Reviewerly/exHarmony huggingface dataset:
Exploration and Practice of the Acquisition Path of Spoken English for Special Purposes with the Blockchain Technology

With the increasing trend of globalization, English, as the most widely used language in the world, has become a subject that many people must learn. English for special purposes is mainly applied to some specialized occupations or English in specific professional skill fields. Due to the increasing national demand for professional compound talents, the status of English for special purposes is also getting higher and higher. However, there are still many problems in the curriculum setting of English for special purposes, for example, outdated teaching mode, lack of oral language skills training, lack of students’ ability to cope with actual situations, weak teaching awareness of teachers, and lack of analysis of students’ needs for English for special purposes, resulting in poor students’ enthusiasm for learning and so on. With the support of blockchain technology, combined with consensus algorithm, hash function algorithm, and smart contract, this paper analyzes English for special purposes, establishes an ESP management system and a corpus of spoken English for special purposes, and analyzes the integrity of the corpus data. The test found that when the number of error data blocks is 10, the total number of damaged data blocks and verification accuracy data blocks is about 4% in order to achieve 99% accuracy. Therefore, the integrity of the system is higher when the damage ratio is lower. The system integrity test finds that, with the number of wrong data being larger, when the wrong data block is at 100, 50% of the data blocks of the system integrity are about 1%. The database is complete, which is more helpful for students to study systematically.
MAIN PROVISIONS OF THE QUALITY CONCEPT PERSON-ORIENTED EDUCATION

The article deals with the problems of personality-oriented education as well as the possibility of realization of the main criterion of quality of education.
Introduction to Blockchain and Terminologies

This chapter covers basics of blockchain. It discusses the anatomy, attributes and types of blockchain. The chapter is organized according to need, introduction, background history and basic concepts of blockchain. Explanations are accompanied with relevant diagrams and tables. The chapter should enable the reader to get familiarity with blockchain technology, understand and visualize fundamentals of blockchain. Various issues and challenges related to blockchain technology are listed towards the end of the chapter. This will help the researchers formulate and explore the problem statements related to various issues and challenges related to blockchain technology. At the end, a case study on blockchain is discussed.
An Intelligent Disease Prediction System for Psychological Diseases by Implementing Hybrid Hopfield Recurrent Neural Network Approach

Machine learning approaches for automatic disease prediction from amorphous clinical data items has become a popular study topic in recent years. Existing electronic health record analysis work in the medical area improves early-stage illness diagnosis by substantiating relevant information connected to patients with disease in huge quantities. However, when standard rule-based approaches are employed, which are unable to handle amorphous clinical data items, the advantages of analysis are not accomplished adequately. Because the utilization of all text written in doctorâ€™s prescription is not equal in the detection of diseases, a single technique will not be able to manage all problems associated with the analysis of amorphous data. As a result, there is a requirement to build a hybrid-model that solves these potential difficulties, which is an intriguing subject matter that should be investigated further. In light of the aforementioned issues, this study offers a hybrid Hopfield recurrent neural network (H2RN2) technique based on amorphous clinical data items taken from Kaggle database. Using fivefold cross validation technique within a recurrent neural network detracts over fitting of the model. The proposed model automatically learns inherent semantic characteristics from available clinical data items. In addition to effective learning during training of the model, the hybrid approach also helps in accurate prediction of the disease with improved accuracy. The developed model is assessed using three measuring parameters, accuracy, recall and F1 score while using available amorphous clinical dataset. The proposed model yielded an accuracy of 97.53% in experimental evaluation, which is superior to several existing approaches for psychological diseases. The hybrid Hopfield recurrent neural network (H2RN2) approach for prediction of psychological diseases is presented in this paper. The results demonstrate that the proposed model outperforms several current techniques in predicting the risk of psychiatric disorders. The similar approach may be used to anticipate the risk of different diseases.
Review of Musso's Broca’s Area and the Language Instinct

This paper is a review of Mariachristina Musso et al.'s Broca's area and the language instinct.
A Deep Learning-Based Approach to Similarity Calculation for Uml Use Case Models

