Reviewerly/exHarmony huggingface dataset:
Differentially Private Guarantees for Analytics and Machine Learning on Graphs: A Survey of Results

We study the applications of differential privacy (DP) in the context of graph-structured data and discuss the formulations of DP applicable to the publication of graphs and their associated statistics as well as machine learning on graph-based data, including graph neural networks (GNNs). Interpreting DP guarantees in the context of graph-structured data can be challenging, as individual data points are interconnected (often non-linearly or sparsely). This connectivity complicates the computation of individual privacy loss in differentially private learning. The problem is exacerbated by an absence of a single, well-established formulation of DP in graph settings. This issue extends to the domain of GNNs, rendering private machine learning on graph-structured data a challenging task. A lack of prior systematisation work motivated us to study graph-based learning from a privacy perspective. In this work, we systematise different formulations of DP on graphs, discuss challenges and promising applications, including the GNN domain. We compare and separate works into graph analytics tasks and graph learning tasks with GNNs. We conclude our work with a discussion of open questions and potential directions for further research in this area.
Compensation Method for Correcting the Topography Convolution of the 3D AFM Profile Image of a Diffraction Grating

Any 3D AFM image is a convolution of the geometry of the AFM tip and the profile of the scanned sample, especially when the dimensions of the scanned sample are comparable to those of the AFM tip shape. The precise profile of the scanned sample can be extracted from the 3D AFM image if the geometry of the AFM tip is known. Therefore, in order to separate the geometry of the AFM probe tip from the 3D AFM image of a diffraction grating with a rectangular profile and to correct for the topographic convolutions induced by the AFM probe tip, a method is used to quantitatively evaluate the geometry of the AFM probe tip, including the tip radius and the included angle. A model for reconstructing the measured AFM image is proposed to correct topography convolutions caused by the AFM tip shape when scanning a diffraction grating with rectangular profiles. A series of experiments were performed to verify the effectiveness of the proposed AFM tip geometry evaluation method, and comparison experiments were conducted to demonstrate the feasibility and reliability of the proposed reconstruction model.
HUMAN ACTIVITY RECOGNITION IMPROVEMENT ON SMARTPHONE ACCELEROMETERS USING CIMA

Human activity recognition (HAR) is a research field that focuses on detecting user activities and has wide applications. However, the problems that need to be solved are real-time constraints and imbalanced datasets due to different activity frequencies. Our research aims to apply classification integrated moving averages (CIMA) to HAR by evaluating its performance regarding real-time constraints and imbalanced datasets. We achieved the smartphone accelerometer dataset from Kaggle, which consists of several activities: walking, jogging, climbing, and descending stairs. We develop a general CIMA windowing algorithm with hyperparameters J and W. We benchmark CIMA with two state-of-the-art HAR methods: distributed online activity recognition system (DOLARS) and convolutional neural network (CNN). We conducted some imbalance and model size analysis. The test results show that, with J = 10 and W = 240, CIMA performs better than DOLARS and CIMA with recall, precision, and f1-score of 0.996, 0.993, and 0.994. We also prove that CIMA, assisted by quantization, has the smallest model size compared to the CNN and DOLARS model sizes. Finally, we demonstrate that CIMA performs well for imbalanced datasets, where CIMA’s recall on upstairs and downstairs activities is better than DOLARS and CNN, with values of 1.00 and 0.98, respectively. Key Words: classification integrated moving average, human activity recognition, smartphone, accelerometer, imbalanced dataset
Breast Cancer Classification Based on KNNI Imputation of Missing Data

Some disease datasets have different degrees of missing, which will lead to the problem of low classification accuracy. To improve the effectiveness of breast cancer disease detection and diagnosis, a classification prediction method combining KNNI and XGBoost was proposed and applied to the classification and analysis of breast cancer data. First, the KNNI method is used to impute the missing data in the breast cancer patient dataset; Then, the original dataset is equalized by the SMOTE oversampling method; Finally, XGBoost is used to extract features that are strongly related to breast cancer malignancies as the input of the model, optimize the XGBoost model by grid search algorithm, find the optimal model parameters, and classify and diagnose the breast cancer dataset. The experimental results show that KNNI can effectively recover the lost data, improve the data quality, and improve the subsequent classification accuracy. Applying imputation methods to flexibly apply missing data to machine learning methods holds great promise.
Machine Learning System for the Effective Diagnosis and Survival Prediction of Breast Cancer Patients

