Reviewerly/exHarmony huggingface dataset:
An Approach for Aspect Extraction Using Double Embedding Technique based Machine Learning Model

Objectives:To perform aspect extraction for opinion mining of learner comments in the online teaching-learning scenario.Methods : A machine learning model is developed for aspect extraction.The authors collected the dataset consisting of around 5000 learner comments from Coursera and performed analysis on the dataset.To validate the results, the standard SemEval2014 dataset is used.Findings: For both the contextualised and non contextualised word embeddings, the authors compiled the results of their model for two different datasets, viz., the SemEval 2014 dataset and the collected dataset.The proposed model provided an accuracy of 92.78%.Novelty: For the first time in the literature, double embedding is used for aspect extraction.Using domain-specific embedding provided good results, but the proposed model (using double embedding) is far superior in accuracy.
Perankingan Dosen Berbasis Aktifitas Forum Moodle Menggunakan Metode Fuzzy Tsukamoto

The online learning process currently utilizes a Learning Management System, one of which is Moodle. Moodle provides a forum menu where the forum is a digital forum for asking questions and answering and sharing between lecturers and students. Lecturer and student activity can be seen from the forum menu. For this reason, research was conducted on the ranking of lecturer activities in the moodle forum. The ranking determination uses the Fuzzy Tsukamoto decision making system. Fuzzy Tsukamoto has step efficiency in determining rules and processing data that has a taboo range and produces crips output. The processing steps carried out are determining the variables, giving weights, creating a fuzzy set of each variable, then the fuzzification process and creating a rule base and finally, defuzzification, namely the calculation of negligence and output. Based on the rule and the predicate alpha value, the appropriate value is obtained to rank the quality of lecturers based on their activities in the moodle forum.&#x0D; Keywords — database, forum, fuzzy, moodle, tsukamoto
Functional Status of ICt tools and its Usage in Government Schools: A rural-Urban Comparison in telangana State, India

The application of Information and Communication Technology (ICT) has been rapidly increasing in all the sectors including the education system.There is deliberate need of technology integration in school education for new emerging societies to facilitate significant learning needs for holistic development.The main objective of this paper is assessing the availability and functional Information and Communication Technology (ICT) tools in government schools in rural and urban areas.This study has collected primary data from 72 government secondary schools in Warangal Rural and Urban districts of Telangana State.The data reveals that of the total sample schools 43 percent schools are not using computers and chi-square test results (p>0.05) shows that there is no significant difference between rural and urban schools.Also, regarding other functional ICT devices like projectors, K-YANs, printers, television, UPS it is found that there is no significant difference between the schools of rural and urban area.Overall, it is very disheartening to found that more than four-fifth (80 percent) schools had very poor performance in using ICT tools for teaching and learning.
Technical Perspective: Revisiting Runtime Dynamic Optimization for Join Queries in Big Data Management Systems

Query optimization is the process of finding an efficient query execution plan for a given SQL query. The runtime difference between a good and a bad plan can be tremendous. For example, in the case of TPC-H query 5, a query with 5 joins, the difference between the best and the worst plan is more than 10,000×. Therefore, it is vital to avoid bad plans. The dominating factor which differentiates a good from a bad plan is their join order and whether this join order avoids large intermediate results.
Analisis Intensitas Cahaya Lampu Pijar dengan Menerapkan Metode Gray Level Co-occurence Matrik

This paper proposes primary dataset with 13 images thermal capture, 8 high frequency and 4 low frequency. We utilize thermal images fluorescent lamp and using image processing with extraction feature GLCM method. Furthermore, Contrast, Correlation. Energy, Homogeinity dan sudut 0°, 45°, 90°, 135°, these feature texture using for calculated validation compare with both exsperiment qualitative results in Table1 and Table2. Therefore, exsperiment with fluorescent lamp Figure 2 quantitative results significant in Table1. Quantitative results with fluorescent lamp in Table2 extraction feature GLCM method with angle 0°, 45°, 90°, 135° and in Table1 quantitaive result with low frequency 50 Hz with T (oC) 50 is significantly robust. Comparable quantitative results in Table2 with low frequency 50 Hz from extraction feature mean value angle 0°, 45°, 90°, 135° Contrast (0.0363), Correlation (0.9959), Energy (0.1353), and Homogeneity (0.9832).
Applied Music Department Improvements due to Decrease in College Admission Resources

