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The aim of this paper is to improve the parameter estimation accuracy of the system to be identified by using measurements from a known system. By introducing the transfer gain matrix and setting the effective identification criterion, a novel transfer sparse identification method is raised, which deals with the sparse issues more precise. Besides, the unbiased form is given in the parameter analysis and the recursion form can prevent the dimension catastrophe related problems. Moreover, in order to test the effects of the transfer and avoid bad performance, a negative transfer analysis condition is carried out. Finally, the simulation verifies the enhancements and benefits of the proposed transfer sparse identification method, confirming that the transfer performance outperforms better than that of no transfer, especially on the zero parameters identification. [ABSTRACT FROM AUTHOR]

Building on the cross-linguistic variability in the meaning of vague quantifiers, this study explores the potential for negative transfer in Italian-Slovenian bilinguals concerning the use of quantificational determiners, specifically the translational equivalents of the English “many”, that is the Slovenian "precej" and "veliko". The aim is to identify relevant aspects of pragmatic knowledge for cross-linguistic influence. The study presents the results of a sentence-picture verification task in which Slovenian native speakers and Italian-Slovenian bilinguals evaluated sentences of the form "Quantifier X are Y" in relation to visual contexts. The results suggest that Italian learners of Slovenian, unlike Slovenian native speakers, fail to distinguish between "precej" and "veliko". This finding aligns with the negative transfer hypothesis. The study highlights the potential role of pragmatic knowledge in cross-linguistic transfer, particularly in the context of vague quantifiers. [ABSTRACT FROM AUTHOR]

Multi-task learning (MTL) is widely used in the online recommendation and financial services for multi-step conversion estimation, but current works often overlook the sequential dependence among tasks. In particular, sequential dependence multi-task learning (SDMTL) faces challenges in dealing with complex task correlations and extracting valuable information in real-world scenarios, leading to negative transfer and a deterioration in the performance. Herein, a systematic learning paradigm of the SDMTL problem is established for the first time, which applies to more general multi-step conversion scenarios with longer conversion paths or various task dependence relationships. Meanwhile, an SDMTL architecture, named Task-Aware Feature Extraction (TAFE), is designed to enable the dynamic task representation learning from a sample-wise view. TAFE selectively reconstructs the implicit shared information corresponding to each sample case and performs the explicit task-specific extraction under dependence constraints, which can avoid the negative transfer, resulting in more effective information sharing and joint representation learning. Extensive experiment results demonstrate the effectiveness and applicability of the proposed theoretical and implementation frameworks. Furthermore, the online evaluations at MYbank showed that TAFE had an average increase of 9.22% and 3.76% in various scenarios on the post-view click-through & conversion rate (CTCVR) estimation task. Currently, TAFE is deployed in an online platform to provide various traffic services. [ABSTRACT FROM AUTHOR]

Multitask optimization algorithms can simultaneously derive the best solution for different tasks; however, the convergence speed of such algorithms is slow when frequent negative transfers occur. This is primarily because the similarity function of different tasks is designed only in the decision or target space. Moreover, an algorithm is prone to fall into local optima when population diversity is lost. To reduce negative migration and balance diversity and enhance convergence of multitask optimization algorithms, a multitask particle swarm optimization algorithm based on dual spatial similarity (MTPSO-DSS) is developed in this study. A new similarity function is built into the algorithm for the different tasks based on both decision and target spaces, whereby the transfer probability is adaptively adjusted. The new similarity function, which is more rigorous and accurate, can reduce the probability of negative migration and maintain the convergence speed. Furthermore, a new updating method is designed to handle negative migration and increase diversity of search directions. Adaptive mutation and non-allelic gene crossover strategies are designed to increase the diversity of the algorithm and help it escape from local optima. To verify the performance of the proposed algorithm, nine general multitasking optimization test functions are tested via the proposed algorithm, and the results are compared with other eight multitasking algorithms. The proposed algorithm outperformed the other algorithms for most functions in terms of convergence accuracy and speed, and the average improvement in the convergence accuracy compared with the other eight algorithms is between 23.35 and 99.99%. [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. [ABSTRACT FROM AUTHOR]

