Search

A variety of real-world situations take the form of repeated principal-agent problems with binary evaluation. I evaluate the principal's optimal evaluation effort and quality threshold for acceptance in the setting of dynamic agency with binary evaluation, focusing specifically on the evaluation by a top academic journal of papers submitted by economists. In the baseline model, the journal should statistically discriminate in favour of high-status economists, by setting an acceptance threshold that declines with their prior estimate of the economist's quality, while evaluation effort should likely increase with perceived author quality, as this provides incentives for authors to exert more effort, which is particularly valuable from the highest-quality authors. However, if a first good publication is more valuable than subsequent publications by the same economist, acceptance thresholds will tend to increase after publication success, relative to the threshold that would have followed a rejection. • Considers repeated principal-agent problem with binary evaluation. • Specifically, evaluation by a top academic journal of papers submitted by economists. • Journal should statistically discriminate in favour of high-status economists. • Acceptance threshold declines with prior estimate of economist's quality. • But threshold could increase after publication if first publication is most valuable. [ABSTRACT FROM AUTHOR]

This paper overviews an assortment of recent research work undertaken on recommender system models based on using multiple views of user and item-related data across the recommendation process. A summary of representative literature on multi-view recommender approaches is provided, describing their main characteristics, such as: their potential to overcome most common shortcomings in conventional recommender systems, as well as the use of data science, learning techniques and aggregation processes to combine information stemming from multiple views. A tabular summary is provided to facilitate the comparison of the similarities and differences among the surveyed works, along with commonly identified directions for future research in the topic. [ABSTRACT FROM AUTHOR]

The faculty clinical practice model provides dedicated time for nursing lecturers and educators in a university school of nursing to work with supervision in the clinical environments for an agreed amount of time each year. Allowing academics to partake in faculty clinical practice this way has been shown to update skills and retain clinical competency. Some nursing and midwifery academics believe it is essential to remain clinically current and up-to-date with professional issues in the clinical environments, whereas other academics believe reading current research maintains clinical competency. This discussion paper will explore the authors' own experiences of faculty clinical practice as an opportunity to enhance their learning. Narrative accounts of time spent in the clinical areas being expressed as invaluable as it allowed the authors to become part of the health professional team, refine clinical skills, gain clinical confidence, and share knowledge. This, in turn, impacted upon the academic's teaching style as well as redefined it by introducing incidents and stories from their experience. It has been concluded by the authors that faculty clinical practice allows academics to increase confidence, encourage leadership skills, and improve their teaching abilities in their clinical area of expertise. [ABSTRACT FROM AUTHOR]

Summary: High-fidelity patient simulation is a method of education increasingly utilised by educators of nursing to provide authentic learning experiences. Fidelity and authenticity, however, are not conceptually equivalent. Whilst fidelity is important when striving to replicate a life experience such as clinical practice, authenticity can be produced with low fidelity. A challenge for educators of undergraduate nursing is to ensure authentic representation of the clinical situation which is a core component for potential success. What is less clear is the relationship between fidelity and authenticity in the context of simulation based learning. Authenticity does not automatically follow fidelity and as a result, educators of nursing cannot assume that embracing the latest technology-based educational tools will in isolation provide a learning environment perceived authentic by the learner. As nursing education programmes increasingly adopt simulators that offer the possibility of representing authentic real world situations, there is an urgency to better articulate and understand the terms fidelity and authenticity. Without such understanding there is a real danger that simulation as a teaching and learning resource in nurse education will never reach its potential and be misunderstood, creating a potential barrier to learning. This paper examines current literature to promote discussion within nurse education, concluding that authenticity in the context of simulation-based learning is complex, relying on far more than engineered fidelity. [Copyright &y& Elsevier]

Sharing new medical information that is perceived as seriously effecting people's lives, i.e., breaking bad news (BBN) is important in caring for patients and relatives and is challenging for healthcare professionals. Optimal BBN requires incorporation and implementation of multiple professional competencies acquired gradually throughout years of training. The BBN encounter has implications for all participants: the patient, family members, their close social environments, and the deliverer of the news. Due to these implications and the accountability involved, medical schools invest educational resources in helping medical students develop this competency. The current paper summarizes literature, research, and teaching experiences while suggesting practical guidelines for designing and teaching a BBN course to undergraduate students. The following principles lie behind the recommendations: stepwise spiral continuity of exposure to and teaching of communication skills in various contexts while focusing on BBN in the advanced clinical years; relating the developing skills to broader humanistic studies; enhancing awareness of self-perspectives and beliefs regarding BBN; connecting to patients' and family members experiences and needs; providing a BBN protocol and opportunities for structured experiential learning followed by reflection and feedback; using observation and reflection to address gaps between theory and real-life practice; and creating continuity of learning about BBN through undergraduate, graduate, and continuing medical education. Applying this learning process can help enhance the management of these difficult conversations to improve patients' care during these difficult, life-changing encounters, and physicians' well-being. [ABSTRACT FROM AUTHOR]

Alphabet books are an important instructional text used in early education. Advances in mobile technology have led to alphabet books of an electronic format with accompanying sound, animations, and interactive hot spots. This study investigates the differential effectiveness of paper alphabet books and alphabet eBooks in training alphabetic knowledge in 4-year-olds. Three groups of approximately 30 children were assigned to one of three conditions: paper alphabet book, alphabet eBook, or storybook control. Book reading sessions composed of three to four children were run twice a week over eight weeks, with child-book behaviours coded at each session. Measures of early reading ability were collected pre and post-intervention. Children in all conditions improved over time in emergent literacy but no significant differences between conditions were found. Children using paper alphabet books were more likely to say letter names, and their time oriented to the books predicted post-test letter-name and phonological awareness after controlling for pre-test scores. In contrast, time oriented to the alphabet eBooks made no prediction to post-test sores. [ABSTRACT FROM AUTHOR]

