Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence最新文献_第2页

Prediction of Hepatocellular Carcinoma Diseases Based on Methylation Data and Screening of Hub Genes 基于甲基化数据和枢纽基因筛选的肝癌疾病预测

Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence Pub Date : 2021-12-04 DOI: 10.1145/3507548.3507577

Yawei Zhang, Fangtao Ren, Xi Liu, Fan Zhang

{"title":"Prediction of Hepatocellular Carcinoma Diseases Based on Methylation Data and Screening of Hub Genes","authors":"Yawei Zhang, Fangtao Ren, Xi Liu, Fan Zhang","doi":"10.1145/3507548.3507577","DOIUrl":"https://doi.org/10.1145/3507548.3507577","url":null,"abstract":"DNA methylation is of great significance to the diagnosis, treatment and disease prediction of hepatocellular carcinoma (HCC). The commonly used DNA methylation microarrays have high data dimensions, and different CpG sites detected may map to the same gene. To extract more effective features for HCC disease prediction, this study uses a linear regression model that integrates TCGA database methylation data and gene expression data, based on the DNA methylation microarray data of the GEO database (GSE113017), the corresponding gene expression data was predicted and the differentially expressed genes (3766) with significant differences were screened out, which was used as the feature of the data set. Constructing an Artificial Neural Network (ANN) to train a machine learning model for HCC disease prediction and perform 10-fold cross-validation. The resulting model has an accuracy of 95.1% for HCC disease prediction based on DNA methylation microarray data. Compared with other HCC prediction methods, this method has better performance. Then analyze the differentially expressed genes with protein-protein interaction network (PPI network), and use the top five connected genes in the network as hub genes, namely: GNGT2, GNB4, FPR2, CDC20, NMUR1, which can be used as biomarkers for the diagnosis, treatment and prognosis of HCC.","PeriodicalId":414908,"journal":{"name":"Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125227787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on Key Technology of Auto-driving Based on Machine Vision 基于机器视觉的自动驾驶关键技术研究

Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence Pub Date : 2021-12-04 DOI: 10.1145/3507548.3507614

Zhang bo chuan, Liu Guo ming, Pei Sheng wang, Yu li, Li Hai peng

引用次数: 0

Deep Spatial and Temporal Information based QoE Evaluation Model for HTTP Adaptive Streaming 基于深度时空信息的HTTP自适应流QoE评价模型

Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence Pub Date : 2021-12-04 DOI: 10.1145/3507548.3507608

L. Du, L. Zhuo, Jiafeng Li, Hui Zhang

{"title":"Deep Spatial and Temporal Information based QoE Evaluation Model for HTTP Adaptive Streaming","authors":"L. Du, L. Zhuo, Jiafeng Li, Hui Zhang","doi":"10.1145/3507548.3507608","DOIUrl":"https://doi.org/10.1145/3507548.3507608","url":null,"abstract":"The content characteristics of video is one of the important influencing factors affecting the user's Quality of Experience (QoE). In this paper, deep spatial and temporal information are extracted to characterize the content characteristics of video, which are then used to establish a QoE evaluation model for HTTP adaptive streaming. Firstly, a Gabor convolutional layer and Channel Attention (CA) are incorporated into ResNet18 to construct the Gabor-CA-ResNet18 network, which is used to capture the Deep Spatial Information (DSI) of video. To avoid the problem of the \"curse of dimensionality\", LargeVis is applied to reduce the dimensionality of the DSI features to improve the representative and discriminative ability, obtaining a compact feature representation vector. Secondly, 3D Convolutional Neural Networks (3D CNN) and Gated Recurrent Unit (GRU) are used together to capture the Deep Temporal Information (DTI) of video, named 3D CNN-GRU. And finally, the DSI and DTI features are combined with the statistical features of other influencing factors, including video quality level, re-buffering duration, re-buffering frequency, and so on, to form the feature parameter vector. The Gradient Boosting method is adopted to establish the mapping relationship model between the feature parameter vector and Mean Opinion Score (MOS), which can be used to predict the user's QoE. Experimental results on SQoE-III and SQoE-IV datasets demonstrate that the proposed QoE model can achieve the state-of-the-art performance compared with the existing QoE evaluation models.","PeriodicalId":414908,"journal":{"name":"Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134449979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AMOD-Net: Attention-based Multi-Scale Object Detection Network for X- Ray Baggage Security Inspection 基于注意力的X射线行李安检多尺度目标检测网络

Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence Pub Date : 2021-12-04 DOI: 10.1145/3507548.3507552

Xiao-lin Zhu, Jitong Zhang, Xiaopan Chen, Danyang Li, Yufei Wang, Minghao Zheng

{"title":"AMOD-Net: Attention-based Multi-Scale Object Detection Network for X- Ray Baggage Security Inspection","authors":"Xiao-lin Zhu, Jitong Zhang, Xiaopan Chen, Danyang Li, Yufei Wang, Minghao Zheng","doi":"10.1145/3507548.3507552","DOIUrl":"https://doi.org/10.1145/3507548.3507552","url":null,"abstract":"X-ray baggage security checking is an extremely important task, which can detect various dangerous objects in airports, stations and other public places to prevent crimes and protect personal safety. However, at present, most of the recognition is done manually, which is inefficient and error-prone. As a complementary, object detection algorithm is beneficial to avoiding errors caused by manual detection. Although the universal object detection is well developed and the performance of the universal detectors is very advanced, the performance of these detectors in X-ray image detection is mediocre. In this paper, we propose an Attention-based Multi-Scale Object Detection Network (called AMOD-Net) for X-ray baggage security inspection. To solve the problems of stacking and occlusion existed in the X- ray baggage image, we design a channel selection attention module for AMOD-Net. To make better use of the feature information, we construct a deep feature fusion structure for AMOD-Net. Experiments on the X-ray baggage dataset demonstrate that our approach achieves very competitive results.","PeriodicalId":414908,"journal":{"name":"Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117125204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Modified Kernelized Correlation Filter Tracker Based on Saliency Detection and Reliability Judgment 基于显著性检测和可靠性判断的改进核相关滤波跟踪器

Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence Pub Date : 2021-12-04 DOI: 10.1145/3507548.3507553

Haipeng Li, Wenjuan Zheng, Bin Zhou, YanYangshuo Liu

{"title":"Modified Kernelized Correlation Filter Tracker Based on Saliency Detection and Reliability Judgment","authors":"Haipeng Li, Wenjuan Zheng, Bin Zhou, YanYangshuo Liu","doi":"10.1145/3507548.3507553","DOIUrl":"https://doi.org/10.1145/3507548.3507553","url":null,"abstract":"With the rapid development of the correlation filter and deep learning technology, object tracking has been applied widely in the field of autonomous driving and video surveillance. It is challenging to achieve robust and efficient object tracking due to the variability of scenes and the complexity of the background. Considering real-time and computation limitations, correlation filter based tracker is still a good solution. In this paper, we proposed a modified tracking algorithm based on kernelized correlation filter. To distinguish the object from the background noise and eliminate unreasonably high energy values of the correlation filter in the boundary region, the saliency detection of object candidate regions is adopted. Besides, a compound discrimination method is proposed considering both the maximum correlation peak and the average peak-to-correlation energy to judge the reliability of tracking results accurately. Our approach is evaluated on OTB-2015 dataset and the experimental results show that our approach achieves outstanding performance than the classical algorithm KCF. Moreover, it is robust and efficient enough for occlusion and illumination change.","PeriodicalId":414908,"journal":{"name":"Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125349584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Construction and Practice of Multimedia Intelligent Classroom in the Information Age 信息时代多媒体智能课堂的建设与实践

Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence Pub Date : 2021-12-04 DOI: 10.1145/3507548.3507605

