ICITR - 2021

Permanent URI for this collectionhttp://192.248.9.226/handle/123/19432

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    Adapting marytts for synthesizing sinhalese speech to communicate with children
    (Faculty of Information Technology, University of Moratuwa., 2021-12) Lakmal, MAJA; Methmini, KADG; Rupasinghe, DMHM; Hettiarachchi, DI; Piyawardana, V; Senarathna, M; Reyal, S; Pulasinghe, K; Ganegoda, GU; Mahadewa, KT
    The majority of the Sri Lankan population speak Sinhala, which is also the country's mother tongue. Sinhala is a difficult language to learn by children aged between 1–6 years when compared to other languages. Text to speech system is popular among children who have difficulties with reading, especially those who struggle with decoding. By presenting the words auditorily, the child can focus on the meaning of words instead of spending all their brainpower trying to sound out the words. In Sri Lanka, however, computer systems based on the Sinhala language especially for children are extremely rare. In this situation having a Sinhala text-to-speech technology for communicating with children is a helpful option. Intelligibility should be considered deeply in this system because this is specific for children. Recordings of a native Sinhalese speaker were used to synthesize a natural-sounding voice, rather than a robotic voice. This paper proposes an approach of implementing a Sinhalese text-to-speech system for communicating with children using unit selection and HMM -based mechanisms in the MaryTTS framework. Although a work in progress, the intermediate findings have been presented.
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    Automated question and answer generating system for educational platforms
    (Faculty of Information Technology, University of Moratuwa., 2021-12) Thiruvanantharajah, M; Hangarangoda, N; Rajapakshe, S; IT; Ganegoda, GU; Mahadewa, KT
    Learning through the web gets to be well known which encourages learners to learn any kind of stuff at any time from the internet assets. In exam preparing questions and answering is have moved into the technology world. In Many industries, more activities have begun to shift as a result of the increased changes brought about by the Covid-19 virus to people's usual livelihoods, and one significant component whose technologization has created concerns is education. This paper presents a novel system that has been introduced to improve the standards of instructing via virtual and non-virtual platforms by ensuring that both the educational staff and the students are provided with the same level of understanding of their education. The support system ensures that the students and educational staff are provided with an automatic question and answer generation mechanism which will thereby improve the quality of education by presenting a standardized method of preparing questions to the educational staff, while similarly providing a better opportunity to improve study methods for the students.
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    Cheating detection in browser-based online exams through eye gaze tracking
    (Faculty of Information Technology, University of Moratuwa., 2021-12) Dilini, N; Senaratne, A; Yasarathna, T; Warnajith, N; Seneviratne, L; Ganegoda, GU; Mahadewa, KT
    Eye-tracking can detect and examine human visual attention, emotional conditions, latent cognitive processes such as efforts to recall a concept or the fear of running out of time, and so on. Hence, we can use eye-tracking to identify deviant behavior patterns in learning and problem-solving. At present, given the existence of a global pandemic, online exams are widely used by educational institutions to evaluate students' performance. However, identifying cheating is challenging due to the absence of a human (invigilator) monitoring students' behavior as done in exams held in a physical location. In an online environment, students' behavior, and attempts to cheat, can only be captured via a computer, thus requiring a mechanism for online proctoring with capabilities for cheating detection. In this research paper, we present a browser based cheating detection approach in online examinations through eye gaze tracking. We developed a browser plugin to track the eye gaze movements through the in-built web camera. Using the plugin, we generate an eye gaze dataset while a student faces an online examination. We then process and analyze this dataset to detect any misbehavior during an online examination. The underlying research work of this paper identifies different eye gaze patterns during online examinations and present a cheating detection mechanism. For anomaly detection in the eye gaze data, we use a One-Class Support Vector Machine (OCSVM). We then use these identified anomalies to predict cheating behaviors of the test takers. The given approach can be used for any web-based quiz examination such as academic institutions' exams, company recruitment exams, and overseas testing exams to detect any anomalous behaviors of the test takers during the examination period. The given eye tracking approach can also be applied to other research domains such as online gaming, and web usability studies to capture information related to user behaviors.
