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3rd International Symposium on computer Applications and Information Systems, ISCAIS 2024

作者： Zhao, Jiangfeng College of Computer Science and Software Engineering Shenzhen University Shenzhen518057 China

ISBN: (纸本)9781510681316

Multiplayer Online Battle Arena (MOBA) games currently dominate the esports landscape, offering a concrete and vivid embodiment for team comparisons, where accurately predicting the winning team is both important and challenging. Recent studies often rely on the static features of team members to evaluate a team’s capabilities and predict their likelihood of winning. However, such approaches fail to consider the critical game-contextual influences of interactions with teammates and opponents on individuals. During a match, an individual’s performance is consistently influenced by teammates and opponents, leading to variations in individual features across different games. Moreover, the measurement of team capabilities also needs improvement, as the single scalar rating employed is too simplistic for sophisticated MOBA games. To this end, we propose a novel Context-Enhanced Match Outcome Prediction model (CEMOP), which incorporates the effect of both teammates and opponents for a better measurement of team capabilities. Specifically, we designed a match context encoder for MOBA games to evaluates the impact of teammates and opponents on individuals separately and embeds the information into the individual representations. After that the single team member’s representations which contain the influence of teammates and opponents are aggregated based on their importance to create a comprehensive representation of the team capabilities, enables CEMOP to adaptively adjust team evaluations. Experimental results from three real-world MOBA games clearly verify the effectiveness of CEMOP compared with several state-of-the-art methods. © 2024 SPIE.

关键词： Data mining

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MVDC: A Multi-view Dental Completion Model Based on Contrastive Learning

MVDC: A Multi-view Dental Completion Model Based on Contrast...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yang, Xunyu Deng, Qingxin Huang, Minghan Jiang, Landu Zhang, Dian College of Computer Science and Software Engineering Shenzhen University Guangdong China Guangdong China

ISBN: (纸本)9798350368741

Restoring the patient's occlusal function of broken teeth is a challenging task since tooth texture is very complex, a slight deviation may affect the patient's chewing function and temporomandibular joint function. Therefore, how to efficiently repair the complete shape and real surface of the crown is a critical problem. Traditional technologies are hard to restore complete shape of the dental crown or lack inlay surface details, due to dataset limitations and complexity of missing parts. In this paper, we propose a multi-view crown restoration framework MVDC based on contrastive learning. Specifically, MVDC contains: 1) a multi-view generator with a specially designed loss measurement by using contrastive learning;2) a multi-scale discriminator mechanism able to consider relation and consistency between teeth from different scales;3) an occlusal groove extraction network to extract the occlusal details. We conducted extensive experiments on existing public datasets. The results showcase the superior performance of MVDC. © 2025 IEEE.

关键词： Contrastive learning Multi-view Teeth completion

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Rainfall Process Similarity Algorithm Research Based on Deep Learning by Using Precipitation Images 7

Rainfall Process Similarity Algorithm Research Based on Deep...

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7th International Conference on computer Information science and Application Technology, CISAT 2024

作者： Yu, Yufeng Wu, Panqi Cai, Difei Liu, Weifu Wang, Xuhang Hohai University College of Computer Science and Software Engineering Nanjing210098 China

ISBN: (纸本)9798350375107

Rainfall is the main cause of flood disasters, and analyzing its features plays a crucial role in preventing flood disasters. How to extract rainfall process features and conduct rainfall similarity analysis is a challenging work. Based on the similarity search method for daily precipitation images, this paper proposes a rainfall process similarity search method, which extracts rainfall duration, cumulative precipitation, temporal distribution, spatial distribution and center movement trajectory as the features of rainfall process, and defines a set-weighting method based on the Grey Wolf Optimization algorithm (IGWO) to weight and fuse the five extracted features as the similarity measure of rainfall process. The similarity search results of rainfall process images on the Jialing Basin illustrate that the method proposed in this paper can efficiently and accurately identify similar rainfall processes in historical scenes, thereby providing a new technical support for intelligent rainfall-flood forecasting. © 2024 IEEE.

关键词： Rain

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DeSAM2: Detection-Driven SAM2 for Hyperspectral Object Tracking 14

DeSAM2: Detection-Driven SAM2 for Hyperspectral Object Track...

