772 research outputs found

    Image-based Decision Support Systems: Technical Concepts, Design Knowledge, and Applications for Sustainability

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    Unstructured data accounts for 80-90% of all data generated, with image data contributing its largest portion. In recent years, the field of computer vision, fueled by deep learning techniques, has made significant advances in exploiting this data to generate value. However, often computer vision models are not sufficient for value creation. In these cases, image-based decision support systems (IB-DSSs), i.e., decision support systems that rely on images and computer vision, can be used to create value by combining human and artificial intelligence. Despite its potential, there is only little work on IB-DSSs so far. In this thesis, we develop technical foundations and design knowledge for IBDSSs and demonstrate the possible positive effect of IB-DSSs on environmental sustainability. The theoretical contributions of this work are based on and evaluated in a series of artifacts in practical use cases: First, we use technical experiments to demonstrate the feasibility of innovative approaches to exploit images for IBDSSs. We show the feasibility of deep-learning-based computer vision and identify future research opportunities based on one of our practical use cases. Building on this, we develop and evaluate a novel approach for combining human and artificial intelligence for value creation from image data. Second, we develop design knowledge that can serve as a blueprint for future IB-DSSs. We perform two design science research studies to formulate generalizable principles for purposeful design — one for IB-DSSs and one for the subclass of image-mining-based decision support systems (IM-DSSs). While IB-DSSs can provide decision support based on single images, IM-DSSs are suitable when large amounts of image data are available and required for decision-making. Third, we demonstrate the viability of applying IBDSSs to enhance environmental sustainability by performing life cycle assessments for two practical use cases — one in which the IB-DSS enables a prolonged product lifetime and one in which the IB-DSS facilitates an improvement of manufacturing processes. We hope this thesis will contribute to expand the use and effectiveness of imagebased decision support systems in practice and will provide directions for future research

    Stem Cells in Domestic Animals

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    Stem cells are an attractive tool for cell-based therapies in regenerative medicine, both for humans and animals. The research and review articles published in this first book of the Collection “Stem Cells in Domestic Animals: Applications in Health and Production” are excellent examples of the recent advances made in the field of stem/stromal cell research in veterinary medicine. In this field, sophisticated and new treatments are now required for improving patients’ quality of life; in livestock animals, the goal of regenerative medicine is to improve not only animal welfare but also the quality of production, aiming to preserve human health. The contributions collected in this book concern both laboratory research and clinical applications of mesenchymal stem/stromal cells. The increasing knowledge of cell-based therapies may constitute an opportunity for researchers, veterinary practitioners, and animal owners to contribute to animal and human health and well-being

