Research Publications

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Start date: 2021End date: 2021

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Now showing 1 - 10 of 544
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    PublicationOpen Access
    LEXISGURU: Mobile Application for Learning Basic Lexis in English for Kids
    (Springer Science and Business Media Deutschland GmbH, 2021-11-05) Jayasinghe, M. J. W; Hennayaka, W. H. M. A. D. H; Fernando, M. P. M; Thilakarathne, K. N. U; Samarakoon, U; Kumari, S
    Lexis is an essential part of English vocabulary that puts a good foundation on a child’s English knowledge. In this rapidly globalizing world, it is fundamentally essential to learn English from a young age. In recent years eLearning, mobile applications have been developed for teaching Lexis to children. The market of educational mobile apps, especially for English language learning, has been rapidly growing. Especially in a country like Sri Lanka, English is not the mother tongue, it is the second language. So, when that second language is not taught right the child will lose interest in learning that language. The problem is that the existing lexical learning mobile applications does not aim at keeping the child interested and interactive in the learning process and in Sri Lanka, children find it difficult to understand these lexical parts. As a result, teachers and parents had to spend a lot of time to teach them those lexical parts. We designed and developed a mobile application called “LexisGuru” that uses interactive and effective ways to teach three lexical parts that are homophones, synonyms, and antonyms to children aged between 8–10 in Sri Lanka. This mobile application uses Machine Learning (ML), Image Processing (IP), gamification that includes collaborative environments, and speech recognition techniques. The developed mobile application was introduced to primary level learners, and they were all very attracted and interested while using this application. The attractive user interfaces, the pretests, and posttests, notifying the child when he loses focus while learning, using interesting stories and activities to teach lexis, playing a game with multiple players, and asking questions from the lesson and taking the voice inputs gave a new experience and showed that making the mobile application interactive as possible is an effective way to teach lexis to children.
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    ARCSECURE: Centralized Hub for Securing a Network of IoT Devices
    (Springer, Cham, 2021-07-06) Yapa Abeywardena, K; Abeykoon, A. M. I. S; Atapattu, A. M. S. P. B; Jayawardhane, H. N; Samarasekara, C. N
    As far as it is considered, IoT has been a game changer in the advancement of technology. In the current context, the major issue that users face is the threat to their information stored in these devices. Modern day attackers are aware of vulnerabilities in existence in the current IoT environment. Therefore, securing information from being gone into the hands of unauthorized parties is of top priority. With the need of securing the information came the need of protecting the devices which the data is being stored. Small Office/Home Office (SOHO) environments working with IoT devices are particularly in need of such mechanism to protect the data and information that they hold in order to sustain their operations. Hence, in order come up with a well-rounded security mechanism from every possible aspect, this research proposes a plug and play device “ARCSECURE”.
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    Use of Interpretable Evolved Search Query Classifiers for Sinhala Documents
    (Springer, Cham, 2021-01) Haddela, P; Hirsch, L; Brunsdon, T; Gaudoin, J
    Document analysis is a well matured yet still active research field, partly as a result of the intricate nature of building computational tools but also due to the inherent problems arising from the variety and complexity of human languages. Breaking down language barriers is vital in enabling access to a number of recent technologies. This paper investigates the application of document classification methods to new Sinhalese datasets. This language is geographically isolated and rich with many of its own unique features. We will examine the interpretability of the classification models with a particular focus on the use of evolved Lucene search queries generated using a Genetic Algorithm (GA) as a method of document classification. We will compare the accuracy and interpretability of these search queries with other popular classifiers. The results are promising and are roughly in line with previous work on English language datasets.
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    An Automated System for Estimating GSM Value of Fabrics using Beta Particle Absorption Characteristics
    (IEEE, 2021-11-22) Dias, S; Sandaruwan, K. G. D; Jayasekara, S
    Grams per square meter (GSM) or Grammage is an ISO-recommended term to express the mass per unit area of papers, metal sheets, plastic-made products, and fabric materials. GSM tests are widely used in the textile industry to measure GSM of knitted fabrics, and to ensure quality and other standard fabrics' specifications. Meanwhile, most textile organizations prefer the manual GSM measuring procedure, which leads to many disadvantages, including fabric wastage. An automated non-contact type fabric Grammage measuring method is proposed as an alternative solution for this industrial issue. This paper introduces mathematical models for determining GSM values based on beta particle absorption characteristics of three selected fabrics. Moreover, this study discusses a real-time GSM measuring system, which utilizes the generated mathematical models. Through this paper, developed mathematical models are investigated over the goodness of fit parameters. Developed models are appropriate for GSM value estimation with R2 values over 0.99, and lower Root Mean Square Error (RMSE) Values. Error percentages obtained during the validation of these mathematical models are less than 1% for all fabric types.
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    Optimum Music: Gesture Controlled, Personalized Music Recommendation System
    (IEEE, 2021-12-09) Wijekoon, R; Ekanayaka, D; Wijekoon, M; Perera, D; Samarasinghe, P; Seneweera, O; Peiris, A
    Music plays an important role in everyone’s life since it helps to relax the mind when appropriate music is played. This paper presents a music recommendation system based on the user’s current emotions, activities as well as demographic information such as age, gender, and ethnicity. In addition, the system can be controlled by hand gestures and vocal commands. Unsupervised learning methods in were used to recommend music according to the demographic data and emotions of the user. Finally, the important idea is to recommend music based on all of the user’s data, such as demographics, emotions, and activities. The overall system performance was manually tested and evaluated with a group of individuals, yielding a 70% satisfaction rate for the recommendation; additionally, supporting models such as demographic identification, emotion identification, and hand gesture identification have received a higher proportion of accuracies, contributing to the research’s success. Unlike other systems, ours utilizes all of the user’s information while making music recommendations.
