Approach to nocturnal enuresis in children Ong, Li Ming; Chan, Joel Meng Fai; Koh, Gabrielle Eloise Ming Yen ...
Singapore medical journal,
04/2024, Volume:
65, Issue:
4
Journal Article
Innovation starts with people, making the human capital within the workforce decisive. In a fast-changing knowledge economy, 21st-century digital skills drive organizations' competitiveness and ...innovation capacity. Although such skills are seen as crucial, the digital aspect integrated with 21st-century skills is not yet sufficiently defined. The main objectives of this study were to (1) examine the relation between 21st-century skills and digital skills; and (2) provide a framework of 21st-century digital skills with conceptual dimensions and key operational components aimed at the knowledge worker. A systematic literature review was conducted to synthesize the relevant academic literature concerned with 21st-century digital skills. In total, 1592 different articles were screened from which 75 articles met the predefined inclusion criteria. The results show that 21st-century skills are broader than digital skills – the list of mentioned skills is far more extensive. In addition, in contrast to digital skills, 21st-century skills are not necessarily underpinned by ICT. Furthermore, we identified seven core skills: technical, information management, communication, collaboration, creativity, critical thinking and problem solving. Five contextual skills were also identified: ethical awareness, cultural awareness, flexibility, self-direction and lifelong learning.
•Conducted a systematic literature review to examine 21st-century digital skills for work.•1592 different articles were screened from which 75 articles were included.•21st-century skills are broader and more often on conceptual level than digital skills.•We propose a framework of 7 core and 5 contextual 21st-century digital skills.
We start this article with the exploration of similarities between the resource-based view of the firm (RBV) and stakeholder theory at the time of their origination and then proceed with the ...conversation on what led to distinct developmental trajectories of the two theories. Though RBV has become a leading paradigm in the strategic management field, we argue that in its current form, RBV is yet incomplete. We suggest there are four aspects that stakeholder theory can offer to inform RBV: normativity, sustainability, people, and cooperation. Reconciling stakeholder theory and RBV is a promising path to advancing our understanding of management, and we provide a two-part guideline to management scholars and practitioners who would be willing to take this path.
As higher education institutions compete to gain competitive advantage in the areas of student enrolment, engagement, graduate numbers, and programme delivery, are these institutes at risk of missing ...out on one of the largest growing markets? The cohort of persons aged 65 years and over, is expected to double in size by 2040. But the cohort of those who are currently aged 55+ years is not extensively targeted by higher education institutions globally. Platitudes about ‘mature students’ are often used when discussing this demographic, but educational departments identify any person over 23 years as a ‘mature’ student. Hence, is this term truly targeting the senior population and lifelong learning curiosity? Persons who are 65 years have a lifetime of experience at the time of their retirement, and on average have an additional 20 years in which to share that experience with rates of volunteering highest among the 65 to 74 year old age group 1. By identifying the barriers of the 50+ year’s cohort to transition to education in different forms and different levels, we can prepare the ground work for easing their inclusion in higher education institutions at, or before retirement.
This paper presents a novel safe integral reinforcement learning (IRL)-based optimal trajectory tracking scheme for nonlinear systems with uncertain dynamics that is subject to constraints. We ...leverage multilayer neural networks (MNNs) for actor-critic MNNs along with an NN identifier in the backstepping process for minimizing a discounted value function. A time-varying barrier Lyapunov function (TVBLF) is utilized for handling constraints and to provide safety assurances. Online weight update laws for the actor and critic MNNs are derived that are driven by Bellman error and control input error. We introduce an online lifelong learning (LL) method in the critic NN, utilizing the Bellman error in MNNs to address catastrophic forgetting. The method’s effectiveness is demonstrated through simulations on mobile robot multitask tracking. The paper concludes with a stability analysis of the closed-loop system.
Continual Learning (CL) trains models on streams of data, with the aim of learning new information without forgetting previous knowledge. However, many of these models lack interpretability, making ...it difficult to understand or explain how they make decisions. This lack of interpretability becomes even more challenging given the non-stationary nature of the data streams in CL. Furthermore, CL strategies aimed at mitigating forgetting directly impact the learned representations. We study the behavior of different explanation methods in CL and propose CLEX (ContinuaL EXplanations), an evaluation protocol to robustly assess the change of explanations in Class-Incremental scenarios, where forgetting is pronounced. We observed that models with similar predictive accuracy do not generate similar explanations. Replay-based strategies, well-known to be some of the most effective ones in class-incremental scenarios, are able to generate explanations that are aligned to the ones of a model trained offline. On the contrary, naive fine-tuning often results in degenerate explanations that drift from the ones of an offline model. Finally, we discovered that even replay strategies do not always operate at best when applied to fully-trained recurrent models. Instead, randomized recurrent models (leveraging on an untrained recurrent component) clearly reduce the drift of the explanations. This discrepancy between fully-trained and randomized recurrent models, previously known only in the context of their predictive continual performance, is more general, including also continual explanations.
