Reviewerly/exHarmony huggingface dataset:
A P2P Electricity Trading System by Integrating Blockchain with Optimization

With numerous distributed energy sources as participants, blockchain shows great potential in the peer-to-peer electricity markets. However, blockchain originally is designed for finical system, and are not fully suitable for cyber-physical systems. This paper proposes a P2P power trading method, in which an optimal solution proof mechanism is designed. It combines the consensus mechanism with the optimization problems of the physical system which are difficult to solve but easy to verify, saving computing power therefore. Meanwhile, we redesign the blockchain structure. The power grid operation status is recorded in the block body, so that the link between blockchains is integrated into the physical state transfer constraints. As for the trading mechanism, a real-time transaction mechanism based on smart contract is proposed according to the mechanism and mode of energy trading. The simulation of transactions in a decentralized power trading system validates the proposed power trading method.
SRUH-GNN: Social Recommendation of User Homophily based on Graph Neural Network

Social recommendation is an effective method to improve recommendation accuracy and recommendation system performance in recommender systems, attempting to combine user-item interactions with social links to reduce data sparsity and cold-start problems. Previous research approaches on social recommendation model the fusion of social information and user-item interactions, however, they ignore the problem of inconsistent social relationships, which affects the accuracy of recommendations. To consider the consistency of user social relationships, this paper proposes a graph neural network-based social recommendation model for user homogeneity, which obtains consistent embeddings of users at the contextual and relational levels through graph neural networks and relational attention. In social recommendation, through experiments on two mainstream datasets, we can demonstrate that our model outperforms the comparison model.
A Novel Deep Belief Network Based on Shallow Feature Regression

To address the problem that deep belief network (DBN) does not fully utilize the feature information of hidden layer in the application process, which leads to the degradation of model prediction accuracy. A novel DBN based on shallow feature regression (SDBN) is proposed in this paper. Firstly, the initial parameters of SDBN are obtained by unsupervised pre-training. All hidden layers are added to the fully connected layer for regression, and the final prediction output is obtained by linear weighted summation of the hidden layer outputs. Then, backpropagation algorithm (BP) is applied to the supervised fine-tuning process of SDBN. Finally, experimental testing in the debutanizer column validates the validity of the SDBN model. The experimental results show that the prediction accuracy of the SDBN model is reduced by 0.053and 0.071 compared with DBN and PLSR, respectively, under the premise of guaranteeing the model speed.
Features Masked Auto-Encoder-Based Anomaly Detection in Process Industry

With the development of the modern process industry, accurate anomaly detection methods are increasingly needed. However, identifying anomalies from high-dimensional data continues to be a challenge for the process industry. In this work, a feature masked autoencoder (FMAE) method is proposed to meet this challenge. As a masked-reconstruction task, a high mask rate is first adopted to mask the feature of data. Then, the intrinsic information of unmasked features is extracted through the encoder and masked features are recovered through the decoder. This task forces the intrinsic information learned by the model to increase while alleviating the high-dimensional problem. Additionally, anomaly tends to be reconstructed to a normal by FMAE because only normal data is used for training. Thus, anomalies are detected and localized by computing input-output residuals. Finally, the effectiveness of the model is verified on the Lublin Sugar Factory dataset.
FedVPS: Federated Learning for Privacy and Security of Internet of Vehicles on Non-IID Data

The 6th generation of mobile networks (6G) offers many advantages, including fast transmission rates, low delays, and ultra-dense networks. These benefits can solve the communication problems in the Internet of Vehicles (IoV). However, auto-mobiles as IoV terminals generate Non-Independent Identically Distributed (Non-IID) data while driving, making it necessary to introduce distributed machine learning into the IoV as a network that integrates intelligent computing and vehicle networking. To address the Non-IID of local data and the heterogeneity of local models, we propose a heterogeneous Federated Learning scheme in this paper. Based on the distributed architecture (Terminal - Edge device - Cloud) in the IoV, we designed a privacy protection scheme using Secure Multi-Party Computation (SMPC). This ensures that the terminals participating in Federated Learning get accurate calculation results without revealing useful information, thus preserving the privacy of local datasets. The privacy and security of the IoV based on Federated Learning (FedVPS) not only protect the privacy of the terminals but also improve communication efficiency, enabling accurate and efficient distributed machine learning. The aggregation method of Federated Learning is a prototype-based scheme that utilizes the effective information stored in local datasets. In the BIT-Vehicle dataset, FedVPS is not only more robust but also has excellent prediction accuracy. Compared to FedAvg, FedVPS has advantages in communication efficiency and model prediction accuracy.
Multi-dimensional Visualization Method of SCD File Information of Intelligent Substation Based on Knowledge Graph

