As our dependence on intelligent machines continues to grow, so does the demand for more
transparent and interpretable models. In addition, the ability to explain the model generally …

In line with the general trend in artificial intelligence research to create intelligent systems
that combine learning and symbolic components, a new sub-area has emerged that focuses …

Understanding black box models has become paramount as systems based on opaque
Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response …

In the emerging world of metaverses, it is essential for speech communication systems to be
aware of context to interact with virtual assets in the 3D world. This paper proposes the …

We design a predictive layer for structured-output prediction (SOP) that can be plugged into
any neural network guaranteeing its predictions are consistent with a set of predefined …

Social media platform such as Twitter provides a space where users share their thoughts
and opinion as well as connect, communicate, and contribute to certain topics using short …

Since its emergence in the 1960s, Artificial Intelligence (AI) has grown to conquer many
technology products and their fields of application. Machine learning, as a major part of the …

Research in both ecology and AI strives for predictive understanding of complex systems,
where nonlinearities arise from multidimensional interactions and feedbacks across multiple …

SenticNet is the knowledge base which the sentic computing framework leverages on for
concept-level sentiment analysis. This chapter illustrates how such a resource is built. In …

Neuro-symbolic artificial intelligence refers to a field of research and applications that
combines machine learning methods based on artificial neural networks, such as deep …