Parallel corpora for low-resource Arabic dialects and English are limited and small-scale, and most neural machine translation models, including Google Translate, rely mainly on parallel corpora of standard Arabic and English to train for dialectal Arabic translation. A model well trained to translate to and from standard Arabic is believed to efficiently translate dialectal Arabic, given their similarities. This study demonstrates the impact of not using large-scale, dialect-specific parallel corpora by quantitatively and qualitatively analyzing the performance of Google Translate in translating Egyptian Arabic adjuncts. Compared to human reference translation, Google Translate achieved a low BLEU score of 14.69. Qualitative analysis showed that reliance on standard Arabic parallel corpora caused a negative transfer problem manifested in the literal translation of idiomatic adjuncts, the misinterpretation of dialectal adjuncts as main clause constituents, the translation of dialectal adjuncts after orthographically similar standard Arabic words, and the use of standard Arabic common lexical meanings to translate dialect-specific adjuncts. This study’s findings will be relevant for researchers interested in dialectal Arabic neural machine translation and has implications for investment in the development of large-scale, dialect-specific corpora to better process the peculiarities of Arabic dialects and reduce the effect of negative transfer from standard Arabic. [ABSTRACT FROM AUTHOR]

The visions of Industry 4.0 and 5.0 have reinforced the industrial environment. They have also made artificial intelligence incorporated as a major facilitator. Diagnosing machine faults has become a solid foundation for automatically recognizing machine failure, and thus timely maintenance can ensure safe operations. Transfer learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model, which typically involves two datasets. In response to the availability of multiple datasets, this paper proposes using selective and adaptive incremental transfer learning (SA-ITL), which fuses three algorithms, namely, the hybrid selective algorithm, the transferability enhancement algorithm, and the incremental transfer learning algorithm. It is a selective algorithm that enables selecting and ordering appropriate datasets for transfer learning and selecting useful knowledge to avoid negative transfer. The algorithmalso adaptively adjusts the portion of training data to balance the learning rate and training time. The proposed algorithm is evaluated and analyzed using ten benchmark datasets. Compared with other algorithms fromexisting works, SA-ITL improves the accuracy of all datasets. Ablation studies present the accuracy enhancements of the SA-ITL, including the hybrid selective algorithm (1.22%--3.82%), transferability enhancement algorithm (1.91%--4.15%), and incremental transfer learning algorithm (0.605%--2.68%). These also show the benefits of enhancing the target model with heterogeneous image datasets that widen the range of domain selection between source and target domains. [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.)

Transfer learning is an essential tool for improving the performance of primary tasks by leveraging information from auxiliary data resources. In this work, we propose Adaptive Robust Transfer Learning (ART), a flexible pipeline of performing transfer learning with generic machine learning algorithms. We establish the nonasymptotic learning theory of ART, providing a provable theoretical guarantee for achieving adaptive transfer while preventing negative transfer. Additionally, we introduce an ART‐integrated‐aggregating machine that produces a single final model when multiple candidate algorithms are considered. We demonstrate the promising performance of ART through extensive empirical studies on regression, classification, and sparse learning. We further present a real‐data analysis for a mortality study. [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]

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]

Copyright of Journal of Guangdong University of Technology is the property of Journal of Guangdong University of Technology 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.)

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]

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]

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]

This article demonstrates how Cognitive Grammar and Construction Grammar can prevent Chinese students learning Spanish from fossilizing mistakes in restrictive relative clauses at the A2-B1 level of the European Framework of Reference for Languages. To address this issue, first, relative clauses in Spanish and Chinese were contrasted and, second, tailored solutions based on Cognitive Grammar were proposed. Among the cognitive based tailored solutions, certain geometry forms, colours and basic mathematics metaphors were compared with syntactic characteristics such as noun order, subordination hierarchy or resumption. To elucidate the impact of such teaching methods, an experiment with 74 Chinese students was performed. The results indicate that the efficacy of the proposed materials is statistically significant and as such, the Chinese students avoid fossilized mistakes while producing subject, object and locative relative clauses in Spanish. [ABSTRACT FROM AUTHOR]

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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]

This article demonstrates how Cognitive Grammar and Construction Grammar can prevent Chinese students learning Spanish from fossilizing mistakes in restrictive relative clauses at the A2-B1 level of the European Framework of Reference for Languages. To address this issue, first, relative clauses in Spanish and Chinese were contrasted and, second, tailored solutions based on Cognitive Grammar were proposed. Among the cognitive based tailored solutions, certain geometry forms, colours and basic mathematics metaphors were compared with syntactic characteristics such as noun order, subordination hierarchy or resumption. To elucidate the impact of such teaching methods, an experiment with 74 Chinese students was performed. The results indicate that the efficacy of the proposed materials is statistically significant and as such, the Chinese students avoid fossilized mistakes while producing subject, object and locative relative clauses in Spanish. [ABSTRACT FROM AUTHOR]

