Reviewerly/exHarmony huggingface dataset:
A hybrid model with dual channel feature processing for short-term photovoltaic power prediction

Abstract Adverse effects of random fluctuations and intermittent characteristics of solar irradiance usually hamper the proper operation of the photovoltaic power grid. It is therefore desirable to improve the accuracy of photovoltaic (PV) power prediction. In this work, PV forecasting is realized through a Bayesian optimized model which combines the long short-term memory and radial basis function neural network (BOA-LSTM-RBF). The hybrid model presents a dual channel feature processing by extracting the historical data of PV generation via long-short-term memory network (LSTM) and extracting the forecasted weather conditions via radial basis function neural network (RBF). Then the number of hidden layer neurons and the training batch size are simultaneously optimized by &amp; the Bayesian optimization algorithm (BOA). The testing results of three stations demonstrate that, compared with other available models, the RMSE values of BOA-LSTM-RBF model decreased by 2% ∼ 17%, which has striking advantages in prediction precision and generalizability. More interestingly, high-precision PV power forecasting can be achieved even under dramatic weather changes.
Semantically Disentangled Variational Autoencoder for Modeling 3D Facial Details

Parametric face models, such as morphable and blendshape models, have shown great potential in face representation, reconstruction, and animation. However, all these models focus on large-scale facial geometry. Facial details such as wrinkles are not parameterized in these models, impeding accuracy and realism. In this article, we propose a method to learn a Semantically Disentangled Variational Autoencoder (SDVAE) to parameterize facial details and support independent detail manipulation as an extension of an off-the-shelf large-scale face model. Our method utilizes the non-linear capability of Deep Neural Networks for detail modeling, achieving better accuracy and greater representation power compared with linear models. In order to disentangle the semantic factors of identity, expression and age, we propose to eliminate the correlation between different factors in an adversarial manner. Therefore, wrinkle-level details of various identities, expressions, and ages can be generated and independently controlled by changing latent vectors of our SDVAE. We further leverage our model to reconstruct 3D faces via fitting to facial scans and images. Benefiting from our parametric model, we achieve accurate and robust reconstruction, and the reconstructed details can be easily animated and manipulated. We evaluate our method on practical applications, including scan fitting, image fitting, video tracking, model manipulation, and expression and age animation. Extensive experiments demonstrate that the proposed method can robustly model facial details and achieve better results than alternative methods.
Localization of Moving Sources: Uniqueness, Stability, and Bayesian Inference

.We consider the subsonic moving point source problem for the scalar wave equation in \(\boldsymbol{R}^3\) , proving a regularity result for the direct problem and uniqueness and stability results for the inverse problem. We then present and investigate numerically a Bayesian framework for the inference of the source trajectory and intensity from wave field measurements. The framework employs Gaussian process priors, the preconditioned Crank–Nicolson scheme with Markov chain Monte Carlo sampling, and conditioning on functionals to include prior information on the source trajectory.Keywordsinverse problemsmoving sourcessubsonicwave equationBayesian inferenceMSC codes35R3035L0562F15
Activation Distribution-based Layer-wise Quantization for Convolutional Neural Networks

As convolutional neural network (CNN) research accelerates with advances in GPU s, the accuracy of CNN models has been continuously improved. However, in proportion to the enhancement of model accuracy, the computational amount of the CNN models increases, which causes a problem that it is difficult to practically use the CNN models in mobile/embedded platforms. To address this problem, optimization and weight reduction methods for CNN models have been actively studied. This paper proposes a new scale factor for layer-specific quantization considering the activation distribution of CNN s. The proposed method has the advantage that it is possible to minimize the accuracy drop for each layer and is friendly to hardware accelerator design. As a result, the proposed quantization method achieves much higher accuracy compared to the quantization studies on the conventional accelerator design while maintaining low hardware resources.
[Pose Testing Method for Medical Service Robots Based on Vision System].

