ELASTIC: Edge Workload Forecasting based on Collaborative Cloud-Edge Deep Learning

With the rapid development of edge computing in the post-COVID19 pandemic period, precise workload forecasting is considered the basis for making full use of the edge limited resources, and both edge service providers (ESPs) and edge service consumers (ESCs) can benefit significantly from it. Existing paradigms of workload forecasting (i.e., edge-only or cloud-only) are improper, due to failing to consider the inter-site correlations and might suffer from significant data transmission delays. With the increasing adoption of edge platforms by web services, it is critical to balance both accuracy and efficiency in workload forecasting. In this paper, we propose ELASTIC, which is the first study that leverages a cloud-edge collaborative paradigm for edge workload forecasting with multi-view graphs. Specifically, at the global stage, we design a learnable aggregation layer on each edge site to reduce the time consumption while capturing the inter-site correlation. Additionally, at the local stage, we design a disaggregation layer combining both the intra-site correlation and inter-site correlation to improve the prediction accuracy. Extensive experiments on realistic edge workload datasets collected from China's largest edge service provider show that ELASTIC outperforms state-of-the-art methods, decreases time consumption, and reduces communication cost. © 2023 ACM.

SignalR based Real-Time applications in education

Before Covid, we introduced our own classroom response system to improve the effectiveness of our teaching. To this end, we adopted an open-source technique, SignalR, which provides a framework for building real-time web applications. Overnight, due to the emergency situation starting in 2019, education was moved to the virtual space. Both students and professors had to learn how to teach or learn using only online facilities, without a testing period. During the emergency, a synchronous online teaching mode was required by our university, so the choice was made to use Microsoft Teams, implemented with SignalR for real-time functionality. After the emergency, we were all happy to have our 'old life' back and return to our personal teaching style, but is it possible, is it possible to continue teaching in the same way as before Covid-19 - is it possible to step into the same river twice? Students have become accustomed to convenient, modern, digital options during the online education period and now that we are back in school, they insist that we continue to use the new tools. In this essay, we want to describe the changes in students' attitudes that we can usefully build on in the future and that will influence the further development of our project. © 2023 IEEE.

Audio Streams Synchronization for Music Performances

For many years, the mechanisms of transmitting audio streams have been gaining popularity. The SARS-COV-2 pandemic completely remodeled people's habits by completely preventing participation in concerts. The technical possibilities of the musicians' remote cooperation have not been fully used yet.The popularity of remote communication is unquestionable. However, so far this type of communication has been based on a one-to-many model. In the case of music events, or music production in general, a many-to-one or generally many-to-many model must be implemented. For this to be possible, it is necessary to solve the problem of synchornization of streams originating sequentially from many creators. In addition to the aspect of audio stream synchronization discussed in this article, one of the assumptions was also the ease of adapting the proposed solution as part of a web application. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Real Time Web-based Facemask Detection

The widespread spread of the Covid-19 virus in 2020-2021 is very worrying for all people around the world, coupled with the spread of a new variant of the Covid-19 virus, which is more aggressive and easily transmitted, causing public unrest about when this pandemic will end. The policy of using masks to reduce the spread of the virus has been made to minimize the spread. But even if there is a policy, there are still people who don't want to wear masks. Therefore, a mask detection system is needed to help differentiate whether someone uses a mask or not by displaying alerts in a form of web application. This research was conducted using several data augmentation techniques to increase the variation of the data to be used before training the algorithm model using the Convolutional Neural Network (CNN) algorithm with MobileNetV2 and VGG19 architectures. Both models are then evaluated where the architecture with the best performance will be implemented in the form of a web application. The accuracy of both models was compared, with the result of MobileNetV2 being 99% accurate and VGG19 being 98%. MobileNetV2 as the model that has the best accuracy value will be implemented in the form of a web application using the Haar Feature-Based Cascade to detect masks. The web application will be publicly accessed local at Universitas Multimedia Nusantara. © 2022 IEEE.

