Voting Method Research Articles

Information and computing system based on smart contracts for online voting

The purpose of research.The actual problem in voting systems is not the prevention of fraud and the failure to ensure the integrity of elections. Therefore, the task of building a system of safe and transparent voting is urgent.The purpose of the research is design and develop an information and computing system based on smart contacts for online voting.Methods. The presented system has the form of a decentralized system that can overcome a number of limitations, such as the likelihood of human error, falsification of voting results and problems with accessibility, using blockchain technology, which provides protection against unauthorized access and transparent record keeping.The use of smart contract technology also ensures that the voting process will be automated, transparent and secure. Thanks to the ability to execute self-executing contracts based on predefined rules, smart contracts can eliminate the need for intermediaries, reduce the likelihood of errors and ensure a high degree of accountability.Results. In order to evaluate the effectiveness of the developed system, testing was carried out, after which a comparative analysis was made with other similar systems.The system can ensure that votes are counted accurately and cannot be manipulated, thereby increasing confidence in the results of the voting process. In addition, the system can potentially reduce the costs associated with traditional voting methods and improve accessibility for voters, since it can be accessed remotely via a web interface.In general, the system will be of interest to various organizations and individuals seeking to conduct safe and transparent voting processes.Conclusion. The use of this software product will provide security and convenience when participating in various online voting, reliable storage of votes and the provision of detailed statistics on the conducted voting.

Exploring legitimacy in a municipal budget decision in Switzerland: empirical insights into citizens' perceptions.

This study examines legitimacy in municipal budgeting decisions, focusing on input, throughput and output dimensions. Using data from four Swiss studies, we explore how citizens assess these dimensions across traditional and innovative decision-making processes and investigate the impact of different voting methods on legitimacy perceptions. Our findings reveal that in routine processes using traditional voting, legitimacy dimensions are considered collectively. Conversely, in innovative participatory budgeting, dimensions are judged separately, involving more active evaluation. Throughput legitimacy (perceived fairness) emerges as crucial in both contexts, while input and output legitimacy's importance varies by process type. The Method of Equal Shares voting system shifts focus towards procedural fairness, increases representation and is perceived as fairer than the traditional Greedy method. However, even fair processes cannot fully compensate for outcome dissatisfaction, highlighting the complex interplay of legitimacy dimensions. This research contributes to understanding legitimacy construction in municipal decision-making, offering insights into the relationship between voting methods and legitimacy perceptions. The findings have implications for policy-makers seeking to enhance the effectiveness and acceptance of budgeting processes. This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'.

Fair voting outcomes with impact and novelty compromises? Unravelling biases in electing participatory budgeting winners.

Participatory budgeting, as a paradigm for democratic innovations, engages citizens in the distribution of a public budget to projects, which they propose and vote for implementation. So far, voting algorithms have been proposed and studied in social choice literature to elect projects that are popular, while others prioritize a proportional representation of voters' preferences, for instance, the rule of equal shares. However, the anticipated impact and novelty in the broader society by the winning projects, as selected by different algorithms, remains totally under-explored, lacking both a universal theory of impact for voting and a rigorous unifying framework for impact and novelty assessments. This article tackles this grand challenge towards new axiomatic foundations for designing effective and fair voting methods. This is through new and striking insights derived from a large-scale analysis of biases over 345 real-world voting outcomes, characterized for the first time by a novel portfolio of impact and novelty metrics. We find strong causal evidence that equal shares comes with impact loss in several infrastructural projects of different cost levels that have been so far over-represented. However, it also comes with a novel, yet over-represented, impact gain in welfare, education and culture. We discuss the broader implications of these results and how impact loss can be mitigated at the stage of campaign design and project ideation.This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'.

DeepBP: Ensemble deep learning strategy for bioactive peptide prediction.

