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Radio Frequency Fingerprint Identification of WiFi Signals Based on Federated Learning for Different Data Distribution Scenarios 基于联合学习的 WiFi 信号射频指纹识别,适用于不同数据分布场景
Pub Date : 2024-05-17 DOI: 10.1007/s11036-023-02229-0
Jibo Shi, Bin Ge, Qiong Wu, Ruichang Yang, Yan Sun

The number of terminal devices has skyrocketed along with the quick growth of cognitive radio networks. Massive equipment produce a lot of data that should not be shared, often WiFi signals. The radio frequency (RF) fingerprint identification approach for WiFi signals proposed in this research is based on federated learning and trains a collaborative model to complete RF fingerprint without transferring privacy-sensitive data. Aiming at the lack of labeled data and heterogeneous distribution of labeled data in actual situations, a federated transfer learning mechanism is designed. The technique suggested in this paper increases the accuracy of RF fingerprint at various sizes and assures that data privacy is not compromised, according to experimental results on real-world datasets.

随着认知无线电网络的快速发展,终端设备的数量也在激增。海量设备会产生大量不应共享的数据,通常是 WiFi 信号。本研究提出的 WiFi 信号射频(RF)指纹识别方法基于联合学习,在不传输隐私敏感数据的情况下,训练一个协作模型来完成射频指纹识别。针对实际情况中标签数据的缺乏和标签数据的异构分布,设计了一种联合转移学习机制。根据在真实世界数据集上的实验结果,本文提出的技术提高了各种规模的射频指纹的准确性,并确保数据隐私不受损害。
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引用次数: 0
Collaborative Localization Strategy Based on Node Selection and Power Allocation in Resource-Constrained Environments 资源受限环境中基于节点选择和功率分配的协作定位策略
Pub Date : 2024-05-14 DOI: 10.1007/s11036-024-02345-5
Geng Chen, Qingbin Wang, Xiaoxian Kong, Qingtian Zeng

Accurate positioning in the constrained environment of Global Navigation satellite Systems (GNSS) is a challenging problem, especially in resource-constrained urban canyon environments. In order to incentivize collaborative agency, this paper, grounded in an economic framework, proposes the utilization of auction mechanisms to address issues pertaining to collaboration and power allocation among agents. For different types of agents, different auction methods are designed according to their own resources for collaborative positioning. Firstly, an Iterative Bidirectional Auction (IBA) cooperative localization algorithm is proposed to solve the problem of cooperation and power allocation among agents in resource-constrained environments. Secondly, in order to ensure the fairness of power distribution, the auction reserve price is introduced, and the relationship between the auction reserve price and power distribution is deduced. Then, considering that there are different types of agents in the actual scenario, One-Shot Auction (OSA) algorithm is proposed to realize the cooperation between user agents and vehicle agents. Finally, analysis and numerical results demonstrate that under the proposed collaborative strategy, agents with better network conditions are more likely to participate in cooperation. Compared to non-cooperative positioning (NC), each agent experiences an improvement in position accuracy of over 60%. The performance of the proposed algorithm is approximately 43% better than uniform power allocation (UPA), and the position accuracy approaches that of the full power allocation (FPA) algorithm. Our algorithm outperforms OSA, PAR and BACL in positioning accuracy with the same agent nodes, and is the most power-efficient. This is pivotal for collaborative positioning under resource constraints.