Motor skills are required for activities of daily living. Transcranial direct current stimulation (tDCS) applied in association with motor skill learning has been investigated as a tool for enhancing training effects in health and disease. Here, we review the published literature investigating whether tDCS can facilitate the acquisition, retention or adaptation of motor skills. Work in multiple laboratories is underway to develop a mechanistic understanding of tDCS effects on different forms of learning and to optimize stimulation protocols. Efforts are required to improve reproducibility and standardization. Overall, reproducibility remains to be fully tested, effect sizes with present techniques vary over a wide range, and the basis of observed inter-individual variability in tDCS effects is incompletely understood. It is recommended that future studies explicitly state in the Methods the exploratory (hypothesis-generating) or hypothesis-driven (confirmatory) nature of the experimental designs. General research practices could be improved with prospective pre-registration of hypothesis-based investigations, more emphasis on the detailed description of methods (including all pertinent details to enable future modeling of induced current and experimental replication), and use of post-publication open data repositories. A checklist is proposed for reporting tDCS investigations in a way that can improve efforts to assess reproducibility. [ABSTRACT FROM AUTHOR]

A review of publications about educational research in Guinea-Bissau in the last 40 years is the subject of this paper. Published articles have permitted us to map the research terrain and produce new knowledge without collecting new empirical data. We reviewed 148 studies published between 1975 and 2015 in journal articles, theses, monographs, research reports, conference papers, books, edited volumes and grey literature. Moving from title to introduction/abstract and then the entire text we explored thematic focuses, methods, findings, conclusions and funding. This paper argues that education policy receives more attention followed by language issues and it also suggests that the research agenda is mainly due to external pressure with a tendency for applied research. [ABSTRACT FROM AUTHOR]

Abstract: People often attribute their reluctance to study texts on screen to technology-related factors rooted in hardware or software. However, previous studies have pointed to screen inferiority in the metacognitive regulation of learning. The study examined the effects of time pressure on learning texts on screen relative to paper among undergraduates who report only moderate paper preference. In Experiment 1, test scores on screen were lower than on paper under time pressure, with no difference under free regulation. In Experiment 2 the time condition was manipulated within participants to include time pressure, free regulation, and an interrupted condition where study was unexpectedly stopped after the time allotted under time pressure. No media effects were found under the interrupted study condition, although technology-related barriers should have taken their effect also in this condition. Paper learners who preferred this learning medium improved their scores when the time constraints were known in advance. No such adaptation was found on screen regardless of the medium preference. Beyond that, paper learning was more efficient and self-assessments of knowledge were better calibrated under most conditions. The results reinforce the inferiority of self-regulation of learning on screen and argue against technology-related factors as the main reason for this. [Copyright &y& Elsevier]

Abstract: Game theory provides a solution to the problem of finding a set of optimal decision-making strategies in a group. However, people seldom play such optimal strategies and adjust their strategies based on their experience. Accordingly, many theories postulate a set of variables related to the probabilities of choosing various strategies and describe how such variables are dynamically updated. In reinforcement learning, these value functions are updated based on the outcome of the player''s choice, whereas belief learning allows the value functions of all available choices to be updated according to the choices of other players. We investigated the nature of learning process in monkeys playing a competitive game with ternary choices, using a rock–paper–scissors game. During the baseline condition in which the computer selected its targets randomly, each animal displayed biases towards some targets. When the computer exploited the pattern of animal''s choice sequence but not its reward history, the animal''s choice was still systematically biased by the previous choice of the computer. This bias was reduced when the computer exploited both the choice and reward histories of the animal. Compared to simple models of reinforcement learning or belief learning, these adaptive processes were better described by a model that incorporated the features of both models. These results suggest that stochastic decision-making strategies in primates during social interactions might be adjusted according to both actual and hypothetical payoffs. [Copyright &y& Elsevier]

This paper analyses farmers’ networking strategies for sustainable agricultural development. The focus is on internal barriers of farmers’ networks with respect to the management of the group and collective learning processes. These aspects determine to an extent the success or failure of collective strategies. Based upon case study material we will draw lessons and present ideas on how management and learning can be enhanced. We argue that in collective strategies the focus should not only be on the goal of the group, but that attention to the process of cooperation such as safeguarding coherence, monitoring and evaluation of goals and views, shared responsibility and balanced leadership is a prerequisite for effective collective action. [Copyright &y& Elsevier]

Gastrointestinal ultrasound (GIUS) is an ultrasound application that has been practiced for more than 30 years. Recently, GIUS has enjoyed a resurgence of interest, and there is now strong evidence of its utility and accuracy as a diagnostic tool for multiple indications. The method of learning GIUS is not standardised and may incorporate mentorship, didactic teaching and e-learning. Simulation, using either low- or high-fidelity models, can also play a key role in practicing and honing novice GIUS skills. A course for training as well as establishing and evaluating competency in GIUS is proposed in the manuscript, based on established learning theory practice. We describe the broad utility of GIUS in clinical medicine, including a review of the literature and existing meta-analyses. Further, the manuscript calls for agreement on international standards regarding education, training and indications. [ABSTRACT FROM AUTHOR]

This paper brings novel insights into group coordination and price dynamics in complex environments. We implement an overlapping-generation model in the lab, where the output dynamics is given by the well-known chaotic quadratic map. This model structure allows us to study previously unexplored parameter regions where the perfect-foresight dynamics exhibits chaotic dynamics. This paper highlights three key findings. First, the price converges to the simplest equilibria, namely the monetary steady state or the two-cycle, in all markets. Second, we document a novel and intriguing finding: we observe a non-monotonicity of the behavior when complexity increases. Convergence to the two-cycle occurs for the intermediate parameter range, while both the extreme scenarios of a simple stable two-cycle and highly non-linear dynamics (with chaos) lead to coordination on the steady state in the lab. All indicators of coordination and convergence significantly exhibit this non-monotonic relationship in the learning-to-forecast experiments and this non-monotonicity persists in the learning-to-optimize design. Third, convergence in the learning-to-optimize experiment is more challenging to achieve: coordination on the two-cycle is never observed, although the two-cycle Pareto-dominates the steady state in our design. [ABSTRACT FROM AUTHOR]

