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The vaccination of children is a crucial tool to protect both individuals and the world in general from various diseases and pathogens. Unfortunately, the vaccination procedure is not a pleasant one for all children, with many experiencing various levels of discomfort, sometimes reaching intolerable levels. In the first part of this work, we develop VACS, a tool that measures the discomfort children experience during vaccination. VACS takes into consideration the complete timeline of the vaccination experience from the perspective of the child, starting from the moment the child enters the doctor's office through to their departure, and also the complete range of manifestations of discomfort, ranging from moaning and crying to facial expressions and posture. Their discomfort is quantified as a number from 0 to 25, with zero corresponding to a smooth vaccination and 25 to maximal/unbearable discomfort. In the second part of the work, we apply VACS to 40 vaccinations of children aged 2 to 12. Our findings show that approximately 40% of the children do not face discomfort during vaccination, but for the rest discomfort of varying degrees is observed. We also find that doctors are content with their patients facing considerably higher discomfort levels than what the children themselves are willing to withstand: doctors are content with VACS values up to 19 whilst children start to suffer when the VACS value exceeds 11. Surprisingly, characteristics such as (a) gender, (b) whether the state's recommended vaccination program has been implemented in full, and even (c) prior negative vaccination experiences are found to be poor predictors of vaccination discomfort. Age on the other hand may be a factor, with younger children experiencing discomfort more often and more intensely; more research is required in order to validate this with higher confidence. The formulation of VACS opens the door for more systematic work towards the mitigation of vaccination discomfort for children. [ABSTRACT FROM AUTHOR]

In this work we present an updated version of "Safe University", a protocol aimed to ensure the safe operation of academic institutions during the SARS-CoV-2 pandemic. The protocol is detailed, addressing all aspects of the actions that are required, ranging from controlled access to the university premises and tracking of immunity status to air quality provisions and organization of classes and teaching methods and more. A step-by-step guide and a sample timeline are included, to facilitate practical implementation. The protocol was first developed in the summer of 2021, when the Delta variant had first emerged but not yet dominated, with the aim to support the operation of Greek universities at the beginning of the 2021–2022 academic year. Since then, it has been updated to reflect the evolution of the virus and the pandemic, as well as the developments in the relevant scientific knowledge and additional monitoring, safeguarding and treatment tools that humanity now possesses. It has also been given a more generic form, making it suitable and adjustable for other countries and cultural/political environments as well as other respiratory viruses. With some additional adjustments, it can also be suitable to deal with epidemics from non-respiratory viruses that may arise in the future. [ABSTRACT FROM AUTHOR]

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Abstract: The property of transitivity is one of the most important for fuzzy binary relations, especially in the cases when they are used for the representation of real-life similarity or ordering information. As far as the algorithmic part of the actual calculation of the transitive closure of such relations is concerned, works in the literature mainly focus on crisp symmetric relations, paying little attention to the case of general fuzzy binary relations. Most works that deal with the algorithmic part of the transitive closure of fuzzy relations focus only on the case of max–min transitivity, disregarding other types of transitivity. In this paper, after formalizing the notion of sparseness and providing a representation model for sparse relations that displays both computational and storage merits, we propose an algorithm for the incremental update of fuzzy sup-t transitive relations. The incremental transitive update (ITU) algorithm achieves the re-establishment of transitivity when an already transitive relation is only locally disturbed. Based on this algorithm, we propose an extension to handle the sup-t transitive closure of any fuzzy binary relation, through a novel incremental transitive closure (ITC) algorithm. The ITU and ITC algorithms can be applied on any fuzzy binary relation and t-norm; properties such as reflexivity, symmetricity and idempotency are not a requirement. Under the specified assumptions for the average sparse relation, both of the proposed algorithms have considerably smaller computational complexity than the conventional approach; this is established both theoretically and verified via appropriate computing experiments. [Copyright &y& Elsevier]

In the last few years, numerous multimedia archives have made extensive use of digitized storage and annotation technologies. Still, the development of single points of access, providing common and uniform access to their data, despite the efforts and accomplishments of standardization organizations, has remained an open issue as it involves the integration of various large-scale heterogeneous and heterolingual systems. This paper describes a mediator system that achieves architectural integration through an extended three-tier architecture and content integration through semantic modeling. The described system has successfully integrated five multimedia archives, quite different in nature and content from each other, while also providing easy and scalable inclusion of more archives in the future. [ABSTRACT FROM AUTHOR]

Abstract: In any neural network system, proper parameter initialization reduces training time and effort, and generally leads to compact modeling of the process under examination, i.e. less complex network structures and better generalization. However, in cases of multi-dimensional data, parameter initialization is both difficult and time consuming. In the proposed scheme a novel, multi-dimensional, unsupervised clustering method is used to properly initialize neural network architectures, focusing on resource allocating networks (RAN); both the hidden and output layer parameters are determined by the output of the clustering process, without the need for any user interference. The main contribution of this work is that the proposed approach leads to network structures that are compact, efficient and achieve best classification results, without the need for manual selection of suitable initial network parameters. The efficiency of the proposed method has been tested on several classes of publicly available data, such as iris, Wisconsin and ionosphere data. [Copyright &y& Elsevier]

Digital assistants—such as chatbots—facilitate the interaction between persons and machines and are increasingly used in web pages of enterprises and organizations. This paper presents a methodology for the creation of chatbots that offer access to museum information. The paper introduces an information model that is offered through the chatbot, which subsequently maps the museum's modeled information to structures of DialogFlow, Google's chatbot engine. Means for automating the chatbot generation process are also presented. The evaluation of the methodology is illustrated through the application of a real case, wherein we developed a chatbot for the Archaeological Museum of Tripolis, Greece. [ABSTRACT FROM AUTHOR]