24,993 research outputs found
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
The Metaverse offers a second world beyond reality, where boundaries are
non-existent, and possibilities are endless through engagement and immersive
experiences using the virtual reality (VR) technology. Many disciplines can
benefit from the advancement of the Metaverse when accurately developed,
including the fields of technology, gaming, education, art, and culture.
Nevertheless, developing the Metaverse environment to its full potential is an
ambiguous task that needs proper guidance and directions. Existing surveys on
the Metaverse focus only on a specific aspect and discipline of the Metaverse
and lack a holistic view of the entire process. To this end, a more holistic,
multi-disciplinary, in-depth, and academic and industry-oriented review is
required to provide a thorough study of the Metaverse development pipeline. To
address these issues, we present in this survey a novel multi-layered pipeline
ecosystem composed of (1) the Metaverse computing, networking, communications
and hardware infrastructure, (2) environment digitization, and (3) user
interactions. For every layer, we discuss the components that detail the steps
of its development. Also, for each of these components, we examine the impact
of a set of enabling technologies and empowering domains (e.g., Artificial
Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on
its advancement. In addition, we explain the importance of these technologies
to support decentralization, interoperability, user experiences, interactions,
and monetization. Our presented study highlights the existing challenges for
each component, followed by research directions and potential solutions. To the
best of our knowledge, this survey is the most comprehensive and allows users,
scholars, and entrepreneurs to get an in-depth understanding of the Metaverse
ecosystem to find their opportunities and potentials for contribution
Recommended from our members
Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era
OpenAI has recently released GPT-4 (a.k.a. ChatGPT plus), which is
demonstrated to be one small step for generative AI (GAI), but one giant leap
for artificial general intelligence (AGI). Since its official release in
November 2022, ChatGPT has quickly attracted numerous users with extensive
media coverage. Such unprecedented attention has also motivated numerous
researchers to investigate ChatGPT from various aspects. According to Google
scholar, there are more than 500 articles with ChatGPT in their titles or
mentioning it in their abstracts. Considering this, a review is urgently
needed, and our work fills this gap. Overall, this work is the first to survey
ChatGPT with a comprehensive review of its underlying technology, applications,
and challenges. Moreover, we present an outlook on how ChatGPT might evolve to
realize general-purpose AIGC (a.k.a. AI-generated content), which will be a
significant milestone for the development of AGI.Comment: A Survey on ChatGPT and GPT-4, 29 pages. Feedback is appreciated
([email protected]
Modularizing and Assembling Cognitive Map Learners via Hyperdimensional Computing
Biological organisms must learn how to control their own bodies to achieve
deliberate locomotion, that is, predict their next body position based on their
current position and selected action. Such learning is goal-agnostic with
respect to maximizing (minimizing) an environmental reward (penalty) signal. A
cognitive map learner (CML) is a collection of three separate yet
collaboratively trained artificial neural networks which learn to construct
representations for the node states and edge actions of an arbitrary
bidirectional graph. In so doing, a CML learns how to traverse the graph nodes;
however, the CML does not learn when and why to move from one node state to
another. This work created CMLs with node states expressed as high dimensional
vectors suitable for hyperdimensional computing (HDC), a form of symbolic
machine learning (ML). In so doing, graph knowledge (CML) was segregated from
target node selection (HDC), allowing each ML approach to be trained
independently. The first approach used HDC to engineer an arbitrary number of
hierarchical CMLs, where each graph node state specified target node states for
the next lower level CMLs to traverse to. Second, an HDC-based
stimulus-response experience model was demonstrated per CML. Because
hypervectors may be in superposition with each other, multiple experience
models were added together and run in parallel without any retraining. Lastly,
a CML-HDC ML unit was modularized: trained with proxy symbols such that
arbitrary, application-specific stimulus symbols could be operated upon without
retraining either CML or HDC model. These methods provide a template for
engineering heterogenous ML systems
