Reviewerly/exHarmony huggingface dataset:
Chinese Text Sentiment Classification Based on LSTM Word-Tag Relational Network Model

In the task of Chinese text sentiment classification, the method based on sentiment dictionary and machine learning is suitable for the situation of small amount of text corpus data and simple text semantics. With the explosive growth of online text information and the continuous enrichment of expression methods, many researchers have gradually applied deep learning methods to text sentiment classification and made breakthrough progress. Convolutional neural network (CNN) can effectively capture the local feature information of spatial structure, but it lacks the ability to learn the contextual relevance of words. Recurrent Neural Network (RNN) can solve the semantic context problem well, but the phenomenon of gradient explosion and gradient disappearance is easy to occur in the training process. To solve these problems, this paper designs a word-tag relation network model based on LSTM (Long Short-Term Memory) Chinese text sentiment classification model, which can acquire deeper tag dependency and enrich the feature information of the text.
The Algorithm Based on Supervised Time Series Segmentation of Researching Dynamic Gesture Recognition

The Algorithm use the changes of pixels in the time domain in image sequences and the correlation between adjacent frames, find the corresponding relationship between the previous frame and the current frame to collect samples, ensure that the whole gesture can be collected without too many redundant frames. Use two-way propagation search mechanism, shortest length constraint l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</inf> and maximum length constraint l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</inf> are introduced to reduce the search space. By calculating an estimate ei of a point, determinate of segmentation points for sequence segmentation quantitatively, then calculate the joint probability of each characteristic distribution under each state, category matching based on maximum probability. The experiment uses a large number of samples to train the model and compares it with other algorithms, it shows that the algorithm has achieved good results in dynamic gesture recognition.
Deep Neural Network Copyright Protection Method Based on Fingerprinted Adversarial Samples

Neural network watermarking is currently the mainstream deep neural network (DNN) model copyright protection method, and the embedding of the watermark is accomplished by changing the internal structure of the model or introducing special input-output relations. The current watermarking method has weak defense against ambiguity attacks and only solves the attribution problem of the model, which is difficult to help users to make accurate rights recovery. In order to solve the above problems, we propose a DNN model copyright protection method based on fingerprinted adversarial samples. This method uses asymmetric digital fingerprints to solve the authentication and pirate tracking problems, generates adversarial samples with better mobility by iterative and momentum methods, fine-tunes the model so that the adversarial samples clamp the decision boundary of the model to complete the embedding of the watermark, and finally gives the treatment of piracy behavior. VGG-16 is chosen as the baseline network, and experiments are conducted on three publicly available datasets to evaluate whether the method meets the various performance requirements of neural network watermarking and to derive the appropriate weighting key length. The method outperforms the comparison method in all metrics, while achieving pirate tracking.
Gesture Recognition Using Inertial Sensors with 1D Convolutional Neural Network

Nowadays, smart wearable devices are widely used and Micro-Electro-Mechanical Systems technology (MEMS) sensors are usually embedded in them. In this paper, a novel gesture recognition framework based on the 3D accelerometer and 3D gyroscope of smart phone is prpposed. Currently, most of the effort is focused on manually extracting features from MEMS sensors. However, without relevant domain knowledge, it is difficult to extract valid features to classify specific tasks. Instead of manual feature extraction, we propose 1D Convolutional Neural Network model to automatically extract features and gesture recognition from acceleration and gyroscope data. The best average recognition of ten Arabic numerals was 89.63%. The experimental results show that gesture-based Human-computer Interaction (HCI) can be applied as a new type of HCI for consumer electronics and mobile devices.
Network Equipment Fault Maintenance Decision System Based on Bayesian Decision Algorithm

Bayesian network is a directional loop topology composed of graph theory and probability principle. The input data can be classified by expressing the causal relationship between variables in the way of directed connection and expressing the correlation degree between variables in the way of probability. It avoids complicated operation and improves the efficiency of fault diagnosis. The performance of network devices deteriorates gradually. After a certain period of time, an alarm is generated indicating that the performance deteriorates. If the network devices are not repaired in time, the network devices will be faulty soon. Based on Bayesian decision algorithm, the hierarchical structure of network equipment fault maintenance decision system is designed, and the criteria of Bayesian decision algorithm and the decision model of network equipment fault maintenance are constructed. The research results can make decision on network equipment fault maintenance and improve the level of network equipment fault diagnosis and maintenance.
Service Dynamic Mapping Algorithm for Industry Chain Knowledge Graph

