Reviewerly/exHarmony huggingface dataset:
A Study on the Relationship between "Five Education" Inputs and Employment Satisfaction of College Students Based on SEM Model Relationship with Employment Satisfaction

In recent years, with the increasing popularity of higher education, more and more college students are entering the talent market, and the employment pressure is multiplying, and the overall employment satisfaction is decreasing sharply.Therefore, college students should continuously improve their comprehensive strengths during their school years in order to increase their relative satisfaction during employment.This paper discusses the connection between "five education" and college students' employment satisfaction from the perspective of students' investment in moral, intellectual, physical, aesthetic and labor, so as to put forward specific suggestions for students' cultivation and development.
Building Segmentation from Remote Sensing Data using Enhanced U-Net

In this paper, a simple boundary-enhanced network and a new multi-task loss function are proposed for building segmentation from multiple remote sensing sources. The experimental results on MapAI-challenge dataset demonstrates that our network can segment buildings from remote sensing data, especially on the image &amp; laser multi-source dataset. The mean score of IoU and BIoU is 0.8995 for task 1 and 0.9155 for task 2 on the MapAI validation dataset and 0.5239 for task 1, and 0.7038 for task 2 on the MapAI test dataset.
Complex background information slows down parallel search efficiency by reducing the strength of interitem interactions.

In the laboratory, visual search is often studied using uniform backgrounds. This contrasts with search in daily life, where potential search items appear against more complex backgrounds. In the present study, we examined the effects of background complexity on a parallel visual search under conditions where objects are easily segregated from the background. Target-distractor similarity was sufficiently low such that search could unfold in parallel, as indexed by reaction times that increase logarithmically with set size. The results indicate that when backgrounds are relatively simple (sandy beach with water elements), search efficiency is comparable to search using a solid background. When backgrounds are more complex (child bedroom or checkerboard), logarithmic search slopes increase compared to search on solid backgrounds, especially at higher levels of target-distractor similarity. The results are discussed in terms of different theories of visual search. It is proposed that the complex visual information occurring in between distractors slows down individual distractor rejection times by weakening the strength of interitem interactions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Machine Learning and NLP based Sentiment Analysis

In today's digital world, it is highly important to know how sentiment analysis is important and how it is implemented on website building and creating any company profile in any social media and digital platform. It helps to tackle customer inquiries digitally and determine whether to purchase any particular company product and maintain the review platform good to protect the face of the company. This research study discusses how NLP and ML work together to implement the sentiment analysis. It also discusses about how NLP use certain tools and how it codes the human language and their way of transferring the data to meaningful conclusion and also discusses about the use of python in ML model to perform sentiment analysis.
A Comparative Study on Sense Utilization of Cathedral and Escorted by Light: Focused on the New Function of Expressionism

This thesis analyzes how ‘expressionism’ is used in contemporary literature through reading Cathedral by Raymond Carver and Escorted by Lighted by hae-jin Cho. ‘Expressionism’ is an art period that appeared in the 20th century and has the aim to express a subjective psychological condition of a character. This thesis limits ‘expressionism’ to the ‘senses’ of humans. The first person and the main character of each two short-story portrays vividly the inner world of themselves through the ‘five senses’, especially the ‘sense of touch’ and the ‘sense of sight’. Furthermore, the utilization of ‘sense of touch’ and ‘sense of sight’ have the feature of the medium of the narrator to understand other people. Eventually, the ‘expressionism’ in contemporary literary works has the hopeful function of suggesting ‘the possibility of solidarity’ and beyond ‘the connection to the community’.
Sentiment analysis of Chinese microblog based on BiGRU-attention

