7,704 research outputs found

    Cloud Computing Service for Managing Large Medical Image Data-Sets Using Balanced Collaborative Agents

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    Managing large medical image collections is an increasingly demanding important issue in many hospitals and other medical settings. A huge amount of this information is daily generated, which requires robust and agile systems. In this paper we present a distributed multi-agent system capable of managing very large medical image datasets. In this approach, agents extract low-level information from images and store them in a data structure implemented in a relational database. The data structure can also store semantic information related to images and particular regions. A distinctive aspect of our work is that a single image can be divided so that the resultant sub-images can be stored and managed separately by different agents to improve performance in data accessing and processing. The system also offers the possibility of applying some region-based operations and filters on images, facilitating image classification. These operations can be performed directly on data structures in the database

    2011 Strategic roadmap for Australian research infrastructure

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    The 2011 Roadmap articulates the priority research infrastructure areas of a national scale (capability areas) to further develop Australia’s research capacity and improve innovation and research outcomes over the next five to ten years. The capability areas have been identified through considered analysis of input provided by stakeholders, in conjunction with specialist advice from Expert Working Groups   It is intended the Strategic Framework will provide a high-level policy framework, which will include principles to guide the development of policy advice and the design of programs related to the funding of research infrastructure by the Australian Government. Roadmapping has been identified in the Strategic Framework Discussion Paper as the most appropriate prioritisation mechanism for national, collaborative research infrastructure. The strategic identification of Capability areas through a consultative roadmapping process was also validated in the report of the 2010 NCRIS Evaluation. The 2011 Roadmap is primarily concerned with medium to large-scale research infrastructure. However, any landmark infrastructure (typically involving an investment in excess of $100 million over five years from the Australian Government) requirements identified in this process will be noted. NRIC has also developed a ‘Process to identify and prioritise Australian Government landmark research infrastructure investments’ which is currently under consideration by the government as part of broader deliberations relating to research infrastructure. NRIC will have strategic oversight of the development of the 2011 Roadmap as part of its overall policy view of research infrastructure

    Cloud Computing en salud: Sistema para Administrar Imagenes Biomedicas

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    En el campo de la biomedicina se genera una inmensa cantidad de imágenes diariamente. Para administrarlas es necesaria la creación de sistemas informáticos robustos y ágiles, que necesitan gran cantidad de recursos computacionales. El presente artículo presenta un servicio de cloud computing capaz de manejar grandes colecciones de imágenes biomédicas. Gracias a este servicio organizaciones y usuarios podrían administrar sus imágenes biomédicas sin necesidad de poseer grandes recursos informáticos. El servicio usa un sistema distribuido multi agente donde las imágenes son procesadas y se extraen y almacenan en una estructura de datos las regiones que contiene junto con sus características. Una característica novedosa del sistema es que una misma imagen puede ser dividida, y las sub-imágenes resultantes pueden ser almacenadas por separado por distintos agentes. Esta característica ayuda a mejorar el rendimiento del sistema a la hora de buscar y recuperar las imágenes almacenadas

    Taking Computation to Data: Integrating Privacy-preserving AI techniques and Blockchain Allowing Secure Analysis of Sensitive Data on Premise

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    PhD thesis in Information technologyWith the advancement of artificial intelligence (AI), digital pathology has seen significant progress in recent years. However, the use of medical AI raises concerns about patient data privacy. The CLARIFY project is a research project funded under the European Union’s Marie Sklodowska-Curie Actions (MSCA) program. The primary objective of CLARIFY is to create a reliable, automated digital diagnostic platform that utilizes cloud-based data algorithms and artificial intelligence to enable interpretation and diagnosis of wholeslide-images (WSI) from any location, maximizing the advantages of AI-based digital pathology. My research as an early stage researcher for the CLARIFY project centers on securing information systems using machine learning and access control techniques. To achieve this goal, I extensively researched privacy protection technologies such as federated learning, differential privacy, dataset distillation, and blockchain. These technologies have different priorities in terms of privacy, computational efficiency, and usability. Therefore, we designed a computing system that supports different levels of privacy security, based on the concept: taking computation to data. Our approach is based on two design principles. First, when external users need to access internal data, a robust access control mechanism must be established to limit unauthorized access. Second, it implies that raw data should be processed to ensure privacy and security. Specifically, we use smart contractbased access control and decentralized identity technology at the system security boundary to ensure the flexibility and immutability of verification. If the user’s raw data still cannot be directly accessed, we propose to use dataset distillation technology to filter out privacy, or use locally trained model as data agent. Our research focuses on improving the usability of these methods, and this thesis serves as a demonstration of current privacy-preserving and secure computing technologies

