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This article introduces the possibility of studying graffiti using a colorimeter developed with Arduino hardware technology according to the Do It Yourself (DIY) philosophy. Through the obtained Red Green Blue (RGB) data it is intended to study and compare the information extracted from each of the graffiti present on different walls. The same color can be found in different parts of a single graffiti, but also in other graffiti that could a priori be of different authorship. Nevertheless, graffiti may be related, and it may be possible to group graffiti artists and "gangs" that work together. The methodology followed for the construction of the colorimeter and its real application in a practical case are described in four case studies. The case studies describe how graffiti were identified and recognized and they provide a comparison of the collected color samples. The results show the added value of the colorimeter in the graffiti recognition process, demonstrating its usefulness on a functional level. Finally, the contributions of this research are outlined, and an analysis is carried out of the changes to be made to the proposed method in the future, for improved graffiti color identification.

Physical aggression is a serious and widespread problem in society, affecting people worldwide. It impacts nearly every aspect of life. While some studies explore the root causes of violent behavior, others focus on urban planning in high-crime areas. Real-time violence detection, powered by artificial intelligence, offers a direct and efficient solution, reducing the need for extensive human supervision and saving lives. This paper is a continuation of a systematic mapping study and its objective is to provide a comprehensive and up-to-date review of AI-based video violence detection, specifically in physical assaults. Regarding violence detection, the following have been grouped and categorized from the review of the selected papers: 21 challenges that remain to be solved, 28 datasets that have been created in recent years, 21 keyframe extraction methods, 16 types of algorithm inputs, as well as a wide variety of algorithm combinations and their corresponding accuracy results. Given the lack of recent reviews dealing with the detection of violence in video, this study is considered necessary and relevant.

The 2030 Agenda is a clear development milestone for humanity in its ambition to care for our future generations. Smart Cities play an essential role in this development, the development direction of which is based, among others factors, on the fulfilment of the criteria set by the various Sustainable Development Goals (SDGs). The purpose of the current research is to verify the current state of science concerning the development of the SDGs in Smart Cities to establish a theoretical framework for the development and integration of data specific to a Smart City to participate in homogeneous monitoring of these indicators and to compare with other Smart Cities. Once a robust bibliographic study has been carried out, we proceed to establish a theoretical study, the application methodology of which follows a process developed in various phases to obtain an integrated system for monitoring and linking of the development of Smart Cities according to the criteria set out in the applicable SDGs. The clear implication is establishing an objective and efficient process that covers collection to integrate and compare the obtained data. One of the limitations of this process is overcoming the technological barrier in specific less developed environments that may not have sufficient funding. With respect to future lines of research, the current study opens the door to studying the implications of this type of process on the management and elaboration of specific public policies, for example, concerning the time taken to establish and resolve them.

Research in Agents and Multiagent Systems has matured significantly in recent years, representing one of the main branches of Artificial Intelligence and currently there are numerous effective applications of these technologies combined with Deep Learning, Computer Vision or Natural Language Processing, including areas such as healthcare and Ambient Intelligence, smart cities and mobility, Industry 4.0, educational technology, and fintech, among many others. In this regard, the International Conference on Practical Applications of Agents and Multi-Agent System (PAAMS) provides an international forum to present and discuss the latest scientific advances and their effective applications in different sectors, evaluate the impact of the approach and facilitate technology transfer among different stakeholders. Currently, a series of co-located events specialized in different areas of research are held simultaneously with PAAMS, these being the International Congress on Blockchain and Applications (BLOCKCHAIN), the International Conference on Distributed Computing and Artificial Intelligence (DCAI), the International Conference on Decision Economics (DECON), the International Symposium on Ambient Intelligence (ISAmI), the International Conference on Methodologies and Intelligent Systems for Technology Enhanced Learning (MIS4TEL), and the International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB). In this regard, the present Special Issue includes a selection of extended papers presented at the 20th International Conference PAAMS 22 and its co-located events and held in L’Aquila (Italy), July 13-15, 2022.

