691 research outputs found

    Revisiting the capitalization of public transport accessibility into residential land value: an empirical analysis drawing on Open Science

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    Background: The delivery and effective operation of public transport is fundamental for a for a transition to low-carbon emission transport systems’. However, many cities face budgetary challenges in providing and operating this type of infrastructure. Land value capture (LVC) instruments, aimed at recovering all or part of the land value uplifts triggered by actions other than the landowner, can alleviate some of this pressure. A key element of LVC lies in the increment in land value associated with a particular public action. Urban economic theory supports this idea and considers accessibility to be a core element for determining residential land value. Although the empirical literature assessing the relationship between land value increments and public transport infrastructure is vast, it often assumes homogeneous benefits and, therefore, overlooks relevant elements of accessibility. Advancements in the accessibility concept in the context of Open Science can ease the relaxation of such assumptions. Methods: This thesis draws on the case of Greater Mexico City between 2009 and 2019. It focuses on the effects of the main public transport network (MPTN) which is organised in seven temporal stages according to its expansion phases. The analysis incorporates location based accessibility measures to employment opportunities in order to assess the benefits of public transport infrastructure. It does so by making extensive use of the open-source software OpenTripPlanner for public transport route modelling (≈ 2.1 billion origin-destination routes). Potential capitalizations are assessed according to the hedonic framework. The property value data includes individual administrative mortgage records collected by the Federal Mortgage Society (≈ 800,000). The hedonic function is estimated using a variety of approaches, i.e. linear models, nonlinear models, multilevel models, and spatial multilevel models. These are estimated by the maximum likelihood and Bayesian methods. The study also examines possible spatial aggregation bias using alternative spatial aggregation schemes according to the modifiable areal unit problem (MAUP) literature. Results: The accessibility models across the various temporal stages evidence the spatial heterogeneity shaped by the MPTN in combination with land use and the individual perception of residents. This highlights the need to transition from measures that focus on the characteristics of transport infrastructure to comprehensive accessibility measures which reflect such heterogeneity. The estimated hedonic function suggests a robust, positive, and significant relationship between MPTN accessibility and residential land value in all the modelling frameworks in the presence of a variety of controls. The residential land value increases between 3.6% and 5.7% for one additional standard deviation in MPTN accessibility to employment in the final set of models. The total willingness to pay (TWTP) is considerable, ranging from 0.7 to 1.5 times the equivalent of the capital costs of the bus rapid transit Line-7 of the Metrobús system. A sensitivity analysis shows that the hedonic model estimation is sensitive to the MAUP. In addition, the use of a post code zoning scheme produces the closest results compared to the smallest spatial analytical scheme (0.5 km hexagonal grid). Conclusion: The present thesis advances the discussion on the capitalization of public transport on residential land value by adopting recent contributions from the Open Science framework. Empirically, it fills a knowledge gap given the lack of literature around this topic in this area of study. In terms of policy, the findings support LVC as a mechanism of considerable potential. Regarding fee-based LVC instruments, there are fairness issues in relation to the distribution of charges or exactions to households that could be addressed using location based measures. Furthermore, the approach developed for this analysis serves as valuable guidance for identifying sites with large potential for the implementation of development based instruments, for instance land readjustments or the sale/lease of additional development rights

    SoK: Distributed Computing in ICN

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    Information-Centric Networking (ICN), with its data-oriented operation and generally more powerful forwarding layer, provides an attractive platform for distributed computing. This paper provides a systematic overview and categorization of different distributed computing approaches in ICN encompassing fundamental design principles, frameworks and orchestration, protocols, enablers, and applications. We discuss current pain points in legacy distributed computing, attractive ICN features, and how different systems use them. This paper also provides a discussion of potential future work for distributed computing in ICN.Comment: 10 pages, 3 figures, 1 table. Accepted by ACM ICN 202

    Machine learning as a service for high energy physics (MLaaS4HEP): a service for ML-based data analyses

