Incivility as a Violation of Communication Norms—A Typology Based on Normative Expectations toward Political Communication

Research on incivility in political communication usually defines uncivil communication as a violation of established norms. Few studies, however, have specified these norms and corroborated them using relevant theoretical concepts. This article aims at strengthening the foundations of incivility research by analytically reconstructing the potential normative expectations of communication participants toward the behavior of others in offline and online political communication. We propose that these expectations can be considered as communication norms, which enable cooperative communication in political debates and conflicts. We use action theory, evolutionary anthropology, and linguistics to propose a norm concept that differentiates five communication norms: an information norm, a modality norm, a process norm, a relation norm, and a context norm. Drawing on these norms, we propose new definitions of incivility and civility. We also provide a comprehensive typology of norm violations that can be used as a heuristic for empirical research.

An Automatic Modulation Classification Network for IoT Terminal Spectrum Monitoring under Zero-sample Situations

Rely on powerful computing resources, a large number of internet of things (IoT) sensors are placed in various locations to sense the environment around where we live and improve the service. The proliferation of IoT end devices has led to the misuse of spectrum resources, making spectrum regulation an important task. Automatic modulation classification (AMC) is a task in spectrum monitoring, which senses the electromagnetic space and is carried out under non-cooperative communication. However, DL-based methods are data-driven and require large amounts of training data. In fact, under some non-cooperative communication scenarios, it is challenging to collect the wireless signal data directly. How can the DL-based algorithm complete the inference task under zero-sample conditions? In this paper, a signal zero-shot learning network (SigZSLNet) is proposed for AMC under the zero-sample situations firstly. Specifically, for the complexity of the original signal data, SigZSLNet generates the convolutional layer output feature vector instead of directly generating the original data of the signal. The semantic descriptions and the corresponding semantic vectors are designed to generate the feature vectors of the modulated signals. The generated feature vectors act as the training data of zero-sample classes, and the recognition accuracy of AMC is greatly improved in zero-sample cases as a consequence. The experimental results demonstrate the effectiveness of the proposed SigZSLNet. Simultaneously, we show the generated feature vectors and the intermediate layer output of the model.