Relevant bibliographies by topics / Cross-learning

Academic literature on the topic 'Cross-learning'

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

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Contents

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

Journal articles on the topic "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.
8

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.
9

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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You might also be interested in the extended bibliographies on the topic 'Cross-learning' for particular source types:
Journal articles Dissertations / Theses Books

Dissertations / Theses on the topic "Cross-learning":

1

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 problem is cross-view matching for person re-identification which a person is captured by multiple non-overlapping camera views, the objective is to match him/her across views among a large number of imposters. Typically a person's appearance is represented using features of thousands of dimensions, whilst only hundreds of training samples are available due to the difficulties in collecting matched training samples. With the number of training samples much smaller than the feature dimension, the existing methods thus face the classic small sample size (SSS) problem and have to resort to dimensionality reduction techniques and/or matrix regularisation, which lead to loss of discriminative power for cross-view matching. To that end, this thesis proposes to overcome the SSS problem in subspace learning by matching cross-view data in a discriminative null space of the training data. The second problem is cross-view matching for zero-shot learning where data are drawn from different modalities each for a different view (e.g. visual or textual), versus single-modal data considered in the first problem. This is inherently more challenging as the gap between different views becomes larger. Specifically, the zero-shot learning problem can be solved if the visual representation/view of the data (object) and its textual view are matched. Moreover, it requires learning a joint embedding space where different view data can be projected to for nearest neighbour search. This thesis argues that the key to make zero-shot learning models succeed is to choose the right embedding space. Different from most existing zero-shot learning models utilising a textual or an intermediate space as the embedding space for achieving crossview matching, the proposed method uniquely explores the visual space as the embedding space. This thesis finds that in the visual space, the subsequent nearest neighbour search would suffer much less from the hubness problem and thus become more effective. Moreover, a natural mechanism for multiple textual modalities optimised jointly in an end-to-end manner in this model demonstrates significant advantages over existing methods. The last problem is cross-view generation for image captioning which aims to automatically generate textual sentences from visual images. Most existing image captioning studies are limited to investigate variants of deep learning-based image encoders, improving the inputs for the subsequent deep sentence decoders. Existing methods have two limitations: (i) They are trained to maximise the likelihood of each ground-truth word given the previous ground-truth words and the image, termed Teacher-Forcing. This strategy may cause a mismatch between training and testing since at test-time the model uses the previously generated words from the model distribution to predict the next word. This exposure bias can result in error accumulation in sentence generation during test time, since the model has never been exposed to its own predictions. (ii) The training supervision metric, such as the widely used cross entropy loss, is different from the evaluation metrics at test time. In other words, the model is not directly optimised towards the task expectation. This learned model is therefore suboptimal. One main underlying reason responsible is that the evaluation metrics are non-differentiable and therefore much harder to be optimised against. This thesis overcomes the problems as above by exploring the reinforcement learning idea. Specifically, a novel actor-critic based learning approach is formulated to directly maximise the reward - the actual Natural Language Processing quality metrics of interest. As compared to existing reinforcement learning based captioning models, the new method has the unique advantage of a per-token advantage and value computation is enabled leading to better model training.
2

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 of machine-readable, domain-specific semantic knowledge is time consuming and prone to inconsistencies. The field of ontology learning therefore provides tools for automating the construction of domain ontologies (ontologies describing the entities and relations within a particular field of interest), by analyzing large quantities of domain-specific texts. This thesis studies three main topics within the field of ontology learning. First, we examine which sources of information are useful within an ontology learning system and how the information sources can be combined effectively. Secondly, we do this with a special focus on cross-language text collections, to see if we can learn more from studying several languages at once, than we can from a single-language text collection. Finally, we investigate new approaches to formal and automatic evaluation of the quality of a learned ontology. We demonstrate how to combine information sources from different languages and use them to train automatic classifiers to recognize lexico-semantic relations. The cross-language data is shown to have a positive effect on the quality of the learned ontologies. We also give theoretical and experimental results, showing that our ontology evaluation method is a good complement to and in some aspects improves on the evaluation measures in use today.
För att köpa boken skicka en beställning till exp@ling.su.se/ To order the book send an e-mail to exp@ling.su.se
4

