Majority Voting Research Articles

Mapping the dynamics of aquatic vegetation in Lake Kyoga and its linkages to satellite lakes

Lake Kyoga is a shallow, young, flooded basin just north of and about 30m lower than Lake Victoria. The catchment encompasses Lake Kyoga itself, and a constellation of several dozen small satellite lakes following valley contours mostly to its east. The Kyoga basin fish fauna shares many non-cichlid species plus a spectacular, partially endemic radiation of haplochromine cichlids most similar to but still largely distinct from those in Lake Victoria. This fish fauna is of high conservation concern, as it preserves remnants of the regional species flock that have disappeared from Lake Victoria and Lake Kyoga, leaving small remnant populations in some of the satellite lakes. Now, these too are imperiled by limnological dynamics, including fluctuations in the nature and extent of aquatic vegetation. The water bodies in the Kyoga Basin are highly dynamic due both to fluctuation in water level and large amplitude variation in marginal and floating vegetation. This variation has profound evolutionary and conservation implications, since it can create and destroy critical aquatic habitat. It can also alternately anneal and cleave gene flow over time, both between the main lake and its satellites, and among the satellite lakes. The aquatic vegetation cluttering these linkages can create a spatial refugium for many native fish species that are more tolerant of hypoxia than an introduced macropredator, the Nile perch. Anthropogenic impacts to this region have greatly increased in recent years, altering relationships between aquatic vegetation and endangered species, fisheries and other ecosystem services provided by the lake. Understanding these dynamics require a means of mapping aquatic vegetation, connectivity, and habitat through time. Here we develop a new and improved algorithm to map the spatial distribution and dynamics of floating and emergent aquatic vegetation via remote sensing. We utilize a time series of 440 Landsat images dating from 1986 to 2020. A series of water and vegetation indices are designed to reveal change in the aquascape over time. First, two types of water masks are derived using a majority rule - a separate water mask for each image and a composite water mask of the region over the study period. Second, the difference between the two masks is then used to delineate the potential location of macrophytes over the image. Third, an algorithm is developed to separate the floating vegetation from emergent vegetation; this algorithm uses Landsat spectral bands and two additional spatial and temporal metrics that considerably improve classification accuracy. A more extensive analysis of aquascape trajectories using remote sensing can inform fish conservation strategies and fisheries management and illuminate the role of landscape dynamics in macroevolutionary patterns of aquatic taxa.

A differentiable quantum phase estimation algorithm

Abstract The simulation of electronic properties is a pivotal issue in modern electronic structure theory, driving significant efforts over the past decades to develop protocols for computing energy derivatives. In this work, we address this problem by developing a strategy to integrate the quantum phase estimation algorithm within a fully differentiable framework. This is accomplished by devising a smooth estimator able to tackle arbitrary initial states. We provide analytical expressions to characterize the statistics and algorithmic cost of this estimator. Furthermore, we provide numerical evidence that the estimation accuracy is retained when an arbitrary state is considered and that it exceeds the one of standard majority rule. 
We explicitly use this procedure to estimate chemically relevant quantities, demonstrating our approach through ground-state and triplet excited state geometry optimization with simulations involving up to 19 qubits. This work paves the way for new quantum algorithms that combine interference methods and quantum differentiable programming.

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%.

A Novel Image Processing Approach for Colloid Detection in Saturated Porous Media.

Over recent decades, natural and artificial colloids, as well as nanoparticles, have been increasingly used in various applications. Consequently, with this rising consumption, surface and subsurface environments are more exposed to these particles. The presence of these particles and the colloid-facilitated transport of microorganisms, the interactions between dissolved contaminants and mobile colloids in porous media, and the fate and transport of colloids through groundwater-one of the primary sources of water supply for human societies-have attracted extensive research. This study investigates the performance of several image processing methods in the field of colloid detection, which is a prerequisite for the subsequent steps in porous media research. We employed four different categories of image processing approaches on microscopy images-segmentation-based methods, background-detection-based methods, filter-based methods, and morphology-based methods-to conduct the detection process of colloids. Eight methods were applied and subsequently analyzed in terms of their drawbacks and advantages to determine the best ones in this domain. Finally, we proposed an ensemble approach that leverages the strengths of the three best methods using a majority vote to detect colloids more accurately. In experiments, Precision, Recall, F-measure, and TCR criteria were considered as evaluation tools. Experimental results demonstrate the high accuracy of image processing methods in recognizing colloids. Among all these methods, morphology-based methods were the most successful, achieving the best detection performance and improving the limited distinguishing features of small colloids. Moreover, our ensemble approach, achieving perfect scores across all evaluation criteria, highlights its superiority compared with other detection methods.

