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"SOS Signal" in Breathing Sound - Rapid COVID-19 Diagnosis Based on Machine Learning 呼吸声中的“SOS信号”——基于机器学习的COVID-19快速诊断
Hanxiang Wang
Abstract—The severe acute respiratory syndrome coronavirus 2 is a novel type of coronavirus that causes COVID-19. The COVID-19 virus has recently infected more than 590 million individuals, resulting in a global pandemic. Traditional diagnosis methods are no longer effective due to the exponential rise in infection rates. Quick and accurate COVID-19 diagnosis is made possible by machine learning (ML), which also assuages the burden on healthcare systems. After the effective utilization of Cough Audio Signal Classification in diagnosing a number of respiratory illnesses, there has been significant interest in using ML to enable universal COVID-19 screening. The purpose of the current study is to determine people's COVID-19 status through machine learning algorithms. We have developed a Random Forest based model and achieved an accuracy of 0.873 on the COUGHVID dataset, demonstrates the potential of using audio signals as a cheap, accessible, and accurate COVID-19 screening tool.
重症急性呼吸综合征冠状病毒2型是引起COVID-19的新型冠状病毒。COVID-19病毒最近感染了5.9亿多人,导致全球大流行。由于感染率呈指数级上升,传统的诊断方法已不再有效。通过机器学习(ML)可以快速准确地诊断COVID-19,这也减轻了医疗系统的负担。在有效利用咳嗽音频信号分类诊断多种呼吸系统疾病后,人们对使用ML实现普遍的COVID-19筛查产生了浓厚的兴趣。本次研究的目的是通过机器学习算法确定人们的COVID-19状态。我们开发了一个基于随机森林的模型,并在COUGHVID数据集上实现了0.873的准确率,证明了使用音频信号作为廉价、可获取和准确的COVID-19筛查工具的潜力。
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引用次数: 0
CTI-GAN: Cross-Text-Image Generative Adversarial Network for Bidirectional Cross-modal Generation 双向跨模态生成的跨文本-图像生成对抗网络
Changhong Jing, Bing Xue, Ju-dong Pan
Cross-modal tasks between text and images are increasingly a research hotspot. This paper proposed a cross-text-image generative adversarial network(CTI-GAN). This model can complete the cross-modal bidirectional generation task between image and text. The method effectively connects text and image modeling to realize bidirectional generation between image and text. The extraction effect of text features is improved by hierarchical LSTM encoding. Through feature pyramid fusion, the features of each layer are fully utilized to improve the image feature representation. In this paper, experiments are conducted to verify the effectiveness of the above improvements for image text generation. The improved algorithm can efficiently complete the task of cross-modal image text generation and improve the accuracy of the generated samples. In the text description generation image task, the inception score of CTI-GAN is improved by about 2% compared with StackGAN++, HDGAN, GAN-INT-CLS, and other models under the same conditions of the same dataset.
文本与图像之间的跨模态任务日益成为研究热点。提出了一种跨文本-图像生成对抗网络(CTI-GAN)。该模型可以完成图像和文本之间的跨模态双向生成任务。该方法有效地将文本和图像建模连接起来,实现了图像和文本之间的双向生成。采用分层LSTM编码,提高了文本特征的提取效果。通过特征金字塔融合,充分利用每一层的特征,提高图像的特征表示。本文通过实验验证了上述改进对图像文本生成的有效性。改进后的算法可以有效地完成跨模态图像文本生成任务,提高生成样本的准确性。在文本描述生成图像任务中,在相同数据集的相同条件下,CTI-GAN的初始得分比StackGAN++、HDGAN、GAN-INT-CLS等模型提高了约2%。
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引用次数: 1
Accelerating minimap2 for long-read sequencing on NUMA multi-core CPU 在NUMA多核CPU上加速minimap2的长读排序
Qisheng Xu, Y. Dou, Yanjie Sun
Recent advances in three-generation sequencing technology allow for the rapid generation of large throughput of long reads, and mapping these long reads to a reference sequence is one of the first and most time-consuming steps in the downstream application of genomics. Minimap2, the state-of-the-art long-read sequencing aligner available today, has the advantage of being fast and accurate. However, as NUMA multi-core CPU gradually becomes the processors of mainstream computers, minimap2 is not specifically optimised and adapted for the NUMA multi-core architecture. Frequent remote memory accesses, resource contention and idle hardware resources result in a performance far below the theoretical peak performance of NUMA multi-core CPU. Based on the above problems, we propose three optimisation strategies, namely copying index at each NUMA node and binding threads to the cores of NUMA node, designing new IO and computation overlap mechanism, and adaptively adjusting batch_size based on IO and computation time, to achieve full utilisation of resources. We obtain three sets of human genome sequencing data from the ENA database and performed performance tests on the FT 2000+ MCD-FP92 NUMA multi-core CPU system. The three-point strategies proposed in this paper are effective in improving the performance of minimap2, with a maximum speedup of 13 percentage points.
