ABSTRACT
Web applications have experienced a widespread adaptation owing to the agile Service Oriented Architecture (SOA) reflecting the ever-changing software needs of users. Google Meet is one of the top video conferencing applications, especially in the post-COVID19 era. Security and privacy concerns are therefore critical. This paper presents an extensive digital forensic analysis of Google Meet running on multiple browsers and software platforms including Google Chrome, Mozilla Firefox, and Microsoft Edge browsers in Windows 10 and Linux. Artifacts, traces of potential evidence, are extracted from different locations on a client's desktop, including the memory and browser. These include meeting records, communication records, email addresses, profile pictures, history, downloads, bookmarks, cache, cookies, etc. We explore how different Random Access Memory (RAM) sizes of client devices impact the persistence and format of extracted memory artifacts. A memory artifact extraction tool is developed to automate the extraction of artifacts identified via unstructured string analysis. Google Meet forensic artifacts are critical in that they are potential digital evidence in relevant criminal investigations. Additionally, they highlight that user data can be extracted despite implementing multiple privacy and security mechanisms. © 2022 The Author(s)
ABSTRACT
The Internet of Things (IoT) has revamped service-oriented architectures by enabling edge-based devices to collect and share information that is vital for the service provisioning process. IoT devices have evolved from simple data acquirers and have become part of the service provisioning process. These devices are now able to sense, acquire, communicate, and process data in an intelligent manner. With the support of Artificial Intelligence (AI), IoT devices can now support users with minimal reliance on centralized entities, such as the Cloud. IoT devices are now able to share raw and processed information securely, without or with minimal reliance on centralized devices. This paper proposes a general framework for Health 4.0 to provide edge-based health services with the support of AI. IoT devices collect and share patient information in a secure manner to enable user-side disease diagnosis. The solution enables both federated and centralized learning to coexist under one framework. As a proof-of-concept, the solution considers a COVID-19 diagnosis use-case. A Machine Learning (ML) web-based user application is developed to analyze frontal chest X-ray (CXR) images and make predictions on whether patients' lungs are damaged. The solution provides an experimental study on mechanisms and approaches needed to increase learning accuracy with reduced dataset sizes and image quality through Federated Learning (FL). © 2022 IEEE.
ABSTRACT
Rural tourism has become a way for people to pursue a simple life and relax, thanks to the variety of tourism forms available. Rural tourism, on the other hand, lacks a unified information platform for providing services to users and is unable to provide humanized services based on tourism experience. Rural tourism around the city has become the main force of tourism recovery because outbound tourism has yet to begin and domestic interprovincial tourism has only recently recovered. The development of rural tourism can be aided by an information service system that can effectively improve the utilization efficiency of tourism resources and explore new and reasonable paths for the development of rural tourism. This paper examines the opportunities and challenges that rural tourism faces in the face of the COVID-19 epidemic, as well as actionable strategies for the rapid growth of rural tourism. This paper introduces the Service-Oriented Architecture (SOA) technology required in the construction of the tourism service information sharing platform as the research object. In addition, the rural tourism information service system was established. So that rural tourism can be at the forefront of tourism's rapid recovery and revitalization, it should be promoted to become more vibrant. © 2022 Yukun You.
ABSTRACT
In the situation of an epidemic outbreak, a contact tracing tool is preferred to alert the infection status of daily encountered people. Since we are in the era that smartphone is carried everywhere and embedded with Bluetooth technology, a Bluetooth-based mobile app is proposed in this paper for advanced contact tracing. The proposed app can not only trace the infectious people contacted with the user but also label the danger level by scanning the proximity and lingering time for each case. It is simple yet efficient to apply as it does not employ any new Bluetooth protocol but only requires basic inputs that are acquirable from any smartphone with Bluetooth 2.0 and above. This application is built using service-oriented architecture which helps mobile devices to communicate with a data collection server as well as each other. The collected data will be shown in a web application and used to further study the propagation characteristics of new infectious viruses. It also comprises a daily survey that users answer, and which will be used by health officials for early prognosis. The app is currently tested campus-wide and showed salient features in terms of scalability, mobility, and sensing inaccuracy-proof, which has the potential to be applied in larger populations with more complicated scenarios. © 2021 IEEE.