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Flow prediction of mountain cities arterial road network for real-time regulation 山区城市干线路网流量预测与实时调控
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-09-25 DOI: 10.1016/j.jksuci.2024.102190
Xiaoyu Cai , Zimu Li , Jiajia Dai , Liang Lv , Bo Peng
This study aims to enhance the understanding of vehicle path selection behavior within arterial road networks by investigating the influencing factors and analyzing spatial and temporal traffic flow distributions. Using radio frequency identification (RFID) travel data, key factors such as travel duration, route familiarity, route length, expressway ratio, arterial road ratio, and ramp ratio were identified. We then proposed an origin–destination path acquisition method and developed a route-selection prediction model based on a multinomial logit model with sample weights. Additionally, the study linked the traffic control scheme with travel time using the Bureau of Public Roads function—a model that illustrates the relationship between network-wide travel time and traffic demand—and developed an arterial road network traffic forecasting model. Verification showed that the prediction accuracy of the improved multinomial logit model increased from 92.55 % to 97.87 %. Furthermore, reducing the green time ratio for multilane merging from 0.75 to 0.5 significantly decreased the likelihood of vehicles choosing this route and reduced the number of vehicles passing through the ramp. The flow prediction model achieved a 97.9 % accuracy, accurately reflecting actual volume changes and ensuring smooth operation of the main airport road. This provides a strong foundation for developing effective traffic control plans.
本研究旨在通过调查影响因素和分析时空交通流分布,加深对干道网络内车辆路径选择行为的理解。利用无线射频识别(RFID)出行数据,确定了出行时长、路线熟悉程度、路线长度、快速路比例、干道比例和匝道比例等关键因素。然后,我们提出了一种起点-终点路径获取方法,并开发了一个基于带样本权重的多叉 Logit 模型的路线选择预测模型。此外,该研究还利用公共道路局函数将交通管制方案与旅行时间联系起来--该函数模型说明了整个网络的旅行时间与交通需求之间的关系,并开发了一个干道网络交通量预测模型。验证结果表明,改进后的多叉 logit 模型的预测准确率从 92.55% 提高到 97.87%。此外,将多车道并线的绿灯时间比从 0.75 降低到 0.5,大大降低了车辆选择该路线的可能性,并减少了通过匝道的车辆数量。流量预测模型的准确率达到 97.9%,准确反映了实际流量变化,确保了机场主干道的顺畅运行。这为制定有效的交通管制计划奠定了坚实的基础。
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引用次数: 0
The evolution of the flip-it game in cybersecurity: Insights from the past to the future 网络安全翻转游戏的演变:从过去到未来的启示
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-09-25 DOI: 10.1016/j.jksuci.2024.102195
Mousa Tayseer Jafar , Lu-Xing Yang , Gang Li , Xiaofan Yang
Cybercrime statistics highlight the severe and growing impact of digital threats on individuals and organizations, with financial losses escalating rapidly. As cybersecurity becomes a central challenge, several modern cyber defense strategies prove insufficient for effectively countering the threats posed by sophisticated attackers. Despite advancements in cybersecurity, many existing frameworks often lack the capacity to address the evolving tactics of adept adversaries. With cyber threats growing in sophistication and diversity, there is a growing acknowledgment of the shortcomings within current defense strategies, underscoring the need for more robust and innovative solutions. To develop resilient cyber defense strategies, it remains essential to simulate the dynamic interaction between sophisticated attackers and system defenders. Such simulations enable organizations to anticipate and effectively counter emerging threats. The Flip-It game is recognized as an intelligent simulation game for capturing the dynamic interplay between sophisticated attackers and system defenders. It provides the capability to emulate intricate cyber scenarios, allowing organizations to assess their defensive capabilities against evolving threats, analyze vulnerabilities, and improve their response strategies by simulating real-world cyber scenarios. This paper provides a comprehensive analysis of the Flip-It game in the context of cybersecurity, tracing its development from inception to future prospects. It highlights significant contributions and identifies potential future research avenues for scholars in the field. This study aims to deliver a thorough understanding of the Flip-It game’s progression, serving as a valuable resource for researchers and practitioners involved in cybersecurity strategy and defense mechanisms.
