International Evidence Review of Conditional (Suspended) Sentences: Final Report

This report reviews international use of conditional sentences, specifically understood to mean a criminal sentence of imprisonment which is suspended pending a person's satisfaction of specific criteria. These sentences are being considered in jurisdictions seeking ways of managing prison population growth. The laws and any information about use of such sentences among selected countries is considered in the first part. The second part reviews research on perceptions of such sentences, and community-based sentences generally. This research was requested by and submitted to the Scottish Government

Healthcare Assistant role in Advance Care Planning Discussions: A QUALITATIVE STUDY INFORMED BY THE THEORY OF PLANNED BEHAVIOUR

Advance care planning is recognised as optimal in promoting patient autonomy and shared decision making, aiming to improve quality of care at end of life. Healthcare assistants are an integral part of the multi-disciplinary team, playing a pivotal role in the provision and application of advance care planning, though they are largely unrecognised and unsupported in this role. In this study we aimed to explore health care assistants’ beliefs, intentions, and behaviour regarding advance care planning via the Theory of Planned Behaviour framework with palliative care patients and their caregivers in the community.  We conducted a qualitative study using semi-structured interviews. From a potential pool of 95 HCAs employed in Northern Ireland, a purposive sample of 9 agreed to take part. Interviews were undertaken online, transcribed, and subject to thematic analysis and informed by the Theory of Planned Behaviour coding process. Nine health care assistants participated. Analysis revealed, healthcare assistants believed they were best placed to hold intimate advance care planning conversations, due to the personal relationship they built with patients and families, however their role was believed to be undervalued and unseen in policy and practice. Normative beliefs identified a lack of role definition and boundaries in advance care planning within the multi-disciplinary team. Participants behavioural intentions were to support patients and families, through barriers relating to role definition and boundaries created uncertainty. Findings of this study inform our understanding of the role of the healthcare assistants in promoting and implementing advance care planning conversations. Results reflect previous research which indicate key barriers in the lack of role clarity for healthcare assistants to promoting advance care planning. Overall, the findings highlight the need for recognition to be given to the healthcare planning role and interventions to be developed to expand this role. <br/

A Comparative Study of Existing and New Deep Learning Methods for Detecting Knee Injuries using the MRNet Dataset

This work presents a comparative study of existing and new techniques to detect knee injuries by leveraging Stanford's MRNet Dataset. All approaches are based on deep learning and we explore the comparative performances of transfer learning and a deep residual network trained from scratch. We also exploit some characteristics of Magnetic Resonance Imaging (MRI) data by, for example, using a fixed number of slices or 2D images from each of the axial, coronal and sagittal planes as well as combining the three planes into one multi-plane network. Overall we achieved a performance of 93.4% AUC on the validation data by using the more recent deep learning architectures and data augmentation strategies. More flexible architectures are also proposed that might help with the development and training of models that process MRIs. We found that transfer learning and a carefully tuned data augmentation strategy were the crucial factors in determining best performance

A comparative study of existing and new deep learning methods for detecting knee injuries using the MRNet dataset

This work presents a comparative study of existing and new techniques to detect knee injuries by leveraging Stanford's MRNet Dataset. All approaches are based on deep learning and we explore the comparative performances of transfer learning and a deep residual network trained from scratch. We also exploit some characteristics of Magnetic Resonance Imaging (MRI) data by, for example, using a fixed number of slices or 2D images from each of the axial, coronal and sagittal planes as well as combining the three planes into one multi-plane network. Overall we achieved a performance of 93.4% AUC on the validation data by using the more recent deep learning architectures and data augmentation strategies. More flexible architectures are also proposed that might help with the development and training of models that process MRIs. We found that transfer learning and a carefully tuned data augmentation strategy were the crucial factors in determining best performance

Exploring EEG for Object Detection and Retrieval

This paper explores the potential for using Brain Computer Interfaces (BCI) as a relevance feedback mechanism in content-based image retrieval. We investigate if it is possible to capture useful EEG signals to detect if relevant objects are present in a dataset of realistic and complex images. We perform several experiments using a rapid serial visual presentation (RSVP) of images at different rates (5Hz and 10Hz) on 8 users with different degrees of familiarization with BCI and the dataset. We then use the feedback from the BCI and mouse-based interfaces to retrieve localized objects in a subset of TRECVid images. We show that it is indeed possible to detect such objects in complex images and, also, that users with previous knowledge on the dataset or experience with the RSVP outperform others. When the users have limited time to annotate the images (100 seconds in our experiments) both interfaces are comparable in performance. Comparing our best users in a retrieval task, we found that EEG-based relevance feedback outperforms mouse-based feedback. The realistic and complex image dataset differentiates our work from previous studies on EEG for image retrieval.Comment: This preprint is the full version of a short paper accepted in the ACM International Conference on Multimedia Retrieval (ICMR) 2015 (Shanghai, China

Formulating queries for collecting training examples in visual concept classification

Video content can be automatically analysed and indexed using trained classifiers which map low-level features to semantic concepts. Such classifiers need training data consisting of sets of images which contain such concepts and recently it has been discovered that such training data can be located using text-based search to image databases on the internet. Formulating the text queries which locate these training images is the challenge we address here. In this paper we present preliminary results on TRECVid data of concept classification using automatically crawled images as training data and we compare the results with those obtained from manually annotated training sets

K-Space at TRECVid 2007

In this paper we describe K-Space participation in TRECVid 2007. K-Space participated in two tasks, high-level feature extraction and interactive search. We present our approaches for each of these activities and provide a brief analysis of our results. Our high-level feature submission utilized multi-modal low-level features which included visual, audio and temporal elements. Specific concept detectors (such as Face detectors) developed by K-Space partners were also used. We experimented with different machine learning approaches including logistic regression and support vector machines (SVM). Finally we also experimented with both early and late fusion for feature combination. This year we also participated in interactive search, submitting 6 runs. We developed two interfaces which both utilized the same retrieval functionality. Our objective was to measure the effect of context, which was supported to different degrees in each interface, on user performance. The first of the two systems was a ‘shot’ based interface, where the results from a query were presented as a ranked list of shots. The second interface was ‘broadcast’ based, where results were presented as a ranked list of broadcasts. Both systems made use of the outputs of our high-level feature submission as well as low-level visual features