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256 Metaverse Records Dataset 24

256 Metaverse Records Dataset

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32nd ACM International Conference on Multimedia, MM 2024

作者： Steinert, Patrick Wagenpfeil, Stefan Frommholz, Ingo Hemmje, Matthias L. Faculty of Mathematics and Computer Science University of Hagen Hagen Germany Faculty of Business Computing and Software Engineering Pfh University of Applied Sciences Göttingen Germany School of Mathematics and Computer Science University of Wolverhampton Wolverhampton United Kingdom

ISBN: (纸本)9798400706868

The metaverse is a developing field that crosses several areas of multimedia research. In this paper, we introduce the 256-MetaverseRecords dataset, a novel and extensive collection of annotated screen recordings in the form of videos from various virtual worlds of the metaverse. We describe the process of creating the dataset, the quality criteria for the annotations, and the exploration of the dataset. We also show four experiments to evaluate the performance of different feature extraction methods for Metaverse Recordings (MVRs): MVR segmentation, audio event detection, and object and interaction detection based on this dataset. Our results demonstrate that existing methods have limitations and leave challenges in dealing with the diversity and complexity of metaverse data, and that more research is needed to develop metaverse-specific techniques. Our dataset can serve as a valuable resource for the research community and foster the development of new applications and solutions for the metaverse. © 2024 Owner/Author.

关键词： dataset

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WIND SPEED FORECASTING IN HIGH-SPEED RAILWAY SYSTEM BASED ON SECONDARY DECOMPOSITION AND GATED RECURRENT UNIT NETWORK 10

WIND SPEED FORECASTING IN HIGH-SPEED RAILWAY SYSTEM BASED ON...

引用

10th International Symposium on Test Automation and Instrumentation, ISTAI 2024

作者： Gu, Wei Xu, Chunjie Yang, Guoyuan Xing, Hongyan Liu, Tongyuan School of Electronics & Information Engineering Nanjing University of Information Science & Technology Nanjing210044 China Institute of Computing Technology China Academy of Railway Sciences Corporation Limited Beijing100081 China

ISBN: (纸本)9781837242672

Accurately predicting wind speed (WS) is crucial for the operational safety of high-speed railway (HSR) systems in strong wind environments. However, the prediction task is challenging due to WS data's nonlinearity and intermittency nature. To address this issue, this study proposes a hybrid prediction model SD-BGRU, which combines secondary decomposition (SD), sample entropy (SE), and gated recurrent unit (GRU). Specifically, the raw WS signal is first decomposed using complete ensemble empirical mode decomposition adaptive noise (CEEMDAN). The decomposed data are clustered into three groups based on their SE. VMD is then applied to further decompose sub-signals with high SE values. The remaining sub-signals obtained from CEEMDAN and sub-signals obtained from VMD are fed into bidirectional GRU (BGRU) to generate individual predictions. These prediction results are combined to produce a final prediction result. We conducted extensive experiments on a real-world WS dataset to evaluate the performance of SD-BGRU. The experiment results demonstrate that SD-BGRU outperforms other comparative models, with a mean absolute error of 0.3395, root mean square error of 0.4571, and mean absolute percentage error of 8.88%. These promising results indicate that SD-BGRU is an effective way for guiding decision-making in the HSR system. © The Institution of engineering & Technology 2024.

关键词： Wind forecasting

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Ethereum Fraud Detection: A comparative analysis of supervised learning approach 1

Ethereum Fraud Detection: A comparative analysis of supervis...

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1st IEEE International Conference on Advances in Signal Processing, Power, Communication, and computing, ASPCC 2024

作者： Tripathy, Nrusingha Chaudhury, Kumar Surjeet Nibedita, Arpita Nayak, Subrat Kumar Behera, Manjushree Jena, Baidehi Siksha O Anusandhan Deemed to Be University Department of Computer Science and Engineering Odisha Bhubaneswar India Kalinga Institute of Industrial Technology Deemed to Be University School of Computer Engineering Odisha Bhubaneswar India Trident Academy of Technology School of Computing Odisha Bhubaneswar India P.N College Department of Computer Science Khordha India

ISBN: (纸本)9798350355093

Ethereum is an application platform that distributes versions of intelligent contracts to thousands of people globally, utilizing blockchain to decentralize data. Ethereum is a global currency that is used to exchange value without requiring supervision or outside involvement. However, as e-commerce grows, the biggest threat to trade security is the proliferation of illegal activities like phishing, money laundering, and bribery. The need for strong defenses is highlighted by the inherent hazards of blockchain technology, such as the potential for fraud and cyberattacks. The stability and preservation of confidence in the financial system depend on an accessible network that is impervious to these kinds of assaults. Different classification algorithms, including Logistic Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) are being used in this work to detect Ethereum fraud. We are utilizing a dataset that includes rows of Ethereum cryptocurrency transactions along with rows of known unauthorized transactions. Notably, the 'XGBoost' model identifies differences that might draw attention and avert possible problems in this task. Supervised learning for Ethereum fraud detection improves the ecosystem's security and integrity. © 2024 IEEE.

