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Journal articles on the topic 'Cross-learning'

Author: Grafiati

Published: 7 July 2024

Last updated: 31 July 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Miller, Anne. "Design for cross-cultural learning." International Journal of Intercultural Relations 12, no. 3 (1988): 296–97. http://dx.doi.org/10.1016/0147-1767(88)90022-3.

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12

Bitter, Christian, Jannik Peters, Hasan Tercan, and Tobias Meisen. "Industrial Cross-Robot Transfer Learning." Procedia CIRP 120 (2023): 1297–302. http://dx.doi.org/10.1016/j.procir.2023.09.166.

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13

Providel, Eliana, Marcelo Mendoza, and Mauricio Solar. "Cross-Lingual Cross-Domain Transfer Learning for Rumor Detection." Future Internet 17, no. 7 (2025): 287. https://doi.org/10.3390/fi17070287.

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This study introduces a novel method that merges propagation-based transfer learning with word embeddings for rumor detection. This approach aims to use data from languages with abundant resources to enhance performance in languages with limited availability of annotated corpora in this task. Furthermore, we augment our rumor detection framework with two supplementary tasks—stance classification and bot detection—to reinforce the primary task of rumor detection. Utilizing our proposed multi-task system, which incorporates cascade learning models, we generate several pre-trained models that are

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14

Blythe, Richard A., Kenny Smith, and Andrew D. M. Smith. "Learning Times for Large Lexicons Through Cross-Situational Learning." Cognitive Science 34, no. 4 (2010): 620–42. http://dx.doi.org/10.1111/j.1551-6709.2009.01089.x.

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15

Liu, Yan, Lei Zhang, Pingling Deng, and Zheng He. "Common Subspace Learning via Cross-Domain Extreme Learning Machine." Cognitive Computation 9, no. 4 (2017): 555–63. http://dx.doi.org/10.1007/s12559-017-9473-5.

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16

Ding, Mingyu, Zhe Wang, and Zhiwu Lu. "Cross-domain mapping learning for transductive zero-shot learning." Computer Vision and Image Understanding 187 (October 2019): 102784. http://dx.doi.org/10.1016/j.cviu.2019.07.004.

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17

Gao, Zan, T. T. Han, Lei Zhu, Hua Zhang, and Yinglong Wang. "Exploring the Cross-Domain Action Recognition Problem by Deep Feature Learning and Cross-Domain Learning." IEEE Access 6 (2018): 68989–9008. http://dx.doi.org/10.1109/access.2018.2878313.

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18

Kadu, Sandeep S., Pritish K. Raut, and Shamkumar U. Burungale. "Perceptions about the Virtual Learning amongst Medical students: A Cross Sectional Study." Indian Journal of Forensic Medicine and Pathology 14, no. 4 (2021): 831–34. http://dx.doi.org/10.21088/ijfmp.0974.3383.14421.8.

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Background: Covid-19 pandemic has forced the medical education to switch from traditional learning to virtual learning. Many different platforms are available for virtual learning. And like any other teaching-learning method, this new method also has its pros and cons. But the important thing is how the medical students perceive this technique. Hence, the study was planned to understand the student’s perception regarding Virtual learning. Methods: A cross-sectional study was conducted among under-graduate medical students across various medical colleges in India, via a Google form. Total 824 m

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19

Zhang, Chengyuan, Jiayu Song, Xiaofeng Zhu, Lei Zhu, and Shichao Zhang. "HCMSL: Hybrid Cross-modal Similarity Learning for Cross-modal Retrieval." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 1s (2021): 1–22. http://dx.doi.org/10.1145/3412847.

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The purpose of cross-modal retrieval is to find the relationship between different modal samples and to retrieve other modal samples with similar semantics by using a certain modal sample. As the data of different modalities presents heterogeneous low-level feature and semantic-related high-level features, the main problem of cross-modal retrieval is how to measure the similarity between different modalities. In this article, we present a novel cross-modal retrieval method, named Hybrid Cross-Modal Similarity Learning model (HCMSL for short). It aims to capture sufficient semantic information

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20

Jiang, Lin, Jigang Wu, Shuping Zhao, and Jiaxing Li. "Cross-Scatter Sparse Dictionary Pair Learning for Cross-Domain Classification." IEEE Transactions on Multimedia 27 (2025): 371–84. https://doi.org/10.1109/tmm.2024.3521731.

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21

Borsea, Manish, Tejas Barhateb, Ujjwal Amodkarc, Sumit Borsed, and Prof N. L. Bhale. "Cross-Camera Person Re-Identification in Urban Networks Using Machine Learning." International Journal of Research Publication and Reviews 6, no. 6 (2025): 1926–29. https://doi.org/10.55248/gengpi.6.0625.2048.

