration—is a complex task. To overcome this challenge, this paper proposes a method that involves several steps. First, the measurement data are preprocessed to enhance its quality. Next, features are extracted from the acquired point clouds using the Fast Point Feature Histogram method, which categorizes important characteristics of the data. Finally, the extrinsic parameters are computed using the Fast Global Registration technique. The best set of parameters for the pipeline and the calibration success are evaluated using the normalized root mean square error. In a static real-world indoor scenario, a minimum root mean square error of 7 cm was achieved. Importantly, the paper demonstrates that the presented approach is suitable for online use, indicating its potential for real-time applications. By effectively calibrating the solid-state LIDAR systems and establishing point correspondences, this research contributes to the advancement of multi-LIDAR fusion and facilitates accurate perception and mapping in various fields such as autonomous driving, robotics, and environmental monitoring.
Focusing on the triangulation of detective fiction, masculinity studies and disability studies, "Investigating the Disabled Detective – Disabled Masculinity and Masculine Disability in Contemporary Detective Fiction" shows that disability challenges common ideals of (hegemonic) masculinity as represented in detective fiction. After a theoretical introduction to the relevant focal points of the three research fields, the dissertation demonstrates that even the archetypal detectives Dupin and Holmes undermine certain nineteenth-century masculine ideals with their peculiarities. Shifting to contemporary detective fiction and adopting a literary disability studies perspective, the dissertation investigates how male detectives with a form of neurodiversity or a physical impairment negotiate their masculine identity in light of their disability in private and professional contexts. It argues that the occupation as a detective supports the disabled investigator to achieve ‘masculine disability’. Inversing the term ‘disabled masculinity’, predominantly used in research, ‘masculine disability’ introduces a decisively gendered reading of neurodiversity and (acquired) physical impairment in contemporary detective fiction. The term implies that the disabled detective (re)negotiates his masculine identity by implementing the disability in his professional investigations and accepting it as an important, yet not defining, characteristic of his (gender) identity. By applying this approach to five novels from contemporary British and American detective fiction, the dissertation demonstrates that masculinity and disability do not negate each other, as commonly assumed. Instead, it emphasises that disability allows the detective, as much as the reader, to rethink masculinity.
In zeitgenössischen Entscheidungssystemen ist die Integration von maschinellen Lernmodellen (ML) wie CatBoost, Random Forest und Entscheidungsbäumen allge- genwärtig und übt erheblichen Einfluss auf gesellschaftliche Dynamiken aus. Diese weitverbreitete Anwendung betont die kritische Notwendigkeit wirksamer Fairness- Interventionen, um inhärente Verzerrungen und Diskriminierungen zu mildern. Allerdings adressieren vorherrschende Ansätze überwiegend binäre Klassifikationen und stützen sich häufig auf begrenzte, regionsspezifische Datensätze, was ihre Relevanz und Anwendbarkeit einschränkt. Um diese Mängel zu beheben, schlagen wir eine Erweiterung des Fairness-Projektionsmodells vor, das Ensemble-Learning-basierten Klassifikatoren als Basis Klassifizierungsmodell verwendet. Das vorgeschlagene Modell wird Fairness Projection with Ensemble Trees (FPET) genannt, eine innovative Nachbearbeitungsintervention, die speziell für Multi- Class-Klassifikationsaufgaben entwickelt wurde. Fairness Projection with Ensemble Trees ist einzigartig darauf ausgelegt, mehrere und sich überschneidende geschützte Gruppen zu berücksichtigen, was es vielseitig und inklusiv macht. Ein herausragendes Merkmal von FPET ist seine Modellagnostik und Skalierbarkeit auf große Datensätze, erleichtert durch ein informationstheoretisches Framework, das auf Informationsprojektion basiert. Dieser Ansatz liefert robuste theoretische Garantien hinsichtlich Konvergenz und Stichprobenkomplexität und gewährleistet somit seine praktische Umsetzbarkeit. Darüber hinaus wird das Design von FPET durch die Unterstützung für parallele Verarbeitung verstärkt, was seine Eignung für groß angelegte Anwendungen weiter erhöht.
