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Groundbreaking taxonomy of metaverse characteristics

Artificial Intelligence Review 2025年第8期58卷 1-50页

作者： Sadeghi-Niaraki, Abolghasem Rahimi, Fatema Azlan, Nur Alya Emanuelle Binti Song, Houbing Ali, Farman Choi, Soo-Mi Department of Computer Science and Engineering and Convergence Engineering for Intelligent Drone XR Research Center Sejong University Seoul Korea Republic of BaltimoreMD21250 United States Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

The Metaverse, a dynamic and immersive virtual realm, has captured the imagination of researchers and enthusiasts worldwide. This survey paper aims to introduce a groundbreaking taxonomy for the characteristics of the Metaverse offering a structured and adaptable framework that extends beyond existing categorizations by incorporating dynamic transformations. Unlike prior taxonomies, which often focus on fixed attributes, our approach emphasizes the dynamic evolution of Metaverse characteristics. Through an extensive review of published literature, this study explores key technological, social, economic, and ethical dimensions of the Metaverse. It introduces a process-oriented classification based on 23 distinct characteristics, including immersification, spatiotemporalification, interactification, persistentification, presentification, personification, unification, imaginification, economification, uncertaintification, and credification. By mapping these evolving aspects, we provide a structured and future-proof foundation for understanding the Metaverse’s continuous development. This survey establishes a new standard for comprehensiveness and innovation, shedding light on the diverse facets that have been explored in literature. Through this novel taxonomy, we provide a detailed map of the current landscape and offer insights that pave the way for future research and development in this burgeoning digital frontier. © The Author(s) 2025.

关键词： Comprehensive analysis Futuristic exploration Metaverse characteristics Paradigm-shifting taxonomy Revolutionary categorization

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DMM: Disparity-guided Multispectral Mamba for Oriented Object Detection in Remote Sensing

arXiv

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arXiv 2024年

作者： Zhou, Minghang Li, Tianyu Qiao, Chaofan Xie, Dongyu Wang, Guoqing Ruan, Ningjuan Mei, Lin Yang, Yang The Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China The Beijing Institute of Space Mechanics and Electricity Beijing100094 China Donghai Laboratory Zhejiang Zhoushan316021 China The Beijing Institute of Space Mechanics and Electricity Beijing100094 China The Center for Future Media The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China The Institute of Electronic and Information Engineering University of Electronic Science and Technology of China Guangdong523808 China

Multispectral oriented object detection faces challenges due to both inter-modal and intra-modal discrepancies. Recent studies often rely on transformer-based models to address these issues and achieve cross-modal fusion detection. However, the quadratic computational complexity of transformers limits their performance. Inspired by the efficiency and lower complexity of Mamba in long sequence tasks, we propose Disparity-guided Multispectral Mamba (DMM), a multispectral oriented object detection framework comprised of a Disparity-guided Cross-modal Fusion Mamba (DCFM) module, a Multi-scale Target-aware Attention (MTA) module, and a Target-Prior Aware (TPA) auxiliary task. The DCFM module leverages disparity information between modalities to adaptively merge features from RGB and IR images, mitigating inter-modal conflicts. The MTA module aims to enhance feature representation by focusing on relevant target regions within the RGB modality, addressing intra-modal variations. The TPA auxiliary task utilizes single-modal labels to guide the optimization of the MTA module, ensuring it focuses on targets and their local context. Extensive experiments on the DroneVehicle and VEDAI datasets demonstrate the effectiveness of our method, which outperforms state-of-the-art methods while maintaining computational efficiency. Code will be available at https://***/Another-0/DMM. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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Reveal the Mathematical Structures of Honeyword Security Metrics

arXiv

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arXiv 2023年

作者： Su, Pengcheng Cheng, Haibo Li, Wenting Wang, Ping Peking University School of Computer Science Beijing China Peking University National Engineering Research Center for Software Engineering Beijing China Peking University School of Software and Microelectronics Beijing China

