In this short paper, we present some initial work on Mobile Assisted Language Learning (MALL) undertaken by the ATLAS research group. ATLAS embraced this multidisciplinary field cutting across Mobile Learning and Computer Assisted Language Learning (CALL) as a natural step in their quest to find learning formulas for professional English that adapt to the changing profiles and needs of our modern society. A needs-analysis undertaken by group members highlights the way in which professionals need to have language learning activities available on their mobile devices. The SO-CALL-ME project has been established to enable such MALL apps, designed and developed within the ATLAS group, to be studied with real users to explore the way in which they can improve their oral language skills. Here one such app, ANT-Audio News Trainer, is presented as an example of the development being undertaken.
The world is facing the COVID-19 pandemic, leading to an unprecedented change in the lifestyle routines of millions. Beyond the general physical health, financial, and social repercussions of the pandemic, the adopted mitigation measures also present significant challenges in the population’s mental health and health programs. It is complex for public organizations to measure the population’s mental health in order to incorporate it into their own decision-making process. Traditional survey methods are time-consuming, expensive, and fail to provide the continuous information needed to respond to the rapidly evolving effects of governmental policies on the population’s mental health. A significant portion of the population has turned to social media to express the details of their daily life, rendering this public data a rich field for understanding emotional and mental well-being. This study aims to track and measure the sentiment changes of the Mexican population in response to the COVID-19 pandemic. To this end, we analyzed 760,064,879 public domain tweets collected from a public access repository to examine the collective shifts in the general mood about the pandemic evolution, news cycles, and governmental policies using open sentiment analysis tools. Sentiment analysis polarity scores, which oscillate around -0.15, show a weekly seasonality according to Twitter’s usage and a consistently negative outlook from the population. It also remarks on the increased controversy after the governmental decision to terminate the lockdown and the celebrated holidays, which encouraged the people to incur social gatherings. These findings expose the adverse emotional effects of the ongoing pandemic while showing an increase in social media usage rates of 2.38 times, which users employ as a coping mechanism to mitigate the feelings of isolation related to long-term social distancing. The findings have important implications in the mental health infrastructure for ongoing mitigation efforts and feedback on the perception of policies and other measures. The overall trend of the sentiment polarity is 0.0001110643.
New language technologies are coming, thanks to the huge and competing private investment fuelling rapid progress; we can either understand and foresee their effects, or be taken by surprise and spend our time trying to catch up. This report scketches out some transformative new technologies that are likely to fundamentally change our use of language. Some of these may feel unrealistically futuristic or far-fetched, but a central purpose of this report - and the wider LITHME network - is to illustrate that these are mostly just the logical development and maturation of technologies currently in prototype. But will everyone benefit from all these shiny new gadgets? Throughout this report we emphasise a range of groups who will be disadvantaged and issues of inequality. Important issues of security and privacy will accompany new language technologies. A further caution is to re-emphasise the current limitations of AI. Looking ahead, we see many intriguing opportunities and new capabilities, but a range of other uncertainties and inequalities. New devices will enable new ways to talk, to translate, to remember, and to learn. But advances in technology will reproduce existing inequalities among those who cannot afford these devices, among the world’s smaller languages, and especially for sign language. Debates over privacy and security will flare and crackle with every new immersive gadget. We will move together into this curious new world with a mix of excitement and apprehension - reacting, debating, sharing and disagreeing as we always do. Plug in, as the human-machine era dawns.
and UAE, 86% of all respondents have two or more devices that can connect to the internet, and nearly two thirds (64%) already own three or more mobile devices with this feature; another 39% own four or more (p. 4). We live on the move, and this includes mobility, as well as working anytime, anywhere and lifelong learning. Thus, research on language teaching and/or learning should focus on the ways to get adapted to the specific new needs of our modern society (e.g. mobility). Accordingly, for instance, mobile-assisted language learning (MALL) activities should be appbased; "this is not a trend language educators can ignore" (Godwin-Jones, 2011, p. 8). In this paper, we present some preliminary results and conclusions after the design and implementation of some MALL apps carried out by the ATLAS research group. They have been developed in the context of the SO-CALL-ME project, in order to enable the members of ATLAS to explore the way in which students can improve their oral language skills "on the move".
Desde la publicación, en 2008, del primer monográfico sobre aprendizaje de lenguas mediante tecnología móvil (en inglés conocido con el acrónimo MALL: Mobile Assisted Language Learning), bajo la coordinación de Shield y KukulskaHulme en la prestigiosa revista ReCALL (Shield y Kukulska-Hulme, 2008), son muchos los cambios que afectan a la forma de aprender lenguas extranjeras a través de dispositivos móviles como los smartphones o las tabletas.El avance de la tecnología, el abaratamiento de las tarifas de datos a nivel mundial, la presencia de redes inalámbricas en la mayoría de lugares de paso, transportes y edificios públicos, así como la amplísima difusión de este tipo de dispositivos móviles (por ejemplo, véase la gráfica incluida en la figura 1), han sido factores determinantes para la progresiva utilización de los mismos en actividades dedicadas al aprendizaje de lenguas extranjeras. Cabe destacar asimismo el ascenso vertiginoso que han experimentado las suscripciones a anchos de banda para dispositivos móviles en los últimos años.
Maritime safety and security are being constantly jeopardized. Therefore, identifying maritime flow irregularities (semi-)automatically may be crucial to ensure maritime security in the future. This paper presents a Ship Semantic Information-Based, Image Similarity Calculation System (Ship-SIBISCaS), which constitutes a first step towards the automatic identification of this kind of maritime irregularities. In particular, the main goal of Ship-SIBISCaS is to automatically identify the type of ship depicted in a given image (such as abandoned, cargo, container, hospital, passenger, pirate, submersible, three-decker, or warship) and, thus, classify it accordingly. This classification is achieved in Ship-SIBISCaS by finding out the similarity of the ship image and/or description with other ship images and descriptions included in its knowledge base. This similarity is calculated by means of an LSA algorithm implementation that is run on a parallel architecture consisting of CPUs and GPUs (i.e., a heterogeneous system). This implementation of the LSA algorithm has been trained with a collection of texts, extracted from Wikipedia, that associate some semantic information to ImageNet ship images. Thanks to its parallel architecture, the indexing process of this image retrieval system has been accelerated 10 times (for the 1261 ships included in ImageNet). The range of the precision of the image similarity method is 46% to 92% with 100% recall (that is, a 100% coverage of the database).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.