Obstacle and Change Detection Using Monocular Vision

We explore change detection using videos of change-free paths to detect any changes that occur while travelling the same paths in the future. This approach benefits from learning the background model of the given path as preprocessing, detecting changes starting from the first frame, and determining the current location in the path. Two approaches are explored: a geometry-based approach and a deep learning approach. In our geometry-based approach, we use feature points to match testing frames to training frames. Matched frames are used to determine the current location within the training video. The frames are then processed by first registering the test frame onto the training frame through a homography of the previously matched feature points. Finally, a comparison is made to determine changes by using a region of interest (ROI) of the direct path of the robot in both frames. This approach performs well in many tests with various floor patterns, textures and complexities in the background of the path. In our deep learning approach, we use an ensemble of unsupervised dimensionality reduction models. We first extract feature points within a ROI and extract small frame samples around the feature points. The frame samples are used as training inputs and labels for our unsupervised models. The approach aims at learning a compressed feature representation of the frame samples in order to have a compact representation of background. We use the distribution of the training samples to directly compare the learned background to test samples with a classification of background or change using a majority vote. This approach performs well using just two models in the ensemble and achieves an overall accuracy of 98.0% with a 4.1% improvement over the geometry-based approach

Retracing my steps:Taking an autoethnographic journey down the online interprofessional learning pathway

This work builds on a sustained and coherent research corpus which has been developed over the last twenty years, commencing in 2002. During this period, the focus of my research has been interprofessional learning (IPL) characterised by the interactions that occur between students of different professional groups (Barr, et al. 2005). This has been explored both in university and practice settings, and has included substantial team awards of £1,000,000 (2002-2005) and £5,000,000 (2005-2010). In the first decade (2002 onwards), I undertook extensive primary research thoroughly immersed in the field (Bluteau & Jackson, 2005; Jackson & Bluteau, 2007; Bluteau & Krumins, 2008; Bluteau & Jackson, 2009a; Jackson & Bluteau, 2009a; 2009b). The second decade (2012-present) has enriched, deepened and consolidated my role as a leader within this arena, and has produced a second wave of publications which form the basis of this critical overview and portfolio. My portfolio of research is highly original, with models and theory drawn from outside the studied domain (Bronfenbrenner (1979; 1986; 1995); Garrison & Archer (2000); Garrison, Anderson & Archer (2000); Mausse, 1954; Rogers (1951; 1957; 1974; 1980; 1983; Winnicott, 1971). These innovative articles have brought fresh insights into the studied context, redefining the concerns and challenges regarding the creation and implementation of sustainable online IPL. The portfolio has been explored by employing an autoethnographic approach, characterised by self–reflecting upon my personal journey and critically analysing how this experience has led me to understand the culture and influence of online IPL. This has illustrated the coherence of my work by retracing my steps through my journey as a leader in the field, and as a research apprentice, to re-examine the ‘golden thread’ of online IPL

Econometrics meets sentiment : an overview of methodology and applications

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

The Role of Twitter in Cryptocurrency Pump-and-Dumps

We examine the influence of Twitter promotion on cryptocurrency pump-and-dump events. By analyzing abnormal returns, trading volume, and tweet activity, we uncover that Twitter effectively garners attention for pump-and-dump schemes, leading to notable effects on abnormal returns before the event. Our results indicate that investors relying on Twitter information exhibit delayed selling behavior during the post-dump phase, resulting in significant losses compared to other participants. These findings shed light on the pivotal role of Twitter promotion in cryptocurrency manipulation, offering valuable insights into participant behavior and market dynamics

The secondary structures of the Xenopus laevis and human mitochondrial small ribosomal subunit RNA are similar

AbstractExtensive corrections of the nucleotide sequence of the Xenopuslaevis mitochondrial small ribosomal subunit RNA gene [Roe et al. (1985) J. Biol. Chem. 260, 9759-9774] are reported. We found an additional fragment of 142 nucleotides and describe 25 nucleotide differences scattered in the gene. The nucleotide sequence of the X. laevis mitochondrial 12 S rRNA gene presents 80% homology with that of the same gene of bovine mitochondrion. We propose a new secondary structure for the product of the X. laevis gene. Contrary to the finding of Roe et al., we observed the same general organization of stems and loops as for the human mitochondrial 12 S rRNA gene product. On the other hand, the structural homology observed between the mitochondrial and cytoplasmic small subunit rRNAs of X. laevis appears much lower. These results strongly suggest that animal vertebrate mitochondrial DNAs have followed the same evolutionary pathway

Interprofessional Working in Practice – Avoiding a Theory-Practice Gap

This paper aims to encourage and promote further discussion around the theme of the theory and practice gap in the teaching and practice of interprofessional education (IPE) in pre-registration health and social care. Following a brief history of IPE, we consider the importance of providing students with supported opportunities to observe, learn and put into practice IPE. We also highlight the necessity of involving practitioners in creating health professionals who are ‘fit for purpose’ at qualification

Questioning the news about economic growth : sparse forecasting using thousands of news-based sentiment values

The modern calculation of textual sentiment involves a myriad of choices as to the actual calibration. We introduce a general sentiment engineering framework that optimizes the design for forecasting purposes. It includes the use of the elastic net for sparse data-driven selection and the weighting of thousands of sentiment values. These values are obtained by pooling the textual sentiment values across publication venues, article topics, sentiment construction methods, and time. We apply the framework to the investigation of the value added by textual analysis-based sentiment indices for forecasting economic growth in the US. We find that the additional use of optimized news-based sentiment values yields significant accuracy gains for forecasting the nine-month and annual growth rates of the US industrial production, compared to the use of high-dimensional forecasting techniques based on only economic and financial indicators. (C) 2018 The Author(s). Published by Elsevier B.V. on behalf of International Institute of Forecasters