Faculty of Computing

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Open Access
Cognitive Complexity Applied to Software Development: An Automated Procedure to Reduce the Comprehension Effort
(Institute for Research and Community Services, Institut Teknologi Bandung, 2023-05) Wijendra, D.R; Hewagamage, K.P
The cognitive complexity of a software application determines the amount of human effort required to comprehend its internal logic, which results in a subjective measurement. The quantification process of the cognitive complexity as a metric is problematic since the factors representing the computation do not represent the exact human cognition. Therefore, the determination of cognitive complexity requires expansion beyond its quantification. The human comprehension effort related with a software application is associated with each phase of its development process. Correct requirements identification and accurate logical diagram generation prior to code implementation can lead to proper logical identification of software applications. Moreover, human comprehension is essential for software maintenance. Defect identification, correction and handling of code quality issues cannot be maintained without good comprehension. Therefore, cognitive complexity can be effectively applied to demonstrate human understandability inside the respective phases of requirements analysis, design, defect tracking, and code quality optimization. This study involved automation of the above-mentioned phases to reduce the manual human cognitive load and reduce cognitive complexity. It was found that the proposed system could enhance the average accuracy of requirements analysis and class diagram generation by 14.44% and 9.89% average accuracy incrementation through defect tracking and code quality issues compared to manual procedures.
Open Access
COVID-19 symptom identification using Deep Learning and hardware emulated systems
(Elsevier, 2023-06-28) Liyanarachchi, R; Wijekoon, J; Premathilaka, M; Vidhanaarachchi, S
The COVID-19 pandemic disrupted regular global activities in every possible way. This pandemic, caused by the transmission of the infectious Coronavirus, is characterized by main symptoms such as fever, fatigue, cough, and loss of smell. A current key focus of the scientific community is to develop automated methods that can effectively identify COVID-19 patients and are also adaptable for foreseen future virus outbreaks. To classify COVID-19 suspects, it is required to use contactless automatic measurements of more than one symptom. This study explores the effectiveness of using Deep Learning combined with a hardware-emulated system to identify COVID-19 patients in Sri Lanka based on two main symptoms: cough and shortness of breath. To achieve this, a Convolutional Neural Network (CNN) based on Transfer Learning was employed to analyze and compare the features of a COVID-19 cough with other types of coughs. Real-time video footage was captured using a FLIR C2 thermal camera and a web camera and subsequently processed using OpenCV image processing algorithms. The objective was to detect the nasal cavities in the video frames and measure the breath cycles per minute, thereby identifying instances of shortness of breath. The proposed method was first tested on crowd-sourced datasets (Coswara, Coughvid, ESC-50, and a dataset from Kaggle) obtained online. It was then applied and verified using a dataset obtained from local hospitals in Sri Lanka. The accuracy of the developed methodologies in diagnosing cough resemblance and recognizing shortness of breath was found to be 94% and 95%, respectively.
Open Access
Derivation of Bessel function closed-form solutions in zero dimensional φ4-field theory
(Elsevier Ltd, 2022-02) Munasinghe, R. M.
The integral is used as an introductory learning tool in the study of Quantum Field Theory and path integrals. Typically, it is analyzed via perturbation theory. Closed-form solutions have been quoted for which I could not find any derivation. Using a simple and elegant transformation, the close form solutions for the integral and its even positive integer moments can be obtained in terms of Bessel functions.
Embargo
Development of a risk model for different innovator types in textile and apparel industries
(Emerald Publishing, 2023-01) Kumarapeli, U; Ratnayake, V; Jayawardana, S. S
Purpose – Technological innovation is one of the strongest driving forces in the survival and growth of any organization, including textile and apparel industries. However, technological innovation inherits a wide array of risks due to the uncertainty involved in it. In-depth research reveals the existence of a significant relationship between innovation failures and the approach used to innovate, that is, the organization’s innovator type. However, quantitative evidence supporting this concern is still lacking. Hence, the purpose of this paper is to bridge the existing gap in the literature on effective management of technological innovation risk factors and the innovator type of textile and apparel industries. Design/methodology/approach – The risk factors related to technological innovations are identified under different innovator types. Analytic network process (ANP) has been used to evaluate the contribution of risk factors according to the innovator type of the organization. Data was gathered through the literature review and structured and semi structured interviews with textile and apparel industry experts. The contribution of risk factors was determined through priorities, derived according to the ANP using Super Decision software. Findings – Contribution of risk factors takes different values according to innovator type. This provides comprehensive knowledge on developing a risk management strategy according to the innovator type of the organization. Furthermore, this provides insight into the fact that a generalized risk management strategy will not be effective and sensible for all innovator types. Originality/value – The findings provide a thorough understanding of developing a customized risk management strategy by determining the “most to least” criticality of risks based on the innovator type of the organization. Furthermore, findings can be used to adopt the most appropriate innovator type based on the organization’s key competencies. Moreover, this guides the organization in making the best use of internal resources during risk management. Furthermore, this provides insight into the risk factors that must be addressed prior to embarking on new innovative approaches
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Driving Innovative Culture with Emotional Intelligence
(IEEE, 2023-06-12) Rizwi, A; Lokuliyana, S
This research aims to examine the relationship between employee innovation and positive and negative contagion within supervising roles. Establishing an innovative culture within the organization and having managers with a high level of Emotional Intelligence are essential. As a result, this enables the study to examine the effects of these factors on employees. The study is evaluated the effects of adopting an innovation culture and working with managers who are emotionally quotient on the performance of the employees. In the corporate sector, innovation takes place under different conditions than in the private sector. Human beings experience emotions daily. An employee survey of 40 items (5-point Likert Scale) is distributed. A total of 200 surveys have been evaluated. The validity and reliability of the data were checked using SPSS, and the results were assessed using regression analysis. It involves constructing a confidence interval based on a single sample and a given level of confidence. The findings indicate that Emotional Intelligence, innovative organizational culture, and employee performance are meaningfully related. In conclusion, organizations must create innovative institution cultures and employ managers that have high levels of Emotional Intelligence to increase their employees' performance using the application of innovation.
