Reviewerly/exHarmony huggingface dataset:
EVALUATION OF REFLECTION SUPPORT SYSTEM FOR SIMULATED LESSONS BY CLASSIFYING AND VISUALIZING FEATURES UTILIZING CLUSTERING

EVALUATION OF REFLECTION SUPPORT SYSTEM FOR SIMULATED LESSONS BY CLASSIFYING AND VISUALIZING FEATURES UTILIZING CLUSTERING
REPORT ON NSF-FUNDED GRADUATE TRAINEESHIP IN DATA SCIENCE TECHNOLOGIES AND APPLICATIONS

Appears in: ICERI2023 Proceedings Publication year: 2023Pages: 5163-5172ISBN: 978-84-09-55942-8ISSN: 2340-1095doi: 10.21125/iceri.2023.1292Conference name: 16th annual International Conference of Education, Research and InnovationDates: 13-15 November, 2023Location: Seville, Spain
Deep Learning for an Automated Image-Based Stem Cell Classification

Hematopoiesis is a process in which hematopoietic stem cells produce other mature blood cells in the bone marrow through cell proliferation and differentiation. The hematopoietic cells are cultured on a petri dish to form a different colony-forming unit (CFU). The idea is to identify the type of CFU produced by the stem cell. Several software has been developed to classify the CFU automatically. However, an automated identification or classification of CFU types has become the main challenge. Most of the current software has common drawbacks, such as the expensive operating cost and complex machines. The purpose of this study is to investigate several selected convolutional neural network (CNN) pre-trained models to overcome these constraints for automated CFU classification. Prior to CFU classification, the images are acquired from mouse stem cells and categorized into three types which are CFU-erythroid (E), CFU-granulocyte/macrophage (GM) and CFU-PreB. These images are then pre-processed before being fed into CNN pre-trained models. The models adopt a deep learning neural network approach to extract informative features from the CFU images Classification performance shows that the models integrated with the pre-processing module can classify the CFUs with high accuracies and shorter computational time with 96.33% on 61 minutes and 37 seconds, respectively. Hence, this work finding could be used as the baseline reference for further research.
Information System for Calculating the Shortest Route for a Mobile Robot in a Multilevel Environment Based on Unity

An integral part of the global task of increasing the efficiency of movement processes and performing specified technological operations by a mobile robot is the choice of a movement path from the initial (starting) to the final (target) point. At the same time, a preliminary assessment of the proposed route of movement is an important stage in the development of a program for its movement. In this paper, we consider the problem of finding the shortest path for moving the mobile robot in a multilevel environment, taking into account the features of transitions between levels, restrictions, the presence or absence of obstacles along the way. An information system for calculating the shortest route from the starting point to the target point is proposed based on the NavMesh navigation system in the Unity virtual environment. The results of computer simulation of robot movement in a multilevel environment show that the system successfully finds the shortest route for various scenarios.
PEMBERDAYAAN PETUGAS PEMADAM KEBAKARAN DALAM BANTUAN HIDUP DASAR PADA KASUS GAWAT DARURAT

In conditions where accidents and various disasters occur in the community, the people often contacted by the community are the firefighters. Empowering firefighters on basic life support in this case becomes very important due to their important role in the community. Community service methods by providing guidance and simulations to firefighters in Banjar Regency. Community service results after being assessed by all firefighters in Banjar Regency can recognize signs of heart and lung emergency and can correctly simulate CPR assistance. Building the capacity of firefighters and other community groups in Banjar Regency to provide CPR assistance is very important given that Banjar Regency is a disaster prone area.&#x0D; &#x0D; Keywords: empowerment, BHD, firefighter
DILAN (DETEKSI DINI-LANJUT) NARKOBA PELAJAR SMPN-3 KECAMATAN CEMPAKA KOTA BANJARBARU

Drug abuse in students are increase. Banjarbaru City shows 305 case drug abuse in 65% students in the range 10-19 years old covers third highest district are Cempaka district, Landasan Ulin district and Banjarbaru district. Objective : supportive knowledge early teenage traits of drug abuser, how to prevent drug abuse by reinforcement of assertive behavior and self concept. Method : screening drug abuse randomly to 100 students, leaflet distribution, practical simulation and health education to 55 participants of community school. Result : urine test to 95 students show negative, health promotion by DILAN drug abuse shows difference between pre and post test. Training introduction of DILAN drug abuse will help community school to care with drug abuse. Getting early to know potential deviation will be easy to early detection and prevention by teacher and family in school level. Recommendation : the need to continuity of program by monitoring aplication and tools by online program.&#x0D; Keyword : early detection, drug abuse, students
Development of an Interactive Android-Based Ayo-Olopon Game

