Voting Mechanism Research Articles

A two-stage domain alignment method for multi-source domain fault diagnosis

The issue of restricted target domain tags and constrained information offered by a single source domain in the intelligent fault diagnosis may be successfully resolved by multi-source domain adaptation. Compared with traditional domain adaptation methods, multisource domain adaptation methods face more difficult challenges: the differences between domains are more complex. Hence, a two-stage domain alignment method for multi-source domain fault diagnosis is proposed. The method is accomplished by developing a common feature extractor and several domain feature extractors and classifiers, along with the two-stage distribution adaptation method. Finally, the classifier is used to forecast target samples, while the voting mechanism is utilized to construct the target samples' pseudo labels. With the resulting pseudo labels, the final model training is completed.

Ultra-low complexity random forest for optical fiber communications.

In this paper, we present an efficient equalizer based on random forest for channel equalization in optical fiber communication systems. The results are experimentally demonstrated in a 120 Gb/s, 375 km, dual-polarization 64-quadrature magnitude modulation (QAM) optical fiber communication platform. Based on the optimal parameters, we choose a series of deep learning algorithms for comparison. We find that random forest has the same level of equalization performance as deep neural networks as well as lower computational complexity. Moreover, we propose a two-step classification mechanism. We first divide the constellation points into two regions and then use different random forest equalizers to compensate the points in different regions. Based on this strategy, the system complexity and performance can be further reduced and improved. Furthermore, due to the plurality voting mechanism and two-stage classification strategy, the random forest-based equalizer can be applied to actual optical fiber communication systems.

Imbalanced data based fault diagnosis of the chiller via integrating a new resampling technique with an improved ensemble extreme learning machine

Fault diagnosis of the chiller is essential to guarantee chiller's safe operation and reduce building energy consumption. However, the existing fault diagnosis methods rarely consider the chiller's imbalanced data conditions, which always leads to low diagnosis accuracy of the minority class samples. To figure out the issue of imbalanced fault pattern data during the chiller fault diagnosis, a hybrid resampling based improved extreme learning machine (HRIELM) is developed in our work. A new hybrid resampling technique (HRT) is first proposed to balance the majority and minority classes in the imbalanced fault pattern datasets. The HRT is then carried out repetitively to obtain multiple diverse rebalanced training datasets using different benchmark datasets. Subsequently, various primary extreme learning machine (ELM) models are established utilizing these rebalanced training datasets. Furthermore, an improved ELM model based on a novel selective ensemble learning strategy is presented to enhance the effectiveness of the chiller fault diagnosis. The basic ELM models with superior performance for diagnosing the neighbor subset constructed according to the fault snapshot sample are first picked out, and a weight factor is further defined to build the final selective ensemble ELM model. Finally, the fault pattern of the snapshot sample is identified according to the weighted voting mechanism. Detailed experimental results on the ASHRAE Research Project RP-1043 experimental datasets certify the effectiveness of the presented HRIELM scheme for the chiller fault diagnosis under the imbalanced data environments.

Matching method for mutated veneer sheet images using gray-level co-occurrence matrix features

This paper studies the tracking of wooden veneer sheets by matching their respective wet and dry colour images. The tracking of veneer sheets has proved to be a challenging task due to random mutations during processing in terms of color changes, the emergence of defects, and, occasionally, lost pieces of the veneer surface. The proposed matching procedure involves image segmentation with five different sizes, followed by segment-wise extraction of Gray Level Co-occurrence Matrix (GLCM) textural feature arrays, and their similarity comparisons respectively. A voting mechanism is introduced that allocates the correct match based on the majority. An optional shifting procedure is applied to match candidates with missing areas. The method is demonstrated and benchmarked using a real-world dataset sourced from the industry, comprising 2579 high-quality images of spruce veneer pairs obtained from peeling and drying. In comparison to earlier studies that employed randomized 50 pair sampling on the same dataset, our approach yields a matching accuracy of 99.41%, outperforming the previously reported 84.93%. These findings have relevance for researchers in wood image analytics and carry practical implications for large-scale, automated veneer production facilities seeking innovative ways to optimize their raw material usage.