网络犯罪统计数据凸显了数字威胁对个人和组织的严重影响,而且这种影响还在不断加剧,经济损失也在迅速攀升。随着网络安全成为一项核心挑战,一些现代网络防御战略被证明不足以有效应对复杂攻击者带来的威胁。尽管网络安全技术在不断进步,但许多现有框架往往无法应对精明对手不断变化的战术。随着网络威胁的复杂性和多样性不断增加,人们越来越认识到当前防御战略的不足之处,强调需要更强大和创新的解决方案。要制定有弹性的网络防御战略,模拟复杂的攻击者和系统防御者之间的动态互动仍然至关重要。这种模拟使组织能够预测并有效应对新出现的威胁。Flip-It 游戏是公认的捕捉复杂攻击者和系统防御者之间动态互动的智能模拟游戏。它能够模拟错综复杂的网络场景,使企业能够通过模拟真实世界的网络场景,评估其针对不断演变的威胁的防御能力、分析漏洞并改进应对策略。本文全面分析了网络安全背景下的 "Flip-It "游戏,追溯了它从诞生到未来的发展前景。论文强调了该游戏的重大贡献,并为该领域的学者指出了潜在的未来研究途径。本研究旨在全面了解 Flip-It 游戏的发展过程,为网络安全战略和防御机制方面的研究人员和从业人员提供有价值的资源。
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引用次数: 0
Framework to improve software effort estimation accuracy using novel ensemble rule 利用新型集合规则提高软件工作量估算准确性的框架
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-09-20 DOI: 10.1016/j.jksuci.2024.102189
Syed Sarmad Ali , Jian Ren , Ji Wu
<div><div>This investigation focuses on refining software effort estimation (SEE) to enhance project outcomes amidst the rapid evolution of the software industry. Accurate estimation is a cornerstone of project success, crucial for avoiding budget overruns and minimizing the risk of project failures. The framework proposed in this article addresses three significant issues that are critical for accurate estimation: dealing with missing or inadequate data, selecting key features, and improving the software effort model. Our proposed framework incorporates three methods: the <em>Novel Incomplete Value Imputation Model (NIVIM)</em>, a hybrid model using <em>Correlation-based Feature Selection with a meta-heuristic algorithm (CFS-Meta)</em>, and the <em>Heterogeneous Ensemble Model (HEM)</em>. The combined framework synergistically enhances the robustness and accuracy of SEE by effectively handling missing data, optimizing feature selection, and integrating diverse predictive models for superior performance across varying project scenarios. The framework significantly reduces imputation and feature selection overhead, while the ensemble approach optimizes model performance through dynamic weighting and meta-learning. This results in lower mean absolute error (MAE) and reduced computational complexity, making it more effective for diverse software datasets. NIVIM is engineered to address incomplete datasets prevalent in SEE. By integrating a synthetic data methodology through a Variational Auto-Encoder (VAE), the model incorporates both contextual relevance and intrinsic project features, significantly enhancing estimation precision. Comparative analyses reveal that NIVIM surpasses existing models such as VAE, GAIN, K-NN, and MICE, achieving statistically significant improvements across six benchmark datasets, with average RMSE improvements ranging from <em>11.05%</em> to <em>17.72%</em> and MAE improvements from <em>9.62%</em> to <em>21.96%</em>. Our proposed method, CFS-Meta, balances global optimization with local search techniques, substantially enhancing predictive capabilities. The proposed CFS-Meta model was compared to single and hybrid feature selection models to assess its efficiency, demonstrating up to a <em>25.61%</em> reduction in MSE. Additionally, the