Negative Transfer Research Articles

Writing in English and Writing in Standard Arabic in Higher Education: A Correlational Study

Writing as a mental and cognitive process entails bringing into light students’ written products. Writing in higher education is considered one of the most important skills that students should develop. In this regard, this study investigates the correlation between writing in Standard Arabic and writing in English among university students in Morocco. To verify this link, two tests were used as research instruments. Students were asked to write a paragraph in Standard Arabic and another in English. The participants were 33 male and female university students. To analyse the scores that students obtained in the two tests, a Pearson Product Moment correlation was used as a statistical procedure. The results indicate that there is a significant correlation between writing in Standard Arabic and writing in English. The students who performed well in Standard Arabic also did well in writing in English. It has also been found that there is a 21.20% relationship between the two variables under examination. The findings of this study support the Chomskyan Universal Grammar perspective. It also supports the notion of positive transfer. The results of this study have different implications for language teaching and learning.

IF-EDAAN: An information fusion-enhanced domain adaptation attention network for unsupervised transfer fault diagnosis

Unlike domain adaptation methods that rely on single-source information for transfer diagnosis, multi-source information-based domain adaptation methods can leverage the extensive diagnostic features derived from multiple sources of data. However, the issues of potential feature conflicts, the critical fault information loss, and high computational burdens still hinder effective applications of multi-source information domain adaptation for transfer diagnosis. For resolving these issues, this study proposes an unsupervised multi-source information domain adaptation approach for transfer fault diagnosis, which utilizes an information fusion-enhanced domain adaptation attention network (IF-EDAAN). Firstly, an information fusion method that converts multi-source information into a fused image using principal component analysis and signal-to-image conversion is employed to enhance and compress data from both the source and target domains. Then, a parameter-free attention mechanism (PFAM) module is proposed to adaptively focus on the domain-invariant temporal and spatial features of information fusion samples. Subsequently, the weight assignment module and joint maximum mean discrepancy metric strategy are proposed to mitigate negative transfer, thus enabling the effective extraction and alignment of domain-invariant temporal and spatial features. Finally, experiment validations on two rotating machinery datasets have been comprehensively elaborated to verify the efficacy and advantages of our proposed IF-EDAAN approach for transfer fault diagnosis across different working conditions. Experiment results have proved that our proposed IF-EDAAN approach can rapidly adapt to new transfer diagnostic scenarios with impressive performance and outperform several mainstream unsupervised domain adaptation approaches.

Federated Adaptive Causal Estimation (FACE) of Target Treatment Effects

Federated learning of causal estimands may greatly improve estimation efficiency by leveraging data from multiple study sites, but robustness to heterogeneity and model misspecifications is vital for ensuring validity. We develop a Federated Adaptive Causal Estimation (FACE) framework to incorporate heterogeneous data from multiple sites to provide treatment effect estimation and inference for a flexibly specified target population of interest. FACE accounts for site-level heterogeneity in the distribution of covariates through density ratio weighting. To safely incorporate source sites and avoid negative transfer, we introduce an adaptive weighting procedure via a penalized regression, which achieves both consistency and optimal efficiency. Our strategy is communication-efficient and privacy-preserving, allowing participating sites to share summary statistics only once with other sites. We conduct both theoretical and numerical evaluations of FACE and apply it to conduct a comparative effectiveness study of BNT162b2 (Pfizer) and mRNA-1273 (Moderna) vaccines on COVID-19 outcomes in U.S. veterans using electronic health records from five VA regional sites. We show that compared to traditional methods, FACE meaningfully increases the precision of treatment effect estimates, with reductions in standard errors ranging from 26 % to 67 % .

