Reviewerly/exHarmony huggingface dataset:
Construction of business English corpus under internet information technology

With the development of social science and technology and the continuous progress of information technology, China&apos;s Internet era has gradually begun. In recent years, it has developed into the Internet era, representing a new social form. In view of the problems existing in traditional business English teaching, we should pay full attention to the integration of the Internet and the economy and society, improve teaching innovation and teaching rate, provide a more convenient and effective network platform channel for the society, and propose solutions to the problems and promotion of business English in the network environment. Multimodal interactive teaching mode. The results show that the investment in the national education and training industry has increased year by year, and the investment in the education and training industry in 2020 will reach a maximum of 41.57 billion yuan.
Design of foreign language intelligent translation recognition system based on improved GLR algorithm

With the rapid development of computer science, artificial intelligence technology has been more and more applied in all aspects of people&apos;s production and life, which has greatly changed people&apos;s lifestyle and promoted the development of society. The communication in this field at home and abroad is more frequent and in-depth, which puts forward higher requirements for the corresponding translation quality. This paper discusses the design of foreign language intelligent translation recognition system(FLITRS) based on the improved GR algorithm, and explores the current situation of syntax analysis of GLR algorithm. Taking English translation as an example, this paper mainly analyzes and discusses the processing of language translation and its recognition. Through experiments, the recognition accuracy and recognition rate of dynamic memory, statistics, GLR algorithm and improved GLR algorithm are compared. The experimental results show that the score of language translation based on dynamic memory and statistical algorithm is less than 85, while the score of machine translation(MT) based on improved GLR algorithm is more than 92. When compiling, only the MT through the improved GLR algorithm is closest to the HT. The recognition accuracy is more than 94%, even more than the same level as manual translation, which proves the high efficiency and feasibility of the improved GLR algorithm in foreign language intelligent translation recognition.
Indoor gymnasium virtual system using data mining algorithm

With the call for national health and people&apos;s attention to sports events, more and more people are participating in sports. However, the increasing user demand and problems such as insufficient venues and insufficient funds have reduced the chances of people entering the gymnasium. In order to enable more people to understand the layout of the gymnasium and participate in sports activities at home, this paper uses virtual reality technology to develop an indoor sports virtual system, which not only allows people to roam in the gymnasium without leaving their homes, but also optimizes the allocation of venue resources. The system uses the free and open source Away3D engine as the development platform, uses 3DSMAX software to model, and processes gym pictures or videos through Photoshop software to restore the real gym scene. In order to verify the cross-platform and efficient rendering of the system, this paper tests the scene rendering frame rate of the system running on different terminals. The experimental results show that the system has the highest rendering frame rate on the computer terminal whether it is a single-person roaming process or a multi-person roaming process. And the survey experiment using data mining technology to analyze user satisfaction also proves that the system meets the design requirements and reaches the user&apos;s use standard.
Algorithms for detection and tracking of human targets downhole based on deep learning

With the development of human society, the development and utilization of resources such as oil and coal has become more and more in-depth. In the mining process, deep excavation and transportation of the stratum are often required. However, the underground environment is complex. In the process of mining and construction, the underground operating conditions are getting worse and worse, and it will also consume a lot of time and human resources. At the same time, deep learning (DL) has become a hot research topic, and it has made great progress and achieved great results in image recognition and other fields. Therefore, in order to ensure the safety and efficiency of production, this article combines DL technology, and explores target detection and tracking algorithms, and explores its application in detecting and tracking pedestrian targets, so as to discover potential safety hazards and issue warnings in time. This article uses experimental analysis and data analysis to better understand the tracking effects of these methods through experimental research, so as to better complete the task of pedestrian target tracking in the underground environment. According to the experimental results, compared with other methods, the feature fusion tracking method proposed in this paper produces smaller errors, higher recognition accuracy, shorter computing time, and produces better results in pedestrian target tracking in underground environments.
Generative adversarial networks based on target hash attacks

Due to the powerful learning capabilities of deep learning, deep hashing based retrieval models have been widely used in large-scale image retrieval systems. There is, however, little research on the security of deep hashing based retrieval models, which is a practical security issue. In this paper, we propose a target hash supervised adversarial network (TarS-GAN), which can perform targeted hashing attack efficiently and effectively. As part of this effort, we select a target hash code to supervise the generator in order to construct the adversarial sample by minimizing the Hamming distance between the hash code of the adversarial sample and the target hash code. In addition, the generator and discriminator counterbalance each other to promote more realistic adversarial examples. Numerous experiments demonstrate that TarS-GAN is effective in attacking deep hashing based image retrieval.
Intelligent recognition English translation system based on machine learning algorithm