The structure of intelligent substation SCD file is complex, and there are many layers of information. Different functions have corresponding information basis, and often need to be associated across multiple information nodes, resulting in poor visualization. This paper presents a multi-dimensional visualization method of SCD file information of smart substation based on knowledge graph. Firstly, the file structure and function structure of SCD text expression are analyzed. Then the SCD file node is extended and the seven-step method is adopted to construct the intelligent substation ontology, which is realized by Protégé. The knowledge graph of substation is constructed by using ontology and relationship between SCD nodes. The graph expression of SCD is completed, and the visualization of file structure information is realized. The information path of functions such as the correlation between primary and secondary devices and the correlation between virtual and real circuits is mined. The information nodes in the path are searched in the SCD knowledge graph to realize the visualization of functional structure information. The above work was done through Neoj4, Python. The simulation results show that knowledge graph can decouple SCD information and realize multi-dimensional visualization of information with a high degree of visualization, which can provide guidance for operation and maintenance and information retrieval.
Camera Field Calibration Method Using Collinear Point Target Joint Constraints

A method based on a specially designed coplanar point target and multiple projection invariants is proposed for efficient and rapid on-site calibration of visual measurement systems. The main features of this method include the separation of the distortion model from the camera model, the accurate determination of the distortion center using the fundamental matrix, and the definition of the distortion measure function based on constraints such as coplanar vanishing point, weighted line distance, and cross ratio invariance. The optimal distortion coefficients are then obtained through optimization using the Levenberg-Marquardt algorithm, followed by linear calibration of the camera intrinsic and extrinsic parameters. Experimental results demonstrate that this algorithm can rapidly and accurately obtain camera parameters, and it is easy to implement on-site in industrial settings.
Interactively Rejioning 2D Oracle Bone Fragments Based on Contour Matching

The oracle bone fragments are usually rejoined manually by experts. This is a very challenging task because of the huge number of fragments and the complexity of the breakage situation, which leads to low efficiency of manual search and low accuracy of results. In this paper, we explore a computer-aided technology in 2D oracle rubbings rejoining to accelerate the matching and searching process. We provide an oracle rejoining pipeline and interactive interfaces to integrate contour extraction, matching, searching, and merging of fragments. Besides, we conduct experiments on a real oracle bone dataset, and the results demonstrate that our technology finds the correct matching fragments within a certain rank and achieves satisfactory final matching results.
Substation helmet detection based on improved YOLOX-S algorithm

The improved YOLOX-S algorithm is proposed for the detection of small helmet targets based on an improved YOLOX-S algorithm for the detection of helmets worn by relevant personnel in hazardous scenarios in substations. First, the ECA attention mechanism is introduced into the CSPLayer structure in YOLOX-S to direct the model to pay more attention to channel features of small target information and enhance the model's ability to utilize useful features. Secondly, the addition of the ConvNext Block module after the three feature layers of the backbone feature extraction network to enhance the model's ability to exploit useful features. Finally, the weighted feature fusion mechanism of BiFPN is introduced in the enhanced feature extraction network by changing the original concat to BiFPN_concat, adding learnable weights to each input feature to learn the importance of different input features, distinguishing the importance of different features in the feature fusion process, and better focusing on the target information to be detected. The experimental results show that the mAP of the improved algorithm is 92.65%, which is an average accuracy improvement of 2.55% over the original YOLOX-S algorithm and meets the practical requirements.
Exploring Selection and Search Usability Across Desktop, Tablet, and Head-Mounted Display WebXR Platforms

We present a comparative evaluation of a VR learning application on desktop, head-mounted displays, and tablet platforms. We first evaluated fundamental interaction, including selection and search, and general usability across these platforms using Circles, our custom-built WebXR application. We developed two virtual environments for the study: (1) a selection and search testbed, and (2) a virtual learning environment developed for use within a post-secondary gender diversity workshop. Performance and general usability results were consistent with past studies, suggesting that WebXR offers adequate performance to support learning applications. However, designing a compelling user experience in VR remains challenging, although web-based VR offers accessibility benefits due to its multi-platform design. Finally, as this study was conducted remotely during the COVID-19 pandemic, we also reflect on how our system and study accommodate remote participation, similar to a traditionally lab-based experience.
Data-driven Adaptive Control of CRAC in Data Center Based on Online Incremental RVFL

