Min-Ling Zhang
2018-11-20 16:38:11 UTC
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PAKDD 2019 Call for Workshop Submissions
The 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2019)
April 14-17, 2019
Macau, China
http://www.pakdd2019.org
===========================================================
The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is one of the longest established and leading international conferences in the areas of data mining and knowledge discovery. It provides an international forum for researchers and industry practitioners to share their new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems, and the emerging applications.
The PAKDD workshops provide an informal and vibrant opportunity for researchers and industry practitioners to share their research positions, original research results and practical development experiences on specific challenges and emerging issues. We cordially solicit submissions to PAKDD workshops where each workshop will have a right to include its outstanding papers in a LNCS/LNAI post Proceedings of PAKDD Workshops published by Springer. Submitting a paper to the workshop means that the authors agree that at least one author should attend the workshop to present the paper, if the paper is accepted.
List of PAKDD 2019 workshops include:
*** PAISI ¨C The 14th Pacific Asia Workshop on Intelligence and Security Informatics ***
URL: http://www.business.hku.hk/paisi/2019/
Organisers
¨C Michael Chau (The University of Hong Kong, China)
¨C G. Alan Wang (Virginia Tech, United States)
¨C Hsinchun Chen (The University of Arizona, United States)
Contact: ***@business.hku.hk
Intelligence and Security Informatics (ISI) is concerned with the study of the development and use of advanced information technologies and systems for national, international, and societal security-related applications. Submissions may include systems, methodology, testbed, modeling, evaluation, and policy papers. Research should be relevant to both informatics and national/international security. Topics include but are not limited to: Information Sharing and Big Data Analytics, Infrastructure Protection and Emergency Responses, Cybercrime and Terrorism Informatics and Analytics, and Enterprise Risk Management, IS Security, and Social Media Analytics. PAISI 2019 will be held in conjunction with PAKDD and will provide a stimulating forum for ISI researchers in Pacific Asia and other regions of the world to exchange ideas and report research progress. Selected PAISI 2019 papers will be published in Springer's Lecture Notes in Artificial Intelligence (LNAI) series, which is in!
dexed by EI Compendex, ISI Proceedings, and Scopus.
*** WeL - PAKDD 2019 Workshop on Weakly Supervised Learning: Progress and Future ***
URL: http://lamda.nju.edu.cn/conf/wel19/
Organisers
- Yu-Feng Li (Nanjing University, China)
- Sheng-Jun Huang (Nanjing University of Aeronautics and Astronautics, China)
Contact: ***@nju.edu.cn; ***@nuaa.edu.cn
The aim of the workshop is to highlight the current research related to weakly supervised learning techniques in different types of weak supervision and their applications in real problems. The workshop will also emphasize a discussion for the major challenges for the future of weakly supervised learning and provide an opportunity to researchers for related fields such as optimization, statistical learning to get a feedback from other community.
*** LDRC - Learning Data Representation for Clustering ***
URL: https://sites.google.com/view/pakdd-workshop-ldrc2019
Organisers
- Yu-Feng Li (Nanjing University, China)
- Sheng-Jun Huang (Nanjing University of Aeronautics and Astronautics, China)
Contact: ***@parisdescartes.fr
The aim of the workshop is to highlight the current research related to weakly supervised learning techniques in different types of weak supervision and their applications in real problems. The workshop will also emphasize a discussion for the major challenges for the future of weakly supervised learning and provide an opportunity to researchers for related fields such as optimization, statistical learning to get a feedback from other community.
*** BDM ¨C The 8th Workshop on Biologically-inspired Techniques for Knowledge Discovery and Data Mining ***
URL: https://bdm19.blogs.auckland.ac.nz/
Organisers
¨C Shafiq Alam (University of Auckland, New Zealand)
¨C Gillian Dobbie (University of Auckland, New Zealand)
Contact: ***@cs.auckland.ac.nz
BDM to highlight the current research related to biologically inspired techniques in different data mining domains and their implementation in real life data mining problems. The workshop will also give an opportunity to the researcher from computational intelligence and evolutionary computation to get a feedback on their work from data mining community, machine learning, and computational intelligence and other KDD community.
