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706 results on '"Xing, Chunxiao"'

155. An Effective Algorithm for Dimensional Reduction in Collaborative Filtering

Author

Gao, Fengrong, Xing, Chunxiao, Zhao, Yong, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goh, Dion Hoe-Lian, editor, Cao, Tru Hoang, editor, Sølvberg, Ingeborg Torvik, editor, and Rasmussen, Edie, editor

Published
2007
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156. Research and Implementation of a Personalized Recommendation System

Author

Dong, Li, Nie, Yu, Xing, Chunxiao, Wang, Kehong, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Sugimoto, Shigeo, editor, Hunter, Jane, editor, Rauber, Andreas, editor, and Morishima, Atsuyuki, editor

Published
2006
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157. Managing and Querying of Videos by Semantics in Digital Library – A Semantic Model SemTTE and Its XML-Based Implementation

Author

Wang, Yu, Xing, Chunxiao, Zhou, Lizhu, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Sugimoto, Shigeo, editor, Hunter, Jane, editor, Rauber, Andreas, editor, and Morishima, Atsuyuki, editor

Published
2006
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158. Managing and Querying Video by Semantics in Digital Library

Author

Wang, Yu, Xing, Chunxiao, Zhou, Lizhu, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Gonzalo, Julio, editor, Thanos, Costantino, editor, Verdejo, M. Felisa, editor, and Carrasco, Rafael C., editor

Published
2006
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159. DRMS: Massive Digital Resource Management System Based on OSS

Author

Xing, Chunxiao, Gao, Fengrong, Zhou, Lizhu, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Fox, Edward A., editor, Neuhold, Erich J., editor, Premsmit, Pimrumpai, editor, and Wuwongse, Vilas, editor

Published
2005
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160. The PARNEM: Using Network Emulation to Predict the Correctness and Performance of Applications

Author

Li, Yue, Qian, Depei, Xing, Chunxiao, He, Ying, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Zhuge, Hai, editor, and Fox, Geoffrey C., editor

Published
2005
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161. An Evaluation Method for Video Semantic Models

Author

Wang, Yu, Zhou, Lizhu, Xing, Chunxiao, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Candan, K. Selçuk, editor, and Celentano, Augusto, editor

Published
2005
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162. Cataloging and Preservation Toolkit of a Chinese Mathematics Ancient Books Digital Library

Author

Dong, Li, Xing, Chunxiao, Zhou, Lizhu, Zhang, Bei, Jiang, Airong, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Chen, Zhaoneng, editor, Chen, Hsinchun, editor, Miao, Qihao, editor, Fu, Yuxi, editor, Fox, Edward, editor, and Lim, Ee-peng, editor

Published
2005
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165. A Cache Replacement Algorithm in Hierarchical Storage of Continuous Media Object

Author

Xu, Yaoqiang, Xing, Chunxiao, Zhou, Lizhu, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Li, Qing, editor, Wang, Guoren, editor, and Feng, Ling, editor

Published
2004
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170. Gromov-Wasserstein Guided Representation Learning for Cross-Domain Recommendation

Author

Li, Xinhang, Qiu, Zhaopeng, Zhao, Xiangyu, Wang, Zihao, Zhang, Yong, Xing, Chunxiao, Wu, Xian, Li, Xinhang, Qiu, Zhaopeng, Zhao, Xiangyu, Wang, Zihao, Zhang, Yong, Xing, Chunxiao, and Wu, Xian

Abstract

Cross-Domain Recommendation (CDR) has attracted increasing attention in recent years as a solution to the data sparsity issue. The fundamental paradigm of prior efforts is to train a mapping function based on the overlapping users/items and then apply it to the knowledge transfer. However, due to the commercial privacy policy and the sensitivity of user data, it is unrealistic to explicitly share the user mapping relations and behavior data. Therefore, in this paper, we consider a more practical cross-domain scenario, where there is no explicit overlap between the source and target domains in terms of users/items. Since the user sets of both domains are drawn from the entire population, there may be commonalities between their user characteristics, resulting in comparable user preference distributions. Thus, without the mapping relations at user level, it is feasible to model this distribution-level relation to transfer knowledge between domains. To this end, we propose a novel framework that improves the effect of representation learning on the target domain by aligning the representation distributions between the source and target domains. In addition, GWCDR can be easily integrated with existing single-domain collaborative filtering methods to achieve cross-domain recommendation. Extensive experiments on two pairs of public bidirectional datasets demonstrate the effectiveness of our proposed framework in enhancing the recommendation performance. © 2022 Owner/Author.

