Computational Science and Technology

Table of Contents

1. Frontmatter

2. Building a Knowledge Graph of Vietnam Tourism from Text

   Phuc Do, Hung Le

   Abstract

   Most data in the world is in form of text. Therefore, we can say text stores large amount of the knowledge of human beings. Extracting useful knowledge from text, however, is not a simple task. In this paper, we present a complete pipeline to extract knowledge from paragraph. This pipeline combines state-of-the-art systems in order to yield optimal results. There are some other Knowledge Graphs such as Google Knowledge Graph, YAGO, or DBpedia. Most of the data in these Knowledge Graphs is in English. On the other hand, the results from our system is used to build a new Knowledge Graph in Vietnamese of Vietnam Tourism. We use the rich resources language like English to process a low resources language like Vietnamese. We utilize the NLP tools of English such as Google translate, Stanford parser, Co-referencing, ClausIE, MinIE. We develop Google Search to find the text describing the entities in the Internet. This text is in Vietnamese. Then, we translate the Vietnamese text into English text and use English NLP tools to extract triples. Finally, we translate the triples back into Vietnamese and build the knowledge graph of Vietnam tourism. We conduct experiment and discover the advantages and disadvantages of our method.

3. Technology Adoption Models: Users’ Online Social Media Behavior Towards Visual Information

   Irma Syarlina Binti Che Ilias, Suzaimah Ramli, Muslihah Wook, Nor Asiakin Hasbullah

   Abstract

   Technology Adoption Model is used in various technology fields to understand and predict users’ intentions and behaviors. However, the Technology Adoption Model used in Social Media, which explains users’ intentions and behaviors needs to be investigated. Nevertheless, there is little understanding of users’ intentions and behaviors towards visual information, which plays an important role in effective communication. This study reviewed a considerable amount of past studies on the use of the technology adoption model by users’ online social media behaviors towards visual information. Based on the literature survey from FOUR (4) databases; ACM, IEEE, Scopus, and Science Direct; TWELVE (12) articles have been reviewed. The study found that Uses and Gratifications Theory (UGT) is the most adopted model due to the motivation mechanism applied. Most importantly, the review managed to discuss the models, factors, visual information, and methods in relation to users’ social media intention and behavior. An Integrated Adoption Model could be developed to examine the consequences of the technology adopted to create a holistic understanding of how technology influences the users’ intentions and behaviors towards visual information. This is one of the recommendations presented at the end of this research for the reference of future scholars.

4. A Pedagogical Framework with Integration of TPACK for Mobile Interactive System in Teaching Mathematics

   Daniel Lai, Lew Sook Ling, Ooi Shih Yin

   Abstract

   Although there is a variety of technology available in 21st century, the way classes and lesson being conducted are still mostly remaining the same which is teaching via one-way communication. One-way communication teaching process surfastces issues like lack of interaction where minimal discussion is going on during teaching session, Since the interaction between teachers and students are fairly poor, limited classroom activity can be expected such as “Question and Answer” which lead to the increment of boredom in classes and lessons among students. As the teaching process is leaning towards instructional, students’ feedback is usually being overlooked. Hence, since the availability of educational technology is getting more common nowadays, the integration of technology in classroom is encouraged changing the teaching and learning environment including knowledge delivery method from teacher perspective. Introducing mobile interactive system allows teachers deliver their knowledge differently, however, they are required to adapt and get familiar with the educational technology for improving teaching experience. Therefore, Technological Pedagogical Content Knowledge (TPACK) framework is applied in this paper to assess teachers using the seven elements of the framework. This paper is aimed to address the issues faced by conventional classroom and identify the effectiveness of teachers conducting classes using educational technology with the application of TPACK framework. Proposed TPACK framework is formed with the integration of three elements which are teachers’ efficiency, students’ performance and students’ engagement representing the outcome of current TPACK framework.

5. Towards Palm Bunch Ripeness Classification Using Colour and Canny Edge Detection

   Ian K. T. Tan, Yue-Hng Lim, Nyen-Ho Hon

   Abstract

   The ripeness of the farm-able palm fruits is an important factor in the production of quality palm oil. The work presented is an image processing implementation in the palm oil industry to eliminate human errors in the judgment of the ripeness of palm fruit bunches as well as to introduce automation. Various techniques were employed to obtain data from the images provided for the data mining process. The features used are the colour of the palm fruit bunches and the amount of edges representing visible leaves in the palm fruit bunches, indicating empty sockets. The project is able to achieve an accuracy of up to 79.11%.

