13,419 research outputs found

    Annual report of the officers of the town of Jackson, New Hampshire for the fiscal year ending December 31, 2022.

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    This is an annual report containing vital statistics for a town/city in the state of New Hampshire

    Reinforcement Learning-based User-centric Handover Decision-making in 5G Vehicular Networks

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    The advancement of 5G technologies and Vehicular Networks open a new paradigm for Intelligent Transportation Systems (ITS) in safety and infotainment services in urban and highway scenarios. Connected vehicles are vital for enabling massive data sharing and supporting such services. Consequently, a stable connection is compulsory to transmit data across the network successfully. The new 5G technology introduces more bandwidth, stability, and reliability, but it faces a low communication range, suffering from more frequent handovers and connection drops. The shift from the base station-centric view to the user-centric view helps to cope with the smaller communication range and ultra-density of 5G networks. In this thesis, we propose a series of strategies to improve connection stability through efficient handover decision-making. First, a modified probabilistic approach, M-FiVH, aimed at reducing 5G handovers and enhancing network stability. Later, an adaptive learning approach employed Connectivity-oriented SARSA Reinforcement Learning (CO-SRL) for user-centric Virtual Cell (VC) management to enable efficient handover (HO) decisions. Following that, a user-centric Factor-distinct SARSA Reinforcement Learning (FD-SRL) approach combines time series data-oriented LSTM and adaptive SRL for VC and HO management by considering both historical and real-time data. The random direction of vehicular movement, high mobility, network load, uncertain road traffic situation, and signal strength from cellular transmission towers vary from time to time and cannot always be predicted. Our proposed approaches maintain stable connections by reducing the number of HOs by selecting the appropriate size of VCs and HO management. A series of improvements demonstrated through realistic simulations showed that M-FiVH, CO-SRL, and FD-SRL were successful in reducing the number of HOs and the average cumulative HO time. We provide an analysis and comparison of several approaches and demonstrate our proposed approaches perform better in terms of network connectivity

    Neural Architecture Search: Insights from 1000 Papers

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    In the past decade, advances in deep learning have resulted in breakthroughs in a variety of areas, including computer vision, natural language understanding, speech recognition, and reinforcement learning. Specialized, high-performing neural architectures are crucial to the success of deep learning in these areas. Neural architecture search (NAS), the process of automating the design of neural architectures for a given task, is an inevitable next step in automating machine learning and has already outpaced the best human-designed architectures on many tasks. In the past few years, research in NAS has been progressing rapidly, with over 1000 papers released since 2020 (Deng and Lindauer, 2021). In this survey, we provide an organized and comprehensive guide to neural architecture search. We give a taxonomy of search spaces, algorithms, and speedup techniques, and we discuss resources such as benchmarks, best practices, other surveys, and open-source libraries

    Countermeasures for the majority attack in blockchain distributed systems

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    La tecnología Blockchain es considerada como uno de los paradigmas informáticos más importantes posterior al Internet; en función a sus características únicas que la hacen ideal para registrar, verificar y administrar información de diferentes transacciones. A pesar de esto, Blockchain se enfrenta a diferentes problemas de seguridad, siendo el ataque del 51% o ataque mayoritario uno de los más importantes. Este consiste en que uno o más mineros tomen el control de al menos el 51% del Hash extraído o del cómputo en una red; de modo que un minero puede manipular y modificar arbitrariamente la información registrada en esta tecnología. Este trabajo se enfocó en diseñar e implementar estrategias de detección y mitigación de ataques mayoritarios (51% de ataque) en un sistema distribuido Blockchain, a partir de la caracterización del comportamiento de los mineros. Para lograr esto, se analizó y evaluó el Hash Rate / Share de los mineros de Bitcoin y Crypto Ethereum, seguido del diseño e implementación de un protocolo de consenso para controlar el poder de cómputo de los mineros. Posteriormente, se realizó la exploración y evaluación de modelos de Machine Learning para detectar software malicioso de tipo Cryptojacking.DoctoradoDoctor en Ingeniería de Sistemas y Computació

    Annual SHOT Report 2018

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    SHOT is affiliated to the Royal College of PathologistsAll NHS organisations must move away from a blame culture towards a just and learning culture. All clinical and laboratory staff should be encouraged to become familiar with human factors and ergonomics concepts. All transfusion decisions must be made after carefully assessing the risks and benefits of transfusion therapy. Collaboration and co-ordination among staff is vital

