2021 7th International Conference on Computing and Artificial Intelligence最新文献

Robust Facial Manipulation Detection via Domain Generalization 基于域泛化的鲁棒面部动作检测

2021 7th International Conference on Computing and Artificial Intelligence Pub Date : 2021-04-23 DOI: 10.1145/3467707.3467736

Pengxiang Xu, Xue Mei, Yi Wei, Tiancheng Qian

{"title":"Robust Facial Manipulation Detection via Domain Generalization","authors":"Pengxiang Xu, Xue Mei, Yi Wei, Tiancheng Qian","doi":"10.1145/3467707.3467736","DOIUrl":"https://doi.org/10.1145/3467707.3467736","url":null,"abstract":"Face generation and forgery algorithms are available on the Internet, which promotes facial manipulation detection to be an important topic. Recently, many methods have been presented to detect facial manipulation images and videos. Most of which focus on specific datasets and achieve promising results on them. However, it is hard for them to detect the facial images manipulated by unknown face synthesis algorithms. In this paper, we present a method to improve the generalization ability of the detection models using one class domain generalization. Unlike the methods using datasets to train deep neural networks directly, we propose to shape the problem to domain generalization. The images manipulated by different algorithms are regarded as different domains. To obtain domain-invariant features, we take the fake facial images from multiple domains into the domain discriminator for domain adversarial training. The models can discriminate between the real and fake facial images from different domains, even the fake images generated by unknown algorithms. The experiments implemented on FaceForensics++ dataset demonstrate that the proposed method achieves outstanding performance and improves the robustness of the detection models.","PeriodicalId":145582,"journal":{"name":"2021 7th International Conference on Computing and Artificial Intelligence","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114164889","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: 2

Improved Data Mining Method for Class-Imbalanced Financial Distress Prediction 类不平衡财务困境预测的改进数据挖掘方法

2021 7th International Conference on Computing and Artificial Intelligence Pub Date : 2021-04-23 DOI: 10.1145/3467707.3467754

Tingting Ren, Tongyu Lu, Yuanyuan Yang

引用次数: 2

Bigraphical Modelling and Design of Multi-Agent Systems 多智能体系统的图形建模与设计

2021 7th International Conference on Computing and Artificial Intelligence Pub Date : 2021-04-23 DOI: 10.1145/3467707.3467762

A. Dib, R. Maamri

引用次数: 1

Fundamental contemplation on the adequacy of the analysis of brain waves: case of EEG 对脑电波分析充分性的基本思考:以脑电图为例

2021 7th International Conference on Computing and Artificial Intelligence Pub Date : 2021-04-23 DOI: 10.1145/3467707.3467742

A. Thapaliya, Rozaliya Amirova, S. Busechian, Vladimir Ivanov, Sergey Masyagin, Ruslan Shakirov, G. Succi, Herman Tarasau, A. Tormasov, Oydinoy Zufarova

引用次数: 0

Students’ Debugging Behavior Analysis in Game-Based Learning 游戏学习中学生调试行为分析

2021 7th International Conference on Computing and Artificial Intelligence Pub Date : 2021-04-23 DOI: 10.1145/3467707.3467760

Fan Yang, Z. Dong, Zhong Wu

{"title":"Students’ Debugging Behavior Analysis in Game-Based Learning","authors":"Fan Yang, Z. Dong, Zhong Wu","doi":"10.1145/3467707.3467760","DOIUrl":"https://doi.org/10.1145/3467707.3467760","url":null,"abstract":"As programing became more and more important, people are taking a large amount of work to help students to learn programming skills effectively. This paper applies a programming learning game called May's Journey to fit 5 debugging types including syntax, logical, structure, reasoning, and undefined debugging errors into programming levels. Then we can find out the reason why students make mistakes, and which debugging type would cause the mistakes of other debugging types. And we have 6 findings, (1) This paper proposes a student debugging model to describe how students make debugging errors, which is used for further analysis on student debugging behaviors. (2) This paper proposes to use group mean and with-in group variance based on student debugging model, which finds out the common debugging errors and personal debugging errors. (3) This paper proposes to extract student debugging patterns using Random forest, which identifies student debugging behaviors, so that students who have the same debugging pattern can be trained together. (4) This paper also proposes to use student debugging model-based SVM to extract student performance patterns, which identifies student performance changing over programming levels in terms of a specific debugging type. (5) This paper proposes to apply mean decrease accuracy and mean decrease Gini to identify the effectiveness of debugging types; and (6) this paper proposes to use a classification-based LSTM algorithm to predict debugging errors, which improves the predication accuracy a lot. Experiments and results are also provided to prove that our methods are valid and better.","PeriodicalId":145582,"journal":{"name":"2021 7th International Conference on Computing and Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124369869","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 Fire Prediction Algorithm Based on Thermal Infrared Image 基于热红外图像的火灾预测算法研究

