Reviewerly/exHarmony huggingface dataset:
Validitas dan reliabilitas instrumen penilaian berbasis android untuk menilai kemampuan siswa SMK dalam mendesain busana secara digital

&lt;p&gt;The aims of this research and development are (1) to develop an android-based assessment instrument to assess the ability to design clothes digitally, (2) to test the validity and reliability of the instruments developed, (3) to test the practicality of using android-based assessment instruments. The research model uses the Borg &amp;amp; Gall model which consists of ten development steps, namely: (1) research and data collection (2) planning, (3) product draft development, (4) field trials, (5) initial product improvement, ( 6) field trials, (7) improvement of the results of field tests, (8) field implementation tests, (9) improvement of the final product, and (10) dissemination and implementation. The data analysis technique uses the Aikens'V formula to test content validity, Cronbach's Alpha to analyze reliability, and percentages to test practicality. The results of the study (1) obtained an assessment instrument in the form of a Likert scale, 4 options, equipped with a rubric for assessing fashion design products digitally. The instrument is constructed on 4 dimensions/aspects of 15 indicators. (2) the assessment instrument developed is valid with a Vcount of 0.93 and reliable with a reliability coefficient of 0.91. (3) The use of instruments in the good category with an average score of 87.5%.&lt;/p&gt;
UPAYA MENINGKATKAN AKTIVITAS BELAJAR PESERTA DIDIK MELALUI MODEL PEMBELAJARAN PROJECT BASED LEARNING DAN MEDIA TANGRAM PADA MATA PELAJARAN MATEMATIKA MENYUSUN BERBAGAI BANGUN DATAR

Designing mathematics learning for elementary school students must be able to give a pleasant impression on students. In general, elementary school students still like to play. Therefore, learning designs that involve games can be expected to increase student learning activities. In this study, learning design on flat building material was carried out with the Project Based Learning (PJBL) model with the help of tangram media. This study aims to improve student learning activities in mathematics subjects, the subject of flat-building. The type of research chosen is PTK. The research cycle carried out consists of two cycles. The research site was conducted at SDN Belotan 1 in the 2022/2023 school year. The subjects of the study were selected in grade IV students. The results showed that the percentage of student learning activities in the first cycle was 62.1875%. In the second cycle, improvements are made to the weaknesses that appear in the first cycle. The results of this improvement showed that in the second cluster, the percentage of student learning activity was 77.5%. Based on the results of data analysis, it was concluded that the Project Based Learning (PJBL) learning model with the help of tangram media can increase student learning activities.
General treatment of Gaussian trusted noise in continuous variable quantum key distribution

Continuous Variable (CV) quantum key distribution (QKD) is a promising candidate for practical implementations due to its compatibility with the existing communication technology. A trusted device scenario assuming that an adversary has no access to imperfections such as electronic noises in the detector is expected to provide significant improvement in the key rate, but such an endeavor so far was made separately for specific protocols and for specific proof techniques. Here, we develop a simple and general treatment that can incorporate the effects of Gaussian trusted noises for any protocol that uses homodyne/heterodyne measurements. In our method, a rescaling of the outcome of a noisy homodyne/heterodyne detector renders it equivalent to the outcome of a noiseless detector with a tiny additional loss, thanks to a noise-loss equivalence well-known in quantum optics. Since this method is independent of protocols and security proofs, it is applicable to Gaussian-modulation and discrete-modulation protocols, to the finite-size regime, and to any proof techniques developed so far and yet to be discovered as well.
Reinforcement Learning With Reward Machines in Stochastic Games

