Economic Impacts of COVID-19 on Microfinance Institutions and Small Businesses: Empirical Survey from Somalia

The microfinance institutions promote the entrepreneurial innovations that contribute the fundamentals of a healthy economy and job creation opportunities. In Somalia, the small businesses have ever been valuable resources to encourage the economic sustainability of Somali country. After the destruction in January 26, in 1991, the people of Somalia have relied on the entrepreneurial skills to develop small businesses on which the lives of many Somali people have hinged. Therefore, this study assesses the economic impacts of covid-19 on microfinance institutions and small businesses. The study follows descriptive correlation. The overall sample consisted of 40 participants in Benadir region.  The study found that covid-19 has negative impact on both microfinance institutions and small businesses as shown by the mean index of microfinance institutions 1.72 and standard deviation 0.48, likewise, the mean index of small businesses scored 1.86 and standard deviation 0.69. Key words: Economic impacts, Covid -19, Microfinance Institutions, Small Businesses, Benadir region DOI: 10.7176/EJBM/13-6-21 Publication date:March 31st 2021

MasakhaNEWS: News Topic Classification for African languages

African languages are severely under-represented in NLP research due to lack of datasets covering several NLP tasks. While there are individual language specific datasets that are being expanded to different tasks, only a handful of NLP tasks (e.g. named entity recognition and machine translation) have standardized benchmark datasets covering several geographical and typologically-diverse African languages. In this paper, we develop MasakhaNEWS -- a new benchmark dataset for news topic classification covering 16 languages widely spoken in Africa. We provide an evaluation of baseline models by training classical machine learning models and fine-tuning several language models. Furthermore, we explore several alternatives to full fine-tuning of language models that are better suited for zero-shot and few-shot learning such as cross-lingual parameter-efficient fine-tuning (like MAD-X), pattern exploiting training (PET), prompting language models (like ChatGPT), and prompt-free sentence transformer fine-tuning (SetFit and Cohere Embedding API). Our evaluation in zero-shot setting shows the potential of prompting ChatGPT for news topic classification in low-resource African languages, achieving an average performance of 70 F1 points without leveraging additional supervision like MAD-X. In few-shot setting, we show that with as little as 10 examples per label, we achieved more than 90\% (i.e. 86.0 F1 points) of the performance of full supervised training (92.6 F1 points) leveraging the PET approach.Comment: Accepted to IJCNLP-AACL 2023 (main conference

MasakhaNEWS:News Topic Classification for African languages

African languages are severely under-represented in NLP research due to lack of datasets covering several NLP tasks. While there are individual language specific datasets that are being expanded to different tasks, only a handful of NLP tasks (e.g. named entity recognition and machine translation) have standardized benchmark datasets covering several geographical and typologically-diverse African languages. In this paper, we develop MasakhaNEWS -- a new benchmark dataset for news topic classification covering 16 languages widely spoken in Africa. We provide an evaluation of baseline models by training classical machine learning models and fine-tuning several language models. Furthermore, we explore several alternatives to full fine-tuning of language models that are better suited for zero-shot and few-shot learning such as cross-lingual parameter-efficient fine-tuning (like MAD-X), pattern exploiting training (PET), prompting language models (like ChatGPT), and prompt-free sentence transformer fine-tuning (SetFit and Cohere Embedding API). Our evaluation in zero-shot setting shows the potential of prompting ChatGPT for news topic classification in low-resource African languages, achieving an average performance of 70 F1 points without leveraging additional supervision like MAD-X. In few-shot setting, we show that with as little as 10 examples per label, we achieved more than 90\% (i.e. 86.0 F1 points) of the performance of full supervised training (92.6 F1 points) leveraging the PET approach

Parachute tricuspid valve unique case and the first case in Somalia

Atrioventricular valve parachute deformity is rare and is generally seen in the mitral position, called the parachute mitral valve. It is rare to see it in the tricuspid valve and up to now, there have been approximately 14 cases of parachute abnormalities in tricuspid valves in a literature review. We present here a 21-year-old male who presented to the emergency department with progressive shortness of breath over several months. Despite the absence of chest pain, palpitations, or weight changes, examination revealed a soft, holosystolic murmur at the left lower sternal border. Further investigation through electrocardiogram and echocardiogram identified a rare structural abnormality known as parachute tricuspid valve, resulting in significant tricuspid regurgitation with mild right chamber enlargement. The patient was recommended to go abroad for further management since a cardiothoracic surgeon is not available in our country

MasakhaNEWS:News Topic Classification for African languages

African languages are severely under-represented in NLP research due to lack of datasets covering several NLP tasks. While there are individual language specific datasets that are being expanded to different tasks, only a handful of NLP tasks (e.g. named entity recognition and machine translation) have standardized benchmark datasets covering several geographical and typologically-diverse African languages. In this paper, we develop MasakhaNEWS -- a new benchmark dataset for news topic classification covering 16 languages widely spoken in Africa. We provide an evaluation of baseline models by training classical machine learning models and fine-tuning several language models. Furthermore, we explore several alternatives to full fine-tuning of language models that are better suited for zero-shot and few-shot learning such as cross-lingual parameter-efficient fine-tuning (like MAD-X), pattern exploiting training (PET), prompting language models (like ChatGPT), and prompt-free sentence transformer fine-tuning (SetFit and Cohere Embedding API). Our evaluation in zero-shot setting shows the potential of prompting ChatGPT for news topic classification in low-resource African languages, achieving an average performance of 70 F1 points without leveraging additional supervision like MAD-X. In few-shot setting, we show that with as little as 10 examples per label, we achieved more than 90\% (i.e. 86.0 F1 points) of the performance of full supervised training (92.6 F1 points) leveraging the PET approach