• What is currently known about this topic. • Various instructional approaches and impact on learning. • What the paper adds to the existing knowledge. • Conceptual framework of online interactive instruction and its impact on learning. • How the information in your manuscript can be applied to practice. • Shows trends in instructional approaches for undergraduate and graduate curricula. This paper discusses the underlying conceptual theories that support the use of technology-based educational tools and their implications on student learning outcomes. The use of technology-based educational tools has expanded where virtual classrooms, digital simulations, real-time feedback, interactive quizzes, podcasts, and other multimedia tools are integrated into instruction, especially throughout higher education. These educational tools are hypothesized to augment both student learning and attitudes towards learning. The instructional components in technology-based learning are engrained in theory and supported by empirical research. The relationship between active learning and student success, how technology-based educational tools can be designed to facilitate active learning, and how they positively impact student self-efficacy and self-regulation, including metacognitive awareness will be explicated. The theoretical underpinnings of online, interactive learning will also be applied to trends in nursing curricula. [ABSTRACT FROM AUTHOR]

Despite lacking a generally accepted definition, Artificial General Intelligence (AGI) is commonly understood to refer to artificial agents possessing the capacity to build up a context-independent understanding of itself and the world and to generalize this knowledge across a multitude of contexts. In human agents, this capacity is, to a large degree, facilitated by processes of self-directed learning , during which agents voluntarily control the conditions under which episodes of learning and problem solving occur. Since self-directed learning depends on the degree of knowledge the agent has about various aspects of themselves (their bodily skills, their learning goal, etc.), an AGI implementation of this type of learning must build on a theory of how this self-knowledge is actualized and modified during the learning process. In this paper, we employ the pattern theory of self in order to characterize different aspects of an agent's self that are relevant for self-directed learning. Such aspects include agent-internal cognitive states such as thoughts, emotions, and intentions, but also relational states such as action possibilities in the environment. Combinations of these aspects form a characteristic pattern, which is unique to each individual agent, with no one aspect being necessary or sufficient for the individuation of that agent's self. Here, we focus on the interdependence of narrative and embodied aspects of the self-pattern, since they involve particularly salient challenges consisting in conceptualizing the interaction between propositional and motor representations. In our paper, we model the reciprocal interaction of these aspects of the self-pattern within an individual cognitive agent. We do so by extending an approach by Ryan, Agrawal, & Franklin (2020), who laid the groundwork for the implementation of the pattern theory of self in the LIDA (Learning Intelligent Decision Agent) model. We describe how embodied and narrative aspects of an agent's self-pattern are realized by patterns of interaction between different LIDA modules over time, and how interactions at multiple temporal scales allow the agent's self-pattern to be both dynamically variable and relatively stable. Finally, we investigate the implications this view has for the creation of artificial agents that can benefit from self-directed learning, both in the context of deliberate planning and adaptive motor execution. [ABSTRACT FROM AUTHOR]

Historically, economic models of memory have neglected a pivotal aspect: decay, the established notion that memories deteriorate over time, resulting in a loss of fidelity. In this paper we show that a framework of rational memory with decay can produce the recency effect and other intuitively appealing memory phenomena. We apply the framework to both finite time and dynamic programming settings, deriving a wide range of results with a focus on comparative statics and asymptotic behaviors. Many of our results can also be applied to multi-dimensional rational inattention settings where individuals have to split their attention across several information sources. [ABSTRACT FROM AUTHOR]

Contemporary societal challenges put in evidence the need to improve the hard and soft skills of chemical engineering students. To promote a more student-centered approach, active-based learning, and improved assessment strategies, the Brazilian government approved the so-called New National Curriculum Guidelines (NCG) for engineering courses. To comply with those guidelines, the Department of Chemical Engineering of the Polytechnic School of the University of São Paulo (USP) is currently developing an educational modernization process sponsored by the Fulbright Commission in Brazil, called Special Program for Modernization of Undergraduate Education (PMG). The project is based on three pillars of modernization: content (what), form (how), and infrastructure (where). This paper describes initiatives in each of those pillars: content and format changes in Chemical Reaction Engineering and Process Safety courses and the creation of new spaces for a student-centered approach (an innovative classroom layout and a makerspace). By gathering two concrete classroom experiences guided by a broader institutional educational policies (the PMG project and the NCG), this paper highlights that slight changes can lead to great improvements in the learning process, leading to more engagement in the development of hard skills while favoring improvements in soft skills, such as communication, team-based work, and critical thinking. • Presents a modernization program under implementation at Universidade de São Paulo. • Discusses modernization from a triaxial perspective of content, form and infrastructure. • Reports the feedback of a PBL initiative for soft-skills development in a CRE course. • An active-based methodology in a Process Safety course is presented and discussed. • Presents two new infrastructural changes to facilitate the development of soft-skills. [ABSTRACT FROM AUTHOR]

It is observed that in economics education research "there are few lines of research that lead to definitive conclusions" (Allgood & Schaur, 2019). This can be explained by the dominant use of the input–output model in economics education research studies. According to the Student Experience of Learning (SEL) framework, the links between learning inputs and output are mediated by students' approaches to learning. This paper argues that to fully investigate learning in economics, the researcher should go beyond the input–output framework. While empirical studies confirm the theoretical association of approach and outcome based on the SEL framework, education researchers tend to focus on the overall approaches students utilise at the unit or course level. There is ample evidence that students utilise different learning approaches in different assessment regimes. If a study fails to investigate learning approaches students utilise in different assessment regimes, important assessment-specific relationships between approach and outcome may be missed. This paper documents the construction of an instrument for measuring learning activities in three traditional assessment regimes, viz. MCQ exam, essay assignment and essay exam in economics. The instrument was found to possess sound psychometric properties and ecological (post-dictive) validity, capable of discriminating approaches to learning for different assessments. Given the use of innovative assessments nowadays, researchers are encouraged to reword the instrument to make it more relevant to other assessment types. [ABSTRACT FROM AUTHOR]

