Reviewerly/exHarmony huggingface dataset:
An Adaptive Optimization Method Based on Learning Rate Schedule for Neural Networks

Artificial intelligence (AI) is achieved by optimizing the cost function constructed from learning data. Changing the parameters in the cost function is an AI learning process (or AI learning for convenience). If AI learning is well performed, then the value of the cost function is the global minimum. In order to obtain the well-learned AI learning, the parameter should be no change in the value of the cost function at the global minimum. One useful optimization method is the momentum method; however, the momentum method has difficulty stopping the parameter when the value of the cost function satisfies the global minimum (non-stop problem). The proposed method is based on the momentum method. In order to solve the non-stop problem of the momentum method, we use the value of the cost function to our method. Therefore, as the learning method processes, the mechanism in our method reduces the amount of change in the parameter by the effect of the value of the cost function. We verified the method through proof of convergence and numerical experiments with existing methods to ensure that the learning works well.
IMAGERY IDENTIFICATION OF TOMATOES WHICH CONTAIN PESTICIDES USING LEARNING VECTOR QUANTIZATION

Tomatoes have a risk of carrying pesticides above the maximum residue limit (MRL) because the fruit is directly sprayed with pesticides during its production process. Pesticide residue in farmers’ produce pose indirect effects to the consumers, but in the long run, it may cause health problems such as neural disorders as well as enzyme metabolism. This research identifies the image of tomatoes containing pesticides by using two types of tomatoes were used as samples, namely tomatoes which contain pesticides, and those which do not contain pesticides. This research aims to develop an algorithm to identify tomatoes that contain pesticides and those which do not contain pesticides using Learning Vector Quantization (LVQ). The characteristics used to identify tomato images are average, variant, and standard deviation. This research consisted of two classes and used 40 training image data and 40 test image data for each class. During the training process using LVQ parameters, there were 98.75% best percentage at alpha 0.001 and decalpha 0.9 with the lowest iteration of 3. The final weight obtained from the parameters was then used to perform test data identification. In terms of the best performance on the test data, it was with alpha 0.001 and decalpha 0.9, which reached 97.5%.
Multimodal Fusion Algorithm and Reinforcement Learning-Based Dialog System in Human-Machine Interaction

Studies on human-machine interaction system show positive results on system development accuracy. However, there are problems, especially using certain input modalities such as speech, gesture, face detection, and skeleton tracking. These problems include how to design an interface system for a machine to contextualize the existing conversations. Other problems include activating the system using various modalities, right multimodal fusion methods, machine understanding of human intentions, and methods for developing knowledge. This study developed a method of human-machine interaction system. It involved several stages, including a multimodal activation system, methods for recognizing speech modalities, gestures, face detection and skeleton tracking, multimodal fusion strategies, understanding human intent and Indonesian dialogue systems, as well as machine knowledge development methods and the right response. The research contributes to an easier and more natural humanmachine interaction system using multimodal fusion-based systems. The average accuracy rate of multimodal activation, testing dialogue system using Indonesian, gesture recognition interaction, and multimodal fusion is 87.42%, 92.11%, 93.54% and 93%, respectively. The level of user satisfaction towards the multimodal recognition-based human-machine interaction system developed was 95%. According to 76.2% of users, this interaction system was natural, while 79.4% agreed that the machine responded well to their wishes.
The affective factors influencing students’ speaking ability

Considering speaking ability is one of the important abilities that students have to master in learning English. However, most of their speaking ability is low. It is caused by several factors, one of those factors is affective factors (motivation, anxiety, and self-confidence). The research aims to know what affective factors that mostly influence students’ speaking ability. This research applied quantitative design with simple linear regression analysis. The sample of the research is 39 students from two classes X science class and X social class at SMA Muhammadiyah 9 Surabaya. Questionnaire and speaking test used as the instrument of the research. The result of the speaking test showed that the students’ speaking ability is below average. It is proven by the scores of students speaking tests are mostly below the minimal completeness criteria that is said by the school. The result shows that among three affective factors, the most influential factor is self-confidence, and the second influential factor is motivation. Meanwhile anxiety is proven not to influence the students speaking ability. The findings show that the total significant value of motivation is 0,011, anxiety is 0.063, and self-confidence is 0.007. It shows that speaking ability is influenced by motivation and self-confidence, but the result of the regression test shows that anxiety does not influence students' speaking ability even though the data result of anxiety is appropriate with the theory of the research. It can be caused by various aspects such as students’ characteristics, class atmosphere during the speaking test, and teacher attitude. So, the teacher should be fun when setting up the atmosphere of speaking, no pressure, and make students less anxious, so they don’t feel to be tested and to be assessed.
Co-Designing Visualizations for Information Seeking and Knowledge Management

