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Автор: Grafiati
Опубліковано: 11 вересня 2023

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Статті в журналах з теми "TRANSFER LEARNING APPROACH"

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Durgut, Rafet, Mehmet Emin Aydin, and Abdur Rakib. "Transfer Learning for Operator Selection: A Reinforcement Learning Approach." Algorithms 15, no. 1 (January 17, 2022): 24. http://dx.doi.org/10.3390/a15010024.

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Анотація:
In the past two decades, metaheuristic optimisation algorithms (MOAs) have been increasingly popular, particularly in logistic, science, and engineering problems. The fundamental characteristics of such algorithms are that they are dependent on a parameter or a strategy. Some online and offline strategies are employed in order to obtain optimal configurations of the algorithms. Adaptive operator selection is one of them, and it determines whether or not to update a strategy from the strategy pool during the search process. In the field of machine learning, Reinforcement Learning (RL) refers to goal-oriented algorithms, which learn from the environment how to achieve a goal. On MOAs, reinforcement learning has been utilised to control the operator selection process. However, existing research fails to show that learned information may be transferred from one problem-solving procedure to another. The primary goal of the proposed research is to determine the impact of transfer learning on RL and MOAs. As a test problem, a set union knapsack problem with 30 separate benchmark problem instances is used. The results are statistically compared in depth. The learning process, according to the findings, improved the convergence speed while significantly reducing the CPU time.
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Zhao, Peng, Guoqin Wu, Sheng Yao, and HuiTing Liu. "A Transductive Transfer Learning Approach Based on Manifold Learning." Computing in Science & Engineering 22, no. 1 (January 1, 2020): 77–87. http://dx.doi.org/10.1109/mcse.2018.2882699.

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Mishra, Bishwas, and Abhishek Samanta. "Quantum Transfer Learning Approach for Deepfake Detection." Sparklinglight Transactions on Artificial Intelligence and Quantum Computing 02, no. 01 (2022): 17–27. http://dx.doi.org/10.55011/staiqc.2022.2103.

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Deepfake image manipulation has achieved great attention in the previous year’s owing to brings solemn challenges from the public self-confidence. Forgery detection in face imaging has made considerable developments in detecting manipulated images. However, there is still a need for an efficient deepfake detection approach in complex background environments. This paper applies the state-of-the-art quantum transfer learning approach for classifying deepfake images from original face images. The proposed model comprises classical pre-trained ResNet-18 and quantum neural network layers that provide efficient features extraction to learn the different patterns of the deepfake face images. The proposed model is validated on a real-world deepfake dataset created using commercial software. An accuracy of 96.1 % was obtained.
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Huang, Shuai, Jing Li, Kewei Chen, Teresa Wu, Jieping Ye, Xia Wu, and Li Yao. "A transfer learning approach for network modeling." IIE Transactions 44, no. 11 (January 2, 2012): 915–31. http://dx.doi.org/10.1080/0740817x.2011.649390.

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Raza, Noman, Asma Naseer, Maria Tamoor, and Kashif Zafar. "Alzheimer Disease Classification through Transfer Learning Approach." Diagnostics 13, no. 4 (February 20, 2023): 801. http://dx.doi.org/10.3390/diagnostics13040801.

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Alzheimer’s disease (AD) is a slow neurological disorder that destroys the thought process, and consciousness, of a human. It directly affects the development of mental ability and neurocognitive functionality. The number of patients with Alzheimer’s disease is increasing day by day, especially in old aged people, who are above 60 years of age, and, gradually, it becomes cause of their death. In this research, we discuss the segmentation and classification of the Magnetic resonance imaging (MRI) of Alzheimer’s disease, through the concept of transfer learning and customizing of the convolutional neural network (CNN) by specifically using images that are segmented by the Gray Matter (GM) of the brain. Instead of training and computing the proposed model accuracy from the start, we used a pre-trained deep learning model as our base model, and, after that, transfer learning was applied. The accuracy of the proposed model was tested over a different number of epochs, 10, 25, and 50. The overall accuracy of the proposed model was 97.84%.
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Cao, Bin, Sinno Jialin Pan, Yu Zhang, Dit-Yan Yeung, and Qiang Yang. "Adaptive Transfer Learning." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 3, 2010): 407–12. http://dx.doi.org/10.1609/aaai.v24i1.7682.

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Анотація:
Transfer learning aims at reusing the knowledge in some source tasks to improve the learning of a target task. Many transfer learning methods assume that the source tasks and the target task be related, even though many tasks are not related in reality. However, when two tasks are unrelated, the knowledge extracted from a source task may not help, and even hurt, the performance of a target task. Thus, how to avoid negative transfer and then ensure a "safe transfer" of knowledge is crucial in transfer learning. In this paper, we propose an Adaptive Transfer learning algorithm based on Gaussian Processes (AT-GP), which can be used to adapt the transfer learning schemes by automatically estimating the similarity between a source and a target task. The main contribution of our work is that we propose a new semi-parametric transfer kernel for transfer learning from a Bayesian perspective, and propose to learn the model with respect to the target task, rather than all tasks as in multi-task learning. We can formulate the transfer learning problem as a unified Gaussian Process (GP) model. The adaptive transfer ability of our approach is verified on both synthetic and real-world datasets.
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Rani, Rajneesh, and Harpreet Singh. "Fingerprint Presentation Attack Detection Using Transfer Learning Approach." International Journal of Intelligent Information Technologies 17, no. 1 (January 2021): 53–67. http://dx.doi.org/10.4018/ijiit.2021010104.

