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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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Research on Image Classification Combining Wavelet Analysis and Spiking Neural Network 结合小波分析和峰值神经网络的图像分类研究
Xiao Fei, Liao Jianping, Tian Jie, Wang Guangshuo
Wavelet analysis is a variant of Fourier analysis, which can be used for time-frequency analysis of signal processing, and has achieved remarkable results in the field of image processing. The spike neural network is called the third-generation neural network, which is different from the previous generation, the neural network of the spike neural network is more inspired by neuroscience, and this neural network is constructed in a way closer to the human brain mechanism, which can be applied to many machine learning tasks. Image classification is one of the basic tasks in the field of computer vision. We explore the application of wavelet analysis to the training process of the spiking neural network, before the original data is input into the neural network, we process it with wavelet transform, so that the characteristics of the input data are easier to be learned by the neural network.
小波分析是傅里叶分析的一种变体,可用于信号处理的时频分析,在图像处理领域取得了显著的成果。尖峰神经网络被称为第三代神经网络,与上一代不同的是,尖峰神经网络的神经网络更多地受到神经科学的启发,这种神经网络的构建方式更接近于人脑机制,可以应用于许多机器学习任务。图像分类是计算机视觉领域的基本任务之一。我们探索了小波分析在尖峰神经网络训练过程中的应用,在将原始数据输入神经网络之前,对其进行小波变换处理,使输入数据的特征更容易被神经网络学习。
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引用次数: 0
Freeway Free-Flow Payment System Based On Beidou 基于北斗的高速公路自由流支付系统
Xia Peng, Z. Di, Gao Ming
With the continuous increase of highway ETC access to the national toll stations entrance, some private cars use the ETC rule loopholes to rub ETC situation more and more. Therefore, to completely solve this problem and speed up toll collection, we have developed this system, which can record the vehicle’s current latitude and longitude in real time through BDS positioning, and compare it with the latitude and longitude record in the system, when the vehicle passes through the longitude and latitude of the junction will be automatically judged, and in accordance with the requirements of the high-speed automatic toll payment, a little bit, to completely avoid traffic jams and rub ETC situation.
随着高速公路ETC进入国家收费站入口的不断增加,一些私家车利用ETC规则漏洞蹭ETC的情况越来越多。因此,为了彻底解决这一问题,加快收费速度,我们开发了这个系统,它可以通过BDS定位实时记录车辆当前的经纬度,并与系统中的经纬度记录进行对比,当车辆经过路口的经纬度时会自动判断,并按照高速自动收费的要求,一点点,完全避免了交通堵塞和摩擦等情况。
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引用次数: 0
EVA: Evolving Giant Pretrained Model for Few-Shot Conditional Image Generation EVA:用于少数镜头条件图像生成的进化巨型预训练模型
Zhixin Zhai
Few-shot conditional image generation, which refers to synthesizing images from new conditions based on few training samples, is challenging. Previous works either stick to learning from scratch with enlarged training samples by data augmentation or transferring fixed knowledge grasped from source data to describe target data distribution. They often result in unsatisfied performance due to the overfitting or underfitting brought by rare samples. To address the above issue, we propose a simple yet effective approach, namely EVolving giAnt (EVA), to make the pretrained giant generative model evolve to properly fit target data distribution with few samples (e.g., 1-shot, 10-shot and 50-shot). Specifically, we maintain most prior knowledge stored in model parameters to prevent overfitting, add a new module to accept target conditions, and adapt few parameters for out-bounded features extracted from the target data to avoid underfitting. Extensive experiments confirm the high diversity and quality of our synthesized samples as well as the practicality of our approach in the extreme 1-shot generation.
少镜头条件图像生成是一种基于少量训练样本的新条件合成图像的方法,是一个具有挑战性的问题。以往的工作要么坚持通过数据扩充来扩大训练样本,从零开始学习,要么将从源数据中掌握的固定知识转移到目标数据分布中。由于样本稀少带来的过拟合或欠拟合,往往导致性能不理想。为了解决上述问题,我们提出了一种简单而有效的方法,即进化巨人(EVA),使预训练的巨人生成模型进化到合适地拟合样本较少的目标数据分布(例如,1次、10次和50次)。具体而言,我们保留了存储在模型参数中的大部分先验知识以防止过拟合,增加了一个新的模块来接受目标条件,并对从目标数据中提取的外界特征采用少量参数来避免欠拟合。大量的实验证实了我们合成样品的高多样性和质量,以及我们的方法在极端1次生成中的实用性。
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引用次数: 0
Android Malware Detection Based on Heterogeneous Information Network with Cross-Layer Features 基于跨层异构信息网络的Android恶意软件检测
Ren Xixuan, Zhao Lirui, Wang Kai, Xue Zhixing, Hou Anran, Shao Qiao
As a mature and open mobile operating system, Android runs on many IoT devices, which has led to Android-based IoT devices have become a hotbed of malware. Existing static detection methods for malware using artificial intelligence algorithms focus only on the java code layer when extracting API features, however there is a lot of malicious behavior involving native layer code. Thus, to make up for the neglect of the native code layer, we propose a heterogeneous information network-based Android malware detection method with cross-layer features. We first translate the semantic information of apps and API calls into the form of meta-paths, and construct the adjacency of apps based on API calls, then combine information from different meta-paths using multi-core learning. We implemented our method on the dataset from VirusShare and AndroZoo, and the experimental results show that the accuracy of our method is 93.4%, which is at least 2% higher than other related methods using heterogeneous information networks for malware detection.