출산율 감소와 노령화에 따른 인구문제는 전 세계적으로 주목되고 있는 사회적 문제이다. 최근 우리나라 출산율은 2022년 기준 0.78명 OECD 가입국 중 최하위를 기록하고 있으며 대학 정원보다 입학 예정자가 줄어들어 서울 수도권을 제외한 대학들은 폐교 위기를 맞이하고 있다. 한편 2000년 중반부터 시작된 각종 오디션 프로그램의 인기로 대중음악에 대한 인식이 변하면서 전국적으로 실용음악과의 수가 늘어났으며, 최근 한국 아이돌 그룹의 빌보드 차트 상위권 진입 등 한국 대중음악의 세계적인 인기는 매년 높아져만 가고 있다. 학령인구 감소에도 실용음악과 보컬전공 입시 경쟁률은 347:1이라는 이상 현상을 나타내고 있으며 몇몇 인기 학교만 집중되는 지원자 증가로 수도권 밖 대학들은 정원모집에 어려움을 호소하고 있다. 연구의 목적을 달성하기 위해 학령인구 감소에 따른 입학 자원의 변화와 입시 결과를 분석하여 연주 위주로 치우친 입시제도의 문제점과 교육과정의 문제점을 찾아내고 개선방안을 제안해 보았다. 또한, 설문을 통하여 입시, 교육 제도의 문제점을 파악하고 빠르게 변화하는 음악 시장을 파악하여 차별화된 운영 방법과 체계화된 시스템을 통하여 실용음악 교육이 대응할 방안을 제언한다. Population problems caused by declining fertility rates and aging are social issues that have attracted worldwide attention. Korea"s birth rate (0.78) in 2022 was the lowest among OECD member countries. The number of expected admissions has already decreased compared to the university quota, and universities outside the Seoul metropolitan area are facing a crisis of closing. With the popularity of various audition programs that started in the mid-2000s, the perception of popular music has changed, and the number of applied music departments has increased nationwide. The worldwide popularity of Korean popular music, such as the recent entry of Korean Idol groups to the top of the Billboard charts, is increasing yearly. Despite the decrease in the school-age population, the competition rate for the applied music and vocal majors is an abnormal phenomenon of 347:1, and local universities, which are concentrated in popular schools in the metropolitan area, are complaining of difficulties in recruiting quotas. This study analyzed the changes in admission resources and the entrance exam results due to the decreasing school-age population. The results revealed problems in the entrance exam system that was biased towards performance and the problems in the curriculum and suggested improvement plans. In addition, through surveys, problems in the entrance examination and education system are identified. The fast-changing music market is identified, and a method for practical music education is proposed through a differentiated operating method and a systematic system.
INCREASING STUDENT UNDERSTANDING ON PPKN STUDY THROUGH THE DEBATE MODEL TO THE LEGISLATION MATERIALS IN CLASS VIII A SMP NEGERI 4 BANDUNG

This study is Classroom Action Research (PTK – Penelitian Tindakan Kelas) aims to improve learning outcomes and activities of class VIII A students in PPKn lessons on legislative material through the application of debate model learning. The results of the pre-study obtained an average value of 55.87, complete 2 people or 5%. The results of the research that have been carried out, in the first cycle obtained an average value of 75.06 categorized as “Good” completed 22 people or 55%. After reflection, the acquisition of the average value in the second cycle increased, to 86.34 categorized as “Very Good” completed 38 people or 95%. The increase in the percentage of achievement of the KKM (Kriteria Ketuntasan Minimal – Minimum Completeness Criteria) value from pretest to cycle ranged from 50%. It can be conthated that the application of the debate learning model is effectively used in the learning process of PPKn on legllative meal because can significantly increase activities, and student learning outcomes. The obstacles found in this study are requires time, extra energy, and patience from the teacher, and overall orang and guidance is peaded by teacher .
Implementasi Metode K-Means Untuk Memprediksi Status Kredit Macet

