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With an ever-increasing abundance of English Language Teaching materials, there is always a need for research to unearth the possible gendered discourses in/around them. This investigation sought to uncover patterns a Systemic Functional Linguistic reading of Ministry-produced textbooks for the Iranian schools might yield vis-a-vis gender representation. To this end, all the clauses in the Vision series, containing references to males and females, were analyzed in terms of their Transitivity System elements. Subsequent to this phase, which rendered an imbalanced view of gendered identities, van Leeuwen’s categories of Identification and Functionalization were applied to fine-tune the subjectivities arrived at. The study found men as transacting in more domains of life, as being more rational and vocal, and as being described more fully and attributed more lifelike qualities. As for van Leeuwen’s resources, in addition to men being rendered in more occupations, they were displayed as being more innovative and technological. Con una abundancia cada vez mayor de materiales para la enseñanza del idioma inglés, existe la necesidad de realizar investigaciones para descubrir los posibles discursos de género en ellos y alrededor de ellos. Esta investigación buscó descubrir patrones que una lectura lingüística sistémica funcional de libros de texto producidos por el Ministerio para las escuelas iraníes podría generar en relación con la representación de género. Para ello, se analizaron todas las cláusulas de la serie Visión, que contienen referencias a hombres y mujeres, en términos de sus elementos del Sistema de Transitividad. Posteriormente a esta fase, que generó una visión desequilibrada de las identidades de género, se aplicaron las categorías de Identificación y Funcionalización de van Leeuwen para afinar las subjetividades a las que se llegó. El estudio encontró que los hombres realizan transacciones en más ámbitos de la vida, son más racionales y vocales, y se les describe más completamente y se les atribuyen cualidades más realistas. En cuanto a los recursos de van Leeuwen, además de que los hombres se desempeñaban en más ocupaciones, se los mostraba como más innovadores y tecnológicos.

An engaging and enjoyable atmosphere for learning English as a second language can provide the right setting for promoting language proficiency. It appears that textbooks are essential in this regard. This study aimed to investigate how learners' multiple intelligences were affected by the supplementary materials designed for them. To do so, a three-phase study was designed. In Phase 1, a triangulation model based on a checklist, teacher interviews, and researchers' experience was employed to evaluate the Vision series based on multiple intelligence factors. In the second phase, supplementary materials were designed based on the results obtained in the first phase, in accordance with Jones' (2017) guidelines and Christion's (1997) taxonomy of language learning activities for multiple intelligences. The designed tasks and activities were implemented in a class (experimental group, N = 60) during the third phase. The Babel proficiency test was used, and The Persian version of Mckenzie's multiple intelligences inventory (Hajhashemi & Bee Eng, 2010) were administered to both experimental and control groups as the pretest and posttest to ensure the homogeneity of two groups at first and examine the impact of these tasks on students’ multiple intelligences. The result of the first phase revealed that Vision series did not have enough tasks to fulfil learners' needs based on multiple intelligences abilities, and there is a need to provide supplementary tasks in order to teach with multiple intelligences. In the second phase, the designed tasks were implemented in experimental groups for four months as supplementary material. The t-test result indicated that the designed tasks positively and significantly affected learners' multiple intelligences

Pragmatic competence is an essential language pedagogy component represented in English textbooks. The paper attempts to examine the extent to which pragmatic knowledge was reflected in Iranian high school English textbooks (Vision series). Hence, Halliday’s (1973) model and Halliday, et al.’s (1964) model were used to investigate subcategories of functional knowledge and sociolinguistic knowledge. The data was described by descriptive statistics. The findings revealed the Iranian culture-deprived representation (e.g., traditional customs, and ceremonies such as Nowruz, Chaharshanbesori, and Yalda). Moreover, the results highlighted only a limited number of pragmatic components implicitly introduced in the Vision series, and attention was given to their representation and problematic distribution. Furthermore, the English sociocultural patterns were limited and attempts were made to reflect the Iranian culture and convection of daily communication. Thus, some pedagogical implications were offered to improve the Vision series, using authentic pragmatic content instead of proclaiming the fictitious prescription of its authors.

Financial time series are chaotic that, in turn, leads their predictability to be complex and challenging. This paper presents a novel financial time series prediction hybrid that involves Chaos Theory, Convolutional neural network (CNN), and Polynomial Regression (PR). The financial time series is first checked in this hybrid for the presence of chaos. The chaos in the series of times is later modeled using Chaos Theory. The modeled time series is input to CNN to obtain initial predictions. The error series obtained from CNN predictions is fit by PR to get error predictions. The error predictions and initial predictions from CNN are added to obtain the final predictions of the hybrid model. The effectiveness of the proposed hybrid (Chaos+CNN+PR) is tested by using three types of Foreign exchange rates of financial time series (INR/USD, JPY/USD, SGD/USD), commodity prices (Gold, Crude Oil, Soya beans), and stock market indices (S&P 500, Nifty 50, Shanghai Composite). The proposed hybrid is superior to Auto-regressive integrated moving averages (ARIMA), Prophet, Classification and Regression Tree (CART), Random Forest (RF), CNN, Chaos+CART, Chaos+RF and Chaos+CNN in terms of MSE, MAPE, Dstat, and Theil’s U .