The industrial chain knowledge graph is an industrial chain analysis method based on knowledge graph technology, which can present knowledge, entities, and relationships in a structured way in a specific field. Through the industrial chain knowledge graph, the various links in the industrial chain can be clearly understood, thus enabling the quick discovery of relevant entities and relationships in the user's desired field. Service mapping is crucial for service organization, discovery, and management. However, there are currently many problems with service mapping, as services in various fields on the Internet exist in a chaotic manner, making it difficult for users to quickly discover relevant service information in their desired field. Currently, methods for organizing and managing services based on the industrial chain knowledge graph are not widely available. To improve the efficiency and accuracy of service mapping, this article proposes a dynamic service mapping algorithm based on the industrial chain knowledge graph. This method organizes and manages services in various fields through the industrial chain knowledge graph, and calculates the similarity between feature words based on the word2vec model, combined with the TF-IDF inverse document frequency to comprehensively calculate the similarity between industrial chain segments and services. When the similarity is greater than a specified threshold, the segment and the service are automatically mapped. The results show that this method can greatly improve the efficiency and accuracy of service mapping, thereby greatly improving the efficiency of service organization and discovery.
Research on the Application of Computer Virtual Reality Technology in the Simulation System of Physical Fitness Parameters of Sprinters

Quantitative analysis and evaluation of the change process of athlete’s performance ability in spring training practice is the main way for coaches to understand the training effect, revise the training plan, and scientifically control the training process. In this paper, a simulation system of physical fitness parameters of sprinters is proposed based on the innovation of computer virtual reality technology. The system includes perception interaction subsystem and model calculation subsystem. The perception and interaction subsystem produces and constructs a three-dimensional training scene through the virtual environment, and then renders the picture in the three-dimensional scene through the view module in the model calculation subsystem. The model is established based on the improvement of Banister’s motion adaptation model, and the limited growth parameter of the adaptation is added. The parameter estimation results showed that the fit of the model to the data was 0.882±0.068. The response curve of the model to the analog input signal conforms to the theoretical assumptions. The similarity of the distance correlation between the predicted value of the model and the measured value of the physical state of sprinters is 0.914±0.079. The new model can be adapted for short-term prediction of fitness status.
Design and implementation of quickly building artificial intelligence training environment on high-performance server

In the artificial intelligence algorithm training process, it is a very time-consuming thing for researchers to configure the environment required by the code on the shared servers. In view of the difficulty to configure software operating environment on the server side and the pain that different researchers easy to conflict with each other in the research of big data and artificial intelligence when multiple people share multiple high-performance servers, a cooperation training platform between desktop and high-performance server based on SLURM, Singularity and Anaconda was proposed. At first, Anaconda was used to complete the software prototype development and simple running test in the desktop environment; Then a single file created by the Singularity software which packaged the existing environment and code was uploaded to a remote high-performance server. At last, SLURM and Singularity technology are used to start the operation of the single file on some high-performance servers and calculate the experimental results. A typical deep neural network training process was taken as an example to demonstrate the feasibility of the experimental data platform. The experiment shows that the experimental data platform can quickly configure the same experimental environment on the server and desktop, and has the characteristics of security, easy management and high efficiency.
Research on Improving Personalized Recommendation Accuracy Based on NLP Semantic Analysis

In order to improve the accuracy of English machine translation under the deep learning mode, an improved personalized recommendation model for English machine translation based on NLP(natural language processing) semantic analysis is proposed. According to the semantic mapping structure characteristics of the source language words, the NLP semantic analysis model of English machine translation under the deep learning mode is established. The parameters of the training model with large-scale corpus are taken as the initial parameters of the scarce corpus model. The neural network parameters of the deep learning are used to learn multiple English machine translation tasks at the same time, and the meta learning is introduced into machine translation. The NLP semantic learning task of English machine translation is solved by using a small amount of computational gradient iteration. The NLP semantic analysis information is introduced into the translation model to expand the existing corpus resources and improve the personalized recommendation accuracy based on NLP semantic analysis. The test results show that the personalized recommendation learning ability of English machine translation using this method is strong, which can effectively use the contribution of different parameters to the loss function to compare whether the parameters are beneficial to the training process, and improve the efficiency of recommendation and the vocabulary matching ability in translation.
Convolutional Neural Network in Face Recognition Application Analysis

