Maintaining Voting Integrity using Blockchain

The potentials of using blockchains and distributed ledgers to support voting processes have attracted significant attention in the electronic voting community. Most of these recent ideas are centered on blockchain-based e-voting protocols. Others focus on how blockchain can be exploited to simultaneously deliver auditability and anonymity of voters in the voting process. A common feature of these research efforts is the use of blockchain within e-voting contexts. We elaborate in this work the integrity requirements that must be supported by blockchain in online voting as well as offline voting prevalent in developing countries. The framework conditions for blockchain-based voting are also discussed

Factors for e-voting adoption - analysis of general elections in Nigeria

The adoption and use of e-voting technologies in major elections remain largely problematic regardless of where they are implemented. This has motivated a number of survey-based empirical studies on determining important factors for e-voting adoption based on existing technology adoption models. However, there is a paucity of studies, which provide deep insights and understanding of core issues involved in e-voting adoption success or failures in different contexts. This article describes an ethnography carried out with the goal to understand factors that support or inhibit e-voting adoption based on detailed data collected during the 2011 Nigerian General Elections. By consolidating existing e-voting adoption models and a multi-level innovation adoption model into an analytical framework, we analysed the observations made by one of the authors as a participant in the adoption and implementation of the e-voting initiative as well as the post-election reports. Our findings are synthesized into a multi-level e-voting adoption model. In addition, we catalog a number of factors that could negatively affect e-voting adoption in a similar environment. Our results contribute to advancing theory building in e-voting adoption while it provides practitioners with a concrete checklist of success factors and barriers for adopting e-voting technologies

Design imperatives for e-voting as a sociotechnical system

There are a number of past and ongoing research efforts on the development of e-voting systems. These works largely focus on requirements, technical specification and implementation technologies to support different aspects of the elections from registration and verification through balloting to counting and result. A major shortcoming of these studies is their sole focus on technical aspect of e-voting solution wit/lOut significant attention paid to human and environment factors that arguably determine the successful adoption of such e-voting solutions. This paper addresses this design gap in three steps. First, it provides a conceptualization of e-voting system as a socio-technical system. Second, it elaborates a set of principles to guide a socioteclmical design for e-voting. Third, it provides concrete implications of these principles. The paper concludes on the pragmatics of this approach to e-voting adoption particularly in environment such as Nigeria

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care

Bag of Tricks for Improving Deep Learning Performance on Multimodal Image Classification

A comprehensive medical image-based diagnosis is usually performed across various image modalities before passing a final decision; hence, designing a deep learning model that can use any medical image modality to diagnose a particular disease is of great interest. The available methods are multi-staged, with many computational bottlenecks in between. This paper presents an improved end-to-end method of multimodal image classification using deep learning models. We present top research methods developed over the years to improve models trained from scratch and transfer learning approaches. We show that when fully trained, a model can first implicitly discriminate the imaging modality and then diagnose the relevant disease. Our developed models were applied to COVID-19 classification from chest X-ray, CT scan, and lung ultrasound image modalities. The model that achieved the highest accuracy correctly maps all input images to their respective modality, then classifies the disease achieving overall 91.07% accuracy

Local and global election result collation & transmission system

We present a technique for election results collation and transmission in largely populated areas. In our earlier works we presented an electronic balloting and result transmission systems [1] [2]. Whereas these systems perform creditably well where voters are within estimated number of between 500 and 1000, a problem of local collation was introduced where the population exceeds 2000 where multiple devices have to be introduced. We achieved local collation by providing an admin device for result collation from each e-balloting devices at various voting points. The results collated at local level were further transmitted to central collation center from each polling units after local result have been announced. The system provided way to ensure local result display at individual Polling Units (a requirement of Nigerian laws) and reduces congestion during results’ transmissio