Reviewerly/exHarmony huggingface dataset:
Study on Issue and Challenges of Government Schools in Karnataka

Education - in its broadest sense imparts a formative effect on the mind, character, and physical ability of an individual. This is particularly so in case of primary education. In its technical sense, education is the process by which society deliberately transmits its accumulated knowledge, skills and values from one generation to another through institutions. Teacher’s quality is the overarching important factor in the entire process of education system. To improve the quality of school education system today we need teachers who can perform not only inside the classroom but also with the entities outside the school system. The current focus of educational administrators and policymakers is rightly laid on developing the required competencies and skills. The aims of education reflect the current needs and aspirations of a society as well as its lasting values, and the immediate concerns of a community as well as broad human ideals. Very little research efforts have been carried out to delve into these issues. This article presents the discussion of the varied demographic and other discussion of the Karnataka states, status of dropout students, government initiatives regarding primary education in the study area. The present study focuses to explore the issues &amp; challenges of government schools in Karnataka.
Formal Specification and Verification of JDK’s Identity Hash Map Implementation

Hash maps are a common and important data structure in efficient algorithm implementations. Despite their wide-spread use, real-world implementations are not regularly verified. In this article, we present the first case study of the IdentityHashMap class in the Java JDK. We specified its behavior using the Java Modeling Language (JML) and proved correctness for the main insertion and lookup methods with KeY, a semi-interactive theorem prover for JML-annotated Java programs. Furthermore, we report how unit testing and bounded model checking can be leveraged to find a suitable specification more quickly. We also investigated where the bottlenecks in the verification of hash maps lie for KeY by comparing required automatic proof effort for different hash map implementations and draw conclusions for the choice of hash map implementations regarding their verifiability.
Efektivitas Pembelajaran Secara Daring dengan Menggunakan Aplikasi Whatapp terhadap Hasil Belajar

process is very different from the offline or face-to-face learning process, such as the way of delivering material and how to assess it. It is also of course that there are many obstacles experienced by educators and students during the online learning process. This study aims to see whether online learning using the Whatsapp application on learning outcomes is effective or not. Based on the results of research and hypothesis testing that has been discussed, it can be concluded that the data analysis used is a hypothesis test, where the hypothesis test is used to see the effectiveness of online learning using the Whatsapp application on learning outcomes. In this study hypothesis testing used a one-sample t-test. Based on the results of the study, a p-value or sig value was obtained. = 0.000 &lt; α = 0.05 So it can be said H_1 accepted, it can be concluded that online learning using the Whatsapp application on learning outcomes is effective.
PEMBUATAN VIDEO EXPLAINER TENTANG TRIPUTRA DNA SEBAGAI MEDIA INFORMASI KARYAWAN DAYA MOTOR ASIA AFRIKA

In this digital era, finding various things on the internet is effortless, especially finding information in form of explainer video. Explainer video in this digital era are quite important, it makes messages or information easy to convey with visuals that look neat and organized. This final project aims to deliver information about Triputra DNA's Guiding Values through explainer video. The author uses multi panel layout in making explainer video to produce a neat and organized visual. From the video that has been made and the results of the questionnaire received, messages or information is easy to convey by using explainer video and by using multi panel layout creates a neat and orderly visual.
The Research on the Communication Features of Michael Jordan’s Documentary the Last Dance

As the well-deserved GOAT of the NBA, Michael Jordan and his Bulls team left the best of the NBA in the 1990s and also promoted the NBA to the whole world. But unfortunately, many young fans have never gotten the opportunity to see Jordan play though wearing Jordan's sneakers. The emergence of The Last Dance presented the audience with a most realistic Jordan including his many dark sides, and eventually achieved great success in the field of sports documentaries. This paper will use the example of The Last Dance to explore the characteristics of documentary film distribution in the network age, including how it has been successful and what still needs to be improved. These studies will be of great help to documentary filmmakers as well as any communication researcher. By looking at the narrative approach of The Last Dance, the hottest topics about The Last Dance on social media, the timing of The Last Dance's release, and The Last Dance's ratings in the Chinese market. This research found that The Last Dance used a relatively clever narrative technique that interspersed secondary thread and backstories with the main thread, and that The Last Dance also gained more attention by revealing an unknown side of Jordan, in addition, The Last Dance was released during the NBA lockout, however due to the timing of the Chinese television broadcast and political reasons, The Last Dance failed to sell well in the Chinese market.
Combining Different Data Sources for IIoT-Based Process Monitoring

