Reviewerly/exHarmony huggingface dataset:
An Empirical Study on the Driving Factors of Financial Management Behavior of the Middle-Aged and Elderly Based on Logistic Model

With the rapid development of economy and society, people's demand and participation in investment and financial management are also gradually increasing. With the advent of an aging society, the participation of the middle-aged and elderly people in investment and financial management behavior is also gradually increasing, but so far, there is still a large space for development. This paper takes the middle-aged and elderly people as the research object, analyzes the basic situation of the middle-aged and elderly people's investment and financial decision-making through the literature analysis method, collects the specific data of the factors affecting the investment and financial decision-making through the questionnaire survey, and uses the logistic model and other methods to analyze the data. Finally, the regression analysis results show that age, financial knowledge, herding, risk preference and future expectation will have a significant impact on the investment and financial decision-making of the elderly. Through this research, this paper hopes to provide relevant financial institutions with opinions and references related to products, help the middle-aged and elderly people avoid the risk of financial fraud, and make a certain contribution to the development of financial market and the improvement of the participation of the middle-aged and elderly people in investment and financial management.
Leveraging deep survival models to predict quality of care risk in diverse hospital readmissions

Hospital readmissions rate is reportedly high and has caused huge financial burden on health care systems in many countries. It is viewed as an important indicator of health care providers' quality of care. We examine the use of machine learning-based survival analysis to assess quality of care risk in hospital readmissions. This study applies various survival models to explore the risk of hospital readmissions given patient demographics and their respective hospital discharges extracted from a health care claims dataset. We explore advanced feature representation techniques such as BioBERT and Node2Vec to encode high-dimensional diagnosis code features. To our knowledge, this study is the first to apply deep-learning based survival-analysis models for predicting hospital readmission risk agnostic of specific medical conditions and a fixed window for readmission. We found that modeling the time from discharge date to readmission date as a Weibull distribution as in the SparseDeepWeiSurv model yields the best discriminative power and calibration. In addition, embedding representations of the diagnosis codes do not contribute to improvement in model performance. We find dependency of each model's performance on the time point at which it is evaluated. This time dependency of the models' performance on the health care claims data may necessitate a different choice of model in quality of care issue detection at different points in time. We show the effectiveness of deep-learning based survival-analysis models in estimating the quality of care risk in hospital readmissions.
[Research on mild cognitive impairment diagnosis based on Bayesian optimized long-short-term neural network model].

The recurrent neural network architecture improves the processing ability of time-series data. However, issues such as exploding gradients and poor feature extraction limit its application in the automatic diagnosis of mild cognitive impairment (MCI). This paper proposed a research approach for building an MCI diagnostic model using a Bayesian-optimized bidirectional long short-term memory network (BO-BiLSTM) to address this problem. The diagnostic model was based on a Bayesian algorithm and combined prior distribution and posterior probability results to optimize the BO-BiLSTM network hyperparameters. It also used multiple feature quantities that fully reflected the cognitive state of the MCI brain, such as power spectral density, fuzzy entropy, and multifractal spectrum, as the input of the diagnostic model to achieve automatic MCI diagnosis. The results showed that the feature-fused Bayesian-optimized BiLSTM network model achieved an MCI diagnostic accuracy of 98.64% and effectively completed the diagnostic assessment of MCI. In conclusion, based on this optimization, the long short-term neural network model has achieved automatic diagnostic assessment of MCI, providing a new diagnostic model for intelligent diagnosis of MCI.循环神经网络结构极大地优化了时间序列数据的处理能力，但是其网络梯度爆炸以及特征提取能力较差等问题，影响了它在轻度认知障碍（MCI）自动诊断中的应用。针对这一问题，本文提出贝叶斯优化双向长短时神经网络（BO-BiLSTM）构建MCI诊断模型的研究思路。诊断模型基于贝叶斯算法，结合先验分布与后验概率结果共同作用寻优BO-BiLSTM网络超参数，并采用功率谱密度、模糊熵以及多重分形谱等能够充分反映MCI脑认知状态的多角度特征量作为诊断模型的输入，实现MCI自动诊断。结果表明：基于特征融合的贝叶斯优化BiLSTM网络模型，MCI诊断正确率可达到98.64%，能够有效地完成MCI的诊断评估。综上，基于此优化的长短时神经网络模型，实现了MCI的自动诊断评估，为MCI智能诊断提供了一种新的模型。.
Automated detection of causal relationships among diseases and imaging findings in textual radiology reports

