8,681 research outputs found

    Learning Fashion Compatibility with Bidirectional LSTMs

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    The ubiquity of online fashion shopping demands effective recommendation services for customers. In this paper, we study two types of fashion recommendation: (i) suggesting an item that matches existing components in a set to form a stylish outfit (a collection of fashion items), and (ii) generating an outfit with multimodal (images/text) specifications from a user. To this end, we propose to jointly learn a visual-semantic embedding and the compatibility relationships among fashion items in an end-to-end fashion. More specifically, we consider a fashion outfit to be a sequence (usually from top to bottom and then accessories) and each item in the outfit as a time step. Given the fashion items in an outfit, we train a bidirectional LSTM (Bi-LSTM) model to sequentially predict the next item conditioned on previous ones to learn their compatibility relationships. Further, we learn a visual-semantic space by regressing image features to their semantic representations aiming to inject attribute and category information as a regularization for training the LSTM. The trained network can not only perform the aforementioned recommendations effectively but also predict the compatibility of a given outfit. We conduct extensive experiments on our newly collected Polyvore dataset, and the results provide strong qualitative and quantitative evidence that our framework outperforms alternative methods.Comment: ACM MM 1

    Natural Notation for the Domestic Internet of Things

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    This study explores the use of natural language to give instructions that might be interpreted by Internet of Things (IoT) devices in a domestic `smart home' environment. We start from the proposition that reminders can be considered as a type of end-user programming, in which the executed actions might be performed either by an automated agent or by the author of the reminder. We conducted an experiment in which people wrote sticky notes specifying future actions in their home. In different conditions, these notes were addressed to themselves, to others, or to a computer agent.We analyse the linguistic features and strategies that are used to achieve these tasks, including the use of graphical resources as an informal visual language. The findings provide a basis for design guidance related to end-user development for the Internet of Things.Comment: Proceedings of the 5th International symposium on End-User Development (IS-EUD), Madrid, Spain, May, 201

    ‘My favourite things to do’ and ‘my favourite people’: Exploring salient aspects of children’s self-concept

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    This study explores the potential of the ‘draw-and-write’ method for inviting children to communicate salient aspects of their self-concept. Irish primary school children aged 10–13 years drew and wrote about their favourite people and things to do (social and active self). Children drew and described many salient activities (39 in total) and people – including pets. Results suggest that widely used, adult-constructed self-esteem scales for children, while multidimensional, are limited, and that ‘draw-and-write’ is an effective multimodal method with which children can express their social and active self-concepts

    Everyday writing in Graeco-Roman and late antique Egypt : outline of a new research programme

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    In October 2017, the European Research Council awarded a Starting Grant to Klaas Bentein for his project EVWRIT: Everyday writing in Graeco-Roman and Late Antique Egypt: A socio-semiotic study of communicative variation. In what follows, the research goals, methodology, and corpus of this new project are briefly outlined

    Speech-driven Animation with Meaningful Behaviors

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    Conversational agents (CAs) play an important role in human computer interaction. Creating believable movements for CAs is challenging, since the movements have to be meaningful and natural, reflecting the coupling between gestures and speech. Studies in the past have mainly relied on rule-based or data-driven approaches. Rule-based methods focus on creating meaningful behaviors conveying the underlying message, but the gestures cannot be easily synchronized with speech. Data-driven approaches, especially speech-driven models, can capture the relationship between speech and gestures. However, they create behaviors disregarding the meaning of the message. This study proposes to bridge the gap between these two approaches overcoming their limitations. The approach builds a dynamic Bayesian network (DBN), where a discrete variable is added to constrain the behaviors on the underlying constraint. The study implements and evaluates the approach with two constraints: discourse functions and prototypical behaviors. By constraining on the discourse functions (e.g., questions), the model learns the characteristic behaviors associated with a given discourse class learning the rules from the data. By constraining on prototypical behaviors (e.g., head nods), the approach can be embedded in a rule-based system as a behavior realizer creating trajectories that are timely synchronized with speech. The study proposes a DBN structure and a training approach that (1) models the cause-effect relationship between the constraint and the gestures, (2) initializes the state configuration models increasing the range of the generated behaviors, and (3) captures the differences in the behaviors across constraints by enforcing sparse transitions between shared and exclusive states per constraint. Objective and subjective evaluations demonstrate the benefits of the proposed approach over an unconstrained model.Comment: 13 pages, 12 figures, 5 table

    Using Sound to Enhance Users’ Experiences of Mobile Applications

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    The latest smartphones with GPS, electronic compass, directional audio, touch screens etc. hold potentials for location based services that are easier to use compared to traditional tools. Rather than interpreting maps, users may focus on their activities and the environment around them. Interfaces may be designed that let users search for information by simply pointing in a direction. Database queries can be created from GPS location and compass direction data. Users can get guidance to locations through pointing gestures, spatial sound and simple graphics. This article describes two studies testing prototypic applications with multimodal user interfaces built on spatial audio, graphics and text. Tests show that users appreciated the applications for their ease of use, for being fun and effective to use and for allowing users to interact directly with the environment rather than with abstractions of the same. The multimodal user interfaces contributed significantly to the overall user experience

    Taking It to the Street: How Roadway Design Helped Shape a Neighborhood's Development

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    Summarizes lessons learned during the process of planning the redesign of one of the region's most historic corridors in Minneapolis -- Lake Street -- and illustrates how roadway design can help shape a neighborhood's development
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