Since the beginning of the 21st century, in training, life and work, people have become more and more aware of the increasing frequency of communication between different languages. Whether they are idiosyncrasies or forms of communication, there are rigorous issues and increasing demands on the accuracy of translation communication. The main purpose of this paper is to design and research a system for intelligent recognition of English translation based on machine learning algorithms. Rapid advances in computing power, growth and adoption of the Internet, and multilingual knowledge bases in both countries and the United Nations have given us millions of bilingual institutions. More and more researchers are devoted to computational engineering training with success. Experiments show that the NP-sequencing pattern library plays the most important role, and its translation results are improved by 3 percentage points compared to the baseline system Moses. And about 2876 English terms in the whole test corpus can find the same syntactic structure in the pattern library, accounting for about 88.7% of the test corpus.
Construction cost optimization technology based on decision tree CHAID algorithm

With the rapid growth of the construction engineering industry, the construction industry has made great progress, but in the process of its development, there have been many irrational growth models such as extensive growth and real estate bubbles. In addition, with the continuous advancement of the development of construction projects, the types and modes of construction projects have become more and more complex, and the importance of construction project management has become more and more prominent. This article will discuss the cost of construction projects. Make a relative cost optimization technology research, this paper adopts the decision tree CHAID algorithm to solve the cost management problem of construction projects, and proposes corresponding cost optimization technical solutions, which is conducive to the integration of China&apos;s construction industry with the international industry and the promotion of my country&apos;s industrial structure transformation. From the perspective of decision tree structure, the earliest decision tree is a multi-forked tree structure, which is it brings some trouble to interpreting the rules of the decision tree, while the decision tree CHAID algorithm generates rules that are easy to understand, easy to implement, fast, able to process data-type and general-type attributes at the same time and calculate massive data good. This paper mainly studies the design of mechanical equipment fault detection robot, proposes a series of data model analysis, and analyzes the main theories and characteristics of the electronic tracing algorithm. 3.98s, the running time of single-machine multi-threading is 3.87s, while the running time of decision tree CHAID is 3.79s, the relative acceleration is slightly higher than that of single-machine single-threaded and single-machine multi-threaded.
Construction of intelligent elderly care service platform based on neural network algorithm

With the gradual affirmation of family informatization and the networking with the community, caring for community-based care for the elderly will become an inevitable practice for elderly care in the information age. Therefore, it is imperative to develop an intelligent elderly care service platform based on neural network algorithms, and in view of the local sound information management of the elderly, improve the existing local elderly care service model to solve the problem of inappropriate local elderly care services. Let the elderly also enjoy the convenience of information technology services. The purpose of this paper is to study the intelligent elderly care service platform based on neural network algorithm. This paper introduces the neural network model and analyzes the applicability of intelligent decision-making. Through the comparison and selection of neural network, it is applied to the system modeling of home intelligent decision-making and the decision-making algorithm design. Finally, build a platform for the intelligent decision-making system in the entire home environment, and determine the evaluation standards and methods of intelligent decision-making. The experimental research shows that the number of requests per second that can be processed by the main functional interfaces of the platform designed in this paper is mainly between 5000-10000, which can basically meet thousands of community elderly groups.
Audit stratified sampling algorithm based on neural network

Computerized accounting and electronic business data have been widely used in many units. Sampling auditing of financial and business data of the audited unit is the basis for daily audit work. In the information age, it is very important to extract and mine useful information from massive data. The main purpose of this paper is to study the hierarchical algorithm of audit sampling based on neural network. This paper mainly describes the audit sampling system researched and developed in detail, and promotes the audit stratified sampling analysis technology to a new field through data mining technology. Experiments show that the K-means algorithm of hierarchical division and center division has only a slight difference in data processing results, and most of the variance results are different from 7-20, but the difference between the original K-means algorithm is large, and the advantages are more obvious.
Gynecological surgery safety monitoring system based on hybrid cuckoo algorithm

