Reviewerly/exHarmony huggingface dataset:
An Efficient Neural Network Model for Sentiment Analysis using Bi-LSTM and CNN

These days, internet users are extremely keen in expressing their thoughts on a variety of goods or products in social networking sites, which has led to an increase in the amount of human generated content found on the web. Analysis of user sentiment on social media platforms like Twitter has emerged as a highly essential and difficult job in recent years. A nontraditional technique is required in order to complete the job of sentiment analysis in such a scenario due to the features of the data being used. In addition, analysis of sentiment on social media platforms is a basic topic that has a wide range of fascinating applications. The majority of the currently available algorithms for classifying sentiment on social media determine the polarity of sentiment solely based on the textual content of posts and ignore additional information available mainly on the following platforms. This In this work recommends an architecture for combining CNN and Bi-LSTM models into one another. The implementation of BILCNN which is a combined approach includes a word embedding concept that transforms tweets into numerical form, a CNN layer that accepts attribute embedding as source and generates features with a smaller criterion, and a Bi-LSTM model that takes input again from CNN layer and generates classification results. GloVe and Word2Vec were both used in their own right in order to determine the effect that the word embedding response had on the model that was suggested. The datasets for Yelp and Tweets were obtained, and BILCNN was applied to them. The findings suggest that the suggested system is capable of providing suggestions by a precision of 94.8 percent, offering consumers a satisfactory performance in their recommendations.
Exploring Earth’s Deep Water Cycle using Sublithospheric Diamonds

Sublithospheric diamonds, which may form within the transition zone and lower mantle, are the very deepest direct samples of the Earth's interior.[e.g., 1, 2, 3].Diamond can protect mineral inclusions from reactions during ascent to the surface.Therefore, the study of inclusions in diamond often provides invaluable insight into the geochemical and physical conditions of their formation and more broadly planetary volatile cycling [4].Combining synchrotron X-ray computed microtomography and X-ray diffraction at the GSECARS sector of the Advanced Photon Source, we are analyzing mineral inclusions in-situ within a suite of about fifty diamonds from a known super-deep diamond locality in Juina, Brazil.Pink beam microtomography first enables high resolution mapping (1.24 microns/pixel) of mineral inclusions within host diamonds.The tomography data also reveal microcracks within the diamonds that may lead to secondary alteration of their inclusions.After locating pristine inclusions of interest, we employ single-crystal and powder Xray diffraction to identify individual inclusions.The primary objective of this research is to identify and characterize silicate inclusions within these diamonds and determine their degree of hydration to understand Earth's deep water cycle.We will present the results of our mineral inclusion work to date, including the range of mineral phases identified, their likely origin, and their implications for deep Earth composition and dynamics.
Data Enhancement Method Based on Generative Adversarial Network for Small Transmission Line Detection

Unmanned aerial vehicle (UAV) patrol inspection is an important means of transmission line detection. However, during UAV patrol, due to the UAV or the bad weather, there may be droplets on the camera lens, which will especially reduce the effect of small target detection. In this paper, a data enhancement method based on improved generative adversarial network (GAN) is proposed for automatically removing droplets from UAV images of transmission lines. In order to take more account of the context information of small targets when removing droplets, this method integrates the attention mechanism in the design of generator. Experiment shows that compared with the benchmark method, the proposed method can make the image after removing droplets more similar to the original image, and increase the structural similarity (SSIM) by 8.69%. The practical value of this method is further proved in the process of object detection of droplets removed images.
METHODS OF TEACHING THE EDUCATIONAL DISCIPLINE "ROAD SAFETY" FOR STUDENTS OF HIGHER PROFESSIONAL EDUCATION ORGANIZATIONS

The article outlines the problem of meeting the need of society in highly qualified specialists in the field of life safety, road safety, in particular, that is very relevant for our country today. The article presents the organization of a creative approach to implement the educational process, to combine traditional and new teaching methods, due to their influence on the results of training specialists. The article deals with the issues of teaching methods of the academic discipline Road Safety. The article presents the characteristics of the stages of the lesson and an exemplary lesson plan on the topic "Fundamentals of Traffic Safety". The article is addressed to teachers who teach the discipline Road Safety in the system of higher and secondary vocational education, as well as students enrolled in this program.
PENGENALAN KOSAKATA BAHASA INGGRIS ANAK USIA 4-5 TAHUN DI TK BRUDER NUSA INDAH