关键词： Ethereum

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Comparative Analysis of Password Storage Security using Double Secure Hash Algorithm

Comparative Analysis of Password Storage Security using Doub...

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2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022

作者： Chethana, Savarala Charan, Sreevathsa Sree Srihitha, Vemula Radha, D. Kavitha, C.R. Amrita School of Computing Bengaluru Dept. of Computer Science & Engineering Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9781665453424

Passwords are generally used to keep unauthorized users out of the system. Password hacking has become more common as the number of internet users has extended, causing a slew of issues. These problems include stealing the confidential information of a company or a country by adversaries which harm the economy or the security of the organization. Hackers often use password hacking for criminal activities. It is indispensable to protect passwords from hackers. There are many hacking methods such as credential stuffing, social engineering, traffic interception, and password spraying for hacking the passwords. So, in order to control hacking, there are hashing algorithms that are mostly used to hash passwords making password cracking more difficult. In this proposed work, different hashing algorithms such as SHA-1, MD-5, Salted MD-5, SHA-256, and SHA-512 have been used. And the MySQL database is used to store the hash values of passwords that are generated using various hash functions. It is proven that SHA is better than MD-5 and Salted MD-5. Whereas in the SHA family, SHA-512 and SHA-256 have their own benefits. Four new hashing functions have been proposed using the combination of existing algorithms like SHA-256, and SHA-512 namely SHA-256-with-SHA-256, SHA-256- With-SHA-512,SHA-512-With-SHA-512,and SHA-512- With-SHA-256. They provide strong hash value for passwords by which the security of passwords increases, and hacking can be controlled to an extent. © 2022 IEEE.

关键词： Hash functions

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Secure Encryption of Satellite Images Using S-Box-Based Two-Fish Algorithm and A Novel Chaotic Map 3rd

Secure Encryption of Satellite Images Using S-Box-Based Two-...

引用

3rd International Conference on Signal and Data Processing, ICSDP 2023

作者： Jansi, R. Ashwini, K. Aishwarya, N. Muthulakshmi, M. Logeshwari, M. Kanna, Mukesh Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai India Department of Mathematics Sathyabama Institute of Science and Technology Semmancheri Tamil Nadu Chennai600119 India

ISBN: (纸本)9789819795772

In recent days, encryption of satellite imagery has attracted a wide range of researchers since these images contain confidential information. Thereby, it is necessary to preserve the privacy of this data to avoid unauthorized access. Two-fish algorithm is one of the widely used image encryption schemes. However, the main drawback of this scheme lies with the static nature of the S-box. To avoid this issue and to improve the quality of encryption, in this paper, we propose a novel algorithm based on chaotic map to create a dynamic S-box structure. In the proposed Chaotic S-Box-based Two-Fish (CSTF) algorithm, a novel chaotic map with improved Lyapunov exponent and approximate entropy has been proposed. The sequences produced by the proposed chaotic map are used to randomize the values inside the static S-box structure of Two-fish algorithm. This encryption scheme is suitable for the encryption of satellite images. The proposed algorithm is validated in terms of S-box evaluation metrics like nonlinearity, bit independence criteria-nonlinearity (BIC-nonlinearity), strict Avalanche criterion (SAC) and bit independence criteria-strict Avalanche criterion (BIC-SAC). To validate the proposed algorithm in terms of key sensitivity, tests like Unified Average Changing Intensity (UACI), Hamming Distance (HD) and Number of Pixels Change Rate (NPCR) have been performed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lyapunov functions

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Advancing In-Situ Bioprinting Through Ant Colony Optimisation: Resolving Dead Zone Challenges in Path Planning 2nd

Advancing In-Situ Bioprinting Through Ant Colony Optimisatio...

引用

2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Liu, Keyu Huang, Long Feng, Zhiming Ren, Xianlin Chen, Yi School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou China School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou China Department of Chemical Engineering University of Manchester Manchester United Kingdom School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9789819639489

Organ transplantation faces significant challenges due to the limited availability of donor organs, necessitating the exploration of innovative alternatives. Bioprinting, a dynamic form of 3D printing, offers a promising solution by creating complex biological structures layer by layer. This study focuses on addressing the dynamic optimization challenges in in-situ bioprinting through the integration of Ant Colony Optimization (ACO) within the Computational Intelligence Aided Design (CIAD) framework. The dynamic nature of bioprinting environments, characterized by shifting obstacles and changing targets, requires robust algorithms that can adapt to these variations in real-time. ACO, inspired by the foraging behavior of ant colonies, provides effective global optimization with low complexity, making it well-suited for these dynamic conditions. The methodology includes environmental modeling, adaptive path selection, and dynamic dead zone escape through improved state transition rules. Simulations conducted using MATLAB demonstrate that ACO ensures complete area coverage, minimizes track repetition, and significantly reduces the number of turns, thus enhancing the efficiency and success rate of bioprinting. These findings highlight the potential of ACO in advancing dynamic optimization techniques for bioprinting, contributing to the broader fields of evolutionary dynamic optimization and regenerative medicine. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ant colony optimization

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Face Recognition in Digital Documents with Live Image 7

