Automatic image enhancement using intrinsic geometrical information

collaboration

Illumination robust face representation based on intrinsic geometrical information

collaboration: keywords: Illumination robust face representation; intrinsic geometrical information; naturalistic human-robot interaction system; human-computer interaction system; binary non-subsampled contourlet transform; B-NSCT; multidirectional contour information; multiscale contour information; facial texture; CMU PIE databases; Yale B databasescollaboration: keywords: Illumination robust face representation; intrinsic geometrical information; naturalistic human-robot interaction system; human-computer interaction system; binary non-subsampled contourlet transform; B-NSCT; multidirectional contour information; multiscale contour information; facial texture; CMU PIE databases; Yale B databasescollaboration: keywords: Illumination robust face representation; intrinsic geometrical information; naturalistic human-robot interaction system; human-computer interaction system; binary non-subsampled contourlet transform; B-NSCT; multidirectional contour information; multiscale contour information; facial texture; CMU PIE databases; Yale B databasescollaboration: keywords: Illumination robust face representation; intrinsic geometrical information; naturalistic human-robot interaction system; human-computer interaction system; binary non-subsampled contourlet transform; B-NSCT; multidirectional contour information; multiscale contour information; facial texture; CMU PIE databases; Yale B databasescollaboration: keywords: Illumination robust face representation; intrinsic geometrical information; naturalistic human-robot interaction system; human-computer interaction system; binary non-subsampled contourlet transform; B-NSCT; multidirectional contour information; multiscale contour information; facial texture; CMU PIE databases; Yale B database

From Personalised Predictions to Targeted Advice: Improving Self-Management in Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease, that can lead to joint damage but also affects quality of life (QoL) including aspects such as self-esteem, fatigue, and mood. Current medical management focuses on the fluctuating disease activity to prevent progressive disability, but practical constraints mean periodic clinic appointments give little attention to the patient's experience of managing the wider consequences of chronic illness. The main aim of this study is to explore how to use patient-derived data both for clinical decision-making and for personalisation, with the first steps towards a platform for tailoring self-management advice to patients' lifestyle changes. As a result, we proposed a Bayesian network model for personalisation and have obtained promising outcomes

Attitudes to technology supported rheumatoid arthritis care: investigating patient and clinician perceived opportunities and barriers

Globally, demand outstrips capacity in rheumatology services, making Mobile Health (mHealth) attractive, with the potential to improve access, empower patient self-management and save costs. Existing mHealth interventions have poor uptake by end-users. This study was designed to understand existing challenges, and opportunities and barriers for computer technology in the rheumatoid arthritis (RA) care pathway

Tuzluluğun Penaeus semisulcatus (Decapoda: Penaeidae)' ta postlarval büyüme ve yaşama oranı üzerine etkileri

Yaşama ve büyüme oranları açısından, düşük tuzluluklarla kıyaslandığında, P. semisulcatus post-larvaları (PL), PL20 ve PL60 dönemleri arasında, yüksek tuzluluklarda daha iyi bir performans göstermişlerdir. Yüksek tuzluluklarda (%o30-40) deneme sonuna kadar yaşayanların yüzdeleri (%19-23) düşük tuzluluklardakinden (0.05). Tuzluluğun artması biyomasın 0.020 g'dan (%o 10'da) 0.317 g'a (%o40'ta) çıkmasına neden olmuştur (P 0.05). A rise in salinity resulted in an increase in the biomass from 0.020 g at 10 ppt to 0.317 g at 40 ppt (P < 0.05). Optimum salinity for the nursery culture of P. semisulcatus PLs appeared to be about 40 ppt at 28 $\circ$C. Hence, the results of this study demonstrate that P. semisulcatus inhabiting the Mediterranean Sea is not a good candidate for culture in waters of low salinity

Predicting Social Dynamics in Child-Robot Interactions with Facial Action Units

We examine the extent to which task engagement, social engagement, and social attitude in child-robot interaction can be predicted on the basis of Facial Action Unit (FAU) intensity. The analyses were based on child-robot and child-child interaction data from the PInSoRo dataset [1]. We applied Logistic Regression, Naive Bayes, and Probabilistic Neural Networks to these data. Results indicated that FAU intensities have potential to predict social dynamics in child-robot interactions (average balanced accuracy scores up to 84%), and illustrate a difference in behavior of children towards other children when compared to their interaction with robots