Relevant bibliographies by topics / Cross-learning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Cross-learning'

Author: Grafiati
Published: 7 July 2024
Last updated: 31 July 2025

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Journal articles on the topic "Cross-learning"

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Abarghooei, Majid. "Designing a Cross-Platform Mobile Learning System." Lecture Notes on Software Engineering 3, no. 3 (2015): 195–98. http://dx.doi.org/10.7763/lnse.2015.v3.189.

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Chok, S. "Cross organisational learning." BMJ 322, no. 7293 (2001): 2. http://dx.doi.org/10.1136/bmj.322.7293.s2-7293.

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Colonnelli, Iacopo, Robert Birke, Giulio Malenza, et al. "Cross-Facility Federated Learning." Procedia Computer Science 240 (2024): 3–12. http://dx.doi.org/10.1016/j.procs.2024.07.003.

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Newell, Sue. "Enhancing Cross-Project Learning." Engineering Management Journal 16, no. 1 (2004): 12–20. http://dx.doi.org/10.1080/10429247.2004.11415234.

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Petersen, Maya L., Annette M. Molinaro, Sandra E. Sinisi, and Mark J. van der Laan. "Cross-validated bagged learning." Journal of Multivariate Analysis 98, no. 9 (2007): 1693–704. http://dx.doi.org/10.1016/j.jmva.2007.07.004.

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Nayan, Surina, Hariharan N. Krishnasamy, and Latisha Asmaak Shafie. "A Cross-National Study of Motivation in Language Learning." International Journal of Information and Education Technology 4, no. 2 (2014): 194–97. http://dx.doi.org/10.7763/ijiet.2014.v4.397.

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Nie, Weizhi, Anan Liu, Wenhui Li, and Yuting Su. "Cross-view action recognition by cross-domain learning." Image and Vision Computing 55 (November 2016): 109–18. http://dx.doi.org/10.1016/j.imavis.2016.04.011.

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Eliawati, Titim. "CROSS CULTURAL UNDERSTANDING LEARNING METHOD." Journal MELT (Medium for English Language Teaching) 3, no. 1 (2019): 17. http://dx.doi.org/10.22303/melt.3.1.2018.14-26.

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Cross cultural understanding is one of the courses that require lecturers' creativity in their teaching in the classroom. Students difficulties in understanding cross cultural until they can feel the cultural confusion experienced by a stranger who enters a region that has a different culture with him/ her. The purpose of this study is to get the best method used in course learning cross cultural understanding. The method used is research literature by comparing and analyzing some cross-cultural understanding of learning methods in several past studies. The results of this study are 1) The use
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Han, Pi-Chi, and John A. Henschke. "Cross-Cultural Learning and Mentoring." International Journal of Adult Vocational Education and Technology 3, no. 3 (2012): 26–36. http://dx.doi.org/10.4018/javet.2012070103.

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Dr. Malcolm Shepherd Knowles popularized andragogy as the theory of adult learning and was referred to as the Father of Adult Education in the United States (US). As his doctoral students, the authors had extensive personal contacts with him. This paper utilizes the method of autoethnography to explore how cross-cultural learning and cross-cultural mentoring facilitate transformative learning with the development of intercultural competencies for sojourners when they interact with a significant human being in cross-cultural settings.
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Bonometti, Stefano. "Learning in Cross-Media Environment." International Journal of Web-Based Learning and Teaching Technologies 12, no. 4 (2017): 48–57. http://dx.doi.org/10.4018/ijwltt.2017100105.

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The aim of this paper to reflect on the definition of a cross-media learning environment by analyzing two training approaches to the professional development of teachers. The first approach centers around curricular internships as training for future teachers, the second focuses on professional development for teachers in service. The aim of the author's analysis was to identify the factors that contribute to overcoming the 'real' vs. 'online' and 'theory' vs. “practice” gap, opting for an integrated cross-media learning environment.
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Dissertations / Theses on the topic "Cross-learning"

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Zhang, Li. "Cross-view learning." Thesis, Queen Mary, University of London, 2018. http://qmro.qmul.ac.uk/xmlui/handle/123456789/43185.

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Key to achieving more efficient machine intelligence is the capability to analysing and understanding data across different views - which can be camera views or modality views (such as visual and textual). One generic learning paradigm for automated understanding data from different views called cross-view learning which includes cross-view matching, cross-view fusion and cross-view generation. Specifically, this thesis investigates two of them, cross-view matching and cross-view generation, by developing new methods for addressing the following specific computer vision problems. The first pro
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Si, Si, and 斯思. "Cross-domain subspace learning." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B44912912.

