Accessible charts are part of the equation of accessible papers: a heuristic evaluation of the highest impact LIS Journals

Purpose Statistical charts are an essential source of information in academic papers. Charts have an important role in conveying, clarifying and simplifying the research results provided by the authors, but they present some accessibility barriers for people with low vision. This article aims to evaluate the accessibility of the statistical charts published in the library and information science (LIS) journals with the greatest impact factor. Design/methodology/approach A list of heuristic indicators developed by the authors has been used to assess the accessibility of statistical charts for people with low vision. The heuristics have been applied to a sample of charts from 2019 issues of ten LIS journals with the highest impact factor according to the ranking of the JCR. Findings The current practices of image submission do not follow the basic recommended guidelines on accessibility like color contrast or the use of textual alternatives. On the 2 other hand, some incongruities between the technical suggestions of image submission and their application in analyzed charts also emerged. The main problems identified are: poor text alternatives, insufficient contrast ratio between adjacent colors, and the inexistence of customization options. Authoring tools do not help authors to fulfill these requirements. Research limitations The sample is not very extensive; nonetheless, it is representative of common practices and the most frequent accessibility problems in this context. Social implications The heuristics proposed are a good starting point to generate guidelines for authors when preparing their papers for publication and to guide journal publishers in creating accessible documents. Low vision users, a highly prevalent condition, will benefit from the improvements. Originality/value The results of this research provide key insights into low vision accessibility barriers, not considered in previous literature and can be a starting point for their solution.This research has been done in the framework of the PhD Programme in Engineering and Information Technology of the Universitat de Lleida (UdL). This work has been partially supported by the Spanish project PID2019-105093GB-I00 (MINECO/FEDER, UE) and CERCA Programme/Generalitat de Catalunya

Building Machines That Learn and Think Like People

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar

Web Search, Web Tutorials & Software Applications: Characterizing and Supporting the Coordinated Use of Online Resources for Performing Work in Feature-Rich Software

Web search and other online resources serve an integral role in how people learn and use feature-rich software (e.g., Adobe Photoshop) on a daily basis. Users depend on web resources both as a first line of technical support, and as a means for coping with system complexity. For example, people rely on web resources to learn new tasks, to troubleshoot problems, or to remind themselves of key task details. When users rely on web resources to support their work, their interactions are distributed over three user environments: (1) the search engine, (2) retrieved documents, and (3) the application's user interface. As users interact with these environments, their actions generate a rich set of signals that characterize how the population thinks about and uses software systems "in the wild," on a day-to-day basis. This dissertation presents three works that successively connect and associate signals and artifacts across these environments, thereby generating novel insights about users and their tasks, and enabling powerful new end-user tools and services. These three projects are as follows: Characterizing usability through search (CUTS): The CUTS system demonstrates that aggregate logs of web search queries can be leveraged to identify common tasks and potential usability problems faced by the users of any publicly available interactive system. For example, in 2011 I examined query data for the Firefox web browser. Automated analysis uncovered approximately 150 variations of the query "Firefox how to get the menu bar back", with queries issued once every 32 minutes on average. Notably, this analysis did not depend on direct access to query logs. Instead, query suggestions services and online advertising valuations were leveraged to approximate aggregate query data. Nevertheless, these data proved to be timely, to have a high degree of ecological validity, and to be arguably less prone to self-selection bias than data gathered via traditional usability methods. Query-feature graphs (QF-Graphs): Query-feature graphs are structures that map high-level descriptions of a user's goals to the specific features and commands relevant to achieving those goals in software. QF-graphs address an important instance of the more general vocabulary mismatch problem. For example, users of the GIMP photo manipulation software often want to "make a picture black and white", and fail to recognize the relevance of the applicable commands, which include: "desaturate", and "channel mixer". The key insights for building QF-graphs are that: (1) queries concisely express the user's goal in the user's own words, and (2) retrieved tutorials likely include both query terms, as well as terminology from the application's interface (e.g., the names of commands). QF-graphs are generated by mining these co-occurrences across thousands of query-tutorial pairings. InterTwine: InterTwine explores interaction possibilities that arise when software applications, web search, and online support materials are directly integrated into a single productivity system. With InterTwine, actions in the web browser directly impact how information is presented in a software application, and vice versa. For example, when a user opens a web tutorial in their browser, the application's menus and tooltips are updated to highlight the commands mentioned therein. These embellishments are designed to help users orient themselves after switching between the web browser and the application. InterTwine also augments web search results to include details of past application use. Search snippets gain before and after pictures and other metadata detailing how the user's personal work document evolved the last time they visited the page. This feature was motivated by the observation that existing mechanisms (e.g., highlighting visited links) are often insufficient for recalling which resources were previously helpful vs. unhelpful for accomplishing a task. Finally, the dissertation concludes with a discussion of the advantages, limitations and challenges of this research, and presents an outline for future work

Gráficos estadísticos accesibles para personas con baja visión: desarrollo de una metodología para su evaluación heurística

