An Extension to the Predictive Capability Maturity Model to Assess Model Credibility Using Dempster-Shafer Theory
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The initial goals of the Space Shuttle Program required that the avionics and software systems blaze new trails in advancing avionics system technology. Many of the requirements placed on avionics and software were accomplished for the first time on this program. Examples include comprehensive digital fly-by-wire technology, use of a digital databus for flight critical functions, fail operational/fail safe requirements, complex automated redundancy management, and the use of a high-order software language for flight software development. In order to meet the operational and safety goals of the program, the Space Shuttle software had to be extremely high quality, reliable, robust, reconfigurable and maintainable. To achieve this, the software development team evolved a software process focused on continuous process improvement and defect elimination that consistently produced highly predictable and top quality results, providing software managers the confidence needed to sign each Certificate of Flight Readiness (COFR). This process, which has been appraised at Capability Maturity Model (CMM)/Capability Maturity Model Integration (CMMI) Level 5, has resulted in one of the lowest software defect rates in the industry. This paper will present an overview of the evolution of the Primary Avionics Software System (PASS) project and processes over thirty years, an argument for strong statistical control of software processes with examples, an overview of the success story for identifying and driving out errors before flight, a case study of the few significant software issues and how they were either identified before flight or slipped through the process onto a flight vehicle, and identification of the valuable lessons learned over the life of the project.
This slide presentation reviews the experience NASA has had in using Capability Maturity Model (CMM) and Capability Maturity Model Integration (CMMI). In particular this presentation reviews the agency's experience within the software engineering discipline and the lessons learned and key impacts from using CMMI.
Artificial Intelligence (AI) is a powerful emerging technology area which requires special attention to using it ethically. AI ethics is still an emerging field, and the partners for this workshop and report seek to move AI ethics discussion ahead by experimenting with ways to measure AI ethics criteria. The following document describes the outcomes and learnings from The Ethical Artificial Intelligence Quantification Workshop held at the National Institute for Aerospace (NIA), Hampton, Virginia on May 12th, 2022. The purpose of the workshop was for participants to evaluate and experiment-with the methodology and process presented by AIEthics.World in cooperation with Intel Corporation. The meeting participants learned about the Ethical AI Certification and Maturity Model™ and applied the methodology to selected notional AI systems. The workshop facilitated the evaluation of the maturity of the AI system according to ethical considerations relevant to NASA, NIA and other participants. The workshop consisted of three main phases. The first phase focused on understanding and summarizing NASA’s ethical approaches, mission and values based on published documentation, discussions and individual insights & opinions of participants. This information was prioritized, weighted, ordered, and quantified in phase two, to formulate an alignment between human values (ethics) and their applicability to AI systems during all lifecycle phases. The first two phases were summarized as a form of ethical genealogy for artificial intelligence, specific to NASA’s ethical approaches. In the third and last phase of the workshop the participants evaluated notional examples of artificial intelligence to qualify and quantify its ability to adhere to the organizational ethics approaches, using the Ethical AI Certification and Maturity Model™. The workshop uses the concept of genealogy, in the traditional sense: the study and traceability of lines of ancestors in the process of evolutionary development from earlier forms. However, as it is applied to an Ethical AI definition, it is providing the insights to the necessary and mandatory traceability of content, data, metrics, telemetry, elements, and structures which are used in the AI’s lifecycle to foster and measure AI ethics in all steps of its lifecycle. The Ethical Artificial Intelligence Quantification Workshop provided NASA with the opportunity to apply the Ethical AI Certification and Maturity Model™, in combination with existing and well-known decision-making and quality control methods to identify the metrics and measurements for an Ethical AI and assess its ethical condition and quality aligned with NASA ethics approaches. The result of the workshop is the capacity for NASA to apply the maturity model assessment to its AI Systems as desired and if necessary, publish the ability of these AI Systems to adhere to the organizational ethical goals. AI ethics frameworks need to be customized for each application domain, for example, individual NASA Mission Directorates. General principles that work in one area such as AI/Machine Learning-based text analysis (the ethics of information-extraction) may need to be adapted for another such as sense-and-avoid decision-making in a flight environment. The workshop was conducted among approximately twenty NASA subject matter experts, so the elements noted above should be considered examples, not definitive NASA ethical AI principles, genealogy, etc. Generating a definitive AI ethics framework for an organization as diverse as NASA would require far more discussion, debate, review, etc. However, the workshop provided valuable insight into mechanisms and processes for quantifying AI ethical qualities.
