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Present CFP : 2017
HASE 2017 programme will be divided into the following tracks.
Track A: Theoretical foundations of assurance
Contributions under this track will focus on formal methods that aid in modeling and validating new and existing designs of complex systems.
Track B: The practice of assurance
Contributions under this track will focus on methods that have been applied in the design of high assurance systems, or have been tested in realistic testbeds.
Track C: Tools
Contributions under this track will focus on new or existing tools and their effectiveness in creating high assurance designs.
Track D: Ideas under trial
Short papers under this track will focus on new ideas that fall under design innovation. Such ideas might not have undergone a rigorous test but are worthy of discussion.
Papers that cut across Tracks A, B, and C are welcome.
Track E: Student Session
This track will feature research presentations by undergraduate and graduate students. Papers under this category must have a student as the first author who is enrolled full time at a recognised university. Student authors will be asked to provide a letter from the university confirming their full time enrolment.
Limited travel support is available for students with accepted papers under this session to partially cover the air fare and hotel costs.
Systems of interest [not limited to]:
- Cyber-physical Systems (including public infrastructure such as power grid, water treatment and distribution, mass transportation, digital manufacturing systems)
- Internet of Things
- Distributed Systems
- Web Services
- Embedded Systems
- Autonomous vehicles
- Robot swarms
- High Assurance Complex Networks
- Topics of interest [not limited to]
- Design languages
- Formal Methods
- Domain Specific Languages
- Evolution and Change
- Verification and Validation
- Software Analysis and Visualisation
- Transformation-based Development
- Security and Privacy
- Reliability and Safety
- Tools for High Assurance Systems
- Artificial Intelligence in High Assurance
- Machine Learning in High Assurance