A Learning Ecosystem for Linemen Training based on Big Data Components and Learning Analytics

Linemen training is mandatory, complex, and hazardous. Electronic technologies, such as virtual reality or learning management systems, have been used to improve such training, however these lack of interoperability, scalability, and do not exploit trace data generated by users in these systems. In this paper we present our ongoing work on developing a Learning Ecosystem for Training Linemen in Maintenance Maneuvers using the Experience API standard, Big Data components, and Learning Analytics. The paper describes the architecture of the ecosystem, elaborates on collecting learning experiences and emotional states, and applies analytics for the exploitation of both, legacy and new data. In the former, we exploit legacy e-Learning data for building a Domain model using Text Mining and unsupervised clustering algorithms. In the latter we explore self-reports capabilities for gathering educational support content, and assessing students emotional states. Results show that, a suitable domain model for personalizing maneuvers linemen training path can be built from legacy text data straightforwardly. Regarding self reports, promising results were obtained for tracking emotional states and collecting educational support material, nevertheless, more work around linemen training is required

Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges

Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas. This fascination extends particularly to the Internet of Things (IoT), a landscape characterized by the interconnection of countless devices, sensors, and systems, collectively gathering and sharing data to enable intelligent decision-making and automation. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the IoT. Specifically, it starts by outlining the fundamental principles of IoT and the critical role of Artificial Intelligence (AI) in IoT systems. Subsequently, it delves into AGI fundamentals, culminating in the formulation of a conceptual framework for AGI's seamless integration within IoT. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education. However, adapting AGI to resource-constrained IoT settings necessitates dedicated research efforts. Furthermore, the paper addresses constraints imposed by limited computing resources, intricacies associated with large-scale IoT communication, as well as the critical concerns pertaining to security and privacy

Fifth Conference on Artificial Intelligence for Space Applications

The Fifth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: automation for Space Station; intelligent control, testing, and fault diagnosis; robotics and vision; planning and scheduling; simulation, modeling, and tutoring; development tools and automatic programming; knowledge representation and acquisition; and knowledge base/data base integration

A framework for operative and social sustainability functionalities in Human- Centric Cyber-Physical Production Systems

In a near future where manufacturing companies are faced with the rapid technological developments of Cyber-Physical Systems (CPS) and Industry 4.0, a need arises to consider how this will affect human operators remaining as a vital and important resource in modern production systems. What will the implications of these orchestrated and ubiquitous technologies in production – a concept we call Cyber-Physical Production Systems (CPPS) – be on the health, learning and operative performance of human workers? This paper makes three main contributions to address the question. First, it synthesizes the diverse literature regarding CPS and social sustainability in production systems. Second, it conceptualizes a holistic framework, the CyFL Matrix, and outlines a guideline to analyze how the functionalities of a CPPS relate to operational and social sustainability-related performance impacts at different levels of analysis. Finally, it presents an industrial use case, which the CyFL Matrix and the related guidelines are applied to. In doing so, the study offers first support to researchers and managers of manufacturing companies willing to define suitable operational and social sustainability-related performances for Human-centric Cyber-Physical Production Systems of the future