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Hjelm, Hans. "Cross-language Ontology Learning : Incorporating and Exploiting Cross-language Data in the Ontology Learning Process." Doctoral thesis, Stockholms universitet, Institutionen för lingvistik, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-8414.

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An ontology is a knowledge-representation structure, where words, terms or concepts are defined by their mutual hierarchical relations. Ontologies are becoming ever more prevalent in the world of natural language processing, where we currently see a tendency towards using semantics for solving a variety of tasks, particularly tasks related to information access. Ontologies, taxonomies and thesauri (all related notions) are also used in various variants by humans, to standardize business transactions or for finding conceptual relations between terms in, e.g., the medical domain. The acquisition
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Zhu, Xiaodan. "On Cross-Series Machine Learning Models." W&M ScholarWorks, 2020. https://scholarworks.wm.edu/etd/1616444550.

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Sparse high dimensional time series are common in industry, such as in supply chain demand and retail sales. Accurate and reliable forecasting of high dimensional time series is essential for supply chain planning and business management. In practical applications, sparse high dimensional time series prediction faces three challenges: (1) simple models cannot capture complex patterns, (2) insufficient data prevents us from pursuing more advanced models, and (3) time series in the same dataset may have widely different properties. These challenges prevent the currently prevalent models and theo
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Fohlin, Robert. "A cross-media game environment for learning." Thesis, Linnéuniversitetet, Institutionen för datavetenskap, fysik och matematik, DFM, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-9314.

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Cross-media games are evolving as a new exciting platform for gaming where different devices are used to create a type of game play were a variant of devices, such as mobile phones and laptops are used. This thesis investigates the possibility of merging cross-media games into the domain of Mobile Learning to create a type of mobile learning game where collaboration becomes a vital part of the game play and style enhances collaboration between the users. By studying cross-media games, key features are captured and converted into requirements that are realised in a prototype that enables cross-
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Kodirov, Elyor. "Cross-class transfer learning for visual data." Thesis, Queen Mary, University of London, 2017. http://qmro.qmul.ac.uk/xmlui/handle/123456789/31852.

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Automatic analysis of visual data is a key objective of computer vision research; and performing visual recognition of objects from images is one of the most important steps towards understanding and gaining insights into the visual data. Most existing approaches in the literature for the visual recognition are based on a supervised learning paradigm. Unfortunately, they require a large amount of labelled training data which severely limits their scalability. On the other hand, recognition is instantaneous and effortless for humans. They can recognise a new object without seeing any visual sam
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Porto, Faimison Rodrigues. "Cross-project defect prediction with meta-Learning." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-21032018-163840/.

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Defect prediction models assist tester practitioners on prioritizing the most defect-prone parts of the software. The approach called Cross-Project Defect Prediction (CPDP) refers to the use of known external projects to compose the training set. This approach is useful when the amount of historical defect data of a company to compose the training set is inappropriate or insufficient. Although the principle is attractive, the predictive performance is a limiting factor. In recent years, several methods were proposed aiming at improving the predictive performance of CPDP models. However, to the
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Ciucanu, Radu. "Cross-model queries and schemas : complexity and learning." Thesis, Lille 1, 2015. http://www.theses.fr/2015LIL10056/document.

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La spécification de requêtes est généralement une tâche difficile pour les utilisateurs non-experts. Le problème devient encore plus difficile quand les utilisateurs ont besoin d'interroger des bases de données de grande taille et donc difficiles à visualiser. Le schéma pourrait aider à cette spécification, mais celui-ci manque souvent ou est incomplet quand les données viennent de sources hétérogènes. Dans cette thèse, nous abordons le problème de la spécification de requêtes pour les utilisateurs non-experts. Nous identifions deux approches pour attaquer ce problème : apprendre les requêtes
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Weatherholtz, Kodi. "Perceptual learning of systemic cross-category vowel variation." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1429782580.

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Nerantzi, Chrissi. "Towards a framework for cross-boundary collaborative open learning for cross-institutional academic development." Thesis, Edinburgh Napier University, 2017. http://researchrepository.napier.ac.uk/Output/1025583.

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This phenomenographic study, explores the collaborative open learning experience of academic staff and open learners in cross-institutional academic development settings, and adds to what is known in these settings. It provides new insights for academic developers and course designers about the benefits of crossing boundaries (i.e. open learning) in an academic development context and proposes an alternative model to traditional academic Continuing Professional Development (CPD). It engages academic staff in experiencing novel approaches to learning and teaching and developing as practitioners
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Books on the topic "Cross-learning"

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Korhonen, Vesa. Cross-cultural lifelong learning. Tampere University Press, 2010.

