Reviewerly/exHarmony huggingface dataset:
Diversity-based active learning: creating a representative object detection dataset in 3D point clouds

Object detection in 3D point clouds is essential in fields such as geospatial intelligence and autonomous driving. The common machine learning problem of scarce labeled training data is even more acute with 3D point cloud data. Active learning provides a framework to prioritize the additional effort to manually annotate unlabeled training data. Most active learning methods for deep learning fall into one of two categories: uncertainty methods and diversity methods. Uncertainty methods select data by assessing model outputs for their confidence and consistency and are therefore dependent on the expected output of each deep learning task. These methods tend to select batches of informative yet highly similar samples to label. Diversity-based active learning aims to create a labeled dataset that is both varied and representative of the remaining unlabeled data. Diversity methods operate directly on the feature representations of the inputs and are thus more flexible with respect to the specifics of the deep learning task. Our current work explores applying diversity methods and uncertainty-diversity hybrid methods to 3D object detection. We evaluate various approaches to incorporate diversity, including K-Medoids clustering, core set selection, and furthest nearest neighbors. We address the high dimensionality of the features extracted from a VoxelNet-based object detector by varying the distance metric used in the active learning algorithms. Furthermore, we compare our results to those obtained using only uncertainty methods. We assess the performance and efficiency of each active learning method in addition to the representativeness and diversity of the labeled datasets produced. We find that hybrid uncertainty-diversity methods outperform other methods in terms of object detection AP50 throughout active learning, annotation efficiency, and class balance.
ANALYZING JUNIOR HIGHSCHOOL STUDENTS’ MOTIVATION IN LEARNING ENGLISH IN NEW NORMAL

The purpose of this research is to design a qualitative research method. In collecting data 30 students and five students were randomly. The research design used questionnaires and interviews. The results of data analysis show that the motivation to learn English in the new normal is high, making them want to go back to school; compared to online learning, students are easier to understand and can achieve learning achievement by returning to school. Based on the statement above, the researcher concludes that it can help students with their willingness to learn again in school in the new normal
PERCEPTION ON THE USE OF ZOOM IN SPEAKING CLASS OF THE THIRD YEAR STUDENTS AT TADULAKO UNIVERSITY

This study aims to know students' perceptions on the use of zoom in speaking class as an alternative solution. Whether the use of zoom as a media of online learning in speaking class is perceived positive or negative during pandemic eras of covid-19. The subjects of this research were 47 students of class 2019, the academic year 2021–2022, at Tadulako University of English Education Study Program. The research was mix method research with embedded design. The instruments used to collect data are questionnaires and interviews. The results of data analysis show that the use of zoom in speaking class perceived negatively by the student, there are many aspects influenced such as: the use of zoom application in speaking class could not fulfill the evaluation aspects of speaking skills itself, instability of internet connection interfere in the midst of knowledge transfer, financial problem in purchasing internet package, and students lack of initiative in upgrading their speaking achievement. However, there are also some positive perception that is found in this study regarding the use of zoom as a media of learning. The students agreed that the use of zoom application is helpful and could help the student learn autonomously which change their learning habit and support students to be actively engaged during speaking class. This implies that the use of the zoom application is not appropriate to be use in speaking classes. In addition, students agree that the zoom application cannot help them improve their speaking skills.
A distributed objects’ information safe access and data privacy protection algorithm in smart grid

For the problems of large communication load and low security in the transmission process of distributed smart grid terminal data, based on the blockchain technology, this paper proposed a novel data privacy protection algorithm for distributed smart grid terminal. To be specific, by judging whether there are malicious relay nodes in the transmission path, the relay nodes whose trust degree does not satisfy the threshold are isolated from the whole blockchain network, which can ensure the security of the public ledger and realizing that private data will not be disclosed. The experimental results show that, compared with the traditional data privacy protection technology, the proposed algorithm has strong practicability, effectively improves the security factor of data in the transmission process, and provides effective protection for the safe access of information and data privacy of distributed objects in smart grid. And compared with the traditional methods, the distributed data storage effectively solves the single point of failure problem and enhances the resilience of the network. And the combination with blockchain technology provides powerful support for more complex smart grid environments.
A Chinese Entity-Relation Extraction Method Via Improved Machine Reading Comprehension