Honeyword is a representative "honey" technique to detect intruders by luring them with decoy data. This kind of honey technique blends a primary object (from distribution P) with decoy samples (from distribution Q). In this research, we focus on two key Honeyword security metrics: (1) flatness function (ϵA(i)): the probability that a distinguishing attacker A (a probabilistic Turing Machine) succeeds in picking up the real one from honey objects of a user within i chances, and (2) success-number function (λAU(i)): in a Honeyword system of U users, the expectation of A’s success number (the number of passwords submitted by A) before its failure number (the number of honeywords submitted by A) reaches a threshold i. Previous researchers are engaged in designing experimental methods to estimate their values. We’ve derived theoretical formulas on both metrics of the strongest A using the optimal guessing strategy, marking a first in the field. The mathematical structures of these metrics are intriguing: the flatness function has an expression as (Formula Presented). In particular, the most important one, (Formula Presented)b, where M(Formula Presented), and G is a cumulative distribution function derived from P and Q. This formula provides a criterion to compare different honey distributions: the one with smaller M and b is more satisfactory. The mathematical structure of the success-number function is a series of convolutions with beta distribution kernels: (Formula Presented), where U is the number of users in the system and (x) is a monotonically increasing function. For further elaboration, we made some representative calculations. Our findings offer insights into security assessments for Honeyword and similar honey techniques, contributing to enhanced security measures in these systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Distribution functions

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Right Frontal Gamma Transcranial Alternating Current Stimulation Modulates Optimism Biases

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Neuroscience Bulletin 2025年第1期41卷 172-176页

作者： Ziqing Yao Jinwen Wei Gan Huang Linling Li Zhen Liang Li Zhang Haiyan Wu Tifei Yuan Zhiguo Zhang Xiaoqing Hu Department of Psychology The State Key Laboratory of Brain and Cognitive SciencesThe University of Hong KongHong KongSARChina School of Biomedical Engineering Medical SchoolShenzhen UniversityShenzhen518060China Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging Shenzhen UniversityShenzhen518060China Centre for Cognitive and Brain Sciences and Department of Psychology University of MacaoMacao SARChina Shanghai Key Laboratory of Psychotic Disorders Brain Health InstituteNational Center for Mental DisordersShanghai Mental Health CenterShanghai Jiaotong University School of MedicineShanghai210109China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China Peng Cheng Laboratory Shenzhen518055China HKU-Shenzhen Institute of Research and Innovation Shenzhen518063China

Dear Editor,People are often optimistic when forecasting the future,such that they believe they are less likely to encounter adverse life *** phenomenon,known as optimism bias,emerges due to preferential encoding and consolidation of desirable over undesirable information[1,2],engaging frontal brain regions such as the anterior cingulate cortex and inferior frontal gyrus[1].An optimism bias is instrumental in supporting mental health:reduction or absence of optimism biases is associated with mood disorders such as depression,characterized by pessimistic thinking about the future[3].This relationship highlights the potential value of interventions that could enhance optimism bias[4].

关键词： ism such likely

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MULTIMODAL TRANSFORMER NETWORKS WITH LATENT INTERACTION FOR AUDIO-VISUAL EVENT LOCALIZATION

MULTIMODAL TRANSFORMER NETWORKS WITH LATENT INTERACTION FOR ...

引用

2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： He, Yixuan Xu, Xing Liu, Xin Ou, Weihua Lu, Huimin Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China China Glasgow College University of Electronic Science and Technology of China China Department of Computer Science Huaqiao University China School of Big Data and Computer Science Guizhou Normal University China Department of Mechanical and Control Engineering Kyushu Institute of Technology Japan

ISBN: (纸本)9781665438643

The task of audio-visual event localization (AVEL) aims to localize a visible and audible event in a video. Previous methods first divide a video into segments and then fuse visual and acoustic features at the segment level via a co-attention mechanism. However, existing methods mostly model relations between individual visual and audio segments in a limitedly short period, which may not cover a longer video duration for better high-level event information modeling. In this paper, we proposed a novel model termed Multimodal Transformer Network with Latent Interaction (MTNLI) to tackle this problem. The proposed MTNLI model employs a multimodal Transformer structure to learn the cross-modality relationships between latent visual and audio summarizations in long segment sequences, which summarize the visual and audio segments into a small number of latent representations to avoid modeling uninformative individual visual-audio relations. The cross-modality information between the latent summarizations is propagated to fuse valuable information from both modalities, which can effectively handle large temporal inconsistent between vision and audio. Our MTNLI method achieves state-of-the-art performance on the benchmark AVE (Audio-Visual Event) dataset for the event localization task. © 2021 IEEE

