Reviewerly/exHarmony huggingface dataset:
From sentiment to style: Charles Stewart Parnell’s rhetoric in the first crisis of the UK

Abstract Charles Stewart Parnell was the dominant Irish political figure of the late-nineteenth century. His campaigns for land reform and Home Rule changed Ireland’s relationship with Britain and reshaped north-south relations on the island. Despite his remarkable stature and influence, his legacy continues to be contested among scholars and his political stance is widely described as an ‘enigma’. His speeches were the main instrument through which he mobilized and polarized opinion throughout the Anglo-world, yet they have never been edited or even collected, and their impact—always presumed to be considerable—has not been studied in any systematic way. In this article, we describe the application of a combination of sentiment analysis (SA) and topic modelling to Parnell’s speeches to investigate how, when, and why he used emotive language, and which topics were utilized in the emotional manipulation of his audience. We will discuss the issues which arose in our experiments and how adopting a more nuanced approach to language could provide solutions to these challenges. We will also talk about how our investigation lead us away from ‘pure’ SA (i.e. positive or negative attitudes towards a particular aspect) and towards ‘style’ as a better framework for understanding and comparing the texts of the speeches. Finally, we will outline our plans for further work in this area.
EFEKTIVITAS APE KARTU KATA BERGAMBAR DALAM PENGEMBANGAN BAHASA ANAK USIA 5-6 TAHUN DI RA MIFTAHUL HUDA CEMORO

Language development in children is the ability to express language orally, both recognizing pictures, symbols of the letters of the alphabet, spelling and reading vocabulary as well as the ability to communicate. This study aims to describe the effectiveness of the picture word card educational game tool in language development for children aged 5-6 years at RA Miftahul Huda Cemoro. The place and time of this research was conducted on Jl. General Sudirman No. 02 Cemoro Hamlet, Balak Village, Songgon District, Banyuwangi Regency. This study uses qualitative descriptive research from data obtained through interviews, observation and documentation. The data analysis technique used the Miles and Huberman model. The stages in learning activities carried out through the application of pictorial word card educational game tools, namely the planning stage, the implementation stage and the evaluation stage. At the evaluation stage the teacher uses a checklist assessment. From the results of the final accumulation of assessments using 4 indicators that have been determined as a reference for assessment, there were 8 children who achieved BSB scores (developed very well) and 2 children achieved BSH scores (developed as expected). This shows that the application of picture word card educational games can stimulate the development of language skills in children optimally.
Adversarial Detection by Approximation of Ensemble Boundary

&lt;p&gt;A spectral approximation of a Boolean function is proposed for approximating the decision boundary of an ensemble of Deep Neural Networks (DNNs) solving two-class pattern recognition problems. The Walsh combination of relatively weak DNN classifiers is shown experimentally to be capable of detecting adversarial attacks. By observing the difference in Walsh coefficient approximation between clean and adversarial images, it appears that transferability of attack may be used for detection. Approximating the decision boundary may also aid in understanding the learning and transferability properties of DNNs. While the experiments here use images, the proposed approach of modelling two-class ensemble decision boundaries could in principle be applied to any application area.&lt;/p&gt;
Quantum Graph Neural Networks on a Single Qubit

Machine learning based on graph data is a hard task due to the complex structure of graph data.When this topic comes to quantum machine learning, it becomes more challenging due to the limited resources in the current NISQ era, in particular when a neural network can have thousands or even millions of parameters.In this paper, to address these challenges, we proposed a novel quantum graph neural network that uses only a single qubit.In our novel work, a graph embedding method was leveraged to waive the needs of hyperparameters, leading to the single-qubit based implementation with the least number of parameters in the network.Our experiments show that our method can be adapted to graph data of different structures and sizes, and can be efficiently deployed on real quantum computers.Our sQGNN quantum graph neural network (sQGNN) exhibits a high capability of fault tolerance towards noisy NISQ devices.In the current NISQ era, our single-qubit-based quantum neural networks can maximize the use of limited qubits and enable the implementation of large quantum graph neural networks on resourcelimited VQCs, showing a new promising pathway to overcome the current major shortcomings of VQCs facing real-world applications.
Research on the School-Enterprise Cooperation Training Mode for Music Professionals in Colleges and Universities

In order to meet the needs of enterprises, adapt to the development of the social era, and cultivate practical talents in line with the market, adopting and promoting the school-enterprise cooperation mode have become important reform measures of higher education for music professionals in colleges and universities. In the context of the continuous optimization and development of national higher education, clarifying the significance of deepening the reform, exploring the drawbacks of the current mode and the areas that can be developed, seeking an effective way to combine music education with social needs, tapping the unlimited potential of professionals, and guiding the training of professionals are all constructive guarantees for ensuring sufficient national human resources in the future.
Patterns of errors in texts written by Costa Rican university English learners: A corpus-aided study