Bioactive peptides are important bioactive molecules composed of short-chain amino acids that play various crucial roles in the body, such as regulating physiological processes and promoting immune responses and antibacterial effects. Due to their significance, bioactive peptides have broad application potential in drug development, food science, and biotechnology. Among them, understanding their biological mechanisms will contribute to new ideas for drug discovery and disease treatment. This study employs generative adversarial capsule networks (CapsuleGAN), gated recurrent units (GRU), and convolutional neural networks (CNN) as base classifiers to achieve ensemble learning through voting methods, which not only obtains high-precision prediction results on the angiotensin-converting enzyme (ACE) inhibitory peptides dataset and the anticancer peptides (ACP) dataset but also demonstrates effective model performance. For this method, we first utilized the protein language model-evolutionary scale modeling (ESM-2)-to extract relevant features for the ACE inhibitory peptides and ACP datasets. Following feature extraction, we trained three deep learning models-CapsuleGAN, GRU, and CNN-while continuously adjusting the model parameters throughout the training process. Finally, during the voting stage, different weights were assigned to the models based on their prediction accuracy, allowing full utilization of the model's performance. Experimental results show that on the ACE inhibitory peptide dataset, the balanced accuracy is 0.926, the Matthews correlation coefficient (MCC) is 0.831, and the area under the curve is 0.966; on the ACP dataset, the accuracy (ACC) is 0.779, and the MCC is 0.558. The experimental results on both datasets are superior to existing methods, demonstrating the effectiveness of the experimental approach. In this study, CapsuleGAN, GRU, and CNN were successfully employed as base classifiers to implement ensemble learning, which not only achieved good results in the prediction of two datasets but also surpassed existing methods. The ability to predict peptides with strong ACE inhibitory activity and ACPs more accurately and quickly is significant, and this work provides valuable insights for predicting other functional peptides. The source code and dataset for this experiment are publicly available at https://github.com/Zhou-Jianren/bioactive-peptides .

An Effective Ensemble Approach for Preventing and Detecting Phishing Attacks in Textual Form

Phishing email assaults have been a prevalent cybercriminal tactic for many decades. Various detectors have been suggested over time that rely on textual information. However, to address the growing prevalence of phishing emails, more sophisticated techniques are required to use all aspects of emails to improve the detection capabilities of machine learning classifiers. This paper presents a novel approach to detecting phishing emails. The proposed methodology combines ensemble learning techniques with various variables, such as word frequency, the presence of specific keywords or phrases, and email length, to improve detection accuracy. We provide two approaches for the planned task; The first technique employs ensemble learning soft voting, while the second employs weighted ensemble learning. Both strategies use distinct machine learning algorithms to concurrently process the characteristics, reducing their complexity and enhancing the model’s performance. An extensive assessment and analysis are conducted, considering unique criteria designed to minimize biased and inaccurate findings. Our empirical experiments demonstrates that using ensemble learning to merge attributes in the evolution of phishing emails showcases the competitive performance of ensemble learning over other machine learning algorithms. This superiority is underscored by achieving an F1-score of 0.90 in the weighted ensemble method and 0.85 in the soft voting method, showcasing the effectiveness of this approach.

Paradoxical oddities in two multiwinner elections from Scotland

If you were a candidate in an election, would you prefer more support from voters or less? Put another way: would you prefer that your campaign staff persuade more voters to vote for you, or fewer? These questions seem silly, because of course you would want more support from voters. Surprisingly, when using certain voting methods it is actually possible for more voter support to produce a worse result for a candidate; such an outcome is a type of monotonicity paradox. In [1], we searched for various types of monotonicity paradoxes in 1079 single-transferable vote (STV) elections from a database of Scottish local government elections. The purpose of this article is to present in detail two of the most interesting elections revealed by our search. These two elections are arguably the most paradox-riddled real-world political ranked choice elections ever documented, perhaps rivalled only by four single-winner examples from the United States: the 2009 mayoral election in Burlington, Vermont ([2, 3]); a 2021 city council race in Minneapolis, Minnesota [4]; the 2022 Special Election for US House in Alaska [5]; and the 2022 District 4 School Director election in Oakland, California [6]. The first election we present is the 2017 council election in the Buckie Ward of the Moray Council Area, which demonstrated the most extreme instance of a committee size monotonicity paradox ever observed in an actual election. The second election is the 2012 council election in the Steòrnabhagh a Tuath Ward of the Nah-Eileanan Siar Council Area, which demonstrated upward and downward monotonicity paradoxes, as well as a no-show paradox. To contextualise these elections, as part of our discussion we indicate how often these kinds of paradoxes occur in Scottish local government elections.