UML models are main artifacts of software applications and valuable assets of software projects. The similarity calculation of UML models is an effective way to model reuse, model validation, model retrieval and pattern detection, which can help developers reduce development cost. Existing studies mainly focus on UML design models such as class diagrams and object diagrams. Yet, there is little research on calculating similarity for use case models despite their importance for software requirements reuse and validation. Thus, this paper proposes a similarity calculation approach for UML use case models. This approach first transforms the use case models into use case graphs and then adopts the deep learning technique -- SimGNN neural network to calculate the structural similarity between use case graphs. Then it calculates the semantic similarity of use case models with TF-IDF and Cosine similarity. Finally it syntheses the results of semantic similarity and structural similarity of use case graphs. The effectiveness and efficiency of this approach was evaluated by comparing to two other approaches -- 1) Cosine similarity+TF-IDF and 2) Cosine similarity+edit distance. The results showed that the our approach has better performance in terms of both effectiveness and efficiency.
Optimization and Design of General Machinery Production Line Management Process Based on Intelligent Computing Model

Machinery is the most important basic tool for modern times, and it is also an indispensable sharp weapon for creating big country projects. The production line management of general machinery is particularly important. Traditional production line management causes a lot of labor waste, time waste, and low production efficiency, so the efficiency of production line management also determines the quality and output of general machinery. Therefore, it has become an industry consensus to realize intelligent management in the manufacturing process of general machinery. General machinery is not only a complex industrial structure but also has a series of preconditions that are not conducive to production, such as the diversity of parts and the low precision of preparation in the early stage. Therefore, to realize automation in the field of construction machinery manufacturing, it usually faces more challenges. Whether it is traditional production process or automatic production process, the realization of intelligent production line is the primary problem, because both production efficiency and product quality are determined by the efficient production line management efficiency and exquisite process. The workload of production line management is heavy, which is time-consuming, labor-consuming, and expensive. Adhere to the goal of quality first, and how to improve the efficiency of the production line has become the biggest problem at present. Based on the above problems, this paper adopts intelligent computing model to improve the efficiency of production line management and optimize the process. A series of tedious processes from product adoption to final shipment of production lines need intelligent technology to simplify the process, which can effectively improve the production efficiency of general machinery, reduce production costs, and improve the production quality of machinery. Supply chain management theory is used to manage suppliers’ production behavior, so as to reduce costs and improve quality and service, thus improving the competitiveness of batch production lines and enterprises. Advanced manufacturing technology is used to realize automation and flexible production of batch production lines, thus improving the rapid response ability of production lines to market demand.
Prediction of Local Concentration Fields in Porous Media with Chemical Reaction Using a Multi Scale Convolutional Neural Network

The study of solute transport in porous media is of interest in many chemical engineering systems. The pore scale modeling of these systems is time consuming, for this reason computational fluid dynamics (CFD) simulations are not practical if a large number of simulations is required, like in optimization workflows or in multiscale modeling, where a model at a large scale calls for pore scale simulations. It has been shown that neural networks can be trained with a dataset of CFD flow simulations to perform fast inference for new data. However, it is crucial to provide the network with an appropriate description of the studied system in order to achieve good generalization capability, so additional research is needed to employ neural networks for new complex systems. The appropriate training of a network to predict coupled flow and solute transport phenomena is still an outstanding problem, and in this work we do just that. We train a multi scale convolutional neural network (MSNet) with a diverse dataset of simulations of transport and chemical reaction in porous media to predict the local concentration fields in 2D images of porous media. Our dataset contains a wide diversity of sphere pack arrangements under different operating conditions (Péclet and Reynolds numbers). We train a robust model by employing different input descriptors that represent the medium and the different operating conditions of each system. Our trained model is able to provide nearly instantaneous predictions, compared to around twenty hours of the CFD workflow, with less than 3.5% error on new geometries and transport conditions. Thus the model could be easily integrated in a multiscale workflow where fast response is needed.
Research on Implicit Intent Recognition Method Based on Prompt Learning

As one of the core modules of the dialogue system, intent recognition plays an important role in human-computer interaction. Most of the existing intent recognition research is limited to simple, direct, and explicit intent recognitions. However, the natural human-computer interactions are flexible and diverse, and the expressions are often the euphemistic implicit intentions. Therefore, the implicit intent recognition brings new research challenges in this field. This paper pioneers a Chinese Implicit Intent Dataset CIID, which covers 7 common intents from different fields, and the data is the text containing the user's implicit intent. Based on this corpus, it is the first time prompt learning is employed for implicit intent recognition and by constructing a suitable prompt template, the model can get 'relevant hints' to dig out the true intention of the user. Finally, this paper evaluates a range of classification models on CIID dataset. Experimental results show that the recognition rate of the proposed model is 97.6%, and achieves the state-of-the-art recognition accuracy. Furthermore, since it is difficult to collect the user's implicit intention data,this paper also explores the performance of these classification models on the CIID dataset with few-shot settings, and the experimental results show when the training data is reduced to 4.7%, the recognition rate of the proposed model can still keep 92.4%, which is significantly higher than other baseline models, the results further prove this proposed method is advanced and robust.
Learning Just-in-Time Code Duplicates Extraction