Abstract Objective Textual radiology reports contain a wealth of information that may help understand associations among diseases and imaging observations. This study evaluated the ability to detect causal associations among diseases and imaging findings from their co-occurrence in radiology reports. Materials and Methods This IRB-approved and HIPAA-compliant study analyzed 1 702 462 consecutive reports of 1 396 293 patients; patient consent was waived. Reports were analyzed for positive mention of 16 839 entities (disorders and imaging findings) of the Radiology Gamuts Ontology (RGO). Entities that occurred in fewer than 25 patients were excluded. A Bayesian network structure-learning algorithm was applied at P &amp;lt; 0.05 threshold: edges were evaluated as possible causal relationships. RGO and/or physician consensus served as ground truth. Results 2742 of 16 839 RGO entities were included, 53 849 patients (3.9%) had at least one included entity. The algorithm identified 725 pairs of entities as causally related; 634 were confirmed by reference to RGO or physician review (87% precision). As shown by its positive likelihood ratio, the algorithm increased detection of causally associated entities 6876-fold. Discussion Causal relationships among diseases and imaging findings can be detected with high precision from textual radiology reports. Conclusion This approach finds causal relationships among diseases and imaging findings with high precision from textual radiology reports, despite the fact that causally related entities represent only 0.039% of all pairs of entities. Applying this approach to larger report text corpora may help detect unspecified or heretofore unrecognized associations.
[A multimodal medical image contrastive learning algorithm with domain adaptive denormalization].

Recently, deep learning has achieved impressive results in medical image tasks. However, this method usually requires large-scale annotated data, and medical images are expensive to annotate, so it is a challenge to learn efficiently from the limited annotated data. Currently, the two commonly used methods are transfer learning and self-supervised learning. However, these two methods have been little studied in multimodal medical images, so this study proposes a contrastive learning method for multimodal medical images. The method takes images of different modalities of the same patient as positive samples, which effectively increases the number of positive samples in the training process and helps the model to fully learn the similarities and differences of lesions on images of different modalities, thus improving the model's understanding of medical images and diagnostic accuracy. The commonly used data augmentation methods are not suitable for multimodal images, so this paper proposes a domain adaptive denormalization method to transform the source domain images with the help of statistical information of the target domain. In this study, the method is validated with two different multimodal medical image classification tasks: in the microvascular infiltration recognition task, the method achieves an accuracy of (74.79 ± 0.74)% and an F1 score of (78.37 ± 1.94)%, which are improved as compared with other conventional learning methods; for the brain tumor pathology grading task, the method also achieves significant improvements. The results show that the method achieves good results on multimodal medical images and can provide a reference solution for pre-training multimodal medical images.最近，深度学习在医学图像任务中取得了令人瞩目的成果。然而，这种方法通常需要大规模的标注数据，而医学图像的标注成本较高，因此如何从有限的标注数据中进行高效学习是一个难题。目前，常用的两种方法是迁移学习和自监督学习，然而这两种方法在多模态医学图像中的研究却很少，因此本研究提出了一种多模态医学图像对比学习方法。该方法将同一患者不同模态的图像作为正样本，有效增加训练过程中的正样本数量，有助于模型充分学习病灶在不同模态图像上的相似性和差异性，从而提高模型对医学图像的理解能力和诊断准确率。常用的数据增强方法并不适合多模态图像，因此本文提出了一种域自适应反标准化方法，借助目标域的统计信息对源域图像进行转换。本研究以两个不同的多模态医学图像分类任务对本文方法展开验证：在微血管浸润识别任务中，本文方法获得了（74.79 ± 0.74）%的准确率和（78.37 ± 1.94）%的F1分数，相比其它较为熟知的学习方法有所提升；对于脑肿瘤病理分级任务，本文方法也取得了明显的改进。结果表明，本文方法在多模态医学图像数据上取得了良好的结果，可为多模态医学图像的预训练提供一种参考方案。.
Tuning Privacy-Utility Tradeoff in Genomic Studies Using Selective SNP Hiding.

Researchers need a rich trove of genomic datasets that they can leverage to gain a better understanding of the genetic basis of the human genome and identify associations between phenol-types and specific parts of DNA. However, sharing genomic datasets that include sensitive genetic or medical information of individuals can lead to serious privacy-related consequences if data lands in the wrong hands. Restricting access to genomic datasets is one solution, but this greatly reduces their usefulness for research purposes. To allow sharing of genomic datasets while addressing these privacy concerns, several studies propose privacy-preserving mechanisms for data sharing. Differential privacy is one of such mechanisms that formalize rigorous mathematical foundations to provide privacy guarantees while sharing aggregated statistical information about a dataset. Nevertheless, it has been shown that the original privacy guarantees of DP-based solutions degrade when there are dependent tuples in the dataset, which is a common scenario for genomic datasets (due to the existence of family members). In this work, we introduce a new mechanism to mitigate the vulnerabilities of the inference attacks on differentially private query results from genomic datasets including dependent tuples. We propose a utility-maximizing and privacy-preserving approach for sharing statistics by hiding selective SNPs of the family members as they participate in a genomic dataset. By evaluating our mechanism on a real-world genomic dataset, we empirically demonstrate that our proposed mechanism can achieve up to 40% better privacy than state-of-the-art DP-based solutions, while near-optimally minimizing utility loss.
Analysis of AI using Education Competency according to AI Value Perception and AI Self-efficacy Cluster Types of Students in Graduate School of Education

