Journal articles: 'Cross-learning'

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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Chok, S. "Cross organisational learning." BMJ 322, no. 7293 (April 28, 2001): 2. http://dx.doi.org/10.1136/bmj.322.7293.s2-7293.

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Newell, Sue. "Enhancing Cross-Project Learning." Engineering Management Journal 16, no. 1 (March 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 (October 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 (January 29, 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 used learning method must involve students in the foreign culture studied; 2) Internet is one of the best learning media in the process of learning cross cultural understanding; 3) Students at least understand the language of the culture studied. The conclusion of this study is that cross cultural understanding learning is not easy to do if it only provides theory because students need an in-depth understanding of the cross-cultural process which experienced by someone.

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

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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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Blythe, Richard A., Kenny Smith, and Andrew D. M. Smith. "Learning Times for Large Lexicons Through Cross-Situational Learning." Cognitive Science 34, no. 4 (January 21, 2010): 620–42. http://dx.doi.org/10.1111/j.1551-6709.2009.01089.x.

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

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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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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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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 (December 15, 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 medical students participated in the study. Results: Out of 824 participants, 36.2% feel that students do not listen to the virtual class attentively from start to end. 79.2% agree that there is more distraction during virtual learning and 77.1% students feel that virtual learning is boring because of lack of interaction between teacher and students. Also 47.3% students don’t think that it is easy to clear doubts during virtual learning. 77.5% agree that there are more chances of scrolling the social media during virtual learning. According to 67.7% students, only the knowledge aspect is better learnt during the virtual learning. But the 50.1% students agree that virtual learning is cost-effective and time-saving. Still, 90.9% students prefer traditional learning considering all aspects of medical education Conclusion: Despite having certain advantages, medical students still prefer conventional method of learning to acquire different competencies, while the virtual learning remains supportive method. Many issues need to be addressed for wide acceptance of virtual learning.

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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 (April 20, 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 from both labeled and unlabeled cross-modal pairs and intra-modal pairs with same classification label. Specifically, a coupled deep fully connected networks are used to map cross-modal feature representations into a common subspace. Weight-sharing strategy is utilized between two branches of networks to diminish cross-modal heterogeneity. Furthermore, two Siamese CNN models are employed to learn intra-modal similarity from samples of same modality. Comprehensive experiments on real datasets clearly demonstrate that our proposed technique achieves substantial improvements over the state-of-the-art cross-modal retrieval techniques.

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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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Solodka, Anzhelika, Oksana Filatova, Oksana Hinkevych, and Oleksandr Spanatiy. "Cross-cultural Language Learning: Interpretative Engagement." Arab World English Journal 12, no. 3 (September 15, 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 study investigates how the process of interaction shapes the engagement of learners in practices of noticing, reflection, and comparison of cross-cultural situations. The data came from a case study on cross-cultural language learning within the second semester, 2021. The study analyzes the audio-recording of the classes, researcher notes, and post-course interviews of 24 participants. This research used a method of the content analysis. The study of the results, based on six categories (narrative analysis, discourse analysis, semiotic analysis, interpretative analyses, conversation analysis, and critical analysis), showed that the learners started to consider the nature of their cross-cultural mediation. The research proved that through such an interpretative engagement, students become engaged into working with languages and cultures. The study presents some recommendations for language teachers to create a meaning-making process from multiple perspectives.

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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 (April 2015): 2416. http://dx.doi.org/10.1121/1.4920809.

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Mavritsaki, Eirini, Stephanie Chua, Zoe Kourtzi, Maxine Lintern, and Panagiotis Rentzelas. "Cross-cultural differences in perceptual learning." Journal of Vision 18, no. 10 (September 1, 2018): 277. http://dx.doi.org/10.1167/18.10.277.

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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 (December 3, 2013): e81038. http://dx.doi.org/10.1371/journal.pone.0081038.

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

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

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Xue, Bao-Xin, Mario Barbatti, and Pavlo O. Dral. "Machine Learning for Absorption Cross Sections." Journal of Physical Chemistry A 124, no. 35 (August 6, 2020): 7199–210. http://dx.doi.org/10.1021/acs.jpca.0c05310.

