Editorial

The metaverse has been described as the third Internet revolution, a virtual society and social network that mirrors real scenarios through a decentralized architecture. It is a new future form of the Internet that integrates various new technologies, and its combination with the education field has great potential. In the post-epidemic era, the shape of education has undergone significant changes, evolving continuously towards large-scale, digital, virtualized and intelligent

Exploring the Design Space of Immersive Urban Analytics

Recent years have witnessed the rapid development and wide adoption of immersive head-mounted devices, such as HTC VIVE, Oculus Rift, and Microsoft HoloLens. These immersive devices have the potential to significantly extend the methodology of urban visual analytics by providing critical 3D context information and creating a sense of presence. In this paper, we propose an theoretical model to characterize the visualizations in immersive urban analytics. Further more, based on our comprehensive and concise model, we contribute a typology of combination methods of 2D and 3D visualizations that distinguish between linked views, embedded views, and mixed views. We also propose a supporting guideline to assist users in selecting a proper view under certain circumstances by considering visual geometry and spatial distribution of the 2D and 3D visualizations. Finally, based on existing works, possible future research opportunities are explored and discussed.Comment: 23 pages,11 figure

Low Distortion Shell Map Generation

A shell map (Porumbescu et al., 2005) is a bijective mapping between shell space (the space between a base surface and its offset) and texture space. It can be used to generate small-scale features on surfaces using a variety of modeling techniques. In this paper, we present an efficient algorithm, which reduces distortion by construction, for the offset surface generation of triangular meshes. The basic idea is to independently offset each triangle of the base mesh, and then stitch them up by solving a Poisson equation. We then introduce the details for computation of a stretch metric, which measures the distortion of shell maps. Our results show a substantial improvement compared to previous results

BAAP: A Behavioral Animation Authoring Platform for Emotion Driven 3D Virtual Characters

Abstract. Emotion, as an important aspect of human intelligence, has been playing a significant role in virtual characters. We propose an improved threelevel structure of affective model as "personality-emotion-mood" for intelligent and emotional virtual characters. We also present the emotion state space, as well as the emotion updating functions, to generate authentic and expressive emotions. In order to achieve the complexity and variety of behaviors, we bring forward a behavior organizing structure as the behavior tree, which defines four kinds of behavior organizations as well as the behavior tag and behavior message, to manage virtual characters' behaviors. At the end, we achieve an experimental platform BAAP, which prove our emotion model and behavior organizing structure to be effective and practical in generating intelligent and emotional behavioral animations. Keywords: virtual character, affective computing, behavior tree, behavioral animation, authoring platform Introduction In previous work [1], we proposed a computational emotion model which can be incorporated into physiological and social components of emotions. We improve it in this paper and present an experimental platform BAAP. We mainly focus on two subjects: emotion modeling and behavior organizing. Based on the OCEAN mode

Multi-Agent Deep Reinforcement Learning for Multi-Object Tracker

Multi-object tracking has been a key research subject in many computer vision applications. We propose a novel approach based on multi-agent deep reinforcement learning (MADRL) for multi-object tracking to solve the problems in the existing tracking methods, such as a varying number of targets, non-causal, and non-realtime. At first, we choose YOLO V3 to detect the objects included in each frame. Unsuitable candidates were screened out and the rest of detection results are regarded as multiple agents and forming a multi-agent system. Independent Q-Learners (IQL) is used to learn the agents' policy, in which, each agent treats other agents as part of the environment. Then, we conducted offline learning in the training and online learning during the tracking. Our experiments demonstrate that the use of MADRL achieves better performance than the other state-of-art methods in precision, accuracy, and robustness

A Convenient and Low-Cost Model of Depression Screening and Early Warning Based on Voice Data Using for Public Mental Health

Depression is a common mental health disease, which has great harm to public health. At present, the diagnosis of depression mainly depends on the interviews between doctors and patients, which is subjective, slow and expensive. Voice data are a kind of data that are easy to obtain and have the advantage of low cost. It has been proved that it can be used in the diagnosis of depression. The voice data used for modeling in this study adopted the authoritative public data set, which had passed the ethical review. The features of voice data were extracted by Python programming, and the voice features were stored in the format of CSV files. Through data processing, a big database, containing 1479 voice feature samples, was generated for modeling. Then, the decision tree screening model of depression was established by 10-fold cross validation and algorithm selection. The experiment achieved 83.4% prediction accuracy on voice data set. According to the prediction results of the model, the patients can be given early warning and intervention in time, so as to realize the health management of personal depression