Context: Refactoring is a critical task in software maintenance, and is usually performed to enforce better design and coding practices, while coping with design defects. The Extract Method refactoring is widely used for merging duplicate code fragments into a single new method. Several studies attempted to recommend Extract Method refactoring opportunities using different techniques, including program slicing, program dependency graph analysis, change history analysis, structural similarity, and feature extraction. However, irrespective of the method, most of the existing approaches interfere with the developer's workflow: they require the developer to stop coding and analyze the suggested opportunities, and also consider all refactoring suggestions in the entire project without focusing on the development context. Objective: To increase the adoption of the Extract Method refactoring, in this paper, we aim to investigate the effectiveness of machine learning and deep learning algorithms for its recommendation while maintaining the workflow of the developer.Method: The proposed approach relies on mining prior applied Extract Method refactorings and extracting their features to train a deep learning classifier that detects them in the user's code. We implemented our approach as a plugin for IntelliJ IDEA called AntiCopyPaster. To develop our approach, we trained and evaluated various popular models on a dataset of 18,942 code fragments from 13 Open Source Apache projects. Results: The results show that the best model is the Convolutional Neural Network (CNN), which recommends appropriate Extract Method refactorings with an F-measure of 0.82. We also conducted a qualitative study with 59 developers to evaluate the usefulness of the developed plugin. Conclusion: The results show that developers tend to appreciate the idea of the approach and are satisfied with various aspects of the plugin's operation.
Modelling of Top-Down Visual Attention

The aim of this paper is to provide evidence for the proposition that people allocate attention more on cognitive relevant objects, specifically faces, humans and vehicles, than at random. Experiments show that such high-level features explain allocation of attention by the participants of the study. 20 subjects have watched videos containing different types of relevant objects, and from that a attention map has been generated. This is compared with a top-down prediction map generated from GT tracking data on a subset of the sequences, using correlation. Previous work is improved with regards to the validation method, and the method is analysed to clarify the interpretation of the results.
Wim-Based Group Navigation for Collaborative Virtual Reality

We propose a group World-in-Miniature (group WiM) navigation technique that allows a guide to control and navigate a team within a miniature replica of the entire virtual environment. We evaluated the technique's usability, user performance, and discomfort with both user roles of the group guide and members and compared it with a state-of-the-art group teleportation technique in a user study (n = 20). No significant differences were found on the placement error and their interaction effect. However, descriptive results reveal the advantages of the group WiM in terms of task completion time and its usability. The results of discomfort questionnaires indicate the benefits of group teleportation for the guide's role. For group members, the group WiM induced less discomfort. Qualitative feedback on perceived advantages and disadvantages of both techniques was collected and summarized. In conclusion, the group WiM is an effective tool for navigation in complex virtual environments. Moreover, the results of the comparative evaluation reveal valuable insights and show potential research directions for group navigation.
Optomechanical Industrial-Level Camera Modifications for Repeatable Thermal Image Drift

Thermal image drift is observed in prevalent industrial-level cameras because their optomechanical design is not optimized to reduce this phenomenon . In this paper, the effect of temperature on industrial-level cameras is investigated, focusing on the thermal image drift resulting from ambient temperature changes and warming-up process. Standard methods for reducing thermal image drift are reviewed, concentrating on the lack of repeatability aspect of this drift. Repeatable thermal image drift is crucial for applying a compensation model as random thermal deformations in sensors cannot be compensated. Moreover, the possible cause of this issue is explored, and novel optomechanical camera modifications are proposed that maintain the thermal degrees of freedom for the deforming sensor, limitingÂ the lack of repeatability aspect of thermal image drift to a low level. The improvement is verified by conducting experiments using a specialized test stand equipped with an invar frame and thermal chamber. Considering the results from the application of the polynomial compensation model, the standard deviation of the central shifts of image drift is reduced by x3.99, and the absolute range of image drift is reduced by x2.53.
An Infrared Small Target Polarization Enhancement Algorithm Based on Physical Model of Atmospheric Scattering

