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Trust Model for Effective Consensus in Blockchain 区块链中有效共识的信任模型
IF 1.3 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-02-01 DOI: 10.4108/eai.1-2-2022.173294
R. Shalini, R. Manoharan
. Abstract Blockchain technology is a revolution started as a new economy with an alternative currency namely Bitcoin. Besides the economical aspect, the technological capabilities of Blockchain such as distributed computing, record keeping, irrecoverability of transactions, reliability and etc., are harnessed by variety of real-world applications. Blockchain is a rising pool of records known as blocks linked using security procedure. It is typically managed by a group of nodes in a distributed network technology which integrates technologies such as distributed ledger, security and consensus algorithm to ensure reliability and immutability. In Blockchain, the access privileges are determined by a set of nodes called miners, which run the consensus algorithm to access and submit transactions in to the block after authentication. However, in the existing Blockchain, there is no mechanism to ensure the trust and robustness of the miners and eliminate the malicious miners which runs the consensus algorithm. Therefore, this paper proposes a trust model with an objective of eliminating untrusted nodes from the mining process to enhance the reliability and security of the Blockchain. Further, the proposed trust model is suitably analysed for transaction rate, efficiency and scalability with Hyper Ledger framework to ensure the robustness.
. 区块链技术是一场以比特币为替代货币的新经济革命。除了经济方面,区块链的技术能力,如分布式计算、记录保存、交易的不可恢复性、可靠性等,都被各种现实世界的应用所利用。区块链是一个不断增长的记录池,称为使用安全程序链接的块。它通常由分布式网络技术中的一组节点进行管理,该技术集成了分布式账本、安全性和共识算法等技术,以确保可靠性和不变性。在区块链中,访问权限由一组称为矿工的节点决定,这些节点运行共识算法,在身份验证后访问并提交交易到块中。然而,在现有的区块链中,没有机制来确保矿工的信任和鲁棒性,并消除运行共识算法的恶意矿工。因此,本文提出了一种信任模型,旨在从挖掘过程中消除不可信节点,以增强区块链的可靠性和安全性。此外,在超级账本框架下,对所提出的信任模型进行了交易率、效率和可扩展性分析,以确保其鲁棒性。
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引用次数: 0
Scene Classification of Remotely Sensed Images using Optimized RSISC-16 Net Deep Convolutional Neural Network Model 基于优化rssc -16 Net深度卷积神经网络模型的遥感图像场景分类
IF 1.3 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-02-01 DOI: 10.4108/eai.1-2-2022.173292
P. Deepan, L. R. Sudha, K. Kalaivani, J. Ganesh
Remote Sensing Image (RSI) analysis has seen a massive increase in popularity over the last few decades, due to the advancement of deep learning models. A wide variety of deep learning models have emerged for the task of scene classification in remote sensing image analysis. The majority of these models have shown significant success. However, we found that there is significant variability, in order to improve the system efficiency in characterizing complex patterns in remote sensing imagery. We achieved this goal by expanding the architecture of VGG-16 Net and fine-tuning hyperparameters such as batch size, dropout probabilities, and activation functions to create the optimized Remote Sensing Image Scene Classification (RSISC-16 Net) deep learning model for scene classification. Using the Talos optimization tool, the results are carried out. This will increase efficiency and reduce the risk of over-fitting. Our proposed RSISC-16 Net model outperforms the VGG-16 Net model, according to experimental results.
