COVID-19 causes a jarring impact on the livelihoods of people in Malaysia and globally. To prevent an outbreak in the community, identifying the likely sources of infection (hotspots) of COVID-19 is important. The goal of this study is to formulate a bipartite network model of COVID-19 transmissions by incorporating patient mobility data to address the assumption on population homogeneity made in the conventional models and focus on indirect transmission. Two types of nodes - human and location - are the main concern in the research scenario. 21 location nodes and 31 human nodes are identified from a patient's pre-processed mobility data. The parameters used in this study for location node and human node quantifications are the ventilation rate of a location and the environmental properties of the location that affect the stability of the virus such as temperature and relative humidity. The summation rule is applied to quantify all nodes in the network and the link weight between the human node and the location node. The ranking of location and human nodes in this network is computed using a web search algorithm. This model is considered verified as the error obtained from the comparison made between the benchmark model and the COVID-19 bipartite network model is small. As a result, the higher ranking of the location is denoted as a hotspot in this study, and for a human node attached to this node will be ranked higher in the human node ranking. Consequently, the hotspot has a higher risk of transmission compared to other locations. These findings are proposed to provide a framework for public health authorities to identify the sources of infection and high-risk groups of people in the COVID-19 cases to control the transmission at the initial stage.

This paper presents the formulated 'play-to-engage' model for community engagement that incorporates factors in cultural protocols and the game design thinking approach. The model was experimented in an indigenous rural village in Borneo. Engagement was done with a group of indigenous community leaders, teachers and indigenous students from the village. It is a known fact that all indigenous cultures have overwhelming cultural protocols and rural custom practices for researchers to adhere to and follow. Since game is a universal language, the creation of a trustworthy partnership between the community and researchers was made easier using play during the engagement process. Incorporating a fun gameplay approach could also be a solution to thrive on reflection and creativity of those individuals in capturing the needs of the study. The outcome of the engagement was positive and the communities' needs, issues, experiences and motivation were collected during the play.

Features detection and matching are the essential processes in image mosaicing and computer vision applications. Our work intend to find descriptors that are obtained by considering all interest/feature points and its locations on images, and then form a set of corresponding spatial relations based on the interest points between images. Hence in this paper, we will evaluate and present the performance of a few detectordescriptor-matcher approaches on raw aerial images for stitching image purposes. We have experimented on Canny Edge Detector, SIFT and SURF approaches to extract feature points. The extracted descriptors are then matched using FLANN based matcher. Finally, the RANSAC Homography is used to estimate the transformation model so stitching procedure could be applied in order to produce a mosaic aerial image. The results have shown that SURF approach outperforms the others in terms of its robustness of the method and higher speed in execution time.

Myriad of layout and clustering algorithms exist to generate visual graphs of named entities. Consequently, it is hard for researchers to select the appropriate algorithms that fulfill their needs. This paper intends to assist the researchers by presenting the performance evaluation of the combination of graph layout algorithm followed by a clustering algorithm. The layout algorithms are OpenORD and Hu’s algorithms, and the clustering algorithms are Chinese Whispers and GivanNewman algorithms. The evaluation is carried out on bio-named entities that are linked by some annotated relations. The results of the experimentations highlight the strengths and weaknesses of the four combinations regarding running time, loss of relations (or edges), edge crossing, and cluttered display.

In this paper, we present and discuss our proposed method on landmarking on 2.5-dimensional (2.5D) face range images. Face landmarking plays an important role as an intermediary component in several face processing operation applications. Locating facial landmarks automatically remains a challenge. Detecting and localizing landmarks from raw face data are often performed manually by trained and experienced scientists or clinicians, and the process is usually lengthy, laborious and tedious. In order to overcome these challenges, we introduce a method that employs geometric approach, through utilizing the mean and Gaussian curvatures, primitive surfaces information to identify and label features as anatomical landmarks. In addition, comparative experiments on both automatic landmarking and manual landmarking were also performed and the results have demonstrated that the proposed method outperforms the manual landmarking in terms of obtaining distinct facial landmarks correctly and accurately.

The procedure for counting colonies is often performed manually and the process is lengthy and tedious. For that reason, several methods that rely on digital images for automatically counting cells and bacteria colonies have been proposed. Fully automated and high throughput hardware imaging instruments are also available, but such machines are extremely costly. In this paper, we introduce a mobile based computer vision algorithm for automatic bacteria colony counting using morphological operations and transforms in image processing, on a custom Android mobile cross-platform open source software and written in Java, C++ and Open CV computer vision library. The results have shown are promising given that the acquisition and detection were done in a non-controlled environment.