proposed CFS-Meta achieves a <em>10%</em> (MAE) improvement against the hybrid PSO-SA model, an <em>11.38%</em> (MAE) improvement compared to the Hybrid ABC-SA model, and <em>12.42%</em> and <em>12.703%</em> (MAE) improvements compared to the hybrid Tabu-GA and hybrid ACO-COA models, respectively. Our third method proposes an ensemble effort estimation (EEE) model that amalgamates diverse standalone models through a Dynamic Weight Adjustment-stacked combination (DWSC) rule. Tested against international benchmarks and industry datasets, the HEM method has improved the standalone model by an average of <em>21.8%</em> (Pred()) and the homogeneous ensemble model by <em>15%</em> (Pred()). This
这项研究的重点是改进软件工作量估算(SEE),以便在软件行业快速发展的过程中提高项目成果。准确估算是项目成功的基石,对于避免预算超支和最大限度降低项目失败风险至关重要。本文提出的框架解决了对准确估算至关重要的三个重要问题:处理缺失或不充分的数据、选择关键功能以及改进软件工作量模型。我们提出的框架包含三种方法:新颖的不完整值估算模型(NIVIM)、使用元启发式算法(CFS-Meta)的基于相关性特征选择的混合模型以及异构集合模型(HEM)。组合框架通过有效处理缺失数据、优化特征选择和整合不同的预测模型,在不同的项目场景中实现卓越性能,从而协同提高 SEE 的稳健性和准确性。该框架大大减少了估算和特征选择的开销,而集合方法则通过动态加权和元学习优化了模型性能。这就降低了平均绝对误差(MAE),减少了计算复杂性,使其对各种软件数据集更加有效。NIVIM 专为解决 SEE 中普遍存在的不完整数据集而设计。通过变异自动编码器(VAE)整合合成数据方法,该模型结合了上下文相关性和项目固有特征,显著提高了估算精度。对比分析表明,NIVIM 超越了 VAE、GAIN、K-NN 和 MICE 等现有模型,在六个基准数据集上实现了统计意义上的显著改进,平均 RMSE 提高了 11.05% 到 17.72%,MAE 提高了 9.62% 到 21.96%。我们提出的 CFS-Meta 方法兼顾了全局优化和局部搜索技术,大大提高了预测能力。为了评估 CFS-Meta 模型的效率,我们将其与单一特征选择模型和混合特征选择模型进行了比较,结果表明,CFS-Meta 模型的 MSE 降低了 25.61%。此外,与混合 PSO-SA 模型相比,提议的 CFS-Meta 模型实现了 10%(MAE)的改进;与混合 ABC-SA 模型相比,实现了 11.38%(MAE)的改进;与混合 Tabu-GA 模型和混合 ACO-COA 模型相比,分别实现了 12.42% 和 12.703%(MAE)的改进。我们的第三种方法提出了一种集合努力估算(EEE)模型,该模型通过动态权重调整堆叠组合(DWSC)规则合并了多种独立模型。通过对国际基准和行业数据集的测试,HEM 方法将独立模型平均改进了 21.8%(Pred()),将同质集合模型平均改进了 15%(Pred())。这种全面的方法强调了我们的模型通过先进的预测建模为推进软件项目管理(SPM)所做的贡献,为软件工程工作量估算设定了新的基准。
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引用次数: 0
Heterogeneous emotional contagion of the cyber–physical society 网络物理社会的异质情绪传染
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-09-18 DOI: 10.1016/j.jksuci.2024.102193
Heqi Gao , Jiayi Zhang , Guijuan Zhang , Chengming Zhang , Zena Tian , Dianjie Lu

When emergencies occur, panic spreads quickly across cyberspace and physical space. Despite widespread attention to emotional contagion in cyber–physical societies (CPS), existing studies often overlook individual relationship heterogeneity, which results in imprecise models. To address this issue, we propose a heterogeneous emotional contagion method for CPS. First, we introduce the Strong–Weak Emotional Contagion Model (SW-ECM) to simulate the heterogeneous emotional contagion process in CPS. Second, we formulate the mean-field equations for the SW-ECM to accurately capture the dynamic evolution of heterogeneous emotional contagion in the CPS. Finally, we construct a small-world network based on strong–weak relationships to validate the effectiveness of our method. The experimental results show that our method can effectively simulate the heterogeneous emotional contagion and capture changes in relationships between individuals, providing valuable guidance for crowd evacuations prone to emotional contagion.

