Reviewerly/exHarmony huggingface dataset:
Deep learning with noisy labels: Learning True Labels as Discrete Latent Variable

In recent years, learning from data with noisy labels (Label Noise) has emerged as a critical issue for supervised learning. This issue has become even more concerning as a result of recent concerns about Deep Learning's generalization capabilities. Indeed, deep learning necessitates a large amount of data, which is typically gathered by search engines. However, these engines frequently return data with Noisy labels. In this study, the variational inference is used to investigate Label Noise in Deep Learning. (1) Using the Label Noise concept, observable labels are learned discriminatively while true labels are learned using reparameterization variational inference. (2) The noise transition matrix is learned during training without the use of any special methods, heuristics, or initial stages. The effectiveness of our approach is shown on several test datasets, including MNIST and CIFAR32, and theoretical results show how variational inference in any discriminating neural network can be used to learn the correct label distribution.
Virtual Excitations and Entanglement Dynamics and Polygamy in Three Ultra‐Strongly Coupled Systems

The Milburn dynamics of three nonresonant ultra-strongly coupled oscillators are resolved by using symplectic geometry. We look at the Milburn dynamics of virtual excitations and how they affect pairwise entanglement. It is found that the dynamics of excitations and entanglement experience similar profiles against time, physical parameters, and decoherence rate. Furthermore, we show that the extinction of excitations entails separability, which demonstrates the hierarchy between entanglement and virtual excitations. Additionally, we analyze the effects of physical parameters on the redistribution of virtual excitations among the three bi-partitions. As a result, we show the violation of the monogamy of excitations as in quantum discord. This implies that excitations can be considered as signatures of quantum correlations beyond entanglement. Besides, we emphasize that our treatment can be used to model coupled quantum circuits in real situations (with decoherence).
A Novel Incremental Framework for Building Classifier Using Constraint Class Association Rules

Associative classification (AC) performs much better than other traditional classifiers. It generates a huge number of class association rules (CARs). Since users are interested in the subset of rules, constraints are introduced in the generation of CARs. Real-world databases are record-based in which data is continuously added which demands incremental mining. Hence, constraint class association rules (CCAR) is mined from incremental data. To limit the number of rules and to remove the duplicate rules, redundant rule pruning and duplicate rule pruning techniques are applied. To improve the accuracy of the classifier, the rule selection using principality metric has been applied and the classifier is constructed with rules possessing high principality. Then, classifier is evaluated using single rule and multiple rule prediction methods and the accuracy of the proposed classifier are measured. Experimental results show that the accuracy of the proposed classifier is relatively higher when compared to other algorithms.
GMSK Demodulation Combining 1D‐CNN and Bi‐LSTM Network Over Strong Solar Wind Turbulence Channel

Abstract To solve the problem of bit error rate (BER) performance degradation over strong solar wind turbulence channel, this paper addresses to Gaussian minimum frequency shift keying (GMSK) demodulation using machine learning. First, by analyzing the scintillation characteristics of the telemetry signal caused by solar wind turbulence during the solar superior conjunction, the K distribution channel model is innovatively established over strong solar wind turbulence channel. Then, the approximate probability density function of the K distribution of the established channel is studied by using Laguerre orthogonal polynomial for convenience. Additionally, the BER performance of GMSK over strong solar wind turbulence channel is analyzed. Second, using a one‐dimensional convolutional neural network (1D‐CNN) and bidirectional long short‐term memory network (Bi‐LSTM), two demodulators for GMSK are proposed. By integrating the 1D‐CNN and LSTM to extract local features and process the dynamic information of GMSK signals, we propose a novel GMSK demodulator over strong solar wind turbulence channel with combining 1D‐CNN and Bi‐LSTM. The simulation results show that, even if the solar wind turbulence has serious effects on the signal at a low signal‐to‐noise ratio, the proposed combining neural network demodulation can obtain better BER performance than the classical Viterbi algorithm, demodulation using only a single 1D‐CNN or Bi‐LSTM. The proposed GMSK demodulator is more suitable for the deep space exploration of the solar system.
Real estate risk analysis based on support vector machine

Real estate investment risk is an unpredictable factor affecting development profit, so it is very important to analyze real estate investment risk. In this paper, by considering the real estate decision-making stage, preparation stage, construction stage and the stage of management risk, real estate investment risk analysis index system is established, based on the practical engineering project construction sample, risk factors and expectations index based on support vector machine is the response of the relationship, and the actual project risk analysis.
Research on target detection based on clustering and Hough transform