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Анотація:
In this busy world, biometric authentication methods are serving as fast authentication means. But with growing dependencies on these systems, attackers have tried to exploit these systems through various attacks; thus, there is a strong need to protect authentication systems. Many software and hardware methods have been proposed in the past to make existing authentication systems more robust. Liveness detection/presentation attack detection is one such method that provides protection against malicious agents by detecting fake samples of biometric traits. This paper has worked on fingerprint liveness detection/presentation attack detection using transfer learning for which the authors have used a pre-trained NASNetMobile model. The experiments are performed on publicly available liveness datasets LivDet 2011 and LivDet 2013 and have obtained good results as compared to state of art techniques in terms of ACE(average classification error).
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Aswathi, T., T. R. Swapna, and S. Padmavathi. "Transfer Learning approach for grading of Diabetic Retinopathy." Journal of Physics: Conference Series 1767, no. 1 (February 1, 2021): 012033. http://dx.doi.org/10.1088/1742-6596/1767/1/012033.

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Oh, YongKyung, Namu Kim, and Sungil Kim. "Transfer Learning based Approach for Mixture Gas Classification." Journal of the Korean Institute of Industrial Engineers 47, no. 2 (April 30, 2021): 144–59. http://dx.doi.org/10.7232/jkiie.2021.47.2.144.

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Cvetkovic, Stevica, Nemanja Savic, and Ivan Ciric. "Deep Transfer Learning Approach for Robust Hand Detection." Intelligent Automation & Soft Computing 36, no. 1 (2023): 967–79. http://dx.doi.org/10.32604/iasc.2023.032526.

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Дисертації з теми "TRANSFER LEARNING APPROACH"

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Andersen, Linda, and Philip Andersson. "Deep Learning Approach for Diabetic Retinopathy Grading with Transfer Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279981.

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Diabetic retinopathy (DR) is a complication of diabetes and is a disease that affects the eyes. It is one of the leading causes of blindness in the Western world. As the number of people with diabetes grows globally, so does the number of people affected by diabetic retinopathy. This demand requires that better and more effective resources are developed in order to discover the disease in an early stage which is key to preventing that the disease progresses into more serious stages which ultimately could lead to blindness, and streamline further treatment of the disease. However, traditional manual screenings are not enough to meet this demand. This is where the role of computer-aided diagnosis comes in. The purpose of this report is to investigate how a convolutional neural network together with transfer learning can perform when trained for multiclass grading of diabetic retinopathy. In order to do this, a pre-built and pre-trained convolutional neural network from Keras was used and further trained and fine-tuned in Tensorflow on a 5-class DR grading dataset. Twenty training sessions were performed and accuracy, recall and specificity were evaluated in each session. The results show that testing accuracies achieved were in the range of 35% to 48.5%. The average testing recall achieved for class 0, 1, 2, 3 and 4 was 59.7%, 0.0%, 51.0%, 38.7% and 0.8%, respectively. Furthermore, the average testing specificity achieved for class 0, 1, 2, 3 and 4 was 77.8%, 100.0%, 62.4%, 80.2% and 99.7%, respectively. The average recall of 0.0% and average specificity of 100.0% for class 1 (mild DR) were obtained because the CNN model never predicted this class.
Diabetisk näthinnesjukdom (DR) är en komplikation av diabetes och är en sjukdom som påverkar ögonen. Det är en av de största orsakerna till blindhet i västvärlden. Allt eftersom antalet människor med diabetes ökar, ökar även antalet med diabetisk näthinnesjukdom. Detta ställer högre krav på att bättre och effektivare resurser utvecklas för att kunna upptäcka sjukdomen i ett tidigt stadie, vilket är en förutsättning för att förhindra vidareutveckling av sjukdomen som i slutändan kan resultera i blindhet, och att vidare behandling av sjukdomen effektiviseras. Här spelar datorstödd diagnostik en viktig roll. Syftet med denna studie är att undersöka hur ett faltningsnätverk, tillsammans med överföringsinformation, kan prestera när det tränas för multiklass gradering av diabetisk näthinnesjukdom. För att göra detta användes ett färdigbyggt och färdigtränat faltningsnätverk, byggt i Keras, för att fortsättningsvis tränas och finjusteras i Tensorflow på ett 5-klassigt DR dataset. Totalt tjugo träningssessioner genomfördes och noggrannhet, sensitivitet och specificitet utvärderades i varje sådan session. Resultat visar att de uppnådda noggranheterna låg inom intervallet 35% till 48.5%. Den genomsnittliga testsensitiviteten för klass 0, 1, 2, 3 och 4 var 59.7%, 0.0%, 51.0%, 38.7% respektive 0.8%. Vidare uppnåddes en genomsnittlig testspecificitet för klass 1, 2, 3 och 4 på 77.8%, 100.0%, 62.4%, 80.2% respektive 99.7%. Den genomsnittliga sensitiviteten på 0.0% samt den genomsnittliga specificiteten på 100.0% för klass 1 (mild DR) erhölls eftersom CNN modellen aldrig förutsåg denna klass.
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2

Xue, Yongjian. "Dynamic Transfer Learning for One-class Classification : a Multi-task Learning Approach." Thesis, Troyes, 2018. http://www.theses.fr/2018TROY0006.

