Hypergraphs have attracted increasing attention in recent years thanks to their flexibility in naturally modeling a broad range of systems where high-order relationships exist among their interacting parts. This survey reviews the newly born hypergraph representation learning problem, whose goal is to learn a function to project objects—most commonly nodes—of an input hyper-network into a latent space such that both the structural and relational properties of the network can be encoded and preserved. We provide a thorough overview of existing literature and offer a new taxonomy of hypergraph embedding methods by identifying three main families of techniques, i.e., spectral, proximity-preserving, and (deep) neural networks. For each family, we describe its characteristics and our insights in a single yet flexible framework and then discuss the peculiarities of individual methods, as well as their pros and cons. We then review the main tasks, datasets, and settings in which hypergraph embeddings are typically used. We finally identify and discuss open challenges that would inspire further research in this field.

The widespread use of mobile devices is leading towards their adoption in the learning process, even if some pedagogical challenges are still not fully addressed when integrating mobile-assisted activities into regular curricula activities. In this paper, we first define some guidelines to design a general, tailorable, and platform-independent mobile learning system. Second, we present the aCME system, a mobile infrastructure, developed by adhering to the defined guidelines, to provide a general-purpose system, easy to start up with and friendly to user, and finally, tailorable, i.e., that can be easily adapted to the needs of different learning environments. The aCME system has been implemented as a Web-based architecture, to provide content and functionalities for different contexts, accessible from any location and by using any communication device. Finally, we describe a tool, deployed into aCME, that allows responding to quiz-based questionnaires during learning activities. A preliminary evaluation was performed to analyze usability and user satisfaction when interacting with the system we developed. The paper is concluded with some comments and future research directions.