Reviewerly/exHarmony huggingface dataset:
Research on an integrated index prediction model based on RF-XGBOOST-ANN

At present, the performance of each machine learning algorithm is different in the exponential prediction research, and they work independently without fusion, and cannot complement each other, resulting in low accuracy, generalization and stability of each machine learning algorithm. This paper aims to find a better prediction algorithm by comparison. In this paper, by constructing a prediction method based on Random Forest-XGBOOST-Artificial Neural Network and integrated learning based on BAGGING, MSE, R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and MAPE are used as test indicators. The conclusions are as follows: The RF-XGBOOST-ANN integrated algorithm has great advantages over single machine learning, with small error and accurate prediction. It stands out among many integrated prediction models and has broad application prospects. In this paper, the ensemble algorithm is used to improve the prediction accuracy. The prediction results are more accurate. The machine learning algorithms are effectively integrated, and the prediction performance is significantly improved.
Research on Chinese Sentiment Classification Based on BERT-wwm-BiLSTM-SVM

Music reviews help to understand the specific information in the recording, and use this to judge the listening intentions and recommendation plans of different individuals. By analyzing the emotional polarity of comments, it can help users get better personalized recommendation schemes, and the platform can also adjust and optimize the content according to user feedback. Therefore, this study proposes a sentiment classification model based on BERT-wwm-BiLSTM-SVM. The model uses web crawler technology to crawl relevant comments from mainstream music software. The obtained data is cleaned and organized to form a music comment dataset. Firstly, the BERT-wwm model is used to process word vectors on music review data Obtain the characteristics of words and sentences, while avoiding ambiguity caused by word polysemy, then use BiLSTM to capture the context sequence information, and finally the output of BiLSTM is combined with SVM model for sentiment classification, and the accuracy of BERT-wwm-BiLSTM-SVM model is improved compared with BERT, BILSTM, LSTM and other models.
An Inverse Propensity Score Framework for Breaking Filter Bubble Recommendation

Recommendation systems often amplify biases in data, leading to the creation of filter bubbles. Models learned from historical interactions with an imbalanced distribution of items will amplify this imbalance by over-recommending popular items. In the long term, addressing this issue is crucial for a healthy recommendation ecosystem. Existing work has not addressed the issue of bias amplification with respect to the quality of items, and definitions of item quality are typically global and do not account for user differences. In this work, we carefully analyze how bias amplification leads to filter bubbles. To address this, we propose an IPS-based sequence recommendation system (IPS-seqRS) that leverages both user local popularity and item group quality to eliminate filter bubbles. We conduct extensive experiments on a large real-world industrial dataset to validate the superiority of our proposedIPS-seqRS method in eliminating filter bubbles.
A Temporal Convolutional Knowledge Tracing Model Integrating Forgetting Factors and Item Response Theory

As the current mainstream model in the field of knowledge tracking, the deep knowledge tracking model based on RNN has insufficient interpretability, long sequence dependence, gradient disappearance and gradient explosion. Aiming at the above problems, a Temporal Convolutional Knowledge Tracing Model Integrating Forgetting Factors and Item Response Theory(TCKT-FI) was proposed. Firstly, TCN is used to extract the students’ knowledge states. Then the exercise embedding vector is obtained, and the relevant weight vector is obtained through calculation. Next combined with the forgetting factors summarized in pedagogy, the knowledge state is forgotten. Finally, the difficulty of the exercises and the students' ability are obtained by using the embedding vector of the exercises and the mastery of the knowledge points, and the probability of the correct answer to the next question is predicted based on the above factors. Comparing the TCKT-FI model with the DKVMN model, CKT model and SAKT model, the results show that the accuracy rate (ACC) and area under the curve (AUC) of the prediction results are significantly improved, and the best effect is increased by about 14 percentage points, and The calculation speed has been improved by 3-6 times.
Stability of Generalized Continuous Hopfield Network

The Hopfield Neural Network (HNN) is a significant neural network which is applied in diverse applications such as signal reconstruction, image analysis, and optimization. In this paper, we propose a novel method for analyzing or determining the stability of generalized continuous feedback neural networks, which abandons the traditional approach of constructing Lyapunov energy function. The paper also provides relevant proofs for this method. By mathematical proof, it is concluded that the continuous Hopfield network is globally asymptotically stable, and this result is consistent with the result obtained by constructing the energy function.
Trajectory planning of 6-DOF manipulator based on spatial ant colony algorithm and obstacle avoidance

