No abstract

Human activity recognition (HAR) is an important research area due to its potential for building context-aware interactive systems. Though movement-based activity recognition is an established area of research, recognising sedentary activities remains an open research question. Previous works have explored eye-based activity recognition as a potential approach for this challenge, focusing on statistical measures derived from eye movement properties---low-level gaze features---or some knowledge of the Areas-of-Interest (AOI) of the stimulus---high-level gaze features. In this paper, we extend this body of work by employing the addition of mid-level gaze features; features that add a level of abstraction over low-level features with some knowledge of the activity, but not of the stimulus. We evaluated our approach on a dataset collected from 24 participants performing eight desktop computing activities. We trained a classifier extending 26 low-level features derived from existing literature with the addition of 24 novel candidate mid-level gaze features. Our results show an overall classification performance of 0.72 (F1-Score), with up to 4% increase in accuracy when adding our mid-level gaze features. Finally, we discuss the implications of combining low- and mid-level gaze features, as well as the future directions for eye-based activity recognition.

No abstract

Ancient and modern stromatolites are potentially a challenge for petrophysicists when characterizing biosediments of microbial origin. Because of the heterogeneity, sometimes very cemented and lacking porosity, sometimes highly porous, these widely differing states can be used to develop techniques that can have wider application to addressing the representative elementary volume (REV -single or multiple REVs) challenge in microbial carbonates. Effective media properties -like porosity -need to be defined on REV scales and the challenge is that this scale is often close to or significantly larger than the traditional core plugs on which properties are traditionally measured. A combination of outcrop images, image analysis techniques, micro-computed tomography (CT) and modelling have been used to capture the porosity (or in some cases, precursor porosity) architecture and provide a framework for estimating petrophysical property sensitivities in a range of situations that can be subjected to further calibration by measurements in relevant microbial reservoir rocks. This work will help guide the sampling approach along with the interpretation and use of petrophysical measurements from microbial carbonates. The bioarchitectural component, when controlling porosity in microbial carbonates, presents a significant challenge as the REV scale is often much larger than core plugs, requiring careful screening of existing data and measurement and additional geostatistical model-based approaches (with further calibration).