Reviewerly/exHarmony huggingface dataset:
Smart contract transaction model of blockchain for multi-source and heterogeneous distribution network

Aiming at the transaction problem of multi-source heterogeneous distribution network, a P2P intelligent contract based on blockchain technology is proposed. Firstly, a multi-source heterogeneous distribution network model based on Intelligent contract is established. Secondly, a multi-source heterogeneous distribution network market transaction mechanism is proposed. The mechanism designs different kinds of transaction members, frameworks and processes. Transaction members can participate in the market quotation and determine the transaction unit price according to their own power surplus and shortage state. Finally, a smart contract solution with blind beat mechanism is designed for the proposed transaction mechanism. The effectiveness of the transaction mechanism is verified by an example and deployed on the Ethereum private chain. The effectiveness of the transaction mechanism is verified by an example and deployed on the Ethereum private chain. Through the simulation transaction analysis, it can be seen that the above transaction mechanism effectively improves the economic benefits of trading members and effectively ensures the market security and free competition.
Intelligent unmanned medicine cabinet based on 51 single-chip microcomputer

With the relatively uneven distribution of medical resources in China and a new outbreak of COVID-19 at the end of 2019, we developed an intelligent medicine cabinet to alleviate the problem of high pressure and difficulty in accessing medical care in hospitals around the country.The medicine cabinet has a signal transmission circuit system based on 51 microcontroller and a new X-Y trajectory control module, which controls the Pulse Width Modulation (PWM) signal by Proportion Integration Differentiation (PID) algorithm to improve the accuracy of the DC motor.It has the functions of online drug selection, drug sales, drug transmission, etc.Meanwhile, the online drug purchase system based on the WeChat applet can reduce the probability of infection by a new coronavirus.And the new X-Y cargo track will significantly improve the safety of fragile drugs while ensuring their delivery.The development of this medicine cabinet will greatly reduce the operating cost of pharmacies and meet the demand of people to purchase medicine at night.
Person re-identification method based on multi-scale residual pooling

Person re-identification is a cross camera pedestrian retrieval problem. The data retrieved by pedestrians can be images, videos, and text. The current person re-identification methods are insufficient in expression of pedestrian features and poor robustness, resulting in low model accuracy. This paper proposes a Multi-scale Residual Pooling for person re-identification. ResNet50 is used as the basic network to obtain the multi-scale features. Global average pooling and maximum average pooling are performed on the input features at different network levels. Each group of average pooled and maximum pooled features is subtracted to remove the influence of image background clutter. The subtracted difference features are added to the maximum pooled features to obtain a more discriminative residual pooled fusion feature, making the network focus on the whole body contour of pedestrians and the difference between pedestrians and background. On this basis, triplet loss and cross-entropy loss are combined to optimize the model, and reordering technology is used to optimize the network. The experimental results showed that the Rank1 index of this paper’s method tested on the Market1501 and Duke MTMC-reID datasets reaches 96.41% and 91.43%, respectively, and mAP (mean Average Precision) reaches 94.52% and 89.30%, respectively. which is better than the current mainstream algorithms.
Research on cloud computing resource scheduling strategy based on genetic ant colony fusion algorithm

In this paper, ant colony algorithm and genetic algorithm are combined to improve the stability and efficiency of cloud computing resource scheduling. The strategy of this algorithm is to use the local optimal solution of genetic algorithm as the initial pheromone of ant colony algorithm, and introduce load balancing adjustment factor and transfer probability into ant colony algorithm. The experimental results show that the genetic ant colony fusion algorithm in this paper has better advantages in the number of iterations, time cost, power cost and load balancing, and achieves the goal of uniform distribution of cloud computing resources.
Research and exploration for deep learning of artificial intelligence based on intelligent voice interaction in engineering experiment project guidance

With the deep exploration and application of AI deep learning in the field of education, deep learning is widely used and promoted especially in the experimental projects of engineering in colleges and universities so that many projects’ intelligent characteristics can be accurately described and simulated by machines. Nowadays, deep learning has been widely used in image, speech, natural language processing, CTR evaluation, big data feature extraction and so on. This paper discusses the exploration and application of deep learning based on intelligent voice interaction to solve the students’ repeated problems in the guidance of engineering laboratories through the solutions provided by machine itself. This could help to reduce the workload of teachers, make the experimental guidance more efficient and improve the learning effect of students more obviously.
Baseline detection based on local connection and evaluation for Tibetan historical document text line

