Reviewerly/exHarmony huggingface dataset:
Implementation Of Tree Model In The Development Of E-Mantram Android Application

Hindu Mantram is chants of speech with supernatural powers, which should not be done carelessly. The Balinese Hindu Mantram is a modified form of the Hindu Mantram that adapts to the local wisdom of the Balinese Hindu Community. The problem is that there is no digital education platform regarding the Balinese Hindu Mantram. Based on these problems, a mobile-based information system was built that integrates the Balinese Hindu Mantram and Yadnya Ceremony with its ceremonial procession. This information system applied Model Tree and UAT with PSSUQ Method. This research aimed to develop an application that can be a platform to provide education about the Balinese Hindu Mantram and its relationships. The results obtained from this research were the E-Mantram Android mobile application that implemented the Tree Model and UAT results with a System Usefulness value of 1.94, Information Quality of 2.06, Interface Quality of 2.06, and Overall of 2.01.
Alleviating Mode Collapse Problem in Deep Bayesian Active Learning

Deep Bayesian Active Learning is an Active Learning technique that proves a success in high dimensional datasets. Standard Deep Models require a large amount of data to achieve high performance. On the contrary, Deep Bayesian Neural Networks facilitates high accuracy while utilizing fewer data. In addition, they outperform regular deep models during the selection process of data points since they consider the uncertainty of both model parameters and data points. Nevertheless, Deep Bayesian Active Learning is not robust against the Mode Collapse problem. In other words, it samples data points that are poor in diversity since it only considers the uncertainty of data points. This work proposes a method that combines Deep Bayesian Active Learning with a clustering based approach and alleviates the Mode Collapse problem. The proposed method outperforms the baseline model in terms of accuracy.
The Performance Comparison of Different Training Strategies for Reinforcement Learning on DeepRTS

In this paper, we train reinforcement learning agents on the game of DeepRTS under different training strategies, which are i) training against rule based agents, ii) self-training and iii) training by adversarial attack to another agent. We perform certain modifications on the DeepRTS game and the reinforcement learning framework to make it closer to real life decision making problems. For this purpose, we allow agents take macro actions based on human heuristics, where these actions may last multiple time steps and the durations for these actions may differ from each other. In addition, the agents simultaneously take actions for each available unit at a time step. We train the reinforcement learning based agents under three different training strategies and we provide a detailed performance analysis of these agents against several reference agents.
Neural Machine Translation Approaches for Post-OCR Text Processing

Optical Character Recognition (OCR) is the process of extracting the texts from the images by means of some special programs and transferring them to the computer environment. OCR quality directly affects the quality of most natural language processing processes. Many applications such as text classification, information extraction, text summarization with texts extracted from images are used in daily life. Therefore, detecting and correcting incorrectly translated texts after OCR is a topic that researchers are working on with many methods today. In this study, it is aimed to apply and observe the results on the dataset presented in the International Conference on Document Analysis and Recognition (ICDAR) 2019 OCR Post Error Detection and Correction competition, using the latest neural machine translation methods to find and correct post-OCR text errors.
A Sliding Window Approach for Early Prediction of Sepsis

In this study, it was aimed to detect sepsis before it occurs and thus reduce the mortality rate by enabling early treatment. The inference performance of the machine learning method was increased by monitoring the demographics and vital signs of the patients and various laboratory test results collected at regular intervals with time-based feature modeling. A sliding window approach was proposed for this modeling so as to include the past six hours, and this feature vector has been used to classify whether or not sepsis will develop in patients. Model performance was evaluated from different perspectives on the basis of numerous studies proposed in recent years, and it was concluded that the proposed approach resulted in a significant performance improvement.
Deblurring in Color Images Using General Tikhonov Method Based on FFT Algorithm

During recording an image, it is inevitable that it will not be affected by corruptions such as blurring and noise. Many methods have been developed in the literature to remove such corruptions from the image. In this study, a method using a larger size Laplace filter in the general Tikhonov method based on fast Fourier transform is proposed to remove such corruptions in color images. The proposed method is compared with similar methods in the literature, and it is shown that it produces efficient results.
A New Initialization Technique: Truncated Towers

Artificial Neural Networks (ANN) can perform various tasks by modifying their parameters, determined by initialization methods, using activation functions and learning algorithms. Alongside chosen training dataset, suitability and success of the chosen methods determine how well they can fulfill these tasks. With the &#x2018;Truncated Tower Distribution&#x2019;, which we developed to find a better initialization method, a partial and visible increase in success rates have been achieved in classification tasks on different data sets, using neuron number-independent and neuron-number-dependent initialization methods.
The relationship between structural dimensions and information overload