在全球导航卫星系统(GNSS)的受限环境中进行精确定位是一个具有挑战性的问题,尤其是在资源受限的城市峡谷环境中。为了激励协作代理,本文以经济学框架为基础,提出利用拍卖机制来解决代理之间的协作和权力分配问题。针对不同类型的代理,根据其自身的协作定位资源设计了不同的拍卖方法。首先,提出了一种迭代双向拍卖(IBA)合作定位算法,以解决资源受限环境下代理间的合作和权力分配问题。其次,为了保证电量分配的公平性,引入了拍卖底价,并推导了拍卖底价与电量分配之间的关系。然后,考虑到实际场景中存在不同类型的代理,提出了单次拍卖(OSA)算法,以实现用户代理和车辆代理之间的合作。最后,分析和数值结果表明,在所提出的合作策略下,网络条件较好的代理更有可能参与合作。与非合作定位(NC)相比,每个代理的定位精度提高了 60% 以上。拟议算法的性能比统一功率分配(UPA)高出约 43%,定位精度接近全功率分配(FPA)算法。在相同代理节点的情况下,我们的算法在定位精度上优于 OSA、PAR 和 BACL,而且是最省电的算法。这对于资源限制下的协同定位至关重要。
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引用次数: 0
A Sentiment Analysis Method for Big Social Online Multimodal Comments Based on Pre-trained Models 基于预训练模型的大型社交网络多模态评论情感分析方法
Pub Date : 2024-05-13 DOI: 10.1007/s11036-024-02303-1
Jun Wan, Marcin Woźniak

In addition to a large amount of text, there are also many emoticons in the comment data on social media platforms. The multimodal nature of online comment data increases the difficulty of sentiment analysis. A big data sentiment analysis technology for social online multimodal (SOM) comments has been proposed. This technology uses web scraping technology to obtain SOM comment big data from the internet, including text data and emoji data, and then extracts and segments the text big data, preprocess part of speech tagging. Using the attention mechanism-based feature extraction method for big SOM comment data and the correlation based expression feature extraction method for SOM comment, the emotional features of SOM comment text and expression package data were obtained, respectively. Using the extracted two emotional features as inputs and the ELMO pre-training model as the basis, a GE-Bi LSTM model for SOM comment sentiment analysis is established. This model combines the ELMO pre training model with the Glove model to obtain the emotional factors of social multimodal big data. After recombining them, the GE-Bi LSTM model output layer is used to output the sentiment analysis of big SOM comment data. The experiment shows that this technology has strong extraction and segmentation capabilities for SOM comment text data, which can effectively extract emotional features contained in text data and emoji packet data, and obtain accurate emotional analysis results for big SOM comment data.

在社交媒体平台的评论数据中,除了大量文本外,还有许多表情符号。在线评论数据的多模态特性增加了情感分析的难度。有人提出了一种针对社交网络多模态(SOM)评论的大数据情感分析技术。该技术利用网络搜刮技术从互联网上获取 SOM 评论大数据,包括文本数据和表情符号数据,然后对文本大数据进行提取和分割,对部分语音标签进行预处理。利用基于注意力机制的 SOM 评论大数据特征提取方法和基于相关性的 SOM 评论表情特征提取方法,分别获得了 SOM 评论文本和表情包数据的情感特征。以提取的两个情感特征为输入,以 ELMO 预训练模型为基础,建立了用于 SOM 评论情感分析的 GE-Bi LSTM 模型。该模型将 ELMO 预训练模型与 Glove 模型相结合,获得了社会多模态大数据中的情感因素。重新组合后,利用 GE-Bi LSTM 模型输出层输出 SOM 评论大数据的情感分析结果。实验表明,该技术对SOM评论文本数据具有较强的提取和分割能力,能有效提取文本数据和表情包数据中包含的情感特征,并获得准确的SOM评论大数据情感分析结果。
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引用次数: 0
A Pruning Method Combined with Resilient Training to Improve the Adversarial Robustness of Automatic Modulation Classification Models 剪枝法与弹性训练相结合,提高自动调制分类模型的对抗鲁棒性
Pub Date : 2024-05-13 DOI: 10.1007/s11036-024-02333-9
Chao Han, Linyuan Wang, Dongyang Li, Weijia Cui, Bin Yan

In the rapidly evolving landscape of wireless communication systems, the vulnerability of automatic modulation classification (AMC) models to adversarial attacks presents a significant security challenge. This study introduces a pruning and training methodology tailored to address the nuances of signal processing within these systems. Leveraging a pruning method based on channel activation contributions, our approach optimizes adversarial training potential, enhancing the model’s capacity to improve robustness against attacks. Additionally, the approach constructs a resilient training method based on a composite strategy, integrating balanced adversarial training, soft target regularization, and gradient masking. This combination effectively broadens the model’s uncertainty space and obfuscates gradients, thereby enhancing the model’s defenses against a wide spectrum of adversarial tactics. The training regimen is carefully adjusted to retain sensitivity to adversarial inputs while maintaining accuracy on original data. Comprehensive evaluations conducted on the RML2016.10A dataset demonstrate the effectiveness of our method in defending against both gradient-based and optimization-based attacks within the realm of wireless communication. This research offers insightful and practical approaches to improving the security and performance of AMC models against the complex and evolving threats present in modern wireless communication environments.