Motivation—Industrial internet of things (IIoT) refers to interconnected sensors, instruments, and other devices networked together with computers’ industrial applications, including manufacturing and energy management. This connectivity allows for data collection, exchange, and analysis, potentially facilitating improvements in productivity and efficiency, as well as other economic benefits. IIoT provides more automation by using cloud computing to refine and optimize process controls. Problem—Detection and classification of events inside industrial settings for process monitoring often rely on input channels of various types (e.g. energy consumption, occupation data or noise) that are typically imprecise. However, the proper identification of events is fundamental for automatic monitoring processes in the industrial setting, allowing simulation and forecast for decision support. Methods—We have built a framework where process events are being collected in a classic cars restoration shop to detect the usage of equipment such as paint booths, sanders and polishers, using energy monitoring, temperature, humidity and vibration IoT sensors connected to a Wifi network. For that purpose, BLE beacons are used to locate cars being repaired within the shop floor plan. The InfluxDB is used for monitoring sensor data, and a server is used to perform operations on it, as well as run machine learning algorithms. Results—By combining location data and equipment being used, we are able to infer, using ML algorithms, some steps of the restoration process each classic car is going through. This detection contributes to the ability of car owners to remotely follow the restore process, thus reducing the carbon footprint and making the whole process more transparent.
Cancer Classification from High-Dimensional Multi-omics Data Using Convolutional Neural Networks, Recurrence Plots, and Wavelet-Based Image Fusion

High-dimensional and multi-modal data pose an exceptional challenge in machine learning. With the number of features vastly exceeding the number of training instances, such datasets often bring established pattern recognition techniques to an awkward position: Traditional, shallow models crumble under the sheer complexity of the data, but deep neural networks will helplessly overfit. In this study, an innovative methodology takes up the task, in the case study of using multi-modal biological data for binary classification of cancer types. Our deep learning approach entails transforming the data into images, integrating different modalities via wavelet-based image fusion, then extracting features, and classifying the data with pretrained convolutional neural networks. The results reveal that this framework has the potential to tackle high-dimensional data efficiently and effectively, learning from a low volume of complex data without overfitting, suggesting this to be a promising direction for further research.
Data Augmentation to Reduce Computational Complexity of Neural-Network-Based Soft-Failure Cause Identifier

We investigated data augmentation to train neural networks (NNs) for soft-failure cause identification, demonstrating its impacts on NN complexity. Results indicate up to 68% reduction in the computational complexity of NN for each inference.
Modulator-free intensity- and phase-modulated optical transmitter for quantum communications

We demonstrate a simplified optical transmitter design for generating intensity-and phase-modulated pulses, using injection locking and pulse interference. The transmitter is applied for proof-of-principle quantum key distribution, achieving Mbps secure bit rates.
Teknologi React Native Pada Aplikasi Mobile Pemilihan Anggota Terbaik dan Pencatatan Point Bank Sampah

The increase in people's consumption patterns in the Kalirejo area, Central Lampung has an impact on increasing the volume of waste. The Sidodadi Trash Bank is an alternative in the context of waste management at the community level. Sidodadi Trash Bank in its management requires recording activities and rankings for all its members which are calculated every year with the aim of providing rewards for the best members. Recording and ranking activities are still done manually, therefore a Decision Support System (SPK)-based application is presented to facilitate the activities of the Sidodadi Trash Bank. This application can be used for administrators and also all members of the Sidodadi Trash Bank. This application was created using visual studio software and Simple Additive Weighting (SAW) method. The results of the calculation of the SAW method on the data of five members manually proved to be the same as the results of the system calculations, namely 0,99, which was won by Novita Sari. Testing Device was carried out on Android versions 7.1.1 Nougat, 7.1.2 Nougat, 8.0.0 Oreo, 8.1 Oreo, and 9.0.0 Pie and all worked. From the test results, it is found that the application runs well at least on the Android 5.0 Lollipop version.
Forensic Analysis of Drone Collision with Transfer Learning

Drones are one of devices that are used in many different activities. There is a time when drones have accidents, and authorities need to find the cause. Drone forensics is used to determine the cause of an accident. The analysis phase of drone forensics is one of the most important steps in determining accident causes. In this paper, we applied deep learning technique to classify drone collisions. We investigate the use of the InceptionV3 as the deep learning framework. Additionally, this study compares the performance of the proposed method with other techniques, such as MobileNet, VGG, and ResNet, in classifying drone collisions. In this experiment, we also implement transfer learning as well as its fine tuning to speed up the training process and to improve the accuracy value. Additionally, our investigation shows that InceptionV3 outperforms others in terms of accuracy, precision, and F1 scores.
Penerapan Teknik Neural Network dalam memprediksi Perkembangan Impor Kelompok Industri Tekstil dengan Metode Backpropagation