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Le but de cette thèse est de minimiser la perte de performance d'un système de détection lorsqu'il rencontre un changement de distribution de données à la suite d’un événement connu (maintenance, ajout de capteur etc.). L'idée est d'utiliser l'approche d'apprentissage par transfert pour exploiter l'information apprise avant l’événement pour adapter le détecteur au système modifié. Un modèle d'apprentissage multitâche est proposé pour résoudre ce problème. Il utilise un paramètre pour équilibrer la quantité d'informations apportées par l'ancien système par rapport au nouveau. Ce modèle est formalisé de manière à pouvoir être résolu par un SVM mono-classe classique avec une matrice de noyau spécifique. Pour sélectionner le paramètre de contrôle, une méthode qui calcule les solutions pour toutes les valeurs du paramètre introduit et un critère de sélection de sa valeur optimale sont proposés. Les expériences menées dans le cas de changement de distribution et d’ajout de capteurs montrent que ce modèle permet une transition en douceur de l'ancien système vers le nouveau. De plus, comme le modèle proposé peut être formulé comme un SVM mono-classe classique, des algorithmes d'apprentissage en ligne pour SVM mono-classe sont étudiés dans le but d'obtenir un taux de fausses alarmes stable au cours de la phase de transition. Ils peuvent être appliqués directement à l'apprentissage en ligne du modèle proposé
The aim of this thesis is to minimize the performance loss of a one-class detection system when it encounters a data distribution change. The idea is to use transfer learning approach to transfer learned information from related old task to the new one. According to the practical applications, we divide this transfer learning problem into two parts, one part is the transfer learning in homogenous space and the other part is in heterogeneous space. A multi-task learning model is proposed to solve the above problem; it uses one parameter to balance the amount of information brought by the old task versus the new task. This model is formalized so that it can be solved by classical one-class SVM except with a different kernel matrix. To select the control parameter, a kernel path solution method is proposed. It computes all the solutions along that introduced parameter and criteria are proposed to choose the corresponding optimal solution at given number of new samples. Experiments show that this model can give a smooth transition from the old detection system to the new one whenever it encounters a data distribution change. Moreover, as the proposed model can be solved by classical one-class SVM, online learning algorithms for one-class SVM are studied later in the purpose of getting a constant false alarm rate. It can be applied to the online learning of the proposed model directly
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Severan, Debra Devillier. "A Qualitative Approach to Transfer of Training for Managers in Leadership Development." ScholarWorks, 2019. https://scholarworks.waldenu.edu/dissertations/7570.

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Learning and development (L&D) professionals in a Fortune 500 company were unable to determine whether managers who completed leadership development courses were transferring what they learned to their work practices. The purpose of this qualitative single instrumental case study was to uncover the factors that accelerated or impeded the transfer of training for employees in the workplace. The conceptual framework was social cognitive learning theory with emphasis on the triadic reciprocal causation model. Guiding questions were used to explore 2 areas: (a) how managers described their preparedness to transfer the training to their jobs, and (b) how managers described their perceptions of the transfer of training from the concepts learned in class to practical job application. Data were collected through one-on-one online interviews with 12 managers who had completed a leadership development course. Data analysis included organizing the data; reading them multiple times; developing codes, categories, and themes; and interpreting the findings. Over 90% of the participants stated that they felt prepared to implement the training after the class. However, only half reported a moderate to high level of confidence incorporating the training into their work. A 3-day professional development project was designed to heighten awareness of the benefits of advancing the transference and application of training with a strong focus on driving social change in the workplace through improved interpersonal skills between managers and their direct reports.
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Wu, Michael. "Transfer Learning Approach to Powder Bed Fusion Additive Manufacturing Defect Detection." DigitalCommons@CalPoly, 2021. https://digitalcommons.calpoly.edu/theses/2324.

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Laser powder bed fusion (LPBF) remains a predominately open-loop additive manufacturing process with minimal in-situ quality and process control. Some machines feature optical monitoring systems but lack automated analytical capabilities for real-time defect detection. Recent advances in machine learning (ML) and convolutional neural networks (CNN) present compelling solutions to analyze images in real-time and to develop in-situ monitoring. Approximately 30,000 selective laser melting (SLM) build images from 31 previous builds are gathered and labeled as either “okay” or “defect”. Then, 14 open-sourced CNN were trained using transfer learning to classify the SLM build images. These models were evaluated by F1 score and down selected to the top 3 models. The top 3 models were then retrained and evaluated using Dietterich’s 5x2 cross-validation and compared with pairwise student t-tests. The pairwise t-test results show no statistically significant difference in performance between VGG- 19, Xception, and InceptionResNet. All models are strong candidates for future development and refinement. Additional work addresses the entire model development process and establishes a foundation for future work. Collaborations with computer science students has produced an image pre-processing program to enhance as-taken SLM images. Other outcomes include initial work to overlay CAD layer images and preliminary hardware integration plan for the SLM machine. The results from this work have demonstrated the potential of an optical layer-wise image defect detection system when paired with a CNN.
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Węckowska, Dagmara Maria. "Learning the ropes of the commercialisation of academic research : a practice-based approach to learning in knowledge transfer offices." Thesis, University of Sussex, 2013. http://sro.sussex.ac.uk/id/eprint/45183/.

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Exploitation of the knowledge generated by university research can bring social and economic benefits; thus, knowledge transfer between universities and industry is an important aspect of public policy. In many countries, including the United Kingdom (UK), universities have been developing the capacity to support the commercialisation of publicly funded research, typically by setting up centralised Knowledge Transfer Offices (KTOs). Previous studies have revealed that KTOs need a wide range of abilities to support the commercialisation of academic research, but our understanding of how these abilities are developed and have evolved over time remains limited. In order to address this identified gap in the literature, this thesis examines the questions: What do KTOs learn? How do KTOs learn? and Why do KTOs learn? To address these questions, the thesis adopts a practice-based view of organisational knowledge and learning. The conceptual framework developed to investigate learning by KTOs assumes that their commercialisation practice is learnt through the interactions of their staff within communities of practice, within networks of practice and across communities of practice, and that this learning can be initiated by KTO staff or by targeted strategies devised by the KTO and the university's management. This conceptual framework guides the case studies of six purposefully selected KTOs in the UK. The selection of KTOs is aimed at identifying cases with different learning patterns in order to maximise insights gained from cross-case comparisons as well as at literal replication of the findings. The analysis is based on data collected from semi-structured interviews with key staff in selected KTOs and on information from relevant documents, and follows the ‘explanation building' technique (Yin, 2009). The findings reveal that KTOs tend to develop one of two types of commercialisation practice – each of which is based on different implicit assumptions about generating science-based innovation, and associated with a different set of abilities. Moreover, the findings demonstrate the processes by which changes in practice come about, highlighting the interplay between situated learning and strategic practices of management. The results presented address the aforementioned gap in the literature on university-industry knowledge transfer and contribute to the developing situated learning theory by shedding light on how incremental and more radical changes in practice emerge. The findings should be useful to policy-makers who seek to support universities to build capability for knowledge transfer.
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Allworth, James William. "A Machine Learning Approach to Space Debris Characterisation and Classification using Ground Based Optical Observations." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29185.