Driver impairment detection using decision tree based feature selection and classification

The driver's control authority plays a crucial role at commanding safely a vehicle that is the technical system. And the driver monitoring systems need to detect and identify whether the driver is impaired and able to percept and react possible hazards. In this paper, the driver's impairment detection system is presented. The procedure of fusing driving data constituted by a large set of vehicle sensors in addition to driver's eye glance location is elaborated. A batch data to train, validate and test the impairment detection system is constituted by co-existing 14 location information and 76 different sensors for trips that contain a crash or near crash instances. The procedure of aligning vehicle motion sensor data logged at fixed sampling rate with eye glance location information is presented by applying a sliding window approach. The window is slided when eye glance location is changed and mean of sensor values is used to present the batch data in this window. Each instance of the batch data is classified. A two stage detection algorithm is tested and evaluated. Each instance is classified as impair or non-impair using Extreme Gradient (xg) boosted trees and a voting mechanism for the instances is used to decide whether driver is impaired or not. The results illustrate the effectiveness of the impairment detection system.

Tight distortion bounds for distributed metric voting on a line

We consider a voting problem where agents and alternatives are on the line of real numbers, the agents are partitioned into disjoint districts, and the goal is to choose one alternative using a distributed voting mechanism. Such mechanisms select a representative alternative for each district and then choose one of them as the winner. We design simple mechanisms with distortion at most 2+5 for the average-of-max and max-of-average cost objectives, matching the corresponding lower bound shown in previous work.

Proxy voting in the U.S. House of Representatives: Legislative shirking in new clothing?

New technological developments have heightened interest in understanding and evaluating new tools of participatory and representative engagement in the political sphere. Recent academic research in this area is mainly theoretical, and focuses on voters rather than legislators. This study addresses this gap in the literature by empirically exploring the unique case of proxy voting by members of the U.S. House of Representatives that was introduced as an emergency measure to allow the Congress to function during COVID-19. The core result from this study, given its finding that vote skipping during 2019 is positively correlated with the use of proxy voting during 2021, indicates that proxy voting in this case is connected to legislative shirking. Thus, it appears that the proxy voting mechanism introduced in 2020 via a U.S. House resolution represents a new configuration of traditional legislative shirking or vote skipping.

Evaluation and classification of obfuscated Android malware through deep learning using ensemble voting mechanism

With the rise in popularity and usage of Android operating systems, malicious applications are targeted by applying innovative ways and techniques. Today, malware becomes intelligent that uses several ways of obfuscation techniques to hide its functionality and evade anti-malware engines. For mainstream smartphone users, Android malware poses a severe security danger. An obfuscation approach, however, can produce malware versions that can evade current detection strategies and dramatically lower the detection accuracy. Attempting to identify Android malware obfuscation variations, this paper proposes an approach to address the challenges and issues related to the classification and detection of malicious obfuscated variants. The employed detection and classification scheme uses both static and dynamic analysis using an ensemble voting mechanism. Moreover, this study demonstrates that a small subset of features performs consistently well when they are derived from the basic malware (non-obfuscated), however, after applying a novel feature-based obfuscation approach, the study shows a drastic change indicating the relative importance of these features in obfuscating benign and malware applications. For this purpose, we present a fast, scalable, and accurate mechanism for obfuscated Android malware detection based on the Deep learning algorithm using real and emulator-based platforms. The experiments show that the proposed model detects malware effectively and accurately along with the identification of features that are usually obfuscated by malware attackers.