Umfassende Bewertungen an diversen Datensätzen, darunter das ENEM- Prüfungsdatensatz aus Brasilien, HSLS und COMPAS, zeigen die überlegene Leistung unseres vorgeschlagenen Modells, Fairness Projection with Ensemble Trees (FPET), das den CatBoost-Klassifikator sowohl für binäre als auch für Multi- Class- Klassifikationsaufgaben verwendet. In allen Datensätzen zeigte CatBoost herausragende Leistungen. Unsere Fairness-Methode übertraf auch andere Benchmark Modelle wie Equality of Odds (EqOdds), Level Equal Opportunity (LevEqOpp), Reduktionsmethode und Ablehnungsverfahren. Die Ergebnisse wurden anhand von zwei Metriken verglichen: Mean Equal Opportunity und Statistical Parity. Diese Ergebnisse unterstreichen die Wirksamkeit von FPET in verschiedenen Kontexten und führen einen neuartigen Ansatz zur Fairness im maschinellen Lernen ein, der gerechte und inklusive Entscheidungsfindungen sicherstellt.
Empirische Studien in der Softwaretechnik verwenden Software Repositories als Datenquellen, um die Softwareentwicklung zu verstehen. Repository-Daten werden entweder verwendet, um Fragen zu beantworten, die die Entscheidungsfindung in der Softwareentwicklung leiten, oder um Werkzeuge bereitzustellen, die bei praktischen Aspekten der Entwicklung helfen. Studien werden in die Bereiche Empirical Software Engineering (ESE) und Mining Software Repositories (MSR) eingeordnet. Häufig konzentrieren sich Studien, die mit Repository-Daten arbeiten, auf deren Ergebnisse. Ergebnisse sind aus den Daten abgeleitete Aussagen oder Werkzeuge, die bei der Softwareentwicklung helfen. Diese Dissertation konzentriert sich hingegen auf die Methoden und High-Order-Methoden, die verwendet werden, um solche Ergebnisse zu erzielen. Insbesondere konzentrieren wir uns auf inkrementelle Methoden, um die Verarbeitung von Repositories zu skalieren, auf deklarative Methoden, um eine heterogene Analyse durchzuführen, und auf High-Order-Methoden, die verwendet werden, um Bedrohungen für Methoden, die auf Repositories arbeiten, zu operationalisieren. Wir fassen dies als technische und methodische Verbesserungen zusammen um zukünftige empirische Ergebnisse effektiver zu produzieren. Wir tragen die folgenden Verbesserungen bei. Wir schlagen eine Methode vor, um die Skalierbarkeit von Funktionen, welche über Repositories mit hoher Revisionszahl abstrahieren, auf theoretisch fundierte Weise zu verbessern. Wir nutzen Erkenntnisse aus abstrakter Algebra und Programminkrementalisierung, um eine Kernschnittstelle von Funktionen höherer Ordnung zu definieren, die skalierbare statische Abstraktionen eines Repositorys mit vielen Revisionen berechnen. Wir bewerten die Skalierbarkeit unserer Methode durch Benchmarks, indem wir einen Prototyp mit MSR/ESE Wettbewerbern vergleichen. Wir schlagen eine Methode vor, um die Definition von Funktionen zu verbessern, die über ein Repository mit einem heterogenen Technologie-Stack abstrahieren, indem Konzepte aus der deklarativen Logikprogrammierung verwendet werden, und mit Ideen zur Megamodellierung und linguistischen Architektur kombiniert werden. Wir reproduzieren bestehende Ideen zur deklarativen Logikprogrammierung mit Datalog-nahen Sprachen, die aus der Architekturwiederherstellung, der Quellcodeabfrage und der statischen Programmanalyse stammen, und übertragen diese aus der Analyse eines homogenen auf einen heterogenen Technologie-Stack. Wir liefern einen Proof-of-Concept einer solchen Methode in einer Fallstudie. Wir schlagen eine High-Order-Methode vor, um die Disambiguierung von Bedrohungen für MSR/ESE Methoden zu verbessern. Wir konzentrieren uns auf eine bessere Disambiguierung von Bedrohungen durch Simulationen, indem wir die Argumentation über Bedrohungen operationalisieren und die Auswirkungen auf eine gültige Datenanalysemethodik explizit machen. Wir ermutigen Forschende, „gefälschte“ Simulationen ihrer MSR/ESE-Szenarien zu erstellen, um relevante Erkenntnisse über alternative plausible Ergebnisse, negative Ergebnisse, potenzielle Bedrohungen und die verwendeten Datenanalysemethoden zu operationalisieren. Wir beweisen, dass eine solche Art des simulationsbasierten Testens zur Disambiguierung von Bedrohungen in der veröffentlichten MSR/ESE-Forschung beiträgt.
