While no one seems to believe that business schools or their faculties bear entire responsibility for the ethical decision-making processes of their students, these same institutions do have some burden of accountability for educating students surrounding these skills. To that end, the standards promulgated by the Association to Advance Collegiate School of Business, their global accrediting body, require that students learn ethics as part of a business degree. However, since the AACSB does not require the inclusion of a specific course (...) to achieve this objective, it may be satisfied by establishing a stand-alone course in ethical decision-making, by integrating ethical decision-making into the existing curricula, by some combination of the two strategies, or through some alternative mechanism. Notwithstanding the choice of delivery process, though, the institution must ensure that it is able to demonstrate the students' achievement of learning with regard to ethics, a bar that was raised, or arguably simply modified, in 2003. With learning objectives designed precisely to measure the student delta based on content, process and engagement in a particular class, those programs that have opted for stand-alone ethics courses may be more prepared to respond to assessment-related inquiries regarding their programs or satisfaction of the standards. The relevance of the AACSB standards modification to the current efforts at ethics integration in business programs is instead a re-examination of how to create a program of integration that is designed to ensure the most effective learning results possible, while responding to the challenges presented by the integrated approach. The purpose of this article is to explore some of those challenges that may be somewhat universal to business school programs implementing the integrated approach, and to share one large university's response to those challenges, along with lessons learned. (shrink)

In this dissertation I argue for a wider and more robust notion of the modularity of mind thesis. The developmental disorder of autism is the prime analytic tool for developing this approach. I argue that a variety of other approaches are deeply flawed in that they cannot account for the autistic spectrum disorder. I mean by this the autistic profile of deficits such as the lack of social interaction and the avoidance of social contact. I begin with Fodorian modularity. (...) I argue that autism presents us with a case that threatens the division Fodor has between modules and central systems. The autistic disorder exemplifies an area of higher cognition that has many of the properties commonly associated with modular processing. Since Fodor cannot opt for a modular account of theory of mind it must be that his account of central systems is incorrect. I next argue that Baron-Cohen's amended modular architecture cannot explain autism since the autistic deficit cannot be due to a defective module for processing intentional action. Furthermore, his use of modularity threatens to make his view of cognition incoherent. Finally I take up Gopnik and Meltzoff's approach that eschews any type of modularity and instead posits a general learning mechanism. If autism, as they claim, were a general theory-building problem, then one should expect to see other behavioral deficits in other areas of autistic cognition. We do not. I then offer an alternative version of modularity inspired by Karmiloff-Smith . It gives us advantages. On Karmiloff-Smith's account we would expect the autistic deficit to have more perceptually basic components and recent research is bearing this out. Progressive modularity also provides us with a framework in which to understand the ways autistic persons understand the social world. This approach also seeks to unify the cognitive work being done on development with burgeoning work on development in neuroscience. (shrink)

This paper shows how to define probability distributions over linguistically realistic syntactic structures in a way that permits us to define language learning and language comprehension as statistical problems. We demonstrate our approach using lexical‐functional grammar (LFG), but our approach generalizes to virtually any linguistic theory. Our probabilistic models are maximum entropy models. In this paper we concentrate on statistical inference procedures for learning the parameters that define these probability distributions. We point out some of the (...) practical problems that make straightforward ways of estimating these distributions infeasible, and develop a “pseudo‐likelihood” estimation procedure that overcomes some of these problems. This method raises interesting questions concerning the nature of the data available to a language learner and the modularity of language learning and processing. (shrink)

In order to extract the regularities underlying a continuous sensory input, the individual elements constituting the stream have to be encoded and their transitional probabilities (TPs) should be learned. This suggests that variance in statistical learning (SL) performance reflects efficiency in encoding representations as well as efficiency in detecting their statistical properties. These processes have been taken to be independent and temporally modular, where first, elements in the stream are encoded into internal representations, and then the co‐occurrences between (...) them are computed and registered. Here, we entertain a novel hypothesis that one unifying construct—the rate of information in the sensory input—explains learning performance. This theoretical approach merges processes related to encoding of events and those related to learning their regularities into a single computational principle. We present data from two large‐scale experiments with over 800 participants tested in support for this hypothesis, showing that rate of information in a visual stream clearly predicts SL performance, and that similar rate of information values leads to similar SL performance. We discuss the implications for SL theory and its relation to regularity learning. (shrink)

Recent years have yielded many discussions on how to endow autonomous agents with the ability to make ethical decisions, and the need for explicit ethical reasoning and transparency is a persistent theme in this literature. We present a modular and transparent approach to equip autonomous agents with the ability to comply with ethical prescriptions, while still enacting pre-learned optimal behaviour. Our approach relies on a normative supervisor module, that integrates a theorem prover for defeasible deontic logic within (...) the control loop of a reinforcement learning agent. The supervisor operates as both an event recorder and an on-the-fly compliance checker w.r.t. an external norm base. We successfully evaluated our approach with several tests using variations of the game Pac-Man, subject to a variety of “ethical” constraints. (shrink)

