Reviewerly/exHarmony huggingface dataset:
Comparison of Scenario Pre-processing Performance on Support Vector Machine and Naïve Bayes Algorithms for Sentiment Analysis

Television shows need a rating in their assessment, but public opinion is also required to complete it. Sentiment analysis is necessary for its completion. An essential step in sentiment analysis is pre-processing because, in public opinion, there are still many inappropriate writings. This study aims to compare the performance results using different pre-processing scenarios to get the best pre-processing performance on Support Vector Machine (SVM) and Naïve Bayes (NB) on sentiment analysis about the television show X Factor Indonesia. The stages used to start from literature study, problem analysis, design, data collection, pre-processing with two scenarios, word weighting with TF-IDF, classification using SVM and NB, then resulting accuracy from Confusion Matrix. The findings of this research are that optimal performance can be achieved using a comprehensive pre-processing scenario. This scenario should include the following steps: case-folding, removing emoji, cleansing, removing repetition characters, word normalization, negation handling, stopwords removal, stemming, and tokenization, with an accuracy of 79.44% on the SVM algorithm. This research shows that the complete pre-processing of the SVM algorithm is better in terms of accuracy, precision, recall, and F1-score.&#x0D;
Error Mitigation Thresholds in Noisy Quantum Circuits

Abstract Extracting useful information from noisy near-term quantum simulations requires error mitigation strategies. A broad class of these strategies rely on precise characterization of the noise source. We study the performance of such strategies when the noise is imperfectly characterized. We adapt an Imry-Ma argument to predict the existence of an error mitigation threshold for random spatially local circuits in spatial dimensions D ≥ 2: characterization disorder below the threshold rate allows for error mitigation up to times that scale with the number of qubits. For one-dimensional circuits, by contrast, mitigation fails at an O(1) time for any imperfection in the characterization of disorder. We discuss implications for tests of quantum computational advantage, fault-tolerant probes of measurement-induced phase transitions, and quantum algorithms in near-term devices.
Designing Information System Inventory and Transaction Reports Web-Based Using ICONIX Process Method

Tjangkir Kopi is a small-medium enterprise (MSMEs) engaged in food and beverage in West Java, precisely in City. There is a problem in the current business process, namely in managing the stock of materials that are still done manually through the Microsoft Excel application, which is very likely to occur misunderstandings. Competition in the Food and Beverage Industry is getting tougher, making MSMEs Tjangkir Kopi have to improve their business processes by creating an information system that helps develop their business to achieve a competitive advantage. In addition, there is still a lack of fulfillment of job descriptions for each employee and centralized report data. The System to be Built helps quickly and directly integrate with material stock and expenditure data in the database, so there won't be material stock problems in the warehouse. This system makes transaction reports on business processes much more effective because the data is automated. This research aims to analyze and design systems to support better inventory management and transaction reports and to support business processes with more complete and clear diagrams using the ICONIX Process method. This research explains the stages of analysis by collecting data through interviews and literature studies. The stages of system design produce business process proposals supported by GUI design, use case diagrams, domain models, robustness diagrams, sequence diagrams, and class diagrams. The results of research through analysis and system design using the ICONIX process method, it is hoped that Tjangkir Kopi can use it as an illustration of business processes to develop the business to be better and able to compete with competitors.&#x0D;
Optimizing Distributed Face Recognition Systems through Efficient Aggregation of Facial Embeddings

Biometrics are one of the most privacy-sensitive data. Ubiquitous authentication systems with a focus on privacy favor decentralized approaches as they reduce potential attack vectors, both on a technical and organizational level. The gold standard is to let the user be in control of where their own data is stored, which consequently leads to a high variety of devices used. Moreover, in comparison with a centralized system, designs with higher end-user freedom often incur additional network overhead. Therefore, when using face recognition for biometric authentication, an efficient way to compare faces is important in practical deployments, because it reduces both network and hardware requirements that are essential to encourage device diversity. This paper proposes an efficient way to aggregate embeddings used for face recognition based on an extensive analysis on different datasets and the use of different aggregation strategies. As part of this analysis, a new dataset has been collected, which is available for research purposes. Our proposed method supports the construction of massively scalable, decentralized face recognition systems with a focus on both privacy and long-term usability.
Unsupervised Learning vs. Round-Tripping in SpanishTurkish Machine Translation

