9,856 research outputs found
Investigation into Mobile Learning Framework in Cloud Computing Platform
AbstractâCloud computing infrastructure is increasingly
used for distributed applications. Mobile learning
applications deployed in the cloud are a new research
direction. The applications require specific development
approaches for effective and reliable communication. This
paper proposes an interdisciplinary approach for design and
development of mobile applications in the cloud. The
approach includes front service toolkit and backend service
toolkit. The front service toolkit packages data and sends it
to a backend deployed in a cloud computing platform. The
backend service toolkit manages rules and workflow, and
then transmits required results to the front service toolkit.
To further show feasibility of the approach, the paper
introduces a case study and shows its performance
Adapting e-learning and learning services for people with disabilities
Providing learning materials and support services that are adapted to the needs of individuals has the potential to enable learners to obtain maximal benefit from university level studies. This paper describes EU4ALL project which has been exploring how to present customized learning materials and services for people with disabilities. A number of the technical components of the EU4ALL framework are described. This is followed with a brief description of prototype implementations. This is then followed by a discussion of a number of research directions that may enhance the adaptability, usability and accessibility of information and support systems can be used and consumed by a diverse user population
Models of everywhere revisited: a technological perspective
The concept âmodels of everywhereâ was first introduced in the mid 2000s as a means of reasoning about the
environmental science of a place, changing the nature of the underlying modelling process, from one in which
general model structures are used to one in which modelling becomes a learning process about specific places, in
particular capturing the idiosyncrasies of that place. At one level, this is a straightforward concept, but at another
it is a rich multi-dimensional conceptual framework involving the following key dimensions: models of everywhere,
models of everything and models at all times, being constantly re-evaluated against the most current
evidence. This is a compelling approach with the potential to deal with epistemic uncertainties and nonlinearities.
However, the approach has, as yet, not been fully utilised or explored. This paper examines the
concept of models of everywhere in the light of recent advances in technology. The paper argues that, when first
proposed, technology was a limiting factor but now, with advances in areas such as Internet of Things, cloud
computing and data analytics, many of the barriers have been alleviated. Consequently, it is timely to look again
at the concept of models of everywhere in practical conditions as part of a trans-disciplinary effort to tackle the
remaining research questions. The paper concludes by identifying the key elements of a research agenda that
should underpin such experimentation and deployment
Applying digital content management to support localisation
The retrieval and presentation of digital content such as that on the World Wide Web (WWW) is a substantial area of research. While recent years have seen huge expansion in the size of web-based archives that can be searched efficiently by commercial search engines, the presentation of potentially relevant content is still limited to ranked document lists represented by simple text snippets or image keyframe surrogates. There is expanding interest in techniques to personalise the presentation of content to improve the richness and effectiveness of the user experience. One of the most significant challenges to achieving this is the increasingly multilingual nature of this data, and the need to provide suitably localised responses to users based on this content. The Digital Content Management (DCM) track of the Centre for Next Generation Localisation (CNGL) is seeking to develop technologies to support advanced personalised access and presentation of information by combining elements from the existing research areas of Adaptive Hypermedia and Information Retrieval. The combination of these technologies is intended to produce significant improvements in the way users access information. We review key features of these technologies and introduce early ideas for how these technologies can support localisation and localised content before concluding with some impressions of future directions in DCM
Engineering Crowdsourced Stream Processing Systems
A crowdsourced stream processing system (CSP) is a system that incorporates
crowdsourced tasks in the processing of a data stream. This can be seen as
enabling crowdsourcing work to be applied on a sample of large-scale data at
high speed, or equivalently, enabling stream processing to employ human
intelligence. It also leads to a substantial expansion of the capabilities of
data processing systems. Engineering a CSP system requires the combination of
human and machine computation elements. From a general systems theory
perspective, this means taking into account inherited as well as emerging
properties from both these elements. In this paper, we position CSP systems
within a broader taxonomy, outline a series of design principles and evaluation
metrics, present an extensible framework for their design, and describe several
design patterns. We showcase the capabilities of CSP systems by performing a
case study that applies our proposed framework to the design and analysis of a
real system (AIDR) that classifies social media messages during time-critical
crisis events. Results show that compared to a pure stream processing system,
AIDR can achieve a higher data classification accuracy, while compared to a
pure crowdsourcing solution, the system makes better use of human workers by
requiring much less manual work effort
Orchestration of machine learning workflows on Internet of Things data
Applications empowered by machine learning (ML) and the Internet of Things (IoT) are changing the way people live and impacting a broad range of industries. However, creating and automating ML workflows at scale using real-world IoT data often leads to complex systems integration and production issues. Examples of challenges faced during the development of these ML applications include glue code, hidden dependencies, and data pipeline jungles. This research proposes the Machine Learning Framework for IoT data (ML4IoT), which is designed to orchestrate ML workflows to perform training and enable inference by ML models on IoT data. In the proposed framework, containerized microservices are used to automate the execution of tasks specified in ML workflows, which are defined through REST APIs. To address the problem of integrating big data tools and machine learning into a unified platform, the proposed framework enables the definition and execution of end-to-end ML workflows on large volumes of IoT data. In addition, to address the challenges of running multiple ML workflows in parallel, the ML4IoT has been designed to use container-based components that provide a convenient mechanism to enable the training and deployment of numerous ML models in parallel. Finally, to address the common production issues faced during the development of ML applications, the proposed framework used microservices architecture to bring flexibility, reusability, and extensibility to the framework. Through the experiments, we demonstrated the feasibility of the (ML4IoT), which managed to train and deploy predictive ML models in two types of IoT data. The obtained results suggested that the proposed framework can manage real-world IoT data, by providing elasticity to execute 32 ML workflows in parallel, which were used to train 128 ML models simultaneously. Also, results demonstrated that in the ML4IoT, the performance of rendering online predictions is not affected when 64 ML models are deployed concurrently to infer new information using online IoT data
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