Negative Transfer of Changzhou Dialect to English Phonetic Acquisition of Primary School Students and Its Teaching Implications

With China's ongoing opening up and steady progress in internationalization, English, as an international common language, has become a bridge for everyone to communicate across cultures with the world. However, many Chinese learners focus more on reading and writing skills while neglecting speaking proficiency. Speaking, as the most basic pattern of communication, plays a vital role in English second language acquisition. China is a vast country with many dialects. However, there is limited research on how the Changzhou dialect influences phonetic transfer in second language acquisition. Therefore, this paper focuses on Changzhou dialect to explore its negative transfer effect on primary school students' English phonetics acquisition and the corresponding teaching implications. This paper analyzes the negative transfer effect based on former research, online resources, and teaching and learning experiences. In addition, it examines the extent of the negative transfer of Changzhou dialect on English phonetics acquisition through questionnaire survey. Research has shown that the Changzhou dialect exerts a negative transfer effect on the English phonetic acquisition of primary school students. The primary causes of students' pronunciation errors are phoneme substitution, phoneme incompleteness and the inability to distinguish between long and short sounds. Improving students' English accents requires joint efforts from schools, teachers, and students.

The Interference of The Indonesian Language in The Daily Arabic Conversation Among Female Students at an Islamic High School in Indonesia

Interference is the process of language transfer, where elements from the mother tongue (first language) are integrated into the second language (target language). This research aims to determine (1) the forms of Indonesian language interference in daily Arabic conversations among female students at Madrasah Aliyah Islamiyah Nurul Haramain NWDI Narmada, West Lombok, and (2) the factors causing Indonesian language interference in these daily conversations. A qualitative research approach using the case study method was employed, with data collection techniques including observation, interviews, and documentation. The findings of this study indicate that (1) two types of language interference occur among the female students: syntactic interference and phonological interference, and (2) five factors contribute to the Indonesian language interference in daily Arabic conversations, including (1) difficulties in understanding language grammar, (2) limited vocabulary memorization, (3) lack of awareness in using the target language, (4) teaching methods in the classroom, and (5) beginners in learning Arabic.

Multi-view domain adaption based multi-scale convolutional conditional invertible discriminator for cross-subject electroencephalogram emotion recognition.

Cross subject Electroencephalogram (EEG) emotion recognition refers to the process of utilizing electroencephalogram signals to recognize and classify emotions across different individuals. It tracks neural electrical patterns, and by analyzing these signals, it's possible to infer a person's emotional state. The objective of cross-subject recognition is to create models or algorithms that can reliably detect emotions in both the same person and several other people. Accurately predicting emotions poses challenges due to dynamic traits. Models struggle with feature extraction, convergence, and negative transfer issues, hindering cross subject emotion recognition. The proposed model employs thorough signal preprocessing, Short-Time Geodesic Flow Kernel Fourier Transform (STGFKFT) for feature extraction, enhancing classifiers' accuracy. Multi-view sheaf attention improves feature discrimination, while the Multi-Scale Convolutional Conditional Invertible Puma Discriminator Neural Network (MSCCIPDNN) framework ensures generalization. Efficient computational techniques and the puma optimization algorithm enhance model robustness and convergence. The suggested framework demonstrates extraordinary success with high accuracy, of 99.5%, 99% and 99.50% for SEED, SEED-IV, and DEAP dataset sequentially. By incorporating these techniques, the proposed method aims to precisely recognition emotions, and accurately captures the features, thereby overcoming the limitations of existing methodologies.

Multimodal optimal matching and augmentation method for small sample gesture recognition

In human-computer interaction, gesture recognition based on physiological signals offers advantages such as a more natural and fast interaction mode and less constrained by the environment than visual-based. Surface electromyography-based gesture recognition has significantly progressed. However, since individuals have physical differences, researchers must collect data multiple times from each user to train the deep learning model. This data acquisition process can be particularly burdensome for non-healthy users. Researchers are currently exploring transfer learning and data augmentation techniques to enhance the accuracy of small-sample gesture recognition models. However, challenges persist, such as negative transfer and limited diversity in training samples, leading to suboptimal recognition performance. Therefore, We introduce motion information into sEMG-based recognition and propose a multimodal optimal matching and augmentation method for small sample gesture recognition, achieving efficient gesture recognition with only one acquisition per gesture. Firstly, this method utilizes the optimal matching signal selection module to select the most similar signals from the existing data to the new user as the training set, reducing inter-domain differences. Secondly, the similarity calculation augmentation module enhances the diversity of the training set. Finally, the Modal-type embedding enhances the information interaction between each mode signal. We evaluated the effectiveness on Self-collected Stroke Patient, the Ninapro DB1 dataset and the Ninapro DB5 dataset and achieved accuracies of 93.69%, 91.65% and 98.56%, respectively. These results demonstrate that the method achieved performance comparable to traditional recognition models while significantly reducing the collected data.