The aim of this research is to analyze the development of the textile industry group in Indonesia using Artificial Intelligence. The analysis is conducted through a predictive model that will be used to predict the import development of the textile industry group. The dataset is sourced from the Indonesian Central Bureau of Statistics through the website https://www.bps.go.id/. The technique used is neural network with backpropagation method, and the analysis is conducted using Matlab. Backpropagation is a training method that has a target to be sought. This method is also a multilayer method, which has input, hidden, and output layers. The research process consists of two stages, namely the training stage and the testing stage. Out of several architecture models tested (3-10-1, 3-25-1, 3-50-1, 3-80-1, and 3-100-1), the best architecture model obtained is 3-100-1 with an MSE of 0.000999996 and an accuracy value of 100 percent.
Assessment of academic work in AI using Bibliometrics: The Case of Lund University

Artificial Intelligence (AI) is considered as “General Purpose Technology” that could have large impact effects in several sectors and cause disruptive changes in societies. Universities are often seen as the seedbeds of new knowledge and scientific research, as a point of departure, by using bibliometric approach we aim to assess the scientific output for AI, to understand the characteristics and potential of AI research at Lund University. The preliminary findings showed the diverse partnerships of LU scientists with other universities, companies, research institutes, and hospitals, funding agencies, and international partnerships. Our study aims to establish the groundwork for future assessments of AI research in a sustainable and transparent manner. We plan to identify both traditional scientific inputs and outputs, to unpack the unknown dimensions of academic work at universities, with a more holistic approach to assess academic work and pave the way for future studies.
Design and Implementation of Biometric-Based Student Information Retrieval System Using Finger Prints Recognition

This study designed and implements a biometric-based student information retrieval system using finger print recognition. The findings showed that the existing manual system is prone to lots of errors such difficulties in accessing records, wrong data entry, difficulties in data manipulation, identity management and difficulties in updating students’ records. Structured system analysis and design methodology was adopted for this research. The design and implementation of the developed application software showed that computerized student information retrieval system using biometrics greatly enhanced the documentation, manipulation and retrieval of student information in a more accurate, fast and secured manner.
MULTIPLE DISEASE DETECTION USING MACHINE LEARNING

Because of numerous contributing risk factors, including diabetes, high blood pressure, high cholesterol, irregular pulse rate, and many other factors, it is challenging to diagnose heart disease.The severity of cardiac disease in humans has been determined using various data mining and neural network techniques.According to the research paper the power of the proposed model was quite satisfactory and it was able to predict the evidence of heart disease in a particular individual using KNN and logistic regression which showed good accuracy compared to previously used classifiers like naïve bayes etc.Healthcare is a very prominent research field with rapid technological advancement and increasing data day by day.In order to deal with a large volume of healthcare data we need Big Data Analytics which is an emerging approach in the Healthcare domain.Millions of patients seek treatments around the globe with various procedures.Making informed and effective decisions to enhance the general standard of healthcare will be aided by analyzing the trends in patient treatment for the diagnosis of a specific condition.According to the research report, to forecast diabetes mellitus, we have used decision trees, random forests, and neural networks.As given in the paper Pima Indian Diabetes Dataset is also used for the prediction.Diagnosis of Parkinson disease through a machine learning approach provides better understanding from PD dataset in the present decade.Orange v2.0b and weka v3.4.10 have been used in the present experimentation for the statistical analysis, classification, Evaluation and unsupervised learning methods.The Centre for Machine Learning and Intelligent Systems has retrieved a voice dataset for Parkinson's disease from the UCI Machine learning repository.
The effects of cognitive- and affective-based trust on students’ knowledge sharing and learning performance during the Covid-19 pandemic

This study examined the effects of cognitive and affective-based trust on knowledge sharing among students, which influences learning performance during the COVID-19 pandemic. A survey was conducted with 730 participants, and analysis was carried out using structural equation modeling (SEM) based on the uses and gratifications (U&amp;G) theory. The results showed that cognitive and affective trust significantly affects students’ knowledge sharing behavior on Facebook, which further influences learning performance. This study also showed that social media had become a tool for social interaction and learning, which is crucial to students during the COVID-19 pandemic.
APPLYING SMALL GROUP DISCUSSION COMBINED WITH PICTURE TO IMPROVE WRITING SKILL OF THE EIGHTH-GRADE STUDENTS OF SMP CIPTA DHARMA DENPASAR