In accordance with the 2022 curriculum revision, artificial intelligence (AI) basic knowledge was included in the school curriculum, and AI education began to be introduced in the school field.Accordingly, as the role of graduate schools of education in fostering pre-service teachers and teachers is emphasized, it is time for AI-related basic research.The purpose of this study was to identify the AI value perception and AI self-efficacy cluster types of education graduate students, and to analyze the difference in AI-based education competency according to these cluster types.To this end, the results were analyzed based on the data of 155 students who faithfully responded to the questionnaire among the students of A graduate school of education located in the metropolitan area.For data analysis, cluster analysis, one-way analysis of variance (ANOVA), and multivariate analysis of variance (MANOVA) were conducted with AI value perception and AI self-efficacy cluster type as independent variables, and AI using education competency and its sub-areas as dependent variables.As a result of the study, AI value perception and AI self-efficacy cluster types showed four clusters.According to these clusters, there were differences between groups in both the AI-based education competency and its sub-areas.In particular, the results commonly found in the difference showed that group 1 (AI value perception and AI self-efficacy high) had higher score of AI using education competency and its sub-areas than group 4 (AI value percpetion and AI self-efficacy low).Based on this, the direction and implications for improving future AI using educational competencies in graduate schools of education were proposed.
A Meta-Analysis on the Effects of The Early Childhood Art Education

Objectives The purpose of this study is to present the progress of efficient art education programs for the development of art skills in infants using meta-analysis to the field of early childhood education and to present comprehensive implications and directions for art education.&#x0D; Methods From 2000 to March 2021, 69 academic journals and 168 degree papers related to early childhood art education were published in Korea. Comprehensive Meta Analysis (CMA) was used for data analysis. From a total of 237 papers, 250 sample-level effect sizes and 364 variable-level effect sizes were extracted. As for the analysis model of the effect size, a wireless model was selected, and the analysis level was analyzed at the sample level and the variable level.&#x0D; Results As a result of the study, the overall average effect size was 1.207, showing a large effect size, and the overall average effect size of the result variable showed the highest art attitude, and the lowest expression ability. The average effect size of the outcome variable according to the integrated variable was the highest in the art area and the lowest in the aesthetic response. In the integration of other areas, the aesthetic response was the highest and the activity process was the lowest. In the case of the average effect size for each control variable, the publication year was significant for each thesis characteristic, but the publication type was not significant. The study target area and infant institutions were not significant, and the type of kindergarten was significant. There was no significant difference between age and age composition. The number of treatments and time per session were significant, and the treatment period was not significant. The activity content of the content variable and the integrated variable were not significant, and the integrated area was significant.&#x0D; Conclusions This study is meaningful in that it analyzed the effect of the early childhood art education program on children's art-related abilities and provided comprehensive implications for art education.
Robust Seeded Image Segmentation Using Adaptive Label Propagation and Deep Learning-Based Contour Orientation

Deep Learning has become a popular tool for addressing complex tasks in many computer vision applications. Label diffusion methods have also been a very effective technique for getting accurate segmentations of real-world images, as they combine user autonomy, versatility and accurateness through a user-friendly interface. In this paper, we propose a seeded segmentation framework for partitioning real-world images by combining deep contour learning and graph-based label propagation models. More precisely, our approach takes a CNN-type contour detection network to learn graph edge weights, which are used as input to solve a coupled energy minimization problem that diffuses the user-selected annotations to the desired targets. To accurately extract deep features from image contours while generating diffusion maps, we train a deep learning architecture that integrates a hierarchical neural network, a graph-based label propagation model and a loss function, allowing the coupled training mechanism to refine the results until convergence. We attest to the effectiveness and accuracy of the proposed approach by conducting both quantitative and qualitative assessments with existing seeded image segmentation methods.
Credit Card Fraud Detection Using Logistic Regression and Synthetic Minority Oversampling Technique (SMOTE) Approach.