Moving in an environment with obstacles is a common operating condition for manipulators. How to adjust the obstacle avoidance planning path according to the position of obstacles in time is the key to achieving the safe operation of manipulators in obstacle environments. The trajectory of a 6- DOF manipulator is proposed to be planned using a spatial ant colony method. By establishing an improved D-H motion model to solve the forward and inverse kinematic equations, movement from a specified point to a specified point at the end of the manipulator is achieved. To create collision-free routes, a modified ant colony algorithm is paired with a collision detection technique. The position of each arm is determined by solving the inverse solution of the manipulator motion through the manipulator's end path nodes, collision detection is done to assess the viability of each set of inverse solutions, and then the obstacle avoidance path is obtained. The effectiveness and feasibility of this method for 6-DOF manipulator trajectory planning and obstacle avoidance are verified through simulation experiments.
Design of Translation Accuracy Correction Algorithm for English Translation Software Based on Semantic Relations

Up to now, a fully automatic and high-quality software translation system has not appeared, which is due to the complexity of language knowledge and the limitation of human understanding of language laws. At present, the research of software translation is not limited to the syntactic and semantic analysis of a single sentence, but also to fully explore the internal laws of language and deeply explore various contextual information including sentence groups, paragraphs, chapters and genres. Therefore, this paper studies the accuracy methods of software translation in English language and literature. This paper describes the implementation process of similar instance retrieval module and translation generation module in software translation, and further proofreads the accurate translation of English language through data-oriented translation model.
An Improved Wavelet Denoising Method Based on Acoustic Echo Analysis

Acoustic echo detection is a method of echo analysis using active acoustic signal. There are a lot of same frequency noises in the echo, the acoustic imaging quality is poor, and the detection effect is greatly affected. The traditional noise reduction methods have some problems, such as poor suppression effect of same frequency noise and so on. In this paper, the characteristics of signal and noise are studied by using the collected target echo information and analyzing the echo of target position. The improved threshold wavelet denoising method is proposed, and the segmented threshold denoising is carried out according to the signal-to-noise ratio. After echo denoising, the full matrix imaging based on Hilbert transform is applied to target detection and analysis, and the imaging effect is obvious. The experimental results show that the improved wavelet denoising method greatly improves the echo signal-to-noise ratio and the relative identification of target position.
Architecture Design of Electronic Evidence Trusted Deposits and Traceability Application System for Safety Production in Power Grid Enterprises

For power grid enterprises in the development of power engineering infrastructure, line equipment operation and inspection and other production and management activities, often due to evidence collection is not timely, lack of effective evidence and other reasons lead to the inability to prove, weak defense of rights, to the legitimate rights and interests of power grid enterprises caused losses. In this context, this paper carries out the technical research on the whole life cycle management scheme of electronic evidence for power grid enterprises' safety production, designs the architecture of electronic evidence credible storage and traceability application service system, and realizes the whole life cycle credible management of electronic evidence from collection, curing, transmission, sealing to checking and identification. Enhance the credibility of electronic evidence, access to evidence from the traditional "after the fact evidence" to "before the evidence" mode change, and promote the company's safety production management level.
Application and research of interpretable real-time software defect detection method

This paper proposes an ex ante interpretable real-time software defect prediction method based on formal functions. The method mainly consists of three parts, metric definition, defect prediction modeling and interpretability analysis. The interpretability analysis includes the reduction of polynomial functions and the ordering of importance of metric elements. In order to verify the interpretability and accuracy of the instant defect prediction method proposed in this paper, the experimental results show that the interpretable instant software defect detection method proposed in this paper has good feasibility and application value.
Auction Pricing for Bandwidth across Multiple Datacenters with Traffic Engineering

