359,204 research outputs found

    Capture and Reuse of Knowledge in ICT-based Decisional Environments

    Get PDF
    Health care practitioners continually confront with a wide range of challenges, seeking to making difficult diagnoses, avoiding errors, ensuring highest quality, maximizing efficacy and reducing costs. Information technology has the potential to reduce clinical errors and to im-prove the decision making in the clinical milieu. This paper presents a pilot development of a clinical decision support systems (CDSS) entitled MEDIS that was designed to incorporate knowledge from heterogeneous environments with the purpose of increasing the efficiency and the quality of the decision making process, and reducing costs based on advances of in-formation technologies, especially under the impact of the transition towards the mobile space. The system aims to capture and reuse knowledge in order to provide real-time access to clinical knowledge for a variety of users, including medical personnel, patients, teachers and students.Clinical Decision Support Systems, Knowledge Management, Knowledge Interoperability, Mobile Interface, Object-relational Mapping

    Capture and Reuse of Knowledge in ICT-based Decisional Environments

    Get PDF
    Health care practitioners continually confront with a wide range of challenges, seeking to making difficult diagnoses, avoiding errors, ensuring highest quality, maximizing efficacy and reducing costs. Information technology has the potential to reduce clinical errors and to im-prove the decision making in the clinical milieu. This paper presents a pilot development of a clinical decision support systems (CDSS) entitled MEDIS that was designed to incorporate knowledge from heterogeneous environments with the purpose of increasing the efficiency and the quality of the decision making process, and reducing costs based on advances of in-formation technologies, especially under the impact of the transition towards the mobile space. The system aims to capture and reuse knowledge in order to provide real-time access to clinical knowledge for a variety of users, including medical personnel, patients, teachers and students

    Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

    Get PDF
    The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

    Towards Semantic Integration of Heterogeneous Sensor Data with Indigenous Knowledge for Drought Forecasting

    Full text link
    In the Internet of Things (IoT) domain, various heterogeneous ubiquitous devices would be able to connect and communicate with each other seamlessly, irrespective of the domain. Semantic representation of data through detailed standardized annotation has shown to improve the integration of the interconnected heterogeneous devices. However, the semantic representation of these heterogeneous data sources for environmental monitoring systems is not yet well supported. To achieve the maximum benefits of IoT for drought forecasting, a dedicated semantic middleware solution is required. This research proposes a middleware that semantically represents and integrates heterogeneous data sources with indigenous knowledge based on a unified ontology for an accurate IoT-based drought early warning system (DEWS).Comment: 5 pages, 3 figures, In Proceedings of the Doctoral Symposium of the 16th International Middleware Conference (Middleware Doct Symposium 2015), Ivan Beschastnikh and Wouter Joosen (Eds.). ACM, New York, NY, US

    Learning to infer: RL-based search for DNN primitive selection on Heterogeneous Embedded Systems

    Full text link
    Deep Learning is increasingly being adopted by industry for computer vision applications running on embedded devices. While Convolutional Neural Networks' accuracy has achieved a mature and remarkable state, inference latency and throughput are a major concern especially when targeting low-cost and low-power embedded platforms. CNNs' inference latency may become a bottleneck for Deep Learning adoption by industry, as it is a crucial specification for many real-time processes. Furthermore, deployment of CNNs across heterogeneous platforms presents major compatibility issues due to vendor-specific technology and acceleration libraries. In this work, we present QS-DNN, a fully automatic search based on Reinforcement Learning which, combined with an inference engine optimizer, efficiently explores through the design space and empirically finds the optimal combinations of libraries and primitives to speed up the inference of CNNs on heterogeneous embedded devices. We show that, an optimized combination can achieve 45x speedup in inference latency on CPU compared to a dependency-free baseline and 2x on average on GPGPU compared to the best vendor library. Further, we demonstrate that, the quality of results and time "to-solution" is much better than with Random Search and achieves up to 15x better results for a short-time search

    Language technologies and the evolution of the semantic web

    Get PDF
    The availability of huge amounts of semantic markup on the Web promises to enable a quantum leap in the level of support available to Web users for locating, aggregating, sharing, interpreting and customizing information. While we cannot claim that a large scale Semantic Web already exists, a number of applications have been produced, which generate and exploit semantic markup, to provide advanced search and querying functionalities, and to allow the visualization and management of heterogeneous, distributed data. While these tools provide evidence of the feasibility and tremendous potential value of the enterprise, they all suffer from major limitations, to do primarily with the limited degree of scale and heterogeneity of the semantic data they use. Nevertheless, we argue that we are at a key point in the brief history of the Semantic Web and that the very latest demonstrators already give us a glimpse of what future applications will look like. In this paper, we describe the already visible effects of these changes by analyzing the evolution of Semantic Web tools from smart databases towards applications that harness collective intelligence. We also point out that language technology plays an important role in making this evolution sustainable and we highlight the need for improved support, especially in the area of large-scale linguistic resources
    • 

    corecore