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Extended context-based semantic communication system for text transmission 扩展的基于上下文的文本传递语义通信系统
IF 7.5 2区 计算机科学 Q1 TELECOMMUNICATIONS Pub Date : 2024-06-01 DOI: 10.1016/j.dcan.2022.09.023
Yueling Liu , Shengteng Jiang , Yichi Zhang , Kuo Cao , Li Zhou , Boon-Chong Seet , Haitao Zhao , Jibo Wei

Context information is significant for semantic extraction and recovery of messages in semantic communication. However, context information is not fully utilized in the existing semantic communication systems since relationships between sentences are often ignored. In this paper, we propose an Extended Context-based Semantic Communication (ECSC) system for text transmission, in which context information within and between sentences is explored for semantic representation and recovery. At the encoder, self-attention and segment-level relative attention are used to extract context information within and between sentences, respectively. In addition, a gate mechanism is adopted at the encoder to incorporate the context information from different ranges. At the decoder, Transformer-XL is introduced to obtain more semantic information from the historical communication processes for semantic recovery. Simulation results show the effectiveness of our proposed model in improving the semantic accuracy between transmitted and recovered messages under various channel conditions.

上下文信息对于语义提取和恢复语义通信中的信息非常重要。然而,在现有的语义通信系统中,上下文信息并没有得到充分利用,因为句子之间的关系往往被忽视。在本文中,我们提出了一种用于文本传输的基于上下文的扩展语义通信(ECSC)系统,在该系统中,句子内部和句子之间的上下文信息被用于语义表示和恢复。在编码器中,自我注意和句段级相对注意分别用于提取句内和句间的上下文信息。此外,编码器还采用了门机制,以纳入来自不同范围的上下文信息。在解码器中,引入 Transformer-XL 从历史通信过程中获取更多语义信息,以进行语义恢复。仿真结果表明,在各种信道条件下,我们提出的模型都能有效提高传输信息与恢复信息之间的语义准确性。
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引用次数: 0
Dynamics modeling and optimal control for multi-information diffusion in Social Internet of Things 社交物联网中多信息扩散的动力学建模与最优控制
IF 7.5 2区 计算机科学 Q1 TELECOMMUNICATIONS Pub Date : 2024-06-01 DOI: 10.1016/j.dcan.2023.02.014
Yaguang Lin, Xiaoming Wang, Liang Wang, Pengfei Wan

As an ingenious convergence between the Internet of Things and social networks, the Social Internet of Things (SIoT) can provide effective and intelligent information services and has become one of the main platforms for people to spread and share information. Nevertheless, SIoT is characterized by high openness and autonomy, multiple kinds of information can spread rapidly, freely and cooperatively in SIoT, which makes it challenging to accurately reveal the characteristics of the information diffusion process and effectively control its diffusion. To this end, with the aim of exploring multi-information cooperative diffusion processes in SIoT, we first develop a dynamics model for multi-information cooperative diffusion based on the system dynamics theory in this paper. Subsequently, the characteristics and laws of the dynamical evolution process of multi-information cooperative diffusion are theoretically investigated, and the diffusion trend is predicted. On this basis, to further control the multi-information cooperative diffusion process efficiently, we propose two control strategies for information diffusion with control objectives, develop an optimal control system for the multi-information cooperative diffusion process, and propose the corresponding optimal control method. The optimal solution distribution of the control strategy satisfying the control system constraints and the control budget constraints is solved using the optimal control theory. Finally, extensive simulation experiments based on real dataset from Twitter validate the correctness and effectiveness of the proposed model, strategy and method.

