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Recently, transfer learning has gained popularity in the machine learning community. Transfer Learning (TL) has emerged as a promising paradigm that leverages knowledge learned from one or more related domains to improve prediction accuracy in a target domain with limited data. However, for time series forecasting (TSF) applications, transfer learning is relatively new. This paper addresses the need for empirical studies as identified in recent reviews advocating the need for practical guidelines for Transfer Learning approaches and method designs for time series forecasting. The main contribution of this paper is the suggestion of a comprehensive framework for Transfer Learning Sensitivity Analysis (SA) for time series forecasting. We achieve this by identifying various parameters seen from various angles of transfer learning applied to time series, aiming to uncover factors and insights that influence the performance of transfer learning in time series forecasting. Undoubtedly, symmetry appears to be a core aspect in the consideration of these factors and insights. A further contribution is the introduction of four TL performance metrics encompassed in our framework. These TL performance metrics provide insight into the extent of the transferability between the source and the target domains. Analyzing whether the benefits of transferred knowledge are equally or unequally accessible and applicable across different domains or tasks speaks to the requirement of symmetry or asymmetry in transfer learning. Moreover, these TL performance metrics inform on the possibility of the occurrence of negative transfers and also provide insight into the possible vulnerability of the network to catastrophic forgetting. Finally, we discuss a sensitivity analysis of an Ensemble TL technique use case (with Multilayer Perceptron models) as a proof of concept to validate the suggested framework. While the results from the experiments offer empirical insights into various parameters that impact the transfer learning gain, they also raise the question of network dimensioning requirements when designing, specifically, a neural network for transfer learning. [ABSTRACT FROM AUTHOR]

In this work, we study the transfer learning problem under high-dimensional generalized linear models (GLMs), which aim to improve the fit on target data by borrowing information from useful source data. Given which sources to transfer, we propose a transfer learning algorithm on GLM, and derive its l 1 / l 2 -estimation error bounds as well as a bound for a prediction error measure. The theoretical analysis shows that when the target and sources are sufficiently close to each other, these bounds could be improved over those of the classical penalized estimator using only target data under mild conditions. When we don't know which sources to transfer, an algorithm-free transferable source detection approach is introduced to detect informative sources. The detection consistency is proved under the high-dimensional GLM transfer learning setting. We also propose an algorithm to construct confidence intervals of each coefficient component, and the corresponding theories are provided. Extensive simulations and a real-data experiment verify the effectiveness of our algorithms. We implement the proposed GLM transfer learning algorithms in a new R package glmtrans, which is available on CRAN. for this article are available online. [ABSTRACT FROM AUTHOR]

Copyright of Arts for Linguistic & Literary Studies is the property of Thamar University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Currently, most models rarely consider the negative transfer problem in the research field of cross-subject EEG emotion recognition. To solve this problem, this paper proposes a semi-supervised domain adaptive algorithm based on few labeled samples of target subject, which called multi-domain geodesic flow kernel dynamic distribution alignment (MGFKD). It consists of three modules: 1) GFK common feature extractor: projects the feature distribution of source and target subjects to the Grassmann manifold space, and obtains the latent common features of the two feature distributions through GFK method. 2) Source domain selector: obtains pseudo-labels of the target subject through weak classifier, finds ''golden source subjects'' by using few known labels of target subjects. 3) Label corrector: uses a dynamic distribution balance strategy to correct the pseudo-labels of the target subject. We conducted comparison experiments on the SEED and SEED-IV datasets, and the results show that MGFKD outperforms unsupervised and semi-supervised domain adaptation algorithms, achieving an average accuracy of 87.51±7.68% and 68.79±8.25% on the SEED and SEED-IV datasets with only one labeled sample per video for target subject. Especially when the number of source domains is set as 6 and the number of known labels is set as 5, the accuracy increase to 90.20±7.57% and 69.99±7.38%, respectively. The above results prove that our proposed algorithm can efficiently and quickly improve the cross-subject EEG emotion classification performance. Since it only need a small number of labeled samples of new subjects, making it has strong application value in future EEG-based emotion recognition applications.

The present study aimed to explore learners’ perceptions while using Google Translate (GT) during online English-Writing lessons and to evaluate its effectiveness on learners’ English writing skills. Online questionnaires and individual interviews were used to collect self-reported opinions from 24 Vietnamese students. Learner writing samples from the 12-week online session were also collected in order to identify possible improvements in writing. The findings revealed that the learners generally reported a positive effect on their writing from using Google Translate. In contrast, textual analysis of learners’ writing samples indicated that although there were slight improvements in their writing skills, problems still exist. According to an error analysis that was conducted, negative transfer of structures from the students’ L1 (Vietnamese) accounted for most of the lexical and syntactic errors identified. The findings demonstrate that GT is a useful support tool for teaching English writing. The contrastive analysis in the present study contributes to language interference studies and discussions on bilingual and multilingual education in the Vietnamese context.

