2019 National Information Technology Conference (NITC)最新文献

{"title":"Security Awareness among Smart Speaker Users","authors":"A. Jayatilleke, S. Thelijjagoda, Parakum Pathirana","doi":"10.1109/NITC48475.2019.9114497","DOIUrl":"https://doi.org/10.1109/NITC48475.2019.9114497","url":null,"abstract":"High-speed internet together with advanced manufacturing processes have brought about the “Internet of Things” - everyday equipment which have now become part of a larger ecosystem, communicating with one another via the internet, to provide a unique experience to the end user. ‘Smart’ speakers, lighting systems and even fitness monitors coupled with smartphones offering fast processing and internet communication speeds, now allow individuals to virtually control all aspects of their personal and official lives, at the convenience of their fingertips. However, as with all technological innovations, the Internet of Things too is vulnerable to security breaches. These devices have access to vast amounts of data, some of which may be unknown to the end users themselves. Recent findings have shown that Smart Speakers could be ‘listening’ to user conversations even when they are disabled. A confidentiality breach of user conversations could not only affect an individual's personal life, but also jeopardise their work lives, extending up to even compromises in national security. This study focuses on the level of information security awareness amongst users of Smart Speakers. Smart Speakers have been increasing in use over the past several years, aided by the simplicity of configuration and the wide range of possibilities. This research also attempts to identify if users exercise the same level of security and caution given to internet banking accounts, with their Smart Speaker.","PeriodicalId":386923,"journal":{"name":"2019 National Information Technology Conference (NITC)","volume":"122 23","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120967883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RSSI and Feed Forward Neural Network (FFNN) Based Indoor Localization in WSN","authors":"Y. Weerasinghe, M.W.P Maduranga, M. B. Dissanayake","doi":"10.1109/NITC48475.2019.9114515","DOIUrl":"https://doi.org/10.1109/NITC48475.2019.9114515","url":null,"abstract":"In the advent of Internet of Things (IoT), Wireless Sensor Network (WSN) technologies play an important role in acquisition of different physical quantities for different applications. The Received Signal Strength Indicator (RSSI) based indoor localization is a well-known localization method used in WSN technologies due to its low complexity, availability and low energy consumption. In this research we explore the possibility of applying RSSI value based Feed Forward Neural Network (FFNN) jointly to identify the correct location of a moving object or a person, which is an important requirement of IoT-based Ambient Assisted Living (AAL) applications. We setup an experimental test bed for the acquisition of RSSI data remotely, which contained two types of nodes called beacon node and the mobile node. The ESP 8266 is used as the controller for nodes, which is based on IEEE 802.11 standard. The RSSI values from the beacon nodes will be sent to a remote server via Mosquitto Message Queuing Telemetry Transport (MQTT) broker, and then the RSSI values will be secondhand utilized by the FFNN supervised learning model that we developed at the remote server. Output of FFNN model gives the location of the object or person in two dimensional (2D) space. In the end of the research, the validity is checked by using the statistical assessment models and the results substantiate the significance of using supervised learning method in RSSI based indoor positioning.","PeriodicalId":386923,"journal":{"name":"2019 National Information Technology Conference (NITC)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123129628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Intelligent Approach to Segmentation and Classification of Common Skin Diseases in Sri Lanka","authors":"L. Wijesinghe, Dmr Kulasekera, W. Ilmini","doi":"10.1109/NITC48475.2019.9114507","DOIUrl":"https://doi.org/10.1109/NITC48475.2019.9114507","url":null,"abstract":"Skin diseases prevail worldwide, and the quality of life and overall health of patients are often hindered as a result. Early detection and treatment are key to a quick recovery. An automated system to identify skin diseases can act as a tool to assist doctors and healthcare workers. This paper presents an intelligent system to segment and classify three common skin diseases in Sri Lanka - tinea versicolor, atopic dermatitis and psoriasis - using image processing, genetic algorithm and machine learning. YUV -based color segmentation was applied to extract the affected region, then the texture and color features were extracted for classification. Genetic algorithm was utilized to obtain the optimized feature subset. An SVM based classifier was then trained and succeeded in classifying the three skin diseases with an overall accuracy of 86.7%.","PeriodicalId":386923,"journal":{"name":"2019 National Information Technology Conference (NITC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134397205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real Time Deception Detection for Criminal Investigation","authors":"Imasha Lakshan, L. Wickramasinghe, Sandaru Disala, Smirithika Chandrasegar, P. Haddela","doi":"10.1109/NITC48475.2019.9114422","DOIUrl":"https://doi.org/10.1109/NITC48475.2019.9114422","url":null,"abstract":"Deception Detection System (PREDICTOR) is a solution to support the criminal investigation process by providing a technological analysis in justifying the guilt of an accused criminal in the investigation process. This study gives guidelines to substantiate decision making in the interrogation. In judicature, the importance of a platform that is capable of analyzing the genuineness and the (a) reliability of a lie and a truth, (b) emotion of the suspect and the (c) attentiveness has been recognized for a long period. The feasibility of using Machine Learning (ML) techniques to build such platforms has been explored before. However, no known platform could identify the suspect's authenticity, emotion, and attentiveness. The goal is to analyze the brain waves and build a real-time deception detection application to analyze lie/truth, emotion and the attentiveness, which will support the investigation process in a wide range of angles to decision making. Electroencephalogram (EEG) based real-time lie detection, emotion detection, and attention detection will be implemented using ML tools and techniques along with the help of special hardware equipment called MUSE 2 headband. Especially this equipment is required for the data acquisition as well as the creation of the final application. The outcome of this system is a solution to be used during the criminal investigation process as a deception detection system for lie, emotion and attentiveness of the suspect. This is more effective in the questioning process to get an idea of the suspect. This system will have a major impact on the Police Department, Criminal Investigation Department, and Judicial System to ensure the real criminal and reduce the workload of Criminal Investigation officers.","PeriodicalId":386923,"journal":{"name":"2019 National Information Technology Conference (NITC)","volume":"129 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116577763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid Approach to Face Recognition System using PCA & LDA in Border Control","authors":"Susara S. Thenuwara, C. Premachandra, S. Sumathipala","doi":"10.1109/NITC48475.2019.9114426","DOIUrl":"https://doi.org/10.1109/NITC48475.2019.9114426","url":null,"abstract":"In this paper, we have proposed a hybrid approach to face recognition using PCA and LDA in border control like time-critical application. Duplicate passports, unauthorized VISA, fake identities, and border criminals have dramatically increased within the last few years in Sri Lanka due to a lack of proper identification system at the borders. Even though in the traditional VISA granting process biometrics are sent to borders and final destinations such as immigration counters, there are no proper face recognition measures at airports, to prove that it is the same person who comes to apply VISA that is identified at the airport. The proposed system uses Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) to compare updated face biometrics and the physical appearance at the airport in a novel hybrid way. After the face recognition is done by a hybrid solution with a confidence level, the candidate will proceed to the manual process as usual. The proposed system speeds up the traditional process, and increasing the accuracy of identification and smooth adaptation to the traditional method can be identified as the main benefits of the system. Face biometrics is the main ingredient of the proposed system. The system has been analysed with the traditional model and evaluated with authentic biometric sample and identified with 98% accuracy in face recognition with less average time. A nearest mean hybrid approach in the time-critical application can be identified as the novelty of the proposed system.","PeriodicalId":386923,"journal":{"name":"2019 National Information Technology Conference (NITC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122937275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HateSense: Tackling Ambiguity in Hate Speech Detection","authors":"K. Kumaresan, Kaneeka. Vidanage","doi":"10.1109/NITC48475.2019.9114528","DOIUrl":"https://doi.org/10.1109/NITC48475.2019.9114528","url":null,"abstract":"Hate speech propagated online has been a long-trailing issue which induces several negative effects on society. The current efforts for the automated detection of hate speech online have utilized machine learning techniques in order to try and solve the issue as a classification problem. However, the significant drawback that has been identified in existing literature is that the inability of existing systems to tackle the ambiguity when it comes to hate speech detection, more specifically differentiating between hateful and offensive content. This research aims to tackle