Illegal Community Detection in Bitcoin Transaction Networks

Community detection is widely used in social networks to uncover groups of related vertices (nodes). In cryptocurrency transaction networks, community detection can help identify users that are most related to known illegal users. However, there are challenges in applying community detection in cryptocurrency transaction networks: (1) the use of pseudonymous addresses that are not directly linked to personal information make it difficult to interpret the detected communities; (2) on Bitcoin, a user usually owns multiple Bitcoin addresses, and nodes in transaction networks do not always represent users. Existing works on cluster analysis on Bitcoin transaction networks focus on addressing the later using different heuristics to cluster addresses that are controlled by the same user. This research focuses on illegal community detection containing one or more illegal Bitcoin addresses. We first investigate the structure of Bitcoin transaction networks and suitable community detection methods, then collect a set of illegal addresses and use them to label the detected communities. The results show that 0.06% of communities from daily transaction networks contain one or more illegal addresses when 2,313,344 illegal addresses are used to label the communities. The results also show that distance-based clustering methods and other methods depending on them, such as network representation learning, are not suitable for Bitcoin transaction networks while community quality optimization and label-propagation-based methods are the most suitable

Visualising M-learning system usage data

Data storage is an important practice for organisations that want to track their progress. The evolution of data storage technologies from manual methods of storing data on paper or in spreadsheets, to the automated methods of using computers to automatically log data into databases or text files has brought an amount of data that is beyond the level of human interpretation and comprehension. One way of addressing this issue of interpreting large amounts of data is data visualisation, which aims to convert abstract data into images that are easy to interpret. However, people often have difficulty in selecting an appropriate visualisation tool and visualisation techniques that can effectively visualise their data. This research proposes the processes that can be followed to effectively visualise data. Data logged from a mobile learning system is visualised as a proof of concept to show how the proposed processes can be followed during data visualisation. These processes are summarised into a model that consists of three main components: the data, the visualisation techniques and the visualisation tool. There are two main contributions in this research: the model to visualise mobile learning usage data and the visualisation of the usage data logged from a mobile learning system. The mobile learning system usage data was visualised to demonstrate how students used the mobile learning system. Visualisation of the usage data helped to convert the data into images (charts and graphs) that were easy to interpret. The evaluation results indicated that the proposed process and resulting visualisation techniques and tool assisted users in effectively and efficiently interpreting large volumes of mobile learning system usage data

Semi-Supervised Semantic Segmentation of Remote Sensing Images Based on Dual Cross-Entropy Consistency

Semantic segmentation is a growing topic in high-resolution remote sensing image processing. The information in remote sensing images is complex, and the effectiveness of most remote sensing image semantic segmentation methods depends on the number of labels; however, labeling images requires significant time and labor costs. To solve these problems, we propose a semi-supervised semantic segmentation method based on dual cross-entropy consistency and a teacher–student structure. First, we add a channel attention mechanism to the encoding network of the teacher model to reduce the predictive entropy of the pseudo label. Secondly, the two student networks share a common coding network to ensure consistent input information entropy, and a sharpening function is used to reduce the information entropy of unsupervised predictions for both student networks. Finally, we complete the alternate training of the models via two entropy-consistent tasks: (1) semi-supervising student prediction results via pseudo-labels generated from the teacher model, (2) cross-supervision between student models. Experimental results on publicly available datasets indicate that the suggested model can fully understand the hidden information in unlabeled images and reduce the information entropy in prediction, as well as reduce the number of required labeled images with guaranteed accuracy. This allows the new method to outperform the related semi-supervised semantic segmentation algorithm at half the proportion of labeled images

Evaluation of the Effectiveness of Environmental and Social Impact Assessment Compliance on the Mining Sector in Rwanda

This research evaluates the mining sector of Rwanda in terms of compliance, enforcement challenges and implementation of the Environmental and Social Impact Assessment (ESIA) regulatory requirements.  As at 2022, the sector was one of key economic drivers contributing hugely about $733 million in exports and 3.6% of GDP to the growth, however, the sector is undoubtedly grappling with obvious environmental and social challenges. The effectiveness ESIA compliance in the mining sector was evaluated using a mixed-methods approach, integrating qualitative and quantitative data from the government, companies, and communities. Data were analyzed using frequencies, percentages, means, regression and correlation analysis. The results indicated that males across all age groups constituted 62% of the miners while female constituted only 38% demonstrating a higher participation of males in the mining sector compared to females. Also, various levels of inconsistency in compliance with proper ESIA procedures and guidelines were observed with only 28% of the companies in Full Compliance (FU), 48% Partial Compliance (PC), and 24% showed Non-Compliance (NC).  It was also observed   that a statistically significant negative correlation was observed between deforestation and ESIA compliance (r = -0.62, p < 0.01), and a strong inverse relationship was found between ESIA compliance and water contamination (r = -0.58, p < 0.01). Similarly, a stronger ESIA enforcement corresponds with lower biodiversity loss (r = -0.55, p < 0.01). ESIA compliance and stakeholder participation were correlated and the result showed (r = 0.67, p < 0.01), also, the relationship between ESIA compliance and conflict reduction was (r = -0.54, p < 0.05). Again, significant positive relationship between ESIA compliance and mining sector profitability (β=0.42, p<0.01 & beta = 0.42, p < 0.01 β=0.42, p<0.01) was observed. While ESIA compliance initially imposes higher operational costs, the regression analysis indicated that it ultimately results in long-term cost savings (β=−0.38, p<0.05 & beta = -0.38, p < 0.05 β=−0.38, p<0.05). The findings highlighted a strong association between ESIA compliance and a company's market reputation (β=0.55, p<0.01 & beta = 0.55, p < 0.01β=0.55, p<0.01). The study recommends strengthening regulatory oversight, implementing digital compliance tracking, enhancing community participation, and enforcing stricter penalties for non-compliance. Addressing these challenges is essential for aligning Rwanda's obligated best practices in the mining sector with Vision 2050 and the Sustainable Development Goals (SDGs), ensuring responsible resource management, proper environmental conservation, and responsible social justice. Key words : Mining, Compliance, Environment, Sustainability, Impact, Assessmen