Aiming at the medical service robot used in the indoor environment, this study proposes a method to test the pose accuracy of its working surface based on the binocular vision system. This method uses a binocular vision coordinate system to measure targets fixed on the working surface of the service robot, aligns the measurement system with the robot base coordinate system through the nonlinear least squares method, and integrates the multi-eye image data to achieve the accuracy test of the working surface. Finally, the vision test program was tested and verified on a mobile service robot model ABIR X8. According to the accuracy index given in GB/T 38124 Service Robot Performance Test Method, the test results of its pose accuracy were obtained.
Query-focused summarisation in research articles based on semantic function of sentences

Query-focused summarisation (QFS) in research articles is usually used to help researchers to sum up content related to specific aspects of research articles. However, most QFS approaches failed to consider the inherent structure of research articles to speculate the semantic functions of sentences to make summarisations relate to the given aspect more precisely. Systematic functional linguistic studies suggested that research articles contain inherent structures in which sections and sentences have their specific functions. We suppose these structures can be used as auxiliary information for scientific summarisation. In this article, we seek to improve existing extractive QFS methods by using the macrostructure and discourse segment structure of research articles. We categorise sentences in research articles into different types according to their semantic functions and assign different weights to each type of sentence using an action-based relevance calculation method. We show that our system outperforms the baseline system on a benchmark dataset. Our findings suggest that using the inherent structure of research articles as assistance is practical for scientific summarisation.
Design Of Information System To Support Movie Ticket Booking And Cinema Operations

Due to the amazing development of the internet, today if you don't transfer your business to the web it's like you don't even exist. That is why we are witnessing the growing development of web applications that allow users to complete all their tasks with only one click. Cinema is a partly complex system because many processes take place within it, so the focus of this paper is the automation of the process of booking and selling tickets for movie screenings in cinemas. This paper presents the development of a web-based information system that will enable its users to quickly and easily book tickets for movie screenings in the selected cinema.
Survey on In-vehicle Datasets for Human Pose Estimation

Computer vision techniques are essential in autonomous driving applications. Consequently, many deep learning-based methods have been developed using a large number of outdoor images captured from vehicle-mounted cameras. However, less attention has been paid to the inside of vehicles, although in-vehicle applications such as driver monitoring and action recognition are also essential in the autonomous driving scenario. In this paper, we investigate the existing in-vehicle datasets, especially on human pose estimation, and provide their details.
"Core Strength" of Dance Lala Training Considering the Body Motion Tracking Video and Predictive Model

"Core Strength" of the dance Lala training considering the body motion tracking video and predictive model is studied in this paper. When constructing the training sample template, it is necessary to set the value of the number of poses in advance. Too small a value will increase the error of the training action behavior template and reduce the recognition accuracy; too large a value will also increase the complexity of model training, training and recognition execution time will also increase. The function of the pose estimation module is to complete the description and expression of the specific posture of the human body at the current moment according to the frame image. After processing by the pose estimation module, each frame in the sequence can be calculated to obtain a feature vector, so that the image sequence can be converted into a feature vector sequence for the next step of action recognition. With these models, the applications on the dance Lala training is tested.
Code Generation with Hybrid of Structural and Semantic Features Retrieval

Due to the growing need for faster software delivery, code generation has attracted more and more attention, since it could improve code maintainability by providing suggestions for coding. In the model of generating program source code from natural language (NL), the most effective method is to generate an intermediate architecture (such as Abstract Syntax Tree) combined with a deep learning model. However, these models have the following drawbacks: (1) The data structural information is underutilized and the correlation between samples is not considered. (2) Lack of the ability to memorize large and complex structures, so that complex codes cannot be generated correctly. To address these issues, we propose HRCODE model, a code generation architecture based on Hybrid of structural and semantic features Retrieval CODE model. We transform the NL description into an intermediate structure with structural features. Then, the NL and the intermediate structure are embedded into a vector through weight mixing, and we calculate the similarity score between each vector to retrieve the most relevant samples. Finally, the new input is brought into the PLBART model to generate code. Experiments show that HRCODE is at least 4.7% higher than the state-of-the-art models in the ACC metric and at least 10.3% higher in the BLEU-4 score. We have released our code at https://github.com/jesokang/HRCODE.
Development of a Thermal and Electrical Performance Prediction Model for a Photovoltaic-thermal System Using an Artificial Neural Network

Recently, Zero Energy Building (ZEB) that reduces the energy consumption and energy demand is becoming major issue. To realize ZEB, high energy efficiency renewable energy systems are actively applied in the building. Among the renewable energy systems, the photovoltaic-thermal (PVT) systems can respond heat and electricity demands of the buildings with one facility. Although, it is necessary to accurately predict the performance of the PVT system, most studies are conducted at the mock-up test level, through energy simulation and laboratory-scale experiment. In this study, the real-scale experiment plant that comprises the PVT module, heat storage tank, and actual building was constructed to collect the database of the PVT system under actual conditions. Moreover, based on the real-scale experimental data, the performance prediction model was constructed using artificial neural network (ANN). To verify the accuracy of the performance prediction model based on ANN, the coefficient of variation root mean square error (Cv(RMSE)) proposed by ASHRAE Guideline 14 was used. The Cv(RMSE) for the predicted result by ANN was calculated as 10% of the thermal efficiency, and 16% of the electrical production. Through the Cv(RMSE) result, it was confirmed that the performance prediction model based on ANN is reliable.
System Design for Detecting Real Estate Speculation Abusing Inside Information: For the Fair Reallocation of Land