The quality of data-driven Machine Translation (MT) strongly depends on the quantity as well as the quality of the trainingdataset. However, collecting a large set of training parallel texts is not easy in practice. Although various approaches have already beenproposed to overcome this issue, the lack of large parallel corpora still poses a major practical problem for many language pairs. Sincemonolingual data plays an important role in boosting fluency for Neural MT (NMT) models, this paper investigates and compares theperformance of two learning-based translation approaches for Spanish-Turkish translation as a lowresource setting in case we only haveaccess to large sets of monolingual data in each language; 1) Unsupervised Learning approach, and 2) Round-Tripping approach. Eitherapproach completely removes the need for bilingual data or enables us to train the NMT system relying on monolingual data only. Weutilize an Attention-based NMT (Attentional NMT) model, which leverages a careful initialization of the parameters, the denoisingeffect of language models, and the automatic generation of bilingual data. Our experimental results demonstrate that the UnsupervisedLearning approach outperforms the Round-Tripping approach in SpanishTurkish translation and vice versa. These results confirm thatthe Unsupervised Learning approach is still a reliable learning-based translation technique for Spanish-Turkish low-resource NMT.
Examine the advantages of an integrated scalability approach at various cloud stack layers

The development of cloud computing has significantly altered how services are built, deployed, and made accessible to users outside of the organization. In actuality, the pay-as-you-go model of dispersed IT supported by the cloud computing paradigm calls for the outsourcing of software services and applications. In this situation, the capacity to ensure effective cloud performance management and to facilitate automated scalability become fundamental prerequisites. Users of the cloud are becoming more and more interested in a transparent and coherent image of the cloud, where performance is guaranteed in a variety of situations and under a variety of loads. In this essay, We examine the advantages of an integrated scalability approach at various cloud stack layers, concentrating on the database and compute infrastructure layers. In order to achieve this, we offer various performance measurements and a set of rules based on them to assess the cloud stack’s condition and scale it as needed to maintain stable performance. Then, using a proof-of-concept architecture, we empirically investigate three scaling scenarios for cloud performance: database only, computing infrastructure solely, and the scenario where computing infrastructure and database compete for resources.
The Effect of Acceleration on Continuous Variable Quantum Key Distribution with Discrete Modulation

The influence of gravity on quantum key distribution cannot be ignored in practical space communication, and this paper provides a thorough analysis of the effectiveness of discrete modulated states. By calculating the acceleration in both inertial and non-inertial reference systems with expectation values of the computational Heisenberg field respectively, the gain coefficient is obtained. Based on this parameter, an improved protocol with a high key rate over a transmission distance of 390 km is proposed, enabling the practical application of quantum key distribution techniques. Furthermore, the results obtained can be extended to the eight-state schemes. This paper not only extends the range of discrete modulation states, but also enhances the impact of relativistic quantum information.
Demand Response systems distributed on the cloud, offering a security &amp; privacy framework for data flow

Demand Response (DR) is quickly becoming a critical component of the contemporary energy industry, notably in EU energy markets. As a result, substantial work has gone into standardizing demand response data models. As a result, an increasing number of demand response concepts are based on these standards. As a result, an increasing number of demand response concepts are based on these standards. These approaches, however, are often centralized, and those that rely on cloud solutions employ the cloud as a centralized data repository, assuming that the data is already homogenised when saved, i.e. all data has the same structure and type. In practice, however, DR plans rely on a number of components that deliver data in a variety of forms and types. Furthermore, the various DR standards establish models for various data formats, which impede data sharing between different DR systems. This article introduces CIM, a generic technology that allows current disaster recovery systems to disperse their components in the cloud while providing a robust security and privacy foundation for data interaction. Furthermore, the CIM includes a semantic interoperability layer capable of transforming data into a normalised form when transferred, allowing it to be consumed transparently by DR components. Experiments support the CIM as a solution for DR systems to decentralize their designs and share heterogeneous data with other DR systems that adhere to other DR standards.
An Association of Twitter Derived Measures of Discrimination and Race-Motivated Hate Crimes

In 2020, there was a sharp rise in Anti-Asian American hate crimes. While the causes are still being explored, one of the main reasons this occurred was due to the rise in Anti-Asian American rhetoric connected to the COVID-19 pandemic. This exemplifies how prejudices often can turn into violence, more specifically hate crimes. And hateful speech, oftentimes spread through the internet, can help spread and deepen that prejudice to a wider audience. In previous research, prejuidice and discrimination has largely been studied through self-reported measures. While those studies are necessary, they are susceptible to many pitfalls and should be supplemented with statistics-based measures. This study employs one of these emerging measures. This study used the Twitter Streaming API to collect tweets containing a racial slur, an extension of hateful speech, geo-located from users in the United States over a period of two months. With these, this study compared statewide Twitter racial slur usage and statewide average sentiment polarity of the tweets with race-motivated hate crimes from the state. Though no relationship was found between statewide sentiment expressed and race-motivated hate crimes, this study did find a significant positive relationship between statewide Twitter slur usage and hate crimes. These findings support previous studies that have connected Internet-based measures of discrimination with aspects of general well-being. and even specifically hate crimes. This study shows the potential of using data derived from social media to create cost effective and expansive indicators of an area’s social climate around a topic, even race.
Quantum Particle Swarm Optimization with Deep Learning-Based Arabic Tweets Sentiment Analysis

