Reviewerly/exHarmony huggingface dataset:
KEMAMPUAN LITERASI SAINS SISWA PADA MATERI VIRUS PANDEMI COVID-19 DI SMAN 3 LUWUK

Students' scientific literacy in virus material is important to know their ability to read, write, or speak to get information or knowledge from various sources on virus material. the purpose of this study was to look at the scientific literacy skills of Luwuk 3 Public High School students during the Covid-19 pandemic. The data collection technique used in this study consisted of observation, filling out the instrument by students. This type of research questionnaire is in the form of multiple choice questions that are used to measure students' scientific literacy abilities, namely students' understanding in identifying scientific problems, explaining phenomena scientifically and using scientific evidence. data analysis using descriptive statistical analysis. Based on the results of the research and discussion, the researcher can conclude that of the 3 indicators measured, namely, the first identifies a scientific issue with a bad category present value (35.57%), the second explains the phenomenon scientifically with a moderate present value (50.75%) , and third, utilizing scientific evidence with a percentage value in the sufficient category (54.73%). The present results for all categories show that the scientific literacy skills of SMAN 3 Luwuk students regarding the Covid-19 virus are in the sufficient category with a percentage value of 47.39%.
Research on communication emitter identification based on semi-supervised dimensionality reduction in complex electromagnetic environment

The individual identification of communication emitters is a process of identifying different emitters based on the radio frequency fingerprint features extracted from the received signals.Due to the inherent non-linearity of the emitter power amplifier, the fingerprints provide distinguishing features for emitter identification.In this study, Approximate Entropy is introduced into Variational Mode Decomposition whose features performed in each mode which is decomposed from the reconstructed signal are extracted while the local minimum removal method is to filter out the noise mode to improve SNR.We proposed a semi-supervised dimensionality reduction method named Exponential Semi-supervised Discriminant Analysis to reduce the high-dimensional feature vectors of the signals, and LightGBM is applied to build a classifier for communication emitter identification.The experimental results show that the method performs better than the state-of-art individual communication emitter identification technology for the steady signal data set of radio stations with the same plant, batch, and model.
Analysis of Audio Steganography combined with Cryptography for RC4 and 3DES Encryption

Digital data security is of utmost importance in today's world, both cryptography and steganography are important tools to ensure the confidentiality and integrity of the data.Cryptography involves transforming the plaintext into ciphertext using encryption techniques and then decrypting it back into plaintext using decryption techniques and steganography involves hiding data in plain sight by embedding it in cover media such as images, audio, or video.Both cryptography and steganography have their own strengths and weaknesses, they can be combined to provide even better protection for digital data.By encrypting the data before embedding it in cover media using steganography, we can ensure that even if the cover media is intercepted, the data remains secure as it is encrypted.Regarding audio steganography, the techniques you mentioned, such as LSB, Echo hiding, Phase coding, and Tone insertion, are commonly used.For example, LSB is a simple technique that involves replacing the least significant bits of audio samples with data bits, while Echo hiding involves modifying the echo of an audio signal to hide the data.The combination of cryptography and steganography can provide better protection for digital data.Audio steganography techniques such as LSB, Echo hiding, Phase coding, and Tone insertion can be used depending on the specific requirements of the application.
Pharma Software - A Complete Overview

With the recent advancements in Pharmaceutical Industry, there comes an increased complexity in data and products. In a Pharmaceutical Industry, various aspects are considered simultaneously. So, nowadays various advanced softwares are used to handle these parameters. A Software is a set of programs that tells a computer what tasks it should perform. It consists of a series of code written by a programming language, which is then interpreted by a computer. This process is known as compilation. Thus, it makes our work easier by maintaining all the records and performing necessary calculation and much more. In a pharmaceutical industry variety of software are used as the tasks that needs to be performed are also of various types. For example, Record keeping of a Batch of Tablet in a manufacturing section, Quality Control Test Results, Regulatory Management, Marketing And Sales Data Handling, Clinical Trial Data Management, etc. Sometimes, a single software is used and sometimes a combination of two or more are used. There are roughly 19 types of software used in the whole Pharmaceutical Industry, and among them various companies have their wide range of products. Currently, after the implementation of GMP, GLP and GCP, the use of the software has marginally increased. Keywords: Pharmaceutical, Software, Industry, Recent software, Pharma- Industry
Smart Phone Product Review Using SVM Technique of Sentiment Analysis

