Reviewerly/exHarmony huggingface dataset:
Mi-Botway: a Deep Learning-based Intelligent University Enquiries Chatbot

Intelligent systems for universities that are powered by artificial intelligence have been developed on a large scale to help people with various tasks. The chatbot concept is nothing new in today's society, which is developing with the latest technology. Students or prospective students often need actual information, such as asking customer service about the university, especially during the current pandemic, when it is difficult to hold a personal meeting in person. Chatbots utilized functionally as lecture schedule information, student grades information, also with some additional features for Muslim prayer schedules and weather forecast information. This conversation bot was developed with a deep learning model adopted by an artificial intelligence model that replicates human intelligence with a specific training scheme. The deep learning implemented is based on RNN which has a special memory storage scheme for deep learning models, in particular in this conversation bot using GRU which is integrated into RASA chatbot framework. GRU is also known as Gated Recurrent Unit, which effectively stores a portion of the memory that is needed, but removes the part that is not necessary. This chatbot is represented by a web application platform created by React JavaScript, and has 0.99 Average Precision Score.
How is the role of Digital Leadership and Knowledge Sharing on Performance? An Empirical Study on SMEs in Bandung West Java

The purpose of this study was to analyze the effect of Digital Leadership on the performance of SMEs and the effect of Knowledge Sharing on the Performance of SMEs in Bandung West Java. The research was conducted using a quantitative approach through a survey. The population of this study were all SMEs employees in Bandung, while the number of samples in this study was 400 people who were selected using simple random sampling. Testing the hypothesis in this study using SEM (Structural Equation Modeling) with the PLS program. The research data was obtained through the distribution of online questionnaires through social media. The results of this study are that Digital Leadership has a positive and significant effect on the performance of SMEs and the variable Knowledge Sharing has a positive and significant effect on Performance SMEs in Bandung West Java.
BlockTrust-RPL: distributed blockchain based authentication and trust validation for secure objective function formulation in RPL-IoT

Routing protocol for low power lossy network (RPL) faces many challenges such as high energy consumption and high congestion and so on. Mainly IoT environment suffers by various attacks such as rank, Sybil and DDoS because of insufficient security provisions. The proposed BlockTrust-RPL focuses on detecting security threats without increasing energy consumption and congestion. For that, the proposed BlockTrust-RPL consists of three phases: 1) distributed authentication; 2) valid trust-based parent selection; 3) trickle timer optimisation and mobile sink movement. The overall network is connected with blockchain thorough block gateway. Blockchain is constructed with TrustBlock and AuthBlock where, TrustBlock records trust values and Authblock records authentication credentials. The simulation is done by using NS3.26 network simulator, which evaluates the performance of the proposed BlockTrust-RPL in terms of packet delivery ratio, delay, attack detection accuracy and average energy consumption with respect to number of nodes and number of malicious nodes.
IMPLEMENTASI METODE LONG SHORT TERM MEMORY UNTUK MEMPREDIKSI PERGERAKAN NILAI HARGA EMAS

Investment is an activity that is invested or financed with the hope of profit or return in the future. One form of investment is to use gold. Many people are interested in investing in gold. Apart from being quite affordable, investing in gold is also very easy to do and flexible. Gold is included in the low risk investment class because its price movements tend to follow the rate of inflation. This study uses the Long Short Term Memory (LSTM) method to predict the movement of gold prices which will visualize the data in the form of a Line Chart. This study aims to provide decision support material regarding the movement of buying and selling prices of gold through the presentation of data aimed at people who invest in gold. However, real gold price movements cannot be separated from external factors such as the exchange rate of the rupiah against foreign currencies, import export policies and other government policies. There are many influencing factors such as opening price, closing price, adjust close, volume, highs, lows recorded over a certain period of time. Another factor is the Daily Return which can be positive (Grown In Value) and negative (Lost In Value).
About the Truth in the Scientific and Everyday Cognition

