Reviewerly/exHarmony huggingface dataset:
Automated identification of cardiomyocyte nuclei in H and E-stained heart tissue with CycleGAN

Purpose: Identification of cardiomyocyte nuclei is of great importance when assessing heart tissue for cardiovascular disease. However, manual identification of cardiomyocyte nuclei in hematoxylin & eosin (H&E)-stained heart tissue is particularly complex due to extensive heterogeneity in morphology. While expensive and time-consuming staining processes can be utilized to identify cardiomyocyte nuclei, this study proposes to test the feasibility of using a cycle-consistent generative adversarial network (CycleGAN) to automate the identification of cardiomyocyte nuclei in H&E-stained heart tissue without the need for additional staining. Materials and Methods: 100 H&E-stained heart tissue sample regions from cancer patients were manually labeled with the input from an expert pathologist. Sample regions were processed into 3,584 labeled tiles and 3,584 tiles without labels, and then sorted, augmented, and run through a CycleGAN to facilitate the identification of cardiomyocyte nuclei. Performance was assessed with Fréchet Inception Distance (FID), Fréchet ResNet-50 Distance (FRD), sensitivity, specificity, and accuracy. Results: Sensitivity and specificity were 61.6% sensitivity, 96.3% specificity with an accuracy of 85.0% for 30 randomly selected samples. FID and FRD reached as low as 64.8 and 2.46, respectively, with downward trend for FID and high volatility with a slight downward trend for FRD. Notably, the best results were achieved when FRD was at its lowest value. Conclusion: The results suggest cardiomyocyte nuclei can be identified by a CycleGAN. However, targeted improvements are necessary to ensure accurate identification. An enhanced version of this approach may facilitate the automation of identifying cardiomyocyte nuclei in H&E-stained heart tissue, facilitating cardiovascular research.
Space-filling curves for modeling spatial context in transformer-based whole slide image classification

The common method for histopathology image classification is to sample small patches from large whole slide images and make predictions based on aggregations of patch representations. Transformer models provide a promising alternative with their ability to capture long-range dependencies of patches and their potential to detect representative regions, thanks to their novel self-attention strategy. However, as a sequence-based architecture, transformers are unable to directly capture the two-dimensional nature of images. While it is possible to get around this problem by converting an image into a sequence of patches in raster scan order, the basic transformer architecture is still insensitive to the locations of the patches in the image. The aim of this work is to make the model be aware of the spatial context of the patches as neighboring patches are likely to be part of the same diagnostically relevant structure. We propose a transformer-based whole slide image classification framework that uses space-filling curves to generate patch sequences that are adaptive to the variations in the shapes of the tissue structures. The goal is to preserve the locality of the patches so that neighboring patches in the one-dimensional sequence are closer to each other in the two-dimensional slide. We use positional encodings to capture the spatial arrangements of the patches in these sequences. Experiments using a lung cancer dataset obtained from The Cancer Genome Atlas show that the proposed sequence generation approach that best preserves the locality of the patches achieves 87.6% accuracy, which is higher than baseline models that use raster scan ordering (86.7% accuracy), no ordering (86.3% accuracy), and a model that uses convolutions to relate the neighboring patches (81.7% accuracy).
Tuning of Turbulence Model Closure Coefficients Using an Explainability Based Machine Learning Algorithm

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;This article discusses an application of Machine Learning (ML) tools to improve the prediction accuracy of Computational Fluid Dynamics (CFD) for external aerodynamic workflows. The Reynolds Averaged Navier-Stokes (RANS) approach to CFD has proved to be one of the most popular simulation methodologies due to its quick turnaround times and acceptable level of accuracy for most applications. However, in many cases the accuracy for the RANS models can prove to be suboptimal that can be significantly improved with model closure coefficient tuning. During the original turbulence model creation, these closure coefficients were chosen by somewhat ad hoc methods using simple canonical flows that do not transfer well to flows involving more complex objects, like the automotive bodies used in this work. This work presents a novel method of applying ML tools to CFD to optimize the turbulence closure coefficients by using model explainability tools such as Shapley Values, Shapley Additive exPlanations (SHAP), and ML surrogate models. The 25-degree slant Ahmed body model was used to obtain sampling data to tune closure coefficient in the Menter Shear Stress Transport (SST) turbulence model implemented in the open source CFD code, OpenFOAM v2012. Shapley additive values were then calculated using the samples which showed that &lt;i&gt;β&lt;/i&gt;&lt;sup&gt;∗&lt;/sup&gt; has the strongest influence over the model predictions of lift and drag. ML surrogate models were then applied alongside SHAP providing a better overall sampling efficiency with Shapley additive values and more complete explanations of the model. The SHAP explanations showed that &lt;i&gt;β&lt;/i&gt;&lt;sup&gt;∗&lt;/sup&gt; had the most influence on the force predictions followed by &lt;i&gt;σ&lt;/i&gt;&lt;sub&gt;&lt;i&gt;ω&lt;/i&gt;2&lt;/sub&gt;, while &lt;i&gt;σ&lt;/i&gt;&lt;sub&gt;&lt;i&gt;ω&lt;/i&gt;1&lt;/sub&gt;, &lt;i&gt;σ&lt;/i&gt;&lt;sub&gt;&lt;i&gt;k&lt;/i&gt;1&lt;/sub&gt;, and &lt;i&gt;σ&lt;/i&gt;&lt;sub&gt;&lt;i&gt;k&lt;/i&gt;2&lt;/sub&gt; were shown to have little impact. The surrogate model was then used along with its explanations to provide optimized coefficients that reduced the error in the drag and lift predictions to -3.67% and -2.49% respectively, from -9.67% and -75.8%.&lt;/div&gt;&lt;/div&gt;
Liver Tumor Segmentation using Deep Learning Techniques