The present study aimed at investigating the effect of the L1 negative transfer on the writing of EFL Saudi students. It examined the common grammatical errors that Saudi students made while writing in English and analyzed the sources of their errors. The samples consisted of English essays written by 74 freshmen female students enrolled at Majmaah University in Saudi Arabia. The samples were analyzed and the errors classified according to grammatical sub-categories including tenses, singular/plural markers, prepositions, articles and pronouns. This study found that 81% of the students' errors can be accounted for in terms of L1 transfer. Grammatical errors were the most frequent ones, recording 67% of the total rate of errors. More specifically, preposition (40%) and tense (32%) errors constituted the most frequent subcategories of the grammatical errors. It was concluded that learning basic linguistic differences between Saudi students' L1 and English language is a necessary condition for helping the learners to overcome the problem of first language interference in their writing.

This study explores how gender and first language (L1) conceptual transfer influence the use of English prepositions among 50 Vietnamese university students aged 18 to 25, enrolled in an English language program. The participants' comprehension of English prepositions in terms of usage, meaning, and context was assessed using pre- and post-tests consisting of 20 multiple-choice questions. The data analysis employed descriptive and inferential statistics, including chi-square tests and independent samples t-tests. Both the preand post-test results revealed a moderate level of English prepositional usage, with all participants displaying improvement. Female participants performed slightly better than males on the post-test, while there were no notable gender differences in pre-test scores. In both tests, participants with high levels of L1 conceptual transfer performed significantly poorer compared to those with low levels. These findings highlight the significant role of L1 conceptual transfer in the English prepositional usage of Vietnamese language learners, while suggesting a minor impact of gender. Further research is needed to delve deeper into this relationship. The results emphasize the importance of addressing L1 conceptual transfer in English language instruction, specifically regarding prepositions, within the context of Vietnam. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the acquisition of liaison in English by Chinese-speaking learners. Ten second-year postgraduate students of non-English majors in Tongji University, China, were invited to take part in an experiment. They were asked to prepare recordings of a set of English materials, including phrases, dialogues, and a talking topic, before and after a self-study training on liaison. To examine every type of liaison in their speech, the study analysed the recordings using the speech analysis software Praat. The results showed that before the training, the students negatively transferred the native language (L1) pattern to the target second language (L2). This kind of negative transfer of L1 Chinese to the acquisition of liaison in L2 English could be explained by the differences between English and Chinese syllables. After the training, the students showed substantial improvement in phrase and dialogue reading. The findings are expected to help both teachers and students gain a better understanding of liaison and the differences between English and Chinese syllables, thus contributing to English teaching and learning.

A vast body of available literature presents controversial perspectives on the role of L1 in learning and communicating an L2. The current exploratory qualitative study attempted to investigate the reported experiences of ten Afghan EFL learners regarding their L1 (Farsi Dari) role in communicating an L2 (English). The data collected through Skype interviews with ten participants were analyzed thematically. The findings indicated that both positive and negative transfers of L1 occur at different levels in L2. The emerging themes revealed that L2 is scaffolded with L1 proficiency; further, it helps generate ideas, improve self-esteem, and reduce anxiety. The findings also indicated some L1 interferences in L2 communication particularly pronunciation, grammar, and vocabulary. The current study's findings suggest that the language instructors should be aware of the language transfers, both positive and negative, to provide quality teaching to EFL learners. Further research studies can be conducted through a different research design such as quantitative or mixed-method exploring the EFL perspectives.

