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نتیجه جستجو - یادگیری آنلاین

تعداد مقالات یافته شده: 15
ردیف عنوان نوع
1 Becoming a scholarly management practitioner – Entanglements between the worlds of practice and scholarship
تبدیل شدن به یک متخصص مدیریت علمی - درهم تنیدگی بین دنیای عمل و بورسیه-2021
Our contribution in this paper is to elucidate how doctoral education can enable professionals to develop through an experiential pedagogy that is based on a theoretical model of scholarly management practice. It will draw from our experience of designing and running a large online DBA with participants from across the world. We present a model of Scholarly Management Practice and explain how its use differentiates this approach to doctoral education from others in that there is a clear focus on how holders of the DBA enact their management practice, charac- terized by an orientation to problematization, inquiry, dialogue and critical reflection. We describe the design and underlying theoretical and philosophical rationale for how the program elements articulate together to stimulate the development of scholarly management practitioners. The implications for teaching and learning are presented in the form of a description and ratio- nale for the design of the program in its three stages. We illustrate the trajectory of potential development as a doctoral practitioner through the vignette of one student’s journey. We also reflect on the limitations and lessons learned of our own theorising and practice in the devel- opment and delivery of this DBA.
keywords: Doctoral education | Scholarly practice | Online learning | Management education | DBA
مقاله انگلیسی
2 Improving learning in the management of gender violence: Educational impact of a training program with reflective analysis of dramatized video problems in postgraduate nurses
بهبود یادگیری در مدیریت خشونت جنسیتی:تأثیر آموزشی یک برنامه آموزشی با تجزیه و تحلیل بازتابنده مشکلات ویدئویی دراماتیک در پرستاران کارشناسی ارشد-2021
Background: Most gender-based violence victims who sought help in Spain did so through health services. Training on gender-based violence with active learning methodologies promotes the management of knowledge, reflection, and adaptation to change. Nurses, along with an educator, can construct knowledge with the same strategies they will use professionally. Purpose: To evaluate the knowledge, skills, and attitudes associated of postgraduate nurses on gender-based violence before and after a reflection-based training program with dramatized problem-videos. The secondary objectives were to evaluate the knowledge in the activation of protocols, skills, and attitudes in the management of women who are victims of gender-based violence, the consolidation of learning, and the applicability to the workplace. Methods: Pre-post quasi-experimental study without a control group. A specifically validated and designed in- strument was utilized to evaluate the dimensions of knowledge, skills, and attitudes when facing gender-based violence, before and after the training sessions, along with additional questions to assess if the participants possessed better tools to address gender-based violence. Results: The difference between the pre and post-tests was statistically significant for the dimensions knowledge, skills, and attitude (p < 0.05), with a smaller effect size in the dimensions skills and attitude. Also, high scores were observed in the consolidation of learning and applicability to the workplace. Conclusion: Reflection-based training with dramatized problem-videos improved the acquisition of tools neces- sary for the detection and management of gender-based violence of nurses.
keywords: پرستاری آموزش و پرورش | خشونت بر اساس جنسیت | یادگیری فعال | یادگیری آنلاین | ارزیابی کمی | Education nursing | Gender-based violence | Active learning | Online learning | Quantitative evaluation
مقاله انگلیسی
3 A self-organizing developmental cognitive architecture with interactive reinforcement learning
یک معماری شناختی توسعه ای خود سازمان دهی شده با یادگیری تقویتی تعاملی-2020
Developmental cognitive systems can endow robots with the abilities to incrementally learn knowledge and autonomously adapt to complex environments. Conventional cognitive methods often acquire knowl- edge through passive perception, such as observing and listening. However, this learning way may gener- ate incorrect representations inevitably and cannot correct them online without any feedback. To tackle this problem, we propose a biologically-inspired hierarchical cognitive system called Self-Organizing De- velopmental Cognitive Architecture with Interactive Reinforcement Learning (SODCA-IRL). The architec- ture introduces interactive reinforcement learning into hierarchical self-organizing incremental neural networks to simultaneously learn object concepts and fine-tune the learned knowledge by interacting with humans. In order to realize the integration, we equip individual neural networks with a memory model, which is designed as an exponential function controlled by two forgetting factors to simulate the consolidation and forgetting processes of humans. Besides, an interactive reinforcement strategy is designed to provide appropriate rewards and execute mistake correction. The feedback acts on the for- getting factors to reinforce or weaken the memory of neurons. Therefore, correct knowledge is preserved while incorrect representations are forgotten. Experimental results show that the proposed method can make effective use of the feedback from humans to improve the learning effectiveness significantly and reduce the model redundancy.
