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Coverage and similarity of bibliographic databases to find most relevant literature for systematic reviews in education
Get PDFSystematic literature reviews in educational research have become a popular research method. A key point hereby is the choice of bibliographic databases to reach a maximum probability of finding all potentially relevant literature that deals with the research question analyzed in a systematic literature review. Guidelines and handbooks on review recommend proper databases and information sources for education, along with specific search strategies. However, in many disciplines, among them educational research, there is a lack of evidence on the relevance of databases that need to be considered to find relevant literature and lessen the risk of missing relevant publications. Educational research is an interdisciplinary field and has no core database. Instead, the field is covered by multiple disciplinary and multidisciplinary information sources that have either a national or international focus. In this article, we discuss the relevance of seven databases in systematic literature reviews in education, based on results of an empirical data analysis of three recently published reviews. To evaluate the relevance of a database, the relevant literature of those reviews served as the gold standard. Results indicate that discipline-specific databases outperform international multidisciplinary sources, and a combination of discipline-specific international and national sources is most efficient in finding a high proportion of relevant literature. The article discusses the relevance of the databases in relation to their coverage of relevant literature, while considering practical implications for researchers performing a systematic literature search. We, thus, present evidence for proper database choices for educational and discipline-related systematic literature reviews
On the Potential of Algorithm Fusion for Demographic Bias Mitigation in Face Recognition
Get PDFWith the rise of deep neural networks, the performance of biometric systems has increased tremendously. Biometric systems for face recognition are now used in everyday life, e.g., border control, crime prevention, or personal device access control. Although the accuracy of face recognition systems is generally high, they are not without flaws. Many biometric systems have been found to exhibit demographic bias, resulting in different demographic groups being not recognized with the same accuracy. This is especially true for facial recognition due to demographic factors, e.g., gender and skin color. While many previous works already reported demographic bias, this work aims to reduce demographic bias for biometric face recognition applications. In this regard, 12 face recognition systems are benchmarked regarding biometric recognition performance as well as demographic differentials, i.e., fairness. Subsequently, multiple fusion techniques are applied with the goal to improve the fairness in contrast to single systems. The experimental results show that it is possible to improve the fairness regarding single demographics, e.g., skin color or gender, while improving fairness for demographic subgroups turns out to be more challenging
Neutron Yield Predictions with Artificial Neural Networks: A Predictive Modeling Approach
Get PDFThe development of compact neutron sources for applications is extensive and features many approaches. For ion-based approaches, several projects with different parameters exist. This article focuses on ion-based neutron production below the spallation barrier for proton and deuteron beams with arbitrary energy distributions with kinetic energies from 3 MeV to 97 MeV. This model makes it possible to compare different ion-based neutron source concepts against each other quickly. This contribution derives a predictive model using Monte Carlo simulations (an order of 50,000 simulations) and deep neural networks. It is the first time a model of this kind has been developed. With this model, lengthy Monte Carlo simulations, which individually take a long time to complete, can be circumvented. A prediction of neutron spectra then takes some milliseconds, which enables fast optimization and comparison. The models’ shortcomings for low-energy neutrons (<0.1 MeV
) and the cut-off prediction uncertainty (±3 MeV
) are addressed, and mitigation strategies are proposed
The catalytic domain of free or ligand bound histone deacetylase 4 occurs in solution predominantly in closed conformation
Get PDFHuman histone deacetylase 4 (HDAC4) is a key epigenetic regulator involved in a number of important cellular processes. This makes HDAC4 a promising target for the treatment of several cancers and neurodegenerative diseases, in particular Huntington's disease. HDAC4 is highly regulated by phosphorylation and oxidation, which determine its nuclear or cytosolic localization, and exerts its function through multiple interactions with other proteins, forming multiprotein complexes of varying composition. The catalytic domain of HDAC4 is known to interact with the SMRT/NCOR corepressor complex when the structural zinc-binding domain (sZBD) is intact and forms a closed conformation. Crystal structures of the HDAC4 catalytic domain have been reported showing an open conformation of HDAC4 when bound to certain ligands. Here, we investigated the relevance of this HDAC4 conformation under physiological conditions in solution. We show that proper zinc chelation in the sZBD is essential for enzyme function. Loss of the structural zinc ion not only leads to a massive decrease in enzyme activity, but it also has serious consequences for the overall structural integrity and stability of the protein. However, the Zn2+ free HDAC4 structure in solution is incompatible with the open conformation. In solution, the open conformation of HDAC4 was also not observed in the presence of a variety of structurally divergent ligands. This suggests that the open conformation of HDAC4 cannot be induced in solution, and therefore cannot be exploited for the development of HDAC4-specific inhibitors