The article explores a particular case of negative transfer of the Surrealist artistic project during the early phase of the French-centred movement within the Romanian avant-garde magazines network. Dismissed as “inauthentic” or mediated for its scandalous allure, Surrealism is unsuccessful at making inroads into the Romanian avant-garde press, closely connected to International Constructivism. This process triggers a delayed, albeit impressive, response to the Surrealist aesthetic and political thought, toward the end of the 1920s, and is, initially, largely regarded as a fraught association. Arguing that artistic circulation and transmission is not always a synchronous or symmetrical process in a centre-periphery direction, the article opens an inquiry into the motivations and implications of the Romanian negative response to early Surrealism. Additionally, the article provides insights into the complex interplay between Constructivism and Surrealism in the space of the Romanian avant-garde periodicals, essential for the understanding of Central and Eastern European avant-gardes, and a new perspective on how the periphery moderates the Surrealist label and its artistic project. [ABSTRACT FROM AUTHOR]

The typical way to conduct data-driven prognosis is to train a degradation model with historical data, then apply the model to predict failure for in-service units. Most existing works assume the historical data and in-service data are from the same process. In practice, however, different but related processes can share similar degradation patterns. Thus, the historical data from these processes are expected to provide useful prognosis information for each other. In this article, we propose a data-level transfer learning framework to extract useful and shared information from different processes to benefit the prognosis of in-service units. In this framework, the degradation data in each process is modeled by a mixed effects model. To facilitate the information sharing among different mixed effects models, a hierarchical Bayesian structure is proposed to model and connect the distributions of mixed effects in different mixed models. Because the degradation paths in different processes are rarely the same, the dimension of the mixed effects/regressor in each process can be different. To handle this issue, we propose a tailored linear transformation to marginalize or expand the distributions of mixed effects in different degradation processes to achieve consistent dimensions. The transferred information is finally incorporated with the degradation data from in-service units to conduct prognosis. The proposed method is validated and compared with various benchmarks in extensive numerical studies and two case studies. The results show the proposed method can successfully transfer useful information in different processes to benefit the prognosis. [ABSTRACT FROM AUTHOR]

Introduction: Boxing as a sport is growing on Chinese campuses, resulting in a coaching shortage. The human pose estimation technology can be employed to estimate boxing poses and teach interns to relieve the shortage. Currently, 3D cameras can provide more depth information than 2D cameras. It can potentially improve the estimation. However, the input channels are inconsistent between 2D and 3D images, and there is a lack of detailed analysis about the key point location, which indicates the network design for improving the human pose estimation technology. Method: Therefore, a model transfer with channel patching was implemented to solve the problems of channel inconsistency. The differences between the key points were analyzed. Three popular and highly structured 2D models of OpenPose (OP), stacked Hourglass (HG), and High Resolution (HR) networks were employed. Ways of reusing RGB channels were investigated to fill up the depth channel. Then, their performances were investigated to find out the limitations of each network structure. Results and discussion: The results show that model transfer learning by the mean way of RGB channels patching the lacking channel can improve the average accuracies of pose key points from 1 to 20% than without transfer. 3D accuracies are 0.3 to 0.5% higher than 2D baselines. The stacked structure of the network shows better on hip and knee points than the parallel structure, although the parallel design shows much better on the residue points. As a result, the model transfer can practically fulfill boxing pose estimation from 2D to 3D. [ABSTRACT FROM AUTHOR]

Recently, transfer learning has gained popularity in the machine learning community. Transfer Learning (TL) has emerged as a promising paradigm that leverages knowledge learned from one or more related domains to improve prediction accuracy in a target domain with limited data. However, for time series forecasting (TSF) applications, transfer learning is relatively new. This paper addresses the need for empirical studies as identified in recent reviews advocating the need for practical guidelines for Transfer Learning approaches and method designs for time series forecasting. The main contribution of this paper is the suggestion of a comprehensive framework for Transfer Learning Sensitivity Analysis (SA) for time series forecasting. We achieve this by identifying various parameters seen from various angles of transfer learning applied to time series, aiming to uncover factors and insights that influence the performance of transfer learning in time series forecasting. Undoubtedly, symmetry appears to be a core aspect in the consideration of these factors and insights. A further contribution is the introduction of four TL performance metrics encompassed in our framework. These TL performance metrics provide insight into the extent of the transferability between the source and the target domains. Analyzing whether the benefits of transferred knowledge are equally or unequally accessible and applicable across different domains or tasks speaks to the requirement of symmetry or asymmetry in transfer learning. Moreover, these TL performance metrics inform on the possibility of the occurrence of negative transfers and also provide insight into the possible vulnerability of the network to catastrophic forgetting. Finally, we discuss a sensitivity analysis of an Ensemble TL technique use case (with Multilayer Perceptron models) as a proof of concept to validate the suggested framework. While the results from the experiments offer empirical insights into various parameters that impact the transfer learning gain, they also raise the question of network dimensioning requirements when designing, specifically, a neural network for transfer learning.

