Reviewerly/exHarmony huggingface dataset:
DanZero: Mastering GuanDan Game with Reinforcement Learning

The use of artificial intelligence (AI) in card games has been a widely researched topic in the field of AI for an extended period. Recent advancements have led to AI programs exhibiting expert-level gameplay in complex card games such as Mahjong, DouDizhu, and Texas Hold’em. This paper aims to develop an AI program, named DanZero, for GuanDan, an exceptionally complex card game that involves four players competing and cooperating in a long process to upgrade their level quickly. Developing AI for GuanDan is challenging due to its large state and action space, long episode length, and uncertainty in the number of players. To address these challenges, we propose DanZero, the first AI program for GuanDan, that employs reinforcement learning using a distributed framework for training. Our framework consists of two processes: the Actor Process and the Learner Process. In the Actor Process, we design state features and generate samples through agents’ self-play. In the Learner Process, we update the model using the Deep Monte-Carlo Method. We trained DanZero for 30 days, utilizing 160 CPUs and 1 GPU to develop the program successfully. We compared DanZero’s performance with eight baseline AI programs based on heuristic rules, and our results indicate DanZero’s exceptional performance. We further tested DanZero with human players and demonstrated its ability to perform at a human level. The code for DanZero can be found in the supplementary material.
The MagicBook Revisited

Twenty years ago the MagicBook demonstrated an early collaborative cross-reality system allowing users to transition along the reality-virtuality continuum. This current application reproduces the key elements of the MagicBook using mobile phones rather than high end graphics computers and custom handheld displays. Just like the original, people can use a handheld viewer to see virtual content superimposed over real book pages, and then fly into the scenes and experience them immersively. Multi-scale collaboration is also supported enabling people in the AR view to see the users in VR, or to experience the scenes together at the same scale. We also add new features, such as realistic avatars, tangible interaction, use of a handheld controller, and support for remote participants. Overall, we have created a cross reality platform that fits in a person’s pocket, but enables them to collaborate with dozens of people in AR and VR in a very natural way. This is demonstrated in a command and control use case, showing its application in a fire-fighting scenario. We also report on pilot study results from people that have tried the platform.
Mixture of Public and Private Distributions in Imperfect Information Games

In imperfect information games (e.g. Bridge, Skat, Poker), one of the fundamental considerations is to infer the missing information while at the same time avoiding the disclosure of private information. Disregarding the issue of protecting private information can lead to a highly exploitable performance. Yet, excessive attention to it leads to hesitations that are no longer consistent with our private information. In our work, we show that to improve performance, one must choose whether to use a player’s private information. We extend our work by proposing a new belief distribution depending on the amount of private and public information desired. We empirically demonstrate an increase in performance and, with the aim of further improving performance, the new distribution should be used according to the position in the game. Our experiments have been done on multiple benchmarks and in multiple determinization-based algorithms (PIMC and IS-MCTS).
Weighting Information Sets with Siamese Neural Networks in Reconnaissance Blind Chess

Research in Game Artificial Intelligence distinguishes between fully observable, perfect-information games and imperfect-information games, which hide part of the game’s full information. In games with imperfect information, all possible game states that are consistent with a player’s currently available information about the progress of the game are called the information set for that player. This information set can be used for multiple purposes such as determining the expected outcome of a certain move by evaluating it on all possible states in the information set. While in theory there is no way to distinguish states within an information set, players can use experience and other context information to estimate which states are the most likely. In this paper, we estimate a probability distribution over an information set from historic data such that we can assign a weight to each individual state. We achieve this by training a Siamese neural network with triplets of comparisons between different states in the information set given the context of the previously obtained information. A first evaluation in the game of Reconnaissance Blind Chess shows that we can learn to identify the one true game state in a large information set with high probability. In addition, when used within a naively constructed RBC agent, this approach shows promising gameplay performance. At the time of writing, a simple agent based on the Siamese neural network is ranked #6 of all agents on the public RBC leaderboard.
Spaces to Think: A Comparison of Small, Large, and Immersive Displays for the Sensemaking Process