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    With the CERN LHC program underway, there has been an acceleration of data growth in the High Energy Physics (HEP) field and the usage of Machine Learning (ML) in HEP will be critical during the HL-LHC program when the data that will be produced will reach the exascale. ML techniques have been successfully used in many areas of HEP nevertheless, the development of a ML project and its implementation for production use is a highly time-consuming task and requires specific skills. Complicating this scenario is the fact that HEP data is stored in ROOT data format, which is mostly unknown outside of the HEP community. The work presented in this thesis is focused on the development of a ML as a Service (MLaaS) solution for HEP, aiming to provide a cloud service that allows HEP users to run ML pipelines via HTTP calls. These pipelines are executed by using the MLaaS4HEP framework, which allows reading data, processing data, and training ML models directly using ROOT files of arbitrary size from local or distributed data sources. Such a solution provides HEP users non-expert in ML with a tool that allows them to apply ML techniques in their analyses in a streamlined manner. Over the years the MLaaS4HEP framework has been developed, validated, and tested and new features have been added. A first MLaaS solution has been developed by automatizing the deployment of a platform equipped with the MLaaS4HEP framework. Then, a service with APIs has been developed, so that a user after being authenticated and authorized can submit MLaaS4HEP workflows producing trained ML models ready for the inference phase. A working prototype of this service is currently running on a virtual machine of INFN-Cloud and is compliant to be added to the INFN Cloud portfolio of services

    Specialized IoT systems: Models, Structures, Algorithms, Hardware, Software Tools

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    Монография включает анализ проблем, модели, алгоритмы и программно- аппаратные средства специализированных сетей интернета вещей. Рассмотрены результаты проектирования и моделирования сети интернета вещей, мониторинга качества продукции, анализа звуковой информации окружающей среды, а также технология выявления заболеваний легких на базе нейронных сетей. Монография предназначена для специалистов в области инфокоммуникаций, может быть полезна студентам соответствующих специальностей, слушателям факультетов повышения квалификации, магистрантам и аспирантам

    Digital 3D reconstruction as a research environment in art and architecture history: uncertainty classification and visualisation

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    The dissertation addresses the still not solved challenges concerned with the source-based digital 3D reconstruction, visualisation and documentation in the domain of archaeology, art and architecture history. The emerging BIM methodology and the exchange data format IFC are changing the way of collaboration, visualisation and documentation in the planning, construction and facility management process. The introduction and development of the Semantic Web (Web 3.0), spreading the idea of structured, formalised and linked data, offers semantically enriched human- and machine-readable data. In contrast to civil engineering and cultural heritage, academic object-oriented disciplines, like archaeology, art and architecture history, are acting as outside spectators. Since the 1990s, it has been argued that a 3D model is not likely to be considered a scientific reconstruction unless it is grounded on accurate documentation and visualisation. However, these standards are still missing and the validation of the outcomes is not fulfilled. Meanwhile, the digital research data remain ephemeral and continue to fill the growing digital cemeteries. This study focuses, therefore, on the evaluation of the source-based digital 3D reconstructions and, especially, on uncertainty assessment in the case of hypothetical reconstructions of destroyed or never built artefacts according to scientific principles, making the models shareable and reusable by a potentially wide audience. The work initially focuses on terminology and on the definition of a workflow especially related to the classification and visualisation of uncertainty. The workflow is then applied to specific cases of 3D models uploaded to the DFG repository of the AI Mainz. In this way, the available methods of documenting, visualising and communicating uncertainty are analysed. In the end, this process will lead to a validation or a correction of the workflow and the initial assumptions, but also (dealing with different hypotheses) to a better definition of the levels of uncertainty

    Using machine learning for automated de-identification and clinical coding of free text data in electronic medical records

