Reviewerly/exHarmony huggingface dataset:
Keynote Speech Title: Intelligent Agent with Reasoning under Uncertainty

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. An intelligent agent is a program that can make decisions or perform a service based on its environment, user input and experiences. These programs can be used to autonomously gather information on a regular, programmed schedule or when prompted by the user in real time. Agents in real environments are inevitably forced to make decisions based on incomplete information. Even when an agent senses the world to find out more information, it rarely finds out the exact state of the world. For example, a doctor does not know exactly what is going on inside a patient, a teacher does not know exactly what a student understands, and a robot does not know what is in a room it left a few minutes ago. When an intelligent agent must act, it has to use whatever information it has. This lecture consider reasoning with uncertainty that arises whenever an agent is not omniscient.
AN EMPIRICAL STUDY OF THE DEVELOPMENT OF STUDENT TOLERANCE IN A MULTIETHNIC SOCIETY

The existence of tolerant relations in a multi-ethnic society leads to natural, non-coercive cooperation, the rapprochement of people from different cultures. Great hopes are placed on the development of tolerance in a multi-ethnic society as one of the main conditions for the prevention of various interpersonal conflicts, peace and harmony. Therefore, in the current situation, the results of the study of socio-psychological features of tolerance of the younger generation of different social, age, tender and other groups are a guarantee of successful development and implementation of psychological and pedagogical programs of conformity formation.The object of study of the article are male and female students of universities in Almaty.The aim of the author's research is to study the features of the development of student tolerance in a multi-ethnic society.The empirical research in the article includes the development of an empirical research program to study and describe the features of the development of student tolerance in a multi-ethnic society.The reliability of the results and conclusions of the study and consistent application of the rules of empirical research were ensured during the study. In addition, a sufficient number of samples, mathematical statistics and computer data processing devices were used to compare with the results of other researchers and to identify statistically significant patterns.
Low-Cost Smart COVID-19 Patient Monitoring and Support System

Social distancing, isolation, and quarantining are very familiar words since the outbreak of the coronavirus (COVID-19). COVID - 19 is a highly contagious pathogenic viral infection. It is very risky to get close contact with people who have COVID-19 symptoms or COVID-19 positive; nevertheless, covid patient monitoring is also significant for saving his/her life. To solve the Covid-19 pandemic situation accentuates a focus on remote patient monitoring. A small smart healthcare support system is built to monitor COVID-19 patients' health status and the patient emergency abet. This system can also trace the patient location; thus, aid can be provided to the patient promptly. This system uses a respiration sensor, oxygen saturation sensor, temperature sensor, heart rate sensor, GPS. All the sensors, as well as GPS, are connected with Arduino-Uno. By processing sensor data, the smart system can discern the patient's critical condition and forward this information to the doctor/nurse or hospital in charge and patient relative's smartphone as a text message. This paper aims to develop a system to support COVID-19 patients and develop a remote healthcare platform for monitoring pandemic situations and providing emergency aid promptly as a text to the smartphone.
Remote Health Monitoring with Cloud Based Adaptive Data Collection and Analysis

An e-health monitoring system with wearable sensors, smart phone and an intelligent web server is proposed in this paper in cloud environment. An adaptive data collection technique is included in the system to investigate the patient's health condition interactively. In the system the sensors gather the patient's physiologic parameters (e.g., heart rate, body temperature etc.) periodically and transfer the data to the smart phones via Bluetooth module. The patient data are then uploaded to the cloud server database using an android application. The server monitors the database intelligently to find abnormality in the database. If the server finds abnormality, then it sends a notification to the doctor android application to notify the condition of the patient. Based on the patient condition the doctor may ask anything about the disease symptom or suggest anything. We propose periodically data collection and monitoring the database adaptive to the patient condition. Moreover an on-demand real-time investigation technique is integrated in the system for doctor to check real time patient health condition. The proposed system is evaluated with the patients of Medical Center of University of Rajshahi. The results show the effectiveness of the proposed system.
PSYCHOLOGICAL AND PEDAGOGICAL SUPPORT OF INNOVATION-EDUCATIONAL ACTIVITY OF SCHOOL: ASPECT OF TRAINING OF FUTURE TEACHER-PSYCHOLOGIST