COST Action "European network for Web centred linguistic data science” (NexusLinguarum)

We present the current state of the large "European network for Web-centred linguistic data science”. In its first phase, the network has put in place several working groups to deal with specific topics. The network also already implemented a first round of Short Term Scientific Missions (STSM), which was unfortunately hindered by the Covid-19 outbreak.

Comunicación científica tras la crisis del COVID-19: estrategias de publicación en TikTok en el tablero transmedia

Introducción: La presente investigación analiza la visión de destacados periodistas y comunicadores sobre la adaptación del periodismo científico al entorno digital: por un lado, se abordan las posibilidades del ecosistema web para el uso de las narrativas transmedia y, por otro, se explora el potencial de TikTok como plataforma divulgativa. Metodología: El trabajo incluye un corpus de entrevistas semiestructuradas a profesionales vinculados a instituciones y medios junto a investigadores del área de Comunicación. Resultados: El estudio evidencia la oportunidad que supone para el periodismo científico el entorno transmedia como aliado para acercar el conocimiento a nuevos públicos. A recomendaciones sobre contenidos, estilo y relación con los usuarios de los mensajes que se divulgan en TikTok. Discusión: La investigación también conlleva un sentido crítico ante la necesidad de que profesionales y medios se adapten a los nuevos entornos como un factor vital de sostenibilidad. Entendiendo que no se trata de tendencias pasajeras sino del reto de recuperar la confianza de las audiencias y garantizar su viabilidad. Conclusiones: Además de informar con rigor sobre temas de interés en un momento crítico de infodemia, se debe aspirar a crear comunidades en torno a la ciencia y generar contenidos atractivos para públicos no habituados a estas informaciones. Evitando un uso replicante de las redes sociales, aprovechando todas sus posibilidades narrativas y, en última instancia, contribuyendo a reforzar el valor del ejercicio profesional en un contexto de desprestigio mediático.Alternate :Introduction: This research aims to show the vision of prominent j ournalists in regards to the adaptation of science journalism to digital contexts. In addition, it addresses the possibilities offered by this web ecosystem to use transmedia narratives and TikTok as platforms for dissemination. Methodology: The methodology applied included semi-structured interviews with professionals linked to institutions, media, and university researchers in the area of Communication. Results: The study demonstrates the opportunity that the transmedia environment represents for science journalism as an ally to bring knowledge closer to new audiences. On a practical level, they point out its graphic potential, access and immediacy, in addition to proposing recommendations on content, style and relationship with the users of the messages that are disseminated in TikTok. Discussion: The research also includes a critical sense of the need for professionals and the media to adapt to new environments as a vital factor for sustainability. Understanding that it is not a question of passing trends but of the challenge of recovering the confidence of audiences and guaranteeing their viability. Conclusions: In addition to reporting rigorously on topics of interest at a critical moment of misinformation, it must aspire to create communities around science and generate attractive content for audiences not accustomed to this information. Avoiding a replicating use of social networks, taking advantage of all their narrative possibilities and, ultimately, contributing to reinforce the value of the professional practice in a context of media discrediting.

Editorial

Ganesan and Gunasekaran presented "Assessment of information literacy skills and knowledge-based competencies in using electronic resources among medical students,” in which they administered a questionnaire to 120 medical students enrolled at the Mahatma Gandhi Medical College and Research Institute (India) to inquire about their acquisition of information literacy skills, centering on use, purpose for using information, search strategies and the information sources that they used. In another COVID-19 related article, Singh's "Role of National Digital Library of India (NDLI) for facilitating open access resources (OARs): an investigation on COVID-19 research repository,” provides insights about the availability of COVID-19 related information resources and argues about the importance of such digital resources, and specifically of the NDLI repository, to support research and education. Porsche, Suchá and Martinek contributed ‘The potential of Google Analytics for tracking the reading behavior in web books', in which they conducted a pilot quantitative study on 190 web book users regarding their reading behavior, by employing Google Analytics.