当紧急情况发生时,恐慌会在网络空间和物理空间迅速蔓延。尽管网络物理社会(CPS)中的情绪传染受到广泛关注,但现有研究往往忽略了个体关系的异质性,从而导致模型不精确。为了解决这个问题,我们提出了一种适用于 CPS 的异质性情感传染方法。首先,我们引入强弱情感传染模型(SW-ECM)来模拟 CPS 中的异质情感传染过程。其次,我们提出了 SW-ECM 的均场方程,以准确捕捉 CPS 中异质情绪传染的动态演化过程。最后,我们构建了一个基于强弱关系的小世界网络来验证我们方法的有效性。实验结果表明,我们的方法可以有效地模拟异质情绪传染并捕捉个体间关系的变化,为容易发生情绪传染的人群疏散提供有价值的指导。
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引用次数: 0
Enhanced prediction model of short-term sea surface wind speed: A multiscale feature extraction and selection approach coupled with deep learning technique 短期海面风速增强预测模型:结合深度学习技术的多尺度特征提取和选择方法
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-09-17 DOI: 10.1016/j.jksuci.2024.102192
Jin Tao , Jianing Wei , Hongjuan Zhou , Fanyi Meng , Yingchun Li , Chenxu Wang , Zhiquan Zhou
Accurate prediction of short-term sea surface wind speed is essential for maritime safety and coastal management. Most conventional studies encounter challenges simply in analyzing raw wind speed sequences and extracting multiscale features directly from the original received data, which result in lower efficiency. In this paper, an enhanced hybrid model based on a novel data assemble method for original received data, a multiscale feature extraction and selection approach, and a predictive network, is proposed for accurate and efficient short-term sea surface wind speed forecasting. Firstly, the received original data including wind speed are assembled into correlation matrices in order to uncover inherent associations over varied time spans. Secondly a novel Multiscale Wind-speed Feature-Enhanced Convolutional Network (MW-FECN) is designed for efficient and selective multiscale feature extraction, which can capture comprehensive characteristics. Thirdly, a Random Forest Feature Selection (RF-FS) is employed to pinpoint crucial characteristics for enhanced prediction of wind speed with higher efficiency than the related works. Finally, the proposed hybrid model utilized a Bidirectional Long Short-Term Memory (BiLSTM) network to achieve the accurate prediction of wind speed. Experimental data are collected in Weihai sea area, and a case study consist of five benchmarks and three ablation models is conducted to assess the proposed hybrid model. Compared with the conventional methods, experiment results illustrate the effectiveness of the proposed hybrid model and demonstrate effective balancing prediction accuracy and computational time. The proposed hybrid model achieves up to a 28.45% MAE and 27.27% RMSE improvement over existing hybrid models.
准确预测短期海面风速对海上安全和海岸管理至关重要。大多数传统研究仅在分析原始风速序列和直接从原始接收数据中提取多尺度特征方面遇到挑战,导致效率较低。本文提出了一种基于新颖的原始接收数据组装方法、多尺度特征提取和选择方法以及预测网络的增强型混合模型,用于准确高效的短期海面风速预报。首先,将接收到的包括风速在内的原始数据组装成相关矩阵,以发现不同时间跨度上的内在联系。其次,设计了一种新颖的多尺度风速特征增强卷积网络(MW-FECN),用于高效、有选择性地提取多尺度特征,从而捕捉综合特征。第三,采用随机森林特征选择(RF-FS)来精确定位关键特征,以提高风速预测的效率。最后,所提出的混合模型利用双向长短期记忆(BiLSTM)网络实现了风速的精确预测。在威海海域收集了实验数据,并进行了由五个基准和三个消融模型组成的案例研究,以评估所提出的混合模型。与传统方法相比,实验结果表明了所提出的混合模型的有效性,并有效地平衡了预测精度和计算时间。与现有的混合模型相比,所提出的混合模型的 MAE 和 RMSE 分别提高了 28.45% 和 27.27%。
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引用次数: 0
Multi-objective optimization in order to allocate computing and telecommunication resources based on non-orthogonal access, participation of cloud server and edge server in 5G networks 基于非正交访问、云服务器和边缘服务器在 5G 网络中的参与,进行多目标优化以分配计算和电信资源
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-09-16 DOI: 10.1016/j.jksuci.2024.102187
Liying Zhao , Chao Liu , Entie Qi , Sinan Shi
Mobile edge processing is a cutting-edge technique that addresses the limitations of mobile devices by enabling users to offload computational tasks to edge servers, rather than relying on distant cloud servers. This approach significantly reduces the latency associated with cloud processing, thereby enhancing the quality of service. In this paper, we propose a system in which a cellular network, comprising multiple users, interacts with both cloud and edge servers to process service requests. The system assumes non-orthogonal multiple access (NOMA) for user access to the radio spectrum. We model the interactions between users and servers using queuing theory, aiming to minimize the total energy consumption of users, service delivery time, and overall network operation costs. The problem is mathematically formulated as a multi-objective, bounded non-convex optimization problem. The Structural Correspondence Analysis (SCA) method is employed to obtain the global optimal solution. Simulation results demonstrate that the proposed model reduces energy consumption, delay, and network costs by approximately 50%, under the given assumptions.