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 theoretically successful advanced models (e.g., neural networks) from working in actual use. We focus our research on a pharmaceutical (pharma) demand forecasting problem. To overcome the challenges faced by sparse high dimensional time series, we develop a cross-series learning framework that trains a machine learning model on multiple related time series and uses cross-series information to improve forecasting accuracy. Cross-series learning is further optimized by dividing the global time series into subgroups based on three grouping schemes to balance the tradeoff between sample size and sample quality. Moreover, downstream inventory is introduced as an additional feature to support demand forecasting. Combining the cross-series learning framework with advanced machine learning models, we significantly improve the accuracy of pharma demand predictions. To verify the generalizability of cross-series learning, a generic forecasting framework containing the operations required for cross-series learning is developed and applied to retail sales forecasting. We further confirm the benefits of cross-series learning for advanced models, especially RNN. In addition to the grouping schemes based on product characteristics, we also explore two grouping schemes based on time series clustering, which do not require domain knowledge and can be applied to other fields. Using a retail sales dataset, our cross-series machine learning models are still superior to the baseline models. This dissertation develops a collection of cross-series learning techniques optimized for sparse high dimensional time series that can be applied to pharma manufacturers, retailers, and possibly other industries. Extensive experiments are carried out on real datasets to provide empirical value and insights for relevant theoretical studies. In practice, our work guides the actual use of cross-series learning.
5

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-media gaming with the intention of creating an environment in which learning could be supported. The development process of the prototype is described and evaluated in the thesis. The result presents a categorization of the key features for cross-media gaming and a prototype of a cross-media game. The thesis investigates which are the key technical features for creating cross-medial games for learning that can be identified for supporting the development process? The results presents a categorization of identified features along with potential future work based on the thesis. It is shown that features related to data sharing are highly prioritized and that certain features are absolutely required to enable cross-media gaming whilst others have less priority.
6

