International Research Symposium on Pure and Applied Sciences (IRSPAS)
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Item Generalizability of Music Emotion Classifiers: An Evaluation of the Applicability of miremotion in Emotion Classification of Sri Lankan Folk Melodies(4th International Research Symposium on Pure and Applied Sciences, Faculty of Science, University of Kelaniya, Sri Lanka, 2019) Charles, J.; Lekamge, S.Music conveys and evokes powerful emotions, owing to various musical characteristics such as rhythm, melody, and orchestration. This amazing ability has motivated the researchers worldwide to discover relationships between music and emotion. As a result, various data mining tasks are carried out where state-of-the-art machine learning techniques are utilized in music emotion classification. However, the literature reveals that these studies frequently employ western or western classical music. Since the emotional expression in music is carried out through various musical characteristics which are cultural-specific, generalizability of classifiers trained using different ground-truth music in new contexts warrants further research. Therefore, in our study miremotion which is an existing classification model defined in MATLAB MIRToolbox was investigated for its applicability in Sri Lankan folk melodies which is an abundant source of emotion expression. miremotion is a classification model trained using Western film music which is presented by previous scholars after extensive research. Our study comprised of a listening experiment, subjects being thirty university students (age 30-35 years; from non-music disciplines) in which subjective ratings for ‘happy’, ‘sad’, ‘tender’, ‘anger’, and ‘fear’ were obtained using a seven point Likert scale. Thirty music files (30s; 44100Hz; stereo; 32bit; .wav) were employed as the music stimuli. Objective predictions using miremotion were obtained for the same on the above-mentioned emotion categories. To identify the differences among the subjective ratings and the objective predictions, paired t-tests were performed. Between the two groups no significant difference was noted only for ‘anger’ (p > 0.05) where significant differences were noted for all other emotions: ‘happy’ (p < 0.001), ‘sad’ (p < 0.05), ‘tender’ (p < 0.05), and ‘fear’ (p < 0.05). The findings reveal that music-emotion classification models cannot always be generalized and that their applicability varies depending on the emotion. Findings are also in support of the need for developing classification models for cultural-specific melodies. The study further aims to apply machine learning techniques for emotion classification of Sri Lankan folk melodies followed by a comparison among different standard classification algorithms. Use of a limited number of thirty music files in this study was due to the need for considering a reasonable number of stimuli for human listeners in an experiment. The study was also limited by the inclusion of a homogeneous study population where further studies are needed which pay higher emphasis on the annotator profiles, as music-emotion perception is highly influenced by sociocultural and educational background.Item Machine learning based model for Android malware analysis and detection.(International Research Symposium on Pure and Applied Sciences, 2017 Faculty of Science, University of Kelaniya, Sri Lanka., 2017) Kavneth, G. A. S.; Jayalal, S.Rapid advancement of technology has enabled smartphones to become extremely powerful. They are capable of attracting a considerable amount of users with new features provided by mobile device operating systems such as Android and iOS. Android extended its lead by capturing 86 percent of the total market in 2017, and became the most popular mobile operating system. The Android operating system, which is found on a wide range of devices is owned by Google and powered by the Linux kernel. It is an open source operating system that enables mobile application developers to access unlocked hardware and develop new apps as they wish. However, this huge demand and freedom has made the hackers and cybercriminals more curious to generate malicious apps towards the Android operating system. They constantly target the security vulnerabilities in the operating system to gain access within the system. Even though, Google provides a primary set of security services, there are possibilities for potentially harmful applications in the Google Play store and other third party application stores. Thus, research on effective and efficient mobile threat analysis becomes an emerging and important topic in cybersecurity research area. Many researchers proposed various security analysis and evaluation strategies such as static analysis and dynamic analysis. In this research, we propose a hybrid approach, which aggregates the static and dynamic analysis for detecting security threats and attacks by Android malware application. This approach has two phases. First phase is the static analysis for applications, which will analyze the mobile application without execution. This focuses on extracting app APK file and examining permission requests made by Android apps that have declared in AndroidManifest.xml, as a means for detecting malwares. Because, in most of cases extra permissions granted by applications will allow the attacker to exploit the device. As the next phase, we perform dynamic analysis for mobile application. This phase focuses on runtime data obtained from the applications such as CPU, scheduler information from every running application, network calls, sensor data and so forth. For both phases, we have used supervised, machine learning based algorithms to train models and recognize