Face Recognition has proven to be one of the most successful technology. In this study, the convolutional neural network is applied to the field of face recognition, which greatly improves the accuracy of recognition. In this study, by obtaining higher quality face image data source, select a better face recognition optimizer, adjust some of the hyperparameters, and then improve the ability of face data processing. In this study, convolution neural is used to improve the algorithm accuracy of feature extraction, which improves the recognition effect of the model.
Consortium Blockchain Efficient Storage Access Control Solution

The consortium blockchain platform Hyperledger Fabric provides a channel mechanism to isolate and protect the data, but the nodes inside the channel access the chain data at will and the centralization of management make the consortium blockchain face security problems such as data leakage, confusion of node access rights, and inability to update access rights. In order to solve the above problems in the consortium blockchain, an efficient storage access control scheme for the consortium blockchain applicable to Hyperledger Fabric is designed. The scheme is studied from several aspects of access control, attribute update, access policy update, key distribution and efficient and secure storage of data, using a combination of data chain and attribute set chain through the channel mechanism provided by Fabric, using database to store update records to achieve access policy update, using Fabric-CA component to achieve node attribute private key generation and distribution, proposing IPFS chunking and negotiation mechanism to reduce the I/O performance consumption of nodes. Experiments and security analysis show that the proposed scheme is efficient and secure.
Design and Implementation of a Cyber Rang with Emulated Network Security Devices

When universities conduct network attack and defence experiments, window, Linux systems and vulnerable applications are simulated in the Cyber Security Range, while the simulation of network security devices is missing. As a result, the experimental process is quite different from the process in the real production environment. EVE-NG (Emulated Virtual Environment) is an emerging virtual simulation platform that incorporates Dynamips, IOL, and QEMU simulation technologies. Based on the EVE-NG platform, this paper not only simulates network security hardware devices such as firewall, web firewall, logging platform, but also builds an infrastructure environment for network attack and defence. Based on this environment, a complete network attack and defence experimental environment can be constructed by adding various host systems with vulnerabilities, applications, and even vulnhub target machines. Using the method in this paper, the experimental environment can be established by using a highly configured computer without purchasing any hardware security device, which reduces the cost of establishing a laboratory. In addition, this kind of Cyber Security Range is closer to the real production environment, which is conducive to improving students' network attack and defence skills.
HSVAugRegNet: Random Augmentation to Rotate Image Recognition

Ship detection technology has always played an important role in operations such as maritime traffic management, ship protection and identification of noncompliant ships. When conducting water surveillance, ship target detection is carried out not only to ensure ship safety but also to supervise non-compliant ships. Due to the current improvements in physical imaging technology and developments in the field of machine vision, it is now possible to use deep learning methods to train neural networks for ship inspection. In all types of ship inspection tasks, ship targets are variable in direction and vary greatly in scale, and the detection accuracy is not high if the generic traditional horizontal frame detection method is made to work. Therefore, this paper proposes the HSVAugRegNet based on the framework of RegNet, which deepens the network depth based on RegNet and also adds data enhancement means HSV random perturbation to change the image brightness and improve the network generalization ability. After conducting experiments on the well-known dataset HRSC2016, it was found that compared to the original method, the results obtained by HSVAugRegNet improved 3.41% of the map, satisfying the need to improve recognition accuracy.
Research on Photovoltaic Power Forecasting Based on SOM Weather Clustering

Changeable weather leads to random output from Photovoltaic (PV). Therefore, it is difficult to forecast PV power generation accurately by manual forecasting or traditional forecasting methods. We proposed a new method to improve forecast accuracy based on SOM-BP Neural Network. Firstly, we clustered different weather types by Self-Organizing Maps (SOM) to solve the problem of inaccurate PV power forecasting in the conditions of changeable weather. Secondly, Back Propagation (BP) Neural Network was applied to predict PV power generation considering the result of weather clustering from SOM methods. Finally, we relied on the PV power generation data and meteorological data in Qingpu District, Shanghai, trained the overall model and acquired the forecast results. The results showed that the SOM-BP model had the greatest forecast accuracy through the error comparison analysis among different models.
Research on Mobile Robot Path Planning Based on Fuzzy Artificial Potential Field Algorithm