在快速发展的无线通信系统中,自动调制分类(AMC)模型容易受到恶意攻击,这给安全带来了巨大挑战。本研究针对这些系统中信号处理的细微差别,介绍了一种剪枝和训练方法。利用基于信道激活贡献的剪枝方法,我们的方法优化了对抗性训练潜力,增强了模型的能力,提高了对抗攻击的鲁棒性。此外,该方法还构建了一种基于复合策略的弹性训练方法,整合了平衡对抗训练、软目标正则化和梯度掩蔽。这种组合有效地拓宽了模型的不确定性空间,混淆了梯度,从而增强了模型对各种对抗策略的防御能力。训练方案经过精心调整,既能保持对敌方输入的敏感性,又能保持对原始数据的准确性。在 RML2016.10A 数据集上进行的综合评估证明,我们的方法在无线通信领域能有效抵御基于梯度和优化的攻击。这项研究为提高 AMC 模型的安全性和性能,抵御现代无线通信环境中复杂且不断变化的威胁提供了具有洞察力的实用方法。
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引用次数: 0
DGNet: A Handwritten Mathematical Formula Recognition Network Based on Deformable Convolution and Global Context Attention DGNet:基于可变形卷积和全局上下文关注的手写数学公式识别网络
Pub Date : 2024-05-10 DOI: 10.1007/s11036-024-02315-x
Cuihong Wen, Lemin Yin, Shuai Liu

The Handwritten Mathematical Expression Recognition (HMER) task aims to generate corresponding LATEX sequences from images of handwritten mathematical expressions. Currently, the encoder-decoder architecture has made significant progress in this task. However, the architecture based on the DenseNet encoder fails to adequately consider the unique features of handwritten mathematical expressions (HME) and the similarity between different characters. Additionally, the decoder, with its small receptive field during the decoding process, fails to effectively capture the spatial positional information of the targets, resulting in a lack of global contextual information during decoding. To address these issues, this paper proposes a neural network called DGNet based on deformable convolution and global contextual attention. Our network takes into full consideration the sparse nature of handwritten mathematical formulas and utilizes the properties of deformable convolution, allowing the convolution kernel to deform based on the content of the neighborhood. This enables our model to better adapt to geometric changes and other deformations in handwritten mathematical expressions. Simultaneously, we introduce GCAttention in optimizing the feature part to fully aggregate global contextual features of both position and channel. In experiments, our model achieved accuracies of 58.51%, 56.32%, and 56.1% on the CROHME 2014, 2016, and 2019 datasets, respectively.

手写数学表达式识别(HMER)任务旨在从手写数学表达式的图像中生成相应的 LATEX 序列。目前,编码器-解码器架构在这项任务中取得了重大进展。然而,基于 DenseNet 编码器的架构未能充分考虑手写数学表达式(HME)的独特特征和不同字符之间的相似性。此外,解码器在解码过程中的感受野较小,无法有效捕捉目标的空间位置信息,导致解码过程中缺乏全局上下文信息。为了解决这些问题,本文提出了一种基于可变形卷积和全局上下文关注的神经网络,即 DGNet。我们的网络充分考虑了手写数学公式的稀疏性,并利用了可变形卷积的特性,允许卷积核根据邻域的内容进行变形。这使得我们的模型能够更好地适应手写数学表达式中的几何变化和其他变形。同时,我们在优化特征部分时引入了 GCAttention,以充分聚合位置和通道的全局上下文特征。在实验中,我们的模型在 CROHME 2014、2016 和 2019 数据集上的准确率分别达到了 58.51%、56.32% 和 56.1%。
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引用次数: 0
Distributionally Robust Federated Learning for Mobile Edge Networks 移动边缘网络的分布式稳健联合学习
Pub Date : 2024-05-03 DOI: 10.1007/s11036-024-02316-w
Long Tan Le, Tung-Anh Nguyen, Tuan-Dung Nguyen, Nguyen H. Tran, Nguyen Binh Truong, Phuong L. Vo, Bui Thanh Hung, Tuan Anh Le

