Use of reusable audiovisual elements as means to improve learning of Mathematics in Engineering. Experience based on teaching mathematics in engineering degrees

After the COVID-19 pandemic, learning content virtualization has been promoted by educational institutions, leading to the development of audiovisual resources to support teaching. These materials often consist of videos of variable duration envisaged as a complement to other training strategies in order to facilitate the acquisition of specific learning contents. This work presents a collaborative experience among teachers for audiovisual content generation, tailored to the specific contents of different math-related basic subjects in five different engineering degrees at the University of Cantabria. © GKA Ediciones, authors.

TEDGE-Caching: Transformer-based Edge Caching Towards 6G Networks

As a consequence of the COVID-19 pandemic, the demand for telecommunication for remote learning/working and telemedicine has significantly increased. Mobile Edge Caching (MEC) in the 6G networks has been evolved as an efficient solution to meet the phenomenal growth of the global mobile data traffic by bringing multimedia content closer to the users. Although massive connectivity enabled by MEC networks will significantly increase the quality of communications, there are several key challenges ahead. The limited storage of edge nodes, the large size of multimedia content, and the time-variant users' preferences make it critical to efficiently and dynamically predict the popularity of content to store the most upcoming requested ones before being requested. Recent advancements in Deep Neural Networks (DNNs) have drawn much research attention to predict the content popularity in proactive caching schemes. Existing DNN models in this context, however, suffer from long-term dependencies, computational complexity, and unsuitability for parallel computing. To tackle these challenges, we propose an edge caching framework incorporated with the attention-based Vision Transformer (ViT) neural network, referred to as the Transformer-based Edge (TEDGE) caching, which to the best of our knowledge, is being studied for the first time. Moreover, the TEDGE caching framework requires no data pre-processing and additional contextual information. Simulation results corroborate the effectiveness of the proposed TEDGE caching framework in comparison to its counterparts. © 2022 IEEE.

Feasible Blackboard implementations, challenging blended learning through agile Academic Learning Management Systems in Transnational Higher Education

Blended learning is an adaptive (and necessary) response adopted by most universities to face the challenges posed by the COVID-19 ongoing pandemic. Academic Learning Management Systems (ALMSs) played a strategic and pivotal role under high pressure. Online learning can occur by implementing integrated solutions embedded in the Academic portals or subscriptions to other videoconferencing solutions. Blending online and face-to-face teaching proved challenging from different perspectives. This approach has been considered essential to ensure an adequate level of service and avoid outflows of students, but it led to increased teaching workload, improved infrastructure, flexible pedagogic approach, enhanced technologies, specific training, and IT solutions development. The authors perform a SWOT analysis of blended learning, in a transnational Higher Education Context, to match the findings with the most relevant and critical challenges. The results, therefore, support the Information System analysis, focusing on one of the most widespread ALMSs. The case of Blackboard is finally considered, and some implementations are suggested. © 2022 Association for Computing Machinery. All rights reserved.

Multi-Media Content Clustering and Computer Intelligent Analysis by Text Mining

The epidemic situation of covid-19 spread all over the world, which is not optimistic. In order to extract valuable information for the epidemic from the numerous Internet data. With data mining technology, this paper crawls more than 10000 pieces of data from the microblog platform of overseas anti epidemic diary topic, and preprocesses the obtained text data set with word segmentation, removing stop words and other data, extracts the keywords of each microblog through word vector model, counts word frequency, and clustes text. In addition, the emotional value of the text is analyzed. Finally, the data were grouped into seven categories, and the trend chart of emotion value was drawn, and each result was displayed in the way of graph. By analysing, on the one hand, valuable information can be extracted from the micro blog data generated by overseas Chinese to help the domestic people understand the real situation of the overseas epidemic and adjust the risk response measures;on the other hand, the general situation of social media data during the epidemic can be generally understood from the macro perspective to provide reference for government departments in terms of management of entry-exit and epidemic prevention and control. It is helpful to further improve the governance system and the modernization of governance capacity in response to public health emergencies in China. © 2021 ACM.

Delay-Sensitive Wireless Content Delivery: An Interpretable Artificial Intelligence Approach

The COVID-19 emergency has made the consumption of multimedia content skyrocket in all contexts, including education. Many universities leverage hybrid learning models, in which students join a real-time video session via Wi-Fi from several classrooms to ensure safety and social distancing. This is creating a significant strain on the wireless access network, which is required to deliver an unusually high level of traffic. Artificial Intelligence (AI) and Machine Learning (ML) solutions have emerged as a way to make networks easier to control and to manage. However, their black box nature and in general their fire and forget approach has generated considerable skepticism over the entire value chain, from vendors to network administrators. This situation has led to a new interest in interpretable AI solutions, which aim at making the decisions taken by AI/ML models intelligible to a domain expert. In this article, we review the concept of interpretable AI and analyze the challenges, requirements, and benefits it can bring to delay-sensitive content delivery in 802.11 Wi-Fi networks. Furthermore, we apply these requirements to a use case in which we focus on advanced Quality of Service (QoS) provision, and we propose an interpretable and low-complexity ML model that addresses those requirements. The results demonstrate performance gains up to 60% in the sensitive traffic and up to 20% at network-wide level. © 2021 IFIP.

Automated crisis content categorization for COVID-19 tweet streams

Social media platforms, like Twitter, are increasingly used by billions of people internationally to share information. As such, these platforms contain vast volumes of real-time multimedia content about the world, which could be invaluable for a range of tasks such as incident tracking, damage estimation during disasters, insurance risk estimation, and more. By mining this real-time data, there are substantial economic benefits, as well as opportunities to save lives. Currently, the COVID-19 pandemic is attacking societies at an unprecedented speed and scale, forming an important use-case for social media analysis. However, the amount of information during such crisis events is vast and information normally exists in unstructured and multiple formats, making manual analysis very time consuming. Hence, in this paper, we examine how to extract valuable information from tweets related to COVID-19 automatically. For 12 geographical locations, we experiment with supervised approaches for labelling tweets into 7 crisis categories, as well as investigated automatic priority estimation, using both classical and deep learned approaches. Through evaluation using the TREC-IS 2020 COVID-19 datasets, we demonstrated that effective automatic labelling for this task is possible with an average of 61% F1 performance across crisis categories, while also analysing key factors that affect model performance and model generalizability across locations. © 2021 Information Systems for Crisis Response and Management, ISCRAM. All rights reserved.