Virtual environments will deeply alter the way we conduct scientific studies on human behavior. Possible applications range from spatial navigation over addressing moral dilemmas in a more natural manner to therapeutic applications for affective disorders. The decisive factor for this broad range of applications is that virtual reality (VR) is able to combine a well-controlled experimental environment together with the ecological validity of the immersion of test subjects. Until now, however, programming such an environment in Unity® requires profound knowledge of C# programming, 3D design and computer graphics. In order to give interested research groups access to a realistic VR environment which can easily adapt to the varying needs of experiments, we developed a large, open source, scriptable and modular VR city. It covers an area of 230 hectare, up to 150 self-driving vehicles and 655 active and passive pedestrians and thousands of nature assets to make it both highly dynamic and realistic. Furthermore, the repository presented here contains a stand-alone City AI toolkit for creating avatars and customizing cars. Finally, the package contains code to easily set up VR studies. All main functions are integrated into the graphical user interface of the Unity® Editor to ease the use of the embedded functionalities. In summary, the project named Westdrive is developed to enable research groups to access a state-of-the-art VR environment that is easily adapted to specific needs and allows focus on the respective research question.
Collaborative Mixed-Reality (CMR) applications are gaining interest in a wide range of areas including games, social interaction, design and health-care. The vast majority of published work to date has focused on display technology advancements, software, collaboration architectures and applications. However, the potential security concerns that affect collaborative platforms have received limited research attention. In this position paper, we investigate the challenges posed by cyber-security threats to CMR systems. We focus on how typical network architectures facilitating CMR and how their vulnerabilities can be exploited by attackers, and discuss the degree of potential social, monetary impacts, psychological and other harms that may result from such exploits. The main purpose of this paper is to provoke a discussion on CMR security concerns. We highlight insights from a cyber-security threat modelling perspective and also propose potential directions for research and development towards better mitigation strategies. We present a simple, systematic approach to understanding a CMR attack surface through an abstraction-based reasoning framework to identify potential attack vectors. Using this framework, security analysts, engineers, designers and users alike (stakeholders) can identify potential Indicators of Exposures (IoE) and Indicators of Compromise (IoC). Our framework should allow stakeholders to reduce their CMR attack surface as well as understand how Intrusion Detection System (IDS) approaches can be adopted for CMR systems. To demonstrate the validity to our framework, we illustrate several CMR attack surfaces with a set of use-cases. Finally, we also present a discussion on the future directions this line of research should take.
Name-based forwarding plane is a critical but challenging component for Named Data Networking (NDN), where the hash table is an appealing candidate for data structure utilized in FIB on the benefit of its fast lookup speed. However, the hash table is flawed that it does not naturally support the longest-prefix-matching (LPM) algorithm for name-based forwarding. To support LPM in the hash table, besides the linear lookup, the random search (such as binary search) aims at increasing the lookup speed by reconstructing the FIB and optimizing the search path. We propose a composite data structure for random search based on the combination of hash table and trie; the latter is introduced preserve the logical associations among names, so as to recycle memory and prevent the so-called backtracking problem, thus enhancing the lookup efficiency. The experiment indicates the superiority of our scheme in lookup speed, the impact on memory consumption has also been evaluated.
Traditional types of mind maps involve means of visually organizing information. They can be created either using physical tools like paper or post-it notes or through the computer-mediated process. Although their utility is established, mind maps and associated methods usually have several shortcomings with regards to effective and intuitive interaction as well as effective collaboration. Latest developments in virtual reality demonstrate new capabilities of visual and interactive augmentation, and in this paper, we propose a multimodal virtual reality mind map that has the potential to transform the ways in which people interact, communicate, and share information. The shared virtual space allows users to be located virtually in the same meeting room and participate in an immersive experience. Users of the system can create, modify, and group notes in categories and intuitively interact with them. They can create or modify inputs using voice recognition, interact using virtual reality controllers, and then make posts on the virtual mind map. When a brainstorming session is finished, users are able to vote about the content and export it for later usage. A user evaluation with 32 participants assessed the effectiveness of the virtual mind map and its functionality. Results indicate that this technology has the potential to be adopted in practice in the future, but a comparative study needs to be performed to have a more general conclusion.
We learn and/or relearn motor skills at all ages. Feedback plays a crucial role in this learning process, and Virtual Reality (VR) constitutes a unique tool to provide feedback and improve motor learning. In particular, VR grants the possibility to edit 3D movements and display augmented feedback in real time. Here we combined VR and motion capture to provide learners with a 3D feedback superimposing in real time the reference movements of an expert (expert feedback) to the movements of the learner (self-feedback). We assessed the effectiveness of this feedback for the learning of a throwing movement in American football. This feedback was used during (concurrent feedback) and/or after movement execution (delayed feedback), and it was compared with a feedback displaying only the reference movements of the expert. In contrast with more traditional studies relying on video feedback, we used the Dynamic Time Warping algorithm coupled to motion capture to measure the spatial characteristics of the movements. We also assessed the regularity with which the learner reproduced the reference movement along its path. For that, we used a new metric computing the dispersion of distance around the mean distance over time. Our results show that when the movements of the expert were superimposed on the movements of the learner during learning (i.e., self + expert), the reproduction of the reference movement improved significantly. On the hand, providing feedback about the movements of the expert only did not give rise to any significant improvement regarding movement reproduction.
