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2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)最新文献

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Non-Autoregressive Machine Translation with a Novel Masked Language Model 基于掩码语言模型的非自回归机器翻译
Li Ke, Liao Jie, Wan Wangjun
Non-autoregressive translation (NAT) has become a hot direction for its acceleration on decoding. Conditional masked language model (CMLM) performs excellently in NAT models. We review and extend the CMLM in some strategy: (1) N-gram mask strategy, which can help model to learn coarse sematic information of target language; (2) top-k decoding strategy, our model generates the top-k probability words in each step so that it can generate the final sentence in constant number steps. Extensive experiments demonstrate that our method is progressive compared with CMLM and some other NAT models. Specially, on the dataset WMT 14 EN-DE, our approach can achieve 27.24 BLEU score with only 0.1 BLEU sacrifice compared with the autoregressive counterpart base Transformer while speeding up 3 times on decoding.
非自回归翻译(Non-autoregressive translation, NAT)因其对译码速度的加快而成为研究的热点。条件掩码语言模型(CMLM)在NAT模型中表现优异。我们对CMLM的一些策略进行了回顾和扩展:(1)N-gram掩码策略,它可以帮助模型学习目标语言的粗语义信息;(2) top-k解码策略,我们的模型每步生成top-k个概率词,从而以常数步长生成最终句子。大量的实验表明,与CMLM和其他NAT模型相比,我们的方法是渐进的。特别是在WMT 14 EN-DE数据集上,与自回归的base Transformer相比,我们的方法可以获得27.24 BLEU分数,仅牺牲0.1 BLEU,解码速度提高3倍。
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引用次数: 0
Underwater Acoustic Sensing with Rational Orthogonal Wavelet Pulse and Auditory Frequency Cepstral Coefficient-Based Feature Extraction 基于合理正交小波脉冲和听觉倒谱系数的水声传感特征提取
Guo Tiantian, E. Lim, M. López-Benítez, Ma Fei, Yu Limin
Active pulse design, target detection and classification play an essential role in underwater acoustic sensing. This paper addresses the system design with three kinds of pulse signals, including continuous wave (CW), linear frequency modulation (LFM) signal and rational orthogonal wavelet (ROW) signal. The detector design has an architecture of feature extraction and convolutional neural network (CNN) based classification. A geometric underwater channel model is adopted to facilitate the generation of training datasets with designated geometric underwater environment parameters. The simulated received pulse signals are converted into feature maps as the input of the classifier. This paper applies the acoustic features, Short Time Fourier Transform (STFT), Mel Frequency Cepstral Coefficients (MFCC) and Gammatone Frequency Cepstral Coefficients (GFCC) to construct different feature maps. A lightweight CNN model is used as the classifier. Experiments demonstrate the superiority of the ROW wavelet pulse signals and the proposed algorithm in target localization and underwater signal classification.
主动脉冲设计、目标检测与分类在水声传感中起着至关重要的作用。本文采用连续波(CW)、线性调频(LFM)和有理正交小波(ROW)三种脉冲信号进行系统设计。该检测器设计具有特征提取和基于卷积神经网络(CNN)分类的结构。采用几何水下通道模型,便于生成具有指定几何水下环境参数的训练数据集。将接收到的模拟脉冲信号转换成特征映射作为分类器的输入。本文利用声学特征、短时傅里叶变换(STFT)、Mel频率倒谱系数(MFCC)和γ酮频率倒谱系数(GFCC)来构建不同的特征映射。使用轻量级CNN模型作为分类器。实验证明了ROW小波脉冲信号及其算法在目标定位和水下信号分类方面的优越性。
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引用次数: 1
RIDRL: A Deep Reinforcement Learning Based on Multiple Dispatching Rules and IGA Algorithm for JSP RIDRL:基于多调度规则和IGA算法的JSP深度强化学习
Su Jin, Lyu Shubin, Lu Xin, Wan You, Liao Fusheng
Job shop scheduling problem (JSP) is a very general and complex scheduling problem in the manufacturing industry. The traditional priority dispatching rule (PDR) can get the approximate solution for some specific problems. Nevertheless, for the complex and changing realistic factory floor, the quality of the existing solution fluctuates significantly. To solve the problem, this paper fuse multiple dispatching rules and the Insertion Greedy Algorithm (IGA) to deep reinforcement learning (DRL), namely RIDRL, to solve the job shop scheduling problem. In this method, we manually choose five generalizable state features as the states of the workshop environment. Employing 18 scheduling rules as the action space in the agent while designing a quick converge reward function. Additionally, we use a Proximal Policy Optimization Algorithm (PPO) to train the DRL agent with minimizing makespan as the optimization objective. Several simulation experiments on many standard instances indicate that the proposed method obtains competitive solutions for problems of different sizes.