The phenomenon of truth is an eternal philosophical problem. Aristotle also claimed in Metaphysics that “philosophy is called the knowledge of truth”, and Plato considered it the object of the desires of genuine philosophers. For episte­mology, truth is the foundation of human existence in all its manifestations, in­cluding everyday life. Truth is the goal and the highest value for philosophy and science. There are different concepts of truth. Correspondent and coherent theo­ries of truth are more widespread than others, the problem of truth in everyday cognition has not been fully developed, the relevance of which is related to the search for “reliable ways to orient a person in the world of pseudo-realities and flows of disinformation” (V.A. Lectorsky), with the search for the founda­tions of everyday life, order in the chaos of information and events, as well as with the search for the meaning of life. The very recognition of the truth and the desire to know it affect the character, interests, position of people, form the inner world of a person. The most fundamental and little-explored character­istics of the truth of everyday cognition are revealed on the basis of comparison of understanding truth in scientific and everyday cognition. The prerequisites of the classical (correspondent) and coherent concepts of truth containing the im­plicit idea of their synthesis are clarified. The features of understanding absolute and relative truth in everyday cognition and the criteria for the reliability of ev­eryday cognition are explicated. It is concluded that the truth of everyday cogni­tion is an empirically proven knowledge of the real state of affairs in the human life world, based on the available experience, the experience of communication, as well as the socio-cultural translation of the experience of generations.
Automatic Thai Ticket Classification By Using Machine Learning For IT Infrastructure Company

Ticket classification is a process to define the category name of each ticket before assigning the resolution team to serve each ticket. It is an important process to support the customers inside and outside the company. It can make customer dissatisfaction if the processing time is high or delayed. Based on the recording data in 2019-2021 at the studying company, we found that the manual ticket classification got an error rate about 53 percent because the office workers misunderstand. To alleviate this problem, we propose the methodology for automatic Thai ticket classification by using Term Frequency-Inverse Document Frequency with Support Vector Machine. The experimental result shows that the performance of the proposed methodology is higher than the manual classification by 2 times or 41 percent.
Robust self-training strategy for various molecular biology prediction tasks

Molecular biology prediction tasks suffer the limited labeled data problem since it normally demands a series of professional experiments to label the target molecule. Self-training is one of the semi-supervised learning paradigms that utilizes both labeled and unlabeled data. It trains a teacher model on labeled data, and uses it to generate pseudo labels for unlabeled data. The labeled and pseudo-labeled data are then combined to train a student model. However, the pseudo labels generated from the teacher model are not sufficiently accurate. Thus, we propose a robust self-training strategy by exploring robust loss function to handle such noisy labels, which is model and task agnostic, and can be easily embedded with any prediction tasks. We have conducted molecular biology prediction tasks to gradually evaluate the performance of proposed robust self-training strategy. The results demonstrate that the proposed method consistently boosts the prediction performance, especially for molecular regression tasks, which have gained a 41.5% average improvement.
Analysis of Research Trends Related to Teacher Efficacy of Early Childhood Teachers: From 1998 to 2021

Objectives The purposes of this study is to provide basic data for the systematic development of the field of teacher efficacy in the future by examining domestic research trends related to teacher efficacy in early childhood teachers.&#x0D; Methods For this purpose, 274 papers related to teaching efficacy of early childhood teachers published in the registered journals from 1998 to 2021 were selected for analysis, and research on teaching efficacy of early childhood teachers was selected by research period, research type, teaching efficacy type, and measurement tool., trends were analyzed by research topic.&#x0D; Results By study period, the trend has continued to increase since 2005. By research type, quantitative studies were the most common, followed by mixed studies, qualitative studies, and literature studies. By type of teacher efficacy, overall teacher efficacy was higher than specific teaching efficacy study. By measurement tool, foreign scale adaptations and reconstructions were more numerous than those developed by domestic researchers, and Riggs and Enochs (1990)'s science teaching efficacy scale was used the most. By research topic, the largest number of studies on variables related to teacher efficacy were found.&#x0D; Conclusions Based on these results, implications for the development of research in the field of teacher efficacy for early childhood teachers and improvement of teacher efficacy for early childhood teachers were presented.
SOLVING THE INVERSE PROBLEM FOR RECURRENT COMPOSITION OF FUZZY CORRESPONDENCES

The paper proposes a formal formulation of the problem of fuzzy diagnostics based on rules in the form of a recurrent composition of fuzzy correspondences. The methodology of solving the inverse problem by reducing it to an optimization problem is described. The practical implementation of the methodology of synthesis of solutions in the environment of the analytical platform of symbolic mathematics is presented.
Land, Letṧema and Leola: Digital Transformation on a Rural Community’s Own Terms