Deepfake detection using convolutional vision transformers and convolutional neural networks

Deepfake technology has rapidly advanced in recent years, creating highly realistic fake videos that can be difficult to distinguish from real ones. The rise of social media platforms and online forums has exacerbated the challenges of detecting misinformation and malicious content. This study leverages many papers on artificial intelligence techniques to address deepfake detection. This research proposes a deep learning (DL)-based method for detecting deepfakes. The system comprises three components: preprocessing, detection, and prediction. Preprocessing includes frame extraction, face detection, alignment, and feature cropping. Convolutional neural networks (CNNs) are employed in the eye and nose feature detection phase. A CNN combined with a vision transformer is also used for face detection. The prediction component employs a majority voting approach, merging results from the three models applied to different features, leading to three individual predictions. The model is trained on various face images using FaceForensics++ and DFDC datasets. Multiple performance metrics, including accuracy, precision, F1, and recall, are used to assess the proposed model’s performance. The experimental results indicate the potential and strengths of the proposed CNN that achieved enhanced performance with an accuracy of 97%, while the CViT-based model achieved 85% using the FaceForences++ dataset and demonstrated significant improvements in deepfake detection compared to recent studies, affirming the potential of the suggested framework for detecting deepfakes on social media. This study contributes to a broader understanding of CNN-based DL methods for deepfake detection.

DeepIOD: Towards A Context-Aware Indoor-Outdoor Detection Framework Using Smartphone Sensors.

Accurate indoor-outdoor detection (IOD) is essential for location-based services, context-aware computing, and mobile applications, as it enhances service relevance and precision. However, traditional IOD methods, which rely only on GPS data, often fail in indoor environments due to signal obstructions, while IMU data are unreliable on unseen data in real-time applications due to reduced generalizability. This study addresses this research gap by introducing the DeepIOD framework, which leverages IMU sensor data, GPS, and light information to accurately classify environments as indoor or outdoor. The framework preprocesses input data and employs multiple deep neural network models, combining outputs using an adaptive majority voting mechanism to ensure robust and reliable predictions. Experimental results evaluated on six unseen environments using a smartphone demonstrate that DeepIOD achieves significantly higher accuracy than methods using only IMU sensors. Our DeepIOD system achieves a remarkable accuracy rate of 98-99% with a transition time of less than 10 ms. This research concludes that DeepIOD offers a robust and reliable solution for indoor-outdoor classification with high generalizability, highlighting the importance of integrating diverse data sources to improve location-based services and other applications requiring precise environmental context awareness.

Automatic Age and Gender Recognition Using Ensemble Learning

The use of speech-based recognition technologies in human–computer interactions is increasing daily. Age and gender recognition, one of these technologies, is a popular research topic used directly or indirectly in many applications. In this research, a new age and gender recognition approach based on the ensemble of different machine learning algorithms is proposed. In the study, five different classifiers, namely KNN, SVM, LR, RF, and E-TREE, are used as base-level classifiers and the majority voting and stacking methods are used to create the ensemble models. First, using MFCC features, five base-level classifiers are created and the performance of each model is evaluated. Then, starting from the one with the highest performance, these classifiers are combined and ensemble models are created. In the study, eight different ensemble models are created and the performances of each are examined separately. The experiments conducted with the Turkish subsection of the Mozilla Common Voice dataset show that the ensemble models increase the recognition accuracy, and the highest accuracy of 97.41% is achieved with the ensemble model created by stacking five classifiers (SVM, E-TREE, RF, KNN, and LR). According to this result, the proposed ensemble model achieves superior accuracy compared to similar studies in recognizing age and gender from speech signals.

An extended belief rule-based system with hybrid sampling strategy for imbalanced rule base

Imbalanced dataset is an important focus for classification. As the mainstream of addressing imbalanced dataset, data-level methods are trapped in facing difficultly-determined subjective parameters, and the inconsistency between new minority samples and original minority samples simultaneously. To address it, this paper develops an extended belief-rule-based (EBRB) system with hybrid sampling strategy, which is a white-box classifier. The hybrid sampling strategy is composed of an under-sampling process and an oversampling process, in which subjective parameters are not involved. The under-sampling is to identify and remove overlapping majority rules by iteratively determining an appropriate objective threshold for calculating the inconsistency degree of rule base, and to determine and remove redundant non-overlapping majority rules by using the density of non-overlapping rules in clustered groups. The oversampling is to design a differential evolution based iterative process to generate new minority rules in groups by minimizing the inconsistency of rule base. The distribution of original dataset is maintained extremely by balancing rules in clustered majority and minority groups, respectively. This EBRB system is used for the auxiliary diagnosis of thyroid nodules, and its superior performance is highlighted by the comparison with existing EBRB systems, representative data-level methods, and algorithm-level methods.