作业车间调度问题(Job shop scheduling problem, JSP)是制造业中一个非常普遍和复杂的调度问题。传统的优先级调度规则(PDR)可以得到一些具体问题的近似解。然而,对于复杂和不断变化的现实工厂车间,现有解决方案的质量波动很大。为了解决这一问题,本文将多调度规则和插入贪婪算法(IGA)融合到深度强化学习(DRL)中,即RIDRL,来解决作业车间调度问题。在这种方法中,我们手动选择五个可概括的状态特征作为车间环境的状态。采用18条调度规则作为智能体的动作空间,同时设计快速收敛的奖励函数。此外,我们使用了一种近端策略优化算法(PPO)来训练DRL代理,以最小化makespan为优化目标。在多个标准实例上的仿真实验表明,该方法对不同规模的问题都能得到竞争性的解。
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引用次数: 0
AW-PCNN: Adaptive Weighting Pyramidal Convolutional Neural Network for Fine-Grained Few-Shot Learning AW-PCNN:用于细粒度少镜头学习的自适应加权金字塔卷积神经网络
Li Hengbai
Due to the unique challenges of high intra-class variation and low inter-class variation, Fine-Grained Visual Classification (FGVC) tasks extremely need to extract multilevel semantic features of fine-grained images for classification. Moreover, labor-intensive annotations and the existence of long-tailed distribution of fine-grained images in real life make fine-grained few-shot learning an urgent problem to be solved. In this paper, we propose an Adaptive Weighting Pyramidal Convolutional Neural Network (AW-PCNN) for fine-grained few-shot learning. Our AW-PCNN consists of a PCNN module and a AW module, which are improved in two aspects. First, our PCNN module extracts the features of each layer in CNN to obtain both high-level global and low-level local subtle features of images to overcome the challenge of FGVC tasks. Second, We employ the metric learning approach for few-shot learning, and our AW module improves it by selecting decisive pairs and adaptively weighting the pairs based on their similarity scores to mitigate the challenge of FGVC tasks and learn a better embedding space. Our AW-PCNN achieves state-of-the-art performance on three benchmark fine-grained datasets, which proves the effectiveness and superiority of our model.
由于类内变化大、类间变化小的独特挑战,细粒度视觉分类(FGVC)任务极其需要提取细粒度图像的多层次语义特征进行分类。此外,现实生活中精细粒度图像标注的劳动密集型和长尾分布的存在,使得精细粒度少射学习成为一个亟待解决的问题。在本文中,我们提出了一种用于细粒度少镜头学习的自适应加权金字塔卷积神经网络(AW-PCNN)。我们的AW-PCNN由一个PCNN模块和一个AW模块组成,在两个方面进行了改进。首先,我们的PCNN模块提取CNN中每一层的特征,同时获得图像的高级全局和低级局部细微特征,以克服FGVC任务的挑战。其次,我们采用度量学习方法进行少镜头学习,我们的AW模块通过选择决定性对并根据它们的相似度评分自适应加权来改进它,以减轻FGVC任务的挑战并学习更好的嵌入空间。我们的AW-PCNN在三个基准细粒度数据集上取得了最先进的性能,证明了我们模型的有效性和优越性。
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引用次数: 0
Feature Modeling and Dimensionality Reduction to Improve ML-Based DDOS Detection Systems in SDN Environment 特征建模和降维改进SDN环境下基于ml的DDOS检测系统
Mohamed Ali Setitra, Ilyas Benkhaddra, Zine El Abidine Bensalem, Mingyu Fan
Distributed Denial of Service (DDoS) attacks are one of the most significant challenges in network security, especially in the Software-Defined Network (SDN) environment, due to the centralized network management provided by the Control Plane. Considering the insufficiency of traditional detection approaches because of the growth and sophistication of DDoS attacks, exploiting Machine Learning (ML) techniques is in high demand. For this, feature modeling is essential to obtain an effective ML-based DDoS detection system, especially in the pre-processing phase. In this paper, we proposed and implemented a pre-processing model based on deep studying the dataset, going so far as to increase the features number for a better representation and, if necessary, minimize the data dimension by exploring some dimensionality reduction techniques such as Principal Component Analysis (PCA) or t-distributed Stochastic Neighbor Embedding (t-SNE). Moreover, to invest even more in our conceptual aspect relating to SDN environments, as specified in the above-cited challenge, we have chosen to implement our proposed model using an open-source SDN dataset created specially in an SDN environment. Then, the statistical characteristics of these correlations are analyzed. In addition, eight ML techniques between supervised and unsupervised models were used in our work to detect DDoS attacks. Finally, we compared our proposed model with other existing approaches. The outcome showed that the detecting reliability is improved, and the method has a good effect on detecting DDoS attacks compared with other methods.