The paper presents an approach for developing multi-agent reinforcement learning systems that are made up of a coalition of modular agents. We focus on learning to segment sequences (sequential decision tasks) to create modular structures, through a bidding process that is based on reinforcements received during task execution. The approach segments sequences (and divides them up among agents) to facilitate the learning of the overall task. Notably, our approach does not rely on (...) a priori knowledge or a priori structures. Initial experiments demonstrated the basic promise of the approach. This work shows how bidding and reinforcement learning can be usefully combined, thus pointing to a new research direction. (shrink)

A new method for evaluating aircraft engine monitoring data is proposed. Commonly, prognostics and health management systems use knowledge of the degradation processes of certain engine components together with professional expert opinion to predict the Remaining Useful Life (RUL). New data-driven approaches have emerged to provide accurate diagnostics without relying on such costly processes. However, most of them lack an explanatory component to understand model learning and/or the nature of the data. A solution based on a novel recurrent version (...) of a VAE is proposed in this paper to overcome this gap. The latent space learned by the model, trained with data from sensors placed in different parts of these engines, is exploited to build a self-explanatory map that can visually evaluate the rate of deterioration of the engines. Besides, a simple regressor model is built on top of the learned features of the encoder in order to numerically predict the RUL. As a result, remarkable prognostic accuracy is achieved, outperforming most of the novel and state-of-the-art approaches on the available modular aero-propulsion system simulation data (C-MAPSS dataset) from NASA. In addition, a practical real-world application is included for Turbofan engine data. This study shows that the proposed prognostic and explainable framework presents a promising new approach. (shrink)

The article examines the question of how learning multiple tasks interacts with neural architectures and the flow of information through those architectures. It approaches the question by using the idealization of an artificial neural network where it is possible to ask more precise questions about the effects of modular versus nonmodular architectures as well as the effects of sequential versus simultaneous learning of tasks. A prior work has demonstrated a clear advantage of modular architectures when the (...) two tasks must be learned at the same time from the start, but this advantage may disappear when one task is first learned to a criterion before the second task is undertaken. Indeed, in some cases of sequential learning, nonmodular networks achieve success levels comparable to those of modular networks. In particular, if a nonmodular network is to learn two tasks of different difficulty and the more difficult task is presented first and learned to a criterion, then the network will learn the second, easier one without permanent degradation of the first one. In contrast, if the easier task is learned first, a nonmodular task may perform significantly less well than a modular one. It seems that the reason for this difference has to do with the fact that the sequential presentation of the more difficult task first minimizes interference between the two tasks. More broadly, the studies summarized in this article seem to imply that no single learning architecture is optimal for all situations. (shrink)

The presence of general intelligence poses a major evolutionary puzzle, which has led to increased interest in its presence in nonhuman animals. The aim of this review is to critically evaluate this question and to explore the implications for current theories about the evolution of cognition. We first review domain-general and domain-specific accounts of human cognition in order to situate attempts to identify general intelligence in nonhuman animals. Recent studies are consistent with the presence of general intelligence in mammals (rodents (...) and primates). However, the interpretation of a psychometricgfactor as general intelligence needs to be validated, in particular in primates, and we propose a range of such tests. We then evaluate the implications of general intelligence in nonhuman animals for current theories about its evolution and find support for the cultural intelligence approach, which stresses the critical importance of social inputs during the ontogenetic construction of survival-relevant skills. The presence of general intelligence in nonhumans implies that modular abilities can arise in two ways, primarily through automatic development with fixed content and secondarily through learning and automatization with more variable content. The currently best-supported model, for humans and nonhuman vertebrates alike, thus construes the mind as a mix of skills based on primary and secondary modules. The relative importance of these two components is expected to vary widely among species, and we formulate tests to quantify their strength. (shrink)

Machine generated contents note: -- Preface -- Acknowledgements -- Notes on Contributors -- PART I: COMPLEXITY IN ANIMAL MINDS -- Introduction: M.McGonigle-Chalmers -- Relational and Absolute Discrimination Learning by Squirrel Monkeys: Establishing a Common Ground with Human Cognition; B.T.Jones -- Serial List Retention by Non-Human Primates: Complexity and Cognitive Continuity; F.R.Treichler -- The Use of Spatial Structure in Working Memory: A Comparative Standpoint; C.De Lillo -- The Emergence of Linear Sequencing in Children: A Continuity Account and a Formal Model; (...) M.McGonigle-Chalmers&I.Kusel -- Sensitivity to Quantity: What Counts Across Species?; S.T.Boysen&A.M.Yocom -- PART II: COMPLEXITY IN ROBOTS -- Editorial Introduction; D.McFarland -- Towards Cognitive Robotics: Robotics, Biology and Developmental Psychology; M.Lee, U.Nehmzow&M.Rodriguez -- Structuring Intelligence: The Role of Hierarchy, Modularity and Learning in Generating Intelligent Behaviour; J.J.Bryson -- Epistemology, Access, and Computational Models; G.Luger -- Reasoning About Representations in Autonomous Systems: What P´Olya and Lakatos Have To Say; A.Bundy -- PART III: LANGUAGE, EVOLUTION AND THE COMPLEX MIND -- Editorial Introduction; K.Stenning -- How to Qualify for a Cognitive Upgrade: Executive Control, Glass Ceilings, and the Limits of Simian Success; A.Clark -- Private Codes and Public Structures; C.Allen -- The Emergence of Complex Language; W.Hinzen -- Language Evolution: Enlarging the Picture; K.Stenning&M.Van Lambalgen -- Epilogue: Reminiscences of Brendan McGonigle -- Index. (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)