Students with proficient writing skills can articulate their thoughts, beliefs, knowledge, emotions, and experiences. Students should be familiar with paragraph arrangement and general structure. However, they continued to struggle with writing paragraphs, particularly descriptive paragraphs. In addition, they were unable to organize it in terms of general structures, coherence, and unity. The primary purpose of this study was to examine whether the writing skills of the eighth-grade students of SMP Cipta Dharma Denpasar might be enhanced through small group discussion combined with picture. This research employed a two-cycle classroom action research design. The relevant data were obtained using instruments, including paragraph construction tests and a structured questionnaire. Following a cyclical research procedure, it was determined that small group discussion combined with picture was suitable for enhancing the writing skills of the subjects. It was evident from the findings of the pre-test, post-test 1, and post-test 2 that their writing skills had improved significantly. In addition, it was supported by the structured questionnaire, which revealed positive responses to the technique. It may be inferred that the writing skills of the eighth-grade students of SMP Cipta Dharma Denpasar could be improved by small group discussion combined with picture
ANALISIS KLASIFIKASI SENTIMEN PENGGUNA MEDIA SOSIAL TWITTER TERHADAP PENUNDAAN PEMILU PRESIDEN TAHUN 2024

The discourse of postponing the 2024 election and extending the term of office of the president has stimulated sentiment in some regions in Indonesia. The discourse has implications for the extension of the president's term of office which is considered unconstitutional. The issue of postponing the election is clearly not the aspiration of the people, but only the interests of political passion and lust among the rulers who want to perpetuate power so that it is considered to violate and insult the constitution which stipulates that elections are held every five years. This study aims to determine how to analyze the sentiment of postponement the 2024 election on comments from the Indonesian people on Twitter or called tweets. The number of comments that will be used in the study is 1826 consisting of 710 positive sentiments and 1116 negative sentiments. The research method used is the Cross-Industry Standard Process for Data Mining (CRISP-DM) method. With this method, the stages of research carried out are data collection, business understanding, data understanding, data preprocessing, data labeling, modeling, evaluation, and deployment. Based on the results obtained at the modeling stage, the tweet data that has been collected is then processed and analyzed for sentiment with a train/test split data model and k-fold cross validation using the Naive Bayes (NB) algorithm, Support Vector Machine (SVM), Deep Learning (DL) ), and Desicious Tree (DT) and a comparison of 80:20 for training and testing data, the highest accuracy value is obtained by using the train/test split data model using the Naive Bayes algorithm to produce an accuracy of 80,55%.
Implementasi Teknologi Informasi dan Komunikasi New Media Digital sebagai Konsep Model Pembelajaran Siswa di SMPN 101 Jakarta

After the Covid-19 pandemic, various parts of the world began to pursue and experience improvements in the world of education, the learning concept that provides various facilities makes students look for something that makes it easy and understandable for the material conveyed by the teacher in the learning model.Digital Information and Communication Technology New Media offers a very interesting learning concept among students, new application platforms have sprung up to facilitate learning such as mentorship, Quizizz, profs and others.The purpose of this study is to see and know the concept of a learning model that can be easily understood by students at SMPN 101 Jakarta.As is known, in the last two years students have been presented with a learning model that makes students understand without being able to understand the concept of the learning model as a whole.The method used was quantitative with a comparative causal approach using a field survey with 94 students at SMPN 101 Jakarta.Testing the truth of the hypothesis of 94 student respondents at SMPN 101 Jakarta with a simple linear regression analysis.The results obtained can be seen based on research data that new digital media has a great influence on the concept of learning models 71.3% and 63.8% of the concept of learning models with the help of digital media.It can be seen that new digital media information and communication technology is very helpful in determining the concept of student learning models at SMPN 101 Jakarta.
Optimal strategies for the static black-peg AB game with two and three pegs

The AB Game is a game similar to the popular game Mastermind. We study a version of this game called Static Black-Peg AB Game. It is played by two players, the codemaker and the codebreaker. The codemaker creates a so-called secret by placing a color from a set of [Formula: see text] colors on each of [Formula: see text] pegs, subject to the condition that every color is used at most once. The codebreaker tries to determine the secret by asking questions, where all questions are given at once and each question is a possible secret. As an answer the codemaker reveals the number of correctly placed colors for each of the questions. After that, the codebreaker only has one more try to determine the secret and thus to win the game. For given [Formula: see text] and [Formula: see text], our goal is to find the smallest number [Formula: see text] of questions the codebreaker needs to win, regardless of the secret, and the corresponding list of questions, called a [Formula: see text]-strategy. We present a [Formula: see text]-strategy for [Formula: see text] for all [Formula: see text], and a [Formula: see text]-strategy for [Formula: see text] for all [Formula: see text] and show the optimality of both strategies, i.e., we prove that no [Formula: see text]-strategy for a smaller [Formula: see text] exists.