Identification of a multi-input/single-output system enables the creation of a mathematical model that as accurately as possible represents the input-output relation induced by a physical system. When there is little information about the physical laws related to the system or when the system is too complex, methods such as parametric identification are used to define the system model. In that case, preliminary assumptions can be made about the system leading to a parametric aggregation function based model. This parametric model may be learned by estimating the parameters of this aggregation function from a representative set of inputs/outputs. A major difficulty is to design a model that is relatively simple yet precise enough to meet the user's needs. Linear models are commonly used because they meet these two contradictory constraints. However, use of a linear model is often at the expense of the accuracy of input-output relationship description. In a recent paper, a new modeling approach was proposed, under the name of macsum modeling [31] , which aims at replacing the linear model concept by a set of linear models. The obtained aggregation function leads to an interval-valued output. This represents the lack of accuracy of the model to predict the system output when the inputs are known. An interesting feature of this model is that it is ruled by a single precise parametric vector whereas the output is imprecise. Moreover, the vector dimension is equal to that of the input space. In this paper, we propose a method to learn such a model so that it best reflects the input-output relationship of the considered system. This method is based, as in the case of linear modeling, on a regression method. This is particularly new approach because the macsum aggregation is based on a Choquet integral and very few authors have proposed learning of an aggregation function based on the Choquet integral. [ABSTRACT FROM AUTHOR]

This paper investigates the role of learning pathway design in a web-based 360° virtual laboratory safety training. A linearly structured virtual laboratory safety training was designed and implemented. Student experiences with the linear learning pathway were compared with a previously implemented non-linear learning pathway. In the linear pathway, students complete the virtual laboratory tour in a predetermined order, while in a non-linear pathway the students can complete the virtual laboratory tour in any order. Student feedback was collected from over 900 students and the experiences from the linearly structured virtual laboratory were highly positive. Compared to the previously implemented non-linear learning pathway, the student feedback related to the learning experience improved significantly. The feedback also showed a difference between preferred learning styles, highlighting the importance of choosing the learning pathway based on the intended learning outcomes and offering different types of learning materials for different learners. Overall, the findings of this study indicate that the linearly structured virtual laboratory offers an effective and motivating learning environment for laboratory safety training. [Display omitted] • The role of learning pathway design in a web-based 360∘ virtual laboratory safety training was studied. • Significant proportion of students favored a linearly structured learning pathway over of free roaming. • Catering to diverse learning styles was important when designing virtual laboratories for safety education. • Students reported several benefits for the virtual laboratory training, including refreshing experience and interactivity. [ABSTRACT FROM AUTHOR]

In this study, we address the issue of limited generalization capabilities in intelligent diagnosis models caused by the lack of high-quality fault data samples for aero-engine rolling bearings. We provide a fault anomaly detection technique based on distillation learning to address this issue. Two Vision Transformer (ViT) models are specifically used in the distillation learning process, one of which serves as the teacher network and the other as the student network. By using a small-scale student network model, the computational efficiency of the model is increased without sacrificing model accuracy. For feature-centered representation, new loss and anomaly score functions are created, and an enhanced Transformer encoder with the residual block is proposed. Then, a rolling bearing dynamics simulation method is used to obtain rich fault sample data, and the pre-training of the teacher network is completed. For anomaly detection, the training of the student network is completed based on the proposed loss function and the pre-trained teacher network, using only the vibration acceleration samples obtained from the normal state. Finally, the trained completed network and the designed anomaly score function are used to achieve the anomaly detection of rolling bearing faults. The experimental validation was carried out on two sets of test data and one set of real vibration data of a whole aero-engine, and the detection accuracy reached 100 %. The results show that the proposed method has a high capability of rolling bearing fault anomaly detection. • The dynamic numerical simulation method is used to obtain fault sample data sets to train the T-Net. • By using a small-scale S-Net, the computational efficiency of the model is increased without sacrificing model accuracy. • A new loss function and anomaly score function of feature center representation are proposed. • A new enhanced Transformer encoder that fuses residual blocks is proposed to solve the problem of attention collapse. • The method in this paper has been applied to the real aero-engine rolling bearing fault detection. [ABSTRACT FROM AUTHOR]

Multi-view representation learning aims to integrate multiple data information from different views to improve the task performance. The information contained in multi-view data is usually complex. Not only do different views contain different information, but also different samples of the same view contain different information. In the multi-view representation learning, most existing methods either simply treat each view/sample with equal importance, or set fixed or dynamic weights for different views/samples, which is not accurate enough to capture the information of dimensions of each sample and causes information redundancy, especially for high-dimensional samples. In this paper, we propose a novel unsupervised multi-view representation learning method based on instance-wise feature selection. A main advantage of instance-wise feature selection in this paper is that one can dynamically select dimensions that favor both view-specific representation learning and view-shared representation learning for each sample, thereby improving the performance from the perspective of model input. The proposed method consists of selector network, view-specific network and view-shared network. Specifically, selector network is used to obtain the selection template, which selects different number of dimensions conducive to representation learning from different samples to solve the sample heterogeneity problem; the view-specific network and view-shared network are used to extract the view-specific and view-shared representations, respectively. The selector network, view-shared network, and view-specific network are optimized alternately. Extensive experiments on various multi-view datasets with clustering and multi-label classification tasks demonstrate that the proposed method outperforms the state-of-the-art multi-view learning methods. • Improving multi-view learning performance from the perspective of model input. • Dynamically selecting dimensions for different samples in representation learning. • Adopting an alternative learning mechanism to optimize all networks. • Experiments on various datasets with various tasks to verify the model effectiveness. [ABSTRACT FROM AUTHOR]