Breast cancer is one of the most significant global health challenges. Effective diagnosis and prognosis prediction are crucial for improving patient outcomes in the case of this disease. As machine learning (ML) has significantly improved prediction models in many disciplines, the goal of this study is to develop a ML system for medical specialists that can accurately predict tumor diagnosis and patient survival for breast cancer patients. For the training of diagnosis and survival prediction, five algorithmic models—decision tree (DT), random forest (RF), naive bayes (NB), support vector machines (SVMs), and gradient boosting—were trained with 569 records from the Breast Cancer Wisconsin dataset and 1,980 records from the Breast Cancer Gene Expression Profiles dataset. The results showed that the NB model exhibited better performance for tumor diagnosis, achieving an accuracy of 95.0%, while RF presented the best results for patient survival, with an accuracy of 76.0%. A survey of medical experts’ experience with the resulting system showed high scores in reliability, performance, satisfaction, usability, and efficiency, confirming that ML systems have the potential to improve breast cancer patient outcomes.
Retraction: Increasing interpersonal trust through divergent thinking

Citation: Frontiers Editorial Office (2024) Retraction: Increasing interpersonal trust through divergent thinking. Front. Psychol. 15:1384407. doi: 10.3389/fpsyg.2024.1384407
Sentiment Analysis using Support Vector Machine and Random Forest

Sentiment analysis, is commonly known as opinion mining, is a vital field in natural language processing (NLP) that claims to find out the sentiment or emotion expressed in a given text. This research paper demonstrates an exhaustive survey of sentiment analysis, focusing on the application of machine learning techniques. Comprehensive parametric literature review has been completed to determine the sentiment analysis using SVM and Random Forest. Additionally, the paper covers preprocessing techniques, feature extraction, model training, evaluation, and challenges encountered in sentiment analysis. The findings of this research contribute to a deeper understanding of sentiment analysis and provide insights into the effectiveness of machine learning approaches in this domain. Based on the results obtained, two machine learning algorithms named as Random Forest and SVM were evaluated based on their accuracy in a classification task. The Random Forest algorithm achieved an accuracy of 0.78564, while SVM outperformed it slightly with an accuracy of 0.80394. Both Random Forest and SVM have demonstrated their strengths in achieving respectable accuracies in the given classification task. These results suggest that SVM, with its slightly higher accuracy of 0.80394, may be a more suitable choice when accuracy is the primary concern. However, the basic configuration need and characteristics of the problem at hand should be considered when choosing the better algorithm with better results.
Adaptive Gaussian Wiener Filter for CT-Scan Images with Gaussian Noise Variance

Medical imaging plays an important role in modern healthcare, with Computed Tomography (CT) being essential for high-resolution cross-sectional imaging. However, Gaussian noise often occurs within the CT scan images and makes it difficult for image interpretation and reduces the diagnostic accuracy, creating a significant obstacle to fully utilizing CT scanning technology. Existing denoising techniques have a hard time balance between noise reduction and preserving the important image details, failing to enable the optimal diagnostic precision. This study introduces Adaptive Gaussian Wiener Filter (AGWF), a novel filter aims to denoise CT scan images that have been corrupted with various Gaussian noise variance without compromising the image details. The AGWF combines the Gaussian filter for initial noise reduction, followed by the implementation of Wiener filter, which can adaptively estimate noise variance and signal power in localized regions. This approach not only outperforms other existing techniques but also showcases a remarkable balance between noise reduction and image detail preservation. The experiment evaluates 300 images from the dataset and each image is corrupted with Gaussian noise variance to ensure a comprehensive evaluation of the AGWF’s performance. The evaluation indicated that AGWF can improve the Signal-to-Noise Ratio (SNR) value and reduce the Root Mean Square Error (RMSE) and Mean Square Error (MSE) value, showing a qualitative improvement in CT scan imagery. The proposed method holds promising potential for advancing medical imaging technology with the implementation of deep learning.
Changes in patterns of peer relationships in primary education classroom networks through cooperative learning.

We studied the impact of cooperative learning on positive peer relationships, that is, liking to work together, in classroom networks. Cooperative learning was implemented as part of the "Success for All" program. Longitudinal social network analysis was used to investigate the development of structures and patterns of relationships in 16 intervention and 16 control classrooms, including a total of 791 students. Results showed significantly less reciprocation and transitivity in the dynamics of the intervention networks, while the number of nominations in the intervention classes was at least not smaller than in the control classes, indicating less tendency toward the formation of small clusters or cliques. We did not find that peer relationships became more diverse with regard to gender, socioeconomic status, or academic performance through the implementation of cooperative learning. In intervention classrooms, children tended to nominate their teammates with whom they work together in cooperative learning activities; however, also in control classrooms, children tended to nominate children with whom they are seated in the classroom. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
Research on the dehazing effect of orthogonal polarization method based on atmospheric scattering model and considering extinction ratio parameter