关键词： Benchmarking

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A Track-type Inverted Climbing Robot with Bio-inspired Spiny Grippers

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Journal of Bionic engineering 2020年第5期17卷 920-931页

作者： Yanwei Liu Limeng Wang Fuzhou Niu Pengyang Li Yan Li Tao Mei School of Mechanical and Precision Instrument Engineering Xi'an University of TechnologyXi'an 710048China School of Mechanical Engineering Suzhou University of Science and TechnologySuzhou 215009China Robotics Research Center Peng Cheng LaboratoryShenzhen 518055China

To enable the capacity of climbing robots to work on steep surfaces,especially on inverted surfaces,is a fundamental but challenging *** capacity can extend the reachable workspace and applications of climbing robots.A track-type inverted climbing robot called SpinyCrawler was developed in this *** a spiny track with an opposed gripping mechanism,the robot was experimentally demonstrated to have the ability of generating considerable adhesion to achieve stable inverted ***,to guarantee reliable attachment of the robot on rough ceilings,a spiny gripper inspired by the opposed gripping prolegs of caterpillars is designed,and a gripping model of the interaction between spines and the ceiling asperities is established and ***,a spiny track is developed by assembling dozens of spiny grippers to enable continuous attachment.A cam mechanism is introduced in the robot design without extra actuators to achieve stable attachment and easy detachment during continuous ***,climbing experiments are conducted on different surfaces,using a SpinyCrawler *** results demonstrated stable climbing ability on various rough inverted and vertical surfaces,including concrete walls,crushed stone walls,sandpaper walls,brick walls,and brick ceilings.

关键词： inverted climbing track-type robot rough ceiling bio-inspired spiny gripper

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A hierarchical bagging-SVM human behavior recognition method

A hierarchical bagging-SVM human behavior recognition method

引用

2021 International Conference on Electronic Information engineering and computer Technology, EIECT 2021

作者： Liang, Yulong Dang, Xiaochao Hao, Zhanjun School of Computer Science and Engineering Northwest Normal University Lanzhou730070 China Gansu Internet of Things Engineering Research Center Lanzhou730070 China

ISBN: (数字)9781510650510

ISBN: (纸本)9781510650503

Aiming at the problems of using mobile device sensors to recognize human behaviors such as low accuracy, few types, and no consideration of transition actions, this paper proposes a hierarchical Bagging-SVM human behavior recognition method. This method first applies the moving average filtering algorithm to the collected sensor data for noise reduction and smoothing;then calculates the amplitude of the acceleration vector sum to characterize human motion behavior, extracts time domain features to build a layered model;finally uses the Bagging-SVM algorithm Perform hierarchical recognition of behaviors in hierarchical models. Experimental results show that compared with other recognition methods, this method can recognize human behavior more accurately and has higher robustness. © 2021 SPIE.

关键词： Behavioral research

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Multimodal Detection and Identification of Robot Manipulation Failures

arXiv

引用

arXiv 2023年

作者： Inceoglu, Arda Aksoy, Eren Erdal Sariel, Sanem Artificial Intelligence and Robotics Laboratory Faculty of Computer and Informatics Engineering Istanbul Technical University Maslak Turkey School of Information Technology Center for Applied Intelligent Systems Research Halmstad University Halmstad Sweden

An autonomous service robot should be able to interact with its environment safely and robustly without requiring human assistance. Unstructured environments are challenging for robots since the exact prediction of outcomes is not always possible. Even when the robot behaviors are well-designed, the unpredictable nature of physical robot-object interaction may prevent success in object manipulation. Therefore, execution of a manipulation action may result in an undesirable outcome involving accidents or damages to the objects or environment. Situation awareness becomes important in such cases to enable the robot to (i) maintain the integrity of both itself and the environment, (ii) recover from failed tasks in the short term, and (iii) learn to avoid failures in the long term. For this purpose, robot executions should be continuously monitored, and failures should be detected and classified appropriately. In this work, we focus on detecting and classifying both manipulation and post-manipulation phase failures using the same exteroception setup. We cover a diverse set of failure types for primary tabletop manipulation actions. In order to detect these failures, we propose FINO-Net [1], a deep multimodal sensor fusion based classifier network. Proposed network accurately detects and classifies failures from raw sensory data without any prior knowledge. In this work, we use our extended FAILURE dataset [1] with 99 new multimodal manipulation recordings and annotate them with their corresponding failure types. FINO-Net achieves 0.87 failure detection and 0.80 failure classification F1 scores. Experimental results show that proposed architecture is also appropriate for real-time use. Copyright © 2023, The Authors. All rights reserved.