We investigate multi-agent reinforcement learning for stochastic games with complex tasks, where the reward functions are non-Markovian. We utilize reward machines to incorporate high-level knowledge of complex tasks. We develop an algorithm called Q-learning with reward machines for stochastic games (QRM-SG), to learn the best-response strategy at Nash equilibrium for each agent. In QRM-SG, we define the Q-function at a Nash equilibrium in augmented state space. The augmented state space integrates the state of the stochastic game and the state of reward machines. Each agent learns the Q-functions of all agents in the system. We prove that Q-functions learned in QRM-SG converge to the Q-functions at a Nash equilibrium if the stage game at each time step during learning has a global optimum point or a saddle point, and the agents update Q-functions based on the best-response strategy at this point. We use the Lemke-Howson method to derive the best-response strategy given current Q-functions. The three case studies show that QRM-SG can learn the best-response strategies effectively. QRM-SG learns the best-response strategies after around 7500 episodes in Case Study I, 1000 episodes in Case Study II, and 1500 episodes in Case Study III, while baseline methods such as Nash Q-learning and MADDPG fail to converge to the Nash equilibrium in all three case studies.
GUI-VP Kit: A RISC-V VP Meets Linux Graphics - Enabling Interactive Graphical Application Development

Today, Virtual Prototypes (VPs) are heavily used to enable early software development and to accelerate the design process. The aim of this work is twofold: (i) enable the early development of interactive graphical applications running on Linux, and (ii) provide an easy-to-use and configurable solution for RISC-V. In this paper, we present GUI-VP Kit. GUI-VP Kit includes GUI-VP, a greatly extended and improved RISC-V VP, as well as configurations to build a runnable Linux environment, and input/output drivers that form the interface between peripherals and Linux applications. In our experiments employing GUI-VP Kit, we show that well-known X-applications can be executed in GUI-VP using a VNC network connection. Moreover, we demonstrate reasonable speed for a Linux port of a classic first-person 3D-game.
Transforming Technical Services through Training and Development.

"Transforming Technical Services through Training and Development.." Journal of Electronic Resources Librarianship, 35(2), pp. 167–168
AI vendors could accept EU's code of conduct idea

Headline INT: AI vendors could accept EU's code of conduct idea
Study on Cloud Based Integrated Bhulekh Model Transforming Revenue Administration and Digitally Empowering Society

The goal of the government is to deliver numerous citizen-centric services in a cost-effective and transparent manner. To do this, the government must leverage cutting-edge technology in novel ways and combine it with process reengineering models that include a range of stakeholders in service delivery. Additionally, the government can perform claim validation by directly verifying documentary proofs through created databases. The study highlights the remarkable initiative taken by the Government of Uttar Pradesh to transform Bhulekh 1.0 to Bhulekh 2.0, by employing cloud-based integration of stakeholders in revenue administration, including the registry office, revenue courts, banks, Aadhar, e-District, and Common Service Center (CSC). This transformation has set a new standard of digital delivery of services and improved the performance of Revenue Administration through MeghRaj Cloud. The cloud powered Bhulekh 2.0 is making significant progress towards creating a digitally empowered society by providing services from anywhere at any time in a responsive manner.
Outlook: Future and Development of Digital Psychology

It is no longer a question of whether, but how we deal with the advancing digitalization and how psychologists will position themselves here. There will be individual, economic and social improvements, but also “digital gaps”. Where innovations and improvements arise in the digital space, it is also important to look at things critically.
The Effect Of Show And Tell Method On Students’ Speaking Ability At Eighth Grade Of Mts Diniyah Pandai Sikek

The purpose of this research was to find out the effect of using Show and Tell method on students’ speaking ability for the eighth-grade students of MTs Diniyah Pandai Sikek. This research was experimental and was conducted in MTs Diniyah Pandai Sikek of Eighth Grade during the 2021/2022 Academic Years. There were two classes: experimental class and control class, in which each class consists of 23 students. The total population was 69 students. This research used purposive sampling. The researchers use an experimental quantitative method for collecting the data. Based on the statement above, the researchers take the whole of two classes or 46 students as a sample. The instrument in collecting the data was an oral test: namely by asking the students to present the information about themselves and asking them one by one to describe their object while the researcher listen to them. Technique of collecting the data by comparing the result of pre-test and post-test both of the classes. Helped by the English teacher, then the scores were classified based on components of speaking, they were vocabulary, pronunciation, grammar, fluency, and comprehension. The result shows that test of t-obtained &gt; t-table or 5.369 &gt; 1.960. It means the hypothesis that there was a significant effect of the Show and Tell method on students’ speaking ability in junior high school.
Adaptive Modeling for Rainfall Prediction Using Ensemble Machine Learning and Bayesian Optimization