ABSTRACT The research aimed to determine the English vocabulary recognition of children ages 4-5 years in Bruder Nusa Indah Kindergarten Pontianak. The method of this research was qualitative in phenomenology form. The data source was an English teacher who teaches a group of children ages 4-5 years in Bruder Nusa Indah Kindergarten Pontianak. This research used observation with observatory guidelines, interviews with interview guidelines, and documentations with documentation guidelines as the research technique. The process in analyzing the data was through data collection, data reductions, data display, and conclusion drawing/ verification. Based on the result of research and through general data analysis, it can be concluded that the teacher had been conducted each step of English vocabulary recognitions’ learning process properly from the beginning to the end of the activity, used a variety of media in teaching, and evaluated each children using observation scoring and checklist. Keyword: English Vocabulary Recognition to Children ages 4-5 years
Characteristics, Technologies and Applications of Energy Internet Digital Twins

Digital revolution and energy revolution drive the Chinese power system upgrading to the Energy Internet (EI). Digital twins (DT) build a real-time and complete mapping digital image through intelligent perception and real-time connection of physical system, driving efficient collaboration and intelligent linkage of multiple elements in the physical world, it is one of the key supporting technologies of EI. This paper summarizes the characteristics of EI and Energy Internet digital twins (EIDT), proposes the key technologies of EIDT applied in El, and analyzes the specific application scenarios.
Research on CGAN-BERT and RGAN-BERT Models for Short Text Classification based on Semi-Supervised Model

With the rapid development of artificial intelligence, a large number of short texts cause a certain degree of information redundancy. Text classification technology can help people classify and process information, and has important applications in the fields of recommendation system, public opinion monitoring and information retrieval. However, short text information in different fields has the characteristics of industry professionalism and fast updating of language style. The resulting problems such as poor applicability of the model and bottlenecks in annotation make the effect of traditional classification methods in short text analysis limited. Therefore, we propose semi supervised text classification models CGAN-BERT and RGAN-BERT based on convolutional neural network and cyclic neural network respectively. The newly designed generator and discriminator are more conducive to the game process. We evaluate our model and other classical models on several public data sets. The experimental results show that our proposed model is better than other models.
Profile analysis in clustering with Hotelling’s T-square statistics

Cluster analysis is a multivariate technique that groups objects based on their characteristics. For instance, it groups the most closely similar objects in the same cluster, thereby forming high internal homogeneity and external heterogeneity. Validation of the grouping results, carried out through profile analysis, is important to obtain the best partition that fits the basic data. Therefore, this study determined the profile analysis in clustering using Hotelling's T-square statistics on profile analysis and its application to rainfall data. Equivalent profile analysis with mixed ANOVA was used to test for hypothesis on the mean value of multiple variables (multivariate) using graph principles. In profile analysis, data plots were carried out to compare between groups of 3 patterns visually, namely, profile alignment, coincide, and alignment with the flat axis. These patterns were further validated using Hotelling's T-squared statistical test, which is a multivariate extension of the common one-sample or paired student t-test and used when the number of response variables is one or more. The result showed that the data is close to normal multivariate.
Hybrid Image Denoising with Wavelet and Deep Learning Model

Underwater image enhancement restores corrupted underwater photos so that clean images can be produced.Deep learning-based image super resolution reconstruction has been a prominent issue in the near past.To enhance the efficiency and effectiveness of deep learningbased solutions, this research puts forth an enhanced picture deep convolutional neural networks-based super-resolution reconstruction technique.Deep learning-based image improvement techniques normally use paired data to train the model; however, in the underwater environment, paired data, such as damaged photographs and their corresponding clean images, is difficult to collect simultaneously.Another significant issue is how to effectively keep specific information in the improved image.We provide a neural network-based model for an approach to unpaired underwater picture enhancement to overcome these issues.
Sourcing Data

Although setting up a structure and layout is a key part of the process for creating a site, no site will be complete without data. Eleventy is no different - over the next few pages, I will show you how Eleventy consumes data, before we add features such as site metadata and connect to data sources both internally and externally.
MODERN MODELS OF MIXED EDUCATION