The present corpus-aided study sought to identify the grammatical and non-grammatical second language (L2) error patterns of Costa Rican university English learners at all academic levels of a public university. Specifically, a total of 360 English as a foreign language learners, who were enrolled in the B.A in English or B.A. in English Teaching during the second semester of 2019, took the Quick Oxford Placement Test to ascertain their English Proficiency level and composed an argumentative text to elicit their written errors. Results from the placement test showed that the participants’ proficiency level ranged between B1 (low intermediate) and C1 (low advanced). In addition, the quantitative nature of the study required not only converting the handwritten compositions with a speech recognition software but also identifying and tagging all L2 errors with a tagging system. Analyses of a statistical software for data management revealed that the learner corpus contained a total of 33 L2 error patterns, which were classified as follows: 17 grammatical, 10 stylistics, and 6 lexical. Main descriptive findings indicated that although some error frequencies lowered to the point of having none as learners advanced in the major (e.g., capitalization and superlatives), other linguistic problem areas persisted all throughout (e.g., word form errors, fragments, and word order). Concluding remarks highlight that because the error frequencies of some L2 error categories still ranked high over time, learners’ L2 knowledge of lexical, syntactic, morphological, and stylistic domains could need more expert input (in the form of explicit instruction and/or feedback) depending on the complexity of the target structure.
Analysis of the Development Trend and Competitive Strategies of Cloud Service Companies - Take Tencent Cloud as an Example

Internet enterprises with the rapid development of computer networks, small and medium-sized enterprises usually can not afford to build and maintain the cost of the server, the cost of using cloud services for large enterprises, security, and post-maintenance is usually more cost-effective than the formation of their servers. The main enterprises in the domestic cloud service market are Alibaba Cloud, Tencent Cloud, Huawei Cloud, and Baidu AI Cloud. Based on market research data, statistical data, and the use of SWOT analysis, this paper analyzes the current situation of the domestic cloud services market and the competitive environment. The study concludes that the current cloud service market is still in the development stage, but due to the rapid development of computer technology and the Internet, future development will accelerate more and more. Cloud service providers should improve service quality and users' consumption experience to protect their market share and prepare for future competition with other cloud service providers.
Development of Human Resource Management in Off-Campus Educational Institutions in the Context of COVID-19：Take an off-campus educational institution in Beijing as an example

In recent years, due to the increasing demand for education by parents and students, a large number of off-campus education and training institutions have been created, and a large number of people have also been attracted to join the industry. The rapid development of this industry has created tens of millions of employment opportunities, but due to the short development time of the industry, there is a lack of a reasonable personnel management system. In the wake of the COVID-19 outbreak in 2020, many industries have been hit. In the off-campus education and training industry, due to improper personnel management and communication, a large number of personnel were lost. This article will take an off-campus educational institution in Beijing as an example to study the company's human resource management methods and policies in the "post-epidemic era", and find out the problems and solve them. The study found that such enterprises currently do not pay enough attention to grass-roots employees, lack of respect, inadequate communication, perfunctory manpower adjustment policies, and inadequate incentives for personnel, and put forward corresponding solutions according to each type of problem.
Blind detection of circular image rotation angle based on ensemble transfer regression and fused HOG

Introduction Aiming at the problems of low accuracy in estimating the rotation angle after the rotation of circular image data within a wide range (0°–360°) and difficulty in blind detection without a reference image, a method based on ensemble transfer regression network, fused HOG, and Rotate Loss is adopted to solve such problems. Methods The proposed Rotate Loss was combined to solve the angle prediction error, especially the huge error when near 0°. Fused HOG was mainly used to extract directional features. Then, the feature learning was conducted by the ensemble transfer regression model combined with the feature extractor and the ensemble regressors to estimate an exact rotation angle. Based on miniImageNet and Minist, we made the circular random rotation dataset Circular-ImageNet and random rotation dataset Rot-Minist, respectively. Results Experiments showed that for the proposed evaluation index MSE_Rotate, the best single regressor could be as low as 28.79 on the training set of Circular-ImageNet and 2686.09 on the validation set. For MSE_Rotate, MSE, MAE, and RMSE on the test set were 1,702.4325, 0.0263, 0.0881, and 0.1621, respectively. And under the ensemble transfer regression network, it could continue to decrease by 15%. The mean error rate on Rot-Minist could be just 0.59%, significantly working easier in a wide range than other networks in recent years. Based on the ensemble transfer regression model, we also completed the application of image righting blindly.
Contributions of lexicometry to perceptions of the energy transition