Formation target track sorting is an important problem in multi-target track processing. Unlike the processing of periodic scans and point tracks of active radar, passive positioning data has the problems of irregular positioning period and difficulty in distinguishing multiple targets by target signal attributes. In this paper, we propose a track initiation algorithm for passive positioning system based on density clustering and Hough transform for the characteristics of passive positioning system. The algorithm first uses the density clustering algorithm to identify the cluster centers of targets with large measurement errors, and then uses the Hough transform method to initiate a track.
A snapshot cluster segmentation method based on dynamic network

Dynamic graph neural networks are a research hotspot in the field of deep learning, especially in social networks, recommendation systems, traffic networks and other fields with very wide applications. At present, most of the commonly used deep learning-based network analysis methods are focused on discrete dynamic graph neural networks. However, the effectiveness of discrete graph neural network methods is based on the degree of good or bad snapshot segmentation of dynamic networks, so this paper proposes a Snapshot Clustering Segmentation Method (SCSM) for dynamic networks, and researchers can effectively segment the snapshots of dynamic networks by the SCSM method in this paper to make the downstream tasks can get good results.
Smart learning of foreign languages: rationale, framework, and path

Smart learning is an important means of foreign language learning in the era of education informatization 2.0. Smart Learning of Foreign Languages (SLFL) is based on students' metacognition, multiple intelligence, and meaning construction. With the support of information technology, teachers, students, learning resources and the learning environment are interrelated and interact with each other to form the framework of SLFL. Under the guidance of this framework, five paths of SLFL are constructed, namely: personalized learning, learning from crowds, learning in the real-life context, embodied learning, and blended learning, and a case study was implemented to examine the effect of SLFL.
An improved sparrow search algorithm based on weighted mutation and neighborhood perturbation

Aiming at the problems that sparrow search algorithm is easy to fall into local optimum, slow convergence speed and insufficient convergence accuracy, an Improved Sparrow Search Algorithm (ISSA) with weighted mutation and neighborhood perturbation is proposed. Improve the follower position update formula and add the weighted mutation differential evolution algorithm to maintain the population diversity and balance the global exploration and local development ability of the algorithm. Combining attack with reverse search and variable spiral position update, the algorithm perturbs the individual position around the optimal neighborhood, jumps out of the local optimization, and improves the convergence speed and accuracy of the algorithm. The performance of the improved algorithm is tested on 12 benchmark functions. The experimental results show that the improved algorithm has better optimization accuracy, convergence performance and stability.
Analysis of Face Data Augmentation in Various Poses for Face Recognition Model

When constructing a face recognition model, many face images for each person are required for the model to perform well. Face data augmentation is a popular technique to create variations of face poses via angular head movements, e.g., yaw, pitch, and roll. This study used convolutional neural networks, ResNet-50, MobileNetV3-Large, and SeResNet18 models to evaluate the impact of face data augmentation on the performance of the models. The training was performed using the FaceScrub dataset to evaluate the performance of the face data augmentation approach using a batch size of 20, a learning rate of 0.001, and 150 epochs. The results demonstrate that the SeResNet18 model obtained the highest accuracy (0.8719) with a loss of 1.5106. Before increasing the amount of face data through various poses, the accuracy of the SeResNet18 model was only 0.8108, with a loss of 1.5513. We also discovered that the original data combined with all angular head movements (yaw, pitch, and roll) produced the best result for all models.
Machine Learning Model Based on REST API for Predicting Tenders Winner

Machine Learning is a tool that can provide the ability to make good predictions using experience data. The problem found from the results of research observations is that the marketing department reports that PT Rekayasa Industri is currently experiencing a decline in revenue that has the potential to harm the company, one of the factors is lack of understanding and deciding whether the company should participate in project tenders or not with aspects namely price, partners, and competitors. The purpose of this study is to utilize machine learning in developing a back-end system to help the marketing department to determine the probability of winning the upcoming tender so that it can be integrated with internally owned applications. The purpose of this research is to utilize machine learning to determine the probability of winning the upcoming tender by developing a back-end system using 3 (three) classification algorithms, namely Logistic Regression, Decision Tree, and Random Forest with REST API technology. The results of this study indicate that Random Forest is the appropriate model in this case, namely predicting victory, being able to study experience data well, and of course being a model that can help minimize the analysis process carried out by the marketing department. This is evidenced by the results of the tests carried out, namely the accuracy value of 96.15%, f1 score of 97.14% and the AUC value of 0.97 (very good classification). Followed by Decision Tree with an accuracy value of 84.62%, an f1 score of 89.47% and an AUC value of only 0.78 (classification is quite good). Meanwhile, Logistic Regression resulted in less than optimal data learning performance with an accuracy value of 80.77 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , an f1 score of 87.18% and the AUC value which was quite good in the classification task, which was only 0.72.
Augmented Reality English Education Based iOS with MobileNetV2 Image Recognition Model

