Analysis The Effect of e-Budgeting and Government Internal Control System on The Quality of Financial Reporting of Local Government in Indonesia in Islamic Perspectives

The objective of this study is to determine the impact of e-budgeting and internal government control systems to financial reporting of  local governments in Indonesia as measured by the achievement of the unqualified opinion from the report of the Financial Audit Board (LHP BPK) on the Regional Government Financial Reports (LKPD). Financial reporting is a mechanism in the delivery of financial information and a representation of the financial position of transactions carried out by local governments , as an obligation of responsibility for the allocation and use of resources that are useful for accountability and transparency purposes.The implementation of e-budgeting and the government internal control system in the preparation of financial reporting is a means to realize accountability and transparency in governance in bureaucratic reform. This is consistent with the perspective of Islam provides guidance in the embodiment of the system of good governance government in the form of three pillars, namely transparency , accountability and participation . The study population was all local governments in Indonesia with a sample that local governments in Indonesia are already implementing e-budgeting in the 2017 -2018 . This research method is a quantitative empirical research method using secondary data with data analysis performed statistical tests using multiple linear regression with Eviews 9.0. The results of hypothesis testing and multiple regression analysis with  eviews 9.0 shows that e-budgeting and government internal control systems are simultaneously influenced positively to financial reporting of  local governments in Indonesia in the form of unqualified opinion.  Keywords :  E-Budgeting, Government Internal Control, Financial Reporting, Islamic Perspective

Covid-19: reinforcing the impact of Islamic banking through value-based intermediation

The novel Covid-19 pandemic has caused an unprecedented human crisis around the globe. The necessary actions implemented to contain the virus have sparked both economic and social downturn. It shows the fragility and unpreparedness of the economy to face such a pandemic. Significant weakening of economic conditions has escalated the pressure on households, businesses and financial markets. However, before the Covid-19 outbreak, Bank Negara Malaysia has taken a new initiative by introducing Value-Based Intermediation (VBI). VBI’s strategy opens up a new holistic layer for Islamic banks in providing the public at large with impactful and profitable services. This paper discusses VBI’s strategy and its potential application from the viewpoint of Sharīʽah. This paper also discusses Islamic banks' activities in implementing VBI as well as their response to the Covid-19 pandemic, based on qualitative inquiry. The paper concludes that VBI is a long journey that requires significant transformation of mindset among key stakeholders. As Covid-19 has adversely impacted communities in several ways, Islamic banks could empower communities through provision of financial solutions that create positive impact

The Debt-Equity Ratio Choice: Risk Sharing Instruments, A Viable Alternative

Evidence has been documented in the literature that the interest based debt financing system is experiencing continuous discomfort. The outcome of the 2008 global financial crisis has further create fresh vigor to the assertion. Also, these authors have submitted that debt and leveraging are the two major causes of financial instability in the present system. This paper claims that the existence of the interest-based debt regime is becoming less acceptable, as excessive debt can affect the whole economic system, even in a developed country like United States. From an economic viewpoint, therefore, by banning interest rate-based contracts and decreeing exchange contracts, Islamic financeinspires risk sharing and prohibits risk transfer, risk shedding, and risk shifting. The paper proposes risk sharing based Islamic financing as a suitable alternative to the interest based debt financing. This study concludes that risk-sharing finance has several benefits, especially its potential to minimize, if not circumvent, the debt prompted financial crises that have beset the world..Keywords :  Debt-Equity Ratio; Risk Sharing Instruments; Islamic Financ

HERDPhobia: A Dataset for Hate Speech against Fulani in Nigeria

Social media platforms allow users to freely share their opinions about issues or anything they feel like. However, they also make it easier to spread hate and abusive content. The Fulani ethnic group has been the victim of this unfortunate phenomenon. This paper introduces the HERDPhobia - the first annotated hate speech dataset on Fulani herders in Nigeria - in three languages: English, Nigerian-Pidgin, and Hausa. We present a benchmark experiment using pre-trained languages models to classify the tweets as either hateful or non-hateful. Our experiment shows that the XML-T model provides better performance with 99.83% weighted F1. We released the dataset at https://github.com/hausanlp/HERDPhobia for further research.Comment: To appear in the Proceedings of the Sixth Workshop on Widening Natural Language Processing at EMNLP202

Achieving the Sustainable Development Goals: the role of Islamic social finance towards realizing financial inclusion in the unprecedented Covid-19

The purpose of this study is to explore the role of Islamic social finance towards realising financial inclusion in achieving the Sustainable Development Goals (SDGs) in the 2030 agenda for SDGs, as propagated by United Nations Member States in 2015. A critical analysis is made to explain the possible contribution of Islamic social finance in achieving financial inclusion which is aligned with SDGs that brings balanced to the physical, emotional, mental and spiritual of the community in supporting overall economic growth which finally combats the economic impact of the COVID-19 pandemic. The results may also motivate the financial industries to promote Islamic social finance products and corporate social responsibilities as well as enhance the development of ISF towards achieving financial inclusion in fulfilling SDGs which is seen as being parallel with Maqᾱṣid al-Sharῑ῾ah especially in resolving unpredictable economic issuesand hiccups during COVID-19