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    Computational modelling of synaptic plasticity: a review of models, parameter estimation using deep learning, and stochasticity
    (Faculty of Information Technology, University of Moratuwa., 2021) Kumarapathirana, KPSD; Kulasiri, D; Samarasinghe, S; Liang, J; Ganegoda, GU; Mahadewa, KT
    It is imperative to understand the human memory formation and impairment to treat dementia effectively. There is ample scientific evidence that memory formation is strongly correlated to synaptic connections. Synaptic plasticity reflects the strength of these connections and is strongly related to memory formation and impairment. The complexity in the signalling pathways and interactions among proteins demands a systemic approach to study synaptic plasticity. Hence systems biology approaches are used in computational neuroscience. In this paper, we review the key computational models related to synaptic plasticity, the use of deep learning in parameter estimation, and the incorporation of epistemic stochasticity in the models.
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    Convolutional neural network to reproduce selfie images after removing supportive hand
    (Faculty of Information Technology, University of Moratuwa., 2021-12) Weerakoon, WMHI; Meegama, RGN; Ganegoda, GU; Mahadewa, KT
    Although selfie images have become popular among smartphone users, the supportive hand that holds the camera ruins the beauty of the picture. The captured images will look more realistic if the supporting hand is removed from the original image. This paper proposes a machine learning and a computer vision based approach to remove the supportive hand and reconstruct the removed hand that matches with the person who took the picture. A fully convolution neural network (FCN) and a partial convolution neural network (PConv Net) have been used to accomplish this task. Results indicate that the FCN gives 94.58% validation accuracy with the PConv Net is utilized to train the model for background matching and hand creation. The FCN and PCovNet models minimize validation lose up to 1.73 and 1.95, respectively.
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    A data driven approach for detection and correction of spelling errors in sinhala essays
    (Faculty of Information Technology, University of Moratuwa., 2021-12) Samarasinghe, PM; Sewwandi, WBI; Ranathunga, L; Wijetunge, WASN; Ganegoda, GU; Mahadewa, KT
    This paper proposes novel approaches for checking and correcting spelling errors in Sinhala essays written by candidates of grade five scholarship examination. They don't have a proper mechanism to identify their spelling mistakes in essays by themselves. Spelling errors by such students may occur due to the violation of spelling rules, missing or adding of letters, missing modifiers, inaccurate spelling in a similar structure, and similar sound letters]. To mitigate such challenges, the Sinhala corpus file has been developed to identify the accurate and inaccurate spellings of the written words. The role of this application is to identify the correct and incorrect words which are entered by the user and generate the most correct words as suggestions for the incorrect words. This paper introduces three new novel approaches to detect the correctly spelled words in Sinhala essays namely object word checker method, suffixes checker method and similar word checker method. With addition to that this paper discusses three approaches to generate accurate suggestions including one novel approach. When evaluating the accuracy of the spelling error detection and correction module the overall results for precision, recall, and the f -measure were recorded as 83.05%, 85.57%, and 86.62% respectively.
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    Design of a novel current controlling module for functional electrical stimulation (fes) system
    (Faculty of Information Technology, University of Moratuwa., 2021-12) Chamal, GRP; Fernando, MIM; Kulathunga, WPMW; Pathirana, KD; Prins, NW; Ganegoda, GU; Mahadewa, KT
    Functional electrical stimulation (FES) is widely used in rehabilitative therapies such as restoration of motor function for post-stroke and other types of paralysis. Here we present the design of an FES current controlling module which can be directly connected with micro-controllers to provide repetitive rehabilitation therapies. The key features in this module are the ability to generate customized pulse trains, interfacing with the computer, and low cost. The results show that the output current is independent of the load and the current can be controlled 0 – 25 mA at 40 Hz frequency.
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    Detection of suicide ideation in twitter using ann
    (Faculty of Information Technology, University of Moratuwa., 2021-12) Yatapala, KDYHT; Kumara, BTGS; Ganegoda, GU; Mahadewa, KT
    Suicide is considered one of the leading problems in the present. Detecting suicide earlier and providing a solution is considered the most successful way to suicide ideation and suicide attempts prevention. At present, online communication channels are used to express the suicidal tendencies of some people. This paper presents a machine learning approach to identify suicide pattern and detect suicide ideation or thoughts by considering online user-generated content with the aim of suicide ideation detection. People who have suicidal ideations, express strong negative feelings. Here, an Artificial Neural Network is used as a machine learning algorithm. To detect ideations of suicide, we generate feature vectors using different techniques including Word2Vec, Doc2Vec, and TF-IDF features. As the online user communication channel, we select Twitter.
