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BigComp 2017 : The 4th International Conference on Big Data and Smart Computing

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Link: http://conf2017.bigcomputing.org/
 
When Feb 13, 2017 - Feb 16, 2017
Where Jeju Island
Submission Deadline Sep 30, 2016
Notification Due Dec 16, 2016
Final Version Due Jan 13, 2017
Categories    computer science   data mining   machine learning   big data
 

Call For Papers

CALL FOR PARTICIPATION

2017 IEEE International Conference on Big Data and Smart Computing (IEEE BigComp 2017)
(http://www.bigcomputing.org)
Jeju Island, Jeju, KOREA
February 13-16, 2017

You are invited to attend the 2017 IEEE International Conference on Big Data and Smart Computing 2017 (IEEE BigComp2017). It

provides an international forum for exchanging ideas and information on big data and smart computing fields which have recently

drawn much attention and interests.

[Keynote Speech 1]
Title : Bio-Synergy Analysis with a Virtual Human System CODA
Speaker : Doheon Lee (Bio-Synergy National Research Center/KAIST, Korea)
http://biosoft.kaist.ac.kr/~dhlee
Abstract: Recently, there are growing interests in combinational bio-agents interacting with multiple targets to overcome the limitations of the current single target approaches. Many drug development efforts based on the Paul Ehrlich’s magic bullet principle, where a single therapeutic agent with ideal selectively could successfully regulate a single target causing a particular disease, have been suffering critical hindrances including unwanted off-target effects and degraded efficacy. Synergistic regulation of multiple targets with multiple agents is expected to remedy those hindrances. Furthermore, recent trends of 4P healthcare require more comprehensive spectrum of bio-agents for disease prevention as well as treatment. Functional food and ingredients have drawing increasing attention especially for preventive medicine and life-time healthcare. As they are composed of multiple components inherently, their precise interactions with human physiology are thought to be synergistic regulation of multiple targets with multiple agents This talk introduces a national initiative where multipleagent-multiple-target systems biology technology for natural product-based healthcare is being developed. Core components of the technology platform are virtual cell and human systems, which are computational models of molecular, cellular, and organ-level physiological mechanisms. The synergistic effects of multiple agents on multiple targets are simulated and predicted with those virtual systems, and validated in real systems including model cells and animals.

[Keynote Speech 2]
Title : Machine Learning: Status and Perspectives
Speaker : Zhi-Hua Zhou (Department of Computer Science & Technology, Nanjing University, China)
http://cs.nju.edu.cn/zhouzh/
Abstract: Machine learning has achieved great success in both research and application during the past decade. It originated as a research branch of artificial intelligence (AI), and becomes the mainstream of current AI research. In this talk, we will briefly introduce the progress and status of machine learning, and discuss on some future perspectives. We will comment on strengths and weakness of deep learning. Then, we will talk about challenges and opportunities introduced by open environment machine learning tasks. Moreover, considering that in its current form of "data + algorithm", machine learning suffers from many weakness or even bottlenecks, such as the need of large amount of training data, the difficulty of adapting to environmental change, the incomprehensibility, etc., we advocate to explore the form of learnware, which is a well-performed pre-trained learning model
with a specification explaining its purpose and/or specialty. Learnwares can be put into a market, such that when one is going to tackle a machine learning task, rather than building his model from scratch, he can do it in this way: Figure out his own requirement, and then browse/search the market, identify and adopt a good learnware whose specification matches his requirement. In some cases he can use the learnware directly, whereas in more cases he may need to use his own data to adapt/polish the learnware. Nevertheless, the whole process can be much less expensive and more efficient than building a model from scratch by himself. If learnwares come to reality, strong machine learning models can be achieved even for tasks with small data, and data privacy will become a less serious issue for machine learning tasks. More importantly, it will enable common end users to achieve tricky
learning performances that previously can only be achieved by machine learning experts.

[Invited Speech]
Title : Computational Methods for Large-Scale Microbiome Data Analysis
Speaker : Xiaohua Tony Hu (College of Computing & Informatics, Drexel University, USA)
http://www.cis.drexel.edu/faculty/thu/
Abstract: We know little about microbes. Recently, huge amounts of data are generated from many microbiome projects such as the Human Microbiome Project (HMP), Metagenomics of the Human Intestinal Tract (MetaHIT),etc. These datasets provide opportunities to study the mystery of the microbial world, and analyzing these data will help us to better understand the function and structure of the microbial community of the human body, earth and other environmental eco-systems. However, the huge data volume, the complexity of the microbial community and the intricate data properties have created a lot of opportunities and challenges for data analysis and mining. In this talk, I will discuss a computational framework to tackle these challenging issues, focusing on the following three tasks: 1) visualization approaches to visualize microbiome data and to infer microbial interactions and relations; 2) computational methods for identifying and visualizing higher-order microbial interactions and relations from three types of microbiome datasets: metagenomes, bacterial genomes and literatures respectively; 3) the extracted interactions and relations from different knowledge sources will be integrated in a knowledge graph. Statistical and machine learning approaches is discussed forconsistency checking of inferred microbial interactions and relations.

[Co-located Workshops]
1. The 2nd International Workshop on Big Data Analytics for Healthcare and Well-being (BigData4Healthcare 2017)
- Date : February 13, 2017
- Organizer : Ho-Jin Choi, Lingyun Zhu and Min Song
- Website : http://sigai.or.kr/workshop/bigcomp/2017/big-data-for-healthcare/
2. The 3rd Exobrain Workshop ? Natural language question answering for human-machine knowledge communication (Exobrain 2017)
- Date : February 13, 2017
- Organizer : Sang-Kyu Park and Ho-Jin Choi
- Website : http://sigai.or.kr/workshop/bigcomp/2017/exobrain/
3. The International Workshop on Affective and Sentimental Computing (ASC 2017)
- Date : February 13, 2017
- Organizer : Haoran Xie, Tak-Lam Wong, Fu Lee Wang, Raymond Wong and Xiaohui Tao
- Website : http://www.cihe.edu.hk/asc2017/

[Registration]
* Author Registration Deadline: January 16, 2017
* Early Registration Deadline: February 1, 2017
* Conference Registration Fee
Author Early Late
(by Jan 16) (by Feb 1) (or On-Site)
Regular Member USD 650 USD 650 USD 750
Regunlar Non-member USD 850 USD 850 USD 950
Student Member USD 300 USD 300 USD 400
Student Non-member USD 400 USD 400 USD 500

[Registration Guideline]
1. All accepted paper registration should be made by Jan. 16, 2017. Manuscripts received after this date will NOT be published in the proceedings of this conference.
2. All accepted paper must be registered as Regular registration. If multiple papers are accepted, each paper SHOULD be registered as Regular registration.

[Useful Information]
For a detailed program and information, please visit the BigComp 2017 web site at http://conf2017.bigcomputing.org/

[For more information]
- Home Page : http://conf2017.bigcomputing.org/
- Contacts : BigComp 2017 secretariat (ejmoon@kiise.or.kr)

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