VOTEMAT: A Blockchain Based Voting System

This study aims to show that a secure, trustable and immutable voting system can be established with Blockchain technology. Decentralized structure of the Blockchain excludes the central authority and provides transparency. Moreover, its cryptographic functions enable secure transactions. Therefore, the operation is prevented from potential frauds, such as multiple votes, fake vote attempts, and fraudulent vote counts. The proposed method, VOTEMAT, covers both electronic voting and paper ballot as a complete solution. A mobile application and a Website, connected to Ethereum private Blockchain network, were developed for the voters who prefer to cast their votes remotely. It is also possible to vote in the voting centres via the mobile device or paper ballot placed in the vote boxes; but these votes are also recorded in the same Blockchain and equally secure. For the remote users, a two-step authentication is designed, based on the information on the national identity card and face recognition. An additional encryption based security measure is used to avoid hacking attempts, such as man in the middle attacks. Since the proposed system is more practical than the traditional voting methods, it can increase the participation and be utilized in all kinds of local or national elections.

Prediction of aviation safety incident risk level based on ECSDNN

Prediction of aviation safety incident risk level is an important means of active risk management. Considering the high-dimensional complexity and class imbalance of massive aviation safety incident data, a prediction method for aviation safety incident risk level based on integrated cost-sensitive deep neural network (ECSDNN) is proposed. The feature representation of aviation safety incident data is realized by using the method of embedded coding of categorical attributes and splicing of numerical attributes; the cost-sensitive matrix and cost-sensitive loss function are designed by comprehensively considering the misclassification ratio and fixed cost, and the base classifier model based on cost-sensitive deep neural network (CSDNN) is constructed; the hard voting method is used to integrate multiple base classifiers with different parameters and performances to construct the integrated prediction model ECSDNN for aviation safety incident risk level. The experimental results on the Aviation Safety Incident Reporting System (ASRS) dataset show that compared with the optimal prediction ability of the baseline algorithm, the prediction accuracy of the ECSDNN model is improved 4.51%; compared with the single CSDNN base classifier, the prediction accuracy is improved 3.17%.

Deep hybrid architecture with stacked ensemble learning for binary classification of retinal disease

PurposeEarly retinal disease identification is vital since symptoms are passive at initial stages but lead to irreversible vision loss at advanced stages. Globally, a substantial population is at risk of vision impairment, prompting researchers to investigate methods for efficient classification. MethodsThis work experimented one hundred and forty-four different hybrid architectures amalgamating each of the eight convolutional neural architectures (VGG, EfficientNet, Inception, ResNet, NasNet, DenseNet, InceptionResNet, Xception) with seven classifiers (Logistic regression, K-Nearest Neighbours, Support Vector Classifier, Decision Tree, Bagging classifier, Random Forest, Adaptive Boosting, Light Gradient Boost and Extra tree classifier). The top performing n (n=3,4,5) classifiers were ensembled with meta-learner using stacking strategy. The performance of the pipeline is evaluated with two distinct meta-learners, three different image sizes, and feature counts on the RFMiD dataset. ResultsThe architectures were assessed using (1) the performance metrics – accuracy, precision, recall, and F1-score, (2) statistical graphics to understand the prevalence of classifiers, Borda count voting method to identify the best CNN model, (3) Tukey's honestly significance difference test to identify best-performing architecture. Two architectures achieved a high accuracy of 92.34 percent and F1 scores of 95.11 and 95.19. ConclusionThis is the first work to experiment with 144 combinations to identify suitable deep architecture for binary retinal disease classification. The study recommends Xception for feature extraction ensembled with ExtraTreeClassifier, Light gradient boosting machine, Random Forest, AdaBoost classifiers, and meta-learner as Logistic Regression. Another best-performing architecture is similar with a variation in DenseNet121 for feature extraction and support vector as a meta-learner.