Globally, teaching and learning in contact face-to-face universities had to transition to online learning during 2020, following the disruptions caused by COVID-19 pandemic lockdowns imposed in many countries around the world. The purpose of this paper is to provide insights from preliminary results of ongoing research focusing on students' personal concerns raised about online learning during the early stages of the COVID-19 pandemic in South Africa. Data were collected using a web-based survey in 2020, sampling one group of second-year university students. Internationally, the COVID-19 pandemic has led to an acceleration in digital transformation for teaching and learning in many face-to-face contact universities. The key findings from the survey reported in this paper are twofold: firstly, it was found that COVID-19 has fundamentally changed both teaching and learning space geographies with large proportions of university students having to study from their homes during the lockdown; secondly, students recognised that access to Information and Communication Technology (ICT) infrastructure and the unaffordability of internet connections were significant concerns for most respondents who took part in the study. The study seeks to highlight that even though the COVID-19 pandemic has led to an acceleration in the digital transformation underway in tertiary education and has drawn university teaching and learning more comprehensively into the digital era, the uneven distribution of ICT infrastructure accentuates the barriers to and inequalities that exist for students and for practical home studying. The study makes initial policy suggestions for assisting this acceleration into the digital world. Future research can build on this platform by analysing the post-COVID-19 effects in the university teaching and learning environment. [ABSTRACT FROM AUTHOR]

• This paper proposes a novel multivariate dictionary learning framework. • A coherence enhancement term to improve the coherent representing of the coding coefficients under different resolutions. • A multivariate dictionary optimization method to solve dictionaries involving the calculation of fractional norm. • The proposed method achieves the state-of-the-art performance on several benchmark datasets. Off-the-shelf dictionary learning algorithms have achieved satisfactory results in small sample face recognition applications. However, the achieved results depend on the facial images obtained at a single resolution. In practice, the resolution of the images captured on the same target is different because of the different shooting equipment and different shooting distances. These images of the same category at different resolutions will pose a great challenge to these algorithms. In this paper, we propose a Joint Coupled Representation and Homogeneous Reconstruction (JCRHR) for multi-resolution small sample face recognition. In JCRHR, an analysis dictionary is introduced and combined with the synthetic dictionary for coupled representation learning, which better reveals the relationship between coding coefficients and samples. In addition, a coherence enhancement term is proposed to improve the coherent representation of the coding coefficients at different resolutions, which facilitates the reconstruction of the sample by its homogeneous atoms. Moreover, each sample at different resolutions is assigned a different coding coefficient in the multi-dictionary learning process, so that the learned dictionary is more in line with the actual situation. Furthermore, a regularization term based on the fractional norm is drawn into the dictionary coupled learning to remove the redundant information in the dictionary, which can reduce the negative impacts of the redundant information. Comprehensive results demonstrate that the proposed JCRHR method achieves better results than the state-of-the-art methods, on several small sample face databases. [ABSTRACT FROM AUTHOR]

Learning in this modern era can use various kinds of technology. One of them is virtual reality. Virtual Reality is an environment generated by a computer that makes the user feel immersed with the object that is generated in their surroundings. This paper presents a previous review to understand the implementation of virtual reality in learning. This paper uses SLR as a research method. The study concludes that there is one device for implementing virtual reality called Head Mounted Displays (HMD) and Google Cardboard, two types of user experience, and how far virtual reality has been applied in learning so far. To this day the implementation of virtual reality for learning is still a bit difficult to do because the lack of suffice facilities due to limited funds. [ABSTRACT FROM AUTHOR]

This paper presents a philosophical exploration for understanding language and intercultural competence from the Taoist philosophy, focusing on three Taoist concepts of Yin-Yang, He (harmony) and Sanbao (three-treasures). The philosophical insights were applied to an ethnographic study that was conducted to investigate the participants' intercultural learning outcomes during an EU-exchange sail-training voyage across the North Sea. The sailing space enables convergence and interaction, minimises boundaries and fosters the co-construction of a sense of community. This paper acknowledges the complexity of dynamic and relation-oriented intercultural learning and communication, and proposes the concept of Dual Harmony of Intercultural Cultivation (DHIC), including the dual dimensions of personal harmony and relational harmony, based on the critical analysis of the interculturality in the sail-training context. [ABSTRACT FROM AUTHOR]

In this paper, we propose the application of strategic analysis tools to design an educational program. Findings are based on the case of the Master in Innovative Manufacturing Systems (MIMS), a unique training offer to achieve a multidisciplinary expertise in smart manufacturing, in a two-year program simulating the journey of innovation. Building on novel educational paradigms and recent developments in the area of manufacturing, the main goal of the program is to provide the European industry with outstanding experts willing, able, and trained to bring novel solutions that address relevant societal challenges. Special emphasis is placed on real-world contexts and proactive activities, as well as strategies to continuously update the program. We aim to contribute to the educational facet of smart manufacturing. [ABSTRACT FROM AUTHOR]

In recent years, there has been an increasing transition towards the Industry 5.0 paradigm, which focuses on technology and sustainability but also on the resilience and human-centricity of the production and logistics processes. These changes require investment in equipment, process change, and new skills. To avoid the mismatch between the skills required by industry and the skills offered by university courses, recently, learning factories (LFs) have been spread worldwide, aiming at promoting hand-on learning approaches to manufacturing, logistics and technological processes. This paper illustrates how LFs can support the development of contents and competencies useful in the transition to Industry 5.0 in the industrial engineering courses. Starting from the description of a LF installed in Italy, this study presents a systematisation of the contents that can be addressed by adopting LF in the design of learning modules specifically facing the requirements and skills of the different Industry 5.0 areas. [ABSTRACT FROM AUTHOR]