A recommendation attack detection approach integrating CNN with Bagging

Due to their open architecture, collaborative filtering recommender systems are susceptible to recommendation attacks, in which attackers inject fake rating data into the system to affect the accuracy of recommendation results. To detect these attacks, numerous detection methods have been designed and proven effective. However, in recent years, deep learning-based recommendation attack models such as GSA-GAN have shown higher concealment, posing new challenges to existing detection methods. Motivated by the need for improved detection, in this paper we propose a new approach called CNN-BAG, which integrates convolutional neural network (CNN) and Bagging (BAG) techniques. CNN-BAG can enhance the detection performance by simultaneously leveraging the deep learning capabilities of CNN and the ensemble learning strengths of Bagging. Firstly, we construct a deep neural network based on CNN as the base learner to automatically extract and learn features of recommendation attacks. Secondly, we use the Bagging algorithm to perform bootstrap sampling on the training data to generate multiple diverse training subsets. The above constructed base learners are then trained on these generated training subsets to produce multiple base classifiers for classifying recommendation attacks. Finally, we combine the base classifiers’ outputs using a majority voting method to obtain the final detection results. To assess the performance of CNN-BAG in detecting recommendation attacks, we compared it against several well-established detection methods on the Movielens-10M and Amazon datasets. Our experiments revealed that CNN-BAG is adept at identifying various attack types, including the deep learning-based recommendation attack models.

“Don’t research us”—How Mastodon instance rules connect to research ethics

Mastodon is a decentralized social network that has recently gained in popularity due to the platform changes of Twitter (now X). When it comes to collecting and analyzing data, the technical structure of such a decentralized network not only has methodological but also ethical implications. Mastodon consists of a large number of individual instances (around 17k), and each of these instances typically have their own set of rules, which may also address the use of data. Against this backdrop, we investigated whether and how Mastodon instances address the scientific use of data. Our analyses focused on active instances with English-language rules. Based on a combination of quantitative and qualitative content analysis, our results show that only a small portion of instances mention the scientific use of their data. Instead, the majority of rules rather focuses on user behavior and interactions. Based on the results, we formulate recommendations for researchers who want to work with Mastodon data. The recommendations are informed by the results of our empirical study and guided by general ethical principles for the examination of data from social media.

DepressionEmo: A novel dataset for multilabel classification of depression emotions

Emotions are integral to human social interactions, with diverse responses elicited by various situational contexts. Particularly, the prevalence of negative emotional states has been correlated with negative outcomes for mental health, necessitating a comprehensive analysis of their occurrence and impact on individuals. In this paper, we introduce a novel dataset named DepressionEmo designed to detect 8 emotions associated with depression by 6037 examples of long Reddit user posts. This dataset was created through a majority vote over inputs by zero-shot classifications from pre-trained models and validating the quality by annotators and ChatGPT, exhibiting an acceptable level of inter-rater reliability between annotators. The correlation between emotions, and linguistic analysis are conducted on DepressionEmo. Besides, we provide several text classification methods classified into two groups: machine learning methods such as SVM, XGBoost, and LightGBM; and deep learning methods such as BERT, BART, GAN-BERT, and T5. Despite achieving the same F1 Macro score of 0.76 as BART, the pretrained BERT model, bert-base-uncased, stands out as the most efficient model in our experiments due to its lower number of parameters. Across all emotions, the highest F1 Macro value is achieved by suicide intent, indicating a certain value of our dataset in identifying emotions in individuals with depression symptoms through text analysis. The curated dataset is publicly available at: https://github.com/abuBakarSiddiqurRahman/DepressionEmo.