Open Access
A Graph Pointer Network-Based Multi-Objective Deep Reinforcement Learning Algorithm for Solving the Traveling Salesman Problem
(MDPI, 2023-01-13) Perera, J; Liu, S.H; Mernik, M; Črepinšek, M; Ravber, M
Traveling Salesman Problems (TSPs) have been a long-lasting interesting challenge to researchers in different areas. The difficulty of such problems scales up further when multiple objectives are considered concurrently. Plenty of work in evolutionary algorithms has been introduced to solve multi-objective TSPs with promising results, and the work in deep learning and reinforcement learning has been surging. This paper introduces a multi-objective deep graph pointer network-based reinforcement learning (MODGRL) algorithm for multi-objective TSPs. The MODGRL improves an earlier multi-objective deep reinforcement learning algorithm, called DRL-MOA, by utilizing a graph pointer network to learn the graphical structures of TSPs. Such improvements allow MODGRL to be trained on a small-scale TSP, but can find optimal solutions for large scale TSPs. NSGA-II, MOEA/D and SPEA2 are selected to compare with MODGRL and DRL-MOA. Hypervolume, spread and coverage over Pareto front (CPF) quality indicators were selected to assess the algorithms’ performance. In terms of the hypervolume indicator that represents the convergence and diversity of Pareto-frontiers, MODGRL outperformed all the competitors on the three well-known benchmark problems. Such findings proved that MODGRL, with the improved graph pointer network, indeed performed better, measured by the hypervolume indicator, than DRL-MOA and the three other evolutionary algorithms. MODGRL and DRL-MOA were comparable in the leading group, measured by the spread indicator. Although MODGRL performed better than DRL-MOA, both of them were just average regarding the evenness and diversity measured by the CPF indicator. Such findings remind that different performance indicators measure Pareto-frontiers from different perspectives. Choosing a well-accepted and suitable performance indicator to one’s experimental design is very critical, and may affect the conclusions. Three evolutionary algorithms were also experimented on with extra iterations, to validate whether extra iterations affected the performance. The results show that NSGA-II and SPEA2 were greatly improved measured by the Spread and CPF indicators. Such findings raise fairness concerns on algorithm comparisons using different fixed stopping criteria for different algorithms, which appeared in the DRL-MOA work and many others. Through these lessons, we concluded that MODGRL indeed performed better than DRL-MOA in terms of hypervolumne, and we also urge researchers on fair experimental designs and comparisons, in order to derive scientifically sound conclusions.
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A Methodological Approach for the Quality Assurance of Virtual Platforms Delivering Short-Term Online IT Courses
(IEEE, 2023-06-12) Munasinghe, B.A; Gamage, M. P. A. W.; Dinesh Asanka, P. P. G.
Online course delivery democratizes learning throughout life and creates opportunities for knowledge socialization. Short online IT courses have become famous among the audience. This research paper specifies weights by applying the Best Worst Method (BWM) for the dimensions and indicators determined as criteria a platform needs to meet to be a quality short online IT course delivery platform (SOITCDP). In this context, the paper considers thirteen popular SOITCDP and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to rank them based on their performance. The Sensitivity analysis ensures that the framework universally applies to evaluating any SOITCDP. The evaluation results are presented through comprehensive and interactive dashboards demonstrated on Power BI. The result of the research depicts that the "Teaching Resources" of the dimensions gained the highest weight while the "Attraction" indicator secured the rank one.The overall performance of platforms showcases that "Udemy" has excelled in all dimensions and indicators. The subsequent study also provides directions for implementing a framework to evaluate the performance of the platforms. The novelty of the research is highlighted on the grounds of implementing a framework unique to the evaluation of short-term online IT courses and the demonstration of results on interactive dashboards.