Analysts need to process large amounts of data in order to extract concepts, themes, and plans of action based upon their findings. Different display technologies offer varying levels of space and interaction methods that change the way users can process data using them. In a comparative study, we investigated how the use of single traditional monitor, a large, high-resolution two-dimensional monitor, and immersive three-dimensional space using the Immersive Space to Think approach impact the sensemaking process. We found that user satisfaction grows and frustration decreases as available space increases. We observed specific strategies users employ in the various conditions to assist with the processing of datasets. We also found an increased usage of spatial memory as space increased, which increases performance in artifact position recall tasks. In future systems supporting sensemaking, we recommend using display technologies that provide users with large amounts of space to organize information and analysis artifacts.
Towards More Effective Interactions: A Hybrid Embodied Interaction Approach in Stereoscopic Displays

Head-mounted virtual reality/augmented reality (VR/AR) devices have become prevalent in implementing immersive analytics. However, these devices have certain limitations, such as disconnecting the user from the real working world and possibly causing discomfort. Recently, portable glasses-free stereoscopic display devices have emerged as a potential solution to address these limitations. Unlike head-mounted devices, these displays utilize screens to render the virtual space, resulting in virtual content appearing either inside or outside of the screen. Therefore, designing natural, intuitive, and immersive user interactions in this virtual environment with a physical boundary is crucial for enhancing device usability. This research focuses on developing an effective interaction method for selecting and moving objects within the stereoscopic displays. We propose a hybrid embodied interaction method that leverages gesture controls. Specifically, inside-screen object manipulation is achieved using ray-object intersection and pinch gestures, while outside-screen manipulation involves a grab gesture and hand movements. The transition between these two interaction modes can be accomplished using either the "OK" gesture or an automatic attraction mechanism.We conducted a user study to evaluate our hybrid interaction method, and the results indicate that participants found it to be more natural and capable of accurately selecting and moving objects.
Game-map Pathfinding with Per-Problem Selection of Synthesized Heuristics

Variants of A* search are widely used for video-game pathfinding with a heuristic function that is typically either a generic formula designed by humans (e.g., the Manhattan distance) or pre-computed for a specific video-game map. Recent work attempted to combine portability of the former and higher performance of the latter by automatically synthesizing arithmetic formulae. Such formulae are simple enough to be human-readable, portable enough to provide guidance on novel maps and yet complex enough to notably outperform a baseline. Each formula-represented heuristic was synthesized for a given map, presumably capturing some features of the map. However, maps can be non-uniform and some regions of one map may have features similar to another map. This work uses a portfolio of synthesized heuristics and selects from it on a per-problem basis. The selection is done automatically by determining the pair of map regions in which the start and the goal states of a given problem instance belong. A pre-computed database gives the highest-performing heuristic from the portfolio for that pair of regions. This heuristic is then used to guide A* to solve the problem instance. Empirical evaluation on maps from video games indicates noticeable speed-up compared to using a single synthesized heuristic for all problem instances on a map.
3D Slicer-AR-Bridge: 3D Slicer AR Connection for Medical Image Visualization and Interaction with AR-HMD

In recent years, increasing interest has been revealed in medical Augmented Reality (AR), particularly in medical education, preoperative planning, and intra-operative guidance. However, there lacks a platform to effectively integrate each essential part of surgical AR, including visualization, interaction, tracking, and medical image processing, hindering new beginners or surgeons from easily building up any comprehensive AR applications. Therefore, we introduce 3D Slicer-AR-Bridge, a novel framework taking advantage of open-source medical image processing software 3D Slicer and advanced AR developing toolkits, to enable fast generation of surgical AR applications with high performance, high extensibility, and transparent data flow. The bridge is first built upon a seamless medical data sharing interface supporting volumes, segmentations, models, and annotations, between 3D Slicer and Unity, together with corresponding visualization and interaction methods. Sensor resources from AR devices are then synchronized to the bridge during visualization using holographic remoting to enable in-situ display with high render quality. Meanwhile, all resources are highly accessible in 3D Slicer, while a general user interface is provided to operate all the elements, enabling the creation of an in-situ AR application with little coding, which we believe will greatly benefit the community and clinical research. The performance tests in our paper indicate that 3D Slicer-AR-Bridge can do data exchange efficiently. At the end of our paper, we provide two different applications built with the bridge with little modification to the codes, to exemplify how the framework can help the researchers in surgical AR.
Two-Memory Reinforcement Learning