Within the framework of a research project, belonging to a University, which addresses problems related to the hydrocarbons industry, a survey is carried out during the month of September 2021 to investigate the perception of citizens regarding different issues related to the industry.The energy transition emerges as a solution to climate change that is commonly associated with the burning of fossil fuels.This paper aims to investigate through vocabulary how these ideas are installed in people.The answers to the open questions are analyzed: "Explain in your own words what you understand by energy transition", "What is your opinion about the energy transition?" and "Who must lead this change that leads to the energy transition?", using statistical analysis of textual data.These questions are part of a larger survey.The use of textual statistics in the analysis of answers to open questions provides information about what the investigated subjects think since the ideas are manifested through vocabulary.Additionally, this type of analysis makes it possible to differentiate and group subjects, establishing categories that do without the subjectivity of the researcher.The R language is used, complementing it with the SPSS Statistics software.188 surveys are analyzed in order to identify lexical differences and similarities between age groups.Using Lexicometry, the words are contextualized by building contingency tables of the different groups.These characteristics identified in the lexicon are studied by Correspondence Factor Analysis applied to the added lexical table.During this process, responses are not modified or categorized.This work makes it possible to determine which words are installed in people's minds regarding the energy transition and corroborates that there is a lexical difference in perceptions between different age groups.
Perceptions of EFL Students in Different Proficiency Levels Toward Group Work Experiences During Online Learning

Cooperative learning has frequently been studied experimentally, with evidence of its numerous benefits. However, the extent to which students understand their role in enhancing the method's positives is still being determined, and how learners perceive their interactions should be noticed. This study examined students of English as a Foreign Language perceived group interaction at different proficiency levels (proficient and less proficient) during online learning. The study employed a quantitative research design using a survey technique with statistical data analysis. Data showed insignificant differences in perceptions between the proficient and the less proficient students with (p&gt;0.05). Thus, proficient and less proficient students could provide learning opportunities when they worked collaboratively. Moreover, all students preferred to work collaboratively regardless of their proficiency level. These findings suggest that proficiency differences were not the decisive factor affecting the nature of students’ interaction in groups. Instead, the pattern of interaction co-constructed by learners may have a more significant impact on students learning performance.
Forensic Accounting and Detection of Occupational Fraud in Nigerian Deposit Money Banks

Banks play a vital role in the intermediation of financial resources in the financial system. However, the recurrent nature of fraud has made deposit money banks less effective. The study aims to examine the effect of forensic accounting on the detection of occupational fraud in Nigerian deposit money banks with a specific focus on data mining techniques, computer-assisted audit techniques, and trend analysis. Data were collected from primary sources using a well-structured questionnaire. Data were analyzed using descriptive statistics and ordinary least square (OLS) regression. The result shows that R2 and R2 Adjusted has the value of 0.917484 and 0.901904. The study then conclude that data mining technique has a significant positive effect on occupational fraud detection while the computer-assisted audit technique has a significant positive effect on occupational fraud detection and that trend analysis has a significant positive effect on occupational fraud detection. This finding implies that the detection of occupational fraud will be enhanced when forensic accounting technique is employed. The study therefore recommends, that deposit money banks should enhance the internal control systems with data mining techniques, computer-assisted audit techniques, and trend analysis feature to uncover asset misappropriation fraud in Nigerian deposit money banks by the central bank of Nigeria.
Web-based geographic information system for sago plant production land in Merauke District

Abstract Sago is also a symbol of indigenous peoples, sago has become one of the important staples. The land tension of sago plant production in Merauke regency is supported by natural conditions and the climate is good for the development of sago plants. in the current condition, the community still feels difficult in the search for sago land because there is no publication media for the location of sago crop production land developed by the Merauke Horticultural Agriculture Office, by building this system will help the community in finding sago land, The system is a geographical information system of sago plant production land in Merauke district based on the web that uses the PHP and Xampp programming languages as a localhost server over the Apache HTTP server program, MySQL database and PHP programming language translator. System testing performed on this system uses the Black Box method and Beta testing. The results of testing using Black Box found that the geographical information system of sago crop production land in Merauke regency based on the web can accelerate the process of searching for sago land developed by the Department of Agriculture and Horticultural. Keywords: geographical information system, sago plant production.
Effect of Oil Spill on Physicochemical Properties of Soil Spilled Sites in Kokori, Ethiope East Local Government Area, Delta State, Nigeria

