Learning to self-manage by intelligent monitoring, prediction and intervention

Despite the growing prevalence of multimorbidities, current digital self-management approaches still prioritise single conditions. The future of outof- hospital care requires researchers to expand their horizons; integrated assistive technologies should enable people to live their life well regardless of their chronic conditions. Yet, many of the current digital self-management technologies are not equipped to handle this problem. In this position paper, we suggest the solution for these issues is a model-aware and data-agnostic platform formed on the basis of a tailored self-management plan and three integral concepts - Monitoring (M) multiple information sources to empower Predictions (P) and trigger intelligent Interventions (I). Here we present our ideas for the formation of such a platform, and its potential impact on quality of life for sufferers of chronic conditions

A Concept of weather window (WW) in managing the rain risks in construction projects of Sri Lanka

Different weather conditions such as rain, wind and snow would directly impact on. , th, e performance of any construction project. Being a tropical country, the effect from rain wou I be experienced mostly in Sri Lanka. Within this context, risks caused from rain can be defined in financial terms as a loss or gain due to a change in weather conditions over a period of time. Weather records available in the Meteorological Department of previous years are analysed to establish the different rain risk categories based on dry spell, rain spell, and wet spell which are derived from a “wet day ” as defined by the Meteorological Department. In this research, the value used to define the wet day is modified to establish the "weather windows (WWs),” under above rain risk categories, namely as major weather window, moderate weather window and minor weather window. These established WWs are applied to a completed project and analyzed at different risk conditions. It was identified that the concept could be used effectively to manage the rain risks. The results showed that 3.5% of the total project cost would have been saved, if the weather sensitive items such as excavation and earth works, landscaping and external works, etc., of the project were sheduled by analysing the WWs, during the planing stage, even though the rain is considered as an Act of God and a totally uncertain event.

A concept of weather window (WW) in managing the rain risks in construction projects of Sri Lanka

Different weather conditions such as rain, wind and snow would directly impact on the performance of any construction project. Being a tropical country, the effect from rain would be experienced mostly in Sri Lanka. Within this context, risks caused from rain can be defined in financial terms as a loss or gain due to a change in weather conditions over a period of time. Weather records available in the Meteorological Department of previous years are analysed to establish the different rain risk categories based on dry spell, rain spell, and wet spell which are derived from a "wet day" as define by the Meteorological Department. In this research the value used' to define the wet day is modified to establish the '"weather windows• (WW.s)," under above rain risk categories, namely as major weather window, moderate weather window and minor weather window. These established WWs are applied to a completed project and analyzed at different risk conditions.lt was identified that the concept could be used effectively to manage the rain risks. The results showed that 3.5% of the total project cost would have been saved, if the weather sensitive items such as excavation and earth works, landscaping and external works, etc., of the project were scheduled by analysing the WWs, during the planning stage, even though the rain is considered as an Act of God and a totally uncertain event

Preface: The 6th International Workshop on Knowledge Discovery in Healthcare Data (KDH)

No abstract available

Aspect selection for social recommender systems

In this paper, we extend our previous work on social recommender systems to harness knowledge from product reviews. By mining product reviews, we can exploit sentiment-rich content to ascertain user opinion expressed over product aspects. Aspect aware sentiment analysis provides a more structured approach to product comparison. However, aspects extracted using NLP-based techniques remain too large and lead to poor quality product comparison metrics. To overcome this problem, we explore the utility of feature selection heuristics based on frequency counts and Information Gain (IG) to rank and select the most useful aspects. Here an interesting contribution is the use of top ranked products from Amazon to formulate a binary classification over products to form the basis for the supervised IG metric. Experimental results on three related product families (Compact Cameras, DSLR Cameras and Point & Shoot Cameras) extracted from Amazon.com demonstrate the effectiveness of incorporating feature selection techniques for aspect selection in recommendation task. © Springer International Publishing Switzerland 2015.This research has been partially supported by AGAUR Scholarship (2013FI-B 00034) and NASAID (CSIC Intramural 201550E022).Peer Reviewe

Unsupervised Feature Selection for Text Data

Feature selection for unsupervised tasks is particularly challenging, especially when dealing with text data. The increase in online documents and email communication creates a need for tools that can operate without the supervision of the user. In this paper we look at novel feature selection techniques that address this need. A distributional similarity measure from information theory is applied to measure feature utility. This utility informs the search for both representative and diverse features in two complementary ways: CLUSTER divides the entire feature space, before then selecting one feature to represent each cluster; and GREEDY increments the feature subset size by a greedily selected feature. In particular we found that GREEDY’s local search is suited to learning smaller feature subset sizes while CLUSTER is able to improve the global quality of larger feature sets. Experiments with four email data sets show significant improvement in retrieval accuracy with nearest neighbour based search methods compared to an existing frequency-based method. Importantly both GREEDY and CLUSTER make significant progress towards the upper bound performance set by a standard supervised feature selection method