Audio-Visual Automatic Speech Recognition Towards Education for Disabilities
Education is a fundamental right that enriches everyone’s life. However, physically challenged people often debar from the general and advanced education system. Audio-Visual Automatic Speech Recognition (AV-ASR) based system is useful to improve the education of physically challenged people by providing hands-free computing. They can communicate to the learning system through AV-ASR. However, it is challenging to trace the lip correctly for visual modality. Thus, this paper addresses the appearance-based visual feature along with the co-occurrence statistical measure for visual speech recognition. Local Binary Pattern-Three Orthogonal Planes (LBP-TOP) and Grey-Level Co-occurrence Matrix (GLCM) is proposed for visual speech information. The experimental results show that the proposed system achieves 76.60 % accuracy for visual speech and 96.00 % accuracy for audio speech recognition
Arts and humanities shaping the AI future
The organisation of this event was motivated by the view there should be more Arts and Humanities (A&H) perspectives, methods and approaches involved in shaping our future relationship with AI technology. Our invitation was sent to the most diverse group we could imagine being interested in this view. Positive responses to the invitation, rich discussions during and critical reflections after the meeting in general confirms this view. Besides facilitating a discussion amongst this group of participants from different disciplines, the event was not outcome-driven. Some information as well as questions were gathered before the meeting. At the meeting, example projects using A&H methods to shape relationships with AI technology were presented as triggers for small group discussions to follow. Note takers collected and summarised discussion highlights at the end of the day, and invitations for post-meeting follow up reflections were sent. This report provides a relatively detailed account of these activities, the conditions and what was shared. Writing this has been useful for considering what might come next, which we are currently reflecting on. Please feel free to contact us with any thoughts or questions
Supplementary materials for the article: Augmented reality interfaces for pedestrian-vehicle interactions: An online study.
Supplementary data for the paper Tabone, W., Happee, R., GarcĂa, J., Lee, Y.M., Lupetti, M.L., Merat, N., & De Winter, J.C.F. (2022). Augmented reality interfaces for pedestrian-vehicle interactions: An online study.
Data includes  an export of the questionnaire questions, the respondent raw data, all the videos utilised, and a supplementary video demonstrating all the interfaces operating in the VR environment. </p
Robotic Bronchoscopy: Review of Three Systems
Robotic bronchoscopy (RB) has been shown to improve access to smaller and more peripheral lung lesions, while simultaneously staging the mediastinum. Pre-clinical studies demonstrated extremely high diagnostic yields, but real-world RB yields have yet to fully matched up in prospective studies. Despite this, RB technology has rapidly evolved and has great potential for lung-cancer diagnosis and even treatment. In this article, we review the historical and present challenges with RB in order to compare three RB systems
Semantic Segmentation Enhanced Transformer Model for Human Attention Prediction
Saliency Prediction aims to predict the attention distribution of human eyes
given an RGB image. Most of the recent state-of-the-art methods are based on
deep image feature representations from traditional CNNs. However, the
traditional convolution could not capture the global features of the image well
due to its small kernel size. Besides, the high-level factors which closely
correlate to human visual perception, e.g., objects, color, light, etc., are
not considered. Inspired by these, we propose a Transformer-based method with
semantic segmentation as another learning objective. More global cues of the
image could be captured by Transformer. In addition, simultaneously learning
the object segmentation simulates the human visual perception, which we would
verify in our investigation of human gaze control in cognitive science. We
build an extra decoder for the subtask and the multiple tasks share the same
Transformer encoder, forcing it to learn from multiple feature spaces. We find
in practice simply adding the subtask might confuse the main task learning,
hence Multi-task Attention Module is proposed to deal with the feature
interaction between the multiple learning targets. Our method achieves
competitive performance compared to other state-of-the-art methods
Can Large Language Models design a Robot?
Large Language Models can lead researchers in the design of robots.Comment: Under revie
- …