Reviewerly/exHarmony huggingface dataset:
AI for AI: Using AI methods for classifying AI science documents

Abstract Subject area classification is an important first phase in the entire process involved in bibliometrics. In this paper, we explore the possibility of using automated algorithms for classifying scientific papers related to Artificial Intelligence at the document level. The current process is semimanual and journal based, a realization that, we argue, opens up the potential for inaccuracies. To counter this, our proposed automated approach makes use of neural networks, specifically BERT. The classification accuracy of our model reaches 96.5%. In addition, the model was used for further classifying documents from 26 different subject areas from the Scopus database. Our findings indicate that a significant subset of existing Computer Science, Decision Science, and Mathematics publications could potentially be classified as AI-related. The same holds in particular cases in other science fields such as Medicine and Psychology or Arts and Humanities. The above indicate that in subject area classification processes, there is room for automatic approaches to be utilized in a complementary manner with traditional manual procedures.
UCloudStack - A Private Cloud Platform for Lightweight Delivery

Cloud computing is a relatively mature business computing model, which is gradually developed from technologies such as distributed computing, parallel processing, and grid computing. Similarly, with the continuous emergence of cloud computing applications, people’s understanding of cloud computing is also constantly changing. This paper designs a private cloud platform called “UCloudStack” based on cloud computing technology. The platform can provide a complete set of cloud resource management capabilities such as unified management of core services such as virtualization, SDN network, and distributed storage, resource scheduling, monitoring logs, and operation and maintenance, helping the digital transformation of government and enterprises.
niDts: A New Generation Intelligent Data Trading System

As an innovative economic form, the digital economy takes data as a new production factor and becomes a new driving force to promote the high-quality development of economy and society. Building a fully digital trading platform plays a very important role in improving the development level and core competitiveness of China’s digital economy.At this stage, there are many problems in data trading, such as insufficient ecosystem, unclear data ownership, imperfect system and mechanism, imperfect supervision system, and shortage of industry professionals.This paper proposes a new generation of Intelligent Data Trading System of niDts, which realizes the liberalization of data trading, providing efficient, convenient, transparent, and safe data product trading services for data circulation transactions. The system has stimulated the multiplier effect of data elements and led to the digital transformation in the fields of economy, life, and governance.
Electric Power Asynchronous Heterogeneous Data Accelerated Compression for Edge Computing

With the development of edge computing and cloud computing in power scenarios, the cloud center collects a large amount of data from edge nodes every day, and the load of edge nodes is overloaded and the transmission delay increases, making it difficult to store and use data in the cloud center. The communication capabilities, storage capabilities and computing capabilities of nodes face greater challenges. Load data is the most important structural data in the asynchronous heterogeneous data of electric power. In order to reduce the amount of data generated during the transmission process of the edge network, compression technology can be used to effectively compress the load data. Before using the traditional integrated neural network model to compress the time series load data, it is necessary to calculate the variance of the window data, and compare the obtained variance with the empirical threshold, so as to divide the load data into stable data and unstable data. Due to the complex logic of data preprocessing, the overall compression calculation is time-consuming, and the robustness of the data classification algorithm is not high due to the need to manually set empirical parameters. In this paper, the multilayer perceptron is applied to load data classification, combined with the integrated neural network model, to construct an edge-side data compression scheme that can be applied to smart grid scenarios. This scheme achieves faster compression speed on the basis of ensuring the original compression ratio.
Transwarp ArgoDB: A Distributed Flash Database

Transwarp ArgoDB is a one-stop distributed flash database newly issued by Transwarp. It can replace the Hadoop + MPP hybrid architecture, satisfying business’s various demands for big data platform. Therefore, businesses can use big data platform more efficiently and make better use of big data’s commercial value. It supports standard SQL syntax and provides advanced technical capabilities such as multi-mode analysis, realtime data processing, storage and calculation decoupling, mixed load, data federation, mixed deployment of heterogeneous servers and so on. Through an ArgoDB database, we can meet the needs of data warehouse, real-time data warehouse, data mart and federal computing. While reducing platform complexity and it total cost of ownership, it improves business response speed. As an excellent database product, it has successfully replaced Oracle, DB2, Teradata and other foreign products in all walks of life.
Unbalanced Data Classification Model in PHM Field Based on Oversampling Algorithm