在过去的几十年里,由于深度学习模型的进步,遥感图像(RSI)分析的普及程度大幅增加。为了解决遥感图像分析中的场景分类问题,出现了各种各样的深度学习模型。这些模式中的大多数都取得了显著的成功。然而,为了提高系统在遥感图像中表征复杂模式的效率,我们发现存在显著的变异性。为了实现这一目标,我们扩展了VGG-16 Net的架构,并对批处理大小、辍学概率和激活函数等超参数进行了微调,以创建优化的遥感图像场景分类(rssc -16 Net)深度学习模型。利用Talos优化工具,对结果进行了验证。这将提高效率并减少过度拟合的风险。实验结果表明,我们提出的rssc -16网络模型优于VGG-16网络模型。
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引用次数: 0
Comparison of Classification Model for the Detection of Cyber-attack using Ensemble Learning Models 基于集成学习模型的网络攻击检测分类模型比较
IF 1.3 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-02-01 DOI: 10.4108/eai.1-2-2022.173293
M. Akhtar, Tao Feng
Incorporating digital technologies into security systems is a positive development. It's time for the digital system to be appropriately protected from potential threats and attacks. An intrusion detection system can identify both external and internal anomalies in the network. There are a variety of threats out there, both active and passive. If these dangers aren't addressed, attacks and data theft could occur from the point of origin all the way to the point of destination. Machine learning is still in its infancy, despite its wide range of applications. It is possible to predict the future by using machine learning. A cyber-attack detection system is depicted in this study using machine learning models. Machine learning algorithms were trained to predict cyber-attack scores using data from prior cyber-attacks on an open source website. In order to detect an attack at its earliest possible stage, this research also examined multiple linear machine learning algorithm-based categorization models. Classifiers' accuracy is also compared in the presentation, as is the presentation itself. Balance procedures were followed. Radio Frequency and GBC have the best accuracy, at 87.93%, followed by ABC at 86.11%, BT at 81.03%, ET at 70.31%, and DT at 70.31 percent (84.48 percent ).
将数字技术纳入安全系统是一项积极的发展。是时候适当地保护数字系统免受潜在的威胁和攻击了。入侵检测系统可以识别网络中的外部异常和内部异常。有各种各样的威胁,有主动的也有被动的。如果不解决这些危险,攻击和数据盗窃可能会从起点一直发生到目的地。尽管机器学习的应用范围很广,但它仍处于起步阶段。利用机器学习来预测未来是可能的。本研究使用机器学习模型描述了一个网络攻击检测系统。机器学习算法被训练来预测网络攻击得分,使用的数据来自一个开源网站先前的网络攻击。为了在尽可能早的阶段检测到攻击,本研究还检查了多个基于线性机器学习算法的分类模型。分类器的准确性也会在演示中进行比较,演示本身也是如此。遵循平衡程序。Radio Frequency和GBC准确率最高,为87.93%,ABC为86.11%,BT为81.03%,ET为70.31%,DT为70.31%(84.48%)。
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引用次数: 5
An Efficient Algorithm for Image De-noising by using Adaptive Modified Decision Based Median Filters 基于自适应修正决策的中值滤波器的图像去噪算法
IF 1.3 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-01-27 DOI: 10.4108/eai.27-1-2022.173163
Faiz Ullah, K. Kumar, M. Khuhro, A. Laghari, A. Wagan, Umair Saeed
INTRODUCTION: In image processing noise removal is a hot research field. Lots of studies have been carried out and many algorithms and filters have been planned to improve the image information. There are various noise removal procedures to identify and remove the corrupted pixels. But several image de-noising algorithms apply the filter to the overall image to filter non-corrupted pixels also. To overcome these weaknesses, we proposed an efficient denoising algorithm by cascading Adaptive Median Filter (AMF) with Modified Decision Based Median Filter (MDBMF). OBJECTIVES: To acquire an efficient denoising algorithm for impulse noise reduction by combining AMF and MDBMF methods which are effective, efficient for denoising various kinds of images. To retain the edges, prevent signal deterioration, and ensure non-corrupted image pixels are remaining intact, regardless of various degrees of noise in the image. To avoid the condition where noisy pixels are replaced by other noisy pixels to maintain the quality of images from its degraded noise version such as blur which often takes place during transmission, acquisition, storage, etc. METHODS, RESULTS