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Non-verbal signals in speech are encoded by prosody and carry information that ranges from conversation action to attitude and emotion. Despite its importance, the principles that govern prosodic structure are not yet adequately understood. This paper offers an analytical schema and a technological proof-of-concept for the categorization of prosodic signals and their association with meaning. The schema interprets surface-representations of multi-layered prosodic events. As a first step towards implementation, we present a classification process that disentangles prosodic phenomena of three orders. It relies on fine-tuning a pre-trained speech recognition model, enabling the simultaneous multi-class/multi-label detection. It generalizes over a large variety of spontaneous data, performing on a par with, or superior to, human annotation. In addition to a standardized formalization of prosody, disentangling prosodic patterns can direct a theory of communication and speech organization. A welcome by-product is an interpretation of prosody that will enhance speech- and language-related technologies.

The manual modeling of complex systems is a daunting task; and although a plethora of methods exist that mitigate this issue, the problem remains very difficult. Recent advances in generative AI have allowed the creation of general-purpose chatbots, capable of assisting software engineers in various modeling tasks. However, these chatbots are often inaccurate, and an unstructured use thereof could result in erroneous system models. In this paper, we outline a method for the safer and more structured use of chatbots as part of the modeling process. To streamline this integration, we propose leveraging scenario-based modeling techniques, which are known to facilitate the automated analysis of models. We argue that through iterative invocations of the chatbot and the manual and automatic inspection of the resulting models, a more accurate system model can eventually be obtained. We describe favorable preliminary results, which highlight the potential of this approach., Comment: This is a preprint version of a paper that will appear at Modelsward 2024

Methodologies for development of complex systems and models include external reviews by domain and technology experts. Among others, such reviews can uncover undocumented built-in assumptions that may be critical for correct and safe operation or constrain applicability. Since such assumptions may still escape human-centered processes like reviews, agile development, and risk analyses, here, we contribute toward making this process more methodical and automatable. We first present a blueprint for a taxonomy and formalization of the problem. We then show that a variety of digital artifacts of the system or model can be automatically checked against extensive reference knowledge. Since mimicking the breadth and depth of knowledge and skills of experts may appear unattainable, we illustrate the basic feasibility of automation with rudimentary experiments using OpenAI's ChatGPT. We believe that systematic handling of this aspect of system engineering can contribute significantly to the quality and safety of complex systems and models, and to the efficiency of development projects. We dedicate this work to Werner Damm, whose contributions to modeling and model-based development, in industry and academia, with a special focus on safety, helped establish a solid foundation to our discipline and to the work of many scientists and professionals, including, naturally, the approaches and techniques described here.

There are many recognized effects of sexual reproduction and other forms of bi-parental reproduction on the sustainment and evolution of species. They include increased diversity, accelerated adaptation, constrained accumulation of deleterious mutations, and the homogenization of species' genotype. Nevertheless, many questions remain open regarding the evolution of bi-parental reproduction. Here we explore some of the finer details of the homogenization effect, which we believe deserve deeper analysis and discussion. We present a first-cut mathematical analysis, which suggests that bi-parental reproduction enhances the retention of shared, common properties of the parent generation in their offspring, as compared with mono-parental, clonal reproduction. For illustration, we accompany the mathematical claim and proof with several simulation examples depicting and comparing bi- and mono-parental reproduction of real numbers, images of printed text, and images of flowers. We argue that the retention of shared traits of preceding generations is an intrinsic effect of merging the encodings of parents' traits, regardless of physical, chemical, biological and social aspects of the reproduction process and of the specific trait at hand. As survival of offspring often depends on their ability to step in and fill voids left by failing, dying or absent members of the species in vital interaction networks, retention of common properties across generations helps sustain the species. This side-effect of sexual reproduction may have also contributed to the very evolution and pervasiveness of sexual reproduction in nature., Comment: This version presents an analytical angle, with some updates relative to V2, and several illustrative simulations

In all software development projects, engineers face the challenge of translating the requirements layer into a design layer, then into an implementation-code layer, and then validating the correctness of the result. Many methodologies, languages and tools exist for facilitating the process, including multiple back-and-forth `refinement trips' across the requirements, design and implementation layers, by focusing on formalizing the artifacts involved and on automating a variety of tasks throughout. In this paper, we introduce a novel and unique development environment, which integrates scenario-based programming (SBP) via the LSC language and the object-oriented, visual Statecharts formalism, for the development of reactive systems. LSC targets creation of models and systems directly from requirement specifications, and Statecharts is used mainly for specifying final component behavior. Our integration enables semantically-rich joint execution, with the sharing and interfacing of objects and events, and can be used for creating and then gradually enhancing testable models from early in requirements elicitation through detailed design. In some cases, it can be used for generating final system code. We describe the technical details of the integration and its semantics and discuss its significance for future development methodologies.