Xibin Xu, Xiaole Zhao

引用次数: 0

Optimal scheme for dynamic adjustment of active mirror in FAST system FAST系统主动反射镜动态调整的优化方案

Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence Pub Date : 2021-12-04 DOI: 10.1145/3507548.3507576

Hongchuan Zhou, Jiaqi Liu, Junyan Song, Benchao Yang

{"title":"Optimal scheme for dynamic adjustment of active mirror in FAST system","authors":"Hongchuan Zhou, Jiaqi Liu, Junyan Song, Benchao Yang","doi":"10.1145/3507548.3507576","DOIUrl":"https://doi.org/10.1145/3507548.3507576","url":null,"abstract":"This paper mainly studies the optimal scheme of dynamic adjustment of active mirror in FAST system by establishing mathematical model. According to the constraints such as distance variation range, a mathematical model describing the dynamic trajectory of the position and angle of the active mirror is established, and the correctness of the solution of the model is verified by simulation. The optimal surface model is selected from all the results. When the measured object is directly above the fast system, it is modeled based on the ideal parabola to ensure that the expansion constraints and threshold conditions of the actuator are met. Because the distribution of actuator and cable in the whole fast system is not a uniform sphere, the change of radial distance of cable can be considered as the expansion of actuator; In addition, the model is optimized from two aspects: adjusting the position of focus on the horizontal plane and changing the size of zoom alignment. From these two aspects, the parabolic focusing range satisfying the expansion range is obtained. Finally, the optimization strategy of dynamic adjustment of active mirror in fast system is given.","PeriodicalId":414908,"journal":{"name":"Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126062866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EmSBoTScript: A Tiny Virtual Machine-Based Embedded Software Framework EmSBoTScript:一个基于微型虚拟机的嵌入式软件框架

Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence Pub Date : 2021-12-04 DOI: 10.1145/3507548.3507592

Long Peng, Hao Xu, Jie Yu, Xiaodong Liu, Fei Guan

引用次数: 0

Developing Digital Magazine on Coffee Industry Information in COVID-19 Pandemic for Tourism Enhancement 开发新冠疫情下咖啡行业信息数字杂志，促进旅游业发展

Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence Pub Date : 2021-12-04 DOI: 10.1145/3507548.3507602

H. Sutopo, Anjar Dwi Astono

引用次数: 0

Script event prediction based on pre-trained model with tail event enhancement 基于尾事件增强预训练模型的脚本事件预测

Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence Pub Date : 2021-12-04 DOI: 10.1145/3507548.3507585

Zhenyu Huang, Yongjun Wang, Hongzuo Xu, Songlei Jian, Zhongyang Wang

{"title":"Script event prediction based on pre-trained model with tail event enhancement","authors":"Zhenyu Huang, Yongjun Wang, Hongzuo Xu, Songlei Jian, Zhongyang Wang","doi":"10.1145/3507548.3507585","DOIUrl":"https://doi.org/10.1145/3507548.3507585","url":null,"abstract":"Script event prediction is a big challenge and its goal is to predict the subsequent event based on the observed events. Since an event is described by text, the pre-trained models have been applied for event representation. However, the embedding based on the pre-trained models is sensitive to the short text format of events, and the existing works do not handle it well. In addition, previous models pay more attention to the semantic similarity but ignore the factors of emergencies. The turning event at the tail of the event chain can easily affect the follow-up direction. This paper proposes a new preprocessing method: cleaning, alignment, and connection, which helps to obtain richer event representations. On this basis, we concatenate the embedding of the CLS token and event sequence to integrate the semantic and temporal features of the event chain. To deal with the problem of event turning, we propose a tail event enhancement module. It adds the transition probability of tail events and candidate events into prediction layer, so as to avoid pay only attention to the semantic feature. The results of a large number of comparative experiments and ablation experiments confirm the superiority of our model compared with the baselines.","PeriodicalId":414908,"journal":{"name":"Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131653705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1