As industrial systems become more complex and monitoring sensors for everything from surveillance to our health become more ubiquitous, multivariate time series prediction is taking an important place in the smooth-running of our society. A recurrent neural network with attention to help extend the prediction windows is the current-state-of-the-art for this task. However, we argue that their vanishing gradients, short memories, and serial architecture make RNNs fundamentally unsuited to long-horizon forecasting with complex data. Temporal convolutional networks (TCNs) do not suffer from gradient problems and they support parallel calculations, making them a more appropriate choice. Additionally, they have longer memories than RNNs, albeit with some instability and efficiency problems. Hence, we propose a framework, called PSTA-TCN, that combines a parallel spatio-temporal attention mechanism to extract dynamic internal correlations with stacked TCN backbones to extract features from different window sizes. The framework makes full use parallel calculations to dramatically reduce training times, while substantially increasing accuracy with stable prediction windows up to 13 times longer than the status quo.

Transformer architectures have widespread applications, particularly in Natural Language Processing and Computer Vision. Recently, Transformers have been employed in various aspects of time-series analysis. This tutorial provides an overview of the Transformer architecture, its applications, and a collection of examples from recent research in time-series analysis. We delve into an explanation of the core components of the Transformer, including the self-attention mechanism, positional encoding, multi-head, and encoder/decoder. Several enhancements to the initial Transformer architecture are highlighted to tackle time-series tasks. The tutorial also provides best practices and techniques to overcome the challenge of effectively training Transformers for time-series analysis.

Machine Learning (ML) methods have been proposed in the academic literature as alternatives to statistical ones for time series forecasting. Yet, scant evidence is available about their relative performance in terms of accuracy and computational requirements. The purpose of this paper is to evaluate such performance across multiple forecasting horizons using a large subset of 1045 monthly time series used in the M3 Competition. After comparing the post-sample accuracy of popular ML methods with that of eight traditional statistical ones, we found that the former are dominated across both accuracy measures used and for all forecasting horizons examined. Moreover, we observed that their computational requirements are considerably greater than those of statistical methods. The paper discusses the results, explains why the accuracy of ML models is below that of statistical ones and proposes some possible ways forward. The empirical results found in our research stress the need for objective and unbiased ways to test the performance of forecasting methods that can be achieved through sizable and open competitions allowing meaningful comparisons and definite conclusions.

There is a need for solutions which assist users to understand long time-series data by observing its changes over time, finding repeated patterns, detecting outliers, and effectively labeling data instances. Although these tasks are quite distinct and are usually tackled separately, we present an interactive visual analytics system and approach that can address these issues in a single system. It enables users to visualize, understand and explore univariate or multivariate long time-series data in one image using a connected scatter plot. It supports interactive analysis and exploration for pattern discovery and outlier detection. Different dimensionality reduction techniques are used and compared in our system. Because of its power of extracting features, deep learning is used for multivariate time-series along with 2D reduction techniques for rapid and easy interpretation and interaction with large amount of time-series data. We deploy our system with different time-series datasets and report two real-world case studies that are used to evaluate our system.

Time series data analysis is crucial for real-world applications. While deep learning has advanced in this field, it still faces challenges, such as limited or poor-quality data. In areas like computer vision, data augmentation has been widely used and highly effective in addressing similar issues. However, these techniques are not as commonly explored or applied in the time series domain. This paper addresses the gap by evaluating basic data augmentation techniques using MLP, CNN, and Transformer architectures, prioritized for their alignment with state-of-the-art trends in time series analysis rather than traditional RNN-based methods. The goal is to expand the use of data augmentation in time series analysis. The paper proposed EMixup, which adapts the Mixup method from image processing to time series data. This adaptation involves mixing samples while aiming to maintain the data's temporal structure and integrating target contributions into the loss function. Empirical studies show that EMixup improves the performance of time series models across various architectures (improving 23/24 forecasting cases and 12/24 classification cases). It demonstrates broad applicability and strong results in tasks like forecasting and classification, highlighting its potential utility across diverse time series applications.

In this article, we want to discuss the use of deep learning model to predict potential vibrations of high-speed trains. In our research, we have tested and developed deep learning model to predict potential vibrations from time series of recorded vibrations during travel. We have tested various training models, different time steps and potential error margins to examine how well we are able to predict situation on the track. Summarizing, in our article we have used the RNN-LSTM neural network model with hyperbolic tangent in hidden layers and rectified linear unit gate at the final layer in order to predict future values from the time series data. Results of our research show the our system is able to predict vibrations with Accuracy of above 99% in series of values forward.