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 samples by just knowing the description of it, leveraging similarities between the description of the new object and previously learned concepts. Motivated by humans recognition ability, this thesis proposes novel approaches to tackle cross-class transfer learning (crossclass recognition) problem whose goal is to learn a model from seen classes (those with labelled training samples) that can generalise to unseen classes (those with labelled testing samples) without any training data i.e., seen and unseen classes are disjoint. Specifically, the thesis studies and develops new methods for addressing three variants of the cross-class transfer learning: Chapter 3 The first variant is transductive cross-class transfer learning, meaning labelled training set and unlabelled test set are available for model learning. Considering training set as the source domain and test set as the target domain, a typical cross-class transfer learning assumes that the source and target domains share a common semantic space, where visual feature vector extracted from an image can be embedded using an embedding function. Existing approaches learn this function from the source domain and apply it without adaptation to the target one. They are therefore prone to the domain shift problem i.e., the embedding function is only concerned with predicting the training seen class semantic representation in the learning stage during learning, when applied to the test data it may underperform. In this thesis, a novel cross-class transfer learning (CCTL) method is proposed based on unsupervised domain adaptation. Specifically, a novel regularised dictionary learning framework is formulated by which the target class labels are used to regularise the learned target domain embeddings thus effectively overcoming the projection domain shift problem. Chapter 4 The second variant is inductive cross-class transfer learning, that is, only training set is assumed to be available during model learning, resulting in a harder challenge compared to the previous one. Nevertheless, this setting reflects a real-world setting in which test data is available after the model learning. The main problem remains the same as the previous variant, that is, the domain shift problem occurs when the model learned only from the training set is applied to the test set without adaptation. In this thesis, a semantic autoencoder (SAE) is proposed building on an encoder-decoder paradigm. Specifically, first a semantic space is defined so that knowledge transfer is possible from the seen classes to the unseen classes. Then, an encoder aims to embed/project a visual feature vector into the semantic space. However, the decoder exerts a generative task, that is, the projection must be able to reconstruct the original visual features. The generative task forces the encoder to preserve richer information, thus the learned encoder from seen classes is able generalise better to the new unseen classes. Chapter 5 The third one is unsupervised cross-class transfer learning. In this variant, no supervision is available for model learning i.e., only unlabelled training data is available, leading to the hardest setting compared to the previous cases. The goal, however, is the same, learning some knowledge from the training data that can be transferred to the test data composed of completely different labels from that of training data. The thesis proposes a novel approach which requires no labelled training data yet is able to capture discriminative information. The proposed model is based on a new graph regularised dictionary learning algorithm. By introducing a l1- norm graph regularisation term, instead of the conventional squared l2-norm, the model is robust against outliers and noises typical in visual data. Importantly, the graph and representation are learned jointly, resulting in further alleviation of the effects of data outliers. As an application, person re-identification is considered for this variant in this thesis.
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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 best of our knowledge, there is no evidence of which CPDP methods typically perform best. Moreover, there is no evidence on which CPDP methods perform better for a specific application domain. In fact, there is no machine learning algorithm suitable for all domains. The decision task of selecting an appropriate algorithm for a given application domain is investigated in the meta-learning literature. A meta-learning model is characterized by its capacity of learning from previous experiences and adapting its inductive bias dynamically according to the target domain. In this work, we investigate the feasibility of using meta-learning for the recommendation of CPDP methods. In this thesis, three main goals were pursued. First, we provide an experimental analysis to investigate the feasibility of using Feature Selection (FS) methods as an internal procedure to improve the performance of two specific CPDP methods. Second, we investigate which CPDP methods present typically best performances. We also investigate whether the typically best methods perform best for the same project datasets. The results reveal that the most suitable CPDP method for a project can vary according to the project characteristics, which leads to the third investigation of this work. We investigate the several particularities inherent to the CPDP context and propose a meta-learning solution able to learn from previous experiences and recommend a suitable CDPD method according to the characteristics of the project being predicted. We evaluate the learning capacity of the proposed solution and its performance in relation to the typically best CPDP methods.