Automated identification of the preclinical stage of coal workers' pneumoconiosis from digital chest radiography using three-stage cascaded deep learning model

ObjectiveCoal workers’ pneumoconiosis (CWP) is a broad and serious occupational disease caused by inhaling coal dust, which can cause permanent physical injury. There is no effective treatment currently. Screening of the preclinical stage of CWP can earlier detect the risk of CWP. However, it is laborious for radiologists to screen for CWP from a large number of chest radiographs. Missed diagnoses and misdiagnoses often occur. To address this problem, a novel three-stage cascaded learning model for screening the preclinical stage of CWP on digital chest radiography (DR) was proposed in this paper. Methods1447 DRs of coal workers from two hospitals were used in the study. At the first stage, a YOLOv2 network was trained for detecting lung boxes on DR images. At the second stage, six convolutional neural network (CNN) models were trained to identify the preclinical stage of CWP. At the third stage, an ensemble learning (EL) model based on the soft voting mechanism was implemented to integrate the outputs of the six CNN models. ResultsTest results on the test set generated the area under the receiver characteristic operating curve (AUC), accuracy, sensitivity and specificity of 0.931, 84.7%, 75.0% and 95.7%, respectively. ConclusionThe proposed three-stage cascaded learning model could effectively screen coal workers at the preclinical stage. SignificanceThis is the first study for identifying the preclinical stage of pneumoconiosis on DR images, which could facilitate the secondary prevention of pneumoconiosis.

Investigation on the use of ensemble learning and big data in crop identification

The agriculture sector in Egypt faces several problems, such as climate change, water storage, and yield variability. The comprehensive capabilities of Big Data (BD) can help in tackling the uncertainty of food supply occurs due to several factors such as soil erosion, water pollution, climate change, socio-cultural growth, governmental regulations, and market fluctuations. Crop identification and monitoring plays a vital role in modern agriculture. Although several machine learning models have been utilized in identifying crops, the performance of ensemble learning has not been investigated extensively. The massive volume of satellite imageries has been established as a big data problem forcing to deploy the proposed solution using big data technologies to manage, store, analyze, and visualize satellite data. In this paper, we have developed a weighted voting mechanism for improving crop classification performance in a large scale, based on ensemble learning and big data schema. Built upon Apache Spark, the popular DB Framework, the proposed approach was tested on El Salheya, Ismaili governate. The proposed ensemble approach boosted accuracy by 6.5%, 1.9%, 4.4%, 4.9%, 4.7% in precision, recall, F-score, Overall Accuracy (OA), and Matthews correlation coefficient (MCC) metrics respectively. Our findings confirm the generalization of the proposed crop identification approach at a large-scale setting.

P-PBFT: An improved blockchain algorithm to support large-scale pharmaceutical traceability

To solve the problems of high latency, high system overhead, and small supported scale in the current application of pharmaceutical traceability combined with blockchain technology, an algorithm called Pharmaceutical-Practical Byzantine Fault Tolerance (P-PBFT) based on PBFT, grouping, and credit voting is proposed. The algorithm combines the characteristics of a pharmaceutical supply chain, optimizes the consistency protocol in the original algorithm, divides large-scale network nodes into different consensus sets by response speed, and performs grouping consensus. The algorithm's credit model and voting mechanism dynamically updates user status according to the behavior of nodes in consensus, evaluates the reliability of users, and also serves as a basis for electing management nodes. Experimental results show that the improved P-PBFT consensus algorithm provides smaller latency and higher throughput for pharmaceutical traceability systems, supports larger-scale traceability, effectively alleviates the dramatic increase in communication among network nodes, and reduces the influence of malicious nodes.

Automated Student Classroom Behaviors' Perception and Identification Using Motion Sensors.

With the rapid development of artificial intelligence technology, the exploration and application in the field of intelligent education has become a research hotspot of increasing concern. In the actual classroom scenarios, students' classroom behavior is an important factor that directly affects their learning performance. Specifically, students with poor self-management abilities, particularly specific developmental disorders, may face educational and academic difficulties owing to physical or psychological factors. Therefore, the intelligent perception and identification of school-aged children's classroom behaviors are extremely valuable and significant. The traditional method for identifying students' classroom behavior relies on statistical surveys conducted by teachers, which incurs problems such as being time-consuming, labor-intensive, privacy-violating, and an inaccurate manual intervention. To address the above-mentioned issues, we constructed a motion sensor-based intelligent system to realize the perception and identification of classroom behavior in the current study. For the acquired sensor signal, we proposed a Voting-Based Dynamic Time Warping algorithm (VB-DTW) in which a voting mechanism is used to compare the similarities between adjacent clips and extract valid action segments. Subsequent experiments have verified that effective signal segments can help improve the accuracy of behavior identification. Furthermore, upon combining with the classroom motion data acquisition system, through the powerful feature extraction ability of the deep learning algorithms, the effectiveness and feasibility are verified from the perspectives of the dimensional signal characteristics and time series separately so as to realize the accurate, non-invasive and intelligent children's behavior detection. To verify the feasibility of the proposed method, a self-constructed dataset (SCB-13) was collected. Thirteen participants were invited to perform 14 common class behaviors, wearing motion sensors whose data were recorded by a program. In SCB-13, the proposed method achieved 100% identification accuracy. Based on the proposed algorithms, it is possible to provide immediate feedback on students' classroom performance and help them improve their learning performance while providing an essential reference basis and data support for constructing an intelligent digital education platform.