The phenomenon of information overload is one of the common problems facing decision makers in the information age as a result of dealing with a huge amount of data and information that exceeds their ability to process and understand it, as well as the difficulty in distinguishing between necessary and unnecessary information. The current study aimed to analyze the relationship between the organizational structure represented by its dimensions (centralization, formalization, complexity) and information overload. Data were collected by adopting a questionnaire. The study sample consisted of (84) members of the examination committees at Duhok Technical University. The results showed a positive significant relationship between centralization, formality, complexity, and information overload. Also, among the three dimensions of the organizational structure, centralization is the most contributing dimension to the phenomenon of information dumping. Based on the results, the conclusions and suggestions were made. The study also included suggestions for future research in light of the limitations of the study. The originality of this study stems from the paucity of research on the relationship between organizational structure and information overload.
Relationship Between Information Moral Behavior and Risk Awareness Associated With SNS Use

Social networking services (SNS) are valuable tools for disseminating information if used following the rules, but there is a risk of getting into trouble if they are misused. On the other hand, although unqualified, students in the medical field inevitably handle patients' personal information through on-site training. Therefore, each student must be aware of and responsible for information morals. In this study, to clarify effects of risk awareness on information moral behavior, we surveyed nursing students about their behaviors related to information morality and the risks associated with SNS use during on-site training. This survey revealed that the information moral behavior of nursing students in clinical practice is related to their risk awareness associated with SNS use. The results suggest that risk awareness education is effective for nursing students to acquire correct information moral behavior.
Determining source code repetitiveness on various types of programming assignments

Software projects code duplication and plagiarism are very important in various test cases. The purpose of the work presented in this paper is to observe how various software architectures, project structures, and coding approaches generate different views on code changes. In this paper, code plagiarism -code comparing, in different types of projects has been analyzed through two different approaches. Pythonscript based on the sequence matcher function and the GitLab compare tool are analyzed and compared.Results are presented and discussed in the paper.
Implementation of embedded messages using steganography in the PHP software package

The term steganography is usually associated with hiding and concealing information and messages. People, and even IT professionals, very rarely come into contact with steganography and steganalysis. Only messages are protected by cryptographic protection, while steganography can be said to protect both messages and parties participating in the communication. Steganography also means hiding messages inside computer files and data streams. This paper provides an overview of the implementation of embedded messages using steganography in the PHP software package. Emphasis is placed on hiding information, i.e. messages in JPEG images. As well as decoding or reading a hidden message. The field of steganography is naturally linked to the field of steganalysis, the primary goal of which is the detection of a hidden message, and then its extraction from the object of the message carrier. The most commonly used method for hiding messages is LSB, it is a method that changes the least significant bits to match the secret message. Then, by passing the steganographic key, the message is encrypted using the RC4 algorithm.
Exploring AI-Infused Products Qualities to Unleash AmI

Artificial Intelligence (AI) is entering our daily life and personal environments, as AI-infused products such as smart speakers are entering millions of houses worldwide. Despite the success and their potential to unleash Ambient Intelligence, they are still considered useless or close to a gadget dimension. Accordingly, we consider it mandatory for the design discipline to acknowledge those products, trying to understand and frame the User Experience (UX) they entail. The present study explores the UX dimensions that could describe and assess the experience enabled by such devices. It is the first step of a research program to create a novel UX evaluation method for AI-infused products. In the study, we employed a multi-method approach to (i) understand the main UX dimensions commonly assessed, (ii) identify specific UX dimensions for AI-infused products, and (iii) verify the assumptions with a sample of advanced users. The results are described, discussed, and framed within the current literature.
Implementasi Learning Management System untuk Mendukung Pembelajaran Jarak Jauh Sekolah Menengah Kejuruan

Around 19.8 million high school children in Indonesia are currently studying under the threat of the COVID-19 pandemic. To prevent the transmission of the COVID-19 in the school environment, the Minister of Education and Culture has issued a circular letter regarding online learning and work from home, which requires all schools to implement the “Learn from Home” policy. This policy must be applied from the new semester of 2020-2021 and needs to be supported by a Learning Management System (LMS) along with the competence of all stakeholders. Moodle was chosen because it is free, customizable, and can be deployed in a dedicated server to be fully managed by the school. Implementation stages include requirements gathering, installation and configuration, testing, training, and evaluation. The evaluation noted some needs for the next program, such as simplifying the input process for quiz questions, exploring deeper features, and providing more informative tutorials.
PENGARUH MODIFIKASI MEDIA PEMBELAJARAN BOLA VOLI TERHADAP PENINGKATAN KEMAMPUAN TEKNIK DASAR BERMAIN BOLA VOLI