As the downstream task of building a knowledge graph, Chinese entity relationship extraction from unstructured texts plays an important role in the field of natural language processing. There are two main ways for Chinese entity relation extraction: joint extraction method and pipeline extraction method. The joint extraction method outputs the relation triples contained in the texts directly in a row, which causes two problems: the lack of external knowledge and the nesting of entities. This article proposes a method to take the advantage of the similarity between the span extraction task and the information extraction task, and transforms entity relation extraction problem into a task similar to machine reading comprehension. This method first uses the Roberta pre-training model to obtain word representation with relation information, and then identifies entity pairs that may exists under per relation through a global pointer network, which outperforms better than the normal pointer network. By comparing different models’ performance on the same dataset, the results show the accuracy, recall and F1 scores of our method are higher than other methods, which proves the effectiveness of our method.
Construction of Knowledge Graph Based on Traffic Violations in Beijing

In response to the country’s call to promote the modernization of governance with wisdom, the data published by the information platform is used to build a traffic safety knowledge graph, reveal the logical relationship between the cases of road traffic violations, punishment results, and punishment basis, which can quickly and effectively assist the handling of traffic violations. This paper uses the Selenium crawler technology to obtain the Beijing traffic violation dataset from the comprehensive traffic safety service management platform, uses the Jaccard similarity text matching method to normalize the marked entities, uses the custom rule method for relationship matching, and uses Neo4j to visualize the triples, 196 nodes and 465 edges are formed to represent the knowledge graph of traffic violations, which lays the foundation for the traffic violation retrieval and question answering system.
Aspect-level Sentiment Classification Method Based on Graph Convolutional Network and Interactive Attention Mechanism

Aspect-level sentiment classification often uses syntactic information such as dependency trees to improve model performance, which makes aspect words far away from emotional words; When the text is modeled as a graph model, the interactive information between context and aspect words is ignored in the graph convolutional network, resulting in insufficient semantic information and local feature information. To solve these two problems, an aspect-level sentiment classification method based on graph convolutional network and interactive attention mechanism is proposed. By fine-tuning RoBERTa, the model strengthens the connection between aspect words and emotional words, shortening the distance. The graph convolutional network constructs the induced tree with syntactic dependence between framework words and aspect words into dependency tree. Finally, the interactive attention mechanism is used to study the interactive information between context and aspect words. The experimental results show that contrast to the baseline model, this model improves the accuracy and F1 value.
STUDY ON THE USE OF SUBSTITUTION DRILL TECHNIQUE IN TEACHING GRAMMAR

This study was conducted through Library Research Design. The objectives of this study are to analyze, describe, and explain about the implementation of substitution drill technique in teaching grammar. In this study, the data described was taken from the ones conducted within 2011-2020. There are ten studies used as the data sources in this recent study. The result of descriptive data analysis showed that the implementation of substitution drill can improve the students’ ability in English , such as the ability in conditional sentence, simple present tense, past tense, continuous tense, degrees of comparison, and the ability in using auxiliary do and does. Based on the results of the implementation of substitution drill by previous researchers, it was found that most of the students on different academic levels within 2011-2020 experienced improvement on their ability in English after the implementation of substitution drill. All the secondary data sources used different procedure but in general all drill procedure are similar using the procedure by Decanay &amp; Donald model with the following procedure: a model, a cue, and response.
ANALYSIS OF TEACHING STRATEGIES IN READING COMPREHENSION

This study is to investigate the strategies mostly applied by teachers in teaching reading comprehension in scientific writing/journal articles. This research formulate from library research used scientific writing (journal articles) 2010 – 2021 and how is the implementation of the teaching strategies in reading comprehension the most frequently used on journal articles. This study used qualitative research design. The data were collected by library research the documentation. The data was collected from journal articles that related with the topic. To collected data is used five steps: 1) Searching valid data and resources relevant with the topic, 2) Finding the key information from the data, 3) Taking notes from the essentials information, 4) Organizing the described data into their classifications, 5) Choosing the topic to be analyzed. Based on the findings from ten journal articles, the researchers concludes that the teaching strategies QARs, Scaffolding, Memorizing strategy, Discussion strategy, Reading Aloud, Question generating, Encouraging the use dictionary, Evaluating comprehension in particular task, Brainstorming and Understanding text .From the ten teaching strategies in reading comprehension researcher found the most frequently strategies used in teaching reading comprehension namely, Question-Answer relationships (80%). In accordance with the statement of Adler (2003) QARs is the effective strategy were used in teaching reading comprehension. Teaching strategies has different ways of learning but has the same goals, which is to help students in understanding reading difficulties.
STUDY ON THE IMPLEMENTATION OF ENGLISH SONGS IN TEACHING VOCABULARY MASTERY

