Reviewerly/exHarmony huggingface dataset:
Multi-label Search Model for Open Educational Resources Based on Learning Purpose

The Open Educational Resources (OER) repositories store a large quantity and variety of data from multiple sources, and that present relevant and useful information for the student training process, improving student performance thanks to more up-to-date and didactic educational content, this generates the need for a more specialized Learning Objects (LO) search system. In this research work, a multi-label search platform is proposed based on the learning purpose that unifies the information from different OER in a single platform, using an information extraction process based on Web Scraping techniques and generating a selection of one of them. Or multiple tags, based on the SOLO taxonomy, using an MLP-based multi-tag classifier. As a result, we obtained the improvement of the most specialized search and content according to what teachers really need, up to 85% accuracy of the resources they needed and approval of the use of the platform up to 78.57%.
An AI-Based Model for the Prediction of a Newborn’s Sickle Cell Disease Status

Sickle cell disease remains a global public health problem. In Senegal, a neonatal screening and early follow-up program is conducted at the CERPAD. Such a program, started in April 2017, implements the strategy of systematic screening at birth and concerns children born in the maternity wards of the CHRSL as well as from the reference health center of the city of Saint-Louis. However, out of 18 257 newborns screened since the beginning of the program, only 49 (less than 0.5%) are pathological (SS, SC, etc.) which is extremely low compared to the cost in terms of human resources, working time and use of laboratory consumables. To mitigate the impacts of these limitations of the actual early detection and follow-up approach, we therefore propose in this paper a new approach to targeted screening based on artificial intelligence. We tested and compared the performances of five machine learning algorithms for the prediction of sickle cell status. The preliminary results are promising for the task of whether or not a given newborn has a potentially pathological profile, with the majority of the models showing a high prediction accuracy.
Recommendation System for Carbon Reduction

Nigeria is one of the country’s most vulnerable to carbon emission (CO2). A basic understanding of public perception on vulnerability, attitude and the risk in relation to Carbon Emissions and health provide strategic directions for government policy, adaptation strategies and development of guidelines. The aim of this research was to collect data on people’s knowledge and perception about Carbon Emissions and their impact on health. A cross-sectional survey was conducted online from 20th to 31 August 2021. A structured questionnaire was used to collect data among 129 people online. This research revealed that respondents had a low and inconsistent understanding of climate change and its impact on health. To reduce carbon emission, we built a carbon emission recommender system, which would calculate carbon emitted by individuals and industries and then suggest alternative resources that can be used to reduce or mitigate carbon emission.
SenTekki: Online Platform and Restful Web Service for Translation Between Wolof and French

In this article, we propose a machine translation Web Service and Platform for Wolof, a low-resource Niger-Congolese language. We first developed a Transformer model, and different configurations adapted to these models to translate between Wolof and French. We trained the model on a very limited amount of French-Wolof parallel data of approximately 85,000 parallel sentences. The best model was able to achieve a good performance of around 28 BLUE score. We then developed a web platform that allows text to be translated between French and Wolof, but also a RestFull Web Service that will allow other applications to benefit from the translation service.
A New Wavelet Based Steganography Method for Securing Medical Data

The transmission of confidential information over an open communication channel is susceptible to many threats like copyright infringement, eavesdropping and hacking. In this paper, we propose a solution combining data encryption techniques and multiscale signal analysis for securing patients’ confidential data. Discrete Wavelet Transform (DWT) is first applied to an ECG signal. The confidential patient information and the electrocardiogram (ECG) signal are then encrypted with the Advanced Encryption Standard (AES) method. Finally, the output of the encryption is hidden in an image to form the stego image and transferred to a medical server. While cryptography ensures the confidentiality of the data modified by the encryption process, steganography enhance the security. The evaluation of the proposed system was performed with real data and quantitative parameters such as Percent Residual Difference (PRD), Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR). The experimental results show the proposed scheme has a good encryption effect and a strong ability to resist detection compared with the existing methods.
Affective Human-Robot Interaction with Multimodal Explanations

