Emotional recognition has arisen as an essential field of study that can expose a variety of valuable inputs. Emotion can be articulated in several means that can be seen, like speech and facial expressions, written text, and gestures. Emotion recognition in a text document is fundamentally a content-based classification issue, including notions from natural language processing (NLP) and deep learning fields. Hence, in this study, deep learning assisted semantic text analysis (DLSTA) has been proposed for human emotion detection (...) using big data. Emotion detection from textual sources can be done utilizing notions of Natural Language Processing. Word embeddings are extensively utilized for several NLP tasks, like machine translation, sentiment analysis, and question answering. NLP techniques improve the performance of learning-based methods by incorporating the semantic and syntactic features of the text. The numerical outcomes demonstrate that the suggested method achieves an expressively superior quality of human emotion detection rate of 97.22% and the classification accuracy rate of 98.02% with different state-of-the-art methods and can be enhanced by other emotional word embeddings. (shrink)

Although learning English as a Foreign Language is needed all over the world nowadays, it is still difficult for some Spanish students to learn it. Considering that teacher’s decisions on the use of methodologies is essential in class, we look at future teachers.In this study we focus on future teachers’ training as a key element to match theory and practice and bring to Foreign Language (FL) classes innovative approaches such as Project Based Learning (PBL). A recent experienced (...) (2021-22) in the Faculty of Education of Málaga has been analysed and important conclusions have been drawn from results obtained. (shrink)

The aim of this research was to determine the degree to which undergraduate students’ learning approach, academic achievement and satisfaction were determined by the combination of an intrapersonal factor (self-regulation) and a interpersonal factor (contextual or regulatory teaching). The hypothesis proposed that greater combined regulation (internal and external) would be accompanied by more of a deep approach to learning, more satisfaction and higher achievement, while a lower level of combined regulation would determine a surface approach, less satisfaction and (...) lower achievement. Within an ex post facto design by selection, 1036 university students completed validated questionnaires using an online tool. Several multivariate analyses were conducted. Results showed that the combination of self-regulation and external regulation can be ordered as levels along a five-point scale or heuristic. These levels linearly determine type of learning approach, academic achievement and satisfaction. Implications are established for quality and improvement of the teaching and learning process at university. (shrink)

Spam electronic mails (emails) refer to harmful and unwanted commercial emails sent to corporate bodies or individuals to cause harm. Even though such mails are often used for advertising services and products, they sometimes contain links to malware or phishing hosting websites through which private information can be stolen. This study shows how the adaptive intelligent learning approach, based on the visual anti-spam model for multi-natural language, can be used to detect abnormal situations effectively. The application of this approach (...) is for spam filtering. With adaptive intelligent learning, high performance is achieved alongside a low false detection rate. There are three main phases through which the approach functions intelligently to ascertain if an email is legitimate based on the knowledge that has been gathered previously during the course of training. The proposed approach includes two models to identify the phishing emails. The first model has proposed to identify the type of the language. New trainable model based on Naive Bayes classifier has also been proposed. The proposed model is trained on three types of languages (Arabic, English and Chinese) and the trained model has used to identify the language type and use the label for the next model. The second model has been built by using two classes (phishing and normal email for each language) as a training data. The second trained model (Naive Bayes classifier) has been applied to identify the phishing emails as a final decision for the proposed approach. The proposed strategy is implemented using the Java environments and JADE agent platform. The testing of the performance of the AIA learning model involved the use of a dataset that is made up of 2,000 emails, and the results proved the efficiency of the model in accurately detecting and filtering a wide range of spam emails. The results of our study suggest that the Naive Bayes classifier performed ideally when tested on a database that has the biggest estimate (having a general accuracy of 98.4%, false positive rate of 0.08%, and false negative rate of 2.90%). This indicates that our Naive Bayes classifier algorithm will work viably on the off chance, connected to a real-world database, which is more common but not the largest. (shrink)

At its strongest, Hume's problem of induction denies the existence of any well justified assumptionless inductive inference rule. At the weakest, it challenges our ability to articulate and apply good inductive inference rules. This paper examines an analysis that is closer to the latter camp. It reviews one answer to this problem drawn from the VC theorem in statistical learning theory and argues for its inadequacy. In particular, I show that it cannot be computed, in general, whether we are (...) in a situation where the VC theorem can be applied for the purpose we want it to. (shrink)