A compound analysis of medical device clinical trials registered in Africa on clinicaltrials.gov

BACKGROUND: Africa, specifically the Sub-Saharan region, has had numerous medical technology clinical trials to address the various healthcare challenges around infectious diseases, non-communicable diseases, and nutritional disorders it is facing. Medical device clinical trials provide performance data in terms of safety, efficacy, and efficiency, which is a requirement before commercialization. Key players such as academicians, governments, international organizations, and funders collaborate to drive these trials, but their growth in Africa remains slower compared to other parts of the globe. This paper aims to evaluate the number of medical device clinical trials conducted in different African countries that are registered on the clinicaltrials.gov website.METHODS: Data on medical device clinical trials was mined from clinicaltrials.gov website accessed on 22nd September, 2022. The data extracted was analyzed and cleaned in Microsoft Excel and R. Countries were grouped into regions and descriptive statistical analyses for each region were done. Additionally, frequency distributions were also generated and no inferential statistical tests were performed, as the primary focus of this analysis was to describe the distribution of medical conditions across regions.RESULTS: Thirty-one African countries had registered medical device clinical trials on the website with the majority taking place in Egypt and South Africa. Medical device trials for heart related issues took longer to complete compared to other conditions. Malaria, HIV, and male circumcision related device trials were mainly conducted in Eastern and Southern Africa while trials related to dental, fertility, and obesity were concentrated in Northern Africa. Female reproductive health issues were studied equally across all regions. Some African countries did not have any trials registered on clinicaltrials.gov website.CONCLUSION: Findings from this study clearly show the disparity in the number, status, and duration of medical device clinical trials across various African countries

Assessment of the Influence of Environmental and Anthropogenic Factors on Malaria Transmission in Ngoma District, Rwanda

Malaria continues to remain a major health problem in Ngoma district and Rwanda at large despite of various measures that have been geared towards treatment and control of the disease. The present study was aimed at determining the influence of environmental and anthropogenic factors on malaria transmission in Ngoma district, Rwanda. A cross-section survey employing Questionnaires, observation and interviews were used to collect primary data while secondary data on seasonality of malaria transmission were collected from published and unpublished hospital reports and nearby weather station. The results showed that hospital admission rates for malaria in adults and children was highest in 2014 at 51.0% and 64.6%, respectively. Hospital admission rates in adults was lowest in 2016 at 18.5% and lowest in children in 2017 at 13.2%. There was a positive relationship between malaria admission rates and rainfall and temperature (p = 0.001). The most appreciated night-time outdoor activities were evening parties (Chi-squared value 184.068, p = 0.000) where it is ranked by 3.57). The main reason for not owning the LLINs was that the LLINs were not available as noted by 26.0% (p = 0.000). Irrigation for Rice cultivation and slow flowing fresh water from the extensive anastomosis of tributaries of River Kagera were the most dominant malaria transmission factor (66.1%, p = 0.000). Malaria transmission was significantly associated with non-windows screening (92.9%, p = 0.000). General sanitation is effective in reducing malaria transmission (55.9%). Livestock keeping had a significant impact on malaria transmission increase (38.6%) (Chi-square: 81.506, Std. Dev = 0.489 and p = 0.000) due to increasing mosquitoes density. This study validates anthropogenic factors notably rice farming, poor housing, inappropriate use of bed nets, night parties, irrigation agriculture and improper waste management as the main malaria causing factors in Ngoma district in Rwanda

Index of information about the conflicts in the Great Lakes Region published in the New Vision NewsPaper ; a dissertation for the award of the degree of Bachelor in Library and Information Science at Makerere University

Document collected by the University of Texas Libraries from the web-site of the Reseau Documentaire International Sur La Region Des Grands Lacs Africains (International Documentation Network on the Great African Lakes Region). The Reseau distributes "gray literature", non-published or limited distribution government or NGO documents regarding the Great Lakes area of central Africa including Rwanda, Burundi, and the Democratic Republic of Congo.UT Librarie