Context-aware systems testing and validation

Newer systems are still tested and validated following techniques which have been developed decades ago when systems were of a different nature. We report on an attempt to define a new method which is practical and focus on the concept of ‘context’ as a system aspect which have become more relevant in the development of the subsystem category called Intelligent Environments

Context-aware systems testing and validation

Newer systems are still tested and validated following techniques which have been developed decades ago when systems were of a different nature. We report on an attempt to define a new method which is practical and focus on the concept of ‘context’ as a system aspect which have become more relevant in the development of the subsystem category called Intelligent Environments

Quality traceability for user-centric context-aware systems in intelligent environments

Context-awareness is an important component of modern software systems. For example, in Ambient Assisted Living (AAL), the concept of context-awareness empowers users by reducing their dependence on others. Due to this role in healthcare, such systems need to be reliable and usable by their intended users. Our research addresses the development, testing and validation of context-aware systems in an emerging field which currently lacks sufficient systems engineering processes and disciplines. One specific issue being that developers often focus on delivering a system that works at some level, rather than engineering a system that meets a specified set of system requirements and their corresponding qualities. Our research aims to contribute towards improving the delivery of system quality by tracing, developing and linking systems development data for requirements, contexts including sensors, test cases and their results, and user validation tests and their results. We refer to this approach as the “quality traceability of context-aware systems”. In order to support the developer, the quality traceability of context-aware systems introduces a systems development approach tailored to context-aware systems in intelligent environments, an automated system testing tool and system validation process. We have implemented a case study to inform the research. The case study is in healthcare and based on an AAL system used to remotely monitor and manage, in real time, an individual prone to depressive symptoms

An Extension of Class Diagram to Model the Structure of Context-Aware Systems

Context-aware systems (CASs) have become a reality thanks to the development of smart software and hardware to assist the users in various real life activities. The proliferation of context-aware services has led to the emergence of environments where services are made available for usage anywhere and at any time. CASs have the ability to capture users’ contexts and use their instance values to provide self-adaptive services in response to context changes. Modelling and documenting the structure of such a system during the design phase is vital for system validation, testing, maintenance and version management. The Unified Modelling Language (UML) is the de facto industrial standard for system modelling and development. The UML class diagrams provide notations for modelling graphically the structure of a system in terms of classes and the relationships between them. However, these notations are insufficient to model the structure of CASs. This paper proposes a new set of notations to represent context and context-awareness and their relationships with classes in class diagrams. Hence, the structure of CASs can be specified, visualized, constructed, and documented distinctively during system development. The proposed approach is evaluated using real-world case studies

Towards an Efficient Context-Aware System: Problems and Suggestions to Reduce Energy Consumption in Mobile Devices

Looking for optimizing the battery consumption is an open issue, and we think it is feasible if we analyze the battery consumption behavior of a typical context-aware application to reduce context-aware operations at runtime. This analysis is based on different context sensors configurations. Actually existing context-aware approaches are mainly based on collecting and sending context data to external components, without taking into account how expensive are these operations in terms of energy consumption. As a first result of our work in progress, we are proposing a way for reducing the context data publishing. We have designed a testing battery consumption architecture supported by Nokia Energy Profiler tool to verify consumption in different scenarios

Named Entity Recognition in Electronic Health Records Using Transfer Learning Bootstrapped Neural Networks

Neural networks (NNs) have become the state of the art in many machine learning applications, especially in image and sound processing [1]. The same, although to a lesser extent [2,3], could be said in natural language processing (NLP) tasks, such as named entity recognition. However, the success of NNs remains dependent on the availability of large labelled datasets, which is a significant hurdle in many important applications. One such case are electronic health records (EHRs), which are arguably the largest source of medical data, most of which lies hidden in natural text [4,5]. Data access is difficult due to data privacy concerns, and therefore annotated datasets are scarce. With scarce data, NNs will likely not be able to extract this hidden information with practical accuracy. In our study, we develop an approach that solves these problems for named entity recognition, obtaining 94.6 F1 score in I2B2 2009 Medical Extraction Challenge [6], 4.3 above the architecture that won the competition. Beyond the official I2B2 challenge, we further achieve 82.4 F1 on extracting relationships between medical terms. To reach this state-of-the-art accuracy, our approach applies transfer learning to leverage on datasets annotated for other I2B2 tasks, and designs and trains embeddings that specially benefit from such transfer.Comment: 11 pages, 4 figures, 8 table