Heavy rainfall in Indonesia will usually be followed by news of a flood disaster. Flood is a natural phenomenon that is detrimental to social life. There are various approaches to studying rainfall to forecasting floods disaster, one of which is ensemble machine learning. Ensemble machine learning produces better performance models than single machine learning when analyzing the occurrence of floods through rainfall. However, one model does not seem sufficient to analyze the overall occurrence of rainfall in different areas. This happens because the datasets in each region will have different characteristics, so the models generated by machine learning need to be distinct from one another. Furthermore, manual tuning in ensemble machine learning is sometimes difficult due to a large number of hyperparameters, the complexity of a given problem domain, and ineffective model evaluation. This paper proposes an adaptive modeling approach for rainfall prediction based on these problems using stacking ensemble machine learning Bayesian Optimization. The mean square error (MSE) result of Surabaya, Bandung, Jakarta, Semarang, and Banten are 14.755, 0.249, 3.310, 0.243, and 0.917 respectively. The results of the study indicate that this research provides a more dynamic and reliable approach to determining the best tuning model to predict rainfall. We provide some models in one time running with the tunning configuration in each dataset dynamically.
Study Of Flood Disaster Risk Delta In Bojonegoro Regency

A disaster is an unexpected and often sudden event that causes destruction, serious damage, and human suffering. According to the Law of the Republic of Indonesia Number 24 of 2007 concerning Disaster Management, disasters are caused by natural factors and/or non-natural and human factors so that disasters can result in loss of life, environmental damage, loss of property, and psychological impacts. Bojonegoro Regency which has the widest area crossed by the Bengawan Solo river and 24,753 hectares of its area is a watershed so that almost every year Bojonegoro Regency is flooded when the Bengawan Solo river overflows. Floods in Bojonegoro Regency almost occur every year. Where the flood that occurred in Bojonegoro Regency was caused by the overflow of the Bengawan Solo river. As much as 63% of the total population of Bojonegoro Regency is exposed or at risk of flooding in 2012 to 2020. This study is to determine the Delta (Δ) risk of flood-prone areas in Bojonegoro Regency based on trends in land use change with a multitemporal perspective in Bojonegoro Regency. This study uses the latest methods, especially in terms of spatial modeling, namely TIN and Raster databases. The first step is to predict the trend of land use in Bojonegoro Regency. Which is then continued to identify flood hazards and land use vulnerabilities. Flood disaster-prone areas each year are predicted based on the previous stages using the Analytic hierarchy process which then produces disaster risk in each prediction year. The last step is to calculate the amount of change or Delta (∆) of Flood Disaster Risk in Bojonegoro Regency. The results of this study are Delta (∆) Low Flood Disaster Risk: experienced a change of -1.62% or decreased by 49.66 Ha from 2020 to 2040. Delta (∆) Moderate Flood Disaster Risk: experienced a change of 2.85% or experienced an increase of 17.67 Ha from 2020 to 2040. Delta (∆) High Flood Risk: experienced a change of 23.96% or an increase of 31.99 Ha from 2020 to 2040.
A Hybrid Deep Learning Framework to Predict Alzheimer’s Disease Progression Using Generative Adversarial Networks and Deep Convolutional Neural Networks

Abstract The full text of this preprint has been withdrawn by the authors due to author disagreement with the posting of the preprint. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author.
Smart Online Exam Proctoring System