作为物联网与社交网络的巧妙融合,社交物联网(SIoT)能够提供高效、智能的信息服务,已成为人们传播和共享信息的主要平台之一。然而,SIoT 具有高度开放性和自主性的特点,多种信息可以在 SIoT 中快速、自由、合作地传播,这给准确揭示信息传播过程的特征并有效控制其传播带来了挑战。为此,本文以探索 SIoT 中多信息协同扩散过程为目标,首先基于系统动力学理论建立了多信息协同扩散的动力学模型。随后,从理论上研究了多信息协同扩散动态演化过程的特点和规律,并预测了扩散趋势。在此基础上,为进一步有效控制多信息协同扩散过程,我们提出了两种具有控制目标的信息扩散控制策略,建立了多信息协同扩散过程的最优控制体系,并提出了相应的最优控制方法。利用最优控制理论求解了满足控制系统约束和控制预算约束的控制策略的最优解分布。最后,基于 Twitter 真实数据集的大量仿真实验验证了所提模型、策略和方法的正确性和有效性。
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引用次数: 0
Network traffic classification: Techniques, datasets, and challenges 网络流量分类:技术、数据集和挑战
IF 7.5 2区 计算机科学 Q1 TELECOMMUNICATIONS Pub Date : 2024-06-01 DOI: 10.1016/j.dcan.2022.09.009
Ahmad Azab , Mahmoud Khasawneh , Saed Alrabaee , Kim-Kwang Raymond Choo , Maysa Sarsour

In network traffic classification, it is important to understand the correlation between network traffic and its causal application, protocol, or service group, for example, in facilitating lawful interception, ensuring the quality of service, preventing application choke points, and facilitating malicious behavior identification. In this paper, we review existing network classification techniques, such as port-based identification and those based on deep packet inspection, statistical features in conjunction with machine learning, and deep learning algorithms. We also explain the implementations, advantages, and limitations associated with these techniques. Our review also extends to publicly available datasets used in the literature. Finally, we discuss existing and emerging challenges, as well as future research directions.

在网络流量分类中,了解网络流量与其因果应用、协议或服务组之间的相关性非常重要,例如,在促进合法拦截、确保服务质量、防止应用堵塞点以及促进恶意行为识别方面。本文回顾了现有的网络分类技术,如基于端口的识别技术、基于深度数据包检测的识别技术、结合机器学习的统计特征以及深度学习算法。我们还解释了这些技术的实现、优势和局限性。我们的综述还扩展到文献中使用的公开可用数据集。最后,我们讨论了现有的和新出现的挑战,以及未来的研究方向。
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引用次数: 0
Behaviour recognition based on the integration of multigranular motion features in the Internet of Things 物联网中基于多粒度运动特征集成的行为识别
IF 7.5 2区 计算机科学 Q1 TELECOMMUNICATIONS Pub Date : 2024-06-01 DOI: 10.1016/j.dcan.2022.10.011
Lizong Zhang , Yiming Wang , Ke Yan , Yi Su , Nawaf Alharbe , Shuxin Feng

With the adoption of cutting-edge communication technologies such as 5G/6G systems and the extensive development of devices, crowdsensing systems in the Internet of Things (IoT) are now conducting complicated video analysis tasks such as behaviour recognition. These applications have dramatically increased the diversity of IoT systems. Specifically, behaviour recognition in videos usually requires a combinatorial analysis of the spatial information about objects and information about their dynamic actions in the temporal dimension. Behaviour recognition may even rely more on the modeling of temporal information containing short-range and long-range motions, in contrast to computer vision tasks involving images that focus on understanding spatial information. However, current solutions fail to jointly and comprehensively analyse short-range motions between adjacent frames and long-range temporal aggregations at large scales in videos. In this paper, we propose a novel behaviour recognition method based on the integration of multigranular (IMG) motion features, which can provide support for deploying video analysis in multimedia IoT crowdsensing systems. In particular, we achieve reliable motion information modeling by integrating a channel attention-based short-term motion feature enhancement module (CSEM) and a cascaded long-term motion feature integration module (CLIM). We evaluate our model on several action recognition benchmarks, such as HMDB51, Something-Something and UCF101. The experimental results demonstrate that our approach outperforms the previous state-of-the-art methods, which confirms its effectiveness and efficiency.