Substrate prediction for RiPP biosynthetic enzymes via masked language modeling and transfer learning.

Ribosomally synthesized and post-translationally modified peptide (RiPP) biosynthetic enzymes often exhibit promiscuous substrate preferences that cannot be reduced to simple rules. Large language models are promising tools for predicting the specificity of RiPP biosynthetic enzymes. However, state-of-the-art protein language models are trained on relatively few peptide sequences. A previous study comprehensively profiled the peptide substrate preferences of LazBF (a two-component serine dehydratase) and LazDEF (a three-component azole synthetase) from the lactazole biosynthetic pathway. We demonstrated that masked language modeling of LazBF substrate preferences produced language model embeddings that improved downstream prediction of both LazBF and LazDEF substrates. Similarly, masked language modeling of LazDEF substrate preferences produced embeddings that improved prediction of both LazBF and LazDEF substrates. Our results suggest that the models learned functional forms that are transferable between distinct enzymatic transformations that act within the same biosynthetic pathway. We found that a single high-quality data set of substrates and non-substrates for a RiPP biosynthetic enzyme improved substrate prediction for distinct enzymes in data-scarce scenarios. We then fine-tuned models on each data set and showed that the fine-tuned models provided interpretable insight that we anticipate will facilitate the design of substrate libraries that are compatible with desired RiPP biosynthetic pathways.

Transfer fault prognosis of bearing across different machines with the application of negative transfer mitigation

Transfer fault prognosis of bearing across different machines with the application of negative transfer mitigation

A Study on the Phenomenon of Negative Transfer in Online English Vocabulary Learning—A Case Study of Senior Three Students in Tuhuang High School

A Study on the Phenomenon of Negative Transfer in Online English Vocabulary Learning—A Case Study of Senior Three Students in Tuhuang High School

THE INFLUENCE OF NATIVE LANGUAGE ON YOUNG INDONESIAN LEARNERS’ ABILITY TO PRONOUNCE ENGLISH SHORT VOWELS

This study examines the influence of native language on the ability of young Indonesian learners to pronounce English short vowels. Focusing on kindergarten-level students in Badung, Bali, the research explores difficulties in producing English vowel sounds absent in the Indonesian vowel system. Through observation and analysis of students’ pronunciation recordings of consonant-vowel-consonant (CVC) words, the findings reveal that vowel sounds such as /æ/, /ʌ/, and /ɒ/ are often substituted with more familiar vowels from the Indonesian language, such as /e/ and /o/. These mispronunciations are attributed to the limited vowel system of Indonesian, which consists of only five primary vowels. The results support the theory of first language (L1) interference, which posits that differences in phonological structures between the native and target languages pose challenges in language learning. This study highlights the importance of increased exposure to English pronunciation and phonetic practice to enhance learners' pronunciation skills.

Contrastive Analysis of Noun-Adjective Word Order in Students' Writing: A Case Study of Common Core Classes in a Moroccan Public High School

<em>This study explores word order patterns, specifically focusing on the noun-adjective inversion observed in the written language among EFL learners in a Moroccan public high school class. In a case study design with 102 participants, error analysis of learners’ produced language reveals a consistent and frequent deviating pattern as a result of negative transfer from L1 to L2, aligning with the interlanguage hypothesis. The findings enable Moroccan English teachers to predict and address language errors through tailored teaching strategies. Notably, a cross-sectional approach using controlled words unscrambling activity demonstrates the learners’ consistent deviation from English word order, except for instances of familiar phrases, suggesting exposure plays a key role in language accuracy. English teachers in Morocco can significantly profit from these findings by using them to anticipate, react, and modify their teaching strategies. Crucially, using well-known sentences correctly emphasizes how important repeated exposure is to language acquisition</em>

TEMPORAL MISMATCHES: UNDERSTANDING TENSE ERRORS IN ALBANIAN L2 ENGLISH LEARNERS

This study explores the challenges Albanian learners in Kosovo face while acquiring the English tense system, focusing specifically on common difficulties with tense usage. The research gathered data from questionnaires and written texts, revealing a high incidence of errors in tenses such as the Past Simple, Present Simple, Present Perfect, Past Perfect, and Future Simple. Incorrect tense constructions emerged as the most frequent issue, with native language interference and rule overgeneralization contributing to the learners’ difficulties. Mistakes were particularly prominent in the Past and Present Simple tenses, suggesting that these areas require targeted attention in language instruction. The study emphasizes the need for more comprehensive research, especially through longitudinal and spoken data analysis, to improve teaching strategies for Albanian ESL learners in addressing tense-related errors.

