Reviewerly/exHarmony huggingface dataset:
Analysis and CFD Modeling of Thermal Collectors with a Tracker System

Thermal solar systems are currently one of the most suitable ways to convert solar radiation into usable energy. To ensure maximum energy gain, it is necessary to correctly adjust the direction of the collectors in the southern direction. The deviation from the south is energy acceptable, but there are losses that reduce the overall efficiency of the system. A suitable tool for increasing the efficiency of solar thermal collectors is the usage of tracker systems which track the position of the Sun along the ecliptic orbit. The system is directed south towards the equator. It is important to know the effectiveness of the system in terms of assessment. For the monitored period of the equinox, the increase of the solar thermal gain of the system with rotation of the collectors represents 16.23%. One of the methods is to use simulation tools to simulate different conditions of solar energy flow. A model in the Ansys software was developed for solar thermal flat collectors. The model is based on setting the material parameters of the collector construction, the properties of the absorber in terms of absorption as well as the materials and its geometry, the properties of the heat transfer medium, and the setting of the volume flows. At the same time, it is possible to define the change in the energy flow of the incident radiation. The model is a useful tool for evaluating collectors used by different tracking systems. The model was experimentally verified on a tracker system and compared with experimental data with good agreement. For sunny days, the model shows an accuracy rate of 98.7% in the condition without rotation, and 99.1% in the condition with rotation of the collector. After including the 5% degree of uncertainty for the condition of the rotated collectors, the accuracy of the model is 94.2% and for the condition without rotation 93.8%.
Training of an Extreme Learning Machine Autoencoder Based on an Iterative Shrinkage-Thresholding Optimization Algorithm

Orthogonal transformations, proper decomposition, and the Moore–Penrose inverse are traditional methods of obtaining the output layer weights for an extreme learning machine autoencoder. However, an increase in the number of hidden neurons causes higher convergence times and computational complexity, whereas the generalization capability is low when the number of neurons is small. One way to address this issue is to use the fast iterative shrinkage-thresholding algorithm (FISTA) to minimize the output weights of the extreme learning machine. In this work, we aim to improve the convergence speed of FISTA by using two fast algorithms of the shrinkage-thresholding class, called greedy FISTA (G-FISTA) and linearly convergent FISTA (LC-FISTA). Our method is an exciting proposal for decision-making involving the resolution of many application problems, especially those requiring longer computational times. In our experiments, we adopt six public datasets that are frequently used in machine learning: MNIST, NORB, CIFAR10, UMist, Caltech256, and Stanford Cars. We apply several metrics to evaluate the performance of our method, and the object of comparison is the FISTA algorithm due to its popularity for neural network training. The experimental results show that G-FISTA and LC-FISTA achieve higher convergence speeds in the autoencoder training process; for example, in the Stanford Cars dataset, G-FISTA and LC-FISTA are faster than FISTA by 48.42% and 47.32%, respectively. Overall, all three algorithms maintain good values of the performance metrics on all databases.
Evaluation Method and Implementation of Vocal Music Teaching Performance under a Wireless Communication Environment

With the development of society, modern educational technology and electronic information technology are more and more closely combined. Auxiliary teaching products based on computer technology, multimedia technology, and network technology emerge one after another. How to combine modern wireless communication technology with modern educational technology to evaluate the teaching effect has become one of the hot topics in the field of modern teaching research. In view of the lack of communication between teachers and students and evaluation methods of the teaching effect in traditional vocal music teaching, this paper designs a method of evaluation of vocal music teaching effect in a wireless communication environment. First, this paper designs a feedback system of vocal music teaching based on wireless communication. Second, due to the influence of vocal music teaching effect, the factors are diverse, and the neural network has the advantages in processing the data with nonlinear strong robustness and dynamic characteristics. And we use BP (back propagation) neural network as a mathematical model to receive teaching feedback information. Then, the simulation results show that the proposed method can not only evaluate the effect of vocal music teaching but also has good feasibility and practicability. Finally, the method proposed in this paper can help improve the teaching level and efficiency of the school and has a great theoretical value and practical potential.
Artificial Intelligence in Breast Ultrasound: The Emerging Future of Modern Medicine

