Reviewerly/exHarmony huggingface dataset:
Metode Forward Chaining Untuk Mendiagnosa Kerusakan Mesin Mobil Toyota Innova Berbasis Web

The growing development of the automotive industry in Indonesia has an impact on increased car sales. More and more people are buying cars because they have become daily transportation. Damage experienced in the car is handled by experts or expert technicians. Diagnosis of damage is carried out by a technician based on signs of initial damage to the car. This encourages experts to develop new computer systems to support human work. The development of computers today has undergone many very rapid changes, along with the increasing and complex human needs. then we need a method or system that can diagnose damage to the car. One of them is by utilizing artificial intelligence technology. One sub-field of artificial intelligence is an expert system. By utilizing expert system technology can be produced a system to diagnose car engine damage. This expert system discusses the diagnosis of web-based Toyota Innova damage using the forward chaining method. With the aim of helping the community to diagnose the damage to the Toyota Innova car engine.
A Perspective Review on Hyperledger Fabric Access Control System

Blockchain technology has enormous potential and is currently seen as a new technical trend with a quickening growth rate. Supply chain, health care, and finances are just a few of the application areas wherein blockchain-based systems are gaining interest. Whenever it comes to public and private management, data integrity is crucial and becoming more essential. Data integrity can be achieved in an access control system which is built on the blockchain. By using encryption protocols, access controls, and cryptography techniques, the Hyper ledger Fabric is created to be safe against unauthorised access and use. Systems for access control exist to safeguard resources against unwanted access. Platforms for blockchain systems, such as Hyperledger Fabric, are built with data integrity, complete transparency, and confidentiality. This article provides an in-depth analysis of a Hyperledger enterprise blockchain technology.
Procedure-Aware Pretraining for Instructional Video Understanding

Our goal is to learn a video representation that is useful for downstream procedure understanding tasks in instructional videos. Due to the small amount of available annotations, a key challenge in procedure understanding is to be able to extract from unlabeled videos the procedural knowledge such as the identity of the task (e.g., 'make latte'), its steps (e.g., 'pour milk'), or the potential next steps given partial progress in its execution. Our main insight is that instructional videos depict sequences of steps that repeat between instances of the same or different tasks, and that this structure can be well represented by a Procedural Knowledge Graph (PKG), where nodes are discrete steps and edges connect steps that occur sequentially in the instructional activities. This graph can then be used to generate pseudo labels to train a video representation that encodes the procedural knowledge in a more accessible form to generalize to multiple procedure understanding tasks. We build a PKG by combining information from a text-based procedural knowledge database and an unlabeled instructional video corpus and then use it to generate training pseudo labels with four novel pre-training objectives. We call this PKG-based pre-training procedure and the resulting model Paprika, Procedure-Aware PRe-training for Instructional Knowledge Acquisition. We evaluate Paprika on COIN and CrossTask for procedure understanding tasks such as task recognition, step recognition, and step forecasting. Paprika yields a video representation that improves over the state of the art: up to 11.23% gains in accuracy in 12 evaluation settings. Implementation is available at https://github.com/salesforce/paprika.
Towards Flexible Multi-modal Document Models

Creative workflows for generating graphical documents involve complex inter-related tasks, such as aligning elements, choosing appropriate fonts, or employing aesthetically harmonious colors. In this work, we attempt at building a holistic model that can jointly solve many different design tasks. Our model, which we denote by FlexDM, treats vector graphic documents as a set of multi-modal elements, and learns to predict masked fields such as element type, position, styling attributes, image, or text, using a unified architecture. Through the use of explicit multi-task learning and in-domain pre-training, our model can better capture the multi-modal relationships among the different document fields. Experimental results corroborate that our single FlexDM is able to successfully solve a multitude of different design tasks, while achieving performance that is competitive with task-specific and costly baselines.
Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistency

Deep neural networks are proven to be vulnerable to backdoor attacks. Detecting the trigger samples during the inference stage, i.e., the test-time trigger sample detection, can prevent the backdoor from being triggered. However, existing detection methods often require the defenders to have high accessibility to victim models, extra clean data, or knowledge about the appearance of backdoor triggers, limiting their practicality. In this paper, we propose the test-time corruption robustness consistency evaluation (TeCo), a novel test-time trigger sample detection method that only needs the hard-label outputs of the victim models without any extra information. Our journey begins with the intriguing observation that the backdoor-infected models have similar performance across different image corruptions for the clean images, but perform discrepantly for the trigger samples. Based on this phenomenon, we design TeCo to evaluate test-time robustness consistency by calculating the deviation of severity that leads to predictions' transition across different corruptions. Extensive experiments demonstrate that compared with state-of-the-art defenses, which even require either certain information about the trigger types or accessibility of clean data, TeCo outperforms them on different backdoor attacks, datasets, and model architectures, enjoying a higher AUROC by 10% and 5 times of stability.
Design of unsigned 2n+1 parallel residue arithmetic multiplier