In this study the effects of learning approaches, locus of control (LOC), socio-economic status and self-efficacy on undergraduate students in North Cyprus was investigated. Four questionnaires were administered on 99 students in order to collect data regarding the learning approaches, LOC, self-efficacy and demographic factors. High cumulative grade point average and self-efficacy were shown to be an indicator of academic achievement and high self-efficacy was related to the use of deep approach (DA). Students, whose mothers had (...) lower levels of education, were found to also predict academic success. No direct significant relationship between DA and academic achievement was found. (shrink)

The COVID-19 pandemic has caused tremendous loss starting from early this year. This article aims to investigate the change of anxiety severity and prevalence among non-graduating undergraduate students in the new semester of online learning during COVID-19 in China and also to evaluate a machine learning model based on the XGBoost model. A total of 1172 non-graduating undergraduate students aged between 18 and 22 from 34 provincial-level administrative units and 260 cities in China were enrolled onto this study (...) and asked to fill in a sociodemographic questionnaire and the Self-Rating Anxiety Scale twice, respectively, during February 15 to 17, 2020, before the new semester started, and March 15 to 17, 2020, 1 month after the new semester based on online learning had started. SPSS 22.0 was used to conduct t-test and single factor analysis. XGBoost models were implemented to predict the anxiety level of students 1 month after the start of the new semester. There were 184 and 221 students who met the cut-off of 50 and were screened as positive for anxiety, respectively, in the two investigations. The mean SAS scores in the second test was significantly higher than those in the first test. Significant differences were also found among all males, females, and students majoring in arts and sciences between the two studies. The results also showed students from Hubei province, where most cases of COVID-19 were confirmed, had a higher percentage of participants meeting the cut-off of being anxious. This article applied machine learning to establish XGBoost models to successfully predict the anxiety level and changes of anxiety levels 4 weeks later based on the SAS scores of the students in the first test. It was concluded that, during COVID-19, Chinese non-graduating undergraduate students showed higher anxiety in the new semester based on online learning than before the new semester started. More students from Hubei province had a different level of anxiety than other provinces. Families, universities, and society as a whole should pay attention to the psychological health of non-graduating undergraduate students and take measures accordingly. It also confirmed that the XGBoost model had better prediction accuracy compared to the traditional multiple stepwise regression model on the anxiety status of university students. (shrink)

There are several challenges associated with traditional business ethics education. While case studies have been used extensively in ethics education, such use can be complemented by using Problem Based Learning (PBL). PBL represents a pedagogy employing more collaborative tools that involve students more extensively in the learning process. A well-designed teaching approach based on PBL can have significant positive impact on students’ learning. This paper supplies a representative teaching interaction based on PBL, and discusses the implications of (...) its structure. Introducing PBL is challenging and comes with significant costs as faculty members have to be trained, roles have to be exchanged, powers have to be relinquished, and learning materials have tobe written. We conclude that PBL is suitable for an ethics course as most ethical situations require introspection, reflection, discovery, and concern for other viewpoints, all of which can be facilitated in a PBL environment. (shrink)

There are several challenges associated with traditional business ethics education. While case studies have been used extensively in ethics education, such use can be complemented by using Problem Based Learning (PBL). PBL represents a pedagogy employing more collaborative tools that involve students more extensively in the learning process. A well-designed teaching approach based on PBL can have significant positive impact on students’ learning. This paper supplies a representative teaching interaction based on PBL, and discusses the implications of (...) its structure. Introducing PBL is challenging and comes with significant costs as faculty members have to be trained, roles have to be exchanged, powers have to be relinquished, and learning materials have tobe written. We conclude that PBL is suitable for an ethics course as most ethical situations require introspection, reflection, discovery, and concern for other viewpoints, all of which can be facilitated in a PBL environment. (shrink)