An adaptive control method based on online identification of controller parameters is proposed for the cooling process of Computer Room Air Conditioning(CRAC) in data centers with complex nonlinear and dynamic characteristics. Firstly, the nonlinear system is divided into low-order linear part and high-order unmodeled dynamic term. The random vector functional link network is used to identify the controller parameters. The unmodeled dynamic compensator is introduced into the control strategy to offset the influence of uncertain dynamic term on the closed-loop control system of CRAC-Cabinet system. Then, in order to solve the problem that the identification ability of the offline model is reduced due to the changes of the location layout of the cabinet, the number and placement of electronic devices and other factors in the data center, the online addition of hidden layer nodes is added, and the output weight of the original hidden layer is recalculated and updated when new nodes are added. The simulation results show that the control and tracking ability of the nonlinear adaptive controller with unmodeled dynamic compensation is better than that of the adaptive controller based on linear model, and the experimental results show that the incremental learning network model has faster response speed and higher tracking accuracy than the original model.
The Design and Study of Facial Color Diagnosis System Based on Virtual Reality

Using the development of virtual simulation technology, the application of virtual reality technology and auxiliary design is mature, and the application of acupuncture point display and medical education is applied. It greatly simplifies the difficulty of the model and the difficulties Chinese medicine faces. This paper provides a technical solution for the facial color diagnosis system based on a new perspective, and the model of some Chinese medical facial color diagnosis is constructed, and the interactive process and query framework are designed by using the virtual interactive platform quest3d as the scene drive. This paper mainly discusses the design of other development function modules in the basis of the construction plan of the facial color diagnosis platform of traditional Chinese medicine, and the application of the technology and facial color diagnosis system is prospected in the virtual museum of traditional Chinese medicine.
Insulator defect detection based on improved YOLOv5 algorithm

Due to the issues with a high number of parameters and the sluggish detection speed of the present insulator defect identification algorithm, an enhanced YOLOv5 target detection approach is proposed. To begin, replacing the YOLOv5 backbone network with the lightweight network MobileNetV3 minimizes the amount of processing and the number of parameters used during feature extraction, resulting in faster detection. Afterward, clustering with K-means++ is used to produce anchor frames that are better suited for insulators on transmission lines with thin structures. The network model can then be used to discover small targets for insulator flaws by adding a coordinate attention mechanism at the feature fusion stage. Last but not least, the edge regression is optimized for accurate target localization, which raises the recognition rate and improves the loss function. The revised YOLOv5 network model has a file size of just 7.2 MB, which is only 51% smaller than that of the original YOLOv5 network. This results in a reduction in the number of parameters, an increase in detection speed and accuracy, and a 98% average detection accuracy for mAP. The inference time is also reduced by 21 ms.
Privacy-Preserving PBFT Based on a New BFT Asynchronous Verifiable Secret Sharing

Many types of research on PBFT in the past focused on improving the efficiency of the algorithm, but with the development of the times, people have higher requirements for data privacy, which poses new challenges to the design of the PBFT algorithm. Considering the issue of data privacy, a natural idea is to use the method of secret sharing to let each node only store the secret share locally, and use the binding between the secret share and the polynomial to ensure the consistency of the system. In this line, integrating VSS into the BFT algorithm is a common method, but we found that when the traditional VSS is integrated into the BFT algorithm, there is a problem that the misbehavior of the data writer cannot be identified. To solve this problem, we design a new BFT AVSS based on ZKIPCP. Our scheme itself is not excellent, but it is a good choice when integrated into the BFT algorithm, and it can solve the problem that traditional schemes cannot identify malicious data writers. We describe how to integrate our BFT AVSS scheme into the PBFT algorithm for data privacy protection. Finally, we implemented our privacy-preserving PBFT and proves through experiments that it can meet our original purpose, that is, privacy protection and malicious data writer identification.
SurfChessVR: Deploying Chess Game on Parametric Surface in Virtual Reality

This paper proposes SurfChessVR, a novel gaming interface that deploys chess games on parametric surfaces in Virtual Reality (VR). Unlike traditional chess in VR where all chessmen are placed on a plane chessboard, our approach automatically places chessmen on arbitrary parametric surfaces such as spheres, torus, cones, etc. More specifically, given a mathematical equation that describes a parametric surface as input, our approach automatically generates the chessboard in the shape of that parametric surface and places the chessmen on that surface at different squares with corresponding orientations that align with the surface’s normal directions. In order to deliver players with immersive gaming experiences of playing chess on a parametric surface, we implemented a game AI algorithm called the Min-Max algorithm that enables the computer to game against the player in VR. Experiment results and user studies validate the effectiveness and correctness of our approach.
A Soft Sensor Method based on Unsupervised Multi-layer Domain Adaptation for Batch Processes

