Mobile Intellectual Property Marketplace Model for Commercialization of Intellectual Property Rights

This study discusses the development of the intellectual property (IP) marketplace model based on mobile location-aware computing. Referring to statistics released by the Directorate General of Intellectual Property, there has been a growth in the number of intellectual property rights (IPR) applications in recent years, even during the Covid-19 pandemic. On the other hand, after IPR protection, the commercialization of IPR is one of the pillars of the IP system. Nevertheless, research institutions such as LIPI/BRIN indicate that the potential for commercializing IPR is still low. Furthermore, the opportunity is that cellular networks have covered almost all parts of Indonesia, and there has been significant growth in smartphone users. The method utilized in this research is prototyping. This research results from an IP marketplace model based on mobile location-aware computing in Indonesia. Using the smartphone user's location, contextual IPR information from the user's location related to IPR will enter their smartphone. The experimental results indicate that the application can display a list of IPR information according to the smartphone user's location. Furthermore, the search feature can forage IPR listing information based on user queries. © 2022 IEEE.

Covid Contact Tracing Integrated with Blockchain Technology

The product to be developed in this project is concerned to be a Mobile Application for route tracing based on user's location history. Each user is tracked using the application using GPS location. If a person tests positive for covid-19 then all person in contact with that person will be notified. User can register to this app using his/her Aadhar Card number. A unique id is generated for each user's while registering in this app. So, that with this unique id only, each person is identified and their information is updated. Here blockchain is used for storing user's data. When a person registers to the system their information will be stored in blockchain. Each person will get unique QR code so that when this person enters a store or organization they can scan QR code and get information about the user. The information about the containment Zone will be updated and if the person is coming from containment zone, organization can know about that. A machine learning tool is implemented to detect covid-19 from user's chest CT-scan image. User can upload image and check whether they have covid-19. Resnext network will be used for machine learning. Resnext is the neural network architecture for image classification. © 2022 IEEE.

The Influence of Social Factors on Mental Health and Wellbeing during the COVID-19 Pandemic

This study uses Natural Language Processing and Machine Learning techniques to understand the effect of the COVID-19 pandemic on mental wellbeing. We considered different user groups and locations in the USA to analyze the influence contrasting social factors, such as political stance, had on wellbeing. We measured the mental wellbeing of the social media users through understanding negative sentiment and mental health topic discussion in Twitter posts added by users from the top 10 Democrat and top 10 Republican cities in the USA. To measure the topic discussion, we used a mental health keyword list and developed machine learning models to classify the topic of a tweet. The primary findings include the similarity of the effect the pandemic had on Republican and Democrat cities when considering a timeline of tweets, whilst an increase in 'Anxiety' was discussed across different user groups and cities. Enforcement strategies had an influence on mental wellbeing with results differing for Republican and Democrat cities. An accurate text classifier was developed and used to categorize tweets into different mental health topics. The results showed how medical and unemployed users discussed topics like 'anxiety' and 'depression' more than a control set of users. The best machine learning model was developed using a Decision Tree algorithm which achieved an accuracy of 87% on unseen data. © 2021 IEEE.

Approximate Nearest Neighbour Search on Privacy-aware Encoding of User Locations to Identify Susceptible Infections in Simulated Epidemics

Amidst an increasing number of infected cases during the Covid-19 pandemic, it is essential to trace, as early as possible, the susceptible people who might have been infected by the disease due to their close proximity with people who were tested positive for the virus. This early contact tracing is likely to limit the rate of spread of the infection within a locality. In this paper, we investigate how effectively and efficiently can such a list of susceptible people be found given a list of infected persons and their locations. By using the locations of the given list of infected persons as queries, we investigate the feasibility of applying approximate nearest neighbour (ANN) based indexing and retrieval approaches to obtain a list of top-k suspected users in real-time. Since leveraging information from true user location data can lead to privacy concerns, we also investigate the effectiveness of the ANN methods on privacy-aware encoding of the input data. Experiments conducted on real and synthetic datasets demonstrate that the top-k susceptible users retrieved with existing ANN approaches (KD-tree and HNSW) yield satisfactory recall values and achieves up to 21000 × speed-gain compared to exhaustive search, thus indicating that ANN approaches can potentially be applied, in practice, to facilitate real-time contact tracing even under the presence of imposed privacy constraints. © 2021 ACM.