The hazy weather is currently one of the most common atmospheric environments. It is an urgent problem to realize clear imaging in hazy environment. Orthogonal polarization dehazing is one of the classical dehazing techniques attracting a widespread attention from research workers. However, while using this method, the extinction ratio (ER) parameter of the polarizer was neglected leading to poor dehazing. Therefore, we have introduced the ER of polarizers into the orthogonal polarization dehazing technique in this paper. In the process of sky region division, this study proposes to use HSV, RGB and Lab colour channels to create a comprehensive mask to select the sky region. And the morphological corrosion operation is utilized to increase the region connectivity for better sky region division. Based on subjective visualization, it can be found that the dehazing effect is significantly enhanced with the introduction of ER. The dehazing effect was evaluated based on four parameters of contrast (C0), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM) and information entropy (En). It is further demonstrated the necessity of introducing ER. The experimental results show that the ER can make the dehazing effect of the orthogonal polarization dehazing technique improved, which is valuable for the engineering application of image processing in hazy environment. The experimental results proved to be encouraging.
Underwater Image Enhancement Based on Multiscale Fusion

To improve the visibility and clarity of images in underwater environment, this paper proposes an underwater image enhancement method based on multi-scale fusion. Firstly, the white balance method is used to correct the color of the underwater image, and the original color of the underwater elements is restored as much as possible. Secondly, the dark channel prior algorithm is used to solve the underwater image blur problem. Then, the CLAHE algorithm is used to enhance the contrast of the image. Finally, using weight allocation or fusion rules, multi-scale information and multi-stage fusion of the image are used to generate the final enhanced underwater image. Quantitative image quality evaluation indexes PSNR, SSIM and UIQE are used to evaluate underwater image quality. The results show that the proposed method can effectively solve the problem of underwater image color deviation, make the color of the image more accurate and natural, improve the contrast and brightness of the underwater image, and have better visual effect and richer detail information.
Strong quantum nonlocality with genuine entanglement in an <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math> -qutrit system

In this paper, we construct orthogonal genuinely multipartite entangled bases in ${({\mathbb{C}}^{3})}^{\ensuremath{\bigotimes}N}$ for $N\ensuremath{\ge}3$, where every state is a one-uniform state. By modifying this construction, we successfully obtain strongly nonlocal orthogonal genuinely entangled sets and strongly nonlocal orthogonal genuinely entangled bases, which provide an answer to the problem raised by Halder et al. [Phys. Rev. Lett. 122, 040403 (2019)]. The strongly nonlocal orthogonal genuinely entangled set we constructed in ${({\mathbb{C}}^{3})}^{\ensuremath{\bigotimes}N}$ contains much fewer quantum states than all known ones. Meanwhile, our results also answer the question given by Wang et al. [Phys. Rev. A 104, 012424 (2021)].
Variations of nursing care in patients with COVID-19 in the northern region of Brazil

Although nurses are directly involved in the treatment of patients with COVID-19, their possibilities and limits in dealing with the disease are not well defined. This is at least partly due to the fact that nursing procedures are still being modified. This study aimed to analyze the variations in nursing care for patients with COVID-19 in the public health network in a municipality in the northern region of Brazil. This is a retrospective observational study in which data from the medical records of 499 patients hospitalized in the public health network of the city of Marabá, Brazil, between March and October 2020 were cataloged. The sample was divided into two groups: the first group consisted of the medical records of patients hospitalized between March and June (1st semester, n = 327) and the second group consisted of the medical records of patients hospitalized between July and October 2020 (2nd semester, n = 172). Regarding the 1st semester of 2020, it was observed that 16 nursing care (59.3%) were statistically lower than those of the 2nd semester of 2020 and eight nursing care (29.6%) were statistically higher than those of the 2nd semester of 2020. Furthermore, three nursing care (11.1%) did not show significant differences between the 2 semesters. Overall, the percentage of nursing care increased from the 1st to the 2nd semester (52.8 ± 17.6% vs. 62.7 ± 5.1%, P &lt; 0.001); the greatest changes occurred in relation to the musculoskeletal system (37.3 ± 22.9% vs. 53.3 ± 7.7%, P &lt; 0.001). In conclusion, in the initial months of the pandemic, nursing care was less intense, which may have been a consequence of the lack of knowledge about COVID-19. This may have had a negative impact on epidemiological data from the beginning of the pandemic.
Technologies applied to the curriculum for the recomposition of learning in the post-pandemic period