Bandwidth between servers becomes valuable and congested for network users because of the gradual development of datacenter networks recently. The bandwidth is selling at a fixed price per GB in traditional methods. Traffic engineering technique is commonly used for route scheduling. The main shortcoming of traditional pricing strategies is the low usage rate of costly connections and mendacious transactions of strategic users. Auction principle is able to overcome shortcomings in these aspects, but the unacceptable efficiency in large-scale networks limits the usability. And, existing pricing strategies do not take the importance of data and transaction itself into consideration in scheduling. In order to take advantage of the auction principle and avoid the shortcomings, MAPS, a pricing system for bandwidth across multiple datacenters based on auction principle, is designed. MAPS combines auction pricing with traffic engineering technique in order to achieve high performance. MAPS aims to achieve considerable efficiency and provides fairness guarantee for normal bidders and against malicious bidders. Algorithms in MAPS are well-designed based on the formulation of the problem. Our evaluation is based on simulation and the result proves that MAPS performs well in network utility and time efficiency. Important transactions are prioritized and the value of data transferred is improved up to 60 percent.
Java card competitive product analysis method and device

This article explains the methods and devices of Java Card competitive product analysis from many aspects, such as function, performance, reliability. This article also proposes specific means to optimize the Java card competitive product analysis process, including garbage collection and transaction protection, to increase the technical depth and efficiency of Java Card competitive product analysis. Through the methods and devices, the competitive product capability can be evaluated more comprehensively and deeply to guide the improvement methods.
Design of Computer Intelligent Scoring System for English Translation Based on Markov Model

The existing scoring methods have many shortcomings, such as long calculation time and significant differences from manual scoring results. A machine correction system based on Markov model is proposed to address the problems in English translation correction, in order to reduce manual correction work during the correction process and improve correction efficiency. A hierarchical structure system consisting of modules such as translation data collection, information feature extraction, analysis model construction, and evaluation result feedback has been established for the English translation evaluation system; A language model for evaluating English translations has been proposed, and this model is used to calculate the specific sentence order and word count that appear in the translation. Statistics were conducted on English translation data, and statistical analysis was conducted on the translation data. Intelligent scoring of English translations. Experiments have shown that the difference between the intelligent scoring system and manual scoring is only 0.1 points.
Multiple ensemble learning methods for Mandarin multisyllabic pronunciation assessment

The automatic pronunciation assessment technology based on artificial intelligence can well help language learners understand their pronunciation level and give timely feedback. The main reasons for the lack of relevant datasets and the immaturity of multisyllabic pronunciation assessment techniques are that there are few studies on the assessment of Mandarin multisyllabic pronunciation. In order to solve the above problems, this paper establishes a phoneme- level Mandarin multi-syllable assessment annotated dataset, designs various acoustic models with different network structures, and combines ensemble Learning algorithms to conduct a multifaceted pronunciation assessment comparison experiment on the self-built Mandarin multi-syllable assessment dataset. The experimental results show that the performance of the model with ensemble Learning algorithms is significantly improved compared with the traditional regression model.
Research on Welding Robot Path Planning Based on Genetic Algorithm

Nowadays, welding robots are widely used in the automobile manufacturing industry, but the current solder joint path planning mainly depends on the experience of technicians, and an automatic path planning method is urgently needed. The traditional algorithm can not adapt to the complex working environment of welding robot. In the intelligent algorithm, the advantage of genetic algorithm (GA) is that its overall retrieval level is high and the robustness is good. Therefore, in this research, GA is selected for path planning of welding robot. In this research, firstly, the working conditions of the welding robot are established by the grid map method, and then the adaptive level function is created by referring to the needs of its motion smoothing level and route distance, and the weighting factors of the appropriate smoothing level and route distance and the relevant parameters of the GA are found. Finally, MATLAB is used for simulation verification. Through simulation experiments, it can be concluded that the change of different parameters will have a greater impact on the path of welding robot. By adjusting the parameters, the welding robot can find the optimal path in different environments, which verifies the feasibility and effectiveness of the algorithm.
Treatment of OOV Words in Neural Machine Translation

With the development of the Internet, the cross-language information retrieval technology is growing. In neural machine translation (NMT) system, there are always OOV (Out-of-Vocabulary) words which have never appeared in the training set and it has a direct impact on the performance of translation system. This paper proposes data generalization method to deal with the OOV problem to improve the performance of the system and the result shows that the approach can solve the OOV problem to some extent and improve the performance of the system.
Improved A-star Algorithm in Efficiency Enhancing and Path Smoothing