In this article, we focus on prediction of a target model by transferring the information of source models. To be flexible, we use semiparametric additive frameworks for the target and source models. Inheriting the spirit of parameter-transfer learning, we assume that different models possibly share common knowledge across parametric components that is helpful for the target predictive task. Unlike existing parameter-transfer approaches, which need to construct auxiliary source models by parameter similarity with the target model and then adopt a regularization procedure, we propose a frequentist model averaging strategy with a J-fold cross-validation criterion so that auxiliary parameter information from different models can be adaptively transferred through data-driven weight assignments. The asymptotic optimality and weight convergence of our proposed method are built under some regularity conditions. Extensive numerical results demonstrate the superiority of the proposed method over competitive methods. [ABSTRACT FROM AUTHOR]

This paper examines the use of prepositions in the L2 English speech production of L1 speakers of Czech. The data is sourced from a spoken corpus comprising forty c.15-minute interviews. L2 English preposition use was studied by means of potential occasion analysis, and the results show that prepositions pose a challenge to EFL learners. Careful scrutiny of the data revealed an increasing tendency toward preposition accuracy in speech across proficiency levels A2 to B2. Moreover, it is hypothesized that the participants' incorrect EFL preposition selection is influenced by their L1 knowledge. [ABSTRACT FROM AUTHOR]

Copyright of Frontiers of Information Technology & Electronic Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Language teachers and researchers argue that the process of learning a language is better understood if the errors that language learners make in constructing the new language system are carefully analyzed. Hence, analyzing errors has become essential for facing and overcoming problems and suggesting solutions regarding different aspects of language teaching and learning. Accordingly, this study seeks to examine the errors that Moroccan high school students of English make in writing, and investigate the reasons behind the occurrence of such errors. The sample of the study consists of students' essays collected from different classes. The data collected were analyzed using Corder's (1967) model, which includes three stages; data collection, description, and explanation. The study's findings showed that Moroccan EFL students make different types of errors in writing. The most significant number of the errors found in the students' essays are grammatical errors, and most of these errors are due to intralingual factors. [ABSTRACT FROM AUTHOR]

In the context of data-driven deep learning, employing multi-fidelity methods for swift and precise wake field prediction is a novel attempt. Current Multi-Fidelity Neural Networks (MFNNs) face accuracy loss due to structure theory limitations, and performance degradation from negative transfer. To address these issues and enhance wake prediction performance, we propose a Dual-Adaptive Multi-Fidelity Neural Network (Ada2MF) framework. This framework features an adaptive multi-fidelity (AMF) module that integrates three sub-networks via a learnable weighted gate, effectively capturing the linear, nonlinear, and residual characteristics of high-fidelity data and mitigating theoretical accuracy loss. Additionally, the adaptive fast weighting (AFW) module employs a dynamic loss-weighting algorithm to optimally balance multi-fidelity losses and prevent negative transfer. Initial validation on benchmark functions and further evaluation using a multi-fidelity wind turbine wake field database confirm the effectiveness of Ada2MF. Specifically, with a complete dataset, Ada2MF achieves 66% and 41% improvements in wake prediction accuracy over single-fidelity neural networks and MFNN, respectively. Even with an 80% reduction in data volume, these improvements escalate to 82% and 78%, without incurring significant accuracy loss. Such results underscore Ada2MF's remarkable ability to improve prediction accuracy while substantially reducing the dependency on high-fidelity data. [Display omitted] [ABSTRACT FROM AUTHOR]

Copyright of Z Teorii I Praktyki Dydaktycznej Języka Polskiego is the property of Wydawnictwo Uniwersytetu Slaskiego and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Here, the authors focus on incrementally acquiring heterogeneous knowledge from both internet and publicly available datasets to reduce the tedious and expensive labelling efforts required in video annotation. An incremental transfer learning framework is presented to integrate heterogeneous source knowledge and update the annotation model incrementally during the transfer learning process. Under this framework, web images and existing action videos form the source domain to provide labelled static and motion information of the target domain videos, respectively. Moreover, according to the semantic of the source domain data, all the source domain data are partitioned into several groups. Different from traditional methods, which compare the entire target domain videos with each source group from the source domain, the authors treat the group weights as sample‐specific variables and optimise them along with new adding data. Two regularisers are used to prevent the incremental learning process from negative transfer. Experimental results on the two large‐scale consumer video datasets (i.e. multimedia event detection (MED) and Columbia consumer video (CCV)) show the effectiveness of the proposed method.