A critical step for SARS-CoV-2 assembly and maturation involves the autoactivation of the main protease (MPro WT ) from precursor polyproteins. Upon expression, a model precursor of MPro WT mediates its own release at its termini rapidly to yield a mature dimer. A construct with an E290A mutation within MPro exhibits time dependent autoprocessing of the accumulated precursor at the N-terminal nsp4/nsp5 site followed by the C-terminal nsp5/nsp6 cleavage. In contrast, a precursor containing E290A and R298A mutations (MPro M ) displays cleavage only at the nsp4/nsp5 site to yield an intermediate monomeric product, which is cleaved at the nsp5/nsp6 site only by MPro WT . MPro M and the catalytic domain (MPro 1-199 ) fused to the truncated nsp4 region also show time-dependent conversion in vitro to produce MPro M and MPro 1-199 , respectively. The reactions follow first-order kinetics indicating that the nsp4/nsp5 cleavage occurs via an intramolecular mechanism. These results support a mechanism involving an N-terminal intramolecular cleavage leading to an increase in the dimer population and followed by an intermolecular cleavage at the C-terminus. Thus, targeting the predominantly monomeric MPro precursor for inhibition may lead to the identification of potent drugs for treatment.
This viewgraph presentation discusses the Capability Maturity Model Integrated (CMMI) is an integrated framework for maturity models and associated products that integrates the two key disciplines that are inseparable in a systems development activity: software engineering and systems engineering.
Cost modeling techniques and factors which either add to or subtract from these estimates are examined. The most important factors for increasing costs are interfacing subsystems, subsystem design and software maturity. Cost decrease depends on hardware, software, and support equipment availability. A cost modeling analysis for reentry shield and aerodynamic decelerator subsystems of a reentry vehicle is presented. Integration problems for the subsystems are also discussed.
The cyber-physical security threat to buildings is complex, non-linear, and rapidly evolving as operational and information technologies converge and connect buildings to cyberspace. Cyberattacks on buildings can exploit smart building controls and breach corporate networks, causing financial and reputational damage. This may result in the loss of sensitive building information or the disruption of, or damage to, the systems necessary for the safe and efficient operation of buildings. For the buildings and facility infrastructure, there is a need for a robust national cybersecurity strategy for buildings, guidance on the selection and implementation of appropriate cybersecurity controls for buildings, an approach to evaluate the maturity and adequacy of the cybersecurity programs. To provide an approach for evaluating the maturity of the cybersecurity programs for building control systems, the US Department of Energy’s widely used Cybersecurity Capability and Maturity Model (C2M2) has been adapted into a building control systems version. The revised model, the Buildings-C2M2 (B-C2M2) provides maturity level indicators for cybersecurity programmatic domains. A “B-C2M2 Lite” version allows facility managers and building control system engineers, or information technology personnel to perform rapid self-assessments of their cybersecurity program. Both tools have been pilot tested on several facilities. This paper outlines the concept of a maturity model, describes the B-C2M2 tools, presents results and observations from the pilot assessments, and lays out plans for future work.
In general, Dakota is the Consortium for Advanced Simulation of Light Water Reactors (CASL) delivery vehicle for verification, validation, and uncertainty quantification (VUQ) algorithms. It permits ready application of the VUQ methods described above to simulation codes by CASL researchers, code developers, and application engineers. More specifically, the CASL VUQ Strategy [33] prescribes the use of Predictive Capability Maturity Model (PCMM) assessments [37]. PCMM is an expert elicitation tool designed to characterize and communicate completeness of the approaches used for computational model definition, verification, validation, and uncertainty quantification associated with an intended application. Exercising a computational model with the methods in Dakota will yield, in part, evidence for a predictive capability maturity model (PCMM) assessment. Table 1.1 summarizes some key predictive maturity related activities (see details in [33]), with examples of how Dakota fits in. This manual offers CASL partners a guide to conducting Dakota-based VUQ studies for CASL problems. It motivates various classes of Dakota methods and includes examples of their use on representative application problems. On reading, a CASL analyst should understand why and how to apply Dakota to a simulation problem.
This project has progressed several physics models in the EMPIRE plasma simulation code to achieve higher fidelity simulations of high-current diode operation in pulsed-power accelerators. In this report, we present details for the following major work products: (1) a set of verification problems covering all key processes involved in gap closure physics has been designed; this suite has facilitated feature vetting and overall model maturation, (2) a new EMPIRE exemplar has been developed: the Radiographic Integrated Test Stand 6 (RITS-6) diode, and (3) An exemplar for the Saturn accelerator exemplar was enabled by the models matured under this work to self-consistently simulate further into the diode pulse than previously possible (bipolar flow regime). These developments have lead to the highest confidence EMPIRE power flow predictions of the Saturn accelerator to date. Additionally, three modeling approaches for simulating electrode plasmas have been investigated. We report on these results and provide recommendations.