6. Attention Models for Sentiment Analysis Using Objectivity and Subjectivity Word Vectors

   Wing Shum Lee, Hu Ng, Timothy Tzen Vun Yap, Chiung Ching Ho, Vik Tor Goh, Hau Lee Tong

   Abstract

   In this research, we look at the notions of objectivity and subjectivity and create word embeddings from them for the purpose of sentiment analysis. We created word vectors from two datasets, the Wikipedia English Dataset for objectivity and the Amazon Product Reviews Data dataset for subjectivity. A model incorporating an Attention Mechanism was proposed. The proposed Attention model was compared to Logistic Regression, Linear Support Vector Classification models, and the former was able to achieve the highest accuracy with large enough data through augmentation. In the case of objectivity and subjectivity, models trained with the objectivity word embeddings performed worse than their counterpart. However, when compared to the BERT model, a model also with Attention Mechanism but has its own word embedding technique, the BERT model achieved higher accuracy even though model training was performed with only transfer learning.

7. A Question-Answering System that Can Count

   Abbas Saliimi Lokman, Mohamed Ariff Ameedeen, Ngahzaifa Ab. Ghani

   Abstract

   This paper proposes a conceptual architectural design of Question-Answering (QA) system that can solve “counting” problem. Counting problem is the inability of QA system to produce numerical answer based on retrieved rationale (in text passage) containing list of items. For example, consider “How many items are on sale?” as question and “Currently shampoo, soap and conditioner are on sale” as retrieved rationale from text passage. Normally, system will produce “shampoo, soap and conditioner” as an answer while the ground truth answer is “three”. In other words, system is simply unable to perform the counting process needed in order to correctly answer such questions. To solve this problem, QA system architecture with following components is proposed: (1) A classifier to determine if given question requires a counting answer, (2) A classifier to determine if current system’s answer is not numeric, and (3) A counting method to produce numerical answer based on given rationale. Despite looking like a whole system, the proposed architecture is actually a modular system whereby each component can operate independently (allowing each component to be separately implemented by other systems). In essence, this paper intents to demonstrate a general idea of how the defined problem can be solved using a modular system, that hopefully also opens up more flexible enhancements in the future.

8. Contactless Patient Authentication for Registration Using Face Recognition Technology

   Kian Yang Tay, Ying Han Pang, Shih Yin Ooi, Fan Ling Goh

   Abstract

   Patient registration is an essential process in every clinic and hospital before services are provided to patients. Usually, patient’s identity card or fingerprint (through a fingerprint scanner) will be requested for identity authentication in order to retrieve medical records of the patient. However, the current global health crisis of COVID-19 pandemic is raising concern on the hygiene and safety of sharing objects or touching surfaces. Same worry is also occurred towards the patient registration interaction process; further, hospitals and clinics are classified as high risk premises. Therefore, a contactless patient authentication for registration using face recognition technology is proposed in this work. In this system, a face is scanned and processed. If the face exists in the database indicating that the subject is an established patient, the patient’s records will be retrieved. Else, a new patient registration will be performed to register a new account. The efficiency of the system is assessed using our self-collected database. Empirical results show that the proposed system is able to attain 94% accuracy. But, an inferior performance is obtained, especially dealing with makeup variation.

9. Drawing and Recognising Simple Shapes with Real-Time Feedback Using Pattern Recognition

   Juharizal Adi Jen, Norizan Mat Diah, Zaidah Ibrahim

   Abstract

   Pattern recognition is a mature but exciting and fast-developing field concerning computer vision, image processing, shape drawing, and text analysis. The shape pattern can be recognised easily using this technique. Some children have difficulties in identifying shapes. Therefore, through shape drawing exercises, it helps children to understand better. This project aims to develop a mobile application, assisting children in practising drawing using pattern recognition. It can identify shape types by matching the shape pattern with the given input. It will classify the information provided based on the feature extraction using the Freeman Chain Code. Then, each shape pattern is recognised using regular expression tools. Functionality testing has been conducted on this application with an accuracy of 80%. The application will encourage children to draw more by giving feedback on the exercise that they do. It may assist children in learning a new, better way of drawing shape accurately while improving children’s fine motor skills.