Examination of Sporting Successes of European Football Clubs with Positive and Negative Transfer Balance Sheet

This study aims to examine the relationship between football clubs' transfer expenditures and their sporting success. As football has become an integral part of the global economy, the transfer strategies of clubs play a significant role in both economic and sporting success. Various analyses have been conducted in the literature regarding the relationship between football clubs' positive and negative transfer balances, their league performance, and success in European competitions. The study compares the success of clubs with negative and positive transfer balances between 2019 and 2023, revealing that transfer expenditures do not always directly correlate with sporting success. The results show that while some clubs with high transfer expenditures perform poorly, others with lower spending are able to achieve success. Clubs following positive transfer policies tend to achieve more efficient results, highlighting the importance of long-term strategic investments. Additionally, the research emphasizes that managing transfer expenditures in a balanced and strategic manner is critical for financial sustainability and sporting success. These findings suggest that football clubs should reassess their transfer strategies and adopt a more cost-effective approach.

L1 Interference in English Major Students’ Pronunciation at Technical University of Cotopaxi

This study aims to investigate the impact of mother tongue interference (Spanish) on the pronunciation of the phoneme /d/ in different positions among seventh-semester students majoring in English at the Technical University of Cotopaxi. The study employs a mixed approach with a correlational design. The correlation between the variables allows for the analysis of the quantifiable relationship between Spanish as a mother tongue interference and the quality of the pronunciation of the phoneme /d/ in English. The degree and direction of this correlation will help determine whether and to extent the interference has a significant impact on pronunciation. The findings of the study shed light on the challenges faced by students when pronouncing words with the phoneme /d/ in different positions. In the initial position, the interference is characterized by the transference of Spanish tongue positions and aspiration tendencies, leading to the production of [d] instead of the standard English sound, information detailed in the results and discussion section. In the middle position, students exhibit a tendency to apply Spanish flapping and elision patterns, resulting in the articulation of a tap or alveolar flap [ɾ] instead of the expected English sound. In the final position, the influence of Spanish leads some students to voice the English final /d/ even when English requires voicelessness, indicating the persistence of L1 interference. The results emphasize the importance of addressing specific phonetic and phonological aspects that arise due to mother tongue interference in pronunciation training. Understanding the intricate interplay between L1 influence and the articulation of the phoneme /d/ can lead to targeted interventions that enhance students' pronunciation skills and contribute to their overall communicative competence in English

Agreement Errors in Norwegian L1, English L2 Quantity Pseudopartitives

The pseudopartitive is a construction which is used by L2 learners but is not taught explicitly in Norwegian schools. Further, it shows non-canonical agreement properties (agreement with the embedded noun), which may make it challenging for learners. In this study, we investigated subject-verb agreement accuracy in quantity pseudopartitives among Norwegian L1, English L2 learners. English and Norwegian show similar headedness properties for the pseudopartitive, but unlike English, Norwegian lacks a system of verbal agreement. We collected learner data from the Tracking Written Learner Language corpus (Dirdal et al. 2022), and found a subject-verb agreement error rate of 29.1%, suggesting that despite the overlap in headedness in the two languages, learners struggle to produce agreement correctly. We also found cases of negative transfer, mostly related to specific lexical items (lack, pair). Altogether, this shows that the negative transfer of some properties, e.g. agreement marking, may limit the positive transfer of other structures, e.g. headedness in pseudopartitives.

