596 research outputs found

    Measuring the Health and Development of School-age Zimbabwean Children

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    Health, growth and development during mid-childhood (from 5 to 14 years) are poorly characterised, and this period has been termed the ‘missing middle’. This thesis describes the piloting and application of the School-Age Health, Activity, Resilience, Anthropometry and Neurocognitive (SAHARAN) toolbox to measure growth, cognitive and physical function amongst the SHINE cohort in rural Zimbabwe. The SHINE cluster-randomised trial tested the effects of a household WASH intervention and/or infant and young child feeding (IYCF) on child stunting and anaemia at age 18 months in rural Zimbabwe. SHINE showed that IYCF modestly increased linear growth and reduced stunting by age 18 months, while WASH had no effects. The SAHARAN toolbox was used to measure 1000 HIV-unexposed children (250 in each intervention arm), and 275 HIV-exposed children within the SHINE cohort to evaluate long-term outcomes. Children were re-enrolled at age seven years to evaluate growth, body composition, cognitive and physical function. Four main findings are presented from the SAHARAN toolbox measurements of this cohort. Firstly, child sex, growth and contemporary environmental conditions are associated with school-age physical and cognitive function at seven years. Secondly, early-life growth and baseline environmental conditions suggest the impact of early-life trajectories on multiple aspects of school-age growth, physical and cognitive function. Thirdly, the long-term impact of HIV-exposure in pregnancy is explored, which indicate reduced cognitive function, cardiovascular fitness and head circumference by age 7 years. Finally, associations with the SHINE trial early life interventions are explored, demonstrating that the SHINE early-life nutrition intervention has minimal impact by 7 years of age, except marginally stronger handgrip strength. The public health implications advocate that child interventions need to be earlier (including antenatal), broader (incorporating nurturing care), deeper (providing transformational WASH) and longer (supporting throughout childhood), as well as targeting particularly vulnerable groups such as children born HIV-free

    AI Lifecycle Zero-Touch Orchestration within the Edge-to-Cloud Continuum for Industry 5.0

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    The advancements in human-centered artificial intelligence (HCAI) systems for Industry 5.0 is a new phase of industrialization that places the worker at the center of the production process and uses new technologies to increase prosperity beyond jobs and growth. HCAI presents new objectives that were unreachable by either humans or machines alone, but this also comes with a new set of challenges. Our proposed method accomplishes this through the knowlEdge architecture, which enables human operators to implement AI solutions using a zero-touch framework. It relies on containerized AI model training and execution, supported by a robust data pipeline and rounded off with human feedback and evaluation interfaces. The result is a platform built from a number of components, spanning all major areas of the AI lifecycle. We outline both the architectural concepts and implementation guidelines and explain how they advance HCAI systems and Industry 5.0. In this article, we address the problems we encountered while implementing the ideas within the edge-to-cloud continuum. Further improvements to our approach may enhance the use of AI in Industry 5.0 and strengthen trust in AI systems

    Quality of experience and access network traffic management of HTTP adaptive video streaming