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    Determining flood risk vulnerability using factor analysis approach
    (Faculty of Information Technology, University of Moratuwa., 2021-12) Karunarathne, AWSP; Piyatilake, ITS; Ganegoda, GU; Mahadewa, KT
    Floods are becoming a frequent natural disaster in Sri Lanka. Although it is an uncontrollable natural event, it is essential to pay attention in necessary policy making in order to control floods in the future. Therefore, this study targets to develop a framework to identify the high risk areas in Sri Lanka. This provides useful information to the government as well as the policymakers to take necessary preparedness, prevention, and awareness actions to lessen the risk from floods. This study uses principal component analysis (PCA) to explore the flood situation in Sri Lanka and to rank the main regions according to the risk level by considering flood risk and controlling factors. Twelve factors including both flood risk and controlling indicators are identified based on prior literatures and then the original data set is constructed. With the aid of the PCA method, four principal components are identified, and they are human induced factor, natural induced factor, human induced controlling factor, and natural induced controlling factor. Based on the weights of the principal components a comprehensive score is derived. Finally, the main regions are ranked using the comprehensive scores. The results reveal that Rathnapura, Kalutara, Colombo, Kurunegala and Matara regions have a high-risk chance of having floods.
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    Development of digital storytelling platform for children based on emotions
    (Faculty of Information Technology, University of Moratuwa., 2021-12) Vijayakumaran, T; Thavachelvam, T; Gnaeswaran, A; Sandanayake, TC; Sumathipala, K; Bandara, S; Ganegoda, GU; Mahadewa, KT
    Storytelling is one of the means to make Gen Alpha children attractive to books. This is becoming a challenging task for parents, as they have to spend time telling stories to children. This research proposed a system that can emotionally tell stories as a human storyteller tells stories with twists and suspense. This research involves the implementation of technologies related to Natural Language Processing and Deep Learning, especially the inclusion of Convolutional Neural Networks, Transformers. Camera images of the storybook are the input for optical character recognition (OCR) of the Storybook. Here, various complex algorithms are used throughout the processes of optical character recognition (OCR), sentence-level contextual emotion recognition(CER), and emotional text to speech(TTS) synthesis to implement the Emotional digital storyteller. In addition, immense testing is carried out to explore the effectiveness and characteristics of the implemented model. We achieve 75% and 72.09% accuracy in OCR and CER respectively and MOS values of 3.85 and 1.65 in neutral and emotional TTS respectively. The experiment results show that each model performs well in the children's domain, specifically storytelling books.
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    Diagnostic intervention for mental disorder
    (Faculty of Information Technology, University of Moratuwa., 2021-12) Senanayake, S; Karunanayaka, C; Dananjaya, L; Chamodya, L; Kumari, S; Chandrasiri, S; Ganegoda, GU; Mahadewa, KT
    Mental health is one of the essential factors in the topic of healthcare and wellbeing. However, mental health disorders could cause severe damage, even loss of life to the person or the surroundings, if mental health disorders were not identified and appropriately cured. Unfortunately, though there is good help there, some people have a hard time detecting whether they are suffering from mental health disorders or not. In this study, the team proposes a system to detect mental health issues using facial emotion recognition (FER), sleeping patterns, social media web scraping, and heart rate. The intention is to give an accurate prediction of the mental health status of a person using these three nodes.
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    Digital platform to empower the self-employment in Sri Lanka
    (Faculty of Information Technology, University of Moratuwa., 2021-12) Wickramasinghe, HCP; Thebuwana, TD; Wijesinghe, GKHS; Dissanayake, UN; Kodagoda, N; Suriyawansa, K; Ganegoda, GU; Mahadewa, KT
    Unemployment is a huge problem around the world because a lack of job opportunities. People are unable to find the job opportunities according to their preferences and qualifications. As a solution for this, many countries are attempting to empower self-employment. Most of current world problems have been solved using modern technologies. Therefore, the development of self-employment also can be achieved through modern technology. The objective of our proposed platform, HIRELANCER, is empowering self-employment using modern technologies. HIRELANCER is bringing the consumers, service providers, and suppliers into the same platform. HIRELANCER will consist of innovative features that go beyond comparatively to other platforms such as an advanced mechanism to find best suitable service providers/suppliers for the service, handling the virtual front-desk, cost estimation for the services prior to contacting a service provider, and advanced facility to find a suitable career path for the people who are seeking career guidance. This research paper discusses how the innovative features of HIRELANCER will be beneficial for consumers, service providers, and suppliers and ultimately achieve our main objective, which is empowering self-employment in Sri Lanka.