this issue of ambiguity in hopes of improving hate speech detection in general. The proposed system will utilize human reasoning techniques such as ontologies and fuzzy logics along with sentiment analysis in order to detect hate speech and deconstruct the ambiguity present. The results of the proposed approach show that the system can perform well when it comes to differentiating between hateful and offensive content and it is able to outperform existing systems in crucial factors. Yet, the deconstruction of ambiguity becomes difficult when there are a smaller number of hateful keywords present although the fuzzy control system was able to compensate in most cases. Thereby this research stresses the need for considering the disambiguation between hateful and offensive content when it comes to hate speech detection and utilization of human reasoning techniques to further facilitate this process.","PeriodicalId":386923,"journal":{"name":"2019 National Information Technology Conference (NITC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123145465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cloud-Based Open Source Primary Care Electronic Patient Record System for Sri Lankan Citizens","authors":"K. Mendis, Tharindu Cyril Weerasooriya, S. Withana, Prabath Liyanage, Aruni Weerakoon Silva, R. Wickramasinghe, C. Weerabaddana","doi":"10.1109/NITC48475.2019.9114518","DOIUrl":"https://doi.org/10.1109/NITC48475.2019.9114518","url":null,"abstract":"Sri Lankans made over 100 million visits to public and private outpatient departments (OPD) during 2015, which is estimated to double in 2027. However, these visits have no records, either paper or electronic. Medical records are essential to provide continuity of care, and computer-based medical records were identified as essential technology in 1990 by the Institute of Medicine. The main initiative of the Ministry of Health addresses either OPD health information system or inward system, but it is limited to a few selected hospitals. There are no electronic health records (EHR) that can track patients as they crisscross between different primary care providers in public and private sectors, which is the normal behaviour of the majority of our patients. This paper gives a snapshot of the current healthcare system in Sri Lanka, notes the existing projects related to primary care health information systems, briefly reviews the current status of the global primary care EHR and describes our solution of a generic, cloud-based, open source EHR for use across public and private sectors focusing on a patient-centred electronic ‘personal health record’. We opted to modify a time-tested software solution OpenEMR - https://www.open-emr.org/ OpenEMR is a free and open source, ONC certified, electronic health records and medical practice management application featuring fully integrated electronic health records, practice management, scheduling, electronic billing, internationalization, and multi-lingual support. Sri Lanka OpenEMR (SLOEMR) is now used at the University Family Medicine Centre, Faculty of Medicine, University of Kelaniya at Ragama. Paper medical records of more than a decade were converted to the electronic format. We are in the planning process of piloting the SLOEMR in the Ragama Medical Officer of Health Area with a population of 70,000, with a single electronic record for each person across all private and public sector healthcare providers.","PeriodicalId":386923,"journal":{"name":"2019 National Information Technology Conference (NITC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128659946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evolutionary Algorithm for Sinhala to English Translation","authors":"J. K. Joseph, W. M. T. Chathurika, A. Nugaliyadde, Y. Mallawarachchi","doi":"10.1109/NITC48475.2019.9114453","DOIUrl":"https://doi.org/10.1109/NITC48475.2019.9114453","url":null,"abstract":"Machine Translation (MT) is an area in natural language processing, which focuses on translating from one language to another. Many approaches ranging from statistical methods to deep learning approaches are used in order to achieve MT. However, these methods either require a large number of data or a clear understanding about the language. Sinhala language has less digital text which could be used to train a deep neural network. Furthermore, Sinhala has complex rules, and therefore, it is harder to create statistical rules in order to apply statistical methods in MT. This research focuses on Sinhala to English translation using an Evolutionary Algorithm (EA). EA is used to identifying the correct meaning of Sinhala text and to translate it into English. The Sinhala text is passed to identify the meaning in order to get the correct meaning of the sentence. With the use of the EA the translation is carried out. The translated text is passed on to grammatically correct the sentence. This has shown to achieve accurate results.","PeriodicalId":386923,"journal":{"name":"2019 National Information Technology Conference (NITC)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122004145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}