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Agnes, Niyekawa, ed. Design for cross-cultural learning. Intercultural Press, 1987.

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Stephen, Bochner, Brislin Richard W. 1945-, Lonner Walter J, and East-West Culture Learning Institute, eds. Cross-cultural perspectives on learning. University Microfilms International, 1987.

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1937-, Berendt Erich Adalbert, ed. Metaphors for learning: Cross-cultural perspectives. John Benjamins Publishing, 2008.

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Westwood, Peter S. Teaching and learning difficulties: Cross-curricular perspectives. ACER Press, 2006.

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Zhu, Sijia Cynthia, Shu Xie, Yunpeng Ma, and Douglas McDougall, eds. Reciprocal Learning for Cross-Cultural Mathematics Education. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56838-2.

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Davis, Sarah H. Being there: Learning to live cross-culturally. Harvard University Press, 2011.

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Ringbom, Håkan. Cross-linguistic similarity in foreign language learning. Multilingual Matters, 2007.

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Yihong, Fan, ed. Assuring university learning quality: Cross-boundary collaboration. Tapir Academic Press, 2006.

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Odlin, Terence. Language transfer: Cross-linguistic influence in language learning. Cambridge University Press, 1989.

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Book chapters on the topic "Cross-learning"

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Delaney, Laurel J. "Cross-Cultural Learning." In Exporting. Apress, 2013. http://dx.doi.org/10.1007/978-1-4302-5792-9_24.

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Skocaj, Danijel, Ales Leonardis, and Geert-Jan M. Kruijff. "Cross-Modal Learning." In Encyclopedia of the Sciences of Learning. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_239.

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Delaney, Laurel J. "Cross-Cultural Learning." In Exporting. Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2193-8_24.

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Hibbert, Liesel, and Gregory Kerr. "Cross-disciplinary learning." In English as a Language of Learning, Teaching and Inclusivity. Routledge, 2023. http://dx.doi.org/10.4324/9781003382645-9.

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Smith, Andrew D. M., and Kenny Smith. "Cross-Situational Learning." In Encyclopedia of the Sciences of Learning. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_1712.

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Schaffer, Scott P. "Cross-Disciplinary Team Learning." In Handbook of Improving Performance in the Workplace: Selecting and Implementing Performance Interventions. John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470587102.ch25.

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Schaffer, Scott P. "Cross-Disciplinary Team Learning." In Handbook of Improving Performance in the Workplace: Volumes 1-3. John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470592663.ch44.

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Apfelthaler, Gerhard. "Cross-Cultural Learning Styles." In Encyclopedia of the Sciences of Learning. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_455.

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LaFever, Marcella. "Cross-Cultural Learning Styles." In Encyclopedia of Cross-Cultural School Psychology. Springer US, 2010. http://dx.doi.org/10.1007/978-0-387-71799-9_102.

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Yao, Yuan, Zhiyuan Liu, Yankai Lin, and Maosong Sun. "Cross-Modal Representation Learning." In Representation Learning for Natural Language Processing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1600-9_7.

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AbstractCross-modal representation learning is an essential part of representation learning, which aims to learn semantic representations for different modalities including text, audio, image and video, etc., and their connections. In this chapter, we introduce the development of cross-modal representation learning from shallow to deep, and from respective to unified in terms of model architectures and learning mechanisms for different modalities and tasks. After that, we review how cross-modal capabilities can contribute to complex real-world applications.
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Conference papers on the topic "Cross-learning"

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Xiang, Tian-Yu, Xiao-Hu Zhou, Xiao-Liang Xie, et al. "Cross-Modal Motor Representation Learning." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650264.

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FAYE, Bilal, Hanane Azzag, Mustapha Lebbah, and Djamel BOUCHAFFRA. "Lightweight Cross-Modal Representation Learning." In ESANN 2024. Ciaco - i6doc.com, 2024. http://dx.doi.org/10.14428/esann/2024.es2024-96.

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Berger, Alexander H., Laurin Lux, Suprosanna Shit, et al. "Cross-Domain and Cross-Dimension Learning for Image-to-Graph Transformers." In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025. https://doi.org/10.1109/wacv61041.2025.00017.

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Fisch, Shalom M., Richard Lesh, Beth Motoki, Sandra Crespo, and Vincent Melfi. "Cross-platform learning." In the 10th International Conference. ACM Press, 2011. http://dx.doi.org/10.1145/1999030.1999036.

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Fu, Eugene Yujun, Michael Xuelin Huang, Hong Va Leong, and Grace Ngai. "Cross-Species Learning." In MM '18: ACM Multimedia Conference. ACM, 2018. http://dx.doi.org/10.1145/3240508.3240710.