    Cybersecurity applications of Blockchain technologies

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    With the increase in connectivity, the popularization of cloud services, and the rise of the Internet of Things (IoT), decentralized approaches for trust management are gaining momentum. Since blockchain technologies provide a distributed ledger, they are receiving massive attention from the research community in different application fields. However, this technology does not provide cybersecurity by itself. Thus, this thesis first aims to provide a comprehensive review of techniques and elements that have been proposed to achieve cybersecurity in blockchain-based systems. The analysis is intended to target area researchers, cybersecurity specialists and blockchain developers. We present a series of lessons learned as well. One of them is the rise of Ethereum as one of the most used technologies. Furthermore, some intrinsic characteristics of the blockchain, like permanent availability and immutability made it interesting for other ends, namely as covert channels and malicious purposes. On the one hand, the use of blockchains by malwares has not been characterized yet. Therefore, this thesis also analyzes the current state of the art in this area. One of the lessons learned is that covert communications have received little attention. On the other hand, although previous works have analyzed the feasibility of covert channels in a particular blockchain technology called Bitcoin, no previous work has explored the use of Ethereum to establish a covert channel considering all transaction fields and smart contracts. To foster further defence-oriented research, two novel mechanisms are presented on this thesis. First, Zephyrus takes advantage of all Ethereum fields and smartcontract bytecode. Second, Smart-Zephyrus is built to complement Zephyrus by leveraging smart contracts written in Solidity. We also assess the mechanisms feasibility and cost. Our experiments show that Zephyrus, in the best case, can embed 40 Kbits in 0.57 s. for US1.64,andretrievethemin2.8s.SmartZephyrus,however,isabletohidea4Kbsecretin41s.Whilebeingexpensive(aroundUS 1.64, and retrieve them in 2.8 s. Smart-Zephyrus, however, is able to hide a 4 Kb secret in 41 s. While being expensive (around US 1.82 per bit), the provided stealthiness might be worth the price for attackers. Furthermore, these two mechanisms can be combined to increase capacity and reduce costs.Debido al aumento de la conectividad, la popularización de los servicios en la nube y el auge del Internet de las cosas (IoT), los enfoques descentralizados para la gestión de la confianza están cobrando impulso. Dado que las tecnologías de cadena de bloques (blockchain) proporcionan un archivo distribuido, están recibiendo una atención masiva por parte de la comunidad investigadora en diferentes campos de aplicación. Sin embargo, esta tecnología no proporciona ciberseguridad por sí misma. Por lo tanto, esta tesis tiene como primer objetivo proporcionar una revisión exhaustiva de las técnicas y elementos que se han propuesto para lograr la ciberseguridad en los sistemas basados en blockchain. Este análisis está dirigido a investigadores del área, especialistas en ciberseguridad y desarrolladores de blockchain. A su vez, se presentan una serie de lecciones aprendidas, siendo una de ellas el auge de Ethereum como una de las tecnologías más utilizadas. Asimismo, algunas características intrínsecas de la blockchain, como la disponibilidad permanente y la inmutabilidad, la hacen interesante para otros fines, concretamente como canal encubierto y con fines maliciosos. Por una parte, aún no se ha caracterizado el uso de la blockchain por parte de malwares. Por ello, esta tesis también analiza el actual estado del arte en este ámbito. Una de las lecciones aprendidas al analizar los datos es que las comunicaciones encubiertas han recibido poca atención. Por otro lado, aunque trabajos anteriores han analizado la viabilidad de los canales encubiertos en una tecnología blockchain concreta llamada Bitcoin, ningún trabajo anterior ha explorado el uso de Ethereum para establecer un canal encubierto considerando todos los campos de transacción y contratos inteligentes. Con el objetivo de fomentar una mayor investigación orientada a la defensa, en esta tesis se presentan dos mecanismos novedosos. En primer lugar, Zephyrus aprovecha todos los campos de Ethereum y el bytecode de los contratos inteligentes. En segundo lugar, Smart-Zephyrus complementa Zephyrus aprovechando los contratos inteligentes escritos en Solidity. Se evalúa, también, la viabilidad y el coste de ambos mecanismos. Los resultados muestran que Zephyrus, en el mejor de los casos, puede ocultar 40 Kbits en 0,57 s. por 1,64 US$, y recuperarlos en 2,8 s. Smart-Zephyrus, por su parte, es capaz de ocultar un secreto de 4 Kb en 41 s. Si bien es cierto que es caro (alrededor de 1,82 dólares por bit), el sigilo proporcionado podría valer la pena para los atacantes. Además, estos dos mecanismos pueden combinarse para aumentar la capacidad y reducir los costesPrograma de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: José Manuel Estévez Tapiador.- Secretario: Jorge Blasco Alís.- Vocal: Luis Hernández Encina

    Modern meat: the next generation of meat from cells

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    Modern Meat is the first textbook on cultivated meat, with contributions from over 100 experts within the cultivated meat community. The Sections of Modern Meat comprise 5 broad categories of cultivated meat: Context, Impact, Science, Society, and World. The 19 chapters of Modern Meat, spread across these 5 sections, provide detailed entries on cultivated meat. They extensively tour a range of topics including the impact of cultivated meat on humans and animals, the bioprocess of cultivated meat production, how cultivated meat may become a food option in Space and on Mars, and how cultivated meat may impact the economy, culture, and tradition of Asia

    Towards trustworthy computing on untrustworthy hardware

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    Historically, hardware was thought to be inherently secure and trusted due to its obscurity and the isolated nature of its design and manufacturing. In the last two decades, however, hardware trust and security have emerged as pressing issues. Modern day hardware is surrounded by threats manifested mainly in undesired modifications by untrusted parties in its supply chain, unauthorized and pirated selling, injected faults, and system and microarchitectural level attacks. These threats, if realized, are expected to push hardware to abnormal and unexpected behaviour causing real-life damage and significantly undermining our trust in the electronic and computing systems we use in our daily lives and in safety critical applications. A large number of detective and preventive countermeasures have been proposed in literature. It is a fact, however, that our knowledge of potential consequences to real-life threats to hardware trust is lacking given the limited number of real-life reports and the plethora of ways in which hardware trust could be undermined. With this in mind, run-time monitoring of hardware combined with active mitigation of attacks, referred to as trustworthy computing on untrustworthy hardware, is proposed as the last line of defence. This last line of defence allows us to face the issue of live hardware mistrust rather than turning a blind eye to it or being helpless once it occurs. This thesis proposes three different frameworks towards trustworthy computing on untrustworthy hardware. The presented frameworks are adaptable to different applications, independent of the design of the monitored elements, based on autonomous security elements, and are computationally lightweight. The first framework is concerned with explicit violations and breaches of trust at run-time, with an untrustworthy on-chip communication interconnect presented as a potential offender. The framework is based on the guiding principles of component guarding, data tagging, and event verification. The second framework targets hardware elements with inherently variable and unpredictable operational latency and proposes a machine-learning based characterization of these latencies to infer undesired latency extensions or denial of service attacks. The framework is implemented on a DDR3 DRAM after showing its vulnerability to obscured latency extension attacks. The third framework studies the possibility of the deployment of untrustworthy hardware elements in the analog front end, and the consequent integrity issues that might arise at the analog-digital boundary of system on chips. The framework uses machine learning methods and the unique temporal and arithmetic features of signals at this boundary to monitor their integrity and assess their trust level

