Reviewerly/exHarmony huggingface dataset:
Realization Path of the Social Development of Meteorological Services Based on Intelligent Data Analysis

In recent years, with the development of social economy, the working efficiency of meteorological system has been gradually improved. The socialization of meteorological service refers to the use of intelligent data analysis technology to achieve accurate forecast and real-time monitoring of weather forecast and provide personalized services for users based on this information. This paper studies the realization path of meteorological service socialization in order to bring convenience to people’s life. This paper mainly uses a simulation experiment method, investigation method, and statistical method to conduct in-depth research on the social development of meteorological services. Experimental data show that more than 70 percent of people get weather information from their smartphones. The system designed in this paper can basically meet the requirements.
Anomaly Detection Method for Missile Flight Data by Attention-CNN Architecture

This study proposes a new deep learning-based anomaly detection algorithm in a supervised framework for the Monte-Carlo (MC) simulation data of a missile control system. First, in a similar fashion to the famous BERT(Bidirectional Encoder Representations from Transformers) algorithm in NLP(Natural Language Programming), a pre-trained transformer-based encoder is utilized to provide an alternative representation of time-series data, replacing conventional data-preprocessing. For a more direct perception of differences in normal and abnormal data, a multi-head attention score is also considered as an input of a two-dimensional convolutional neural network classifier along with the output of transformer encoder. The training of the proposed framework is comprised of two steps: unsupervised pre-training of the transformer encoder with all generated MC simulation data and supervised training of the classifier with labelled data. To generate the MC simulation data, the high-fidelity missile 6DOF simulation program is developed with random variables that represent the uncertainties of realistic scenarios. The resulting anomaly detection performance from the generated MC simulation dataset indicates that the proposed framework provides significantly better detection performance than other supervised machine/deep learning-based algorithms. Furthermore, using a multi-head attention score from the transformer encoder as an input of the classifier not only improves the detection performance but also contributes to reducing the necessity of labelled train data. Hence, the proposed method has the potential to greatly reduce the human effort in detecting anomalies in MC simulation data.
BestOf: an online implementation selector for the training and inference of deep neural networks

Abstract Tuning and optimising the operations executed in deep learning frameworks is a fundamental task in accelerating the processing of deep neural networks (DNNs). However, this optimisation usually requires extensive manual efforts in order to obtain the best performance for each combination of tensor input size, layer type, and hardware platform. In this work, we present , a novel online auto-tuner that optimises the training and inference phases of DNNs. automatically selects at run time, and among the provided alternatives, the best performing implementation in each layer according to gathered profiling data. The evaluation of is performed on multi-core architectures for different DNNs using , a lightweight library for distributed training and inference. The experimental results reveal that the auto-tuner delivers the same or higher performance than that achieved using a static selection approach.
Financial and Economic Sequence Forecasting Based on Time Slot Allocation Algorithm

Financial and economic series prediction was very important for the study of economic time series. Because the traditional time series analysis method was mainly suitable for stable data and cannot be applied to unstable economic variables, this paper proposed a knowledge model of financial and economic series prediction time slot algorithm, which provided the basis for solving unstable financial data. Firstly, based on the analysis of relevant financial and economic series prediction methods at home and abroad, this paper applied the time slot allocation algorithm to economic series analysis and prediction and designed a path level time slot allocation algorithm, which introduced a fixed time slot to shorten the time required to complete the path level time slot allocation. Secondly, this paper abstracted the calculation of slot allocation as a convex optimization problem and proposed a round slot allocation algorithm based on constraints and optimal solution. The algorithm can improve the end-to-end throughput and the effective utilization of slots. Finally, the model proposed in this paper was used to predict the indicators of relevant industries, and the simulation results were compared with the standard data. The experimental results showed that the financial and economic series prediction system based on time slot allocation algorithm proposed in this paper can effectively improve the accuracy of financial and economic series prediction, which verified the effectiveness and feasibility of this model.
An emerging treatment technology

