Shri Rai

Vaccine hesitancy is still a significant barrier to achieving widespread immunity in many communities. In this paper, we evaluated a serious game fo-cusing on vaccination against COVID-19. This study investigates the potential of virtual reality (VR) as an innovative educational tool to address this issue. Focusing on the serious game " Spike Force " , which simulates the mechanisms of the mRNA COVID-19 vaccine, this research evaluates the game's effectiveness in enhancing participants' understanding, altering attitudes, and influencing behaviours related to vaccination. Participants engaged with " Spike Force, " and their knowledge, attitudes, and behaviours were assessed through pre-and post-gameplay questionnaires. The findings show that immersive VR experiences can significantly improve vaccine literacy, increase confidence in vaccine-related discussions, and promote positive behavioural changes toward vaccination. These results suggest that VR could play an effective advocacy role for public health education, particularly in combating vaccine hesitancy.

Vaccine hesitancy and uptake have been important issues in controlling the current COVID-19 pandemic in many regions around the globe, but the increase in vaccination rates has been slow or even halted in some countries. Therefore, people who have hesitated in getting the vaccine need to be addressed. One driver influencing vaccination uptake is closing the knowledge gap among the public by equipping them with a deeper understanding of how a vaccine works inside our cells to activate the immune system and develop immunity. Viral immunology is highly conceptual and requires an appreciation of molecular biology in the cell. To give individuals an intuitive awareness of the operation of a mRNA-type virus vaccine for COVID-19, we designed and developed a Virtual Reality (VR) based serious game called ‘Cell Traveler’. Through this innovative VR serious game, the player can control and interact with a sequence of critical real-life events inside a cell triggered by the injected mRNA COVID-19 vaccine. In this paper, we describe the prototype of the ‘Cell Traveler’. We utilize the concepts of serious game to create an experience to encourage students and the public to develop deeper mRNA vaccine knowledge through a memorable and fun experience.

Engagement with one's rehabilitation is crucial for stroke survivors. Serious games utilising desktop Virtual Reality could be used in rehabilitation to increase stroke survivors' engagement. This paper discusses the results of a user experience case study that was conducted with six stroke survivors to determine which game design principles are or would be important for engaging them with a desktop VR serious games designed for the upper limb rehabilitation. The results of our study showed the game design principles that warrant further investigation are awareness, feedback, interactivity, flow and challenge; and also important to a great extent are attention, involvement, motivation, effort, clear instructions, usability, interest, psychological absorption, purpose and a first-person view.

Collaborative filtering (CF) technique in recommender systems (RS) is a well-known and popular technique that exploits relationships between users or items to make product recommendations to an active user. The effectiveness of existing memory based algorithms depend on the similarity measure that is used to identify nearest neighbours. However, similarity measures utilize only the ratings of co-rated items while computing the similarity between a pair of users or items. In most of the e-commerce applications, the rating matrix is too sparse since even active users of an online system tend to rate only a few items of the entire set of items. Therefore, co-rated items among users are even sparser. Moreover, the ratings a user gives an individual item tells us nothing about his comprehensive interest without which the generated recommendations may not be satisfactory to a user. In order to be able to address these issues, a comprehensive study is made of the various existing measures of similarity in a collaborative filtering recommender system (CFRS) and a hierarchical categorization of products has been proposed to understand the interest of a user in a wider scope so as to provide better recommendations as well as to alleviate data sparsity.

Stroke is a very debilitating disease that a stroke survivor can suffer. It can result in both physical and cognitive deficits in the survivor. Without regular rehabilitation, a survivor will reach a chronic condition resulting in a huge burden. Rehabilitation is expansive, and a major issue is access as care givers are needed to take survivors to rehabilitation centres. This paper presents Neuromender::FlexiBrains, a novel game-based rehabilitation system with autonomous adjustment capabilities that can be used in a stroke survivor's home. The system was developed in collaboration with clinicians, neuroscientists and stroke survivors. Stroke clinicians can customise their rehabilitation to cater for each stroke survivor's deficits. The system enables clinicians to remotely monitor and track their clients' progress. Rehabilitation regimes can also be adjusted remotely. The system enables prescribed monitored rehabilitation to be carried out, and it permits clinicians to take care of more survivors without sacrificing the quality of care.