    Examples of works to practice staccato technique in clarinet instrument

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    Klarnetin staccato tekniğini güçlendirme aşamaları eser çalışmalarıyla uygulanmıştır. Staccato geçişlerini hızlandıracak ritim ve nüans çalışmalarına yer verilmiştir. Çalışmanın en önemli amacı sadece staccato çalışması değil parmak-dilin eş zamanlı uyumunun hassasiyeti üzerinde de durulmasıdır. Staccato çalışmalarını daha verimli hale getirmek için eser çalışmasının içinde etüt çalışmasına da yer verilmiştir. Çalışmaların üzerinde titizlikle durulması staccato çalışmasının ilham verici etkisi ile müzikal kimliğe yeni bir boyut kazandırmıştır. Sekiz özgün eser çalışmasının her aşaması anlatılmıştır. Her aşamanın bir sonraki performans ve tekniği güçlendirmesi esas alınmıştır. Bu çalışmada staccato tekniğinin hangi alanlarda kullanıldığı, nasıl sonuçlar elde edildiği bilgisine yer verilmiştir. Notaların parmak ve dil uyumu ile nasıl şekilleneceği ve nasıl bir çalışma disiplini içinde gerçekleşeceği planlanmıştır. Kamış-nota-diyafram-parmak-dil-nüans ve disiplin kavramlarının staccato tekniğinde ayrılmaz bir bütün olduğu saptanmıştır. Araştırmada literatür taraması yapılarak staccato ile ilgili çalışmalar taranmıştır. Tarama sonucunda klarnet tekniğin de kullanılan staccato eser çalışmasının az olduğu tespit edilmiştir. Metot taramasında da etüt çalışmasının daha çok olduğu saptanmıştır. Böylelikle klarnetin staccato tekniğini hızlandırma ve güçlendirme çalışmaları sunulmuştur. Staccato etüt çalışmaları yapılırken, araya eser çalışmasının girmesi beyni rahatlattığı ve istekliliği daha arttırdığı gözlemlenmiştir. Staccato çalışmasını yaparken doğru bir kamış seçimi üzerinde de durulmuştur. Staccato tekniğini doğru çalışmak için doğru bir kamışın dil hızını arttırdığı saptanmıştır. Doğru bir kamış seçimi kamıştan rahat ses çıkmasına bağlıdır. Kamış, dil atma gücünü vermiyorsa daha doğru bir kamış seçiminin yapılması gerekliliği vurgulanmıştır. Staccato çalışmalarında baştan sona bir eseri yorumlamak zor olabilir. Bu açıdan çalışma, verilen müzikal nüanslara uymanın, dil atış performansını rahatlattığını ortaya koymuştur. Gelecek nesillere edinilen bilgi ve birikimlerin aktarılması ve geliştirici olması teşvik edilmiştir. Çıkacak eserlerin nasıl çözüleceği, staccato tekniğinin nasıl üstesinden gelinebileceği anlatılmıştır. Staccato tekniğinin daha kısa sürede çözüme kavuşturulması amaç edinilmiştir. Parmakların yerlerini öğrettiğimiz kadar belleğimize de çalışmaların kaydedilmesi önemlidir. Gösterilen azmin ve sabrın sonucu olarak ortaya çıkan yapıt başarıyı daha da yukarı seviyelere çıkaracaktır

    Deep Transfer Learning Applications in Intrusion Detection Systems: A Comprehensive Review

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    Globally, the external Internet is increasingly being connected to the contemporary industrial control system. As a result, there is an immediate need to protect the network from several threats. The key infrastructure of industrial activity may be protected from harm by using an intrusion detection system (IDS), a preventive measure mechanism, to recognize new kinds of dangerous threats and hostile activities. The most recent artificial intelligence (AI) techniques used to create IDS in many kinds of industrial control networks are examined in this study, with a particular emphasis on IDS-based deep transfer learning (DTL). This latter can be seen as a type of information fusion that merge, and/or adapt knowledge from multiple domains to enhance the performance of the target task, particularly when the labeled data in the target domain is scarce. Publications issued after 2015 were taken into account. These selected publications were divided into three categories: DTL-only and IDS-only are involved in the introduction and background, and DTL-based IDS papers are involved in the core papers of this review. Researchers will be able to have a better grasp of the current state of DTL approaches used in IDS in many different types of networks by reading this review paper. Other useful information, such as the datasets used, the sort of DTL employed, the pre-trained network, IDS techniques, the evaluation metrics including accuracy/F-score and false alarm rate (FAR), and the improvement gained, were also covered. The algorithms, and methods used in several studies, or illustrate deeply and clearly the principle in any DTL-based IDS subcategory are presented to the reader