随着 5G/6G 系统等尖端通信技术的采用和设备的广泛开发,物联网(IoT)中的群感系统正在执行复杂的视频分析任务,如行为识别。这些应用大大增加了物联网系统的多样性。具体来说,视频中的行为识别通常需要对物体的空间信息和物体在时间维度上的动态行为信息进行组合分析。行为识别甚至可能更依赖于包含短程和长程运动的时间信息建模,而涉及图像的计算机视觉任务则侧重于理解空间信息。然而,目前的解决方案无法联合全面地分析视频中相邻帧之间的短程运动和大尺度的长程时间聚合。在本文中,我们提出了一种基于多粒度(IMG)运动特征整合的新型行为识别方法,可为在多媒体物联网人群感应系统中部署视频分析提供支持。特别是,我们通过整合基于通道注意力的短期运动特征增强模块(CSEM)和级联长期运动特征整合模块(CLIM),实现了可靠的运动信息建模。我们在几个动作识别基准(如 HMDB51、Something-Something 和 UCF101)上评估了我们的模型。实验结果表明,我们的方法优于之前的先进方法,这证明了它的有效性和高效性。
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引用次数: 0
A linkable signature scheme supporting batch verification for privacy protection in crowd-sensing 一种支持批量验证的可链接签名方案,用于人群感知中的隐私保护
IF 7.5 2区 计算机科学 Q1 TELECOMMUNICATIONS Pub Date : 2024-06-01 DOI: 10.1016/j.dcan.2023.02.015
Xu Li , Gwanggil Jeon , Wenshuo Wang , Jindong Zhao

The maturity of 5G technology has enabled crowd-sensing services to collect multimedia data over wireless network, so it has promoted the applications of crowd-sensing services in different fields, but also brings more privacy security challenges, the most commom which is privacy leakage. As a privacy protection technology combining data integrity check and identity anonymity, ring signature is widely used in the field of privacy protection. However, introducing signature technology leads to additional signature verification overhead. In the scenario of crowd-sensing, the existing signature schemes have low efficiency in multi-signature verification. Therefore, it is necessary to design an efficient multi-signature verification scheme while ensuring security. In this paper, a batch-verifiable signature scheme is proposed based on the crowd-sensing background, which supports the sensing platform to verify the uploaded multiple signature data efficiently, so as to overcoming the defects of the traditional signature scheme in multi-signature verification. In our proposal, a method for linking homologous data was presented, which was valuable for incentive mechanism and data analysis. Simulation results showed that the proposed scheme has good performance in terms of security and efficiency in crowd-sensing applications with a large number of users and data.

5G 技术的成熟使得众感服务可以通过无线网络采集多媒体数据,从而推动了众感服务在不同领域的应用,但同时也带来了更多隐私安全方面的挑战,其中最常见的就是隐私泄露。环签名作为一种集数据完整性检查和身份匿名性于一体的隐私保护技术,在隐私保护领域得到了广泛应用。然而,引入签名技术会带来额外的签名验证开销。在人群感应场景中,现有签名方案的多签名验证效率较低。因此,有必要在确保安全的前提下设计一种高效的多重签名验证方案。本文提出了一种基于人群感知背景的批量可验证签名方案,支持感知平台对上传的多重签名数据进行高效验证,从而克服传统签名方案在多重签名验证中的缺陷。在我们的建议中,提出了一种链接同源数据的方法,这对激励机制和数据分析很有价值。仿真结果表明,在具有大量用户和数据的人群感应应用中,所提出的方案在安全性和效率方面都有良好的表现。
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引用次数: 0
Detection and defending the XSS attack using novel hybrid stacking ensemble learning-based DNN approach 基于混合堆叠集成学习的深度神经网络检测和防御XSS攻击
IF 7.5 2区 计算机科学 Q1 TELECOMMUNICATIONS Pub Date : 2024-06-01 DOI: 10.1016/j.dcan.2022.09.024
Muralitharan Krishnan , Yongdo Lim , Seethalakshmi Perumal , Gayathri Palanisamy