Transfer learning algorithm can provide a framework to utilize the previous knowledge to train fuzzy neural network (FNN). However, the performance of TL-based FNN will be destroyed by the knowledge over-fitting problem in the learning process. To solve this problem, an interactive transfer learning (ITL) algorithm, which can alleviate the negative transfer among different domains to improve the learning performance of FNN, is designed and analyzed in this article. This ITL-assisted FNN (ITL-FNN) contains the following advantages. First, a knowledge filter algorithm is developed to reconstruct the knowledge in source scene by balancing the matching accuracy and diversity. Then, the knowledge from source scene can fit the instance of target scene with suitable accuracy. Second, a self-balancing mechanism is designed to balance the driven information between the source and target scenes. Then, the knowledge can be refitted to reduce the useless information. Third, a structural competition algorithm is proposed to adjust the knowledge of FNN. Then, the proposed ITL-FNN can achieve compact structure to improve the generalization performance. Finally, some benchmark problems and industrial applications are provided to demonstrate the merits of ITL-FNN. [ABSTRACT FROM AUTHOR]

The objective of this study is to develop an adaptive software sensor technique that can predict objective process variables for a target grade in a plant while also considering information related to various other grades. We use a dataset of the target grade as the target domain and those of the other grades as source domains to perform transfer learning. Multiple models or sub‐models are constructed by setting a source domain for each grade and changing the number of samples used as the source domain. Furthermore, to prevent the negative transfer, the use of a source domain is automatically judged. In this study, we constructed sub‐models using the locally weighted partial least squares approach as an adaptive soft sensor technique. The values of an objective variable were predicted with ensemble learning using sub‐models. The effectiveness of the proposed method was verified using a dataset measured in an actual incineration plant, and the proposed method was able to accurately predict the product quality even when the plant was operated in five grades and when a new grade was produced. [ABSTRACT FROM AUTHOR]

Particle swarm optimization algorithm has become a promising approach in solving multitask optimization (MTO) problems since it can transfer knowledge with easy implementation and high searching efficiency. However, in the process of knowledge transfer, negative transfer is common because it is difficult to evaluate whether knowledge is effective for population evolution. Therefore, how to obtain and transfer the effective knowledge to curb the negative transfer is a challenging problem in MTO. To deal with this problem, a self-adjusting multitask particle swarm optimization (SA-MTPSO) algorithm is designed to improve the convergence performance in this article. First, a knowledge estimation metric, combining the decision space knowledge and the target space knowledge for each task, is designed to describe the effectiveness of knowledge. Then, the effective knowledge is obtained to promote the knowledge transfer process. Second, a self-adjusting knowledge transfer mechanism, based on the effective knowledge and the self-adjusting transfer method, is developed to achieve effective knowledge transfer. Then, the ineffective knowledge is removed to solve the negative transfer problem. Third, the convergence analysis is given to guarantee the effectiveness of the SA-MTPSO algorithm theoretically. Finally, the proposed algorithm is compared with some existing MTO algorithms. The results show that the performance of the proposed algorithm is superior to most algorithms on negative transfer suppression and convergence. [ABSTRACT FROM AUTHOR]

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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]

Multi-mission grouping improves the bunching execution of every undertaking through swapping searching for throughout over linked commitments. Most present multi-strategic frameworks depend upon the perfect presumption that the obligations are totally associated. Notwithstanding this, in real highlights, the responsibilities are maximum intense generally incompletely associated [1, 2]. In the one's occasions, brute propels alternate might also intent unsafe have an effect on that spoils the bunching execution. On this paper, we will be slanted to demonstrate an multi-project bunching techniques for halfway associated duties: oneself balanced multi-adventure grouping (SAMTC) approach and moreover the thoughtsboggling normal cryptography multi-challenge grouping (MRCMTC) way, that is in a scenario to consequently understand thoughts-boggling alternate associated sporting events some of the obligations, as a final product keeping off from risky alternate. Each SAMTC and MRCMTC accumulate the equality network for each factor assignment via abusing fundamental statistics from the supply errands via related models exchange and acquire apparition grouping to incite the rest of the bunching outcomes. Be that when you consider that it's going to, they maintain the related occasions from the accessibility responsibilities in a very stack of techniques. Preliminary effects on proper enlightening statistics display the superiorities of the projected calculations over customary single-essential frameworks and present multi-undertaking grouping processes on each absolutely and restriction of the timerelated commitments [3, 4]. [ABSTRACT FROM AUTHOR]