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

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

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Peñaloza, Claudia, Daniel Mirman, Pedro Cardona, Montserrat Juncadella, Nadine Martin, Matti Laine, and Antoni Rodríguez-Fornells. "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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Abood, Ahid. "Cross cover: learning to change hats." BMJ 330, no. 7484 (January 22, 2005): s38—s39. http://dx.doi.org/10.1136/bmj.330.7484.s38.

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

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32

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

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

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Wehling, Susan. "Cross-Cultural Competency Through Service-Learning." Journal of Community Practice 16, no. 3 (September 17, 2008): 293–315. http://dx.doi.org/10.1080/10705420802255080.

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Yu, Zheng, and Wenmin Wang. "Learning DALTS for cross‐modal retrieval." CAAI Transactions on Intelligence Technology 4, no. 1 (February 18, 2019): 9–16. http://dx.doi.org/10.1049/trit.2018.1051.

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Benkeser, David, Cheng Ju, Sam Lendle, and Mark van der Laan. "Online cross-validation-based ensemble learning." Statistics in Medicine 37, no. 2 (May 4, 2017): 249–60. http://dx.doi.org/10.1002/sim.7320.

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Titus, Simone, and Dick Ng'ambi. "Digital Gaming for Cross-Cultural Learning." International Journal of Game-Based Learning 13, no. 1 (October 10, 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 is considered as the theoretical lens, as it purports that recursive social activities of humans are continually recreated by human agents. The paper concludes that when participants engaged in a cross-cultural game-based learning environment, the social practices acquired through their academic career were interrupted, reshaped, and reproduced into new practices. A social constructivist game-based learning model to foster interaction within multi-cultural higher education classrooms is offered.

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Huafeng, Liu, Chen Jingjing, Li Liang, Bao Bingkun, Li Zechao, Liu Jiaying, and Nie Liqiang. "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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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 (December 9, 2010): 480–98. http://dx.doi.org/10.1111/j.1551-6709.2010.01158.x.

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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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vogt, paul, and andrew d. m. smith. "learning colour words is slow: a cross-situational learning account." Behavioral and Brain Sciences 28, no. 4 (August 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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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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Yu, Huachen, and Jianming Yang. "An Adversarial Self-Learning Method for Cross-City Adaptation in Semantic Segmentation." International Journal of Machine Learning and Computing 10, no. 5 (October 5, 2020): 648–53. http://dx.doi.org/10.18178/ijmlc.2020.10.5.986.

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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 (April 3, 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 well as strengthen relations between input data and semantic space to generalize across seen and unseen classes. In this paper, we propose a novel method termed Learning Cross-Aligned Latent Embeddings (LCALE) as an alternative to these GAN based methods for ZS-CMR. Unlike using the class-embeddings as the semantic space, our method seeks for a shared low-dimensional latent space of input multimodal features and class-embeddings by modality-specific variational autoencoders. Notably, we align the distributions learned from multimodal input features and from class-embeddings to construct latent embeddings that contain the essential cross-modal correlation associated with unseen classes. Effective cross-reconstruction and cross-alignment criterions are further developed to preserve class-discriminative information in latent space, which benefits the efficiency for retrieval and enable the knowledge transfer to unseen classes. We evaluate our model using four benchmark datasets on image-text retrieval tasks and one large-scale dataset on image-sketch retrieval tasks. The experimental results show that our method establishes the new state-of-the-art performance for both tasks on all datasets.

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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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Ran, Hongyan, and Caiyan Jia. "Unsupervised Cross-Domain Rumor Detection with Contrastive Learning and Cross-Attention." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (June 26, 2023): 13510–18. http://dx.doi.org/10.1609/aaai.v37i11.26584.