Existing web-based security applications have failed in many situations due to the great intelligence of attackers. Among web applications, Cross-Site Scripting (XSS) is one of the dangerous assaults experienced while modifying an organization's or user's information. To avoid these security challenges, this article proposes a novel, all-encompassing combination of machine learning (NB, SVM, k-NN) and deep learning (RNN, CNN, LSTM) frameworks for detecting and defending against XSS attacks with high accuracy and efficiency. Based on the representation, a novel idea for merging stacking ensemble with web applications, termed “hybrid stacking”, is proposed. In order to implement the aforementioned methods, four distinct datasets, each of which contains both safe and unsafe content, are considered. The hybrid detection method can adaptively identify the attacks from the URL, and the defense mechanism inherits the advantages of URL encoding with dictionary-based mapping to improve prediction accuracy, accelerate the training process, and effectively remove the unsafe JScript/JavaScript keywords from the URL. The simulation results show that the proposed hybrid model is more efficient than the existing detection methods. It produces more than 99.5% accurate XSS attack classification results (accuracy, precision, recall, f1_score, and Receiver Operating Characteristic (ROC)) and is highly resistant to XSS attacks. In order to ensure the security of the server's information, the proposed hybrid approach is demonstrated in a real-time environment.

由于攻击者的高智商,现有的基于网络的安全应用程序在很多情况下都失效了。在网络应用程序中,跨站脚本攻击(XSS)是修改组织或用户信息时遇到的危险攻击之一。为了避免这些安全挑战,本文提出了一种新颖的、全方位的机器学习(NB、SVM、k-NN)和深度学习(RNN、CNN、LSTM)框架组合,用于高精度、高效率地检测和防御 XSS 攻击。在此基础上,提出了将堆叠集合与网络应用相结合的新思路,即 "混合堆叠"。为了实现上述方法,我们考虑了四个不同的数据集,每个数据集都包含安全和不安全内容。混合检测方法可以自适应地识别来自 URL 的攻击,其防御机制继承了 URL 编码与基于字典的映射的优点,从而提高了预测精度,加快了训练过程,并有效地删除了 URL 中不安全的 JScript/JavaScript 关键字。仿真结果表明,所提出的混合模型比现有的检测方法更有效。它的 XSS 攻击分类结果(准确率、精确度、召回率、f1_score 和接收器工作特征(ROC))准确率超过 99.5%,并且具有很强的抗 XSS 攻击能力。为了确保服务器信息的安全,我们在实时环境中演示了所提出的混合方法。
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引用次数: 0
An improved pulse coupled neural networks model for semantic IoT 一种改进的用于语义物联网的脉冲耦合神经网络模型
IF 7.5 2区 计算机科学 Q1 TELECOMMUNICATIONS Pub Date : 2024-06-01 DOI: 10.1016/j.dcan.2023.06.010
Rong Ma , Zhen Zhang , Yide Ma , Xiping Hu , Edith C.H. Ngai , Victor C.M. Leung

In recent years, the Internet of Things (IoT) has gradually developed applications such as collecting sensory data and building intelligent services, which has led to an explosion in mobile data traffic. Meanwhile, with the rapid development of artificial intelligence, semantic communication has attracted great attention as a new communication paradigm. However, for IoT devices, however, processing image information efficiently in real time is an essential task for the rapid transmission of semantic information. With the increase of model parameters in deep learning methods, the model inference time in sensor devices continues to increase. In contrast, the Pulse Coupled Neural Network (PCNN) has fewer parameters, making it more suitable for processing real-time scene tasks such as image segmentation, which lays the foundation for real-time, effective, and accurate image transmission. However, the parameters of PCNN are determined by trial and error, which limits its application. To overcome this limitation, an Improved Pulse Coupled Neural Networks (IPCNN) model is proposed in this work. The IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons, and all its parameters are set adaptively, which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of images. Experimental segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation Datasets. The IPCNN method achieves a better segmentation result without training, providing a new solution for the real-time transmission of image semantic information.