Researchers have investigated the use of a green approach to synthesize nanoparticles for the remediation of emerging pollutants such as pesticides and dyes. Colloidal nanoparticle solution after synthesis is generally characterized by UV-Vis spectroscopic techniques for surface plasmon resonance (SPR) information and scanning electron microscopy (SEM) for size and shape information as per the traditional approach. Even though it is a known fact that nanoparticle characterization by SEM is widely accepted worldwide and is worth crores of rupees, it may not be in the reach of every individual. This may lead to a lot of delay in sample analysis, thereby hampering the sample quality. Hence the present study focuses on the applying deep learning approach for colloidal nanoparticle image classification and computer vision techniques for determining the size of the nanoparticles in each SEM image. The image generated by the proposed model will be highly useful in finding out the nanoparticle type and size in comparison with ImageJ software. This could be explored during the optimization studies for nanoparticle analysis, thereby avoiding repetitive SEM analysis, which is highly expensive. Once the synthesis protocol is achieved, nanoparticles could be used to efficiently remediate toxic pollutants.
Information Collection, Analysis, and Monitoring System of Children’s Physical Training Based on Multisensor

In order to obtain more children’s physical training information and improve the accuracy of children’s physical training monitoring, a multisensor-based children’s physical training information collection, analysis, and monitoring system is proposed. In the process of physical training and sports training, people’s physical training information collection is directly related to the level and effectiveness of physical training. With the combination of multisensor concept and sports training information collection, it can collect the key index data of sports mobilization in real time with the help of multiple sensors and information technology. Taking children’s physical training as the object, this paper designs a multisensor physical training data information acquisition terminal, collects different training characteristic data with the help of multisensor equipment, and then comprehensively analyzes and monitors the physical information with the help of certain fusion technology, so as to construct a human posture recognition algorithm based on children’s physical training information acquisition. Support vector machine and decision tree are used to classify children’s different physical exercise states, and a relatively perfect algorithm architecture of human posture recognition is constructed. The results show that for two decision trees, each decision tree is trained with a total of 675 groups of data, and a total of 342 groups of data are verified and pruned. The two decision trees take 7.17 s and 7.32 s to complete the training process, respectively. It can be seen that when the number of training groups is equal, the training time of the two placement methods is close, so it can be considered that the two placement methods have little effect on the training speed of decision tree. The experimental data show that the design of children’s physical training monitoring system in this paper has a certain market value.
Twitter based sentimental analysis of Covid-19 observations

The emergence of social media has provided people with the opportunity to express their feelings and thoughts about everything and everything in their lives. There is a massive amount of textual stuff available, and approaches are required to make meaningful use of the information provided by isolating and evaluating the different types of text. Sentimental Analysis is a method of obtaining a human being's point of view through mining his or her emotions. The entire world is sharing their thoughts on social media on the Corona Pandemic that is now underway. This research presents an analysis of attitudes in order to determine whether or not people are optimistic in the face of a difficult circumstance. The technique of polarity is employed by the paper in order to determine if an opinion is positive, negative, or nonpartisan [1]. In order to determine the polarity, the following three major keywords are used: "COVID", "Corona virus," and "COVID-19."
Research on investment value system of power grid emerging business based on index screening and G1-entropy weight method

The emerging business investment evaluation system is established based on fuzzy comprehensive evaluation. The subjective weight and objective weight of each index are determined by using G1-entropy weight synthesis method, combined to get the comprehensive weight of subjective and objective integration, and the comprehensive score of each business is calculated. Three key emerging businesses, namely smart grid and value-added services, integrated energy services and high-end equipment manufacturing, are selected for quantitative investment value evaluation algorithm validation to illustrate the effectiveness of the investment value evaluation model.
Thermal Straightening Control System for Variable-Section Automotive Leaf Springs Rolling Based on IoT Edge Computing

With the rapid development of social economy in recent years, people’s living standards are also improving. The use of automobiles is becoming increasingly frequent, and people’s requirements for the safety, comfort, and energy saving of automobiles are also getting higher. This paper mainly studies the thermal straightening control system after the rolling of variable-section automotive leaf springs through edge computing based on the Internet of Things. This paper presents the basic concepts of IoT edge computing and the role they play in various aspects. The percentage of IoT development trends in 2011 was 6.7%. By 2020, the development trend percentage of IoT reached 68%, an increase of 61.3%. It can be seen that the development of the Internet of Things is very rapid. It can be seen that the straightening accuracy of the thermal straightening control system based on edge computing after the rolling of variable-section automotive leaf springs reaches 78%, and it is 29% higher than the traditional system straightening accuracy, which is only 49%. The safety of the thermal straightening control system of the variable-section automotive leaf spring after rolling based on edge computing reaches 95%, which is 33% higher than the safety of the traditional system. The thermal alignment control system for variable-section automotive leaf springs after rolling based on the edge computing of the Internet of Things is not only safer than the traditional system but also much higher in comfort and alignment accuracy than the traditional system. It can be seen that the thermal straightening control system for variable cross section automotive leaf springs after rolling based on IoT edge computing is more conducive to the development of the automotive industry.
MuCPAD: A Multi-Domain Chinese Predicate-Argument Dataset