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    Eketh: a machine learning-based mobile platform to facilitate the paddy cultivation process in Sri Lanka
    (Faculty of Information Technology, University of Moratuwa., 2021-12) Premachandra, JSANW; Kumara, PPNV; Ganegoda, GU; Mahadewa, KT
    Agriculture is a significant source of human survival and it accounts for the socio-economic growth in many developing countries including Sri Lanka. Paddy Cultivation occupies a remarkable place in Sri Lankan agricultural sector. Unpredictable climatic change has become a critical issue for paddy farmers while unawareness on pest, diseases, new technologies, etc. have also adversely affected Paddy Cultivation productivity. As a solution, the focus on the requirement of accurate weather predictions and timely access to the information for decision-making in Paddy Cultivation is highly progressive. This study introduces eKeth: a mobile platform that provides proper guidance for Sri Lankan paddy farmers through allowing timely access to data enhanced with machine learning. A weather prediction model based on machine learning has been developed to recommend the most suitable days for each farming task in paddy cultivation. The application includes several other features integrated with this machine learning model. Farmers can directly reach help from agriculture experts by posting a query on pest and disease-based issues. Fertilizer management feature allows calculating the amount of fertilizers upon different paddy types and growth stages. Buy and sell feature integrated with this mobile solution guide farmers on newly available machineries and the places where they can make purchases. Farmers can stay updated with the latest agriculture news though the news module while maintaining communications with other farmers and agriculture experts through the community forum empowered by this application. Machine Learning Model used in weather prediction achieved 89% accuracy for Random Forest. Statistical analysis of the user testing results recognizes that the system has been able to achieve a higher user satisfaction.
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    Exploring unorthodox predictors of smartphone addiction during the covid-19 outbreak
    (Faculty of Information Technology, University of Moratuwa., 2021-12) De Silva, GHBA; Sandanayaka, TC; Firdhous, MHM; Ganegoda, GU; Mahadewa, KT
    Smartphones became an integral part of household & corporate management across all industries which resulted in high screen time, & smartphone addiction during the pandemic. This study attempts to examine the association between sociodemographic factors, & perceived smartphone addiction towards real smartphone addiction. Kwon's (2013) validated Smartphone Addiction Survey was used to collect data from the identified subjects (n = 192), and descriptive analyzes and statistical crosstabs were used to infer the associations. The results portray that Sex and Age are strong predictors of smartphone addiction: females over males tend to get addicted to smartphones, while age below 25 is highly addicted to smartphones, and age over 41 is less smartphone addict. The level of education is a moderately fair predictor of smartphone addiction. The higher the level of education, the higher the tendency to become addicted to smartphones. Marital status is not a good predictor of smartphone addiction in context, and there is no difference between being married or not of smartphone addiction. Perceived smartphone addiction is a good predictor of smartphone addiction, who believe they are addicted are more likely to become addicted to smartphones, and vice versa.
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    Fleet management with real-time data analytics
    (Faculty of Information Technology, University of Moratuwa., 2021-12) Rathnayaka, RPDT; Ekanayake, KVJP; Rathnayake, HUW; Jayetileke, HR; Ganegoda, GU; Mahadewa, KT
    Significant effort has to be devoted to surviving the businesses relying on fleet vehicles in the year 2020 and ahead as the novel coronavirus (COVID-19) epidemic became pandemic. Executing profitable business while keeping the staff safe and productive is a critical challenge to deal with. To find a solution, we focus on driver management out of major functions in fleet management such as vehicle, driver, and operation management. We were unable to identify a study conducted to capture real-time data on a ride in a fleet. Therefore, to fill that gap we implemented a cost-effective real-time Fleet Management System (FMS) using data analytics with the use of ESP32 SIM800L with reprogrammable capabilities. Fleet can use this system to monitor real-time data on vehicle location, remaining time to the destination, vehicle speed, and distance traveled. Moreover, the system can be personalized as it has reprogrammable features to be enabled or disabled based on the customer's preference. Once the data is captured through the Global Positioning System (GPS) receiver, data will be transmitted via General Packet Radio Service (GPRS) to two remote servers. One server is hosted locally with SQL and where the other is hosted in a cloud environment with a Firebase realtime database. The vehicle location is tracked using GPS. For fast data transfer, 3G Global System for Mobile communications (GSM) with ESP32 800L microprocessor was used. A web-based graphical user interface is developed to analyse and present the transmitted data. Vehicle information can be viewed and located on the web application in form of google maps. Real-time data analytics is used with Firebase's real-time database. Furthermore, Short Message Service (SMS) facility is made available for the driver to communicate with configured mobile numbers
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    A framework to detect sale forecasting with optimum batch size
    (Faculty of Information Technology, University of Moratuwa., 2021-12) Saradha, RMS; Samadhi, MA; Manawadu, I; Ganegoda, GU; Ganegoda, GU; Mahadewa, KT
    Today, sales forecasting plays a key role for each business. To maintain the sales process successfully, every manufacture focus on retaining optimum production batch size. Therefore, this study aims to develop a framework to detect sale forecasting with optimum batch size. This work focuses on predict future sales and optimum production batch size by using different machine learning techniques and trying to determine the best algorithm suited to the problem. Here, Auto-Regressive Integrated Moving Average (ARIMA) model is used to predict future sales and Artificial Neural Network (ANN) model is developed to determine the optimum level of production as a function of product unit, setup cost, and holding cost in our approach and have found these models have better result than other machine learning models.