Eurocentric paradigms for technology continue to dominate in Africa yet can impede digital transformation by perpetuating senses of inferiority in societies that have endured colonialism and apartheid. This chapter describes how an African creative pedagogy, Mandhwane, is enabling inhabitants of Mamaila, in rural South Africa, to negotiate the meaning of transformation on their own terms. Since 2018 inhabitants have been establishing their own telecommunications system, or Community Network (CN), to provide local internet access and digital services www.mamailanetwork.co.za/ . The CN acts as a “land” where inhabitants freely co-create, which is a vital aspect of Mama Tshepo Khumbane’s philosophy of doing Mandhwane for transformation. The first author applied Matshepo’s techniques to help inhabitants recognise their existing capability to solve their problems by Letṧema, or collective work. Our analysis focuses on designing an app to support Leola, a locally created community scheme in which households collaborate to support bereaved families with funding, equipment and human resources, for funerals and burials. We reflect on the ways that locating design within a rural CN and framing innovation with Mandhwane fosters communal and individualised agency, embeds a social relational ontology in innovation, and can tackle tensions that often arise in digital transformation, such as those that result from differences between older and younger people and between local and externally imposed timescales.
Building a Professional Career of a University Teacher Based on the Information Engineering

The issue of a university teacher’s professional career is considered. The relevance of the study is determined by the fact that the chosen subject is actively developed by supporters of the personality-activity theory, which underlies the design of the educational process in the substantive and methodological aspects. The notion of “career-forming education” (CFE) is interpreted as a non-situational amount of knowledge, applied skills and cognitive scenarios aimed at achieving high results in the profession (recognition within the community, well-paid and socially significant positions) and receiving positive emotions from the work. We understand the system of career growth as a subsystem that is a part of professional education. When studying this system, the authors of the article used the concept of Russian professional education proposed. According to this concept, there are four main ideas: the humanization of education, its democratization and an emphasis on continuity and being ahead of the time. The novelty of the study and its theoretical and practical significance consists in defining three conditions favouring the university teacher’s career growth: the integration of pedagogical work into the system of life meanings and the value hierarchy of the person as their most important component; constant creative search with a critical attitude to one’s professional level; the teacher’s readiness to implement innovative activities. Conditions for the university teacher’s professional development are the following: integration of pedagogical work into the system of life meanings; constant creative search; readiness for the implementation of innovative activities.
Accelerating trail navigation for unmanned aerial vehicle: A denoising deep-net with 3D-NLGL

Waypoints have enhanced the prospect of fully autonomous drone applications. However, Geographical Position System (GPS) spoofing and signal interferences are key issues in waypoint-based drone applications. Also, conceptual waypoint-based drone applications require accurate awareness of waypoints based on environmental cues and integration of additional sensing modalities. Additional sensor modalities may overwhelm drones’ processing resources, reducing operational time. This study proposes W-MobileNet, a denoising model for autonomous trajectory trail navigation based on precision control of a path planner, denoising capabilities of Weiner filters, and perceptual knowledge of convolutional neural networks. Creatively integrating the modules of W-MobileNet results in an intuitive drone navigation controller characterized by position, orientation, and speed estimation. Further, a generic loss function that significantly aids models to converge faster during training is proposed based on adaptive weights. An extensive evaluation of a simulated and real-world experiment shows that W-MobileNet is more favorable in precision and robustness than contemporary state-of-the-art models. W-MobileNet has the potential to become one of the standards for autonomous drone applications.
Comparison of Different Authentication Techniques and Steps to Implement Robust JWT Authentication

From small websites to large e-commerce web applications every software requires some sort of user information to cater to the user in a personalised manner. This sensitive information needs to be protected without enforcing an overhead on the server. One such widely used technique employs JWT (JSON Web Token) to implement authentication and authorization for the end-user. In this paper, the use of JWT is implemented in a streamlined manner to provide a more robust and structured security module that can easily be implemented in various technologies. The proposed methodology will aim towards reducing server overheads by reducing database calls, focus on proper storage of tokens, providing automatic token refresh, and ways to invalidate tokens in case of logout or data breach.
Product Reviews on Opinion Mining using NLP Techniques

Opinion mining is a natural language processing (NLP) based automated text analysis to identify emotions and opinions (positive and negative comments) present in a text. Opinion mining is the technique used for automatically gathering knowledge from different user opinions on a certain topic or situation. The amount of data available on the internet is growing at a high rate. Every day, social media generates a massive quantity of data, such as reviews, comments, and customer feedback. This huge volume of user-generated data is unintelligible unless specific mining techniques are applied to it. Since there are so many fake reviews, there is a need for the opinion mining approach to integrate a spam detection module to deliver a legitimate opinion. People nowadays rely on social media opinions to decide whichever products or services to purchase. Since there are numerous bogus or fraudulent reviews issued by organizations or individuals for a number of reasons, spam detection will emerge as a complex and time-consuming task. The proposed system includes ontology, geolocation and IP address monitoring, a lexicon of spam phrases using naive bayes, comment detection, and account tracking.
FPGA Implementation of a Modified SMS4-BSK Cipher with Novel Efficient XOR Gate Design