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Space debris is becoming an increasingly prevalent issue through a combination of the recent rise in the accessibility of space and the difficulty in actively removing space debris from orbit. The high relative velocity between orbital objects and the difficulty in maintaining their state, results in space debris posing a significant collision risk to active satellites. Risk mitigation strategies rely on space situational awareness, which focuses on tracking space objects and predicting their future states to then inform satellite operators of potential future conjunctions. However, the accuracy of these predictions is limited by a lack of knowledge about the physical characteristics of space debris. This thesis outlines a data-driven approach to space object characterisation through the application of neural networks to light curves extracted from non-resolved ground based optical observations. A light curve is a temporal history of an object's brightness, which contains information about its physical characteristics. Neural networks are more effective when they are trained on a large well-labelled dataset, enabling the complex non-linear relationships within the data to be learned. This has been a limiting factor when applying deep learning to light curve based object classification as light curves are difficult to obtain and label, so real world datasets remain small. This thesis presents simulation-based transfer learning as a method for overcoming this limitation and improving shape classification performance on real world light curve datasets. To further improve performance on challenging cases, a framework for effectively combining multiple light curve observations of a single object is also developed. Finally, a targeted scheduling process has been developed to utilise this framework efficiently, using uncertainty quantification of the neural network output, to selectively prioritise the re-observation of challenging cases and thus reduce misclassifications.
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Lopez, Lira Arjona Alfonso. "Inter-firm knowledge transfer and experiential learning| A business sustainability approach on SME's absorptive capacity." Thesis, Instituto Tecnologico y de Estudios Superiores de Monterrey (Mexico), 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3570884.

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In emerging economies, Small and Medium-Sized Enterprises (SMEs) are threatened by continuous political and economic changes. In such uncertain environments, knowledge is the distinctive factor for the achievement of a competitive advantage. However, limited funds and pressure from competitors force SMEs to seek for external sources of knowledge.

The Multinational Corporation (MNC) represents an alternative for business sustainability within the value chain, including both suppliers and clients. In the aim for pursuing such endeavor, a conceptual framework including inter-firm knowledge transfer processes from the MNC and experiential learning enhanced by the Academia is explored.

In sum, this dissertation is intended to examine the MNC’s and Academia’s role on the procurement of SMEs’ business sustainability through inter-firm knowledge transfer and experiential learning, in terms of absorptive capacity. More specifically, the impact of technical and technological knowledge transferred from the MNC on one side; and reflective learning on managerial skills and business vision from the Academia on the other side, is analyzed through SMEs’ absorptive capacity. Regarding business sustainability, the effect of the application of newly absorbed knowledge is analyzed in terms of SMEs’ selected indicators for business improvements. As a complement, a qualitative study is included in order to provide support for findings hereby obtained.

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Söderdahl, Fabian. "A Cross-Validation Approach to Knowledge Transfer for SVM Models in the Learning Using Privileged Information Paradigm." Thesis, Uppsala universitet, Statistiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385378.

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The learning using privileged information paradigm has allowed support vector machine models to incorporate privileged information, variables available in the training set but not in the test set, to improve predictive ability. The consequent introduction of the knowledge transfer method has enabled a practical application of support vector machine models utilizing privileged information. This thesis describes a modified knowledge transfer method inspired by cross-validation, which unlike the current standard knowledge transfer method does not create the knowledge transfer function and the approximated privileged features used in the support vector machines on the same observations. The modified method, the robust knowledge transfer, is described and evaluated versus the standard knowledge transfer method and is shown to be able to improve the predictive performance of the support vector machines for both binary classification and regression.
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Kraft, Erin. "Planning, Promoting and Assessing Social Learning in Sport: A Landscapes of Practice Approach." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42009.

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In recent years, there has been an increase in women securing leadership positions across Canadian sport. However, when compared with their male counterparts, there continues to be an imbalance of women in these roles. The purpose of this doctoral dissertation was to evaluate a social learning initiative implemented in the province of Alberta to address these existing gender disparities by increasing gender equity, leadership development/diversity, and knowledge transfer across sport systems. The Alberta Women in Sport Leadership Impact Program (AWiSL) was framed using Wenger’s (1998) concept Communities of Practice and consisted of 12 sport leaders (from various PSOs, clubs, and other sport organizations) and six mentors (with leadership expertise). Each sport leader planned and implemented a project in their home sport organizations to support the increase of gender equity and leadership development/diversity. The mentors were responsible for supporting the sport leaders in achieving their project goals and facilitating leadership development opportunities to inspire growth in the sport leaders. Accordingly, an evaluation was conducted using the Value Creation Framework (Wenger-Trayner et al., 2011) to examine the perceived value of participating in this social learning initiative. Data were collected over a year and a half period, from the 18 members who made up the AWiSL group and other important stakeholders. The data included in-depth interviews, informal conversations, observations, surveys, and collecting organizational documents resulting in over 700 pages of transcribed data. The findings are presented in four articles and an additional findings section. The first article focuses on one of the sport leader’s projects which aimed to foster a collaborative women-only training program for 10 women to become certified coach developers. The second article examines the development of the AWiSL mentors’ social learning leadership capabilities during their first attempt at facilitating a CoP to promote gender equity and leadership development/diversity, through an action learning approach. The third article delves into the sport leaders’ perceptions of their leadership skill development through their participation in the two and a half year social learning initiative, specifically a CoP of femininity. Finally, the fourth article highlights the 12 sport leaders’ projects to examine the impacts of the AWiSL in terms of moving gender equity forward across the province. The additional findings section touches on the knowledge transfer outcome of the AWiSL, including the development of a how-to model for organizations wishing to implement a similar initiative and the overall perceived value of this initiative. The dissertation is concluded with a general discussion highlighting the theoretical contributions and practical implications, along with future recommendations for research.
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10