    Status report on the NCRIS eResearch capability summary

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    Preface The period 2006 to 2014 has seen an approach to the national support of eResearch infrastructure by the Australian Government which is unprecedented. Not only has investment been at a significantly greater scale than previously, but the intent and approach has been highly innovative, shaped by a strategic approach to research support in which the critical element, the catchword, has been collaboration. The innovative directions shaped by this strategy, under the banner of the Australian Government’s National Collaborative Research Infrastructure Strategy (NCRIS), have led to significant and creative initiatives and activity, seminal to new research and fields of discovery. Origin This document is a Technical Report on the Status of the NCRIS eResearch Capability. It was commissioned by the Australian Government Department of Education and Training in the second half of 2014 to examine a range of questions and issues concerning the development of this infrastructure over the period 2006-2014. The infrastructure has been built and implemented over this period following investments made by the Australian Government amounting to over $430 million, under a number of funding initiatives

    Edge Computing for Internet of Things

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    The Internet-of-Things is becoming an established technology, with devices being deployed in homes, workplaces, and public areas at an increasingly rapid rate. IoT devices are the core technology of smart-homes, smart-cities, intelligent transport systems, and promise to optimise travel, reduce energy usage and improve quality of life. With the IoT prevalence, the problem of how to manage the vast volumes of data, wide variety and type of data generated, and erratic generation patterns is becoming increasingly clear and challenging. This Special Issue focuses on solving this problem through the use of edge computing. Edge computing offers a solution to managing IoT data through the processing of IoT data close to the location where the data is being generated. Edge computing allows computation to be performed locally, thus reducing the volume of data that needs to be transmitted to remote data centres and Cloud storage. It also allows decisions to be made locally without having to wait for Cloud servers to respond

    ERP implementation methodologies and frameworks: a literature review

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    Enterprise Resource Planning (ERP) implementation is a complex and vibrant process, one that involves a combination of technological and organizational interactions. Often an ERP implementation project is the single largest IT project that an organization has ever launched and requires a mutual fit of system and organization. Also the concept of an ERP implementation supporting business processes across many different departments is not a generic, rigid and uniform concept and depends on variety of factors. As a result, the issues addressing the ERP implementation process have been one of the major concerns in industry. Therefore ERP implementation receives attention from practitioners and scholars and both, business as well as academic literature is abundant and not always very conclusive or coherent. However, research on ERP systems so far has been mainly focused on diffusion, use and impact issues. Less attention has been given to the methods used during the configuration and the implementation of ERP systems, even though they are commonly used in practice, they still remain largely unexplored and undocumented in Information Systems research. So, the academic relevance of this research is the contribution to the existing body of scientific knowledge. An annotated brief literature review is done in order to evaluate the current state of the existing academic literature. The purpose is to present a systematic overview of relevant ERP implementation methodologies and frameworks as a desire for achieving a better taxonomy of ERP implementation methodologies. This paper is useful to researchers who are interested in ERP implementation methodologies and frameworks. Results will serve as an input for a classification of the existing ERP implementation methodologies and frameworks. Also, this paper aims also at the professional ERP community involved in the process of ERP implementation by promoting a better understanding of ERP implementation methodologies and frameworks, its variety and history

    Trustworthy Edge Machine Learning: A Survey

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    The convergence of Edge Computing (EC) and Machine Learning (ML), known as Edge Machine Learning (EML), has become a highly regarded research area by utilizing distributed network resources to perform joint training and inference in a cooperative manner. However, EML faces various challenges due to resource constraints, heterogeneous network environments, and diverse service requirements of different applications, which together affect the trustworthiness of EML in the eyes of its stakeholders. This survey provides a comprehensive summary of definitions, attributes, frameworks, techniques, and solutions for trustworthy EML. Specifically, we first emphasize the importance of trustworthy EML within the context of Sixth-Generation (6G) networks. We then discuss the necessity of trustworthiness from the perspective of challenges encountered during deployment and real-world application scenarios. Subsequently, we provide a preliminary definition of trustworthy EML and explore its key attributes. Following this, we introduce fundamental frameworks and enabling technologies for trustworthy EML systems, and provide an in-depth literature review of the latest solutions to enhance trustworthiness of EML. Finally, we discuss corresponding research challenges and open issues.Comment: 27 pages, 7 figures, 10 table
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