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1. From Child-Centered to Family-Centered Interaction Design

   https://doi.org/10.1145/3585088.3589930  

   Cagiltay, Bengisu; Ibtasar, Rabia; Michaelis, Joseph E; Sebo, Sarah; Mutlu, Bilge (June 2023, IDC '23: Proceedings of the 22nd Annual ACM Interaction Design and Children Conference)

   The goal of this workshop is to have interdisciplinary discussions on family-centered interaction design of technology as an extension to child-centered design. The workshop will discuss the potential benefits of a family-centered approach to design, as well as the challenges and open questions that designers may face when adopting this approach. Through discussions and interactive activities, participants will have the opportunity to discuss and share ideas on how to effectively incorporate a family-centered perspective into their own design processes. A family-centered approach to design has the potential to create more meaningful and contextual experiences for children and their families. 

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2. Operationalizing Human-Centered Perspectives in Explainable AI

   https://doi.org/10.1145/3411763.3441342  

   Ehsan, Upol; Wintersberger, Philipp; Liao, Q. Vera; Mara, Martina; Streit, Marc; Wachter, Sandra; Riener, Andreas; Riedl, Mark O. (January 2021, CHI EA '21: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing SystemsMay 2021)

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3. HUMAN-CENTERED EMOTION RECOGNITION IN ANIMATED GIFS

   Yang, Zhengyuan; Zhang, Yixuan; Luo, Jiebo (January 2019, Proceedings of IEEE International Conference on Multimedia and Expo)

   As an intuitive way of expression emotion, the animated Graphical Interchange Format (GIF) images have been widely used on social media. Most previous studies on automated GIF emotion recognition fail to effectively utilize GIF’s unique properties, and this potentially limits the recognition performance. In this study, we demonstrate the importance of human related information in GIFs and conduct humancentered GIF emotion recognition with a proposed Keypoint Attended Visual Attention Network (KAVAN). The framework consists of a facial attention module and a hierarchical segment temporal module. The facial attention module exploits the strong relationship between GIF contents and human characters, and extracts frame-level visual feature with a focus on human faces. The Hierarchical Segment LSTM (HSLSTM) module is then proposed to better learn global GIF representations. Our proposed framework outperforms the state-of-the-art on the MIT GIFGIF dataset. Furthermore, the facial attention module provides reliable facial region mask predictions, which improves the model’s interpretability. 

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4. Is CoMP Beneficial In User-Centered Wireless Networks?

   https://doi.org/10.1109/6GNet54646.2022.9830168  

   Kasi, Shahrukh Khan; Sajid Hashmi, Umair; Nabeel, Muhammad; Ekin, Sabit; Imran, Ali (July 2022, 2022 1st International Conference on 6G Networking (6GNet))
5. Unstable twin in body-centered cubic tungsten nanocrystals

   https://doi.org/10.1038/s41467-020-16349-8  

   Wang, Xiang; Wang, Jiangwei; He, Yang; Wang, Chongmin; Zhong, Li; Mao, Scott X. (December 2020, Nature Communications)