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22

Zhang, Beichen, and Yue Bao. "Cross-Dataset Learning for Age Estimation." IEEE Access 10 (2022): 24048–55. http://dx.doi.org/10.1109/access.2022.3154403.

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23

Solodka, Anzhelika, Oksana Filatova, Oksana Hinkevych, and Oleksandr Spanatiy. "Cross-cultural Language Learning: Interpretative Engagement." Arab World English Journal 12, no. 3 (2021): 82–96. http://dx.doi.org/10.24093/awej/vol12no3.6.

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Conceptualization of foreign language teaching as a cross-cultural interaction means engaging learners in various cultural mediations. Language use becomes a form of interpretative architecture of a target language. Understanding language use from a discursive perspective develops meta-pragmatic awareness and interpretative capacities of learners. The study answers the question of how to design the architecture of context analysis. This research aims to determine the effective ways of interpretative engagement of learners with aspects of pragmatics in the Ukrainian university setting. The stud

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24

Simmons, Dominique C., James W. Dias, Josh Dorsi, and Lawrence D. Rosenblum. "Cross-modal transfer of talker learning." Journal of the Acoustical Society of America 137, no. 4 (2015): 2416. http://dx.doi.org/10.1121/1.4920809.

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25

Mavritsaki, Eirini, Stephanie Chua, Zoe Kourtzi, Maxine Lintern, and Panagiotis Rentzelas. "Cross-cultural differences in perceptual learning." Journal of Vision 18, no. 10 (2018): 277. http://dx.doi.org/10.1167/18.10.277.

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26

Lauber, Benedikt, Jesper Lundbye-Jensen, Martin Keller, Albert Gollhofer, Wolfgang Taube, and Christian Leukel. "Cross-Limb Interference during Motor Learning." PLoS ONE 8, no. 12 (2013): e81038. http://dx.doi.org/10.1371/journal.pone.0081038.

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27

Bourdin, Béatrice, and Alexandre Aubry. "High learning potential and cross-culturality." Revue de neuropsychologie 16, no. 3 (2024): 199–205. http://dx.doi.org/10.1684/nrp.2024.0798.

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28

Luckmann, Charles. "Cross Cultural Learning with the Navajo." Journal of Experiential Education 12, no. 3 (1989): 23–27. http://dx.doi.org/10.1177/105382598901200305.

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29

Levin, Aaron. "Psychiatrists Cross Borders for Collaborative Learning." Psychiatric News 52, no. 17 (2017): 1. http://dx.doi.org/10.1176/appi.pn.2017.8a1.

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30

Xue, Bao-Xin, Mario Barbatti, and Pavlo O. Dral. "Machine Learning for Absorption Cross Sections." Journal of Physical Chemistry A 124, no. 35 (2020): 7199–210. http://dx.doi.org/10.1021/acs.jpca.0c05310.

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31

Massoli, Fabio Valerio, Giuseppe Amato, and Fabrizio Falchi. "Cross-resolution learning for Face Recognition." Image and Vision Computing 99 (July 2020): 103927. http://dx.doi.org/10.1016/j.imavis.2020.103927.

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32

Levinson, Nanette S., and Minoru Asahi. "Cross-national alliances and interorganizational learning." Organizational Dynamics 24, no. 2 (1995): 50–63. http://dx.doi.org/10.1016/0090-2616(95)90071-3.

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33

Leach, Stephen, and Sue Hopgood. "Cross Sector Action Learning for leaders." International Journal of Leadership in Public Services 2, no. 1 (2006): 38–42. http://dx.doi.org/10.1108/17479886200600007.

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34

Peñaloza, Claudia, Daniel Mirman, Pedro Cardona, et al. "Cross-situational word learning in aphasia." Cortex 93 (August 2017): 12–27. http://dx.doi.org/10.1016/j.cortex.2017.04.020.

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35

Abood, Ahid. "Cross cover: learning to change hats." BMJ 330, no. 7484 (2005): s38—s39. http://dx.doi.org/10.1136/bmj.330.7484.s38.

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36

Yang, Xiaoshan, Tianzhu Zhang, and Changsheng Xu. "Cross-Domain Feature Learning in Multimedia." IEEE Transactions on Multimedia 17, no. 1 (2015): 64–78. http://dx.doi.org/10.1109/tmm.2014.2375793.

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37

Wilson, Angene H. "Cross‐cultural experiential learning for teachers." Theory Into Practice 26, sup1 (1986): 519–27. http://dx.doi.org/10.1080/00405848709543321.

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38

Tanita, Keiko. "Metaphors for Learning: Cross-cultural Perspectives." Asian Englishes 11, no. 1 (2008): 56–60. http://dx.doi.org/10.1080/13488678.2008.10801228.

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39

Wehling, Susan. "Cross-Cultural Competency Through Service-Learning." Journal of Community Practice 16, no. 3 (2008): 293–315. http://dx.doi.org/10.1080/10705420802255080.