Android作为一个成熟开放的移动操作系统,运行在许多物联网设备上,这导致基于Android的物联网设备成为恶意软件的温床。现有的基于人工智能算法的恶意软件静态检测方法在提取API特征时只关注java代码层,而大量恶意行为涉及原生层代码。因此,为了弥补对本机代码层的忽视,我们提出了一种基于异构信息网络的具有跨层特征的Android恶意软件检测方法。我们首先将应用程序和API调用的语义信息转换成元路径的形式,并基于API调用构建应用程序的邻接关系,然后利用多核学习将来自不同元路径的信息进行组合。我们在VirusShare和AndroZoo的数据集上实现了我们的方法,实验结果表明,我们的方法的准确率为93.4%,比其他使用异构信息网络进行恶意软件检测的相关方法高出至少2%。
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引用次数: 0
Deep Learning Based Sensitive Data Detection 基于深度学习的敏感数据检测
Peng Chong
The growing popularity of edge techniques, such as IoT, 5G, blockchain, make it increasingly challenging to protect sensitive data due to the amount of data increases and the growing volume of regulatory policies. To properly protect sensitive data, it is very important to identify sensitive data and implement data anonymization to ensure the quality and proper use of data anonymization techniques. This work focuses on proactively sensitive data identification, classification and anonymization using machine learning techniques. We first investigated the sensitive data extraction from both structured data and unstructured data, in which Bert models and Regular expressions were used to achieve the identification of sensitive data in real-time. Meanwhile, we propose a comprehensive sensitive detection framework combining the Bert model with regular expressions that can achieve high precision and good generalization capability with not so large corpus. The experimental results demonstrate the effectiveness of proposed solution.
随着物联网、5G、区块链等边缘技术的日益普及,由于数据量的增加和监管政策的增加,保护敏感数据变得越来越具有挑战性。为了正确保护敏感数据,识别敏感数据并实现数据匿名化是保证数据匿名化技术质量和正确使用的关键。这项工作的重点是使用机器学习技术主动识别敏感数据,分类和匿名化。首先研究了从结构化数据和非结构化数据中提取敏感数据的方法,利用Bert模型和正则表达式实现敏感数据的实时识别。同时,我们提出了一种Bert模型与正则表达式相结合的综合敏感检测框架,该框架可以在不太大的语料库下实现高精度和良好的泛化能力。实验结果证明了该方法的有效性。
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引用次数: 1
Machine Learning Base Methods for Breast Cancer Diagnose 基于机器学习的乳腺癌诊断方法
Deng Yang, Yang Yujun, Qiu Laixiang, Zhouyi Wang
Cancer is a serious threat to people's health, and its heterogeneous nature and its ability to divide and proliferate make it difficult to cure. For women around the world, breast cancer has been affecting their health and even the risk of life. Therefore, earlier and more accurate diagnosis can save patient's lives. As research into machine learning has become more advanced, different algorithms have been applied to various datasets, including medical data. In this paper, mainly introduce three algorithms that are commonly used and superior in cancer diagnosis, K-Nearest Neighbor algorithm, Naive Bayesian algorithm based on Bayes' theorem and Support Vector Machine. An experimental case is used to illustrate the F1 score, accuracy and recall rate of these two algorithms on the same data set.
癌症是对人类健康的严重威胁,其异质性及其分裂和增殖能力使其难以治愈。对于世界各地的女性来说,乳腺癌一直在影响她们的健康,甚至危及生命。因此,更早、更准确的诊断可以挽救患者的生命。随着对机器学习的研究越来越先进,不同的算法已经应用于各种数据集,包括医疗数据。本文主要介绍了三种在癌症诊断中较为常用和优越的算法:k -最近邻算法、基于贝叶斯定理的朴素贝叶斯算法和支持向量机。通过一个实验案例,说明了这两种算法在同一数据集上的F1分数、准确率和召回率。
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引用次数: 0
Detection of Melanoma Skin Cancer Using Capsule Network and Multi-Task Learning Framework 基于胶囊网络和多任务学习框架的黑色素瘤皮肤癌检测
Esther Stacy E. B. Aggrey, Qin Zhen, Seth Larweh Kodjiku, K. Asamoah, Obed Barnes, Linda Delali Fiasam, Evans Aidoo, Henrietta Aggrey
Melanoma is the most dangerous and aggressive kind of skin cancer, which is also the most frequent form of cancer worldwide. Given the complexities involved, automatic melanoma detection using skin imaging has lately received interest within the machine learning field. Convolutional neural network has widely been employed in recent years to address this problem. However, existing CNN models for skin cancer classification have the drawback of ignoring crucial spatial relationship between features. They are only able to perform accurate classifications provided a predetermined set of features are present in the test data, regardless of how those features are distributed, which leads to false negatives. Furthermore, the CNN pooling layers responsible for down-sampling in these networks also result in loss of data and poor generalization performance. This study proposes a combination of convolutional block and Capsule Neural Network with a multi-task learning framework to address the aforementioned challenges and boost skin cancer classification. The model’s efficiency was measured by a number of metrics, including accuracy, specificity, recall, and F1 score. The accuracy of the proposed model achieved 98.93%, 98.52%, 95.7%, and 98.87%, respectively, indicating great efficiency when compared to other existing networks. As a result, the proposed method offers less sophisticated and robust architecture for automating the process of melanoma diagnoses and accelerating detection procedures in order to save a life.