The problem of training a future teacher-psychologist in the context of psychological-pedagogical support of the innovative and educational environment of the school, simulating the activities of the school psychologist in this contextis considered in this article. The competences of future teacher-psychologiston psychological-pedagogical support of innovation-forming activity atschool, and peculiarities of their formation process are defined. Methodological conditions of preparation of future teacher-psychologist for psychological and pedagogical support of innovation and treatment activity in school have been identified.
MODELING OF PSYCHOLOGICAL AND PEDAGOGICAL SUPPORT OF STUDENTS WITH SPECIAL EDUCATIONAL NEEDS AT THE UNIVERSITY IN THE CONDITIONS OF INCLUSIVE EDUCATION

In Kazakhstan, there is a significant activation of inclusive processes in the higher education system. It is impossible to consider teaching students with special educational needs at a university without creating an inclusive educational environment and implementing psychological and pedagogical support measures. In this regard, there is a need to study the basics of psychological and pedagogical support of persons with disabilities at the university. Inclusive education involves the formation and support of such an integrative psychological and pedagogical space in which neither students with special needs nor students with conditionally normative development will experience difficulties in the process of interaction. The purpose of the study is to analyze the activities of a teacher-psychologist and tutor when accompanying students with special needs in higher education. Based on the data obtained, a model of psychological and pedagogical support of students with special educational needs in higher educational institutions was developed. The implementation of an effective model of support for students with disabilities at universities determines appropriate psychological and pedagogical prerequisites for the development of inclusive higher education. The successful integration of persons with disabilities into the social space of the university has an immense impact on the formation of a positive perception in the learning environment of students. Moreover, it contributes to their positive assimilation of the training program. Within the framework of this article tries to investigate the regulatory and legal framework of inclusive education in the Republic of Kazakhstan, its approaches, principles, and methods of implementing psychological and pedagogical support for students with special needs at universities.
Students' perceptions of listening learning using the bottom-up strategy

Listening learning using a bottom-up strategy provides a positive perception for students. The lack of research on students' perceptions of the bottom-up strategy in listening learning is the purpose of this research. The perceptions explored focused on student interest, student motivation, learning effects, problems faced, and solutions to problems students faced. The benefit of this research is to become a reference for the implementation of listening learning using a bottom-up strategy. This study uses a descriptive qualitative method with in-depth interviews with 4 students who have been learning listening using a bottom-up strategy for 2 years. The results of the interviews were analyzed using the Colaizzi method. The themes that emerged from this research were students felt this strategy was interesting, students felt this strategy was motivating in learning listening, students felt this strategy could improve their English skills apart from listening, but students felt that this strategy still had problems such as unclear sound. Students said that the problem could be fixed by making the class conducive and improving the quality of the audio that would be used.
Surviving from Child Abuse in novel Sing, Unburied, Sing (2017) by Jesmyn Ward

This research is an analysis of the novel Sing, Unburied, Sing (2017) by Jesmyn Ward. The purpose of this study is to reveal the issue of surviving child abuse in the novel Sing, Unburied, Sing (2017) by Jesmyn Ward. The issue of surviving from child abuse refers to the type of abuse experienced by the character, the impact of the abuse on the character, and the character's efforts to overcome the abuse. This study uses the concept of life instinct by Sigmund Freud. This study uses the text and context contained in the novel. As a result, the character can go through his difficult time and can rise from the abuse he experienced.
A Study of Human State Pattern Acquisition Method for Partner Robots

For a partner robot that supports a human, it is important to know the state pattern of the human in the environment so that the robot can generate personalized motions according to the state. In this study, we studied a human state classification method to enable the system to acquire human state patterns, which are difficult to design in advance. As a result, we showed that state classification is possible using human skeletal information and density-based clustering.
Context-Based Email Ranking System for Enterprise

This paper describes an approach for an automated context-based email ranking system for enhancing enterprise communication. An average day of an employee working in an organization includes cramming the inbox, to find and reply to important emails, sort them, organize them. All this is extremely stressful if the user has a deadline to meet. As they say, "A cluttered inbox, like the cluttered desk, hampers productivity". Hence, to address this problem, we have proposed a system, to organize the mails according to their priority based on the context and communication history between the clients. An algorithm was designed using machine learning and natural language processing to recommend Top ‘N’ emails from bulk so that the person can focus on important tasks at hand and get them done as fast as possible. The model was trained using Enron Email Dataset. For discovering topics from the document, LDA (Latent Dirichlet Allocation) Topic Modeling was used. During model training, a maximum coherence score of 0.53987 was observed and the minimum perplexity score was -13.985 for the Number of Topics equal to 20. For the experimentation purpose, a proportional slice was selected from the main dataset. The final email ranking was done using the LDA topic modelling and the communication frequency between users.
Generalization of LSTM CNN ensemble profiling method with time-series data normalization and regularization