Sentiment Analysis (SA), a Machine Learning (ML) technique, is often applied in the literature. The SA technique is specifically applied to the data collected from social media sites. The research studies conducted earlier upon the SA of the tweets were mostly aimed at automating the feature extraction process. In this background, the current study introduces a novel method called Quantum Particle Swarm Optimization with Deep Learning-Based Sentiment Analysis on Arabic Tweets (QPSODL-SAAT). The presented QPSODL-SAAT model determines and classifies the sentiments of the tweets written in Arabic. Initially, the data pre-processing is performed to convert the raw tweets into a useful format. Then, the word2vec model is applied to generate the feature vectors. The Bidirectional Gated Recurrent Unit (BiGRU) classifier is utilized to identify and classify the sentiments. Finally, the QPSO algorithm is exploited for the optimal fine-tuning of the hyperparameters involved in the BiGRU model. The proposed QPSODL-SAAT model was experimentally validated using the standard datasets. An extensive comparative analysis was conducted, and the proposed model achieved a maximum accuracy of 98.35%. The outcomes confirmed the supremacy of the proposed QPSODL-SAAT model over the rest of the approaches, such as the Surface Features (SF), Generic Embeddings (GE), Arabic Sentiment Embeddings constructed using the Hybrid (ASEH) model and the Bidirectional Encoder Representations from Transformers (BERT) model.
Estimation of Brake Pad Wear Using Fuzzy Logic in Real Time

Brake pad components are important in two-wheeled vehicles because they concern the driver's and others' safety. Brake lining wear is an unavoidable phenomenon. This is because of the concept of braking, which involves bringing two things into contact with each other such that they press against each other and rub against each other. Brake pads that have not been replaced make the brakes unable to work normally, so the potential for accidents is even greater. One of the factors causing the problem is negligence and ignorance of the condition of the ream linings, which should be time for the change. This paper proposes a tool that can estimate the condition of the brake pads based on the level of wear in real-time using the fuzzy logic method. Fuzzy logic will estimate the degree of wear of brake pads based on speed, brake fluid pressure, and braking duration parameters. The type of brake used in this paper is the type of disk brake used on two-wheeled vehicles. The test is not carried out or applied to two-wheeled vehicles but is applied to brake pad wear test equipment that works like a two-wheeled vehicle. Based on the test results, the fuzzy logic implanted into the Arduino microcontroller can provide information on the estimated condition of the brake pads on LCDs in real time based on fuzzy set datasets obtained through experimental tests. Based on the experimental results, the brake lining wear test was carried out for 30 minutes with a pressure of 10 and 17 psi. The results showed that the thickness of the brake linings decreased by around 21.66% and 26.68%, respectively.&#x0D;
Teen Perception of Felon Disenfranchisement: Exploring Ideologies of Future Voters

Felon disenfranchisement continues to have a significant impact on the function of our democracy. While some argue that committing a felony constitutes a breach of the social contract warranting the denial of voting rights, others argue that such a denial is itself a violation of democratic principles that undermines the foundation of the social contract. As public opinion influences state laws on felon disenfranchisement (FD) policies, there has been significant research on public opinion on FD laws. The current research focuses on adolescent opinions about FD policies in an attempt to capture future directions of voter opinion on this critical issue.
Comparing and Reviewing Modern Primality Tests

Primality tests refer to algorithms that determine whether a number is prime or composite. These tests are essential to modern encryption algorithms, including the widely used RSA public key encryption algorithm. However, with so many different primality tests out there, choosing the correct one for a given application can be challenging. This paper provides an overview of modern primality tests, detailing the differences between different types of tests and when one test may be preferable to another. Furthermore, implementations of popular primality tests are written and compared to one another graphically to better understand their differing performances. Lastly, we look at next steps for the field of primality tests due to the rise of quantum computing, which could serve as a means of creating even better primality tests.
Banknote Authentication Using Logistic Regression and Artificial Neural Networks

Banknotes are special notes authorized by the government and carry monetary value. As a result, there are incentives for criminals to create counterfeit. The goal of this study is to create models using machine learning techniques that can accurately classify a banknote as authentic or fake. The methods used were logistic regression and artificial neural networks. An open-source data set was obtained and split into 7 sub-datasets, and multiple models were created to model the data. There was a total of 7 logistic regression models, each corresponding to one of the 7 sub-datasets. Additionally, an artificial neural networks model was used on the 7th sub-dataset. Both the neural networks model and the logistic regression model achieved accuracies greater than 99%.
Designing a Unified Healthcare Data System with Blockchain and Cryptography