Abstract: Sentiment Analysis (SA) is a task of identifying positive and negative opinions, emotion and evaluation in text available over the social networking sites and the world wide web have been gained quite a popularity in the recent years. The analysis serves as an important feedback for further improvement in the offered services and user experiences. Several techniques have been used recently including machine learning approaches and vocabulary orientated semantic algorithms. This report presents a survey of various techniques and tools have been used in the previous research sentiment analysis process. There is a massive increase in number of people who access various social networking and micro-blogging websites that gives new shapes the impression of today’s generation. Many reviews for a specific product, brand, individual, and movies etc. are helpful in directing the perception of people thus the analysts are begun to create algorithms to automate the classification of distinctive reviews on the basis of their polarities in particular : Positive, Negative and Neutral. This machine-driven classification mechanism is referred as Sentiment Analysis. The ultimate aim of this paper is to use support vector machine (SVM) classification technique to classify the feelings of good phone product review that analyses datasets used for classification of sentiments and texts. Also, data sets are used for training as well as testing and implemented through SVM technique for finding the polarity of the ambiguous tweets. The obtain results show to achieve high accuracy as predicted on the basis of reviews of smart phone.
The Use of Social Deixis in EFL Classroom Interactions at SD Islam Al Azhar 60 Pekalongan Jawa Tengah

This study aimed to identify the use of social deixis in EFL classroom interactions at SD Islam Al Azhar 60 Pekalongan Jawa Tengah. It used a descriptive-qualitative study that implement social deixis analysis by Levinson’s theory. The subjects of this study are the third graders that consist of 62 students and 3 teachers who teach EFL learners in SD Islam Al Azhar 60 Pekalongan Jawa Tengah. This study used two instruments, namely observation and interview. The researcher observed all teaching and learning activities in the classroom from the first to the end of the EFL class. The interview is to complete the information needed directly from the respondents about the use of social deixis in the study. Then, the data is analyzed by using Levinson’s (1983) theory. The findings of the study showed that there are four types data of social deixis in EFL interaction. They are 28 data of addressee, 2 data of referent, 2 data of bystander, and 22 data and formality level of honorific. Besides, there are two types of absolute social deixis that are used in EFL classrooms, namely 4 data of authorized speakers and 2 data of authorized recipients. The researcher also discovered seven sentences that contain the relationship between relational and absolute social deixis. This relationship is happened to maintain politeness function and used to respect participants. Keywords: Social deixis, English as a foreign language, classroom interactions
Donation based System using Blockchain

A donation-based system using blockchain technology can provide transparency and accountability in charitable giving, addressing concerns about the misuse of donated funds. The system would allow donors to contribute directly to specific causes or organizations, with each transaction recorded on a public blockchain ledger, ensuring that funds are used as intended. Smart contracts could be used to automate the distribution of funds according to predetermined rules and conditions, reducing administrative overhead and ensuring that funds are distributed fairly. In addition, the use of cryptocurrency could eliminate the need for intermediaries such as banks, reducing transaction fees and increasing the speed of transfers. The system would also allow for real-time tracking of donations and impact, providing donors with greater visibility into how their contributions are being used and the overall effectiveness of the charitable organizations they support. Overall, a blockchain-based donation system has the potential to improve transparency, efficiency, and accountability in charitable giving, ultimately helping to create a more equitable and sustainable world.
Technological Adaptation Trends for Sociology Teachers in Online Learning during Covid-19 Pandemic

Online learning during the COVID-19 pandemic has forced sociology teachers to adapt to technology as a supporting medium, so that teaching and learning activities can be carried out correctly. The research method used in this research is the descriptive qualitative method. The research data are the results of interviews and observations. Sources of research data are sociology teachers in Singaraja City High School. Data collection techniques using observation and interviews. The data analysis technique is triangulation using (1) data reduction, (2) data presentation, and (3) concluding. This research indicates that sociology teachers use some aplications to support their online learning activities. However, some of these applications that are most often used include WhatsApp Group, Google Classroom, and Educandy as a game education. The main reasons why they use this application because it was faster, more effective and efficiently. The obstacles experienced by sociology teachers are the lack of ability to adapt to available technology so that sociology teachers only use three supporting application, and students who do not have signals, quotas, and cell phones become obstacles for sociology teachers in delivering material and assignments to the students concerned. And student delays in collecting assignments are obstacles for sociology teachers in providing assessments. The solutions taken are IT mastery training, the use of learning videos, educational games, and students who are constrained by signals and quotas can visit schools.
SigOpt Mulch: An intelligent system for AutoML of gradient boosted trees

