Application of majority voting and consensus voting algorithms in N-version software

Voting is an important operation in multichannel computation paradigm and realization of ultrareliable and real-time control systems that arbitrates among the results of N redundant variants. These systems include N-modular redundant (NMR) hardware systems and diversely designed software systems based on N-version programming (NVP). Depending on the characteristics of the application and the type of selected voter, the voting algorithms can be implemented for either hardware or software systems. In this paper, a novel voting algorithm is introduced for real-time fault-tolerant control systems, appropriate for applications in which N is large. Then, its behavior has been software implemented in different scenarios of error-injection on the system inputs. The results of analyzed evaluations through plots and statistical computations have demonstrated that this novel algorithm does not have the limitations of some popular voting algorithms such as median and weighted; moreover, it is able to significantly increase the reliability and availability of the system in the best case to 2489.7% and 626.74%, respectively, and in the worst case to 3.84% and 1.55%, respectively.

A TMR Genetic Voting Algorithm for Fault-tolerant Medical Robot

Biopsy is considered the gold standard for diagnosing brain tumors, but its invasive nature can pose risks to patients. Additionally, tissue analysis can be cumbersome and inconsistent among observers. This research aims to develop a cost-effective, non-invasive, MRI-based computer-aided diagnosis tool that can reliably, accurately and swiftly identify brain tumor grades. Our system employs ensemble deep learning (EDL) within an MRI multiclass framework that includes five datasets: two-class (C2), three-class (C3), four-class (C4), five-class (C5) and six-class (C6). The EDL utilizes a majority voting algorithm to classify brain tumors by combining seven renowned deep learning (DL) models-EfficientNet, VGG16, ResNet18, GoogleNet, ResNet50, Inception-V3 and DarkNet-and seven machine learning (ML) models, including support vector machine, K-nearest neighbour, Naïve Bayes, decision tree, linear discriminant analysis, artificial neural network and random forest. Additionally, local interpretable model-agnostic explanations (LIME) are employed as an explainable AI algorithm, providing a visual representation of the CNN's internal workings to enhance the credibility of the results. Through extensive five-fold cross-validation experiments, the DL-based majority voting algorithm outperformed the ML-based majority voting algorithm, achieving the highest average accuracies of 100 ± 0.00%, 98.55 ± 0.35%, 98.47 ± 0.63%, 95.34 ± 1.17% and 96.61 ± 0.85% for the C2, C3, C4, C5 and C6 datasets, respectively. Majority voting algorithms typically yield consistent results across different folds of the brain tumor data and enhance performance compared to any individual deep learning and machine learning models.

Abstract. Crown area is one of the key parameters in determining tree growth and an important basis for estimation of biophysical characteristics at single-tree levels in natural and man-made forests. Therefore, the present study was aimed to improve the estimation of crown area on unmanned aerial vehicle (UAV) data using a novel method in a Pinus eldarica man-made forest. The UAV-based RGB images with spatial resolution of 2 cm were acquired from the study area and then resampled to four pixel sizes of 10, 30, 50 and 70 cm. The resampled images were classified by three methods, i.e., Support vector machine (SVM), Random forest (RF), and Artificial neural network (ANN), which are all ensemble (bagging) classification methods. In the next step, the maps of three classification methods for each pixel size were combined by majority voting algorithm at pixel level. The results showed the robustness of ANN in all pixel sizes compared to RF and SVM. Additionally, the combination of the machine learning method by majority voting algorithms had significantly improved the accuracy of P. eldarica crown delineation and its area estimation on the UAV orthoimages with the investigated pixel sizes.

Voting algorithms play a significant role in most fault-tolerant and control systems so these algorithms are continually in progress and with regard to specific situations in different systems, new voting algorithms are growing and increase the reliability and fault tolerability of systems. In this paper, a new combined algorithm using a weighted voter based on Markov chain is presented that is increased the reliability of system than both of median voter and average voter. In this paper, the proposed algorithm on average weather data of a year in Tehran was tested and simulation results show the effectiveness of the method than the other methods.

An important problem in fault-tolerant distributed computer systems is maintaining agreement between nonfaulty processes in the presence of undiagnosed faults. Approximate agreement defines a condition in which it is not necessary for the agreed values to be numerically identical. Rather, processes need only agree with each other to within a predefined numerical tolerance. Convergent voting algorithms which achieve approximate agreement have been studied in the context of two classes of systems, synchronous and asynchronous. Studies have also addressed both completely connected and partially connected systems. Together, the two properties of synchrony and connectivity yield 4 different voting domains. In all studies to date, each voting domain has been treated as a separate problem. This paper: shows that for at least one broad family of voting algorithms, the 4 domains are special cases of a more general convergent voting problem; analyzes convergent voting under the 3-mode hybrid fault model of Thambidurai and Park; and presents a set of unifying relations applicable to all 4 voting domains. These relations are used to specify voting algorithms which optimize fault-tolerance, convergence rate, or computational overhead in any given voting domain. The task of designing a voting algorithm for a particular fault-tolerant system is thus greatly simplified.

Estimation of solubility of acid gases in ionic liquids using different machine learning methods

In the last decade, the rapid growth of the Internet and email, there has been a dramatic growth in spam. Spam is commonly defined as unsolicited email messages and protecting email from the infiltration of spam is an important research issue. Classifications algorithms have been successfully used to filter spam, but with a certain amount of false positive trade-offs, which is unacceptable to users sometimes. This paper presents an approach to overcome the burden of GL (grey list) analyzer as further refinements to our multi-classifier based classification model (Islam, M. and W. Zhou 2007). In this approach, we introduce a ldquomajority voting grey list (MVGL)rdquo analyzing technique which will analyze the generated GL emails by using the majority voting (MV) algorithm. We have presented two different variations of the MV system, one is simple MV (SMV) and other is the ranked MV (RMV). Our empirical evidence proofs the improvements of this approach compared to the existing GL analyzer of multi-classifier based spam filtering process.