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Traditional clustering algorithms for medical image segmentation can only achieve satisfactory clustering performance under relatively ideal conditions, in which there is adequate data from the same distribution, and the data is rarely disturbed by noise or outliers. However, a sufficient amount of medical images with representative manual labels are often not available, because medical images are frequently acquired with different scanners (or different scan protocols) or polluted by various noises. Transfer learning improves learning in the target domain by leveraging knowledge from related domains. Given some target data, the performance of transfer learning is determined by the degree of relevance between the source and target domains. To achieve positive transfer and avoid negative transfer, a negative-transfer-resistant mechanism is proposed by computing the weight of transferred knowledge. Extracting a negative-transfer-resistant fuzzy clustering model with a shared cross-domain transfer latent space (called NTR-FC-SCT) is proposed by integrating negative-transfer-resistant and maximum mean discrepancy (MMD) into the framework of fuzzy c-means clustering. Experimental results show that the proposed NTR-FC-SCT model outperformed several traditional non-transfer and related transfer clustering algorithms. [ABSTRACT FROM AUTHOR]

To maximize the influence across multiple heterogeneous networks, we propose an innovative cross-network learning model to study the influence maximization problem from two perspectives, namely, seed selection and graph sparsification. On one hand, we consider seed selection as a cross-network node prediction task, by leveraging the greedy seed selection knowledge prelearned in a smaller source network, to heuristically select the nodes most likely to act as seed for the target networks. On the other hand, we consider graph sparsification as a cross-network edge prediction problem, by adapting the influence propagation knowledge previously acquired in the source network to remove the edges least likely to contribute to influence propagation in the target networks. To address domain discrepancy, a fuzzy self-learning algorithm is proposed to iteratively train the prediction model by leveraging not only the fully labeled data in the source network, but also the most confident predicted instances with their predicted fuzzy labels in the target network. With such fuzzy labels, we can differentiate the confident levels of predictions generated by different self-training iterations, thus lowering the negative effects caused by less confident predictions. The performance of the proposed model is benchmarked with the popular influence maximization algorithms for seed selection; and also competed with several graph sparsification algorithms for inactive edge prediction. Experimental results on the real-world datasets show that the proposed cross-network learning model can achieve a good tradeoff between the efficiency and effectiveness of the influence maximization task in the target networks. [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]

This paper analyses the mastery of English word stress of China's Tibetan Amdo English learners, by means of acoustic phonetics. According to the "Negative Transfer" theory, as the mother language of Amdo doesn't have word stress, this will put negative influence to the learning of English stress and their pronunciation of it will be poor. However, the result of this study shows that these learners' grasp of English word stress is better than prediction, with an overall accuracy of 70% percent. Among the findings, two noticeable research result was discovered, which are the Amdo speakers' pronunciation of English words with stress on the first syllable (for words with multi-syllables), compound words with stress on the first word are quite problematic, and these speakers has no awareness of "stress shift". These findings are very helpful to Amdo English learners and their eachers and could be further used in pedagogy designs. [ABSTRACT FROM AUTHOR]

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]

Telemonitoring is the use of electronic devices to remotely monitor patients. Taking the Parkinson’s disease (PD) as an example, the use of at-home testing device (AHTD) enables remote, internet-based measurement of PD vocal symptoms. Translating AHTD measurement into a unified PD rating scale (UPDRS) through predictive analytics enables cost-effective, convenient, and close tracking of PD progression. Building a predictive model between AHTD measurement and UPDRS is not straightforward because PD patients are highly heterogeneous, which requires patient-specific models. Learning a patient-specific model faces the challenge of limited data. Transfer learning (TL) tackles this challenge by leveraging other patients’ information to make up the data shortage when modeling a target patient. Among different TL methods, the category of parameter transfer methods is more appropriate for the telemonitoring application because it transfers patient-specific model parameters but not patients’ data. However, existing parameter transfer methods fall short because not every other patient’s information is helpful and blind transfer causes the problem of negative transfer. To tackle this limitation, we propose a positive TL (PTL) method. We provide an in-depth theoretical study on the risk and condition for negative transfer to happen, which further drive the development of novel PTL algorithms that are robust to negative transfer. We apply PTL to predict UPDRS of 42 PD patients using their AHTD vocal measurement. PTL achieves significantly better accuracy compared with single learning and one-model-fits-all approaches. Note to Practitioners—This paper was motivated by the growing use of telemonitoring devices to facilitate remote, close tracking of disease progression. These devices are usually paired up with predictive analytics to translate the measurement signals into a clinical indicator of disease progression. This is a challenging task because the predictive model needs to be patient specific in order to account for patient heterogeneity. This paper proposes a TL method called PTL that can leverage other patients’ information when building a predictive model for a target patient. The unique feature of PTL is that it can intelligently select which patients to transfer from, and thus preventing negative transfer. PTL is a just-in-time development for the emerging healthcare paradigm of telemonitoring and telemedicine by providing predictive analytics models and algorithms to address the unique challenges and contributing to the use of these remote devices to improve patient care. [ABSTRACT FROM AUTHOR]