Child face recognition at scale: synthetic data generation and performance benchmark
Get PDFWe address the need for a large-scale database of children’s faces by using generative adversarial networks (GANs) and face-age progression (FAP) models to synthesize a realistic dataset referred to as “HDA-SynChildFaces”. Hence, we proposed a processing pipeline that initially utilizes StyleGAN3 to sample adult subjects, which is subsequently progressed to children of varying ages using InterFaceGAN. Intra-subject variations, such as facial expression and pose, are created by further manipulating the subjects in their latent space. Additionally, this pipeline allows the even distribution of the races of subjects, allowing the generation of a balanced and fair dataset with respect to race distribution. The resulting HDA-SynChildFaces consists of 1,652 subjects and 188,328 images, each subject being present at various ages and with many different intra-subject variations. We then evaluated the performance of various facial recognition systems on the generated database and compared the results of adults and children at different ages. The study reveals that children consistently perform worse than adults on all tested systems and that the degradation in performance is proportional to age. Additionally, our study uncovers some biases in the recognition systems, with Asian and black subjects and females performing worse than white and Latino-Hispanic subjects and males
Automated Classification of Physiologic, Glaucomatous, and Glaucoma-Suspected Optic Discs Using Machine Learning
Get PDFIn order to generate a machine learning algorithm (MLA) that can support ophthalmologists with the diagnosis of glaucoma, a carefully selected dataset that is based on clinically confirmed glaucoma patients as well as borderline cases (e.g., patients with suspected glaucoma) is required. The clinical annotation of datasets is usually performed at the expense of the data volume, which results in poorer algorithm performance. This study aimed to evaluate the application of an MLA for the automated classification of physiological optic discs (PODs), glaucomatous optic discs (GODs), and glaucoma-suspected optic discs (GSODs). Annotation of the data to the three groups was based on the diagnosis made in clinical practice by a glaucoma specialist. Color fundus photographs and 14 types of metadata (including visual field testing, retinal nerve fiber layer thickness, and cup–disc ratio) of 1168 eyes from 584 patients (POD = 321, GOD = 336, GSOD = 310) were used for the study. Machine learning (ML) was performed in the first step with the color fundus photographs only and in the second step with the images and metadata. Sensitivity, specificity, and accuracy of the classification of GSOD vs. GOD and POD vs. GOD were evaluated. Classification of GOD vs. GSOD and GOD vs. POD performed in the first step had AUCs of 0.84 and 0.88, respectively. By combining the images and metadata, the AUCs increased to 0.92 and 0.99, respectively. By combining images and metadata, excellent performance of the MLA can be achieved despite having only a small amount of data, thus supporting ophthalmologists with glaucoma diagnosis
On application of a surrogate model to numerical evaluation of effective elastic properties of composites with 3D rotationally symmetric particles
Get PDFMicromechanical modelling of particulate composites with non-ellipsoidal particle shapes presents significant challenges because analytical approaches based on the fundamental results of Eshelby cannot be used. On the other side, direct numerical evaluations by finite element analysis can involve high computational cost in the case when particle features have small radius of curvature, sharp edges and require extremely fine meshes. This paper proposes substituting the exact particle shape with a surrogate model producing approximately the same contribution to the effective elastic moduli. We illustrate our approach by considering rotationally symmetric 3D particle shapes with the external surface defined by the Laplace's spherical harmonics. In this case, spherical layered surrogates offer good accuracy of approximation, especially when the material parameters of each layer are determined by the particle swarm optimization algorithm. The proposed approach is presented by considering several highly undulated particle shapes and comparing the surrogate model results with direct finite element simulations of the original microstructure
Creating Resilient Smart Homes with a Heart: Sustainable, Technologically Advanced Housing across the Lifespan and Frailty through Inclusive Design for People and Their Robots
Get PDFThe design of age-friendly homes benefits vulnerable groups, such as frail people and older adults. Advances in smart home technologies, including robots, have important synergies with homes designed for health needs. Yet, focus on environmental and sustainable housing design and improvements misses important opportunities for collective impact. Stronger involvement of disciplines, such as those from the built environment for technological integration within homes and effects on space and the community, is needed. There is a need for a unified framework integrating the needs and factors of the resident, smart home technologies and robots, and the built environment, and that includes the concept of a “home”. With the remodeling of housing towards sustainable and environmental targets, as well as advances in smart home technologies such as robots, the timeliness of shared input for the benefit of residents now and in the future is of the essence. This would help target future research into effective and optimized cohabitation with technology within homes for the purpose of improving the wellbeing of residents
Campus Praxis
Get PDFDie Uniklinik RWTH Aachen plant seit Ende 2022 die „Campuspraxis“, eine allgemeinmedizinische Praxis auf ihrem Gelände, um eine hochwertige medizinische Versorgung für Mitarbeitende, Studierende und Anwohnende sicherzustellen. Die Integration der Praxis in den bestehenden Krankenhausbau stellt aufgrund architektonischer Besonderheiten und strenger Auflagen eine Herausforderung dar.