An Empirical Study on How Well Do COVID-19 Information Dashboards Service Users' Information Needs

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since its first being reported in December 2019, COVID-19 has spread quickly around the world, and becomes a global pandemic. Previous information sources have a number of problems when providing COVID-19 information web services. First, the information from government or traditional media (i.e., TV and newspaper) is not frequently updated. Second, different layers of the government (state and federal government) may provide contradictory information. Also, there are many rumours spread on social media, which makes it difficult for people to know who and what to trust. Finally, the current information about COVID-19 is fragmented. It takes effort for people to aggregate the information they need to see from different places. © 2022 IEEE.

Oral Presentation: Performance of an Artificial Intelligence System Tool for the Detection of ADRs from Patients' Reports During the Covid-19 Vaccination Campaign: An International Journal of Medical Toxicology and Drug Experience

Introduction: During the recent covid-19 vaccination campaign, the number of ICSRs reported by patients and professionals has dramatically increased, reaching up to almost 1 M declarations only in Europe (EMA numbers). To deal with such growing amount of data, Synapse Medicine®, in collaboration with The French National Agency for Medicines and Health Products Safety (ANSM), have developed an artificial intelligence (AI) tool, the Medication Shield, which, based on a natural language processing algorithm, is able to detect ADRs from patients' reports and to code them into an appropriate MedDRA preferred term (PT). Before the covid-19 pandemic, this system was successful in detecting ADRs from the patient reports declared through the French web national reporting system (1, 2). However, how it behaves in conditions of higher reporting flow rate is unknown at present. Objective: To evaluate the performance of the Medication Shield in detecting vaccine-related ADRs from patients' ICSRs declared across the covid-19 vaccination campaign. Methods: A machine learning (ML) pipeline composed by a light Gradient Boosting Machine ensemble model was employed to detect and code covid-19 vaccine-related ADRs from patients' ICSRs declared through the web reporting system during the vaccination campaign (Jan 2021-Apr 2022). The encoding of regional pharmacovigilance centers was employed as the reference ground truth to train the algorithm in a supervised manner. Moreover, a panel of three pharmacologists, with significant experience in ADRs encoding, was set-up to perform a case-by-case analysis of 200 hundreds reports for which the algorithm provided improper encoding. Results: Overall, 65.191 ICSRs were extracted and used to train our ML algorithm. Of this, 54.987 were employed to validate the system. Importantly, almost 86% of the ICSRs were related to covid vaccines. Because the percentage of newly reported ADRs increased over time and was higher for vaccine than not-vaccine related reports, we split the training and validation sets in batches with similar ADRs distribution. Performance evaluation is currently under process. Initial feedbacks from the analysis performed by the experts are showing an uneven distribution of false positive and false negative across samples. Results from the other experts are needed to confirm this finding. Conclusion: The core findings of this study will be gathered in the forthcoming weeks and be ready for the ISoP meeting in September. This work will provide new insights about the effectiveness of deploying AI as a support to treat real world data in a context of sanitary crisis.

Dispersal of bacteria and stimulation of permafrost decomposition by Collembola

Contrary to most soils, permafrost soils have the atypical feature of being almost entirely deprived of soil fauna. Abiotic constraints on the fate of permafrost carbon after thawing are increasingly understood, but biotic constraints remain scarcely investigated. Incubation studies, essential to estimate effects of permafrost thaw on carbon cycling, typically measure the consequences of permafrost thaw in isolation from the topsoil and thus do not account for the effects of altered biotic interactions because of e.g. colonization by soil fauna. Microarthropods facilitate the dispersal of microorganisms in soil, both on their cuticle (ectozoochory) and through their digestive tract (endozoochory), which may be particularly important in permafrost soils, considering that microbial community composition can strongly constrain permafrost biogeochemical processes.Here we tested how a model species of microarthropod (the CollembolaFolsomia candida) affected aerobic CO2 production of permafrost soil over a 25 d incubation. By using Collembola stock cultures grown on permafrost soil or on an arctic topsoil, we aimed to assess the potential for endo- and ectozoochory of soil bacteria, while cultures grown on gypsum and sprayed with soil suspensions would allow the observation of only ectozoochory.The presence of Collembola introduced bacterial amplicon sequence variants (ASVs) absent in the no-Collembola control, regardless of their microbiome manipulation, when considering presence–absence metrics (unweighted UniFrac metrics), which resulted in increased species richness. However, these introduced ASVs did not induce changes in bacterial community composition as a whole (accounting for relative abundances, weighted UniFrac), which might only become detectable in the longer term.CO2 production was increased by 25.85 % in the presence of Collembola, about half of which could be attributed to Collembola respiration based on respiration rates measured in the absence of soil. We argue that the rest of the CO2 being respired can be considered a priming effect of the presence of Collembola, i.e. a stimulation of permafrost CO2 production in the presence of active microarthropod decomposers. Overall, our findings underline the importance of biotic interactions in permafrost biogeochemical processes and the need to explore the additive or interactive effects of other soil food web groups of which permafrost soils are deprived.