Herein, the particle size distributions (PSDs) and shape analysis of in vivo bioproduced particles from aqueous Au3+ and Eu3+ solutions by the cyanobacterium Anabaena sp. are examined in detail at the nanoscale. Generally, biosynthesis is affected by numerous parameters. Therefore, it is challenging to find the key set points for generating tailored nanoparticles (NPs). PSDs and shape analysis of the Au and Eu-NPs were performed with ImageJ using high-resolution transmission electron microscopy (HR-TEM) images. As the HR-TEM image analysis reflects only a fraction of the detected NPs within the cells, additional PSDs of the complete cell were performed to determine the NP count and to evaluate the different accuracies. Furthermore, local PSDs were carried out at five randomly selected locations within a single cell to identify local hotspots or agglomerations. The PSDs show that particle size depends mainly on contact time, while the particle shape is hardly affected. The particles formed are distributed quite evenly within the cells. HR-PSDs for Au-NPs show an average equivalent circular diameter (ECD) of 8.4 nm (24 h) and 7.2 nm (51 h). In contrast, Eu-NPs preferably exhibit an average ECD of 10.6 nm (10 h) and 12.3 nm (244 h). Au-NPs are classified predominantly as “very round” with an average reciprocal aspect ratio (RAR) of ~0.9 and a Feret major axis ratio (FMR) of ~1.17. Eu-NPs mainly belong to the “rounded” class with a smaller RAR of ~0.6 and a FMR of ~1.3. These results show that an increase in contact time is not accompanied by an average particle growth for Au-NPs, but by a doubling of the particle number. Anabaena sp. is capable of biosorbing and bioreducing dissolved Au3+ and Eu3+ ions from aqueous solutions, generating nano-sized Au and Eu particles, respectively. Therefore, it is a low-cost, non-toxic and effective candidate for a rapid recovery of these sought-after metals via the bioproduction of NPs with defined sizes and shapes, providing a high potential for scale-up.
This thesis explores and examines the effectiveness and efficacy of traditional machine learning (ML), advanced neural networks (NN) and state-of-the-art deep learning (DL) models for identifying mental distress indicators from the social media discourses based on Reddit and Twitter as they are immensely used by teenagers. Different NLP vectorization techniques like TF-IDF, Word2Vec, GloVe, and BERT embeddings are employed with ML models such as Decision Tree (DT), Random Forest (RF), Logistic Regression (LR) and Support Vector Machine (SVM) followed by NN models such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) to methodically analyse their impact as feature representation of models. DL models such as BERT, DistilBERT, MentalRoBERTa and MentalBERT are end-to-end fine tuned for classification task. This thesis also compares different text preprocessing techniques such as tokenization, stopword removal and lemmatization to assess their impact on model performance. Systematic experiments with different configuration of vectorization and preprocessing techniques in accordance with different model types and categories have been implemented to find the most effective configurations and to gauge the strengths, limitations, and capability to detect and interpret the mental distress indicators from the text. The results analysis reveals that MentalBERT DL model significantly outperformed all other model types and categories due to its specific pretraining on mental data as well as rigorous end-to-end fine tuning gave it an edge for detecting nuanced linguistic mental distress indicators from the complex contextual textual corpus. This insights from the results acknowledges the ML and NLP technologies high potential for developing complex AI systems for its intervention in the domain of mental health analysis. This thesis lays the foundation and directs the future work demonstrating the need for collaborative approach of different domain experts as well as to explore next generational large language models to develop robust and clinically approved mental health AI systems.