Case law retrieval is the task of locating truly relevant legal cases given an input query case. Unlike information retrieval for general texts, this task is more complex with two phases (legal case retrieval and legal case entailment) and much harder due to a number of reasons. First, both the query and candidate cases are long documents consisting of several paragraphs. This makes it difficult to model with representation learning that usually has restriction on input length. Second, the concept (...) of relevancy in this domain is defined based on the legal relation that goes beyond the lexical or topical relevance. This is a real challenge because normal text matching will not work. Third, building a large and accurate legal case dataset requires a lot of effort and expertise. This is obviously an obstacle to creating enough data for training deep retrieval models. In this paper, we propose a novel approach called supporting model that can deal with both phases. The underlying idea is the case–case supporting relation and the paragraph–paragraph as well as the decision-paragraph matching strategy. In addition, we propose a method to automatically create a large weak-labeling dataset to overcome the lack of data. The experiments showed that our solution has achieved the state-of-the-art results for both case retrieval and case entailment phases. (shrink)

In the current performance evaluation works of commercial banks, most of the researches only focus on the relationship between a single characteristic and performance and lack a comprehensive analysis of characteristics. On the other hand, they mainly focus on causal inference and lack systematic quantitative conclusions from the perspective of prediction. This paper is the first to comprehensively investigate the predictability of multidimensional features on commercial bank performance using boosting regression tree. The dimensionality in the financial-related fields is relatively high. (...) There are not only observable price data, financial fundamentals data, etc., but also many unobservable undisclosed data and undisclosed events; more sources of income cannot be explained by existing models. Aiming at the characteristics of commercial bank data, this paper proposes an adaptively reduced step size gradient boosting regression tree algorithm for bank performance evaluation. In this method, a random subsample sampling is performed before training each regression tree. The adaptive reduction step size is used to replace the reduction step size setting of the original algorithm, which overcomes the shortcomings of low accuracy and poor generalization ability of the existing regression decision tree model. Compared to the BIRCH algorithm for classification of existing data, our proposed gradient boosting regression tree algorithm with adaptively reduced step size obtains better classification results. This paper empirically uses data from rural banks in 30 provinces in China to classify the different characteristics of rural banks’ performance in order to better evaluate their performance. (shrink)

Abstract:There can be ethical understanding of organizational policy issues and that is important. However, there can be policy understanding about what the organization should do without understanding of individual level responsibility. There can be cognitive understanding of both policy and individual level ethics responsibilities and that is important. However, there can be cognitive understanding without affective, emotive concern. Intellectual understanding without affective concern can lead to understanding without motivation. There can be cognitive understanding and affective concern and that is important, (...) but not enough. There can be cognitive understanding and affective concern without effective political method. An action-learning approach to organizational ethics can join cognitive understanding of policy and individual level issues with both affective concern and effective political method. Joining of cognitive understanding, affective concern, and effective political method can stimulate and enable ethical character. (shrink)

Abstract:There can be ethical understanding of organizational policy issues and that is important. However, there can be policy understanding about what the organization should do without understanding of individual level responsibility. There can be cognitive understanding of both policy and individual level ethics responsibilities and that is important. However, there can be cognitive understanding without affective, emotive concern. Intellectual understanding without affective concern can lead to understanding without motivation. There can be cognitive understanding and affective concern and that is important, (...) but not enough. There can be cognitive understanding and affective concern without effective political method. An action-learning approach to organizational ethics can join cognitive understanding of policy and individual level issues with both affective concern and effective political method. Joining of cognitive understanding, affective concern, and effective political method can stimulate and enable ethical character. (shrink)

Automated classification of legal documents has been the subject of extensive research in recent years. However, this is still a challenging task for long documents, since it is difficult for a model to identify the most relevant information for classification. In this paper, we propose a two-stage supervised learning approach for the classification of petitions, a type of legal document that requests a court order. The proposed approach is based on a word-level encoder–decoder Seq2Seq deep neural network, such as (...) a Bidirectional Long Short-Term Memory (BiLSTM) or a Bidirectional Encoder Representations from Transformers (BERT) model, and a document-level Support Vector Machine classifier. To address the challenges posed by the lengthy legal documents, the approach introduces a human-in-the-loop approach, whose task is to localize and tag relevant segments of text in the word-level training part, which dramatically reduces the dimension of the document classifier input vector. We performed experiments to validate our approach using a real-world dataset comprised of 270 intermediate petitions, which were carefully annotated by specialists from the 15th civil unit of the State of Alagoas, Brazil. Our results revealed that both BiLSTM and BERT-Convolutional Neural Networks variants achieved an accuracy of up to 95.49%, and also outperformed baseline classifiers based on the Term Frequency–Inverse Document Frequency test vectorizer. The proposed approach is currently being utilized to automate the aforementioned justice unit, thereby increasing its efficiency in handling repetitive tasks. (shrink)