2021 7th International Conference on Computing and Artificial Intelligence Pub Date : 2021-04-23 DOI: 10.1145/3467707.3467739

Qijun Wang, Chao Yang, Shujun Duan, Shiqing Wei

引用次数: 1

Online Audio-Visual Speech Separation with Generative Adversarial Training 基于生成对抗训练的在线视听语音分离

2021 7th International Conference on Computing and Artificial Intelligence Pub Date : 2021-04-23 DOI: 10.1145/3467707.3467764

Peng Zhang, Jiaming Xu, Yunzhe Hao, Bo Xu

{"title":"Online Audio-Visual Speech Separation with Generative Adversarial Training","authors":"Peng Zhang, Jiaming Xu, Yunzhe Hao, Bo Xu","doi":"10.1145/3467707.3467764","DOIUrl":"https://doi.org/10.1145/3467707.3467764","url":null,"abstract":"Audio-visual speech separation has been demonstrated to be effective in solving the cocktail party problem. However, most of the models cannot meet online processing, which limits their application in video communication and human-robot interaction. Besides, SI-SNR, the most popular training loss function in speech separation, results in some artifacts in the separated audio, which would harm downstream applications, such as automatic speech recognition (ASR). In this paper, we propose an online audio-visual speech separation model with generative adversarial training to solve the two problems mentioned above. We build our generator (i.e., audio-visual speech separator) with causal temporal convolutional network block and propose a streaming inference strategy, which allows our model to do speech separation in an online manner. The discriminator is involved in optimizing the generator, which can reduce the negative effects of SI-SNR. Experiments on simulated 2-speaker mixtures based on challenging audio-visual dataset LRS2 show that our model outperforms the state-of-the-art audio-only model Conv-TasNet and audio-visual model advr-AVSS under the same model size. We test the running time of our model on GPU and CPU, and results show that our model meets online processing. The demo and code can be found at https://github.com/aispeech-lab/oavss.","PeriodicalId":145582,"journal":{"name":"2021 7th International Conference on Computing and Artificial Intelligence","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131574246","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

Research on UAV Detection of Threat Target around oil Pipeline Based on Deep Learning 基于深度学习的无人机石油管道周围威胁目标检测研究

2021 7th International Conference on Computing and Artificial Intelligence Pub Date : 2021-04-23 DOI: 10.1145/3467707.3467714

Qiang Wu, Xuegang Wu, Xin Zheng, Bin Yue

{"title":"Research on UAV Detection of Threat Target around oil Pipeline Based on Deep Learning","authors":"Qiang Wu, Xuegang Wu, Xin Zheng, Bin Yue","doi":"10.1145/3467707.3467714","DOIUrl":"https://doi.org/10.1145/3467707.3467714","url":null,"abstract":"With the development of UAV, UAV has been applied to various projects with its advantages of low construction cost,low safety risk coefficient and convenient operation.In terms of UAV platform, currently composite wing and multi-rotor UAV are typically adopted, which can realize basic flight route. In terms of image detection, neural network is mainly used to classify and recognize the target in the image. In this paper, the YOLOV4 algorithm is improved to make it more suitable for UAV detection of ground targets.In the ground detection of UAV, most of them are small targets, so clustering method is used to redesign anchor for small targets. Because the features of small targets have more details in the shallow feature layer, the shallow feature is superimposed into the feature extraction layer, and the shallow feature and the deep feature are fused.In the data processing, data enhancement, color dithering, flipping, cutting of the data set for expansion. Through the test of the modified network, the following results are obtained: the overall mAP is improved by 9.3%, the detection mAP for small targets such as people is improved by 23.75%, and the detection mAP for working vehicles is improved by 15.4%. The detection efficiency of small targets is improved, and the speed can meet the real-time requirements, and it can be deployed in the UAV for UAV detection.","PeriodicalId":145582,"journal":{"name":"2021 7th International Conference on Computing and Artificial Intelligence","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131749900","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

Improving Efficient Neural Architecture Search Using Out-net 利用Out-net改进高效神经结构搜索

2021 7th International Conference on Computing and Artificial Intelligence Pub Date : 2021-04-23 DOI: 10.1145/3467707.3467747

Cong Liu, Q. Miao, Min Huang

引用次数: 0

Modeling and Parameter Optimization of Anti-Missile System Combat Network 反导系统作战网络建模与参数优化

2021 7th International Conference on Computing and Artificial Intelligence Pub Date : 2021-04-23 DOI: 10.1145/3467707.3467755

Huachao Li, Qingsong Zhao, Jianbin Sun, Boyuan Xia, Junyi Ding

引用次数: 1