The Covid-19 pandemic has been one of the defining events in recent history. Education and company hiring processes has been impacted most adversely due to the pandemic. Disrupting the age-old class-room setup, the pandemic has forced educational institutions like schools and universities to implement online classes and online examinations. This paper proposes a smart online proctoring system which is the most effective method to reduce the percentage of cheating in online examinations. One can easily take an online proctored exam at any quiet environment. The system uses real time video from the webcam which is then passed onto the ML models to detect any malpractice performed by the examinee. The online proctoring system can be used by the educational institutes for conducting online examinations in a supervised manner. It can also be used by the companies for initial hiring processes through which the candidates can be filtered. This approach is cost effective and less time consuming.
Natural Language Processing for the Turkish Academic Texts in the Engineering Field: Key-Term Extraction, Similarity Detection, Subject/Topic Assignment

The information retrieved from texts plays crucial roles in many aspects. Although there are significant attempts on natural language processing for various types of texts in Turkish, none of them deals with academic texts. This study mainly aims to retrieve precise key terms from Turkish academic texts in the field of engineering and develops algorithms for similarity detection and automatic classification based on these key terms. In the first step of this study: a library and customized templates, that can transform the n-grams into structured forms, are created by considering the features of engineering terminology and the grammar of the Turkish language. Then, a customized similarity detection algorithm is developed. Finally, the Naïve Bayes Classifier is used to assign the documents to the appropriate engineering sub-fields. The project proposals submitted to The Scientific and Technological Research Council of Turkey (TÜBİTAK) Academic Research Funding Program Directorate (ARDEB) are analyzed as a case study. The results indicate that the proposed similarity algorithm correctly detects almost all of the re-submitted proposals while the accuracy of the classifier is 83.3% in the first prediction and reaches up to 96.4% in the first three predictions over a sample of 1255 proposals.
Analysis of Language Errors on The Thematic Semester Final Examination Problems 1 and 2 Elementary School Level

In the 2013 curriculum, which applies integrated thematic learning, Indonesian plays an essential role as a vehicle for carrying knowledge, from being a means of communication and a means of thinking. Therefore, language errors must be minimized in teaching and learning. This study aims to describe the types of errors and the causes of errors in writing the Final Semester Examination questions in Thematic 1 and Thematic 2. This research was carried out at Baitur Rahmah Elementary School. This type of research is descriptive qualitative research. Data collection methods used are observation and interviews. The data analysis technique was carried out through three stages: note, listen, and record. This research uses data source and method triangulation to test data validity. The study results illustrate that there are still many spelling, word, and sentence errors. This is caused by several sources, namely the last educational background of the teacher who teaches, the lack of understanding of the rules in writing, and the lack of accuracy in making the questions.
Generalization Ability of Wide Residual Networks

In this paper, we study the generalization ability of the wide residual network on $\mathbb{S}^{d-1}$ with the ReLU activation function. We first show that as the width $m\rightarrow\infty$, the residual network kernel (RNK) uniformly converges to the residual neural tangent kernel (RNTK). This uniform convergence further guarantees that the generalization error of the residual network converges to that of the kernel regression with respect to the RNTK. As direct corollaries, we then show $i)$ the wide residual network with the early stopping strategy can achieve the minimax rate provided that the target regression function falls in the reproducing kernel Hilbert space (RKHS) associated with the RNTK; $ii)$ the wide residual network can not generalize well if it is trained till overfitting the data. We finally illustrate some experiments to reconcile the contradiction between our theoretical result and the widely observed ``benign overfitting phenomenon''
Improving Deep Representation Learning via Auxiliary Learnable Target Coding

Deep representation learning is a subfield of machine learning that focuses on learning meaningful and useful representations of data through deep neural networks. However, existing methods for semantic classification typically employ pre-defined target codes such as the one-hot and the Hadamard codes, which can either fail or be less flexible to model inter-class correlation. In light of this, this paper introduces a novel learnable target coding as an auxiliary regularization of deep representation learning, which can not only incorporate latent dependency across classes but also impose geometric properties of target codes into representation space. Specifically, a margin-based triplet loss and a correlation consistency loss on the proposed target codes are designed to encourage more discriminative representations owing to enlarging between-class margins in representation space and favoring equal semantic correlation of learnable target codes respectively. Experimental results on several popular visual classification and retrieval benchmarks can demonstrate the effectiveness of our method on improving representation learning, especially for imbalanced data.
Monitoring Evaluation of Teaching Process Based on Grey Cluster and Association Rules