Liver tumor segmentation plays a critical role in medical imaging analysis, especially in cancer diagnosis and treatment planning.Accurate segmentation of liver tumors is essential.There has been growing interest in developing automated methods for liver tumor segmentation, practically using deep learning algorithms.In this paper, we use the convolutional neural networks (CNNs), on large datasets of liver images, labelled with the tumor regions.A U-net architecture is builtwith cross connections.The encoder and decoder portion of the Unet is built using a 34 layerResNet.The model was trained on Liver Tumor Segmentation challenge (LITS) dataset of liver CT scans and evaluated on a separate test dataset.The results demonstrated that the proposed method produced great segmentation accuracy and an average dice score of 0.95
Variational quantum simulation of the quantum critical regime

The quantum critical regime marks a zone in the phase diagram where quantum fluctuation around the critical point plays a significant role at finite temperatures. While it is of great physical interest, simulation of the quantum critical regime can be difficult on a classical computer due to its intrinsic complexity. Herein, we propose a variational approach, which minimizes the variational free energy, to simulate and locate the quantum critical regime on a quantum computer. The variational quantum algorithm adopts an ansatz by performing an unitary operator on a product of a single-qubit mixed state, in which the entropy can be analytically obtained from the initial state, and thus the free energy can be accessed conveniently. With numeral simulation, using the one-dimensional Kitaev model as a demonstration we show that the quantum critical regime can be identified by accurately evaluating the temperature crossover line. Moreover, the dependencies of both the correlation length and the phase coherence time with temperature are evaluated for the thermal states. Our work suggests a practical way as well as a first step for investigating quantum critical systems at finite temperatures on quantum devices with few qubits.
Quantum interferometric power and non-Markovianity in the decoherence channels

In quantum open systems, non-Markovianity is an important phenomenon that allows a backflow of information from the environment to the system. In this work, we investigate the non-Markovianity problems in two different types of channels, where the system–environment interactions are treated with and without the rotating-wave approximation (RWA). We employ the quantum interferometric power (QIP) to quantify the non-Markovian dynamics, which is the minimal quantum Fisher information obtained by the local unitary evolution in a bipartite system. By the hierarchy equation method, we calculate the dynamical evolution of the QIP in the non-RWA case. The results show that the dynamical behavior under the non-RWA is significantly different from that under the RWA in both weak and strong coupling. Moreover, in the non-RWA case, we also find the nonmonotonic behavior of the non-Markovianity measure with the variation of coupling strength, which is caused by the competition between the rotating-wave terms and the counterrotating-wave terms. As a result, we highlight the importance of the counterrotating-wave terms for the influence of non-Markovianity.
HUMAN IDENTIFYING SYSTEM USING BIOMETRICS

Human identification system using biometrics is software that enables the users, notably the hospital management staff and admin team, to make sure that the other processes take place promptly without any time lag and issues owing to which there will be any delay in the further procedure.We may perhaps save the lives of the individuals on their deathbeds and even notify their relatives of the present position and situation without any time lag or delay, which will aid in prompt action and help to accomplish everything that is required by the relatives.The tool helps to determine the individual by utilizing their unique biometrics and utilizes them to identify the perfect identity of the person with exact results and about a 100 percent unique and correct match.The arduous clerical process of form filling is mechanized by employing this program.The patient's specific information is pulled from the database, and an automated admission form is prepared, which will facilitate the process of hospital management and even the police in the case of a catastrophic accident in which the identification of a person is required to be known for another procedure
Types and sentences structure charged with character education values essays of grade IV elementary school students in Bali Province