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Smart Waste Segregation for Home Environment
(IEEE, 2023-06-12) Abeygunawardhana, P.K. W; Muhammed Rijah, U.L
The segregation of waste and recycling is essential for effective waste management. Due to the busy schedule most of the people do not have a time to separate their waste. However, there is a significant issue with the segregation of the collected garbage. The implementation of an intelligent trash-management architecture is essential for the removal or reduction of waste and the maintenance of a clean, corporate environment. An IoT-stationed smart waste management device is proposed in this study that uses sensor devices to identify rubbish in the dustbins. With the aid of sensors, the waste substances in it will be separated through IoT as soon as it is discovered. Sensors and the IoT module are connected through a microcontroller. To detect the presence of garbage, an ultrasonic sensor is used. All garbage entered will be caught by the web camera and processed by the machine learning module once it has been processed. Using the model, we can identify the many forms of waste, such as paper, plastic, and glass, which account for most of the garbage materials found in a home area. This aids in removing the trash from the trash can in the most efficient and effective manner possible. This research presents an IoT, and Machine Learning based completely intelligent trash segregation and management System that recognizes the dustbins' wastes using sensor systems. This project aims to develop an automated waste segregation system using a CNN algorithm that will capture waste images from a camera with object detection and classify waste materials such as paper, plastic, and glass so that the waste can be recycled appropriately. The proposed architecture with CNN gives an accuracy of 84.67%. This system will help in garbage disposal by categorizing it, contributing to a cleaner environment.
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Static Code Analyser to Enhance Developer Productivity
(IEEE, 2023-05-23) Peiris, D. R. I.; Kodagoda, N
One of the main metrics important to software development is productivity. The measure of productivity helps a organization/ individual or team identify how well their employees/ software runs and how they could improve. In this research paper, a new software is proposed along with a background study of how improving code quality will improve developers productivity and efficiency. The software proposed to aid developer productivity is a custom built static code analyser using python and it’s rule set has been tailored through carefully selected research papers and with feedback from experts of the software industry
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"Talking Books" : A Sinhala Abstractive Text Summarization Approach for Sinhala Textbooks
(IEEE, 2023-05-23) Rathnayake, B.R.M.S.R.B.; Manathunga, K; Kasthurirathna, D
The ability for books to talk would be an exciting concept, and this research discussion paves the path for an identical approach. The research objectives discussed in this paper address several burning problems, solve them and adapt them to future technological enhancements from a Sri Lankan context. Burning problems include reducing printing costs for textbooks, addressing students’ health, promoting green technology, and identifying a suitable summarising approach to the native language, Sinhala resulting in students’ learning ease. Other symptoms for the betterment indicate paths taken to reduce the weight of school bags carried by students, reduce paper usage by the government on printing textbooks, and spread technological awareness to teenagers regarding e-Learning. Textbooks issued by the government will be digitized and centralized into a single system that the government officials themselves can administer. The paper discusses limited hindsight literature and proposes 2 new algorithms for abstractive and extractive summarization for Sinhala text. The 2 algorithms are compared against one another in terms of performance, efficiency, precision and accuracy. Experts in the education domain have verified the derived summary of both algorithms. The deliverable artefacts are the mobile application, a RESTful auto-summarization plugin service, and new data sets extracted to train the GPT-3 models.
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Tamil Grammarly – A Typing Assistant for Tamil Language using Natural Language Processing
(IEEE, 2023-06-12) Mahadevan, P; Srihari, P; Seyon, S; Vasavan, P; Panchendrarajan, R
Tamil is one of the ancient and most convoluted languages in the world. Although it is being the official language of many Asian countries, even native speakers tend to find difficulties in writing Tamil due to its morphologically rich nature. While there are various studies focusing on automatically identifying and correcting a specific typing error, very limited effort has been made to develop a comprehensive solution to assist the native and non-native writers of Tamil. In this paper, we propose a typing assistant tool Tamil Grammarly using Natural Language Processing (NLP) techniques. Specifically, the tool aims to aid the user to fix grammatical errors and spelling errors and recommend the next words and synonyms of the current word in real-time while typing. The NLP-based typing assistant functions of Tamil Grammarly were developed using a transformer-based model, LSTM model, and Word2Vec model. Extensive evaluation performed shows that our tool can assist the users in real-time with an accuracy of 73% - 93% within 0.4 to 5.3 seconds.
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WONGA: The Future of Personal Finance Management – A Machine Learning-Driven Approach for Predictive Analysis and Efficient Expense Tracking
(IEEE, 2023-07-10) Uyanahewa, M.I.R; Jayawardana, G.V.H.D; Bandara, M.B.D.N; Hapugala, H.A.V.V; Attanayaka, B
The financial literacy of Sri Lankans is relatively low, leading to difficulties in managing personal finances. This research presents a smart solution to simplify the complexities associated with money management and assist individuals in managing their finances more efficiently to achieve better financial health without requiring a comprehensive knowledge of money management from the user. The proposed system automates personal finance management with minimal user effort, reducing manual data entry by tracking cash flow by utilizing SMS messages and expense bills to extract bank transaction data and cash expenditures. Each extracted expense will automatically be categorized into the correct expense category. The system also generates a custom budget plan for each user based on spending patterns to help them stay on the budget throughout the month and avoid irrational overspending. Furthermore, the system provides a mechanism to predict future expenses associated with upcoming events based on calendar events, allowing users to devise the most efficient budget plan and avoid facing financially unprepared events in the upcoming month. All these smart solutions are bundled up in the "Wonga" mobile application to help users make better financial decisions to achieve personal financial success.