A song is a type of teaching media that can be used to teach any subject. It can also be used to teach vocabulary because song lyrics contain a variety of vocabularies. The goals of this study are to describe the steps of the implementation of English Song in teaching vocabulary mastery and to describe how successful the implementation of English Song in teaching vocabulary mastery was. The study applied qualitative design that implemented Library Research Method. The data were collected from scientific writing on the implementation of English songs in teaching vocabulary between 2011 and 2021. The scientific writing were found from the library and internet. The data were analyzed using content analysis based on Dale (1992). The results reveal that there are five scientific writings from Widhiastuti (2012), Febria (2015), Gushendra (2017), Bahri (2018), and Sari (2019). The results of the data analyzed show that the implementation of English Song in five different studies by five different experts used different steps in implementing the procedure. One of them was followed the procedure suggested by Dale (1992) which is play the song with no preparation and no hand-outs. Three of them give the materials before the listening selection, and other researcher play the video or singing the song by the teacher. Based on second research question, the vocabulary mastery of students’ improve after the implementation of English Song in the classroom. The researcher conclude that the implementation of English Song in teaching vocabulary mastery has a positive effect because the students’ vocabulary has improved as a result of the research.
Abstract 15673: AI-Enabled Prediction of Bleeding in Patients Supported on Extracorporeal Membrane Oxygenation

Introduction: Clinical data within the electronic health record (EHR) combined with artificial intelligence (AI) and machine learning (ML) offer the promise of rapid advancement of knowledge at the point of care. Hypothesis: We hypothesized that aggregating clinical data with preservation of relational elements will yield a more precise and less biased representation of patient data for predictive analytics. We leveraged these data to predict clinically significant bleeding in the ECMO-supported population. Methods: The data of children 0-19 years managed on ECMO for first run cardiac indications in a single center between 1/2010-12/2020 were analyzed. Bleeding Day was defined as any 24-hour period which included pulmonary, gastrointestinal, intracranial or surgical wound hemorrhage, and/or surgical intervention or Factor VIIa administration for hemorrhage control. EHR data documenting the ECMO episode and embedding relational elements were used to inform 1) a multivariable logistic regression model matching a previously- performed analysis using manually extracted data [1], and 2) a feature-limited graph neural network (GNN) model of bleeding. Results: There were 272 patients supported with ECMO for total 2,012 days which informed the analysis, with median age 0.4 [IQR 0.03, 3.30] years, 56% male and 14% of days including a bleeding event. Direct comparison of the EHR-derived logistic regression model with previously published manual results are presented in Table 1. Inclusion of the EHR-derived covariates in a GNN model achieved similar operating characteristics. Conclusions: EHR-derived AI-prediction models of bleeding in this complex patient population are at least as accurate as models with manual data and traditional statistical analysis, but with a known pathway to potentially more accurate predictions. This methodology can be replicated to other conditions, allowing rapid insights towards a learning healthcare system.
Abstract 14400: Predicting In-Hospital Mortality Among Patients Admitted With Heart Failure: A Machine Learning Approach

Introduction: Patients hospitalized with heart failure (HF) experience high in-hospital mortality. Machine learning predictive models have the potential to identify heart failure patients at risk of adverse outcomes. Methods: Using the electronic medical record (EMR) at a large academic medical center, we identified 49,457 patients who were admitted with heart failure from 2011 to 2022. 71% were admitted with HF with reduced ejection fraction (HFrEF, EF≤40%) and 29% with HF with mildly reduced or preserved ejection fraction (HFmrEF/HFpEF, EF &gt;40%). Using patient demographics, comorbidities, admission medications, labs, and vitals throughout the hospitalization, we constructed random forest machine learning models to predict in-hospital mortality. We compared model performance to that of logistic regression utilizing the same variables. Results: The median age (25th-75th percentile) of the cohort was 72 (59-83) years, and 45% of the group was female. 71% patients were admitted with HFrEF and 29% patients were admitted with HFmrEF/HFpEF. For the overall cohort, our machine learning model was able to predict in-hospital mortality with a sensitivity (SN) of 91%, a specificity (SP) of 85%, and an area under the curve (AUC) of 0.95 (Figure 1). For those with HFrEF, our model demonstrated a SN of 91%, SP of 87% and AUC of 0.95, and for those with HFmrEF/HFpEF, a SN of 87%, a SP of 89%, and an AUC of 0.94. For all three cohorts, our models outperformed logistic regression. The top predictors of in-hospital mortality were BNP, BUN, and Systolic BP for those admitted with HFrEF, and sex, creatinine, and diastolic BP for those with HFmrEF/HFpEF. Conclusions: Machine learning models built from EMR data have the ability to accurately predict in-hospital mortality among patients admitted for HF. Such prediction tools, when used in the appropriate context, may help flag hospitalized heart failure patients at high risk for in-hospital mortality.
FIODC Architecture: The Architecture for Fashion Image Annotation

