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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Kwon, Soonhong Son, Wooyoung Lee, Jong-Hyouk Sejong University Dept. of Computer and Information Security and Convergence Engineering for Intelligent Drone Seoul Korea Republic of Sejong University Protocol Engineering Lab. Seoul Korea Republic of

ISBN: (纸本)9798350333336

With the recent surge in interest surrounding the metaverse environment, the significance of effective copyright license management has grown considerably. While systematic copyright management is crucial, the limitations of existing copyright identification systems pose challenges to maintaining proper control. To address these issues, this paper proposes an integrated model combining Unique Content Identifier (UCI) and Extended Integrated Copyright Number (EICN) to develop a robust copyright identification system that safeguards the rights of copyright holders. © 2023 IEEE.

关键词： Copyrights

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Fractal Measurement of Images Human Brain from MRI

Fractal Measurement of Images Human Brain from MRI

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2024 Cognitive Models and Artificial Intelligence Conference, AICCONF 2024

作者： Anarova, Shahzoda Jabbarov, Jamoliddin Uralova, Iroda Tashkent University of Information Technologies Department of Convergence of digital technologies Computer Engineering Faculty Tashkent Uzbekistan Department of Computer science and technology Intelligent systems and computer technologies Faculty Samarkand State University Samarkand Uzbekistan

ISBN: (纸本)9798400716928

This article is devoted to the determination of the fractal size of the damaged part of the human brain on the basis of images obtained from MRI (magnetic resonance imaging). There are various mathematical methods for determining the size of fractal structures, this article uses two variants of the cell counting method and describes the analysis of errors in determining the fractal size of the damaged part of the human brain. The article shows that the damaged part of the human brain has a fractal structure and that it has a fractal dimension. Fractal measurement has also been used to determine the size of the damaged part of the human brain, unlike common methods. In determining the fractal scale, cases of changes in human body organs were not taken into account. In the article, the determination of the size of the damaged part of the human brain is studied on the basis of mathematical formulas. Two different variants of the special cell counting method were used to calculate the fractal measurements, as well as the corresponding number of experimental results. In addition, these measurements were used to assess the extent to which patients suffered brain damage and to provide information on the practical application of these fractal measurements. © 2024 ACM.

关键词： Magnetic resonance imaging

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Tjong:A transformer‐based Mahjong AI via hierarchical decision‐making and fan backward

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 982-995页

作者： Xiali Li Bo Liu Zhi Wei Zhaoqi Wang Licheng Wu School of Information and Engineering Minzu University of ChinaBeijingChina Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE Minzu University of ChinaBeijingChina Department of Computer Science New Jersey Institute of TechnologyNewarkNew JerseyUSA

Mahjong,a complex game with hidden information and sparse rewards,poses significant *** Mahjong AIs require substantial hardware resources and extensive datasets to enhance AI *** authors propose a transformer‐based Mahjong AI(Tjong)via hierarchical decision‐*** utilising self‐attention mechanisms,Tjong effectively captures tile patterns and game dynamics,and it decouples the decision pro-cess into two distinct stages:action decision and tile *** design reduces de-cision complexity ***,a fan backward technique is proposed to address the sparse rewards by allocating reversed rewards for actions based on winning *** consists of 15M parameters and is trained using approximately 0.5 M data over 7 days of supervised learning on a single server with 2 *** action decision achieved an accuracy of 94.63%,while the claim decision attained 98.55%and the discard decision reached 81.51%.In a tournament format,Tjong outperformed AIs(CNN,MLP,RNN,ResNet,VIT),achieving scores up to 230%higher than its ***-more,after 3 days of reinforcement learning training,it ranked within the top 1%on the leaderboard on the Botzone platform.

关键词： decision making deep learning deep neural networks

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DIG: Improved DINO for Graffiti Detection

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3557-3569页

作者： Wang, Bingshu Mao, Qianchen Liu, Aifei Chen, Long Chen, C. L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China University of Macao Department of Computer and Information Science China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Graffiti detection is essential in historic building protection and urban neighborhood management. Graffiti detection has made significant progress in recent years based on the development of deep learning. However, small-scale graffiti, interference from the background, and the false detection of word parts in graffiti make it a challenging problem. This article proposes a Transformer-based high-precision graffiti detection method, namely DIG. Precisely, it consists of three modules: 1) Spatial query selection (SQS), scale-aware IoU loss (SIL);2) Denoising Task with binary contrastive denoising (BCDN);and 3) IoU-guided box denoising (IBD) modules. To detect small-scale graffiti, this paper proposes SIL to help the loss function to perceive small-scale graffiti and large-scale graffiti fairly. To reduce the false detection of word parts, this article presents the SQS module, which integrates spatial information into the query selection process of the Encoder to filter out falsely detected bounding boxes within the graffiti. To reduce the interference from the background, this article introduces a denoising task with BCDN and IBD modules, improving the model’s ability to distinguish graffiti from the background and accurately select appropriate bounding boxes. A large number of experimental results on the STORM dataset show that our method achieves state-of-the-art results with an AP50 of 87.9%. Moreover, DIG achieved competitive results on the FineFM dataset for mask detection. This indicates that DIG can also be conveniently transferred to detect other scenarios. © 2013 IEEE.