The ability to speak English is a must-have currently. Along with the development of the times, the ability to speak English is very important for everyone. Learning English is very important, especially in terms of education and work. Therefore, with the urgent need for English in the national and international arena, English must be introduced from an early age. By taking advantage of technological advances, especially iOS, education can be done using educational applications. This English-language object recognition educational application is designed by utilizing Augmented Reality (AR) technology by utilizing ARKit, and CoreML for object recognition models, and AVKit for text-to-speech. To find out the name of an object in English, the user is required to point his cellphone at the object he wants to know the name, then click on the cellphone screen to bring up the text of the object name in English above the object and a sound will sound the pronunciation of the text. This educational application is done using XCode software and the Swift programming language. Its development uses the waterfall method. This object recognition educational application in English can run well according to the design, starting from identifying objects and displaying text on objects with test results using the Black Box method, which is valid as a whole.
The Influence of The COVID-19 Pandemics in Indonesia On Predicting Economic Sectors

Several studies have tried to prove the link between the economic sectors in Indonesia with the COVID-19 pandemic. However, research has yet to observe the influence of the COVID-19 pandemic on the predicted performance of regression models. This study proposes the development of previous research following the impact of the COVID-19 pandemic on machine learning performances in predicting economic sectors in Indonesia. The economic sectors mentioned include the exchange rate, CPI, and stock price. The proposed methods for comparison are decision tree (DST) and random forest (RF). Comparison of prediction performance with legacy uses root mean squared error (RMSE), mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Test results show that the RF regression model has superior performance compared to DST with the best MSE, RMSE, MAPE, MAE, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r^{2}$</tex> value of 0.010, 0.102, 0.64%, 0.100, and 0.89, respectively. Using the T-Test, we prove that the COVID-19 pandemic does not significantly affect machine learning predictions on the exchange rate but significantly affects machine learning predictions on CPI and stock prices.
Low Cloud Type Classification System Using Convolutional Neural Network Algorithm

Clouds are one of the most important parameters in doing weather observations. Meteorologist and atmospheric experts are able to determine short-term and long-term weather conditions by observing atmospheric parameters, one of which is cloud types. The goal of this research is to develop a system which is able to recognize cloud patterns through imagery to determine the types so that the subjective factor in cloud observations can be decreased and help observers to observe cloud types more easily. This system was built by implementing the Python programming language and one of the deep learning models, named as Convolutional Neural Network (CNN). The data used in the system are low cloud image data which are divided into 5 (five) classes, namely Cumulunimbus, Cumulus, Stratus, Stratocumulus, and Nombostratus. The result shows that the accuracy achieved the number of 92.22% in the training process with the total loss of 0.20%.
Interaction Design of Indonesian Anti Hoax Chatbot using User Centered Design

The increasing use of social media has led to an increase in the spread of hoaxes in Indonesia. To overcome this, there are several anti-hoax applications available for Indonesians. Even though these applications can be accessed freely, the number of usages is still low. People tend not to use it due to the application's poor interaction design, such as difficult to understand, etc. Therefore, the low usage number causing the urgency to solve the application's interaction design. This research aims to create an interaction design for an application that can overcome the users problems to increase the number of usages of the anti-hoax application. User needs and preferences for the anti-hoax application interaction design are determined through research using questionnaire and interview methods. The platform chosen as the solution is a chatbot on the Whatsapp application. The method used to design the anti-hoax chatbot's interaction design is user-centered design, so the focus is on the user needs and problems. This research resulted a high-fidelity prototype that meets the usability goals which are effective to use and easy to learn and the user experience goals which is helpful. The result of the usability testing shows that the interaction design meets the usability goals and user experience goals that has been determined with a completion rate of 100%, SEQ 6.7 out of 7, SUS 87.7 out of 100, CUQ 89 out of 100, and IMI 6.4 out of 7.
DeepRec: Efficient Product Recommendation Model for E-Commerce using CNN

