دانلود و نمایش مقالات مرتبط با Online learning::صفحه 1
دانلود بهترین مقالات isi همراه با ترجمه فارسی 2

با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد). 

نتیجه جستجو - Online learning

تعداد مقالات یافته شده: 24
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 Adaptive early classification of temporal sequences using deep reinforcement learning
طبقه بندی اولیه انطباقی توالی های زمانی با استفاده از یادگیری تقویتی عمیق-2020
In this article, we address the problem of early classification (EC) of temporal sequences with adaptive prediction times. We frame EC as a sequential decision making problem and we define a partially observable Markov decision process (POMDP) fitting the competitive objectives of classification earliness and accuracy. We solve the POMDP by training an agent for EC with deep reinforcement learning (DRL). The agent learns to make adaptive decisions between classifying incomplete sequences now or delaying its prediction to gather more measurements. We adapt an existing DRL algorithm for batch and online learning of the agent’s action value function with a deep neural network. We propose strategies of prioritized sampling, prioritized storing and random episode initialization to address the fact that the agent’s memory is unbalanced due to (1): all but one of its actions terminate the process and thus (2): actions of classification are less frequent than the action of delay. In experiments, we show improvements in accuracy induced by our specific adaptation of the algorithm used for online learning of the agent’s action value function. Moreover, we compare two definitions of the POMDP based on delay reward shaping against reward discounting. Finally, we demonstrate that a static naive deep neural network, i.e. trained to classify at static times, is less efficient in terms of accuracy against speed than the equivalent network trained with adaptive decision making capabilities
Keywords: Early classification | Adaptive prediction time | Deep reinforcement learning | Temporal sequences | Double DQN | Trade-off between accuracy vs. speed
مقاله انگلیسی
6 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
7 Intelligent handover decision scheme using double deep reinforcement learning
طرح تصمیم گیری واگذاری هوشمند با استفاده از یادگیری تقویتی عمیق دو برابر-2020
Handovers (HOs) have been envisioned to be more challenging in 5G networks due to the inclusion of millimetre wave (mm-wave) frequencies, resulting in more intense base station (BS) deployments. This, by its turn, increases the number of HOs taken due to smaller footprints of mm-wave BSs thereby making HO management a more crucial task as reduced quality of service (QoS) and quality of experience (QoE) along with higher signalling overhead are more likely with the growing number of HOs. In this paper, we propose an offline scheme based on double deep reinforcement learning (DDRL) to minimize the frequency of HOs in mm-wave networks, which subsequently mitigates the adverse QoS. Due to continuous and substantial state spaces arising from the inherent characteristics of the considered 5G environment, DDRL is preferred over conventional Q-learning algorithm. Furthermore, in order to alleviate the negative impacts of online learning policies in terms of computational costs, an offline learning framework is adopted in this study, a known trajectory is considered in a simulation environment while ray-tracing is used to estimate channel characteristics. The number of HO occurrence during the trajectory and the system throughput are taken as performance metrics. The results obtained reveal that the proposed method largely outperform conventional and other artificial intelligence (AI)-based models.
Keywords: Double deep reinforcement learning | Handover management | Millimetre-wave communication
مقاله انگلیسی
8 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
9 A novel optimal bipartite consensus control scheme for unknown multi-agent systems via model-free reinforcement learning
یک طرح کنترل اجماع دو طرفه بهینه جدید برای سیستم های چند عامل ناشناخته از طریق یادگیری تقویتی بدون مدل-2020
In this paper, the optimal bipartite consensus control (OBCC) problem is investigated for unknown multi-agent systems (MASs) with coopetition networks. A novel distributed OBCC scheme is proposed based on model-free reinforcement learning method to achieve OBCC, where the agent’s dynamics are no longer required. First, The coopetition networks are applied to establish the cooperative and competitive interactions among agents, and then the OBCC problem is formulated by introducing local neighbor bipartite consensus errors and performance index functions (PIFs) for each agent. Second, in order to obtain the OBCC laws, a policy iteration algorithm (PIA) is employed to learn the solutions to discrete-time (DT) Hamilton-Jacobi-Bellman (HJB) equations. Third, to implement the pro- posed methods, we adopt a data-driven actor-critic-based neural networks (NNs) frame- work to approximate the control laws and the PIFs, respectively, in an online learning manner. Finally, some simulation results are given to demonstrate the effectiveness of the developed approaches.
Keywords: Optimal bipartite consensus control | Multi-agent systems | Coopetition network | Model-free | Reinforcement learning
مقاله انگلیسی
10 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
rss مقالات ترجمه شده rss مقالات انگلیسی rss کتاب های انگلیسی rss مقالات آموزشی
logo-samandehi
بازدید امروز: 508 :::::::: بازدید دیروز: 0 :::::::: بازدید کل: 508 :::::::: افراد آنلاین: 40