This paper analyses morpho-syntactic interference errors committed by learners of French as a foreign language in four Tanzanian universities: UDSM, UDOM, DUCE and Makumira. The paper has three specific objectives: (i) to identify morpho-syntactic interference errors, (ii) to account for their sources and (iii) to recommend a corrective treatment. The study included a total of 61 respondents. The data was collected through learners’ written texts in French from which a corpus was developed. The study was guided by the interlanguage theory and the error analysis approach. Data analysis was qualitative. The findings reveal that errors included the use of nouns with English origins (18.87%), omission of prepositions (36.79%) and absence of determiners (44.34%). The findings further show that these errors are due to previously acquired or learned languages: Swahili, ethnic community languages and English. Different recommendations are given following the findings. As regards the use of definite and indefinite articles in French, teachers should provide a guided reading of different French texts through which learners will be able to understand how articles are used. To master the use of prepositions, teachers should encourage learners to read a variety of texts in French as this can make them understand and internalize the different prepositions. Moreover, through regular exercises on word formation in French, learners may be able to familiarize themselves with French nouns, hence internalizing their forms. Finally, the learning of French nouns should be done in context.

As an effective learning paradigm against insufficient training samples, multi-task learning (MTL) encourages knowledge sharing across multiple related tasks so as to improve the overall performance. In MTL, a major challenge springs from the phenomenon that sharing the knowledge with dissimilar and hard tasks, known as negative transfer, often results in a worsened performance. Though a substantial amount of studies have been carried out against the negative transfer, most of the existing methods only model the transfer relationship as task correlations, with the transfer across features and tasks left unconsidered. Different from the existing methods, our goal is to alleviate negative transfer collaboratively across features and tasks. To this end, we propose a novel multi-task learning method called task-feature collaborative learning (TFCL). Specifically, we first propose a base model with a heterogeneous block-diagonal structure regularizer to leverage the collaborative grouping of features and tasks and suppressing inter-group knowledge sharing. We then propose an optimization method for the model. Extensive theoretical analysis shows that our proposed method has the following benefits: (a) it enjoys the global convergence property and (b) it provides a block-diagonal structure recovery guarantee. As a practical extension, we extend the base model by allowing overlapping features and differentiating the hard tasks. We further apply it to the personalized attribute prediction problem with fine-grained modeling of user behaviors. Finally, experimental results on both simulated dataset and real-world datasets demonstrate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Recently there has been an increasing interest in the multivariate Gaussian process (MGP) which extends the Gaussian process (GP) to deal with multiple outputs. One approach to construct the MGP and account for non-trivial commonalities amongst outputs employs a convolution process (CP). The CP is based on the idea of sharing latent functions across several convolutions. Despite the elegance of the CP construction, it provides new challenges that need yet to be tackled. First, even with a moderate number of outputs, model building is extremely prohibitive due to the huge increase in computational demands and number of parameters to be estimated. Second, the negative transfer of knowledge may occur when some outputs do not share commonalities. In this paper we address these issues. We propose a regularized pairwise modeling approach for the MGP established using CP. The key feature of our approach is to distribute the estimation of the full multivariate model into a group of bivariate GPs which are individually built. Interestingly pairwise modeling turns out to possess unique characteristics, which allows us to tackle the challenge of negative transfer through penalizing the latent function that facilitates information sharing in each bivariate model. Predictions are then made through combining predictions from the bivariate models within a Bayesian framework. The proposed method has excellent scalability when the number of outputs is large and minimizes the negative transfer of knowledge between uncorrelated outputs. Statistical guarantees for the proposed method are studied and its advantageous features are demonstrated through numerical studies. [ABSTRACT FROM AUTHOR]

Cross-domain object detection in images has attracted increasing attention in the past few years, which aims at adapting the detection model learned from existing labeled images (source domain) to newly collected unlabeled ones (target domain). Existing methods usually deal with the cross-domain object detection problem through direct feature alignment between the source and target domains at the image level, the instance level (i.e., region proposals) or both. However, we have observed that directly aligning features of all object instances from the two domains often results in the problem of negative transfer, due to the existence of (1) outlier target instances that contain confusing objects not belonging to any category of the source domain and thus are hard to be captured by detectors and (2) low-relevance source instances that are considerably statistically different from target instances although their contained objects are from the same category. With this in mind, we propose a reinforcement learning based method, coined as sequential instance refinement, where two agents are learned to progressively refine both source and target instances by taking sequential actions to remove both outlier target instances and low-relevance source instances step by step. Extensive experiments on several benchmark datasets demonstrate the superior performance of our method over existing state-of-the-art baselines for cross-domain object detection. [ABSTRACT FROM AUTHOR]