In today's world, progressively enormous popularity prevails around artificial intelligence (AI). AI is gaining popularity in the identification of various images. Therefore, it has been widely used in the ultrasound of the breast. Furthermore, AI can perform a quantitative evaluation, which further helps maintain the diagnosis's accuracy. Moreover, breast cancer is the most common cancer in women, posing a severe threat to women's health. Hence, its early detection is usually associated with a patient's prognosis. As a result, using AI in breast cancer screening and detection is highly crucial. The concept of AI in the perspective of breast ultrasound has been highlighted in this brief review article. It tends to focus on early AI, i.e., traditional machine learning and deep learning algorithms. Also, the use of AI in ultrasound and the use of it in mammography, magnetic resonance imaging, nuclear medicine imaging, and classification of breast lesions is broadly explained, along with the challenges faced in bringing AI into daily practice.
Classification of Animals Using MobileNet with SVM Classifier

This paper proposes classification of different animal images comprising dog, cat, elephant, panda, and monkey. The most significant task in computer vision and deep learning is animal image classification. This work helps the forest service in further developing their alarm systems to the people who stay close to the forest area. Animal classification is beneficial in research allied to focused animal behavior, and also dangerous animal disturbance in domestic location can be avoided. This paper is focused on MobileNet Architecture which is implemented over Animal Classification Dataset for categorization of distinct animals. The extracted features from MobileNet Architecture are then used to classify the different categories of animal images with the utilization of an SVM classifier. Extensive experimentation on an Animal Image Dataset of 800 images with various classes are used to evaluate the performance. From the results of MobileNet with SVM has achieved a good performance of 99%.
Intelligent Identification and Order-Sensitive Correction Method of Outliers from Multi-Data Source Based on Historical Data Mining

In recent years, outliers caused by manual operation errors and equipment acquisition failures often occur, bringing challenges to big data analysis. In view of the difficulties in identifying and correcting outliers of multi-source data, an intelligent identification and order-sensitive correction method of outliers from multi-data sources based on historical data mining was proposed. First, an intelligent identification method of outliers of single-source data is proposed based on neural tangent kernel K-means (NTKKM) clustering. The original data is mapped to high-dimensional feature space using Neural Tangent Kernel, where the features of outliers are acquired by K-means clustering to realize the accurate identification of outliers. Second, an order-sensitive missing value imputation framework for multi-source data (OMSMVI) was proposed. The similarity graph of sources with missing data was constructed based on multidimensional similarity analysis, and the filling order decision was transformed into an optimization problem to realize the optimal filling order decision of missing values in multi-source data. Finally, a neighborhood-based imputation (NI) algorithm is proposed. Based on the traditional KNN filling algorithm, neighboring nodes of sources with missing data are flexibly selected to the achieve accurate correction of outliers. The case experiment was operated on actual power grid data, and the results show that the proposed clustering method can identify outliers more accurately, and the determined optimal imputation sequence has higher accuracy, which provide a feasible new idea for the identification and correction of outliers in the process of data preprocessing.
An Effective Model of Confidentiality Management of Digital Archives in a Cloud Environment

Aiming at the problem of confidentiality management of digital archives on the cloud, this paper presents an effective solution. The basic idea is to deploy a local server between the cloud and each client of an archive system to run a confidentiality management model of digital archives on the cloud, which includes an archive release model, and an archive search model. (1) The archive release model is used to strictly encrypt each archive file and archive data released by an administrator and generate feature data for the archive data, and then submit them to the cloud for storage to ensure the security of archive-sensitive data. (2) The archive search model is used to transform each query operation defined on the archive data submitted by a searcher, so that it can be correctly executed on feature data on the cloud, to ensure the accuracy and efficiency of archive search. Finally, both theoretical analysis and experimental evaluation demonstrate the good performance of the proposed solution. The result shows that compared with others, our solution has better overall performance in terms of confidentiality, accuracy, efficiency and availability, which can improve the security of archive-sensitive data on the untrusted cloud without compromising the performance of an existing archive management system.
Construction of a Basic Japanese Teaching Resource Base Based on a Deep Neural Network under a Big Data Environment