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]

In our modern society, English is becoming increasingly important. As is known to all, pronunciation is a significant part of a language, which is complex and flexible. Chinese learners with a fluent and native English will benefit a lot. But the fact is that Chinese learners who have learned English as a second language for a long time still have lots of different problems on English pronunciation because of the interference of mother tongue. Therefore, based on language transfer theory, this paper intends to show different English pronunciation problems of learners in several areas--Northwest China, Central China, Northeast China and Southern China. By comparing Chinese and English phoneme families, the reasons of the problems in these areas will be analyzed. This paper aims to alleviate English pronunciation problems in these areas. What's more, it is expected that this paper will be useful in teaching of English pronunciation. [ABSTRACT FROM AUTHOR]

Drawing from organizational learning theory, extant literature argues that a pre‐acquisition alliance between an acquirer and its target is usually expected to have a positive impact on subsequent acquisition performance. Yet, since alliances and acquisitions are partially dissimilar events, negative transfer effects can bias inter‐partner learning and negatively affect acquisition performance. To fill this gap, our paper explores pre‐acquisition alliance duration and examines its effects on acquisition outcomes. We argue that a pre‐acquisition alliance can have either beneficial or detrimental effects on subsequent acquisition performance, depending on the duration of the pre‐acquisition alliance. We examine our hypotheses using a (1988–2008) panel database covering acquisitions by French firms in the US. We show an inverted U‐shaped relationship between the duration of a pre‐acquisition alliance and the probability of acquisition failure. Our results also indicate that this non‐monotonic relationship is partially moderated by the degree of partner commitment to the alliance. [ABSTRACT FROM AUTHOR]

In this paper we deal with the problem of measuring the similarity between training and tests datasets in the context of transfer learning (TL) for image classification. TL tries to transfer knowledge from a source domain, where labelled training samples are abundant but the data may follow a different distribution, to a target domain, where labelled training samples are scarce or even unavailable, assuming that the domains are related. Thus, the requirements w.r.t. the availability of labelled training samples in the target domain are reduced. In particular, if no labelled target data are available, it is inherently difficult to find a robust measure of relatedness between the source and target domains. This is of crucial importance for the performance of TL, because the knowledge transfer between unrelated data may lead to negative transfer, i.e. to a decrease of classification performance after transfer. We address the problem of measuring the relatedness between source and target datasets and investigate three different strategies to predict and, consequently, to avoid negative transfer in this paper. The first strategy is based on circular validation. The second strategy relies on the Maximum Mean Discrepancy (MMD) similarity metric, whereas the third one is an extension of MMD which incorporates the knowledge about the class labels in the source domain. Our method is evaluated using two different benchmark datasets. The experiments highlight the strengths and weaknesses of the investigated methods. We also show that it is possible to reduce the amount of negative transfer using these strategies for a TL method and to generate a consistent performance improvement over the whole dataset. [ABSTRACT FROM AUTHOR]

Transfer learning method has been widely used in machine learning when training data is limited. However, class noise accumulated during learning iterations can lead to negative transfer which can adversely affect performance when more training data is used. In this paper, we propose a novel method to identify noise samples for noise reduction. More importantly, the method can detect the point where negative transfer happens such that transfer learning can terminate at the near top performance point. In this method, we use the sum of the Rademacher distribution to estimate the class noise rate of transferred data. Transferred data having high probability of being labeled wrongly is removed to reduce noise accumulation. This negative sample reduction process can be repeated several times during transfer learning until we find the point where negative transfer occurs. As we can detect the point where negative transfer occurs, our method not only has the ability to delay the point where negative transfer happens, but also the ability to stop transfer learning algorithms at the right place for top performance gain. Evaluation based on cross-lingual/domain opinion analysis evaluation data set shows that our algorithm achieves the state-of-the-art result. Furthermore, our system shows a monotonic increase trend in performance improvement when more training data are used beating the performance degradation curse of most transfer learning methods when training data reaches certain size. [ABSTRACT FROM AUTHOR]

Copyright of Istraživanja: Casopis Fakulteta Humanističkih Nauka is the property of Dzemal Bijedic University of Mostar, Faculty of Humanities 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.)