10. Information Technology Students’ Preferences on Blended Learning

    Choo-Kim Tan, Choo-Peng Tan, Ng Shaun Wes

    Abstract

    The advancement of technology nowadays provides opportunities in education including the adoption of blended learning. The objective of this project was to examine Information Technology students’ preferences on blended learning. Findings found that most students preferred to use it in Science subjects/courses, both inside the class and outside the classroom, and learn via online with a blended learning method as revision after the class. However, students of low and medium math anxious students had no significant difference in their preferences. It is hoped that the findings of the project will help researchers to customise the incorporation of blended learning tools in students learning as well help educators in planning and adopting blended learning, and benefit students in their learning. The system developers also should consider designing and develop more suitable systems/apps for students in learning Language subjects.

11. Improved Facial Recognition Algorithms Based on Dragonfly and Grasshopper Optimization

    Dyala Rasheed Ibrahim, Je Sen Teh, Rosni Abdullah

    Abstract

    In this paper, we investigate two relatively new optimization algorithms in facial recognition, the grasshopper optimization algorithm (GOA) and binary dragonfly algorithm (BDA) which had the best performance out of 13 optimization algorithms that were compared. We investigate the effectiveness of both optimization algorithms alongside two classifiers, k-nearest neighbor (KNN) and support vector machine (SVM). Performance evaluation of the four combinations, BDA-KNN, BDA-SVM, GOA-KNN and GOA-SVM, indicate near-ideal recognition rates, with the GOA variants slightly outperforming their BDA counterparts. When compared to other recently proposed facial recognition approaches, the proposed algorithms depict improved accuracy.

12. Optimization on the Financial Management of Banks with Two-Stage Goal Programming Model

    Lam Weng Siew, Lam Weng Hoe, Chen Jia Wai

    Abstract

    The strategic planning is important in bank financial management. The banks and financial institutions have to achieve multiple goals in optimizing asset, liability, equity, earnings, profit and optimum management item. The subjective judgment in assigning weight of the goals is a drawback in financial management of the banks since it may cause inconsistent judgment. In addition, there are no comprehensive studies done on comparison among the banks for benchmarking based on the past studies in optimizing the financial management. Hence, this paper aims to improve the bank financial management by proposing a two-stage goal programming (GP) model to compare and optimize the bank financial management. The proposed model is developed based on entropy method in determining the weight of the goal at the first stage before optimizing the financial management with GP model at the second stage. Four listed banks in Malaysia are investigated in this study. The results indicate that the goal for asset, equity and optimum management item have been achieved by all banks. Furthermore, the target value of asset, equity, earning and profit can be increased according to the optimal solution of the proposed model. The significance of this paper is to provide insights to the banks for further improvement based on the optimal solution of the proposed model.

13. Evaluating the Performance of Selected Mortality Forecasting Models: A Malaysia Case Study

    Khairunnisa Mokhtar, Syazreen Niza Shair, Norazliani Md Lazam

    Abstract

    The study of human mortality is growing in Malaysia, as accurate mortality rates are classified important especially for social policy planning. This research aims at evaluating the performance of three selected mortality forecasting models, namely the Lee-Carter, CBD and M8 model in which the two latter models are from Cairns, Blake and Dowd. We applied the Malaysian central death rates and the number of mid-year exposures to the models and estimate the goodness of fits of all models using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). In addition, the 20-year out-samples forecast errors are estimated from 1999 to 2019 using the Root Mean Square Errors (RMSEs) and the Mean Absolute Percentage Errors (MAPEs). The findings of this study suggest that the M8 model is the best model for fitting Malaysian mortality data with minimum AIC and BIC values, and by far the most accurate model with the lowest out-sample errors, particularly for higher age category.

14. Assessing Python Programming Through Personalised Learning Styles Model

    Sin-Ban Ho, Sek-Kit Teh, Ian Chai, Chuie-Hong Tan, Swee-Ling Chean, Nur Azyyati Ahmad

    Abstract

    Learning styles, cognitive traits, personality, and learning preferences can vary greatly. That is why there is a great variety in how people receive and process information. Personalizing learning materials according to learner’s learning styles could enhance learner’s learning motivation and lead to better learning performance. This paper examines the relationship between learner’s learning styles and learning performance by proposing three different sets of documentation to test the relationship between the two learning styles of Felder-Silverman and learning performance. To test the proposed documentations and hypotheses, 182 participants in Multimedia University, Cyberjaya, Malaysia answered the Index of Learning Styles (ILS) questionnaire by Felder-Silverman and participated in a documentation experiment in Python programming. The data gathered was analysed using statistical Chi-square test. The results showed that learning performance was enhanced when the documentation was provided in a learning style that matched the subject’s learning style. The confirmed personalised learning styles model can be beneficial to teachers and e-learning recommendation systems when they provide students with materials that are personalised.

