Measuring Task Performance Using Gaze Regions

We present a novel method for measuring task performance using gaze regions, i.e., scene regions fixated by a subject as he or she performs a familiar manual task. The scene regions are learned as a bag of features representation, using library lookup based on the Histogram of Oriented Gradients feature descriptor [1]. By establishing a set of task-specific exemplar models, i.e., models sourced from Pareto optimal sequences, the approach recognizes the local optima within a set of task-specific unlabeled models by estimating the distance (of each unlabeled model) to the exemplar models. During testing, the method is evaluated against a dataset of egocentric sequences, each containing gaze data, belonging to three manual skill-based activities. The results show perfect classification’s accuracy on several proposed schemes

Spatio-Temporal Semantic Representation of Cardiac MRI in Heart Attack Patients

Semantic Web technologies, applications and tools have made great steps forward in the life science and health care data exchange. However, developing appropriate semantic representations, including designing spatio-temporal ontologies, remains difficult and challenging. In this paper, we describe a framework to engineer a spatio-temporal semantic representation for the Cardiac MRI images using the current existing case studies conducted in Sarawak General Hospital Heart Centre

Unsupervised Classification of Intrusive Igneous Rock Thin Section Images using Edge Detection and Colour Analysis

Classification of rocks is one of the fundamental tasks in a geological study. The process requires a human expert to examine sampled thin section images under a microscope. In this study, we propose a method that uses microscope automation, digital image acquisition, edge detection and colour analysis (histogram). We collected 60 digital images from 20 standard thin sections using a digital camera mounted on a conventional microscope. Each image is partitioned into a finite number of cells that form a grid structure. Edge and colour profile of pixels inside each cell determine its classification. The individual cells then determine the thin section image classification via a majority voting scheme. Our method yielded successful results as high as 90% to 100% precision

Towards Automated Biometric Identification of Sea Turtles (Chelonia mydas)

Passive biometric identification enables wildlife monitoring with minimal disturbance. Using a motion-activated camera placed at an elevated position and facing downwards, images of sea turtle carapaces were collected, each belonging to one of sixteen Chelonia mydas juveniles. Then, co-variant and robust image descriptors from these images were learned, enabling indexing and retrieval. In this paper, several classification results of sea turtle carapaces using the learned image descriptors are presented. It was found that a template-based descriptor, i.e. Histogram of Oriented Gradients (HOG) performed much better during classification than keypoint-based descriptors. For our dataset, a high-dimensional descriptor is a must because of the minimal gradient and color information in the carapace images. Using HOG, we obtained an average classification accuracy of 65%.

Iban Plaited Mat Motif Classification with Adaptive Smoothing

Decorative mats plaited by the Iban communities in Borneo contains motifs that reflect their traditional beliefs. Each motif has its own special meaning and taboos. A typical mat motif contains multiple smaller patterns that surround the main motif hence is likely to cause misclassification. We introduce a classification framework with adaptive sampling to remove smaller features whilst retaining larger (and discriminative) image structures. Canny filter and Probabilistic Hough Transform are gradually applied to a clean greyscale image until a threshold value pertaining to the image’s structural information is reached. Morphological dilation is then applied to improve the appearance of the retained edges. The resulting image is described using Binary Robust Invariant Scalable Keypoints (BRISK) features with Random Sample Consensus (RANSAC). We reported the classification accuracy against six common image deformations at incremental degrees: Scale+Rotation, Viewpoint, Image Blur, Joint Photographic Experts Group (JPEG) Compression, Scale and Illumination. From our sensitivity analysis, we found the optimal threshold for adaptive smoothing to be 75.0%. The optimal scheme obtained 100.0% accuracy for JPEG Compression, Illumination, and Viewpoint set. Using adaptive smoothing, we achieved an average increase in accuracy of 11.0% compared to the baseline

User Interface/User Experience (UI/UX) Analysis & Design of Mobile Banking App for Senior Citizens: A Case Study in Sarawak, Malaysia

Smartphones are having such a huge impact to our society and in our daily lives. However, most smartphone applications are not that user-friendly for a senior-aged person. Due to the COVID-19 pandemic, everything now is done online including mobile banking services. There are seniors who refuse to use mobile banking applications in Malaysia because they are not familiar nor comfortable with the app's interface and flow. This study aims to perform a need analysis on user interface and user experience (UI/UX) design for Malaysian seniors when using a mobile banking app. A questionnaire was used in this research as a quantitative research tool, involving 36 respondents aged 55 years old and above, and currently a resident of Sarawak. The questionnaire is split into 5 sections, i.e., demographic, technology background, task, task rating, and preferences. We observed that “Fast loading time” is ranked as the most important feature with the highest mean value of 5.0. The least important feature is “Payment via QR Code” with a mean value of 2.7. Our findings can be used as a basis to prioritize features when designing a mobile banking app to accommodate senior users

Accreditation document tracking system using Scrum approach

Document tracking which involves recording and monitoring the movement of documents has been a time-consuming task for staff. Dislocation and overlooking of the timeline have always been the problems in document control. An effective tool such as a web-based system is the easiest way to be implemented in the workplace. The Accreditation Document Tracking System (ADTS) is designed to monitor the movement and timeline of the document from a department to another department throughout the accreditation process efficiently. The main objective of this project is to develop a system that can track the location of a document and its status of submission at every stage. This advantageous system is developed through scrum approach, which is the most widespread agile methodology used in the industry. It offers flexibility and simplicity to the system developer in upgrading the system. Furthermore, the Unified Modelling Language (UML) is used to describe the interaction between user and proposed system. UML consists of three visual diagrams: (i) Use case diagram, (ii) activity diagram, and (iii) sequence diagram. By following each stage of the diagrams, the proposed system is able to be developed in order to achieve the objective of this project within within the university as well Malaysian Qualification Agency (MQA) and Ministry of Higher Education (MOHE)