Abstract Creativity is a crucial factor in finding novel and useful visualization and interaction techniques, but its emergence is contingent on the right conditions. The focus of visualization research has traditionally been on techniques, and to a lesser degree on the process of creating them with domain experts and end users. This paper focuses on the collaborative design of visualizations for information seeking and knowledge management. The difficult, yet common challenge in any visualization project is to find meaningful visual representations and useful interaction techniques to carry out complex analysis tasks. The unique difficulty for preparing co-design activities for visualization lies in the gap between the abstract nature of data and the concrete form of visual representations. To bridge this gap, our co-design framework for visualization places particular emphasis on actors, activities, and artifacts as categories that expand the focus of visualization design beyond the traditional triad of users, tasks, and data. Drawing from general co-design principles, the framework is developed and validated during the course of two case studies in the context of information management systems and library collection databases. Based on observed patterns during the case studies, practical tactics provide advice on carrying out co-design in information visualization.
The students’ extrovert and introvert personality toward speaking performance

This research aims to analyze the influence of personality in speaking performance. The focus was an analysis of the influence of the extrovert and introvert personality of the student toward speaking performance. This research employed a descriptive qualitative research method with a case study approach. The data was collected through questionnaires, interviews, and speaking tests. The subject of this research was students at MA Baytul Mukarromah Welalange. There were 10 consisted of 5 extrovert and 5 introvert students that had been divided by using the Eysenck Personality Questionnaire to acquire the data needed. The data analysis was employed based on the procedure of data analysis suggested by Miles and Huberman that consisted of step reduction, display, and verification data. The result of this data described that there was no influence between extrovert and introvert personality toward speaking performance, but it is influenced by different levels of knowledge and frequent participation or well preparation. It showed that personality types are not a determiner factor for the success of Speaking Performance, it can be suggested that students with different personality types may employ different kinds of strategies for learning. Consequently, whether extrovert or introvert as long as the student had enough knowledge about the topic and also was supported by frequent participation or well preparation, the speaking performance would be better.
Clustering Penerima Beasiswa Yayasan Untuk Mahasiswa Menggunakan Metode K-Means

Grouping of scholarship recipients Scholarship assistance will be made based on the accumulated value using clustering where the scholarship recipients will be given scholarships with different amounts and sizes, because scholarships from foundations are limited and have levels of distribution. The division of groups to students who receive scholarships from foundations uses the clustering method of data mining where the function of clustering is a cluster or the task of grouping something is using the clustering algorithm approach, namely the K-means algorithm. The results of this clustering show that students based on their groups are divided into four groups based on the number of criteria, the results of the grouping show the number and decision of the foundation on granting foundation scholarships to students.
DIGITAL TRANSFORMATION OF HIGHER EDUCATION: A NEW LOOK AT THE CONFIGURATION OF THE EDUCATIONAL PROCESS AND INTERACTION

Based on the analysis of the existing practice of organizing the educational process in the higher education system, the article examines the features of its transformation and restructuring in the context of digitalization as a global trend in the development of all spheres of human activity. At the same time, it is noted that digitalization allows a new approach to achieving the planned educational results, to implementing the content of education, methods and organizational forms of educational work, as well as evaluating the results achieved. The article emphasizes that digitalization, on the one hand, provides increased openness, flexibility of education, and greater involvement of students in the educational process. It is also noted that the education digitalization allows you to design new educational situations, involve new actors in the learning process, while transforming the configuration of relations between its main participants. Digitalization makes significant changes in the role of the teacher and the student in the learning process, which requires appropriate adaptation. Special attention is paid to clarifying the main definitions related to the process of education digitalization and the implementation of e-learning. The main requirements for designing e-learning components such as video lectures, webinars, and e-courses are considered. The roles of the teacher that allow implementing e-learning in the higher education system are identified separately. In conclusion, it is noted that the digital transformation of higher education provides a new round of its development in modern conditions and is expressed in the formation of a new – mixed – learning model that allows building a higher education system at a qualitatively different level, in which educational results are formed in digital conditions with a rational combination of classical and innovative didactic solutions.
Secured Data Storage in Cloud Using Homomorphic Encryption