三代测序技术的最新进展允许快速生成大通量的长reads,将这些长reads映射到参考序列是基因组学下游应用中最耗时的步骤之一。Minimap2是当今最先进的长读测序仪,具有快速和准确的优势。然而,随着NUMA多核CPU逐渐成为主流计算机的处理器,minimap2并没有针对NUMA多核架构进行专门的优化和适应。频繁的远程内存访问、资源争用和空闲的硬件资源导致性能远远低于NUMA多核CPU的理论峰值性能。针对上述问题,我们提出了三种优化策略,即在每个NUMA节点复制索引并将线程绑定到NUMA节点的核心,设计新的IO和计算重叠机制,以及根据IO和计算时间自适应调整batch_size,以实现资源的充分利用。我们从ENA数据库中获取了三组人类基因组测序数据,并在FT 2000+ MCD-FP92 NUMA多核CPU系统上进行了性能测试。本文提出的三点策略可以有效地提高minimap2的性能,最大加速提高了13个百分点。
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引用次数: 0
ACT-SAGAN: Automatic Configuration Tuning for Kafka with Self-Attention Generative Adversarial Networks ACT-SAGAN:基于自关注生成对抗网络的Kafka自动配置调优
Yating Huang, Chunhai Li, Mingfeng Chen, Zhaoyu Su
When Kafka is used in production environments, a large number of parameters are provided to facilitate user configuration for specific application environments in order to obtain better performance. However, configuring Kafka's parameters requires in-depth knowledge of the user, which is far beyond the ability of the average user and prevents Kafka from obtaining better performance. To address this problem, we propose an ACT-SAGAN method that adds a self-attention mechanism to the generative adversarial network model to capture the associations between hidden structures in good configuration combinations and configuration parameters, which uses these hidden structures and associations to generate better configuration combinations to improve Kafka's performance. Experimental results show that the algorithm improves Kafka's throughput and reduces latency after deployment for the configuration combinations generated by Kafka.
在生产环境中使用Kafka时,为了获得更好的性能,提供了大量的参数,方便用户针对特定的应用环境进行配置。然而,配置Kafka的参数需要对用户有深入的了解,这远远超出了普通用户的能力,也阻碍了Kafka获得更好的性能。为了解决这个问题,我们提出了一种ACT-SAGAN方法,该方法在生成对抗网络模型中添加了自关注机制,以捕获良好配置组合和配置参数中隐藏结构之间的关联,并使用这些隐藏结构和关联来生成更好的配置组合,以提高Kafka的性能。实验结果表明,对于Kafka生成的配置组合,该算法提高了Kafka的吞吐量,减少了部署后的延迟。
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引用次数: 0
Hybrid Sampling Light Graph Collaborative Filtering for Social Recommendation 社会推荐的混合采样光图协同过滤
Yefan Zhu, Li Zhang, Siqi Yang
• The use of graph neural networks has been widely adopted in recommender systems as a state-of-the-art collaborative filtering mechanism. In graph neural collaborative filtering, extracting negative signals from implicit feedback aris-ing from the interaction between users and items is a ma-jor challenge. The negative sampling aspect has not been fully explored in the use of graph neural collaborative filtering for the social recommendation. This study explores negative sampling by combining a graph neural network aggregation procedure with social recommendation graph structures. A system called Hybrid Sampling Light Graph Convolution Collaborative Filtering for Social Recommendations (HLCS) is proposed in this paper. Through the propagation and fusion of embedded representations of users and items in the item domain and social domain, hard negative samples are generated by the hybrid sampling technique to optimize the recommendation model’s performance. Using two real-world datasets, we conducted comprehensive experiments and showed that the HLCS approach was superior to the SOTA approach, particularly in cold-start situations. ;
•图神经网络作为一种最先进的协同过滤机制已被广泛应用于推荐系统中。在图神经协同过滤中,从用户与物品交互产生的隐式反馈中提取负面信号是一个重大挑战。在使用图神经协同过滤进行社交推荐时,负采样方面的研究还没有得到充分的探讨。本研究将图神经网络聚合过程与社会推荐图结构相结合,探索负抽样。提出了一种基于混合采样光图卷积的社会推荐协同过滤系统。通过在项目域和社交域对用户和项目的嵌入表示进行传播和融合,利用混合采样技术生成硬负样本,优化推荐模型的性能。使用两个真实数据集,我们进行了全面的实验,并表明HLCS方法优于SOTA方法,特别是在冷启动情况下。;
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引用次数: 0
Blockchain-based cable supply chain traceability system 基于区块链的电缆供应链可追溯系统
Xiaohong Qiu, Z. Tian
