4,264 research outputs found

    Towards an Expert System for the Analysis of Computer Aided Human Performance

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    Wizundry: A Cooperative Wizard of Oz Platform for Simulating Future Speech-based Interfaces with Multiple Wizards

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    Wizard of Oz (WoZ) as a prototyping method has been used to simulate intelligent user interfaces, particularly for speech-based systems. However, as our societies' expectations on artificial intelligence (AI) grows, the question remains whether a single Wizard is sufficient for it to simulate smarter systems and more complex interactions. Optimistic visions of 'what artificial intelligence (AI) can do' places demands on WoZ platforms to simulate smarter systems and more complex interactions. This raises the question of whether the typical approach of employing a single Wizard is sufficient. Moreover, while existing work has employed multiple Wizards in WoZ studies, a multi-Wizard approach has not been systematically studied in terms of feasibility, effectiveness, and challenges. We offer Wizundry, a real-time, web-based WoZ platform that allows multiple Wizards to collaboratively operate a speech-to-text based system remotely. We outline the design and technical specifications of our open-source platform, which we iterated over two design phases. We report on two studies in which participant-Wizards were tasked with negotiating how to cooperatively simulate an interface that can handle natural speech for dictation and text editing as well as other intelligent text processing tasks. We offer qualitative findings on the Multi-Wizard experience for Dyads and Triads of Wizards. Our findings reveal the promises and challenges of the multi-Wizard approach and open up new research questions.Comment: 34 page

    Speech-to-text models to transcribe emergency calls

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    This thesis is part of the larger project “AI-Support in Medical Emergency Calls (AISMEC)”, which aims to develop a decision support system for Emergency Medical Communication Center (EMCC) operators to better identify and respond to acute brain stroke. The system will utilize historical health data and the transcription from the emergency call to assist the EMCC operator in whether or not to dispatch an ambulance and with what priority and urgency. Our research primarily focuses on adapting the Automatic Speech Recognition (ASR) model, Whisper, to create a robust and accurate ASR model to transcribe Norwegian emergency calls. The model was fine-tuned on simulated emergency calls and recordings done by ourselves. Furthermore, a proof-of-concept ASR web application was developed with the goal of streamlining the manual task of transcribing emergency calls. After demonstrating the application to the involved researchers in AISMEC, and the potential users, both suggested optimism about the potential of this solution to streamline the transcription process. As part of our research, we conducted an experiment where we utilized the suggested transcriptions provided by the application and then corrected them for accuracy. This approach showed a notable reduction in our transcription time. We also found that establishing a machine learning pipeline to fine-tune the model on historical emergency calls was feasible. Further work would involve training the model on actual emergency calls. To investigate the efficiency of the ASR web application further, a larger scale of the semi-automatic transcription experiment could be conducted by the professional audio transcribers at Haukeland universitetssjukehus.Master's Thesis in Joint Master's Programme in Software Engineering - collaboration with HVLPROG399MAMN-PRO

    Third Conference on Artificial Intelligence for Space Applications, part 1

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    The application of artificial intelligence to spacecraft and aerospace systems is discussed. Expert systems, robotics, space station automation, fault diagnostics, parallel processing, knowledge representation, scheduling, man-machine interfaces and neural nets are among the topics discussed

    Design principles of integrated information platform for emergency responses: The case of 2008 Beijing Olympic Games

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    This paper investigates the challenges faced in designing an integrated information platform for emergency response management and uses the Beijing Olympic Games as a case study. The research methods are grounded in action research, participatory design, and situation-awareness oriented design. The completion of a more than two-year industrial secondment and six-month field studies ensured that a full understanding of user requirements had been obtained. A service-centered architecture was proposed to satisfy these user requirements. The proposed architecture consists mainly of information gathering, database management, and decision support services. The decision support services include situational overview, instant risk assessment, emergency response preplan, and disaster development prediction. Abstracting from the experience obtained while building this system, we outline a set of design principles in the general domain of information systems (IS) development for emergency management. These design principles form a contribution to the information systems literature because they provide guidance to developers who are aiming to support emergency response and the development of such systems that have not yet been adequately met by any existing types of IS. We are proud that the information platform developed was deployed in the real world and used in the 2008 Beijing Olympic Games. © 2012 INFORMS

    Seventh Annual Workshop on Space Operations Applications and Research (SOAR 1993), volume 2

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    This document contains papers presented at the Space Operations, Applications and Research Symposium (SOAR) Symposium hosted by NASA/Johnson Space Center (JSC) and cosponsored by NASA/JSC and U.S. Air Force Materiel Command. SOAR included NASA and USAF programmatic overviews, plenary session, panel discussions, panel sessions, and exhibits. It invited technical papers in support of U.S. Army, U.S. Navy, Department of Energy, NASA, and USAF programs in the following areas: robotics and telepresence, automation and intelligent systems, human factors, life support, and space maintenance and servicing. SOAR was concerned with Government-sponsored research and development relevant to aerospace operations
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