Craig, Malcolm. "Factors that influence the receptivity to fault diagnostic learning when a systems approach is applied : a technical transfer study." Thesis, Cranfield University, 1992. http://hdl.handle.net/1826/4153.

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This thesis is concerned with receptivity and response encountered at different levels within organisations when a novel approach to the learning of fault diagnosis skills is introduced. Essentially, the work involved the transfer of a learning technology from research and development on the one hand to the workplace on the other. With only a few exceptions, previous research had taken a highly focused, machinecentred view of fault diagnosis. The same view has been adopted towards the limited range of training that is currently offered in this subject. The overall aim here was to introduce a holistic approach by viewing fault diagnosis as a social process that is conducted within a technical context. To do this, account had to be taken of the complex interactions found between a number of disciplines such as, design, production, quality assurance, buying, maintenance and management. The learning technology that served as a vehicle for the transfer of this systems approach was a series of open learning modules. The modules were produced as part of the project. The methodology was based upon an inductive approach that involved the interpretation of qualitative data; this was done using a triangulation of research methods: case studies, critical incidents, and survey questionnaire. The sample, of both large and small organisations, was designed to provide a mix of different types of manufacturing and service industries. In each case, the practice of fault diagnosis skills continues to be a critical influence upon business performance. Different factors arose at different levels within each organisation, and betweenorganisation factor differences are also identified. Apart from the production of open learning material, the contribution made to the subject area is of new insights into the mechanism used for technology transfer within companies, and the identification of factors that either facilitate or hinder transfer of this kind. There is also a contribution to the debate about how the theory of systems thinking can be applied in a prescriptive way as opposed to the more common descriptive delivery. Recommendations are made for further developmento f the learning technology.
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Книги з теми "TRANSFER LEARNING APPROACH"

1

Hunt, John P. Strategic processing underlying transfer of learning: a modelling approach. Eugene: Microform Publications, College of Human development and performance, University of Oregon, 1989.

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Northern Ireland Credit Accumulation and Transfer System (Project). Designing learning programmes: A credit-based approach : a practical manual. Belfast: NICATS, 2002.

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3

Collins, Gregg. Learning strategic concepts in competitive planning: An explanation-based approach to the transfer of knowledge across domains. Urbana, IL: Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1988.

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Schneider, Lidz Carol, ed. Dynamic assessment: An interactional approach to evaluating learning potential. New York: Guilford Press, 1987.

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5

Jarvis, Scott. Approaching language transfer through text classification: Explorations in the detection-based approach. Bristol: Multilingual Matters, 2012.

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6

Fernandez, Maria. Farmers leading change: A learning approach to involving smallholders in the revitalization of their production systems. Kampala, Uganda: NARO, 2002.

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7

Peter, Lusembo, ed. Farmers leading change: A learning approach to involving smallholders in the revitalization of their production systems. Kampala, Uganda: NARO, 2002.

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8

Suzanne, Jacob, and Hébert Danièle, eds. Pour guider la métacognition. Sainte-Foy: Presses de l'Université du Québec, 2000.

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9

Fogarty, Robin. Patterns for thinking, patterns for transfer: A cooperative team approach for critical and creative thinking in the classroom. 2nd ed. Palatine, Ill: IRI/SkyLight Educational Training and Publishing Inc., 1993.

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Fogarty, Robin. Patterns for thinking, patterns for transfer: A cooperative team approach for critical and creative thinking in the classroom. 4th ed. Palatine, Ill. (200 E. Wood St., Suite 250, Palatine 60067): IRI Group, 1989.

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Частини книг з теми "TRANSFER LEARNING APPROACH"

1

Zhang, Linrui, Yisheng Zhou, Tatiana Erekhinskaya, and Dan Moldovan. "Emoji Prediction: A Transfer Learning Approach." In Advances in Intelligent Systems and Computing, 864–72. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39442-4_65.

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Khandelwal, Shekhar, and Rik Das. "Transfer Learning Approach in Phishing Detection." In Phishing Detection Using Content-Based Image Classification, 37–46. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003217381-4.

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Brown, Christopher J., and Diane Morrad. "SDL Approach to University-Small Business Learning: Mapping the Learning Journey." In Innovation through Knowledge Transfer 2012, 233–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34219-6_26.

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Agarwal, Nancy, Tuğçe Ünlü, Mudasir Ahmad Wani, and Patrick Bours. "Predatory Conversation Detection Using Transfer Learning Approach." In Machine Learning, Optimization, and Data Science, 488–99. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95467-3_35.

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Li, Wei, Shuai Ding, Yi Chen, and Shanlin Yang. "A Transfer Learning Approach for Credit Scoring." In Advances in Intelligent Systems and Computing, 64–73. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98776-7_8.

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Kajdanowicz, Tomasz, Slawomir Plamowski, Przemyslaw Kazienko, and Wojciech Indyk. "Transfer Learning Approach to Debt Portfolio Appraisal." In Lecture Notes in Computer Science, 46–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28931-6_5.