The traditional cable supply chain industry has problems such as difficulty in product traceability, production monitoring, rights protection and evidence collection, and data sharing, which seriously restrict the further development of the cable supply chain industry. A blockchain-based cable supply chain traceability system is designed using blockchain technology to integrate, share and supervise cable supply chain data. This system includes five functional modules: user management, data upload, two-way traceability, data sharing, and complaint handling. According to the characteristics of the cable traceability cycle, the storage module adopts a combination of blockchain, Inter-planetary File System (IPFS) and MySQL to meet different business needs. The experimental results show that the system basically meets the performance requirements and has good practicability.
传统的电缆供应链行业存在产品溯源难、生产监控难、维权取证难、数据共享难等问题,严重制约了电缆供应链行业的进一步发展。利用区块链技术设计了基于区块链的电缆供应链可追溯系统,实现电缆供应链数据的集成、共享和监督。该系统包括用户管理、数据上传、双向追溯、数据共享、投诉处理五大功能模块。根据电缆可追溯周期的特点,存储模块采用区块链、IPFS (Inter-planetary File System)和MySQL的组合,满足不同的业务需求。实验结果表明,该系统基本满足性能要求,具有良好的实用性。
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引用次数: 0
Electric Bicycle Detection Based on Deep Learning 基于深度学习的电动自行车检测
Jiakang Sun, Yuhan Zhang
In China's urban traffic, the number of electric bicycles is increasing. Therefore, it becomes particularly important to accurately detect the behavior of electric bicycles and their riders through road traffic monitoring and implement efficient supervision to provide technical support. In the actual traffic surveillance video, electric bicycles occupy a small video image area and are easy to block each other, resulting in inaccurate detection and missed detection. To solve these problems, based on the idea of YOLOv4 algorithm, an improved detection algorithm of electric bicycle is proposed in this paper: replace the original YOLOv4 backbone network CSPDarknet-53 with GhostNet to enhance the detection speed. ECA attention mechanism is introduced in front of the three-layer prediction network to enhance the detection accuracy. The SPP module is replaced by the enhanced receptive field RFB module to strengthen the feature extraction ability. The experimental results show that the detection accuracy of the improved YOLOv4 algorithm is increased by 1.53%, and the detection speed is increased by 14FPS.
在中国的城市交通中,电动自行车的数量正在增加。因此,通过道路交通监控,准确检测电动自行车及其骑行者的行为,并实施高效的监管,为其提供技术支持就显得尤为重要。在实际的交通监控视频中,电动自行车占用的视频图像面积较小,容易相互遮挡,导致检测不准确,漏检。针对这些问题,本文基于YOLOv4算法的思想,提出了一种改进的电动自行车检测算法:将原有的YOLOv4骨干网CSPDarknet-53替换为GhostNet,提高检测速度。在三层预测网络前引入了ECA关注机制,提高了检测精度。用增强的感受野RFB模块代替SPP模块,增强特征提取能力。实验结果表明,改进后的YOLOv4算法检测精度提高了1.53%,检测速度提高了14FPS。
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引用次数: 0
Efficient Parallel Computation Of 3D Model Deformation Based On Conformal Mapping 基于保角映射的三维模型变形高效并行计算
Kun Qian, Yinghua Li, Xinggui Xu, Hao Xu, Guochang Hu, Ping-Hui Li
In general, the animation between three-dimensional models uses linear interpolation between models to calculate the intermediate state. Before interpolation, the mapping relationship between the source model and the target model should be calculated to find the one-to-one correspondence of vertices. It is often necessary to traverse all vertices of the mesh in interpolation calculation, it can be difficult to implement parallel operation due to the irregularity of triangular mesh. This paper is aimed to form a regular representation of the 3D model in the conformal parameterization and set up efficient parallel computing under the regular structure so as to improve the efficiency of deformation computing of 3D models.