分布式拒绝服务(DDoS)攻击是网络安全面临的最大挑战之一,特别是在软件定义网络(SDN)环境下,由于控制平面提供了集中的网络管理。由于DDoS攻击的增长和复杂性,传统检测方法的不足,利用机器学习(ML)技术的需求很高。为此,特征建模对于获得一个有效的基于ml的DDoS检测系统至关重要,尤其是在预处理阶段。在本文中,我们提出并实现了一个基于深度研究数据集的预处理模型,以增加特征数量以获得更好的表示,并在必要时通过探索一些降维技术(如主成分分析(PCA)或t分布随机邻居嵌入(t-SNE))来最小化数据维数。此外,为了在与SDN环境相关的概念方面投入更多,如上述挑战中所述,我们选择使用专门在SDN环境中创建的开源SDN数据集来实现我们提出的模型。然后,分析了这些相关性的统计特征。此外,在我们的工作中使用了监督模型和无监督模型之间的八种ML技术来检测DDoS攻击。最后,我们将我们的模型与其他现有的方法进行了比较。实验结果表明,该方法提高了检测可靠性,与其他方法相比,对DDoS攻击的检测效果较好。
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引用次数: 1
Semantic Enhancement Loss Function Based on Attention Mechanism 基于注意机制的语义增强损失函数
Teng Shuhua, Zheng Lidong, Cheng Zhengting, Yuan Zhian, Ma Yanxin
Panoramic segmentation is an important research direction in computer vision. Considering that different applications have different requirements for semantic segmentation accuracy, a semantic enhancement loss function based on attention mechanism is proposed. By adding attention mechanism, it can enhance the sensitivity to the semantic information of task attention and improve the classification accuracy of specific objects and backgrounds. The experimental results show that the semantic enhancement loss function can effectively improve the classification accuracy of semantic categories required by tasks.
全景分割是计算机视觉中的一个重要研究方向。针对不同应用对语义切分精度的要求不同,提出了一种基于注意机制的语义增强损失函数。通过添加注意机制,可以增强对任务注意语义信息的敏感性,提高对特定对象和背景的分类准确率。实验结果表明,语义增强损失函数可以有效地提高任务所需语义类别的分类精度。
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引用次数: 0
Lattice-Based Anonymous Signcryption For Smart Grid 基于格的智能电网匿名签名
Cao Chenchen, X. Chunxiang, Jiang Changsong, Han Yunxia
Smart grid utilizes intelligent metering devices that transmit the energy usage data of an end user to a service provider. During the transmission’ an adversary could eavesdrop’ intercept’ modify and forge the data. Therefore’ it is critically important to make sure that such data is confidential and unforgeable. Signcryption is an effective technique to satisfy the requirements. However’ existing signcryption schemes are generally based on conventional hardness assumptions’ which are vulnerable to adversaries equipped with quantum computers in the near future. Additionally, they fail to consider identity leakage that allows an adversary to target a special end-user group of smart grid for attacks. To solve the problems’ an anonymous signcryption scheme for smart grid, dubbed AS4Smd, is proposed. AS4Smd is based on lattice cryptography’ which is post-quantum secure. In AS4Smd’ an end-user can sign and encrypt data in a logically single step to produce a signcryptext. Additionally’ the end-user’s identity is embedded in the signcryptext to accomplish end-user anonymity. The performance evaluation on AS4Smd shows that it is efficient in terms of computation and communication efficiency.
智能电网利用智能计量设备,将终端用户的能源使用数据传输给服务提供商。在传输过程中,对手可以窃听、拦截、修改和伪造数据。因此,确保这些数据的机密性和不可伪造性至关重要。签名加密是满足这些要求的有效技术。然而,“现有的签名加密方案通常基于传统的硬度假设”,在不久的将来,这些方案很容易受到配备量子计算机的对手的攻击。此外,他们没有考虑到身份泄露,这使得攻击者可以针对智能电网的特殊终端用户组进行攻击。为了解决这一问题,提出了一种智能电网匿名签名加密方案AS4Smd。AS4Smd基于后量子安全的点阵加密。在AS4Smd '中,最终用户可以在逻辑上的单个步骤中对数据进行签名和加密,以生成签名加密文本。此外,最终用户的身份嵌入到签名加密文本中,以实现最终用户的匿名性。对AS4Smd的性能评价表明,AS4Smd在计算效率和通信效率方面都是高效的。
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引用次数: 0
Automatic Molecule Generation Using Manifold Guidance GAN and Genetic Algorithm 基于流形制导GAN和遗传算法的分子自动生成
Yiheng Huang, Lei Huang, Hongguang Fu
Deep generative models used to generate molecules have demonstrated their superior performance in the creation of novel structures. However, mode collapse is a severe and frequent issue in the GAN-based molecule generation models, in which generator learns a few modes of the data distribution while ignoring others. In this work, we introduce Mol Manifold Guidance Generative Adversarial Network (Mol-MGGAN) to solve this problem. Mol-MGGAN extends generative adversarial networks by introducing a manifold guidance network, which contains a graph encoder that maps molecules into a latent manifold space that covers overall modes of the data distribution, and a discriminator that distinguishes molecules in the manifold space. The guidance network can explicitly prevent the generator from mode collapse through forcing the generator to learn the overall modes of the data. We use the genetic algorithm to further enhance the generator’s ability to produce novel and unique molecules. In the experiments on the QM9 chemical database, we demonstrate that Mol-MGGAN generates nearly 100% valid molecules. Most importantly, we generate more unique and novel molecules compared to the previous GAN-based molecule generation model. The result of the experiments shows that Mol-MGGAN reduces mode collapse during the molecular generation.