The introduction of IT resources and the current situation in society and the state involves making certain adjustments to the educational process. The article deals with the issues of mixed education at the present stage. If before the pandemic, the use of IT resources in Russian education was within the framework of fashion trends or innovative developments, then lockdown showed that this toolkit allows us to save the educational process in emergency situations. The results of the study of modern conditions of the educational process and the introduction of IT resources into it, the introduction of certain adjustments and the construction of modern models of mixed education are presented. The strengths and weaknesses of the mixed training format are presented. The strengths of the training include: the development of information and communication channels between students and the teacher; the development of individual routes for monitoring student learning activities; the possibility of integrating traditional and mixed education; the use of a pointrating system as a differentiated scale of the knowledge system, etc. Weaknesses can be considered: insufficient amount of time for communication between the teacher and students; the ability to assess knowledge and insufficient ability to assess skills; difficulty in assessing the analysis of logical thinking of the student, the course of his reasoning; the presence of low motivation to study in a number of students. The article also examines the factors that have a significant negative impact in Russian reality on the development and effectiveness of the use of mixed education and the most commonly used models of mixed education in Russia (“Rotation” and “Personal choice”)
Design of Corpus Based Comprehensive Ability Evaluation System for College English Teaching

At present, the evaluation accuracy of the comprehensive ability evaluation system of College English teaching is low, and the stability of the evaluation process is poor, resulting in greater risks in the evaluation process. In order to solve the above problems, a new comprehensive ability evaluation system for College English teaching is designed based on corpus, and the hardware and software of the system are optimized. The system hardware mainly designs microprocessor, signal modulator, power module, evaluator, single chip microcomputer and collector. The processor selects sep83 microprocessor and introduces sd63c84 chip. At the same time, en, A1 and A0 are designed as the input ports of the program-controlled amplification circuit of the regulator. The main control chip of the single chip microcomputer is ti7392 and the acquisition is realized through snt5428. By introducing corpus, the software workflow is realized through information collection, quantitative evaluation, designing practical and effective evaluation contents of comprehensive ability of College English teaching, reducing evaluation errors. The experimental results show that the corpus based College English teaching comprehensive ability evaluation system can effectively improve the evaluation stability, reduce the evaluation risk and enhance the accuracy of the evaluation process.
Handwritten Mathematical Expression Recognition via GCAttention-Based Encoder and Bidirectional Mutual Learning Transformer

Recognition of handwritten mathematical expressions to is an image-to-sequence task. Recent research has shown that encoder-decoder models are well suited for this challenge. Many innovative models based on this structure have been proposed, especially on the decoder. Such as attention mechanism and bidirectional mutual learning are used in the decoder. And our model also improves the encoder. We use the multi-scale fusion DenseNet as the encoder and add Global Context Attention. This attention mechanism combines the advantages of force-spatial attention and channel attention. The feature maps of the two scales output by the encoder are used as inputs to the two decoder branches. The decoder uses a two-way mutual learning Transformer, which can understand high-level semantic and contextual information, and can handle long sequences of information well. In order to save memory, the two decoder branches use a set of parameters, and the last two branches are distilled and learned from each other. In this way, not only the bidirectional decoders can learn from each other, but also the two decoder branches can learn from each other, which increases the robustness of the model. Our model achieves 56.80%, 53.34% and 54.62% accuracy on CROHME2014, 2016 and 2019, respectively, and 66.22% accuracy on our own constructed dataset HME100k.
Research on digital logistics parks' horizontal cooperation with Cloud platform: from the perspective of tripartite evolutionary game

To meet the massive and emergent logistics demand, learning from Europe and the USA, a cooperation mechanism can form a gathered and scaled logistics park network. Based on the current practice in China, the logistics Cloud platform makes the cooperation from a horizontal perspective possible. A tripartite evolutionary game model consisting of one logistics Cloud platform and two digital logistics parks is proposed. The platform has strategy {compensating, no compensating}, and the parks have strategy {sharing, no sharing}. The revenue function of each party is constructed, applying dynamic replication function based on stag hunt game; all equilibrium points are found. With Jacobi matrix, two asymptotically stable strategies are formed. The study shows that the three game players either choose sharing/compensating or no sharing/no compensating strategy. The strategy is affected by the sharing/compensating probability, the compensation and opportunity cost, and the order loss. The managerial insights are also discussed.
FILMS OF ESCHATOLOGICAL CATASTROPHISM AS CULTURAL ARTIFACTS OF THEIR TIME: CONSIDERATION IN HISTORICAL DYNAMICS

This article is devoted to the identification and research on films of “eschatological catastrophism”, their evolution and features. The concepts of “catastrophism” and “eschatology” and their relationship are indicated, and on this basis the author's concept of “eschatological catastrophism” in cinema and its attributive signs are revealed. The methodology used in this article includes the analysis and synthesis of films, considering the historical dynamics and their relationship with the socio-cultural context of the era. The study covers films produced in the USA, France, Italy, China, and Canada and other countries. The general trends in the development of eschatological catastrophism films, the factors that determined the rise of these films, are reflected. This research explains the reasons behind the popularity of catastrophe films in a particular period, and using the method of sociocultural observation, the philosophy of films and the dynamics of changes in the semantic context of eschatological catastrophism in the period from 1910-2020 are tracked. The possible reasons for the demand for the secular apocalypse films at the intersection of the XX-XXI centuries are discussed.
Exploring the carbon impact of cybercrime and its mitigation