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14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2024

作者： He, Wangquan Ren, Qi Zhang, Hongtao Mo, Ziqian Li, Nanying Xu, Meng Jia, Sen College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9798331513139

Hyperspectral object tracking (HOT) captures subtle object features, enabling precise identification and tracking in complex backgrounds. However, the high-dimensional nature of data and rapid object changes in dynamic scenes make accurate and robust tracking challenging. This paper proposes a detection-driven Segment Anything Model 2 (SAM2) for HOT (named DeSAM2), which enhances tracking accuracy and robustness through the collaboration of initial prompt tracking and a detection-based self-prompt auxiliary mechanism. Specifically, The method first employs a key band selection strategy to ensure that the spectral difference between object and background is maximized in complex backgrounds. Then, initial prompt tracking is used to acquire initial position of the object, and this process uses SAM2 as a base scheme to maintain high accuracy tracking. Furthermore, to address missed and false detections in initial tracking results within complex scenes, a detection-based self-prompt auxiliary mechanism is designed to further refine and correct tracking, thereby enhancing the adaptability and continuity of the model in dynamic environments. Experimental results on the datasets provided by the HOT Challenge 2024 indicate that the proposed method demonstrates superior performance compared to other advanced trackers. © 2024 IEEE.

关键词： Object detection

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VR-Recon: Visual Refiner for Fine-Grained Human Reconstruction 4

VR-Recon: Visual Refiner for Fine-Grained Human Reconstructi...

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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Xie, Hong Zhu, Zibin Zhang, Xiaoyan Shen Zhen University College of Computer Science & Software Engineering Shenzhen China

ISBN: (纸本)9798350349252

To reconstruct 3D human figures with fine-grained details from sparse views, this paper proposes a novel framework called VR-Recon, which is based on a viewpoint refiner. In this framework, we first decouple geometric features from multi-scale feature learning by integrating an MSG image encoder, aiming to extract high-quality multi-scale geometric features. Next, we employ a viewpoint refiner to accurately capture critical fine-grained geometric information in each independent view space, thereby avoiding the weighted average fusion of features and eliminating the influence of invalid information. This method is trained using a consistent cosine loss and a cascaded view-space geometric loss. The former encourages the MSG to accurately perceive fine geometric changes from the image, while the latter helps the viewpoint refiner discern critical information in different view spaces. Experimental results demonstrate that, compared to state-of-the-art methods, VR-Recon exhibits robust performance in capturing fine geometric details. © 2024 IEEE.

关键词： Feature extraction

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Dual-Enhanced High-Order Self-Learning Tensor Singular Value Decomposition for Robust Principal Component Analysis

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3564-3578页

作者： Xu, Honghui Fang, Chuangjie Wang, Renfang Chen, Shengyong Zheng, Jianwei Zhejiang University of Technology College of Computer Science and Technology Hangzhou310023 China Zhejiang Wanli University College of Big Data and Software Engineering Ningbo315200 China Tianjin University of Technology College of Computer Sciences and Engineering Tianjin300384 China

Recently, tensor singular value decomposition (TSVD) within high-order (Ho) algebra framework has shed new light on tensor robust principal component analysis (TRPCA) problem. However, HoTSVD lacks flexibility in handling the hidden correlations along different modes of large-scale multidimensional data. Moreover, the utilization of fixed or data-independent transformations in HoTSVD may result in suboptimality. For a relief, we propose a dual-enhanced self-learning TSVD along all modes to address computational flaws and learn a lossless transformation that induces a lower average-rank tensor. Specifically, we multiply the learnable semiorthogonal matrices obtained through Tucker compression with the original tensor along all modes, thus obtaining a core tensor with more inherent low rankness. Building upon this foundation, a new TNN is introduced by generalizing HoTSVD to Mode-k TSVD, followed by the facilitation to the core tensor, achieving dual-enhancement. Moreover, a reweighting scheme is imposed on the Mode-k HoTSVD to learn the global low-rank correlation and provide an efficient numerical solution. Finally, an alternating direction method of multipliers (ADMM)-based algorithm is developed as a solver. Experimental results on several types of multidimensional visual data, including light field images (LFI) and color videos, demonstrate the superiority of the proposal over previous state-of-the-art methods. © 2020 IEEE.

关键词： Discrete Fourier transforms

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Certrust:An SDN-Based Framework for the Trust of Certificates against Crossfire Attacks in IoT Scenarios

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computer Modeling in engineering & sciences 2023年第3期134卷 2137-2162页

作者： Lei Yan Maode Ma Dandan Li Xiaohong Huang Yan Ma Kun Xie School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijingChina College of Engineering QatarUniversityDohaQatar

The low-intensity attack flows used by Crossfire attacks are hard to distinguish from legitimate *** methods to identify the malicious flows in Crossfire attacks are rerouting,which is based on *** these existing mechanisms,the identification of malicious flows depends on the IP ***,the IP address is easy to be changed by *** the IP address,the certificate ismore challenging to be tampered with or ***,the traffic trend in the network is towards *** certificates are popularly utilized by IoT devices for authentication in encryption *** proposed a new way to verify certificates for resource-constrained IoT devices by using the SDN *** on DTLShps,the SDN controller can collect statistics on *** this paper,we proposeCertrust,a framework based on the trust of certificates,tomitigate the Crossfire attack by using SDN for *** goal is ***,the trust model is built based on the Bayesian trust system with the statistics on the participation of certificates in each Crossfire ***,the forgetting curve is utilized instead of the traditional decay method in the Bayesian trust system for achieving a moderate decay ***,for detecting the Crossfire attack accurately,a method based on graph connectivity is ***,several trust-based routing principles are proposed tomitigate the Crossfire *** principles can also encourage users to use certificates in *** performance evaluation shows that Certrust is more effective in mitigating the Crossfire attack than the traditional rerouting ***,our trust model has a more appropriate decay rate than the traditional methods.