Comparison of Classification Results of SVM, KNN, Decision Tree, and Ensemble Methods in Diabetes Diagnosis

This study aims to determine which algorithms and test techniques are the most optimal in detecting diabetes mellitus and obtaining the best results based on the value of accuracy, precision, and recall. In this study, approaches were used in early diagnosis of diabetes using KNN, SVM, Decision Tree, and Ensemble Majority Voting methods in Percentage Split and K-Fold Cross Validation methods. Diabetes is a disease characterized by high blood sugar (glucose) levels and can cause a variety of disease complications and damage to the body's organs if not treated immediately. Early diagnosis of diabetes is becoming crucial so that people can take immediate action to the hospital for immediate treatment. The data used is Healthcare-Diabetes from Kaggle. The results of this study have found that the K-Fold Cross Validation method is better because it can provide an average improvement in Ensemble accuracy of 13.42% compared to the Percentage Split method which only gives an average increase in Ensamble accuracy of 9.15%. The best algorithm for classifying diabetes disease is the Ensemble Majority Voting algorithm using the K-Fold Cross Validation method with a 98.81% accuracy rate. These excellent research results may contribute to detecting early symptoms of diabetes before it become too severe.

A robust correspondence-based registration method for large-scale outdoor point cloud

ABSTRACT Large-scale outdoor point cloud registration is essential for the 3D reconstruction of outdoor scenes. Its central objective is to achieve accurate point cloud registration by determining accurate spatial transformation parameters. While feature-based methods eliminate the need for initial position estimation, they encounter challenges in handling high outlier rates. Therefore, a method capable of effectively managing outliers is crucial for enhancing the efficiency and accuracy of large-scale outdoor point cloud registration. This paper introduces the maximal clique with adaptive voting (MCAV) method, which leverages graph-based inlier compatibility to optimize potential matches. MCAV employs adaptive parameter voting (APV) to enhance computational efficiency, demonstrating significant speedup characteristics in datasets with a significant number of inliers. To further reduce outliers in potential matches, we integrate Black-Rangarajan Duality (BRD) and graduated non-convexity (GNC) into the truncated least squares (TLS) framework (BG-TLS). Accordingly, we propose the efficient BG-TLS (EBG-TLS) method for computing the registration model. Comparative analyses with traditional and deep learning-based methods across various real-world environments demonstrate that the proposed method outperforms existing algorithms in terms of rotation error, translation error, and efficiency, particularly in complex, high-noise settings. This method finds broad applications in geospatial mapping and surveying, autonomous navigation, and environmental monitoring.

Online Voting System

With rapid growth in technologies the old voting methods can change to advanced voting methods. Online voting software is a modern solution that can efficiently and securely facilitate the voting process for various groups and organizations. The use of such software eliminates the need for physical polling stations, as voters can cast their ballots from anywhere with an internet connection

Analysis of LLMs for educational question classification and generation

Large language models (LLMs) like ChatGPT have shown promise in generating educational content, including questions. This study evaluates the effectiveness of LLMs in classifying and generating educational-type questions. We assessed ChatGPT's performance using a dataset of 4,959 user-generated questions labeled into ten categories, employing various prompting techniques and aggregating results with a voting method to enhance robustness. Additionally, we evaluated ChatGPT's accuracy in generating type-specific questions from 100 reading sections sourced from five online textbooks, which were manually reviewed by human evaluators. We also generated questions based on learning objectives and compared their quality to those crafted by human experts, with evaluations by experts and crowdsourced participants.Our findings reveal that ChatGPT achieved a macro-average F1-score of 0.57 in zero-shot classification, improving to 0.70 when combined with a Random Forest classifier using embeddings. The most effective prompting technique was zero-shot with added definitions, while few-shot and few-shot + Chain of Thought approaches underperformed. The voting method enhanced robustness in classification. In generating type-specific questions, ChatGPT's accuracy was lower than anticipated. However, quality differences between ChatGPT-generated and human-generated questions were not statistically significant, indicating ChatGPT's potential for educational content creation. This study underscores the transformative potential of LLMs in educational practices. By effectively classifying and generating high-quality educational questions, LLMs can reduce the workload on educators and enable personalized learning experiences.