Cloud data storage is a model of data storage where data in its digital form is stored in logical pools across the physical storage which is distributed across multiple servers usually in diverse locations.The full physical environment is owned and managed by an organization called Cloud Service Provider (CSP).In this type of storage, the data is distributed and owned by third party, and there is risk of unauthorized access.This makes security, reliability, confidentiality and privacy of data more important when storage in cloud is thought of.Although there are methods available for providing security, much work is not done with emphasis on dynamic nature of the data.Hence securing the data while updating becomes important.In this paper, a method that ensures confidentiality of data stored in cloud using homomorphic encryption is presented.The paper also provides a technique to ensure confidentiality during data updates in cloud.The presented method includes byte level automorphism for ensuring data integrity and confidentiality.The tabulated experimental results show that the proposed method provides more secure frame work for ensuring confidentiality and integrity of data in cloud.
An Automated Framework for Patient Identification and Verification Using Deep Learning

Automated patient identification and verification are very important at a medical emergency and when patients are not carrying his/her identity. It is a risk factor that identifying the correct patient identity for doctors to provide medical treatment. The majority of the identification or verification is being done by wristbands, RFID tags, fingerprint, face detection by using handcraft feature-based face recognition systems. A new framework based on robust deep learning model and contrast enhancement is proposed in this paper. In the proposed work, the light illumination problem has been addressed by the contrast enhancement technique for deep learning models to recognize the face. It is proved that the inclusion of contrast enhancement is improving patient identification and verification. To evaluate the deep learning framework, the proposed deep learning models have been trained on our own dataset and have been tested with a real-time medical providing agency. The experimental results show that the proposed framework exhibits more robust test results with accuracy than existing hand-crafted techniques under the live webcam video capture for the real-time patient detection system.
Formal Methods with a Touch of Magic

Machine learning and formal methods have complimentary benefits and drawbacks. In this work, we address the controller-design problem with a combination of techniques from both fields. The use of black-box neural networks in deep reinforcement learning (deep RL) poses a challenge for such a combination. Instead of reasoning formally about the output of deep RL, which we call the wizard, we extract from it a decision-tree based model, which we refer to as the magic book. Using the extracted model as an intermediary, we are able to handle problems that are infeasible for either deep RL or formal methods by themselves. First, we suggest, for the first time, a synthesis procedure that is based on a magic book. We synthesize a stand-alone correct-by-design controller that enjoys the favorable performance of RL. Second, we incorporate a magic book in a bounded model checking (BMC) procedure. BMC allows us to find numerous traces of the plant under the control of the wizard, which a user can use to increase the trustworthiness of the wizard and direct further training.
Analyzing the Adoption of Recent IT Technologies in Undergraduate Engineering Project Course

Outcome-based professional education processes are a new standard for all stakeholders and knowledge economy.Majority of the time the IT services/consulting industry demands graduates who shall be billable from day one of joining the enterprise.According to the National Employability Report Engineers 2019, only 3% of engineers possess new-age skills in areas such as AI, data analytics and mobile technologies.Academic project works shall be seen in the light of aforesaid observations and therefore suggests treating this as an opportunity to initiate undergraduates to more hands-on experiential learning considering the advancements in technology and market need.The objective of this paper is to identify the gap between the needs of the market/job trends and undergraduate CS/IT student projects areas.The second objective is to identify the factors affecting the project selection process.Our empirical study in general spanning over seven Engineering Institutes in western Maharashtra.The statistical data is collected from a controlled group of 4000 students.The data consists of project topics completed by students in the last nine years (from the year 2011 to the year 2019).Two indicators are used to understand the IT market.First, Gartner strategic technologies present the worldwide trend of technologies.Second, student project areas are compared with job trends in India.The student's feedback on technology adoption theory presents the challenges in adoption of recent IT technologies in academic projects.62% of students have reported unavailability of resources at the institute to encourage the adoption of the recent technologies.The student's perception about the adoption of recent technologies needs to change.
Technological innovation in Córrego do Sítio Mineração - A study of technical and economic aspects by using sensor-based sorting for refractory gold ore

The mining sector has a fundamental financial and economical role in the country where it practices these activities.In addition to employment creation and socialcultural development, the mining sector brings economic growth to the country.However, the sector is facing challenges due to COVID-19, incidents involving tailings dam facilities, rising energy costs, low grade ore deposits, restrictions in mining regulations and access to the available resources.Currently sensor-based sorting (SBS) is well known and applied worldwide in the food, recycling, grain separation industries, and processing of metals, such as diamond, gold, platinum, and feldspar.In the mining industry, ore sorting technology can result in significant improvement in ore quality by removing gangue, uplifting grades, reducing cost reduction and recovering minerals.In this article, we discuss the ore sorting test work and modeled cost-benefit analysis (CBA) for refractory gold ore at Córrego do Sítio Mineração, based on the test results and operating parameters.
Does the Environmental Internal Audit Impact the Achieving of Sustainable Development in Industrial Companies Listed on the Amman Stock Exchange?