While deep reinforcement learning has shown important empirical success, it tends to learn relatively slow due to slow propagation of rewards information and slow update of parametric neural networks. Non-parametric episodic memory, on the other hand, provides a faster learning alternative that does not require representation learning and uses maximum episodic return as state-action values for action selection. Episodic memory and reinforcement learning both have their own strengths and weaknesses. Notably, humans can leverage multiple memory systems concurrently during learning and benefit from all of them. In this work, we propose a method called Two-Memory reinforcement learning agent (2M) that combines episodic memory and reinforcement learning to distill both of their strengths. The 2M agent exploits the speed of the episodic memory part and the optimality and the generalization capacity of the reinforcement learning part to complement each other. Our experiments demonstrate that the 2M agent is more data efficient and outperforms both pure episodic memory and pure reinforcement learning, as well as a state-of-the-art memory-augmented RL agent. Moreover, the proposed approach provides a general framework that can be used to combine any episodic memory agent with other off-policy reinforcement learning algorithms. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Virtual Reality for Industrial Assembly Training: The Impact of Tool Interaction Realism on Learning Outcomes

Virtual Reality is a transformative technology with potential for industrial training. By offering immersive, safe, and cost-effective simulations, Virtual Reality can greatly enhance learning outcomes and operational efficiency in industrial settings. This paper investigates the use of Virtual Reality to improve manual assembly process training in industry. To date, little research has been done on the impact of realistic tool simulations, especially with the use of standard controllers instead of specialized equipment. To address this research gap, a user study compared two versions of a Virtual Reality application: one with realistic tool interactions and one without. Participants actively engaged with these variants, allowing for a detailed analysis of performance and learning outcomes. The findings reveal no significant difference in learning outcomes between those variants. This challenges the assumption that increased realism automatically improves learning in Virtual Reality for industrial training. However, the qualitative evaluation reveals more user engagement and entertainment when realistic tool interactions were incorporated. In summary, Virtual Reality training offers immersive and cost-effective solutions for industrial applications. These insights contribute to exploring optimal implementation strategies for Virtual Reality trainings in industrial settings, where the balance between realism, complexity, engagement, and learning effectiveness is being investigated.
A Systematic Evaluation of Incongruencies and Their Influence on Plausibility in Virtual Reality

Currently, there is an ongoing debate about the influencing factors of one’s extended reality (XR) experience. Plausibility, congruence, and their role have recently gained more and more attention. One of the latest models to describe XR experiences, the Congruence and Plausibility model (CaP), puts plausibility and congruence right in the center. However, it is unclear what influence they have on the overall XR experience and what influences our perceived plausibility rating. In this paper, we implemented four different incongruencies within a virtual reality scene using breaks in plausibility as an analogy to breaks in presence. These manipulations were either located on the cognitive or perceptual layer of the CaP model. They were also either connected to the task at hand or not. We tested these manipulations in a virtual bowling environment to see which influence they had. Our results show that manipulations connected to the task caused a lower perceived plausibility. Additionally, cognitive manipulations seem to have a larger influence than perceptual manipulations. We were able to cause a break in plausibility with one of our incongruencies. These results show a first direction on how the influence of plausibility in XR can be systematically investigated in the future.
Visual Metaphors for Notification into Virtual Environments

The novel research field of Cross-Reality enables the usage of different stages on the Reality-Virtuality Continuum or to move between them. To facilitate communication between or within these stages, it becomes essential to notify head-mounted display users about ongoing events, distractions, or communication approaches in their current Mixed Reality application. These notifications can originate from any other stage of the continuum. In this position paper we explore how visual notifications can be designed to reflect well-known metaphors. We present five initial concepts for notification metaphors, focusing on seamlessly integrating the notification in the Mixed Reality experience to achieve a high plausibility. An initial implementation shows how such notification could look like.
Project Corvus: A Virtual Reality Horror Tool for Improving Self-Efficacy

Horror is often associated with a negative connotation. Yet, in today’s popular culture, the benefits of thrills have become an engaging topic. Why are people drawn to the supposedly repulsive contents of horror? It is an inherent psychological trait we all possess called morbid curiosity. Can we better investigate this behavior towards horror to confront our fears and help us build confidence? In Project Corvus, we explore the ways in which virtual reality (VR) and psychological programs such as exposure therapy can be combined to inform the psycho-educational opportunities in this horror-themed direction. More specifically, to leverage people’s morbid curiosity towards horror for the development of self-efficacy. Through short repetition of horror virtual experiences, the goal is to design an assistive program that can help the participants develop their skills in controlling their emotions even when facing real crises in the future.
SphereDRUNet: A Spherical Denoiser for Omnidirectional Images