During the past decade, neural network models have made tremendous progress on indomain semantic role labeling (SRL).However, performance drops dramatically under the out-of-domain setting.In order to facilitate research on cross-domain SRL, this paper presents MuCPAD, a multi-domain Chinese predicate-argument dataset, which consists of 30,897 sentences and 92,051 predicates from six different domains.MuCPAD exhibits three important features.1) Based on a frame-free annotation methodology, we avoid writing complex frames for new predicates.2) We explicitly annotate omitted core arguments to recover more complete semantic structure, considering that omission of content words is ubiquitous in multi-domain Chinese texts.3) We compile 53 pages of annotation guidelines and adopt strict double annotation for improving data quality.This paper describes in detail the annotation methodology and annotation process of MuC-PAD, and presents in-depth data analysis.We also give benchmark results on cross-domain SRL based on MuCPAD.
Construction of Knowledge Graph English Online Homework Evaluation System Based on Multimodal Neural Network Feature Extraction

This paper defines the data schema of the multimodal knowledge graph, that is, the definition of entity types and relationships between entities. The knowledge point entities are defined as three types of structures, algorithms, and related terms, speech is also defined as one type of entities, and six semantic relationships are defined between entities. This paper adopts a named entity recognition model that combines bidirectional long short-term memory network and convolutional neural network, combines local information and global information of text, uses conditional random field algorithm to label feature sequences, and combines domain dictionary. A knowledge evaluation method based on triplet context information is designed, which combines triplet context information (internal relationship path information in knowledge graph and external text information related to entities in triplet) through knowledge representation learning. The knowledge of triples is evaluated. The knowledge evaluation ability of the English online homework evaluation system was evaluated on the knowledge graph noise detection task, the knowledge graph completion task (entity link prediction task), and the triplet classification task. The experimental results show that the English online homework evaluation system has good noise processing ability and knowledge credibility calculation ability, and has a stronger evaluation ability for low-noise data. Using the online homework platform to implement personalized English homework is conducive to improving students' homework mood, and students' "happy" homework mood has been significantly improved. The implementation of English personalized homework based on the online homework platform is conducive to improving students' homework initiative. With the help of the online homework platform to implement personalized English homework, students' homework time has been reduced, and the homework has been completed well, achieving the purpose of "reducing burden and increasing efficiency."
Modeling Exemplification in Long-form Question Answering via Retrieval

Exemplification is a process by which writers explain or clarify a concept by providing an example.While common in all forms of writing, exemplification is particularly useful in the task of long-form question answering (LFQA), where a complicated answer can be made more understandable through simple examples.In this paper, we provide the first computational study of exemplification in QA, performing a fine-grained annotation of different types of examples (e.g., hypotheticals, anecdotes) in three corpora.We show that not only do state-of-theart LFQA models struggle to generate relevant examples, but also that standard evaluation metrics such as ROUGE are insufficient to judge exemplification quality.We propose to treat exemplification as a retrieval problem in which a partially-written answer is used to query a large set of human-written examples extracted from a corpus.Our approach allows a reliable rankingtype automatic metrics that correlates well with human evaluation.A human evaluation shows that our model's retrieved examples are more relevant than examples generated from a stateof-the-art LFQA model.
Optimization and Application of Random Forest Algorithm for Applied Mathematics Specialty

For unbalanced data classification, RF (Random forest) algorithm will cause problems such as poor classification performance and a large DT scale. With the advent of the era of big data, RF algorithms should have the ability to process large-scale data. Aiming at the problem that RF cannot handle unbalanced data well, this paper improves the feature selection method built in RF and proposes a new feature selection algorithm. On the basis of feature importance ranking, randomness is introduced to ensure the strength of each tree and reduce the correlation between trees. In the extended transform data set, the sensitivity of the RF model has exceeded 0.8, and that of other models has increased to about 0.65. The prediction accuracy of the centralized RF model for the company’s credit rating reached 100%, while the CART model misjudged companies C6 and C7, while the Logit model misjudged companies C3, C5, and C8. Experiments prove the extrapolation of the RF model and its excellent prediction ability. In the practical application of applied mathematics specialty, the RF optimization algorithm proposed in this study can well handle continuous variables and improve the classification accuracy of RF. This paper holds that the advantages of the RF algorithm in data processing and model performance will make it more widely used in the field of enterprise credit risk evaluation.
Two-Step Question Retrieval for Open-Domain QA