Game-based technologies have been widely used as part of stroke rehabilitation. The Neuromender system utilises game-based technologies and consists of serious games that are designed and developed for the purpose of rehabilitation of stroke survivors. In this paper, one of the modules in the Neuromender system which is the “upper limb” module is described and tested for its usability. The upper limb module primarily focuses on the rehabilitation of the upper body extremities of stroke survivors. An experimental study is designed to test the usability of the upper limb module. Various metrics including the optimal distance between the 3D depth sensor device and the survivor, the optimal position of the 3D depth sensor with respect to the survivor, and the response time of the gestures made by the survivors based on their distance to the sensor are evaluated. At the end of the experiments, the optimal distance and optimal position for the survivors to utilise the upper limb module is determined.

It is challenging to ensure security and to minimize economic impacts due to cyber-attacks because of the heavy reliance on ICT in different organizations and this paper presents an approach to estimate the cost of cyber security in public and private sector organizations. The paper also describes an approach for selecting the type of cyber security improvements to ensure that organizational goals are achieved. Different types of cyber-attacks and the subsequent impacts of these attacks are considered. A Value Analysis method is proposed to support the decision-making process by determining the priorities of deployment of various cyber security technologies. The proposed method is based on security costs related to and the losses due to attacks. Examples are provided in the paper to illustrate the proposed approach.

In universities, the amount of Internet traffic fluctuates over the time of a day and over the period of a semester. With the limited bandwidth resource available and ever increasing demand for high throughput due to multi-media, the Internet bandwidth have to be managed within an organization so that the priority traffic critical to the business is not slowed down by less critical, often entertainment, traffic. An important aspect in setting the policy of the bandwidth management is the knowledge of the Internet usage patterns over a period of time. The ability to predict the usage patterns provides the policy maker a powerful tool to set the rules and policies such that the allocation of Internet bandwidth is conducted dynamically in favor of business critical traffic while at the same time aiming to serve all users subject to the availability of the total available bandwidth. This paper reports the initial experiment of predicting the Internet usage patterns using RapidMiner Machine Learning tools with SVM algorithm for the IT Service Department of Sanata Dharma University in Indonesia. The algorithm used the dataset captured from the university over a period of time as the basis for prediction. The result of the dataset analysis is intended to the basis of policies development relating to bandwidth management.

Data fusion as a formal research area is referred to as multi ‐ sensor data fusion. The premise is that combined data from multiple sources can provide more meaningful, accurate and reliable information than that provided by data from a single source. There are many application areas in military and security as well as civilian domains. Multi ‐ sensor data fusion as applied to biometric authentication is termed multi ‐ modal biometrics. Though based on similar premises, and having many similarities to formal data fusion, multi ‐ modal biometrics has some differences in relation to data fusion levels. The objective of the current study was to apply feature level fusion of fingerprint feature and keystroke dynamics data for authentication purposes, utilizing Artificial Neural Networks (ANNs) as a classifier. Data fusion was performed adopting the complementary paradigm, which utilized all processed data from both sources. Experimental results returned a false acceptance rate (FAR) of 0.0 and a worst case false rejection rate (FRR) of 0.0004. This shows a worst case performance that is at least as good as most other research in the field. The experimental results also demonstrated that data fusion gave a better outcome than either fingerprint or keystroke dynamics alone.

Authentication systems enable the verification of claimed identity; on computer systems these are typically password-based. However, such systems are vulnerable to numerous attack vectors and are responsible for a large number of security breaches. Biometrics is now commonly investigated as an alternative to password-based systems. There are numerous biometric characteristics that can be used for authentication purposes, each with different levels of accuracy and positive and negative implementation factors. The objective of the current study was to investigate fingerprint recognition utilizing Artificial Neural Networks (ANNs) as a classifier. An innovative representation method for fingerprint features was developed to facilitate verification by ANNs. For each participant, the method required the alignment of their fingerprint samples (based on extracted local features), and the selection of 8 of these aligned features common to their samples. The six attributes belonging to each of the selected features were used for ANN input. Unlike the common usage, each participant had one dedicated ANN trained to recognize only their fingerprint samples. Experimental results returned a false acceptance rate (FAR) of 0.0 and a false rejection rate (FRR) of 0.0022, which were comparable to (and in some cases, slightly better than) other research efforts in the field.