One of the most widely acceptable means of entertainment all over the world is Games especially in Africa to ease the mind from stress after a rigorous day’s work. Over the years, there have been several African games nearly going into extinction due to the advancement of more new smartphone-based games and the general public is more comfortable using the modern-day gizmo. This has brought about reduction in the number of people that understands and knows about African traditional games and ethics. A lot of effort has been put in place by many developers and researchers to digitalize these games (like Ayo-Olopon) to improve their popularity and expand their accessibility across the globe. The current versions of this Ayo-Olopon game does not provide room for Multi-player in real time nor presence of third party as viewer during the game session. This research developed an android-based Ayo-Olopon game using a stack of React Native for the frontend design and server-side scripting through Node.Js, Websocket was also incorporated which enables real-time communication for multi-players and a third party to join the game as a viewer. The game was evaluated using feedback from randomly selected user and more than 98% of them ascertained its uniqueness and excellent performance. Response speed and transfer time were also used as the evaluation metrics with varying network technologies and protocols, WLAN network technology with gave the shortest response time and transfer speed of 150ms and 0.263s respectively f. This shows that the developed Ayo-olopon game gave a very satisfactory user experience and good performance with good Computational time. Future work should focus on enabling viewers to use audio or chat to motivate the players and increase their morale.
Data mining applied about state madrasah using sentiment analysis on Twitter in Indonesian perception

&lt;span&gt;Indonesian people prefer private schools with high prices, which is one of the exciting things to study. In fact, in the modern era, public schools have competed with private schools with international categories. The problem in public schools is the parent's perspective on the quality of education in public schools, especially public madrasahs. In addition, cases such as bullying and violence between schools. People on Twitter also have various perceptions of public madrasas, which are considered to have religiosity. This research uses the keywords public madrasah, quality management, and quality taken from Twitter using the Orange application. The amount of data in this research is 300 tweets from Twitter. As a result, there are both negative and positive sentiments toward public madrasas. However, the negative sentiment is higher than the positive sentiment. This means parents have more trust in private schools than in public madrasahs.&lt;/span&gt;
Cutoff estimation and construction of their confidence intervals for continuous biomarkers under ternary umbrella and tree stochastic ordering settings

Tuberculosis (TB) studies often involve four different states under consideration, namely, “healthy,” “latent infection,” “pulmonary active disease,” and “extra‐pulmonary active disease.” While highly accurate clinical diagnosis tests do exist, they are expensive and generally not accessible in regions where they are most needed; thus, there is an interest in assessing the accuracy of new and easily obtainable biomarkers. For some such biomarkers, the typical stochastic ordering assumption might not be justified for all disease classes under study, and usual ROC methodologies that involve ROC surfaces and hypersurfaces are inadequate. Different types of orderings may be appropriate depending on the setting, and these may involve a number of ambiguously ordered groups that stochastically exhibit larger (or lower) marker scores than the remaining groups. Recently, there has been scientific interest on ROC methods that can accommodate these so‐called “tree” or “umbrella” orderings. However, there is limited work discussing the estimation of cutoffs in such settings. In this article, we discuss the estimation and inference around optimized cutoffs when accounting for such configurations. We explore different cutoff alternatives and provide parametric, flexible parametric, and non‐parametric kernel‐based approaches for estimation and inference. We evaluate our approaches using simulations and illustrate them through a real data set that involves TB patients.
Artificial Intelligence Technology in the Field of Broadcasting and Hosting

The most important form of artificial intelligence technology applied in the broadcasting and hosting industry is “artificial intelligence anchor”. In the context of artificial intelligence, today’s artificial intelligence anchors have received great technical improvements in terms of image, language appearance, and humanized demeanor. Artificial intelligence anchors serve the audience as well as traditional hosts. Through the research and analysis of artificial intelligence anchors, summarize the advantages of artificial intelligence anchors, guide the future development direction of traditional anchors, and hand over low-level repetitive work to artificial intelligence. The completion of the anchor allows traditional anchors to have more time and energy to dig deep into the work of high-level broadcasting and hosting. This research can provide practical guidance for AI anchors to be better selected and accepted by the audience, so that traditional anchors and artificial intelligence anchors can find growth points in the development of the times, so that artificial intelligence anchors and traditional anchors can play a role in broadcasting and hosting. A good cooperation model has been formed in the industry.
Personalized Television Program Recommendation Based on Item-Based Collaborative Filtering