Majority vote for distributed differentially private sign selection

Majority vote for distributed differentially private sign selection

Training high-performance deep learning classifier for diagnosis in oral cytology using diverse annotations

The uncertainty of true labels in medical images hinders diagnosis owing to the variability across professionals when applying deep learning models. We used deep learning to obtain an optimal convolutional neural network (CNN) by adequately annotating data for oral exfoliative cytology considering labels from multiple oral pathologists. Six whole-slide images were processed using QuPath for segmenting them into tiles. The images were labeled by three oral pathologists, resulting in 14,535 images with the corresponding pathologists’ annotations. Data from three pathologists who provided the same diagnosis were labeled as ground truth (GT) and used for testing. We investigated six models trained using the annotations of (1) pathologist A, (2) pathologist B, (3) pathologist C, (4) GT, (5) majority voting, and (6) a probabilistic model. We divided the test by cross-validation per slide dataset and examined the classification performance of the CNN with a ResNet50 baseline. Statistical evaluation was performed repeatedly and independently using every slide 10 times as test data. For the area under the curve, three cases showed the highest values (0.861, 0.955, and 0.991) for the probabilistic model. Regarding accuracy, two cases showed the highest values (0.988 and 0.967). For the models using the pathologists and GT annotations, many slides showed very low accuracy and large variations across tests. Hence, the classifier trained with probabilistic labels provided the optimal CNN for oral exfoliative cytology considering diagnoses from multiple pathologists. These results may lead to trusted medical artificial intelligence solutions that reflect diverse diagnoses of various professionals.

Equilibrium analysis in majority-based coalitional bargaining games

This paper introduces majority rule into coalitional bargaining games, adapting traditional models that rely on unanimous consent to more accurately mirror decision-making processes in real-world scenarios. We introduce a majority-based coalitional bargaining game (MBCBG), wherein coalitions pass proposals via majority votes. Our analysis of the stationary subgame perfect equilibrium (SSPE) not only establishes the necessary and sufficient conditions for SSPE strategy profiles but also confirms the existence of no-delay SSPEs in MBCBGs. We further delve into symmetric MBCBGs, delineating conditions that ensure equitable outcomes for homogeneous players. Furthermore, we provide a necessary and sufficient condition for the formation of the grand coalition under SSPEs. Additionally, we briefly explore how asymmetries in coalition values, proposal probabilities, and voting weights may influence both the dynamics of coalition formation and the expected equilibrium payoffs.

Optimal Weighted Voting-Based Collaborated Malware Detection for Zero-Day Malware: A Case Study on VirusTotal and MalwareBazaar

We propose a detection system incorporating a weighted voting mechanism that reflects the vote’s reliability based on the accuracy of each detector’s examination, which overcomes the problem of cooperative detection. Collaborative malware detection is an effective strategy against zero-day attacks compared to one using only a single detector because the strategy might pick up attacks that a single detector overlooked. However, cooperative detection is still ineffective if most anti-virus engines lack sufficient intelligence to detect zero-day malware. Most collaborative methods rely on majority voting, which prioritizes the quantity of votes rather than the quality of those votes. Therefore, our study investigated the zero-day malware detection accuracy of the collaborative system that optimally rates their weight of votes based on their malware categories of expertise of each anti-virus engine. We implemented the prototype system with the VirusTotal API and evaluated the system using real malware registered in MalwareBazaar. To evaluate the effectiveness of zero-day malware detection, we measured recall using the inspection results on the same day the malware was registered in the MalwareBazaar repository. Through experiments, we confirmed that the proposed system can suppress the false negatives of uniformly weighted voting and improve detection accuracy against new types of malware.

Boosting Multimode Ruling in DHR Architecture With Metamorphic Relations

ABSTRACTThe DHR architecture provides a revolutionary security defense structure for cyberspace. The multimode ruling in DHR is expected to alleviate the oracle problem, which still suffers from the existence of common model vulnerability. In this work, we design a test segmentation method to transform multimode ruling to a metamorphic testing problem. The text test input that causes inconsistency of heterogeneous executors is converted to a condition set, and we extract subsets of conditions based on its syntax tree. The original test can exploit a specific vulnerability, the follow‐up tests are composed by different subsets of conditions within the original test. We collect the execution matrix for the follow‐up tests to analyse the impact of each subset of conditions on ruling decision. Metamorphic relations are extracted based on the localization of independent condition, that is, the subsets of conditions that can impact ruling decision independently. The executors in an inconsistent ruling should be examined with metamorphic testing methods, rather than traditional majority voting mechanism. The proposed test segmentation and improved multimode ruling methods are evaluated on two DHR‐based cases, SQL injection in cyber‐range system and deserialization attack in ‐ project. The experimental results show that our test segmentation can help to locate malicious expressions and the metamorphic testing‐based multimode ruling can generate more correct results than majority voting mechanism with an average 15.8% performance loss.