移动边缘处理是一种前沿技术,可解决移动设备的局限性,使用户能够将计算任务卸载到边缘服务器,而不是依赖遥远的云服务器。这种方法大大减少了与云处理相关的延迟,从而提高了服务质量。在本文中,我们提出了一个由多个用户组成的蜂窝网络与云服务器和边缘服务器交互处理服务请求的系统。该系统假定用户访问无线电频谱时使用非正交多址接入(NOMA)。我们使用排队理论对用户和服务器之间的交互进行建模,旨在最大限度地减少用户的总能耗、服务交付时间和整体网络运营成本。该问题在数学上被表述为一个多目标、有界非凸优化问题。采用结构对应分析(SCA)方法获得全局最优解。仿真结果表明,在给定的假设条件下,所提出的模型可将能耗、延迟和网络成本降低约 50%。
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引用次数: 0
A novel edge intelligence-based solution for safer footpath navigation of visually impaired using computer vision 基于边缘智能的新型解决方案,利用计算机视觉为视障人士提供更安全的人行道导航
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-09-16 DOI: 10.1016/j.jksuci.2024.102191
Rashik Iram Chowdhury, Jareen Anjom, Md. Ishan Arefin Hossain

Navigating through a tactile paved footpath surrounded by various sizes of static and dynamic obstacles is one of the biggest impediments visually impaired people face, especially in Dhaka, Bangladesh. This problem is important to address, considering the number of accidents in such densely populated footpaths. We propose a novel deep-edge solution using Computer Vision to make people aware of the obstacles in the vicinity and reduce the necessity of a walking cane. This study introduces a diverse novel tactile footpath dataset of Dhaka covering different city areas. Additionally, existing state-of-the-art deep neural networks for object detection have been fine-tuned and investigated using this dataset. A heuristic-based breadth-first navigation algorithm (HBFN) is developed to provide navigation directions that are safe and obstacle-free, which is then deployed in a smartphone application that automatically captures images of the footpath ahead to provide real-time navigation guidance delivered by speech. The findings from this study demonstrate the effectiveness of the object detection model, YOLOv8s, which outperformed other benchmark models on this dataset, achieving a high mAP of 0.974 and an F1 score of 0.934. The model’s performance is analyzed after quantization, reducing its size by 49.53% while retaining 98.97% of the original mAP.

