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The mobile Internet-accessing the Internet via a mobile device-has become quite popular recently. The mobile Internet is mainly different from the stationary Internet because it may be used in various contexts, whereas the stationary Internet is mostly used in pre-determined environments. However, it is unclear when the mobile Internet is used most frequently, and in what context it is most useful. A greater understanding of the contexts for using the mobile Internet will relieve usability problems that mobile Internet users often encounter.This paper proposes a conceptual framework of use contexts, which includes various facets of contexts related to the mobile Internet. It then presents the results of an exploratory study in which the use contexts for the mobile Internet and corresponding usability problems have been empirically monitored. The results indicate that use of the mobile Internet is heavily clustered around a few key contexts, rather than dispersed widely in diverse contexts. Moreover, different contexts are found to cause different kinds of usability problems. The paper ends with theoretical and practical implications of the study results.

Figure 1: Overview of SANVis. (A) The network view displays multiple attention patterns for each layer according to three type of visualization options: (A-1) the attention piling option, (A-2) the Sankey diagram option, and (A-3) the small multiples option. (A-4) The bar chart shows the average attention weights for all heads (each colored with its corresponding hue) per each layer. (B) The HeadLens view helps the user analyze what the attention head learned by showing representative words and by providing statistical information of part-of-speech tags and positions. ABSTRACTAttention networks, a deep neural network architecture inspired by humans' attention mechanism, have seen significant success in image captioning, machine translation, and many other applications. Recently, they have been further evolved into an advanced approach called multi-head self-attention networks, which can encode a set of input vectors, e.g., word vectors in a sentence, into another set of vectors. Such encoding aims at simultaneously capturing diverse syntactic and semantic features within a set, each of which corresponds to a particular attention head, forming altogether multi-head attention. Meanwhile, the increased model complexity prevents users from easily understanding and manipulating the inner workings of models. To tackle the challenges, we present a visual analytics system called SANVis, which helps users understand the behaviors and the characteristics of multi-head self-attention networks. Using a state-of-the-art self-attention model called Transformer, we demonstrate usage scenarios of SANVis in machine translation tasks. Our system is available at http://short.sanvis.org.

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