Gynecological surgery is a pelvic surgery. The trauma of the operation will lead to a series of changes in the postoperative coagulation and fibrinolysis system. If the balance of coagulation and fibrinolysis system is destroyed and there is a free coagulation state, it will lead to the occurrence of venous thrombotic disease. This paper introduces the principle of hybrid cuckoo algorithm, describes the behavior of cuckoo algorithm, discusses the detection of hemoglobin concentration, and analyzes the common detection methods of respiratory function in surgery. Through the analysis of the occurrence time of venous embolism in the case group, the results showed that patients were prone to venous embolism in the perioperative period (1 week before operation to 2 weeks after operation), followed by patients supplemented with radiotherapy and chemotherapy after operation.
Intelligent recognition model of English translation based on cloud computing GLR algorithm

Language communication and understanding involve more and more fields. Human-computer interaction, translation systems, etc. have entered our lives more and more, and are and will change the way of production of our lives. The GLR algorithm can analyze English translation sentences well, and its analysis time complexity is lower than that of parallel algorithms. The main purpose of this paper is to design and study the intelligent recognition model of English translation based on the GLR of cloud computing. The paper mainly lists the main points of the GLR algorithm, the characteristics of the word algorithm, and the classification of words and collocations through calculation, which is convenient for intelligent and so on. Experiments show that the accuracy of English translation is improved by 24% after proofreading, and there is a difference. Therefore, the intelligent recognition model of English translation of this system is relatively very effective.
Fault Risk Early Warning Method of Distribution Network Based on Multi-source Information Fusion

Based on the multi-source information fusion technology, an early warning method of distribution network fault risk is designed in this paper. The distribution network fault risk characteristics of multi-source information are collected. The DS fusion theory is applied to fuse and process the distribution network fault risk characteristics to obtain a fault risk diagnosis decision. The fault risk early warning of the distribution network is realized by combining with the grade evaluation of the fault risk early warning. In order to test the feasibility of the fault risk early warning method designed in this paper, a simulation experiment is designed. The results show that for different types of distribution network fault risk, the average response time of the proposed method for different types of fault is 0.88s, which is real-time and efficient. The average value of the fault early warning accuracy is 93.04%, which is accurate and feasible. It provides reliable risk early warning technical support for the efficient and safe operation of the power system.
Speech Assistance: Use Cases in Industry

Over the past few years, there has been a boost in the use of commercial virtual assistants in the smart home and entertainment domain. Expectations have risen that this technology from consumer market will soon be adopted in industrial applications. However, the functionality they offer and the adaptability to a specific domain is limited. Companies are afraid to become “vendor-locked”, while data privacy issues remain unsolved. The described approach of the project SPEAKER opens a new path to industry adapted speech assistance that meets the needs for European data privacy regulations. First proof-of-concept implementations and a domain specific benchmarking of the automatic speech recognition component are introduced.
CHARACTER BUILDING POTENTIAL OF SRH-TECHNOLOGY UNDER CONDITIONS OF A COMPREHENSIVE SCHOOL

The purpose of the article is to reveal the possibilities of SRH-technology in the implementation of the educational process in a secondary school. Theoretical and practical research methods were used in the research search, includ- ing analysis, comparison and generalization of scientific and pedagogical materials, modeling of processes taking into account the uncertainty factor. The method of expert assessments, the method of vector preferences and the method of focal objects were used to identify trends and countertrends of education in the context of global chal- lenges of our time. The results of the theoretical and empirical search were the identification of the features of the use of SRH-technology for the organization of the educational process in a secondary school. The theoretical sig- nificance of the work consists in clarifying the methodological characteristics of the use of SRH-technology in the implementation of the educational process in a secondary school. The practical significance of the work lies in the disclosure of ways to use SRH-technology in educational work at school.
COMPETENCE-BASED APPROACH AS A MEANS OF SCHOOL AND LABOR MARKET POSITIONS

Social skills and personal qualities formed by secondary schools do not meet the expectations of employers who give a job to school graduates without a professional education. The article considers the competence approach as a way to study and bring together the attitudes of school teachers and the employers in interdisciplinary competencies that a school graduate must have in order to find a job. The empirical basis for identifying competencies is the data of expert interviews with employers. A questionnaire survey of teachers and representatives of the labor market revealed a high degree of coincidence of their positions on what a school must give to its graduates. The teachers and the employers also have almost equally ranked the list of competencies related to everyday professional duties. The results of the study indicate a different understanding of the essence of competencies, if it concerns everyone personally, and the existence of a sociocultural stereotype of competence perception for all respondents. Such stereotype significantly limits the possibilities of using competencies as a means of identifying differences in various social groups expectations, and as a means of coordinating their positions.
Knowledge Graph Entity Alignment Powered by Active Learning

