Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence最新文献_第6页

Variable and Weighted Granularity "Logic Disjunction" Degree Multi Granularity Rough Set 可变和加权粒度“逻辑析取”度多粒度粗糙集

Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence Pub Date : 2021-12-04 DOI: 10.1145/3507548.3507583

Xiaoyan Wang, Tao Zhu, Yuanxia Shen

引用次数: 0

Bursty Events Detection with the Field of Mobile Customer Service 移动客户服务领域的突发事件检测

Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence Pub Date : 2021-12-04 DOI: 10.1145/3507548.3507622

Lili Kong, Chao Xue, Naiyu Tan

引用次数: 0

LCR-GAN: Learning Crucial Representation for Anomaly Detection LCR-GAN:学习异常检测的关键表征

Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence Pub Date : 2021-12-04 DOI: 10.1145/3507548.3508229

Shuo Liu, Li-wen Xu, Jin-Rong Wang, Yan Sun, Zeran Qin

引用次数: 0

Human Fall Detection Model with Lightweight Network and Tracking in Video 基于轻量级网络和视频跟踪的人体跌倒检测模型

Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence Pub Date : 2021-12-04 DOI: 10.1145/3507548.3507549

Xiaoli Ren, Yunjie Zhang, Yanrong Yang

引用次数: 1

Enhanced Efficient YOLOv3-tiny for Object Detection 增强高效YOLOv3-tiny的目标检测

Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence Pub Date : 2021-12-04 DOI: 10.1145/3507548.3507551

Huanqia Cai, Lele Xu, Lili Guo

{"title":"Enhanced Efficient YOLOv3-tiny for Object Detection","authors":"Huanqia Cai, Lele Xu, Lili Guo","doi":"10.1145/3507548.3507551","DOIUrl":"https://doi.org/10.1145/3507548.3507551","url":null,"abstract":"Lightweight object detection models have great application prospects in resource-restricted scenarios such as mobile and embedded devices, and have been a hot topic in the computer vision community. However, most existing lightweight object detection methods show poor detection accuracy. In this study, we put forward a lightweight objection detection model named Enhanced-YOLOv3-tiny to improve the detection accuracy and reduce the model complexity at the same time. In Enhanced-YOLOv3-tiny, we propose a new backbone named GhostDarkNet based on DarkNet53 and Ghost Module for decreasing the model parameters, which helps to get more representative features compared with YOLOv3-tiny. Furthermore, we put forward a new Multiscale Head, which adds three more heads and includes Ghost Module in each head to fuse multi-scale features. Experiments on the Priority Research Application dataset from the real scenes in driving show that the proposed Enhanced-YOLOv3-tiny outperforms the state-of-the-art YOLOv3-tiny by 8.4% improvement in AP metric and decreases parameters from 8.8M to 3.9M, demonstrating the application potentials of our proposed method in resource-constrained scenarios.","PeriodicalId":414908,"journal":{"name":"Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence","volume":"149 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":"115587522","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

Regression Algorithm Based on Self-Distillation and Ensemble Learning 基于自蒸馏和集成学习的回归算法

Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence Pub Date : 2021-12-04 DOI: 10.1145/3507548.3507580

Yaqi Li, Qiwen Dong, Gang Liu

引用次数: 0

The Research of Predicting Student's Academic Performance Based on Educational Data 基于教育数据的学生学习成绩预测研究

Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence Pub Date : 2021-12-04 DOI: 10.1145/3507548.3507578

Yubo Zhang, Yanfang Liu

{"title":"The Research of Predicting Student's Academic Performance Based on Educational Data","authors":"Yubo Zhang, Yanfang Liu","doi":"10.1145/3507548.3507578","DOIUrl":"https://doi.org/10.1145/3507548.3507578","url":null,"abstract":"In recent years, with the continuous improvement of teaching informatization, online teaching or online and offline hybrid teaching has become the new normal of teaching in some schools. However, the biggest problem in online teaching is that it is difficult to predict students' academic performance. Therefore, it is necessary to design an effective method to predict students' academic performance more accurately. In this paper, a student academic level prediction method based on stacking model fusion is proposed. Logistic regression, random forest, XGBoost, and Naive Bayes are selected as base learners according to optimal fusion criteria and model properties. Furthermore, the structure and distribution of the features of the dataset are optimized by data preprocessing, feature coding, and feature selection, and the upper limit of the model expression is effectively raised. On this basis, according to the features of dataset and model performance, we select the appropriate model for model fusion, and further improve the prediction effect. Experiments are conducted on OULAD and xAPI datasets, and the results show that the prediction accuracy of the proposed method is better than that of traditional prediction methods. Finally, we analyze the factors that affect academic performance and give some specific suggestions.","PeriodicalId":414908,"journal":{"name":"Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence","volume":"220 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":"128727897","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