Gradient boosted trees (GBTs) are ubiquitous models used by researchers, machine learning (ML) practitioners, and data scientists because of their robust performance, interpretable behavior, and ease-of-use. One critical challenge in training GBTs is the tuning of their hyperparameters. In practice, selecting these hyperparameters is often done manually. Recently, the ML community has advocated for tuning hyperparameters through black-box optimization and developed state-of-the-art systems to do so. However, applying such systems to tune GBTs suffers from two drawbacks. First, these systems are not model-aware, rather they are designed to apply to a generic model; this leaves significant optimization performance on the table. Second, using these systems requires domain knowledge such as the choice of hyperparameter search space, which is an antithesis to the automatic experimentation that black-box optimization aims to provide. In this paper, we present SigOpt Mulch, a model-aware hyperparameter tuning system specifically designed for automated tuning of GBTs that provides two improvements over existing systems. First, Mulch leverages powerful techniques in metalearning and multifidelity optimization to perform model-aware hyperparameter optimization. Second, it automates the process of learning performant hyperparameters by making intelligent decisions about the optimization search space, thus reducing the need for user domain knowledge. These innovations allow Mulch to identify good GBT hyperparameters far more efficiently—and in a more seamless and user-friendly way—than existing black-box hyperparameter tuning systems.
The effectiveness of Queen's early program on the readiness to enter elementary school and social-emotional competence of preschool children

The effectiveness of Queen's early program on the readiness to enter elementary school and social-emotional competence of preschool children
Remarks on a Direct Controller based on an Echo State Network with Application to Dynamic Systems Control

In this study, the application of an echo state network for controlling a nonlinear dynamic system is investigated. A direct controller in which the echo state network's output is used as the object plant's control input is designed to ensure that the plant output follows the reference output. The echo state network in the controller is trained in advance using input--output datasets of the plant to obtain the inverse transfer function of the plant. A discrete--time nonlinear plant is employed in the computational experiments and the simulation results demonstrate the feasibility of using the echo state network for this task.
Proposal of Automatic Transport System by Recognizing Lanes and AR Markers Using a Fisheye Camera

In recent years, the declining working population due to the declining birthrate and aging population has become a problem in Japan. Automated Guided Vehicles (AGVs) that can be used in factories and warehouses are attracting attention as a solution to this problem. A line-trace type automatic guided vehicle (AGV) is used as one of the methods of automatic guided vehicle (AGV). However, this method has the disadvantage that it takes time to introduce and it is not easy to change the route. In addition, the method using a stereo camera is mainly used for automatic transport robots using image processing such as factories. However, the stereo camera has a problem that the shooting range is narrow. Against this background, we have developed a new approach to automated transport by using a fisheye camera to recognize AR markers and lanes. The use of a fisheye camera expands the recognition range, the use of lanes already used in factories reduces the time and effort required for introduction, and the recognition of AR markers makes it easy to change routes. In this research, we verified the measurement accuracy of AR markers and lanes, made it real-time, implemented it on a robot, and verified the control accuracy of the robot.
Development of Industrial Historical and Cultural Heritage Display System Based on Panoramic VR Tech

With the rapid development of computer network technology, VR tech has been widely used in tourism, engineering construction and education and other industries, realizing all-round interactive viewing and browsing functions. The continuous progress of information resource display technology also provides new opportunities for the dissemination of cultural heritage. Based on the analysis of the advantages of VR tech in the display of industrial historical buildings and cultural relics, this paper studies the application methods of panoramic VR tech in the field of cultural display with the goal of system development. Including the establishment of digital information resource library, panoramic shooting and 3D modeling to achieve 3D scene restoration, virtual interactive development and display system release methods, explore the innovative ideas of panoramic VR tech in the display of industrial history and cultural heritage.
Indonesian EFL Students and Teachers Perceptions of Multimodal Teaching Approach in Writing Skills

Low motivation, poor writing techniques, and lack of vocabulary are a few things that effect how proficient a writer is. Multimodal approach in teaching writing allows for better communication and expression of personal identities through various modes of representation. This study adopts a mixed-method research design utilizing survey and interview as data collection method. Data were analyzed using SPSS version 25 for quantitative data Atlas.Ti for qualitative data. The research was conducted at Hamzanwadi University, the population of the research is all second year of English Language Study students consist of 92 students and 5 teachers. The study discovered positive effects of multimodal teaching approach on writing skills and perceptions of Hamzanwadi’s students and teachers. Then, from the finding suggested that the students who performed multimodal tasks significantly improved their critical writing skills. Furthermore, it can serve as a resource for Indonesian EFL teachers’ continuing professional development.
Unsupervised Learning Based Indoor Personnel Behavior Pattern Mining