In light of the need for curricular adaptations as a strategy to mitigate the impacts caused by the COVID-19 pandemic, this theoretical study aimed to investigate how digital technologies, when applied to the educational curriculum, can contribute to the reconstitution of learning in the post-pandemic period. To support the theories explored in this article, a Systematic Literature Review (SLR) was chosen, using the Google Scholar database as the main source of consultation. The reconstitution of learning is understood as a set of actions that involve prioritizing competencies and skills of the National Common Curricular Base in line with the new curricula that align with them. Technologies emerge as valuable tools for educators, facilitating the implementation of innovative teaching methodologies and corroborating with learning processes and the necessary curricular prioritization. The insertion of education technologies in teaching methodologies assists the learning process by promoting greater interactivity and involvement of students. In addition, it makes learning more dynamic, playful, meaningful, and enriching. Therefore, this study highlights the importance of integrating education technologies into the school curriculum and teaching methodologies, aiming not only to improve the quality of education but also to prepare students for a world increasingly immersed and interconnected in the digital realm.
Application Research of Fish-Plant Symbiosis Technology in a Urban Lake: A Case Study of the Environment Remediation Project with Machine Learning Application

This research examines the Jiefang Park Water Environment Remediation Project, a pioneering initiative in urban lake rehabilitation. The project is anchored in a fish-plant symbiosis system, which has significantly improved water quality, upgrading it from Category V to Category III, and in certain zones, achieving the commendable Category II. This enhancement is quantitatively marked by a notable reduction in nitrogen and phosphorus levels, and a marked increase in water transparency, with submerged plants covering over 70% of the lakebed.A key innovation is the utilization of a machine learning model to predict chlorophyll-a concentration, a vital water quality metric. The model’s accuracy is underscored by its R² values, ranging from 0.23 to 0.99, and RMSE values between 6.921 and 0.237, with the best performance at point 1. Additionally, comprehensive measurement data highlight the project’s effectiveness. Water transparency significantly improved from baseline levels, as evidenced by increased dissolved oxygen levels at various sampling sites, indicative of restored ecological equilibrium in the lake. This study represents an exemplary fusion of ecological restoration and advanced data analytics, signifying a substantial advancement in urban lake restoration. It underscores the potential of combining synergistic ecological approaches with technological innovation to further sustainable urban environmental management.
Autonomous navigation and obstacle avoidance in smart robotic wheelchairs

This review research paper provides a comprehensive analysis of the advancements, challenges, and methodologies in autonomous navigation and obstacle avoidance for smart robotic wheelchairs. The integration of robotics and assistive technology has revolutionized mobility solutions for individuals with impairments, enabling them to navigate complex environments independently. The paper examines the various sensor modalities, machine learning algorithms, and computer vision techniques employed for environment perception and obstacle recognition. It discusses path planning algorithms, motion control strategies, and decision-making processes for autonomous navigation. The review also addresses limitations, such as localization accuracy and dynamic environment modelling, while highlighting recent research advancements and suggesting future directions. Overall, this paper serves as a valuable resource for researchers and practitioners in the field of smart robotic wheelchairs, aiming to enhance mobility and quality of life for individuals with mobility impairments.
Use of metacognitive strategies in the learning of mixture separation processes in elementary school (final years)

In the learning of concepts related to the processes of separating mixtures in elementary school (final years) some difficulties may arise related to the lack of use of some metacognitive strategies that help in the approximation of a meaningful learning. In this context, it becomes relevant to elaborate didactic intervention proposals that show students metacognitive strategies that facilitate learning and provide autonomy. This article aims to raise an environment for reflection and dialogue in the face of an intervention proposal that establishes, in a structured way, a sequence of metacognitive questions linked to the learning of the aforementioned concepts. The results show some potential that can be generated with the implementation of the suggested didactic intervention, except for the necessary adaptations to the school context in which the proposal is intended to be applied.
Substation anomaly detection method based on attention mechanism and feature fusion

To ensure the safe, stable, and reliable operation of substations and improve the intelligent anomaly monitoring ability, this paper proposes an algorithm for detecting abnormal behaviors and equipment defects of substations based on an attention mechanism and feature fusion network. This algorithm designs the feature fusion structure of BiMFPN. It constructs multiple green channels of shallow input to deep output feature fusion, thus fusing a more extensive range of practical features without increasing the cost. At the same time, lightweight CBAM is added to the fusion network to effectively combine the extracted spatial location features with the rich semantic information in the image. In addition, the algorithm also adds a new detection head, which effectively improves the detection performance of multi-scale targets and is more sensitive to small targets. After experimental tests, the mAP and F1 scores of the algorithm are 69.3%and 71.7%, respectively, which are 9% and 8% higher than that of YOLOv5m. The experimental results show that this method can effectively detect abnormal behavior and faulty equipment around the substation and improve the intelligent detection level of the substation.