The Negative Migration of Mother Tongue and Coping Strategies in IELTS Writing of China International High School

This study takes the IELTS writing texts of some senior one students in an international department in Quanzhou as models, analyzes and studies the phenomenon of negative transfer of native language in the texts, and formulates relevant countermeasures with the purpose of minimizing the impact of negative transfer of native language in IELTS writing. It is hoped that this study can provide new ideas and suggestions for IELTS test takers and educators in related industries through the analysis of students' texts and the proposal of countermeasures.

VIETNAMESE UNIVERSITY NON-MAJOR ENGLISH STUDENTS’ WRITING PROBLEMS IN TERMS OF THE FACTORS RELATING TO THEIR META-KNOWLEDGE OF ENGLISH INFORMATION STRUCTURE

This paper reports the results of a study which investigates the problems Vietnamese university non-major English students of two levels of English proficiency encountered in their English writing in terms of the factors relating to their meta-knowledge of English information structure and the extent to which a cognitive meta-linguistic approach to teaching second language (L2) writing can help the learners overcome their writing problems and develop their written communicative ability by first enhancing their meta-knowledge of English information structure. Their problems are conceptualized as being associated with their employing or not employing some writing strategies that can either support or hinder their writing skills development. The analyses of the problems were based on the learners’ responses to the questionnaire and interviews, their writing in the writing tasks and classroom-based worksheets and answer sheets. The findings suggested that the learners in the study encountered the writing problems anticipated before, during and after the intervention. The percentages of the learners encountering the problems decreased over time and the extent to which each problem was solved towards the end of the post-teaching phase varied according to each specific problem. Transfer of first language (L1) strategies was reported in all of the writing problems. The influence of L1 transfer was variable with extremely low evidence of topic-prominent structure. In general, there were no big differences between the two groups in their encountering and overcoming the problems investigated.

Angle-based transfer support matrix machine for roller bearing fault diagnosis under limited labeled data

In real-world fault diagnosis, the process of acquiring fault vibration signals frequently faces various challenges, which lead to certain fault types being represented by a scarcity of vibration signal data. Although support matrix machine (SMM) exhibits excellent classification ability in matrix-based data, its ideal model is difficult to establish with limited labeled samples. To address this limitation, a novel angle-based transfer support matrix machine (ATSMM) is proposed, which integrates matrix-based classifier and transfer learning theory. ATSMM employs an angle-based transfer learning method, which aims to reduce the angle between the normals of the target domain hyperplane and the source domain hyperplane, enabling the transfer of structural information across different domains. Meantime, ATSMM effectively addresses the issue of negative transfer that frequently occurs in transfer learning, which adds the weights calculated by the maximum mean discrepancy method to further improve the transfer effectiveness of the target domain model. The superiority of ATSMM is verified by two different roller bearing datasets, and the outcomes of experiments exhibit that the proposed ATSMM has excellent classification performance compared to alternative methods, especially in scenarios where the labeled sample size is limited.

Division-selection transfer learning for prediction based dynamic multi-objective optimization

Dynamic multi-objective optimization problems (DMOPs) are challenging as they require capturing the Pareto optimal front (POF) and Pareto optimal set (POS) during the optimization process. In recent years, transfer learning (TL) has emerged the empirical knowledge and is an effective approach to solve DMOPs. However, negative transfer can occur when the transfer method is not suitable for the transfer task. It may deviate the search path and seriously reduce the efficiency, so how to reduce the occurrence of negative transfer to save the running time of dynamic multi-objective evolutionary algorithm (DMOEA) is an important issue to be addressed. A division-selection transfer learning evolutionary algorithm for dynamic multi-objective optimization (DST-DMOEA) is designed towards this aim. Specifically, individuals with high Spearman correlation are relatively stable in different environments, selecting them to train the Support Vector Regression (SVR) model ensures a more accurate capture of solution features, predicting the objective values of historical solutions based on the model, and thus divide historical solutions into elite and non-elite solutions. Subsequently, for the elite solutions, individual TL that incorporates local information for optimization and transfer is used, while the non-elite solutions are handled with manifold TL method to obtain the overall data distribution and understand the internal structure. Then, merge the predicted individuals generated by two parts of TL will constitute as the initial population in the optimization process. Compared with other algorithms, the initial solution of DST-DMOEA is closer to the real POF, effectively reducing negative transfer. In addition, in 51 test instances, DST-DMOEA has shown superior performance in over 30 instances.