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    Gis powered an automated generic flood model for river basins in Sri Lanka
    (Faculty of Information Technology, University of Moratuwa., 2021-12) Bandara, MNL; Premasiri, HMR; Sudantha, BH; Ganegoda, GU; Mahadewa, KT
    Flood is the most common and deadliest form of disaster that affects lives and properties all around the world. Predicting natural disasters is very complex due to the lack of proper methods and resources in countries like Sri Lanka. But if there is an efficient prediction system it helps to save not only lives but the environment and infrastructure too. Therefore, the aim of this study is to pave the pathway to build an efficient and effective flood prediction system through analysing available flood modelling techniques and their applications to find their strengths and weaknesses. Then the result of the study could be used to put the foundation for the main requirement of building the system to predict natural disasters. A generic model was developed to take any DEM data and a pour point feature class layer for the specific DEM to generate outputs based on other variables that could be input to the model. It gave model calibration capability as well as significant time saving on tasks. The use of special tools like the ‘Parse Path’ tool in ArcGIS Pro, gave the capability to name output easily and quickly. And it also made saving so efficient because it automatically saves all the results to the file path of the DEM. Due to these factors, when it starts raining in the upper catchment area, could forecast due inundation area in minutes. Including the GIS technology could improve the data quality and availability while incorporating different data sources for more in-depth analysis could give more accurate predictions. Using the GIS-based hydrological model, a suitable system to implement in Sri Lanka could be developed.
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    Heuristics-based sql query generation engine
    (Faculty of Information Technology, University of Moratuwa., 2021-12) Sugandhika, C; Ahangama, S; Ganegoda, GU; Mahadewa, KT
    A database is one of the prime media to store data. Most of the time, relational databases are preferred over other databases due to their ability to represent complex relationships between data. Languages like Structured Query Language (SQL) are used to retrieve data stored in relational databases. Information stored in these databases is often accessed by naïve users who do not possess high competencies in technical database querying. Therefore, Natural Language Interfaces to Databases (NLIDB) are being developed to translate natural language into SQL queries and retrieve the corresponding database results. This paper proposes a novel NLIDB called SQL Query Generation Engine which has been developed using a heuristics-based approach. The system was tested with more than 200 natural language queries and has shown an overall accuracy of 93%.
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    How to pretrain an efficient cross-disciplinary language model: the scilitbert use case
    (Faculty of Information Technology, University of Moratuwa., 2021-12) la Broise, JBD; Bernard, N; Dubuc, JP; Perlato, A; Latard, B; Ganegoda, GU; Mahadewa, KT
    Transformer based models are widely used in various text processing tasks, such as classification, named entity recognition. The representation of scientific texts is a complicated task, and the utilization of general English BERT models for this task is suboptimal. We observe the lack of models for multidisciplinary academic texts representation, and on a broader scale, a lack of specialized models pretrained on specific domains, for which general English BERT models are suboptimal. This paper introduces ScilitBERT, a BERT model pretrained on an inclusive cross-disciplinary academic corpus. ScilitBERT is half as deep as RoBERTa, and has a much lower pretraining computation cost. ScilitBERT obtains at least 96% of RoBERTa's accuracy on two academic domain downstream tasks. The presented cross-disciplinary academic model has been publicly released11https://github.com/JeanBaptiste-dlb/ScilitBERT. The results obtained show that for domains that use a technolect and have a sizeable amount of raw text data; the pretraining of dedicated models should be considered and favored.