Small vehicle detection in aerial images is a challenge in computer vision because small vehicles occupy less pixels and the environment around the small vehicles is complex. To improve the detection performance for the vehicles in aerial images, we propose an improved YOLO V3. The main contributions of our work include: (1) We redesign the backbone of YOLO V3 to select suitable scales for small vehicle detection in aerial images; (2) To make the improved YOLO V3 much stronger, we redesign the loss function of original YOLO V3 by GIOU loss and Focal loss; (3) To verify the performance of improved YOLO V3, we do the comparative experiments on VEDAI dataset. The experimental results show that the proposed method has obtained better performance than original YOLO V3 for small vehicle detection in aerial image.
{"title":"An improved YOLO V3 for small vehicles detection in aerial images","authors":"Moran Ju, Haibo Luo, Zhongbo Wang","doi":"10.1145/3446132.3446188","DOIUrl":"https://doi.org/10.1145/3446132.3446188","url":null,"abstract":"Small vehicle detection in aerial images is a challenge in computer vision because small vehicles occupy less pixels and the environment around the small vehicles is complex. To improve the detection performance for the vehicles in aerial images, we propose an improved YOLO V3. The main contributions of our work include: (1) We redesign the backbone of YOLO V3 to select suitable scales for small vehicle detection in aerial images; (2) To make the improved YOLO V3 much stronger, we redesign the loss function of original YOLO V3 by GIOU loss and Focal loss; (3) To verify the performance of improved YOLO V3, we do the comparative experiments on VEDAI dataset. The experimental results show that the proposed method has obtained better performance than original YOLO V3 for small vehicle detection in aerial image.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"894 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":"130382165","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}
Julia language is a free developing scripting language under the MIT license. Its goal is to case the difficulty of parallel programming. Based on the language mechanisms of Julia, we constructed a use case of computing the average running-time between every two bus stops. And then, we exampled the Julia programming framework and the code refining steps. Julia language supports both multi-cores/CPUs parallel programming mode. To full use all the computing resources, we developed some experiments on new policies about how to improve the computing performance. Experiments show that managing processors in parallel computing model consume working time, but with the increasing of problem size, this impact can be gradually ignored, and gaining nearly linear speedups.
{"title":"Experiment in Parallel Computing for the Julia Programming Language","authors":"Rui Song, Xumin Song, Yasheng Zhang, Yanni Ma","doi":"10.1145/3446132.3446166","DOIUrl":"https://doi.org/10.1145/3446132.3446166","url":null,"abstract":"Julia language is a free developing scripting language under the MIT license. Its goal is to case the difficulty of parallel programming. Based on the language mechanisms of Julia, we constructed a use case of computing the average running-time between every two bus stops. And then, we exampled the Julia programming framework and the code refining steps. Julia language supports both multi-cores/CPUs parallel programming mode. To full use all the computing resources, we developed some experiments on new policies about how to improve the computing performance. Experiments show that managing processors in parallel computing model consume working time, but with the increasing of problem size, this impact can be gradually ignored, and gaining nearly linear speedups.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"50 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":"129460205","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}
R. Maskat, Muhammad Faizzuddin Zainal, Nurrissammimayantie Ismail, N. Ardi, Amirah Ahmad, N. Daud
Automatic labelling is essential in large corpuses. Engaging in human experts to label can be challenging. Semantic understanding can differ from one labeler to another based on individual's language ability. Platforms such as AmazonTurk are not able to ensure the quality of annotations in every domain. Extensive steps such as qualification and counter checking of labels may be implemented which will increase the cost of data annotation. Thus, the higher quality of labelled data expected, the greater the cost that needs to be expended. This scenario is made worse when the language is of low resource where in this work is the Malay language. Malay is a language used mostly in Malaysia, Indonesia, Singapore and Brunei. Unlike English which has large resources to tap into the semantics of sentences, making automatic labelling faster to mature, resources in Malay language are still limited. Further compounded is the use of social media data where the text is short, unnormalized and the inherent presence of code switching. The availability of qualified native Malay labelers is also scarce. To overcome this, we devised a method to automatically label a total of 219,444 Malay tweets by using a combination of sentiment, emotion and toxicity polarities. We extend the work from Arslan et al. who proposed the use of sentiment and emotion to identify cyberbullying text. Our work added toxicity polarity in the context of automatic labelling of cyberbully tweets in Malay. We were able to employ 5 experts with formal degrees in Malay language to label our training set. We applied this method to Malay cyberbullying corpus to determine “bully” and “not bully” labels. We have tested our method on 54,867 manually labelled data and achieved high accuracy.
