Reviewerly/exHarmony huggingface dataset:
Research on the Identification and Evaluation of Aerobics Movements Based on Deep Learning

A deep learning approach is used in this study to provide insight into aerobics movement recognition, and the model is used for aerobics movement recognition. The model complexity is significantly reduced, while the multi-scale features of the target at the fine-grained level are extracted, significantly improving the characterization of the target, by embedding lightweight multi-scale convolution modules in 3D convolutional residual networks to increase the local perceptual field range in each layer of the network. Finally, using the channel attention mechanism, the key features are extracted from the multi-scale features. To create a dual-speed frame rate detection model, the fast-slow combination idea is fused into a 3D convolutional network. To obtain spatial semantic information and motion information in the video, the model uses different frame rates, and the two-channel information is fused with features using lateral concatenation. Following the acquisition of all features, the features are fed into a temporal detection network to identify temporal actions and to design a behavior recognition system for the network model to demonstrate the network model's applicability. The average scores of students in the experimental group were significantly higher than those in the control group in seven areas: set accuracy, movement amplitude, movement strength, body coordination, coordination of movement and music, movement expression, and aesthetics; the average scores of movement proficiency and body control in the experimental group were also significantly higher than those in the control group, but the differences were not significant. The differences between the eight indicators in the experimental group were not significant when compared to those in the preexperimental group, indicating that intensive rhythm training for students improves secondary school students' comprehension, proficiency, and presentation of aerobics sets.
A class-contrastive human-interpretable machine learning approach to predict mortality in severe mental illness

Abstract Machine learning (ML), one aspect of artificial intelligence (AI), involves computer algorithms that train themselves. They have been widely applied in the healthcare domain. However, many trained ML algorithms operate as ‘black boxes’, producing a prediction from input data without a clear explanation of their workings. Non-transparent predictions are of limited utility in many clinical domains, where decisions must be justifiable. Here, we apply class-contrastive counterfactual reasoning to ML to demonstrate how specific changes in inputs lead to different predictions of mortality in people with severe mental illness (SMI), a major public health challenge. We produce predictions accompanied by visual and textual explanations as to how the prediction would have differed given specific changes to the input. We apply it to routinely collected data from a mental health secondary care provider in patients with schizophrenia. Using a data structuring framework informed by clinical knowledge, we captured information on physical health, mental health, and social predisposing factors. We then trained an ML algorithm and other statistical learning techniques to predict the risk of death. The ML algorithm predicted mortality with an area under receiver operating characteristic curve (AUROC) of 0.80 (95% confidence intervals [0.78, 0.82]). We used class-contrastive analysis to produce explanations for the model predictions. We outline the scenarios in which class-contrastive analysis is likely to be successful in producing explanations for model predictions. Our aim is not to advocate for a particular model but show an application of the class-contrastive analysis technique to electronic healthcare record data for a disease of public health significance. In patients with schizophrenia, our work suggests that use or prescription of medications like antidepressants was associated with lower risk of death. Abuse of alcohol/drugs and a diagnosis of delirium were associated with higher risk of death. Our ML models highlight the role of co-morbidities in determining mortality in patients with schizophrenia and the need to manage co-morbidities in these patients. We hope that some of these bio-social factors can be targeted therapeutically by either patient-level or service-level interventions. Our approach combines clinical knowledge, health data, and statistical learning, to make predictions interpretable to clinicians using class-contrastive reasoning. This is a step towards interpretable AI in the management of patients with schizophrenia and potentially other diseases.
Spatial-context-aware RNA-sequence prediction from head and neck cancer histopathology images

Molecular profiling of the tumor in addition to the histological tumor analysis can provide robust information for targeted cancer therapies. Often such data are not available for analysis due to processing delays, cost or inaccessibility. In this paper, we proposed a deep learning-based method to predict RNA-sequence expression (RNA-seq) from Hematoxylin and Eosin whole-slide images (H&E WSI) in head and neck cancer patients. Conventional methods utilize a patch-by-patch prediction and aggregation strategy to predict RNA-seq at a whole-slide level. However, these methods lose spatial-contextual relationships between patches that comprise morphology interactions crucial for predicting RNA-seq. We proposed a novel framework that employs a neural image compressor to preserve the spatial relationships between patches and generate a compressed representation of the whole-slide image, and a customized deep-learning regressor to predict RNA-seq from the compressed representation by learning both global and local features. We tested our proposed method on publicly available TCGA-HNSC dataset comprising 43 test patients for 10 oncogenes. Our experiments showed that the proposed method achieves a 4.12% higher mean correlation and predicts 6 out of 10 genes with better correlation than a state-of-the-art baseline method. Furthermore, we provided interpretability using pathway analysis of the best-predicted genes, and activation maps to highlight the regions in an H&E image that are the most salient of the RNA-seq prediction.Clinical relevance—The proposed method has the potential to discover genetic biomarkers directly from the histopathology images which could be used to pre-screen the patients before actual genetic testing thereby saving cost and time.
A Three-Dimensional Anisotropic Diffusion Equation-Based Video Recognition Model for Classroom Concentration Evaluation in English Language Teaching