The main objective of this descriptive-qualitatively designed study is to describe (1) types of sentences and (2) sentence structures based on character education values contained in the essays of grade IV elementary school students in Bali province. The subject of this study is the essay of grade IV elementary school students in Bali province, while the objects are (1) types of sentences and (2) sentence structures based on character education values contained in the essays of grade IV elementary school students in Bali province. Data is collected by the method of recording documents. Furthermore, the collected data are analyzed descriptively-qualitatively. The results showed that: (1) the types of sentences contained in the essays of grade IV elementary school students in Bali province can be divided based on (a) the number of clauses, there are 804 single sentences, 494 compound sentences, (b) the internal structure of the main clause, there are 1209 complete sentences and 89 incomplete sentences. (2) The sentence structure, both single and compound, contained in the essays of grade IV elementary school students in Bali province is quite varied.
Phonetic Feature Extraction and Recognition Model in Korean Pronunciation Practice Based on AdaBoost

This paper proposes a phonetic feature extraction and recognition model in Korean pronunciation exercises based on AdaBoot moments. Feature extraction algorithms have a great influence on speech emotion recognition algorithms, among which Mel-frequency Cepstral Coefficients (MFCC) is the most commonly used feature in speech emotion recognition. The feature extraction method and the influence of SVM kernel function and parameter selection on the recognition result are studied, and the existing speech feature extraction algorithms and their respective advantages and disadvantages are analyzed, as well as the influence of different kernel functions, kernel parameters and penalty parameters on the recognition performance. An improved grid optimization method is used to further improve the recognition time of voice information.
SENTIMENT ANALYSIS OF CONTENT PERMENKOMINFO NO.5 OF 2020 USING A CLASSIFICATION ALGORITHM

This study aims to evaluate the impact of the policy issued by the Minister of Communication and Information Technology (PERMENKOMINFO No.5 of 2020) on the public's ability to access content through Private Scope Electronic System Providers (PSE). The study uses sentiment analysis and data classification methods to analyze the content of PERMENKOMINFO No.5 of 2020 and provides results on the accuracy of sentiment prediction. The results of the study show that the data classification method in sentiment analysis can provide accurate results in predicting the sentiment towards the content of PERMENKOMINFO No.5 of 2020. The study also highlights the need for improvement and better policy to ensure the interests of the public in accessing online information. The negative sentiment of 80.34% obtained through sentiment analysis provides important contributions for policy evaluation and feedback for improvement. This study provides valuable insights into the public's sentiment towards the PERMENKOMINFO No.5 of 2020 policy and its impact on their ability to access content. It also contributes to understanding the legal uncertainty in accessing content and reinforces the case for better policy to ensure the interests of the public.
Predicting Fake Job Posts with a Voting Classifier of Multiple Classification Models

The detection of fake job posts is becoming increasingly important in the modern job market.With the rise of online job postings, scammers and fraudulent actors are taking advantage of unsuspecting job seekers by posting fake job listings that appear legitimate.This paper proposes a machine learning approach to detect fake job posts using a combination of textual and categorical data.We extract various features from the job post text, such as the presence of certain keywords, as well as features from the job post, such as the job title, employment type, required experience.Models like Logistic regression, SVM, Decision tree, Random forest, Gradient boosting, XGBoost, and MLP with Adam optimizer are compared using various metrics like accuracy, F1 score, ROC AUC score, and more after training.This research can be used to build automated systems to detect fake job posts, helping to protect job seekers from scams and fraudulent activities in the job market.
Building models to predict the diagnosis of emergency department patients using Artificial Neural Networks

There are circumstances in which patients require immediate medical care.Usually, hospitals have a specialized unit ready to take care of emergencies such as: strokes, heart attacks, sepsis, severe wounds, and other medical conditions that are also classifies as emergencies.This research compared models based on artificial neural networks (ANN) to predict the diagnosis of patients received at the emergency department (ED) of a public hospital.The research was carried out following a classic 4-phase methodology: analysis, design, development, and validation.During the analysis, patient records collected by ED personnel during 2020 were thoroughly reviewed and preprocessed.During the design, some of the records were selected and divided into several subsets following specific criteria.Each subset was a set of independent variables used as input to build a prediction model.Technical considerations relate to the artificial neural networks (ANN) such as the number of hidden layers and the number of epochs, batch size, size of the training and test set, and size of the validation dataset were also defined at these point.The phases of construction and the validation are carried out entirely using the WEKA 3.9.6.Numerous experiments, trial and error adjustments, and replications were necessary to produce the results shown in this article.For the purposes is the present work around 15,000 records were considered.The complete dataset was divided in two parts: 80% for training and test, and the remaining 20% for validation.The approach to predict the patient diagnose considered the construction of ANNs with of independent variables.During the construction several configurations of ANNs were tried to achieve better results.Only the records allocated for training and test dataset is used during the construction.The performance of the proposed models was measured in terms of the rate of correct predictions made with records allocated for validation.The experimental result revealed that, depending on the number of diagnoses, the values of the target class, the proposed models were able to predict correctly between 55% and 85% of the cases.In conclusion, ANN-based models can help predict ED patients' diagnosis with a reasonable degree of certainty.However, the success of the model depends greatly on the numbers of values that the target class has.
Perceived Impact of HyFlex Online Class on the Academic Performance of Graduating Engineering Students

