1,997 research outputs found
A Verified Information-Flow Architecture
SAFE is a clean-slate design for a highly secure computer system, with
pervasive mechanisms for tracking and limiting information flows. At the lowest
level, the SAFE hardware supports fine-grained programmable tags, with
efficient and flexible propagation and combination of tags as instructions are
executed. The operating system virtualizes these generic facilities to present
an information-flow abstract machine that allows user programs to label
sensitive data with rich confidentiality policies. We present a formal,
machine-checked model of the key hardware and software mechanisms used to
dynamically control information flow in SAFE and an end-to-end proof of
noninterference for this model.
We use a refinement proof methodology to propagate the noninterference
property of the abstract machine down to the concrete machine level. We use an
intermediate layer in the refinement chain that factors out the details of the
information-flow control policy and devise a code generator for compiling such
information-flow policies into low-level monitor code. Finally, we verify the
correctness of this generator using a dedicated Hoare logic that abstracts from
low-level machine instructions into a reusable set of verified structured code
generators
A Galois Connection for Weighted (Relational) Clones of Infinite Size
A Galois connection between clones and relational clones on a fixed finite
domain is one of the cornerstones of the so-called algebraic approach to the
computational complexity of non-uniform Constraint Satisfaction Problems
(CSPs). Cohen et al. established a Galois connection between finitely-generated
weighted clones and finitely-generated weighted relational clones [SICOMP'13],
and asked whether this connection holds in general. We answer this question in
the affirmative for weighted (relational) clones with real weights and show
that the complexity of the corresponding valued CSPs is preserved
Unplanned dilution and ore-loss optimisation in underground mines via cooperative neuro-fuzzy network
The aim of study is to establish a proper unplanned dilution and ore-loss (UB: uneven break) management system. To achieve the goal, UB prediction and consultation systems were established using artificial neural network (ANN) and fuzzy expert system (FES). Attempts have been made to illuminate the UB mechanism by scrutinising the contributions of potential UB influence factors. Ultimately, the proposed UB prediction and consultation systems were unified as a cooperative neuro fuzzy system
- …