On Epidemic-aware Socio Spatial POI Recommendation

Epidemics such as COVID-19, SARS, H1N1 have highly transmissible viruses and spread wildly through the population with negative consequences. Multiple studies have shown the correlation between the contact networks between individuals and the transmission of infections due to contact between colocated individuals. To mitigate the transmission of the virus, intervention measures have been applied without decisive success. Therefore, reducing transmissions through suitable epidemicaware POI recommendations to users is necessary to cope with user mobility. Current POI recommendation approaches do not take into consideration the transmission of infections between co-located users. In this paper, we formulate a new query named Epidemic-aware POI Recommendation Query (EPQ), to timely recommend a set of POIs to users at different time steps, while considering the spread of infection between co-located users, their social friendships, and their preference. We prove that EPQ is NP-hard and propose an effective and efficient algorithm, Epidemic-aware POI Recommendation (EpRec) to tackle EPQ. We evaluate EpRec on existing location-based social networks and pandemic datasets against state-of-the-art algorithms. The experimental results show that EpRec outperforms the baselines in effectiveness and efficiency. © 2022 IEEE.

Epidemic Spread Optimization for Disease Containment with NPIs and Vaccination

The potential impact of epidemics, e.g., COVID-19, H1N1, and SARS, is severe on public health, the economy, education, and society. Before effective treatments are available and vaccines are fully deployed, combining Non-Pharmaceutical Interventions (NPIs) and vaccination strategies is the main approaches to contain the epidemic or live with the virus. Therefore, research for deciding the best containment operations to contain the epidemic based on various objectives and concerns is much needed. In this paper, we formulate the problem of Containment Operation Optimization Design (COOD) that optimizes the epidemic containment by carefully analyzing contacts between individuals. We prove the hardness of COOD and propose an approximation algorithm, named Multi-Type Action Scheduling (MTAS), with the ideas of Infected Ratio, Contact Risk, and Severity Score to select and schedule appropriate actions that implement NPIs and allocate vaccines for different groups of people. We evaluate MTAS on real epidemic data of a population with real contacts and compare it against existing approaches in epidemic and misinformation containment. Experimental results demonstrate that MTAS improves at least 200% over the baselines in the test case of sustaining public health and the economy. Moreover, the applicability of MTAS to various epidemics of different dynamics is demonstrated, i.e., MTAS can effectively slow down the peak and reduce the number of infected individuals at the peak. © 2022 IEEE.

Cell Based Raft Algorithm for Optimized Consensus Process on Blockchain in Smart Data Market

Due to the explosive increase in IoT devices and traffic, big data is developing into smart data that helps the data science experts understand human activities, through the relationship between mobility and resource application of the users in public spaces. For example, smart data markets help to predict crimes or understand the cause of COVID-19 infections. For these smart services, the users agree to the privacy policy so that the personal and sensitive information can be collected by a third party. But the conditions of the privacy policy do not specify whether the information of the users can be tracked. To ensure data transparency, many systems are applying consortium/private blockchains with raft algorithm. The raft algorithm requires nodes to check countless messages for a single transaction. Eventually, as the number of nodes increases, the overall system degradation is derived from the burden of the leader node. This paper proposes a method to process the collected transactions by dividing a certain amount of transactions into cells, without any extra protocol. The proposed scheme also uses the federated learning model with high accuracy and data privacy, in order to determine the optimized cell size in a blockchain system that should lead to consensus on multiple servers. Therefore, the proposed CBR (Cell-based Raft) consensus algorithm proposes a protocol that reduces the number of messages, without interfering with the concept of the existing raft algorithm, in order to maintain stable throughput in the smart data market where massive transactions occur.

Cloud Security Issues and Log-based Proactive Strategy

Recently, given the current situation the world is undergoing with the COVID-19 pandemic, a lot of companies encourage remote working from home. As a result, the use of the cloud has increased, but as a consequence, cyberattacks on the cloud systems have escalated. However, organizations and companies still have concerns about the use of the cloud as most of them remain unaware of the security threats against cloud system. This work researches through major concerns of the organizations and companies for cloud security and tries to explain why it is important for the organization to be aware of the threats that the cloud is facing. It also proposes a three-step strategy to help companies and organizations to secure their cloud system.

NETWORKING TECHNOLOGIES TO COMBAT THE COVID-19 PANDEMIC

In late 2019, a new virus was discovered, namely SARS-CoV-2. This strain causes severe acute respiratory syndrome coronavirus 2, defined as COVID-19. The disease soon spread all over the world, thus becoming a pandemic. It has been almost two years since worldwide restrictions on our lives started, and the traditional way people live and work has completely changed.