Is There a Role for the International Community in Palestinian State-building?

The European Union is known to be in favour of a two-state solution, but the question arises whether the two-state solution is a pan-European position, or there are Member States that have a "separate" policy, either in support of their national interests or because of historical and religious traditions and might have a different vision of the Middle East peace process. In my research, I am looking for answers to the question about the existing role of the international community in the creation of a Palestinian state. As a precondition for the emergence of an independent state, I hypothesize that EU member states do not recognize unanimously the legitimacy of a Palestinian State, despite the European Union's collective belief in the creation of a two-state solution. The research also highlights that the parties involved in state-building and peacebuilding do not necessarily shape their relations and policies in the interests of the international community. The Israeli-Palestinian peace process can partly facilitate the creation of a Palestinian State. Individual interpretations of the agreements and inflexibility on both sides have meant that neither the peace process nor state-building has made much progress in recent decades. In the research methodology, I conducted theoretical and empirical research and examined the resolutions of the UN General Assembly between 2012 and 2022. For each resolution, I examined the voting mechanism of the EU Member States in terms of whether they support the creation of Palestine or whether they support Israel. The evaluation of these results and the drawing of independent conclusions are the individually added values of the research. I draw conclusions from the voting mechanisms I have evaluated and from the involvement of the international community as to whether the involvement of the international community in state-building has been effective.

SMPT: A Semi-Supervised Multi-Model Prediction Technique for Food Ingredient Named Entity Recognition (FINER) Dataset Construction

To pursue a healthy lifestyle, people are increasingly concerned about their food ingredients. Recently, it has become a common practice to use an online recipe to select the ingredients that match an individual’s meal plan and healthy diet preference. The information from online recipes can be extracted and used to develop various food-related applications. Named entity recognition (NER) is often used to extract such information. However, the problem in building an NER system lies in the massive amount of data needed to train the classifier, especially on a specific domain, such as food. There are food NER datasets available, but they are still quite limited. Thus, we proposed an iterative self-training approach called semi-supervised multi-model prediction technique (SMPT) to construct a food ingredient NER dataset. SMPT is a deep ensemble learning model that employs the concept of self-training and uses multiple pre-trained language models in the iterative data labeling process, with a voting mechanism used as the final decision to determine the entity’s label. Utilizing the SMPT, we have created a new annotated dataset of ingredient entities obtained from the Allrecipes website named FINER. Finally, this study aims to use the FINER dataset as an alternative resource to support food computing research and development.

BIoMT-ISeg: Blockchain internet of medical things for intelligent segmentation.

In the quest of training complicated medical data for Internet of Medical Things (IoMT) scenarios, this study develops an end-to-end intelligent framework that incorporates ensemble learning, genetic algorithms, blockchain technology, and various U-Net based architectures. Genetic algorithms are used to optimize the hyper-parameters of the used architectures. The training process was also protected with the help of blockchain technology. Finally, an ensemble learning system based on voting mechanism was developed to combine local outputs of various segmentation models into a global output. Our method shows that strong performance in a condensed number of epochs may be achieved with a high learning rate and a small batch size. As a result, we are able to perform better than standard solutions for well-known medical databases. In fact, the proposed solution reaches 95% of intersection over the union, compared to the baseline solutions where they are below 80%. Moreover, with the proposed blockchain strategy, the detected attacks reached 76%.