Route planning is a hot issue in mobile robot research. Artificial potential field (APF) method is a classical method to figure out mobile route planning problem. However, the traditional APF method has the disadvantages of being unable to reach and gradually moving away from the target and wandering in situ and falling into local optimum. Therefore, an improved APF method combining fuzzy control with APF method is proposed. Through the design of two double input single output fuzzy controllers, the heading angle and speed are adjusted in real time to help the robot conquers the defects of the traditional APF method in some special cases, reach the target more smoothly and safely, and achieve effective route planning. Simulation Experiment of Robot Route planning in Matlab. The Simulation result indicates that the fuzzy improved APF method can figure out the problem of unreachable target and local optimum, and also shows advantages in obstacle avoidance safety.
A safety helmet recognition algorithm for substation operation site

In the process of electric construction, electric workers are required to wear safety helmets. However, in recent years, production safety incidents in substations have occurred frequently, and the fundamental reason is that operators violated power safety regulations to operate, and one of the important reasons is not wearing safety helmets in accordance with the regulations. In this paper, a pixel-level visual background extraction moving target detection algorithm is proposed to obtain foreground targets. Experiments show that the detection algorithm studied in this paper basically meets the requirements of substation workers' helmet wearing status recognition performance, which is a good way for subsequent safety The application of hat wearing status recognition technology to the intelligent video monitoring system of substations has laid a foundation, and it also provides a new idea for the identification method of helmet wearing status in substations.
Research on sea target detection method based on deep learning

Artificial intelligence technology has made rapid development in recent years, in which the deep learning field has shown strong applicability. In the field of national defense, the war mode has also changed from mechanized combat mode to intelligent combat mode. YOLOv5 network based on target classification and regression is widely used in the field of target detection. This paper proposes a ship detection method based on YOLOv5x video image. We use photographic data annotation and self-made data sets to obtain training data based on video images, and combine them with YOLOv5s model. We use the transfer learning method to train the model and modify the weight parameters. The experimental results show that the detection accuracy of the algorithm can reach 95% for the target class ships and ships in the video images.
High-altitude parabolic detection system based on machine vision

The high-altitude parabolic intelligent detection and early warning system based on machine vision is a high-precision, low-cost, practical, and easy-to-assemble new system designed by combining existing technical means. Combined with traditional video surveillance, the system performs morphological processing on the initial image through grayscale and Gaussian filtering methods, and then transmits data quickly through the 5G communication module. The receiver uses a convolutional neural network algorithm to identify the type of objects falling from high altitudes. When entering the threat mode, while identifying the falling object, through the mathematical model established by analyzing its possible force mode, the Kalman filtering method is used to jointly predict the data received by the dual cameras, and the falling object is predicted. Point coordinates and transfer them. The sound and light early warning module that receives the coordinates quickly processes the data, uses eye-catching laser lights to mark the landing point before the falling object hits the ground, and supplements it with a sound alarm to remind pedestrians, and uses a blocking net to intercept to avoid pedestrians from being hurt. This paper focuses on a high-altitude parabolic detection system based on deep learning and intelligent prediction.
Research on aspect-level sentiment analysis of tourism review text based on joint learning

Aiming at the problem that the current aspect level sentiment analysis model is weak in generalization ability and word perception, a multi-task joint learning model GL-IJM for target word emotional polarity discrimination and target word extraction is proposed, which can learn the relationship between specific aspects and context in the training process, and can be used to capture the information closely related to specific aspects in the context sequence, so as to increase the modeling of specific aspects, compared with the BERT-based method. The F1 values of ATE tasks on camera and mobile phone datasets increased by 0.2 and 0.4 percentage points, respectively, and the F1 values of SPC tasks increased by 0.61 and 0.04 percentage points, respectively.
Discussion on Using Continue Statement to Improve a Descending Algorithm

In essence, the simple selection sort, as an important sort descending algorithm that plays a significant role in data mining, decision analysis and other fields, is to compare, assign subscripts and exchange data, which is designed to sort a set of known disordered data in descending order. The analysis on two different sort descending algorithms of simple selection sort using only the main function and the simple selection sort calling the function reveals that the sort descending algorithm uses an if statement in the inner loop. Discussion on how to control the number of if statements used in descending order using the continue statement. The running results showed that the improved algorithm using the continue statement is feasible. It just realized descending sort, and effectively reduced the times of comparing in sort descending and saved time.