Assessing knee OA severity with CNN attention-based end-to-end architectures

This work proposes a novel end-to-end convolutional neural network (CNN) architecture to automatically quantify the severity of knee osteoarthritis (OA) using X-Ray images, which incorporates trainable attention modules acting as unsupervised fine-grained detectors of the region of interest (ROI). The proposed attention modules can be applied at different levels and scales across any CNN pipeline helping the network to learn relevant attention patterns over the most informative parts of the image at different resolutions. We test the proposed attention mechanism on existing state-of-the-art CNN architectures as our base models, achieving promising results on the benchmark knee OA datasets from the osteoarthritis initiative (OAI) and multicenter osteoarthritis study (MOST).Postprint (published version

Freeing up access to learning: the role for Open Educational Resources

The internet revolution of the last few years has had an impact on how we all live our lives. So it is not surprising that this is also a time of change in attitudes towards how we learn. Free access to information through computer networks has expanded, and part of that information flow are materials designed to help people learn. In addition there are many further online resources that help the learning process, even if that was not the original aim. However, there are risks in this evolution in access to information both for the end user, who can be confused by the options available to them, and to those involved in providing education, who may see their traditional role changing and becoming harder to perform. This situation provides the background for a growing movement to directly consider how education can be provided in a freer and more open way. This has been termed “Open Educational Resources” (OER). The exact definition of the term depends on interpretation, however a useful statement was provided as an outcome from an event organized by UNESCO in 2002 as: “OER are teaching, learning, and research resources that reside in the public domain or have been released under an intellectual property license that permits their free use or re-purposing by others. Open educational resources include full courses, course materials, modules, textbooks, streaming videos, tests, software, and any other tools, materials, or techniques used to support access to knowledge (Atkins, Brown and Hammond, 2007, p4).” Arguably the only difference between an online learning object and an open educational resource is the declaration that it is open. This may be true but that turns out to be a powerful difference. By being open the content can be accessed by any learner who can do so, it can be taken and run in new contexts, it can be reworked by others and adapted for local needs (with the result shared back if desired), it can be made part of shared pool of resources, it can be the shared point of reference for collaboration, and it can be the key to building policies that work in different domain

Understanding and Supporting Directed Content Sharing on the Web

To find interesting, personally relevant web content, we often rely on friends and colleagues to pass links along as they encounter them. In this paper, we study and augment link-sharing via e-mail, the most popular means of sharing web content today. Armed with survey data indicating that active sharers of novel web content are often those that actively seek it out, we present FeedMe, a plug-in for Google Reader that makes directed sharing of content a more salient part of the user experience. Our survey research indicates that sharing is moderated by concern about relevancy to the recipient, a desire to send only novel content to the recipient, and the effort required to share. FeedMe allays these concerns by recommending friends who may be interested in seeing the content, providing information on what the recipient has seen and how many emails they have received recently, and giving recipients the opportunity to provide lightweight feedback when they appreciate shared content. FeedMe introduces a novel design space for mixed-initiative social recommenders: friends who know the user voluntarily vet the material on the userâ s behalf. We present a two week field experiment (N=60) demonstrating that FeedMeâ s recommendations and social awareness features made it easier and more enjoyable to share content that recipients appreciated and would not have found otherwise

Ars Memorativa as the Genesis of Information Design:A Conversation with Manuel Lima

Manuel Lima is one of the most prominent figures of data visualization since the publication of Visual Complexity (Lima 2011). In this conversation, Manuel Lima traces back the origin of data visualization to Ars Memorativa, an ancient mnemonic technique to organize information and facilitate its recall. Going back to the origins is an obsession that brought him to collect and arrange into books images of information design from both physical and digital archives. By doing this, Manuel Lima tackled issues related to the digital objects and their creation, use, and preservation, with a point of view capable of combining the passion for visualizing information and the profession of UX design. This conversation, which took place between Lisbon and Milan on Wednesday 28 July, 2021, comes from a blurb that Manuel Lima wrote for Mapping Affinities (Rodighiero 2021). The discussion is part of the project From Data to Wisdom, and is supported by Fundação para a Ciência e a Tecnologia through the grant POCI-01-0145-FEDER-029717, and the Swiss National Science Foundation through the grant 194442. This text, originally created for the forthcoming book From Data to Wisdom (Higuera Rubio et al. 2022), is published as a preview for Nightingale, the journal of the Data Visualization Society