AND CONCLUSION: The performance corroboration of the proposed efficient denoising algorithmis accomplished employing nine standard grayscale images. The size of all standard images kept 256x256 pixels in this research. The proposed image denoising system has experimented on those images affected with 10% to 90% salt & pepper noise density. Additionally, the performance of the existing state-of-art denoising techniques like AMF, MF, WMF, UMF, and DBMF are contrasted with the proposed hybrid approach. The results showed that de-noised images obtained for 10% to 90% densities levels by proposed hybrid approach are quite better than the quality of denoised images achieved from WMF, UTMF, AMF, and DBMF methods. The proposed algorithm effectively eradicates salt and pepper noise for lower to higher image noise densities
在图像处理中,噪声去除是一个研究热点。人们进行了大量的研究,并设计了许多算法和滤波器来改善图像信息。有各种噪声去除程序来识别和去除损坏的像素。但一些图像去噪算法将滤波器应用于整个图像以过滤未损坏的像素。为了克服这些缺点,我们提出了一种有效的自适应中值滤波器(AMF)与改进的基于决策的中值滤波器(MDBMF)级联去噪算法。目的:将AMF和MDBMF方法相结合,获得一种有效的脉冲降噪算法,对各种类型的图像进行降噪。保留边缘,防止信号恶化,并确保未损坏的图像像素保持完整,无论图像中有不同程度的噪声。为了避免噪声像素被其他噪声像素所取代的情况,以保持图像的质量,而不是在传输、采集、存储等过程中经常发生的噪声退化版本,如模糊。方法、结果与结论:采用9幅标准灰度图像对所提出的高效去噪算法进行了性能验证。在本研究中,所有标准图像的大小都保持在256x256像素。本文提出的图像去噪系统对盐胡椒噪声密度为10% ~ 90%的图像进行了去噪实验。此外,将现有最先进的去噪技术(如AMF、MF、WMF、UMF和DBMF)的性能与提出的混合方法进行了对比。结果表明,混合方法在10% ~ 90%密度水平下得到的去噪图像质量明显优于WMF、UTMF、AMF和DBMF方法得到的去噪图像。该算法能有效地去除图像噪声密度较低或较高的椒盐噪声
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引用次数: 0
Multichannel attention mechanisms fusion based on gate recurrent unit memory network for fine-grained image classification 基于门递归单元记忆网络的多通道注意机制融合用于细粒度图像分类
IF 1.3 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-01-27 DOI: 10.4108/eai.27-1-2022.173165
Rui Yang, Dahai Li
Attention mechanism is widely used in fine-grained image classification. Most of the existing methods are to construct an attention weight map for simple weighted processing of features, but there are problems of low efficiency and slow convergence. Therefore, this paper proposes a multi-channel attention fusion mechanism based on the deep neural network model which can be trained end-to-end. Firstly, the different regions corresponding to the object are described by the attention diagram. Then the corresponding higher order statistical characteristics are extracted to obtain the corresponding representation. In many standard fine-grained image classification test tasks, the proposed method works best compared with other methods.
注意机制广泛应用于细粒度图像分类。现有的方法大多是构造一个关注权图,对特征进行简单的加权处理,存在效率低、收敛慢的问题。为此,本文提出了一种基于端到端训练的深度神经网络模型的多通道注意力融合机制。首先,用注意图描述对象对应的不同区域;然后提取相应的高阶统计特征,得到相应的表示。在许多标准的细粒度图像分类测试任务中,与其他方法相比,该方法效果最好。
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引用次数: 0
Music Emotion Recognition Based on Long Short-Term Memory and Forward Neural Network 基于长短期记忆和前向神经网络的音乐情感识别
IF 1.3 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-01-27 DOI: 10.4108/eai.27-1-2022.173162
Aizhen Liu
In this paper, we propose a new music emotion recognition method based on long short-term memory and forward neural network. First, Mel Frequency Cepstral Coefficient (MFCC) and Residual Phase (RP) are weighted to extract music emotion features, which improves the recognition efficiency of music emotion features. Meanwhile, in order to improve the classification accuracy of music emotion and shorten the training time of the new model, Long short-term Memory network (LSTM) and forward neural network (FNN) are combined. Using LSTM as the feature mapping node of FNN, a new deep learning network (LSTM-FNN) is proposed for music emotion recognition and classification training. Finally, we conduct the experiments on the emotion data set. The results show that the proposed algorithm achieves higher recognition accuracy than other state-of-the-art complex networks.