Federated Learning (FL) revolutionizes data processing in mobile networks by enabling collaborative learning without data exchange. This not only reduces latency and enhances computational efficiency but also enables the system to adapt, learn and optimize the performance from the user’s context in real-time. Nevertheless, FL faces challenges in training and generalization due to statistical heterogeneity, stemming from the diverse data nature across varying user contexts. To address these challenges, we propose (textsf {WAFL}), a robust FL framework grounded in Wasserstein distributionally robust optimization, aimed at enhancing model generalization against all adversarial distributions within a predefined Wasserstein ambiguity set. We approach (textsf {WAFL}) by formulating it as an empirical surrogate risk minimization problem, which is then solved using a novel federated algorithm. Experimental results demonstrate that (textsf {WAFL}) outperforms other robust FL baselines in non-i.i.d settings, showcasing superior generalization and robustness to significant distribution shifts.

联合学习(FL)通过实现无需数据交换的协作学习,彻底改变了移动网络中的数据处理方式。这不仅减少了延迟,提高了计算效率,还使系统能够实时适应、学习和优化用户环境的性能。然而,FL 在训练和泛化方面面临着挑战,原因是不同用户背景下的数据性质各不相同,导致统计异质性。为了应对这些挑战,我们提出了一种基于 Wasserstein 分布鲁棒优化的鲁棒 FL 框架,旨在增强模型泛化能力,以应对预定义的 Wasserstein 模糊集内的所有对抗分布。我们的方法是将(textsf {WAFL})表述为一个经验代用风险最小化问题,然后使用一种新颖的联合算法来解决这个问题。实验结果表明,在非 i.i.d 设置中,(textsf {WAFL}) 优于其他稳健 FL 基线,展示了卓越的泛化能力和对重大分布变化的稳健性。
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引用次数: 0
Probabilistic SAX: A Cognitively-Inspired Method for Time Series Classification in Cognitive IoT Sensor Network 概率 SAX:认知物联网传感器网络时间序列分类的认知启发方法
Pub Date : 2024-05-01 DOI: 10.1007/s11036-024-02322-y
Vidyapati Jha, Priyanka Tripathi

Cognitive Internet of Things (CIoT) is a new subfield of the Internet of Things (IoT) that aims to integrate cognition into the IoT's architecture and design. Various CIoT applications require techniques to inevitably extract machine-understandable concepts from unprocessed sensory data to provide value-added insights about CIoT devices and their users. The time series classification, which is used for the concept's extraction poses challenges to many applications across various domains, i.e., dimensionality reduction strategies have been suggested as an effective method to decrease the dimensionality of time series. The most common approach for time-series classification is the symbolic aggregate approximation (SAX). However, its main drawback is that it does not select the most significant point from the segment during the piecewise aggregate approximation (PAA) stage. The situation is cumbersome when data is heterogeneous and massive. Therefore, this research presents a novel technique for the selection of the most significant point from a segment during the PAA stage in SAX. The proposed technique chooses the maximum informative point as the most significant point using the probabilistic interpretation of sensory data with an appropriate copula design. The appropriate copula is selected using the minimum akaike information criteria (AIC) value. Subsequently, the modified SAX considers the maximum informative points instead of a selection of mean/max/extreme data points on a given segment during the PAA stage. The experimental evaluation of the environmental dataset reveals that the proposed method is more accurate and computationally efficient than classic SAX. Also, for cross-validation it computes the entropy of the information point (i-value) from each dataset to verify the successful transformation of normal data points to information points.