Increased digitalisation of industry, government and society has delivered tremendous benefit, the power of data-driven analysis and control looks to increase. Systems are more valuable to society, government and industry and therefore offer a greater benefit to all. The increased importance of connected systems, and the value of their data has become of significant interest to cyber criminals, online crime accounts for over $1tn pa. These economic activities undoubtedly have a carbon consequence. However current research is primarily focused on the risk and economics of this “push pull” tension between attack and defence with some body of knowledge covering the emotional effects of online crime. There is a gap in the correlation of carbon to cyber impact, cyber defence, and overall risk. This paper describes preliminary research to explore tensions between fit-for-purpose defence and good carbon stewardship and looks at holistic approaches to cyber reduction. It looks at the efficacy of existing risk models and whether or if they can incorporate carbon into the conventional cost and risk tensions which we see today in cyber infrastructures. Rational actor theory has been used in foreign policy and is being applied to cybercrime as well as carbon, to date as isolated discussions.
Evidence Generation in the Fault Type Fusion Diagnosis

Recent years, D-S Evidence theory, as a method of uncertainty reasoning, has flexibility in expression and convenient characteristic in reasoning of uncertain information. Its treatment process to uncertain information is similar to the thinking habit of human experts. In this paper, the evidence generation method of the fault type judgment in the vibration isolation system is well researched. Through the observation of a large number of experimental results, we finally selected 4 common fault types of the vibration isolation system as the frame of discernment. B-P neural network was chosen as a tool for fault diagnosis of multi-sensor information fusion based on D-S evidence theory by steps of building discernment frames and modifying parameters of the neural network. By selecting proper characteristic parameters and generating correct evidence, the ideal result is obtained after fusion, and the purpose of fault type diagnosis is well accomplished, and ideal results are also obtained.
Copying Microsoft Forms Attachments to a SharePoint List Item Using Power Automate

Following up last chapter's discussion of whether to use Microsoft Forms or Power Apps, this chapter covers the situation where Forms is appropriate, but we need to copy the results AND the uploaded attachments to a SharePoint list. It builds on a solution by Norm Young from Canada.
Research on Simulation Sample Generation Technology Based on Multiple Variable Points

In the combat simulation field, how to construct numerous corresponding simulation samples based on the simulation deduction scene, which are used to optimize the combat plan or choose a better one, is an urgent problem to be solved. Current sample generation methods, such as the generative adversarial network, cannot solve the problem of simulation sample generation very well, so a new approach is needed to generate simulation samples. A simulation sample generation technology based on multiple variable points is proposed to solve this problem. This technology can automatically create simulation samples and their corresponding scripts through rule-based means according to a given series of variable points. To verify the effectiveness of the technology, the system effectiveness was compared between the baseline version and the simulation samples generated based on variable points through the AHP, and the combat effectiveness of the generated samples improved by nearly 10%. The simulation sample generation technology based on multi-variable points solves the problem of constructing numerous simulation samples based on the simulation deduction scene and dramatically reduces labor consumption.
Minecraft, The New Border Of The Snap World

The Virtual Snap World is a project that aims to facilitate the teaching of electronics through educational tools. It finds opportunities in the Minecraft video game to connect and promote interest in science and engineering in new generations. This new setting eases the hybrid environment performance which means it provides the face-to-face or virtual system. This presents a solution to the problem the COVID-19 pandemic has created so as to focus on prevention measurements for contagion and the impediment to create events. This project allows having learning spaces considering people’s health now that it can be developed from anywhere in the world.
Analysis on the number of layers in the transformer-based model for neural machine translation

Recently, the transformer-based models have been widely used in sequence-to-sequence (seq2seq) tasks, especially neural machine translation (NMT). In the original transformer, the layer number in encoder is equal to the layer number in decoder. However, the structure is more complex and task is more difficult in decoder than those in encoder, so the layer number should not be same. In order to verify how many layer number in encoder and decoder is properly valued, we improve transformer as our model and conduct four experiments on four translation tasks of IWSLT2017. The experimental results show that the layer number in decoder should be larger than that in encoder, which can bring better translation performance.