黑色素瘤是最危险、最具侵袭性的皮肤癌,也是世界上最常见的癌症。考虑到其中的复杂性,使用皮肤成像的黑色素瘤自动检测最近在机器学习领域引起了人们的兴趣。近年来,卷积神经网络被广泛应用于解决这一问题。然而,现有的用于皮肤癌分类的CNN模型存在忽略特征之间关键空间关系的缺点。它们只能在测试数据中提供一组预先确定的特征时执行准确的分类,而不管这些特征是如何分布的,这将导致假阴性。此外,在这些网络中负责下采样的CNN池化层也会导致数据丢失和泛化性能差。本研究提出了一种结合卷积块和胶囊神经网络的多任务学习框架来解决上述挑战并促进皮肤癌分类。该模型的效率通过一系列指标来衡量,包括准确性、特异性、召回率和F1评分。该模型的准确率分别达到了98.93%、98.52%、95.7%和98.87%,与其他现有网络相比,效率很高。因此,所提出的方法为自动化黑色素瘤诊断过程和加速检测过程提供了不那么复杂和健壮的架构,以挽救生命。
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引用次数: 1
Invariance of K-Frames in Hilbert Spaces Hilbert空间中k -帧的不变性
Li Xujin, L. Jinsong
Frame theory as a further development of wavelet theory, and K-frames are a further expansion of the general concept of frames. This article focuses on the property that K-frames remain invariant under the action of some specific linear operators. Moreover, a perturbation analysis of K-frames with the help of some mathematical tools gives frame-bound estimates for K-frames.
框架理论是对小波理论的进一步发展,而k -框架则是对框架一般概念的进一步扩展。本文研究了k -框架在某些特定的线性算子作用下保持不变的性质。此外,利用一些数学工具对k -帧进行了摄动分析,给出了k -帧的帧界估计。
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引用次数: 0
An AOA-Based IMU Error Suppression Method 一种基于面向对象的IMU误差抑制方法
Jiao Yang, S. Luyao, HU Xinyu, Xing Tian Yi, Li Jun Lin, Li Ya Bin, Wan Qun
In the field of positioning technology, the inertial measurement unit is widely used, it has the advantages of low cost and strong anti-interference. However, inertial navigation positioning produces cumulative errors and poor long-term positioning accuracy. This paper presents a method to suppress the cumulative error by measuring the arrival of the angle of a radiation source. By measuring the direction of a position-known radiation source in the inertial navigation plane at different nodes, the least squares method is used to find the current position coordinates of the node, which reduces the cumulative error of the inertial navigation by about 80%.
在定位技术领域,惯性测量单元应用广泛,具有成本低、抗干扰能力强等优点。但惯性导航定位存在累积误差,长期定位精度差。本文提出了一种通过测量辐射源角度的到达来抑制累积误差的方法。通过测量惯性导航平面中位置已知辐射源在不同节点处的方向,利用最小二乘法求出节点当前位置坐标,使惯性导航的累积误差降低了80%左右。
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引用次数: 0
The Design of a Soccer Robot Game Strategy Based on Fuzzy Decision Algorithms 基于模糊决策算法的足球机器人比赛策略设计
Zhengzhong He
Based on the robot's coordinates and angles, the decision-making system determines what the robot should do in response to the current field situation. The decision-making system of a soccer robot is its brain. Important duties of the football robot system's decision-making subsystem include deploying the players based on the current situation on the field, providing instructions to the players, and assuming the responsibilities of a coach. In this paper, we propose a strategy for decision-making systems competition based on fuzzy decision-making algorithms. A fuzzy comprehensive evaluation is utilized to determine the court's formation and role assignment. This method is capable of integrating a variety of court-related data, does not require the development of a precise mathematical model, and can make sound decisions in real time.
决策系统根据机器人的坐标和角度,确定机器人在应对当前现场情况时应该做什么。足球机器人的决策系统是它的大脑。足球机器人系统决策子系统的重要职责包括根据场上的现状部署球员,为球员提供指导,承担教练的职责。本文提出了一种基于模糊决策算法的决策系统竞争策略。运用模糊综合评价法确定法院的组成和角色分配。这种方法能够整合各种法院相关数据,不需要开发精确的数学模型,并且可以实时做出合理的决策。
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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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