E-commerce, which provides access to millions of things online, has exploded in popularity in recent years. The availability of several options may cause shoppers' heads to spin and make it harder for them to settle on a single purchase. In recent years, many papers proposed the recommendation system as the potential solution to these issues. Hence, this study constructs a novel recommendation model using a learning technique to build an efficient recommender for e-commerce products. To set up our model, we compile a sizable e-commerce dataset. According to our experiments, our technique improves accuracy by 91.47 percent on the training set and 91.17 percent on the testing set. For this reason, the approach we present has the potential to be an effective means of resolving recommendation-related challenges in the actual implementation of e-commerce.
Classification and Sentiment Analysis on Tweets of the Ministry of Health Republic of Indonesia

Government agencies in this modern era are greatly assisted by the rapid development of information technology, so transparency and speed are imperative in providing services to the public. The use of social media by government agencies is one of the innovations by maximizing technology, one of which is the Ministry of Health of the Republic of Indonesia which is the ministry in charge of organizing government affairs in the health sector. The information contained on the Twitter social media account of the Ministry of Health of the Republic of Indonesia is various types of information uploaded randomly, therefore Twitter users often cannot distinguish the types of information provided by the Twitter of the Ministry of Health of the Republic of Indonesia. Based on this case, Twitter users can provide responses or comments that often lead to pros and cons. The extreme gradient boosting (XGBoost) algorithm is a tree-based algorithm such as the decision tree algorithm, which uses an ensemble principle that combines several weak learning sets and makes a new model that is strong to produce strong predictions. In this study, our aim is to implement extreme gradient boosting to classify the tweet topics and analyze the sentiment towards comments made by the public on the tweets of the Ministry of Health of the Republic of Indonesia. The results of this study indicate the highest level of accuracy in the classification, i.e. 89.35% with a precision of 88.76%, and 88.58% for the recall value from 2243 tweets. Furthermore, the best accuracy in sentiment analysis was obtained at 91.22%, 89.17% for precision, and for recall at 89.06% with 304 comment data.
An Autonomous Sensing System for Monitoring Dissolved Carbon Dioxide of Naturl Water for Geochemical Applications

The continuous monitoring and real time analysis of chemical-physical parameters of freshwater flows is of fundamental importance for numerous strategic activities. For instance, the management of water resources, environmental monitoring and the study of chemical-physical and quantitative variations before and after major seismic events affecting areas where important aquifers are located. A low-cost and energy autonomous wireless sensing system has been developed for continuous water chemical-physical monitoring. In particular, the sensing system is capable of measuring the dissolved carbon dioxide (dCO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ) in water along with salinity, conductivity and temperature through a low-power (< 3 mW) infrared sensor.
Mobile Application Performance Improvement with the Implementation of Code Refactor Based on Code Smells Identification: Dutataniku Agriculture Mobile App Case Study

Technology can be a supporter of business processes, even playing an important role in business acceleration. Conventional businesses will slowly move towards technology-oriented businesses. The need for a good application and system becomes a fundamental need today. All sectors are already supported by technology, including the agricultural sector. Agriculture in Indonesia is, slowly but surely, undergoing changes towards modern and precision agriculture. The information system prepared of course needs to have a good code architecture so as to produce a well-performed system. To ensure this, one approach is by identifying the code smell. This study will try to test and compare the code on a mobile-based agricultural information system before and after the code smell-based code refactor is carried out. The code smell of the program will be identified, gets refactored, then its code smell and performance will be re-tested. The result obtained is code smell-based code refactor increases the application's performance. In this study, the average decrease in code smell was 2.9%, and the average increase in application performance was 26%.
Topic Modeling on Covid-19 Vaccination in Indonesia Using LDA Model

According to data from covid19.go.id, there is a lot of hoax news about Covid-19 vaccinations spread across various social media in Indonesia. Meanwhile, the ability to monitor and track misinformation and trends regarding Covid-19 and its spread is an important part of the response process by the media and government to dealing with fake news about Covid-19. Twitter is a social media that is actively used in spreading issues. Twitter users in Indonesia reached 18.45 million users as of January 2022. To find out useful information on Twitter social media comments regarding the Covid-19 Vaccination, a method, namely Topic Modeling, can be used. This study aims to obtain the distribution of the Covid-19 Vaccination topic on Twitter Data in Indonesia to assist the government in knowing the trend of topics related to Covid-19 vaccination and the trend of changing the topic. The dataset on Twitter used is 10,140 pieces about Covid-19 vaccinations in Indonesia in the period August 2021 to April 2022. Based on the Latent Dirichlet Association (LDA), the 5 most popular topics were obtained for each model, and spread in the fields of health, religion, society. Based on the alpha and beta hyperparameter tuning, it was found that the topic with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{K}=5$</tex> , alpha=asymmetric, and beta=0.61 was a relevant LDA topic model for the research dataset because good in topic diversity and have coherence value=0.590.