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PERAN PUSTAKAWAN DALAM MENINGKATKAN MINAT DAN KEBIASAAN MEMBACA SISWA DI SDN GUNUNG BATU 2 KOTA

THE ROLE OF LIBRARIANS IN INCREASING STUDENTS’ READING INTEREST IN SDN GUNUNG BATU 2, BOGOR CITYThe purpose of this study was to determine the Role of Librarians in Increasing Student Reading Interest and Habits at SDN Gunung Batu 2, Bogor City. This research is a qualitative research with a descriptive approach. The subjects of this study were students and school librarians at SDN Gunung Batu 2, Bogor City. This research was conducted in the odd semester of 2019/2020. The data collection techniques used were observation, documentation, and interviews. Data collection techniques using the Miles and Huberman method, namely data reduction, data display, and drawing conclusions are carried out continuously until it is complete so that the data is saturated. Test the validity of the data using triangulation of sources and techniques as well as conducting a member check. And the research results show some findings, namely the background of the role of librarians in increasing students 'reading interest as a form of librarian's success in increasing students' reading interest and habits at SDN Gunung Batu 2, Bogor City. Furthermore, based on the results of observations, interviews, and documentation carried out in the field to related parties who know the problems of this thesis. Librarians provide a mandatory library system for all scheduled classes, hold reading corners, provide good service, hold the latest collections in the library, cooperate with regional libraries, always attend seminars or workshops, always include students in storytelling competitions, read poetry which is related to increasing students' reading interest and habits. In increasing students 'reading interest and habits, librarians certainly have a positive impact on students' reading interests and habits in accordance with the expectations of the librarian's own role. Because the role of the librarian has shown good development for students to be more like, happy, and enthusiastic about visiting the library so that the interest and reading habits of students at SDN Gunung Batu 2 Kota Bogor have increased every year according to the existing percentage.
MALICIOUS URL DETECTION USING MACHINE LEARNING AND DEEP LEARNING

The risk of network information insecurity is increasing rapidly in number and level of danger.The methods mostly used by hackers today is to attack end-to end technology and exploit human vulnerabilities.These techniques include social engineering, phishing, pharming, etc.One of the steps in conducting these attacks is to deceive users with malicious Uniform Resource Locators (URLs).As a result, malicious URL detection is of great interest nowadays.There have been several scientific studies showing a number of methods to detect malicious URLs based on machine learning and deep learning techniques.In this paper, we propose a malicious URL detection method using machine learning techniques based on our proposed URL behaviours and attributes.Moreover, bigdata technology is also exploited to improve the capability of detection malicious URLs based on abnormal behaviours.In short, the proposed detection system consists of a new set of URLs features and behaviours, a machine learning algorithm, and a bigdata technology.The experimental results show that the proposed URL attributes and behaviour can help improve the ability to detect malicious URL significantly.This is suggested that the proposed system may be considered as an optimized and friendly used solution for malicious URL detection.
Artificial Intelligence and Image Analysis for the Identification of Endometrial Malignancies

Abraham Pouliakis et al. compare Machine Learning Algorithms (MLAs) in the discrimination between benign and malignant endometrial nuclei and lesions. Nuclei characteristics are obtained via image analysis and were measured from liquid-based cytology slides. 416 histologically confirmed patients were involved, 168 healthy, and the remaining with pathological endometrium. 50% of the cases were used to three MLAs: a feedforward artificial neural network (ANN) trained by the backpropagation algorithm, a learning vector quantization (LVQ) and a competitive learning ANN. The outcome of this process was the classification of cell nuclei as benign or malignant. Based on the nuclei classification, an algorithm to classify individual patients was constructed. The sensitivity of the MLAs in training set for nuclei classification was in the range of 77%-84%. Patients' classification had sensitivity in the range of 90%-98%. These findings indicate that MLAs have good performance for the classification of endometrial nuclei and lesions.
Few-Shot Semantic Segmentation based on Detail-Preserving-Aware Loss

The vision-based autonomous driving techniques have been activated recently. The pixel-level prediction, semantic segmentation, is widely used in autonomous driving to acquire the pixel-level semantic annotation for further decision-making. However, traditional semantic segmentation requires a large training set to obtain promising performance. It is essential that it is hard to collect such a large dataset for every scenario for autonomous driving, such as outdoor or other bad weather scenarios. In this paper, we propose a novel boundary-aware loss incorporating the rare object augmentation techniques to boost the performance under a limited training set. The conventional edge extraction operator is applied in the ground truth to obtain the boundary information, as well as the detailed branch is proposed to approximate the predicted results to have a similar boundary with the ground truth. Such hard constraints result in the network being hard to overfit, as well as improve the robustness of the semantic segmentation. Extensive experiments demonstrated the proposed method's effectiveness, especially in limited training set scenarios.
DLKN-MLC: A Disease Prediction Model via Multi-Label Learning