关键词： Trust model certificate SDN Crossfire attack bayesian trust system forgetting curve IoT

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DECAY: Dynamic Evaluation and Component Analysis for Enhancing Trust Management

DECAY: Dynamic Evaluation and Component Analysis for Enhanci...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Huber, Brennan Kandah, Farah Auburn University Samuel Ginn College of Engineering Computer Science and Software Engineering United States

ISBN: (纸本)9798350324136

The Internet of Things (IoT) has gained widespread use across various domains, necessitating robust trust management for safeguarding data integrity and device reliability. Existing trust management algorithms often overlook the impact of a device's lifespan on trust adjustments. To address this shortcoming, we propose an innovative trust management strategy that introduces an 'engagement-level' component as a key feature. This component considers a device's past performance, granting established devices milder trust penalties than new ones and facilitating faster trust restoration for devices with low trust levels. Our approach also introduces a trust decay mechanism to handle inactive devices and cease contributing to the system, resulting in decreased trust levels. By discouraging passive behavior and encouraging active participation, this mechanism enhances IoT system trustworthiness. Through simulations and evaluations, we have demonstrated that our approach bolsters system security by scrutinizing malicious devices and rewarding active, trustworthy ones. It ensures vigilant threat monitoring, bolstering system security and improving trust assessment in the IoT environment. By integrating the engagement-level component and trust decay mechanism, our approach offers a dynamic trust management system that addresses existing algorithm limitations, enhancing accuracy and adaptability. © 2024 IEEE.

关键词： Internet of Things (IoT) Trust decay mechanism Trust management

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Theoretical Analysis of an Adaptive Closeness Centrality-Based Algorithm for Dynamic Optimization of Transportation Networks

Theoretical Analysis of an Adaptive Closeness Centrality-Bas...

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2024 International Conference on engineering Management of Communication and Technology, EMCTECH 2024

作者： Mann, Michael Afeka - the Academic College of Engineering School of Software Engineering and Computer Science Tel Aviv Israel

ISBN: (数字)9798331507909

ISBN: (纸本)9798331507909

Purpose: This paper presents a theoretical analysis of the DynaTrans algorithm, a novel approach for dynamic optimization of urban transportation networks. Design/methodology/approach: We introduce an Adaptive Closeness Centrality (ACC) metric and the DynaTrans algorithm, providing formal proofs of correctness, convergence, and efficiency. The analysis employs graph theory, algorithmic complexity theory, and competitive analysis techniques. Findings: We prove that DynaTrans converges to a local optimal state in O(|V|/ϵ) iterations, with each iteration has limited computational and memory requirements. The algorithm achieves solutions within a factor of O(log|V|) from the global optimum, outperforming simple greedy approaches. Practical implications: DynaTrans offers a theoretically sound foundation for real-time traffic management systems, potentially improving urban mobility and reducing congestion. Originality/value: This work introduces a new paradigm for dynamic transportation network optimization, combining adaptive centrality measures with efficient graph algorithms. The rigorous theoretical analysis provides a solid basis for practical implementation and future research in urban traffic management. © 2024 IEEE.

关键词： Traffic congestion

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Zero Trust+: A Trusted-based Zero Trust architecture for IoT at Scale

Zero Trust+: A Trusted-based Zero Trust architecture for IoT...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Huber, Brennan Kandah, Farah Auburn University Computer Science and Software Engineering Samuel Ginn College of Engineering United States

ISBN: (纸本)9798350324136

The Zero Trust security architecture has been widely recognized for its efficacy in strengthening cybersecurity by rejecting the concept of implicit trust and adopting strict identity verification measures. However, as organizations face escalating demands for rapid and scalable access control, the performance limitations of traditional Zero Trust implementations have become a concern. In response to this challenge, we introduce a novel trust management-based solution that significantly improves performance without compromising security integrity. Through a streamlined trust provisioning approach, our solution offers a 20% enhancement in processing efficiency, making it highly suitable for dynamic and large-scale environments. This research paper presents the design, implementation, and evaluation of our trust management-based solution, showcasing its superiority over conventional Zero Trust architectures in addressing performance concerns while delivering the same robust access control capabilities. © 2024 IEEE.

关键词： Internet of Things Security Trust Management Zero Trust Architecture

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