Authors are invited to submit full papers ( with a maximum length of 12 pages, including references and appendices.) using ( Springer LNCS format). Authors must upload their paper as PDF file using the EasyChair platform.
If any problem arises when submitting your paper, please contact: FrankNSW2011@163.com
Each paper will be reviewed by at least three members of the international program committee for ensuring high quality.Paper acceptance will be based on originality, significance, technical soundness, and clarity of presentation. All accepted papers will be included in the workshop proceedings published as part of the Lecture Notes in Computer Science (LNCS) series of Springer.
At least one author of an accepted paper must register and participate in the workshop. Registration is subject to the terms, conditions and procedure of the main ICSOC conference to be found on their website: http://www.icsoc.org/
|Dr Frank Jiang (University of Technology, Sydney, Australia)|
|Dr Haiying Xia (Guangxi Normal University, China)|
The proposed duration of the workshop is one day.
While there are many important topics within the Big Data field, recent research suggested that learning models are critical for many real-world complex systems in associated with data analysis. The current i.i.d. ness-based learning methods cannot handle the coupling relations due to the increased complexity, multiple dimensionalities and heterogeneity of the data, they cannot efficiently find the inter-relationship and intra-relationship with such scale, and they do not scale well and nor do they perform well under highly unstructured, unpredictable conditions (data volume, data variety, data categories etc.). If these problems are resolved, the new learning methods can be the foundation to build up new Big Data applications, to further reduce financial and environmental costs, minimise under-utilised resources, and achieve better performance. Therefore, the new learning systems and applications have drawn growing attention in the community.
To this end, we propose this workshop entitled “Networked learning systems for secured sensing and Its Applications for Big Data Analytics”. The purpose of this workshop is to solicit papers that advance the fundamental theoretical understanding, technological design, and applications related to learning systems for Big Data analytics. The artificial neural network and machine learning methods are promising in solving the wide variety of analytical tasks that are hard to solve using ordinary rule-based programming. More importantly, we explore the new smart learning systems that are of vital importance in such complex, large, heterogeneous, and uncertain Big Data era. This special issue invites paper submissions on the most recent developments in security and learning architectures, neural network design, new data representation, tasks optimization, semi-supervised and coupled learning, and applications to real-world tasks. We also welcome survey and overview papers in these general areas pertaining to learning and neural network architectures, etc. Detailed topics of this workshop include but are not limited to:Cyber Security, CI/Fuzzy -based Learning and Analysis