Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference最新文献

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Highly Accurate Tomato Maturity Recognition: Combining Deep Instance Segmentation, Data Synthesis and Color Analysis 结合深度实例分割、数据合成和颜色分析的高精度番茄成熟度识别
Umme Fawzia Rahim, H. Mineno
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引用次数: 4
Personalized Point-of-Interest Recommendation Based on Social and Geographical Influence 基于社会和地理影响的个性化兴趣点推荐
Chang Su, Bin Gong, Xianzhong Xie
{"title":"Personalized Point-of-Interest Recommendation Based on Social and Geographical Influence","authors":"Chang Su, Bin Gong, Xianzhong Xie","doi":"10.1145/3508259.3508278","DOIUrl":"https://doi.org/10.1145/3508259.3508278","url":null,"abstract":"With the rapid development of location-based social networks (LBSNs), personalized Point-of-Interest (POI) recommendation has become an important personalized service to help users explore the surrounding environment. To better solve the data-sparse problem of POI recommendation, the main idea of existing research is to use neural networks to fuse context information such as social relationships and geographical influence. However, the existing models are still inadequate in integrating context information, and few studies consider privacy protection against users' activity trajectories. To solve these problems, this paper proposes a POI recommendation algorithm, SGGCN, which integrates social relationships and geographical influence. Based on desensitization of user activity trajectory, this method uses a graph convolutional neural network to explicitly learn the collaborative signal between users and users, POIs and POIs, and users and POIs to alleviate the data-sparse problem. Experiments on two real data sets show a 10% improvement over state-of-the-art POI recommendation methods.","PeriodicalId":259099,"journal":{"name":"Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115547122","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
Hacking VMAF and VMAF NEG: Vulnerability to Different Preprocessing Methods 攻击VMAF和VMAF NEG:不同预处理方法的漏洞
Maksim Siniukov, Anastasia Antsiferova, D. Kulikov, D. Vatolin
{"title":"Hacking VMAF and VMAF NEG: Vulnerability to Different Preprocessing Methods","authors":"Maksim Siniukov, Anastasia Antsiferova, D. Kulikov, D. Vatolin","doi":"10.1145/3508259.3508272","DOIUrl":"https://doi.org/10.1145/3508259.3508272","url":null,"abstract":"Video quality measurement plays a critical role in the development of video processing applications. In this paper, we show how popular quality metrics VMAF and its tuning-resistant version VMAF NEG can be artificially increased by video preprocessing. We propose a pipeline for tuning parameters of processing algorithms which allows to increase VMAF by up to 218.8%. A subjective comparison of preprocessed videos showed that with the majority of methods visual quality drops down or stays unchanged. We show that VMAF NEG scores can also be increased by some preprocessing methods by up to 21.9%.","PeriodicalId":259099,"journal":{"name":"Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114513021","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: 6
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