Keywords: Cognitive development | Online learning | Self-organizing neural network | Object recognition | Interactive reinforcement learning
مقاله انگلیسی
4 The impact of learner-, instructor-, and course-level factors on online learning
تأثیر عوامل یادگیرنده ، مربی و دوره در یادگیری آنلاین-2020
The number of K-12 students taking online courses has been increasing tremendously over the past few years. However, most research on online learning either compares its overall effectiveness to that of traditional learning, or examines perceptions or interactions using self-reported data; and very few studies have looked into the relationships between the elements of K-12 online courses and their students’ learning outcomes. Based on student-, instructor-, and course-level data from 919 students enrolled in eight online high-school English language and literature courses, the results of hierarchical linear modeling and content analysis found that project-based assignments and high-level knowledge activities were beneficial to learning outcomes – though not necessarily among students who took these courses for credit-recovery purposes. The paper also discusses implications for both online course-design practices and future research on predictors of online-learning success.
Keywords: K-12 online education | Online course design | English language and literature | Higher-level knowledge activities
مقاله انگلیسی
5 Financial portfolio optimization with online deep reinforcement learning and restricted stacked autoencoder-DeepBreath
بهینه سازی سبد مالی با یادگیری تقویتی عمیق آنلاین و محدود کردن خودکار رمزگذار-DeepBreath-2020
The process of continuously reallocating funds into financial assets, aiming to increase the expected re- turn of investment and minimizing the risk, is known as portfolio management. In this paper, a portfolio management framework is developed based on a deep reinforcement learning framework called Deep- Breath. The DeepBreath methodology combines a restricted stacked autoencoder and a convolutional neu- ral network (CNN) into an integrated framework. The restricted stacked autoencoder is employed in order to conduct dimensionality reduction and features selection, thus ensuring that only the most informative abstract features are retained. The CNN is used to learn and enforce the investment policy which consists of reallocating the various assets in order to increase the expected return on investment. The framework consists of both offline and online learning strategies: the former is required to train the CNN while the latter handles concept drifts i.e. a change in the data distribution resulting from unforeseen circum- stances. These are based on passive concept drift detection and online stochastic batching. Settlement risk may occur as a result of a delay in between the acquisition of an asset and its payment failing to deliver the terms of a contract. In order to tackle this challenging issue, a blockchain is employed. Finally, the performance of the DeepBreath framework is tested with four test sets over three distinct investment periods. The results show that the return of investment achieved by our approach outperforms current expert investment strategies while minimizing the market risk.
Keywords: Portfolio management | Deep reinforcement learning | Restricted stacked autoencoder | Online leaning | Settlement risk | Blockchain
مقاله انگلیسی
6 Multi-objective energy management for fuel cell electric vehicles using online-learning enhanced Markov speed predictor
مدیریت انرژی چند منظوره برای وسایل نقلیه الکتریکی سلول سوختی با استفاده از یادگیری آنلاین ، پیش بینی کننده سرعت مارکوف را افزایش می دهد-2020
As one of promising solutions towards future cleaner transportation, fuel cell electric vehicles have been widely regarded as an attractive technology in both academia and industry. To enhance the vehicle’s operation efficiency, this paper proposes a multi-criteria power allocation strategy for a fuel cell/battery-based plug-in hybrid electric vehicle. Firstly, an adaptive online-learning enhanced Markov velocity-forecast approach is proposed. Its predictive behaviors can be adjusted accordingly under various driving scenarios through the real-time-identified transition probability matrices. Subsequently, based only on the previewed trip duration information and the speed prediction results, a state-of-charge (SOC) reference planning approach is designed to guide the allocation of battery energy. Combining with the velocity-forecast results and the reference SoC, model predictive control derives the optimal power-allocation decision through minimizing the multi-purpose objective function in a finite time horizon. It has been verified that (1) the presented power allocation strategy can reduce over 12.05% H2 consumption and over 94.40% fuel cell power spikes against the commonly used Charge-Depleting/ Charge-Sustaining strategy; (2) despite the existence of mission time estimation errors, the presented control strategy could still bring performance enhancement over the benchmark strategy, thus demonstrating its feasibility for real-world implementations.
Keywords: Energy management strategy | Fuel cell | Plug-in hybrid electric vehicles | Speed forecasting technique | State-of-charge reference generation
مقاله انگلیسی
7 Control-Based Algorithms for High Dimensional Online Learning
الگوریتم های مبتنی بر کنترل برای یادگیری آنلاین با ابعاد بالا-2020
In the era of big data, the high-dimensional online learning problems require huge computing power. This paper proposes a novel approach for high-dimensional online learning. Two new algorithms are developed for online high-dimensional regression and classification problems respectively. The problems are formulated as feedback control problems for some low dimensional systems. The novel learning algorithms are then developed via the control problems. Via an efficient polar decomposition, we derive the explicit solutions of the control problems, substantially reducing the corresponding computational complexity, especially for high dimensional largescale data streams. Comparing with conventional methods, the new algorithm can achieve more robust and accurate performance with faster convergence. This paper demonstrates that optimal control can be an effective approach for developing high dimensional learning algorithms. We have also for the first time proposed a control-based robust algorithm for classification problems. Numerical results support our theory and illustrate the efficiency of our algorithm.