认知物联网(CIoT)是物联网(IoT)的一个新子领域,旨在将认知融入物联网的架构和设计中。各种 CIoT 应用都需要从未经处理的感知数据中提取机器可理解概念的技术,以提供有关 CIoT 设备及其用户的增值见解。用于提取概念的时间序列分类给各个领域的许多应用带来了挑战,即降维策略被认为是降低时间序列维度的有效方法。最常见的时间序列分类方法是符号集合近似法(SAX)。然而,它的主要缺点是在片段聚合逼近(PAA)阶段无法从片段中选择最重要的点。当数据是异构的海量数据时,这种情况就会很麻烦。因此,本研究提出了一种在 SAX 的 PAA 阶段从数据段中选择最重要点的新技术。所提出的技术使用适当的 copula 设计对感官数据进行概率解释,从而选择信息量最大的点作为最重要的点。适当的 copula 是通过最小阿凯克信息准则(AIC)值来选择的。随后,在 PAA 阶段,修改后的 SAX 会考虑信息量最大的点,而不是选择给定片段上的平均/最大/极端数据点。对环境数据集的实验评估表明,与传统的 SAX 相比,所提出的方法更准确,计算效率更高。此外,为了进行交叉验证,它还计算了每个数据集的信息点(i 值)的熵,以验证正常数据点到信息点的转化是否成功。
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引用次数: 0
RIS-Aided MmWave Hybrid Relay Network Based on Multi-Agent Deep Reinforcement Learning 基于多代理深度强化学习的 RIS 辅助毫米波混合中继网络
Pub Date : 2024-04-26 DOI: 10.1007/s11036-024-02323-x
Yifeng Zhao, Xuanhui Liu, Haoran Liu, Xiaoqi Wang, Lianfen Huang

In millimeter wave (mmWave) communication, the utilization of multi-hop relay technology has been regarded as a promising approach to overcome the significant path loss encountered during signal transmission. However, the traditional active relay network suffers from low energy efficiency (EE) and uneven resource distribution. To address these challenges, we introduce Reconfigurable Intelligent Surface (RIS) as a passive relay to the mmWave communication system and create a hybrid relay system that combines passive and active relays, which aims to improve EE through the multi-hop relay. Additionally, with the development of Artificial Intelligence, deep Q-learning (DQN) is applied to optimize the hybrid relay system in this paper, where every transmitted signal of the base station (BS) is considered an agent. In this approach, the network is trained based on the interaction between the collected environment information and the users’ relay allocation strategy. Considering competition-cooperation relationships of multiple users, we propose a multi-agent DQN (MADQN) algorithm to allocate the relay resource where the primary goal is maximizing EE. Simulation results demonstrate that our proposed scheme can effectively converge to the optimal relay link, further improving EE and reducing energy consumption in comparison with conventional schemes.

在毫米波(mmWave)通信中,利用多跳中继技术克服信号传输过程中遇到的巨大路径损耗被认为是一种很有前途的方法。然而,传统的主动中继网络存在能效(EE)低和资源分配不均的问题。为应对这些挑战,我们在毫米波通信系统中引入了可重构智能表面(RIS)作为无源中继,并创建了一个结合无源中继和有源中继的混合中继系统,旨在通过多跳中继提高能效。此外,随着人工智能的发展,本文将深度 Q 学习(DQN)应用于优化混合中继系统,其中基站(BS)的每个传输信号都被视为一个代理。在这种方法中,网络的训练基于收集到的环境信息与用户的中继分配策略之间的相互作用。考虑到多个用户的竞争-合作关系,我们提出了一种多代理 DQN(MADQN)算法来分配中继资源,其主要目标是最大化 EE。仿真结果表明,与传统方案相比,我们提出的方案能有效收敛到最佳中继链路,进一步提高了 EE,降低了能耗。
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引用次数: 0
MalDMTP: A Multi-tier Pooling Method for Malware Detection based on Graph Classification MalDMTP:基于图分类的多层汇集恶意软件检测方法
Pub Date : 2024-04-26 DOI: 10.1007/s11036-024-02318-8
Liang Kou, Cheng Qiu, Meiyu Wang, Hua Liu, Yan Du, Jilin Zhang

