A Semantic Framework for Mode Change Protocols

Chapters 6-12 are driven by questions about the ability to pin down mathematical entities and to articulate mathematical concepts. This chapter is driven by similar questions about the ability to pin down the semantic frameworks of language. It transpires that there are not just non-standard models, but non-standard ways of doing model theory itself. In more detail: whilst we normally outline a two-valued semantics which makes sentences True or False in a model, the inference rules for first-order logic are compatible with a four-valued semantics; or a semantics with countably many values; or what-have-you. The appropriate level of generality here is that of a Boolean-valued model, which we introduce. And the plurality of possible semantic values gives rise to perhaps the ‘deepest’ level of indeterminacy questions: How can humans pin down the semantic framework for their languages? We consider three different ways for inferentialists to respond to this question.

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Semantic Framework in Healthcare

Machine Learning for Healthcare Applications ◽

10.1002/9781119792611.ch7 ◽

2021 ◽

pp. 103-120

Author(s):

Sankar Pariserum Perumal ◽

Ganapathy Sannasi ◽

M. Selvi ◽

Kannan Arputharaj

Keyword(s):

Semantic Framework

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A semantic framework for PEGs

Proceedings of the 13th ACM SIGPLAN International Conference on Software Language Engineering ◽

10.1145/3426425.3426944 ◽

2020 ◽

Author(s):

Sérgio Queiroz de Medeiros ◽

Carlos Olarte

Keyword(s):

Semantic Framework

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Semantic and knowledge based support to business model evaluation to stimulate green behaviour of electric vehicles’ drivers and energy prosumers

Journal of Ambient Intelligence and Humanized Computing ◽

10.1007/s12652-021-03243-4 ◽

2021 ◽

Author(s):

Beniamino Di Martino ◽

Dario Branco ◽

Luigi Colucci Cante ◽

Salvatore Venticinque ◽

Reinhard Scholten ◽

...

Keyword(s):

Business Model ◽

Model Evaluation ◽

Business Models ◽

Real Case ◽

Sustainable Mobility ◽

Semantic Framework ◽

Knowledge Based ◽

Survey Results ◽

Energy Prosumers

AbstractThis paper proposes a semantic framework for Business Model evaluation and its application to a real case study in the context of smart energy and sustainable mobility. It presents an ontology based representation of an original business model and examples of inferential rules for knowledge extraction and automatic population of the ontology. The real case study belongs to the GreenCharge European Project, that in these last years is proposing some original business models to promote sustainable e-mobility plans. An original OWL Ontology contains all relevant Business Model concepts referring to GreenCharge’s domain, including a semantic description of TestCards, survey results and inferential rules.

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Assessing the Effect of Countdown Featured TOR Signal on Drivers in Automated Driving Mode Change

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181320641289 ◽

2020 ◽

Vol 64 (1) ◽

pp. 1213-1217

Author(s):

HyunJoo Park ◽

HyunJae Park ◽

Sang-Hwan Kim

Keyword(s):

Reaction Time ◽

Situation Awareness ◽

Time Preference ◽

Automated Driving ◽

Mode Change ◽

Driving Mode

In conditional automated driving, drivers may be required starting manual driving from automated driving mode after take-over request (TOR). The objective of the study was to investigate different TOR features for drivers to engage in manual driving effectively in terms of reaction time, preference, and situation awareness (SA). Five TOR features, including four features using countdown, were designed and evaluated, consisted of combinations of different modalities and codes. Results revealed the use of non-verbal sound cue (beep) yielded shorter reaction time while participants preferred verbal sound cue (speech). Drivers' SA was not different for TOR features, but the level of SA was affected by different aspects of SA. The results may provide insights into designing multimodal TOR along with drivers' behavior during take-over tasks.

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