anatomical landmarks, which is a vital initial stage for several applications, such as face recognition, facial analysis and synthesis. Locating facial landmarks in images is an important task in image processing and detecting it automatically still remains challenging. The appearance of facial landmarks may vary tremendously due to facial variations. Detecting and extracting landmarks from raw face data is usually done manually by trained and experienced scientists or clinicians, and the landmarking is a laborious process. Hence, we aim to develop methods to automate as much as possible the process of landmarking facial features. In this paper, we present and discuss our new automatic landmarking method on face data using 2.5-dimensional (2.5D) range images. We applied the Scale-invariant Feature Transform (SIFT) method to extract feature vectors and the Otsu’s method to obtain a general threshold value for landmark localisation. We have also developed an interactive tool to ease the visualisation of the overall landmarking process. The interactive visualization tool has a function which allows users to adjust and explore the threshold values for further analysis, thus enabling one to determine the threshold values for the detection and extraction of important keypoints or/and regions of facial features that are suitable to be used later automatically with new datasets with the same controlled lighting and pose restrictions. We measured the accuracy of the automatic landmarking versus manual landmarking and found the differences to be marginal. This paper describes our own implementation of the SIFT and Otsu’s algorithms, analyzes the results of the landmark detection, and highlights future work

It is a well known truth that there is no malaria transmission without blood-sucking bites by mosquitoes. Since there is no blood-sucking without contacts, it follows that the contacts between human beings and mosquitoes can be exploited to generate contact networks. Active computational research in this area is geared at developing new frontiers in modelling malaria transmission through network theory and methods. Ongoing research in this area points to the fact that there are structural similarities between web graph, social network and malaria networks. It is the aim of this paper to explore the structural convergence and to exploit this to build a framework that will pave the way for developing web-hybrid search engines. Like the emergence of search engines revolutionised web research, it is expected that application of search engines in malaria research will make a tremendous impact in malaria control.

This paper presents a method to generate fill-in clues and answers for building automatically a crossword. Answers are capitalised words present in an input sentence and clues are segments of the dependency syntactic structure of that sentence. The pairs (Clue, ANSWER) are extracted from a collection of raw sentences related to the history of Sarawak. This work is at its early stage, and thus the proposed method that generates automatically fill-in clues, was tested on a small set of sentences and the obtained results are promising. Near 53% of the generated fill-in clues are considered correct. The major contribution of this work is the innovative strategy used to read the result of a pre-order depth-first search applied on a dependency graph to generate the clues. The clues and answers generator is implemented in Python.

Since malaria transmission emanates from mosquito bites, there must be points of contact between human beings and mosquitoes. Two of such points of contacts in the malaria life cycle are when malaria infected mosquitoes bite uninfected human beings, and when uninfected mosquitoes bite infected human beings. This obviously implies that there is an interaction between human beings and mosquitoes, which scientists can capitalize on to build network models for the study of malaria transmission. Assuming the issues of determination of network structure, and evolution of requisite algorithm for generating realistic network are successfully tackled, an important challenge that must be dealt with is the determination of strength of the relationship between these interacting nodes of the network. The aim of this paper is to tackle the issue of strength of relationship in the transmission network.

Malaysia is located in Southeast Asia and comprises 13 states and three federal territories. Malaysia has a population of about 28.3 million people (Department of Statistics Malaysia 2009). According to the Department of Statistics, Malaysia, for the year 2009, Malaysia’s gross domestic product (GDP) and gross national income per capita are USD 42.9 billion and USD 7,400 respectively. The latest unemployment rate is 3.6% (Department of Statistics Malaysia 2009).

Understanding the movement pattern of mosquitoes is necessary in malaria research, especially in network modeling, which is based on the fact that the interactions between mosquitoes and human hosts (through blood sucking bites) sustain malaria transmissions. Flight attributes such as direction of flight, length of flight, determinants of flights, among others will affect the overall dynamics of the human-mosquito network. This paper reviews related works on mosquito flight behaviour, gives brief treatment on how this is applied in mosquito traps technology, and as well as outlines the overall implications of mosquito flight patterns to network-based modeling. This work is part of an ongoing research project on network based modeling approach to malaria research with particular interest on the prediction of the public places that act as vector reservoirs.

Even with a lot of efforts by researchers, malaria eradication is yet to become a reality. The link between malaria infected and non-infected human beings, which led to transmission of the disease, is bridged by infected malaria vectors, through blood sucking bites. Such bites take place either in residential homes or public places. Suppose the residential homes are placed under reliable vector control, malaria transmission will still persist unless the public places are taken into consideration. The emerging computational strategy is about leveraging on the interactions between human beings and mosquitoes in public places to build realistic network models with the ultimate aim of applying relevant search techniques to such models, so as to determine the public places which act as reservoir of infected malaria vectors. The overall result will form a key resource for realistic vector control. Hence the idea is to be more proactive and offensive against malaria vectors, by locating the vectors in their hiding places, rather than human beings being located and bitten by the infected vectors. The emerging computational strategy discussed here is part of an ongoing research project in application of computational techniques towards eradication of malaria. This paper documents the result of an initial survey on malaria vector existence in public places.

In this paper, ethical implications of data collection, use and retention of medical and biometric data in biometrics and medical applications are identified. These implications are discussed in the context of five main ethical principles - privacy, confidentiality, security, property and ownership, and reliability and trustworthiness. In addition, to illustrate unethical uses of medical and biometric data, cases of misuse are described. Our research contributes to the European Union's FP7 ETHICAL project, which aims to promote international debate on ethical implications of data collection, use, and retention of biometric and medical data.