Facial expressions are one of the most practical and straightforward ways to communicate emotions. Facial Expression Recognition has been used in lots of fields such as human behaviour understanding and health monitoring. Deep learning models can achieve excellent performance in facial expression recognition tasks. As these deep neural networks have very complex nonlinear structures, when the model makes a prediction, it is not easy for human users to understand what is the basis for the model’s prediction. Specifically, we do not know which facial units contribute to the classification more or less. Developing affective computing models with more explainable and transparent feedback for human interactors is essential for a trustworthy human-robot interaction. Compared to “white-box" approaches, “black-box” approaches using deep neural networks, which have advantages in terms of overall accuracy but lack reliability and explainability. In this work, we introduce a multimodal affective human-robot interaction framework, with visual-based and verbal-based explanation, by Layer-Wise Relevance Propagation (LRP) and Local Interpretable Mode-Agnostic Explanation (LIME). The proposed framework has been tested on the KDEF dataset, and in human-robot interaction experiments with the Pepper robot. This experimental evaluation shows the benefits of linking deep learning emotion recognition systems with explainable strategies.
Machine Learning Approach for Solar Irradiance Estimation on Tilted Surfaces in Comparison with Sky Models Prediction

In this study, two supervised machine learning models (Extreme Gradient Boosting and K-nearest Neighbour) and four isotropic sky models (Liu and Jordan, Badescu, Koronakis, and Tian) were employed to estimate global solar radiation on daily data measured for one year period at the National Center for Energy, Research and Development (NCERD) at the University of Nigeria, Nsukka. Two solarimeters were employed to measure solar radiation: one measured solar radiation on a tilted surface at a 15° angle of tilt, facing south, and the other measured global horizontal solar radiation. The measured global horizontal solar radiation and the time and day number were used as input for the prediction process. Python computational software was used for model prediction, and the performance of each model was assessed using statistical methods such as mean bias error (MBE), mean absolute error (MAE), and root mean square error (RMSE) (RMSE). Compared to the measured data, it was discovered that the Extreme Gradient Boosting (XGBoost) algorithm oﬀered the best performance with the least inaccuracy to sky models.
Clustering Model of English Semantic Text Features Based on Corpus Retrieval

In order to realize English-Chinese automatic translation retrieval, it is necessary to build an English semantic text feature clustering model, propose an English semantic text feature clustering algorithm based on corpus retrieval, analyze the quantitative parameters of English semantic text corpus features, build an English semantic text feature clustering method and scale, use Markov network model structure to build structural expression parameters of semantic text corpus features, and fuse and analyze English semantic text features. A corpus retrieval model of English semantic text is established, comprehensively investigate sentence semantics and cognitive features of the subject by using fuzzy decision method, establish a semantic text corpus, realize translation evaluation and parameter clustering by combining expert evaluation, adopt the method of corpus adaptive retrieval, generate target words and add semantic dynamic components one by one from left to right, and introduce the preference factor of the feature translation quality of semantic text corpus to realize the optimization of text feature clustering model. The simulation results show that the clustering of English semantic text features by this method is better, the quality of translation is improved, and the retrieval accuracy of semantic text corpus is higher.
An Empirical Study on The Comprehensive Quality Evaluation of Chinese College Students Based on Bayesian Algorithm

Under the background of deepening the reform of education evaluation in the new era, by analyzing the current situation and dilemma of College Students' comprehensive quality evaluation, this paper constructs a comprehensive evaluation model from the dimensions of Ideological and political quality, professional knowledge quality, practical innovation ability and physical and mental quality by using Bayesian classification algorithm with small error rate. According to the logical relationship between the evaluation system indicators, the model evaluates the comprehensive quality level of students in the form of probability. Combined with 405 research samples of college students, the prediction results are good, and the model has the ability and expansibility to deal with big data. Finally, the paper suggests that the comprehensive quality evaluation of college students in China should be classified and evaluated combined with the characteristics of disciplines, fuse and optimize multiple heterogeneous data, and construct a multiple evaluation mechanism to improve the scientificity of college education evaluation.
Analyses of Survey Data on Current Textbooks about Relationship between Grades and Attitude of Respondents through Statistical Software SPSS 23