We describe the implementation of Critical Thinking Activities (CTA) designed to encourage 'critical thinking' in an undergraduate engineering Fluid Mechanics course. Critical thinking can be a vague term both difficult to grasp and even more so to measure. Using a longitudinal case study we analyse quantitative and qualitative data collected over three years to explore the overarching question: "how do we know students have thought critically?". We investigate and evaluate the quantitative data that emerged from students undertaking the CTA and the impact of this on their performance. The results indicate that students who performed well in the CTA achieved a final grade for the course of 5 or more (Credit, Distinction or High Distinction). Qualitative data from student feedback demonstrated that the CTA was a significant factor in reinforcing student learning, enabling us to identify areas of misconception and areas in which they could improve. While the study is situated in an engineering context at the University of Queensland, the paper is an exemplar of embedded and sustainable practice, is equally transferable to other disciplinary contexts. • Successful implementation of critical thinking activity (CTA). • CTA used problem-based-learning and reflection through integrated feedback loop. • Three year long studyshows significant impact on student learning • Method used is equally transferable to other disciplines. [ABSTRACT FROM AUTHOR]

• What is currently known about this topic: Active teaching allow the development of soft skills in nursing students • What the paper adds to the existing knowledge: The social representation of active teaching influences the adherence of students • How the information in your manuscript can be applied to practice: Curriculum plans should include active learning methodologies to develop soft skills Tutorial guidance, allows nursing students to develop soft skills. The aim is to understand the social representation of nursing bachelor's degree students on tutorial guidance classes. Qualitative, descriptive-exploratory study, guided by the theory of social representations. Data collection was performed through a free word association test delivered to undergraduate nursing students. The social representation of fits into the framework of an active learning methodology. Students should be prepared for assuming an active role. [ABSTRACT FROM AUTHOR]

Split learning, a distributed learning framework, has garnered significant attention from academic and industrial communities. In contrast to federated learning, split learning offers a more flexible architecture for participants with limited computing resources. However, the security of split learning has been questioned due to the separation of data and model control rights from usage rights. Currently, most research work focuses on inference attacks in split learning. In this paper, we first reveal the vulnerability of split learning to backdoor attacks and present two backdoor attack frameworks from both the server and client perspectives. Regarding the client-side attacker, we insert backdoor samples into the training data by utilizing the client's direct control over local data, and propose two methods for labeling backdoor samples that can be adapted to various application scenarios. Due to the server's lack of control over the client in split learning, it is infeasible for server-side attackers to inject backdoor samples into training data. Our strategy involves leveraging the server's control over the training process to shape the optimization direction of the client model, thereby enabling it to encode backdoor samples. Moreover, we introduce an auxiliary model into the attack framework to enhance the effectiveness of the backdoor attack. The auxiliary model can increase the distinction between backdoor samples and clean samples in the feature space to improve the sensitivity of the client model to backdoor samples. Extensive evaluations demonstrate the high attack accuracy of both proposed attack frameworks without causing any compromise to the performance of the main task. Our research uncovers the potential security risks and rings the alarm for the application of split learning. [ABSTRACT FROM AUTHOR]

Researchers agree that informal professional learning is crucial for developing new EAP teachers' expertise, but there exist few teacher-centred accounts of how context-specific projects and practices create favourable conditions for such learning. Through presenting and analysing a collaborative action research (CAR) project, this paper illustrates how CAR can facilitate informal learning among new EAP teachers. The project was conducted in the context of a large-scale EAP course at a Singaporean university, where three teachers, each possessing under three years' experience teaching EAP, co-created an 11-page analytic rubric and reflected on their collaboration. Drawing on the written reflections of these three teachers, this paper examines how collaborative rubric development created a "backstage" setting where teachers engaged in trusting and stimulating dialogue, aimed at addressing shared concerns surrounding performance expectations and scoring practices. Through the process of drafting, revising, disseminating, and using the rubric, the three teachers clarified and adjusted their own assessment-related beliefs and practices, reflected on the limits of using rubrics to align assessment standards, and experimented with different methods of sharing their findings with other teachers. In the process, the rubric was reinvented as a dialogical tool that enabled teachers to scaffold professional learning for each other. [ABSTRACT FROM AUTHOR]

This paper reflects on the experience of one Scottish University in conducting a face-to-face Objective Structured Examination (OSCE) for large cohorts of student nurses. It outlines the challenges experienced and learning gained. Borton's model of reflection frames this work due to its simplicity, ease of application and cyclical nature. The theoretical framework for the OSCE is critical thinking, enabling students to apply those skills authentically. OSCE's are designed to transfer classroom knowledge to clinical practice and offer an authentic work-based assessment. Validity and robustness are key considerations in any assessment and in OSCE, the number of stations that students encounter is important and debated. We used a case-study based OSCE approach initially over four stations and following reflection, changed to one long station with four phases. In OSCE examinations, interrater reliability is a necessity, and students expect equity of approach. We identified that despite clear marking criteria, marks were polarised, with students achieving high or low marks with little middle ground. Review of examination papers highlighted that although students' overall performance was good, some had failed in at least one station, suggesting a four-station approach may skew results. On reflection we hypothesised that using a one station case study-based, phased approach enabled the examiner to build up a more holistic picture of student knowledge and skills. It also provided the student opportunity to develop a rapport with the examiner and standardised patient, thereby putting them more at ease. We argue that this approach is holistic, authentic and student centred. Our experience highlights that a single station, four phase OSCE is preferrable, enabling students to integrate all aspects of the assessment and provides a holistic view of clinical skills and knowledge. [ABSTRACT FROM AUTHOR]