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    The thesis focuses on Quality of Experience (QoE) of HTTP adaptive video streaming (HAS) and traffic management in access networks to improve the QoE of HAS. First, the QoE impact of adaptation parameters and time on layer was investigated with subjective crowdsourcing studies. The results were used to compute a QoE-optimal adaptation strategy for given video and network conditions. This allows video service providers to develop and benchmark improved adaptation logics for HAS. Furthermore, the thesis investigated concepts to monitor video QoE on application and network layer, which can be used by network providers in the QoE-aware traffic management cycle. Moreover, an analytic and simulative performance evaluation of QoE-aware traffic management on a bottleneck link was conducted. Finally, the thesis investigated socially-aware traffic management for HAS via Wi-Fi offloading of mobile HAS flows. A model for the distribution of public Wi-Fi hotspots and a platform for socially-aware traffic management on private home routers was presented. A simulative performance evaluation investigated the impact of Wi-Fi offloading on the QoE and energy consumption of mobile HAS.Die Doktorarbeit beschäftigt sich mit Quality of Experience (QoE) – der subjektiv empfundenen Dienstgüte – von adaptivem HTTP Videostreaming (HAS) und mit Verkehrsmanagement, das in Zugangsnetzwerken eingesetzt werden kann, um die QoE des adaptiven Videostreamings zu verbessern. Zuerst wurde der Einfluss von Adaptionsparameters und der Zeit pro Qualitätsstufe auf die QoE von adaptivem Videostreaming mittels subjektiver Crowdsourcingstudien untersucht. Die Ergebnisse wurden benutzt, um die QoE-optimale Adaptionsstrategie für gegebene Videos und Netzwerkbedingungen zu berechnen. Dies ermöglicht Dienstanbietern von Videostreaming verbesserte Adaptionsstrategien für adaptives Videostreaming zu entwerfen und zu benchmarken. Weiterhin untersuchte die Arbeit Konzepte zum Überwachen von QoE von Videostreaming in der Applikation und im Netzwerk, die von Netzwerkbetreibern im Kreislauf des QoE-bewussten Verkehrsmanagements eingesetzt werden können. Außerdem wurde eine analytische und simulative Leistungsbewertung von QoE-bewusstem Verkehrsmanagement auf einer Engpassverbindung durchgeführt. Schließlich untersuchte diese Arbeit sozialbewusstes Verkehrsmanagement für adaptives Videostreaming mittels WLAN Offloading, also dem Auslagern von mobilen Videoflüssen über WLAN Netzwerke. Es wurde ein Modell für die Verteilung von öffentlichen WLAN Zugangspunkte und eine Plattform für sozialbewusstes Verkehrsmanagement auf privaten, häuslichen WLAN Routern vorgestellt. Abschließend untersuchte eine simulative Leistungsbewertung den Einfluss von WLAN Offloading auf die QoE und den Energieverbrauch von mobilem adaptivem Videostreaming

    Current Challenges in the Application of Algorithms in Multi-institutional Clinical Settings

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    The Coronavirus disease pandemic has highlighted the importance of artificial intelligence in multi-institutional clinical settings. Particularly in situations where the healthcare system is overloaded, and a lot of data is generated, artificial intelligence has great potential to provide automated solutions and to unlock the untapped potential of acquired data. This includes the areas of care, logistics, and diagnosis. For example, automated decision support applications could tremendously help physicians in their daily clinical routine. Especially in radiology and oncology, the exponential growth of imaging data, triggered by a rising number of patients, leads to a permanent overload of the healthcare system, making the use of artificial intelligence inevitable. However, the efficient and advantageous application of artificial intelligence in multi-institutional clinical settings faces several challenges, such as accountability and regulation hurdles, implementation challenges, and fairness considerations. This work focuses on the implementation challenges, which include the following questions: How to ensure well-curated and standardized data, how do algorithms from other domains perform on multi-institutional medical datasets, and how to train more robust and generalizable models? Also, questions of how to interpret results and whether there exist correlations between the performance of the models and the characteristics of the underlying data are part of the work. Therefore, besides presenting a technical solution for manual data annotation and tagging for medical images, a real-world federated learning implementation for image segmentation is introduced. Experiments on a multi-institutional prostate magnetic resonance imaging dataset showcase that models trained by federated learning can achieve similar performance to training on pooled data. Furthermore, Natural Language Processing algorithms with the tasks of semantic textual similarity, text classification, and text summarization are applied to multi-institutional, structured and free-text, oncology reports. The results show that performance gains are achieved by customizing state-of-the-art algorithms to the peculiarities of the medical datasets, such as the occurrence of medications, numbers, or dates. In addition, performance influences are observed depending on the characteristics of the data, such as lexical complexity. The generated results, human baselines, and retrospective human evaluations demonstrate that artificial intelligence algorithms have great potential for use in clinical settings. However, due to the difficulty of processing domain-specific data, there still exists a performance gap between the algorithms and the medical experts. In the future, it is therefore essential to improve the interoperability and standardization of data, as well as to continue working on algorithms to perform well on medical, possibly, domain-shifted data from multiple clinical centers

    Metodología de auditoría de seguridad intrusiva en redes de sensores inalámbricos WSN para el análisis de vulnerabilidades