Published
2022

172. Learned Index on GPU

Author

Zhong, Xun, primary, Zhang, Yong, additional, Chen, Yu, additional, Li, Chao, additional, and Xing, Chunxiao, additional

Published
2022
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179. Research on Suicide Identification Method Based on Microblog 'Tree Hole' Crowd

Author

Jing, Xiaomin, Lin, Shaofu, Huang, Zhisheng, Siuly, Siuly, Wang, Hua, Chen, Lu, Guo, Yanhui, Xing, Chunxiao, Siuly, Siuly, Wang, Hua, Chen, Lu, Guo, Yanhui, Xing, Chunxiao, Artificial intelligence, Network Institute, and Knowledge Representation and Reasoning

Subjects
Information retrieval, Warning system, Computer science, business.industry, Microblogging, Temporal characteristics, SDG 10 - Reduced Inequalities, Suicide identification, Popularity, Convolutional neural network, Monitoring and rescue, Microblog tree hole, Identification (information), Tree (data structure), Suicide, Social media, The Internet, business
Abstract

Suicide has always been a key issue of social and health organizations and research of scholars. In recent years, with the increasing popularity of Internet and social media, more and more people record their lives and feelings in social media, even more, they publish suicide speeches, especially in the youth. The microblog “tree hole” is generated because a depressed patient has left, a large number of potential depression patients or suicide prone people to leave messages under her account until today, including venting negative emotions, suicide messages and even committing suicide together. In view of this situation, the paper analyzes the actual needs and business scenarios of monitoring and identifying online depression suicide messages. Based on the data of “tree hole” message on microblog, this paper designs and implements a multi-channel convolutional neural network social suicide warning model. By mining the time of message of “tree hole” people in microblog, the feature matrix is obtained after quantifying some knowledge elements; After word orientation quantization, word vector matrix and word vector matrix are obtained. Feature matrix and text vector matrix are used as three inputs of convolutional neural network respectively to identify and warn social suicide. The model is proved to be better by experiments and improve the accuracy of early warning.

Published
2021

180. Research on the Fluctuation Characteristics of Social Media Message Sentiment with Time Before and During the COVID-19 Epidemic

Author

Guo, Chaohui, Lin, Shaofu, Huang, Zhisheng, Shi, Chengyu, Yao, Yahong, Siuly, Siuly, Wang, Hua, Chen, Lu, Guo, Yanhui, Xing, Chunxiao, Siuly, Siuly, Wang, Hua, Chen, Lu, Guo, Yanhui, Xing, Chunxiao, Artificial intelligence, Network Institute, and Knowledge Representation and Reasoning

Subjects
Coronavirus disease 2019 (COVID-19), Computer science, Microblogging, Sentiment analysis, Scrapy-Redis, BERT-BiLSTM, COVID-19 epidemic, Microblog tree hole, Tree (data structure), Message sentiment, Emergency response, SDG 3 - Good Health and Well-being, Multiple time dimensions, Intervention (counseling), Social media, Social psychology
Abstract

“Tree hole” refers to a social media formed after the death of a social media user, in which other users continue to leave messages due to emotional resonance. This paper focuses on exploring the fluctuation of emotions with time in a “tree hole” of social media such as Microblog, and provides ideas and support for suicide warning, rescue, and user portraits of patients with depression in the “tree hole”. In this paper, the dataset of 2,356,066 messages captured from the “tree hole” Microblog with the “tree hole” agent (i.e., an AI program) and pre-processed. Subsequently, the effective dataset was labeled by a text sentiment analysis model based on BERT and BiLSTM, and accordingly the sentiment was scored. Then the scored data was visualized and analyzed in the time dimension. Finally, it was found that the sentiment of the “tree hole” messages reached a trough at 4:00 am and a peak around 8:00 am. In addition, the overall trend of “tree hole” sentiment has fluctuated downwards from Monday to Sunday. We have concluded that the sentiment of patients with depression fluctuates regularly at some special time points, and special events such as the outbreak of COVID-19 and so on, have a great impact on the emotions of patients with depression. Therefore, it is necessary to strengthen warning and intervention for those who has expressed thoughts of suicide at special points to prevent the spread and fermentation of suicidal emotions in the “tree hole” in time. In addition, the rescue volunteers for patients with depression as Tree Hole Rescue Team should make corresponding adjustments to the rescue strategy when special events occur. This research is of great significance for the emergency response of “tree hole” depressed users in major events such as COVID-19 epidemic.

Published
2021

185. PuntTable: A Flexible Repository System for Digital Library

Author

Lan, Chao, Meng, Tianyu, Zhang, Yong, Xing, Chunxiao, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Urs, Shalini R., editor, Na, Jin-Cheon, editor, and Buchanan, George, editor

Published
2013
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192. PuntStore: A Non-relational Database Based Architecture of Data Management in Digital Library

Author

Lan, Chao, Zhang, Yong, Gao, Yang, Xing, Chunxiao, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Chen, Hsin-Hsi, editor, and Chowdhury, Gobinda, editor

Published
2012
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193. Efficient EMD-Based Similarity Search via Batch Pruning and Incremental Computation

Author

Chen, Yu, Zhang, Yong, Wang, Jin, Wu, Jiacheng, and Xing, Chunxiao

Abstract

As a robust similarity measurement, Earth Mover's Distance (EMD) has been widely adopted in many real-world applications, such as machine learning, computer vision and natural language processing. In this paper, we study the problem of EMD-based similarity search, which aims at finding all histogram objects from a dataset whose EMD is within a pre-defined threshold from the given query. Since the time complexity of computing EMD is rather high, it is essential to devise effective techniques to accelerate the query processing. To this end, we propose a filter-and-verification framework: In the filter step, we devise three effective strategies to prune dissimilar objects in batch by sharing the computation between multiple objects. In the verification step, we develop novel flow adjustment techniques to incrementally calculate the EMD of candidates and enable early termination. We justify our proposed framework by conducting both theoretical analysis and extensive experiments. The results on four real world datasets show that our proposed techniques achieve up to an order of magnitude performance gain than state-of-the-art approaches.

Published
2023
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