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 language sciences—Linguistics, Psycholinguistics, and Computational Linguistics—have not been broadly represented at the Cognitive Science Society meetings of the past 30 years, but they are an important part of the heart of cognitive science. This article discusses several major themes that have dominated the controversies and consensus in the study of language and suggests the most pressing issues of the future. These themes include differences among the language science disciplines in their view of numbers and symbols and of modular (...) and distributed cognition, and the need for an increasing prominence of questions concerning language and the brain. (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)

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)

An adaptive cognition approach to evaluative priming is not compatible with the view that the entire process is automatically determined by prime stimulus valence alone. In addition to the evaluative congruity of individual prime–target pairs, an adaptive regulation function should be sensitive to the base rates of positive and negative stimuli as well as to the perceived contingency between prime and target valence. The present study was particularly concerned with pseudocontingent inferences that offer a proxy for the assessment of (...) contingencies from degraded or incomplete stimulus input. As expected, response latencies were shorter for the more prevalent target valence and for evaluatively congruent trials. However, crucially, the congruity effect was eliminated and overridden by pseudocontingencies inferred from the stimulus environment. These strategic inferences were further enhanced when the task called for the evaluation of both prime stimuli and target stimuli. (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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

This paper argues that a key lesson of the GFC of 2008-9 is that our “silo” approach to the disciplines of accounting, finance, and economics (AFE) has not equipped students to deal with complex real world problems such as global financial crises. Such real world problems are interdisciplinary in their causes, effects, and solutions. The paper discusses elements of each of the AFE disciplines that are essential for understanding the GFC, and why courses in economics and finance that seek (...) to address the GFC as a topic need to integrate ideas from these three disciplines. A problem-based learning (PBL) approach is offered as a way forward, through at least one capstone course in a business/commerce degree that brings together the strands from a range of commerce/business disciplines in a case study approach. The paper offers an outline of such a PBL approach to the GFC. (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)

Accurate skin lesion segmentation is an important task in dermatology for facilitating early diagnosis and treatment planning. The challenges in skin lesion segmentation comprehend the variability in lesion, low contrast, heterogeneous backgrounds, overlapping or connected lesions, noise and certain artifacts. Despite of these challenges, Deep learning models accomplish remarkable results for skin lesion segmentation by automatically learning discriminative features. The current research introduces a novel approach utilizing the ASSP-based Deeplabv3+ for skin lesion segmentation along with other UNET-based (...) learners while employing VGG-16, VGG-19 and Dense nets as encoders. In addition, an analysis is conducted on GAN-UNET to evaluate the potential of Generative Artificial Intelligence in generating segmented images of skin lesions. Three benchmark medical image datasets, namely ISIC-2016, ISIC-2018, and HAM10000 Lesion Boundary Segmentation, are used to evaluate all five models. The models are trained exclusively on the ISIC-2018 dataset. A comparative analysis is performed, comparing the performance of these models against state-of-the-art segmentation methods, focusing on standard computer vision metrics. The proposed Deeplabv3+ model outperforms by showcasing its ability to accurately delineate skin lesions and surpassing existing techniques in terms of segmentation accuracy as 0.97, Jaccard coefficient as 0.84 and dice coefficient as 0.91. (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)

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)

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)

The paper explains the application of a Social Innovation Based Transformative Learning (SIBTL) pedagogical approach in an undergraduate, final year business ethics course taught at an Australian university. Using social innovation as an enabling process to extend students’ cognitive, behavioural and managerial competencies in an integrated manner, the paper describes how the SIBTL approach helps ethics teachers to promote students’ ethical action.

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)

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)

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)

In the past, notions of embodiment have been applied to robotics mainly in the realm of very simple robots, and supporting low-level mechanisms such as dynamics and navigation. In contrast, most human-like, interactive, and socially adept robotic systems turn away from embodiment and use amodal, symbolic, and modular approaches to cognition and interaction. At the same time, recent research in Embodied Cognition (EC) is spanning an increasing number of complex cognitive processes, including language, nonverbal communication, learning, and social (...) behavior.This article suggests adopting a modern EC approach for autonomous robots interacting with humans. In particular, we present three core principles from EC that may be applicable to such robots: (a) modal perceptual representation, (b) action/perception and action/cognition integration, and (c) a simulation-based model of top-down perceptual biasing. We describe a computational framework based on these principles, and its implementation on two physical robots. This could provide a new paradigm for embodied human–robot interaction based on recent psychological and neurological findings. (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)