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Register a new userExperiences with advanced CORBA services
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Added by
Grega Milcinski
Publication date
2001
fields
Informatics Engineering
and research's language is
English
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The Common Object Request Broker Architecture (CORBA) is successfully used in many control systems (CS) for data transfer and device modeling. Communication rates below 1 millisecond, high reliability, scalability, language independence and other features make it very attractive. For common types of applications like error logging, alarm messaging or slow monitoring, one can benefit from standard CORBA services that are implemented by third parties and save tremendous amount of developing time. We have started using few CORBA services on our previous CORBA-based control system for the light source ANKA [1] and use now several CORBA services for the ALMA Common Software (ACS) [2], the core of the control system of the Atacama Large Millimeter Array. Our experiences with the interface repository (IFR), the implementation repository, the naming service, the property service, telecom log service and the notify service from different vendors are presented. Performance and scalability benchmarks have been performed.
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AI models and services are used in a growing number of highstakes areas, resulting in a need for increased transparency. Consistent with this, several proposals for higher quality and more consistent documentation of AI data, models, and systems have emerged. Little is known, however, about the needs of those who would produce or consume these new forms of documentation. Through semi-structured developer interviews, and two document creation exercises, we have assembled a clearer picture of these needs and the various challenges faced in creating accurate and useful AI documentation. Based on the observations from this work, supplemented by feedback received during multiple design explorations and stakeholder conversations, we make recommendations for easing the collection and flexible presentation of AI facts to promote transparency.
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Measuring airways in chest computed tomography (CT) images is important for characterizing diseases such as cystic fibrosis, yet very time-consuming to perform manually. Machine learning algorithms offer an alternative, but need large sets of annotated data to perform well. We investigate whether crowdsourcing can be used to gather airway annotations which can serve directly for measuring the airways, or as training data for the algorithms. We generate image slices at known locations of airways and request untrained crowd workers to outline the airway lumen and airway wall. Our results show that the workers are able to interpret the images, but that the instructions are too complex, leading to many unusable annotations. After excluding unusable annotations, quantitative results show medium to high correlations with expert measurements of the airways. Based on this positive experience, we describe a number of further research directions and provide insight into the challenges of crowdsourcing in medical images from the perspective of first-time users.
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