This study concentrated on generalizing an anomaly detection method of time series data using ensemble LSTM CNN network with time series data normalization and regularization. Considering the relevant conditions must meet for time series normalization, an algorithm was proposed for time series normalization. Checking the stationarity and normality of time series data is fundamentally included in the proposed algorithm. Afterward, different types of time series data are visualized with different normalization methods, and the impact of each of these methods is discussed. Normalization techniques like Min - Max, Sigmoid and Tanh are used. When sigmoid normalization used with a dataset where the original data is almost in the range of zero to approximately one was able to normalize well. For the data which is not in the range of [0],[1] this method cannot be used since it tends to overlap data that is not available in original data. The study reveals that a smooth, non-linear sigmoid function performs a better transformation for many anomalous time series data as a normalization factor. The prediction errors of the LSTMCNNkeras (LSTM CNN ensemble neural network was implemented using Keras is called LSTMCNNkeras.) model are discussed and compared with and without proposed normalization approach. Also, the gross effect of both normalization and regularization steps to the prediction errors of the LSTMCNNkeras model is discussed. For the time series data where the range lies somewhere between 0 to 1, produced better predictions in LSTMCNNkeras model along with Sigmoid normalization and Dropout regularization techniques. The gross effect of Layer weight regularization with Tanh normalization was able to produce a foreseeable growth in the performance of LSTMCNNkeras model with more accurate predictions for many time series data.
Suspicious Human Crowd Behaviour Detection - A Transfer Learning Approach

Video surveillance systems play an important role in the public security sector. These systems are used to detect a variety of activities such as suspicious human behaviours, analyze crowd behaviours, manage road traffic and track vehicles. Due to the difficulty in manual monitoring of such activities, several research attempts were carried out to automate information extraction from surveillance systems using machine learning and deep learning approaches. The unavailability of large video data sets for processing purposes, is one of the common barriers in this research domain, since the majority of the existing videos are untrimmed, unannotated, and may contain ambiguous data. The purpose of this research is to propose a machine learning based transfer learning approach, to solve the limitations with the datasets specifically for accurate detection of violent crowd behavior via surveillance systems. Four state-of-the-art models were tested with varied configurations and the proposed prototype achieved highest model accuracy of 97%.
English Language Trainer for Non-Native Speakers using Audio Signal Processing, Reinforcement Learning, and Deep Learning

Lack of basic proficiency and confidence in writing and speaking in English is one of the major social problems faced by most non-native English speakers. Although the general adult literacy rate in Sri Lanka is above average by world standards, the English literacy rate is just 22% among the Sri Lankan adult population. Many individuals face setbacks in achieving their career and academic goals due to these language barriers. In a world where English has become a compulsory requirement to pursue higher education, career development, and performing day-to-day activities, "English Buddy" is a software solution developed to enhance the English learning experience of individuals in a more personalized and innovative way. The system provides an all-in-one solution while filling major research and market gaps in existing solutions in the e-learning domain. The system consists of a personalized learning environment, automated pronunciation error detection system, automated essay evaluation process, automated descriptive answer evaluation component based on semantic similarity, and real-time course content rating system. English Buddy is implemented using state-of-the-art technologies such as Audio Signal Processing, Reinforcement Learning, Deep Learning, and NLP. The LSTM, Sentiment Analysis, and Siamese network models have gained accuracy scores of 0.93, 0.92, and 0.81 respectively. Further, the UAT results proved that the personalized recommendations and pronunciation error detection processes are accurate and reliable. This research has been able to overcome the limitations of most existing solutions that follow traditional approaches and provide better results compared to the recent studies in the e-learning research domain.
Criminal investigation and management system using CCTV footage - "Eagle Eye"

Automated criminal identification is not a very popular topic in Sri Lanka. To identify criminals, the methods which authorities are using are unnecessarily time-consuming. To make this process immovable, identifying a wanted person using an automated system would be a better alternative rather than the current practices. Current practices and techniques such as gathering records from eyewitnesses are not highly reliable. Even though scanning through CCTV camera footage manually is again laborious. Using modern technologies such as biometrics would be the best way to achieve this task in terms of accuracy. We will also use abnormal behavioral detection accompanied by Threatening weapons. As biometric techniques, using face recognition and figure recognition will provide the most promising result. Along with the modified image enhancement method that we are suggesting, the system will be able to capture and process the task in a much better way. Although biometric systems have already been used in society, there is no such system which can be used to identify and verify criminals. we used algorithms such as CNN, DNN, LBPH and Deep learning. As a final result, the system will automatically identify the entire crime incident to improve the quality of the footage, detect the abnormal behaviors accompanied by threatening weapons, identity & recognize the registered criminals using faces and their figures automatically with a minimum amount of time and higher accuracy level.
Period Prediction of Sinhala Epigraphical Scripts using Convolutional Neural Networks