在被各种大小的静态和动态障碍物包围的触觉铺设人行道上导航是视障人士面临的最大障碍之一,尤其是在孟加拉国的达卡。考虑到在这种人口密集的人行道上发生的事故数量,解决这个问题非常重要。我们利用计算机视觉技术提出了一种新颖的深边缘解决方案,让人们意识到附近的障碍物,减少使用手杖的必要性。本研究引入了达卡的各种新型触觉人行道数据集,涵盖了不同的城市区域。此外,还利用该数据集对用于物体检测的现有最先进的深度神经网络进行了微调和研究。开发的基于启发式的广度优先导航算法(HBFN)可提供安全、无障碍的导航指引,然后将其部署到智能手机应用程序中,该应用程序可自动捕捉前方人行道的图像,通过语音提供实时导航指引。研究结果证明了物体检测模型 YOLOv8s 的有效性,该模型在该数据集上的表现优于其他基准模型,mAP 高达 0.974,F1 得分为 0.934。对模型量化后的性能进行了分析,量化后的模型大小减少了 49.53%,同时保留了 98.97% 的原始 mAP。
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引用次数: 0
Graph contrast learning for recommendation based on relational graph convolutional neural network 基于关系图卷积神经网络的推荐图对比学习
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-09-14 DOI: 10.1016/j.jksuci.2024.102168
Xiaoyang Liu , Hanwen Feng , Xiaoqin Zhang , Xia Zhou , Asgarali Bouyer
Current knowledge graph-based recommendation methods heavily rely on high-quality knowledge graphs, often falling short in effectively addressing issues such as the cold start problem and heterogeneous noise in user interactions. This leads to biases in user interest and popularity. To overcome these challenges, this paper introduces a novel recommendation approach termed Knowledge-enhanced Perceptive Graph Attention with Graph Contrastive Learning (KPA-GCL), which leverages relational graph convolutional neural networks. The proposed method optimizes the triplet embedding representation of entity-item interactions based on relationships between adjacent entities in a heterogeneous graph. Subsequently, a graph convolutional neural network is employed for enhanced aggregation. Similarity scores from a contrastive view serve as the selection criterion for high-quality embedded representations, facilitating the extraction of refined knowledge subgraphs. Multiple adaptive contrast-loss optimization functions are introduced by combining Bayesian Personalized Ranking (BPR) and hard negative sampling techniques. Comparative experiments are conducted with ten popular existing methods using real public datasets. Results indicate that the KPA-GCL method outperforms compared methods in all datasets based on Recall, NDCG, Precision, and Hit-ratio measures. Furthermore, in terms of mitigating cold start and noise, the KPA-GCL method surpasses other ten methods. This validates the reasonability and effectiveness of KPA-GCL in real-world datasets.
当前基于知识图谱的推荐方法严重依赖高质量的知识图谱,但往往无法有效解决冷启动问题和用户交互中的异构噪声等问题。这会导致用户兴趣和受欢迎程度出现偏差。为了克服这些挑战,本文介绍了一种新颖的推荐方法,即利用关系图卷积神经网络的知识增强型感知图注意与图对比学习(KPA-GCL)。所提出的方法基于异构图中相邻实体之间的关系,优化了实体-项目交互的三重嵌入表示。随后,采用图卷积神经网络进行增强聚合。来自对比视图的相似性得分可作为高质量嵌入表示的选择标准,从而促进对精细知识子图的提取。通过结合贝叶斯个性化排名(BPR)和硬负采样技术,引入了多种自适应对比度损失优化函数。利用真实的公共数据集,与现有的十种流行方法进行了对比实验。结果表明,基于 Recall、NDCG、Precision 和 Hit-ratio 等指标,KPA-GCL 方法在所有数据集上都优于其他方法。此外,在减少冷启动和噪音方面,KPA-GCL 方法超过了其他十种方法。这验证了 KPA-GCL 在实际数据集中的合理性和有效性。
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引用次数: 0
Improving embedding-based link prediction performance using clustering 利用聚类提高基于嵌入的链接预测性能
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-09-13 DOI: 10.1016/j.jksuci.2024.102181
Fitri Susanti , Nur Ulfa Maulidevi , Kridanto Surendro

Incomplete knowledge graphs are common problem that can impair task accuracy. As knowledge graphs grow extensively, the probability of incompleteness increases. Link prediction addresses this problem, but accurate and efficient link prediction methods are needed to handle incomplete and extensive knowledge graphs. This study proposed modifications to the embedding-based link prediction using clustering to improve performance. The proposed method involves four main processes: embedding, clustering, determining clusters, and scoring. Embedding converts entities and relations into vectors while clustering groups these vectors. Selected clusters are determined based on the shortest distance between the centroid and the incomplete knowledge graph. Scoring measures relation rankings, and link prediction result is selected based on highest scores. The link prediction performance is evaluated using Hits@1, Mean Rank, Mean Reciprocal Rank and prediction time on three knowledge graph datasets: WN11, WN18RR, and FB13. The link prediction methods used are TransE and ComplEx, with BIRCH as the clustering technique and Mahalanobis for short-distance measurement. The proposed method significantly improves link prediction performance, achieving accuracy up to 98% and reducing prediction time by 99%. This study provides effective and efficient solution for improving link prediction, demonstrating high accuracy and efficiency in handling incomplete and extensive knowledge graphs.

