Functional diversity and information units in Extremadura (Spain): problems in the media and political response

The functional diversity in Extremadura is researched by comparing the problem in the media with the norma-tive activity. It also delves into its relationship with in-formation units. The objective is to discover whether the official regulations accompany the social problems reflected in the media. To do this, we investigated the news about functional diversity published in the DOE (Official Gazette of Extremadura), as a normative me-dium, and in the HOY newspaper of Extremadura, as a global communication medium, to see their differences regarding geographical area, terminology and typol-ogy, topics, temporal evolution, and relationship with the information units;and, finally, we delve into this last aspect. The research was carried out during 2020, co-inciding with the crucial period of the health pandemic caused by COVID19, which was a handicap for all citi-zens and, especially, for people with disabilities. Among the general results, a volume of news about functional diversity is obtained, very much in favour of HOY (1344 news) to the detriment of the DOE (29 news). A slight opening towards the outside is also ob-tained, a slight use of the term "functional diversity " and a greater thematic diversity in the HOY newspaper. As for the information units, there is a very low percentage of news (40, all in the HOY, 2.91%), the most frequently mentioned information units are libraries (70%) and the most recurrent theme is "Employment " (42.5%). It con-cludes with a lack of accompaniment on disability be-tween the political regulations and the social problems reflected in the media. It also concludes with the need for greater involvement and visibility in the media of the role played by information units in relation to helping the most vulnerable groups.

Prediction of Hospital Status of COVID-19 Patients from E-Health Records

In the current era of big data, very large amounts of data are generating at a rapid rate from a wide variety of rich data sources. Embedded in these big data are valuable information and knowledge that can be discovered by data science, data mining and machine learning techniques. Electronic health (e-health) records are examples of the big data. With the technological advancements, more healthcare practice has gradually been supported by electronic processes and communication. This enables health informatics, in which computer science meets the healthcare sector to address healthcare and medical problems. As a concrete example, there have been more than 610 millions cumulative cases of coronavirus disease 2019 (COVID-19) worldwide over the past 2.5 years since COVID-19 has declared as a pandemic. As some of these cases require hospitalization. it is important to estimate the demand in hospitalization. Moreover, different levels of hospitalization may require different types of resources (e.g., hospital beds, medical staff). For example, patients admitted into the intensive care unit (ICU) may require assisted ventilation. Hence, in this paper, we present models to make predictions based on e-health records. Specifically, our binary model predicts whether a patient require hospitalization, whereas our multi-class model predicts what level of hospitalization (e.g., regular ward, semi-ICU, ICU) is required by the patient. Our models uses few-shot learning (and may use multi-task learning) with autoencoders (comprising encoders and decoders) and a predictor. Evaluation results on real-life e-health records show the practicality of our models in predicting hospital statuses of COVID-19 cases and the benefits of these models towards effective allocation of resources (e.g., hospital facilities, staff). © 2022 IEEE.

Construction of Cross-Border Food Supply Chain Information Sharing Platform Based on Blockchain Under COVID-19

The pandemic of the COVID-19 has caused many problems in the cross-border food supply chain during transportation, such as food safety is difficult to guarantee due to the infection of people or environmental epidemics, information asymmetry and difficult to share in the food supply chain, and food related information is tampered with and difficult to trace during transportation. The emergence of blockchain technology has brought new solutions to the above problems. Based on the relevant functions and characteristics of the blockchain, this paper constructs a cross-border food supply chain information sharing platform under the blockchain technology, such as using smart contracts to monitor the epidemic security risk in the transportation process of the supply chain, using hash functions and tamper proof features to deal with food information fraud, and using the distributed and decentralized characteristics of the blockchain to solve the sharing problem of information asymmetry;The time stamp in the blockchain is used to trace the information of each link node of food and the initial node for accountability. In this paper, the construction process of the platform is described in detail from the perspective of model, function and subject, and the specific process of the platform is described in detail from the perspective of information storage, information sharing and information traceability. In this paper, the emerging blockchain technology is applied to all links of the food supply chain, and considering the current epidemic problems, the platform can bring new ideas to solve the problems of cross-border food transportation during the epidemic, and has certain theoretical value. © 2022 Association for Computing Machinery.

Digital Humanism and a Quantitative Analysis of Users Behavior in Virtual Exhibitions during the Pandemic Context

The paper highlights the importance of promoting and valorizing cultural heritage collections from cultural institutions, such as art galleries, libraries, archives, and museums, using the most recent I&CTs (Information and Communication Technologies), as well as identifying behavior patterns of visitors inside virtual exhibitions in order to provide them personalized content. The paper's objective is to highlight the influence of modern I&CTs in the cultural field, and the advantages provided by new tools, such as virtual exhibitions, in accessing, promoting, and valorizing cultural collections, especially in pandemic situations. Digital transformation is happening not only in cultural institutions but also in human beings. The concept of Digital Humanism is very popular and developed together with the COVID-19 pandemic evolution. © 2022 The Authors. Published by Elsevier B.V.

Graph Network based Approaches for Multi-modal Movie Recommendation System

The three times increase of SonyLiv viewers during the Tokyo Olympic, the 10% hike of YouTube users during the isolation era of covid-pandemic, and the 19% growth in Netflix user count due to the fastest growth of OTT, etc. have made the digital platform's mode all-time active and specific. The hourly increase of users' interactions and the e-commerce platform's desire of letting users engage on their sites are pushing researchers to shape the virtual digital web as user specific and revenue-oriented. This paper develops a deep learning-based approach for building a movie recommendation system with three main aspects: (a) using a knowledge graph to embed text and meta information of movies, (b) using multi-modal information of movies like audio, visual frames, text summary, meta data information to generate movie/user representations without directly using rating information;this multi-modal representation can help in coping up with cold-start problem of recommendation system (c) a graph attention network based approach for developing regression system. For meta encoding, we have built knowledge graph from the meta information of the movies directly. For movie-summary embedding, we extracted nouns, verbs, and object to build a knowledge graph with head-relation-tail relationships. A deep neural network, as well as Graph attention networks, are utilized for measuring performance in terms of RMSE score. The proposed system is tested on an extended MovieLens-100K data-set having multi-modal information. Experimental results establish that only rating-based embeddings in the current setup outperform the state-of-the-art techniques but usage of multi-modal information in embedding generation performs better than its single-modal counterparts. 1. © 2022 IEEE.