Job seekers find themselves increasingly duped and misled by fraudulent job advertisements, posing a threat to their privacy, security and well-being. There is a clear need for solutions that can protect innocent job seekers. Existing approaches to detecting fraudulent jobs do not scale well, function like a black-box, and lack interpretability, which is essential to guide applicants’ decision-making. Moreover, commonly used lexical features may be insufficient as the representation does not capture contextual semantics of the underlying document. Hence, this (...) paper explores to what extent different categorizations of fraudulent jobs can be classified. In addition, this paper seeks to find what type of features are most relevant in classifying the type of fraudulent job. In this paper, we develop and validate a machine learning system for identifying identity theft, corporate identity theft and multi-level marketing amongst fraudulent job advertisements. We utilized four classes of features: empirical rule set-based features, bag-of-word models, most recent state-of-the-art word embeddings and transformer models for various machine learning classifiers. The machine learning models were validated by evaluating them on a publicly available job description dataset. Our results indicate that the word embeddings and transformer-based features consistently outperformed the handcrafted rule-set based features class. Ultimately, a Gradient Boosting classifier with a combination of empirical rule-set based features, parts-of-speech tags and bag-of-words vectors achieved the best performance with an F1-score of 0.88. (shrink)

Eye-tracking technology has become a powerful tool for biomedical-related applications due to its simplicity of operation and low requirements on patient language skills. This study aims to use the machine-learning models and deep-learning networks to identify key features of eye movements in Alzheimer's Disease (AD) under specific visual tasks, thereby facilitating computer-aided diagnosis of AD. Firstly, a three-dimensional (3D) visuospatial memory task is designed to provide participants with visual stimuli while their eye-movement data are recorded and used to (...) build an eye-tracking dataset. Then, we propose a novel deep-learning-based model for identifying patients with Alzheimer's Disease (PwAD) and healthy controls (HCs) based on the collected eye-movement data. The proposed model utilizes a nested autoencoder network to extract the eye-movement features from the generated fixation heatmaps and a weight adaptive network layer for the feature fusion, which can preserve as much useful information as possible for the final binary classification. To fully verify the performance of the proposed model, we also design two types of models based on traditional machine-learning and typical deep-learning for comparison. Furthermore, we have also done ablation experiments to verify the effectiveness of each module of the proposed network. Finally, these models are evaluated by four-fold cross-validation on the built eye-tracking dataset. The proposed model shows 85% average accuracy in AD recognition, outperforming machine-learning methods and other typical deep-learning networks. (shrink)

This study investigates the use of musical intelligence to improve the English pronunciation of Chinese third level students. It is relevant for a human-centred systems engineering approach to cross-cultural interaction. Language learning is important as valid communication can help interactions and cultural understanding between countries, this also may benefit international stability. There are natural barriers between the English and Chinese language which are reflected in teaching approaches. The teaching of English in Chinese classrooms is removed from real-world English (...) learning environments. The academic environments and approaches focus on the learning of grammar, spelling and writing with little real-life conversation and interaction with native English speakers. English language learning in China is from a more academic perspective rather than practical utilisation. Correct pronunciation and accent is therefore diminished. This study demonstrates that a musical intelligence e-Learning approach can benefit Chinese English language students. (shrink)

The COVID-19 pandemic affects all population segments and is especially detrimental to university students because social interaction is critical for a rewarding campus life and valuable learning experiences. In particular, with the suspension of in-person activities and the adoption of virtual teaching modalities, university students face drastic changes in their physical activities, academic careers, and mental health. Our study applies a machine learning approach to explore the gender differences among U.S. university students in response to the global pandemic. (...) Leveraging a proprietary survey dataset collected from 322 U.S. university students, we employ association rule mining techniques to identify and compare psychological, cognitive, and behavioral patterns among male and female participants. To formulate our task under the conventional ARM framework, we model each unique question-answer pair of the survey questionnaire as a market basket item. Consequently, each participant's survey report is analogous to a customer's transaction on a collection of items. Our findings suggest that significant differences exist between the two gender groups in psychological distress and coping strategies. In addition, the two groups exhibit minor differences in cognitive patterns and consistent preventive behaviors. The identified gender differences could help professional institutions to facilitate customized advising or counseling for males and females in periods of unprecedented challenges. (shrink)