A challenge for education and teaching in universities is posed by "Internet plus," which has made numerous educational resources at universities richer and more accessible. The development of a professional Japanese teaching resource base should be centered on the needs and characteristics of Japanese teaching in universities, as well as establish and enhance the mechanism for resource base construction. All forms of instructional resources should also continuously be updated and improved in order to realize the diversified, systematic, open, and long-term development of Japanese instructional resources. In light of the current state of the information technology industry's rapid expansion, this essay examines a few issues with the building of a Japanese teaching resource database. A fundamental Japanese teaching resource database built on DNN was created as a result. The CNN technology is used in this study to create the Arduino device identification application. Utilizing gadgets in the learning process, learners can obtain learning resources using the Arduino device identification program before engaging in learning activities. The experimental findings also demonstrate that the precision rate and recall rate of the Japanese teaching resource database system developed in this study may achieve about 93 and 94 percent, respectively. Its performance is better than the conventional teaching resource system, and it can offer top-notch teaching resources for teaching fundamental Japanese.
Perception Research of Artificial Intelligence in Environmental Public Health Physiotherapy Nursing for the Elderly

This perceptual study focuses on developing artificial intelligence for elderly care design. It analyses and discusses the role of artificial intelligence in elderly care and its application to physiotherapy care. Artificial intelligence, as an emerging disruptive technology, is releasing the enormous energy accumulated in the technological and industrial revolutions, profoundly transforming how humans produce, live, and think about the world. Economic development and social progress are significantly impacted by it, and it has a great deal of practicality and broad application scope. Although there is a basic consensus in the 18 public understanding of AI, there is still some ambiguity and misunderstanding, knowing what is happening without understanding why. As a result, it is necessary to systematize and gain a comprehensive understanding of this concept and its associated practices.
MPEG-5 LCEVC for 3.0 Next Generation Digital TV in Brazil

TV 3.0 is the next generation digital broadcasting system developed in Brazil by the SBTVD Forum. The ambition of TV 3.0 is significantly higher than that of previous generations as it targets the delivery of IP based signals for applications, such as 8K, HDR, virtual and augmented reality, video enhancement and scalability. To deliver such services, more advanced and flexible compression technologies are required. MPEG-5 Part 2 Low Complexity Enhancement Video Coding (LCEVC) is a new video coding standard which works in combination with a separate video standard (e.g., H.264/AVC [H.264/AVC], H.265/HEVC [H.265/HEVC], H.266/VVC [H.266/VVC], AV1 [AV1]) to enhance the quality of a video. In the typical scenario, the enhanced quality is provided in terms of a higher resolution video obtained by adding details coded through an enhancement layer to a lower resolution version of the same video coded through a base layer. The LCEVC format also provides the ability to signal the bit-depth of the base layer independently from that of the enhancement layer and allowing up to 14-bit depth HDR. MPEG-5 LCEVC has been selected by the SBTVD committee as part of the TV 3.0 in December 2021. In this paper we describe the proposal submitted for LCEVC in response to the SBTVD Call for Proposals (CfP) for TV 3.0.
Towards Verifiable Differentially-Private Polling

Analyses that fulfill differential privacy provide plausible deniability to individuals while allowing analysts to extract insights from data. However, beyond an often acceptable accuracy tradeoff, these statistical disclosure techniques generally inhibit the verifiability of the provided information, as one cannot check the correctness of the participants’ truthful information, the differentially private mechanism, or the unbiased random number generation. While related work has already discussed this opportunity, an efficient implementation with a precise bound on errors and corresponding proofs of the differential privacy property is so far missing. In this paper, we follow an approach based on zero-knowledge proofs (ZKPs), in specific succinct non-interactive arguments of knowledge, as a verifiable computation technique to prove the correctness of a differentially private query output. In particular, we ensure the guarantees of differential privacy hold despite the limitations of ZKPs that operate on finite fields and have limited branching capabilities. We demonstrate that our approach has practical performance and discuss how practitioners could employ our primitives to verifiably query individuals’ age from their digitally signed ID card in a differentially private manner.
Analysis of the Role of Design-Driven Innovation in the Interaction Design of Image Indexing Software under the Background of the Internet of Things

More and more medical images are being produced quickly by modern imaging devices that are connected to the Internet of Things (IoT). For the retrieval of necessary images from a conventional big volume database, the continuous scanning of all image data in an IoT network appears to be ineffective and computationally expensive. Effective image retrieval from a big image database necessitates the use of an effective and scalable image indexing approach. Existing image indexing methods have significant drawbacks, including lower efficiency, constrained scalability, increased computing demands, and longer processing times. To index the medical images obtained by IoT sensors, we suggested a novel, effective Content-Based Cascaded Gabor Wavelet Algorithm (CBCGWA) in this study. Further in this study, the medical images from computed tomography (CT) are employed for medical indexing Totally, the dataset included 168 individually annotated square patches in a subset of 115 high-resolution CT (High-Resolution CT) slices used in this study. An adaptive median filter is used for preprocessing the CT medical images once they are acquired from IoT sensor nodes connected to medical imaging systems. The Gaussian Adaptive Attention Network is then used to cluster together images with comparable attributes (GAAN). The suggested method is used to index the images after image clustering. The cloud database is where the indexed images are eventually maintained. The comparison of the recommended indexing approach to the existing indexing strategies revealed that it is better in terms of processing time, power, and indexing efficiency.
A DNA Sequencing Medical Image Encryption System (DMIES) Using Chaos Map and Knight's Travel Map