Considering the diversity and heterogeneity of different knowledge graphs, it is necessary to logically establish a comprehensive, accurate and unified knowledge repository. We design a framework by importing active learning strategies to neural network models for entity alignment, aiming to create informative seeds for more efficient entity alignment models with lower annotation cost. The model measures the benefit of an entity being selected from the two aspects of its uncertainty and influence. Extensive experiments are conducted on two benchmark datasets, and the results show that our method achieves significant improvement over the existing models.
One-Stage Deep Channels Attention Network for Remote Sensing Images Object Detection

Although existing remote sensing image object detection methods have made significant evolution in deep learning, they did not fully consider the problem of features loss caused by the correspondingly different importance of different channels of feature maps in the convolution pooling. Therefore, a one-stage deep channels attention network for remote sensing images object detection was proposed. First, through a multi-scale feature representation of the Single Shot MultiBox Detector (SSD) Network, the model can combine semantic information with detailed features to better integrate feature layers with different resolutions. Second, for each additional feature extraction layer, the squeeze and excitation (SE) module is introduced, which adaptively re-calibrates the interdependencies between deep channels, then they achieve the response of channel properties in order to learn more efficient feature information. According to experimental results on the RSOD dataset and NWPU VHR-10 dataset, the models proposed in this paper all realize advanced results and achieve state-of-the-art technical performance.
From Less to More: Common-Sense Semantic Perception Benefits Image Captioning

Most recent arts in image captioning rely solely on exploring the information contains in the image or modeling the inner-relations among visual features, which fails to generate informative captions in some cases. Part of what defines humans is the ability of common-sense reasoning behind semantic association, which is different from machines. To this end, we propose a Common-Sense Aware method (CSA) for image captioning, which capitalizes general prior knowledge to associate extra semantic information during generation to infer more informative captions. Specifically, based on ConceptNet, we extract common-sense knowledge features using pre-generated concepts to provide comprehensive associated semantic information for captioning. We conduct extensive experiments on the MS COCO dataset to demonstrate the effectiveness of CSA, results show that it furthers state-of-the-arts.
TraVL: Transferring Pre-trained Visual-Linguistic Models for Cross-Lingual Image Captioning

Visual-Linguistic (VL) pre-training is gaining increasing interest due to its ability to learn generic VL representations that can be used for downstream cross-modal tasks. However, the lack of large-scale and high-quality parallel corpora makes VL pre-training impractical for low-resource languages. Therefore, it is desirable to leverage existing well-trained English VL models for cross-modal tasks in other languages. But a basic approach suffers from its inability to capture the semantic correlation between different modalities and insufficient utilization of the hierarchical representations of VL models. In this work, we propose TraVL, a novel framework for transferring pre-trained VL models for cross-lingual image captioning. To enforce the semantic alignment during modality fusion, TraVL employs joint attention that constructs the key-value pair by concatenating the visual and linguistic representations. To fully exploit the hierarchical visual information, we develop an adjacent layer-fusion mechanism that allows each decoder layer to attend to the encoder’s multilayer representations with similar semantics. Experiments on a Chinese image-text dataset show that TraVL outperforms state-of-the-art captioning models and other transfer learning methods.
Accelerating Hypergraph Motif Counting Based on Hyperedge Relations

Hypergraphs can naturally represent inter-group relations that are prevalent in many application domains by hyperedges. Hypergraph motifs can be described as the structural patterns of three connected hyperedges. As an effective tool, it plays an important role in the local structure analysis of hypergraphs. In this paper, we study exact hypergraph motif counting which is a fundamental problem of hypergraph motif research. Existing algorithms don’t adequately consider hyperedge relations in real-world hypergraphs, which lead to a large number of redundant computations. This motivates us to improve performance by exploiting hyperedge relations. In our work, we classify hypergraph motifs with different hyperedge relations. For different types of motifs, we use set theory to demonstrate and propose different optimization methods to reduce the computation of excessive intersections. We also further reduce the cost of the proposed method by preserving hyperedge intersections when constructing the hyperdege projected graph. Extensive experiments on real datasets validate the superiority of our algorithm compared to existing methods.