Niching is an important technique for multimodal optimization. Most existing niching methods require specification of certain niching parameters in order to perform well. These niching parameters, ...often used to inform a niching algorithm how far apart between two closest optima or the number of optima in the search space, are typically difficult to set as they are problem dependent. This paper describes a simple yet effective niching algorithm, a particle swarm optimization (PSO) algorithm using a ring neighborhood topology, which does not require any niching parameters. A PSO algorithm using the ring topology can operate as a niching algorithm by using individual particles' local memories to form a stable network retaining the best positions found so far, while these particles explore the search space more broadly. Given a reasonably large population uniformly distributed in the search space, PSO algorithms using the ring topology are able to form stable niches across different local neighborhoods, eventually locating multiple global/local optima. The complexity of these niching algorithms is only O ( N ), where N is the population size. Experimental results suggest that PSO algorithms using the ring topology are able to provide superior and more consistent performance over some existing PSO niching algorithms that require niching parameters.
Language learning is an emotional and dynamic process, which is marked by fluctuations in language learners' positive and negative emotional variables (e.g., boredom, enjoyment, anxiety). Presumably, ...evidence can be found for an ecological view of the patterns and variations involved in language learners' emotions under the influence of the interactive individual and contextual elements of classroom learning. The present study contends that an ecological momentary assessment (EMA), which is compatible with the complex dynamic system theory (CDST) can help to explore the dynamics of language learners' emotional variables as they develop out of the process of classroom language learning. EMA is capable of tracing the moment-by-moment changes in a certain emotional trait in language learners as they are learning a foreign or second language. This innovative approach to research compensates for the shortcomings of retrospective studies (the delay of recalls) and also single-shot research designs (for data collection). It is fit for the assessment of the emergent patterns of L2 emotional variables. The distinctive features and pedagogical implications will be further discussed here.
Teachers' apprehension is an indispensable part of the educational context since it impacts the amount to which students are engaged in class activities. Although several studies have been carried ...out considering the role of students' stress in their engagement, it seems extremely vital to conduct such studies among teachers to measure the link between these two variables. In this study, the author has made endeavors to define one of the apprehension's categories named communication apprehension and the antecedents of teachers' apprehension. Then classroom engagement is discussed. Following that, the relevance between the two variables of this research is discussed. Finally, both implications and suggestions for further studies are dealt with.
This paper proposes an improved particle swarm optimizer using the notion of species to determine its neighborhood best values for solving multimodal optimization problems and for tracking multiple ...optima in a dynamic environment. In the proposed species-based particle swam optimization (SPSO), the swarm population is divided into species subpopulations based on their similarity. Each species is grouped around a dominating particle called the species seed. At each iteration step, species seeds are identified from the entire population, and then adopted as neighborhood bests for these individual species groups separately. Species are formed adaptively at each step based on the feedback obtained from the multimodal fitness landscape. Over successive iterations, species are able to simultaneously optimize toward multiple optima, regardless of whether they are global or local optima. Our experiments on using the SPSO to locate multiple optima in a static environment and a dynamic SPSO (DSPSO) to track multiple changing optima in a dynamic environment have demonstrated that SPSO is very effective in dealing with multimodal optimization functions in both environments
Q methodology has been used in a variety of fields to employ a scientific approach to dealing with subjectivity; yet, its use has just gained momentum in the second language acquisition (SLA) domain ...recently (
Damio, 2016
). The present paper argues that Q methodology is remarkably efficient in representing the dynamic quality of complex systems involved in the language learning process, which is, thus, compatible with the complexity and dynamic systems theory (CDST). As Q methodology enjoys advantages of both qualitative and quantitative lines of research (
Irie, 2014
), it helps to explore and reflect L2 learners’ subjective views and perceptions about their emotions in an L2 class in a comprehensive manner. With the current growing attention to individuals’ emotional experiences in recent years, SLA research domain is ripe for many scientific inquiries about L2 learners’ affective variables benefiting from this method. The few existing studies in the L2 domain have had interesting findings, which show the Q methodology should be more extensively used in the field to reveal facts about how learners feel in class from a within-individual point of view. Q methodology can hopefully be capable of representing the dynamicity and complexity of the affective variables language learners experience in the interactive network of classroom learning. Thus, it is expected that innovative research methods such as the Q methodology be employed significantly more than before in the dynamic phase of SLA research in the upcoming years.
Teachers as the most important elements of education constantly need professional development (PD) courses in order to improve their pedagogy and practice. Given this, many educational systems ...worldwide have paid special attention to designing courses by which the quality of teaching and learning raises considerably. This surge of interest has ended in different studies on PD programs in L2 education. However, the pertinent literature lacks a comprehensive review of the models, applications, and impacts of EFL/ESL teachers' PD and various aspects influenced by this construct. To fill this gap and add fresh insights into this strand of research, the present study aimed to review the definitions, characteristics, models, goals, and uses of teacher professional development (TPD) in L2 education. Moreover, several empirical studies were touched on to support the claims of TPD impact on teachers. Finally, the study presented different implications for L2 teachers, teacher trainers, researchers, and policy-makers who can realize the significance and impact of effective TPD courses on the whole process of teaching and learning.