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Kang, Cuicui, Shengcai Liao, Yonghao He, et al. "Cross-Modal Similarity Learning." In CIKM'15: 24th ACM International Conference on Information and Knowledge Management. ACM, 2015. http://dx.doi.org/10.1145/2806416.2806469.

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M’hamdi, Meryem, Xiang Ren, and Jonathan May. "Cross-lingual Continual Learning." In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.acl-long.217.

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Cervino, Juan, Juan Andres Bazerque, Miguel Calvo-Fullana, and Alejandro Ribeiro. "Multi-task Supervised Learning via Cross-learning." In 2021 29th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco54536.2021.9615939.

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Wang, Yabing, Jianfeng Dong, Tianxiang Liang, Minsong Zhang, Rui Cai, and Xun Wang. "Cross-Lingual Cross-Modal Retrieval with Noise-Robust Learning." In MM '22: The 30th ACM International Conference on Multimedia. ACM, 2022. http://dx.doi.org/10.1145/3503161.3548003.

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Johnson, Andrew, Penny Karanasou, Judith Gaspers, and Dietrich Klakow. "Cross-lingual Transfer Learning for." In Proceedings of the 2019 Conference of the North. Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/n19-2023.

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Reports on the topic "Cross-learning"

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Klenk, Matthew, and Ken Forbus. Cross Domain Analogies for Learning Domain Theories. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada471251.

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García Betegón, Mercedes, Eva Perandones Serrano, and Francisco Javier Gayo Santacecilia. Cross-cutting methodologies in learning 3D modeling. Peeref, 2023. http://dx.doi.org/10.54985/peeref.2304p9515916.

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McCloskey, Michael J., Kyle J. Behymer, Elizabeth L. Papautsky, and Aniko Grandjean. Measuring Learning and Development in Cross-Cultural Competence. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada568555.

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Freed, Danielle. K4D Strengthening Cross-sector Learning for Education and FCAS. Institute of Development Studies, 2022. http://dx.doi.org/10.19088/k4d.2022.159.

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This K4D Impact Story shares how the K4D’s Education in Fragile and Conflict Affected States (FCAS) Learning Journey supported the UK Government’s and global partners’ understanding of the subject, facilitated dialogue and learning, and equipped advisors with evidence and ideas that promote ongoing stability. This case study was made possible through the contribution of the K4D Programme team, staff at the Foreign, Commonwealth and Development Office, and others who engaged with the resources and K4D Programme.
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Wang, Zhe, Chao Fan, Xian Min, Shoukun Sun, Xiaogang Ma, and Xiang Que. Cross-scale Urban Land Cover Mapping: Empowering Classification through Transfer Learning and Deep Learning Integration. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317663.

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Thrun, Sebastian, and Joseph O'Sullivan. Clustering Learning Tasks and the Selective Cross-Task Transfer of Knowledge,. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada303253.

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Shevtsiv, Nikita A., and Andrii M. Striuk. Cross platform development vs native development. CEUR Workshop Proceedings, 2021. http://dx.doi.org/10.31812/123456789/4428.

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The paper analyzes the advantages and disadvantages of cross-platform and native mobile application development. The conditions are highlighted in which native and cross-platform development reveal their advantages. These conditions include the project size, work comfort, popularity, relevance. It was concluded that a beginner developer should start learning from native development, and then try cross-platform.
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Aljazzaf, Asma, Michael Wigelsworth, and Juup Stelma. Cultural Adaptations of Social-Emotional Learning in Elementary Education: A Systematic Cross-Cultural Review - Protocol. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2025. https://doi.org/10.37766/inplasy2025.5.0018.

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Bayudan-Dacuycuy, Connie, Paola Ellaine Luzon, and Suzanne Zambrano. Cross-Border Student Mobility and Improvements in the Philippine Tertiary Education Program Relevance and Learning Outcomes. Philippine Institute for Development Studies, 2024. http://dx.doi.org/10.62986/dp2024.21.

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Mainly using qualitative data, this paper analyzes whether and how cross-border student mobility contributes to enhanced Philippine tertiary education programs. The paper recognizes that the intake of international students results from cross-border and home-based internationalization initiatives that mutually reinforce the improvement of programs and learning outcomes. The intake of international students further enhances home-based internationalization initiatives, thus creating a virtuous cycle of effective initiatives, relevant educational products, and increased foreign student enrollment
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Moustafa, Nariman, and Nafisa Waziri. Cross-Country Political Economy Analysis: The status of foundational learning data in Cameroon, Kenya, and Malawi. Unlocking Data, 2025. https://doi.org/10.53832/unlockingdata.1018.

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