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Abou Baker, Nermeen, Jonas Stehr, and Uwe Handmann. "Transfer Learning Approach Towards a Smarter Recycling." In Lecture Notes in Computer Science, 685–96. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15919-0_57.

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Vries, Marc J. de. "Transfer in Technology Through a Concept-Context Approach." In Transfer, Transitions and Transformations of Learning, 13–22. Rotterdam: SensePublishers, 2013. http://dx.doi.org/10.1007/978-94-6209-437-6_2.

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Krishnamoorthy, Sujatha. "Transfer Learning Architecture Approach for Smart Transportation System." In Communications in Computer and Information Science, 162–81. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09469-9_15.

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Yenneti, Shanmukha Sai Sumanth, Riti Kushwaha, Smita Naval, and Gaurav Singal. "Leading Athlete Following UAV Using Transfer Learning Approach." In Communications in Computer and Information Science, 424–33. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0401-0_33.

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Тези доповідей конференцій з теми "TRANSFER LEARNING APPROACH"

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Kicki, Piotr, and Krzysztof Walas. "Friction from Reflectance: Transfer Learning Approach." In 2019 4th International Conference on Robotics and Automation Engineering (ICRAE). IEEE, 2019. http://dx.doi.org/10.1109/icrae48301.2019.9043793.

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Witherow, Megan, Manar D. Samad, and Khan M. Iftekharuddin. "Transfer learning approach to multiclass classification of child facial expressions." In Applications of Machine Learning, edited by Michael E. Zelinski, Tarek M. Taha, Jonathan Howe, Abdul A. Awwal, and Khan M. Iftekharuddin. SPIE, 2019. http://dx.doi.org/10.1117/12.2530397.

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3

Katranji, Mehdi, Etienne Thuillier, Sami Kraiem, Laurent Moalic, and Fouad Hadj Selem. "Mobility data disaggregation: A transfer learning approach." In 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2016. http://dx.doi.org/10.1109/itsc.2016.7795783.

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Upadhyaya, Prashant, Ruchi, and Suniti Dutt. "Transfer Learning Approach for 6G-IoT Applications." In 2022 7th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2022. http://dx.doi.org/10.1109/icces54183.2022.9835931.

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Sakunrasrisuay, Chinapat, Pakarat Musikawan, Anh-Nhat Nguyen, Yanika Kongsorot, Phet Aimtongkham, and Chakchai So-In. "Tomato Maturity Classification: A Transfer Learning Approach." In 2021 25th International Computer Science and Engineering Conference (ICSEC). IEEE, 2021. http://dx.doi.org/10.1109/icsec53205.2021.9684584.

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Singh, Richa, Ashwani Kumar Dubey, and Rajiv Kapoor. "Denoised Autoencoder using DCNN Transfer Learning Approach." In 2022 International Mobile and Embedded Technology Conference (MECON). IEEE, 2022. http://dx.doi.org/10.1109/mecon53876.2022.9751863.

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Sancinetti, Marcelo, Jazmin Vidal, Cyntia Bonomi, and Luciana Ferrer. "A Transfer Learning Approach for Pronunciation Scoring." In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. http://dx.doi.org/10.1109/icassp43922.2022.9747727.

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Koupilová, Zdeňka. "TIPS for active learning approach in distance learning conditions." In DIDACTIC TRANSFER OF PHYSICS KNOWLEDGE THROUGH DISTANCE EDUCATION: DIDFYZ 2021. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0078620.

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Farid, Dewan, Aicha Sekhari, and Ouzrout Yacine. "CLUSTER-BASED KNOWLEDGE TRANSFER APPROACH FOR SMART FARMING." In 12th International Conference on Education and New Learning Technologies. IATED, 2020. http://dx.doi.org/10.21125/edulearn.2020.1867.

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10

Wu, Peilun, Hui Guo, and Richard Buckland. "A Transfer Learning Approach for Network Intrusion Detection." In 2019 IEEE 4th International Conference on Big Data Analytics (ICBDA). IEEE, 2019. http://dx.doi.org/10.1109/icbda.2019.8713213.

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Звіти організацій з теми "TRANSFER LEARNING APPROACH"

1

Roschelle, Jeremy, Britte Haugan Cheng, Nicola Hodkowski, Lina Haldar, and Julie Neisler. Transfer for Future Learning of Fractions within Cignition’s Microtutoring Approach. Digital Promise, April 2020. http://dx.doi.org/10.51388/20.500.12265/95.

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In this exploratory research project, our team’s goal was to design and begin validation of a measurement approach that could provide indication of a student’s ability to transfer their mathematics understanding to future, more advanced mathematical topics. Assessing transfer of learning in mathematics and other topics is an enduring challenge. We sought to invent and validate an approach to transfer that would be relevant to improving Cignition’s product, would leverage Cignition’s use of online 1:1 tutoring, and would pioneer an approach that would contribute more broadly to assessment research.
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2

Lintern, G. A Perceptual Learning Approach to Skill Transfer for Manual Control. Fort Belvoir, VA: Defense Technical Information Center, January 1985. http://dx.doi.org/10.21236/ada154964.

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Lytvynova, Svitlana, Oleksandr Burov, Nataliia Demeshkant, Viacheslav Osadchyi, Сергій Олексійович Семеріков, Світлана Григорівна Литвинова, Олександр Юрійович Буров, Наталія Андріївна Демешкант, and В'ячеслав Володимирович Осадчий. Proceedings of the VI International Workshop on Professional Retraining and Life-Long Learning using ICT: Person-oriented Approach (3L-Person 2021) co-located with 17th International Conference on ICT in Education, Research, and Industrial Applications: Integration, Harmonization, and Knowledge Transfer (ICTERI 2021), Kherson, Ukraine, October 1, 2021. Криворізький державний педагогічний університет, March 2022. http://dx.doi.org/10.31812/123456789/6988.