In this paper, a three-dimensional anisotropic diffusion equation is used to conduct an in-depth study and analysis of students’ concentration in video recognition in English teaching classrooms. A multifeature fusion face live detection method based on diffusion model extracts Diffusion Kernel (DK) features and depth features from diffusion-processed face images, respectively. DK features provide a nonlinear description of the correlation between successive face images and express face image sequences in the temporal dimension; depth features are extracted by a pretrained depth neural network model that can express the complex nonlinear mapping relationships of images and reflect the more abstract implicit information inside face images. To improve the effectiveness of the face image features, the extracted DK features and depth features are fused using a multicore learning method to obtain the best combination and the corresponding weights. The two features complement each other, and the fused features are more discriminative, which provides a strong basis for the live determination of face images. Experiments show that the method has excellent performance and can effectively discriminate the live nature of faces in images and resist forged face attacks. Based on the above face detection and expression recognition algorithms, the classroom concentration analysis system based on expression recognition is designed to achieve real-time acquisition and processing of classroom images, complete student classroom attendance records using face detection and face recognition methods, and analyze students’ concentration from the face integrity and facial expression of students facing the blackboard by combining face detection and expression recognition to visualize and display students’ classroom data for teachers, students, and parents with more data support and help.
Design and Implementation of ImALeG Serious Game: Behavior of Non-playable Characters (NPC)

Non-playable character behavior is necessary to make a faithful simulation situation. In this paper, a computational intelligence method is used to design a behavior models of non-playable character inside a virtual reality serious game which aims to help people learning Amazigh language. The non-playable characters are modeled using the concept of FuSM. Through a survey, we evaluated the emotional impact to identify the player’s feelings toward the virtual environment.
Perbandingan Arsitektur ResNet50 dan ResNet101 dalam Klasifikasi Kanker Serviks pada Citra Pap Smear

Cervical cancer is one of the most deadly types of cancer in women. According to Ferlay et al. (2018) cervical cancer ranks second for the type of cancer that attacks women the most. Data from the Indonesian Ministry of Health, there are at least 15000 cases of cervical cancer every year in Indonesia. This cancer is a type of tumor that develops in the epithelial tissue of the cervix. In addition to HPV vaccination, cervical cancer detection can also be carried out with a Pap smear test and VIA examination supported by medical image tests such as CT scan, microscopic and MRI (Akbar et al. 2021). Pap smear test is a type of test to detect cervical cancer which is quite widely used because the cost of the test is cheaper than the HPV vaccination. This test is carried out by taking samples of uterine cells which are then analyzed for early detection of cervical cancer (BPJS Kesehatan 2020). Through a pap smear can be found the presence of HPV infection and abnormal cells that can turn into cancer cells. The purpose of this research is to apply the ResNet50 and ResNet101 architectures on pap smear images to identify cervical cancer and evaluate the performance of the ResNet50 and ResNet101 architectures in the classification of cervical cancer on pap smear images. In this study, CNN ResNet50 and ResNet101 were used to classify cervical cancer on pap smear images. This study has created two models to predict the grade of cervical cancer on pap smear images. The ResNet50 architecture gets 91% accuracy while the ResNet101 architecture gets 89%. Although the architecture of ResNet101 is more complex than ResNet50, but if viewed from the results of the model evaluation, ResNet101 has a worse performance. This is due to the relatively small training data when trained with a large architecture such as ResNet101, not necessarily resulting in better accuracy.
Improved Model Configuration Strategies for Kannada Handwritten Numeral Recognition