In recent years, the work of organizations in the area of digitization has intensified significantly. This trend is also evident in the field of recruitment where job application tracking systems (ATS) have been developed to allow job advertisements to be published online. However, recent studies have shown that recruiting in most organizations is not inclusive, being subject to human biases and prejudices. Most discrimination activities appear early but subtly in the hiring process, for instance, exclusive phrasing in job advertisement discourages (...) qualified applicants from minority groups from applying. The existing works are limited to analyzing, categorizing and highlighting the occurrence of bias in the recruitment process. In this paper, we go beyond this and develop machine learning models for identifying and classifying biased and discriminatory language in job descriptions. We develop and evaluate a machine learning system for identifying five major categories of biased and discriminatory language in job advertisements, i.e., masculine-coded, feminine-coded, exclusive, LGBTQ-coded, demographic and racial language. We utilized the combination of linguistic features with recent state-of-the-art word embeddings representations as input features for various machine learning classifiers. Our results show that the machine learning classifiers were able to identify all the five categories of biased and discriminatory language with a decent accuracy. The Random Forest classifier with FastText word embeddings achieved the best performance with tenfolds cross-validation. Our system directly addresses the bias in the attraction phase of hiring by identifying and classifying biased and discriminatory language and thus encouraging recruiters to write more inclusive job advertisements. (shrink)

This paper integrates the inclusion and organizational ethics literatures to examine the relationship between employees’ perceptions of an organizational integration and learning approach to diversity and two employee outcomes: organizational citizenship behavior toward the organization and interpersonal workplace deviance. Findings across two field studies from the USA and Germany show that employees’ perceptions of an organizational integration and learning approach to diversity are positively related to perceived organizational ethical virtue. Perceived organizational ethical virtue further transmits the effect of (...) employees’ perceptions of an organizational integration and learning approach to diversity on both organizational citizenship behavior toward the organization and interpersonal workplace deviance. In addition, we find support for a moderated indirect effect model whereby the indirect effect of the perceived integration and learning approach to diversity on the dependent variables through perceived organizational ethical virtue is stronger when employees have high personal value for diversity rather than low personal value for diversity. These results underscore the importance of having a fit between employees’ perceptions of an organization’s approach to diversity and employees’ personal value for diversity in order for inclusion to result in positive employee behaviors. Results emphasize the ethical responsibility of organizations in terms of how they approach diversity. (shrink)

The Air Quality Index (AQI) shows the state of air pollution in a unique and more understandable way. This work aims to forecast the AQI in Algeciras (Spain) 8 hours in advance. The AQI is calculated indirectly through the predicted concentrations of five pollutants (O3, NO2, CO, SO2 and PM10) to achieve this goal. Artificial neural networks (ANNs), sequence-to-sequence long short-term memory networks (LSTMs) and a newly proposed method combing a rolling window with the latter (LSTMNA) are employed as the (...) forecasting techniques. Besides, two input approaches are evaluated: using only the data from the own time series of the pollutant in the first case or adding exogenous variables in the second one. Several window sizes are employed (24, 28 and 72 hours) with ANNs and LSTMNAs. Additionally, several feature ranking methods are applied in the exogenous approach to select the most relevant lagged variables to feed the models. Results show how the proposed exogenous approach increases the performance of the prediction models. Besides, the newly proposed method LSTMNA provides the best performances in most of the cases evaluated. Hence, it constitutes an exciting alternative to standard LSTMs and ANNs to predict pollutants concentrations and, consequently, the AQI. (shrink)

Prosthetic devices that replace a lost limb have become increasingly performant in recent years. Recent advances in both software and hardware allow for the decoding of electroencephalogram signals to improve the control of active prostheses with brain-computer interfaces. Most BCI research is focused on the upper body. Although BCI research for the lower extremities has increased in recent years, there are still gaps in our knowledge of the neural patterns associated with lower limb movement. Therefore, the main objective of this (...) study is to show the feasibility of decoding lower limb movements from EEG data recordings. The second aim is to investigate whether well-known neuroplastic adaptations in individuals with an amputation have an influence on decoding performance. To address this, we collected data from multiple individuals with lower limb amputation and a matched able-bodied control group. Using these data, we trained and evaluated common BCI methods that have already been proven effective for upper limb BCI. With an average test decoding accuracy of 84% for both groups, our results show that it is possible to discriminate different lower extremity movements using EEG data with good accuracy. There are no significant differences in the decoding performance of these movements between healthy subjects and subjects with lower extremity amputation. These results show the feasibility of using BCI for lower limb prosthesis control and indicate that decoding performance is not influenced by neuroplasticity-induced differences between the two groups. (shrink)