A credit card is one of the legal payment media owned by a bank in making a payment transaction within the agreed timeframe. In particular, credit services are provided by institutions or bodies that have the authority to distribute funds in the form of financial assistance to individuals and groups. However, in practice there are bound to be obstacles, especially during payback periods that often occur, such as when a customer wants to submit a Repeat Order or apply for funds again. Obstacles that are usually encountered in the process of granting credit are substandard credit and bad credit payments. Before PT Esta Dana Ventura wants to decide to approve applications for re-granting credit cards from prospective repeat order customers, a classification of assessment criteria is needed to determine the feasibility of granting credit to prospective repeat order customers. This study made the decision to use Data mining clustering classification with Rapidminer tools as a tool to obtain accurate results by processing data using the K-Means clustering method to help PT. Esta Dana Ventura in analyzing potential non-performing loans. By comparing survey data for Repeat Order candidates with previous credit granting data and classifying them in the form of bad or non-bad credit classifications.From the results of research using the k-means method it can produce grouping data into 3 criteria, namely (C0) 69 data with current customers, (C1) 3 data with very current customers, and (C2) 52 data with Bad customers..
C-3PA: Streaming Conformance, Confidence and Completeness in Prefix-Alignments

The aim of streaming conformance checking is to find discrepancies between process executions on streaming data and the reference process model. The state-of-the-art output from streaming conformance checking is a prefix-alignment. However, current techniques that output a prefix-alignment are unable to handle warm-starting scenarios. Further, no indication is given of how close the trace is to termination—a highly relevant measure in a streaming setting. This paper introduces a novel approximate streaming conformance checking algorithm that enriches prefix-alignments with confidence and completeness measures. Empirical tests on synthetic and real-life datasets demonstrate that the new method outputs prefix-alignments that have a cost that is highly correlated with the output from the state-of-the-art optimal prefix-alignments. Furthermore, the method is able to handle warm-starting scenarios and indicate the confidence level of the prefix-alignment. A stress test shows that the method is well-suited for fast-paced event streams.
PEMBUATAN APLIKASI PENYIMPANAN OBAT (APO) DALAM PENGONTROLAN MASA KADALUARSA OBAT DI APOTEK BERBASIS WEB

Digital pharmacy has been growing in the current era, so the need for an application to help the performance of a pharmacist is indispensable. Pharmacy management information system applications that have been circulating are known to have not used a drug expiration date notification system to email. The purpose of this study was to find out whether the drug storage application design can control drug expiration in a website-based pharmacy. The research method is carried out by making website-based software by applying data flow diagrams (DFD). The research procedure is to first design a data flow diagram consisting of drug data input, purchase transaction data input, sales transaction data input, drug storage data input and transaction report input. After that, create a use case design consisting of two, namely admin and cashier. The last step is to create a block diagram flow with notifications of drugs approaching expiration in the form of incoming e-mails to users. The results showed that the design of the drug storage application system that was made could operate properly and had been tested independently. The drug storage application has not yet been tested by the public, so further testing is needed.
DIAGNOSTIC PERFORMANCE OF EXPERTS VERSUS ARTIFICIAL INTELLIGENCE IN READING CHOLANGIOSCOPIC IMAGES OF BILIARY STRICTURES WITH AND WITHOUT PRIOR STENTING

Chulalongkorn University, Thailand.
STICK PUPPETS AS AN ALTERNATIVE MEDIA TO IMPROVE THE SEVENTH GRADERS’ SPEAKING SKILL OF A JUNIOR HIGH SCHOOL IN BANTARAN