The retriever-reader pipeline has shown promising performance in open-domain QA but suffers from a very slow inference speed.Recently proposed question retrieval models tackle this problem by indexing question-answer pairs and searching for similar questions.These models have shown a significant increase in inference speed, but at the cost of lower QA performance compared to the retriever-reader models.This paper proposes a two-step question retrieval model, SQuID (Sequential Question-Indexed Dense retrieval) and distant supervision for training.SQuID uses two bi-encoders for question retrieval.The first-step retriever selects top-k similar questions, and the secondstep retriever finds the most similar question from the top-k questions.We evaluate the performance and the computational efficiency of SQuID.The results show that SQuID significantly increases the performance of existing question retrieval models with a negligible loss on inference speed.
Visualization of images of theoretical physics problems and research assistance supported by Maple

It is a research direction of educational technology that advanced computer language can be used to promote the modernization of physics teaching. Theoretical physics is characterized by abstract concepts, difficult to understand physical images and complex formula derivation, which makes students feel difficult in learning theoretical physics. Among the more popular scientific computing languages, Maple language has unique advantages in the modern application of theoretical physics teaching, including powerful symbolic computing function, intuitive language, a variety of built-in commands and functions, and solving general mathematical problems without complex programming. Taking two typical theoretical physics problems as examples, this paper demonstrates the application of Maple language in theoretical physics teaching techniques. These teaching techniques enable students to intuitively understand physical images. It uses powerful scientific computing functions to make it easier for college students to participate in research training or solve engineering problems.
Measurement of Large-Sized-Pipe Diameter Based on Stereo Vision

To address the lack of fast and high-precision in situ measurement of large-sized pipes in current industrial applications, a pipe-diameter-measurement method based on stereo vision is designed in this paper. By using multiple sets of binocular cameras to perform 3D reconstruction and integration of multiple laser markers projected on measured cross sections of pipes, the pipe diameter can be estimated. In this method, a measurement algorithm is adopted to enable automatic matching of feature points through affine distance transformation, and an optimized point-cloud-registration algorithm with normal-vector constraints is used to ensure measurement robustness. To verify the feasibility of the method, an experimental system was built under laboratory conditions, and three types of pipes with outer diameters from 285 mm to 325 mm were measured. The experimental results show that the relative error is within ±0.570% and the maximum repeatability standard deviation is 0.551 mm. The experimental results basically meet industrial standards, and the proposed method therefore has good application prospects.
Bengali Text Categorization Based on Deep Hybrid CNN–LSTM Network with Word Embedding

With the evolution of the internet and digital network, the amount of textual data is increasing gradually. As a result, it becomes increasingly difficult to categorize vast amounts of data manually. But, with the benefit of the machine learning process, we can categorize these massive amounts of text data automatically according to their contents. Compared with other languages such as English, text categorization in Bangla is one of the challenging tasks. Because a lack of resources is the reason for this. In this research work, we take data from open resources which are containing the Bangla newspaper article corpus with 12 categories. For categorizing these article datasets, we have used word embedding methods to extract features from raw data and then used deep learning methods: Convolutional Neural Network (CNN), Artificial Neural Network (ANN), Long Short-term memory (LSTM) and Hybrid (CNN+Bi-LSTM) methods to classify them based on their contents. Next, we have analyzed the performance of these deep learning based classification models and looked into the reasons for it. Furthermore, the hybrid model has a greater accuracy of 88.56% in 10 categories and 84.93% in 12 categories. Lastly, the limitations of this study as well as future prospects have been discussed.
Research on the Construction of Fintech Information Sharing System and Credit Evaluation Method Based on Internet Platform

Under the background of technology information sharing problems increasing more and more, and financial technology promoting the traditional financial industry, the society needs to speed up the construction of financial technology information sharing credit evaluation system, strengthen the supervision of internet platform and financial technology enterprises, and effectively avoid more risks. Starting from the concept of internet platform and information technology, this paper discusses the current situation of financial science and technology information sharing based on internet platform, puts forward corresponding solutions to the existing problems, and the construction method of financial science and technology information sharing platform and the optimization and adjustment of credit evaluation method.