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Proceedings of the VI International Workshop on Professional Retraining and Life-Long Learning using ICT: Person-oriented Approach (3L-Person 2021) co-located with 17th International Conference on ICT in Education, Research, and Industrial Applications
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Brizard, Jean-Claude. Breaking With the Past: Embracing Digital Transformation in Education. Digital Promise, April 2023. http://dx.doi.org/10.51388/20.500.12265/176.

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Today's businesses know that driving innovation is integral to succeeding not just in the present, but more importantly in the decades to come. Through the years, the innovation of digital technologies has transformed entire industries. Now it’s time to put those technologies to use and apply that same mentality to transform our schools. We need digital transformation of teaching and learning at scale across the United States. This report examines how the traditional one-size-fits-all approach to teaching and learning compares to more learner-centered, personalized frameworks; why we need to transition to them at scale; and how digital technologies can enable that scaling.
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5

González, Javier, Dante Castillo-Canales, Monserrat Creamer, and Magali Ramos Jarrin. Misalignments and Incoherencies within Ecuador's Education System: How Well Are Key Actors and Public Efforts Aligned towards Better Learning Outcomes? Research on Improving Systems of Education (RISE), March 2023. http://dx.doi.org/10.35489/bsg-rise-wp_2023/137.

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This study aims to identify the main critical misalignments and inconsistencies nested in the Ecuadorian education system, which inhibit and limit its capacity to transform efforts, resources, and reforms into better learning outcomes for all students. To do so, it uses an innovative methodology developed by the RISE (Research on Improving Systems of Education) programme based on a 'Systems Thinking' perspective. This approach allows the analysis of key actors, their incentives, and interactions, to assess the overall alignment of the system and the existence of barriers that might prevent the system transitioning towards better learning outcomes. This study is based mainly on qualitative methods and information collected in the field through interviews, focus groups and surveys held in the first semester of 2022 in three cities in Ecuador: Quito, Tena, and Guayaquil. In total, more than 50 stakeholders from different regions and levels of the education system actively participated in this effort, targeted towards the identification and discussion of the inconsistencies and critical issues described in this study. The report has five sections that offer a detailed account of the implementation of the RISE diagnostic framework in the Ecuadorian educational system.
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6

Abdula, Andrii I., Halyna A. Baluta, Nadiia P. Kozachenko, and Darja A. Kassim. Peculiarities of using of the Moodle test tools in philosophy teaching. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3867.

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The paper considers the role of philosophy and philosophical disciplines as the means of forming general cultural competences, in particular, in the development of critical thinking. The article emphasizes that the process of forming over-subject and soft skills, which, as a rule, include also critical thinking, gets much more complicated under the conditions of the reduction in the volume of philosophical courses. The paper grounds that one of the ways to “return” philosophy to educational programmes can be the implementation of training, using the e-learning environment, especially Moodle. In addition, authors point to the expediency of using this system and, in general, e-learning as an instrument for collaborating students to the world’s educational community and for developing their lifelong learning skills. The article specifies the features of providing electronic support in philosophy teaching, to which the following belongs: the difficulty of parametrizing the learning outcomes; plurality of approaches; communicative philosophy. The paper highlights the types of activities that can be implemented by tools of Moodle. The use of the following Moodle test tasks is considered as an example: test control in the flipped class, control of work with primary sources, control of self-study, test implementation of interim thematic control. The authors conclude that the Moodle system can be used as a tools of online support for the philosophy course, but it is impossible to transfer to the virtual space all the study of this discipline, because it has a significant worldview load. Forms of training, directly related to communication, are integral part of the methodology of teaching philosophy as philosophy itself is discursive, dialogical, communicative and pluralistic. Nevertheless, taking into account features of the discipline, it is possible to provide not only the evaluation function of the test control, but also to realize a number of educational functions: updating the basic knowledge, memorization, activating the cognitive interest, developing the ability to reason and the simpler ones but not less important, – the skill of getting information and familiarization with it.
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7

Prisacariu, Roxana. Swiss immigrants’ integration policy as inspiration for the Romanian Roma inclusion strategy. Fribourg (Switzerland): IFF, 2015. http://dx.doi.org/10.51363/unifr.diff.2015.05.

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While the knowledge on immigrants’ integration consolidated through the last 50 years, the Roma studies and the research on the Roma inclusion seems at the beginning. The purpose of this research was to assess if and to what extent the Swiss experience in immigrants’ integration may inspire an efficient approach to Roma inclusion in the Romanian society. After highlighting conceptual vagueness, resemblance and difference in the overall social status of Romanian Roma and immigrants in Switzerland and official approaches to the integration or inclusion of each, the research concludes that the Romanian policy on Roma inclusion presumably can be better anchored in the integration conceptual framework and benefit from immigrants’ integration experience. The Romanian choice for framing its Roma policy as ‘inclusion’ rather than for ‘integration’ may be appropriate as it applies to a historic minority of citizens needing social justice. The use of an immigration integration policy as model for a Roma inclusion strategy is limited due to the stronger legit-imation of historic minorities for shared-ownership of public decision-making. That is the Swiss example of immigrants’ integration could only serve Romania as a minimum standard for its Roma inclusion strategy. It can benefit from the Swiss experience on immigrant's integration policy in terms of conception, coordination, monitoring and transparency may be beneficial, while the Roma political participation may find inspiration from the Swiss linguistic communities’ participatory mechanisms. The on-going reciprocal learning process connecting academia and public authorities able to transform science into action and experience in knowledge may inspire the Romanian authorities.
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Ruvinsky, Alicia, Timothy Garton, Daniel Chausse, Rajeev Agrawal, Harland Yu, and Ernest Miller. Accelerating the tactical decision process with High-Performance Computing (HPC) on the edge : motivation, framework, and use cases. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/42169.