Currently, most models rarely consider the negative transfer problem in the research field of cross-subject EEG emotion recognition. To solve this problem, this paper proposes a semi-supervised domain adaptive algorithm based on few labeled samples of target subject, which called multi-domain geodesic flow kernel dynamic distribution alignment (MGFKD). It consists of three modules: 1) GFK common feature extractor: projects the feature distribution of source and target subjects to the Grassmann manifold space, and obtains the latent common features of the two feature distributions through GFK method. 2) Source domain selector: obtains pseudo-labels of the target subject through weak classifier, finds "golden source subjects" by using few known labels of target subjects. 3) Label corrector: uses a dynamic distribution balance strategy to correct the pseudo-labels of the target subject. We conducted comparison experiments on the SEED and SEED-IV datasets, and the results show that MGFKD outperforms unsupervised and semi-supervised domain adaptation algorithms, achieving an average accuracy of 87.51±7.68% and 68.79±8.25% on the SEED and SEED-IV datasets with only one labeled sample per video for target subject. Especially when the number of source domains is set as 6 and the number of known labels is set as 5, the accuracy increase to 90.20±7.57% and 69.99±7.38%, respectively. The above results prove that our proposed algorithm can efficiently and quickly improve the cross-subject EEG emotion classification performance. Since it only need a small number of labeled samples of new subjects, making it has strong application value in future EEG-based emotion recognition applications. • A semi-supervised DA algorithm for cross-subject emotion recognition is proposed. • The proposed algorithm achieves higher accuracy in SEED and SEED-IV datasets. • It is a non-deep learning method with strong interpretability and short running time. • It could build efficient emotion decoding model for new subject quickly in real-life. [ABSTRACT FROM AUTHOR]

This research intends to find out whether Lithuanians studying English as a foreign language make certain mistakes because of the influence of their native language. It focuses on negative transfer in writing in English and is qualitative rather than quantitative. The article discusses the errors and illustrates them with examples that come from a corpus for which the data was obtained from 34 Moodle forum posts written by English B2 students, native speakers of Lithuanian who were in year one or year two of their studies in various study programmes but also studied English at Vytautas Magnus University as an obligatory subject. The students participated in this forum in October 2018 and reflected on the week of presentations they had recently had: they were asked to write what they liked or did not like in the presentations their colleagues had given in class, what went well and what did not, what they should improve in the future, etc. This study identified the types of errors (based on Camilleri, 2004) that occurred most frequently and their source (based on Camilleri, 2004; Brogan & Son, 2015). Most frequently the students made errors in the cases where there was a specific grammatical category in English, but it was non-existent in Lithuanian, while sometimes the source of errors was literal translation from the native language. The error analysis shows that in the English classroom specific attention should be given to the verb forms “is” and “are”, “was” and “were”, “has” and “have”, articles, collocations, tense agreement, quantifiers, the sentence structure of the English language and the importance of word meanings.

This is a follow-up study that explores if there are any common patterns among the cases of negative transfer in the participants’ answers on implicit differentiation problems using the multiple case study methodology. The data was collected from eighty-one undergraduate students who were in STEM majors in a university in the Midwest. The participants’ answers indicated three main sources of negative transfer including 1) trigonometric identity, 2) u, v substitution technique of integration by parts, and 3) partial derivative. There were also other cases of negative transfer that applied improper concepts or the mixture of negative transfer and failed positive transfer. The findings and the limitation of this study suggest directions of potential future studies.

This paper analyses morpho-syntactic interference errors committed by learners of French as a foreign language in four Tanzanian universities: UDSM15, UDOM16, DUCE17 and Makumira. The paper has three specific objectives: (i) to identify morpho-syntactic interference errors, (ii) to account for their sources and (iii) to recommend a corrective treatment. The study included a total of 61 respondents. The data was collected through learners' written texts in French from which a corpus was developed. The study was guided by the interlanguage theory and the error analysis approach. Data analysis was qualitative. The findings reveal that errors included the use of nouns with English origins (18.87%), omission of prepositions (36.79%) and absence of determiners (44.34%). The findings further show that these errors are due to previously acquired or learned languages: Swahili, ethnic community languages and English. Different recommendations are given following the findings. As regards the use of definite and indefinite articles in French, teachers should provide a guided reading of different French texts through which learners will be able to understand how articles are used. To master the use of prepositions, teachers should encourage learners to read a variety of texts in French as this can make them understand and internalize the different prepositions. Moreover, through regular exercises on word formation in French, learners may be able to familiarize themselves with French nouns, hence internalizing their forms. Finally, the learning of French nouns should be done in context. [ABSTRACT FROM AUTHOR]