A-star algorithm is a classical path planning method, but the generated path is often not smooth enough, which is difficult to be directly applied to cars or other mobile robots. At the same time, the wayfinding efficiency of a* algorithm also needs to be further improved. To solve the above problems, this study was improved by adjusting the way the A- star algorithm evaluated each node and used Bezier curve to smooth the path, thus improve the efficiency of the A-star algorithm and make the resulting path more suitable for the use of automobile robots. After the improvement, the number of search nodes and the time to complete the path are almost reduced by half compared with the traditional A-star algorithm. The simulation results verify the effectiveness of the proposed algorithm, and show that the improved method can generate smooth paths while improving the planning efficiency.
Optimization of machine translation algorithm for English long sentences based on deep learning

Traditional artificial English long sentence translation efficiency is low, and the difference of the translator’s personal level will lead to uneven translation quality. The existing MT can not effectively solve the problems of cross-type ambiguity in English long sentence translation rules. This paper adopts the encoder-decoder structure. In the encoding stage, each word in the Chinese sentence is first mapped to a fixed-length word vector, and all the information of the entire sentence is compressed through the recurrent neural network. The attention model is introduced in the decoding process, so that the decoder pays more attention to the context-dependent words of the current translated word, and selects the translated word with the highest probability to generate the target sentence each time. Compared with traditional English long sentence translation methods, it has higher timeliness, higher accuracy, and stronger translation standards.
Intelligent English Automatic Translation System Based on Improved GLR Algorithm

At present, there are still many problems in the research and application of portable instant translation system. For example, the existing echo cancellation algorithms often have the problem of divergence in the case of two terminal intercom, and the convergence speed of the algorithm can not meet the real-time requirements. Correct and effective shallow parsing plays an important role in the analysis of the whole sentence. In English Chinese machine translation, there is a high degree of grammatical ambiguity between English and Chinese, and the task of sentence analysis is extremely arduous. Parsing model and parsing algorithm are two inseparable problems with statistical parsing research. It reveals that the existing machine translation level can no longer meet the current translation requirements, and the machine translation technology with high translation accuracy.Therefore, this article proposes a method using variable step size, which increases step size without convergence and decreases step size with convergence. The article focuses on analyzing the defects in the GLR algorithm. The research results of this project will provide new research ideas for the intelligent research of machine translation.
Blockchain-Based Decentralized Documentation System

Property enrolment is a salient process. There are several escape clauses and issues. The existing system endures the fabrication of land documents which results in a loss of revenue to the government. So, developing a decentralized online registration using blockchain (BC) makes it comfortable for citizens to transfer property, prevents the touting of the same property to various buyers, and also helps the government to monitor and keep track of property movement to prevent money laundering on real estate. The process of automation eliminates the need for more humans to employ and also reduces the need for storage building which is required for storing physical documents. In this chapter, interplanetary file system (IPFS) is used for connecting all computing devices or nodes with the same system files and storing large files effectively. The land is successfully transferred between users after the successful completion of payment. Moreover, the proposed hybrid system does not require nodes to solve complex puzzles and thus reduces the use of high computational energy.
Speed Controller by Neural Networks Trained by Invasive Weeds for a DC Motor

Throughout history, the implementation of intelligent machines capable of performing activities that help humans has been a complicated task, then the need to seek new sources of inspiration to solve effective control solutions arises, giving rise to the emergence of bio-inspired algorithms which adopt phenomena present in nature. In this research, speed control by Artificial Neural Networks (ANN) or Neuro controller (NC) is developed for its application on an industrial machine such as a DC motor. For the training of the ANN, a novel and almost unexploited algorithm such as the Invasive Weed Optimization (IWO) is used, as a useful tool when training a neuro-controller for complex systems. The neuro controller has superior characteristics to a conventional controller, and if parameterized correctly it does not require a large computational effort. The MatLab/Simulink ANN toolbox is used to develop the basic structure of the ANN and a Control Plant Trainer (CPT) with a DC motor is used as a test plant. An ARDUINO board is used as an acquisition and control board. To validate the performance, the Wilcoxon test is used to compare the Time Weighted Error Integral (TWEI) of an NC trained by Back-propagation with the one trained by IWO and a conventional Proportional Integral and Derivative (PID) controller. The results obtained are good and interesting from the point of view of industrial automatic control.