With the information and data gathered from the survey conducted in advance on higher vocational English textbooks, this paper analyzes different influences that different grades have on respondents’ expectation and satisfaction about higher vocational English textbooks with variance analyses of software SPSS 23. Through software tools of mono-factor variance analyses and tendency tests, influences of different grades on the dependent variables and linear correlation between grades and dependent variables are concretely shown. Based on the accurate analyses conducted with these statistical software tools and the statistics produced by software SPSS, this paper can provide detailed instructions on future construction of higher vocational English textbooks.
Construction and Application of Blended "Golden Course" of College English with OBE and POA Based on MOOC and Teachermate Platform

Guided by the OBE teaching concept and the POA theory, this research attempted to construct a blended "golden course" teaching framework for College English. Following the standard and connotation of "golden course", this paper expounded the teaching design with four specific approaches: setting output of language as teaching objectives , selecting theme-centered topics as teaching contents, conducting flipped classroom mode as major teaching methods, designing multiple teaching formative evaluations. Based on the OBE-POA blended golden course teaching mode, it emphasized an effective connection of "integrating learning into application" and a complete teaching process of "motivating, enabling and assessing" with the cohesive goal of task output, generating the online and offline teaching activities coherent and orderly. The flipped classroom and multiple assessment system also can promote students’ high-level thought pattern, gradually facilitating students to complete speaking and writing output tasks from low stage to high stage. After a semester of teaching practice in an Advanced College English class, it was found that the new "golden course" teaching method can promote faster development of students’ English production proficiency in speaking and writing. The questionnaire survey also showed that most students were pretty satisfied with the effectiveness of the newly upgraded blended "golden course" teaching mode.
Research on College English Education Technology and Teaching Mode in the New Era

This study is based on the present development of technology education in the college English education, combined with the impact of education on college students’ English education technology, analysis to establish the modern education technology environment, put forward two kinds of new era college English teaching mode: Computer Multimedia Teaching Model and Modern Long-distance Teaching Mode. The research also puts forward some prospects for the development of English education technology, such as using mind mapping, building corpus, and using relevant APP.
Research on Translation Quality Control of Computer-Aided Translation Software Trados

With the development of modern translation technology, computer-aided translation software gives excellent technical support to translation quality control, helps translation project teams discover translation product defects as early as possible, and remedy them in time to minimize the negative impact on cost and schedule. In this paper, we take the mainstream translation software SDL Trados Studio as an example to discuss the control of computer-aided translation software on the quality of translated texts.
Research on multidimensional classroom teaching mode in Internet plus Era

This paper explores how to use the Internet platform to carry out network information teaching. Let teachers and students participate in teaching activities more conveniently through their own computers, tablets or smart phones. This paper defines the theoretical basis and practical methods of multi-dimensional classroom construction. Integrate traditional classroom and online classroom, Realize the mixed teaching of online and offline, in class and after class, real and virtual. Let teachers’ teaching and students’ learning break the restrictions of time and space, So as to further promote the reform and innovation of information teaching. Fully embody the educational concept of people-oriented and student-centered, stimulate and mobilize students’ awareness of autonomous learning, emphasize students’ subjective initiative, and realize students’ transformation from passive acceptance learning mode to active inquiry learning mode.
Research on the Construction of Smart Classroom Serving OMO Teaching Reform

With the rapid development of information technology and the increasing demand for educational information application, the concept of "Internet plus higher education" has promoted the integration of information technology and education. It has become an important topic in the new era to enhance the quality of higher education through information technology. The online merge offline teaching model fits well with the teaching concept of the new era and is the mainstream direction of teaching reform. The smart classroom plays a significant role in promoting OMO teaching reform, improving students’ autonomous learning ability and improving learning quality. Based on the goal of helping OMO teaching reform and starting from the functional demands of smart classroom construction, this paper puts forward the system architecture and technical framework of smart classroom, carries out the construction practice of smart classroom, and gives full play to the role of smart classroom in promoting OMO teaching reform.
Analysis of influencing factors of college evaluation based on Speapman rank correlation and Topsis algorithm model