A major assumption in data mining and machine learning is that the training set and test set come from the same domain. They share the same feature space and have the same distribution. However, in many real-world applications, the training set and test set usually come from different domains. Thus, there might be negative similarities between different domains so that the negative transfer problem caused by negative similarity may happen. In this paper, we propose a novel method named active transfer learning (ATL) to solve the above problem. Specifically, the orthogonal projection matrix and the weight coefficient vector are introduced to extend maximum mean discrepancy (MMD) so that it can minimize MMD and simultaneously eliminate the negative transfer. To find the informative and discriminative subsets from the source domain, we then propose an information diversity term by using the local geometric structure information of the source samples. Besides, by using the label information of source samples, our method can guarantee the selected subsets as discriminative as possible. Finally, to efficiently implement the proposed method, an alternating optimization approach, which is based on the alternating direction method of multipliers (ADMM), is designed to solve the optimization problem. To demonstrate the effectiveness of the proposed ATL model, experiments are conducted on five real-world data sets. The experimental results show the superiority of our method over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The present study investigates the correlation between language proficiency, collocations and the role of L1 transfer with collocations. This is a quantitative research. The research places more emphases on collecting data in the form of numbers. It is also experimental research in the sense that it tests participants to measure their variables. The participants of the study were 57 Persian B.A students, both male and female from Islamic Azad University of Bandar Abbas, Iran. The results showed that there is a significant relationship between Iranian subjects’ language proficiency, as measured by the Michigan proficiency test and their knowledge of collocations, as measured by their performance on a collocation test designed for the current study. The results obtained from the research indicate that Iranian EFL learners are more likely to use the right collocation in cases of L1 transfer. This suggests that positive transfer plays a major role when it comes to EFL learners’ ability to produce the right collocations in their L2. The findings of this study have some implications for language teaching. Teachers can put emphasis on the inclusion of selected grammatical and lexical collocations in reading comprehension passages.

This paper draws motivation from the remarkable ability of humans to extract useful building-blocks of knowledge from past experiences and spontaneously reuse them for new and more challenging tasks. It is contended that successfully replicating such capabilities in computational solvers, particularly global black-box optimizers, can lead to significant performance enhancements over the current state-of-the-art. The main challenge to overcome is that in general black-box settings, no problem-specific data may be available prior to the onset of the search, thereby limiting the possibility of offline measurement of the synergy between problems. In light of the above, this paper introduces a novel evolutionary computation framework that enables online learning and exploitation of similarities across optimization problems, with the goal of achieving an algorithmic realization of the transfer optimization paradigm. One of the salient features of our proposal is that it accounts for latent similarities which while being less apparent on the surface, may be gradually revealed during the course of the evolutionary search. A theoretical analysis of our proposed framework is carried out, substantiating its positive influences on optimization performance. Furthermore, the practical efficacy of an instantiation of an adaptive transfer evolutionary algorithm is demonstrated on a series of numerical examples, spanning discrete, continuous, as well as single- and multi-objective optimization. [ABSTRACT FROM AUTHOR]

Individual distribution discrepancy poses significant challenges to cross-subject electroencephalography (EEG) signal decoding. Although transfer learning has emerged as an effective approach to minimize this distribution gap, EEG signals' low voltage amplitude makes them vulnerable to noise and results in abnormal, low-quality samples that cause negative transfer phenomenon. This phenomenon, in turn, undermines the efficacy of transfer learning and impedes brain-computer interface (BCI) applications. To overcome this challenge, we introduce the manifold embedded instance selection (MEIS) algorithm, which addresses negative transfer. The MEIS algorithm operates in two ways: converting raw EEG matrices into manifold embedded vectors that maintain sample discriminability, and designing an evaluator to assess the transferability of samples and filter out negative transfer samples from the source domain. When faced with a large number of source domains, our proposed method employs domain similarity estimation to determine the most beneficial subset of source domains for the target domain. The effectiveness of the proposed method is confirmed via offline and simulated online motor imagery-based BCI experiments where it demonstrates superior accuracy compared to other advanced techniques. Additionally, the MEIS algorithm significantly reduces both training time and the number of required training samples without affecting the model's performance. The code is available at https://github.com/ZilinL/MEIS. [ABSTRACT FROM AUTHOR]