The baseline position is an important reference information of the text line in the document written by Uchen Script.In order to overcome the distortion of text line and obtain accurate baseline position, this paper proposes a baseline detection method based on lines connection local and global evaluation.First, the slanted document image is corrected and the number of text lines is determined by projection.Then the discrete lines are connected locally; Finally, the baseline of text lines is obtained by evaluating the combination of baselines.Experimental results show that the proposed method can effectively overcome document skew and text line distortion, and the baseline position detected is accurate.
Digital audio watermarking algorithm in copyright protection of music works

The Internet and digital technology have impacted all traditional content industries, and the music industry is naturally inevitable. Therefore, research on the Internet, music industry, and digital music copyright has become the focus of academic circles in various countries today. The purpose of this paper is to study the application of copyright protection of musical works based on digital audio watermarking algorithm. Firstly, the theoretical basis of digital music copyright and discrete wavelet transform is expounded. The security of the audio watermarking system is improved by using the pure sequence modulation; the watermark is embedded in the double transform domain according to the energy gathering effect of the discrete cosine transform, and finally the blind extraction of the watermark is realized. Finally, the two algorithms are compared and discussed. The experimental results show that, under the same attack conditions, the normalized correlation coefficient between the original watermark and the extracted watermark of the watermarking algorithm based on pure modulation and double transform domain is larger. The advantages of the watermarking algorithm based on mixed pure modulation and double transform domain are analyzed, which makes the application of wavelet transform coding in music copyright information more practical and extensive.
Practical SS-MPC for collusion whole

Secret sharing (SS) technology is widely used in multi-party computation (MPC) because of its simple secret structure and low computational overhead. Aiming at solving the problem that the existing SS-MPC scheme cannot solve the collusion of all computing parties to recover the original data, this work designs a practical SS-MPC for collusion whole (PSCW) scheme. Through the negotiation of redundant computing nodes and redundant security parameters between computing parties, PSCW scheme can ensure that original data cannot be recovered even if all computing nodes collude to obtain all secret shares, which guarantees the confidentiality of the scheme. At the same time, with the addition of redundant computing nodes, PSCW scheme can correctly realize computing requirements of users and ensure the correctness of the calculation results. Compared with the existing SS-MPC scheme, PSCW scheme has higher security and higher application value in real scenes.
Method of subspace cross-modal retrieval based on hypergraph ranking

Based on the research of subspace cross-modal retrieval method based on high-level semantic correlation, a subspace cross-modal retrieval method based on hypergraph ranking is proposed. The method is mainly divided into two parts: subspace mapping and hypergraph sorting. In the subspace mapping part, the high-order semantic correlation and sparse structural constraints of multi-modal data are considered. The hypergraph sorting part uses hypergraphs to describe multiple targets. The characteristics of multiple correlations of multimodal data are comprehensively considered, and the similarity between multimodal data is comprehensively considered, and the semantic correlation between multimodal data is further mined. Combining hypergraph sorting and subspace learning to further mine semantic associations between cross-modal data can further improve cross-modal retrieval accuracy.
Implementation of multi-domain isolation architecture and communication mechanism in Linux

Multiple Independent Levels of Security (MILS) is widely used in the design of high assurance operating system. By separating the system into components, and making the components run in different domains, the kernel can control and monitor information flow between components to enhance the security and availability of system. However, due to the complexity and certification cost issue associate with large monolithic kernel, MILS architecture is mainly used in microkernel system. But we still want to use the idea of MILS in monolithic kernel system to improve the security. In the Linux, although there are some access control models based on the concept of domain (like SELinux). Limited by the feature of shared kernel, the security of system is affected by the vulnerabilities in itself. Therefore, this paper proposes a scheme of constructing multiple independent isolated domains based on virtualization technology in Linux. We developed on Linux kernel and QEMU/KVM hypervisor, exploiting the isolation feature brought by virtualization to achieve data isolation. We build domain from virtual machine, so that we can separate origin system into components and run them in domains. In the host, we take control of all domains and implements a secure communication mechanism between domains. By using this secure channel, we can monitor the data transmission between domains, and control the information flow according to the security level of the domain. Finally, we evaluated the effectiveness and efficiency of our communication mechanism.
Feature pyramid graph convolutional networks for temporal action detection

