Understanding the multi-modal affective expression of net language in computer-mediated communication.

The fast development of technology and the popularity and prevalence of social media are constantly changing people's way of living especially their communication patterns. Computer-mediated communication facilitates human contact. Meanwhile, net language becomes widely accepted by computer-mediated communicators. Originating from the text-based form, net language evolves into a multi-modal physical form with a combination of texts, symbols, emojis, pictures and other forms of messages. The multi-modality of net language gives rise to difficulties for hearers or readers of the computer-mediated communication to understand the hidden message due to the ambiguous and polysemic nature of symbols. To clarify hearer's understanding and ensure the smooth conduct of computer-mediated communication, the conceptual blending theory will be useful in processing the multi-modal net language. With a four-space network and three operation mechanism, the emergent meaning will be constructed.

Identification of unobservable behavior in stochastic discrete event systems with a low number of sensors.

Dynamic discrete event systems (DDES) are systems that evolve from the asynchronous occurrence of discrete events. Their versatility has become a critical modeling tool in different applications. Finding models that define the behavior of DES is a topic that has been addressed from different approaches, depending on the type of system to be modeled and the model's objective. This article focuses on the identification of timed models for stochastic discrete event systems. The identified model includes both observable and unobservable behavior. The objective of the method is achieved through the following steps:•Identifying the sequences of events observed at different time instances during the closed-loop operation of the system (observed language),•Inferring the stochastic behavior of time between events and modeling the observable behavior as a stochastic timed Interpreted Petri Net (st-IPN),•and finally, inferring the non-observable behavior using the language projection operation between the observed language and the language generated by the st-IPN.This method has novel aspects because it uses timed events, can be applied to systems with a low number of sensors and can infer unobservable behavior for any sequence of events.