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The N-Version Programming (NVP) approach applies the idea of design diversity to obtain fault-tolerant software units, called N-Version Software (NVS) units. The effectiveness of this approach is examined by the software diversity achieved in the member versions of an NVS unit. We define and formalize the concept of design diversity and software diversity in this paper. Design diversity is a property naturally applicable to the NVP process to increase its fault-tolerance attributes. The baseline design diversity is characterized by the employment of independent programming teams in the NVP. More design diversity investigations could be enforced in the NVP design process, including different languages, different tools, different algorithms, and different methodologies. Software diversity is the resulting dissimilarities appearing in the NVS member versions. We characterize it from four different points of view that are designated as: structural diversity, fault diversity, tough-spot diversity, and failure diversity. Our goals are to find a way to quantify software diversity and to investigate the measurements which can be applied during the life cycle of NVS to gain confidence that operation will be dependable when NVS is actually employed. The versions from a six-language N-Version Programming project for fault-tolerant flight control software were used in the software diversity measurement.

Voting algorithms are used to provide an error masking capability in a wide range of highly dependable commercial & research applications. These applications include N-Modular Redundant hardware systems and diversely designed software systems based on N-Version Programming. The most sophisticated & complex algorithms can even tolerate malicious (or Byzantine) subsystem errors. The algorithms can be implemented in hardware or software depending on the characteristics of the application, and the type of voter selected. Many voting algorithms have been defined in the literature, each with particular strengths and weaknesses. Having surveyed more than 70 references from the literature, a functional classification is used in this paper to provide taxonomy of those voting algorithms used in safety-critical applications. We classify voters into three categories: generic, hybrid, and purpose-built voters. Selected algorithms of each category are described, for illustrative purposes, and application areas proposed. Approaches to the comparison of algorithm behavior are also surveyed. These approaches compare the acceptability of voter behavior based on either statistical considerations (e.g., number of successes, number of benign or catastrophic results), or probabilistic computations (e.g., probability of choosing correct value in each voting cycle or average mean square error) during q voting cycles.

In this work, we study the rank aggregation problem based on election voting model. In addition to classical voting rules: Borda, some elimination voting algorithms, including Kemeny methods, are analyzed in a graph theoretic approach. As the Kemeny ranking problem is NP-hard, a new heuristic elimination voting algorithm is proposed in this work. Some experiments have been carried out on TREC data for evaluating these voting algorithms on rank aggregation. The experiments indicate that these elimination algorithms have comparable performance with Borda algorithms, and sometimes outperform it

Metasearch engines are developed to overcome the shortcoming of single search engine and try to benefit from cooperative decision by combining the results of multiple independent search engines that make use of different models and configurations. In this work, we study the metasearch problem via voting that facilities multiple agents making cooperative decision. We can deem the source search engines as voters and all ranked documents as candidates, then metaseach problem is actually to find a voting algorithm to obtain group’s preferences on these documents(candidates). In addition to two widely discussed classical voting rules: Borda and Condorcet, we study another two voting algorithms, Black and Kemeny. Since Kemeny ranking problem is NP-hard, a new heuristic algorithm has been proposed for metasearch. Some experiments have been carried out on TREC2001 data for evaluating these metasearch algorithms coming from voting.KeywordsSearch EngineCondorcet WinnerStrongly Connect ComponentBorda RuleVote AlgorithmThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The rapid proliferation of embedded devices in the Internet of Things (loT) era has intensified security vulnerabilities in edge computing systems. As an emerging hardware security primitive, Semiconductor Superlattice Physical U nclonable Functions (SSLPUF) leverage nanoscale superlattice physical characteristics to provide device authentication and data protection. However, their practical deployment is hindered by inherent instability manifested through Bit Error Rate (BER) degradation and response misalignment. This paper presents a highly robust post-processing algorithm for superlattices, which consists of frame synchronization and Time Majority Voting (TMV) algorithm. The experimental results show that compared with the original response, the maximum BER is reduced by 27.41 % after using precise alignment, and 34.87% after using precise alignment and TMV. The high robust post-processing method combining frame synchronization and TMV algorithm can effectively reduce the instability of SSLPUF, greatly improving the reliability and robustness, and is expected to play an important role in the future Internet of Things.

Biomedical imaging technology has become an important tool for medical research and clinical practice. A large amount of imaging data is generated and collected every day. Managing and analyzing these data sets require the corresponding development of the computer based algorithms for automatic processing. Histology image classification is one of the important tasks in the bio-image informatics field and has broad applications in phenotype description and disease diagnosis. This study proposes a novel framework of histology image classification. The original images are first divided into several blocks and a set of visual features is extracted for each block. An array of C-RSPM (Collateral Representative Subspace Projection Modeling) models is then built that each model is based on one block from the same location in original images. Finally, the C-Value Enhanced Majority Voting (CEWMV) algorithm is developed to derive the final classification label for each testing image. To evaluate this framework, the authors compare its performance with several well-known classifiers using the benchmark data available from IICBU data repository. The results demonstrate that this framework achieves promising performance and performs significantly better than other classifiers in the comparison.