Deep Sequence Models for Text Classification Tasks

The exponential growth of data generated on the Internet in the current information age is a driving force for the digital economy. Extraction of information is the major value in an accumulated big data. Big data dependency on statistical analysis and hand-engineered rules machine learning algorithms are overwhelmed with vast complexities inherent in human languages. Natural Language Processing (NLP) is equipping machines to understand these human diverse and complicated languages. Text Classification is an NLP task which automatically identifies patterns based on predefined or undefined labeled sets. Common text classification application includes information retrieval, modeling news topic, theme extraction, sentiment analysis, and spam detection. In texts, some sequences of words depend on the previous or next word sequences to make full meaning; this is a challenging dependency task that requires the machine to be able to store some previous important information to impact future meaning. Sequence models such as RNN, GRU, and LSTM is a breakthrough for tasks with long-range dependencies. As such, we applied these models to Binary and Multi-class classification. Results generated were excellent with most of the models performing within the range of 80% and 94%. However, this result is not exhaustive as we believe there is room for improvement if machines are to compete with humans

Analyzing COVID-19 Vaccination Sentiments in Nigerian Cyberspace: Insights from a Manually Annotated Twitter Dataset

Numerous successes have been achieved in combating the COVID-19 pandemic, initially using various precautionary measures like lockdowns, social distancing, and the use of face masks. More recently, various vaccinations have been developed to aid in the prevention or reduction of the severity of the COVID-19 infection. Despite the effectiveness of the precautionary measures and the vaccines, there are several controversies that are massively shared on social media platforms like Twitter. In this paper, we explore the use of state-of-the-art transformer-based language models to study people's acceptance of vaccines in Nigeria. We developed a novel dataset by crawling multi-lingual tweets using relevant hashtags and keywords. Our analysis and visualizations revealed that most tweets expressed neutral sentiments about COVID-19 vaccines, with some individuals expressing positive views, and there was no strong preference for specific vaccine types, although Moderna received slightly more positive sentiment. We also found out that fine-tuning a pre-trained LLM with an appropriate dataset can yield competitive results, even if the LLM was not initially pre-trained on the specific language of that dataset

HaVQA: A Dataset for Visual Question Answering and Multimodal Research in Hausa Language

This paper presents HaVQA, the first multimodal dataset for visual question-answering (VQA) tasks in the Hausa language. The dataset was created by manually translating 6,022 English question-answer pairs, which are associated with 1,555 unique images from the Visual Genome dataset. As a result, the dataset provides 12,044 gold standard English-Hausa parallel sentences that were translated in a fashion that guarantees their semantic match with the corresponding visual information. We conducted several baseline experiments on the dataset, including visual question answering, visual question elicitation, text-only and multimodal machine translation.Comment: Accepted at ACL 2023 as a long paper (Findings

Characteristics of COVID-19 cases and factors associated with their mortality in Katsina State, Nigeria, April-July 2020

Introduction: COVID-19 was first detected in Daura, Katsina State, Nigeria on 4 April 2020. We characterized the cases and outlined factors associated with mortality. Methods: We analysed the COVID-19 data downloaded from Surveillance Outbreak Response, Management and Analysis System between 4 April and 31 July 2020. We defined a case as any person with a positive SARS-CoV-2 test within that period. We described the cases in time, person, and place; calculated the crude and adjusted odds ratios and 95% confidence intervals for factors associated with mortality. Results: We analysed 744 confirmed cases (median age 35, range 1-90), 73% males and 24 deaths (Case fatality rate 3.2%, Attack rate 8.5/100,000). The outbreak affected 31 districts, started in week 14, peaked in week 26, and is ongoing. Highest proportion of cases in the age groups were 26.7% (184) in 30-39, 21.7% (153) in 20-29 years, and 18.3% (129) in 40-49 years. While the highest case fatality rates in the age groups were 35.7% in 70-79, 33.3% in 80-89 years, and 19.4% in 60-69 years. Factors associated with death were cough (AOR: 9.88, 95% CI: 1.29-75.79), age ≥60 years (AOR: 18.42, 95% CI: 7.48-45.38), and male sex (AOR: 4.4, 95% CI: 0.98-20.12). Conclusion: Male contacts below 40 years carried the burden of COVID-19. Also, persons 60 years and above, with cough have an increased risk of dying from COVID-19. Risk communication should advocate for use of preventive measures, protection of persons 60 years and above, and consideration of cough as a red-flag sign

AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages

Africa is home to over 2000 languages from over six language families and has the highest linguistic diversity among all continents. This includes 75 languages with at least one million speakers each. Yet, there is little NLP research conducted on African languages. Crucial in enabling such research is the availability of high-quality annotated datasets. In this paper, we introduce AfriSenti, which consists of 14 sentiment datasets of 110,000+ tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yor\`ub\'a) from four language families annotated by native speakers. The data is used in SemEval 2023 Task 12, the first Afro-centric SemEval shared task. We describe the data collection methodology, annotation process, and related challenges when curating each of the datasets. We conduct experiments with different sentiment classification baselines and discuss their usefulness. We hope AfriSenti enables new work on under-represented languages. The dataset is available at https://github.com/afrisenti-semeval/afrisent-semeval-2023 and can also be loaded as a huggingface datasets (https://huggingface.co/datasets/shmuhammad/AfriSenti).Comment: 15 pages, 6 Figures, 9 Table