Statistical charts have a fundamental role in the transmission and simplification of information, to the point that they can be considered in themselves an assistive technology, a type of cognitive accessibility through which we can use our visual system to reduce the effort that involves interpreting tabular data. For this reason, it is important that people with disabilities can access statistical charts on equal terms. Among visual disabilities, low vision stands out due to its high prevalence. Low vision is a visual condition that implies a substantial reduction in the sense of sight that cannot be corrected with corrective lenses, pharmacotherapy, or surgery, and that significantly impacts the daily activities of people who present it. The main objective of this thesis is to identify good and bad practices, as well as to systematize elements and characteristics that can facilitate or hinder the access of people with low vision to statistical charts. From this analysis, a list of heuristic indicators is developed for the evaluation of these elements. The list of indicators was validated through its application in three studies: charts from national and international digital newspapers, library and information science journals, and a final set of charts published by governments and non-governmental organizations in the context of the communication of the pandemic caused by Covid-19

Image Retrieval within Augmented Reality

Die vorliegende Arbeit untersucht das Potenzial von Augmented Reality zur Verbesserung von Image Retrieval Prozessen. Herausforderungen in Design und Gebrauchstauglichkeit wurden für beide Forschungsbereiche dargelegt und genutzt, um Designziele für Konzepte zu entwerfen. Eine Taxonomie für Image Retrieval in Augmented Reality wurde basierend auf der Forschungsarbeit entworfen und eingesetzt, um verwandte Arbeiten und generelle Ideen für Interaktionsmöglichkeiten zu strukturieren. Basierend auf der Taxonomie wurden Anwendungsszenarien als weitere Anforderungen für Konzepte formuliert. Mit Hilfe der generellen Ideen und Anforderungen wurden zwei umfassende Konzepte für Image Retrieval in Augmented Reality ausgearbeitet. Eins der Konzepte wurde auf einer Microsoft HoloLens umgesetzt und in einer Nutzerstudie evaluiert. Die Studie zeigt, dass das Konzept grundsätzlich positiv aufgenommen wurde und bietet Erkenntnisse über unterschiedliches Verhalten im Raum und verschiedene Suchstrategien bei der Durchführung von Image Retrieval in der erweiterten Realität.:1 Introduction 1.1 Motivation and Problem Statement 1.1.1 Augmented Reality and Head-Mounted Displays 1.1.2 Image Retrieval 1.1.3 Image Retrieval within Augmented Reality 1.2 Thesis Structure 2 Foundations of Image Retrieval and Augmented Reality 2.1 Foundations of Image Retrieval 2.1.1 Definition of Image Retrieval 2.1.2 Classification of Image Retrieval Systems 2.1.3 Design and Usability in Image Retrieval 2.2 Foundations of Augmented Reality 2.2.1 Definition of Augmented Reality 2.2.2 Augmented Reality Design and Usability 2.3 Taxonomy for Image Retrieval within Augmented Reality 2.3.1 Session Parameters 2.3.2 Interaction Process 2.3.3 Summary of the Taxonomy 3 Concepts for Image Retrieval within Augmented Reality 3.1 Related Work 3.1.1 Natural Query Specification 3.1.2 Situated Result Visualization 3.1.3 3D Result Interaction 3.1.4 Summary of Related Work 3.2 Basic Interaction Concepts for Image Retrieval in Augmented Reality 3.2.1 Natural Query Specification 3.2.2 Situated Result Visualization 3.2.3 3D Result Interaction 3.3 Requirements for Comprehensive Concepts 3.3.1 Design Goals 3.3.2 Application Scenarios 3.4 Comprehensive Concepts 3.4.1 Tangible Query Workbench 3.4.2 Situated Photograph Queries 3.4.3 Conformance of Concept Requirements 4 Prototypic Implementation of Situated Photograph Queries 4.1 Implementation Design 4.1.1 Implementation Process 4.1.2 Structure of the Implementation 4.2 Developer and User Manual 4.2.1 Setup of the Prototype 4.2.2 Usage of the Prototype 4.3 Discussion of the Prototype 5 Evaluation of Prototype and Concept by User Study 5.1 Design of the User Study 5.1.1 Usability Testing 5.1.2 Questionnaire 5.2 Results 5.2.1 Logging of User Behavior 5.2.2 Rating through Likert Scales 5.2.3 Free Text Answers and Remarks during the Study 5.2.4 Observations during the Study 5.2.5 Discussion of Results 6 Conclusion 6.1 Summary of the Present Work 6.2 Outlook on Further WorkThe present work investigates the potential of augmented reality for improving the image retrieval process. Design and usability challenges were identified for both fields of research in order to formulate design goals for the development of concepts. A taxonomy for image retrieval within augmented reality was elaborated based on research work and used to structure related work and basic ideas for interaction. Based on the taxonomy, application scenarios were formulated as further requirements for concepts. Using the basic interaction ideas and the requirements, two comprehensive concepts for image retrieval within augmented reality were elaborated. One of the concepts was implemented using a Microsoft HoloLens and evaluated in a user study. The study showed that the concept was rated generally positive by the users and provided insight in different spatial behavior and search strategies when practicing image retrieval in augmented reality.:1 Introduction 1.1 Motivation and Problem Statement 1.1.1 Augmented Reality and Head-Mounted Displays 1.1.2 Image Retrieval 1.1.3 Image Retrieval within Augmented Reality 1.2 Thesis Structure 2 Foundations of Image Retrieval and Augmented Reality 2.1 Foundations of Image Retrieval 2.1.1 Definition of Image Retrieval 2.1.2 Classification of Image Retrieval Systems 2.1.3 Design and Usability in Image Retrieval 2.2 Foundations of Augmented Reality 2.2.1 Definition of Augmented Reality 2.2.2 Augmented Reality Design and Usability 2.3 Taxonomy for Image Retrieval within Augmented Reality 2.3.1 Session Parameters 2.3.2 Interaction Process 2.3.3 Summary of the Taxonomy 3 Concepts for Image Retrieval within Augmented Reality 3.1 Related Work 3.1.1 Natural Query Specification 3.1.2 Situated Result Visualization 3.1.3 3D Result Interaction 3.1.4 Summary of Related Work 3.2 Basic Interaction Concepts for Image Retrieval in Augmented Reality 3.2.1 Natural Query Specification 3.2.2 Situated Result Visualization 3.2.3 3D Result Interaction 3.3 Requirements for Comprehensive Concepts 3.3.1 Design Goals 3.3.2 Application Scenarios 3.4 Comprehensive Concepts 3.4.1 Tangible Query Workbench 3.4.2 Situated Photograph Queries 3.4.3 Conformance of Concept Requirements 4 Prototypic Implementation of Situated Photograph Queries 4.1 Implementation Design 4.1.1 Implementation Process 4.1.2 Structure of the Implementation 4.2 Developer and User Manual 4.2.1 Setup of the Prototype 4.2.2 Usage of the Prototype 4.3 Discussion of the Prototype 5 Evaluation of Prototype and Concept by User Study 5.1 Design of the User Study 5.1.1 Usability Testing 5.1.2 Questionnaire 5.2 Results 5.2.1 Logging of User Behavior 5.2.2 Rating through Likert Scales 5.2.3 Free Text Answers and Remarks during the Study 5.2.4 Observations during the Study 5.2.5 Discussion of Results 6 Conclusion 6.1 Summary of the Present Work 6.2 Outlook on Further Wor