Das Projekt folgt einem evidenzbasierten Ansatz (vgl. Rehn, 2019), bei dem empirische Methoden und wissenschaftliche Evidenzen genutzt wurden, um die Bedürfnisse der Nutzenden zu erfassen und ein gesundheitsförderndes Design zu entwickeln. Ein experimenteller und iterativer Ansatz ermöglicht die kontinuierliche Anpassung der Konzepte im realen Setting
CERC 2021 Proceedings
Get PDFCERC 2021, the first online CERC conference, provided an opportunity to welcome not only our European friends and colleagues but also participants from across the globe. Munster Technological University has made a notable impact in Artificial Intelligence, Cybersecurity, and Computer Science research, largely due to national, European, and international funding and partnerships. It feels fitting, therefore, that CERC is once again being hosted by our university.
The selected papers for presentation and publication are covering a wide range of topics like Visual Computing, Data Processing and Machine Learning, E-Healthcare and Smart Diagnostics, E-Learning and Education and Engineering and Society.
Throughout this conference, we have received invaluable support from the program committee and my fellow program chairs, especially Prof. Udo Bleimann for his unwavering support, Prof. Huiru Zheng, Prof. Ingo Stengel, Dr. Haiying Wang, and Prof. Stefanie Regier for their dedication to the review process. We are also grateful to Dirk Burkhardt and Dr. Robert Loew for their efforts in setting up the website, managing the conference system, and preparing the programme and proceedings.
A special thanks goes to Munster Technological University, Ulster University, Hochschule Karlsruhe, and Hochschule Darmstadt for their essential support of this conference.
Dr Haithem AfliCERC 2021, die erste Online-CERC Konferenz, bot die Gelegenheit, nicht nur unsere europäischen Freunde und Kollegen, sondern auch Teilnehmer aus der ganzen Welt zu begrüßen. Die Munster Technological University hat in den Bereichen Künstliche Intelligenz, Cybersicherheit und Informatikforschung einen bemerkenswerten Einfluss ausgeübt, was größtenteils auf nationale, europäische und internationale Finanzierung und Partnerschaften zurückzuführen ist. Es ist daher nur folgerichtig, dass das CERC erneut von unserer Universität ausgerichtet wird.
Die für die Präsentation und Veröffentlichung ausgewählten Beiträge decken ein breites Spektrum an Themen ab, wie Visual Computing, Data Processing und maschinelles Lernen, E-Healthcare und Smart Diagnostics, E-Learning und Bildung sowie Technik und Gesellschaft.
Während der gesamten Konferenz haben wir unschätzbare Unterstützung vom Programmkomitee und meinen Mitstreitern erhalten, insbesondere von Prof. Udo Bleimann für seine unermüdliche Unterstützung, Prof. Huiru Zheng, Prof. Ingo Stengel, Dr. Haiying Wang und Prof. Stefanie Regier für ihr Engagement bei der Begutachtung. Unser Dank gilt auch Dirk Burkhardt und Dr. Robert Loew für ihre Bemühungen bei der Einrichtung der Website, der Verwaltung des Konferenzsystems und der Vorbereitung des Programms und des Tagungsbandes.
Ein besonderer Dank geht an die Munster Technological University, die Ulster University, die Hochschule Karlsruhe und die Hochschule Darmstadt für ihre wesentliche Unterstützung dieser Konferenz
Dr Haithem Afl