Prosthetic devices that replace a lost limb have become increasingly performant in recent years. Recent advances in both software and hardware allow for the decoding of electroencephalogram signals to improve the control of active prostheses with brain-computer interfaces. Most BCI research is focused on the upper body. Although BCI research for the lower extremities has increased in recent years, there are still gaps in our knowledge of the neural patterns associated with lower limb movement. Therefore, the main objective of this (...) study is to show the feasibility of decoding lower limb movements from EEG data recordings. The second aim is to investigate whether well-known neuroplastic adaptations in individuals with an amputation have an influence on decoding performance. To address this, we collected data from multiple individuals with lower limb amputation and a matched able-bodied control group. Using these data, we trained and evaluated common BCI methods that have already been proven effective for upper limb BCI. With an average test decoding accuracy of 84% for both groups, our results show that it is possible to discriminate different lower extremity movements using EEG data with good accuracy. There are no significant differences in the decoding performance of these movements between healthy subjects and subjects with lower extremity amputation. These results show the feasibility of using BCI for lower limb prosthesis control and indicate that decoding performance is not influenced by neuroplasticity-induced differences between the two groups. (shrink)

Research shows that exploring language corpora through data-driven learning plays a significant role in language learning. Nevertheless, it is not clear if using concordancing as an application of DDL affects the learners’ second language motivation. To address this gap, the current study adopted a triangulation design, validating quantitative data model, and a quasi-experimental design. Ninety English-major university students with an intermediate level of English language proficiency, divided into control and experimental groups, took part in the study. Drawing on (...) a second language motivational self-system questionnaire, the findings of the study did not result in any statistically significant differences between the students in their second language motivational self-system. Altogether, the students found learning English through a DDL approach with concordancing less motivating than receiving explicit instruction. The study has implications for language teaching and learning. (shrink)

Abstract:The problem this paper is concerned with is the politics of reforming embedded, parasitic, sometimes predatory, network-based, corruption subsystems. The politics of corruption subsystems is often embedded in social structures sustained by the collective action of interest groups who benefit from the corruption. Therefore, the long-term effectiveness of approaches that focus solely on isolated, individual acts of corruption are limited. The politics of long-term corruption reform can benefit from a combined action-learning and social movement–based collective approach.

Abstract:The problem this paper is concerned with is the politics of reforming embedded, parasitic, sometimes predatory, network-based, corruption subsystems. The politics of corruption subsystems is often embedded in social structures sustained by the collective action of interest groups who benefit from the corruption. Therefore, the long-term effectiveness of approaches that focus solely on isolated, individual acts of corruption are limited. The politics of long-term corruption reform can benefit from a combined action-learning and social movement–based collective approach.

Hypothesis-driven approaches identified important characteristics that differentiate violent from non-violent radicals. However, they produced a mosaic of explanations as they investigated a restricted number of preselected variables. Here we analyzed without a priory assumption all the variables of the “Profiles of Individual Radicalization in the United States” database by a machine learning approach. Out of the 79 variables considered, 19 proved critical, and predicted the emergence of violence with an accuracy of 86.3%. Typically, violent extremists came from criminal (...) but not radical backgrounds and were radicalized in late stages of their life. They were followers in terrorist groups, sought training, and were radicalized by social media. They belonged to low social strata and had problematic social relations. By contrast, non-violent but still criminal extremists were characterized by a family tradition of radicalism without having criminal backgrounds, belonged to higher social strata, were leaders in terrorist organizations, and backed terrorism by supporting activities. Violence was also promoted by anti-gay, Sunni Islam and Far Right, and hindered by Far Left, Anti-abortion, Animal Rights and Environment ideologies. Critical characteristics were used to elaborate a risk-matrix, which may be used to predict violence risk at individual level. (shrink)