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    Establecer una metodología de auditoría de seguridad para redes de sensores inalámbricos bajo el estándar IEEE 802.15.4 basada en técnicas intrusivas para la evaluación de vulnerabilidades.El presente proyecto tiene como objetivo establecer una metodología de auditoría de seguridad para redes de sensores inalámbricos bajo el estándar IEEE 802.15.4 basada en técnicas intrusivas para la evaluación de vulnerabilidades. La metodología corresponde a un enfoque mixto, tipo descriptivo y bibliográfico. La metodología de auditoria seleccionada es Offensive Security se complementa con análisis de riesgos NIST SP 800-30 de las vulnerabilidades según los ataques y el método CVSS para comparar las técnicas. En los resultados se identificó que, en las redes de sensores inalámbricos, los nodos muestran mayor vulnerabilidad debido a que se encuentran instalados en entornos difíciles. Las principales vulnerabilidades con riesgo alto son: rastreo de redes, descifra información sensible y captura activa de tráfico. Por lo que se planificó ataques de confidencialidad, integridad y disponibilidad mediante el uso de herramientas como ZBOSS Sniffer, WireShark y Zigbee-emulador CC. Cabe mencionar que el ataque de escucha presenta mayor vulnerabilidad y con el método CVSS se determinó que la técnica intrusiva sniffing es la más adecuada para identificar vulnerabilidades. Para la validación de la metodología se realizó una prueba maestra de dirección, muestra, ajustes, conexión en red, interfaz RF e interfaz en serie, encontrando vulnerabilidades con mayor precisión. Finalmente, se elaboró medidas de seguridadIngenierí

    Harnessing T cell immunity for the prevention and treatment of liver cancer

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    Hepatocellular carcinoma (HCC) accounts for more than 80% of all diagnosed cases of liver cancer which is a major cause of cancer related fatalities worldwide. With few improvements in the survival rates during the last decades, prevention of HCC is key in reducing its burden globally. Infection with hepatitis B virus (HBV) remains the main etiological risk factor for developing HCC whilst in HBV patients co-infected with hepatitis D virus (HDV), the risk of developing HCC is triplicated due to the accelerated liver disease progression. In studies I and II, we aimed to develop a therapeutic vaccine for chronic HBV and HDV, as a preventive strategy for HCC. In study III, we sought to unlock novel T cell-based immunotherapies, as treatment for advanced HCC through isolation of neoantigen-driven T cell receptors (TCRs). In study I, we show that a homologous preS1-HDAg DNA-based vaccine strategy was able to elicit robust T cell responses to HBV and HDV antigens and entryinhibiting antibodies that could limit HBV monoinfection in liver-humanized mice. In study II, a heterologous DNA prime and protein boost preS1-HDAg vaccine strategy improved immunogenicity and could circumvent the HBV-induced tolerance present in the chronically infected host. Additionally, vaccine-induced antibodies protected liver-humanized mice against a chronic HBV/HDV coinfection and importantly they could protect HBV infected human-liver mice from HDV superinfection. In study III, we studied the cancer-specific T cell responses in patients with HCC, and we could detect T cell reactivity against mutated neoantigens in 4 out of 7 screened HCC patients. We isolated (putative) tumorreactive TCRs for further evaluation of their expression and specificity. Neoantigen-specific TCRs could be utilized to genetically redirect a substantial quantity of T cells against tumor cells, thus offering a potential new treatment for advanced HCC. Taken together, as we continue to unravel the dynamics of the immune system and refine therapies in the context of chronic diseases, this thesis illuminates two promising T cell avenues in the form of active and passive T cell immunotherapy and provides novel insights in the development of preventive and therapeutic tools aiming at combatting liver cancer

    An Intelligent Secure Adversarial Examples Detection Scheme in Heterogeneous Complex Environments

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    Funding Information: Funding Statement: This work was supported in part by the Natural Science Foundation of Hunan Province under Grant Nos. 2023JJ30316 and 2022JJ2029, in part by a project supported by Scientific Research Fund of Hunan Provincial Education Department under Grant No. 22A0686, and in part by the National Natural Science Foundation of China under Grant No. 62172058. This work was also funded by the Researchers Supporting Project (No. RSP2023R102) King Saud University, Riyadh, Saudi Arabia.Peer reviewedPublisher PD

    Federated Learning in Computer Vision

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    Federated Learning (FL) has recently emerged as a novel machine learning paradigm allowing to preserve privacy and to account for the distributed nature of the learning process in many real-world settings. Computer vision tasks deal with huge datasets often with critical privacy issues, therefore many federated learning approaches have been presented to exploit its distributed and privacy-preserving nature. Firstly, this paper introduces the different FL settings used in computer vision and the main challenges that need to be tackled. Then, it provides a comprehensive overview of the different strategies used for FL in vision applications and presents several different approaches for image classification, object detection, semantic segmentation and for focused settings in face recognition and medical imaging. For the various approaches the considered FL setting, the employed data and methodologies and the achieved results are thoroughly discussed
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