Fault detection based on data-driven artificial intelligence has always been a research hotspot. Due to the long- term operation of rotating machinery in a healthy state, the lack of historical data on faults leads to data unbalance problems, which hinder data-driven fault diagnosis and have become one of the stubborn problems in the field of PHM. From the perspective of data preprocessing, this paper explores the effects of SMOTE and LR-SMOTE oversampling algorithms on unbalanced data of rotating machinery. This paper uses the public data of gears and bearings to artificially establish various types of unbalanced data and combines the SMOTE and LR-SMOTE oversampling algorithms with SVM, RF, and GBDT three classifiers into multiple models for experiments. The experimental results show that the algorithm combining LR-SMOTE and SVM can achieve better classification results and has better stability. And compared with the SMOTE algorithm, LR-SMOTE can effectively avoid the overfitting problem of the GBDT classifier.
A Method of Ideological and Political Teaching Resource Accuracy Scheduling and Control Based on MVC Framework

The currently used resource scheduling methods are sorted according to the priority of demand objects, resulting in large computing load, long response delay, low load balance and waste of computing resources on some nodes. Aiming at the above problems, this paper proposes a method of scheduling and controlling ideological and political teaching resources based on MVC framework. After building the MVC framework of Ideological and political teaching resource scheduling, the controller is used to collect the teaching resource scheduling information under the framework. After the establishment of the ideological and political resources scheduling model, the Q-learning algorithm combined with the deep neural network is used to solve the model to achieve the effective scheduling and control of resources. In the experiment of scheduling method, the method under MVC framework reduces the scheduling response time by about 36.85%, improves the load balance, and makes full use of scheduling computing resources.
An Efficient Stable Retrieval Algorithm for English Term Translation Vocabulary Based on Data Mining

In order to ensure the quality of English translation, an efficient retrieval algorithm based on data mining is proposed. Combined with data mining technology, the features of English term translation vocabulary are retrieved, and the similarity evaluation model of English term translation vocabulary is constructed. Based on the similarity characteristics of English terms and translated words, English terms and translated words are reasonably distinguished, and the retrieval process of English terms and translated words is simplified. The experiment proves that the efficient retrieval algorithm based on data mining can meet the research requirements in practical application.
Wildlife detection algorithm based on Inv-YOLOv5m

Wildlife target detection based on infrared camera images is of great significance for biological protection. At present, with the gradual popularization of infrared camera detection, real-time wildlife detection has been widely studied. A series of target detection models have emerged, but the detection accuracy is always not high. In order to realize the effective detection of wildlife, a wild animal detection method based on improved YOLOv5m is proposed in this paper. First, the Focus module in the original feature extraction network is replaced by CBL module. Secondly, the self-attention mechanism is embedded in the feature extraction module to improve the feature extraction ability of the detection network. Finally, the involution operation is introduced into the feature fusion module to improve the detection ability of each feature scale. The experimental results show that the enhanced YOLOv5m wildlife detection model in this paper has greatly improved the accuracy, recall rate and map, and provides a new technical means for wildlife detection.
Hierarchical Optimal Path Optimization Method for Public Building Space Based on Improved Ant Colony Algorithm

Aiming at the problem of indoor optimal path planning, based on the hierarchical characteristics of public building space, a hierarchical optimal path optimization method for public building space based on improved ant colony algorithm is proposed, and the indoor hierarchical optimal path algorithm is implemented. The algorithm regards the road network and floor connections of each floor as an independent structure, and dynamically constructs a structured network model spanning two floors on a floor-by-floor basis according to the floor distribution of the stops. The proposed method uses the network model to analyze the path across floors, thereby obtaining the optimal path traversing all stops in the public building space. The experimental results show that compared with the traditional optimal path algorithm, the time efficiency of this algorithm is significantly improved when the path planning results are more reasonable.
Knowledge representation and application of cloud data center fault diagnosis based on Knowledge Map

The cloud data center of large companies are facing the problems of rapid growth of object scale and the topological relationship of operation and maintenance objects changing from static to dynamic. Based on the sorting out of the cloud data center fault diagnosis knowledge structure, this paper proposes to integrate knowledge and data through a large number of rules, and defines various entities and relationships in the cloud data center operation and maintenance, the cloud data center fault diagnosis knowledge map is constructed. Through the construction and application of the cloud data center operation and maintenance knowledge map, the practicability of the fault diagnosis knowledge map defined in this paper in the operation and maintenance knowledge management and application is verified.
Wireless Communication Power Stable Control Method Based on Adaptive Algorithm

