Proceedings of the ACM Turing Celebration Conference - China最新文献

Ada3D

Proceedings of the ACM Turing Celebration Conference - China Pub Date : 2020-05-22 DOI: 10.1145/3393527.3393544

Muhong Wu, Shuai Yu, Xu Chen

引用次数: 0

SDMA SDMA

Proceedings of the ACM Turing Celebration Conference - China Pub Date : 2020-05-22 DOI: 10.1007/978-3-662-48986-4_313400

Hanjiang Luo, Chao Liu, Yu Luo, Xu Wang, Ziyang Xu, Yongquan Liang

引用次数: 0

Pricing Adjustment System for Spatial-Temporal Load of Charging Station 充电站时空负荷定价调整系统研究

Proceedings of the ACM Turing Celebration Conference - China Pub Date : 2020-05-22 DOI: 10.1145/3393527.3393555

Yunhao Wang, Peng Xu, Wei Zheng, Hengchang Liu

{"title":"Pricing Adjustment System for Spatial-Temporal Load of Charging Station","authors":"Yunhao Wang, Peng Xu, Wei Zheng, Hengchang Liu","doi":"10.1145/3393527.3393555","DOIUrl":"https://doi.org/10.1145/3393527.3393555","url":null,"abstract":"Electric vehicles(EVs) have lately received great attention due to their low emission pollution and recyclability. However, the spatial-temporal charging load has made it challenging to popularize EVs. In the temporal aspect, the extra charging load causes power loss to the power grid, which even results in an overload of the power gird. In the spatial aspect, a long waiting queue during the peak period of charging in some charging stations is detrimental to the development of EVs. Although some encouraging progress has been made, the extra charging load during the peak period of domestic electricity consumption remains an ongoing challenge, and the pricing adjustment system without considering the income of charging station companies is hard to implement and still requires further research. In this paper, we proposed an innovative pricing adjustment system to solve these problems. By studying the charging behaviors and consumption behaviors of drivers, our system will provide each driver with the location, real-time queuing, and customized charging fee of charging stations with the consideration of the income of charging station companies. We use a real data set from Xi'an, e-charge, which contains 241,346 of vehicle charging and driving records for three months with data from 400 charging stations. The experiment shows that our system is effective in decreasing the number of charging vehicles during the peak period of domestic electricity consumption. Also, it reduces the average queue waiting time and the peak waiting time while protecting the profit of charging station operators.","PeriodicalId":364264,"journal":{"name":"Proceedings of the ACM Turing Celebration Conference - China","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129148491","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

Extracting Chinese Domain-specific Open Entity and Relation by Using Learning Patterns 基于学习模式的中文特定领域开放实体和关系提取

Proceedings of the ACM Turing Celebration Conference - China Pub Date : 2020-05-22 DOI: 10.1145/3393527.3393548

Hongying Wen, Zhiguang Wang, Qiang Lu

{"title":"Extracting Chinese Domain-specific Open Entity and Relation by Using Learning Patterns","authors":"Hongying Wen, Zhiguang Wang, Qiang Lu","doi":"10.1145/3393527.3393548","DOIUrl":"https://doi.org/10.1145/3393527.3393548","url":null,"abstract":"Nowadays, Chinese domain-specific relation extraction faces a major challenge, that is the lack of annotation data. To cope with this challenge, the distant supervision which can automatically label large-scale training data was proposed. However, the distant supervision can produce noisy data which will hinder the performance of a model trained on such noisy data. Although significant progress has been made in filtering noisy data, the distant supervision method extracts the relation which already exists in the knowledge base. However, another major challenge in the extraction of domain-specific entities and relations is the diversity of entities and relation, which makes it difficult to accurately predefine relations in the knowledge base. Therefore, the distant supervision does not apply to domain-specific. In order to overcome the above challenges on specific domain, this paper proposes a Chinese Domain-specific Open Entity Relation Extraction Model (DOERE) which learns patterns from a small number of annotated data, and applies extraction patterns to the new domain-specific corpus for extracting entities and relations. Then, this paper proposes a method for automatically labeling data based on patterns. The experimental results show that the model has achieved better precision and recall in large-scale specific domain. And the method of automatically labeling data based on patterns has a good effect on data labeling in specific domain.","PeriodicalId":364264,"journal":{"name":"Proceedings of the ACM Turing Celebration Conference - China","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130373361","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

Delay-sensitive Trajectory Designing for UAV-enabled Data Collection in Internet of Things 基于无人机的物联网数据采集延迟敏感轨迹设计