The importance of speaking has been the main focus on teaching English as a foreign language. The significance of speaking ability can be seen from students’ speaking ability. After conducting preliminary study, it is undeniable that many students have difficulty improving their speaking skills. The problems were devided into five speaking issues; students’ linguistic aspects, psychological aspects, the teacher’s strategy and method in teaching speaking, the lack of media, and students’ speaking achievement. Thus, the researcher proposed stick puppet as an alternative media because it is suitable with the strategy and the aspects that were being improved. This research was Classroom Action Research (CAR) which aims to improve students’ speaking skills. The participants of this research were the Seventh-grade A students’ of SMPN 1 Bantaran in the academic year 2021/2022. The research design applied in this study was a collaborative classroom action research consisting of 1 cycle covering 3 meetings and 1 post-test. The data collection techniques used a speaking test as the primary data to collect students’ speaking scores and teacher’s field note, teacher’s interview, students’ questionnaire, and classroom observation sheet as the secondary data. After implementing stick puppets media, this research found that the speaking skill of seventh-grade A students of SMPN 1 Bantaran improved through stick puppets. The result showed that 22 students or 95,65% could pass the KKM with a mean score improved 90,86 compared to the preliminary speaking test was 16,08. After the research, the data presented that stick puppets media can improve students speaking skills in vocabulary, pronunciation, grammar, and content. Additionally, stick puppets can increase students’ activeness in the classroom. On the other hand, the students were interested, motivated, and confident in using stick puppets which helped students to describe people’s physical appearance.
Analisis Sentimen Covid-19 Pada Media Sosial Dengan Model Neural Machine Translation

Sentiment analysis, also known as opinion mining. Social media offers users to share their conditions and opinions in their daily lives. Much of the vast amount of textual content is currently available and techniques are needed to make meaningful use of the information by separating and examining it. Sentimental analysis can explore the opinions of social media users from the point of view. During the Covid-19 pandemic, the whole world is giving comments on social media. This study aims to understand the awareness and public perception of the Indonesian people about issues related to Covid-19 on social media. Data mining was carried out on comments from users of social media accounts kawalcovid19, opponentcovid19 id, and the ministry of health with a total of 54,250 comments divided into 32,494 in the first week and 21,756 in the second week. The research method of analyzing the data includes data processing and sentiment exploration analysis. Sentiment distribution results based on the first week were positive 75.68%, neutral 16.3%, negative 13.41% while the second week was positive 80.14%, neutral 9.03%, negative 4.84%. The results are the majority of comments in Indonesian and a mixed language. Sentiment analysis shows the positive reaction of the public to the front line and all the efforts of the health ministry in fighting the pandemic. This study concludes that sentiment analysis has proven to be very effective in producing useful knowledge in the discussion of Covid-19, especially on social media, gathering public perceptions and responses about the government's Covid-19 efforts, and providing a different point of view from the current situation on the ground.
Pengelompokkan Data Kemiskinan Provinsi Jawa Barat Menggunakan Algoritma K-Means dengan Silhouette Coefficient

Serious poverty is still one of the problems in Indonesia, especially in West Java Province. The level of underdevelopment and unemployment is still the basis for poverty. Poverty in each region is certainly different. The government needs to know which areas fall into the categories of high poverty levels and low poverty levels so that they can make solutions to set priorities for assisting. Therefore, a data mining technique is needed that can classify the poverty level of areas in West Java, namely the clustering technique with the K-Means algorithm. The purpose of this research is to classify poverty data in West Java Province so that it can be used as information to determine the right policy to distribute aid to the community from the West Java government. The results obtained based on the test, the clusters obtained were 2 clusters with cluster 0 of the high poverty level in as many as 14 regions and cluster 1 of the low poverty level in as many as 13 regions. Based on the test, the K-Means Algorithm obtains a Silhouette Coefficient of 0.576 and is included in the medium structure category. With the results of grouping poverty data, the government can channel aid more precisely.
Pemodelan Sistem Infomasi Keuangan Sekolah Menggunakan Model User Experience Design