本文提出了一种基于长短期记忆和前向神经网络的音乐情感识别方法。首先,对Mel频倒系数(MFCC)和残差相位(RP)进行加权提取音乐情感特征,提高了音乐情感特征的识别效率;同时,为了提高音乐情感的分类精度,缩短新模型的训练时间,将长短期记忆网络(LSTM)和前向神经网络(FNN)相结合。利用LSTM作为FNN的特征映射节点,提出了一种新的用于音乐情感识别和分类训练的深度学习网络(LSTM-FNN)。最后,我们在情感数据集上进行了实验。结果表明,该算法的识别精度高于其他复杂网络。
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引用次数: 0
Multi-attention mechanism based on gate recurrent unit for English text classification 基于门递归单元的英语文本分类多注意机制
IF 1.3 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-01-27 DOI: 10.4108/eai.27-1-2022.173166
Haiying Liu
Text classification is one of the core tasks in the field of natural language processing. Aiming at the advantages and disadvantages of current deep learning-based English text classification methods in long text classification, this paper proposes an English text classification model, which introduces multi-attention mechanism based on gate recurrent unit (GRU) to focus on important parts of English text. Firstly, sentences and documents are encoded according to the hierarchical structure of English documents. Second, it uses the attention mechanism separately at each level. On the basis of the global object vector, the maximum pooling is used to extract the specific object vector of sentence, so that the encoded document vector has more obvious category features and can pay more attention to the most distinctive semantic features of each English text. Finally, documents are classified according to the constructed English document representation. Experimental results on public data sets show that this model has better classification performance for long English texts with hierarchical structure.
文本分类是自然语言处理领域的核心任务之一。针对目前基于深度学习的英语文本分类方法在长文本分类中的优缺点,本文提出了一种英语文本分类模型,该模型引入了基于门递归单元(GRU)的多注意机制,将重点放在英语文本的重要部分。首先,根据英语文档的层次结构对句子和文档进行编码。其次,它在每个层次上分别使用注意机制。在全局对象向量的基础上,利用最大池化方法提取句子的特定对象向量,使编码后的文档向量具有更明显的类别特征,能够更加关注每个英语文本最显著的语义特征。最后,根据构建的英文文档表示对文档进行分类。在公开数据集上的实验结果表明,该模型对具有层次结构的英语长文本具有较好的分类性能。
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引用次数: 0
Region proposal network based on context information feature fusion for vehicle detection 基于上下文信息特征融合的区域建议网络用于车辆检测
IF 1.3 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-01-27 DOI: 10.4108/eai.27-1-2022.173161
Zengyong Xu
By using the traditional methods, the feature information extracted from vehicle target detection is insufficient, which leads to the low accuracy in identifying small target vehicles or blocked targets. Therefore, we propose a region proposal network (RPN) based on context information feature fusion for vehicle detection. RPN obtains feature vectors of fixed length as vehicle target features. Context information fusion network obtains the corresponding context information features on the feature maps of different layers. Finally, the two features are fused. In addition, in order to solve the problem of data imbalance, experiments on PASCAL VOC2007 and PASCAL VOC2012 data sets with difficult sample training show that the proposed method has significantly improved the mean average accuracy (mAP) compared with other methods.
