相关论文: DesCert: Design for Certification

Siren represents a pioneering research effort aimed at fortifying cybersecurity through strategic integration of deception, machine learning, and proactive threat analysis. Drawing inspiration from mythical sirens, this project employs…

We introduce a technology to formally verify that a software system satisfies a temporal specification of functional correctness, without revealing the system itself. Our method combines a deductive approach to model checking to obtain a…

Backtracking (i.e., reverse execution) helps the user of a debugger to naturally think backwards along the execution path of a program, and thinking backwards makes it easy to locate the origin of a bug. So far backtracking has been…

Credential fraud is a widespread practice that undermines investment and confidence in higher education systems and bears significant economic and social costs. Legacy credential verification systems are typically time-consuming, costly,…

Autonomous systems must sustain justified confidence in their correctness and safety across their operational lifecycle-from design and deployment through post-deployment evolution. Traditional assurance methods often separate…

We present our ongoing work on developing an end-to-end verified Rust compiler based on CompCert. It provides two guarantees: one is semantics preservation from Rust to assembly, i.e., the behaviors of source code includes the behaviors of…

The Independent Configurable Architecture for Reliable Operations of Unmanned Systems (ICAROUS) is a software architecture incorporating a set of algorithms to enable autonomous operations of unmanned aircraft applications. This paper…

Safety and reliability play a crucial role when designing Robotic Autonomous Systems (RAS). Early consideration of hazards, risks and mitigation actions -- already in the concept study phase -- are important steps in building a solid…

When designing evidence-based policies and programs, decision-makers must distill key information from a vast and rapidly growing literature base. Identifying relevant literature from raw search results is time and resource intensive, and…

We develop the concept of Trusted and Confidential Program Analysis (TCPA) which enables program certification to be used where previously there was insufficient trust. Imagine a scenario where a producer may not be trusted to certify its…

This study began with a research project, called DISCvR, conducted at the IBM-ILLINOIS Center for Cognitive Computing Systems Reseach. The goal of DISCvR was to build a practical NLP based AI pipeline for document understanding which will…

Cyber-physical systems (CPS) such as autonomous cars, aircraft, and robots are often also safety-critical; thus it is imperative that they operate as intended with a high degree of certainty. Formal verification has been employed to verify…

In the current control design of safety-critical autonomous systems, formal verification techniques are typically applied after the controller is designed to evaluate whether the required properties (e.g., safety) are satisfied. However,…

Human error research on overconfidence supports the benefits of early visibility of defects and disciplined development. If risk to the enterprise is to be reduced, individuals need to become aware of the reality of the quality of their…

While there was great progress regarding the technology and its implementation for vehicles equipped with automated driving systems (ADS), the problem of how to proof their safety as a necessary precondition prior to market launch remains…

Precisely modeling complex systems like cyber-physical systems is challenging, which often render model-based system verification techniques like model checking infeasible. To overcome this challenge, we propose a method called LAR to…

Approximate computing (AC) is an emerging paradigm for energy-efficient computation. The basic idea of AC is to sacrifice high precision for low energy by allowing for hardware which only carries out "approximately correct" calculations.…

Reliable uncertainty quantification is essential for deploying machine learning systems in high-stakes domains. Conformal prediction provides distribution-free coverage guarantees but often produces overly large prediction sets, limiting…

Reproducibility in research remains hindered by complex systems involving data, models, tools, and algorithms. Studies highlight a reproducibility crisis due to a lack of standardized reporting, code and data sharing, and rigorous…

Language model reasoning traces are rarely all-or-nothing; they frequently contain valid intermediate steps before a critical error occurs. Existing uncertainty quantification methods typically certify final answers or entire responses,…