Financial fraud is a serious threat that is expanding effects on the financial sector. The use of credit cards is growing as digitization and internet transactions advance daily. The most common issues in today's culture are credit card scams. This kind of fraud typically happens when someone uses someone else's credit card details. Credit card fraud detection uses transaction data attributes to identify credit card fraud, which can save significant financial losses and affluence the burden on the police. The detection of credit card fraud has three difficulties: uneven data, an abundance of unseen variables, and the selection of an appropriate threshold to improve the models' reliability. This study employs a modified Logistic Regression (LR) model to detect credit card fraud in order to get over the preceding difficulties. The dataset sampling strategy, variable choice, and detection methods employed all have a significant impact on the effectiveness of fraud detection in credit card transactions. The effectiveness of naive bayes, k-nearest neighbour, and logistic regression on highly skewed credit card fraud data is examined in this research. The accuracy of the logistic regression technique will be closer to 0.98%; with this accuracy, frauds may be easily detected. The fact that LR receives the highest classifier score illustrates how well LR predicts credit card theft.
Application of Information Technology in Research &amp; Development of AIoT Energy Curriculum

The purpose of promoting green energy in various countries is to replace traditional hydroelectric or thermal power generation, especially nuclear power, which is a safety concern.However, in exploring the application of green energy, special attention should be paid to not only saving energy, but also inspiring students' recognition of green energy with application of information technology.In addition, the curriculum is designed to provide students with cognitive, technical, and emotional exploration and practical work.This is in line with the spirit of the new curriculum: self-initiative, interaction and mutual benefit.The scale of power generation and energy saving statistics of the solar panels installed in the school are 55,000 kWh of electricity generated per year.The team received a grant from the Sustainable Energy Program (2022) to implement a new syllabus that combines programming and circuit board implementation, and includes modules for the Internet of Things.The faculty community developed a lesson plan to develop an IoT intelligent energy saving c curriculum, which allows students to program power saving switches and develop cross-domain key capabilities to demonstrate smart control via the Internet of Things and APP.
Ensemble Learning Network for Handwritten Digit Recognition Based on Fusion Optimized CNN

&lt;p&gt;Handwritten digit recognition is an active research field. These recognition systems are faced with many challenges, including accuracy, speed and automatic extraction of complex handwriting features. In this paper, a Stacking ensemble learning model based on fusion optimized CNN is proposed, which can be effectively used for handwritten digit recognition. To better extract the features of complex handwritten digital images and maximize the reliability of the model, the Bagging strategy combined with six CNNs is used for feature extraction for the first time, and SVM is used for classification. This not only improves the accuracy and stability of the model, but also effectively avoids over-fitting. In addition, a fusion optimization algorithm based on Adam and SGD is proposed to solve the problem that CNN falls into local optimum due to a large number of iterations. During the process of training, ASCNN can not only speed up the convergence rate in the early stage, but also reduce the oscillation phenomenon in the late stage. Extensive experimental results on the well-known MNIST and USPS handwriting image datasets demonstrate the effectiveness of the proposed model.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Camera Tripod Removal Model in Panoramic Images Based on Generative Adversarial Networks

&lt;p&gt;There are often residual images of the camera tripod in panoramic images, which may reduce the image quality and deteriorate the post-processing speed. To address this problem, a camera tripod removal network (TRNet) based on generative adversarial network&amp;nbsp;is proposed. As an end-to-end model, the generator is designed to include recognition and reconstruction branches, which reduce the number of parameters and improve the training efficiency by sharing the encoder&amp;nbsp;and correspond to scaffold recognition and texture reconstruction respectively. The recognition branch based on the U-Net structure can effectively identify the tripod area, while the reconstruction branch can brilliantly reconstruct the texture details through an intermediate layer formed by stacking dilated convolution residual blocks. Furthermore, spectral normalized Markov discriminator and multiple combined loss function&amp;nbsp;are adopted to promote global texture consistency and thus result in a better texture filling effect. Finally, a data set of 400 panoramic images is constructed and experimental results on this data set demonstrate the better repair ability&amp;nbsp;of TRNet against other state-of-the-art methods.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
The Value and Path of Integrating Red Family Tradition into High School Campus Culture

The construction of campus cultural atmosphere with profound traditional culture is inseparable from the exploration of high-quality cultural resources. Red family tradition is an important part of Chinese excellent traditional culture, and it is the "heirloom" that will never fade in our Party of China, which contains the feelings of the family and the country with the world in mind; The distinctive character of self-denial and public service; The incorruptible style of diligence and frugality; Diligent and progressive academic spirit; Through the second teaching class, the online education platform, and the interaction and cooperation between family and school, the integration of red family traditions into campus culture can enhance the appeal of campus culture, enhance the inheritance of excellent traditional culture, and innovate the content of campus culture, so as to better inherit the red family traditions.
A Statistical Approach to Broken Stick Problems

Let a stick be broken at random at n – 1 points to form n pieces. We consider three problems on forming k-gons with k out of these n pieces, 3≤ k ≤ n, and show how a statistical approach, through a linear transformation of variables, yields simple solutions that also allow fast computation.