In order to meet the objective requirement of the monitoring evaluation of teaching process, a teaching process evaluation method based on grey cluster and association rules is proposed, and the efficiency of operation is improved by the distributed deployment of the method. The results show that the distributed analysis methods based on grey cluster and data mining association rule can reasonably evaluate the students’ learning behavior and teachers’ teaching process. Compared with the single analysis method, the proposed two kinds of evaluation methods can deal with a large number of data better, and can evaluate the quality of the teaching process more comprehensively.
A Case Study on M-Active Learning Application Class in a Mindfulness-based Liberal Art Class of K University

본 연구는 K대학 마음챙김 기반 교양수업에서 M-액티브러닝으로 설계하여 운영해 온 교과목의 학습만족도에 영향을 미치는 변인을 밝히고 M-액티브러닝 수업 경험의 의미 등의 분석을 통해 대학 교양수업에서의 액티브러닝 활용 가능성을 알아보는 것이다. 연구를 위해 K대학교 마음챙김 기반 수업을 진행한 후 학습자 117명을 대상으로 설문을 실시하였고 질적 연구를 위해 소감문, 워크북, 관찰일지 등을 통해 자료를 수집하였다. 연구결과로는 학년별 M-액티브 러닝 과정 중 가장 도움이 되었던 활동으로 1학년은 체험활동, 감사노트, 워크북 순으로, 2학년은 체험활동, 감사노트, 성찰의 순으로 높게 나타났다. 마음교양 수업을 수강한 학습자들은 수업의 즐거움, 적극적인 수업참여, 특별한 경험을 통해 성장이 이루어지는 것으로 나타났다. 이러한 연구결과는 대학의 마음챙김 기반 수업에서 M-액티브러닝 적용을 통해 학습효과를 극대화 할 수 있는 근거자료로 활용할 수 있을 것이다.This study is aimed to clarify the influential variables upon the learning satisfaction with curricular subject, which has been operated by being designed with M-Active Learning in a mindfulness- based liberal art class of K university, and to figure out the applicability of active learning to a university liberal arts class through analyzing the significance of experiencing the M-Active Learning course. For the research, a questionnaire was conducted targeting 117 learners after progressing the classes at K university. For the qualitative research, the materials were collected through reviews, workbooks, and observation logs. As a result of the research, the most helpful activity out of the M-Active Learning courses by school year was indicated to be higher in order of the experience activity, the thank you note, and the workbook as for freshman and of the experience activity, the thank you note, and the self-examination as for sophomore. The learners who took the mind liberal arts course appeared to be grown through the enjoyment, the positive participation, the special experience. These findings will be able to be used as the base data available for maximizing a learning effect through applying M-Active Learning to the mindfulness-based class of a university.
Blockchain Exchange Optimization by Graph Partition and Merging in IoT Scenarios

Blockchain exchanges have been widely used for IoT-based resource sharing. An exchange consists of several related transactions where participants exchange their assets. However, the exchange can take a long time to complete due to different reasons (such as IoT device failure). Meanwhile, exchange fees are an important aspect, because an exchange usually contains multiple transactions, and the exchange fee is the sum of the fees for each transaction. In this article, we explore ways to optimize the exchange process concerning the above issues. The method is based on the graph of an exchange, which is strongly connected. To save exchange time, we propose a way to split the exchange into smaller exchanges, since smaller exchanges have fewer transactions. The method first finds strongly connected subgraphs, separates them from the original graph, and adjusts the edges and their weights. To save transaction fees, we propose a method to reduce the number of transactions. Transactions that meet certain requirements are combined into one transaction, and corresponding weights are adjusted. The verification results show that the proposed method can optimize the exchange process, which can be used to save the exchange time and also can be used to save transaction fees.