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    The widespread adoption of Electronic Medical Records (EMRs) in hospitals continues to increase the amount of patient data that are digitally stored. Although the primary use of the EMR is to support patient care by making all relevant information accessible, governments and health organisations are looking for ways to unleash the potential of these data for secondary purposes, including clinical research, disease surveillance and automation of healthcare processes and workflows. EMRs include large quantities of free text documents that contain valuable information. The greatest challenges in using the free text data in EMRs include the removal of personally identifiable information and the extraction of relevant information for specific tasks such as clinical coding. Machine learning-based automated approaches can potentially address these challenges. This thesis aims to explore and improve the performance of machine learning models for automated de-identification and clinical coding of free text data in EMRs, as captured in hospital discharge summaries, and facilitate the applications of these approaches in real-world use cases. It does so by 1) implementing an end-to-end de-identification framework using an ensemble of deep learning models; 2) developing a web-based system for de-identification of free text (DEFT) with an interactive learning loop; 3) proposing and implementing a hierarchical label-wise attention transformer model (HiLAT) for explainable International Classification of Diseases (ICD) coding; and 4) investigating the use of extreme multi-label long text transformer-based models for automated ICD coding. The key findings include: 1) An end-to-end framework using an ensemble of deep learning base-models achieved excellent performance on the de-identification task. 2) A new web-based de-identification software system (DEFT) can be readily and easily adopted by data custodians and researchers to perform de-identification of free text in EMRs. 3) A novel domain-specific transformer-based model (HiLAT) achieved state-of-the-art (SOTA) results for predicting ICD codes on a Medical Information Mart for Intensive Care (MIMIC-III) dataset comprising the discharge summaries (n=12,808) that are coded with at least one of the most 50 frequent diagnosis and procedure codes. In addition, the label-wise attention scores for the tokens in the discharge summary presented a potential explainability tool for checking the face validity of ICD code predictions. 4) An optimised transformer-based model, PLM-ICD, achieved the latest SOTA results for ICD coding on all the discharge summaries of the MIMIC-III dataset (n=59,652). The segmentation method, which split the long text consecutively into multiple small chunks, addressed the problem of applying transformer-based models to long text datasets. However, using transformer-based models on extremely large label sets needs further research. These findings demonstrate that the de-identification and clinical coding tasks can benefit from the application of machine learning approaches, present practical tools for implementing these approaches, and highlight priorities for further research