Oil pollution might affect soil physicochemical properties. Pore spaces might be clogged, which could reduce soil aeration, water infiltration and increase bulk density, subsequently affecting plant growth. This study investigated the effect of oil spill sites at Kokori, Delta State, Nigeria, analyzing physicochemical properties, total petroleum hydrocarbon, polyaromatic hydrocarbon, benzene, anions and selected heavy metal concentrations using standard methods. Data obtained revealed that the physicochemical properties of the assessed soils were insignificantly altered when compared with their respective DPR intervention values. PH values were in the range of 4.80-5.60 which is considered to be strongly acidic. The electrical conductivity ranged from 122µS/cm to 185µS/cm indicating that the soil is saline. Chloride ion values were between 32.50mg/kg to 82.10mg/kg. Nitrate ranged from 0.85mg/kg to 1.04mg/kg, while phosphate was between 0.34mg/kg and 0.96mg/kg. Total petroleum hydrocarbon varied from 7.12-45.66mg/kg for topsoil and 7.12-37.82mg/kg for bottom soil. It is lowest at 7.12mg/kg, mean at 18.1mg/kg. These values were within the DPR permissible limits. Benzene and Polyaromatic Hydrocarbon (PAH) had a constant value of 0.001mg/kg in all locations. Iron, arsenic, lead, mercury, cadmium, nickel and copper were the heavy metals detected. Their concentrations at spill sites were moderate and generally did not exceed their respective target limits. However, level of lead was as high as 11.6mg/kg in location 3. The soil should be monitored so that it does not exceed the threshold values of the studied parameters.
USING LEGAL ENTITY IDENTIFIER (LEI) ON BLOCKCHAIN FOR REDUCING MONEY LAUNDERING RISKS AT FINANCIAL INSTITUTIONS

The increasing use of blockchain technology nowadays can play a key role in using the decentralized network as a standard database for the unified and secured registry, through which financial institutions can conduct a relevant customer due to diligence measures and business transactions by verifying organizations’ Legal Entity Identifier (LEI) codes, self-sovereign identifiers managed by the Global Legal Entity Identifier Foundation (GLEIF). With proper implementation, this solution can be applied to blockchain networks, offering the additional advantage of quality control and detailed business analysis. Thus, by giving financial institutions the benefit of interacting with legal entities that have proved their reputation and, therefore, by reducing the money laundering risk at financial institutions, blockchain technologies now could reach global standardization and acceptance by financial institutions worldwide.
The Role of Intellectual Capital in Firms’ Performance and Market Value: Evidence from Jordan

The purpose of this study is to examine the association between intellectual capital efficiency and firms’ financial performance and market value. The study employs the value-added intellectual coefficient (VAIC) model to measure intellectual capital efficiency. This model consists of three elements: human capital efficiency, structural capital efficiency, and capital employed efficiency. The study uses the return on assets (ROA) ratio to measure firms' financial performance and the market-to-book value (MBV) ratio as a measure of market value. Data for a sample of 113 manufacturing and service companies listed on the Amman Stock Exchange for 2014 to 2018 were analyzed using multiple regression analysis. The results indicate that VAIC has a significant positive relationship with a firm’s financial performance, while it is unrelated to a firm’s market value. Human and structural capital also have a positive influence on financial performance, but they have no relationship with market valuation. Capital employed is positively related to both financial performance and market value. The findings of this study would enable companies to better understand the role of intellectual capital in adding value for all stakeholders of the company. They may also bring standard setters’ attention to the need for recognizing or disclosing intellectual capital either quantitatively or qualitatively within the accounting financial reports, which may have the potential to increase the credibility of financial reporting.
Assessment of Heavy Metals Content in Soil and Groundwater within the Vicinity of Automobile Mechanic Workshops in Yola and Jimeta Towns, Adamawa State, Nigeria

Although heavy metals are naturally occurring elements that are found throughout the earth’s crust, anthropogenic activities such as mechanic workshops are a source of concern for heavy metal contamination. This paper assessed the heavy metals content in soil and groundwater within the vicinity of automobile mechanic workshops in Yola and Jjimeta Towns, Adamawa State, Nigeria using standard methods. Heavy metals were determined using AAS model 210 VGP while pH was determined by electronic JENWAY glass electrode pH meter (Model 3510). Data obtained revealed that, the pH of the sample ranged from 7.52 to 5.19 (provide the average pH levels in the soil and groundwater). From the study conducted, (provide the mean of the heavy metals here) it can be seen that the level of heavy metals found in the soil is in the order of Zn&gt;Ni&gt;Fe&gt;Pb&gt;Cd. While heavy metals found in the ground water of automobile mechanic locations is in the order Ni&gt;Fe&gt;Zn&gt;Cd&gt;Pb. The levels of metals found in the present study were generally below the Department of Petroleum of Petroleum Resources target values for metals in soils.