In light of the exponential growth of film and television programs, assisting viewers in discovering the desired TV programs from an overwhelming amount of information has become increasingly crucial for television service providers. This study addresses the computational limitations and personal information protection in the dataset by implementing a personalized TV program recommendation based on the item-based collaborative filtering algorithm. In addition to identifying the top-n popular programs, the research also adopts a recommendation strategy for these programs. The accuracy of successfully recommending a single program, which initially stood at 0.34, increases to 0.77 when recommending ten programs. Therefore, effective recommendations bring significant benefits to both television service providers and viewers.
A Dynamic Memory for Reservoir Computing Utilizing Ion Migration in CuInP<sub>2</sub>S<sub>6</sub>

Abstract Time‐series analysis and forecasting play a vital role in the fields of economics and engineering. Neuromorphic computing, particularly recurrent neural networks (RNNs), has emerged as an effective approach to address these tasks. Reservoir computing (RC), a type of RNN, offers a powerful and efficient solution for handling nonlinear information in high‐dimensional spaces and addressing temporal tasks. CuInP 2 S 6 (CIPS), a van der Waals material with ion conductivity, shows promise for sequential task processing. Here, a synapse device based on CIPS is demonstrated that exhibits temporal dynamics under electrical stimulation. By controlling Cu + ion migration, this study successfully emulates synaptic performance, including potentiation and depression characteristics, and RC. Migration of Cu + ions is confirmed using piezoresponse and Kelvin probe force microscopy. The device achieves low normalized root mean square errors (NRMSE) of 0.04762 and 0.01402 for the Hénon map and Mackey‐Glass series tasks, respectively. For real‐life time‐series prediction based on the Jena temperature database, an overall NRMSE of 0.03339 is achieved. These results highlight the potential of CIPS ion conductivity for real‐time signal processing in machine learning, expanding applications in neuromorphic computing.
Integration with Web Services

Today’s digital solutions need to deliver several things, like a rich customer experience, optimized business models, multiple channels to market, and more. The organization’s digital landscape has individual components offering these new capabilities. These components must integrate to deliver the intended digital innovations. Application programming interfaces (APIs) offer a rapid and easy integration solution based on ubiquitous HTTP data exchange. Low-code development platforms (LCDPs) often support data read and write operations over HTTP protocols like REST and GraphQL.
The Effect of TTW With Mobile Games in Learning Vocabulary for English Students of UNIPA Kampus Blitar

Teaching English for English students of Unipa Kampus Blitar is based on vocabulary approach that involves worksheet. The problem is they have low vocabulary mastery. This project aims to investigate the potential of Think Talk Write mobile games for language instruction. It was conducted to find out its effectiveness. The method used is quasi-experimental. The result shows F = 27.108. The probability (P) TTW with Mobile game shows 0.000. Class using TTW with Mobile Games shows mean 74.347 and class learned without treatment shows 59.811. When playing mobile games, the class utilizing TTW has a higher mean value. In conclusion, students can use TTW with Mobile Games to learn English for better vocabulary achievement.
Pupil Control Intelligent Wheelchair based on MediaPipe

In order to solve the problem that some quadriplegics cannot control the wheelchair autonomously, a vision tracking wheelchair control system which combines hardware and software is studied. The Raspberry Pi camera is mounted directly in front of the seat and is used to gather real-time information about the person's face. Using Google pre-trained MobileNetv2 based MediaPipe Face, as well as OpenCV image processing, to achieve the key point of face extraction. In consideration of People's Daily living habits, through the analysis of the direction of sight, eyes closed, mouth closed and other conditions output corresponding commands. The whole system runs on a Raspberry Pi 4B, and the output commands are sent to a motor controller to control the wheelchair. In addition to add automatic obstacle avoidance function to ensure the absolute safety of personnel. The system can operate in a variety of environments, such as background brightness and face occlusion, the system has high robustness.
The Effect of Using LINE Webtoon as a Media in Reading Comprehension at Eleventh Grade of Senior High School Number 8 Jambi City

This research aims to find out whether there is any effect of using LINE Webtoon as a media in reading comprehension of English students at eleventh grade of Senior High School Number 8 Jambi City. This type of research is a quasi-experimental with a total of population 292 people. Through simple random sampling technique, 37 students of Class XI IPA 2 were selected as the control class and 37 students of Class XI IPA 1 were selected as the experimental class. The technique of collecting data is distributing questions to research respondents. In this research, the data analysis technique was using t test on the Statistical Package for the Social Sciences (SPSS) for Windows version 25 program. The result of this research indicated that (1) students’ reading comprehension in English subject in experimental class taught using LINE Webtoon as a media obtained an arithmetic mean of 24.89; (2) students’ reading comprehension in English subject in control class taught by using LINE Webtoon as a media obtained an arithmetic mean of 16.65; (3) students’ reading comprehension in English subject taught using LINE Webtoon as a media in experimental class are higher than students’ reading comprehension in English subject taught by using LINE Webtoon as a media in control class with a value of Sig. 0.000 &gt; 0.05. Furthermore, there is an effect on the use of LINE Webtoon as a media in learning students’ reading comprehension as evidenced by the increase in students’ learning outcomes.
Time series prediction with missing values: a bidirectional fully connected neural network approach

