13,769 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
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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Multi-Graph Convolution Network for Pose Forecasting
Recently, there has been a growing interest in predicting human motion, which
involves forecasting future body poses based on observed pose sequences. This
task is complex due to modeling spatial and temporal relationships. The most
commonly used models for this task are autoregressive models, such as recurrent
neural networks (RNNs) or variants, and Transformer Networks. However, RNNs
have several drawbacks, such as vanishing or exploding gradients. Other
researchers have attempted to solve the communication problem in the spatial
dimension by integrating Graph Convolutional Networks (GCN) and Long Short-Term
Memory (LSTM) models. These works deal with temporal and spatial information
separately, which limits the effectiveness. To fix this problem, we propose a
novel approach called the multi-graph convolution network (MGCN) for 3D human
pose forecasting. This model simultaneously captures spatial and temporal
information by introducing an augmented graph for pose sequences. Multiple
frames give multiple parts, joined together in a single graph instance.
Furthermore, we also explore the influence of natural structure and
sequence-aware attention to our model. In our experimental evaluation of the
large-scale benchmark datasets, Human3.6M, AMSS and 3DPW, MGCN outperforms the
state-of-the-art in pose prediction.Comment: arXiv admin note: text overlap with arXiv:2110.04573 by other author
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
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Economia colaborativa
A importância de se proceder à análise dos principais desafios jurÃdicos que a economia colaborativa coloca – pelas implicações que as mudanças de paradigma dos modelos de negócios e dos sujeitos envolvidos suscitam − é indiscutÃvel, correspondendo à necessidade de se fomentar a segurança jurÃdica destas práticas, potenciadoras de crescimento económico e bem-estar social.
O Centro de Investigação em Justiça e Governação (JusGov) constituiu uma equipa multidisciplinar que, além de juristas, integra investigadores de outras áreas, como a economia e a gestão, dos vários grupos do JusGov – embora com especial participação dos investigadores que integram o grupo E-TEC (Estado, Empresa e Tecnologia) – e de outras prestigiadas instituições nacionais e internacionais, para desenvolver um projeto neste domÃnio, com o objetivo de identificar os problemas jurÃdicos que a economia colaborativa suscita e avaliar se já existem soluções para aqueles, refletindo igualmente sobre a conveniência de serem introduzidas alterações ou se será mesmo necessário criar nova regulamentação.
O resultado desta investigação é apresentado nesta obra, com o que se pretende fomentar a continuação do debate sobre este tema.Esta obra é financiada por fundos nacionais através da FCT — Fundação para a Ciência e a Tecnologia, I.P., no âmbito do Financiamento UID/05749/202
Wav2code: Restore Clean Speech Representations via Codebook Lookup for Noise-Robust ASR
Automatic speech recognition (ASR) has gained a remarkable success thanks to
recent advances of deep learning, but it usually degrades significantly under
real-world noisy conditions. Recent works introduce speech enhancement (SE) as
front-end to improve speech quality, which is proved effective but may not be
optimal for downstream ASR due to speech distortion problem. Based on that,
latest works combine SE and currently popular self-supervised learning (SSL) to
alleviate distortion and improve noise robustness. Despite the effectiveness,
the speech distortion caused by conventional SE still cannot be completely
eliminated. In this paper, we propose a self-supervised framework named
Wav2code to implement a generalized SE without distortions for noise-robust
ASR. First, in pre-training stage the clean speech representations from SSL
model are sent to lookup a discrete codebook via nearest-neighbor feature
matching, the resulted code sequence are then exploited to reconstruct the
original clean representations, in order to store them in codebook as prior.
Second, during finetuning we propose a Transformer-based code predictor to
accurately predict clean codes by modeling the global dependency of input noisy
representations, which enables discovery and restoration of high-quality clean
representations without distortions. Furthermore, we propose an interactive
feature fusion network to combine original noisy and the restored clean
representations to consider both fidelity and quality, resulting in even more
informative features for downstream ASR. Finally, experiments on both synthetic
and real noisy datasets demonstrate that Wav2code can solve the speech
distortion and improve ASR performance under various noisy conditions,
resulting in stronger robustness.Comment: 12 pages, 7 figures, Submitted to IEEE/ACM TASL
Adjacent LSTM-Based Page Scheduling for Hybrid DRAM/NVM Memory Systems
Recent advances in memory technologies have led to the rapid growth of hybrid systems that combine traditional DRAM and Non Volatile Memory (NVM) technologies, as the latter provide lower cost per byte, low leakage power and larger capacities than DRAM, while they can guarantee comparable access latency. Such kind of heterogeneous memory systems impose new challenges in terms of page placement and migration among the alternative technologies of the heterogeneous memory system. In this paper, we present a novel approach for efficient page placement on heterogeneous DRAM/NVM systems. We design an adjacent LSTM-based approach for page placement, which strongly relies on page accesses prediction, while sharing knowledge among pages with behavioral similarity. The proposed approach leads up to 65.5% optimized performance compared to existing approaches, while achieving near-optimal results and saving 20.2% energy consumption on average. Moreover, we propose a new page replacement policy, namely clustered-LRU, achieving up to 8.1% optimized performance, compared to the default Least Recently Used (LRU) policy
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
A Design Science Research Approach to Smart and Collaborative Urban Supply Networks
Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness.
A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense.
Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice
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