Handwritten numeral recognition has been an important area in the domain of pattern classification. The task becomes even more daunting when working with non-Roman numerals. While convolutional neural networks are the preferred choice for modeling the image data, the conception of techniques to obtain faster convergence and accurate results still poses an enigma to the researchers. In this paper, we present new methods for the initialization and the optimization of the traditional convolutional neural network architecture to obtain better results for Kannada numeral images. Specifically, we propose two different methods- an encoderdecoder setup for unsupervised training and weight initialization, and a particle swarm optimization strategy for choosing the ideal architecture configuration of the CNN. Unsupervised initial training of the architecture helps for a faster convergence owing to more task-suited weights as compared to random initialization while the optimization strategy is helpful to reduce the time required for the manual iterative approach of architecture selection. The proposed setup is trained on varying handwritten Kannada numerals. The proposed approaches are evaluated on two different datasets: a standard Dig-MNIST dataset and a custom-built dataset. Significant improvements across multiple performance metrics are observed in our proposed system over the traditional CNN training setup. The improvement in results makes a strong case for relying on such methods for faster and more accurate training and inference of digit classification, especially when working in the absence of transfer learning.
Face occlusion removal for face recognition using the related face by structural similarity index measure and principal component analysis

Facial occlusions like sunglasses, masks, caps etc. have severe consequences when reconstructing the partially occluded regions of a facial image. This paper proposes a novel hybrid machine learning approach for occlusion removal based on Structural Similarity Index Measure (SSIM) and Principal Component Analysis (PCA), called SSIM_PCA. The proposed system comprises two stages. In the first stage, a Face Similar Matrix (FSM) guided by the Structural Similarity Index Measure is generated to provide the necessary information to recover from the lost regions of the face image. The FSM generates Related Face (RF) images similar to the probe image. In the second stage, these RF images are considered as related information and used as input data to generate eigenspaces using PCA to reconstruct the occluded face region exploiting the relationship between the occluded region and related face images, which contain relevant data to recover from the occluded area. Experimental results with five standard datasets viz. Caspeal-R1, IMFDB, and FEI have proven that the proposed method works well under illumination changes and occlusion of facial images.
Variational Gaussian Mixture Models with robust Dirichlet concentration priors for virtual population generation in hypertrophic cardiomyopathy: a comparison study

Nowadays, there is a growing need for the development of computationally efficient virtual population generators for large-scale in-silico clinical trials. In this work, we utilize the Gaussian Mixture Models (GMM) with variational Bayesian inference (BGMM) using robust estimations of Dirichlet concentration priors for the generation of virtual populations. The estimations were based on an exponential transformation of the number of Gaussian components. The proposed method was compared against state-of-the-art virtual data generators, such as, the Bayesian networks, the supervised tree ensembles (STE), the unsupervised tree ensembles (UTE), and the artificial neural networks (ANN) towards the generation of 20000 virtual patients with hypertrophic cardiomyopathy (HCM). Our results suggest that the proposed BGMM can yield virtual distributions with small inter- and intra-correlation difference (0.013 and 0.012), in lower execution time (4.321 sec) than STE which achieved the second-best performance.
The effects of financial development on economic growth in Africa: does the quality of institutions matter

Purpose-The main objective of this paper is twofold.First, the paper aims to examine the effect of financial development on the economic growth of African countries.Second, the paper also aims to examine if institutional quality moderates the nexus between financial development and growth.Methodology-A panel dataset of 35 African countries over the period from 1985 through 2018 is used and to handle the problem of endogeneity and reverse causality the dynamic panel estimation method, GMM estimation, was employed to estimate the relationship while accounting for other control variables that affect economic growth.The data set was retrieved from World Bank world development indicators, international monetary fund, and International Country Risk Guide (ICRG).Findings-The empirical results of the study indicate that financial development has an ambiguous impact on the economic growth of African countries, but it has a significant positive effect on growth if interacted with the institutional quality index such as government stability, rule of law, and corruption.The interaction term between finance and institutional quality indicator is positive and significant, implying that the positive effect of financial development depends on the level of institutional quality of the country.Conclusion-To sum up, in countries where institutional quality is high, the effect of financial development on growth is higher compared to countries where institutional quality is low.This indicates that improving institutional quality is essential to reap the benefit of financial development in Africa.It is thus vital for African countries to engage in drafting various programs and strategies to improve institutional quality so as to improve growth.
Task Tree Partition and Subtree Allocation for Heterogeneous Multiprocessors

