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1. VIRD: Immersive Match Video Analysis for High-Performance Badminton Coaching

   Lin, T.; Aouididi, A.; Chen, Z.; Beyer, J.; Pfister, H.; Wang, J. (January 2023, IEEE transactions on visualization and computer graphics)

   Badminton is a fast-paced sport that requires a strategic combination of spatial, temporal, and technical tactics. To gain a competitive edge at high-level competitions, badminton professionals frequently analyze match videos to gain insights and develop game strategies. However, the current process for analyzing matches is time-consuming and relies heavily on manual note-taking, due to the lack of automatic data collection and appropriate visualization tools. As a result, there is a gap in effectively analyzing matches and communicating insights among badminton coaches and players. This work proposes an end-to-end immersive match analysis pipeline designed in close collaboration with badminton professionals, including Olympic and national coaches and players. We present VIRD, a VR Bird (i.e., shuttle) immersive analysis tool, that supports interactive badminton game analysis in an immersive environment based on 3D reconstructed game views of the match video. We propose a top-down analytic workflow that allows users to seamlessly move from a high-level match overview to a detailed game view of individual rallies and shots, using situated 3D visualizations and video. We collect 3D spatial and dynamic shot data and player poses with computer vision models and visualize them in VR. Through immersive visualizations, coaches can interactively analyze situated spatial data (player positions, poses, and shot trajectories) with flexible viewpoints while navigating between shots and rallies effectively with embodied interaction. We evaluated the usefulness of VIRD with Olympic and national-level coaches and players in real matches. Results show that immersive analytics supports effective badminton match analysis with reduced context-switching costs and enhances spatial understanding with a high sense of presence. 

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2. Playing to Wait: A Taxonomy of Idle Games

   https://doi.org/10.1145/3173574.3174195  

   Alharthi, Sultan A.; Alsaedi, Olaa; Toups Dugas, Phoebe O.; Tanenbaum, Theresa Jean; Hammer, Jessica (January 2018, Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems)

   Idle games are a recent minimalist gaming phenomenon in which the game is left running with little player interaction. We deepen understanding of idle games and their characteristics by developing a taxonomy and identifying game features. This paper examines 66 idle games using a grounded theory approach to analyze play, game mechanics, rewards, interactivity, progress rate, and user interface. To establish a clearly bounded definition of idle games, we analyzed 10 non-idle games with the same approach. We discuss how idle games move players from playing to planning, how they question dominant assumptions about gameplay, and their unusual use of resources such as player attention and computer cycles. Our work illuminates opportunities for the design of idle games, suggests design implications, and provides a framework for researchers to clearly articulate questions about this genre. 

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3. Differential Game Theoretic Models for Designing Water Conservation Incentives

   https://doi.org/10.1287/deca.2024.0208  

   Momeni, Behnam; Mohebbi, Shima (June 2025, Decision Analysis)

   The growing concern over water scarcity and the management of freshwater is common worldwide. Voluntary incentives, such as payments offered to water users, are recognized as a strategy for reducing conflicts and water consumption. A primary challenge lies in determining the allocation of these incentives among water users, taking into account socio-environmental factors. This study leverages differential game theoretic models to design incentive schemes for the network of water users, geographically distributed across a river network. Two networks of water users are considered: those who utilize groundwater and those who rely on surface water for agricultural purposes. A nongovernmental organization (NGO) participates as another key player, providing conservation incentives to encourage water users to reduce their consumption. The proposed model considers both vertical and horizontal interactions within the network of players. By considering the unique characteristics of each water user, the NGO aims to introduce an incentive scheme designed for each water user. Addressing the challenge of model scalability becomes crucial as the number of players or water users within each network increases, in order to identify optimal decisions. Thus, we propose solution methods for both convex and nonconvex decision problems using Karush-Kuhn-Tucker conditions, Value Iteration, and Basis Function Approximation methods. Finally, we perform parametric analyses to examine how parameters influence the choice of solution methods and affect the decision-making processes. Funding: Financial support from the U.S. National Science Foundation [Grant 2108003] is gratefully acknowledged. 

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4. A Taxonomy of Minecraft Activities for STEM

   Lane, H. Chad; Yi, Sherry; Guerrero, Brian; Comins, Neil F. (December 2017, 25th International Conference on Computers in Education Proceedings)

   This paper addresses the relationship between one of the most popular video games in history (Minecraft) and STEM education. It describes a taxonomy of STEM-relevant Minecraft activities (e.g., designing and building automated farms) and their relationship to a wide range of STEM disciplines as defined by Classification of Instructional Programs (CIP; a product of the US Department of Education). Based on interviews with expert Minecraft players, academic research that analyzes Minecraft's educational uses, existing game documentation, and feedback from STEM experts, the taxonomy provides the foundation for better understanding how playing the game may inform the development of STEM interest, and how educators may best leverage those connections. 

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5. POI Recommendation: A Temporal Matching between POI Popularity and User Regularity

   https://doi.org/10.1109/ICDM.2016.0066  

   Yao, Zijun; Fu, Yanjie; Liu, Bin; Liu, Yanchi; Xiong, Hui (December 2016, 2016 IEEE 16th International Conference on Data Mining)

   Point of interest (POI) recommendation, which provides personalized recommendation of places to mobile users, is an important task in location-based social networks (LBSNs). However, quite different from traditional interest-oriented merchandise recommendation, POI recommendation is more complex due to the timing effects: we need to examine whether the POI fits a user’s availability. While there are some prior studies which included the temporal effect into POI recommendations, they overlooked the compatibility between time-varying popularity of POIs and regular availability of users, which we believe has a non-negligible impact on user decision-making. To this end, in this paper, we present a novel method which incorporates the degree of temporal matching between users and POIs into personalized POI recommendations. Specifically, we first profile the temporal popularity of POIs to show when a POI is popular for visit by mining the spatio-temporal human mobility and POI category data. Secondly, we propose latent user regularities to characterize when a user is regularly available for exploring POIs, which is learned with a user-POI temporal matching function. Finally, results of extensive experiments with real-world POI check-in and human mobility data demonstrate that our proposed user-POI temporal matching method delivers substantial advantages over baseline models for POI recommendation tasks. 

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