关键词： Historic preservation

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Adaptive Relay Selection Scheme for Minimization of the Transmission Time

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computers, Materials & Continua 2021年第10期69卷 1361-1373页

作者： Yu-Jin Na Ji-Sung Jung Young-Hwan You Hyoung-Kyu Song Department of Information and Communication Engineering and Convergence for Intelligent DroneSejong UniversitySeoul05006Korea Department of Computer Engineering and Convergence for Intelligent DroneSejong UniversitySeoul05006Korea

As the installation of small cells increases,the use of relay also *** relay operates as a base station as well as just an *** the roles and types of relays become more diverse,appropriate relay selection technology is an effective way to improve communication *** researches for relay selection have been studied to secure the reliability of relay *** this paper,the relay selection scheme is proposed for a cooperative system using decode-and-forward(DF)relaying scheme in the mobile communication *** maintain the transmission rate,the proposed scheme classifies a candidate group considering the outage probability of multiple *** the applicable candidate group,the proposed scheme selects the relay considering the amount of data allocated to each ***,the proposed scheme defines the unit transmission time through each user’s data and relay ***,the proposed scheme selects a relay that minimizes the total transmission time through the relay transmission time that calculates the unit transmission time for all *** this adaptive relay selection scheme,an optimal relay can be assigned for each *** the same transmission rate and the amount of data,the proposed scheme improves the performance of transmission time and *** results show that the proposed scheme reduces the total transmission time for the same amount of data and signal to noise ratio(SNR).

关键词： Relay selection cooperative relay MIMO outage probability transmission time

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Dynamic data summarization for hierarchical spatial clustering

arXiv

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arXiv 2024年

作者： Abduaziz, Kayumov Kim, Min Sik Shin, Ji Sun Department of Computer and Information Security and Convergence Engineering for Intelligent Drones Sejong University Korea Republic of Petabi Inc. Department of Computer and Information Security Sejong University Korea Republic of

Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) finds meaningful patterns in spatial data by considering density and spatial proximity. As the clustering algorithm is inherently designed for static applications, so have recent studies focused on accelerating the algorithm for static applications using approximate or parallel methods. However, much less attention has been given to dynamic environments, where even a single point insertion or deletion can require recomputing the clustering hierarchy from scratch due to the need of maintaining the minimum spanning tree (MST) over a complete graph. This paper addresses the challenge of enhancing the clustering algorithm for dynamic data. We present an exact algorithm that maintains density information and updates the clustering hierarchy of HDBSCAN during point insertions and deletions. Considering the hardness of adapting the exact algorithm to dynamic data involving modern workloads, we propose an online-offline framework. The online component efficiently summarizes dynamic data using a tree structure, called Bubble-tree, while the offline step performs the static clustering. Experimental results demonstrate that the data summarization adapts well to fully dynamic environments, providing compression quality on par with existing techniques while significantly improving runtime performance of the clustering algorithm in dynamic data workloads. © 2024, CC BY-NC-ND.

关键词： Hierarchical clustering

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SEIF: A Semantic-enabled IoT Service Framework for Realizing Interoperable Data and Knowledge Retrieval

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IEIE Transactions on Smart Processing and Computing 2023年第1期12卷 9-22页

作者： Lee, Jieun Gilani, Komal Khatoon, Nargis Jeong, SeungMyeong Song, JaeSeung Department of Computer Security and Convergence Engineering for Intelligent Drones Sejong University Seoul Korea Republic of EGM Sophia Antipolis Valbonne06560 France Autonomous IoT Research Center Korea Electronics Technologies Institute Gyeonggi-do Korea Republic of

The Internet of Things (IoT) has enabled interconnection of embedded computing devices comprised of their own data models in existing infrastructure. Currently, many IoT platforms provide data modeling and information retrieval services. However, the use of various data models and standards causes interoperability issues. Semantic technologies ease such barriers by providing the means to annotate semantic information as IoT data based on common data models. For this reason, semantics is becoming a growing requirement and a major challenge for IoT service layer platforms. The addition of semantic support empowers IoT systems with enhanced meaning in the data gathered from various devices and vertical silos. This paper presents a semantic IoT framework that includes agile support for adding semantic meaning to data, and for knowledge discovery, semantic validation, and accessibility to IoT data through a standardized semantic query language. The proposed evaluation matrix for a possible enhancement-anddevelopment guideline based on a global IoT standard is measured through smart office use cases comprising different sensor information. This paper provides a detailed description of semantic enablement on IoT platforms, including details of its implementation. The results provide a clear guideline for integrating semantic features into the IoT service layer and for data management. © 2023 The Institute of Electronics and information Engineers.

