Text chat applications are an integral part of daily social and professional communication. However, messages sent over text chat applications do not convey vocal or nonverbal information from the sender, and detecting the emotional tone in text-only messages is challenging. In this paper, we explore the effects of speech balloon shapes on the sender-receiver agreement regarding the emotionality of a text message. We first investigated the relationship between the shape of a speech balloon and the emotionality of speech text in Japanese manga. Based on these results, we created a system that automatically generates speech balloons matching linear emotional arousal intensity by Auxiliary Classifier Generative Adversarial Networks (ACGAN). Our evaluation results from a controlled experiment suggested that the use of emotional speech balloons outperforms the use of emoticons in decreasing the differences between message senders’ and receivers’ perceptions about the level of emotional arousal in text messages.

When searching for suitable fonts for a digital graphic, users usually start with an ambiguous thought. For example, they would look for fonts that are suitable for a personal web page or party invitations for children. Their design concept becomes clearer as they interact with external interventions such as exposure to suitable images for use in their web page or the children's preferences regarding the party. Hence, it is important to support users' interactions with unexpected but useful concepts during their search. In this paper, we present a novel framework that helps users to explore a font dataset using the multimodal method that provides unexpected but useful font images or concept words in response to the user's input. We collect a large font dataset and the associated tags and propose the use of unsupervised generative model that jointly learns the correlation between the visual features of a font and the associated tags for the creative process. By examining the results of the model that change with various inputs, we observed that the model produces highly promising results. In the experiment, we verified that the generated concepts by the model are not only new but also relevant to the user input that appears to be useful for inspiring users.

A typical tag/keyword-based search system retrieves documents where, given a query term q, the query term q occurs in the dataset. However, when applying these systems to a real-world font web community setting, practical challenges arise --- font tags are more subjective than other benchmark datasets, which magnify the tag mismatch problem. To address these challenges, we propose a tag dictionary space leveraged by word embedding, which relates undefined words that have a similar meaning. Even if a query is not defined in the tag dictionary, we can represent it as a vector on the tag dictionary space. The proposed system facilitates multi-modal inputs that can use both textual and image queries. By integrating a visual sentiment concept model that classifies affective concepts as adjective--noun pairs for a given image and uses it as a query, users can interact with the search system in a multi-modal way. We used crowd sourcing to collect user ratings for the retrieved fonts and observed that the retrieved font with the proposed methods obtained a higher score compared to other methods.

Instant messaging is a popular form of text-based communication. However, text-based messaging lacks the ability to communicate nonverbal information such as that conveyed through facial expressions and voice tones, although a multitude of emotions may underlie the text of a conversation between participants. In this paper, we propose an approach that uses typefaces to communicate emotions. We investigated which typefaces are useful for delivering emotions and introduced these typefaces into a mobile chat app. We conducted a survey to demonstrate how changes in the typeface of a message affected the meaning of the message conveyed. Our user study provides an understanding of the actual user experience with the application. The results show that the use of multiple typefaces in a message can affect and intensify the valence received by users and the use of multiple typefaces elicited an active response and brought about a livelier mood during texting.

One of the important aspects in graphic design is choosing the font of the caption that matches aesthetically the associated image. To obtain a good match, users would exhaustively examine a long font list requiring them a substantial effort. This paper presents FontMatcher, which supports users to design digital graphic works harmoniously pairing fonts with an image. The system provides three features, recommendation, explaination and feedback. If a warm feeling image is given as input, the system recommends warm feeling fonts, and then explains what is the distinguishing features of the recommendation, e.g. a cursive shape. Users can also provide feedback to find fonts which correspond to their intention. Our evaluation results show that the recommended fonts scored better than selected fonts by novices and provides competing results with the ones chosen by experienced graphic designers. The system also provides explanations that help increasing the reliability of the recommended results.

BACKGROUND: Bioinformatics research for finding biological mechanisms can be done by analysis of transcriptome data with pathway based interpretation. Therefore, researchers have tried to develop tools to analyze transcriptome data with pathway based interpretation. Over the years, the amount of omics data has become huge, e.g., TCGA, and the data types to be analyzed have come in many varieties, including mutations, copy number variations, and transcriptome. We also need to consider a complex relationship with regulators of genes, particularly Transcription Factors(TF). However, there has not been a system for pathway based exploration and analysis of TCGA multi-omics data. In this reason, We have developed a web based system BRCA-Pathway to fulfill the need for pathway based analysis of TCGA multi-omics data. RESULTS: BRCA-Pathway is a structured integration and visual exploration system of TCGA breast cancer data on KEGG pathways. For data integration, a relational database is designed and used to integrate multi-omics data of TCGA-BRCA, KEGG pathway data, Hallmark gene sets, transcription factors, driver genes, and PAM50 subtypes. For data exploration, multi-omics data such as SNV, CNV and gene expression can be visualized simultaneously in KEGG pathway maps, together with transcription factors-target genes (TF-TG) correlation and relationships among cancer driver genes. In addition, 'Pathways summary' and 'Oncoprint' with mutual exclusivity sort can be generated dynamically with a request by the user. Data in BRCA-Pathway can be downloaded by REST API for further analysis. CONCLUSIONS: BRCA-Pathway helps researchers navigate omics data towards potentially important genes, regulators, and discover complex patterns involving mutations, CNV, and gene expression data of various patient groups in the biological pathway context. In addition, mutually exclusive genomic alteration patterns in a specific pathway can be generated. BRCA-Pathway can provide an integrative perspective on the breast cancer omics data, which can help researchers discover new insights on the biological mechanisms of breast cancer.

Nowadays multi-track conferences impose great difficulty to attendees in making a day plan of attending relevant sessions/papers, because a large number of sessions are scheduled on a single day and many of them are at the same time. To address this problem, we introduce MatPlanner, a mobile application, which provides an alternate way of scheduling a day in a conference by understanding the attendee's interest and preferences. MatPlanner learns event-topic interest matrix from few sampled events selected by each user and then recommend relevant events and display the final output. More importantly, MatPlanner's interface enables users to resolve conflicting events by showing them at the same panel which makes it easy to compare. Interview results suggested a high level of satisfaction among participants in scheduling a day.