Impact of Foreign Reserve in Economic Growth: An Empirical Study on Bangladesh

Bangladesh maintained its robust performance in development. There have been upbeat exports and remittances. Overall inflation is slow & the current account deficit was reduced by higher exports and lower import inflation, but the decrease in the financial account surplus diluted the effect of the decline in the current account deficit on the overall balance of payments deficit. In the context of the Error Correction Process, this research studied the impact of economic development on Bangladesh foreign reserve assets using data for the period 1980- 2014. The findings show that economic growth is extremely important. The model's estimate, that economic growth and foreign reserves have a positive long-term relationship. The model checked by error correction estimates for error correction is negative and statistically important. In addition, the model showed that economic growth has short-term relationship too. The adjustment speed is more than 60 percent, suggesting that the term for error correction corrects the imbalance of the previous year. Granger causality test confirm that there is long run & short run causality among the variables. The question is whether the accumulation of foreign currency reserves is a required prerequisite for economic development. The empirical findings in this paper indicate that the rise in foreign exchange reserves induces GDP growth, although causality has not been demonstrated in the opposite direction. Keywords:Bangladesh; Economic Growth; Foreign Reserve, Error correction model; International reserves. DOI: 10.7176/EJBM/13-8-09 Publication date: April 30th 2021

MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification

Recent research in disaster informatics demonstrates a practical and important use case of artificial intelligence to save human lives and suffering during natural disasters based on social media contents (text and images). While notable progress has been made using texts, research on exploiting the images remains relatively under-explored. To advance image-based approaches, we propose MEDIC (Available at: https://crisisnlp.qcri.org/medic/index.html), which is the largest social media image classification dataset for humanitarian response consisting of 71,198 images to address four different tasks in a multi-task learning setup. This is the first dataset of its kind: social media images, disaster response, and multi-task learning research. An important property of this dataset is its high potential to facilitate research on multi-task learning, which recently receives much interest from the machine learning community and has shown remarkable results in terms of memory, inference speed, performance, and generalization capability. Therefore, the proposed dataset is an important resource for advancing image-based disaster management and multi-task machine learning research. We experiment with different deep learning architectures and report promising results, which are above the majority baselines for all tasks. Along with the dataset, we also release all relevant scripts (https://github.com/firojalam/medic).Comment: Multi-task Learning, Social media images, Image Classification, Natural disasters, Crisis Informatics, Deep learning, Datase

MEDIC: a multi-task learning dataset for disaster image classification

Recent research in disaster informatics demonstrates a practical and important use case of artificial intelligence to save human lives and suffering during natural disasters based on social media contents (text and images). While notable progress has been made using texts, research on exploiting the images remains relatively under-explored. To advance image-based approaches, we propose MEDIC (https://crisisnlp.qcri.org/medic/index.html), which is the largest social media image classification dataset for humanitarian response consisting of 71,198 images to address four different tasks in a multi-task learning setup. This is the first dataset of its kind: social media images, disaster response, and multi-task learning research. An important property of this dataset is its high potential to facilitate research on multi-task learning, which recently receives much interest from the machine learning community and has shown remarkable results in terms of memory, inference speed, performance, and generalization capability. Therefore, the proposed dataset is an important resource for advancing image-based disaster management and multi-task machine learning research. We experiment with different deep learning architectures and report promising results, which are above the majority baselines for all tasks. Along with the dataset, we also release all relevant scripts (https://github.com/firojalam/medic).Other Information Published in: Neural Computing and Applications License: https://creativecommons.org/licenses/by/4.0See article on publisher's website: http://dx.doi.org/10.1007/s00521-022-07717-0</p