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40

Yu, Zheng, and Wenmin Wang. "Learning DALTS for cross‐modal retrieval." CAAI Transactions on Intelligence Technology 4, no. 1 (2019): 9–16. http://dx.doi.org/10.1049/trit.2018.1051.

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41

Benkeser, David, Cheng Ju, Sam Lendle, and Mark van der Laan. "Online cross-validation-based ensemble learning." Statistics in Medicine 37, no. 2 (2017): 249–60. http://dx.doi.org/10.1002/sim.7320.

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42

Titus, Simone, and Dick Ng'ambi. "Digital Gaming for Cross-Cultural Learning." International Journal of Game-Based Learning 13, no. 1 (2023): 1–20. http://dx.doi.org/10.4018/ijgbl.331995.

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Although game-based learning has gained significant attention in higher education globally, it is difficult to harness its engagement and interactions to improve student success. This paper argues that the use of digital games has the potential to interrupt social practices and increase engagement and interaction, thereby fostering meaningful learning. Using a mixed-method design, a digital game was used in a sport studies programme, involving 106 participants, over a two-year period. Data were collected through surveys, focus group discussions, and reflective blog posts. Structuration theory

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43

Huafeng, Liu, Chen Jingjing, Li Liang, et al. "Cross-modal representation learning and generation." Journal of Image and Graphics 28, no. 6 (2023): 1608–29. http://dx.doi.org/10.11834/jig.230035.

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44

Smith, Kenny, Andrew D. M. Smith, and Richard A. Blythe. "Cross-Situational Learning: An Experimental Study of Word-Learning Mechanisms." Cognitive Science 35, no. 3 (2010): 480–98. http://dx.doi.org/10.1111/j.1551-6709.2010.01158.x.

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45

Mitchel, Aaron D., and Daniel J. Weiss. "Learning across senses: Cross-modal effects in multisensory statistical learning." Journal of Experimental Psychology: Learning, Memory, and Cognition 37, no. 5 (2011): 1081–91. http://dx.doi.org/10.1037/a0023700.

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46

vogt, paul, and andrew d. m. smith. "learning colour words is slow: a cross-situational learning account." Behavioral and Brain Sciences 28, no. 4 (2005): 509–10. http://dx.doi.org/10.1017/s0140525x0544008x.

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research into child language reveals that it takes a long time for children to learn the correct mapping of colour words. steels & belpaeme's (s&b's) guessing game, however, models fast learning of words. we discuss computational studies based on cross-situational learning, which yield results that are more consistent with the empirical child language data than those obtained by s&b.

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47

Weinstein, Ben G., Sergio Marconi, Stephanie A. Bohlman, Alina Zare, and Ethan P. White. "Cross-site learning in deep learning RGB tree crown detection." Ecological Informatics 56 (March 2020): 101061. http://dx.doi.org/10.1016/j.ecoinf.2020.101061.

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48

A hammood, dalal, Hayder jasim habil, Ibtehal shakir Mahmoud, and Effariza Hanafi. "Deep Learning Cross-Modal Learning for Audio-Visual Speech Recognition." Kerbala Journal for Engineering Sciences 4, no. 1 (2024): 1–14. https://doi.org/10.63463/kjes1090.

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The ability to relate information about languages heard through visual and audio data is a crucial aspect of audio-visual speech recognition (AVSR), which has uses in data manipulation for audio-visual correspondence, including AVE-Net and SyncNet. The technique described in this research uses feature disentanglement to simultaneously handle the tasks listed above. By developing cross-modal standard learning methods, this model can transform visual or aural linguistic characteristics into modality-independent representations. AVE-Net and SyncNet can all be performed with the help of such deriv

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49

Lin, Kaiyi, Xing Xu, Lianli Gao, Zheng Wang, and Heng Tao Shen. "Learning Cross-Aligned Latent Embeddings for Zero-Shot Cross-Modal Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 11515–22. http://dx.doi.org/10.1609/aaai.v34i07.6817.

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Zero-Shot Cross-Modal Retrieval (ZS-CMR) is an emerging research hotspot that aims to retrieve data of new classes across different modality data. It is challenging for not only the heterogeneous distributions across different modalities, but also the inconsistent semantics across seen and unseen classes. A handful of recently proposed methods typically borrow the idea from zero-shot learning, i.e., exploiting word embeddings of class labels (i.e., class-embeddings) as common semantic space, and using generative adversarial network (GAN) to capture the underlying multimodal data structures, as

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50

Cao, Da, Jingjing Chu, Ningbo Zhu, and Liqiang Nie. "Cross-modal recipe retrieval via parallel- and cross-attention networks learning." Knowledge-Based Systems 193 (April 2020): 105428. http://dx.doi.org/10.1016/j.knosys.2019.105428.

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