How to begin? This short question addresses a problem that is anything but simple, especially when regarding something as sophisticated and multilayered as musical theatre. However, scholars of this vast research area have mostly neglected this question so far. This study analyses and compares the initial sections of late Victorian popular musical theatre and is therefore a contribution to several fields of research: the analysis of initial sections of musical theatre in general, the analysis of the music of popular musical theatre in particular, and therefore operetta studies. The 1890s are especially interesting times for popular musical theatre in London: The premiered works include the last collaborations of Gilbert and Sullivan as well as offshoots of Savoy opera; but the so-called ‘naughty nineties’ also saw the emergence of a new genre, musical comedy, which captured the late Victorian zeitgeist like no other. This new form of theatrical entertainment was carefully and consciously constructed and promoted as modern and fashionable, walking a fine line between respectability and mildly risqué excitement.
Because a deep understanding of the developments and new tendencies concerning popular musical theatre in the 1890s is crucial in order to interpret differences as well as similarities, the analyses of the opening numbers are preceded by a detailed discussion of the relevant genres: comic opera, musical comedy, musical play and operetta. Since the producers of the analysed works wanted to distance themselves from former and supposedly old-fashioned traditions, this book also considers influences from their British predecessors, but also from Viennese operetta and French opéra bouffe.
Coat color and pattern are a distinguished feature in mammalian carnivores, shaped by climatic cycles and habitat type. It can be expressed in various ways, such as gradients, polymorphisms, and rare color variants. Although natural selection explains much of the phenotypic variation found in the wild, genetic drift and heterozygote deficiency, as prominent in small and fragmented populations, may also affect phenotypic variability through the fixation of recessive alleles. The aim of this study was to test whether rare color variants in the wild could relate to a deficiency of heterozygotes, resulting from habitat fragmentation and small population size. We present an overview of all rare color variants in the order Carnivora, and compiled demographic and genetic data of the populations where they did and did not occur, to test for significant correlations. We also tested how phylogeny and body weight influenced the presence of color variants with phylogenetic generalized linear mixed models (PGLMMs). We found 40 color-variable species and 59 rare color variants. In 17 variable phenotypic populations for which genetic diversity was available, the average AR was 4.18, HO = 0.59, and HE= 0.66, and FIS= 0.086. We found that variable populations displayed a significant reduction in heterozygosity and allelic richness compared to non-variable populations across species. We also found a significant negative correlation between population size and inbreeding coefficients. Therefore, it is possible that small effective size had phenotypic consequences on the extant populations. The high frequency of the rare color variants (averaging 20%) also implies that genetic drift is locally overruling natural selection in small effective populations. As such, rare color variants could be added to the list of phenotypic consequences of inbreeding in the wild.
The production of isolated metallic nanoparticles with multifunctionalized properties, such as size and shape, is crucial for biomedical, photocatalytic, and energy storage or remediation applications. This study investigates the initial particle formations of gold nanoparticles (AuNPs) bioproduced in the cyanobacteria Anabaena sp. using high-resolution transmission electron microscopy images for digital image analysis. The developed method enabled the discovery of cerium nanoparticles (CeNPs), which were biosynthesized in the cyanobacteria Calothrix desertica. The particle size distributions for AuNPs and CeNPs were analyzed. After 10 h, the average equivalent circular diameter for AuNPs was 4.8 nm, while for CeNPs, it was approximately 5.2 nm after 25 h. The initial shape of AuNPs was sub-round to round, while the shape of CeNPs was more roundish due to their amorphous structure and formation restricted to heterocysts. The local PSDs indicate that the maturation of AuNPs begins in the middle of vegetative cells and near the cell membrane, compared to the other regions of the cell.