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    Guide-Me:Voice authenticated indoor user guidance system
    (IEEE, 2021-12-01) Dissanayake, D. M. L. V; Rajapaksha, R. G. M. D. R. P; Prabhashawara, U. P; Solanga, S. A. D. S.P; Jayakody, A
    Due to a lack of knowledge about the building structure and possible impediments, the majority of blind persons require assistance when traveling through unknown regions. To solve this issue, this paper provides "Guide-Me" as a strategy for indoor navigation with optimum accessibility, usability, and security, decreasing obstacles that the user may meet when traveling through indoor surroundings. Because the intended audience for this research is blind or visually impaired persons, "Guide-Me" makes use of the user’s voice-based inputs. This paper also includes Bluetooth beacon integration for localization, a Smart stick with sensors for obstacle detection, a machine learning model for voice authentication, and an algorithm protocol for a secure connection between server and application Integration driven architecture to assist vision impaired in navigating the known and unknown indoor environment.
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    Traffic Monitoring Related Experimental Study for a Software-Defined Network Based Virtualized Security Functions Platform
    (IEEE, 2021-12-01) Gamage, T. C. T.; Rankothge, W. H.; Gamage, N. D. U.; Jayasinghe, D; Uwanpriya, S. D. L. S.; Amarasinghe, D. A.
    Cloud computing and virtualization technologies are rapidly evolving with new capabilities being added all the time. Security Functions Virtualization (SFV) is the latest addition to cloud services, where Virtualized Security Functions (VSFs) are offered as services by Cloud Service Providers (CSPs). CSPs are focusing more on implementing effective resource management approaches for the cloud infrastructure, considering specific requirements of VSFs. Network traffic monitoring is one of the most crucial aspects of cloud resource management, as monitoring helps CSPs to have a global view of the resource utilization and take necessary proactive management actions, specifically for VSFs.This experimental study focuses on exploring network traffic and resource monitoring for the traffic traverse via a cloud platform where VSFs are offered as a service. We have considered two approaches: periodic monitoring and continuous monitoring. The network traffic is monitored continuously, and resource utilization is monitored periodically. With the implemented monitored framework, CSPs are able to take proactive decisions on resource management, specially towards scale-out/in decisions and security management.
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    Resource Utilization based Load Balancing for a Virtualized Security Functions Platform
    (IEEE, 2021-12-01) Amarasinghe, D. A. H. M.; Rankothge, W. H.; Gamage, N. D. U.; Gamage, T. C. T.; Uwanpriya, S. D. L. S.; Jayasinghe, D.
    Cloud Service Providers (CSPs) are moving to a Software Defined Networks (SDN) based approach to managing their network infrastructure. The introduction of Virtualized Security Functions (VSFs) and offering them as a service has to make SDN more popular among CSPs because SDN brings advantages such as flexibility, elasticity, and easy management. When offering VSFs, resource management is one of the important aspects, especially considering the dynamic nature of the traffic, where traffic increases and decreases dynamically over time. In parallel to the traffic changes, resources also have to increase/ decrease and traffic load must be balanced over the resources. This research focuses on exploring a resource utilization-based load balancing algorithm, that considers memory and CPU utilization of the VSFs. We have used Mininet simulation to build an SDN-based cloud architecture with VSFs, and POX controller to explore the performances of the proposed algorithm, in terms of average workload and average response time. Our results show that the resource utilization-based load balancing algorithm balances the workload and traffic adequately, within an acceptable time.
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    PublicationOpen Access
    The Impact of Cultural Orientation on the Societal Motivations of Luxury Good Consumption of IT Professionals in Sri Lanka
    (researchgate.net, 2021-01) Dissanayake, L. D. A. D.
    The primary objective of the study is to examine the impact of cultural orientation on the societal motivations of luxury good consumption of IT professionals in Sri Lanka. Thus, the study has used a sample of 103 IT practitioners in Sri Lanka. Correlation and regression analysis have been used to achieve the primary objectives. Consequently, among the four independent variables; horizontal individualism, vertical individualism, horizontal collectivism and vertical collectivism, only vertical individualism has a signifcant and positive relationship towards the societal motivation of luxury good consumption. The other variables have negative none signifcant relationship towards social luxury good consumption. Regression analysis concludes that only vertical individualism is a signifcant predictor of social luxury good consumption motivation of IT professionals. The other variables are not signifcant predictors.
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    Deep Learning-Based Surveillance System for Coconut Disease and Pest Infestation Identification
    (IEEE, 2021-12-07) Vidhanaarachchi, S. P.; Akalanka, P. K. G. C.; Gunasekara, R. P. T. I.; Rajapaksha, H. M. U.D; Aratchige, N. S.; Lunugalage, D; Wijekoon, J. L
    The coconut industry which contributes 0.8% to the national GDP is severely affected by diseases and pests. Weligama coconut leaf wilt disease and coconut caterpillar infestation are the most devastating; hence early detection is essential to facilitate control measures. Management strategies must reach approximately 1.1 million coconut growers with a wide range of demographics. This paper reports a smart solution that assists the stakeholders by detecting and classifying the disease, infestation, and deficiency for the sustainable development of the coconut industry. It leads to the early detections and makes stakeholders aware about the dispersions to take necessary control measures to save the coconut lands from the devastation. The results obtained from the proposed method for the identifications of disease, pest, deficiency, and degree of diseased conditions are in the range of 88% - 97% based on the performance evaluations.