In March 2021, a case of speculation that abused private internal information came to light, which involved a group of public officials from the Korea Land and Housing Corporation (LH), and has since been labeled the ‘LH Scandal’. In this scandal, land was misappropriated as a means of creating fraudulent values, instead of returning it to marginalized people in real need of residential space. As a result of this, preventive measures for similar cases have become warranted. Consequently, related laws have been passed, but this is only expected to show its effect as a follow-up response, therefore requiring a preemptive response plan. In this paper, we will propose a conceptual framework that can detect speculation that abuses private internal information, enabling a preemptive response, utilizing outlier detection and Latent Dirichlet Allocation (LDA) methods. The system is designed to create a database (DB) with private inside real estate information, which is linked to another DB with a list of outlier-detected areas that can potentially indicate speculation, and then the system confirms any speculation by comparing the two DBs accordingly. Once a speculation case is confirmed, the system automatically reports the case to the investigative agency. By using this system, we expect to detect hidden speculation cases already committed, as well as speculation cases in real-time. Ultimately, we hope to protect the original purpose of redevelopment and the construction of new towns (housing/retail mixed-use zones), redistributing available land on behalf of marginalized people, who are lacking in residential space, by raising the utility of land.
D-Eazy Donation Platform – An Artificial Intelligence and Video based Mobile Application

About 9.2% of the world, or 689 million people, live in extreme Poverty according to the World Bank. The social agencies or trusts are limited in their capability and coverage to help these underprivileged people for food and main essentials. This paper focuses on the solution which enables mass Donors to seamlessly connect to recipients or Point-of-Contacts (PoCs) for easily donating food and essentials to underprivileged people while sitting at their homes/places. The solution introduces a Video-based Mobile App Platform that can be used by Donors anytime from anywhere to post and schedule their donations. Recipients directly or through agencies/organizations' PoCs are notified of the scheduled donations and pick-up/drop can be arranged internally or through 3rd party pick-up agencies. One of the other key features is SOS (emergency alert). If any poor person or family needs urgent help in terms of expensive medicines, blood, etc., this platform can be used by recipient PoCs to trigger SOS which will be received by all the people registered to this platform and hence can offer help immediately. The Application platform is designed and developed with frontend user interface in React Native/JavaScript and backend server in Python/Django. The application offers AI based Donation Prediction Engine (AIDPE) for adaptive video upload/upload processing and efficient PoC allocation based on multiple factors.
Data-Specific Anisotropic Mexican Hat Wavelets for Structure-Preserving Image Processing

This paper proposes a novel approach for structure-sensitive image processing based on the rigorous mathematical derivation of data-specific anisotropic Mexican hat wavelets (DAM). Our DAM is derived from the negative first-order derivative of the fundamental solution of heat diffusion equation with respect to time, which not only shares similar properties with Mexican hat wavelet but also intrinsically embeds the image-specific properties. Through the scale-aware DAM transform and its inverse transform, we are capable of conducting structure-sensitive image processing. Our key idea is to represent the images as undirected graphs, whose edge weights are governed by the normalized intensity/color differences within the local neighboring pixel window. Based on the rigorous theory of global graph Laplacian and heat diffusion, our original DAM can also encode the local/global structure of images. We employ the Krylov subspace technique to reduce the computational cost of our DAM transform. Furthermore, aiming at various structure-preserving image processing applications such as filtering, detail enhancement, tone manipulation, and stylization, we conduct comprehensive experiments and make quantitative comparisons with other state-of-the-art methods, which demonstrate the versatility and superiority of our method.
On the Byzantine-Fault- Tolerant Consensus in Blockchain Built on Internet of Vehicles