通常,三维模型之间的动画使用模型之间的线性插值来计算中间状态。在插值之前,需要计算源模型和目标模型之间的映射关系,找到顶点的一一对应关系。在插值计算中,往往需要遍历网格的所有顶点,由于三角形网格的不规则性,很难实现并行运算。本文旨在形成三维模型在保形参数化中的规则表示,并在规则结构下建立高效的并行计算,从而提高三维模型变形计算的效率。
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引用次数: 0
Accurate and Time-saving Deepfake Detection in Multi-face Scenarios Using Combined Features 基于组合特征的多人脸场景中精确省时的深度伪造检测
Zekun Ma, B. Liu
There has been an increasing interest in Deepfake detection because of the hidden risks that Deepfake technology poses for social privacy and security. Nowadays, many models achieve impressive performance on existing public benchmarks. However, the majority of existing methods are restricted to single-face scenarios. In this paper, we propose a model that can perform accurate and time-saving Deepfake detection in multi-face scenarios. We fuse different levels of features to improve the performance of the model and use single-face data to aid the training of the multi-face data. Our apporach achieves the state-of-the-art performance in multi-face scenarios and comprehensible experiments have been conducted to demonstrate the soundness and validity of our model.
由于Deepfake技术对社交隐私和安全构成潜在风险,人们对Deepfake检测的兴趣越来越大。如今,许多模型在现有的公共基准测试中取得了令人印象深刻的性能。然而,现有的大多数方法仅限于单面场景。在本文中,我们提出了一种可以在多人脸场景下执行准确且节省时间的Deepfake检测模型。我们融合了不同层次的特征来提高模型的性能,并使用单面数据来辅助多面数据的训练。我们的方法在多面场景下达到了最先进的性能,并且进行了可理解的实验来证明我们模型的合理性和有效性。
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引用次数: 0
The Exponential Dynamic Analysis of Network Attention Based on Big Data 基于大数据的网络注意力指数动态分析
Kaiyong Cheng, Fuxing Liang, Ling Xiao, Huiru Xu
Based on Baidu Index and Internet Big Data, this paper analyzes the overall relationship of information flow spatial network by using social network method, and finds that index-time has dual structural characteristics and keeps changing trend. The research shows that the scale, correlation degree and control efficiency level of information flow show obvious structural characteristics of time dissimilation. Secondly, we study the behavior characteristics of index, dynamically analyze the temporal and spatial changes of big data of network attention and index dynamics, observe the inflow of index data in July and August, and find that the maximum value of a single index reaches 250,457 times. The inflow was the highest in the third quarter, with the maximum index reaching 659,329 times, showing the peak state of the whole period. Through information flow, the correlation between data is revealed, and the time distribution characteristics of monthly peak and quarterly peak are analyzed. Finally, it is concluded that the attention of information flow network has a clear direction in the two dimensions of "index-time", showing its correlation.
基于百度指数和互联网大数据,运用社会网络方法分析了信息流空间网络的整体关系,发现索引时间具有双重结构特征,并呈不断变化的趋势。研究表明,信息流的规模、关联度和控制效率水平表现出明显的时间异化结构特征。其次,研究指数的行为特征,动态分析网络关注大数据和指数动态的时空变化,观察7月和8月指数数据的流入情况,发现单个指数的最大值达到250,457次。第三季度是流入资金最多的季度,最高指数达到659329倍,呈现出整个时期的高峰状态。通过信息流揭示数据间的相关性,分析月高峰和季度高峰的时间分布特征。最后得出信息流网络的注意力在“指标-时间”两个维度上具有明确的方向性,显示出其相关性。
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引用次数: 0
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Proceedings of the 5th International Conference on Computer Science and Software Engineering
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