Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence最新文献_第2页

Harmonic Means between TF-IDF and Angle of Similarity to Identify Prospective Applicants in a Recruitment Setting TF-IDF和相似角的调和方法在招聘环境中识别潜在申请人

Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2020-12-24 DOI: 10.1145/3446132.3446414

Ronie C. Bituin, Ronielle B. Antonio, James A. Esquivel

引用次数: 1

Lane Detection Combining Details and Integrity: an Advanced Method for Lane Detection 结合细节和完整性的车道检测:一种先进的车道检测方法

Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2020-12-24 DOI: 10.1145/3446132.3446145

Xingjian Dai, Jin Xie, J. Qian, Jian Yang

{"title":"Lane Detection Combining Details and Integrity: an Advanced Method for Lane Detection","authors":"Xingjian Dai, Jin Xie, J. Qian, Jian Yang","doi":"10.1145/3446132.3446145","DOIUrl":"https://doi.org/10.1145/3446132.3446145","url":null,"abstract":"Lane detection methods based on convolutional neural network have achieved excellent performance in recent years. Most of them treat lane detection as a semantic segmentation task which judges whether each pixel belongs to a lane. To make full use of the characteristics of lane shape, some researchers proposed to predict the whole lane. In this paper, we propose Lane Detection Combining Details and Integrity (LDCDI) which can explicitly leverage the advantages of both the segmentation-based methods and the regression-based methods. Specifically, we exploit an extra branch with regression-based methods as the auxiliary module after the main module. It not only maintains the advantages of the segmentation-based methods in lane detail segmentation, but also enables the model to have a sufficient understanding of the lane shape. Besides, the auxiliary module only takes part in the training, and there is no extra cost in the prediction. To further improve the quality of lane detection, we introduce a novel direction-sensitive block (DSB) based on ERFNet as the main module, which is more sensitive to the direction information of the image, so as to obtain better performance. Extensive experiments on the CULane dataset can demonstrate that our method outperforms other methods and achieves the state-of-the-art.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":" 33","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113947128","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

Enhancing Prosodic Features by Adopting Pre-trained Language Model in Bahasa Indonesia Speech Synthesis 采用预训练语言模型增强印尼语语音合成中的韵律特征

Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2020-12-24 DOI: 10.1145/3446132.3446196

Lixuan Zhao, Jian Yang, Qinglai Qin

引用次数: 3

Colorful 3d reconstruction from a single image based on deep learning 基于深度学习的单幅图像彩色3d重建

Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2020-12-24 DOI: 10.1145/3446132.3446157

Yuzheng Zhu, Yaping Zhang, Qiaosheng Feng

{"title":"Colorful 3d reconstruction from a single image based on deep learning","authors":"Yuzheng Zhu, Yaping Zhang, Qiaosheng Feng","doi":"10.1145/3446132.3446157","DOIUrl":"https://doi.org/10.1145/3446132.3446157","url":null,"abstract":"Simultaneously recovering the 3D shape and its surface color from a single image has been a very challenging. In this paper, we substantially improve Soft Rasterizer that is a state-of-the art method for 3D color object reconstruction. The model adopts the structure of the encoder and decoder with a single image as input. Firstly, the features are extracted by the encoder, and then they are simultaneously sent to the shape generator and the color generator to obtain the shape estimate and the corresponding surface color, and finally the final colorful 3D model is rendered by the differentiable renderer. In order to ensure the details of the reconstructed 3D model, this paper introduces an attention mechanism into the encoder to further improve the reconstruction effect. For surface color reconstruction, we propose a combination loss. The experimental results show that compared with the 3D reconstruction network models 3D-R2N2 and OccNet, the intersection-over-union (IOU) increases by 10% and 3% in our model. Compared to the open source project SoftRas_O, the model increases by 3.8% on structural similarity (SSIM) and decreases by 1.2% on mean square error (MSE).","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125002107","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

Curve fitting of the user barrage emotional change based on the hybrid kernel PSO_LSSVM model 基于混合核PSO_LSSVM模型的用户弹幕情绪变化曲线拟合

Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2020-12-24 DOI: 10.1145/3446132.3446138

Fulian Yin, Xiaoli Feng, Fangyuan Ju, Yanyan Wang

{"title":"Curve fitting of the user barrage emotional change based on the hybrid kernel PSO_LSSVM model","authors":"Fulian Yin, Xiaoli Feng, Fangyuan Ju, Yanyan Wang","doi":"10.1145/3446132.3446138","DOIUrl":"https://doi.org/10.1145/3446132.3446138","url":null,"abstract":"The prediction of the barrage emotional change is very important for video playback effect and the analysis of user interest. Currently, some existing method including least squares and BP network for data fitting were used. However, these methods often have \"bulging phenomenon\", poor applicability to small samples, and low generalization performance. In order to solve these problems, in this paper, we propose a hybrid kernel PSO_LSSVM model based on least squares support vector machine. The fitting performance of the model is mainly determined by the selected kernel function and its parameters. Considering that the local Gaussian radial basis kernel function has strong learning ability but weak generalization ability, while the global polynomial kernel function has strong generalization ability but weak learning ability. We propose to combine the advantages of the two, build a least squares support vector machine model based on hybrid kernels, and cited the particle swarm optimization algorithm to optimize twice to obtain the optimal parameter value of the model. Hence the model can achieve high fitting accuracy, and can also ensure a higher prediction accuracy. So as to obtain the fitting curve of the user's barrage emotion change, we carried out fitting experiments on the emotional data samples obtained from the barrage comment text, and conducted comparison experiments with unimproved least squares support vector machine, BP neural network and other methods. Verifying the effectiveness and generalization of the model in fitting the barrage emotional change curve.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127692314","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

Network Slimming with Augmented Sparse Training and Optimized Pruning 基于增强稀疏训练和优化剪枝的网络瘦身

Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2020-12-24 DOI: 10.1145/3446132.3446159

Ziliang Guo, Xueming Li

引用次数: 0

A dimension reduction method of situation knowledge based on Sparse Autoencoder 基于稀疏自编码器的情境知识降维方法

Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2020-12-24 DOI: 10.1145/3446132.3446151

Chuang Wang, Song Li, Wenfeng Wei, Shijie Li, Jiayi Liu

引用次数: 1

Multi-constrained Vehicle Routing Problem Solution based on Adaptive Genetic Algorithm 基于自适应遗传算法的多约束车辆路径问题求解

Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2020-12-24 DOI: 10.1145/3446132.3446401

Wen-Qing Fan

引用次数: 0

Research on the application of image processing in improving the reconnaissance efficiency of UAV 图像处理在提高无人机侦察效率中的应用研究

Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2020-12-24 DOI: 10.1145/3446132.3446198

Xiang-hui Shen, Xiaoyang Liu, Pengfei Jiao

引用次数: 1

Transgenerators Transgenerators

Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2020-12-24 DOI: 10.1145/3446132.3446417

Arip Asadulaev, Gideon Stein, A. Filchenkov

引用次数: 0