Tree-shaped task graphs become a paradigm to be utilized in distributed platform for various computational domains, such as the electronic structure calculations and the factorization of sparse matrices. However, the scheduling of the tree-shaped task graph has been rarely studied for the more realistic heterogeneous multiprocessor platform (HEMP). This paper proposes an efficient algorithm named Partition-Allocation (PA) for parallel computing on HEMP with limited memory. Algorithm PA consists of two stages: partitioning and allocation. In the partitioning stage, a task tree is split into several subtrees. In the allocation stage, these subtrees are assigned to different processors for execution. Our algorithm PA can reduce makespan by prioritizing subtrees on the critical path, both in the partitioning and in the allocation. Based on randomly generated trees and real-world dataset, experimental results show that the proposed PA is significantly better than the latest work in terms of average makespan. The proposed algorithm can successfully reduce the average makespan by up to 67.01% on real-world dataset, and 52.35% on randomly generated trees.
From Information Search to the Loss of Personality: The Phenomenon of Dataism

Purpose. The research is devoted to the analysis of the urgent problem of the information society: the overload of a person with information and, as a result, the impossibility of adequate formation and development of the personality; as well as the problem of "digitization" of human existence and the formation of a new reality of dataism. Theoretical basis. A lot of modern scientific works are devoted to the analysis of the information society, its problems and features. The information society is a logical continuation of the scientific and technological revolution, which led to the rapid growth of scientific knowledge and the technology development. In the 20th century, technologies have touched the sphere of knowledge and information, as a result of which the formation and development of the information society, or "knowledge society", takes place. Information becomes the main resource in it, and, one way or another, a person’s life is inextricably connected with the information space. With the information society formation, the problem of information search and processing becomes one of the most urgent. It turns out that despite the abundance and availability of information, it is very difficult to determine its relevance. A lot of effort is spent on developing information retrieval algorithms. Another problem is related to the person’s inability to process large amounts of data. This situation begins to influence not only the education process and professional activity, but also the formation of a person’s personality. A person is "lost" in the information space and gradually loses his/her "I". Algorithms for data analysis come to the rescue, but gradually, instead of giving a person material for thought, they begin to make decisions on their own, and therefore, live life instead of a person. With the advent of Big Data processing algorithms, a new ideological paradigm appears – dataism, which predicts the merger of a person with the general data flow. Originality. The authors make assumptions that the dataistic future is "natural". A lot of works, including in the framework of philosophy, are focused on the problem of "dissolving" a person in the information space and finding ways to overcome it. But, in our opinion, this process is a completely logical continuation of human evolution. Conclusions. Existence of a person as a data flow is not a problem and a threat, but a new dimension of his/her being, and, therefore, requires a careful study and formulation of the main principles of this form of existence.
Comparison of NASA-POWER solar radiation data with ground-based measurements in the south of South America

Accurate information of solar radiation from satellites is crucial for many applications, mainly in regions with lack of ground-based measurements. In this sense, comparison with ground-based measurement is necessary to ensure the reliability of the information. In this work, the daily global solar irradiation data from NASA’s Prediction of Worldwide Energy Resources (NASA-POWER; power.larc.nasa.gov) were compared with ground-based measurements in the 8 stations of the Saver-Net solar irradiance network (http://www.savernet-satreps.org/en) installed in the south of South America. A linear regression analysis was performed to analyze the agreement between satellite data and ground-based measurements. The coefficient of determination shows very good correspondence with a mean value of 0.95. The mean absolute error (0.63 kWh/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /d) and the root mean squared error (0.48 kWh/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /d) reflect a low difference.
Monocular 3D Object Detection Based on Uncertainty Prediction of Keypoints

Three-dimensional (3D) object detection is an important task in the field of machine vision, in which the detection of 3D objects using monocular vision is even more challenging. We observe that most of the existing monocular methods focus on the design of the feature extraction framework or embedded geometric constraints, but ignore the possible errors in the intermediate process of the detection pipeline. These errors may be further amplified in the subsequent processes. After exploring the existing detection framework of keypoints, we find that the accuracy of keypoints prediction will seriously affect the solution of 3D object position. Therefore, we propose a novel keypoints uncertainty prediction network (KUP-Net) for monocular 3D object detection. In this work, we design an uncertainty prediction module to characterize the uncertainty that exists in keypoint prediction. Then, the uncertainty is used for joint optimization with object position. In addition, we adopt position-encoding to assist the uncertainty prediction, and use a timing coefficient to optimize the learning process. The experiments on our detector are conducted on the KITTI benchmark. For the two levels of easy and moderate, we achieve accuracy of 17.26 and 11.78 in AP3D, and achieve accuracy of 23.59 and 16.63 in APBEV, which are higher than the latest method KM3D.