关键词： Robots

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MACRO: Incentivizing Multi-Leader Game-Based Pareto-Efficient Crowdsourcing for Video Analytics

MACRO: Incentivizing Multi-Leader Game-Based Pareto-Efficien...

引用

International Conference on Data engineering

作者： Yu Chen Sheng Zhang Ziying Zhou Xiaokun Wang Yu Liang Ning Chen Yuting Yan Mingjun Xiao Jie Wu Zhuzhong Qian Harry Xu State Key Lab. for Novel Software Technology Nanjing University P.R. China School of Systems Science Beijing Normal University China School of Computer and Electronic Information / School of Artificial Intelligence Nanjing Normal University China School of Computer Science and Technology University of Science and Technology of China P.R. China Center for Networked Computing Temple University USA Computer Science Department Samueli School of Engineering University of California Los Angeles USA

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

In recent years, many crowdsourcing platforms have emerged, using the resources of recruited workers to perform diverse outsourcing tasks, where the video analytics attracts much attention due to its practical implications. For maximum profits, platforms carefully choose the workers and determine the video analytics configurations to ensure accuracy; meanwhile, workers possess the flexibility to tailor the configurations for their indivi-dual gains, which makes it hard for platforms to optimize their profits considering the platform-worker conflicts. In this paper, we design an incentive mechanism for Multi-leader game-based video Analytics upon CROwdsourcing, named MACRO, to over-come the above situation. Under that mechanism, we first formu-late the utility optimization problems for platforms and workers, respectively. We then propose a dual ascent-based method to op-timally determine the video analytics configurations for a multi-platform game, ensuring Pareto efficiency. Moreover, in the context of a multi-leader game involving platform-worker conflicts, we design an incentive function with its incentive factor update strategy and propose an ADMM-based approach for maximizing incentives that motivate workers to contribute to the platforms' profits. Rigorous proofs demonstrate the linear convergence of the MACRO to the multi-leader Stackelberg equilibrium. Trace-driven experiments show that MACRO improves the Pareto efficiency by 26.3%, outperforming other approaches.

关键词： Crowdsourcing Accuracy Visual analytics Games Data engineering Outsourcing Task analysis

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PMDC: Pancreas Segmentation with Mamba Block and Deformable 3D Convolution

PMDC: Pancreas Segmentation with Mamba Block and Deformable ...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Meng, Ziyao Qin, Yiming Wang, Tianyi Shen, Hao Liu, Yang Dong, Jie Song, Haitao Shanghai Jiao Tong University Computer Science and Engineering Shanghai China Shanghai Artificial Intelligence Research Institute Shanghai China Xiangfu Laboratory Jiashan China Medical School of Nanjing University Nanjing Jinling Hospital Department of Critical Care Medicine Nanjing China Shanghai Jiao Tong University National Center for Translational Medicine Shanghai China

ISBN: (纸本)9798350386226

Pancreas segmentation is a task traditionally hindered by the morphological complexity and variability of the pancreas. Transformers have excelled in capturing context information in medical images but are limited by computational resources. Recently, state space models (SSMs), especially Mamba, have demonstrated the ability to linearly scale with input sequences, surpassing transformers in long-sequence data analysis tasks and showing potential in visual tasks as well. Inspired by this, we propose a novel approach for pancreas segmentation using Mamba Block and deformable 3D convolution (PMDC). PMDC integrates Convolutional Neural Networks (CNN) and SSM, capturing both local fine-grained features and long-range dependencies through a Mamba-based U-Net architecture and leveraging deformable 3D convolutions (3DCN) to dynamically adapt to pancreatic shape variations. PMDC also ensures scalability and generalizability by self-configuring across different datasets. Experiments on NIH, MSD, and PancCT datasets demonstrate that PMDC outperforms existing CNNs and transformers, offering a robust solution for pancreas segmentation. © 2024 IEEE.

关键词： Convolutional neural networks

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