Promoting English as a Foreign Language learners’ willingness to communicate and self-efficacy in different contexts has drawn the attention of many investigators. This review explored the effect of digital-based flipped learning classrooms on enhancing learners’ willingness to communicate and self-efficacy. The related literature indicated that learners’ intention to communicate is affected by social media and digitalized materials used in flipped classrooms. Compared to the traditional educational contexts, this review showed higher levels of self-efficacy in flipped classrooms among EFL learners. (...) Furthermore, the review expounded on the implications and future directions for EFL teachers, teacher educators, educational policy-makers, and advisors. The ideas can improve their awareness of learner self-efficacy, willingness to communicate, and the use of flipped learning approach in educational contexts. (shrink)

In air traffic control, the airspace is divided into several smaller sectors for better management of air traffic and air traffic controller workload. Such sectors are usually managed by a team of two air traffic controllers: planning controller and executive controller. D-side controller is responsible for processing flight-plan information to plan and organize the flow of traffic entering the sector. R-side controller deals with ensuring safety of flights in their sector. A better understanding and predictability of D-side controller actions, for (...) a given traffic scenario, may help in automating some of its tasks and hence reduce workload. In this paper, we propose a learning model to predict D-side controller actions. The learning problem is modeled as a supervised learning problem, where the target variables are D-side controller actions and the explanatory variables are the aircraft 4D trajectory features. The model is trained on six months of ADS-B data over an en-route sector, and its generalization performance was assessed, using crossvalidation, on the same sector. Results indicate that the model for vertical maneuver actions provides highest prediction accuracy. Besides, the model for speed change and course change action provides predictability accuracy of 80% and 87%, respectively. The model to predict the set of all the actions for each flight achieves an accuracy of 70% implying for 70% of flights; D-side controller’s action can be predicted from trajectory information at sector entry position. In terms of operational validation, the proposed approach is envisioned as ATCO assisting tool, not an autonomous tool. Thus, there is always ATCO discretion element, and as more ATCO actions are collected, the models can be further trained for better accuracy. For future work, we will consider expanding the feature set by including parameters such as weather and wind. Moreover, human in the loop simulation will be performed to measure the effectiveness of the proposed approach. (shrink)

This paper proposes a nonlinear autoregressive neural network method for the investment performance evaluation of state-owned enterprises. It is different from the traditional method based on machine learning, such as linear regression, structural equation, clustering, and principal component analysis; this paper uses a regression prediction method to analyze investment efficiency. In this paper, we firstly analyze the relationship between diversified ownership reform, corporate debt leverage, and the investment efficiency of state-owned enterprises. Secondly, a set of investment efficiency evaluation index (...) system for SOE was constructed, and a nonlinear autoregressive neural network approach was used for verification. The data of A-share state-owned listed companies in Shanghai and Shenzhen stock exchanges from 2009 to 2018 are taken as a sample. The experimental results show that the output value from the NARNET is highly fitted to the actual data. Based on the neural network model regression analysis, this paper conducts a descriptive statistical analysis of the main variables and control variables of the evaluation indicators. It verifies the direct impact of diversified ownership reform on the investment efficiency of SOE and the indirect impact on the investment efficiency of SOE through corporate debt leverage. (shrink)

An important topic in evolutionary art is the development of systems that can mimic the aesthetics decisions made by human begins, e.g., fitness evaluations made by humans using interactive evolution in generative art. This paper focuses on the analysis of several datasets used for aesthetic prediction based on ratings from photography websites and psychological experiments. Since these datasets present problems, we proposed a new dataset that is a subset of DPChallenge.com. Subsequently, three different evaluation methods were considered, one derived from (...) the ratings available at DPChallenge.com and two obtained under experimental conditions related to the aesthetics and quality of images. We observed different criteria in the DPChallenge.com ratings, which had more to do with the photographic quality than with the aesthetic value. Finally, we explored learning systems other than state-of-the-art ones, in order to predict these three values. The obtained results were similar to those using state-of-the-art procedures. (shrink)