The SMS4-BSK cipher is a secure encryption algorithm that prevents data packets’ security threats over the wireless medium. In this paper, the novel design of a 4Transistor XOR gate structure is proposed. The SMS4-BSK cryptosystem is modified with this novel XOR gates by replacing the standard 12 Transistor XOR gates. Brute-force is an attack that can break any cipher by attempting all the combinations of the Key. The success of a cryptosystem design relies on how much time the brute-force attack takes to decode the secret information. In the defense sector, the cryptosystem should be designed so that it should be more vital to resist security attacks and be faster to encrypt the original message. The enciphering and deciphering process of the Modified SMS4-BSK algorithm uses the same keyspace. The same scheme can also be used for both enciphering and deciphering. The Modified SMS4-BSK algorithm is designed with Verilog HDL using the Xilinx Vivado Design and it is realized in 28nm Kintex-7 Field Programmable Gate Array. The implementation result shows that the delay of the Modified SMS4-BSK algorithm is 1.09 times lesser than the SMS4-BSK encryption architecture.
Effects of Virtual Reality (VR) Video as Asynchronous e-Learning Supplement on Student Learning Outcomes

The purpose of this study is to explore the impact of virtual reality (VR) videos as asynchronous e-learning supplements on improving student learning outcomes. This study is an experimental study, using two sample groups (experimental and control). The experimental group was taught by presenting virtual reality (VR) videos connected to e-learning in the LMS, and the control group by traditional (face-to-face) or expository learning (without VR). Research participants in each sample group were 24 early semester students who programmed a basic natural science course at the Universitas Pendidikan Mandalika (UNDIKMA). The groups were given the initial task as a pretest, learning treatment, and the final task as a posttest. Learning assignments and descriptions of learning materials are related to the theme of 'climate change.' Test instruments (essays) were employed to measure students' cognitive learning outcomes. The test instrument used fulfills the validity aspect as a psychometric property of the instrument. Data were analyzed descriptively (n-gain parameter), and statistically (analysis of mean differences using independent sample t-test) at a significance level of 0.05. The results of the descriptive and statistical tests showed that there was a significant impact from the application of virtual reality (VR) videos as asynchronous e-learning supplements to the improvement of student learning outcomes. Our findings confirm that virtual reality (VR) videos as asynchronous e-learning supplements are better at improving student learning outcomes when compared to traditional face-to-face (expository) learning. More detailed results are presented in this article.
Neural network training for serial multisensor of autonomous vehicle system

&lt;span&gt;This study aims to find the best artificial neural network weight values to be applied to the autonomous vehicle system with ultrasonic multisensor. The implementation of neural network in the system required long time process due to its training process. Therefore, this research is using offline training before implementing to online training by embedding the best network weight values to obtain the outputs faster according to desired targets. Simulink were used to train the system offline. Eight ultrasonic sensors are used on all sides of the vehicle and arranged in a serial multisensory configuration as inputs of neural network. With eight inputs, one sixteen-depth hidden layer, and five outputs, it was trained using the back-propagation algorithm of artificial neural network. By 100000 iterations, the output values and the target values are almost the same, indicating its convergency with minimum of errors. The result of this training is the best weights of the networks. These weight values can be implemented as fixed-weight in online training.&lt;/span&gt;
EXPERIMENTAL ACTIVITIES USING VIRTUAL SIMULATORS TO LEARN CHEMISTRY DURING COVID-19 PANDEMIC

Faced with the COVID-19 pandemic, the education system moved to virtual mode, causing teachers to change methodologies, strategies, and teaching resources in order to be used for teaching-learning processes. The impediment of entering to Chemistry laboratories motivated the use of simulators and virtual laboratories as resources linked to learning. Present study determines the efficacy of these resources, the research is quantitative with a correlational level and a quasiexperimental design with post-test. The applied method was the heuristic and assumes Ausubel's theory of significant learning. The study population comprises 188 students enrolled during the period November 2020- April 2021 in the career of Pedagogy of Experimental Sciences, Chemistry and Biology. The sampling technique was intentional non-probabilistic and corresponds to the second, third and fifth semester students of the subjects of General Chemistry, Inorganic Chemistry and Chemical Physics respectively. At the end of the investigation, it was determined that the PhET simulator and the virtual laboratories: Crocodile Chemistry605 and Yenka allowed feedback and motivation in each guided virtual experimental activity; not existing a significant difference in the academic performance of the students, who in their majority reached the learning results.