Pre-service english teachers' perceptions and strategies implemented in academic writing processes

Conocer las necesidades y cómo los estudiantes perciben su propio proceso de aprendizaje de una lengua extranjera en un aula de clase es muy importante ya que así ellos pueden alcanzar niveles más altos de aprendizaje. Por esta razón, esta investigación informa las percepciones de futuros profesores de un programa de licenciatura acerca de la escritura y de las estrategias usadas al momento de escribir un documento académico. Actualmente no hay un número considerable de estudios en la región en dicho campo. De esta forma, esta investigación intenta contribuir y dar perspectivas en el campo de la escritura académica, cómo es percibida y qué estrategias de escritura son desarrolladas por los estudiantes de pregrado. Apuntando a este objetivo, ocho entrevistas hechas a un mismo número de estudiantes de pregrado quienes asistían a un curso de composición inglesa fueron desarrolladas con el propósito de obtener percepciones acerca de la escritura académica en su proceso de aprendizaje. También la creación de un ensayo y los diarios de campo de los investigadores fueron otros métodos usados para la recolección de datos para este estudio. Este estudio fue conducido en una universidad pública de Pereira Colombia, en el cual, el análisis de los datos mostró una falta de conciencia en el uso de estrategias al momento de crear un producto académico. Por otra parte este estudio comunica percepciones acerca de cómo los estudiantes percibieron su proceso de escritura en relación a sus fortalezas y debilidades.To know students’ necessities and how they perceive their own learning process of a foreign language in a classroom is very important, thus they can achieve upper levels in their learning. For this reason, this research reports the pre-service teachers’ perceptions about writing and their strategies used at the moment of writing an academic paper. Nowadays there are not a considerable number of studies in the region in such field. In this way, this research intends to contribute and give insights in the field of academic writing, how students perceive it and the strategies developed around it. With the purpose of aiming at this objective, eight interviews were developed. One interview to the teacher of the English composition course and seven to the undergraduate students who attended the same subject, in order to get insights about the academic writing in their learning process. Also an essay elaboration and the researchers’ journals were other methods used to collect data for this study. This study was conducted in a state university of Pereira Colombia, in which the data analysis showed a lack of awareness in the use of strategies at the moment of creating an academic product. Moreover this study conveys insights about how students perceived their writing process in relation to their strengths and weaknesses

Affective computing for smart operations: a survey and comparative analysis of the available tools, libraries and web services

In this paper, we make a deep search of the available tools in the market, at the current state of the art of Sentiment Analysis. Our aim is to optimize the human response in Datacenter Operations, using a combination of research tools, that allow us to decrease human error in general operations, managing Complex Infrastructures. The use of Sentiment Analysis tools is the first step for extending our capabilities for optimizing the human interface. Using different data collections from a variety of data sources, our research provides a very interesting outcome. In our final testing, we have found that the three main commercial platforms (IBM Watson, Google Cloud and Microsoft Azure) get the same accuracy (89-90%). for the different datasets tested, based on Artificial Neural Network and Deep Learning techniques. The other stand-alone Applications or APIs, like Vader or MeaninCloud, get a similar accuracy level in some of the datasets, using a different approach, semantic Networks, such as Concepnet1, but the model can easily be optimized above 90% of accuracy, just adjusting some parameter of the semantic model. This paper points to future directions for optimizing DataCenter Operations Management and decreasing human error in complex environments

Ten thousand names : rank and lineage affiliation in the Wenxian covenant texts

This is the publisher's official version, also available electronically from: http://dx.doi.org/10.5169/seals-147840.The following paper looks at evidence of rank distinction and lineage affiliation among partici¬ pants in a covenant recorded on tablets excavated at Wenxian $ 3÷ Henan province, and dated to the fifth century BC. The covenant is in the form of a loyalty oath to a leader, taken to be the head of the Han M§ lineage, one of the ministerial families of Jin The text of the covenant is written in ink on stone tablets, each individualized with the name of a covenantor. Tablets with this partic¬ ular covenant text were found in five separate pits. The number of tablets in each pit ranged from several dozen to more than 5000. The stone- type and shape of the tablets varied within and among pits. I argue that these variations are evidence of distinctions in rank among the covenantors. I dis¬ cuss a set of four related names from the tablets that appear to support this conjecture. I then look at names, of both covenantors and enemies, in which a lineage name is found. I argue that these names show that it was loyalty to the Han leader, not shared lineage affiliation, which was the main requirement for participation in the covenanting group. I conclude with a brief discussion on the size of the covenanting group, lineages within political groups, and the wider significance of these materials

