Call for Papers
Machine Learning and its Application Track
The 35th ACM Symposium on Applied Computing (SAC 2020)
March 30 - April 3, 2020, Brno, Czech Republic
Today’s society generates data at such a speed that huge data are collected within a couple of days which was not possible in last decade. The algorithms of machine learning are used to compute directly from data without relying on a predetermined equation as a model and provide exciting solutions. These algorithms are used in multi-diversified fields and increasing the popularity of machine learning techniques.
The focus of this track is on the application of both novel and well-known techniques to the machine learning algorithms. In this contextual, researchers and practitioners from academia and industry will get a chance to keep in touch with problems, open issues and future directions in the field of development of devoted applications using machine learning techniques.
Topics of interest:
The topics related to this track are listed below but not limited to
- Deep learning
- Transfer learning
- Reinforcement learning
- Planning and learning
- Multi-agent learning
- Online and incremental learning
- Scalability of learning algorithms
- Bayesian networks
- Support vector machines
- Case-based reasoning
- Hybrid learning algorithms
- Machine learning and information retrieval
- Machine learning for web navigation and mining
- Applications of machine learning in medicine, health, bioinformatics, security, game playing, smart factories, smart cities, etc.
Important Dates:
- Paper submission:
September 15, 2019September 29, 2019 - Acceptance notification:
November 10, 2019November 24, 2019 - Camera-ready copies:
November 25, 2019December 9, 2019
Submission Guidelines:
Authors are invited to submit original and unpublished papers of research and applications for this track. The author(s) name(s) and address(es) must not appear in the body of the paper, and self-reference should be in the third person. This is to facilitate double-blind review. Only the title should be shown at the first page without the author's information. Full papers are limited to 8 pages with the option for up to 2 additional pages. Papers must be formatted according to the ACM SAC template (Author Kit of SAC 2020). For full submission guidelines, please follow the instructions on the ACM SAC 2020 website. Please submit regular papers via SAC 2020 Web page. When submitting, select MLA Track (Machine Learning and its Applications).
Poster Publication of Selected Papers: Papers that received high reviews but were not accepted due to space limitations can be invited for the poster session and will be published as short papers in the symposium proceedings. Posters are limited to 3 pages with the option for up to only an additional page.
Registration:
Paper registration is required, allowing the inclusion of the paper/poster in the conference proceedings. An author or a proxy attending SAC MUST present the paper. This is a requirement for the paper/poster to be included in the ACM digital library. No-show of scheduled papers and posters will result in excluding them from the ACM digital library.
Track Co-Chairs:
Keon Myung Lee
Dept. of Computer Science, Chungbuk National University, Republic Korea
Email: kmlee@cbnu.ac.kr
Jee-Hyong Lee
Dept. of Software, Sungkyunkwan University, Republic of Korea
Email: john@skku.edu
Program Committee Member:
Tanir Ozcelebi, Eindhoven University of Technology, The Netherlands
Pradeep Kumar, Oxford University, United Kingdom
Hyunwoo Kim, Korea Institute of Science and Technology, Korea
SeongWoo Kwak, Keimyung University, Korea
Subash Mukhopadhyay, Macquarie University, Australia
Uma Shanker Tiwary, IIIT, Allahabad, India
Dhananjay Singh, Hankuk University of Foreign Studies, Korea
Rakesh Ranjan, Himgiri Zee University, India
Dae-Won Kim, Chungang University, Korea
Sungwon Jung, Gachon University, Korea
Ranjit Biswas, Jamia Hamdard University, India
Il Kyeun Ra, University of Colorado at Denver, USA
Hong-Young Park, Satellite Technology Research Center, KAIST, Korea
Zong-Zin Eun, NAVER Corp., Korea