Proceedings of the ACM Turing Celebration Conference - China Pub Date : 2020-05-22 DOI: 10.1145/3393527.3393541

Pengfei Wu, Chao Sha, Haiping Huang, Haiyan Wang

引用次数: 2

Web Security Education in A Multidisciplinary Learning Context 多学科学习背景下的网络安全教育

Proceedings of the ACM Turing Celebration Conference - China Pub Date : 2020-05-22 DOI: 10.1145/3393527.3393528

Qiang Liu, Wentao Zhao, Minghui Wu, Chengzhang Zhu

引用次数: 2

An Adaptive and Robust Model for WiFi-based Localization 一种基于wifi的自适应鲁棒定位模型

Proceedings of the ACM Turing Celebration Conference - China Pub Date : 2020-05-22 DOI: 10.1145/3393527.3393546

Yajie Song, Xiansheng Guo

{"title":"An Adaptive and Robust Model for WiFi-based Localization","authors":"Yajie Song, Xiansheng Guo","doi":"10.1145/3393527.3393546","DOIUrl":"https://doi.org/10.1145/3393527.3393546","url":null,"abstract":"The fluctuation of received signal strength (RSS) caused by environmental changing and heterogeneous devices severely degenerates the performance of WiFi fingerprint-based positioning methods. Deep Domain Adaptation (DDA) in transfer learning has proven to be an effective strategy to deal with this situation. However, the existing DDA methods show limited improvement in positioning accuracy in presence of the two factors simultaneously. In this study, we propose a new deep adaptation networks by adopting the joint constraints of mean and covariance to reduce domain discrepancy, which shows an excellent adaptability to environmental changing and heterogeneous devices. To further improve the robustness of our network, we design an exponential moving average method to update the parameters of the network, which can be further updated by unlabeled data from target domain, which is highly consistent with the actual application scenario and has practical significance. Experiment results show that the proposed model can reduce domain discrepancy effectively, and achieve lower positioning error than some other existing methods in real complex indoor environments.","PeriodicalId":364264,"journal":{"name":"Proceedings of the ACM Turing Celebration Conference - China","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116950046","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

Context-aware Location Search on Maps 地图上的上下文感知位置搜索

Proceedings of the ACM Turing Celebration Conference - China Pub Date : 2020-05-22 DOI: 10.1145/3393527.3393556

Yufan Sheng, Yu Hao

引用次数: 0

Fast and Precise Energy Consumption Prediction Based on Fully Convolutional Attention Res2Net 基于全卷积注意力的能量消耗快速精确预测

Proceedings of the ACM Turing Celebration Conference - China Pub Date : 2020-05-22 DOI: 10.1145/3393527.3393559

Chao Yang, Zhongwen Guo, Yuan Liu

引用次数: 1

A New Steganography Without Embedding Based on Adversarial Training 一种基于对抗训练的无嵌入隐写新方法

Proceedings of the ACM Turing Celebration Conference - China Pub Date : 2020-05-22 DOI: 10.1145/3393527.3393564

Wenjie Jiang, Donghui Hu, Cong Yu, Meng Li, Zhong-Qiu Zhao

{"title":"A New Steganography Without Embedding Based on Adversarial Training","authors":"Wenjie Jiang, Donghui Hu, Cong Yu, Meng Li, Zhong-Qiu Zhao","doi":"10.1145/3393527.3393564","DOIUrl":"https://doi.org/10.1145/3393527.3393564","url":null,"abstract":"Steganography is an art to hide information in the carriers to prevent from being detected, while steganalysis is the opposite art to detect the presence of the hidden information. With the development of deep learning, several state-of-the-art steganography and steganalysis based on deep learning techniques have been proposed to improve hiding or detection capabilities. Generative Adversarial Networks (GANs) based steganography directly uses the minimax game between the generator and discriminator, to automatically generate steganography algorithms resisting being detected by powerful steganalysis. The steganography without embedding (SWE) based on GANs, where the generated cover images themselves are stego ones carrying secret information has shown its state-of-the-art steganography performance. However, SWE based on GANs has serious weaknesses, such as low information recovery accuracy, low steganography capacity and poor natural showing. To solve these problems, this paper proposes a new SWE based on adversarial training, with carefully designed generator, discriminator and extractor, as well as their loss functions and optimized training mode. The proposed method can achieve a very high information recovery accuracy (100% in some cases), and at the same time improve the steganography capacity and image quality.","PeriodicalId":364264,"journal":{"name":"Proceedings of the ACM Turing Celebration Conference - China","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126865680","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