AdaptiveVLE: an integrated framework for personalised online education using MPS JetBrains domain-specific modelling environment

This paper contains the design and development of an Adaptive Virtual Learning Environment (AdaptiveVLE) framework to assist educators of all disciplines with creating adaptive VLEs tailored to their needs and to contribute towards the creation of a more generic framework for adaptive systems. Fully online education is a major trend in education technology of our times. However, it has been criticised for its lack of personalisation and therefore not adequately addressing individual students’ needs. Adaptivity and intelligence are elements that could substantially improve the student experience and enhance the learning taking place. There are several attempts in academia and in industry to provide adaptive VLEs and therefore personalise educational provision. All these attempts require a multiple-domain (multi-disciplinary) approach from education professionals, software developers, data scientists to cover all aspects of the system. An integrated environment that can be used by all the multiple-domain users mentioned above and will allow for quick experimentation of different approaches is currently missing. Specifically, a transparent approach that will enable the educator to configure the data collected and the way it is processed without any knowledge of software development and/or data science algorithms implementation details is required. In our proposed work, we developed a new language/framework using MPS JetBrains Domain-Specific Language (DSL) development environment to address this problem. Our work consists of the following stages: data collection configuration by the educator, implementation of the adaptive VLE, data processing, adaptation of the learning path. These stages correspond to the adaptivity stages of all adaptive systems such as monitoring, processing and adaptation. The extension of our framework to include other application areas such as business analytics, health analytics, etc. so that it becomes a generic framework for adaptive systems as well as more usability testing for all applications will be part of our future work

Classification Algorithms Framework (CAF) to enable Intelligent Systems using JetBrains MPS domain-specific languages environment

This paper describes the design and development of a Classification Algorithms Framework (CAF) using the JetBrains MPS domain-specific languages (DSLs) development environment. It is increasingly recognized that the systems of the future will contain some form of adaptivity therefore making them intelligent systems as opposed to the static systems of the past. These intelligent systems can be extremely complex and difficult to maintain. Descriptions at higher-level of abstraction (system-level) have long been identified by industry and academia to reduce complexity. This research presents a Framework of Classification Algorithms at system-level that enables quick experimentation with several different algorithms from Naive Bayes to Logistic Regression. It has been developed as a tool to address the requirements of British Telecom’s (BT’s) data-science team. The tool has been presented at BT and JetBrains MPS and feedback has been collected and evaluated. Beyond the reduction in complexity through the system-level description, the most prominent advantage of this research is its potential applicability to many application contexts. It has been designed to be applicable for intelligent applications in several domains from business analytics, eLearning to eHealth, etc. Its wide applicability will contribute to enabling the larger vision of Artificial Intelligence (AI) adoption in context

Waking up the gut in critically ill patients

Multiorgan failure frequently develops in critically ill patients. While therapeutic efforts in such patients are often focused on the lungs, on the cardiovascular system as well as on the kidneys, it is important to also consider the functional alterations in gut motility and hormone secretion. Given the central regulatory functions of many gut hormones, such as glucagon-like peptide 1, glucagon-like peptide 2, ghrelin and others, exogenous supplementation of some of these factors may be beneficial under conditions of critical illness. From a pragmatic point of view, the most feasible way towards a restoration of gut hormone secretion in critically ill patients is to provide enteral nutritional supply as soon as possible

AdaptiveSystems: an integrated framework for adaptive systems design and development using MPS JetBrains domain-specific modelling environment

This paper contains the design and development of an adaptive systems (AdaptiveSystems Domain-Specific Language - DSL) framework to assist language developers and data scientists in their attempt to apply Artificial Intelligence (AI) algorithms in several application domains. Big-data processing and AI algorithms are at the heart of autonomics research groups among industry and academia. Major advances in the field have traditionally focused on algorithmic research and increasing the performance of the developed algorithms. However, it has been recently recognized by the AI community that the applicability of these algorithms and their consideration in context is of paramount importance for their adoption. Current approaches to address AI in context lie in two areas: adaptive systems research that mainly focuses on implementing adaptivity mechanisms (technical perspective) and AI in context research that focuses on business aspects (business perspective). There is currently no approach that combines all aspects required from business considerations to appropriate level of abstraction. In this paper, we attempt to address the problem of designing adaptive systems and therefore providing AI in context by utilising DSL technology. We propose a new DSL (AdaptiveSystems) and a methodology to apply this to the creation of a DSL for specific application domains such as AdaptiveVLE (Adaptive Virtual Learning Environment) DSL. The language developer will be able to instantiate the AdaptiveSystems DSL to any application domain by using the guidelines in this paper with an integrated path from design to implementation. The domain expert will then be able to use the developed DSL (e.g. AdaptiveVLE DSL) to design and develop their application. Future work will include extension and experimentation of the applicability of this work to more application domains within British Telecom (BT) and other areas such as health care, finance, etc