*** DLKT ¨C The 1st Pacific Asia Workshop on Deep Learning for Knowledge Transfer ***
URL: http://www.intsci.ac.cn/users/zhuangfuzhen/DLKT/2019/
Organisers
¨C Fuzhen Zhuang (Institute of Computing Technology, Chinese Academy of Sciences, China)
¨C Deqing Wang (Beihang University, China)
- Pengpeng Zhao (Soochow University, China)
Contact: ***@ict.ac.cm; ***@buaa.edu.cn; ***@suda.edu.cn
Previous supervised learning algorithms mainly assume that there are plenty of i.i.d. sampled labeled data to train a good model for test data. However, this assumption does not always hold in real-world applications, since labeling data is time consuming and labor tedious. Furthermore, the test data are usually sampled the distribution which is different from the one of training data. The advanced algorithms based on knowledge transfer or sharing provide an effective way to handle this issue, e.g., transfer learning, multi-task learning and multi-view learning, since they either try to handle the distribution mismatch problem or the shortage of labeled data. In recent years, deep learning has been proved to have the ability to learn powerful representations for various kinds of tasks. On the one hand, although there are large amount of previous works based on knowledge transfer or sharing, there are only small amount of them applying deep learning techniques. In this worksh!
op, we aim to bring researchers and practitioners who work on various aspects of advanced knowledge transfer algorithms based on deep learning techniques, to discuss on the state-of-the-art and open problems, to share their expertise and exchange the ideas, and to offer them an opportunity to identify new promising research directions.
WORKSHOP CO-CHAIRS
***************
Leong Hou U (University of Macau, China)
Hady W. LAUW (Singapore Management University, Singapore)
_______________________________________________
Please do not post msgs that are not relevant to the database community at large. Go to www.cs.wisc.edu/dbworld for guidelines and posting forms.
To unsubscribe, go to https://lists.cs.wisc.edu/mailman/listinfo/dbworld
PAKDD 2019 Call for Workshop Submissions
The 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2019)
April 14-17, 2019
Macau, China
http://www.pakdd2019.org
===========================================================
The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is one of the longest established and leading international conferences in the areas of data mining and knowledge discovery. It provides an international forum for researchers and industry practitioners to share their new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems, and the emerging applications.
The PAKDD workshops provide an informal and vibrant opportunity for researchers and industry practitioners to share their research positions, original research results and practical development experiences on specific challenges and emerging issues. We cordially solicit submissions to PAKDD workshops where each workshop will have a right to include its outstanding papers in a LNCS/LNAI post Proceedings of PAKDD Workshops published by Springer. Submitting a paper to the workshop means that the authors agree that at least one author should attend the workshop to present the paper, if the paper is accepted.
List of PAKDD 2019 workshops include:
*** PAISI ¨C The 14th Pacific Asia Workshop on Intelligence and Security Informatics ***
URL: http://www.business.hku.hk/paisi/2019/
Organisers
¨C Michael Chau (The University of Hong Kong, China)
¨C G. Alan Wang (Virginia Tech, United States)
¨C Hsinchun Chen (The University of Arizona, United States)
Contact: ***@business.hku.hk
Intelligence and Security Informatics (ISI) is concerned with the study of the development and use of advanced information technologies and systems for national, international, and societal security-related applications. Submissions may include systems, methodology, testbed, modeling, evaluation, and policy papers. Research should be relevant to both informatics and national/international security. Topics include but are not limited to: Information Sharing and Big Data Analytics, Infrastructure Protection and Emergency Responses, Cybercrime and Terrorism Informatics and Analytics, and Enterprise Risk Management, IS Security, and Social Media Analytics. PAISI 2019 will be held in conjunction with PAKDD and will provide a stimulating forum for ISI researchers in Pacific Asia and other regions of the world to exchange ideas and report research progress. Selected PAISI 2019 papers will be published in Springer's Lecture Notes in Artificial Intelligence (LNAI) series, which is in!
dexed by EI Compendex, ISI Proceedings, and Scopus.