Modelos de predição de defeitos auxiliam profissionais de teste na priorização de partes do software mais propensas a conter defeitos. A abordagem de predição de defeitos cruzada entre projetos (CPDP) refere-se à utilização de projetos externos já conhecidos para compor o conjunto de treinamento. Essa abordagem é útil quando a quantidade de dados históricos de defeitos é inapropriada ou insuficiente para compor o conjunto de treinamento. Embora o princípio seja atrativo, o desempenho de predição é um fator limitante nessa abordagem. Nos últimos anos, vários métodos foram propostos com o intuito de melhorar o desempenho de predição de modelos CPDP. Contudo, na literatura, existe uma carência de estudos comparativos que apontam quais métodos CPDP apresentam melhores desempenhos. Além disso, não há evidências sobre quais métodos CPDP apresentam melhor desempenho para um domínio de aplicação específico. De fato, não existe um algoritmo de aprendizado de máquina que seja apropriado para todos os domínios de aplicação. A tarefa de decisão sobre qual algoritmo é mais adequado a um determinado domínio de aplicação é investigado na literatura de meta-aprendizado. Um modelo de meta-aprendizado é caracterizado pela sua capacidade de aprender a partir de experiências anteriores e adaptar seu viés de indução dinamicamente de acordo com o domínio alvo. Neste trabalho, nós investigamos a viabilidade de usar meta-aprendizado para a recomendação de métodos CPDP. Nesta tese são almejados três principais objetivos. Primeiro, é conduzida uma análise experimental para investigar a viabilidade de usar métodos de seleção de atributos como procedimento interno de dois métodos CPDP, com o intuito de melhorar o desempenho de predição. Segundo, são investigados quais métodos CPDP apresentam um melhor desempenho em um contexto geral. Nesse contexto, também é investigado se os métodos com melhor desempenho geral apresentam melhor desempenho para os mesmos conjuntos de dados (ou projetos de software). Os resultados revelam que os métodos CPDP mais adequados para um projeto podem variar de acordo com as características do projeto sendo predito. Essa constatação conduz à terceira investigação realizada neste trabalho. Foram investigadas as várias particularidades inerentes ao contexto CPDP a fim de propor uma solução de meta-aprendizado capaz de aprender com experiências anteriores e recomendar métodos CPDP adequados, de acordo com as características do software. Foram avaliados a capacidade de meta-aprendizado da solução proposta e a sua performance em relação aos métodos base que apresentaram melhor desempenho geral.
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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 à partir d'exemples ou transformer les données dans un format plus facilement interrogeable par l'utilisateur. Nos contributions suivent ces deux directions et concernent trois modèles de données parmi les plus populaires : XML, relationnel et orienté graphe. Cette thèse comprend deux parties, consacrées à (i) la définition et la transformation de schémas, et (ii) l'apprentissage de schémas et de requêtes. Dans la première partie, nous définissons des formalismes de schémas pour les documents XML non-ordonnés et nous analysons leurs propriétés computationnelles; nous étudions également la complexité du problème d'échange de données entre une source relationnelle et une cible orientée graphe. Dans la deuxième partie, nous étudions le problème de l'apprentissage à partir d'exemples pour les schémas XML proposés dans la première partie, ainsi que pour les requêtes de jointures relationnelles et les requêtes de chemins sur les graphes. Nous proposons notamment un scénario interactif qui permet d'aider des utilisateurs non-experts à définir des requêtes dans ces deux classes
Specifying a database query using a formal query language is typically a challenging task for non-expert users. In the context of big data, this problem becomes even harder because it requires the users to deal with database instances of large size and hence difficult to visualize. Such instances usually lack a schema to help the users specify their queries, or have an incomplete schema as they come from disparate data sources. In this thesis, we address the problem of query specification for non-expert users. We identify two possible approaches for tackling this problem: learning queries from examples and translating the data in a format that the user finds easier to query. Our contributions are aligned with these two complementary directions and span over three of the most popular data models: XML, relational, and graph. This thesis consists of two parts, dedicated to (i) schema definition and translation, and to (ii) learning schemas and queries. In the first part, we define schema formalisms for unordered XML and we analyze their computational properties; we also study the complexity of the data exchange problem in the setting of a relational source and a graph target database. In the second part, we investigate the problem of learning from examples the schemas for unordered XML proposed in the first part, as well as relational join queries and path queries on graph databases. The interactive scenario that we propose for these two classes of queries is immediately applicable to assisting non-expert users in the process of query specification
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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 through engagement in academic CPD that stretches beyond institutional boundaries, characterised by diversity and based on collaboration and openness. Data collection was conducted using a collective case study approach to gain insights into the collective lived collaborative open learning experience in two authentic cross-institutional academic development settings with collaborative learning features designed in. At least one of the institutions involved in each course was based in the United Kingdom. Twenty two individual phenomenographic interviews were conducted and coded. The findings illustrate that collaborative open learning was experienced as two dynamic immersive and selective patterns. Boundary crossing as captured in the categories of description and their qualitatively different variations, shaped that experience and related to modes of participation; time, place and space; culture and language as well as diverse professional contexts. Facilitator support and the elasticity of the design also positively shaped this experience. The community aspect influenced study participants' experience at individual and course level and illuminated new opportunities for academic development practice based on cross-boundary community-led approaches. The findings synthesised in the phenomenographic outcome space, depicting the logical relationships of the eleven categories of description in this study, organised in structural factors, illustrate how these contributed and shaped the lived experience, together with a critical discussion of these with the literature, aided the creation of the openly licensed cross-boundary collaborative open learning framework for cross-institutional academic development, the final output of this study. A design tool developed from the results is included that aims to inform academic developers and other course designers who may be considering and planning to model and implement such approaches in their own practice.
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Books on the topic "Cross-learning":