An approach to computer vision known as object detection is utilised to locate items in images or videos. Object detection methods usually use machine learning or deep learning to get effective results. When watching photographs or movies, humans are able to quickly recognise and pin down objects of interest. Object detection attempts to mimic this intelligence through the use of computers. Using this method of localization and identification, object detection is able to count the number of objects in a photo, track their precise locations, label them and monitor their movements. In the case of the fashion sector, this object detection is crucial for identifying fashion clothes and classifying them to enable autonomous labelling. Our innovative multi-stage Fashion Image Object Identification and Classification (FIODC) Architecture for object detection and classification is presented in this study. In order to do the same, we have taken into account the performance of four object detection models on our dataset: SSD Resnet, EfficientDet, CenterNet Resnet and Faster RCNN Resnet. The Faster RCNN Resnet outperforms all other models with the highest MAP and classification accuracy, according to experimental data and comparisons of all four models’ MAPs (Mean Average Precision).
Medical Image Diagnosis Based on Adaptive Hybrid Quantum CNN

Abstract Hybrid quantum systems have shown promise in image classification by combining the strengths of both classical and quantum algorithms. These systems leverage the parallel processing power of quantum computers to perform complex computations while utilizing classical algorithms to handle the vast amounts of data involved in imaging. The hybrid approach is intended to improve accuracy and speed compared to traditional classical methods. Further research and development in this area can revolutionize the way medical images are classified and help improve patient diagnosis and treatment. The use of Conventional Neural Networks (CNN) for the classification and diagnosis of medical images using big datasets requires, in most cases, the use of special high-performance computing machines, which are very expensive and hard to access by most researchers. A new form of Machine Learning (ML), Quantum machine learning (QML), is being introduced as an emerging strategy to overcome this problem. A hybrid quantum-classical CNN uses both quantum and classical convolution layers designed to use a parameterized quantum circuit. This means that the computing model utilizes a quantum circuits approach to construct QML algorithms, which are then used to transform the quantum state to extract image hidden features. This computational acceleration is expected to achieve better algorithm performance than classical CNNs. This study intends to evaluate the performance of a Hybrid Quantum CNN (HQCNN) against a conventional CNN. This will be followed by some optimizer modifications for both proposed and classical CNN methods to investigate the possible further improvement of their performance. The optimizer modification is based on forcing the optimizer to be directly adaptive to model accuracy. The optimizer adaptiveness will be based on the development of an optimizer with a loss base adaptive momentum. Several algorithms will be developed to achieve the above-mentioned goals, including CNN, QCNN, CNN with the adaptive optimizer, and QCNN with the Adaptive optimizer. The four algorithms will be tested against a Kaggle brin dataset containing over 7000 samples. The test results show the hybrid quantum circuit algorithm outperformed the conventional CNN algorithm. The performance of both algorithms was further improved by using a fully adaptive SGD optimizer.
Easy pair selection method for Kinship Verification using fixed age group images

Kinship Verification (KV) by using facial images is a relevant and challenging research problem in the field of computer vision. Facial features are extracted from the input pair of images and subsequently analyzed to find out existence of kin relation between them. Weights of the training model are suitably learned and adapted for the same. In most of the existing schemes, non kin pairs are randomly generated from the existing kin pair database for the learning process of the model. Since non-kin pairs are formed randomly from the true kin pair database itself, hence there is a significant probability that some non kin pairs have more similarity in feature space which is ideally undesirable and leads to improper learning of the model and results into errors. To reduce such errors, we have proposed a new scheme called easy-pair selection method for KV. Kin and non kin pairs are categorized into easy and hard pairs based on sum of square distance with respect to the feature space of the pairs. Suitable kin-margin (KM) value is chosen to classify the pair as easy or hard. Further, selected easy pairs are used to compute the optimized training weights (W) from the training dataset. The obtained weights will ensure to minimize the distance of kin pairs and maximize the distance of non-kin pairs in feature space. A fixed age group image database (FAG) have also been proposed through this article which contains the corresponding facial images of the parents and their children from the same age group to minimize the issue of age in-variance as well. Extensive experiments have been performed to validate the accuracy of the proposed scheme and is found to outperform the existing relevant schemes for KV.
The Use of Artificial Intelligence in Mathematics: Scientific and Social Aspects

Discusses the use of artificial intelligence (AI) in mathematics. The basic principles of AI operation and its application in mathematical research are described. The advantages of using AI in mathematics are analyzed, such as increasing the efficiency and accuracy of calculations, discovering new patterns and patterns in large amounts of data, and others. However, the potential risks and challenges associated with the use of AI in mathematics are also considered, as well as the need to take into account social and ethical issues.