关键词： Internet of things

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A Robust Key Exchange and Tamper-Resistant Protocol for HAN and NAN Networks in Smart Grids

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IEEE Internet of Things Journal 2025年第12期12卷 21394-21403页

作者： Faizan Ayub, Muhammad Li, Xiong Mahmood, Khalid Shamshad, Salman Kumar Das, Ashok Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Korea University College of Informatics Department of Computer Science and Engineering Seoul02841 Korea Republic of Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China

Smart grids (SGs) rely on home area networks (HANs) and neighborhood area networks (NANs) to ensure efficient power distribution, real-time monitoring, and seamless communication between smart devices. Despite these advantages, the use of public communication channels in HAN and NAN networks introduces critical challenges, such as vulnerability to impersonation, physical tampering, and scalability issues in resource-constrained environments. These issues compromise the stability, reliability, and security of SG environments. Existing protocols often fail to adequately address these challenges, particularly in ensuring resistance to physical tampering of supply lines and impersonation attacks. Additionally, they struggle to minimize the computation, communication, and energy costs associated with securing a resource-constrained SG environment. Therefore, we propose a robust key exchange and tamper-resistant protocol for HAN and NAN networks to address these limitations. The proposed protocol leverages the physical unclonable function (PUF) mechanism to offer physical tampering resistance to smart meters. We validate our protocol formally using the Random or Real (RoR) model, which confirms its security robustness. Additionally, our informal security analysis highlights the protocol’s resilience against impersonation, physical tampering attacks, etc. We analyze and compare the performance of the proposed protocol, which further demonstrates our protocol’s effectiveness and security compared to competing protocols. Moreover, the proposed protocol achieves resource efficiency with 45.99% and 58.85% substantial reductions in computation overhead and energy overhead, respectively, compared to competing protocols. These results showcase the protocol’s enhanced security and practicality for resource-constrained SG environments. © 2014 IEEE.

关键词： Authentication Protocol

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Transfer Learning-Based Robust Insider Threat Detection

Transfer Learning-Based Robust Insider Threat Detection

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IEEE International Conference on Trust, security and Privacy in Computing and Communications (TrustCom)

作者： Yujun Kim Deok-Han Kim Young-Gab Kim Dept. of Computer and Information Security and Convergence Engineering for Intelligent Drone Sejong University Seoul Republic of Korea

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

A malicious insider’s threats who has access to the organization’s systems and is familiar with security policies are difficult to detect and can cause significant financial damage. A reconstruction-based anomaly detection method leveraging deep learning is one method for detecting insider threats. The method is employed to solve a data imbalance problem caused by the scarcity of abnormal data in real environments. The method trains a deep learning model to reconstruct normal data received from all users. After training, the model reconstructs normal data more accurately than abnormal data, and the reconstruction-based anomaly detection method can detect anomalies based on the reconstruction difference between normal and abnormal data. However, existing reconstruction-based anomaly detection methods train on all users’ normal data using the same network parameters. Consequently, if a behavior is normal for one user but abnormal for another, the reconstruction model learns this behavior as normal because the reconstruction model trains only on normal behaviors. Since normal behaviors differ for each user depending on their position, role, and other factors within the organization, it is necessary to develop methods for detecting abnormal behaviors specific to each user. To address this problem, we propose a transfer learning-based insider threat detection method. The proposed method consists of 1) a pre-trained encoder that outputs latent representations of normal data for all users and 2) user-specific decoders assigned to each user, which are trained on the corresponding user’s normal data to reconstruct their normal data. We evaluate the proposed method’s detection rate (DR) and area under the curve (AUC) on the CERT dataset and compare it with a deep learning model trained on normal data from all users. The experimental results show that the proposed method achieves higher DR and AUC than the deep learning model. These results indicate that the proposed method en

关键词： Deep learning Training Autoencoders Transfer learning Organizations Threat assessment Data models Decoding security Anomaly detection

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A Method for Quantitative Object De-Identification Analysis of Anonymized Video

A Method for Quantitative Object De-Identification Analysis ...

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IEEE International Conference on Trust, security and Privacy in Computing and Communications (TrustCom)

作者： Deok-Han Kim Yujun Kim Young-Gab Kim Dept. of Computer and Information Security and Convergence Engineering for Intelligent Drone Sejong University Seoul Republic of Korea

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

The rise in video content has amplified the risk of information leakage, prompting the development of various anonymization techniques. These techniques include traditional methods such as pixelation, blurring, and masking, as well as more advanced approaches like video encryption, which involves encrypting parameters during video encoding, and techniques that subtly alter facial features to prevent identification. However, assessing the effectiveness of these anonymization techniques remains challenging. Common metrics such as the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR), often used for object anonymization in videos, primarily assess image quality and lack accuracy for security evaluation. Furthermore, subjective human evaluations fail to provide the consistent, quantitative data necessary for cross-study comparisons. Given that a single identifiable frame in a video can compromise overall security, this study introduces a quantitative method for evaluating the de-identification rate in regions of interest (ROI) within videos. The proposed approach calculates an object de-identification rate (ODR) by combining SSIM and edge detection ratio (EDR) using a harmonic mean and a power mean. Our proposed method enhances the precision and reliability for security of anonymized video.

关键词： Measurement Data privacy Privacy PSNR Power system harmonics Video surveillance information filtering Encryption Reliability information integrity

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