Spearman rank correlation model is introduced into the determination of influencing factors of evaluation results of higher vocational colleges. Taking the "innovation and strengthening school project" in Guangdong Province as a research case, 27 class B higher vocational colleges, 4 first-class indicators and 89 relevant data indicators are selected, and 28 key explanatory variables are selected. The rationality of Spearman results is further verified by Topsis algorithm model. The empirical results show that the through training of secondary and higher vocational colleges, the value of practical teaching equipment provided by enterprises, the number of students ordered by enterprises, the availability of school financial funds, and the expenditure of school financial funds are strongly related to the ranking of colleges and universities; The growth rate of enrollment in ordinary higher vocational colleges, the per capita teaching hours of full-time teachers, the total class hours of professional courses, the proportion of in-service personnel funds in the total expenditure, teachers' Continuing Education and training expenditure and the proportion in the total salary have no substantive impact.
Research on the development of artificial intelligence based on CiteSpace and SPSS technology

In recent years, artificial intelligence technology is booming, and artificial intelligence technology has become a research hotspot in academic circles. The author uses CiteSpace to conduct citation analysis on the research literature of knowledge map in the field of artificial intelligence. Through LLR cluster analysis, it is known that most of the research topics focus on basic theoretical research, method research and application research. This paper analyzes the proportion of artificial intelligence in various industries by using SPSS technology, and forecasts the trend of the scale of China’s artificial intelligence market. On the basis of adhering to the principle of people-oriented and the unity of truth principle and value principle, we should overcome the problems caused by man-machine relationship and promote the harmonious coexistence of artificial intelligence and human intelligence.
Application of finite element analysis software ABAQUS in civil engineering course teaching

Civil engineering is one of the disciplines with close combination of theory and practice. Theory teaching is the main form of imparting knowledge. Mechanics course is abstract and boring, and focuses on theory and formula derivation. The contents of professional courses such as steel structure and concrete structure need to be combined with experiment and theoretical analysis, and the experimental class hours of these courses are very few, so it is difficult for students to have an in-depth understanding of theoretical knowledge. ABAQUS is an engineering simulation software based on finite element method, which can solve various problems from linear analysis to complex nonlinear analysis. Applying ABAQUS finite element technology to the teaching of civil engineering courses helps to stimulate students' interest in learning and enhance students' ability to solve practical problems, so as to improve the teaching effect.
Research On the Application Of Blockchain Technology In Teachers’ File Management

Teachers are pioneers in the field of education and determine the development of education. In order to realize the call of our government departments for educational modernization, it is extremely important for teachers to develop modernization in the future. The management of teachers’ files determines teachers’ work mentality and work efficiency.The application of blockchain technology in teacher file management can be more secure, stable and lasting than the traditional way. Based on the analysis of the present situation and disadvantages of teachers’ archives management, this paper further explains that the advantages of blockchain can solve the problems of teachers’ archives storage at present. As well as a positive influence of blockchain of file data on teachers’ development. Finally, this paper expounds the new model construction of blockchain technology applied to teachers’ file management, analyzes the new challenges in several aspects of the new model construction of blockchain technology applied to teachers’ file management and the effective measures adopted to better realize blockchain of teachers’ files.Comprehensively improve the storage efficiency of teachers’ files.
Research on the Teaching of International Business Negotiation Course Based on Smart Classroom Platform

As an innovative teaching model, smart classrooms make up for the shortcomings of traditional classrooms, focusing on cultivating students’ independent learning and innovation capabilities, and focusing on student-centered teaching ideas. The online and offline hybrid teaching model based on smart classrooms has been used in the course of International Business Negotiation to achieve good results. This article starts from the analysis of the course objectives and teaching objects of International Business Negotiations, expounds the shortcomings of the current traditional teaching methods, and uses the smart classroom platform to reconstruct the course content and course teaching methods. Through practice, this paper analyzes the significance of the hybrid teaching model of the smart classroom platform to improve the course of international business negotiation, so as to promote the reform and innovation of course teaching.
Cognitive psychology as a component of nbics – technologies

From the standpoint of neocognitive psychology, the article examines the active role and impact of technology on all spheres of human existence, including the functional qualities of the human body and brain. The vectors of technomodification and integration of the human psyche are marked. The manifestation of new technological possibilities that allow to modify the sensory-perceptual sphere of man, to change the qualities of his subjective reality and consciousness is shown. The limits of technomodification, within which the human qualities of an artificially created personality are preserved, are discussed. The sphere of activity of neocognitive psychology in researches of the artificial mental organisms generated by technologies of technomodification, social communication, a society and the technogenic environment is considered. The moral and ethical problems that arise when a person intervenes in the processes of life are shown.