Mohd Fairuz Shiratuddin

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.

The gamification of stroke rehabilitation may increase patient's motivation and engagement towards their rehabilitation activity and hence contributes to faster recovery. Apart from the interactive experience that games offer, it also may be used as a persuasive tool. In this view, games may be used to persuade stroke patients in shaping a good behavior towards their rehabilitation activity. This paper reports our proposed conceptual design model of building persuasive game for stroke patients that follows the Persuasive System Design Model. We incorporate the persuasive software features that includes reduction, tailoring, self-monitoring, and rewards. The proposed game architecture is also discussed in this paper.

In a typical image captioning pipeline, a Convolutional Neural Network (CNN) is used as the image encoder and Long Short-Term Memory (LSTM) as the language decoder. LSTM with attention mechanism has shown remarkable performance on sequential data including image captioning. LSTM can retain long-range dependency of sequential data. However, it is hard to parallelize the computations of LSTM because of its inherent sequential characteristics. In order to address this issue, recent works have shown benefits in using self-attention, which is highly parallelizable without requiring any temporal dependencies. However, existing techniques apply attention only in one direction to compute the context of the words. We propose an attention mechanism called Bi-directional Self-Attention (Bi-SAN) for image captioning. It computes attention both in forward and backward directions. It achieves high performance comparable to state-of-the-art methods.

Recently, extensive use of digital music has led to an increase in songs in online music applications and personal music libraries. In large music libraries, songs which are not listened to regularly, most probably will go unnoticed. There are many ways of browsing songs in an online music library. In the field of Music Information Retrieval (MIR), some type of visual forms such as colour, avatar, mood picture and album cover to visualise music, have been introduced. However, there is no research focusing explicitly on textures. To create new method of browsing music, we proposed a framework to visualise music mood using visual texture. In order to determine how well people can interact with the visual texture to browse songs in music library, usability testing was conducted. In this paper, we will present the results of the usability testing.

Existing basic artificial neurons merge multiple weighted inputs and generate a single activated output. This paper explores the applicability of a new structure of a neuron, which merges multiple weighted inputs like existing neurons, but instead of generating single output, it generates multiple outputs. The proposed “Multiple Output Neuron” (MON) can reduce computation in a basic XOR network. Furthermore, a MON based convolutional neural network layer (MONL) is described. Proposed MONL can backpropagate errors, thus can be used along with other CNN layers. MONL reduces the network computations, by reducing the number of filters. Reduced number of filters limits the network performance, thus MON based neuroevolution (MON-EVO) technique is also proposed. MON-EVO evolves the MONs into single output neurons for further improvement in training. Existing neuroevolution techniques do not utilize backpropagation but MONs can utilize backpropagation. Experimental networks trained using the CIFAR-10 classification dataset show that proposed MONL and MON-EVO provide a solution for reduced training computation and neuroevolution using backpropagation.

We present an attention-based image captioning method using DenseNet features. Conventional image captioning methods depend on visual information of the whole scene to generate image captions. Such a mechanism often fails to get the information of salient objects and cannot generate semantically correct captions. We consider an attention mechanism that can focus on relevant parts of the image to generate fine-grained description of that image. We use image features from DenseNet. We conduct our experiments on the MSCOCO dataset. Our proposed method achieved 53.6, 39.8, and 29.5 on BLEU-2, 3, and 4 metrics, respectively, which are superior to the state-of-the-art methods.

The paper reports an empirical investigation of the factors affecting consumer perceptions of the adoption of Electronic Health Records in Australia. This paper also details the processes involved in the pilot testing of the instrument where it has been pilot-tested to a convenience sample by sending individual postal survey envelopes to shortlisted community organisations in Australia. Reliability analysis to check the internal consistency was performed using the Cronbach’s alpha. Content validity was achieved by reviewing the instrument with a panel of experts. The results of this pilot study proved the feasibility of a full-scale study and these could be used as the basis for refinement of the instrument. Based upon the outcome of validity and reliability testing, items for the final instrument were identified. The findings showed that the tested model does fit the data well and has a significant and positive impact on the consumer’s attitude in using the EHR.

Technology plays a major role in discovering and improving accuracies of disorders and difficulties through identification of patterns. This paper attempts to discover such unique brainwave signal patterns found in adults with dyslexia using EEG while performing tasks that are more challenging for individuals with dyslexia. The EEG signals are collected from adults with dyslexia and normal controls during passage reading and rapid automatized naming. EEG signals provide valuable insights into the behaviour of the brain; however, identifying these patterns is not always quite straightforward due to its complexity. We identify these unique patterns and optimal brain regions for classification using machine learning. This study revealed that the greater level of difficulties seen in individuals with dyslexia while performing these tasks compared to normal controls are reflected in the brainwave signal patterns.