The teaching of ethics in teacher education programs is crucial for fostering the moral and ethical development of prospective teachers and shaping them into ethical role models for future students. This study, employing qualitative case study research, gathered data from undergraduates in teacher education programs to explore the best approaches for ethics education. It found that combining digital and case-based pedagogical methods, fostering an open-minded attitude among lecturers, and implementing a blend of Socratic and active learning techniques leads (...) to the most favorable outcomes. Additionally, continuous formative assessment throughout the academic year is essential. To cultivate a teaching workforce with strong ethical and moral values, undergraduate courses in ethics and morality should be thought-provoking, integrating real-life cases with a mix of digital and tangible resources. The ethics course lecturer should not only serve as a role model and exhibit a caring disposition but also use effective teaching methodologies. The paper details key themes and categories for successful ethics instruction and offers specific recommendations. (shrink)

Professional, Ethical, Legal, and Educational Lessons in Medicine: A Problem Based Approach provides a comprehensive review of the complex and challenging field of professional medical practice. Its problem-based format incorporates a vast pool of practical, board-exam-style multiple-choice questions for self-assessment, and is an ideal resource for exam preparation as well as ongoing clinical education among trainees and clinicians The practice of medicine is not only about clinical care of patients. Physicians must navigate ethical conundrums, legal pitfalls, and quality improvement issues (...) while maintaining scrupulous professionalism and being exemplary educators. This volume addresses common issues in the realm of professional medical practice not typically covered in other texts. Each of the 73 chapters presents a problem-based case that branches out into an oral board examination type stem questions. A discussion section with brief review articles summarizes the most recent literature. Multiple-choice questions with answers allows for comprehensive self-assessment or interactive group quizzes. This book will appeal to medical and allied health professionals in all specialties of medicine. It is an excellent and hands-on reference for the related but diverse domains associated with professional healthcare practice by students, physicians, and other healthcare providers compacted into one volume. (shrink)

Starting with the hypothesis that analogical reasoning consists of a search of semantic space, we used eye-tracking to study the time course of information integration in adults in various formats of analogies. The two main questions we asked were whether adults would follow the same search strategies for different types of analogical problems and levels of complexity and how they would adapt their search to the difficulty of the task. We compared these results to predictions from the literature. Machine (...) class='Hi'>learning techniques, in particular support vector machines (SVMs), processed the data to find out which sets of transitions best predicted the output of a trial (error or correct) or the type of analogy (simple or complex). Results revealed common search patterns, but with local adaptations to the specifics of each type of problem, both in terms of looking-time durations and the number and types of saccades. In general, participants organized their search around source-domain relations that they generalized to the target domain. However, somewhat surprisingly, over the course of the entire trial, their search included, not only semantically related distractors, but also unrelated distractors, depending on the difficulty of the trial. An SVM analysis revealed which types of transitions are able to discriminate between analogy tasks. We discuss these results in light of existing models of analogical reasoning. (shrink)

More than three billion users are currently on one of Meta’s online platforms with Facebook being still their most prominent social media service. It is well known that Facebook has designed a highly immersive social media service with the aim to prolong online time of its users, as this results in more digital footprints to be studied and monetized. In this context, it is debated if social media platforms can elicit addictive behaviors. In the present work, we demonstrate in N (...) = 1,094 users that it is possible to predict from digital footprints of the Facebook users their self-reported addictive tendencies toward social media by applying machine-learning strategies. More specifically, we analyzed the predictive power of a set of models based on different sets of features extracted from digital traces, namely posting activity, language use, and page Likes. To maximize the predictive power of the models, we used an ensemble of linear and non-linear prediction algorithms. This work showed also sufficient accuracy rates in distinguishing between disordered and non-disordered social media users. In sum, individual differences in tendencies toward “social networks use disorder” can be inferred from digital traces left on the social media platform Facebook. Please note that the present work is limited by its cross-sectional design. (shrink)

Drawing on a global sample of microfinance institutions (MFIs), this paper offers insights into the trade-off versus synergy debate of adopting multiple institutional goals in hybrid organisations. Additionally, it unravels which organisation- and country-specific determinants associate with top joint performance using machine-learning techniques. We find that the synergy versus trade-off debate is not dichotomous. Rather, MFIs can be strong both socially and financially but not while charging low interest rates. In our sample, 17% of MFIs serve a low-income clientele (...) in need with a diverse range of services while remaining financially sustainable and ask a close-to-average interest rate. These organisations are larger and more mature as well as financially prudent in that they minimize both financial and credit risk. Moreover, they are located in countries where their services can create larger benefits regarding their underlying goals. (shrink)