近年来,物联网(IoT)逐渐发展出收集感知数据、构建智能服务等应用,导致移动数据流量激增。同时,随着人工智能的快速发展,语义通信作为一种新的通信范式备受关注。然而,对于物联网设备来说,实时高效地处理图像信息是快速传输语义信息的必要任务。随着深度学习方法中模型参数的增加,传感器设备中的模型推理时间也在不断增加。相比之下,脉冲耦合神经网络(PCNN)的参数较少,更适合处理图像分割等实时场景任务,为实时、有效、准确地传输图像奠定了基础。然而,PCNN 的参数是通过试错确定的,这限制了它的应用。为了克服这一局限,本文提出了改进脉冲耦合神经网络(IPCNN)模型。IPCNN 构建了输入图像的静态属性与神经元动态属性之间的联系,其所有参数都是自适应设置的,避免了传统方法中手动设置的不便,提高了参数对不同类型图像的适应性。在 Matlab 和伯克利分割数据集的灰度图像和自然图像上的实验分割结果证明了所提出的 IPCNN 自适应参数设置方法的有效性和高效性。IPCNN 方法无需训练即可获得较好的分割效果,为图像语义信息的实时传输提供了新的解决方案。
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引用次数: 0
A survey on semantic communications: Technologies, solutions, applications and challenges 语义通信调查:技术、解决方案、应用和挑战
IF 7.5 2区 计算机科学 Q1 TELECOMMUNICATIONS Pub Date : 2024-06-01 DOI: 10.1016/j.dcan.2023.05.010
Yating Liu , Xiaojie Wang , Zhaolong Ning , MengChu Zhou , Lei Guo , Behrouz Jedari

Semantic Communication (SC) has emerged as a novel communication paradigm that provides a receiver with meaningful information extracted from the source to maximize information transmission throughput in wireless networks, beyond the theoretical capacity limit. Despite the extensive research on SC, there is a lack of comprehensive survey on technologies, solutions, applications, and challenges for SC. In this article, the development of SC is first reviewed and its characteristics, architecture, and advantages are summarized. Next, key technologies such as semantic extraction, semantic encoding, and semantic segmentation are discussed and their corresponding solutions in terms of efficiency, robustness, adaptability, and reliability are summarized. Applications of SC to UAV communication, remote image sensing and fusion, intelligent transportation, and healthcare are also presented and their strategies are summarized. Finally, some challenges and future research directions are presented to provide guidance for further research of SC.

语义通信(Semantic Communication,SC)是一种新型通信范式,它为接收器提供从信源中提取的有意义信息,以最大限度地提高无线网络的信息传输吞吐量,从而超越理论容量极限。尽管对语义通信的研究十分广泛,但目前还缺乏对语义通信的技术、解决方案、应用和挑战的全面调查。本文首先回顾了 SC 的发展,总结了其特点、架构和优势。接着,讨论了语义提取、语义编码和语义分割等关键技术,并总结了其在效率、鲁棒性、适应性和可靠性方面的相应解决方案。此外,还介绍了 SC 在无人机通信、远程图像传感与融合、智能交通和医疗保健方面的应用,并总结了相关策略。最后,提出了一些挑战和未来研究方向,为 SC 的进一步研究提供指导。
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引用次数: 0
Depressive semantic awareness from vlog facial and vocal streams via spatio-temporal transformer 通过时空变换器从vlog面部和声音流中获得抑郁语义意识
IF 7.5 2区 计算机科学 Q1 TELECOMMUNICATIONS Pub Date : 2024-06-01 DOI: 10.1016/j.dcan.2023.03.007
Yongfeng Tao , Minqiang Yang , Yushan Wu , Kevin Lee , Adrienne Kline , Bin Hu