Best Practices of Convolutional Neural Networks for Question Classification

Question Classification (QC) is of primary importance in question answering systems, since it enables extraction of the correct answer type. State-of-the-art solutions for short text classification obtained remarkable results by Convolutional Neural Networks (CNNs). However, implementing such models requires choices, usually based on subjective experience, or on rare works comparing different settings for general text classification, while peculiar solutions should be individuated for QC task, depending on language and on dataset size. Therefore, this work aims at suggesting best practices for QC using CNNs. Different datasets were employed: (i) A multilingual set of labelled questions to evaluate the dependence of optimal settings on language; (ii) a large, widely used dataset for validation and comparison. Numerous experiments were executed, to perform a multivariate analysis, for evaluating statistical significance and influence on QC performance of all the factors (regarding text representation, architectural characteristics, and learning hyperparameters) and some of their interactions, and for finding the most appropriate strategies for QC. Results show the influence of CNN settings on performance. Optimal settings were found depending on language. Tests on different data validated the optimization performed, and confirmed the transferability of the best settings. Comparisons to configurations suggested by previous works highlight the best classification accuracy by those optimized here. These findings can suggest the best choices to configure a CNN for QC

From the Richness of the Signal to the Poverty of the Stimulus: Mechanisms of Early Language Acquisition

1.1 The poverty of stimulus argument and the learnability of lan-guage................................ 12 1.1.1 The induction problem.................. 1

On the Promotion of the Social Web Intelligence

Given the ever-growing information generated through various online social outlets, analytical research on social media has intensified in the past few years from all walks of life. In particular, works on social Web intelligence foster and benefit from the wisdom of the crowds and attempt to derive actionable information from such data. In the form of collective intelligence, crowds gather together and contribute to solving problems that may be difficult or impossible to solve by individuals and single computers. In addition, the consumer insight revealed from social footprints can be leveraged to build powerful business intelligence tools, enabling efficient and effective decision-making processes. This dissertation is broadly concerned with the intelligence that can emerge from the social Web platforms. In particular, the two phenomena of social privacy and online persuasion are identified as the two pillars of the social Web intelligence, studying which is essential in the promotion and advancement of both collective and business intelligence. The first part of the dissertation is focused on the phenomenon of social privacy. This work is mainly motivated by the privacy dichotomy problem. Users often face difficulties specifying privacy policies that are consistent with their actual privacy concerns and attitudes. As such, before making use of social data, it is imperative to employ multiple safeguards beyond the current privacy settings of users. As a possible solution, we utilize user social footprints to detect their privacy preferences automatically. An unsupervised collaborative filtering approach is proposed to characterize the attributes of publicly available accounts that are intended to be private. Unlike the majority of earlier studies, a variety of social data types is taken into account, including the social context, the published content, as well as the profile attributes of users. Our approach can provide support in making an informed decision whether to exploit one\u27s publicly available data to draw intelligence. With the aim of gaining insight into the strategies behind online persuasion, the second part of the dissertation studies written comments in online deliberations. Specifically, we explore different dimensions of the language, the temporal aspects of the communication, as well as the attributes of the participating users to understand what makes people change their beliefs. In addition, we investigate the factors that are perceived to be the reasons behind persuasion by the users. We link our findings to traditional persuasion research, hoping to uncover when and how they apply to online persuasion. A set of rhetorical relations is known to be of importance in persuasive discourse. We further study the automatic identification and disambiguation of such rhetorical relations, aiming to take a step closer towards automatic analysis of online persuasion. Finally, a small proof of concept tool is presented, showing the value of our persuasion and rhetoric studies

MELINDA: A Multimodal Dataset for Biomedical Experiment Method Classification

We introduce a new dataset, MELINDA, for Multimodal biomEdicaL experImeNt methoD clAssification. The dataset is collected in a fully automated distant supervision manner, where the labels are obtained from an existing curated database, and the actual contents are extracted from papers associated with each of the records in the database. We benchmark various state-of-the-art NLP and computer vision models, including unimodal models which only take either caption texts or images as inputs, and multimodal models. Extensive experiments and analysis show that multimodal models, despite outperforming unimodal ones, still need improvements especially on a less-supervised way of grounding visual concepts with languages, and better transferability to low resource domains. We release our dataset and the benchmarks to facilitate future research in multimodal learning, especially to motivate targeted improvements for applications in scientific domains.Comment: In The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), 202