Residue Multiplication operations are extensively used in Residue Number System (RNS) based cryptosystem architecture. Pointing to increase the speed performance of RNS crypto processors, the new parallel unsigned 2n+1 residue multiplier is designed in this work. Mathematical model, Algorithm, Architecture and FPGA implementation is done in this work. The proposed residue multiplier is described in Verilog HDL and synthesized in Application-Specific Integrated Circuits (ASIC) environment. Cadence RTL Compiler estimates the Area, Power and Delay performance parameters using various CMOS libraries. The proposed 2n+1 residue multiplication scheme saves 13% of the area, improves the speed by 19% and PDP by 23% compared to the recent 2n+1 residue multipliers.
The Influence of Information Search Learning Strategies and Student Learning Motivation on Learning Outcomes in PAI Learning

This study aims to determine the influence of the application of Information Search learning strategies and learning motivation on PAI learning outcomes of SMA Negeri 1 Merbau students as well as the interaction between learning strategies and learning motivation on PAI learning outcomes. This research is a quantitative study with a population of as many as all class XI students at SMA Negeri 1 Merbau consisting of 6 study groups. Based on the Cluster Random Sampling technique, one is an Information Search learning class and one is an Expository learning class. The instruments of this study are tests and questionnaires. The test of normality of the learning outcomes data is the Liliefors test, and the homogeneity test is the Fisher test and the Bartlett Test. The data analysis technique is a two-path Anava at a significant α = 0.05 followed by the Schiche test. The results showed that there was a shift in the Information Search learning strategy on the learning outcomes of PAI students of SMA Negeri 1 Merbau with high (X̅= 28.6) and Low (X̅=19.6) learning motivations. In this case, the PAI learning outcomes of students with high motivation are higher than those of pai students with low learning motivation.
Analisis Kesulitan Belajar Siswa Kelas III pada Mata Pelajaran Matematika di SDN 9 Ampenan Tahun Pelajaran 2022/2023

This study aims to determine the types of mathematics learning difficulties experienced by students and the improvement efforts made by teachers to overcome mathematics learning difficulties for third-grade students of SDN 9 Ampenan. This research is qualitative research with the descriptive method and uses the procedure of implementing the diagnosis of learning difficulties. The research subjects were 6 third-grade students who had difficulty learning mathematics and third-grade teachers. Methods of data collection using interviews, observation, and documentation. Data analysis using Interactive Analysis Method. The results showed that students had difficulty learning the subject of arithmetic operations of addition, subtraction, multiplication, and division. The types of mathematics learning difficulties experienced by students are difficulties in understanding mathematical concepts, difficulties in calculations, difficulties in understanding mathematical symbols, and difficulties in working on story problems. Improvement efforts made by classroom teachers are peer tutors, special tutoring, remedial or repairs to students who have grades below the KKM, and communicating and collaborating with parents. To overcome difficulties in learning mathematics that come from within students, it is recommended to create conditionals (reinforcement, rewards, encouragement), while difficulties that come from outside students are carried out by completing facilities and infrastructure in the learning process and using varied methods.
Expert System Prediction of Covid-19 Transmission Vulnerability using Forward Chaining

Covid-19 is a pandemic that is currently sweeping the world, where the number of infected victims has reached millions. The sudden outbreak of the Covid-19 pandemic has caused unprepared communities to panic and worry, although there are also those who are indifferent or unconcerned about this contagious disease, which is a major factor in the rapid spread of the virus. This study aims to build a system to predict the vulnerability of users to contracting Covid-19 through an expert system application. The expert system method uses forward chaining to determine the user's vulnerability level based on factors used and inputted by the user. The large number of patients who still do not believe in the existence of Covid-19 and the spread of hoax news complicates the handling of Covid-19. With an expert system that predicts vulnerability to contracting the Covid-19 virus as a tool, it can increase awareness among low-awareness communities and reduce the spread of the Covid-19 virus.
Hate Speech and Atrocity Prevention in Asia: Patterns, Trends and Strategies