This paper presents an efficient cyberphysical platform for the smart management of smart territories. It is efficient because it facilitates the implementation of data acquisition and data management methods, as well as data representation and dashboard configuration. The platform allows for the use of any type of data source, ranging from the measurements of a multi-functional IoT sensing devices to relational and non-relational databases. It is also smart because it incorporates a complete artificial intelligence suit for data analysis; it includes techniques for data classification, clustering, forecasting, optimization, visualization, etc. It is also compatible with the edge computing concept, allowing for the distribution of intelligence and the use of intelligent sensors. The concept of smart cities is evolving and adapting to new applications; the trend to create intelligent neighbourhoods, districts or territories is becoming increasingly popular, as opposed to the previous approach of managing an entire megacity. In this paper, the platform is presented, and its architecture and functionalities are described. Moreover, its operation has been validated in a case study where the bike renting service of Paris—Vélib’ Métropole has been managed. This platform could enable smart territories to develop adapted knowledge management systems, adapt them to new requirements and to use multiple types of data, and execute efficient computational and artificial intelligence algorithms. The platform optimizes the decisions taken by human experts through explainable artificial intelligence models that obtain data from IoT sensors, databases, the Internet, etc. The global intelligence of the platform could potentially coordinate its decision-making processes with intelligent nodes installed in the edge, which would use the most advanced data processing techniques.

The cloud computing paradigm has the ability to adapt to new technologies and provide consistent cloud services. These features have led to the widespread use of the paradigm, making it necessary for the underlying computer infrastructure to cope with the increased demand and the high number of end users. Platforms often use classical mathematical models for this purpose, helping assign computational resources to the services provided to the final user. Although this kind of model is valid and widespread, it can be refined through intelligent techniques. Therefore, this research presents a novel system consisting of a multi-agent system, which integrates a case-based reasoning system. The resulting system dynamically allocates resources within a cloud computing platform. This approach, which is distributed and scalable, can learn from previous experiences and produce better results in each resource allocation. A model of the system has been implemented and tested on a real cloud platform with successful results.

The paradigm of Learning Object provides Educators and Learners with the ability to access an extensive number of learning resources. To do so, this paradigm provides different technologies and tools, such as federated search platforms and storage repositories, in order to obtain information ubiquitously and on demand. However, the vast amount and variety of educational content, which is distributed among several repositories, and the existence of various and incompatible standards, technologies and interoperability layers among repositories, constitutes a real problem for the expansion of this paradigm. This study presents an agent-based architecture that uses the advantages provided by Cloud Computing platforms to deal with the open issues on the Learning Object paradigm.

The Internet of Things (IoT) has become one of the most widely research paradigms, having received much attention from the research community in the last few years. IoT is the paradigm that creates an internet-connected world, where all the everyday objects capture data from our environment and adapt it to our needs. However, the implementation of IoT is a challenging task and all the implementation scenarios require the use of different technologies and the emergence of new ones, such as Edge Computing (EC). EC allows for more secure and efficient data processing in real time, achieving better performance and results. Energy efficiency is one of the most interesting IoT scenarios. In this scenario sensors, actuators and smart devices interact to generate a large volume of data associated with energy consumption. This work proposes the use of an Edge-IoT platform and a Social Computing framework to build a system aimed to smart energy efficiency in a public building scenario. The system has been evaluated in a public building and the results make evident the notable benefits that come from applying Edge Computing to both energy efficiency scenarios and the framework itself. Those benefits included reduced data transfer from the IoT-Edge to the Cloud and reduced Cloud, computing and network resource costs.

The automatic generation of music is an emergent field of research that has attracted the attention of countless researchers. As a result, there is a broad spectrum of state of the art research in this field. Many systems have been designed to facilitate collaboration between humans and machines in the generation of valuable music. This research proposes an intelligent system that generates melodies under the supervision of a user, who guides the process through a mechanical device. The mechanical device is able to capture the movements of the user and translate them into a melody. The system is based on a Case-Based Reasoning (CBR) architecture, enabling it to learn from previous compositions and to improve its performance over time. The user uses a device that allows them to adapt the composition to their preferences by adjusting the pace of a melody to a specific context or generating more serious or acute notes. Additionally, the device can automatically resist some of the user’s movements, this way the user learns how they can create a good melody. Several experiments were conducted to analyze the quality of the system and the melodies it generates. According to the users’ validation, the proposed system can generate music that follows a concrete style. Most of them also believed that the partial control of the device was essential for the quality of the generated music.