Proceedings of the ACM Turing Celebration Conference - China最新文献_第10页

Progressive deep feature learning for manga character recognition via unlabeled training data 基于未标记训练数据的漫画角色识别的渐进式深度特征学习

Proceedings of the ACM Turing Celebration Conference - China Pub Date : 2019-05-17 DOI: 10.1145/3321408.3322624

Xiaoran Qin, Yafeng Zhou, Yonggang Li, Siwei Wang, Yongtao Wang, Zhi Tang

{"title":"Progressive deep feature learning for manga character recognition via unlabeled training data","authors":"Xiaoran Qin, Yafeng Zhou, Yonggang Li, Siwei Wang, Yongtao Wang, Zhi Tang","doi":"10.1145/3321408.3322624","DOIUrl":"https://doi.org/10.1145/3321408.3322624","url":null,"abstract":"The recognition of manga (Japanese comics) characters is an essential step in industrial applications, such as manga character retrieval, content analysis and copyright protection. However, conventional methods for manga character recognition are mainly based on handcrafted features which are not robust enough for manga of various style. The emergence of deep learning based methods provides representational features, which has a huge demand for labeled data. In this paper, we propose a framework to exploit unlabeled manga data to facilitate the discriminative capability of deep feature representations for manga character recognition (i.e., unsupervised learning on manga images), which does not rely on any manual annotation. Specifically, we first train an initial feature model using an anime character dataset. Then, we adopt a Progressive Main Characters Mining (PMCM) strategy which iterates between two steps: 1) produce selected data with estimated labels from unlabeled data, 2) update the feature model by the selected data. These two steps are mutually promoted in essence. Experimental results on Manga109 dataset, to which we introduce new head annotations, demonstrate the effectiveness of the proposed framework and the usefulness in manga character verification and retrieval.","PeriodicalId":364264,"journal":{"name":"Proceedings of the ACM Turing Celebration Conference - China","volume":"353 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133215668","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: 7

The construction of movie marketing index based on factor analysis 基于因子分析的电影营销指标构建

Proceedings of the ACM Turing Celebration Conference - China Pub Date : 2019-05-17 DOI: 10.1145/3321408.3326688

Yan Wang, Xuteng Wang, Yugang Li, Yufan Zhou

引用次数: 1

PWGAN: wasserstein GANs with perceptual loss for mode collapse PWGAN:具有模态崩溃感知损失的wasserstein gan

Proceedings of the ACM Turing Celebration Conference - China Pub Date : 2019-05-17 DOI: 10.1145/3321408.3326679

Xianyu Wu, Canghong Shi, Xiaojie Li, Jia He, Xi Wu, Jiancheng Lv, Jiliu Zhou

{"title":"PWGAN: wasserstein GANs with perceptual loss for mode collapse","authors":"Xianyu Wu, Canghong Shi, Xiaojie Li, Jia He, Xi Wu, Jiancheng Lv, Jiliu Zhou","doi":"10.1145/3321408.3326679","DOIUrl":"https://doi.org/10.1145/3321408.3326679","url":null,"abstract":"Generative adversarial network (GAN) plays an important part in image generation. It has great achievements trained on large scene data sets. However, for small scene data sets, we find that most of methods may lead to a mode collapse, which may repeatedly generate the same image with bad quality. To solve the problem, a novel Wasserstein Generative Adversarial Networks with perceptual loss function (PWGAN) is proposed in this paper. The proposed approach could be better to reflect the characteristics of the ground truth and the generated samples, and combining with the training adversarial loss, PWGAN can produce a perceptual realistic image. There are two benefits of PWGAN over state-of-the-art approaches on small scene data sets. First, PWGAN ensures the diversity of the generated samples, and basically solve mode collapse problem under the small scene data sets. Second, PWGAN enables the generator network quickly converge and improve training stability. Experimental results show that the images generated by PWGAN have achieved better quality in visual effect and stability than state-of-the-art approaches.","PeriodicalId":364264,"journal":{"name":"Proceedings of the ACM Turing Celebration Conference - China","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121334669","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

An effective feature representation of web log data by leveraging byte pair encoding and TF-IDF 利用字节对编码和TF-IDF的web日志数据的有效特征表示

Proceedings of the ACM Turing Celebration Conference - China Pub Date : 2019-05-17 DOI: 10.1145/3321408.3321568

Junlang Zhan, X. Liao, Yukun Bao, Lu Gan, Zhiwen Tan, Mengxue Zhang, Ruan He, Jialiang Lu

引用次数: 9

A method for online transaction fraud detection based on individual behavior 一种基于个人行为的在线交易欺诈检测方法

Proceedings of the ACM Turing Celebration Conference - China Pub Date : 2019-05-17 DOI: 10.1145/3321408.3326647