The majority of public and private healthcare data systems across the globe are proprietary, making it difficult to coordinate and share data between medical institutions on different systems. For the United States, a technically and economically advanced nation, patients still face significant issues with healthcare data sharing, which contribute to inefficiencies in the healthcare system. Friction in data sharing is also prevalent in developing nations like India, where the lack of shareability of patient data leads to redundant tests and unnecessarily high healthcare costs. To improve the integrity and shareability of patient data, we propose a Unified Healthcare Data System, built with blockchain and cryptography. Under this system, patients are in control of their data, and can choose to share their data with doctors and institutions without traditional barriers. To ensure data integrity, blockchain is used to provide immutability and data ownership while cryptography is used to encrypt patient data and implement permissioned access. In this paper, we provide a working implementation of Unified Healthcare Data System and expand on the viability, impact, and evolution of this blockchain-based healthcare data system.
Penerapan Enterprise Resource Planning (ERP) Menggunakan Odoo 14 (Studi Kasus: Usaha Garmen Pada Club Ink Bali

Abstract&#x0D; Enterprise Resource Planning (ERP) is an information system that is intended for some companies such as Club Ink Bali. Club Ink Bali is a Micro, Small and Medium Enterprise or UMKM which is engaged in the manufacturing of apparel, producing a produc t that is quite productive to make Club Ink Bali often experiences problems such as product ordering systems, and other transaction recordings that do not yet use technology, resulting in a long time to place an order, loss of material information data, and transactions. Therefore, the owner feels the need for an integrated technology system, therefore the researcher feels the right solution to deal with the problems at hand is to use Odoo software to help carry out business processes in the company by using several modules such as the Sales Module, Purchase Module, Point of Sales Module, Inventory Module, Manufacturing Module, Invoice Module, and Website Module.&#x0D;
Pemodelan Matematika Penyebaran Penyakit Toksoplasmosis dengan Pengaruh Vektor

Toxoplasmosis is a sickness as a result of the parasite Toxoplasma gondii, which can attack humans and animals, especially cats.The reason for this investigation is to discover the model, the results of model analysis, and an interpretation of the results of a mathematical model analysis of the spread toxoplasmosis by vector effects.The strategies used on this take a look at are descriptive.A mathematical model of the spread toxoplasmosis by vector influence takes the form of a nonlinear system of equations consisting of eight nonlinear equations.Analysis results of a mathematical model with two fixed points, a disease-free fixed point and a diseased fixed point.Each is asymptotically stable, subject to several conditions.The resulting baseline reproductive numbers indicate that the disease is becoming epidemic as the horizontal transmission rate and transmission rate from the latent population to the infected population within the vector increase.
General Cyclical Training of Neural Networks

This position paper describes the principle of “General Cyclical Training” in machine learning, where training starts and ends with “easy training” and the “hard training” happens during the middle epochs. We propose several manifestations for training neural networks, including algorithmic examples (via hyper-parameters and loss functions), data-based examples, and model-based examples. Specifically, we introduce several novel techniques: cyclical weight decay, cyclical batch size, cyclical focal loss, cyclical softmax temperature, cyclical data augmentation, cyclical gradient clipping, and cyclical semi-supervised learning. In addition, we demonstrate that cyclical weight decay, cyclical softmax temperature, and cyclical gradient clipping (as three examples of this principle) are beneficial in the test accuracy performance of a trained model. Furthermore, we discuss model-based examples (such as pretraining and knowledge distillation) from the perspective of general cyclical training and recommend some changes to the typical training methodology. In summary, this paper defines the general cyclical training concept and discusses several specific ways in which this concept can be applied to training neural networks. In the spirit of reproducibility, the code used in our experiments is available at https://github.com/lnsmith54/CFL.
Simulasi Monte Carlo dan Penerapannya dalam Menentukan Probabilitas Pergerakan Saham Indeks LQ-45

The problem for investors when investing is determine stocks that have the opportunity to move up that they get maximum profits in the future.The purpose of this study is to determine the opportunities for movement of the LQ-45 index stock.This study uses data on closing prices for daily stocks listed the LQ-45 index in the period February 2021-January 2022.To find out the opportunities for stock movements, a monte carlo simulation is used which based on the stock price movement model, the stock price movement model is influenced by the latest stock price and brown geometry motion 100 times and 10000 times simulation for 10 days of stock trading.Based on the results of the simulation analysis, the average share price was Rp. 916 -Rp.23000, the value of forecasting accuracy namely very good forecasting accuracy with an average error of less than 10%.Companies that have increased opportunities for 10 trading days in February