Assurance cases offer a structured way to present arguments and evidence for certification of systems where safety and security are critical. However, creating and evaluating these assurance cases can be complex and challenging, even for systems of moderate complexity. Therefore, there is a growing need to develop new automation methods for these tasks. While most existing assurance case tools focus on automating structural aspects, they lack the ability to fully assess the semantic coherence and correctness of the assurance arguments.

In prior work, we introduced the Assurance 2.0 framework that prioritizes the reasoning process, evidence utilization, and explicit delineation of counter-claims (defeaters) and counter-evidence. In this paper, we present our approach to enhancing Assurance 2.0 with semantic rule-based analysis capabilities using common-sense reasoning and answer set programming solvers, specifically s(CASP). By employing these analysis techniques, we examine the unique semantic aspects of assurance cases, such as logical consistency, adequacy, indefeasibility, etc. The application of these analyses provides both system developers and evaluators with increased confidence about the assurance case.

Abstraction processes involve two variables that are often confused with one another: concreteness (banana versus belief) and specificity (chair versus furniture or Buddhism versus religion). Researchers are investigating the relationship between them, but many questions remain open, such as: What type of semantics characterizes words with varying degrees of concreteness and specificity? We tackle this topic through an in-depth semantic analysis of 1049 Italian words for which human-generated concreteness and specificity ratings are available. Our findings show that (as expected) the semantics of concrete and abstract concepts differs, but most interestingly when specificity is considered, the variance in concreteness ratings explained by semantic types increases substantially, suggesting the need to carefully control word specificity in future research. For instance, mathematical concepts (phase) are on average abstract and generic, while behavioral qualities (arrogant) are on average abstract but specific. Moreover, through cluster analyses based on concreteness and specificity ratings, we observe the bottom-up emergence of four subgroups of semantically coherent words. Overall, this study provides empirical evidence and theoretical insight into the interplay of concreteness and specificity in shaping semantic categorization.

To objectively and quantitatively study transcribed protocols of design conversations, we apply a semantic analysis approach based on dynamic semantic networks of nouns. We examined the applicability of the approach focused on a dynamic evaluation of the design problem solving process in engineering design educational settings. Using a case of real-world case, we show that the approach is able to determine the time dynamics of semantic factors such as level of abstraction, polysemy, information content, and quantify convergence/divergence in engineering design conversations.

Analysing verbal data produced during the design activity is helpful to gain a better understanding of design creativity. To understand exchange of information in terms of creative outcomes, a semantic analysis approach was used to measure the semantic content of communications between students and teachers. The goal was to use this tool to analyse design conversations, and to investigate their relation to design creativity, assessed in terms of originality, usability, feasibility, aesthetics, elaboration, overall value and overall creativity. Abstraction, Polysemy, Information Content and Semantic Similarity were employed to explore 35 design conversations from the DTRS10 dataset. Main findings suggest that a significant relationship exists between Information Content and Originality, and between Information Content and Overall creativity of the produced design outcomes. Significant relations were also found between Abstraction, Polysemy, Information Content, and Feasibility, as well as between Semantic Similarity and Overall Value of the outcomes. Implications for the use of semantic measures for encouraging creativity in the design studio are discussed.

To objectively and quantitatively study transcribed protocols of design problem solving conversations, we propose a semantic analysis approach based on dynamic semantic networks of nouns constructed with WordNet 3.1 lexical database. We examined the applicability of the semantic approach focused on a dynamic evaluation of the design problem solving process in educational settings. Using a case of real- world design problem-solving conversations, we show that the approach is able to determine the time dynamics of semantic factors such as level of abstraction, polysemy or information content, and quantify convergence/divergence of semantic similarity in design conversations between students, instructors and real clients. The approach can also be used to evaluate the aforementioned semantic factors for successful and unsuccessful ideas generated in the process of design problem solving, or to assess the effect of external feedback on the developed design solution. The proposed semantic analysis approach allows fast computation of the semantic factors in real time thereby demavonstrating a potential for both monitoring and support of the design problem solving process.

Investigations of the relationships between climate and human history often place more emphasis on the science of climate change than on understanding human socio-economic processes, and therefore suffer from superficial results and an unbalanced perspective. This is partly due to the lack of high-resolution data concerning long-term socio-economic processes. Here, we base our study of climate and society on a series of 2130-yr-long economic proxy data from China with decadal resolution. The economy was associated significantly with temperature and precipitation at the two predominant bands of 100 and 320 yr. The phase transition of economic states was influenced positively by long-term temperature change combined with triggering effects of short-term changes in precipitation. However, climatic impact on economy should not be recognized as simple causality but some driving-response relation coupled with meditation by human agency at multiple scales. A model of ‘adaptive cycles’ implies, in relative to the developing phases, climate–economy relationship during the declining phases was more easily moderated by slower processes like rigidity and faster processes like unrest. From a more-macro perspective, climatic driving for the macro-economic cycles was moderated by larger and slower processes like social memory, spatial shifting of key economic areas, and social–technical advance.