Face Recognition in Digital Documents with Live Image

引用

7th IET Smart Cities Symposium, SCS 2023

作者： Ajitha, P. Venkateswaran, H. Tamilvizhi, T. Sadish Sendil, M. Surendran, R. School of Computing Sathyabama Institute of Science and Technology Chennai India Department of Mathematics Sapthagiri Engineering College Krishnagiri India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Emerging Technologies Guru Nanak Institute of Technology Telegana India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9781839539831

The primary point of this undertaking is to take care of the issue of duplicating testaments we are proposing a computerized authentication framework dependent on square chain innovation and to confirm the explorer's personality utilizing live camera, which permits quicker union and increasingly generalizable portrayals. So as to take care of the issue of duplicating testaments, the advanced authentication framework dependent on block chain innovation would be presented. The framework produces the electronic record of a paper statement that goes into the database with other related information, and ascertains its hash esteem. It stores the hash in the chain frame an opportunity into the square at that point. The structure will generate a related QR code and request string code to enter the paper testament, thus verifying the paper statement’s validity. The platform enhances efficiency activities at each level by organising block chain highlights. The invention of the Block chain gives changelessness and freely undeniable exchanges, these Block chain properties can be used to create the advanced testament that is hostile to false and easy to confirm. © The Institution of engineering & Technology 2023.

关键词： Authentication

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PipeEdge: A Trusted Pipelining Collaborative Edge Training based on Blockchain 23

PipeEdge: A Trusted Pipelining Collaborative Edge Training b...

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32nd ACM World Wide Web Conference, WWW 2023

作者： Yuan, Liang He, Qiang Chen, Feifei Dou, Ruihan Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia Faculty of Mathematics University of Waterloo Canada National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Powered by the massive data generated by the blossom of mobile and Web-of-Things (WoT) devices, Deep Neural Networks (DNNs) have developed both in accuracy and size in recent years. Conventional cloud-based DNN training incurs rapidly-increasing data and model transmission overheads as well as privacy issues. Mobile edge computing (MEC) provides a promising solution by facilitating DNN model training on edge servers at the network edge. However, edge servers often suffer from constrained resources and need to collaborate on DNN training. Unfortunately, managed by different telecoms, edge servers cannot properly collaborate with each other without incentives and trust. In this paper, we introduce PipeEdge, a scheme that promotes collaborative edge training between edge servers by introducing incentives and trust based on blockchain. Under the PipeEdge scheme, edge servers can hire trustworthy workers for pipelined DNN training tasks based on model parallelism. We implement PipeEdge and evaluate it comprehensively with four different DNN models. The results show that it outperforms state-of-the-art schemes by up to 173.98% with negligible overheads. © 2023 ACM.

关键词： Deep neural networks

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What Factors Affect Multi-Modal In-Context Learning? An In-Depth Exploration 38

What Factors Affect Multi-Modal In-Context Learning? An In-D...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Qin, Libo Chen, Qiguang Fei, Hao Chen, Zhi Li, Min Che, Wanxiang School of Computer Science and Engineering Central South University China Research Center for Social Computing and Information Retrieval China Harbin Institute of Technology China Tsinghua University China ByteDance China

Recently, rapid advancements in Multi-Modal In-Context Learning (MM-ICL) have achieved notable success, which is capable of achieving superior performance across various tasks without requiring additional parameter tuning. However, the underlying rules for the effectiveness of MM-ICL remain under-explored. To fill this gap, this work aims to investigate the research question: "What factors affect the performance of MM-ICL?" To this end, we investigate extensive experiments on the three core steps of MM-ICL including demonstration retrieval, demonstration ordering, and prompt construction using 6 vision large language models and 20 strategies. Our findings highlight (1) the necessity of a multi-modal retriever for demonstration retrieval, (2) the importance of intra-demonstration ordering over inter-demonstration ordering, and (3) the enhancement of task comprehension through introductory instructions in prompts. We hope this study can serve as a foundational guide for optimizing MM-ICL strategies in future research. © 2024 Neural information processing systems foundation. All rights reserved.

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Improved Performance of Canny Edge Detection in Low-Light Conditions 3

Improved Performance of Canny Edge Detection in Low-Light Co...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Dadi, Pradeep Surya Saranya, G. Tamilvizhi, T. Nelson, Leema Surendran, R. Chennai Institute of Technology Department of Mechatronics Engineering Chennai India Amrita Vishwa Vidyaapeetham Chennai Campus Amrita School of Computing Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665462167

Visual simultaneous localization and mapping (SLAM), which has numerous potential uses, is swiftly emerging as a significant development in embedded vision. In our analysis, there remain several unsolved dilemmas about SLAM that are visual, making it less favored over Lidar SLAM. Because it offers an affordable solution for mapping and navigation in mobile robots. These mobile robots can be defined as robots whose unique base is not fixed, so as technology establishes and advances, we. In this report, a real-time light, that is, dim or image or video in low light problem enhancement algorithm is proposed that significantly improves the overall performance of the Canny Edge part detection algorithm, which usually may be useful for benefit recognition in low light issues in cellular robots for several mapping and navigation tasks. © 2023 IEEE.

关键词： Mobile robots

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