Ligong Chen, Zhaohui Zhang, Qiuwen Liu, Lijun Yang, Ying Meng, P. Wang

{"title":"A method for online transaction fraud detection based on individual behavior","authors":"Ligong Chen, Zhaohui Zhang, Qiuwen Liu, Lijun Yang, Ying Meng, P. Wang","doi":"10.1145/3321408.3326647","DOIUrl":"https://doi.org/10.1145/3321408.3326647","url":null,"abstract":"Nowadays, judging the current transaction based on user history transactions is an important detection method. However, different users have different transaction behaviors, when all users use the same limit to judge whether the transaction is abnormal, it will result in higher misjudgment for some users. Aiming at the above problems, this paper proposes an individual behavior transaction detection method based on hypersphere model. In this model, considering multiple dimensions of normal historical transaction records, the characteristics of user's transaction behavior is generated with the trend of transaction. Then, the user optimal risk threshold algorithm is proposed to determine the optimal risk threshold for each user. Finally combining the transaction behavior and the optimal risk threshold, the user behavior benchmark is formed, which is used to construct the multidimensional hypersphere model. On this basis, a mapping method for transforming transaction detection into midpoint in multidimensional space is proposed. The experiment proves that the proposed method is superior to other models, and it is found that the characterization effect of user behavior is related to the frequency of users' transactions.","PeriodicalId":364264,"journal":{"name":"Proceedings of the ACM Turing Celebration Conference - China","volume":"194 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128810628","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: 9

A memory-compact and fast sketch for online tracking heavy hitters in a data stream 一个内存紧凑和快速的草图，用于在线跟踪数据流中的重量级人物

Proceedings of the ACM Turing Celebration Conference - China Pub Date : 2019-05-17 DOI: 10.1145/3321408.3323084

Zhiying Tang, Qingjun Xiao, Junzhou Luo

{"title":"A memory-compact and fast sketch for online tracking heavy hitters in a data stream","authors":"Zhiying Tang, Qingjun Xiao, Junzhou Luo","doi":"10.1145/3321408.3323084","DOIUrl":"https://doi.org/10.1145/3321408.3323084","url":null,"abstract":"Network traffic measurement is important for network management, including bandwidth management to mitigate network congestion, and security management to detect DDOS attacks and worm spreading. However, with the explosive volume of network data and the fast transmission speed of network packets (in giga or even tera bps), it is a challenging task to measure the size of each network flow both accurately and memory-efficiently, using the size-limited SRAM memory of line card. Therefore, many sublinear space algorithms for processing data streams have been proposed, such as CountMin (CM), Count Sketch (CS) and Virtual Active Counters (VAC), which achieve extreme memory compactness by providing probabilistic guarantees on flow size measurement accuracy. However, these existing algorithms can still be greatly improved as to the performance of both online recording and querying the per-flow size, which is needed for online tracking heavy hitters. Our paper proposes a highly compact and efficient counter architecture, called CountMin virtual active counter (CM-VAC), which provides more accurate measurement results than CM and CS under a very tight memory space. We also achieve higher query speed than VAC by modifying its query policy. We demonstrate the superior performance of our algorithm by both experimental results and theoretical analysis based on CAIDA network traces.","PeriodicalId":364264,"journal":{"name":"Proceedings of the ACM Turing Celebration Conference - China","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116367642","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

A novel data hiding algorithm for game texture based on Hamming+1 一种基于Hamming+1的游戏纹理数据隐藏算法

Proceedings of the ACM Turing Celebration Conference - China Pub Date : 2019-05-17 DOI: 10.1145/3321408.3326648

Jianfeng Lu, Weiling Cheng, Shanqing Zhang, Li Li

引用次数: 0

Seeing eye drone: a deep learning, vision-based UAV for assisting the visually impaired with mobility 视眼无人机:一种深度学习、基于视觉的无人机，用于帮助视障人士移动

Proceedings of the ACM Turing Celebration Conference - China Pub Date : 2019-05-17 DOI: 10.1145/3321408.3321414

L. Grewe, Garrett Stevenson

引用次数: 8

Source detection in the bitcoin network: a multi-reporting approach 比特币网络中的源检测:一种多报告方法

Proceedings of the ACM Turing Celebration Conference - China Pub Date : 2019-05-17 DOI: 10.1145/3321408.3321609

Chong Zhang, Xiaoying Gan

引用次数: 0

UrbanEdge

Proceedings of the ACM Turing Celebration Conference - China Pub Date : 2019-05-17 DOI: 10.1145/3321408.3323089

Xiaochen Fan, Chaocan Xiang, Liangyi Gong, Xiangjian He, Chao Chen, Xiang Huang

{"title":"UrbanEdge","authors":"Xiaochen Fan, Chaocan Xiang, Liangyi Gong, Xiangjian He, Chao Chen, Xiang Huang","doi":"10.1145/3321408.3323089","DOIUrl":"https://doi.org/10.1145/3321408.3323089","url":null,"abstract":"The revolution of smart city has led to rapid development and proliferation of Internet of Things (IoT) technologies, with the focus on transmitting raw sensory data into valuable knowledge. Meanwhile, the ubiquitous deployments of IoT are raising the importance of processing data in real-time at the edge of networks rather than in remote cloud data centers. Based on above, edge computing has been proposed to exploit the capabilities of edge devices in providing in-proximity computing services for various IoT applications. In this paper, we present UrbanEdge, a conceptual edge computing architecture empowered by deep learning for urban IoT time series prediction. We design a hierarchical architecture to process correlated IoT time series and illustrate the work-flow of UrbanEdge in data collection, data transmission and data processing. As a core component of UrbanEdge, a deep learning model is developed with attention-based recurrent neural networks. Composed with multiple processing layers, the deep learning model can extract feature representations from raw IoT data for monitoring and prediction. We evaluate the designed deep learning model of UrbanEdge on real-world datasets, evaluation results show that the UrbanEdge outperforms other baseline methods in time series prediction.","PeriodicalId":364264,"journal":{"name":"Proceedings of the ACM Turing Celebration Conference - China","volume":"637 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115113174","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