Patients rehabilitation with musculoskeletal sequel from covid-19 using vision artificial in the city of Huancayo

Technology and its applications are here to improve our lives, it is used ever more these days with the pandemic Covid-19. This article is aimed to reduce the attendance to Hospitals and clinics where you would be treated with musculoskeletal muscular treatments in the city of Huancayo. With the help of modern technology it is offered an alternative software with artificial vision in order to monitor most patients in real time. The development of this investigation is set in 5 stages, the first stage talks about a posture recognition with artificial vision with framework mediapipe. The second stage explains the design interface and the mathematics formula which controls a patient development, the third stage describes the integration from the first and the second stage with a treat method. The fourth stage describes de development of a webpage using services to develop and monitor in real time. The last stage describes the process of the software validation having the last usuary with a chart of questions. Finally, the results of validations show the patient acceptation, as so 63.6% of patients who had no difficulties doing the software exercises. As Such a monitoring from the initial stage from the patien is hey factor before starting the therapy. © 2022 IEEE.

Ivory: Learning Network Adaptive Streaming Codes

With the growing interest in web services during the current COVID-19 outbreak, the demand for high-quality low-latency interactive applications has never been more apparent. Yet, packet losses are inevitable over the Internet, since it is based on UDP. In this paper, we propose Ivory, a new real-world system framework designed to support network adaptive error control in real-time communications, such as VoIP, using a recently proposed low-latency streaming code. We design and implement our prototype over UDP that can correct or retransmit lost packets conditional on network conditions and application requirements.To maintain the highest quality, Ivory attempts to correct as many lost packets as possible on-the-fly, yet incurring the smallest footprint in terms of coding overhead over the network. To achieve such an objective, Ivory uses a deep reinforcement learning agent to estimate the best coding parameters in real-time based on observed network states and experience learned. It learns offline the best coding parameters to use based on previously observed loss patterns and takes into account the round-trip time observed to decide on the optimum decoding delay for a low-latency application. Our extensive array of experiments shows that Ivory achieves a better trade-off between recovering packets and using lower redundancy than the state-of-the-art network adaptive streaming codes algorithms. © 2022 IEEE.

A Free Web Service for Fast COVID-19 Classification of Chest X-Ray Images with Artificial Intelligence

The coronavirus outbreak became a major concern for society worldwide. Technological innovation and ingenuity are essential to fight COVID-19 pandemic and bring us one step closer to overcome it. Researchers over the world are working actively to find available alternatives in different fields, such as the Healthcare System, pharmaceutic, health prevention, among others. With the rise of artificial intelligence (AI) in the last 10 years, IA-based applications have become the prevalent solution in different areas because of its higher capability, being now adopted to help combat against COVID-19. This work provides a fast detection system of COVID-19 characteristics in X-Ray images based on deep learning (DL) techniques. This system is available as a free web deployed service for fast patient classification, alleviating the high demand for standards method for COVID-19 diagnosis. It is constituted of two deep learning models, one to differentiate between X-Ray and non-X-Ray images based on Mobile-Net architecture, and another one to identify chest X-Ray images with characteristics of COVID-19 based on the DenseNet architecture. For real-time inference, it is provided a pair of dedicated GPUs, which reduce the computational time. The whole system can filter out non-chest X-Ray images, and detect whether the X-Ray presents characteristics of COVID-19, highlighting the most sensitive regions. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A User Self-Design Collaborative Web System for Self-directed Health Learning: A Covid-19 Awareness Project