15. The Programming Learning Assessment Model for Measuring Student Performance

    Swee-Ling Chean, Sin-Ban Ho, Ian Chai, Chuie-Hong Tan, Sek-Kit Teh, Nur Azyyati Ahmad

    Abstract

    With recent pandemic, many students cannot join the class in physical classroom. The needs for e-learning and self-assessment become more salient than before. The teaching mode has been changing from teacher-centered to student-centered method. E-learning environment is practically a highly essential software application in the education field. However, programming-specific functionalities are hardly to be found on most of the general-purpose learning platforms, which may be unwieldy and unnecessarily complex to instructors and students in the programming learning process. This research aims to design a self-assessment model for a better support of programming e-learning, especially with exist of mandatory programming-specific functionalities. It’s believed that student background and effort have close correlation with their programming performance. More data to verify the correlations associated with positive learning outcome. In this research, we highlight the relationship between student background and student performance levels for introducing personalised self-assessment sets for students to learn programming. We propose and discuss Language, Education, Achievement, and Programming (LEAP) and Programming Learning Assessment (PLA) models to fill in the gap between the background knowledge and student competencies. To measure the correlation between proposed models and student performance, an experiment that involves 65 respondents was conducted. The data was analysed with structured and statistical approaches. Preliminary study shows that there are multivariate effects of the English fluency on PLA model. With the increasing demands of IT and software development skills, this research will help in motivating and encouraging more people to learn programming.

16. Design and Functionality of a University Academic Advisor Chatbot as an Early Intervention to Improve Students’ Academic Performance

    Mei Shyan Lim, Sin-Ban Ho, Ian Chai

    Abstract

    This paper introduces the design and functionality of a university academic advisor chatbot, which leverages on the result of a prediction model to predict students’ academic performance, to do early intervention to assist students who may need academic guidance. The prediction model is based on students’ attendance and scores of formative assessments to predict the score of the final summative assessment using a suitable machine learning algorithm. Scikit-learn library using Python will be used in this research to run the machine learning algorithms. The chatbot will be developed using Dialogflow which is integrated with one of the text messaging apps and established connection to a database. The database stores students’ attendance, scores of formative assessments, scores of final summative assessments and the status of students whom the chatbot has reached out to. This research aims to reduce the workload of lecturers to reach out to every student who is predicted to have problems in their academic studies and at the same time, be able to assist students using a chatbot.

17. Multiprocessing Implementation for Building a DNA q-gram Index Hash Table

    Candace Claire Mercado, Aaron Russell Fajardo, Saira Kaye Manalili, Raphael Zapanta, Roger Luis Uy

    Abstract

    Over the past few years, next-generation sequencing has become an invaluable technology for numerous applications in the field of genomics. The success of these applications are dependent on the performance of each phase in the genomic sequence pipeline, which starts with read mapping. However, read mapping is computationally intensive since it requires mapping billions of reads to numerous locations in a large reference genome. Building a q-gram index hash table has proven to be an efficient alternative to reduce the repetitive scanning of the reference during the verification step. A q-gram index hash table stores the locations of each q-gram in the reference genome. To accelerate the process of building this data structure and to exploit the multi-core architecture, instructions can be executed in parallel and distributed to multiple CPU cores. This paper performs a comparison analysis between the sequential and multiprocessing implementation of the index build time of the three methods for building a q-gram index hash table. The implementation results show that all multiprocessing versions are faster than sequential ones, with speedups ranging from 1.53 to 2.57. Although the open addressing method yields the fastest index build time, the best speedup is achieved by the minimizer-based method.

18. Predicting Chart Difficulty in Rhythm Games Through Classification Using Chart Pattern Derived Attributes

    Arturo P. Caronongan III, Nelson A. Marcos

    Abstract

    Rhythm games are music-themed games that challenge players’ sense of rhythm and reaction skills. One such popular rhythm-based video game is Dance Dance Revolution, where players perform steps on a dance platform that is synchronized with music as directed by on-screen step charts. An issue that exists, not just in Dance Dance Revolution, but in rhythm games in general is the estimation of a chart’s difficulty level. While many methods and studies exist in generating and predicting chart attributes, there is no clear methodology existing in determining the optimal difficulty of a given chart. This paper aims to address the aforementioned issue in the game of Dance Dance Revolution by proposing a methodology that involves extracting patterns and common attributes in step charts that enable more accuracy in determining a chart’s difficulty level. The resulting methodology achieved an average True-Positive rating of 0.683 and an overall model accuracy of 74.82% for classifying charts according to levels in Dance Dance Revolution.