Lately, the continuous temporal data from motion sensors in wearable devices has been great interest for the research community due to its demand for analyzing human activities in several applications e.g. healthcare, sports, and surveillance. Numerous solo deep learning models have been proposed in the literature to extract an adequate feature representation from temporal sensory data, however, they are limited to encode only one aspect of the data, and not sufficient to capture the in-depth association between the patterns. This paper presents an ensemble deep model to encode the complex patterns and dependencies in temporal data. An ensemble model refers to the merging or blending of separate deep models, aiming to utilize their unique strengths and abilities to construct a more resilient and effective solution. The contributions of this paper are two folds: First, a systematic evaluation of five different ensemble models is presented to encode the raw sensory data. Secondly, this study proposed a lightweight, multi-layer, and hybrid LSTM-GRU model to recognize the human activities using time series sensory data. Specifically, the raw sensory data is fed into a two-layer LSTM followed by GRU layers. To avoid over-fitting and accelerate convergence, we employed dropout and batch normalization layers. An extensive evaluation of all the five ensemble models is carried out on two large benchmark datasets, namely: UCI-HAR and WISDM. The proposed LSTM-GRU architecture achieved the recognition accuracy of 99.06% and 96.61% on the WISDM and UCI-HAR, respectively. The performance comparison with existing state-of-the-art techniques confirms the superiority and efficacy of the proposed algorithm. • A comprehensive literature review of existing HAR techniques is conducted. • A systematic evaluation of five different ensemble models is presented. • The optimal network configuration is investigated to improve accuracy and efficiency. • The performance of proposed model is evaluated on two large and diverse datasets. • A comparison of various architectures is presented to analyze the sensory data. [ABSTRACT FROM AUTHOR]

In the presence of uncertainty, the optimum obtained based on a nominal identified model can neither provide any performance guarantee nor ensure that critical constraints are satisfied, which is crucial for e.g., bioprocess applications characterized by a high degree of complexity combined with costly experiments. Hence, uncertainty should be considered in the optimization and, furthermore, experiments designed to reduce the uncertainty most important for optimization. Herein, we propose a general framework that combines model-based robust optimization with optimal experiment design. The proposed framework can take advantage of prior knowledge in the form of a mechanistic model structure, and the importance of this is demonstrated by comparing it to more standard black-box models typically employed in learning. Through optimal experiment design, we repeatedly reduce the uncertainty most relevant for optimization so as to maximize the potential for improving the worst-case performance by balancing between exploration and exploitation. This makes the proposed method an efficient model-based robust optimization framework, especially in cases with limited experimental resources. The main part of the paper focuses on the case with modeling uncertainty that can be reduced with the availability of more experimental data. Towards the end of the paper, we consider extending the method to also include inherent uncertainty, such as input uncertainty and unmeasured disturbances. The effectiveness of the method is illustrated through a realistic simulation case study of medium optimization of Chinese hamster ovary cell cultivation in continuous monoclonal antibody production, where the metabolic network consists of 23 extracellular metabolites and 126 reactions. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of hotel employee's thriving at work (including learning and vitality) on their service performance, and explores how leader-member exchange (LMX) moderates this influence. The sampling object of the questionnaire is the employees of 5-star hotels in the north, central, and south of Taiwan who are willing to answer the questionnaires. Purposive sampling is employed. In total, 900 surveys were disseminated throughout Taiwan., including 300 each in the north, central, and south. Out of these, 657 acceptable surveys were collected, resulting in a 73% response rate. The research findings show that: (1) learning positively affect service performance; (2) vitality positively affect service performance; (3) LMX negatively moderates the influence of learning on service performance; (4) LMX negatively moderates the influence of vitality on service performance. At last, the paper proposes managerial implications and subsequent recommendations derived from the research findings. • leader-member exchange = LMX. • Hotel employees' learning has a positive influence on their service performance. • Hotel employees' vitality has a positive influence on their service performance. • LMX moderates the influence of hotel employees' learning on service performance. • LMX moderates the influence of hotel employees' vitality on service performance. [ABSTRACT FROM AUTHOR]

Recently, Social Media (SM)'s adoption as educational instruments in Higher Education (HE) has been gaining popularity among researchers and practitioners. Nonetheless it has been adopted to support various learning approaches such as Cooperative Learning (CL). Despite the growing research in this field, few studies have proposed a comprehensive SM integration framework as well as empirical studies to evaluate its effectiveness. This paper examines how SM is being used as a pedagogical tool in HE and identifies major factors that drives SM's inclusion in HE classrooms. In addition, the paper investigates the effectiveness of a previously proposed integration framework which was developed to promote CL using a popular SM platform, LinkedIn. A survey was administered to collect data from students in different subjects at a university in Australia. Structural Equation Modelling (SEM) was adopted for data analysis using IBM's SPSS26 and AMOS26. One of the notable results of this study is the significant positive relation found between the use of the adopted integration framework and CL, SM challenges' mitigation, students' intention to use SM and their learning process. The findings of this study have important implications for practice and pave the way for future empirical studies. [ABSTRACT FROM AUTHOR]

Federated learning is a novel distribute machine learning paradigm to support cooperative model training among multiple participant clients, where each client keeps its private data locally to protect its data privacy. However, in practical application domains, Federated learning still meets several heterogeneous challenges such data heterogeneity, model heterogeneity, and computation heterogeneity, significantly decreasing its global model performance. To the best of our knowledge, existing solutions only focus on one or two challenges in their heterogeneous settings. In this paper, to address the above challenges simultaneously, we present a novel solution called Full Heterogeneous Federated Learning (FHFL). Firstly, we propose a synthetic data generation approach to mitigate the Non-IID data heterogeneity problem. Secondly, we use knowledge distillation to learn from heterogeneous models of participant clients for model aggregation in the central server. Finally, we produce an opportunistic computation schedule strategy to exploit the idle computation resources for fast-computing clients. Experiment results on different datasets show that our FHFL method can achieve an excellent model training performance. We believe it will serve as a pioneer work for distributed model training among heterogeneous clients in Federated learning. [ABSTRACT FROM AUTHOR]

• History-dependent monetary policy (AIT) lacks empirical evidence. • In a forecasting lab experiment, history-dependent alternatives are compared. • The performances of average inflation-targeting depend on its exact specification. • Longer histories are destabilizing, stronger reaction coefficients stabilizing. This paper compares alternative monetary policy regimes within a controlled lab environment, where groups of participants are tasked with repeatedly forecasting inflation in a simple macroeconomic model featuring only the dynamics of interest rates, inflation and inflation expectations. Average-inflation targeting can replicate the price path observed under price-level targeting in the presence of disinflationary shocks and enable subjects to coordinate on simple heuristics that reflect the concern of the central bank for past inflation gaps. However, this depends on the exact specification of the policy rule. In particular, if the central bank considers more than two lags, subjects fail to form expectations that are consistent with the monetary policy rule, which results in greater inflation volatility. Reinforcing communication around the target helps somewhat anchor long-run inflation expectations. [ABSTRACT FROM AUTHOR]