X-ray computed tomography (XRT) is a three-dimensional (3D), non-destructive, and reproducible investigation method capable of visualizing and examining internal and external structures of components independent of the material and geometry. In this work, XRT with its unique abilities complements conventionally utilized examination methods for the investigation of microstructure weakening induced by hydrogen corrosion and furthermore provides a new approach to corrosion research. The motivation for this is the current inevitable transformation to hydrogen-based steel production. Refractories of the system Al2O3-SiO2 are significant as lining materials. Two exemplary material types A and B, which differ mainly in their Al2O3:SiO2 ratio, are examined here using XRT. Identical samples of the two materials are measured, analyzed, and then compared before and after hydrogen attack. In this context, hydrogen corrosion-induced porosity and its spatial distribution and morphology are investigated. The results show that sample B has an higher resistance to hydrogen-induced attack than sample A. Furthermore, the 3D-representation revealed a differential porosity increase within the microstructure.
The title compound, [Fe(C5H5)(C21H24NO2)], which is produced by the oxidation of 1-(4-tert-butylphenyl)-2-ethyl-3-ferrocenylpyrrole, crystallizes as a racemic mixture in the centrosymmetric space group P21/n. The central heterocyclic pyrrole ring system subtends dihedral angles of 13.7 (2)° with respect to the attached cyclopentadienyl ring and of 43.6 (7)° with the major component of the disordered phenyl group bound to the N atom. The 4-tert-butylphenyl group, as well as the non-substituted Cp ring are disordered with s.o.f. values of 0.589 (16) and 0.411 (16), respectively. In the crystal, molecules with the same absolute configuration are linked into infinite chains along the b-axis direction by O—H···O hydrogen bonds between the hydroxy substituent and the carbonyl O atom of the adjacent molecule.
Reducing gender bias in STEM is key to generating more equality and contributing to a more balanced workforce in this field. Spatial ability and its components are cognitive processes crucial to success in STEM education and careers. Significant gender differences have consistently been found in mental rotation (MR), the ability to mentally transform two- and three-dimensional objects. The aim of this pilot study is to examine factors in psychological assessment which may contribute to gender differences in MR performance. Moreover, findings will inform the development of the new approaches to assessment using computer adaptive testing (CAT). (1) Background: The study examines the impact of emotional regulation on MR performance in primary school children whose mean age was 9.28 years old. (2) Methods: Skin conductance was measured to assess the impact of emotional reactivity (ER) on performance during an MR task. (3) Results: Patterns of ER influence response time (RT) on specific items in the task. (4) Conclusions: Identifying the effects of emotional arousal and issues of test construction such as stereotyped stimuli and item difficulty in tests of spatial ability warrants ongoing investigation. It is vital to ensure that these factors do not compromise the accurate measurement of performance and inadvertently contribute to the gender gap in STEM.