Time series prediction with missing values is a universal problem in various fields, while existing models cannot satisfy the requirements of engineering systems. To solve this challenge, an effective model is proposed: Bi-FCNN, which combines the bidirectional fully connected neural networks (FCNN) and the back propagation (BP) algorithm with linear propagation and nonlinear mapping capabilities, and performs well in temporal information with missing values forecasting tasks. Analysis of comparative experiment indicates that Bi-FCNN could be competent in missing data environment for predicting deletion that is inescapable due to natural or artificial factors.
Optimization of Extreme Learning Machine Based on Improved Beetle Antennae Search for Slot Die Coating Prediction

In the actual production of slot die coating, the minimum coating thickness and the maximum substrate moving speed could only be judged by production experience, and there was no accurate prediction model due to the nonlinear characteristics of fluid motion. Therefore, building a reasonable and efficient prediction model for slot die coating is now an urgent and challenging task. In this paper, an optimized extreme learning machine (ELM) based on improved beetle antennae search (IBAS) algorithm is proposed for slot die coating prediction. The optimized ELM model can well learn the nonlinear characteristics of the system and make accurate predictions, thus solving the traditional inaccurate empirical judgment. As the prediction accuracy of ELM depends on the selection of weights and biases, the IBAS optimization algorithm is used to quickly search for the optimal value of weights and biases in the ELM network. IBAS algorithm improves the generation mechanism of antennae on the basis of the original algorithm, so that the algorithm can converge quickly. At the same time, the search strategy of the algorithm is improved to avoid falling into the local optimal solution. By predicting the production data of slit coating, the feasibility and effectiveness of IBAS-ELM model are proved.
Inverse kinematics of 7-Dof robot based on pelican optimization algorithm

Robots are more and more applied to the automation of fruit picking, for the picking of large fruit trees, 7-Dof robots with excellent obstacle avoidance ability have a useful place. However, the inverse kinematics solution of 7-Dof robot kinematics has the problem of low precision and many iterations. In order to solve these problems, Pelican Optimization Algorithm is introduced to optimize the redundant robot. Pelican optimization algorithm is a new meta-heuristic algorithm, which has high development ability and high space exploration ability, and is suitable for solving optimization problems. In this paper, 30 groups of positions in the motion space of 7-Dof robot are randomly selected, and the inverse kinematics is solved by Pelican optimization algorithm. The simulation results show that using Pelican optimization algorithm to solve the inverse kinematics of 7-Dof robot can basically reduce the solving error to less than 10^(-7) mm after 250 iterations, and the iterative calculation time is only about 0.26s, which has better comprehensive solving effect compared with other intelligent algorithms.
Lightweight facial attribute editing with separable latent vector

There are two main challenges faced by generative adversarial networks (GANs) for facial attribute editing. The first challenge is how to perform targeted attribute editing in a controllable manner during the editing process, preserving the variability of relevant attributes while ensuring the invariance of irrelevant attributes. The second challenge is the high computational resource requirements of GANs, making them demanding on hardware performance for almost all image editing GANs. To address these challenges, we propose our SLGAN model based on StyleGAN2, which incorporates separable loss and knowledge distillation methods. Experimental results demonstrate that our proposed model achieves promising performance on relevant tasks.
The Journey to Serverless Migration: An Empirical Analysis of Intentions, Strategies, and Challenges

Serverless is an emerging cloud computing paradigm that facilitates developers to focus solely on the application logic rather than provisioning and managing the underlying infrastructure. The inherent characteristics such as scalability, flexibility, and cost efficiency of serverless computing, attracted many companies to migrate their legacy applications toward this paradigm. However, the stateless nature of serverless requires careful migration planning, consideration of its subsequent implications, and potential challenges. To this end, this study investigates the intentions, strategies, and technical and organizational challenges while migrating to a serverless architecture. We investigated the migration processes of 11 systems across diverse domains by conducting 15 in-depth interviews with professionals from 11 organizations. We also presented a detailed discussion of each migration case. Our findings reveal that large enterprises primarily migrate to enhance scalability and operational efficiency, while smaller organizations intend to reduce the cost. Furthermore, organizations use a domain-driven design approach to identify the use case and gradually migrate to serverless using a strangler pattern. However, migration encounters technical challenges i.e., testing event-driven architecture, integrating with the legacy system, lack of standardization, and organizational challenges i.e., mindset change and hiring skilled serverless developers as a prominent. The findings of this study provide a comprehensive understanding that can guide future implementations and advancements in the context of serverless migration.