The system aims to direct the user to create their network system collaboratively for a case of Covid-19 health throughout a case study in a Web System and E-Commerce course. The framework of directed health learning is based on 7x2C content knowledge established criteria to assess the effectiveness and efficiency of the system. The self-design frame framework is plan-oriented and based on the concept of a plan and plans integrations and special relationships. The user is directed to break the system into plans and the design is to self-guide the user to build a system to comprehend, combat, coexist, cope, and trace COVID-19 with four layers of diagnostics, simulation, and pattern matching database. With the collaboration of users' systems, a fact from one user as output can be transferred to another user as an input in a circulation forming a general fact. Consequently, the transfer of learning from one system flows into another system resulting in a pattern to be found. Based on the pattern an algorithm will formulate to tackle a solution to COVID19. The implication of this study will be a guideline for others to initiate their own participant's system to find a pattern and formulate an algorithm for the pandemic. The idea of self-design and self-directed learning can be transferred to other fields of study covid-19 health. At present time, a parallel case study of goods and services on farming of Sunchoke plant has been directed with three plan themes of Grow, Eat, and Heal.) © 2021 IEEE.

Construction of Disease-Symptom Knowledge Graph from Web-Board Documents

Featured ApplicationAuthors are encouraged to provide a concise description of the specific application or a potential application of the work. This section is not mandatory.The research aim is to construct a disease-symptom knowledge graph (DSKG) as a cause-effect knowledge graph containing disease-symptom relations as a cause-effect relation type determined from downloaded documents on medical web-board resources. Each disease-symptom relation connects a disease-name concept node (a causative-concept node) to a corresponding node having a group of correlated symptom-concept/effect-concept features as common symptom-concept/effect-concept features among some disease-name concepts. The DSKG benefits non-professionals in preliminary diagnosis through a recommender web-board. There are three main problems: how to determine symptom concepts from sentences without annotation on the documents having disease-name concepts as the documents’ topic-names;how to determine the disease-symptom relations from the documents with/without complications;and how to construct the DSKG involving high dimensional symptom-concept features after union of the correlated symptom-concept groups. Therefore, we apply a word co-occurrence pattern including medical-symptom expressions from Wikipedia including MeSH and the Lexitron Dictionary to determine the symptom concepts. The Cartesian product is applied for automatic-supervised machine learning to determine the disease-symptom relation. We propose using Principal Component Analysis for constructing the DSKG by dimensionality reduction in the symptom-concept features with minimized information loss. In contrast to previous works, the proposed approach enables the DSKG construction with precise and concise representation scores of 7.8 and 9, respectively.

A Benchmark of Non-intrusive Parametric Audio Quality Estimation Models for Broadcasting Systems and Web-casting Applications

Due to the rising usage of various broadcasting systems and web-casting applications, a measurement of audio quality has become an essential task. This paper presents a benchmark of the parametric models for non-intrusive estimation of the audio quality perceived by the end user. The proposed solution is based on machine learning techniques for broadcasting systems and web-casting applications. The main goal of this study is to assess the performance of the non-intrusive parametric models as well as to evaluate a statistical significance of the performance differences between those models. The paper provides a comparison of several models based on the Support Vector Regression, Genetic Programming, Multigene Symbolic Regression, Neural Networks and Random Forest. The obtained results indicate that among the investigated models the most accurate, although not the fastest ones, are the model based on Random Forest (a broadcast scenario) and the SVR-based model (a web-cast scenario). These models represent promising candidates for non-intrusive parametric audio quality assessment in the context of broadcasting systems and web-casting applications.