用于生成分子的深度生成模型在创建新结构方面显示出其优越的性能。然而,在基于gan的分子生成模型中,模式崩溃是一个严重而频繁的问题,在这种模型中,生成器只学习了数据分布的一些模式,而忽略了其他模式。在这项工作中,我们引入摩尔流形制导生成对抗网络(Mol- mggan)来解决这个问题。Mol-MGGAN通过引入流形引导网络扩展了生成式对抗网络,该网络包含一个将分子映射到覆盖数据分布整体模式的潜在流形空间的图形编码器,以及一个区分流形空间中的分子的鉴别器。引导网络可以通过强制生成器学习数据的整体模式来明确地防止生成器模式崩溃。我们使用遗传算法进一步增强生成器产生新颖独特分子的能力。在QM9化学数据库的实验中,我们证明了Mol-MGGAN生成的有效分子接近100%。最重要的是,与之前基于gan的分子生成模型相比,我们生成了更多独特和新颖的分子。实验结果表明,Mol-MGGAN可以减少分子生成过程中的模式坍缩。
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引用次数: 0
ICCWAMTIP 2022 Cover Page ICCWAMTIP 2022封面页
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引用次数: 0
Wb-Proxshare: A Warrant-Based Proxy Re-Encryption Model for Secure Data Sharing in Iot Networks Via Blockchain Wb-Proxshare:一种基于授权的代理再加密模型,用于通过区块链实现物联网网络中的安全数据共享
Collins Sey, Hang Lei, Xiaoyu Li, Weizhong Qian, Obed Barnes, Linda Delali Fiasam, Cong Zhang, Seth Larweh Kodjiku, Isaac Osei Agyemang, Isaac Adjei-Mensah, Xiaolei Shang
The Internet of Things (IoT) has become increasingly popular due to the enormous growth in the number of smart devices and the massive amount of data these devices generate. Data access and sharing has been one of the most valuable services of the IoT network. For this reason, the security and privacy of the data are of great essence to harnessing the full potential of the IoT network. Existing security measures for data access and sharing have proven to be insufficient. They usually need more credibility due to centralization and single-point-of-failure problems. In this paper, we propose WB-Proxshare, a data access and sharing model based on warrant and proxy re-encryption (PRE) with blockchain. We present a mechanism that further enhances collusion-resistance in data storage, access and sharing. Our model ensures tamper-proof, data privacy, provenance and auditing. We set a proxy server that issues warrant for data storage and sharing and re-encrypts data owners’ encrypted data to grant access to legitimate data users. Our security analysis and evaluation show that the proposed model has high efficiency and ensures strong data security, integrity, and confidentiality guarantee for data access and sharing in the IoT environment and is practicable.
由于智能设备数量的巨大增长以及这些设备产生的大量数据,物联网(IoT)变得越来越受欢迎。数据访问和共享一直是物联网网络中最有价值的服务之一。因此,数据的安全性和隐私性对于充分利用物联网网络的潜力至关重要。现有的数据访问和共享安全措施已被证明是不够的。由于集中化和单点故障问题,它们通常需要更高的可信度。本文提出了一种基于区块链授权和代理重加密(PRE)的数据访问和共享模型WB-Proxshare。我们提出了一种在数据存储、访问和共享方面进一步增强抗合谋的机制。我们的模型确保了防篡改、数据隐私、来源和审计。我们设置了一个代理服务器,为数据存储和共享颁发授权,并重新加密数据所有者的加密数据,以授予合法数据用户访问权限。我们的安全性分析和评估表明,该模型具有较高的效率,为物联网环境下的数据访问和共享提供了强有力的数据安全性、完整性和保密性保障,是切实可行的。
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引用次数: 0
期刊
2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)
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