title: Did COVID-19 tourism sector supports alleviate investor fear?

authors: Corbet, Shaen

subjects: Social Sciences

abstract: The COVID-19 pandemic presented a dynamic black-swan event to which governments implemented support programmes to reduce sectoral probability of default. This research analyses investor response to such assistance, designed to mitigate the effects of the pandemic upon international aviation and tourism. Investor confidence in such support schemes is estimated through short-term abnormal returns. Results indicate significant differential behaviour, with fiscal policy found to be a dominant and largely effective mechanism generating median abnormal returns of 2.17 %. Specific assistance programmes relating to COVID-19 loan facilities, and the provision of pandemic relief packages significantly alleviated short-term investor concerns with median abnormal returns estimated between 2.87 % and 3.89 % respectively. Our empirical results offer investors and policymakers an additional layer of information.

text: Did COVID-19 tourism sector supports alleviate investor fear? The COVID-19 pandemic presented a dynamic black-swan event to which governments implemented support programmes to reduce sectoral probability of default. This research analyses investor response to such assistance, designed to mitigate the effects of the pandemic upon international aviation and tourism. Investor confidence in such support schemes is estimated through short-term abnormal returns. Results indicate significant differential behaviour, with fiscal policy found to be a dominant and largely effective mechanism generating median abnormal returns of 2.17 %. Specific assistance programmes relating to COVID-19 loan facilities, and the provision of pandemic relief packages significantly alleviated short-term investor concerns with median abnormal returns estimated between 2.87 % and 3.89 % respectively. Our empirical results offer investors and policymakers an additional layer of information.

year: 2022

title: Deep feature engineering using full-text publications

authors: Safder, Iqra

abstract: We have observed a rapid proliferation in scientific literature and advancements in web technologies has shifted information dissemination to digital libraries [1]. In general, the research conducted by scientific community is articulated through scholarly publications pertaining high quality algorithms along other algorithmic specific metadata such as achieved results, deployed datasets and runtime complexity. According to estimation, approximately 900 algorithms are published in top core conferences during the years 2005-2009 [2]. With this significant increase in algorithms reported in these conferences, more efficient search systems with advance searching capabilities must be designed to search for an algorithm and its supported metadata such as evaluation results like precision, recall etc., particular dataset on which an algorithm executed or the time complexity achieved by that algorithm from full body text of an article. Such advanced search systems could support researchers and software engineers looking for cutting edge algorithmic solutions. Recently, state designed to search for an algorithm from full text articles [3-5].
In this work, we designed an advanced search engine for full text publications that leverages the deep learning techniques to classify algorithmic specific metadata and further to improve searching capabilities for a search system.

text: Deep feature engineering using full-text publications We have observed a rapid proliferation in scientific literature and advancements in web technologies has shifted information dissemination to digital libraries [1]. In general, the research conducted by scientific community is articulated through scholarly publications pertaining high quality algorithms along other algorithmic specific metadata such as achieved results, deployed datasets and runtime complexity. According to estimation, approximately 900 algorithms are published in top core conferences during the years 2005-2009 [2]. With this significant increase in algorithms reported in these conferences, more efficient search systems with advance searching capabilities must be designed to search for an algorithm and its supported metadata such as evaluation results like precision, recall etc., particular dataset on which an algorithm executed or the time complexity achieved by that algorithm from full body text of an article. Such advanced search systems could support researchers and software engineers looking for cutting edge algorithmic solutions. Recently, state designed to search for an algorithm from full text articles [3-5].
In this work, we designed an advanced search engine for full text publications that leverages the deep learning techniques to classify algorithmic specific metadata and further to improve searching capabilities for a search system.

year: 2018

title: A pedagogical framework for embedding computational thinking in authentic technology practice

authors: Fox-Turnbull, Wendy

subjects: authentic technology practice

abstract: Computational thinking is an aspect of digital technologies in the New Zealand Curriculum that teaches children to approach problems systematically, using logical and analytical reasoning.  This paper presents a study undertaken in a small-town primary school in New Zealand with a high population of Māori students. The study drew on four aspects from Kotsopoulos et al.’s framework of pedagogical experiences: unplugged, tinkering, making, remixing to investigate pedagogical strategies that facilitate the successful embedding of computational thinking within authentic technological practice. The research aimed to identify learning pedagogies that support young learners in their understanding of computational thinking through designing and developing digital technologies. The qualitative methodologies were informed by the three pedagogical principles from Bishop and Berryman to guide the research design and frame learning support for students. Key themes that emerged from the data included the values of local context, self-autonomy, classroom organisation, use of physical and digital manipulatives. Finally, the paper presents a model for the pedagogical delivery of computational thinking when embedded in technological practice in primary schools, drawing from and adding to Kotsopoulos and colleague’s pedagogical framework. The most significant modification situates the existing model within authentic technology practice, providing context for learning. The model shows an adjustment from ‘Making’ to ‘Designing and Making’, thus strengthening the position of digital technologies within the Technology learning area.  The model also signals that authentic technological within a primary classroom practice is heavily impacted by ‘the pragmatics of delivery’ through a range of pedagogical and organisational strategies developed to ensure the needs of all students are met and to support them through their learning journey in computational thinking. The last significant modification of Kotsopoulos and colleagues’ model is the removal of ‘remixing’ because the primary aged students in this study did not reach the level of sophistication required for remixing. The aim of the framework is to assist teachers and teacher educators to design and develop successful teaching and learning strategies for implementing computational thinking into authentic technology practice in primary schools.

text: A pedagogical framework for embedding computational thinking in authentic technology practice Computational thinking is an aspect of digital technologies in the New Zealand Curriculum that teaches children to approach problems systematically, using logical and analytical reasoning.  This paper presents a study undertaken in a small-town primary school in New Zealand with a high population of Māori students. The study drew on four aspects from Kotsopoulos et al.’s framework of pedagogical experiences: unplugged, tinkering, making, remixing to investigate pedagogical strategies that facilitate the successful embedding of computational thinking within authentic technological practice. The research aimed to identify learning pedagogies that support young learners in their understanding of computational thinking through designing and developing digital technologies. The qualitative methodologies were informed by the three pedagogical principles from Bishop and Berryman to guide the research design and frame learning support for students. Key themes that emerged from the data included the values of local context, self-autonomy, classroom organisation, use of physical and digital manipulatives. Finally, the paper presents a model for the pedagogical delivery of computational thinking when embedded in technological practice in primary schools, drawing from and adding to Kotsopoulos and colleague’s pedagogical framework. The most significant modification situates the existing model within authentic technology practice, providing context for learning. The model shows an adjustment from ‘Making’ to ‘Designing and Making’, thus strengthening the position of digital technologies within the Technology learning area.  The model also signals that authentic technological within a primary classroom practice is heavily impacted by ‘the pragmatics of delivery’ through a range of pedagogical and organisational strategies developed to ensure the needs of all students are met and to support them through their learning journey in computational thinking. The last significant modification of Kotsopoulos and colleagues’ model is the removal of ‘remixing’ because the primary aged students in this study did not reach the level of sophistication required for remixing. The aim of the framework is to assist teachers and teacher educators to design and develop successful teaching and learning strategies for implementing computational thinking into authentic technology practice in primary schools.

year: 2025