With the rapid growth of information transmission via the Internet, efforts have been made to reduce network load to promote efficiency. One such application is semantic computing, which can extract and process semantic communication. Social media has enabled users to share their current emotions, opinions, and life events through their mobile devices. Notably, people suffering from mental health problems are more willing to share their feelings on social networks. Therefore, it is necessary to extract semantic information from social media (vlog data) to identify abnormal emotional states to facilitate early identification and intervention. Most studies do not consider spatio-temporal information when fusing multimodal information to identify abnormal emotional states such as depression. To solve this problem, this paper proposes a spatio-temporal squeeze transformer method for the extraction of semantic features of depression. First, a module with spatio-temporal data is embedded into the transformer encoder, which is utilized to obtain a representation of spatio-temporal features. Second, a classifier with a voting mechanism is designed to encourage the model to classify depression and non-depression effectively. Experiments are conducted on the D-Vlog dataset. The results show that the method is effective, and the accuracy rate can reach 70.70%. This work provides scaffolding for future work in the detection of affect recognition in semantic communication based on social media vlog data.

随着互联网信息传输的快速增长,人们一直在努力减轻网络负荷以提高效率。语义计算就是这样一种应用,它可以提取和处理语义通信。社交媒体使用户能够通过移动设备分享他们当前的情绪、观点和生活事件。值得注意的是,有心理健康问题的人更愿意在社交网络上分享他们的感受。因此,有必要从社交媒体(视频日志数据)中提取语义信息来识别异常情绪状态,以便及早识别和干预。大多数研究在融合多模态信息以识别抑郁等异常情绪状态时没有考虑时空信息。为解决这一问题,本文提出了一种提取抑郁语义特征的时空挤压变换器方法。首先,在变压器编码器中嵌入时空数据模块,利用该模块获得时空特征的表示。其次,设计了一个具有投票机制的分类器,以鼓励模型有效地对抑郁和非抑郁进行分类。我们在 D-Vlog 数据集上进行了实验。结果表明,该方法是有效的,准确率可达 70.70%。这项工作为今后基于社交媒体 vlog 数据的语义通信中的情感识别检测工作提供了支架。
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引用次数: 0
Multi-layer network embedding on scc-based network with motif 基于 scc 网络的多层网络嵌入图案
IF 7.5 2区 计算机科学 Q1 TELECOMMUNICATIONS Pub Date : 2024-06-01 DOI: 10.1016/j.dcan.2024.01.002
Lu Sun , Xiaona Li , Mingyue Zhang , Liangtian Wan , Yun Lin , Xianpeng Wang , Gang Xu

Interconnection of all things challenges the traditional communication methods, and Semantic Communication and Computing (SCC) will become new solutions. It is a challenging task to accurately detect, extract, and represent semantic information in the research of SCC-based networks. In previous research, researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node classification. However, the content of semantic information is quite complex. Although graph convolutional neural networks provide an effective solution for node classification tasks, due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures, the extracted feature information is subject to varying degrees of loss. Therefore, this paper extends from a single-layer topology network to a multi-layer heterogeneous topology network. The Bidirectional Encoder Representations from Transformers (BERT) training word vector is introduced to extract the semantic features in the network, and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network. A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node classification. We verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

万物互联对传统通信方式提出了挑战,语义通信与计算(Semantic Communication and Computing,SCC)将成为新的解决方案。在基于 SCC 的网络研究中,如何准确检测、提取和表示语义信息是一项具有挑战性的任务。在以往的研究中,研究人员通常使用卷积法提取图的特征信息,并执行相应的节点分类任务。然而,语义信息的内容相当复杂。虽然图卷积神经网络为节点分类任务提供了有效的解决方案,但由于其在表示多种关系模式方面的局限性,以及不能识别和分析高阶局部结构,提取的特征信息会受到不同程度的损失。因此,本文从单层拓扑网络扩展到多层异构拓扑网络。引入变压器双向编码器表征(BERT)训练词向量来提取网络中的语义特征,并结合网络模型表征网络的高阶局部特征模块对现有图神经网络进行改进。提出了一种基于 SCC 网络的多层网络嵌入算法,以完成端到端的节点分类任务。我们在一个真实的多层异构网络上验证了该算法的有效性。
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引用次数: 0
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Digital Communications and Networks
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