Characterizing HCI Research in China: Streams, Methodologies and Future Directions

This position paper takes the first step to attempt to present the initial characterization of HCI research in China. We discuss the current streams and methodologies of Chinese HCI research based on two well-known HCI theories: Micro/Marco-HCI and the Three Paradigms of HCI. We evaluate the discussion with a survey of Chinese publications at CHI 2019, which shows HCI research in China has less attention to Macro-HCI topics and the third paradigms of HCI (Phenomenologically situated Interaction). We then propose future HCI research directions such as paying more attention to Macro-HCI topics and third paradigm of HCI, combining research methodologies from multiple HCI paradigms, including emergent users who have less access to technology, and addressing the cultural dimensions in order to provide better technical solutions and support

Animal Exercise: A New Evaluation Method

At present, Animal Exercise courses rely too much on teachers’ subjective ideas in teaching methods and test scores, and there is no set of standards as a benchmark for reference. As a result, students guided by different teachers have an uneven understanding of the Animal Exercise and cannot achieve the expected effect of the course. In this regard, the authors propose a scoring system based on action similarity, which enables teachers to guide students more objectively. The authors created QMonkey, a data set based on the body keys of monkeys in the coco dataset format, which contains 1,428 consecutive images from eight videos. The authors use QMonkey to train a model that recognizes monkey body movements. And the authors propose a new non-standing posture normalization method for motion transfer between monkeys and humans. Finally, the authors utilize motion transfer and structural similarity contrast algorithms to provide a reliable evaluation method for animal exercise courses, eliminating the subjective influence of teachers on scoring and providing experience in the combination of artificial intelligence and drama education

Compulsive Smartphone Use: The Roles of Flow, Reinforcement Motives, and Convenience

Along with its rapid growth of penetration, smartphone has become highly prevalent in recent years. Meanwhile, compulsive smartphone use emerges as a rising concern. Given that research on compulsive smartphone use is scarce in the information systems literature, this paper aims to reveal its significant determinants to enrich the theoretical development in this area. In particular, we incorporate flow, reinforcement motives (i.e., instant gratification and mood regulation), and convenience in the research model to examine their influences on compulsive smartphone use. We conduct an empirical online survey with 384 valid responses to assess the model. The findings show that flow and reinforcement motives have direct and significant effects on compulsive use. Convenience affects compulsive use indirectly through flow, while flow further mediates the effects of reinforcement motives on compulsive use. Implications for both research and practice are offered

Learning to Collocate Visual-Linguistic Neural Modules for Image Captioning

Humans tend to decompose a sentence into different parts like \textsc{sth do sth at someplace} and then fill each part with certain content. Inspired by this, we follow the \textit{principle of modular design} to propose a novel image captioner: learning to Collocate Visual-Linguistic Neural Modules (CVLNM). Unlike the \re{widely used} neural module networks in VQA, where the language (\ie, question) is fully observable, \re{the task of collocating visual-linguistic modules is more challenging.} This is because the language is only partially observable, for which we need to dynamically collocate the modules during the process of image captioning. To sum up, we make the following technical contributions to design and train our CVLNM: 1) \textit{distinguishable module design} -- \re{four modules in the encoder} including one linguistic module for function words and three visual modules for different content words (\ie, noun, adjective, and verb) and another linguistic one in the decoder for commonsense reasoning, 2) a self-attention based \textit{module controller} for robustifying the visual reasoning, 3) a part-of-speech based \textit{syntax loss} imposed on the module controller for further regularizing the training of our CVLNM. Extensive experiments on the MS-COCO dataset show that our CVLNM is more effective, \eg, achieving a new state-of-the-art 129.5 CIDEr-D, and more robust, \eg, being less likely to overfit to dataset bias and suffering less when fewer training samples are available. Codes are available at \url{https://github.com/GCYZSL/CVLMN}Comment: Accepted to IJCV. Codes are available at https://github.com/GCYZSL/CVLM