This study aimed to determine the impact of environmental internal auditing with its dimensions represented by (compliance auditing, auditing environmental management systems, and auditing environmental financial statements) in achieving sustainable development in industrial companies listed on the Amman Stock Exchange. To achieve the aim of the study, a questionnaire was designed as a data collection tool for the study and distributed among 154 respondents namely: internal auditors, financial managers, and employees working in the financial departments in these companies. After conducting the necessary statistical analysis using SPSS, a statistical impact was reached at the significance level of α ≤ 0.05 for environmental internal auditing with its dimensions represented by (auditing compliance, auditing environmental management systems, and auditing environmental financial statements) in achieving sustainable development in industrial companies listed on the Amman Stock Exchange. The study suggests that industrial companies must provide appropriate environmental internal audit tools, including providing internal auditors with knowledge of environmental factors that have a fundamental impact on financial statements and understanding of environmental legislation and laws. The findings clarified the importance of training internal auditors on the stages of environmental internal auditing and skills in assessing environmental risks and obligations.
SCIENTIFIC AND METHODOLOGICAL SUPPORT OF TEACHERS

Currently, there is a contradiction between the established traditions of edifying pedagogical mentoring of young teachers and their need for individual scientific and methodological support for the formation of the author's teaching style. The article considers options for scientific and methodological support of teachers: equal age-based cooperation, diverse edifying interaction, equal age-based interaction. It is shown that situations of compatibility, the process of finding a contradiction, observance of the principle of eventuality are important, which will lead to the formation of an author's research handwriting, a style of research activity among the young teachers. The stages of this process are proposed: the stage of reflective familiarization, active imitation, reflective selfknowledge, to form research behavior and author's style, acceptance and presentation of oneself in the new role of researcher/methodologist. Thus, the inevitability of meeting with new traditions, rituals, customs is consistently ensured; the young teacher has performed the first cultural tests of research/methodological behavior; understanding oneself in a new role, exploratory self-determination, and then accepting and presenting oneself in a new role as a researcher/methodist. The process of co-action of a mentor with a young teacher is described: organization of reflection; motivation to independently search for a research task or methodological reception, resources of their own research activities; included observation, readiness to provide pedagogical support, organization of reflection. The functions of the accompanying teacher are shown: facilitation, accompaniment and reflection. The tactics of scientific and methodological support (guardianship, care and protection; mentoring and cooperation; pedagogical assistance, support and real support) in the context of scientific and methodological support. The essence of the scientific and methodological support of teachers is described: creating motivating conditions for the formation of the author's style among teachers, providing various opportunities for professional expression and feeling satisfied with their work. The logic of scientific and methodological support has been clarified: through criticism – to doubt, to acceptance, cultural test, correction and secondary acceptance of oneself; from interaction with a mentor-master-tutor through compatibility and eventuality to independence and scientific and methodological authorship.
A View on Deep Reinforcement Learning in Imperfect Information Games

Many real-world applications can be described as large-scale games of imperfect information. This kind of games is particularly harder than the deterministic one as the search space is even more sizeable. In this paper, I want to explore the power of reinforcement learning in such an environment; that is why I take a look at one of the most popular game of such type, no limit Texas Hold’em Poker, yet unsolved, developing multiple agents with different learning paradigms and techniques and then comparing their respective performances. When applied to no-limit Hold’em Poker, deep reinforcement learning agents clearly outperform agents with a more traditional approach. Moreover, if these last agents rival a human beginner level of play, the ones based on reinforcement learning compare to an amateur human player. The main algorithm uses Fictitious Play in combination with ANNs and some handcrafted metrics. We also applied the main algorithm to another game of imperfect information, less complex than Poker, in order to show the scalability of this solution and the increase in performance when put neck in neck with established classical approaches from the reinforcement learning literature.