ABSTRACT
Governments hiding facts and truth from the public seems to have become a common phenomenon, especially during the social crisis in China. The practice of the public using various media to express dissent and opinions, to overcome government censorship, appears to contribute to freedom of speech. Inspired by widespread online articles during the COVID-19 pandemic in 2020, this paper argues that the flaws in this logic are the dualism, which digital media created (pro-democracy vs authoritarian;freedom vs control), in understanding media in China. By borrowing the discussion of the de-westernization of media and communication studies, the paper argues that the introduc-tion of digital media makes de-westernized studies in China harder because it prompts us to think "digitally."
ABSTRACT
Intrusion detection/prevention systems have attracted much interest in recent years due to increased online connectivity. In recent years due to COVID pandemic and due to the increased number of online users, online data has become more and more exposed to different types of attacks. Hence, in order to keep data safe, it has become quite important to detect/prevent such attacks. An IDS is a sensor that is used for the observation of such attacks on the nodes or the network itself, and in this way, it tries to keep the information safe from possible attacks. However, accurately identifying such attacks so that they can be prevented effectively is a concern. This accuracy is measured by the number of false positive & false negative in a dataset. These days ML/DL algorithms are being significantly utilized for improving the accuracy of different systems (e.g., health care, stock market, forecasting etc.). Considering its importance, the work presented here studies the impact of using ML/DL algorithms on the accuracy of IDS/IPS. The impact of these algorithms is studied by using evaluation metrics for classification of network assaults in the intrusion detection system using different datasets. These algorithms are subject to further changes for improving the accuracy parameters based on evaluation metrics. © 2022 IEEE.
ABSTRACT
With the recent outbreak of the COVID-19 pandemic, major industries and academic institutions throughout the world have moved into a work-from-home situation, which resulted in a huge demand for networking resources. In particular, several home users are demanding simultaneously high data rates to support online video streaming applications, like Webex and Zoom. Therefore, it is expected that in these situations the quality of experience of online users may be severely affected or deteriorated, especially as the number of simultaneous active online users increases. In this paper, we use the Neyman-Scott Cluster Process from Stochastic Geometry to model the spatial patterns of online home users and analyze the throughput that could be achieved by a various numbers of online home users who are accessing the internet concurrently. © 2021 IEEE.
ABSTRACT
Correctness of software is an important concern in many safety-critical areas like aviation and the automotive industry. In order to have skilled developers, teaching formal methods is crucial. In our software quality course, we teach students two techniques for correct software development, post-hoc verification and correctness-by-construction. Due to Covid, the last course was held online. We present our lessons learned of adapting the course to an online format on the basis of two user studies;one user study held in person in 2019 and one online user study held after the online course. For good online teaching, we suggest the use of accessible (web-)tools for active participation of the students to compensate the advantages of teaching in person. © 2021, Springer Nature Switzerland AG.
ABSTRACT
Background: The sharing and utilization of online users' information has become an important resource for governments to manage COVID-19; however, it also involves the risk of leakage of users' personal information. Online users' sharing decisions regarding personal information and the government's COVID-19 prevention and control decisions influence each other and jointly determine the efficiency of COVID-19 control and prevention. Method: Using the evolutionary game models, this paper examines the behavioral patterns of online users and governments with regard to the sharing and disclosure of COVID-19 information for its prevention and control. Results: This paper deduce the reasons and solutions underlying the contradiction between the privacy risks faced by online users in sharing information and COVID-19 prevention and control efforts. The inconsistency between individual and collective rationality is the root cause of the inefficiency of COVID-19 prevention and control. Conclusions: The reconciliation of privacy protection with COVID-19 prevention and control efficiency can be achieved by providing guidance and incentives to modulate internet users' behavioral expectations.