Image denoising is a primary pre-processing task in image processing. Although it has garnered significant research attention in the context of traditional 2D images, omnidirectional image denoising has received relatively limited attention in the literature. Furthermore, extending processing models and tools designed for 2D images to the sphere presents many challenges due to the inherent distortions and non-uniform pixel distributions associated with spherical representations and their underlying projections. In this paper, we address the problem of omnidirectional image denoising and we aim to study the advantage of denoising the spherical image directly rather than its mapping. We introduce a novel network called SphereDRUNet to denoise spherical images using deep learning tools on a spherical sampling. We show that denoising directly the sphere using our network gives better performance, compared to denoising the projected equirectangular images with a similarly learned model.
Challenges and Opportunities for Collaborative Immersive Analytics with Hybrid User Interfaces

Over the past years, we have seen an increase in the number of user studies involving mixed reality interfaces. As these environments usually exceed standardized user study settings that only measure time and error, we developed, designed, and evaluated a mixed-immersion evaluation framework called ReLive. Its combination of in-situ and ex-situ analysis approaches allows for the holistic and malleable analysis and exploration of mixed reality user study data of an individual analyst in a step-by-step approach that we previously described as an asynchronous hybrid user interface. Yet, collaboration was coined as a key aspect for visual and immersive analytics – potentially allowing multiple analysts to synchronously explore mixed reality user study data from different but complementary angles of evaluation using hybrid user interfaces. This leads to a variety of fundamental challenges and opportunities for research and design of hybrid user interfaces regarding e.g., allocation of tasks, the interplay between views, user representations, and collaborative coupling that are outlined in this position paper.
An Asynchronous Hybrid Cross Reality Collaborative System

This work presents a Mixed Reality (MR)-based asynchronous hybrid cross reality collaborative system which supports recording and playback of user actions in three-dimensional task space at different periods in time. Using this system, an expert user can record a task process such as virtual object placement or assembly, which can then be viewed by other users in either Augmented Reality (AR) or Virtual Reality (VR) views at later points in time to complete the task. In VR, the pre-scanned 3D workspace can be experienced to enhance the understanding of spatial information. Alternatively, AR can provide real-scale information to help the workers manipulate real world objects, and complete the task assignment. Users can also seamlessly move between AR and VR views as desired. In this way the system can contribute to improving task performance and co-presence during asynchronous collaboration.
Deep Detector and Optical Flow-based Tracking Approach of Facial Markers for Animation Capture

Marker-based facial motion capture methods are still commonly used in the film and game industry. However, these methods can lose track of target markers under certain conditions such as occlusion and blur, requiring extensive manual revisions. Therefore, there is a need for a more robust marker tracking method that can accurately and stably track markers over a longer period of time, thereby simplifying manual operations. In this paper, we present a new facial marker tracking system that focuses on improving the accuracy and stability of performance capture. Our system integrates a robust optical flow tracking method with the proposed Marker-DETR detector to achieve synthetic analysis of the marker tracking process. To evaluate the performance of our system, we collected a real-world dataset that records the performance of voluntary actors, with ground truth labels provided by artists. Our experimental results demonstrate that our approach outperforms state-of-the-art trackers such as SiamDW and ECO in terms of RMSE (root mean square error) and AUC (area under the curve). These results confirm the improved accuracy and stability of our approach.
Supporting Artefact Awareness in Partially-Replicated Workspaces

Using Cross Reality (CR) approaches for remote collaboration will often result in partially-replicated workspaces. Here, workspace artefacts are not equally accessible—i.e. a physical artefact may only be manipulated by one collaborator—and in general, the artefacts become desynchronised over time. In this paper, we introduce a framework for artefact awareness that can help collaborators maintain an understanding of each others’ manipulations with workspace artefacts. We illustrate our design explorations through sketches, and outline how we aim to study the effectiveness and utility of artefact awareness in cross reality remote collaboration. In our work, we expect to show that effectively supporting artefact awareness will help make cross-reality remote collaboration smoother.
Utilizing a Robot to Endow Virtual Objects with Stiffness

This paper proposes a novel approach to provide stiffness feedback in VR utilizing the inherent characteristics of encounter-type haptic devices (ETHDs). The method aims to provide a sense of object’s deformation and enhance users’ perception of stiffness when touching objects. We explored how to minimize the influence of hardness on stiffness perception and selected materials for the soft, medium, and hard hardness mapping groups to adapt to users’ perceptions when touching objects of different hardness levels. Using this system, we compared and evaluated the method with a touch interaction without stiffness perceptions and found a better performance in terms of realism, possibility to act, attractiveness, efficiency.