Sampling May Not Always Increase Detector Performance: A Study on Collecting Training Examples 抽样不一定能提高检测器的性能:一种收集训练样本的研究

Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence Pub Date : 2021-12-04 DOI: 10.1145/3507548.3507568

Jun Liu, Shuang Lai

{"title":"Sampling May Not Always Increase Detector Performance: A Study on Collecting Training Examples","authors":"Jun Liu, Shuang Lai","doi":"10.1145/3507548.3507568","DOIUrl":"https://doi.org/10.1145/3507548.3507568","url":null,"abstract":"In recent years, the research of computer vision is popular. However, the image data that can be used for computer vision training is very limited, so it is necessary to find an effective method to expand the datasets based on the existing image data. In this paper, we study methods to collect more training data from existing datasets and compare detectors’ performance trained with datasets generated by different methods. One method is to perform sampling-based on statistical properties of feature descriptors. For every feature, the underlying assumption is that a probability density function (PDF) exists, such PDF is approximated with existing training examples and new training examples are sampled from the approximated PDF. The other method is simply to expand the existing datasets by flipping each training example along its symmetric axis. Locally Adaptive Regression Kernel (LARK) feature is used in this paper because it is robust against illumination changes and noise. Our experimental results demonstrate that an expanded training dataset is not always preferable, even if the expanded dataset includes all original training data.","PeriodicalId":414908,"journal":{"name":"Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence","volume":"45 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":"126277872","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

Mining Latent Semantic Correlation inspired by Quantum Entanglement 基于量子纠缠的潜在语义关联挖掘

Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence Pub Date : 2021-12-04 DOI: 10.1145/3507548.3507598

Zan Li, Yuexian Hou, Tingsan Pan, Tian Tian, Yingjie Gao

{"title":"Mining Latent Semantic Correlation inspired by Quantum Entanglement","authors":"Zan Li, Yuexian Hou, Tingsan Pan, Tian Tian, Yingjie Gao","doi":"10.1145/3507548.3507598","DOIUrl":"https://doi.org/10.1145/3507548.3507598","url":null,"abstract":"Text representation learning is the cornerstone of solving downstream problems in Natural Language Processing (NLP). However, mining the potential explanatory factors or semantic associations behind data, rather than simply representing the superficial co-occurrence of words, remains a non-trivial challenge. To this end, we seek inspiration from the Quantum Entanglement (QE) which can effectively provide a complete description for the nature of realities and a globally-determined intrinsic correlation of considered objects, thus proposing a novel representation learning hypothesis called the Latent Semantic Correlation (LSC), namely the implicit internal coherence between the semantic space and its corresponding category space. To construct a multi-granularity representation from sememes to words, phrases, sentences, and higher-level LSC, we implement a QE-inspired Network (QEN) under the constraints of quantum formalism and propose the Local Semantic Measurement (LSM) and Extraction (LSE) for effectively capturing probability distribution information from the entangled state of a bipartite quantum system, which has a clear geometrical motivation but also supports a well-founded probabilistic interpretation. Experimental results conducted on several benchmarking classification tasks prove the validity of the LSC hypothesis and the superiority of QEN.","PeriodicalId":414908,"journal":{"name":"Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence","volume":"15 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":"127757912","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 Digital Exhibition Design of Former Residence Memorial Hall based on IPOP Theory 基于IPOP理论的故居纪念馆数字化展示设计研究

Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence Pub Date : 2021-12-04 DOI: 10.1145/3507548.3507606

Xia Wang, Zhengqing Jiang

引用次数: 0