In batch processes, soft sensors frequently face the problem of irregular distributions between current and past data owing to variations in operating circumstances, as well as and poor model performing resulting from the absence with labels in the current data. This paper proposes a soft sensor method that is founded on dynamic multi-layer domain adaptation (DMDA). The method being proposed first training a convolutional neural network model with a substantial quantity of labeled data in the source domain, and subsequently use the obtained parameters as the beginning stage for the target model. Then, by utilizing multi-kernel maximum mean discrepancy (MK-MMD) and conditional embedding operator discrepancy (CEOD), the multi-layer convolutional neural network can effectively measure the difference in the overall (marginal) and specific (conditional) distributions between the source and target domains. Furthermore, the incorporation of an adaptive factor facilitates the dynamic adjustment of distribution weight, enabling precise fine-tuning of the target model. Finally, a regression model is established using the distribution-adapted historical data to achieve unsupervised soft sensor modeling. The substrate concentration in different fermentation tanks of the chlortetracycline fermentation process can be predicted through the utilization of the proposed approach. The experimental findings indicate that this method can accomplish tank-to-tank knowledge transfer, and significantly outperform traditional transfer learning-based soft sensor methods.
Application of Natural Interaction in Narrative Presentation in Museum Exhibition

Applications based on Virtual Reality (VR) and Augmented Reality (AR) techniques have become mature solutions for museum displays. However, existing applications in museums tend to focus more on the visual presentation effect rather than interactive functions that engage with audiences, thereby limiting the narrative presentation in museums. This paper presents a case study of the display of the Small Wild Goose Pagoda, introducing a virtual exhibition method based on natural gesture interaction. By combining 3D scanning data and literature research, we realized the virtual restoration of the historical appearance of the Small Wild Goose Pagoda, providing rich content for the narrative exhibition. Utilizing the Leap Motion controller and the VR headset, we developed an interactive displaying system for the exhibition. The experimental application results demonstrate that our method can effectively improve the narrative values and the richness of the museum exhibition.
Multi-agent Proximal Policy Optimization via Non-fixed Value Clipping

With the wide application of multi-intelligent reinforcement learning (MARL), its development becomes more and more mature. Multi-agent Proximal Policy Optimization (MAPPO) extended by Proximal Policy Optimization (PPO) algorithm has attracted the attention of researchers with its superior performance. However, the increase in the number of agents in multi-agent cooperation tasks leads to overfitting problems and suboptimal policies due to the fixed clip range that limits the step size of updates. In this paper, MAPPO via Non-fixed Value Clipping (NVC-MAPPO) algorithm is proposed based on MAPPO, and Gaussian noise is introduced in the value function and the clipping function, respectively, and rewriting the clipping function into a form called non-fixed value clipping function. In the end, experiments are conducted on StarCraftII Multi-Agent Challenge (SMAC) to verify that the algorithm can effectively prevent the step size from changing too much while enhancing the exploration ability of the agents, which has improved the performance compared with MAPPO.
A Federated Learning Framework Based on CSP Homomorphic Encryption

In the era of big data, the field of deep learning is developing rapidly. Deep learning model algorithms and a large amount of data make deep learning an effective tool to solve practical problems. However, in the current centralized deep learning model of a large number of client-server models, when we upload our data for training on the server side, there is a risk of leaking privacy. Federated Learning is a type of distributed machine learning that allows multiple institutions or individuals to learn collaboratively without exchanging data. Users only upload their model parameters, and the server aggregates the model parameters of each user into a global model and returns it to the client, and the client updates its local model according to the global model to achieve the global optimal solution, avoiding leakage of private data. However, attackers can still restore user data by obtaining uploaded model parameters from users, which leads to the fact that only transmitting model parameters cannot protect user privacy. Therefore, the issue of privacy protection has become the focus of federated learning. In this paper, the CSP (conjugate search problem) fully homomorphic encryption algorithm is used to encrypt the user model parameters. The CSP encrypt algorithm will not cause the loss of model performance, and at the same time has the characteristics of fast encryption speed, which is very suitable for application in the field of machine learning.
Short-Term Prediction of PV Power Based on Hybrid CNN-BiLSTM-Attention Model and VMD

Accurate and reliable energy forecasting has become a mainstream trend for solving the energy crisis. The potential of databases in energy forecasting is explored by using big data-driven methods. In this paper, we introduce the variational modal decomposition (VMD) to determine the best model for short-term PV power forecasting by comparing the accuracy between VMD and different combinations of models. Each model uses recursive multi-step prediction at the high level and one-dimensional convolutional networks, as well as long short-term memory networks (LSTM), bidirectional long short-term memory networks (Bi-LSTM), and attention mechanisms at the low level. We evaluate the performance of each model strategy by comparing their mean squared error and mean absolute error against the actual values. The final results show that the VMD decomposition method proposed in this paper has the best prediction accuracy in the combined CNN-Bi-LSTM-Attention model.