Microblog short text usually contains rich emotional information. It is a hot research topic in network data mining to grasp the dynamics of network public opinion through microblog emotion analysis. In order to improve the effect of Chinese microblog sentiment analysis, this paper first uses word embedding technology to quantify microblog short text from high dimension to low dimension vector space; Then, the global features of microblog data are extracted through BiGRU, and the Attention mechanism is introduced to obtain important features to build a Chinese microblog emotion analysis model. The feasibility and superiority of the model were verified with the public data set released by SMP2020. The accuracy, recall and F1 values of the model reached 78.65%, 78.57% and 78.41% respectively. The experimental results show that the feature vector of BiGRU combined with attention mechanism contains more rich emotion information of short text of microblog, which can effectively improve the performance of sentiment analysis of Chinese microblog. The experimental results show that the feature vector of Bi-GRU combined with the attention mechanism contains richer semantic information of the text, which can effectively improve the performance of emotion recognition of online public opinions.
Access control strategy based on smart contract in blockchain-based energy market

In recent years, the continuous increase in the number of renewable energy sources and users has led to an increase in the load on the electricity market. In this environment, the personal information data and management flexibility cannot be guaranteed in a market that integrates producers and consumers. It seems that the new energy market will face many challenges, such as how to price the output power of power plants according to different energy sources, which price that consumers can sell to other market users through self-generated electricity, how to communicate with consumers and producers in order to maintain the balance of the market and encourage consumers to reduce electricity consumption, how to calculate the market load in order to reduce carbon emissions and protect the environment. In the face of many of the above challenges, it is beneficial to introduce blockchain technology into the energy market because of the unique characteristics, which effectively reduces the cost of maintaining the market and enhances the flexibility of the market. But different market users are sensitive to market information with the advent of information transparency. Therefore, how to restrain the user behavior of the market and maintain the stability of the market will become a major problem. In this paper, we propose a market-based access control strategy to construct a blockchain framework and design an access control-based smart contract to constrain the behavior of each user. In order to further strengthen the relationship between producers and consumers, the concept of prosumer is used to enable consumers to participate in trading activities as energy producers. The experimental results are proved that the proposed solution works successfully in the blockchain, with timely responses between prosumers and consumers, good execution time and the admission of access control provides better market efficiency.
Abstractive text summarization model based on BERT vectorization and bidirectional decoding

Facing the pressure of the extremely inflated network information and data overload surplus, it is extremely important to locate “valuable information” efficiently and accurately. Text summarization technology in the field of Natural Language Processing (NLP) is an effective means to analyze and process network information. In this paper, we propose an abstractive text summarization model based on bidirectional encoder representations from transformers (BERT) vectorization and bidirectional decoding. The BERT is adopted to obtain a more global vector representation, which helps the subsequent encoder and decoder to fuse the full-text information to generate a summary with high generality. The decoding phase adopts a bidirectional decoding structure and combines the attention mechanism to maintain the bilateral decoding result to generate summaries. The bidirectional decoding structure can be fine-tuned according to the bidirectional results, which can overcome the tilt problem of the unidirectional structure, and the generated summaries are more consistent. The experimental results on the NLPCC2017 text summarization dataset show that the summaries generated by our model have the higher coherence at the word and sentence level, and the stronger generalization of the full text.
Data mining and prediction based on Wordle

For the wordle game, we mainly want to determine whether its daily words have an impact on the proportion of people participating in the difficult mode. We need to predict the distribution of the number of attempts in the future difficult mode, so we can use neural network to establish the MIMO(Multiple-Input-Multiple-Output) BP neural network model. This model encodes words as input. We set the transformation function of the model as tansig and purelin, the integration function as trainlm, and the learning function as learngdm. Then we set the number of training to 1000, and the mean square error of training is 0.005. After that, we can train the model and draw a conclusion.
Professional career in education : the curriculum of the discipline – 011 Educational, pedagogical sciences

The study of the academic discipline "Professional career in education" will contribute to the familiarization of graduate students with the practice of developing a professional career in educational practice and scientific domestic and foreign literature. This approach will contribute to mastering the content of a professional career on the conceptual basis of scientific provisions of pedagogy, psychology, theory of creativity, laws and principles in the field of education, which is realized through a persistent desire to build a professional career, professional self-realization, self-awareness of career orientation, expansion career-oriented knowledge, special abilities, skills, formation of professionally important qualities.
Novelty and ambiguity of testing: remembering Paul Virilio