Inscriptions are important resources to know our history. The study of recognizing epigraphical scripts is a challenging task since the shapes of the characters were changed over the time and different sets of characters were used in different eras. Period prediction of epigraphical scripts is an important initial step in automated inscription character recognition systems, and also it helps archaeologists to find the era of an inscription in real-time. In this paper, we propose a novel approach to classify the era of Sinhala epigraphical scripts into five different periods using the images of Sri Lankan inscriptions. Since no previous studies were conducted, we have constructed a dataset for Sinhala ancient characters. Deep transfer learning techniques are utilized to train a CNN model with fewer numbers of samples. Moreover, a channel attention module is included to boost the character-wise features in classification. Based on the experimental results, the proposed approach showed 90.60% of average classification accuracy.
Lifetime-Driven Scheduling of Security-Critical Internet-of-Things Applications on Real-Time Heterogeneous Multicore Systems

With the rapid advance in information technology, we have witnessed the emergence of diverse promising Internet-of-things (IoT) applications, such as smart transportation, smart home, healthcare, and environmental monitoring. Due to parallel computing advantages, multicore systems are advocated as a good candidate hardware platform to accelerate the computation completion of IoT applications. However, the limited energy budgets and inherent openness characteristics of IoT scenarios pose fundamental challenges to the design of multicore systems. While making salutary attempts on improving the energy efficiency and augmenting the security level of IoT applications, most of the existing research works neglect the negative impacts of their optimization techniques on the lifetime of multicore platforms. Consequently, multicore systems potentially suffer from the earlier occurrence of permanent faults, i.e., have to be replaced in advance before the desired lifecycle ending point. In this paper, we conduct the first study on scheduling precedence constrained IoT applications depicted by directed acyclic graph (DAG) to minimize the energy consumption of multicore platforms under timing, security, and lifetime constraints. To address this problem, we first present a JADE-based offline algorithm to determine the optimal task-to-core allocation, task execution order, core operating frequency, as well as task security level. Following the offline algorithm, we then design a low-complexity dynamic algorithm to achieve extra energy savings by wisely adjusting core operating frequencies. Results reveal that our solution can reduce 41.48&#x0025; energy consumption while meeting all design constraints compared to state-of-the-art schemes.
Latency Minimization in Blockchain-Enabled Fog Computing Networks: A Novel Byzantine Fault Tolerance Approach

Recently, fog computing networks (FCNs) have shown enormous benefits yet suffer from security and privacy issues. Blockchain technology has been introduced as a solution to these issues. Nevertheless, it brought new challenges. In this paper, we propose Smart Byzantine Fault Tolerance (SBFT) algorithm for blockchain-enabled FCNs. The proposed algorithm is designed to overcome the challenges of deploying BFT algorithms into FCNs. Such as high latency, computational cost, and communication overhead. The problem was formulated to achieve low latency for the system by optimizing task computation resource allocation, task partitioning, transmission power control, and blockchain computation resource allocation while ensuring the system&#x0027;s security. Simulation results were conducted to illustrate the effectiveness of the proposed algorithm, which can achieve low latency, computational cost, and communication overhead.
RGC: Reliable Gesture Classification via Wearables Using GANs-Based Data Augmentation

Gesture-related human-computer interaction systems have been developed with different purposes. Wearable-based gesture recognition is well studied and considered to be effective. However, unexpected body movement of a user, such as walking and turning, is one issue that affects the robustness of classification. Collecting a sufficient dataset for every unexpected movement would be time-consuming and labor-intensive. To reduce the burden on users and address the lack of training data, we proposed RGC, which is a framework for IMU data augmentation. RGC adopts Generative-Adversarial-Networks-based and rotation-based augmentation to enhance the diversity of the dataset, which helps the classifier learn more effective representations for gesture recognition. We collected a dataset of 21120 arm gesture samples under different kinds of unexpected movements to evaluate RGC. The experiments showed that our method provides an 8.8% to 1.1% accuracy improvement for different SOTA classifiers with different original dataset sizes.