In some long and untrimmed videos, locating the important and key segments can be a very challenging task for temporal action detection.Current methods make remarkable progress when it comes to RGB images.The aim of this paper is to develop a method with the assistance of dynamic model of human body skeletons to address this problem.To this end, we propose a Feature Pyramid Graph Convolutional Network (FP-GCN).The introduced model contains a Feature Encoding Module to encode skeleton data with graph convolutional networks, a Feature Pyramid Module to exploit the inherent pyramidal hierarchy and an Action Detection Module to generate the final prediction results of the detection.We experiment our approach on NTU RGB+D and THUMOS14 datasets and obtain a satisfactory result.
A text detection algorithm for ancient Chinese writing based on Swin transformer and mask R-CNN

The study of ancient Chinese writing has great cultural and historical value.Text annotation is a time-consuming and laborintensive part of ancient writing research.In this paper, we construct a deep learning model for ancient Chinese text detection which combines Swin Transformer and Mask R-CNN.To solve the overlapping detection problem, we propose Text Non-Maximum Suppression (text-NMS) and text-NMS loss.The former is to weed out redundant subtext bounding boxes in the Non-Maximum Suppression process, and the latter further rectifies the detection failure missed by text-NMS in bounding box regression.Experiments on the Chinese Stone Inscription Dataset show that the proposed algorithm can improve the accuracy of ancient text detection.The text-NMS and text-NMS loss algorithm boost the average precision (AP) of Swin-small and Mask R-CNN from 64.4% to 65.8% with few additional hyper-parameter and computational overhead.
Research on technical architecture of knowledge extraction in vertical field

How to extract high-value knowledge quickly, accurately and comprehensively from high volume, and high variety data in vertical field, is a major issue to be solved in the intelligent construction of various fields. Knowledge extraction, which extracts large-scale structured knowledge from multi-source heterogeneous data, can provide a strong support for many applications, such as knowledge graph construction, automatic question answering, situation awareness and intelligent decision. In this paper, we first summarize the critic problems and challenges of knowledge extraction in vertical field by surveying the key technologies involved in knowledge extraction. Then, we propose a unified framework of knowledge extraction targeting for vertical field. This framework presents a detail solution for the key problems in knowledge extraction and indicates the potential research work to be carried out in the future.
Attention enhanced dynamic kernel convolution for TDNN-based speaker verification

Speaker embedding is a state-of-the-art front-end module, which is used to extract discriminative speaker features for speaker-related tasks. The Time Delay Neural Network (TDNN) has been a classical network architecture since it was first applied on speaker related tasks known as X-vector. In this paper, we propose new network structures based on current popular ECAPA-TDNN. We propose a dynamic kernel convolution module to extract features from short-term and long-term context adaptively, thus achieving multi-scale receptive fields. We also apply three enhanced attention modules instead of plain Squeeze-Excitation (SE) layer to realize more efficient information interaction between channels and spaces. The proposed architectures are superior to the most advanced network, with an optimal Equal Error Rate (EER) of 6.40% and a parameters reduction of 6.32%, they also achieve better performances when speaker utterances are shortened.
Scene graph generation based on global embedding and contextual fusion

As a structured abstraction method for objects and their interactions in visual scene, scene graph captures entities in the scene and the relationships between the entity pairs, and helps in understanding the visual scene better. Currently, scene graphs in most research works are generated by modeling using context information among targets, focusing only on the inference process but ignoring the integrity of the input target information and the impact of the global information of the targets on relationship inference. Therefore, a new scene graph generation method based on global embedding and contextual fusion (GECF) is proposed in this paper. In this method, richer entity information is obtained by embedding global information into the entity features, while more robust inference of entity interaction information and more reasonable relationship fusion are acquired by combining the attention weighting module and the context inference module as a joint inference module, and merging the obtained entity features according to their discrepancy. The experiment on Visual Genome dataset shows that GECF method performs better than the existing methods in scene graph visual relationship detection.
Deep learning algorithm vs XGBoost using Wisconsin breast cancer diagnosis