    A Decision Support System for Economic Viability and Environmental Impact Assessment of Vertical Farms

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    Vertical farming (VF) is the practice of growing crops or animals using the vertical dimension via multi-tier racks or vertically inclined surfaces. In this thesis, I focus on the emerging industry of plant-specific VF. Vertical plant farming (VPF) is a promising and relatively novel practice that can be conducted in buildings with environmental control and artificial lighting. However, the nascent sector has experienced challenges in economic viability, standardisation, and environmental sustainability. Practitioners and academics call for a comprehensive financial analysis of VPF, but efforts are stifled by a lack of valid and available data. A review of economic estimation and horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. This thesis proposes an open-source DSS framework to evaluate business sustainability through financial risk and environmental impact assessments. Data from the literature, alongside lessons learned from industry practitioners, would be centralised in the proposed DSS using imprecise data techniques. These techniques have been applied in engineering but are seldom used in financial forecasting. This could benefit complex sectors which only have scarce data to predict business viability. To begin the execution of the DSS framework, VPF practitioners were interviewed using a mixed-methods approach. Learnings from over 19 shuttered and operational VPF projects provide insights into the barriers inhibiting scalability and identifying risks to form a risk taxonomy. Labour was the most commonly reported top challenge. Therefore, research was conducted to explore lean principles to improve productivity. A probabilistic model representing a spectrum of variables and their associated uncertainty was built according to the DSS framework to evaluate the financial risk for VF projects. This enabled flexible computation without precise production or financial data to improve economic estimation accuracy. The model assessed two VPF cases (one in the UK and another in Japan), demonstrating the first risk and uncertainty quantification of VPF business models in the literature. The results highlighted measures to improve economic viability and the viability of the UK and Japan case. The environmental impact assessment model was developed, allowing VPF operators to evaluate their carbon footprint compared to traditional agriculture using life-cycle assessment. I explore strategies for net-zero carbon production through sensitivity analysis. Renewable energies, especially solar, geothermal, and tidal power, show promise for reducing the carbon emissions of indoor VPF. Results show that renewably-powered VPF can reduce carbon emissions compared to field-based agriculture when considering the land-use change. The drivers for DSS adoption have been researched, showing a pathway of compliance and design thinking to overcome the ‘problem of implementation’ and enable commercialisation. Further work is suggested to standardise VF equipment, collect benchmarking data, and characterise risks. This work will reduce risk and uncertainty and accelerate the sector’s emergence

    Développement d’un système intelligent de reconnaissance automatisée pour la caractérisation des états de surface de la chaussée en temps réel par une approche multicapteurs

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    Le rôle d’un service dédié à l’analyse de la météo routière est d’émettre des prévisions et des avertissements aux usagers quant à l’état de la chaussée, permettant ainsi d’anticiper les conditions de circulations dangereuses, notamment en période hivernale. Il est donc important de définir l’état de chaussée en tout temps. L’objectif de ce projet est donc de développer un système de détection multicapteurs automatisée pour la caractérisation en temps réel des états de surface de la chaussée (neige, glace, humide, sec). Ce mémoire se focalise donc sur le développement d’une méthode de fusion de données images et sons par apprentissage profond basée sur la théorie de Dempster-Shafer. Les mesures directes pour l’acquisition des données qui ont servi à l’entrainement du modèle de fusion ont été effectuées à l’aide de deux capteurs à faible coût disponibles dans le commerce. Le premier capteur est une caméra pour enregistrer des vidéos de la surface de la route. Le second capteur est un microphone pour enregistrer le bruit de l’interaction pneu-chaussée qui caractérise chaque état de surface. La finalité de ce système est de pouvoir fonctionner sur un nano-ordinateur pour l’acquisition, le traitement et la diffusion de l’information en temps réel afin d’avertir les services d’entretien routier ainsi que les usagers de la route. De façon précise, le système se présente comme suit :1) une architecture d’apprentissage profond classifiant chaque état de surface à partir des images issues de la vidéo sous forme de probabilités ; 2) une architecture d’apprentissage profond classifiant chaque état de surface à partir du son sous forme de probabilités ; 3) les probabilités issues de chaque architecture ont été ensuite introduites dans le modèle de fusion pour obtenir la décision finale. Afin que le système soit léger et moins coûteux, il a été développé à partir d’architectures alliant légèreté et précision à savoir Squeeznet pour les images et M5 pour le son. Lors de la validation, le système a démontré une bonne performance pour la détection des états surface avec notamment 87,9 % pour la glace noire et 97 % pour la neige fondante
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