Keywords: Classification | high dimensional dataset | model predictive control | online learning | robust control
مقاله انگلیسی
8 Online RBM: Growing Restricted Boltzmann Machine on the fly for unsupervised representation
آنلاین RBM: در حال رشد محدودیت ماشین بولتزمن در پرواز برای نمایندگی بدون نظارت-2020
In this work, we endeavor to investigate and propose a novel unsupervised online learning algorithm, namely the Online Restricted Boltzmann Machine (O-RBM). The O-RBM is able to construct and adapt the architecture of a Restricted Boltzmann Machine (RBM) artificial neural network, according to the statistics of the streaming input data. Specifically, for a training data that is not fully available at the onset of training, the proposed O-RBM begins with a single neuron in the hidden layer of the RBM, progressively adds and suitably adapts the network to account for the variations in streaming data distributions. Such an unsupervised learning helps to effectively model the probability distribution of the entire data stream, and generates robust features. We will demonstrate that such unsupervised representations can be used for discriminative classifications on a set of multi-category and binary classification problems for unstructured image and structured signal data sets, having varying degrees of class-imbalance. We first demonstrate the O-RBM algorithm and characterize the network evolution using the simple and conventional multi-class MNIST image dataset, aimed at recognizing hand-written digit. We then benchmark O-RBM performance to other machine learning, neural network and Class RBM techniques using a number of public non-stationary datasets. Finally, we study the performance of the O-RBM on a real-world problem involving predictive maintenance of an aircraft component using time series data. In all these studies, it is observed that the O-RBM converges to a stable, concise network architecture, wherein individual neurons are inherently discriminative to the class labels despite unsupervised training. It can be observed from the performance results that on an average O-RBM improves accuracy by 2.5%–3% over conventional offline batch learning techniques while requiring at least 24%–70% fewer neurons.
Keywords: Restricted Boltzmann Machine | Online learning | Unsupervised representation
مقاله انگلیسی
9 Deep online hierarchical dynamic unsupervised learning for pattern mining from utility usage data
یادگیری بدون نظارت سلسله مراتبی عمیق آنلاین برای استخراج الگو از داده های استفاده از ابزار-2019
While most non-intrusive load monitoring (NILM) work has focused on supervised algorithms, unsuper- vised approaches can be more interesting and practical. Specifically, they do not require labelled training data to be acquired from the individual appliances and can be deployed to operate on the measured ag- gregate data directly. We propose a fully unsupervised novel NILM framework based on Dynamic Bayesian hierarchical mixture model and Deep Belief network (DBN). The deep network learns, in unsupervised fashion, low-level generic appliance-specific features from the raw signals of the house utilities usage, then the hierarchical Bayesian model learns high-level features representing the consumption patterns of the residents captured by the correlations among the low-level features. The temporal ordering of the high-level features is captured by the Dynamic Bayesian Model. Using this architecture, we overcome the computational complexity that would occur if temporal modelling was directly applied to the raw data or even to the constructed features. The computational efficiency is crucial as our application involves massive data from different utilities usage. Moreover, we develop a novel online inference algorithm to cope with this big data. Finally, we propose different evaluation methods to analyse the results which show that our algorithm finds useful patterns.
Keywords: Non-intrusive load monitoring | Bayesian modelling | Online learning | Human activity recognition
مقاله انگلیسی
10 Designing online network intrusion detection using deep auto-encoder Q-learning
طراحی تشخیص نفوذ آنلاین به شبکه با استفاده از یادگیری-Q خودرمزگذار عمیق-2019
Because of the increasing application of reinforcement learning (RL), particularly deep Q- learning algorithm, research organizations utilize it with increasing frequency. The predic- tion of cyber vulnerability and development of efficient real-time online network intrusion detection (NID) systems are progressions toward becoming RL-powered. An open issues in NID is the model design and prediction of real-time online data composed of a series of time-related feature patterns. There have been concerns regarding the operation of the developed systems because cyber-attack scenarios vary continuously to circumvent NID. These issues have been related to the human interaction significance and the decrease in accuracy verification. Therefore, we employ an RL that permits a deep auto-encoder in the Q-network (DAEQ-N). The proposed DAEQ-N attempts to achieve the maximum prediction accuracy in online learning systems into which continuous behavior patterns are fed and which are trained with more significant weights by classifying it as either “normal”or “anomalous.”
Keywords: Network anomalies | Online learning systems | Network intrusion detection (NID) | Deep Q-Network (DQN) | Reinforcement learning (RL)
مقاله انگلیسی
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