知识图谱不完整是影响任务准确性的常见问题。随着知识图谱的扩展,不完整的概率也会增加。链接预测可以解决这个问题,但需要准确高效的链接预测方法来处理不完整和广泛的知识图谱。本研究提出利用聚类对基于嵌入的链接预测进行修改,以提高性能。建议的方法包括四个主要过程:嵌入、聚类、确定聚类和评分。嵌入将实体和关系转换为向量,而聚类则将这些向量分组。根据中心点与不完整知识图谱之间的最短距离确定选定的聚类。评分衡量关系排名,并根据最高分选出链接预测结果。在三个知识图谱数据集上,使用点击率@1、平均排名、平均互易排名和预测时间对链接预测性能进行了评估:三个知识图谱数据集:WN11、WN18RR 和 FB13。使用的链接预测方法是 TransE 和 ComplEx,聚类技术是 BIRCH,短距离测量是 Mahalanobis。所提出的方法大大提高了链路预测性能,准确率高达 98%,预测时间缩短了 99%。这项研究为改进链接预测提供了有效和高效的解决方案,在处理不完整和广泛的知识图谱时表现出高精度和高效率。
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引用次数: 0
A sharding blockchain protocol for enhanced scalability and performance optimization through account transaction reconfiguration 通过账户交易重新配置增强可扩展性和性能优化的分片区块链协议
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-09-11 DOI: 10.1016/j.jksuci.2024.102184
Jiaying Wu , Lingyun Yuan , Tianyu Xie , Hui Dai

Sharding is a critical technology for enhancing blockchain scalability. However, existing sharding blockchain protocols suffer from a high cross-shard ratio, high transaction latency, limited throughput enhancement, and high account migration. To address these problems, this paper proposes a sharding blockchain protocol for enhanced scalability and performance optimization through account transaction reconfiguration. Firstly, we construct a blockchain transaction account graph network structure to analyze transaction account correlations. Secondly, a modularity-based account transaction reconfiguration algorithm and a detailed account reconfiguration process is designed to minimize cross-shard transactions. Finally, we introduce a transaction processing mechanism for account transaction reconfiguration in parallel with block consensus uploading, which reduces the reconfiguration time overhead and system latency. Experimental results demonstrate substantial performance improvements compared to existing shard protocols: up to a 34.7% reduction in cross-shard transaction ratio, at least an 83.2% decrease in transaction latency, at least a 52.7% increase in throughput and a 7.8% decrease in account migration number. The proposed protocol significantly enhances the overall performance and scalability of blockchain, providing robust support for blockchain applications in various fields such as financial services, supply chain management, and industrial Internet of Things. It also enables better support for high-concurrency scenarios and large-scale network environments.

分片是提高区块链可扩展性的关键技术。然而,现有的分片区块链协议存在跨分片比率高、交易延迟高、吞吐量提升有限以及账户迁移率高等问题。针对这些问题,本文提出了一种分片区块链协议,通过账户交易重构来增强可扩展性和优化性能。首先,我们构建了区块链交易账户图网络结构,分析交易账户相关性。其次,我们设计了一种基于模块化的账户交易重构算法和详细的账户重构流程,以尽量减少跨分区交易。最后,我们引入了与区块共识上传并行的账户交易重新配置交易处理机制,从而减少了重新配置时间开销和系统延迟。实验结果表明,与现有的分片协议相比,该协议的性能有了大幅提升:跨分片交易比率降低了 34.7%,交易延迟至少减少了 83.2%,吞吐量至少增加了 52.7%,账户迁移数量减少了 7.8%。所提出的协议大大提高了区块链的整体性能和可扩展性,为金融服务、供应链管理和工业物联网等各个领域的区块链应用提供了强有力的支持。它还能更好地支持高并发场景和大规模网络环境。
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Journal of King Saud University-Computer and Information Sciences
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