SELinux (Security-Enhanced Linux) enforces mandatory access control (MAC) according to policies. However, due to the inherent complexity of systems, real-world SELinux policies often become intricate, comprising thousands of statements, making manual verification of their correctness and security challenging. In addition, specific environments with stringent security requirements demand the formal verification of policies. While existing policy tools primarily focus on particular aspects of policy analysis, such as integrity and consistency, there is a lack of a unified approach to satisfy diverse requirements.

Osaka Metropolitan University researchers used GPS trajectory data and quasi-experimental methods to evaluate how the opening of ONIKURU affected citizens’ stay behavior.

Urban forests are essential infrastructure for climate resilience, biodiversity, and public health, argues new essay from group of global researchers which highlights policy priorities to safeguard these vital ecosystems.

Skill-based reinforcement learning has become the mainstream approach to solve sparse-rewards decision making tasks. The skills extracted from the demonstration datasets provide the temporal abstraction. However, in previous skill-based RL methods, the skills are kept fixed during online learning, which brings in sub-optimal asymptotic performance when the dataset contains only sub-optimal behavior modes.