CALL FOR PARTICIPATION
(IEEE BigComp 2017 -- Final Program Uploaded!!!)
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 for
consistency 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