A Cooperative Intrusion Detection System for the Internet of Things Using Convolutional Neural Networks and Black Hole Optimization.

Maintaining security in communication networks has long been a major concern. This issue has become increasingly crucial due to the emergence of new communication architectures like the Internet of Things (IoT) and the advancement and complexity of infiltration techniques. For usage in networks based on the Internet of Things, previous intrusion detection systems (IDSs), which often use a centralized design to identify threats, are now ineffective. For the resolution of these issues, this study presents a novel and cooperative approach to IoT intrusion detection that may be useful in resolving certain current security issues. The suggested approach chooses the most important attributes that best describe the communication between objects by using Black Hole Optimization (BHO). Additionally, a novel method for describing the network's matrix-based communication properties is put forward. The inputs of the suggested intrusion detection model consist of these two feature sets. The suggested technique splits the network into a number of subnets using the software-defined network (SDN). Monitoring of each subnet is done by a controller node, which uses a parallel combination of convolutional neural networks (PCNN) to determine the presence of security threats in the traffic passing through its subnet. The proposed method also uses the majority voting approach for the cooperation of controller nodes in order to more accurately detect attacks. The findings demonstrate that, in comparison to the prior approaches, the suggested cooperative strategy can detect assaults in the NSLKDD and NSW-NB15 datasets with an accuracy of 99.89 and 97.72 percent, respectively. This is a minimum 0.6 percent improvement.

Evaluate the competence of fuzzy preference using statistical conflict in the frame of DS evidence theory

Fuzzy preference based DS evidence theory effectively solves the combined explosion ascribing to the increasing of the cardinality of the frame of discernment in the application. However, limited work has focused on evaluating the performance of the binary classifier (i.e. dynamic improvement of the according fuzzy preference element). The conflict coefficient can well reflect the relationship between various pieces of evidence and effectively evaluate the ability of each classifier. By statistically analyzing prior conflict information, the conflict interval range is determined to more effectively correct the fuzzy preference matrix. Therefore, we proposed a new method to evaluate the competence of fuzzy preference using statistical conflict in the frame of DS evidence theory. First, a binary transformation of the multiclassification problem is performed to generate the BPA and the matrix was generated by orthogonal fusion of multiple BPAs. Second, calculate the conflict coefficients and make statistics. The statistical conflict coefficients are combined and the conflict interval is solved to modify the construction of the fuzzy preference matrix. Third, based on the measured data in the feature space, the matrix is constructed by majority voting using the k-nearest neighbors of correctly classified objects. Finally, the two matrices are fused by determining the relationship between the conflict coefficient and the threshold value of each binary pair. The non-dominance degree is calculated based on the modified fuzzy preference matrix to make the final decision. Analysis and results of application examples and several datasets in practical application are used to demonstrate effectiveness and flexibility of the proposed method.

Collective intelligence facilitates emergent resource partitioning through frequency-dependent learning.

Deciding where to forage must not only account for variations in habitat quality but also where others might forage. Recent studies have suggested that when individuals remember recent foraging outcomes, negative frequency-dependent learning can allow them to avoid resources exploited by others (indirect competition). This process can drive the emergence of consistent differences in resource use (resource partitioning) at the population level. However, indirect cues of competition can be difficult for individuals to sense. Here, we propose that information pooling through collective decision-making-i.e. collective intelligence-can allow populations of group-living animals to more effectively partition resources relative to populations of solitary animals. We test this hypothesis by simulating (i) individuals preferring to forage where they were recently successful and (ii) cohesive groups that choose one resource using a majority rule. While solitary animals can partially avoid indirect competition through negative frequency-dependent learning, resource partitioning is more likely to emerge in populations of group-living animals. Populations of larger groups also better partition resources than populations of smaller groups, especially in environments with more choices. Our results give insight into the value of long- versus short-term memory, home range sizes and the evolution of specialization, optimal group sizes and territoriality. This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.

Politics and income taxes: Progress and progressivity

AbstractThis paper begins with a survey of the literature on the political economy approaches to labor income taxation. We focus on recent progress made by examining in detail the specific properties of nonlinear taxes derived in the context of voting. Next, we present new results on the existence of a majority voting equilibrium that unifies work in the standard framework. Finally, we discuss how recent theoretical results help us uncover empirical patterns from the last 50 years in the US tax system, namely, a sharp decrease in top marginal tax rates, the rise of the Earned Income Tax Credit, and increased progressivity in the middle of the income distribution.