Implementing a Three-Step Security Protocol for Advanced Online Election Systems: A Novel Approach

Organization of voting is the strength of character of all equalitarianism and system. Our country utilize an offline voting systems that are incompetent and disorganized by cause of the demand for abundant man power, and more refinement time for announcement outcome. Our Online Voting System aims at minimizing the division between traditional voting methods by facilitating and updating them. Our system makes it possible for voters to vote their favorable candidates from home through safe and stable online voting. By integrating India's Aadhaar card system and implementing OTPs (One Time Passwords) for better security, ensure a resilient and trustworthy voting process with full integrity. Once they login, voters are led through a strict authentication rules that checks for the genuineness of their ID cards before allowing them to continue. This comprehensive verification mechanism provides that only individuals who meet the requirements are able to participate thereby considerably decreasing the chance for electoral fraud. In addition to that, we also ensure that the system gives space to vote in secret and the votes are protected using strong cryptography measures. Our platform aims at fulfilling this object by leveraging advanced technologies. In this regard, we work towards a better and more egalitarian and democratic electoral system.

Enhancing Electoral Integrity: Implementation of a Secure E-Voting Platform

This paper proposes the design and implementation of a secured e-voting system intended to innovate the electoral process, thereby improving voters' Technologies: I.INTRODUCTION participation in elections. Traditional methods of voting have a number of drawbacks regarding logistics inefficiency, accessibility to voters, and security issues. The proposed e-voting system will help solve these problems by offering a digital platform that will ensure secured, efficient, and accessible voting. The system has been developed in Java, JSP, and MySQL; it has a highly user- friendly interface, a robust backend processing system, and data managed safely. The system is secured to allow access only to the authorized voter by its OTP-based authentication mechanism via SMS gateway. In fact, such a model of two- factor authentication considerably reduces unauthorized access and hence enhances the security for the voting process. All votes are encrypted before storage to maintain confidentiality and integrity of data for the voters using the system. The architecture of the system is such that it is scalable, hence, capable of responding to simultaneous requests from a high volume of users with no degradation of performance. Extensive testing gives evidence of the system's reliability and efficiency in different scenarios, hence allowing it to suit all parameters that ensure a secure, as well as transparent, electoral process. Introducing contemporary technology into the context of voting, this e-voting system has great potential to revolutionize how elections are conducted and guarantee more participation while securing the integrity of the election outcome in the digital era. INDEX TERMS : Online Voting , Secure Voting Platform, Voter Authentication, OTP Verification, SMS Gateway, Encryption, Web Application Development, Voting Results , Election Management System

Identification of millet origin using terahertz spectroscopy combined with ensemble learning

It’s crucial for both producers and consumers to accurately trace the origin of millet, given the significant differences in price and taste that exist between millets from various origins. The traditional method of identifying the origin of millet is time-consuming, laborious, complex, and destructive. In this study, a new method for fast and non-destructive differentiation of millet origins is developed by combining terahertz time domain spectroscopy with ensemble learning. Firstly, three machine learning algorithms, namely support vector machine (SVM), random forest (RF), and kernel extreme learning machine (KELM), were used to build different discriminative models, and then the impact of six different preprocessing methods on the models’ classification performance was compared. It was observed that models employing Savitzky-Golay preprocessing exhibited pronounced superiority in accurately determining the millet’s geographical origins. Building upon these findings, the research introduces an innovative ensemble learning strategy, leveraging both topsis and stacking techniques, to harness the collective strengths of the three algorithms. The outcomes of this approach reveal its remarkable capacity to distinguish millets originating from five distinct locations without the necessity for any parameter fine-tuning. The accuracy, F1 score, and Kappa on the prediction set are all 100 %, which significantly outperforms the single model, traditional voting method, and stacking method. The culmination of this study suggests that the integration of terahertz time-domain spectroscopy and TOPSIS-Stacking ensemble learning emerges as a promising method for the swift and non-intrusive discrimination of millet geographical origins with remarkable precision.