. In this paper I will comment on a few theses of a psychologist Alexander Poddiakov regarding the psychology of acting in new and ambiguous circumstances, as well as that of developing cognitive research abilities. While comparing intelligence, creativity and research abilities testing, Poddiakov reflects upon methodological side effects, namely, doubtful correlation between them, immanent bias on the side of tests’ creators, oxymoronic way to present those tests as a ‘standard list of creative answers’. Notable and worth our reflection is the uncertainty of Poddiakov’s point of view which we intend to scrutinize: while acknowledging the value of intelligence and creativity testing as such, the author occasionally stumbles upon ‘hidden pitfalls’ of this praxis, wherein, undoubtedly, the very ambivalence of the era, which all of us are part of, is revealed — that which has been raised to the level of a civilizational imperative of techno-prosthesization of subjectivity and culture. Mainly I will be alluding to Poddiakov’s article ‘Mind Testing Practices: from regulation to freedom’ [Poddiakov, 2016] and our private correspondence. The aim of a proposed extensive comment is broadening the epistemological space of the discourse presented by Poddiakov, towards questions of philosophical-psychoanalytic kind while focusing on those which consider subjectivity in a broader perspective than that which regards testing practice specifically. I hope that meditations presented below will serve as an inspiring example of a productive trans-disciplinarity within the framework of which it is possible to make a ‘second order’ argument reflecting not only upon the very ‘phenomena’ through the lens of conventional methodologies but also the methodologies themselves. Throughout the article I will address Paul Virilio’s book ‘The Vision Machine’ [Virilio, 2004] wherein the author elaborates on problematics of re-structuring the subject in the context of technologization of visual contact with the world.
Teachers’ Professional Self-Efficacy for Collaboration: A Comparison Between European Countries

Since the understanding of non-cognitive skills and their importance have grown, it becomes more and more significant to measure their impact on different areas of professional life. It has been researched that not only individual teacher’s professional self-efficacy, but also collective self-efficacy has a significant impact on both the teachers’ self-efficacy and students’ achievement. The purpose of this study is to select a set of factors that correlate with teachers’ professional self-efficacy and to analyse if teachers’ professional self-efficacy has an impact on teachers’ cooperation with colleagues and students. Latvian, Lithuanian, Estonian, Danish, Norwegian, Swedish, and Finnish data from the OECD TALIS 2018 teacher questionnaires were used. o analyse which factors correlate with teachers’ professional self-efficacy, the authors of this article selected such variables as the type of school, the type of urbanization, the number of special education students in the class, full time or partial time employment, professional development courses and the professional development at university level. The authors found that there were no significant correlations between the type of school, the type of urbanization, and the number of special education students in the classroom, but there was a significant correlation between professional development courses and the professional development during the university study period. The teachers who worked full time job were more self-efficient than others. To study the impact of self-efficacy on collaboration, the authors of this article selected variables that represented teacher – student collaboration and teacher – teacher collaboration. The professional self-efficacy scale was partitioned into four efficacy levels and each level was analysed with answers from each variable. The group comparison and the linear regression analysis showed that teachers with higher self-efficacy levels cooperated more and better with students and colleagues. Thus, this research adds supplementary evidence to studies showing the importance of professional self-efficacy development.
FORMATION OF A SELF-LEARNING ORGANIZATION MODEL IN THE COMPANY’S PERSONNEL MANAGEMENT SYSTEM

The article proposes the author’s concept of forming a model of a self-learning organization as a tool for effective continuous self-development of a company based on personalized learning using expert design and creatively authorized educational technologies. The key features of methodological approaches to the formation of organizational-pedagogical and organizational-managerial mechanisms for the development of professional skills of an interdisciplinary nature, ensuring professional identity and adaptation of personnel to constant changes in professional activity, are revealed.
Application of Improved SOM Neural Network in Intelligent Auditing of Hospital Financial Vouchers