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Massive rumors usually appear along with breaking news or trending topics, seriously hindering the truth. Existing rumor detection methods are mostly focused on the same domain, thus have poor performance in cross-domain scenarios due to domain shift. In this work, we propose an end-to-end instance-wise and prototype-wise contrastive learning model with cross-attention mechanism for cross-domain rumor detection. The model not only performs cross-domain feature alignment, but also enforces target samples to align with the corresponding prototypes of a given source domain. Since target labels in a target domain are unavailable, we use a clustering-based approach with carefully initialized centers by a batch of source domain samples to produce pseudo labels. Moreover, we use a cross-attention mechanism on a pair of source data and target data with the same labels to learn domain-invariant representations. Because the samples in a domain pair tend to express similar semantic patterns especially on the people’s attitudes (e.g., supporting or denying) towards the same category of rumors, the discrepancy between a pair of source domain and target domain will be decreased. We conduct experiments on four groups of cross-domain datasets and show that our proposed model achieves state-of-the-art performance.

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Wei, Yingchao. "Study on the Cross-language Influence of Japanese Learning and English Learning." International Journal of Education and Humanities 12, no. 1 (January 15, 2024): 227–31. http://dx.doi.org/10.54097/s81axx32.

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The purpose of this study is to explore the interaction between Japanese learning and English learning and its impact on language learners, as well as cross-language teaching strategies and practices. Based on the literature research method and comprehensive analysis method, based on the overview of the cross-language influence theory, this paper analyzes the mutual influence mechanism of Japanese learning and English learning, including the comparison and cross-influence of phonological learning, vocabulary learning and grammar learning. Then, it discusses the influence of cross-language influence on the cognitive mechanism of language learners and analyzes the causes of language transfer. Finally, the paper puts forward cross-language teaching strategies and practices, including the exploration of cross-language teaching models, effective cross-language teaching strategies of Japanese and English, and the application prospects of cross-language teaching in language education. Based on this research, we can better understand the interaction between Japanese and English learning, and provide theoretical support and practical guidance for cross-language teaching.

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Sukadari, Sukadari, and Miftachul Huda. "Culture Sustainability through Co-Curricular Learning Program: Learning Batik Cross Review." Education Sciences 11, no. 11 (November 16, 2021): 736. http://dx.doi.org/10.3390/educsci11110736.

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As the application of colored designs on cloth using wax in certain areas in decorating items of clothing, Batik is made through several steps that come from drawing the pattern on the paper followed by imitating the pattern on fabric, which is called ngeblat. The next phase is followed by drawing the pattern using wax, which is called mencanting. The subsequent step is the process of coloring the pattern of Batik, called pencoletan, and then subsequently followed by color-locking on Batik through covering the Batik with wax, namely basic color dyeing. This process is called menembok. The final step is making the panting process through washing, called nglorod. This process should attempt to maintain Batik with cultural sustainability, as its process contains several values, such as discipline, creativity, independence, patriotism, responsibility, cooperation, and environmental care. Based on this background, this paper attempts to examine the Batik’s cultural design and to explore its sustainability through co-curricular school program activities. This study focuses on examining the potential in the process of the application of colored design through elaborating the supporting and interfering factors in a co-curricular learning program of Batik. The analysis was made through several points, namely a co-curricular learning program of Batik for enhancing cultural sustainability, a co-curricular learning program of Batik for enhancing national culture and community responsibility, and co-curricular learning of Batik for sustainability and environmental accountability.

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Zheng, Wendong, Huaping Liu, Bowen Wang, and Fuchun Sun. "Cross-modal learning for material perception using deep extreme learning machine." International Journal of Machine Learning and Cybernetics 11, no. 4 (May 16, 2019): 813–23. http://dx.doi.org/10.1007/s13042-019-00962-1.

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Mitchell, Barry S., Qin Xu, Lixian Jin, Debra Patten, and Ingrid Gouldsborough. "A cross-cultural comparison of anatomy learning: Learning styles and strategies." Anatomical Sciences Education 2, no. 2 (March 2009): 49–60. http://dx.doi.org/10.1002/ase.73.

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Journal articles on the topic 'Cross-learning'

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