This research aims to devise a method of encrypting medical images based on chaos map, Knight's travel map, affine transformation, and DNA cryptography to prevent attackers from accessing the data. The proposed DMIES cryptographic system performs the chaos intertwining logistic map diffusion and confusion process on chosen pixels of medical images. The DNA structure of the medical image has generated using all eight DNA encoding rules that are dependent on the pixel positions in the medical image. Knight's travel map is decomposed, which helps to prevent tampering and certification after the diffusion process. Finally, to avoid the deformity of medical data, a shear-based affine transformation is used. Compared to existing standard image encryption systems, the extensive and complete security assessment highlights the relevance and benefits of the proposed DMIES cryptosystem. The proposed DMIES can also withstand various attacks like statistical, differential, exhaustive, cropping, and noise attack.
Human activity recognition based on an amalgamation of CEV &amp; SGM features

The method of marking video clips with action symbols is known as vision-based human activity recognition. Robust solutions to this problem have a variety of practical implementations. Due to differences in motion performance, recording environments, and inter-personal differences, the challenge is difficult. We specifically resolve these problems in this study work, and we solve imitations of state-of-the-art research. Projected human activity recognition is based on an amalgamation of CEV &amp; SGM features. The proposed solution outperforms current models and produces state-of-the-art outcomes as compared to the best effectiveness of the control, according to experimental results on the datasets.
Convex Multi-Task Learning with Neural Networks

Multi-Task Learning aims at improving the learning process by solving different tasks simultaneously. The approaches to Multi-Task Learning can be categorized as feature-learning, regularization-based and combination strategies. Feature-learning approximations are more natural for deep models while regularization-based ones are usually designed for shallow ones, but we can see examples of both for shallow and deep models. However, the combination approach has been tested on shallow models exclusively. Here we propose a Multi-Task combination approach for Neural Networks, describe the training procedure, test it in four different multi-task image datasets and show improvements in the performance over other strategies.
Multi-level object detection by multi-sensor perception of traffic scenes

Object detection is a hot research topic in the communities of computer vision and intelligent transportation system. In this paper, we propose a novel framework for multi-level object detection by mutli-sensor perception from road scenes. Firstly, two types of 2D object detection methods are proposed based on multi-level feature processing. Among them, an improved 2D object detection method is designed based on CenterNet, which introduces a centripetal offset module and a deformable convolution module to improve the corner matching accuracy and solve the problem of missed detection. Moreover, an improved 2D object detection method based on RetinaNet is designed by the optimization of the sub-network of ResNet. The coordinate attention mechanism and bidirectional feature fusion mechanism are incorporated. Finally, we propose a 3D object detection method based on an improved PointNet network. The point cloud data are projected into a frustum according to the 2D detection result, then the frustum is segmented according to the linearly increased steps. The experimental results on the KITTI dataset and TSD-max dataset well demonstrate the effectiveness of the proposed framework.
Recent Advances in Psychotherapy with Virtual Reality

With consumer devices becoming increasingly affordable, virtual reality (VR) bears therapeutic promise for psychiatry and psychotherapy. Nevertheless, there remains a research-to-practice gap as advances in VR technology outpace clinical research: VR therapeutics practical for day-to-day clinical care remain largely unavailable, inaccessible, or unaffordable. This article returns to three foundational promises of VR: enhancing treatments already feasible in the clinic, making feasible treatments impractical in the clinic, and making possible “impossible” treatments in the clinic. These are discussed in terms of current state of research; whether VR therapeutics are available, accessible, and affordable to frontline clinicians; limitations; and areas of growth via practical examples. This is followed by a discussion on clinical research approach to further bridge the research-to-practice gap: first, using in research only VR devices that are practical for frontline clinics, and second, developing VR-therapeutics with scalability and excellence in mind—through optimization and a team-science approach.