Itis a critical matter for a blockchain system whether a Byzantine fault tolerance (BFT) can be guaranteed during a consensus process. Can an internet of vehicles (10 V) system achieve the BFT consensus when the vehicles keep mobile? This paper seeks an answer to this fundamental question. It focuses on characterizing the impact of mobility on the performance of a BFT consensus among the vehicles.
Correction: Mining Electronic Health Records for Drugs Associated With 28-day Mortality in COVID-19: Pharmacopoeia-wide Association Study (PharmWAS)

[This corrects the article DOI: 10.2196/35190.].
Application of blockchain technology in the data processing security system of financial enterprises

Abstract Data security is not only to consider administrative management methods and punitive measures, but also technically from multiple perspectives such as data integrity, authorization, controllability, and the ability to trace back history. This article analyzes the application of blockchain technology to the data processing security system of financial enterprises, with the purpose of demonstrating the feasibility of blockchain technology to maintain data security and its specific operation methods. This research refers to a large number of references and understands the related systems of data confidentiality in financial enterprise databases and blockchain technology. According to the needs of the experiment, the article constructs the execution structure model of the financial enterprise database data confidentiality system, the task scheduling operation structure model, and the block. The chain domain authentication model, and so forth, carried out corresponding comparative analysis from the processing dimensions of time and space consumption, data security, and so forth, and verified the comprehensive performance of blockchain technology in the application of data security systems. The reliability factor is called the R factor, which is a parameter representing the degree of conformity of several curves, that is, the reliability degree of the assumed model structure. The R factor should be sensitive to the position of surface atoms but not to unstructured quantities, such as electron‐atom scattering field, electron decay, and so forth, which are all unstructured quantities. The experimental results show that before the participation of blockchain technology, the maximum reliability of financial data is only 4.3, and the reliability of data confidentiality after application is increased to 6.8. Multidimensional data shows that blockchain technology can provide guarantee for the application of data processing security systems for financial enterprises.
Flexible Example-Based Program Synthesis on Tree-Structured Function Compositions

Abstract We introduce a flexible program synthesis system whose task is to predict function compositions that transform given inputs to their corresponding given outputs. We process input lists in a sequential manner, allowing the system to generalize to a wide range of input lengths. We separate the operator and the operand in the lambda functions of the used higher order functions to achieve significantly wider numeric parameter ranges compared to the previous works. The evaluations show that this approach is competitive with state-of-the-art systems, while it is much more flexible in terms of the input length, the parameters of the lambda functions, and the integer range of the inputs and outputs. We extend the system to handle tree-structured function compositions by introducing two additional functions (, copy) and the ability to represent unfinished function compositions during the synthesis process. The extended system achieves state-of-the-art results while synthesizing complex function compositions with multiple forks. We believe that flexibility in these aspects is an important step towards solving real-world problems with example-based program synthesis.
Prevention of Fake Food and Adulteration using Electronic Product Code (EPC) and Machine Learning based Block Chain Technology

Block chain is a widely dispersed huge technology in which transactions are recorded and preserved, compromising them with the built-in cryptographic generation. Recent studies have focused on the critical principle of Block chain and how it may be used in areas such as data protection change, food safety, product growth, and business acceleration in all retail establishments. The approach includes genuine features in security architecture to prevent low levels and counterfeit medications. Even at the level of individual stocks, Block chain will ensure compliance with the acquisition of any goods by improving evidence of ownership through the use of particular sources. The strategy includes genuine variables inside the security systems to prevent low-tier and counterfeit pharmaceuticals. Even at the level of individual stocks, Block chain will ensure compliance with the acquisition of any goods by strengthening evidence of ownership via the use of unique sources. Because every time a product changes hands, transactions can be recorded and verified using a unique ID known as the EPC code, which is issued remotely using FSSAI, from its production to sales, and is clearly called Block chain. it does not change, it provides consistency and meaning. When the block is finished, generate a fully unique code that binds to the next page or inhibits the appearance of a series of blocks. Because the concept is entirely based on a reliable cryptographic hash algorithm, the transaction cannot be copied or encrypted, copies are equivalent, and minimal authorization is required to gain access to the data. This will significantly eliminate human errors, additional expenses, and time delays and might be a global procurement application. The suggested system uses the K- Nearest Neighbour algorithm to monitor food production from farmers through distributors, who then sell it to customers. Expired stock can be reclaimed and redistributed after special authorization by bodies. Notify customers regarding product performance as well. FSSAI provides the EPC code, which may be found in every product from manufacture to wholesaler to retailer. The code contains all the information about the goods, so when the customer types the code into the programme, all information about that specific product can be obtained.