Thinking with data visualisations: cognitive processing and spatial inferences when communicating climate change

Data visualisations can be effective for communicating scientific data, but only if they are understood. Such visualisations (i.e. scientific figures) are used within assessment reports produced by the Intergovernmental Panel on Climate Change (IPCC). However, IPCC figures have been criticised for being inaccessible to non-experts. This thesis presents a thematic analysis of interviews with IPCC authors, finding that a requirement to uphold scientific accuracy results in complex figures that are difficult for non-experts to comprehend, and which therefore require expert explanation. Evidence is subsequently presented showing that figures with greater visual complexity are associated with greater perceived comprehension difficulty among non-experts. Comprehension of complex data visualisations may require readers to make spatial inferences. When interpreting a time-series graph of climate data, it was found that non-experts did not always readily identify the long-term trend. Two experiments then show that linguistic information in the form of warnings can support spatial representations for trends in memory by directing visual attention during encoding (measured using eyetracking). This thesis also considers spatial inferences when forming expectations about future data, finding that expectations were sensitive to patterns in past data. Further, features that act on bottom-up perceptual processes were largely ineffective in supporting spatial inferences. Conversely, replacing spatial inferences by explicitly representing information moderated future expectations. However, replacing spatial inferences might not always be desirable in real-world contexts. The evidence indicates that when information is not explicitly represented in a data visualisation, providing top-down knowledge may be more effective in supporting spatial inferences than providing visual cues acting on bottom-up perceptual processes. This thesis further provides evidence-based guidelines drawn from the cognitive and psychological sciences to support climate change researchers in enhancing the ease of comprehension of their data visualisations, and so enable future IPCC outputs to be more accessible

Privacy Intelligence: A Survey on Image Sharing on Online Social Networks

Image sharing on online social networks (OSNs) has become an indispensable part of daily social activities, but it has also led to an increased risk of privacy invasion. The recent image leaks from popular OSN services and the abuse of personal photos using advanced algorithms (e.g. DeepFake) have prompted the public to rethink individual privacy needs when sharing images on OSNs. However, OSN image sharing itself is relatively complicated, and systems currently in place to manage privacy in practice are labor-intensive yet fail to provide personalized, accurate and flexible privacy protection. As a result, an more intelligent environment for privacy-friendly OSN image sharing is in demand. To fill the gap, we contribute a systematic survey of 'privacy intelligence' solutions that target modern privacy issues related to OSN image sharing. Specifically, we present a high-level analysis framework based on the entire lifecycle of OSN image sharing to address the various privacy issues and solutions facing this interdisciplinary field. The framework is divided into three main stages: local management, online management and social experience. At each stage, we identify typical sharing-related user behaviors, the privacy issues generated by those behaviors, and review representative intelligent solutions. The resulting analysis describes an intelligent privacy-enhancing chain for closed-loop privacy management. We also discuss the challenges and future directions existing at each stage, as well as in publicly available datasets.Comment: 32 pages, 9 figures. Under revie