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September 7, 2020 Devi Parikh, Larry Zitnick
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Exploring Crowd Co-creation Scenarios for Sketches

As a first step towards studying the ability of human crowds and machines to effectively co-create, we explore several human-only collaborative co-creation scenarios. The goal in each scenario is to create a digital sketch using a simple web interface.
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September 7, 2020 X. Alice Li, Devi Parikh
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Lemotif: An Affective Visual Journal Using Deep Neural Networks

We present Lemotif, an integrated natural language processing and image generation system that uses machine learning to (1) parse a text-based input journal entry describing the user’s day for salient themes and emotions and (2) visualize the detected themes and emotions in creative and appealing image motifs.
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September 7, 2020 Devi Parikh
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Predicting A Creator’s Preferences In, and From, Interactive Generative Art

We train machine learning models to predict a subset of preferences from the rest. We find that preferences in the generative art form cannot predict preferences in other walks of life better than chance (and vice versa). However, preferences within the generative art form are reliably predictive of each other.
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September 7, 2020 Gunjan Aggarwal, Devi Parikh
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Neuro-Symbolic Generative Art: A Preliminary Study

As a preliminary study, we train a generative deep neural network on samples from the symbolic approach. We demonstrate through human studies that subjects find the final artifacts and the creation process using our neurosymbolic approach to be more creative than the symbolic approach 61% and 82% of the time respectively.
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September 7, 2020 Purva Tendulkar, Abhishek Das, Aniruddha Kembhavi, Devi Parikh
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Feel The Music: Automatically Generating A Dance For An Input Song

We present a general computational approach that enables a machine to generate a dance for any input music. We encode intuitive, flexible heuristics for what a ‘good’ dance is: the structure of the dance should align with the structure of the music.
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August 17, 2020 Shangchen Han, Beibei Liu, Randi Cabezas, Christopher D. Twigg, Peizhao Zhang, Jeff Petkau, Tsz-Ho Yu, Chun-Jung Tai, Muzaffer Akbay, Zheng Wang, Asaf Nitzan, Gang Dong, Yuting Ye, Lingling Tao, Chengde Wan, Robert Wang
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MEgATrack: Monochrome Egocentric Articulated Hand-Tracking for Virtual Reality

We present a system for real-time hand-tracking to drive virtual and augmented reality (VR/AR) experiences. Using four fisheye monochrome cameras, our system generates accurate and low-jitter 3D hand motion across a large working volume for a diverse set of users.
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August 11, 2020 Eden Litt, Siyan Zhao, Robert Kraut, Moira Burke
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What Are Meaningful Social Interactions in Today’s Media Landscape? A Cross-Cultural Survey

As we increasingly integrate technology into our lives, we need a better framework for understanding social interactions across the communication landscape. Utilizing survey data in which more than 4,600 people across the United States, India, and Japan described a recent social interaction, this article qualitatively and quantitatively explores what makes an interaction meaningful.
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July 6, 2020 Steven A. Sumner, Moira Burke, Farshad Kooti
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Adherence to suicide reporting guidelines by news shared on a social networking platform

We conducted a study to analyze adherence to suicide-reporting guidelines on news shared on social media and to assess how adherence affects reader engagement.
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June 8, 2020 Shankar Iyer, Justin Cheng, Nick Brown, Xiuhua Wang
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When Does Trust in Online Social Groups Grow?

To better test the potential causal pathways between trust and behaviors or group properties, we paired a two-wave longitudinal survey of 2358 participants in Facebook Groups with logged activity on Facebook. Using latent change score modeling, we examined how trust may predict changes in behavior or group properties and how behaviors and group properties may predict changes in trust.
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June 2, 2020 Alexander Goettker, Kevin J. MacKenzie, T. Scott Murdison
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Differences between oculomotor and perceptual artifacts for temporally limited head mounted displays

We used perceptual and oculomotor measures to understand the negative impacts of low (phantom array) and high (motion blur) duty cycles with a high-speed, AR-likehead-mounted display prototype.
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