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Music genre classification is one of the sub-disciplines of music information retrieval (MIR) with growing popularity among researchers, mainly due to the already open challenges. Although research has been prolific in terms of number of published works, the topic still suffers from a problem in its foundations: there is no clear and formal definition of what genre is. Music categorizations are vague and unclear, suffering from human subjectivity and lack of agreement. In its first part, this paper offers a survey trying to cover the many different aspects of the matter. Its main goal is give the reader an overview of the history and the current state-of-the-art, exploring techniques and datasets used to the date, as well as identifying current challenges, such as this ambiguity of genre definitions or the introduction of human-centric approaches. The paper pays special attention to new trends in machine learning applied to the music annotation problem. Finally, we also include a music genre classification experiment that compares different machine learning models using Audioset.

Intelligent tutoring systems (ITSs) are computer programs that provide instruction adapted to the needs of individual students. Dialog systems are computer programs that communicate with human users by using natural language. This paper presents a systematic literature review to address ITSs that incorporate dialog systems and have been implemented in the last twenty years. The review found 33 ITSs and focused on answering the following five research questions. a) What ITSs with natural language dialogue have been developed? b) What is the main purpose of the tutoring dialogue in each system? c) What are the pedagogical features of the teaching process performed by the ITSs with natural language dialogue? d) What natural language understanding approach does each system employ to understand students' utterances? e) What evidence exists related to the evaluation of ITSs with natural language dialogue? The results of this review reveal that most ITSs are directed toward science, technology, engineering, and mathematics (STEM) domains at the university level, and the majority of the selected ITSs implement the expectations and misconceptions tailored approach. Furthermore, most ITSs use dialog to help students learn how to solve a problem by applying rules, laws, etc. (the apply level in Bloom's taxonomy). With regard to the instructional approach, the selected ITSs help students write correct explanations or answers for deep questions; assist students in problem solving; or support a reflective dialogue motivated by either previously provided content or the result of a simulation. Additionally, we found empirical evaluations for 90.91% of the selected ITSs that measure the learning gains and/or assess the impacts of different tutoring strategies.

In recent years, recommender systems have been used as a solution to support tourists with recommendations oriented to maximize the entertainment value of visiting a tourist destination. However, this is not an easy task because many aspects need to be considered to make realistic recommendations: the context of a tourist destination visited, lack of updated information about points of interest, transport information, weather forecast, etc. The recommendations concerning a tourist destination must be linked to the interests and constraints of the tourist. In this research, we present a mobile recommender system based on Tourist Trip Design Problem (TTDP)/Time Depending (TD)-Orienteering Problem (OP)-Time Windows (TW), which analyzes in real time the user's constraints and the points of interest's constraints. For solving TTDP, we clustered preferences depending on the number of days that a tourist will visit a tourist destination using a k-means algorithm. Then, with a genetic algorithm (GA), we optimize the proposed itineraries to tourists for facilitating the organization of their visits. We also used a parametrized fitness function to include any element of the context to generate an optimized recommendation. Our recommender is different from others because it is scalable and adaptable to environmental changes and users' interests, and it offers real-time recommendations. To test our recommender, we developed an application that uses our algorithm. Finally, 131 tourists used this recommender system and an analysis of users' perceptions was developed. Metrics were also used to detect the percentage of precision, in order to determine the degree of accuracy of the recommender system. This study has implications for researchers interested in developing software to recommend the best itinerary for tourists with constraint controls with regard to the optimized itineraries.