    Map Services Management

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    About 20 years ago, Google and other companies introduced the tiled maps, and nowadays, it is possible to produce similar work using open data and open source software. Web Map Service and Tile Map Service are a set of open standards to provide ways for users to access and visualize maps by interacting with geospacial data, over the internet. Most of the solutions to provide maps, make use of geospacial databases like PostgreSQL/PostGIS or MBTiles/PMTiles. Dedicated servers follows the standards specified by organizations such as Open Geospatial Consortium. The main goal of this work is to create a centralized and scalable solution that publish basemaps for a predefined set of geographic regions. These basemaps are displayed as part of a desktop or mobile applications with internet access. In order to fulfill this purpose, the best approach is, for each geographic region, to generate a MBTile database using raw data extract of the OpenStreetMap packed by Geofabrik. The raw data are also combined with a second data source, Natural Earth, to complete the map information at smaller scales. The final result goes through a process of cartographic generalization to be able to access only the relevant geospatial data at a given map scale or zoom level. The data are published as vector tiles, using a tile server, and for legacy applications there’s also the possibility to display the basemaps as raster tiles. Another available option is to use PMTiles files, which are similar to MBTiles but cloud optimized and suitable for serverless solutions. In the interest of ensuring good performance and stability, it is possible to keep everything together behind a reverse proxy, using as an example a Nginx server. Taking advantage of HTTP range requests functionality, also available in Nginx, it is possible to consider the serverless option of PMTiles and the standard tile server under the same umbrella. Finally, two points were considered and explored as opportunities for improvement, however not fully implemented. The first is the ability to cache vector/raster tiles requests, and the second is the ability to deploy the solution supported by a Content Delivery Network.Google e outros serviços introduziram o tiled maps há cerca de 20 anos. Atualmente, é possível produzir trabalhos semelhantes usando dados e software de código abertos. Web Map Service e Tile Map Service são um conjunto de protocolos padrão abertos que fornecem aos utilizadores uma forma de acederem e visualizarem mapas interagindo com dados geoespaciais, através da Internet. A maioria das soluções que fornecem mapas fazem uso de bases de dados geoespaciais PostgreSQL/PostGIS ou MBTiles/PMTiles. Os servi dores são dedicados conforme normas padrão especificadas por instituições como a Open Geospatial Consortium. O principal objetivo deste trabalho é criar uma solução centralizada e escalável que publique mapas de base para um conjunto predefinido de regiões geográficas. Estes mapas de base devem ser mostrados numa aplicação desktop ou mobile com acesso à internet. De forma a atingir este propósito, a melhor abordagem é, para cada região geográfica, gerar uma base de dados MBTile, usando extratos de dados em bruto do OpenStreetMap disponibilizados pela Geofabrik. Os dados em bruto são também combinados com uma segunda fonte de dados, o Natural Earth, para completar a informação do mapa nas escalas menores. O resultado final passa por um processo de generalização cartográfica de forma a disponibilizar os dados geoespaciais relevantes para uma determinada escala ou um determinado nível de zoom do mapa. Os dados são publicados como vector tiles, usando um tile server, e para aplicações legacy também existe a possibilidade de disponibilizar os mapas em formato raster. Existe uma outra opção que consiste na utilização de ficheiros PMTile, que são ficheiros similares aos MBTiles mas otimizados para a cloud e disponibilizados num princípio serverless. De forma a garantir um bom desempenho e estabilidade, é possível agregar toda a solução atrás de uma reverse proxy usando por exemplo um servidor Nginx. Tirando partido da funcionalidade HTTP range requests, disponível também no Nginx, torna-se possível servir PMTiles (serverless) e Tile servers sob a mesma infraestrutura. Por fim, mais dois pontos foram considerados e explorados como oportunidades de melhoria, mas não foram totalmente implementados. O primeiro é a capacidade de armazenar em cache pedidos de Tiles vector/raster e o segundo é a capacidade de disponibilizar a solução apoiada num Content Delivery Network

    Privacy-aware Biometric Blockchain based e-Passport System for Automatic Border Control

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    In the middle of 1990s, World Wide Web technology initially steps into our life. Now, 30 years after that, widespread internet access and established computing technology bring embodied real life into Metaverse by digital twin. Internet is not only blurring the concept of physical distance, but also blurring the edge between the real and virtual world. Another breakthrough in computing is the blockchain, which shifts the root of trust attached to a system administrator to the computational power of the system. Furthermore, its favourable properties such as immutable time-stamped transaction history and atomic smart contracts trigger the development of decentralized autonomous organizations (DAOs). Combining above two, this thesis presents a privacy-aware biometric Blockchain based e-passport system for automatic border control(ABC), which aims for improving the efficiency of existing ABC system. Specifically, through constructing a border control Metaverse DAO, border control workload can be autonomously self-executed by atomic smart contracts as transaction and then immutably recorded on Blockchain. What is more, to digitize border crossing documentation, biometric Blockchain based e-passport system(BBCVID) is created to generate an immutable real-world identity digital twin in the border control Metaverse DAO through Blockchain and biometric identity authentication. That is to say, by digitizing border crossing documentation and automatizing both biometric identity authentication and border crossing documentation verification, our proposal is able to significantly improve existing border control efficiency. Through system simulation and performance evaluation by Hyperledger Caliper, the proposed system turns out to be able to improve existing border control efficiency by 3.5 times more on average, which is remarkable. What is more, the dynamic digital twin constructed by BBCVID enables computing techniques such as machine learning and big data analysis applicable to real-world entity, which has a huge potential to create more value by constructing smarter ABC systems
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