The school financial information system if managed very well will be an advantage in the implementation of activities and become a good spearhead for an educational institution. The current problem of processing school financial administration data is ineffectiveness and efficiency in the processing of school financial data, so it greatly affects the speed in determining a decision by high-level school management. The purpose of this research is the application of the user experience design model in making the modeling of the school financial information system very effective which folds system users in every system design starting from the stages of understand, research, sketch, design, implementation, and evaluate. The results of the school financial information system modeling test using a user experience design model for testing system functionality using blackbox testing got a value of 100% according to the functionality of the system, and the test results of Web Quality 4.0 got a value of 87.69% having excellent criteria from usability testing conducted on users.
Comparative Analysis of Multimedia Compression Algorithms

Data compression reduces the amount of bits needed to encode information, conserving expensive resources like disc space and transmission bandwidth. By using data compression, an information block's real size is kept as little as possible. The compression process consists of two steps: encoding, which uses fewer bits to compress a message, and decoding, which uses the compressed representation to roughly resemble or replicate the original message. The two main types of data compression are lossless compression and lossy approaches. The Huffman, Lempel-Ziv, and Run Length encoding techniques were all examined in this research, along with three other commonly used compression techniques. According to the research, Run Length Encoding was the best method for reducing the size of text, web pages, images, and sound.
Outsourced Privacy-Preserving Data Alignment on Vertically Partitioned Database

In the context of real-world secure outsourced computations, private data alignment has been always the essential preprocessing step. However, current private data alignment schemes, mainly circuit-based, suffer from high communication overhead and often need to transfer potentially gigabytes of data. In this paper, we propose a lightweight private data alignment protocol (called SC-PSI) that can overcome the bottleneck of communication. Specifically, SC-PSI involves four phases of computations, including data preprocessing, data outsourcing, private set member (PSM) evaluation and circuit computation (CC). Like prior works, the major overhead of SC-PSI mainly lies in the latter two phases. The improvement is SC-PSI utilizes the function secret sharing technique to develop the PSM protocol, which avoids the multiple rounds of communication to compute intersection set members. Moreover, benefited from our specially designed PSM protocol, SC-PSI does not to execute complex secure comparison circuits in the CC phase. Experimentally, we validate that compared to prior works, SC-PSI can save around 61.39% running time and 89.61% communication overhead.
Robust trajectory planning for multi-rotor aerial vehicles subject to saturation faults and wind disturbances

In this paper, we propose a trajectory planning approach to accommodate saturation faults associated with the propulsion system in multi-rotor vehicles under wind conditions. The Urban Air Mobility (UAM) paradigm demands strict safety guarantees in the face of unexpected mid-flight events. We propose a supervisory trajectory updating scheme based on reinforcement learning (RL) that operates on a flight computer by interacting with flight controllers available in most multi-rotor aerial vehicles. The RL-based planner is trained in simulation and can be refined based on real trajectory data to account for the sim-to-real transfer gap. The proposed algorithm is designed to trade off a conservative planning approach required when the vehicle experiences wind conditions and a more aggressive response required when one of the motors suffers a saturation fault. We demonstrate satisfactory results under different fault scenarios considering wind conditions in a simulation environment platform validated with real flight data.
Design and Implementation of Text Classifier Based on Convolutional Neural Network

With the continuous development and application of artificial intelligence technology, text classification gradually changes from shallow learning mode to deep learning mode. Deep learning mode has obvious improvement in process design, model construction and accuracy of classification prediction results. Therefore, this paper takes text classification as the research object, based on the deep learning algorithm, builds a text classifier by using TextCNN Convolutional Neural Network, and trains the corresponding model with TensorFlow deep learning framework and Keras advanced neural network API in Python environment, so as to form an intelligent text classifier that can be called by Web Server. In addition, ASP.NET framework and C# language will be selected to complete the development of Web Server, improve the definition and deployment of functions and data interfaces, and generate a Web-based text classification system. The overall design of the system conforms to the B/S architecture standard, which supports users to complete the classification results of the input text content by simple operation of the client interface. After testing, the text classifier based on TextCNN has the ability to quickly extract local features, and it is more suitable for the classification prediction of long-sequence text content, and its execution efficiency and classification accuracy are obviously improved.