First workshop that aims at bringing together leading academic scientists, researchers, and research scholars to exchange and share their experiences and research results on all aspects of the usage of hardware and software systems to support lifelong learning, as well as experiences in related fields.
Lifelong learning is the constant pursuit of knowledge, for either professional or personal reasons, inside and outside of formal educational institutes, such as schools and universities. In the modern world, technology evolves rapidly and there is the risk of being left behind; lifelong learning is greatly important as a way to avoid this threat. Although it is not an easy task, it can be supported with hardware and software systems, which can be seen as enablers for lifelong learning. Also, people might need different types of enablers to achieve lifelong learning, and an important guideline for this is contained in Article 24 of the Convention on the Rights of Persons with Disabilities. It states that “States Parties shall ensure that persons with disabilities are able to access general tertiary education, vocational training, adult education and lifelong learning without discrimination and on an equal basis with others”, highlighting the needing for enablers tools to remove barriers for all kinds of users.
The specific systems heavily depend on the domain under consideration and the skills that are learnt, and usually a single system is not effective on multiple domains. Crucially, both hardware and software systems can be used, often with very different scopes: with hardware systems aimed to provide access to information in every situation and environment, while software systems aimed to provide the best suitable form of information, as continuously needed by the user.
Such systems generally need large amounts of data to be effective, and we need tools of various kinds to organize and manage this data: from dedicated devices to new algorithms, able to provide the right form of the information in the right moment. In this sense, the very process of creating, curating and managing personal knowledge bases can be extremely helpful for lifelong learning, regardless of how such data will be used downstream.
As for software systems, there are many different forms of interaction between users and enablers. From virtual assistants able to provide previously unknown data to gamification-based platforms aimed to produce user engagement during some learning process.
In support of these projects, hardware systems play a fundamental role. Today, edge devices are able to collect and process large amounts of data both on site and online (in connection with the cloud or with local gateways). The development of ad hoc hardware systems (FPGA) and the use of communication architectures (IoT, M2M) can allow the development of specialized supports for lifelong learning activities.
This workshop aims at bringing together leading academic scientists, researchers, and research scholars to exchange and share their experiences and research results on all aspects of the usage of hardware and software systems to support lifelong learning, as well as experiences in related fields. Systems built to support lifelong learning can be very diverse, and this workshop welcomes submissions about all of them, to provide an interdisciplinary platform for researchers, practitioners, and educators to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered within the development and deployment of systems to support lifelong learning.
Prospective authors are invited to prepare submissions according to the Springer LNCS Authors Guidelines, using the following template: [Format template from Springer LNCS]
Papers will be accepted only by electronic submission through the conference website, from which guidelines and templates are available. Submissions will NOT be accepted via e-mail. A blind peer-review process will be used to evaluate all submitted papers.
Paolo Fantozzi obtained his Ph.D. on Machine Learning and Artificial Intelligence at DIAG department of Sapienza University of Rome working on prediction of the best heuristic to solve a problem. He lectured, in LUISS and LUMSA universities, several courses including “Lab of Computer Skills”, “Customer Intelligence and Big Data analysis Logics”, and “Data and social networks analysis”. Since 2018 he has been a tutor of the Italian Olympiads in Informatics. His research interests involve machine learning, artificial intelligence, web-based education, and natural language processing.
Valerio Rughetti received his Master Degree in Computer Science engineering from Sapienza University of Rome. Currently is a Ph.D. student (Uninettuno University) and a researcher at DEWS Center of Excellence (University of L’Aquila) working on the applications and execution of machine learning algorithms on edge platforms. He lectured, in LUISS, LUMSA and Tor Vergata universities, several courses including “Web System Design”, “Cloud Computing”, “Edge Computing”, “Artificial Intelligence”. In Luiss Business School he holds courses in the Specialized Master's laboratories on IoT, Digital Skills and Big Data issues. Since 2019 he has been IT consultant for the Parco Archeologico del Colosseo for the design and implementation of a park monitoring project based on machine learning and edge computing technologies. His research activities regard Artificial Intelligence, Machine Learning and design of accelerators to execute neural networks on edge-computing platforms.
Giacomo Valente received the Laurea degree (i.e. BSc+MSc) in Electronic Engineering in 2014 and the Ph.D. degree in Information and Communication Technology in 2018 from University of L'Aquila. In 2014, he was a researcher at DEWS Center of Excellence, working on the development of run-time monitoring systems for multi-core architectures in the context of CRAFTERS european project. Currently, he is a Research Fellow at the University of L'Aquila, working on the development of EDA tools, mainly oriented to design of accelerator to execute neural networks on edge-computing platforms; moreover, it works on run-time monitoring actions for on-chip embedded systems at software and hardware level and dynamic partial reconfiguration. He has been author (or co-author) of several papers published in international conference proceedings and journals.
Eleonora Veglianti is a Post Doc in the FGES department at the Catholique University of Lille, France. She holds a Ph.D. at University of Perugia, Italy. She collaborates with several universities, also foreigners. She spent different periods abroad to collect data for her researches and she has an expertise also about China. She was a visiting Ph.D. student in Wuhan University, in China. She is author of several scientific papers. Her main focus is on: smart cities, artificial intelligence, digital transformation and innovation issues.