With the development and adoption of cloud platforms in various fields, malware attacks have become a serious threat to the Internet cloud ecosystem. However, the pooling process of existing graph classification techniques for malware variant detection uses only a serial and single strategy, resulting in localized malicious behaviors of malware that may be overlooked. In this paper, we propose MalDMTP, a malware detection framework based on multilevel graph classification learning, which implements the graph pooling process for malware classification in parallel and performs graph instance-based discrimination. In particular, MalDMTP first constructs an API call graph based on results obtained from dynamic execution of malware. Then it combines multiple graph neural network learning strategies through multi-level pooling to learn the global importance of nodes in the pooled graph and extract node representations from multiple perspectives for heterogeneous graphs. After that, MalDMTP is aggregated into graph representations by the graph-level pooling function GMT based on a multi-head attention mechanism, which goes through a classifier in order to obtain malware prediction labels. Experimental results show that the proposed MalDMTP can achieve 96.53% accuracy on the Alibaba cloud malware dataset, which improves 1.9% 7.6% over the previous single-graph pooling methods on the graph classification task of malware detection.

随着云平台在各个领域的发展和应用,恶意软件攻击已成为互联网云生态系统的严重威胁。然而,现有图分类技术在恶意软件变种检测的池化过程中仅采用了序列化的单一策略,导致恶意软件的局部恶意行为可能被忽略。本文提出了基于多级图分类学习的恶意软件检测框架 MalDMTP,该框架并行地实现了恶意软件分类的图池化过程,并执行基于图实例的判别。具体来说,MalDMTP 首先根据恶意软件动态执行的结果构建 API 调用图。然后,它通过多级池化结合多种图神经网络学习策略,学习池化图中节点的全局重要性,并从多个角度提取异构图的节点表征。之后,基于多头关注机制的图级池化函数 GMT 将 MalDMTP 聚合为图表示,并通过分类器获得恶意软件预测标签。实验结果表明,所提出的 MalDMTP 在阿里巴巴云恶意软件数据集上的准确率达到 96.53%,比之前的单图池方法在恶意软件检测的图分类任务上提高了 1.9% 7.6%。
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引用次数: 0
Cooperative Mission Planning of USVs Based on Intention Recognition 基于意图识别的 USV 合作任务规划
Pub Date : 2024-04-18 DOI: 10.1007/s11036-024-02324-w
Changting Shi, Yanqiang Wang, Jing Shen, Junhui Qi

To enhance task completion efficiency and quality, the coordination of Unmanned Surface Vehicle (USV) formations in complex environmental situations often requires user intervention. This paper proposes a human-machine collaborative approach for USV mission planning and explores a method for identifying user intervention intentions. A method for recognizing user intention based on intervention style was proposed. The method utilizes the Improved Particle Swarm Optimization-Support Vector Machine (IPSO-SVM) model to recognize intervention style and emphasizes human intention recognition to enhance the ability of USV in complex environments. The method involves modeling continuous intervention operations and incorporating intervention style features to accurately identify user intent. The study proposes a fusion method that combines feature attention, self-attention, and Fusion of Long Short-Term Memory Networks (FLSTMS) to achieve its purpose. Furthermore, it suggests a cooperative mission planning method based on prospect theory, which integrates user risk propensity and identified intentions to optimize planning. Simulation experiments confirm the effectiveness of this approach, highlighting its advantages over traditional methods.

为了提高任务完成的效率和质量,在复杂环境情况下协调无人水面飞行器(USV)编队往往需要用户干预。本文提出了一种用于 USV 任务规划的人机协作方法,并探索了一种识别用户干预意图的方法。本文提出了一种基于干预风格的用户意图识别方法。该方法利用改进的粒子群优化-支持向量机(IPSO-SVM)模型来识别干预风格,并强调人的意图识别,以提高 USV 在复杂环境中的能力。该方法包括对连续干预操作进行建模,并结合干预风格特征来准确识别用户意图。研究提出了一种融合方法,将特征注意、自我注意和长短期记忆网络(FLSTMS)融合在一起,以实现其目的。此外,研究还提出了一种基于前景理论的合作任务规划方法,该方法综合了用户风险倾向和识别意图,以优化规划。模拟实验证实了这种方法的有效性,凸显了它与传统方法相比的优势。
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引用次数: 0
期刊
Mobile Networks and Applications
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