Breast cancer is causing a significant increase in the number of deaths every year. It is the most common kind of cancer and the leading cause of death in women all over the world. Any improvement in cancer illness prediction and detection is critical for a healthy life. As a result, high accuracy in cancer prediction is critical for keeping patients’ treatment and survival standards up to date. Machine learning and deep learning approaches, which have been shown to have a significant impact on the process of breast cancer prediction and early diagnosis, have become a research hotspot and have been proven to be a powerful technique. We went through one machine learning method, XGBoost and deep neural network in this research on the Wisconsin Breast Cancer Diagnostic (WBCD) dataset. After getting the outcomes, these distinct classifiers’ performance is evaluated and compared. The major goal of this research is to use machine-learning and deep learning algorithms to predict and diagnose breast cancer, and to determine which algorithms are the most efficient in terms of confusion matrix, precision, and accuracy.
Design and implementation of research on dispatching of earthquake emergency materials based on improved article swarm optimization algorithm

China is a high-incidence area of earthquakes, and earthquakes cause huge economic losses and casualties every year. Earthquake disasters have adverse effects on the economic and social development of any country in our country. In the post-earthquake rescue process, the dispatch of emergency rescue teams and materials is very important, which is the basis for rescue and disaster relief, response to emergencies, and disaster relief. This paper is based on the related application research of improved particle swarm optimization algorithm. The emergency material reserve is the key to improving the emergency material support capability and an important guarantee for responding to sudden disasters. Therefore, it is necessary to select the best site selection plan for the emergency material reserve. Based on the background of earthquake disaster, this paper analyzes and studies the location problem of emergency material reserve. At the same time, in order to improve the post-earthquake emergency rescue capability, the reasonable dispatch of post-disaster emergency supplies is particularly important. The purpose of this paper is to design and implement the technology involved in the research and dispatch of earthquake emergency materials. The final result of the research shows that the required quantities of emergency materials dispatch are 9.94 tons, 6.52 tons, 4.98 tons, 7.64 tons and 5.33 tons, respectively. The corresponding emergency material dispatch shortage ratios are 6.74%, 4.33%, 3.41%, 5.17% and 4.05%, respectively. The shortage ratio of emergency material dispatch changes with the change of the required quantity of emergency material dispatch. At the same time, this paper analyzes and researches the location of emergency material reserves, in order to improve the emergency rescue capability after the earthquake and reasonably dispatch the emergency materials after the disaster, which is particularly important.
Abnormal event detection based on appearance repair and motion consistency

The application of abnormal event detection in video surveillance is an active research field, but due to the imbalance of positive and negative samples in surveillance video, abnormal event detection is full of challenges. In this paper, we propose a new abnormal event detection method based on appearance repair and motion consistency for detecting anomalous events. Specifically, the input image is partially masked and then fed into our proposed appearance repair autoencoder for image reconstruction, and then the motion consistency of images is constructed by our proposed optical flow network. The experimental results on the UCSD, CUHK Avenue datasets show the superiority of the detection performance of our method.
Machine learning technology in safety production and maintenance of power plant

Many safety accidents occur in electric power enterprises every year, more than 50% of which are caused by the lack of safety supervision, disregard for safety regulations and non-standard operation when operators are maintaining electric power equipment.In order to improve the professional quality of operators, we developed the power equipment maintenance training system based on virtual reality, which allows operators to operate in a 360-degree immersive virtual environment.In this system, we also developed an automatic online evaluation algorithm based on YOLOv3, and the experiment results shows that this method has good accuracy, intelligence and high reliability.
Behavioral Steganography in Social Networks

Recently, using human behavior to hide the existence of information has been at the center of steganography research. In this study, a behavioral steganography algorithm using CMI (Coded Signal Inversion) coding is proposed to minimize the high bit error rate that occurs when transmitting a large number of continuous and identical confidential information in the knapsack algorithm, which is used to improve information transmission efficiency and flexibility of transmission mode in social networks. In the proposed algorithm; Data redundancy is reduced by reducing the number of mutual friends of the sender and each receiver. Then, the proposed algorithm was applied and the results were analyzed. Experimental analysis shows that this scheme improves the practical value of behavioral steganography in social networks and has high security.
Improved puzzle optimization algorithm with multi-strategy integration and its application

Aiming at the problems of low solution accuracy, slow convergence speed and easy to fall into local optimization, a multi-strategy fusion improved puzzle optimization algorithm (IPOA) is proposed. Improved Circle mapping strategy, dwarf mongoose optimization (DMO) strategy, and adaptive t-distribution variation strategy were used to increase the diversity of puzzle populations, improve global search capabilities and local development performance. Through the analysis of multiple benchmark function simulation experiments and PID controller parameter optimization problems, the results show that the IPOA algorithm has high optimization accuracy, fast convergence speed and effectiveness in solving practical engineering problems.