To improve the classification results of high-resolution remote sensing images (RSIs), it is necessary to use feature transfer methods to mine the relevant information between high-resolution RSIs and low-resolution RSIs to train the classifiers together. Most of the existing feature transfer methods can only handle homogeneous data (i.e., data with the same dimension) and are susceptible to the quality of the RSIs, while RSIs with different resolutions present different feature dimensions and samples obtained from illumination conditions. To obtain effective classification results, unlike existing methods that focus only on the projection transformation in feature space, a joint feature-space and sample-space heterogeneous feature transfer (JFSSS-HFT) method is proposed to simultaneously process heterogeneous multi-resolution images in feature space using projection matrices of different dimensions and reduce the impact of outliers by adaptive weight factors in the sample space simultaneously to reduce the occurrence of negative transfer. Moreover, the maximum interclass variance term is embedded to improve the discriminant ability of the transferred features. To solve the optimization problem of JFSSS-HFT, the alternating-direction method of multipliers (ADMM) is introduced to alternatively optimize the parameters of JFSSS-HFT. Using different types of ship patches and airplane patches with different resolutions, the experimental results show that the proposed JFSSS-HFT obtains better classification results than the typical feature transferred methods.

There is no dearth of publication on pronunciation problems of different L1-bacground learners in EFL contexts; however, research in ESL situations (where English is spoken outside the classroom) in general, and in the Nigerian context, in particular, is scarce. Accordingly, to fill this research gap the present study set out to investigate the pronunciation problems of Hausa speakers of English in Nigeria. To achieve the goals of the research, 60 native speakers of Hausa studying at three universities in Northern Cyprus participated in the study. The participants’ pronunciation problems of English were elicited by mean of a pronunciation test that consisted of a word list, a short paragraph, and 15 individual sentences. Moreover, 15 pictures were shown to the participants to name while being audio-recorded. All the test items contained English consonants and vowels with potential pronunciation difficulties for Hausa speakers of English. The collected data were then transcribed and analyzed, and percentages and frequencies of pronunciation errors were computed. The results revealed that native speakers of Hausa face problems in pronouncing certain English vowels (i.e., /ᴧ/, /ᴐ:/ and /з:/) and consonants ( and /ð/). Theoretically, the findings lend support to the notion of negative transfer as all of the errors were the result of mother tongue interference. The findings are interpreted to have pedagogical implications for ESL teachers and syllabus designers in general and in Hausa- speaking communities, in particular.

Multi-task clustering improves the clustering performance of each task by transferring knowledge across related tasks. Most existing multi-task clustering methods are based on the ideal assumption that the tasks are completely related. However, in real applications, the tasks are usually partially related. In these cases, brute-force transfer may cause negative effect which degrades the clustering performance. In this paper, we propose two multi-task clustering methods for partially related tasks: the self-adapted multi-task clustering (SAMTC) method and the manifold regularized coding multi-task clustering (MRCMTC) method, which can automatically identify and transfer related instances among the tasks, thus avoiding negative transfer. Both SAMTC and MRCMTC construct the similarity matrix for each target task by exploiting useful information from the source tasks through related instances transfer, and adopt spectral clustering to get the final clustering results. But, they learn the related instances from the source tasks in different ways. Experimental results on real data sets show the superiorities of the proposed algorithms over traditional single-task clustering methods and existing multi-task clustering methods on both completely and partially related tasks. [ABSTRACT FROM AUTHOR]

Transfer learning is a machine learning paradigm where knowledge from one problem is utilized to solve a new but related problem. While conceivable that knowledge from one task could help solve a related task, if not executed properly, transfer learning algorithms can impair the learning performance instead of improving it – commonly known as negative transfer. In this paper, we use a parametric statistical model to study transfer learning from a Bayesian perspective. Specifically, we study three variants of transfer learning problems, instantaneous, online, and time-variant transfer learning. We define an appropriate objective function for each problem and provide either exact expressions or upper bounds on the learning performance using information-theoretic quantities, which allow simple and explicit characterizations when the sample size becomes large. Furthermore, examples show that the derived bounds are accurate even for small sample sizes. The obtained bounds give valuable insights into the effect of prior knowledge on transfer learning, at least with respect to our Bayesian formulation of the transfer learning problem. In particular, we formally characterize the conditions under which negative transfer occurs. Lastly, we devise several (online) transfer learning algorithms that are amenable to practical implementations, some of which do not require the parametric assumption. We demonstrate the effectiveness of our algorithms with real data sets, focusing primarily on when the source and target data have strong similarities. [ABSTRACT FROM AUTHOR]