malwares. In the first phase, potentially harmful applications can be identified as well as in the proposed hybrid mechanism, which is a combination of both phases. Data that was collected by several cybersecurity research centers were used for the evaluation of the proposed hybrid approach and both real-life malware and benign app data demonstrated a good detection performance with high scalability. The initial findings have been more accurate in identifying Android malwares rather than separating those two static and dynamic behaviors.Item Detection of cyber bullying on social media networks(Faculty of Science, University of Kelaniya, Sri Lanka, 2016) Priyangika. S.; Jayalal, S.Social Media is becoming an integral part of people’s daily lives today. It is an effective way of sharing one’s life experiences, special occasions, achievements and other events with their friends and family. Although it is a fruitful way to communicate with groups, some people find themselves being insulted or offended by others who are involved in certain post or conversations. These insulations can be based on racism, using profanity or any other vulgar or lewd language. This cyber bullying needs to be monitored and controlled by the social media site owners since it will highly effect on the number and safety of the active site membership. Currently, there is no automated process of identifying offensive comments by the social network site itself. It can be only diagnosed by humans after reading the comments, flagging or reporting them to the owner of the site or blocking the offender. Considering the massive big data set generated in social media daily, automatically detection of offensive statements is required to reduce insulation effectively. For this purpose, text classification approach can be applied where a given text will be categorized as insulting or not, through learning from a pre-learned model. In order to develop the model, data was collected from the popular data repository site named www.kaggle.com. The dataset consists of comments posted on Facebook and Twitter. Firstly the dataset was divided into training data set and test data set. Then the collected data was preprocessed by removing the unwanted strings, correcting words and eliminating duplicate data fields. In the next step, features or keywords were extracted which are qualified to distinguish a statement as ‘insulting’ using N-grams model and counting methods. Feature selection is done using Chi- Squared test and finally apply classification algorithms for separating insulting comments and non-insulting comments from a dataset given. Machine learning algorithms such as Support Vector Machines (SVM), Naïve Bayes, Logistic Regression and Random Forest are used for this. Out of the classification algorithms, SVM is to be performed better than other algorithms since this is a two-class classification problem and a comment is to be classified only into two separate classes which are ‘insulting’ and ‘neutral’. With an exact separation of a given comment into ‘insulting’ and ‘neutral’ category, cyberbullying happening through offensive comments posted on social media sites can be detected.Item WatchDog: An Advanced Surveillance System(Faculty of Science, University of Kelaniya, Sri Lanka, 2016) Ganepola, G.A.U.E.; Wijayasiriwardhane, T.K.Surveillance systems have become an integral part of the business world today due to the intensive care given to ensure the security of properties with a considerable monetary value. As a result, Closed-Circuit Television (CCTV) cameras are widely used in organizations. However, these systems have added an additional complexity to the user’s day-to-day work due to considerations like footage review and storage. The most common solution to this problem is incorporation of intelligence and automation to these systems. Typically, image processing and machine learning concepts are employed to implement such surveillance systems. However, the currently available advanced surveillance systems are not affordable for small and medium enterprises. The most widely used freely available advanced surveillance systems only detect motion. On the other hand, the systems that can identify the presence of people and even recognize them cost a considerable amount that does not fit into the budget of most, small scale businesses. Further, the most of the available free surveillance systems have not been designed in a way to achieve both storage efficiency and giving feedback on footages. In fact, most of them do record the footage 24x7. To address all those issues, in this paper, we present “WatchDog”, an advanced surveillance system that is implemented as a 100% free and open source product with features like detection of human presence, storage efficiency mode where the footage is stored only when there is a human in the frame, feedback and reporting facilities and recognizing people in the footage. The system detects people, and only those frames are recorded in high quality while rest of the video is saved in low quality to achieve storage efficiency. Using facial feature recognition, the system can predict factors such as gender and age of people in the footage. At the end of each day, the system produces a report with detailed information. This report would be a great relief from the user’s point of view since it drastically reduces the time to review the footages when required. Viola Jones algorithm, Haar features, Integral image, Adaboost and Cascading concepts are used for Human detections and facial feature recognition in this system. Our aim of this research is to answer the 3 major problems in surveillance systems such as affordability, storage efficiency and intelligence all at once.