The optimize control of the ultra supercritical (USC) unit has been a major concern in power industry. The intermediate point temperature process is a multi-variable system with strong nonlinearity, large scale and great delay, which greatly affects the safety and economy of the USC unit. Generally, it is difficult to realize effective control by using conventional methods. This paper presents a nonlinear generalized predictive control based on a composite weighted human learning optimization network (CWHLO-GPC) to improve the control performance of intermediate point temperature. Based on the characteristics of the onsite measurement data, the heuristic information is incorporated into the CWHLO network, and expressed by different local linear models. Then, global controller is elaborately constituted based on a scheduling program inferred from the network. Compared with classical generalized predictive control (GPC), the non-convex problem is effectively solved by introducing CWHLO models into the convex quadratic program (QP) routine of local linear GPC. Finally, detailed analysis on set point tracking and interference resisting via simulation is addressed to illustrate the efficiency of the proposed strategy. • A network with self-learning and self-organizing abilities is delicately constructed. Through reasonable partition, the nonlinearity of the object system is diluted to an acceptable range. • Real-coded human learning optimization algorithm is first proposed and applied to local linear modeling, by which more accurate models are obtained. • An improved generalized predictive control algorithm is proposed based on local models. And by the mutual cooperation of local controllers, the problem of non-convex in quadratic programming is effectively solved. [ABSTRACT FROM AUTHOR]

This paper present a multi-adaptive network model integrating multiple adaptation mechanisms, specifically focusing on five types of such adaptation mechanisms. Two of them address first-order adaptation by learning of responding on others and first-order adaptation by bonding with others based on homophily. Three other adaptation mechanisms addressed are second-order adaptation of the speed of both Hebbian learning and bonding by homophily, and second-order adaptation of the homophily tipping point. The paper provides a comprehensive explanation of these concepts and their role in controlled adaptation within the diverse contextual scenarios of the paper. [ABSTRACT FROM AUTHOR]

Accurate estimation of State of Health (SOH) is crucial to ensure optimal performance and safe operation of lithium-ion battery. This paper proposes a Stacking ensemble learning paradigm for SOH estimation. The Stacking ensemble learning increases adaptability to different features by using base learners with different structures, reducing the risk of overfitting. The model utilizes random vector functional link (RVFL) and active state tracking long-short-term memory network (AST-LSTM) as base learners, where AST-LSTM actively tracks long-term information of lithium-ion battery, and RVFL acts as the meta-learner for stacking. The random vector functional link network helps to avoid the problem of gradient vanishing that is commonly encountered in neural networks due to the gradient descent principle. To further improve estimation accuracy, Singer initialization method and dimension learning method are employed to enhance the Heap-based optimization (HBO) algorithm. In this study, the IHBO algorithm is used to optimize the hyperparameters of the model. Comparing with other methods, the hybrid model proposed in this paper demonstrates superior estimation performance under different operating conditions: at a temperature of 24 °C with a discharge current of 1 A, at a temperature of 4 °C with a discharge current of 1 A, and at a temperature of 4 °C with a discharge current of 2 A. The highest RMSE of the proposed method for the three working conditions are 0.006, 0.01, and 0.017, respectively. Therefore, the proposed Stacking ensemble learning is feasible for SOH estimation of lithium-ion battery and can better adapt to lithium-ion battery data under different operating conditions. • A Stacking ensmeble learning strategy is proposed for SOH estimation. • The improved HBO algorithm is introduced to optimize the Stacking model. • SOH for lithium batteries under three different operating conditions is estimated. • Six benchmark models are used to verify the performance of the proposed model. [ABSTRACT FROM AUTHOR]

Resilient performance is a crucial characteristic of complex socio-technical systems, enabling them to sustain essential functionality during changing or stressful conditions. Resilience Engineering (RE), a sub-field of safety research, focuses on this perspective of resilience. RE emphasises its "cornerstone model", presenting the RE system goals of "anticipating, monitoring, responding and learning". The cornerstone of learning remains fragmented and undertheorized in the existing literature. This paper aims to enrich RE research and its practical implications by developing a nuanced and comprehensive understanding of the role of learning from resilient performance. To achieve this aim, a scoping literature review was conducted to assess how learning is conceptualised in the RE literature and the theoretical foundations on which previous work rest. The main findings show that RE researchers view learning as the process of understanding the system, sharing knowledge, and re-designing system properties. The application of established learning theories is limited. This paper contributes to research by proposing an organisational process for the RE cornerstone of learning, paving the way for deeper discussions in future studies about learning from resilient performance within complex socio-technical systems. • Organisational learning is a core requirement for the safety and optimal functionality of complex socio-technical systems. • Conceptualizations of organisational learning from resilient performance are under-developed. • The literature review shows that learning from resilient performance is fragmented and undertheorized. • The research lays groundwork for future discussions on learning from resilient performance • A refined organisational process for learning within RE, grounded in established learning theory, is proposed. [ABSTRACT FROM AUTHOR]

Many system development organizations face difficulties in ensuring innovation in digital innovation processes. This paper reports from an interpretive case study of an experiment where two system development teams compete to design a novel application. The paper explores how variation in seniority can support digital innovations and hence improve organizational learning and practice. The experiment indicates how the rules of engagement play a key role in balancing the challenge within a busy work life and provide positive outcomes in the form of hard and soft skills for individuals and for the case organization. The paper concludes by suggesting a stepwise challenge design guide and conceptual model that illustrates how the challenges concept could be included in the practitioner fields of digital innovation. [ABSTRACT FROM AUTHOR]