Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives

Over the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains. As a potentially crucial technique for the development of the next generation of emotional AI systems, we herein provide a comprehensive overview of the application of adversarial training to affective computing and sentiment analysis. Various representative adversarial training algorithms are explained and discussed accordingly, aimed at tackling diverse challenges associated with emotional AI systems. Further, we highlight a range of potential future research directions. We expect that this overview will help facilitate the development of adversarial training for affective computing and sentiment analysis in both the academic and industrial communities

Living a burdensome and demanding life: a qualitative systematic review of the patients experiences of peripheral arterial disease

<div><p>Background</p><p>Peripheral arterial disease (PAD) has a significant negative impact on the quality of life of individuals. Understanding the experiences of people living with PAD will be useful in developing comprehensive patient-centred secondary prevention therapies for this population.</p><p>Aim</p><p>The aim of this study is to identify first-hand accounts of patients’ experiences of living with PAD.</p><p>Methods</p><p>Six databases (CINALH, PsyclNFO, MEDLINE, AMED, EMBASE, Social citation index/Science citation index via Web of Science (WOS)) and reference lists of identified studies were searched until September 2017 (updated February 2018). Qualitative studies reporting patients’ account of living with PAD were eligible for inclusion. A framework thematic synthesis was implemented.</p><p>Results</p><p>Fourteen studies with 360 participants were included. Pain and walking limitation were recurrent among the varied symptom descriptions. Patients’ ignorance and trivialisation of symptoms contributed to delays in diagnosis. Inadequate engagement in disease understanding and treatment decisions meant patients had poor attitudes towards walking treatments and unrealistic expectations about surgery. Depending on symptom progression, patients battle with walking impairment, powerlessness, and loss of independence which were a source of burden to them. Lack of disease understanding is central through patients’ journey with PAD and, although they subsequently began adaptation to long term living with PAD, many worried about their future.</p><p>Conclusions</p><p>Disease understanding is vital across the illness trajectory in patients with PAD. Although certain experiences are common throughout patient journey, some might be unique to a particular stage (e.g. unrealistic expectation about surgery, or rationale of walking in spite of pain in a supervised exercise program). Given that PAD is an overarching construct ranging from the mildest form of intermittent claudication to severe critical limb ischemia with ulceration and gangrene, consideration of important patient constructs specific to each stage of the disease may enhance treatment success. Systematic review registration CRD42017070417.</p></div

Content-based Propagation of User Markings for Interactive Segmentation of Patterned Images

Efficient and easy segmentation of images and volumes is of great practical importance. Segmentation problems that motivate our approach originate from microscopy imaging commonly used in materials science, medicine, and biology. We formulate image segmentation as a probabilistic pixel classification problem, and we apply segmentation as a step towards characterising image content. Our method allows the user to define structures of interest by interactively marking a subset of pixels. Thanks to the real-time feedback, the user can place new markings strategically, depending on the current outcome. The final pixel classification may be obtained from a very modest user input. An important ingredient of our method is a graph that encodes image content. This graph is built in an unsupervised manner during initialisation and is based on clustering of image features. Since we combine a limited amount of user-labelled data with the clustering information obtained from the unlabelled parts of the image, our method fits in the general framework of semi-supervised learning. We demonstrate how this can be a very efficient approach to segmentation through pixel classification.Comment: 9 pages, 7 figures, PDFLaTe