The traditional audit method relies on manual verification, which has a large workload and low efficiency, and is difficult to meet the work needs. In order to improve the efficiency of hospital financial audit, we studied the improvement of SOM neural network, optimized the initial weight matrix and learning rate function of SOM neural network, improved the operation performance and efficiency of neural network, combined with word co occurrence to build a financial intelligent audit model for accounting vouchers, and used the potential correspondence between voucher abstract text vocabulary and accounting subjects to provide audit suspects clues for auditors. The experimental results show that compared with the traditional SOM neural network algorithm, the precision rate of the improved sneural network model based on word co occurrence is increased by 3%, and the recall rate is increased by 2%, which can effectively improve the efficiency and quality of hospital financial audit.
Research on Optimization Algorithm of Enterprise Financial Data Classification Based on Decision Tree

The classification and prediction of enterprise financial data can improve the cost and benefit optimization management level of enterprises. In order to improve the ability of enterprise financial data classification, an optimization algorithm of enterprise financial data classification based on decision tree is proposed. The global data model is adopted to establish the management model of enterprise financial database. Based on the heterogeneous parameters among enterprise financial data sources, combined with the structural feature analysis of data sources, the characteristic analysis method of dynamic allocation and correlation constraints of resources such as human, material and financial resources is adopted to establish the allocation model of influencing factors of enterprise financial data. Based on the decision tree classification algorithm, the correlation features of cost and income of enterprise financial data are extracted. According to the pattern change of compliance management income, cluster analysis and pattern recognition of expected income dynamic characteristics of enterprise financial data are realized. By constructing a dynamic allocation model of enterprise financial data and enterprise financial cost and income, cash flow data analysis method is adopted, according to quantitative parameter analysis of realtime operating cash flow, semantic similarity measurement method is adopted, and based on online observation data cleaning, correlation characteristics recognition and cluster analysis of enterprise financial data cost and income are realized, and enterprise financial data is optimally classified. The empirical analysis and simulation results show that this method is highly reliable in classifying enterprise financial data, and has strong ability to dynamically allocate resources such as manpower, material resources and financial resources and control income and cost, thus improving the quality level of enterprise financial data management.
Phishing URL recognition based on ordered neurons long short-term memory neural network and attention mechanism feature fusion

Aiming at the increasingly serious problems of phishing websites and the poor characteristics of identifying phishing URL, a phishing URL recognition model based onordered neurons long short-term memory neural network( ON-LSTM) and attention mechanism feature fusion is proposed. Firstly, the URL is divided based on the word level and character level of sensitive words and special characters, and then the URL vector after word segmentation is input into the onlstm model to realize feature extraction. In order to enrich the feature information, the feature fusion layer adopts the feature splicing method to fuse the features of sensitive word level and character level, and introduces the attention mechanism to further enhance the feature ability and depth of the module to extract the URL. Finally, the softmax function of the fully connected neural network is used to realize the classification and recognition of URL. The experimental results show that the phishing URL recognition model based on the fusion of ON-LSTM and attention mechanism features has better ability to identify phishing URLs, with an accuracy of 98.95% and an F1 value of 98.92%. Compared with the comparison model, this method has better recognition performance.
Artificial bee colony optimized VM migration and allocation using neural network architecture

In recent years, cloud computing (CC) has been mostly preferred by users due to its advanced features as compared to other environments.There are many advantages to CC, like security, the privacy of data, fast speed of the internet, reliability, etc. [13].But still, some issues arise in CC, like service attacks, less privacy, etc.The CC architecture is categorized into two parts; the user end and the cloud data center end.The CC architecture, which explains how a user interacts with a cloud data center, is shown in Figure 1.The front end defines the process of user interaction with the cloud by using the internet.Users can interact with any internet-connected device, including personal computers, mobile phones, etc., and the device communicates with the cloud data center in the backend.
Virtual Notes Assistant Using ML

The voice assistance takes the voice input through our microphone (Bluetooth and wired microphone).It converts our voice into computer understandable language gives the required solutions and answers which are asked by the user. Voice assistance makes people smart and gives instant and computed results. The main focus is on user based information through user communication. They are able to interpret human speech. An intelligent software program called a virtual notes assistant (VNA) employing machine learning (ML) offers individualized note-taking and task management solutions. To increase productivity and lessen cognitive burden, it makes use of speech recognition, natural language processing, and user behavior analysis.