{"title":"Automatic Labelling of Malay Cyberbullying Twitter Corpus using Combinations of Sentiment, Emotion and Toxicity Polarities","authors":"R. Maskat, Muhammad Faizzuddin Zainal, Nurrissammimayantie Ismail, N. Ardi, Amirah Ahmad, N. Daud","doi":"10.1145/3446132.3446412","DOIUrl":"https://doi.org/10.1145/3446132.3446412","url":null,"abstract":"Automatic labelling is essential in large corpuses. Engaging in human experts to label can be challenging. Semantic understanding can differ from one labeler to another based on individual's language ability. Platforms such as AmazonTurk are not able to ensure the quality of annotations in every domain. Extensive steps such as qualification and counter checking of labels may be implemented which will increase the cost of data annotation. Thus, the higher quality of labelled data expected, the greater the cost that needs to be expended. This scenario is made worse when the language is of low resource where in this work is the Malay language. Malay is a language used mostly in Malaysia, Indonesia, Singapore and Brunei. Unlike English which has large resources to tap into the semantics of sentences, making automatic labelling faster to mature, resources in Malay language are still limited. Further compounded is the use of social media data where the text is short, unnormalized and the inherent presence of code switching. The availability of qualified native Malay labelers is also scarce. To overcome this, we devised a method to automatically label a total of 219,444 Malay tweets by using a combination of sentiment, emotion and toxicity polarities. We extend the work from Arslan et al. who proposed the use of sentiment and emotion to identify cyberbullying text. Our work added toxicity polarity in the context of automatic labelling of cyberbully tweets in Malay. We were able to employ 5 experts with formal degrees in Malay language to label our training set. We applied this method to Malay cyberbullying corpus to determine “bully” and “not bully” labels. We have tested our method on 54,867 manually labelled data and achieved high accuracy.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"55 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":"121668445","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}
Multiple kernel methods based on k-means aims to integrate a group of kernels to improve the performance of kernel k-means clustering. However, we observe that most existing multiple kernel k-means methods exploit the nonlinear relationship within kernels, whereas the local manifold structure among multiple kernel space is not sufficiently considered. In this paper, we adopt the manifold adaptive kernel, instead of the original kernel, to integrate the local manifold structure of kernels. Thus, the induced multiple manifold adaptive kernels not only reflect the nonlinear relationship but also the local manifold structure. We then perform multiple kernel clustering within the multiple kernel k-means clustering framework. It has been verified that the proposed method outperforms several state-of-the-art baseline methods on a variety of data sets.
{"title":"Manifold Adaptive Multiple Kernel K-Means for Clustering","authors":"Liang Du, Haiying Zhang, Xin Ren, Xiaolin Lv","doi":"10.1145/3446132.3446148","DOIUrl":"https://doi.org/10.1145/3446132.3446148","url":null,"abstract":"Multiple kernel methods based on k-means aims to integrate a group of kernels to improve the performance of kernel k-means clustering. However, we observe that most existing multiple kernel k-means methods exploit the nonlinear relationship within kernels, whereas the local manifold structure among multiple kernel space is not sufficiently considered. In this paper, we adopt the manifold adaptive kernel, instead of the original kernel, to integrate the local manifold structure of kernels. Thus, the induced multiple manifold adaptive kernels not only reflect the nonlinear relationship but also the local manifold structure. We then perform multiple kernel clustering within the multiple kernel k-means clustering framework. It has been verified that the proposed method outperforms several state-of-the-art baseline methods on a variety of data sets.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125364536","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}
{"title":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","authors":"","doi":"10.1145/3446132","DOIUrl":"https://doi.org/10.1145/3446132","url":null,"abstract":"","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114771230","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}
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