Academic competitions

Academic challenges comprise effective means for (i) advancing the state of the art, (ii) putting in the spotlight of a scientific community specific topics and problems, as well as (iii) closing the gap for under represented communities in terms of accessing and participating in the shaping of research fields. Competitions can be traced back for centuries and their achievements have had great influence in our modern world. Recently, they (re)gained popularity, with the overwhelming amounts of data that is being generated in different domains, as well as the need of pushing the barriers of existing methods, and available tools to handle such data. This chapter provides a survey of academic challenges in the context of machine learning and related fields. We review the most influential competitions in the last few years and analyze challenges per area of knowledge. The aims of scientific challenges, their goals, major achievements and expectations for the next few years are reviewed

Automatic generation of natural language descriptions of visual data: describing images and videos using recurrent and self-attentive models

Humans are faced with a constant flow of visual stimuli, e.g., from the environment or when looking at social media. In contrast, visually-impaired people are often incapable to perceive and process this advantageous and beneficial information that could help maneuver them through everyday situations and activities. However, audible feedback such as natural language can give them the ability to better be aware of their surroundings, thus enabling them to autonomously master everyday's challenges. One possibility to create audible feedback is to produce natural language descriptions for visual data such as still images and then read this text to the person. Moreover, textual descriptions for images can be further utilized for text analysis (e.g., sentiment analysis) and information aggregation. In this work, we investigate different approaches and techniques for the automatic generation of natural language of visual data such as still images and video clips. In particular, we look at language models that generate textual descriptions with recurrent neural networks: First, we present a model that allows to generate image captions for scenes that depict interactions between humans and branded products. Thereby, we focus on the correct identification of the brand name in a multi-task training setting and present two new metrics that allow us to evaluate this requirement. Second, we explore the automatic answering of questions posed for an image. In fact, we propose a model that generates answers from scratch instead of predicting an answer from a limited set of possible answers. In comparison to related works, we are therefore able to generate rare answers, which are not contained in the pool of frequent answers. Third, we review the automatic generation of doctors' reports for chest X-ray images. That is, we introduce a model that can cope with a dataset bias of medical datasets (i.e., abnormal cases are very rare) and generates reports with a hierarchical recurrent model. We also investigate the correlation between the distinctiveness of the report and the score in traditional metrics and find a discrepancy between good scores and accurate reports. Then, we examine self-attentive language models that improve computational efficiency and performance over the recurrent models. Specifically, we utilize the Transformer architecture. First, we expand the automatic description generation to the domain of videos where we present a video-to-text (VTT) model that can easily synchronize audio-visual features. With an extensive experimental exploration, we verify the effectiveness of our video-to-text translation pipeline. Finally, we revisit our recurrent models with this self-attentive approach

Evaluating Information Retrieval and Access Tasks

This open access book summarizes the first two decades of the NII Testbeds and Community for Information access Research (NTCIR). NTCIR is a series of evaluation forums run by a global team of researchers and hosted by the National Institute of Informatics (NII), Japan. The book is unique in that it discusses not just what was done at NTCIR, but also how it was done and the impact it has achieved. For example, in some chapters the reader sees the early seeds of what eventually grew to be the search engines that provide access to content on the World Wide Web, today’s smartphones that can tailor what they show to the needs of their owners, and the smart speakers that enrich our lives at home and on the move. We also get glimpses into how new search engines can be built for mathematical formulae, or for the digital record of a lived human life. Key to the success of the NTCIR endeavor was early recognition that information access research is an empirical discipline and that evaluation therefore lay at the core of the enterprise. Evaluation is thus at the heart of each chapter in this book. They show, for example, how the recognition that some documents are more important than others has shaped thinking about evaluation design. The thirty-three contributors to this volume speak for the many hundreds of researchers from dozens of countries around the world who together shaped NTCIR as organizers and participants. This book is suitable for researchers, practitioners, and students—anyone who wants to learn about past and present evaluation efforts in information retrieval, information access, and natural language processing, as well as those who want to participate in an evaluation task or even to design and organize one

Interdisciplinarity in the Age of the Triple Helix: a Film Practitioner's Perspective

This integrative chapter contextualises my research including articles I have published as well as one of the creative artefacts developed from it, the feature film The Knife That Killed Me. I review my work considering the ways in which technology, industry methods and academic practice have evolved as well as how attitudes to interdisciplinarity have changed, linking these to Etzkowitz and Leydesdorff’s ‘Triple Helix’ model (1995). I explore my own experiences and observations of opportunities and challenges that have been posed by the intersection of different stakeholder needs and expectations, both from industry and academic perspectives, and argue that my work provides novel examples of the applicability of the ‘Triple Helix’ to the creative industries. The chapter concludes with a reflection on the evolution and direction of my work, the relevance of the ‘Triple Helix’ to creative practice, and ways in which this relationship could be investigated further