Browsing by Author "Wang, Marx"

Now showing 1 - 4 of 4
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    Echofluid: An Interface for Remote Choreography Learning and Co-creation Using Machine Learning Techniques
    Wang, Marx; Duer, Zachary; Hardwig, Scotty; Lally, Sam; Ricard, Alayna; Jeon, Myounghoon (ACM, 2022-10-29)
    Born from physical activities, dance carries beyond mere body movement. Choreographers interact with audiences’ perceptions through the kinaesthetics, creativity, and expressivity of whole-body performance, inviting them to construct experience, emotion, culture, and meaning together. Computational choreography support can bring endless possibilities into this one of the most experiential and creative artistic forms. While various interactive and motion technologies have been developed and adopted to support creative choreographic processes, little work has been done in exploring incorporating machine learning in a choreographic system, and few remote dance teaching systems in particular have been suggested. In this exploratory work, we proposed Echofuid-a novel AI-based choreographic learning and support system that allows student dancers to compose their own AI models for learning, evaluation, exploration, and creation. In this poster, we present the design, development and ongoing validation process of Echofluid, and discuss the possibilities of applying machine learning in collaborative art and dance as well as the opportunities of augmenting interactive experiences between the performers and audiences with emerging technologies.
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    iThem: Programming Internet of Things Beyond Trigger-Action Pattern
    Wang, Marx; Manesh, Daniel; Hu, Ruipu; Lee, Sang Won (ACM, 2022-10-29)
    With emerging technologies bringing Internet of Things (IoT) devices into domestic environments, trigger-action programming such as IFTTT with its simple if-this-then-that pattern provides an efective way for end-users to connect fragmented intelligent services and program their own smart home/work space automation. While the simplicity of trigger-action programming can be efective for non-programmers with its straightforward concepts and graphical user interface, it does not allow the algorithmic expressivity that a programming language has. For instance, the simple if-this-thenthat structure cannot cover complex algorithms that arise from real world scenarios involving multiple conditions or keeping track of a sequence of conditions (e.g., incrementing counters, triggering one action if two conditions are both true). In this exploratory work, we take an alternative approach by creating a programmable channel between application programming interfaces (APIs), which allows programmers to preserve states and to use them to write complex algorithms. We propose iThem, which stands for intelligence of them—internet of things, that allow programmers to author any complex algorithms that can connect diferent IoT services and fully unleash the freedom of a general programming language. In this poster, we share the design, development, and ongoing validation progress of iThem, which piggybacks on existing programmable IoT system IFTTT, and which allows for a programmable channel that connects triggers and actions in IFTTT with versatility.
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    TaskScape: Fostering Holistic View on To-do List With Tracking Plan and Emotion
    Wang, Marx; Lee, Sang Won (ACM, 2022-10-29)
    Despite advancements with intelligence and connectivity in the workspace, productivity tools, such as to-do list applications, still, measure workers’ performance by a binary state—completed, yet completed, and thus the number of tasks completed. Such quantitative measurements can often overlook human values and individual well-being. While concepts such as positive computing and digital well-being are on the rise in the HCI community, few systems have been proposed to efectively integrate holistic considerations for mental and emotional well-being into productivity tools. In this work, we depart from the classic task list management tool and explore the construction of well-being-centered to-do list software. We propose a task management system–TaskScape—, which allow users to have awareness on the following two aspects: (1) how they plan and complete tasks and (2) how they feel towards their work. With the proposed system, we will investigate if having holistic view on their tasks can facilitate refection on what they work on, how they stick to their plans, and how their tasks portfolio support their emotional well-being, nudging users to refect upon their work, planning performance, and their emotional values towards their work. In this poster, we share the design, development, and ongoing validation progress of TaskScape, which is aimed to nudge workers to holistically view work productivity, reminding users that work is more than just work but life.
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    Understanding the Relationship Between Social Identity and Self-Expression Through Animated GIFs on Social Media
    Wang, Marx; Bhuiyan, Md Momen; Rho, Eugenia; Luther, Kurt; Lee, Sang Won (ACM, 2024-04-23)
    GIFs afford a high degree of personalization, as they are often created from popular movie and video clips with diverse and realistic characters, each expressing a nuanced emotional state through a combination of characters' own unique bodily gestures and distinctive visual backgrounds. These properties of high personalization and embodiment provide a unique window for exploring how individuals represent and express themselves on social media through the lens of the GIFs they use. In this study, we explore how Twitter users express their gender and racial identities through characters in GIFs. We conducted a behavioral study ($n=398$) to simulate a series of tweeting and GIF-picking scenarios. We annotated the gender and race identities of GIF characters, and we found that gender and race identities have significant impacts on users' GIF choices: men chose more gender-matching GIFs than women, and White participants chose more race-matching GIFs than Black participants. We also found that users' prior familiarity with the source of a GIF and perceptions about the composition of the audience (viz., having a matching identity) have significant effects on whether a user will choose race- and gender-matching GIFs. This work has implications for practitioners supporting personalized social identity construction and impression management mechanisms online.