The phenomenon of language transfer in SL learning and use is perennial and cannot be silenced easily. In L2 writing, the phenomenon is found to affect the written products to become nonnative and be ambiguous. It is thought that the transfer occurs at the conceptual and structural level of language use. The present paper examines Kurdish EFL learners' writing aiming at identifying transfer types, particularly, the negative transfer. To this end, 20 university level English major students' argumentative writing are analyzed focusing on the conjuncts and adjuncts to find out any track of L1 concepts. The logical clause relationship of cause-effect was the area of focus. The results of the study showed that L1 concepts have been used in forming the relations between sentences and clauses spelt out by lexical signals of sentence connectors and subordinators. It was also found that L1 concepts were transferred into L2 writing. The insights gained from the results of the study reveal that there is a problem, especially the negative influence of L1, which needs to be attended to. [ABSTRACT FROM AUTHOR]

Upgrades are a common experience for people who use technology for any length of time. Upgrades may bring clear benefits, and some users look forward to new versions of software. However for others, upgrades may be a burden. Upgrades bring costs, imposing a need to refamiliarize ourselves with a new version after mastering the old one. It is important to understand such costs as failure to upgrade can make users vulnerable to security risks. We first review multiple studies showing users' dislike of upgrades resulting from changes introduced to the interface. Then we propose a framework to analyze the nature of upgrade costs, explaining their origins using well-validated cognitive psychology concepts including: cognitive automation, negative transfer, cognitive maps, procedural memory and locus of control. We use these concepts to explain the psychological costs of frequent interface changes while sketching some approaches to addressing them. Although new versions are necessary for progress, this paper provides software developers with a deeper understanding of the sometimes overlooked costs of frequent interface changes. [ABSTRACT FROM AUTHOR]

In La Grandiflora, Gib Mihaescu builds the characters' identity by mentally deconstructing them, employing lucidity (the result of an acerbic, obsessive introspection), in constant competition with the phantasm which dislocates reality and hijacks the banal events, recomposing them according to the criteria of an alienated psychology. The characters' deeds are born in a captivity of primitive emotions. Thusly, the literary couple becomes an association of vices and complexes, a negative transference of states converging to neurosis. The main character induces, against the background of his wife's jealousy and unfaithfulness, similar anguish to the other male characters by bringing disrepute to other wives. A deliberate contamination, in the attempt to vilify women, with the aim of rehabilitating his wife and saving their marriage. The erotic transference, negatively connotative, is a pattern that, apparently, leads to trivial literature, but the insertion of mystical and mythical symbols transfigures the short story artistically. The voluptuousness of the displaying the self, the engine of this universe altered by the interference of a moral code, leads to the emergence of the demonic double. Strain alternates with relaxation, unrest becomes calm, and the visceral revolt emerges and disappears contextually. There is a mystifying game that poisons and, eventually, reality reinstates, in the spirit of righteousness, the trust in a general redemption. [ABSTRACT FROM AUTHOR]

Supervised machine learning needs high quality, densely sampled and labelled training data. Transfer learning (TL) techniques have been devised to reduce this dependency by adapting classifiers trained on different, but related, (source) training data to new (target) data sets. A problem in TL is how to quantify the relatedness of a source quickly and robustly, because transferring knowledge from unrelated data can degrade the performance of a classifier. In this paper, we propose a method that can select a nearly optimal source from a large number of candidate sources. This operation depends only on the marginal probability distributions of the data, thus allowing the use of the often abundant unlabelled data. We extend this method to multi-source selection by optimizing a weighted combination of sources. The source weights are computed using a very fast boosting-like optimization scheme. The run-time complexity of our method scales linearly in regard to the number of candidate sources and the size of the training set and is thus applicable to very large data sets. We also propose a modification of an existing TL algorithm to handle multiple weighted training sets. Our method is evaluated on five survey regions. The experiments show that our source selection method is effective in discriminating between related and unrelated sources, almost always generating results within 3% in overall accuracy of a classifier based on fully labelled training data. We also show that using the selected source as training data for a TL method will additionally result in a performance improvement. [ABSTRACT FROM AUTHOR]

Copyright of Linguistica is the property of Linguistica 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.)