*** WeL - PAKDD 2019 Workshop on Weakly Supervised Learning: Progress and Future ***
URL: http://lamda.nju.edu.cn/conf/wel19/
Organisers
- Yu-Feng Li (Nanjing University, China)
- Sheng-Jun Huang (Nanjing University of Aeronautics and Astronautics, China)
Contact: ***@nju.edu.cn; ***@nuaa.edu.cn
The aim of the workshop is to highlight the current research related to weakly supervised learning techniques in different types of weak supervision and their applications in real problems. The workshop will also emphasize a discussion for the major challenges for the future of weakly supervised learning and provide an opportunity to researchers for related fields such as optimization, statistical learning to get a feedback from other community.
*** LDRC - Learning Data Representation for Clustering ***
URL: https://sites.google.com/view/pakdd-workshop-ldrc2019
Organisers
- Yu-Feng Li (Nanjing University, China)
- Sheng-Jun Huang (Nanjing University of Aeronautics and Astronautics, China)
Contact: ***@parisdescartes.fr
The aim of the workshop is to highlight the current research related to weakly supervised learning techniques in different types of weak supervision and their applications in real problems. The workshop will also emphasize a discussion for the major challenges for the future of weakly supervised learning and provide an opportunity to researchers for related fields such as optimization, statistical learning to get a feedback from other community.
*** BDM ¨C The 8th Workshop on Biologically-inspired Techniques for Knowledge Discovery and Data Mining ***
URL: https://bdm19.blogs.auckland.ac.nz/
Organisers
¨C Shafiq Alam (University of Auckland, New Zealand)
¨C Gillian Dobbie (University of Auckland, New Zealand)
Contact: ***@cs.auckland.ac.nz
BDM to highlight the current research related to biologically inspired techniques in different data mining domains and their implementation in real life data mining problems. The workshop will also give an opportunity to the researcher from computational intelligence and evolutionary computation to get a feedback on their work from data mining community, machine learning, and computational intelligence and other KDD community.
*** DLKT ¨C The 1st Pacific Asia Workshop on Deep Learning for Knowledge Transfer ***
URL: http://www.intsci.ac.cn/users/zhuangfuzhen/DLKT/2019/
Organisers
¨C Fuzhen Zhuang (Institute of Computing Technology, Chinese Academy of Sciences, China)
¨C Deqing Wang (Beihang University, China)
- Pengpeng Zhao (Soochow University, China)
Contact: ***@ict.ac.cm; ***@buaa.edu.cn; ***@suda.edu.cn
Previous supervised learning algorithms mainly assume that there are plenty of i.i.d. sampled labeled data to train a good model for test data. However, this assumption does not always hold in real-world applications, since labeling data is time consuming and labor tedious. Furthermore, the test data are usually sampled the distribution which is different from the one of training data. The advanced algorithms based on knowledge transfer or sharing provide an effective way to handle this issue, e.g., transfer learning, multi-task learning and multi-view learning, since they either try to handle the distribution mismatch problem or the shortage of labeled data. In recent years, deep learning has been proved to have the ability to learn powerful representations for various kinds of tasks. On the one hand, although there are large amount of previous works based on knowledge transfer or sharing, there are only small amount of them applying deep learning techniques. In this worksh!
op, we aim to bring researchers and practitioners who work on various aspects of advanced knowledge transfer algorithms based on deep learning techniques, to discuss on the state-of-the-art and open problems, to share their expertise and exchange the ideas, and to offer them an opportunity to identify new promising research directions.
WORKSHOP CO-CHAIRS
***************
Leong Hou U (University of Macau, China)
Hady W. LAUW (Singapore Management University, Singapore)
_______________________________________________
Please do not post msgs that are not relevant to the database community at large. Go to www.cs.wisc.edu/dbworld for guidelines and posting forms.
To unsubscribe, go to https://lists.cs.wisc.edu/mailman/listinfo/dbworld