10,787 research outputs found
Cardea: An Open Automated Machine Learning Framework for Electronic Health Records
An estimated 180 papers focusing on deep learning and EHR were published
between 2010 and 2018. Despite the common workflow structure appearing in these
publications, no trusted and verified software framework exists, forcing
researchers to arduously repeat previous work. In this paper, we propose
Cardea, an extensible open-source automated machine learning framework
encapsulating common prediction problems in the health domain and allows users
to build predictive models with their own data. This system relies on two
components: Fast Healthcare Interoperability Resources (FHIR) -- a standardized
data structure for electronic health systems -- and several AUTOML frameworks
for automated feature engineering, model selection, and tuning. We augment
these components with an adaptive data assembler and comprehensive data- and
model- auditing capabilities. We demonstrate our framework via 5 prediction
tasks on MIMIC-III and Kaggle datasets, which highlight Cardea's human
competitiveness, flexibility in problem definition, extensive feature
generation capability, adaptable automatic data assembler, and its usability
mRUBiS: An Exemplar for Model-Based Architectural Self-Healing and Self-Optimization
Self-adaptive software systems are often structured into an adaptation engine
that manages an adaptable software by operating on a runtime model that
represents the architecture of the software (model-based architectural
self-adaptation). Despite the popularity of such approaches, existing exemplars
provide application programming interfaces but no runtime model to develop
adaptation engines. Consequently, there does not exist any exemplar that
supports developing, evaluating, and comparing model-based self-adaptation off
the shelf. Therefore, we present mRUBiS, an extensible exemplar for model-based
architectural self-healing and self-optimization. mRUBiS simulates the
adaptable software and therefore provides and maintains an architectural
runtime model of the software, which can be directly used by adaptation engines
to realize and perform self-adaptation. Particularly, mRUBiS supports injecting
issues into the model, which should be handled by self-adaptation, and
validating the model to assess the self-adaptation. Finally, mRUBiS allows
developers to explore variants of adaptation engines (e.g., event-driven
self-adaptation) and to evaluate the effectiveness, efficiency, and scalability
of the engines
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Sensory semantic user interfaces (SenSUI)
Rapid evolution of the World Wide Web with its underlying sources of data, knowledge, services and applications continually attempts to support a variety of users, with different backgrounds, requirements and capabilities. In such an environment, it is highly unlikely that a single user interface will prevail and be able to fulfill the requirements of each user adequately. Adaptive user interfaces are able to adapt information and application functionalities to the user context. In contrast, pervasive computing and sensor networks open new opportunities for context aware platforms, one that is able to improve user interface adaptation reacting to environmental and user sensors. Semantic web technologies and ontologies are able to capture sensor data and provide contextual information about the user, their actions, required applications and environment. This paper investigates the viability of an approach where semantic web technologies are used to maximize the efficacy of interface adaptation through the use of available ontology
A Study on the Field of XR Simulation Creation, Leveraging Game Engines to Develop a VR Hospital Framework
This thesis introduces an adaptable and extensible VR framework designed for clinicians and patients using pre-existing game development software like Blender and Unreal Engine. The framework aids patients in familiarizing themselves with hospital scenarios and environments, reducing anxiety, and improving navigation. Clinicians can use the tool to educate patients and collaboratively design new aspects of the environment. A prototype implementation demonstrates the system\u27s effectiveness, with usability studies indicating that teleport movement is preferred over sliding for locomotion and that navigation speed can improve with subsequent trials in the VR simulator. The framework\u27s potential for enhancing patient experience and facilitating informed consent is also discussed. The research findings provide valuable insights for future VR healthcare applications while affirming the valuable future applications of the hospital framework and development workflow
Modular and composable extensions to smalltalk using composition filters
Current and future trends in computer science require extensions to Smalltalk. Rather than arguing for particular language mechanisms to deal with specific requirements, in this position paper we want to make a case for two requirements that Smalltalk extensions should fulfill. The first is that the extensions must be integrated with Smalltalk without violating its basic object model. The second requirement is that extensions should allow for defining objects that are still adaptable, extensible and reusable, and in particular do not cause inheritance anomalies. We propose the composition filters model as a framework for language extensions that fulfills these criteria. Its applicability to solving various modeling problems is briefly illustrated
A Voice-Enabled Framework for Recommender and Adaptation Systems in E-Learning
With the proliferation of learning resources on the Web, finding suitable content (using telephone) has
become a rigorous task for voice-based online learners to achieve better performance. The problem
with Finding Content Suitability (FCS) with voice E-Learning applications is more complex when the
sight-impaired learner is involved. Existing voice-enabled applications in the domain of E-Learning
lack the attributes of adaptive and reusable learning objects to be able to address the FCS problem.
This study provides a Voice-enabled Framework for Recommender and Adaptation (VeFRA) Systems in
E-learning and an implementation of a system based on the framework with dual user interfaces – voice
and Web. A usability study was carried out in a visually impaired and non-visually impaired school
using the International Standard Organization’s (ISO) 9241-11 specification to determine the level
of effectiveness, efficiency and user satisfaction. The result of the usability evaluation reveals that the
prototype application developed for the school has “Good Usability” rating of 4.13 out of 5 scale. This
shows that the application will not only complement existing mobile and Web-based learning systems,
but will be of immense benefit to users, based on the system’s capacity for taking autonomous decisions
that are capable of adapting to the needs of both visually impaired and non-visually impaired learners
UML-F: A Modeling Language for Object-Oriented Frameworks
The paper presents the essential features of a new member of the UML language
family that supports working with object-oriented frameworks. This UML
extension, called UML-F, allows the explicit representation of framework
variation points. The paper discusses some of the relevant aspects of UML-F,
which is based on standard UML extension mechanisms. A case study shows how it
can be used to assist framework development. A discussion of additional tools
for automating framework implementation and instantiation rounds out the paper.Comment: 22 pages, 10 figure
Emerging from the MIST: A Connector Tool for Supporting Programming by Non-programmers
Software development is an iterative process. As user re-quirements emerge software applications must be extended to support the new requirements. Typically, a programmer will add new code to an existing code base of an application to provide a new functionality. Previous research has shown that such extensions are easier when application logic is clearly separated from the user interface logic. Assuming that a programmer is already familiar with the existing code base, the task of writing the new code can be considered to be split into two sub-tasks: writing code for the application logic; that is, the actual functionality of the application; and writing code for the user interface that will expose the functionality to the end user.
The goal of this research is to reduce the effort required to create a user interface once the application logic has been created, toward supporting scientists with minimal pro-gramming knowledge to be able to create and modify pro-grams. Using a Model View Controller based architecture, various model components which contain the application logic can be built and extended. The process of creating and extending the views (user interfaces) on these model components is simplified through the use of our Malleable Interactive Software Toolkit (MIST), a tool set an infrastructure intended to simplify the design and extension of dynamically reconfigurable interfaces.
This paper focuses on one tool in the MIST suite, a connec-tor tool that enables the programmer to evolve the user interface as the application logic evolves by connecting related pieces of code together; either through simple drag-and-drop interactions or through the authoring of Python code. The connector tool exemplifies the types of tools in the MIST suite, which we expect will encourage collabora-tive development of applications by allowing users to inte-grate various components and minimizing the cost of de-veloping new user interfaces for the combined compo-nents
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