WORKSHOPS

PAISI – The 14th Pacific Asia Workshop on Intelligence and Security Informatics

URL: http://www.business.hku.hk/paisi/2019/

Organisers
– Michael Chau (The University of Hong Kong, China)
– G. Alan Wang (Virginia Tech, United States)
– Hsinchun Chen (The University of Arizona, United States)

Contact: mchau@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 indexed by EI Compendex, ISI Proceedings, and Scopus.

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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: liyf@nju.edu.cn; huangsj@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.

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LDRC - Learning Data Representation for Clustering

URL: https://sites.google.com/view/pakdd-workshop-ldrc2019

Organisers
- Lazhar Labiod (University of Paris Descartes, France)
- Mohamed Nadif (University of Paris Descartes, France)

Contact: lazhar.labiod@parisdescartes.fr

This workshop aims at discovering the recent advanced on data representation for clustering under different approaches. Thereby, the LDRC workshop is an opportunity to:
● present the recent advances in data representation based clustering algorithms,
● outline potential applications that could inspire new data representation approaches for clustering,
● explore benchmark data to better evaluate and study data representation based clustering models.

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BDM – The 8th Workshop on Biologically-inspired Techniques for Knowledge Discovery and Data Mining

URL: https://bdm19.blogs.auckland.ac.nz/

Organisers
– Shafiq Alam (University of Auckland, New Zealand)
– Gillian Dobbie (University of Auckland, New Zealand)

Contact: shafiq@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.

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DLKT – The 1st Pacific Asia Workshop on Deep Learning for Knowledge Transfer

URL: http://www.intsci.ac.cn/users/zhuangfuzhen/DLKT/2019/

Organisers
– Fuzhen Zhuang (Institute of Computing Technology, Chinese Academy of Sciences, China)
– Deqing Wang (Beihang University, China)
- Pengpeng Zhao (Soochow University, China)

Contact: zhuangfuzhen@ict.ac.cm; dqwang@buaa.edu.cn; ppzhao@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 workshop, 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.

Important Dates

* Paper Submission Due

October 10, 2018
October 17, 2018


* Notification to Authors
December 15, 2018

* Camera-ready Due
January 15, 2019

* Conference Date
April 14-17, 2019


All deadlines are 11:59pm Pacific Standard Time (PST)