Die strategische Platzierung von Filialen im Bankensektor ist wichtig für die Verbesserung des Kundenzugangs, die Ausweitung der Marktreichweite und den allgemeinen Geschäftserfolg. In dieser Arbeit werden geografische Informationssysteme (GIS) und Clustering-Algorithmen des maschinellen Lernens wie K-Means, DBSCAN und Hierarchical Clustering eingesetzt, um optimale Standorte für neue Bankfilialen vorherzusagen. Zunächst werden die Points of Interest (POIs) im Umfeld der bestehenden Bankfilialen an verschiedenen Standorten in fünf deutschen Städten analysiert, darunter Koblenz, Dresden, Dortmund, Rostock und Hannover. Durch die Analyse der Arten von POIs im Umfeld dieser Filialen werden Faktoren identifiziert, die zu einer erhöhten Kundenfrequenz und einem möglichen Erfolg der Bank beitragen. Die in dieser Arbeit verwendeten Geodaten stammen aus der OpenStreetMap API. Anschließend wird ein Scoring-Mechanismus entwickelt, der den verschiedenen POIs Punkte von 0 bis 10 zuweist. Diese POI-Liste mit Bewertungen wird dann in Clustering-Algorithmen integriert, um neue Filialstandorte vorherzusagen und die strategische Planung im Bankensektor zu verbessern. Der in dieser Arbeit verwendete Ansatz geht weit über den Bankensektor hinaus und kann in einem breiteren Spektrum von Bereichen angewendet werden, z. B. bei standortbezogenen Diensten und räumlichen Entscheidungsunterstützungssystemen.
Predictive Process Monitoring setzt sich als Hilfsmittel zur Unterstützung der betrieblichen Abläufe in Unternehmen immer mehr durch Die meisten heute verfüg-baren Softwareanwendungen erfordern jedoch ein umfangreiches technisches Know-how des Betreibers und sind daher für die meisten realen Szenarien nicht geeignet. Daher wird in dieser Arbeit eine prototypische Implementierung eines Predictive Process Monitoring Dashboards in Form einer Webanwendung vorgestellt. Das System basiert auf dem von Bartmann et al. (2021) vorgestellten PPM-Camunda-Plugin und ermöglicht es dem Benutzer, auf einfache Weise Metriken, Visualisierungen zur Darstellung dieser Metriken und Dashboards, in denen die Visualisierungen angeordnet werden können, zu erstellen. Ein Usability-Test mit Testnutzern mit unterschiedlichen Computerkenntnissen wird durchgeführt, um die Benutzerfreundlichkeit der Anwendung zu bestätigen.
Challenges of Implementing Innovation Strategies at Large Organizations: A case of Lotte Group
(2023)
For many decades, one of the most important focuses of research has been on determining whether or not there is a correlation between the size of an organization and its level of innovation. Unlike small companies, large companies often have well-established structure that are hard to change and change managements seems to be much more difficult especially related to innovation. Nevertheless, there are many examples to prove the opposites. Some large organization like Apple, Amazon... always show great innovation efforts and keep changing in a much positive way. Therefore, the aim of this thesis is to discuss of how large organization can be able to implement innovation when having much drawbacks compare to SMEs. Through the use of a qualitative research approach, researcher was able to explore essential information on the innovation strategies that large companies are using in order to innovate and how they could overcome existing challenges by studying the working process of Lotte Group – one of the biggest companies in Korea.