Abstract This article introduces the conceptual and analytical framework for this special issue on hate speech and atrocity prevention in Asia. It defines hate speech and incitement, and explains the process and context through which hate speech and incitement operates as a risk factor for atrocities. It also provides an explanation of the international legal framework regulating hate speech and incitement that informs the approach of the articles in this issue.
Pengaruh Kecanduan Game Online terhadap Konsentrasi Belajar Peserta Didik di SMP Negeri 2 Ampek Nagari Kabupaten Agam

This research is motivated by the influence of online game addiction on the learning concentration of students at SMP Negeri 2 Ampek Nagari Kababupaten Agam. The purpose of the research is to describe; 1) Knowing the description of online game addiction at SMP Negeri 2 Ampek Nagari, Agam Regency. 2) Knowing the description of students' learning concentration at SMP Negeri 2 Ampek Nagari, Agam Regency. 3) Knowing the effect of online game addiction on learning concentration at SMP N 2 Ampek Nagari, Agam Regency.​ This research is a type of quantitative descriptive research with a simple linear regression analysis approach. This research collected data using observation, questionnaires and documentation. The population of this research was all class VIII students of SMP Negeri 2 Ampek Nagari, totaling 154 people. The sampling technique used purposive sampling technique as many as 40 people. The instrument used is the questionnaire. Data analysis using percents technique. The results of this study reveal that R Square X to Y is 0.60, so it has an effect of X on Y, namely 60%, the remaining 60% is influenced by other variables that are not visible in this study. Judging from the t count of 1.558 and t table of -3.01 with (α) = 0.05. provisions t table obtained from table t. So it is concluded that the regression coefficient X on Y is significant because tcount &gt; ttable (1.558&gt; -3.01) then the hypothesis is accepted which says there is a significant influence of online game addiction variables on learning concentration.
Analysis of Sentiment Adiraku App Reviews on Google Play Store Using Vector Machine Support Algorithm and Naïve Bayes

The Adiraku application is considered to be able to facilitate and facilitate customers so that there is no need to come to the branch office to get information related to the number of installments that must be paid, due dates, credit simulations, and Adira Finance information offers to customers. A large number of reviews from users received makes it difficult for developers to read them, it will take too much time and effort if they have to read and analyze them manually. To find out which reviews are classified as positive or negative reviews. need a sentiment analysis of the review. This study aims to find out how the opinions or opinions of its users on the services of the application, by analyzing these sentiments through a classification process using two algorithms, namely Support Vector Machine and Naïve Bayes. The data used amounted to 2000 data obtained from Google Playstore. Data is labeled into 2 classes namely positive class and negative. Furthermore, the data is divided into 70% training data and 30% testing data and methods used for testing using Bernoulli Naïve Bayes and Linear Kernel. It was concluded that the number of user reviews of the Adiraku application on the Google Play Store showed more positive comments, amounting to 1412 positive and negative reviews, which was 588 reviews. The Support Vector Machine algorithm performs better by getting the best accuracy value of 96%, while the Naïve Bayes algorithm gets an accuracy value of 85%.
Image Processing Technology in Book Metadata Extraction System Using Optical Character Recognition (OCR)

Extracting book metadata by retyping the identity of the book, such as the author's name, book title, publisher, and several other identities, is a routine that is carried out repeatedly at the Polewali Mandar district library, this activity takes much of time, using several staff and it turns out that this activity has much potential for input errors. Errors in extracting book metadata will result in errors in the book repository system database, resulting in difficulty finding and using books or book data information. This problem can be solved by creating a book metadata extraction system using image processing technology and OCR. This study aims to design a scanner technology to extract book metadata. Accuracy is carried out in 2 stages, the first validation of image extraction results using the ROC method and the second validation by directly matching the result of extracting the book's metadata with the actual book. The results of this study indicate that the system has worked with an accuracy of 98.78% with an average detection time of 1.49 seconds and has succeeded in presenting the extraction results on the website page. Thus the metadata extraction system with the OCR method can be applied to libraries to input book data.
Algoritma K-Means dalam Pengelompokan Surat Keluar pada Program Studi Teknik Informatika STMIK Palangkaraya

K-Means algorithm as a method of grouping a set of data. The purpose of this study is to find out the use of the K-Means algorithm for outgoing mail data. The method used in this study focuses on the K-Means method. The grouping data used is 284 outgoing mail data at the STMIK Palangkaraya Informatics Engineering Study Program. Outgoing mail data is grouped into two groups, namely staffing and academic administration. Grouping based on letter number, status of recipient of letter, subject of letter. Steps in the K-Means algorithm result two iterations because there is a group movement that occurs, namely there is a movement from one group to another because the value of the object function changes. The iteration can be stopped in 2nd iteration because the object function change value is below the given threshold value, which is 0.1 and there is no group movement in the data used. Two iterations that have occurred, it shows a decrease in the value of changes in object function and data transfer in group locations.
Implementasi Data Mining Transaksi Penjualan Menggunakan Algoritma Clustering dengan Metode K-Means

