NLS4IoT: The First Workshop on Networked Learning Systems for Secured IoT Hangzhou Hangzhou, China, November 12, 2018 |
Conference website | http://nls4iot.nullobject.cn/ |
Submission link | https://easychair.org/conferences/?conf=nls4iot |
Abstract registration deadline | July 31, 2018 |
Submission deadline | August 13, 2018 |
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
- Cyber Security, Intrusion Detection Systems, Malware and Botnets
- IoT Security/Privacy preservation
- Security of storage systems, operating systems;
- Intrusion detection, prediction, classification, and their response models for survivable, resilient, and self-healing systems
- Actuate-sensory network security, web security, wireless security, digital forensics, security information analytics
- Neural network-based learning, including the intelligent structures, algorithms and applications.
- Heterogeneous learning on multi-modality data, including Multi-view learning, Multitask learning, Transfer learning, Semi-supervised learning, Active learning; Reinforcement learning
- Data-driven learning and control and goal-oriented learning, prediction, and control
- Data-driven evolutionary neural systems; learning in neural control; Neuro-dynamics and complex systems
- Computational intelligence-based learning
- Combinatorial and numerical optimization approaches
- Data-driven Type-2 fuzzy logic, fuzzy and rough data analysis
- Data-driven lattice theory and multi-valued logics and approximate reasoning Fuzzy information processing
- Data-driven Fuzzy control and intelligent system modeling and identification
- Data-driven Fuzzy decision making and decision support systems and hybrid fuzzy systems (fuzzy-neuro-evolutionary)
Learning-based System: Coupled Learning, Deep Learning
- Complexity analysis of distribution algorithms
- Non-iidness learning; Coupled learning and coupled relationship discovery
- Theory and algorithms of data reduction techniques for Big Data including Online/incremental learning algorithms
- Big Data mining, separation and integration techniques; Data Structure and Data Relationship techniques
- Deep learning and its applications
- Scalable domain discovery and analysis
- Anomaly detection in social networks
Applications
- Novel applications of scalable learning in Neural hardware and applications in
Image Processing, Healthcare, Financial, Cyber-security, Mobile computing, mobile networks, Smart cities, Biological data analysis, financial data analysis, industrial applications - Emerging application domains (e.g. smart grid, intelligent transportation systems, communication systems, robotics, etc)
Important Dates
All papers must be original and not simultaneously submitted to another journal or conference.
Abstract submission deadline : July 31, 2018
Full paper submission deadline : August 13, 2018
Notification to authors : September 14, 2018
Camera ready paper : October 1, 2018
Workshop : November 12, 2018
Contact
All questions about submissions should be emailed to FrankNSW2011@163.com