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Managing the ever-growing volume and velocity of data across the battlefield is a critical problem for warfighters. Solving this problem will require a fundamental change in how battlefield analyses are performed. A new approach to making decisions on the battlefield will eliminate data transport delays by moving the analytical capabilities closer to data sources. Decision cycles depend on the speed at which data can be captured and converted to actionable information for decision making. Real-time situational awareness is achieved by locating computational assets at the tactical edge. Accelerating the tactical decision process leverages capabilities in three technology areas: (1) High-Performance Computing (HPC), (2) Machine Learning (ML), and (3) Internet of Things (IoT). Exploiting these areas can reduce network traffic and shorten the time required to transform data into actionable information. Faster decision cycles may revolutionize battlefield operations. Presented is an overview of an artificial intelligence (AI) system design for near-real-time analytics in a tactical operational environment executing on co-located, mobile HPC hardware. The report contains the following sections, (1) an introduction describing motivation, background, and state of technology, (2) descriptions of tactical decision process leveraging HPC problem definition and use case, and (3) HPC tactical data analytics framework design enabling data to decisions.
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9

Kharkivska, Alla A., Liudmyla V. Shtefan, Muntasir Alsadoon, and Aleksandr D. Uchitel. Technology of forming future journalists' social information competence in Iraq based on the use of a dynamic pedagogical site. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3853.

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The article reveals scientific approaches to substantiating and developing technology to form social information competence of future Iraqi journalists based on using a dynamic pedagogical site. After pre-interviewing students of the Journalism Faculty at Al-Imam Al-Kadhim University College for Islamic Sciences in Baghdad, the authors came to the conclusion there are issues on defining the essence of social information competences. It is established that the majority of respondents do not feel satisfied with the conditions for forming these competences in the education institutions. At the same time, there were also positive trends as most future journalists recognized the importance of these professional competences for their professional development and had a desire to attend additional courses, including distance learning ones. Subsequently, the authors focused on social information competence of future journalists, which is a key issue according to European requirements. The authors describe the essence of this competence as an integrative quality of personality, which characterizes an ability to select, transform information and allows to organize effective professional communication on the basis of the use of modern communicative technologies in the process of individual or team work. Based on the analysis of literary sources, its components are determined: motivational, cognitive, operational and personal. The researchers came to the conclusion that it is necessary to develop a technology for forming social information competence of future journalists based on the use of modern information technologies. The necessity of technology implementation through the preparatory, motivational, operational and diagnostic correction stages was substantiated and its model was developed. The authors found that the main means of technology implementation should be a dynamic pedagogical site, which, unlike static, allows to expand technical possibilities by using such applications as photo galleries, RSS modules, forums, etc. Technically, it can be created using Site builder. Further research will be aimed at improving the structure of the dynamic pedagogical site of the developed technology.
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10

Baliki, Ghassan, Dorothee Weiffen, Melodie Al Daccache, Aysegül Kayaoglu, Lara Sujud, Hadi Jaafar, Hala Ghattas, and Tilman Brück. Seeds for recovery: The long-term impacts of a complex agricultural intervention on welfare, behaviour and stability in Syria (SEEDS). Centre for Excellence and Development Impact and Learning (CEDIL), April 2023. http://dx.doi.org/10.51744/crpp7.

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There is scarce evidence on whether and how assistance in humanitarian emergencies and conflict settings impacts household well-being and behaviour. Conducting rigorous impact evaluations in such settings poses multiple challenges in design and data collection. In SEEDS, we evaluate the impact of a complex large-scale multi-arm agricultural intervention on productivity, food security, and resilience in the context of an on-going humanitarian crisis in Syria. Specifically, we identify the causal impacts of agricultural asset transfers over various time horizons (the short-, medium-, and long-run), and across different conditions and subgroups (gender and conflict intensity) at the household-level. We evaluate the effectiveness of irrigation rehabilitation separately at the community-level. We use and combine various data sources, including a unique survey panel dataset collected over a period of four years from multiple governorates in Syria, satellite remote-sensing data, and publicly available violent conflict incidence and weather data. Our findings from using cutting-edge machine and deep learning approaches together with innovative balancing and analytical methods can be summarised as follows: For average treatment effects at the household-level, we find that the provision of agricultural asset support leads to significant improvements in food security in the short- and long-term, three years after the intervention. The positive and significant effect on food security is driven mainly by the increased consumption of healthy food items such as vegetables. In the long-run, livestock support reduces the use of harmful coping strategies households employ to deal with food shortages. Interestingly, we find that households who received vegetable kits are not just less likely to sell their productive assets in the long-term but also are less likely to marry off their young daughters or send their children to work. Overall, we find that both agricultural and livestock asset support is key to improving households’ resilience in the long-term. The irrigation rehabilitation interventions at the community-level positively affected agricultural productivity compared to the pre-intervention and pre-conflict periods. However, these effects were only significantly pronounced in the spring season. As for the heterogeneity analysis, we find that female-headed households benefit remarkably more in terms of food security in the medium-term compared to male-headed families. Moreover, households residing in areas that are moderately affected by violent conflict show stronger food security improvements compared to households from peaceful or conflict-intense settings. Overall, we draw three overarching lessons from our findings in SEEDS: First, agricultural support in protracted conflict settings effectively improves the long-term welfare and resilience of vulnerable households. In fact, the presence of an ongoing humanitarian operation acts as a social safety net if circumstances deteriorate suddenly. Second, not all interventions are equally effective, and not all households equally benefit, underscoring the need to design and implement inclusive context-specific interventions with detailed targeting. Third, methodologically, using multiple remote data sources and machine learning methods help overcome challenges in conducting rigorous impact evaluations in hard-to-reach humanitarian emergency settings.
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