Infrared target enhancement is a hard task in computer vision. With the development of infrared polarization sensors, enhance algorithms have attracted attention. Although using polarization information quickly distinguishes man-made objects from natural backgrounds, cumulus clouds would become detection noise because of their similar characteristics to targets in the sky scene. In this paper, we propose an image enhancement algorithm based on polarization characteristics and the atmospheric transmission model. The algorithm uses the principle of polarization imaging and atmospheric transmission theory to enhance the target in the image while suppressing the interference of clutter. We compare our algorithm with other algorithms through the collected data. The experimental results show that our algorithm significantly improves the target brightness and reduces clutter at the same time, meanwhile our algorithm guarantees real-time performance.
Creating and Improving the Design of the Library Website and the Opac Online Public Access List Using Zoho. Com Sites and Awesometable. Com: A Study

Library websites can offer Interaction with the library catalog. An Online Public Access Catalog (OPAC) provides the ability to log in to a library account to renew or request items. Libraries may organize the various periodical indexes, electronic reference collections, and other databases they subscribe to. A library site provides a library with a site to provide its administrations and tell its history to its location. Many libraries have access to the web, but have we really wondered why we should be there? Need to land in front of library web locations, anticipate the future, or do both? This study focuses on the purpose and various issues identified with a section of the library website including the theme of what a library webpage should look like if it is to conform to its main purpose and area. It is to clarify how to create it and how to set up an OPAC process to help identify books in the library where people or users are, and how to link it to a library website. Library websites can be connected with the library catalog an online public access catalog (OPAC) provides the ability to log in to a library account to update or request items. Libraries can organize various schedules, electronic reference collections, and other databases they subscribe to.
Motivational Beliefs, an Important Contrivance in Elevating Digital Literacy Among University Students

This paper examined the relationship between motivational belief strategies and digital literacy in digital learning among university students. Motivational belief is an intrinsic motivation that encourages an individual to self-motivate through specific strategies. This paper has its aim to contribute to assisting students, academics, and parents in utilizing the right motivational strategies to elevate students’ digital literacy. Although there are a plethora of studies given students’ motivation, studies of motivational belief strategies for elevating digital literacy are still rudimentary. To achieve the research aim, three hypotheses were formulated. A total of 583 respondents were analysed empirically using structural equation modeling-partial least-squares analysis. The findings of the study corroborate a positive and significant relationship between motivational belief strategies and digital literacy competency which signifies the important role of self-motivation in promoting digital literacy as well as preparing students to be a part of the digital future.
A CNN-Based Multi-Level Face Alignment Approach for Mitigating Demographic Bias in Clinical Populations

The investigation of demographic bias in facial analysis applications is a topic of growing interest with achievements in face recognition and gender classification. State-of-the-art convolutional neural networks (CNN) and traditional machine learning models for locating facial landmarks have reached overall performance levels close to human annotation. However, recent studies demonstrated that these models presented performance gaps when applied to different populations, characterizing bias led by demographic features. Nevertheless, few studies have addressed this problem in face alignment and facial landmarks localization methods. In this work, we propose a multilevel face alignment approach settled on CNN models to reduce performance gaps among different populations. We created facial subunit CNN models tied to a facial subunit detector at a higher level. The proposal seeks to improve bad results caused by facial impairment, guided by the following assumptions: facial unit landmarks localization does not require global texture, and combining different facial unit models can improve the final model’s variability. We applied the models in a balanced dataset mixing healthy controls (HC) and individuals with neurological disorders: amyotrophic lateral sclerosis (ALS) and post-stroke (PS). With fewer samples for training, our approach significantly reduced face alignment performance differences among those groups as compared to a state-of-the-art CNN model.
Reconfigurable and Compact All Optical Reversible Channel Multiplexers Using Si3n4 Based Optical Microring Resonator

The paper reports a novel all-optical 2x1 and 4x1 reversible multiplexer using the optical microring resonator (OMRR). The mathematical analysis of the Silicon Nitride (Si 3 N 4 )-based OMRR has been done to design the reversible multiplexers. The proposed reversible multiplexers are validated through the simulation results. The desired figure of merits like extinction ratio (ER) is 20.29 dB, amplitude modulation (AM) is 0.71 dB, and the contrast ratio (CR) is 19.54 dB of designs obtained through simulation prove the high performance and practical feasibility of the designed models.