ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
In this paper we propose an intelligent chatbot that helps tackle the critical COVID-19 situation in India, which is a detrimental issue affecting the population of the country. Patients wait in long queues outside hospitals to obtain critical resources such as beds but return back in vain due to unavailability. The Covisstance Chatbot that we propose helps users locate available beds and ventilators in all hospitals at the user's location, without the need for them to travel. The proposed virtual assistant is implemented integrating several serverless services from Microsoft Power Virtual Agent, Power Automate flows to define actions, Microsoft Lists as a test database and Microsoft's Language Understanding Artificial Intelligence service (LUIS) that performs language and semantic analysis of user's queries. Experimental results show that the proposed chatbot successfully responds to all users' queries related to hospital information about beds and oxygen cylinders availability. © 2021 Association for Computing Machinery.
ABSTRACT
Analyzing web service logs is an important task to ensure the uninterruptible functioning and security for computer systems. When implementing complicated software systems, it is necessary to pay special attention to collecting, storing, processing, and analyzing logs of various services to identify existing and potential security problems. This paper describes an approach to analyzing the dynamics of web services functioning over two years and identifying security risks, as well as impact of the COVID-19 pandemic on the use of Internet services. Recommendations are given to strengthen the protection of web services and reduce cybersecurity risks. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).