1

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

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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. Ann Arbor, Mich: University Microfilms International, 1987.

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Sikkema, Mildred. Design for cross-cultural learning. Yarmouth, Me: Intercultural Press, 1987.

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

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Westwood, Peter S. Teaching and learning difficulties: Cross-curricular perspectives. Camberwell, Vic: 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. Cham: 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. Cambridge, Mass: Harvard University Press, 2011.

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Ringbom, Håkan. Cross-linguistic similarity in foreign language learning. Clevedon [England]: Multilingual Matters, 2007.

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Davis, Sarah H. Resident aliens: Learning to live cross-culturally. Cambridge, Mass: Harvard University Press, 2011.

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

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Book chapters on the topic "Cross-learning":

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Delaney, Laurel J. "Cross-Cultural Learning." In Exporting, 413–22. Berkeley, CA: 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, 861–64. Boston, MA: 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, 451–61. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2193-8_24.

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Smith, Andrew D. M., and Kenny Smith. "Cross-Situational Learning." In Encyclopedia of the Sciences of Learning, 864–66. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_1712.

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Hibbert, Liesel, and Gregory Kerr. "Cross-disciplinary learning." In English as a Language of Learning, Teaching and Inclusivity, 143–54. London: Routledge, 2023. http://dx.doi.org/10.4324/9781003382645-9.

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Schaffer, Scott P. "Cross-Disciplinary Team Learning." In Handbook of Improving Performance in the Workplace: Selecting and Implementing Performance Interventions, 598–612. Hoboken, NJ, USA: 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, 598–612. Hoboken, NJ, USA: 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, 853–55. Boston, MA: 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, 286–87. Boston, MA: 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, 211–40. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1600-9_7.

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Abstract:
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.

Conference papers on the topic "Cross-learning":

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Fisch, Shalom M., Richard Lesh, Beth Motoki, Sandra Crespo, and Vincent Melfi. "Cross-platform learning." In the 10th International Conference. New York, New York, USA: 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. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3240508.3240710.

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Kang, Cuicui, Shengcai Liao, Yonghao He, Jian Wang, Wenjia Niu, Shiming Xiang, and Chunhong Pan. "Cross-Modal Similarity Learning." In CIKM'15: 24th ACM International Conference on Information and Knowledge Management. New York, NY, USA: 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). Stroudsburg, PA, USA: 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. New York, NY, USA: 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. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/n19-2023.

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Ruder, Sebastian, Anders Søgaard, and Ivan Vulić. "Unsupervised Cross-Lingual Representation Learning." In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/p19-4007.

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Liu, Alexander, SouYoung Jin, Cheng-I. Lai, Andrew Rouditchenko, Aude Oliva, and James Glass. "Cross-Modal Discrete Representation Learning." In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.acl-long.215.

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Mao, WeiYang, and jshardrom xia. "Cross-modal representation learning based on contrast learning." In 4th International Conference on Information Science, Electrical and Automation Engineering (ISEAE 2022), edited by Mengyi (Milly) Cen and Lidan Wang. SPIE, 2022. http://dx.doi.org/10.1117/12.2640128.

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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. Fort Belvoir, VA: Defense Technical Information Center, January 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, April 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. Fort Belvoir, VA: Defense Technical Information Center, September 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, September 2022. http://dx.doi.org/10.19088/k4d.2022.159.

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Abstract:
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, October 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,. Fort Belvoir, VA: Defense Technical Information Center, November 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, March 2021. http://dx.doi.org/10.31812/123456789/4428.

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Abstract:
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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Sakurauchi, Yoko. Teaching and Learning for Intercultural Sensitivity: A Cross-Cultural Examination of American Domestic Students and Japanese Exchange Students. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.1642.

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Chen, Yunxiang, Jie Bao, Jianqiu Zheng, Peiyuan Gao, Qizhi He, James Stegen, Brenda Ng, Xiaofeng Liu, Roman Dibiase, and Chaopeng Shen. Upscaling cross-scale flow and respiration interactions at river sediment interface leveraging observation, numerical models, and machine learning. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769792.

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Freed, Danielle. K4D Learning Journey Strengthens the Mainstreaming of Water Security. Institute of Development Studies, September 2022. http://dx.doi.org/10.19088/k4d.2022.164.

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This K4D Impact Story shares how a K4D’s Water Security Learning Journey supported an extensive set of activities and products to engage stakeholders and increase awareness of water security and its cross-cutting nature. Evidence indicates that the learning journey has provided a valuable vehicle for participants to deepen knowledge on the topic, share experiences, build networks, and develop their ability to integrate water security into policy and programming. 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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