Die Aufmerksamkeit politischer Entscheidungsträger weltweit richtet sich in den letzten 10 Jahren verstärkt auf die Kreativwirtschaft als signifikanter Wachstums- und Beschäftigungsmotor in Städten. Die Literatur zeigt jedoch, dass Kreativschaffende zu den gefährdetsten Arbeitskräften in der heutigen Wirtschaft gehören. Aufgrund des enorm deregulierten und stark individualisierten Umfelds werden Misserfolg oder Erfolg eher individuellen Fähigkeiten und Engagement zugeschrieben und strukturelle oder kollektive Aspekte vernachlässigt. Diese Arbeit widmet sich zeitlichen, räumlichen und sozialen Aspekten digitaler behavioraler Daten, um zu zeigen, dass es tatsächlich strukturelle und historische Faktoren gibt, die sich auf die Karrieren von Individuen und Gruppen auswirken. Zu diesem Zweck bietet die Arbeit einen computergestützten, sozialwissenschaftlichen Forschungsrahmen, der das theoretische und empirisches Wissen aus jahrelanger Forschung zu Ungleichheit mit computergestützten Methoden zum Umgang mit komplexen und umfangreichen digitalen Daten verbindet. Die Arbeit beginnt mit der Darlegung einer neuartigen Methode zur Geschlechtererkennung, welche sich Image Search und Gesichtserkennungsmethoden bedient. Die Analyse der kollaborativen Verhaltensweisen sowie der Zitationsnetzwerke männlicher und weiblicher Computerwissenschaftler*innen verdeutlicht einige der historischen Bias und Nachteile, welchen Frauen in ihren wissenschaftlichen Karrieren begegnen. Zur weiterfuhrenden Elaboration der zeitlichen Aspekte von Ungleichheit, wird der Anteil vertikaler und horizontaler Ungleichheit in unterschiedlichen Kohorten von Wissenschaftler*innen untersucht, die ihre Karriere zu unterschiedlichen Zeitpunkten begonnen haben. Im Weiteren werden einige der zugrunde liegenden Mechanismen und Prozesse von Ungleichheit in kreativen Berufen analysiert, wie der Matthew-Effekt und das Hipster-Paradoxon. Schließlich zeigt diese Arbeit auf, dass Online-Plattformen wie Wikipedia bestehenden Bias reflektieren sowie verstärken können.
The diversity within amphibian communities in cultivated areas in Rwanda and within two selected, taxonomically challenging groups, the genera Ptychadena and Hyperolius, were investigated in this thesis. The amphibian community of an agricultural wetland near Butare in southern Rwanda comprised 15 anuran species. Rarefaction and jackknife analyses corroborated that the complete current species richness of the assemblage had been recorded, and the results of acoustic niche analysis suggested species saturation of the community. Surveys at many other Rwandan localities showed that the species recorded in Butare are widespread in cultivated and pristine wetlands. The species were readily distinguishable using morphological, bioacoustic, and molecular (DNA barcoding) features, but only eight of the 15 species could be assigned unambiguously to nominal species. The remaining represented undescribed or currently unrecognized taxa, including three species of Hyperolius, two Phrynobatrachus species, one Ptychadena species, and one species of Amietia. The diversity of the Ridged Frogs in Rwanda was investigated in two studies (Chapters III and IV). Three species of Ptychadena were recorded in wetlands in the catchment of the Nile. They can be distinguished by morphological characters (morphometrics and qualitative features) as well as by their advertisement calls and genetics. The Rwandan species of the P. mascareniensis group was shown to differ from the topotypic population as well as from other genetic lineages in sub-Saharan Africa and an old available name, P. nilotica, was resurrected from synonymy for this lineage. Two further Ptychadena species were identified among voucher specimens from Rwanda deposited in the collection of the RMCA, P. chrysogaster and P. uzungwensis. Morphologically they can be unambiguously distinguished from each other and the three other Rwandan species. A key based on qualitative morphological characters was developed, which allows unequivocal identification of specimens of all species that have been recorded from Rwanda. DNA was isolated from a Rwandan voucher specimen of P. chrysogaster, and the genetic analysis corroborated the species" distinct status.
A species of Hyperolius collected in the Nyungwe National Park was compared to all other Rwandan species of the genus and to morphologically or genetically similar species from neighbouring countries. Its distinct taxonomic status was justified by morphological, bioacoustic, and molecular evidence and it was described as a new species, H. jackie. A species of the H. nasutus group collected at agricultural sites in Rwanda was described as a new species in the course of a revision of the species of the Hyperolius nasutus group. The group was shown to consist of 15 distinct species which can be distinguished from each other genetically, bioacoustically, and morphologically.