The existing wireless communication power control effect is poor. In order to control the wireless communication power effectively, this paper studies the effective control method of wireless communication power combined with adaptive algorithm. A control model of wireless communication network is established. On this basis, the signal-to-noise ratio of wireless communication with or without delay is calculated. Based on this, a simple power model is established. The adaptive algorithm is used to calculate the control parameters of the time-varying channel of wireless communication, the car model with system parameter identification is obtained, and the signal-to-noise ratio of mobile transmission signal is calculated. Under the influence of network transmission delay, a mathematical model is obtained which is in line with the actual communication environment, and the design of wireless communication power control method based on adaptive algorithm is realized. Comparing the absolute value of signal-to-noise ratio tracking error between this control method and two traditional methods, the larger the error, the worse the control effect. In the experiment, the absolute value of the control method under the adaptive algorithm is the smallest, and the speed to achieve the minimum error is the fastest. It can be seen that this method has the best control effect.
SmartCMP: A Cloud Cost Optimization Governance Practice of Smart Cloud Management Platform

Cloud computing, as a new format of the information industry, is the key technology and means to lead the innovation and development of the information industry in the future. As the global economy continues to decline, enterprises pay more attention to refined operations and cost reduction and efficiency enhancement than before, and cloud computing is no exception. Does cloud computing ultimately increase the cost of business or is it worth the money? In the situation of business homogeneity competition, the cost, investment and operation of cloud infrastructure have also become the key to affecting the market competitiveness of enterprise cloud business. To achieve these issues, we design the platform named SmartCMP, which provides the value of cloud cost analysis and optimization. Firstly, from the perspective of financial, multi-dimensional display and analysis of cloud costs are carried out to find ways to reduce costs. Then, users can customize strategies for daily operation, speed up decision-making and reduce risks. Lastly, our platform will actively monitor, detect and repair risks in real time in accordance with policies to strengthen security. The effectiveness of our platform can be verified by comparing to other strategies.
Local Sharing Method of Accounting Information Based on Optimized Security FOA Algorithm

In order to improve the efficiency of accounting information management and realize the local sharing of accounting information, this paper proposes a local sharing method of accounting information based on optimized fruit fly optimization algorithm (FOA). On the basis of constructing the accounting information sharing model by using the optimized FOA algorithm, the importance evaluation index of local accounting information sharing is set up. Then, in the value identification process of accounting information sharing, FOA information sharing management process is improved according to the needs of different levels of information users, and then the local sharing of accounting information is realized in a modular manner. Experiments show that this method has high practicability, its sharing process is less time-consuming, and the basic carrier capacity of the sharing platform is improved.
A Multi-Rotor UAV Track Accuracy Planning Method Based on Bidirectional Recurrent Neural Network

Multi-rotor UAV is one of the key devices for overhead surveying, and autonomous trajectory planning capability is one of its key performances. In the practical application environment of UAV, there are complexity and uncertainty in the operating environment, which makes the trajectory planning more difficult. Therefore, a multi-rotor UAV trajectory planning method based on bidirectional recurrent neural network is proposed. According to the force situation of UAV, the dynamic model of multi rotor UAV is established firstly, and the flight environment model is established on the basis of drawing digital map. Then, according to the relevant parameters of UAV and obstacles, this study uses two-way recurrent neural network to solve the optimal trajectory planning results of UAV. The experimental data show that: the minimum delay of the track planning of this method is 2.10s, the maximum success probability of avoiding obstacles is 100%, and the minimum value of the track deviation rate is 4.75% which proves that the track planning effect of this method is better.
A Preliminary Study on the Application of Knowledge Graph on Aero Engine Health Management (EHM)

Aero-engine prognostics and health management technology are the main methods to address the operation and maintenance of complex equipment system as an aero-engine. With the development of control system and sensor technology, the monitoring data grows in huge and presents typical characteristics such as high speed, multi-source and heterogeneous, etc. The intelligent and effective analysis of the huge data is the key to health management, and knowledge graph is one of the key technologies to solve the problem that a large amount of unstructured data is not fully applied. This paper summarizes the necessity and research status of knowledge graph in aero-engine health management, and makes a preliminary exploration and outlook on the application of knowledge graph in the field of aeroengine health management.
Effect of Transverse Openings on Structural Behavior of Reinforced Concrete curved Beams