传统方法从车辆目标检测中提取的特征信息不足,导致在识别小目标车辆或被遮挡目标时准确率较低。为此,我们提出了一种基于上下文信息特征融合的区域建议网络(RPN)用于车辆检测。RPN获取固定长度的特征向量作为车辆目标特征。上下文信息融合网络在不同层的特征映射上获得相应的上下文信息特征。最后,将这两个特征进行融合。此外,为了解决数据不平衡的问题,在PASCAL VOC2007和PASCAL VOC2012两组样本训练难度较大的数据集上进行的实验表明,与其他方法相比,本文提出的方法显著提高了平均精度(mAP)。
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引用次数: 0
A novel dilated convolutional neural network model for road scene segmentation 一种新的扩展卷积神经网络道路场景分割模型
IF 1.3 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-01-27 DOI: 10.4108/eai.27-1-2022.173164
Yachao Zhang, Yuxia Yuan
Road scene understanding is one of the important modules in the field of autonomous driving. It can provide more information about roads and play an important role in building high-precision maps and real-time planning. Among them, semantic segmentation can assign category information to each pixel of image, which is the most commonly used method in automatic driving scene understanding. However, most commonly used semantic segmentation algorithms cannot achieve a good balance between speed and precision. In this paper, a road scene segmentation model based on dilated convolutional neural network is constructed. The model consists of a front-end module and a context module. The front-end module is an improved structure of VGG-16 fused dilated convolution, and the context module is a cascade of dilated convolution layers with different expansion coefficients, which is trained by a two-stage training method. The network proposed in this paper can run in real time and ensure the accuracy to meet the requirements of practical applications, and has been verified and analyzed on Cityscapes data set.
道路场景理解是自动驾驶领域的重要模块之一。它可以提供更多的道路信息,在构建高精度地图和实时规划中发挥重要作用。其中,语义分割可以为图像的每个像素分配类别信息,这是自动驾驶场景理解中最常用的方法。然而,大多数常用的语义分割算法无法在速度和精度之间取得很好的平衡。本文建立了一种基于扩展卷积神经网络的道路场景分割模型。该模型由前端模块和上下文模块组成。前端模块是VGG-16融合展开卷积的改进结构,上下文模块是不同展开系数的展开卷积层级联,采用两阶段训练方法进行训练。本文提出的网络能够实时运行,保证准确性,满足实际应用的要求,并在cityscape数据集上进行了验证和分析。
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引用次数: 1
An automatic scoring method for Chinese-English spoken translation based on attention LSTM 基于注意力LSTM的汉英口语翻译自动评分方法
IF 1.3 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-01-13 DOI: 10.4108/eai.13-1-2022.172818
X. Guo
In this paper, we propose an automatic scoring method for Chinese-English spoken translation based on attention LSTM. We select semantic keywords, sentence drift and spoken fluency as the main parameters of scoring. In order to improve the accuracy of keyword scoring, this paper uses synonym discrimination method to identify the synonyms in the examinees' answer keywords. At the sentence level, attention LSTM model is used to analyze examinees' translation of sentence general idea. Finally, spoken fluency is scored based on tempo/rate and speech distribution. The final translation quality score is obtained by combining the weighted scores of the three parameters. The experimental results show that the proposed method is in good agreement with the result of manual grading, and achieves the expected design goal compared with other methods.
本文提出了一种基于注意力LSTM的汉英口语翻译自动评分方法。我们选择语义关键词、句子漂移和口语流利度作为评分的主要参数。为了提高关键词评分的准确性,本文采用同义词辨析法对考生答案关键词中的同义词进行识别。在句子层面,使用注意力LSTM模型分析考生对句子大意的翻译。最后,口语流利度是根据语速/语速和语言分布来评分的。将三个参数的加权得分结合得到最终的翻译质量得分。实验结果表明,该方法与人工分级结果吻合较好,与其他方法相比达到了预期的设计目标。
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引用次数: 2
期刊
EAI Endorsed Transactions on Scalable Information Systems
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