COVID-19: Territorialization, Agriculture and Food, 17 June 2020

The challenge for world agriculture is to continue to increase the production of agricultural commodities while reducing the negative impacts it causes on the environment and biodiversity. However, these impacts are not fundamentally due to excessive productivity of the production systems, but much more to the excessive standardization of the production systems, at all scales, the agricultural plot, the holding, the territories and the regions, which no longer allows: (i) to ensure the coupling of biogeochemical cycles (C, N, P, and water) in space and time between the different agricultural production entities;and (ii) to ensure the connectivity of food webs and habitats necessary for biodiversity. Thus, the spatial separation between plant production and animal production leads to biogeochemical dysfunctions whose global consequences become incompatible with the quality of the environment. Only a reasoned and controlled diversification of agricultural production at the territorial level would make it possible to maintain or even increase the level of agricultural productivity while improving the quality of the environment. This is not a return to the past by forcing each farm to systematically diversify its productions, but to see how to reconnect specialized productions with each other in a territorial framework in order to achieve more optimal management (i) the necessary coupling between the bio-geochemical cycles in order to avoid emissions that deteriorate the quality of the environment, and (ii) the dynamics of biodiversity.

Top COVID-19 100 vaccine papers: An Altmetric study

The high number of COVID-19 studies has attracted scholars to produce many studies on the topic. However, while we know the impact of these studies on academia by analyzing their citation score in different indexing outlets, little is known about their impact on social networks. The current study aims to measure the impact of the top 100 vaccination papers on social networks. An Altmetrics analysis is conducted to measure the Altmetrics attention scores of the paper. We retrieved the data through the Web of Science and Scopus. The researchers selected Altmetric.com as a tool to obtain social media and mainstream internet outlet counts. The findings of the study revealed that there is a significant correlation between the citations and Altmetric indicators. Our findings indicate that Twitter and Mendeley represent the most contributes social networks in the final AAS in almost all journals included in the study. The study’s findings have confirmed that COVID-19 vaccination papers have gained many citations and attention on social networks. The study’s main limitation is that it only measured the Altmetrics score for the top 100 papers. Hence, while the current paper gave us insight into the performance of vaccination papers on the social web, there is a need to conduct further studies covering a larger sample. © 2021 IEEE.

COVID19-OBKG: An Ontology-Based Knowledge Graph and Web Service for COVID-19

Given the huge amount of data from diverse sources and involving various conceptual fields in heterogeneous formats, researchers have encountered challenges in their effort to process, search for, and access knowledge about coronavirus disease 2019 (COVID-19). In this paper, we built COVID19-OBKG, an ontology-based knowledge graph and web service for COVID-19, to enable the access and retrieval of knowledge. First, we built the schema of COVID19-OBKG based on biomedical ontologies to guide the construction of the instance layer of COVID19-OBKG from top to bottom. Secondly, we collected data sources related to COVID-19, including structured databases and web pages. We acquired entities and relationships from data sources through named entity recognition and relation extraction algorithms and merged them with knowledge in biomedical ontologies. Thirdly, we modeled our data in the form of an attribute graph and stored it in Dgraph. Finally, we built a web service to support the retrieval and visualization of COVID19-OBKG, which verified the effectiveness of our approach to constructing a knowledge graph, and the usability of COVID19-OBKG. © 2021 IEEE.

Implementation of RESTful API Web Services Architecture in Takeaway Application Development

The Covid-19 that hit the world had an impact on the economy, especially in the trade sector, one of which was experienced by Small and Medium Enterprises (SMEs). Hanura Takeaway (Haway) is an SME engaged in the delivery of goods and food. To facilitate transactions for goods and food delivery services, it is necessary to develop applications that simplify the transaction process. In developing web services, it is necessary to exchange data that is accessed via standard internet protocols. Therefore, we need a web service in developing this application. Implementing a RESTful API web service will certainly facilitate the development of software applications outside the system or with different programming languages or platforms. This research will develop web service architecture using RESTful API in Takeaway application. To optimize the URI, several parameters are used, including filtering, sorting, selection and pagination. The Takeaway application consists of a website as a backend and an Android-based as a frontend. From the test results based on the function method using the Postman application, it shows that the REST API Sever built on the server has been running well. In testing the response time using the Apache JMeter application, the application shows a good response time. Meanwhile, the comparison of responses and requests to SOAP and REST architectures shows that REST takes faster time. © 2021 IEEE.