Framework for personalised online education based on learning analytics through the use of domain-specific modelling and data analytics

This paper contains a framework for the design and development of adaptive Virtual Learning Environments (VLEs) in order to assist educators of all disciplines with configuring and creating adaptive VLEs tailored to their needs. The proposed work is performed in three stages: In the first stage, development of an adaptive VLE that collects learning analytics and enables the educator to parametrize (turn on/off) their collection. The output of these analytics gets stored for further processing. In the second stage, data analysis and processing has been performed for the collected information. In the third stage, the results have been used as an input to adaptive VLE to enable informed personalisation of the student learning path and other adaptations. In this paper, we have proposed the combined use of two different environments for the different stages to achieve the most from their specialisation. For the first and the third stage, the MPS Jetbrains environment for the development of a domain-specific language (DSL) for adaptive VLEs was utilised. This development environment assists the creation of a new DSL that will enable educators to focus on the domain aspects and configure their adaptive VLE implementation to their needs. For the second stage of data analysis, the weka library was used to process the data, apply a range of classification algorithms and produce/store results that can then be used as an input to the adaptive VLE DSL. Overall, the proposed framework-system is anticipated to isolate the domain problem from the corresponding implementation details of web development and data analysis and give the adaptive VLE developer a seamless environment to experiment with a very quick turn-around time with ideas in their domain. More automation and integration between the VLE and the data science algorithms utilised for learning analytics data are part of our future plans towards the greater vision of more autonomic and personalised VLEs

Development of a neuro-fuzzy technique for automated parameter optimization of inverse treatment planning

<p>Abstract</p> <p>Background</p> <p>Parameter optimization in the process of inverse treatment planning for intensity modulated radiation therapy (IMRT) is mainly conducted by human planners in order to create a plan with the desired dose distribution. To automate this tedious process, an artificial intelligence (AI) guided system was developed and examined.</p> <p>Methods</p> <p>The AI system can automatically accomplish the optimization process based on prior knowledge operated by several fuzzy inference systems (FIS). Prior knowledge, which was collected from human planners during their routine trial-and-error process of inverse planning, has first to be "translated" to a set of "if-then rules" for driving the FISs. To minimize subjective error which could be costly during this knowledge acquisition process, it is necessary to find a quantitative method to automatically accomplish this task. A well-developed machine learning technique, based on an adaptive neuro fuzzy inference system (ANFIS), was introduced in this study. Based on this approach, prior knowledge of a fuzzy inference system can be quickly collected from observation data (clinically used constraints). The learning capability and the accuracy of such a system were analyzed by generating multiple FIS from data collected from an AI system with known settings and rules.</p> <p>Results</p> <p>Multiple analyses showed good agreements of FIS and ANFIS according to rules (error of the output values of ANFIS based on the training data from FIS of 7.77 ± 0.02%) and membership functions (3.9%), thus suggesting that the "behavior" of an FIS can be propagated to another, based on this process. The initial experimental results on a clinical case showed that ANFIS is an effective way to build FIS from practical data, and analysis of ANFIS and FIS with clinical cases showed good planning results provided by ANFIS. OAR volumes encompassed by characteristic percentages of isodoses were reduced by a mean of between 0 and 28%.</p> <p>Conclusion</p> <p>The study demonstrated a feasible way to automatically perform parameter optimization of inverse treatment planning under guidance of prior knowledge without human intervention other than providing a set of constraints that have proven clinically useful in a given setting.</p

Efficacy and Safety Comparison of Liraglutide, Glimepiride, and Placebo, All in Combination With Metformin, in Type 2 Diabetes: The LEAD (Liraglutide Effect and Action in Diabetes)-2 study

OBJECTIVE—The efficacy and safety of adding liraglutide (a glucagon-like peptide-1 receptor agonist) to metformin were compared with addition of placebo or glimepiride to metformin in subjects previously treated with oral antidiabetes (OAD) therapy