The large number of products sold by the Bill Lights Store resulted in a stockpile of several product items due to the large supply of products that were less attractive to customers, resulting in many unsold and under-sold products. Bill Lights struggles with inventory levels of sold and unsold products, as well as shortages and overstocks. Bill Lights stores should rank each product so that they know which products are in the most demand. The purpose of this research is to solve the problem of using inventory information by grouping inventory products based on product characteristics using data mining techniques. The technique used is the K-Means algorithm method. K-Means algorithm clustering method and RapidMiner software processing. The data mining process starts with data processing (selection, cleaning, transformation, data mining and interpretation/evaluation). So if we start with a dataset of 160 products, we get cluster 0 with 88 products classified as sold, cluster 1 with 26 products classified as unsold, and cluster 2 with 46 fewer products classified as sold. The result of using the K-Means method is grouped into three clusters. To enable Bill Lights Store to implement sales and growth strategies based on products that are selling well.
Prediksi Gagal Jantung Menggunakan Artificial Neural Network

Cardiovascular disease or heart problems are the leading cause of death worldwide. According to WHO (World Health Organization) every year there are more than 17.9 million deaths worldwide. In previous studies, there have been many studies related to the application of machine learning to predict heart failure and obtained quite good results, ranging from 85 percent to 90 percent, with sophisticated models optimized using neural networks. In this research, experiments were carried out using similar architectures based on the state of the art from previous research, namely Artificial Neural Networks by conducting several hyperparameter tests, namely the number of hidden layers and the number of neuron units in the hidden layer. Based on the test results, the Artificial Neural Network model get the best results by implementing 2 hidden layers with 15 units of neurons in the first hidden layer and 10 units of neurons in the second hidden layer. This model get accuracy on data testing of 92,032% and AUC of 93%.
Left out of the in: Investigating Accessibility’s Influence on Theatre Audience In-groups

It is well acknowledged that the affective nature of attending theatre offers audience members a sense of community amongst one another (Fischer-Lichte, 2008; Hurley, 2010; Heim, 2020). Gathering together to share time, space, and experience provides theatre audience members an opportunity to be a part of a unique collective for the brief duration of the performance. Given the broadly shared objective and role for attending performance, a psychological in-group is created amongst audience members. These psychological in-groups are established to be an enforcer of perceived safety amongst its members, contributing to understood behavioural expectations, foregrounded trust, and comfort (Morton &amp; Power, 2022). Knowing this, this poster considers nuances to the audience in-group by investigating the ways in which access needs impact how these groups are created. I will examine the limitations to the audience in-group as a result of access needs not being met during the performance. From content warnings to participation expectations to physical access, this poster will explore the creation of psychological in-groups created amongst audiences during theatre performance through a lens of accessibility, perceived safety, and comfort. In doing so, we can consider the ways in which care is established and provided to audience members, what sort of care is being offered, and whose care needs are being prioritized. Works Cited Fischer-Lichte. “Shared Bodies, Shared Spaces: The Bodily Co-Presence of Actors and Spectators.” The Transformative Power of Performance. Routledge, 2008, pp. 38-74. Heim, Caroline. Actors and Audiences: Conversations in the Electric Air. Routledge, 2020. Hurley, Erin. Theatre &amp; Feeling. Macmillan International Higher Education, 2010. Morton, Thomas, and Power, Seamus. "Coming together after standing apart: What predicts felt safety in the post-coronavirus crowd?" Soc Sci Med, vol. 293, 2022, pp. 1-9.
A Comparative Study of Move Analysis in Chinese and International Research Article Abstracts

To raise the authors’ awareness of using the moves and move models in abstract writing, this study compares the frequency, type, and significant difference of moves and move models in 100 abstracts written in English by Chinese scholars and 100 abstracts written by international scholars in 10 prestigious international journals in the field of linguistics. The adapted Hyland’s five-move model was used as research framework, Corpus and statistic software SPSS were used as research instruments. The comparisons report that Chinese scholars tend to use introduction, result, and conclusion moves. International scholars are inclined to use purpose, method, result, and conclusion moves. The results also indicate that the four-move model is the most prevalent in the two groups. Chinese scholars used the two-move model more than the English authors whereas used the five-move model less than the international authors. The findings are intended to provide referential value for the writing and teaching of English for academic purposes.