Last March 2019, Philippines suffered the first wave of COVID-19 resulting in most, if not all, educational facilities seizing operations and swiftly shifting into the online learning environment most of us are familiar with.Currently, the Technological Institute of the Philippines Quezon City (T.I.P QC) has adapted into more flexible learning approaches as the threat of COVID-19 slowly dwindles away.With the standard learning approach shifting once again into the new normal, this study aims to explore the effects and impacts of a once again new modality of learning to a graduating engineering student's academic performance.COVID-19 has caused significant changes in how instructors impart knowledge and how students interact with Higher Education (HE).Different teaching and learning approaches have been adopted to account for social exclusion, learners' varied requirements, and the fact that they may live far from the HE classroom.HyFlex is one such mode.In HyFlex, instructors instruct learners simultaneously in a physical classroom and synchronously online using video-conferencing tools.In this small-scale exploratory study, graduating students who took a six-week course through HyFlex are interviewed about their experiences and opinions.Despite communication difficulties between students who attended through various modalities, individuals did value the freedom it provided, according to the findings.The use of several aspects of video conferencing software and other digital tools was considered crucial to HyFlex's effectiveness.A quantitative study was conducted using a unidimensional 4-point Likert scale questionnaire disseminated through a google form link. Respondents are limited to 50 Graduating Engineering Students of T.I.P. QC Campus.Survey results are further validated through the use of frequency, percentage, weighted mean, T-test, Standard Deviation, interpretation, and ranking.
Sentiment Analysis on Reviews of E-commerce Sites Using BERT

The Internet's widespread use has had a significant impact on electronic commerce.The trend of review-oriented consumption, where consumers rely on customer reviews of a film, is gaining popularity in the market.E-commerce platforms face a significant challenge in accurately interpreting user sentiments from the large volume of customer evaluations.This research suggests a BERT-based ecommerce reviews sentiment analysis algorithm to address the aforementioned issues [3].Our approach to researching sentiment analysis involves analysing annotated data and labelling entities using the BIO (B-begin, I-inside, Ooutside) data labelling pattern.By utilizing this method, we are able to accurately identify and classify entities within the data, and determine their sentiment.Based on experimental findings on the Taobao cosmetics review datasets, our approach has demonstrated significant improvements in both accuracy rate and F1 score when compared to conventional deep learning methods.
FOQUS (Focus And Quota Saver) Application for Effective Learning in Online and Digitalized Classroom (A Study Case in SDS Yayasan Pembangun Didikan Islam)

The world is affected by the COVID-19 pandemic.Indonesia is one of the affected countries.This led to a shift in doing our daily activities from the physical into digital in various industries, including education industries.The digital (online) activity must be done using gadget in teaching and learning process.Teachers and students used Zoom, Gmeet, Whatsapp, and other online platforms to interact with the students.However, students tend to lose their focus easily with this learning method.They have a habit of opening social media ang games while studying.These apps are apps which consume student's limited package data.Therefore, not only are they distracted by opening these applications, the student's quota is also used ineffectively.FOQUS application is an application to block students from opening irrelevant applications during class.This application was made to reduce student's data package consumption and help students in retaining their focus during online class.The implementation method starts from meetings and introductions to establish partnership with students, teachers, and authors in Yayasan Pembangun Didikan Islam (YPDI), application design and development, socialization to teachers and students, and application implementation in teaching and learning activities.Evaluation was carried out periodically during the implementation process.Application design and development activities were carried out for 2 months, socialization was carried out to 6A and 6B grade students and accompanying teachers, then the implementation was carried out 2 times, namely before and after using the FOQUS application.During implementation, it was found that many students wanted to open other applications while studying but could not successfully open them because they were already using the FOQUS application.At the end of each session, a post-test was conducted to students from each class.This study found that students who used the application saved 2,32% data package compared with students who don't.The students score from each class increased.Class 6A average score increased from 61,2 to 64,4.Class 6B average score increased dramatically from 44,1 to 62,7.This result supports FOQUS application use might have an effect in supporting student's retention on the lesson being taught.However, external factor (classroom condition) and internal factor (individual intelligence, habits) might also have an influence in the student's class performance.