The electoral process has suffered from deep political instability following the post-colonial independence of most African nations. Moreover, the electoral process in many countries is characterized by massive rigging, high cost of electoral materials, and declaration of false results. In this paper, we will present a review of the blockchain Technology and some of the potential roles to play in conducting a transparent election. This paper opines that with the emergence of the blockchain technology, African Nations should tap from it and build a reliable, secure, and convenient electoral voting system. It further suggests that a blockchain electoral voting system will eliminate most of the challenges faced by African nations in conducting a free, fair and transparent election with low cost and total security. The issue of election rigging is almost completely eradicated with this technology (if properly installed). An attempt to alter/manipulate records (votes) in the system's database can be spotted easily, because of its rigorous consensus rules, such an attempt is considered void and denied permission to access, alter, or destroy any of the previously saved votes. However, the paper argues that there are institutional challenges to implementing this technology within the continent. Specifically, there is a need to educate the masses as well as create robust policies that can accommodate this technology within the continent. Failure to acknowledge these challenges may well prevent the application of blockchain technology in African electoral process in the foreseeable future.

Completely Automated Public Turing Test To Tell Computer and Humans Apart (CAPTCHA) is a computer program that prevents malicious computer users. Text-CAPTCHA schemes utilize less-computational costs. Hence, they are the most popularly used. This paper investigates the effectiveness of state-of-the-art (SOTA) text-CAPTCHA schemes, proposes a Multiview deep learning system to break them, and highlights their weaknesses. Rather than the usual single-view feature extraction, the proposed model explores correlational features from multiple views to increase the model’s generalization and classification accuracy. The model combines convolutional neural networks and recurrent networks to preserve the input text-CAPTCHA’s spatial and sequential order. The proposed system has successfully achieved average accuracies ranging from 93.6% to 100%, and the average time to break a text-CAPTCHA scheme ranges from 0.0032 to 0.21 seconds on eight different datasets. Furthermore, an ablation study on 71 human users was conducted to evaluate the effectiveness of the schemes. The results demonstrated that the proposed system effectively outperforms the human users whom the schemes were designed to serve. Lastly, when compared with existing systems, the proposed system outperforms existing SOTA systems with an accuracy gap of almost 40% higher.

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