Indoor personnel behavior is an essential part of indoor environmental monitoring. Standard indoor personnel behavior recognition methods have problems, such as invasion of privacy and high equipment costs. This paper proposes a novel, low-privacy indoor-person behavior pattern analysis method. Three types of indoor person behavior recognition methods are formed through time and frequency domain analysis. Moreover, since the model will fail in new rooms, we use unsupervised learning to perform pattern recognition on the analyzed feature data to enhance the generalization and robustness of the model. The implementation results show that our algorithm can recognize people's behavior in rooms and that the improved pattern recognition model achieves 97\% results, which is significantly better than more widely used models.
Alat Tanam Jagung dengan Kontrol Jarak Tanam dan Kualitas Biji Berdasarkan Ukuran

A corn planting tool has been made by controlling spacing and seed quality based on size. The aim of this research is to make a prototype corn planting tool with plant spacing and seed quality control based on microcontroller controlled size. The research method starts from making a prototype, and measuring system performance. The results of measuring the performance of the tool are indicated by an error in the reading of the HC-SR04 ultrasonic sensor at a point of 2 cm with an error value of 10% which is close to the actual measurement value, while for reading the distance on the ultrasonic sensor at a point &gt; 2 cm it can be said to be constant to the actual distance. The test results of the BTS7960 motor driver work according to the system work description and are able to control the pulse width modulation (PWM) and the direction of rotation of the DC motor properly. The 28BYJ-48 stepper motor can operate the seed rationing system properly and accurately to rotate the motor at an angle of 90o takes 30 seconds. Overall the tool can function properly. Suggestions for further research, can be added monitoring of corn planting results based on the Internet of Things (IoT).
Proposal for an Automatic Recall Assistance System Using Face Recognition, Voice Recognition, and Conversational Content

We experience various things in our daily life, and our brain stores the information obtained through these experiences, but it is difficult to keep it for a long period of time. Therefore, we propose a recall assistance system, which uses lifelogs. Related studies in memory recall have examined the usefulness of using only images captured by cameras or combining images with other elements for recall. However, there is an issue that past information cannot be easily recalled. The purpose of this research is to develop a system that can extract and present information about memories and past events based on multiple pieces of information to the user in order to realize a recall assistance system that utilizes lifelogs. We propose a system that facilitates the recall of past memories by pointing a camera at the person with whom the user is conversing. We conducted experiments in which we used face recognition, voice recognition, and language processing to identify the three types of information needed to assist recall: face, voice, and conversational content, and used this information to extract past events from a set of data. The results showed that the combination of the three types of information can extract highly relevant past events. In addition, the addition of various information other than the information used in this study is expected to increase the versatility and reproducibility of the method.
Uncertainty in Deep Learning for Image Processing

Training and validation of Neural Networks (NN) are very computationally intensive. Deep Learning (DL) methods have dominated image processing lately. Most DL models assume that the input data distribution is identical between test and validation, but most often, they are not. In other cases the test data is uncertain and labeling may differ based on different expert individual evaluations. This discrepancy makes DL less reliable for tasks like traffic signal recognition for self-driving cars, medical images where labels are difficult to assign, and other tasks where the precision of the output is crucial. It may be beneficial for the models to output “I don’t know” so human experts can decide instead of allowing the model to make catastrophic predictions. By adding the capability of propagating uncertainty to our results, image processing models can provide not just a single prediction of the identification but a distribution over predictions. In this paper, we present a summary of the main uncertainty estimation methods with the corresponding code so scientists can easily add uncertainty methods to their models. We also present our uncertainty evaluation method based on a Belief Network. The advantage of our proposed solution is that the models need to be trained once, making them more computationally efficient. Results show that our method provides efficient uncertainty information specially in the case where the data set has labels from different experts and there is a ground truth.
Validation Check of Neural Network using Monte Carlo Stochastic Optimization Method (MOST)

Recently, artificial intelligence has been applied in various fields, and the core optimization methods are required as an indispensable tool in engineering design, and the selection of high-precision optimization methods has become important. However, traditional optimization methods present challenges such as guaranteed convergence, convergence speed, and tuning constants. Therefore, we developed the Monte Carlo Stochastic Optimization (MOST), which is an optimization method using the Monte Carlo method. MOST first divides the search region into two parts and integrates them by Monte Carlo method. The integrals are then compared and the side with the smaller integral value is selected. The selected area is then split and integrated again in two, selecting the smaller side of the integration. By repeating the selection and integration in this way, the optimum solution is obtained. In this paper, we apply and validate the proposed method to optimize the weighting factors of neural networks for wine evaluation problem, used car identification problem, and abalone age determination problem.