Investigating the influence of uncertainty on office building energy simulation through occupant-centric control and thermal comfort integration

Buildings with occupant-centric control (OCC) systems can adjust heating, ventilation, and air conditioning (HVAC) operations based on occupant status to minimize unnecessary energy use. However, because occupant behavior is stochastic rather than constant, and preferred setpoints can vary across occupant groups, this randomness significantly impacts energy use in buildings with OCC systems.This study used a stochastic simulation approach to model the uncertainty inherent in occupancy patterns and evaluate its impact on the energy consumption of an office building with OCC. In this case, the setpoint temperature was determined based on the Predicted Mean Vote (PMV) method so that each zone could be controlled to the preferred temperature according to the occupant group.The results showed that the uncertainty of the annualized sum of electricity consumption for heating and cooling was 4.3 % and 1.8 %, respectively, with 95 % confidence intervals. Notably, a negative correlation was observed between the number of occupants and the uncertainty of energy consumption. Thus, as the uncertainty in the setpoint temperature increased during periods of low occupancy, the uncertainty in the energy consumption of the HVAC system also tended to increase. These insights underscore the significant influence of occupancy-related factors on building energy utilization.

Water Wheel Plant Dingo Optimizer enabled Deep Convolutional Neural Network for disease detection using hyperspectral leaf image

ProblemIn many countries, agriculture is themain sourceofpeople’s livelihoodandsatisfiestheir nutritional needs.Earlydetection ofplantdiseases throughagriculturalremote monitoringis important to prevent the disease’s spread. The traditional methods require sampling and can damage the plant, but hyperspectral imaging is non-destructive. AimThe major aim of this research is to devise a Water Wheel Plant Dingo Optimizer_Deep Convolutional Neural Network (WWPDO_Deep CNN) for disease detection using a hyperspectral leaf image. MethodsInitially, the input leaf image is given into the leaf segmentation phase, which is done using the proposed Water Wheel Plant Dingo Optimizer (WWPDO), which is the amalgamation of the Water Wheel Plant Algorithm (WWPA) and Dingo Optimizer (DOX). The selected bands’ outputs are subjected to leaf segmentation and which is carried out by employing Bayesian Fuzzy Clustering (BFC). Thereafter, leaf segmented outputs are fussed using the majority voting method. Fused output and individual leaf segmentation output are given into the feature extraction process to extract features, such as local binary patterns and Weber local descriptors. Finally, leaf disease detection is performed using a deep Convolutional Neural Network (Deep CNN) for normal and abnormal cases. The hyperparameters of the Deep CNN are fine-tuned based on the proposed WWPADO. ResultsThe proposed WWPDO_Deep CNN achieved an excellent performance with an accuracy of 91.35 %, a True Positive Rate (TPR) of 93.13 % and a True Negative Rate (TNR) of 90.76 %. ConclusionThe WWPDO_Deep CNN is applicable for early diagnosis under the new classification system and provides a new direction for early diagnosis based on hyperspectral images. Also, the devised model provides an accurate diagnosis of plant diseases. Early and accurate detection allows targeted treatment, reduces the need for widespread pesticide application and promotes more sustainable farming practices.

Building bridges for consensus via alternative voting methods

The study examines the impact of D21, an alternative voting method, on voter polarization using data from President 21 (P21), an online election game, conducted during the 2018 Presidential election in the Czech Republic. Contrary to the actual election outcome, characterized by extreme voter polarization, the P21 outcome under D21 produced cross-ideological consensus among the voters. D21 allows voters to express not only preferences by casting plus votes but also a disapproval by giving a minus vote. Unlike the actual election outcome, P21 data demonstrates voters’ tendencies to cast additional votes for candidates from opposite political spectra, while not using minus votes in retaliatory fashion. These tendencies appeared to be linked to voters’ issue positions and perceptions of the moral leadership qualities of the running candidates rather than their ideological and political affiliations.

Fire Segmentation with an Optimized Weighted Image Fusion Method

In recent decades, earlier fire detection has become a research priority. Since visible and infrared images cannot produce clear and complete information, we propose in this work to combine two images with an appropriate fusion technique to improve the quality of fire detection, segmentation, and localization. The visible image is at first weighted before being used in the fusion process. The value of the optimal weight is estimated from the mean of the visible image with a second-order polynomial model. The parameters of this model are optimized with the least squares method from the curve of optimal weights according to the mean of visible images. Finally, a major voting method based on deep learning models is used. Experiments include an assessment of the framework’s performance not only with respect to its visual appearance but also across a spectrum of predefined evaluation criteria. The experiments show that the proposed model, which includes an optimized weighted image fusion stage before segmentation, has a high Intersection over Union (IoU) score of more than 94%.