The aerial performance, i.e. parachuting, of the Disc-fingered Reed Frog, Hyperolius discodactylus, was described. It represents a novel observation of a behaviour that has been known from a number of Southeast Asian and Neotropical frog species. Parachuting frogs, including H. discodactylus, exhibit certain morphological characteristics and, while airborne, assume a distinct posture which is best-suited for maneuvering in the air. Another study on the species addressed the validity of the taxon H. alticola which had been considered either a synonym of H. discodactylus or a distinct species. Type material of both taxa was re-examined and the status of H. alticola reassessed using morphological data from historic and new collections, call recordings, and molecular data from animals collected on recent expeditions. A northern and a southern genetic clade were identified, a divide that is weakly supported by diverging morphology of the vouchers from the respective localities. No distinction in advertisement call features could be recovered to support this split and both genetic and morphological differences between the two geographic clades are marginal and not always congruent and more likely reflect population-level variation. Therefore it was concluded that H. alticola is not a valid taxon and should be treated as a synonym of H. discodactylus.
On the recognition of human activities and the evaluation of its imitation by robotic systems
(2023)
This thesis addresses the problem of action recognition through the analysis of human motion and the benchmarking of its imitation by robotic systems.
For our action recognition related approaches, we focus on presenting approaches that generalize well across different sensor modalities. We transform multivariate signal streams from various sensors to a common image representation. The action recognition problem on sequential multivariate signal streams can then be reduced to an image classification task for which we utilize recent advances in machine learning. We demonstrate the broad applicability of our approaches formulated as a supervised classification task for action recognition, a semi-supervised classification task for one-shot action recognition, modality fusion and temporal action segmentation.
For action classification, we use an EfficientNet Convolutional Neural Network (CNN) model to classify the image representations of various data modalities. Further, we present approaches for filtering and the fusion of various modalities on a representation level. We extend the approach to be applicable for semi-supervised classification and train a metric-learning model that encodes action similarity. During training, the encoder optimizes the distances in embedding space for self-, positive- and negative-pair similarities. The resulting encoder allows estimating action similarity by calculating distances in embedding space. At training time, no action classes from the test set are used.
Graph Convolutional Network (GCN) generalized the concept of CNNs to non-Euclidean data structures and showed great success for action recognition directly operating on spatio-temporal sequences like skeleton sequences. GCNs have recently shown state-of-the-art performance for skeleton-based action recognition but are currently widely neglected as the foundation for the fusion of various sensor modalities. We propose incorporating additional modalities, like inertial measurements or RGB features, into a skeleton-graph, by proposing fusion on two different dimensionality levels. On a channel dimension, modalities are fused by introducing additional node attributes. On a spatial dimension, additional nodes are incorporated into the skeleton-graph.
Transformer models showed excellent performance in the analysis of sequential data. We formulate the temporal action segmentation task as an object detection task and use a detection transformer model on our proposed motion image representations. Experiments for our action recognition related approaches are executed on large-scale publicly available datasets. Our approaches for action recognition for various modalities, action recognition by fusion of various modalities, and one-shot action recognition demonstrate state-of-the-art results on some datasets.
Finally, we present a hybrid imitation learning benchmark. The benchmark consists of a dataset, metrics, and a simulator integration. The dataset contains RGB-D image sequences of humans performing movements and executing manipulation tasks, as well as the corresponding ground truth. The RGB-D camera is calibrated against a motion-capturing system, and the resulting sequences serve as input for imitation learning approaches. The resulting policy is then executed in the simulated environment on different robots. We propose two metrics to assess the quality of the imitation. The trajectory metric gives insights into how close the execution was to the demonstration. The effect metric describes how close the final state was reached according to the demonstration. The Simitate benchmark can improve the comparability of imitation learning approaches.
FinTech is deemed to be an underexplored phenomenon even in academic and real environments. Among (1) “Sustainable FinTech” – the application of information technology as innovation in established financial services providers’ business operation; and (2) “Disruptive FinTech” – the provision of financial products and services by non-incumbents which in most cases are information technology entrepreneurs, the former receives more attention. In order to contribute to Disruptive FinTech category, the thesis strive to examine Entrepreneurial Strategy framework applied for technology players taking part in Vietnam financial market.