In this research, the effect of horizontal openings for two models of reinforced concrete was studied, and the original model was without openings, and a weakness was observed in the model with openings. We used an external reinforcement by means of carbon fibers, and an increase in the bearing capacity was observed. Compared to the control CCB, the ultimate load capacity of all specimens (CBH.O and CBH.S) reduced by about (11.7 percent and 5.4 percent, in both).
Research on Stable Prediction Method of Online Learning Effect Based on Decision Tree

In order to better improve the efficiency of online learning, based on the current poor effect of online learning, an online learning effect prediction method based on decision tree is proposed. The decision tree algorithm simplifies the collection process of online learning behavior characteristics, constructs the prediction and evaluation index system of online learning effect, and optimizes the prediction steps of online learning effect. Finally, experiments show that the online learning effect prediction method based on decision tree has high effectiveness. When the background information and interaction time = 101-800, the prediction failure rate is up to 6.5%, which can fully meet the research requirements.
Enrichment Program using PROMETHEE for Decision Support Systems of Prospective Assistance Funds Disabilities

There are several criteria in determining the receipt of funds for people with disabilities, including age, education, causes of disability, type of disability, and even marital status. The Preference Ranking Organization for Enrichment Evaluation (PROMETHEE II) method is used to calculate the criteria previously mentioned so that the final results obtained can be used as a reference for aid distribution. Submissions will be accepted if the PROMETHEE II calculation result is written plus. But if the value generated after the calculation is minus, the request will be rejected. The net flow value will be rated as one better. From all trials, both structural tests, functional tests, and validation tests, it can be concluded that the comparison results reached 85%.
A Full Compensation System for Grounding Residual Current in Distribution Network Based on Real-Time Waveform Tracking and Detection

In order to better ensure the safety of distribution network operation, a full compensation system for grounding residual current in distribution network based on real-time waveform tracking and detection is designed. The system hardware is optimized, and the equipment structure design is improved for the main circuit combined with active power filter and thyristor-controlled reactor. Based on the real-time waveform tracking detection technology, the working principle of the system is analyzed, and the characteristics of resonance suppressing power quality comprehensive compensation system for grounding residual current in the distribution network are optimized. The final simulation results show that the full compensation system of grounding residual current in distribution network based on real-time waveform tracking. At the same time, it can effectively carry out comprehensive dynamic control of harmonics and reactive power in the system, providing a basis for high-quality and energy-saving distribution networks.
Design of Aero-Engine Simulation Demonstration System Based on 3D Virtual Software

In order to avoid excessive power consumption of aero-engine equipment and realize effective protection of the application ability of engine components, an aero-engine simulation demonstration system based on three-dimensional virtual software is designed. After connecting the client server and LAN equipment as required, the system application platform is built by establishing system modules and packaging processing. Then use 3DS Max 3D modeling software to build a mathematical constraint model. After starting the engine, calculate the thrust rod angle and judge whether it is in the reverse thrust state. On the premise of known throttle thrust rod angle and flight environment parameters, the performance parameters of the engine are analyzed. Finally, the aero-engine simulation demonstration is completed by combining all levels of hardware equipment. The experimental results show that after the application of the system, the power signal consumed by the engine components in a unit time can be well controlled, which meets the practical application requirements of effectively ensuring the operating capacity of the engine components.
Accurate Recommendation Algorithm of Preschool Education Network Resources Based on Improved Decision Tree

Aiming at the difficulty of users obtaining preschool education network resources, a precise recommendation algorithm of preschool education network resources based on improved decision tree is proposed. The categories of effective information of preschool education network resources are adjusted by improved decision tree, combined with the reconstruction of preschool education network resources, Extract the effective information of preschool education network resources, set the weight threshold of preschool education network resources similarity through the analysis of the similarity of different preschool education network resources in the data set, calculate the weight value, obtain the similarity between preschool education network resources in the data set, obtain the distribution of the similarity between preschool education network resources, and complete the calculation of the similarity value between preschool education network resources, Through user interest modeling, an accurate recommendation algorithm for preschool education network resources is designed. The experimental results show that the accurate recommendation algorithm of preschool education network resources based on improved decision tree has good effect and performance on preschool education network resources recommendation.