    Cognitive Load Reduction in Commanding Heterogeneous Robotic Teams

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    With the proliferation of multi-robot systems, the interfaces required to operate them have become increasingly complex compared to those used for single robot systems. This can present challenges for operators who need to extract relevant information in order to make informed decisions about how to operate the robots. To address this issue, this thesis explores a variety of strategies aimed at improving the intuitiveness and usability of such systems. These strategies encompass a range of approaches, from designing user interfaces to integrating physical input devices, knowledge representations, and other modalities to assist operators. In this context, the thesis proposes a decision support system that provides operators with additional information in an intuitive way, focusing specifically on handling a set of distinct commands for a heterogeneous robotic team. A key constraint during the development of this system was the lack of historical data available to train the modules on. As a result, the proposed system was tested in a few-shot environment and was specifically designed for this circumstance. The support system comprises two modules: one that probabilistically classifies the next command using a data mining approach called sequence prediction, which is used to reorder the available commands in the interface; and a second that creates higher-level commands by mining frequent sequences from the historical dataset. These command sequences are presented to the operator, who can add them as additional executable commands. To evaluate the advantages and disadvantages of this novel approach, a user study was conducted, which showed that both modules increased the efficiency and usability of the system, while also identifying opportunities for further improvement

    Data science, analytics and artificial intelligence in e-health : trends, applications and challenges

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    Acknowledgments. This work has been partially supported by the Divina Pastora Seguros company.More than ever, healthcare systems can use data, predictive models, and intelligent algorithms to optimize their operations and the service they provide. This paper reviews the existing literature regarding the use of data science/analytics methods and artificial intelligence algorithms in healthcare. The paper also discusses how healthcare organizations can benefit from these tools to efficiently deal with a myriad of new possibilities and strategies. Examples of real applications are discussed to illustrate the potential of these methods. Finally, the paper highlights the main challenges regarding the use of these methods in healthcare, as well as some open research lines

    ACARORUM CATALOGUS IX. Acariformes, Acaridida, Schizoglyphoidea (Schizoglyphidae), Histiostomatoidea (Histiostomatidae, Guanolichidae), Canestrinioidea (Canestriniidae, Chetochelacaridae, Lophonotacaridae, Heterocoptidae), Hemisarcoptoidea (Chaetodactylidae, Hyadesiidae, Algophagidae, Hemisarcoptidae, Carpoglyphidae, Winterschmidtiidae)

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    The 9th volume of the series Acarorum Catalogus contains lists of mites of 13 families, 225 genera and 1268 species of the superfamilies Schizoglyphoidea, Histiostomatoidea, Canestrinioidea and Hemisarcoptoidea. Most of these mites live on insects or other animals (as parasites, phoretic or commensals), some inhabit rotten plant material, dung or fungi. Mites of the families Chetochelacaridae and Lophonotacaridae are specialised to live with Myriapods (Diplopoda). The peculiar aquatic or intertidal mites of the families Hyadesidae and Algophagidae are also included.Publishe

    Large Language Models for Software Engineering: A Systematic Literature Review

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    Large Language Models (LLMs) have significantly impacted numerous domains, notably including Software Engineering (SE). Nevertheless, a well-rounded understanding of the application, effects, and possible limitations of LLMs within SE is still in its early stages. To bridge this gap, our systematic literature review takes a deep dive into the intersection of LLMs and SE, with a particular focus on understanding how LLMs can be exploited in SE to optimize processes and outcomes. Through a comprehensive review approach, we collect and analyze a total of 229 research papers from 2017 to 2023 to answer four key research questions (RQs). In RQ1, we categorize and provide a comparative analysis of different LLMs that have been employed in SE tasks, laying out their distinctive features and uses. For RQ2, we detail the methods involved in data collection, preprocessing, and application in this realm, shedding light on the critical role of robust, well-curated datasets for successful LLM implementation. RQ3 allows us to examine the specific SE tasks where LLMs have shown remarkable success, illuminating their practical contributions to the field. Finally, RQ4 investigates the strategies employed to optimize and evaluate the performance of LLMs in SE, as well as the common techniques related to prompt optimization. Armed with insights drawn from addressing the aforementioned RQs, we sketch a picture of the current state-of-the-art, pinpointing trends, identifying gaps in existing research, and flagging promising areas for future study
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