•Continual learning models with similar accuracy produce different explanations.•Continual learning strategies align explanations to the ones of an offline model.•Randomized networks produce better aligned explanations than fully-trained models.
The use of online collaborative learning activities has been notably supported by cloud computing. Although specific reference has been made to a certain online application or service, there has been ...no clear understanding of how different cloud computing tools have shaped the concept of collaborative learning, and the extent to which these resources are accessible to today's students. Thus, a review of the literature was conducted to identify studies on cloud computing tools for collaborative learning in a blended classroom. The review of the literature led to the inclusion of 29 relevant studies categorized as synchronized tools, Learning Management System (LMS) tools, and social networking tools. The review results revealed a set of evidences supporting the use of certain cloud computing tools for certain collaborative learning activities categorized under sharing, editing, communication and discussion. The key opportunities and challenges associated with the use of these tools in a blended learning context were also identified and discussed. Findings from this study will certainly help academicians, practitioners and researchers to understand the potential of using cloud computing environments from a wider perspective.
•A scoping review on the role of cloud computing tools for collaborative learning was conducted.•Synchronized, LMS, and social networking were the main tools used in a blended collaborative learning environment.•The associations between these tools and certain collaborative learning activities were mapped.•Several opportunities and challenges related to the use of these tools for collaborative learning were identified.
•State of the art on continual / lifelong learning and its implications for robotics.•Proposal of a framework to present Continual Learning algorithms.•Summary of existing metrics for continual ...learning.•Presenting robotics applications for continual learning, current challenges and opportunities.
Continual learning (CL) is a particular machine learning paradigm where the data distribution and learning objective change through time, or where all the training data and objective criteria are never available at once. The evolution of the learning process is modeled by a sequence of learning experiences where the goal is to be able to learn new skills all along the sequence without forgetting what has been previously learned. CL can be seen as an online learning where knowledge fusion needs to take place in order to learn from streams of data presented sequentially in time. Continual learning also aims at the same time at optimizing the memory, the computation power and the speed during the learning process. An important challenge for machine learning is not necessarily finding solutions that work in the real world but rather finding stable algorithms that can learn in real world. Hence, the ideal approach would be tackling the real world in a embodied platform: an autonomous agent. Continual learning would then be effective in an autonomous agent or robot, which would learn autonomously through time about the external world, and incrementally develop a set of complex skills and knowledge.Robotic agents have to learn to adapt and interact with their environment using a continuous stream of observations. Some recent approaches aim at tackling continual learning for robotics, but most recent papers on continual learning only experiment approaches in simulation or with static datasets. Unfortunately, the evaluation of those algorithms does not provide insights on whether their solutions may help continual learning in the context of robotics. This paper aims at reviewing the existing state of the art of continual learning, summarizing existing benchmarks and metrics, and proposing a framework for presenting and evaluating both robotics and non robotics approaches in a way that makes transfer between both fields easier. We put light on continual learning in the context of robotics to create connections between fields and normalize approaches.
Humans and animals have the ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan. This ability, referred to as lifelong learning, is mediated by a ...rich set of neurocognitive mechanisms that together contribute to the development and specialization of our sensorimotor skills as well as to long-term memory consolidation and retrieval. Consequently, lifelong learning capabilities are crucial for computational learning systems and autonomous agents interacting in the real world and processing continuous streams of information. However, lifelong learning remains a long-standing challenge for machine learning and neural network models since the continual acquisition of incrementally available information from non-stationary data distributions generally leads to catastrophic forgetting or interference. This limitation represents a major drawback for state-of-the-art deep neural network models that typically learn representations from stationary batches of training data, thus without accounting for situations in which information becomes incrementally available over time. In this review, we critically summarize the main challenges linked to lifelong learning for artificial learning systems and compare existing neural network approaches that alleviate, to different extents, catastrophic forgetting. Although significant advances have been made in domain-specific learning with neural networks, extensive research efforts are required for the development of robust lifelong learning on autonomous agents and robots. We discuss well-established and emerging research motivated by lifelong learning factors in biological systems such as structural plasticity, memory replay, curriculum and transfer learning, intrinsic motivation, and multisensory integration.
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