With the increasingly available electronic health records (EHR), disease prediction has recently gained immense research attention, where an accurate classifier needs to be trained to map the input prediction signals (e.g., symptoms, auxiliary examination results, etc.) to the estimated diseases for each patient. However, most of the current disease prediction models focus on the prediction of a single disease; in the medical field, a patient often suffers from multiple diseases (especially multiple chronic diseases) at the same time. Therefore, multi-disease prediction is of greater significance for patients’ early intervention and treatment, but there is no doubt that multi-disease prediction has higher requirements for data extraction ability and greater complexity of classification. In this paper, we propose a novel disease prediction model DLKN-MLC. The model extracts the information in EHR through deep learning combined with a disease knowledge network, quantifies the correlation between diseases through NodeRank, and completes multi-disease prediction. in addition, we distinguished the importance of common disease symptoms, occasional disease symptoms and auxiliary examination results in the process of disease diagnosis. In empirical and comparative experiments on real EHR datasets, the Hamming loss, one-error rate, ranking loss, average precision, and micro-F1 values of the DLKN-MLC model were 0.2624, 0.2136, 0.2190, 88.21%, and 87.86%, respectively, which were better compared with those from previous methods. Extensive experiments on a real-world EHR dataset have demonstrated the state-of-the-art performance of our proposed model.
Phonetic Cues in Auditory Identification of Bulgarian, Czech, Polish, and Russian Language of Origin

This work presents the results of an auditory language of origin identification experiment. Disyllabic and trisyllabic logatomes were recorded by speakers of Bulgarian, Czech, Polish, and Russian, and presented to L1 speakers of the abovementioned Slavic languages. The goals of the test were to verify the ability of lay listeners to recognize the linguistic origin of speakers, based on spoken samples with limited segmental and suprasegmental information, and to correlate the signal features with the subjects’ performance. It was found that position of word stress is not an important predictor in language recognition. However, inherent vowel characteristics such as duration and vowel space computed by the means of Pillai scores correlate with subjects’ performance. Both the linguistic profile and the familiarity with closely related languages also appear to be relevant predictors of listeners’ performance. Finally, the information-theoretic notion of surprisal applied on regular cross-linguistic sound correspondences was correlated with recognition scores; though, the correlations did not reach the threshold of statistical significance. We conclude that auditory identification of linguistic origin by lay persons, native speakers of closely related languages, is possible even when exposed to limited segmental information, which can serve as a cue in the identification of linguistic origin.
The process of preparing students for design-projecting in art higher education institutions

International scientific conference «The role of psychology and pedagogy in the spiritual development of modern society» : conference proceedings, July 30–31, 2022. Riga, Latvia : «Baltija Publishing», 2022. 363 pages.
Ways of developing motivation to communicate through active interaction and creating situations of success in English classes with future aircraft maintenance engineers

International scientific conference «The role of psychology and pedagogy in the spiritual development of modern society» : conference proceedings, July 30–31, 2022. Riga, Latvia : «Baltija Publishing», 2022. 363 pages.
Modern psychology: the problem of defining the subject and tasks

International scientific conference «The role of psychology and pedagogy in the spiritual development of modern society» : conference proceedings, July 30–31, 2022. Riga, Latvia : «Baltija Publishing», 2022. 363 pages.
The place and role of corporate culture in organization activities

International scientific conference «The role of psychology and pedagogy in the spiritual development of modern society» : conference proceedings, July 30–31, 2022. Riga, Latvia : «Baltija Publishing», 2022. 363 pages.
Community service at Sanggar Belajar Cakrawangsa in Kucur Village, Dau Subdistrict, Malang

Kucur Village, located in Dau District, Malang Regency, is a village that has a lot of potential. However, the economic condition of most of its residents is still far from prosperous. Through this community service activity, the Ma Chung University community service team aims to develop the potential and empower the young generation in Kucur Village through practical English language basic education in collaboration with the Sanggar Belajar Cakrawasa as a partner. The three main problems faced by the partner are limited teaching staff, monotonous learning activities, and unavailability of English learning modules. The methods offered as solutions to the three problems are providing the Training of Trainers (ToT) program to Ma Chung University students to volunteer as additional teaching staff to the partner, according to the Teaching English for Young Learners (TEYL) theory, using fun learning techniques, develop learning modules. After 8 months of mentoring, the three main partners' problems were finally resolved.