This paper presents a new cooperative coevolving particle swarm optimization (CCPSO) algorithm in an attempt to address the issue of scaling up particle swarm optimization (PSO) algorithms in solving ...large-scale optimization problems (up to 2000 real-valued variables). The proposed CCPSO2 builds on the success of an early CCPSO that employs an effective variable grouping technique random grouping. CCPSO2 adopts a new PSO position update rule that relies on Cauchy and Gaussian distributions to sample new points in the search space, and a scheme to dynamically determine the coevolving subcomponent sizes of the variables. On high-dimensional problems (ranging from 100 to 2000 variables), the performance of CCPSO2 compared favorably against a state-of-the-art evolutionary algorithm sep-CMA-ES, two existing PSO algorithms, and a cooperative coevolving differential evolution algorithm. In particular, CCPSO2 performed significantly better than sep-CMA-ES and two existing PSO algorithms on more complex multimodal problems (which more closely resemble real-world problems), though not as well as the existing algorithms on unimodal functions. Our experimental results and analysis suggest that CCPSO2 is a highly competitive optimization algorithm for solving large-scale and complex multimodal optimization problems.
•Based on both technical indicators and news sentiments, the LSTM models outperform the MKL and the SVM in both prediction accuracy and F1 score.•The LSTM models incorporating both information ...sources outperform the models that only use either technical indicators or news sentiments, in both individual stock level and sector level.•Among the four sentiment dictionaries, finance domain specific sentiment dictionary (Loughran-McDonald Financial Dictionary) models the new sentiments better, which brings at most 120% prediction performance improvement, comparing with the other three dictionaries (at most 50%).
Stock prediction via market data analysis is an attractive research topic. Both stock prices and news articles have been employed in the prediction processes. However, how to combine technical indicators from stock prices and news sentiments from textual news articles, and make the prediction model be able to learn sequential information within time series in an intelligent way, is still an unsolved problem. In this paper, we build up a stock prediction system and propose an approach that 1) represents numerical price data by technical indicators via technical analysis, and represents textual news articles by sentiment vectors via sentiment analysis, 2) setup a layered deep learning model to learn the sequential information within market snapshot series which is constructed by the technical indicators and news sentiments, 3) setup a fully connected neural network to make stock predictions. Experiments have been conducted on more than five years of Hong Kong Stock Exchange data using four different sentiment dictionaries, and results show that 1) the proposed approach outperforms the baselines in both validation and test sets using two different evaluation metrics, 2) models incorporating prices and news sentiments outperform models that only use either technical indicators or news sentiments, in both individual stock level and sector level, 3) among the four sentiment dictionaries, finance domain-specific sentiment dictionary (Loughran–McDonald Financial Dictionary) models the news sentiments better, which brings more prediction performance improvements than the other three dictionaries.
People nowadays care more about the source and outcome of English instruction, resulting in an improvement in the quality of English instruction. As a result, the majority of students’ reading and ...writing levels increase steadily, but their listening ability does not, resulting in an extremely unbalanced development of the “listening, reading, and writing” four skills. The use of language in real-world situations is a reflection of human creativity, and the content and tone of the same speaker’s speech at different times, places, and occasions can vary significantly. Language usage cannot be separated from its context. This study proposes a hybrid online and offline English speaking teaching method based on modern educational technology. The method transforms offline teaching materials into a suitable context to create a rich and diverse online speaking classroom. This new teaching method constructs a teaching platform with online and offline as the primary subjects, replacing the previous single mode of teacher output and passive student input to form an all-round teaching platform distinguished by systematization, standardization, all-round language training, high intensity, accuracy, and fluency in language output. The experimental results demonstrated that our evaluation results are useful for providing feedback on the quality of teaching and promoting learning outcomes.
We study the problem of recovering the phase from magnitude measurements; specifically, we wish to reconstruct a complex-valued signal x ∈ ℂ n about which we have phaseless samples of the form y r = ...|〈a r , x〉| 2 , r = 1, ..., m (knowledge of the phase of these samples would yield a linear system). This paper develops a nonconvex formulation of the phase retrieval problem as well as a concrete solution algorithm. In a nutshell, this algorithm starts with a careful initialization obtained by means of a spectral method, and then refines this initial estimate by iteratively applying novel update rules, which have low computational complexity, much like in a gradient descent scheme. The main contribution is that this algorithm is shown to rigorously allow the exact retrieval of phase information from a nearly minimal number of random measurements. Indeed, the sequence of successive iterates provably converges to the solution at a geometric rate so that the proposed scheme is efficient both in terms of computational and data resources. In theory, a variation on this scheme leads to a near-linear time algorithm for a physically realizable model based on coded diffraction patterns. We illustrate the effectiveness of our methods with various experiments on image data. Underlying our analysis are insights for the analysis of nonconvex optimization schemes that may have implications for computational problems beyond phase retrieval.
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