Traditional poll-site voting methods poise multiple administrative and logistical challenges inter alia scalability, cost and miscount. Moreover, there is a noticeable decline in the turnout rate of eligible voters, particularly the youth. This work proposes a novel mobile voting model that uses common-off-theshelf (COTS) mobile phones, in conjunction with a Near Field Communication (NFC) tag technology and a pragmatic biometric verification scheme. The mobile voting application being proposed in this work is launched by leveraging the autocoupling capability of NFC, which also serves for storing baseline information about voters. The auto-coupling feature mediates device familiarity requirement, which is a limiting factor for using mobile phones to administer elections satisfying transparency and ease of use. The baseline information stored in the NFC tag provides local biometric reference data that mediate intensive bandwidth consumption, computational requirement, provide for match-on-a-card features and satisfy the constraint that only the eligible voter may vote. This work notes all security requirements for this model and addresses some architecture, design and security issues that will arise if such a choice is made.
Objectives: This study focuses on the properties of nanohydroxyapatite (nHAp) in terms of remineralization and acid resistance. The nHAp were produced from waste eggshells via the mechanochemistry process. Materials and methods: The characterization was based on Fourier Transform Spectroscopy, X-ray diffraction, Field Scanning Electron Microscope (FESEM), and High-Resolution Electron Microscope to determine the surface morphology of the nHAp. The acid and remineralization properties were evaluated using bovine enamel and dentine models ( n = 5) while the buffering properties against acids were studied using a pH meter. The biocompatibility of the produce nHAp was assessed in vitro against NIH 3T3. Results: The XRD and FTIR results confirm that nHAp were successfully produced from eggshell waste after 5 h of milling. The HRTEM reveals a semi-sphere morphology with an average dimension of 9 to 20 nm. The buffering test suggests that nHAp were highly effective in neutralizing common dietary acids. Also, the nHAp exhibits outstanding remineralization and occluding properties. The cytotoxicity assay suggests that the nHAp had a low toxicity. Conclusion: The study concludes that using eggshell waste to produce nHAp will help in waste management and at the same time, provide valuable biomaterial for the treatment of tooth sensitivity.
News article classification is a recently growing area of interest in text classification because of its associated multiple matching categories. However, the weak reliability indices and ambiguities associated with state-of-the-art classifiers often employed make success in this domain very limited. Also, the high sensitivity and large disparity in performance results of classifiers to the varying nature of real-world datasets make the need for comparative evaluation inevitable. In this paper, the accuracy and computational time efficiency of the Kolmogorov Complexity Distance Measure (KCDM) and Artificial Neural Network (ANN) were experimentally evaluated for a prototype large dimensional news article classification problem. 2000 News articles from a dataset of 2225 British Broadcasting Corporation (BBC) news documents (including examples from sport, politics, entertainment, education and technology, and business) were used for categorical testing purposes. Porter's algorithm was used for word stemming after tokenization and stop-words removal, and a Normalized Term Frequency-Inverse Document Frequency (NTF-IDF) technique was adopted for feature extraction. Experimental results revealed that ANN performs better in terms of accuracy while the KCDM produced better results than ANN in terms of computational time efficiency.
We propose a secure mobile Internet voting architecture based on the Sensus reference architecture and report the experiments carried out using short-term spectral features for realizing the voice biometric based authentication module of the architecture being proposed. The short-term spectral features investigated are Mel-Frequency Cepstral Coefficients (MFCCs), Mel-Frequency Discrete Wavelet Coefficients (MFDWC), Linear Predictive Cepstral Coefficients (LPCC), and Spectral Histogram of Oriented Gradients (SHOGs). The MFCC, MFDWC, and LPCC usually have higher dimensions that oftentimes lead to high computational complexity of the pattern matching algorithms in automatic speaker recognition systems. In this study, higher dimensions of each of the short-term features were reduced to an 81-element feature vector per Speaker using Histogram of Oriented Gradients (HOG) algorithm while neural network ensemble was utilized as the pattern matching algorithm. Out of the four short-term spectral features investigated, the LPCC-HOG gave the best statistical results withRstatistic of 0.9127 and mean square error of 0.0407. These compact LPCC-HOG features are highly promising for implementing the authentication module of the secure mobile Internet voting architecture we are proposing in this paper.
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