Metaverse cryptocurrencies: An empirical analysis of cybersecurity risks and market dynamics

Samet Gürsoy

doi:10.54517/m3105

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Garment-aware gaussian for clothed human modeling from monocular video

Reconstructing the human body from monocular video input presents significant challenges, including a limited field of view and difficulty in capturing non-rigid deformations, such as those associated with clothing and pose variations. These challenges often compromise motion editability and rendering quality. To address these issues, we propose a cloth-aware 3D Gaussian splatting approach that leverages the strengths of 2D convolutional neural networks (CNNs) and 3D Gaussian splatting for high-quality human body reconstruction from monocular video. Our method parameterizes 3D Gaussians anchored to a human template to generate posed position maps that capture pose-dependent non-rigid deformations. Additionally, we introduce Learnable Cloth Features, which are pixel-aligned with the posed position maps to address cloth-related deformations. By jointly modeling cloth and pose-dependent deformations, along with compact, optimizable linear blend skinning (LBS) weights, our approach significantly enhances the quality of monocular 3D human reconstructions. We also incorporate carefully designed regularization techniques for the Gaussians, improving the generalization capability of our model. Experimental results demonstrate that our method outperforms state-of-the-art techniques for animatable avatar reconstruction from monocular inputs, delivering superior performance in both reconstruction fidelity and rendering quality.

From paper to virtual: The meta-life of a historical cartographic artifact

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Deciphering avian emotions: A novel AI and machine learning approach to understanding chicken vocalizations

In this groundbreaking study, we present a novel approach to interspecies communication, focusing on the understanding of chicken vocalizations. Leveraging advanced mathematical models in artificial intelligence (AI) and machine learning, we have developed a system capable of interpreting various emotional states in chickens, including hunger, fear, anger, contentment, excitement, and distress. Our methodology employs a cutting-edge AI technique we call Deep Emotional Analysis Learning (DEAL), a highly mathematical and innovative approach that allows for the nuanced understanding of emotional states through auditory data. DEAL is rooted in complex mathematical algorithms, enabling the system to learn and adapt to new vocal patterns over time. We conducted our study with a sample of 80 chickens, meticulously recording and analyzing their vocalizations under various conditions. To ensure the accuracy of our system’s interpretations, we collaborated with a team of eight animal psychologists and veterinary surgeons, who provided expert insights into the emotional states of the chickens. Our system demonstrated an impressive accuracy rate of close to 80%, marking a significant advancement in the field of animal communication. This research not only opens up new avenues for understanding and improving animal welfare but also sets a precedent for further studies in AI-driven interspecies communication. The novelty of our approach lies in its application of sophisticated AI techniques to a largely unexplored area of study. By bridging the gap between human and animal communication, we believe our research will pave the way for more empathetic and effective interactions with the animal kingdom.

Metaverse cryptocurrencies: An empirical analysis of cybersecurity risks and market dynamics

Samet Gürsoy

Article ID: 3105
Vol 6, Issue 2, 2025

DOI: https://doi.org/10.54517/m3105

Received: 27 November 2024; Accepted: 14 February 2025; Available online: 20 March 2025; Issue release: 30 June 2025

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Abstract

This research aims at assessing how significant cybersecurity issues affect Metaverse coin price and trading volume with particular regard to the five greatest assets from 2017 till 2024. The study uses event study and impulse response analysis to examine the impact of MANA (Decentraland), SAND (The Sandbox), AXS (Axie Infinity Shards), ENJ (Enjin Coin) and GALA (Gala Games) on nine major security incidents like the Coincheck and Ronin hacks. Dependent variables include the prices and volumes of coins in the Metaverse, while prices of Bitcoin and Ethereum serve as the main independent variables to control for price activity. The results show short-lived but strong effects, depending on event intensity and platform specifics. The Coincheck hack caused a 4.9% price drop and 22% volume decline over 10 days, while BtcTurk had a smaller impact. The current paper enlarges the body of research addressing the cryptocurrency sector’s market stability with the new knowledge about the risks control and investments into the fresh classes of assets.

Keywords

metaverse cryptocurrencies; cybersecurity events; blockchain security; cryptographic algorithms; digital identity

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