In this talk, we will briefly present the proposed structure of the library with a focus on intervals of real numbers, a key ingredient to many QLs, mechanised using the MathComp-analysis library. Then, we will discuss work in progress on mechanising Quantitative Linear Logic within our library, which is a recently proposed QL by Capucci et al. generalising multiplicative-additive linear logic.
This talk presents Ouros, a processor that takes a different approach. Instead of a central logic deciding what happens next, processor components work in a data-driven manner. Ouros also splits work into lightweight threads automatically, without help from the programmer or compiler - something that was previously impractical because managing threads cost too much. In Ouros, creating and synchronising a thread each take a single cycle.
I'll also describe the garbage collector, which cleans up memory in the background, with its algorithm verified by bounded model checking, and close with results from simulation and FPGA prototyping.
Originally, ADTs have been constructed manually in a highly informal manner: the node labels are plain textual descriptions of what the expert has is mind, and the meaning of sub-goal combinators (classically OR, AND and sequentialAND) is left open to the designer intuitive interpretation. The academic world has developed various formal settings to better handle ADTs, hoping for diverse (semi-)automated techniques.
In this talk, we focus on a piece of work, started a decade ago, that shows formal approaches to assist experts in constructing ADTs, with an accurate account of the computational complexity of decision problems relevant to the use of ADTs in practice.
Account ecosystems comprise a set of resources, including online accounts, devices, and credentials, that jointly impact a user's or organisation's security. Prior work shows that users frequently overlook subtle dependencies within these ecosystems, leading to security breaches that are difficult to detect using analysis techniques focused on isolated components.
This talk introduces resource access graphs (RAGs), a formal model for the automated analysis of dynamic account ecosystems. To capture the dynamic nature of account ecosystems, we define a transition system based on graph rewriting with RAGs as states in which both user actions and adversarial capabilities are modelled as graph transformation rules. Security properties are specified in computation tree logic (CTL), allowing automated analysis.
The approach is demonstrated on a simple Apple account ecosystem with the recent security feature Stolen Device Protection (SDP). The analysis reveals limitations in SDP, affecting both the integrity and availability of a user's Apple Account.