Reputation-based Decentralized Autonomous Organization for the non-profit sector: Leveraging blockchain to enhance good governance

The Decentralized Autonomous Organization (DAO), a group organized by governance rules programmed on a blockchain, has recently been attracting attention as a novel organizational form. The effectiveness of a DAO’s decentralized governance mechanism and transparency, as secured by its code, has generally been discussed in contrast with traditional stock companies. However, the potential of a DAO for non-profits, which provide goods and services that profit-seeking organizations do not offer, has been less discussed. This paper presents a proof-of-concept implementation to demonstrate the advantages of utilizing a DAO governance framework for non-profits. To this end, this study developed a DAO governance framework incorporating a reputation-based decision-making system, a peer evaluation system, and a transparent, real-time accounting system for the Ethereum blockchain. Most current decentralized governance systems rely heavily on token-based voting using governance tokens with stock-like features. However, there is a need for a voting mechanism beyond token-based voting for non-profits, which do not have owners. Therefore, the developed application applies an existing reputation-based voting mechanism and integrates additional features, such as a membership system with mutual evaluation and a reputation NFT to visualize contributions. Several exemplar demonstrations were conducted to evaluate its key functionalities. This application enabled discussions across the boundary between technology and society in terms of the key aspects of non-profits: i) transparency of finance and governance, ii) participatory governance by diverse stakeholders, and iii) equity and inclusiveness of the consensus mechanism. The results indicated that blockchain technology compensates for a non-profit’s vulnerabilities, and illustrated that the proposed reputation-based governance mechanisms are well-motivated. However, the results also revealed that blockchain-based governance involves as many potential risks and limitations as it brings benefits. Lastly, by providing several possible solutions to these constraints as well as recommendations for future research, this paper contributes to the sustainable development of non-profits as one of the foundations of democratic governance.

User behavior prediction model based on implicit links and multi-type rumor messages

Traditional prediction models of rumor forwarding are based solely on explicit network topology, and with no consideration for homogeneity and antagonism among multi-type rumor messages. To solve these problems, this study proposes a user behavior prediction model based on implicit links and multi-type rumor messages. First, because most existing studies are based on explicit network topology and ignore the influence of implicit links on information transmission, this study considers the interaction and similarity among users comprehensively and uses the K-dimension-tree algorithm to mine implicit links among non-friends, thereby improving the network topology. Second, given fuzziness and complexity of user forwarding behavior in multi-type rumor messages, considering the advantages of graph convolutional networks (GCNs) model in network representation, rumor information, user characteristics and network structure are fully represented with features. Finally, considering the high integration ability and adaptive ability of model fusion, a softmax layer is added to finalize the basic multi-classification, and then multiple GCN-based models are fused by a voting mechanism to realize the prediction of user forwarding behavior. Experiments show that the proposed model can effectively predict a user’s forwarding behavior under multi-type rumor topics, and the model has improved generalization ability.

An Improved SVM Method for Movement Recognition of Lower Limbs by MIMU and sEMG

Aiming at the problems that the movement recognition accuracy of lower limbs needs to be improved, the optimized SVM recognition method by using voting mechanism is proposed in this paper. First, CS algorithm is applied to optimize the kernel function parameter and the penalty factor for SVM model. And then, voting mechanism is used to ensure the recognition accuracy of SVM classification algorithm. Finally, the experiments have been implemented and different classification algorithms have been compared. The recognition results shows that the movement recognition accuracy for the lower limbs by the optimized SVM recognition algorithm using voting mechanism is about 98.78%, which is higher than other commonly used classification algorithm with or without voting mechanism. The recognition method for the lower limbs proposed in this paper can be used in the field of rehabilitation training, smart healthcare and so on.

Research on fault diagnosis technology of heat meter based on multi classifier fusion of pigeon swarm algorithm.

In order to improve the availability of fault data, the fault data of heat meters had been classified, and balances all kinds of fault data according to interpolation algorithms to meet the needs of fault diagnosis algorithms. Based on the voting mechanism, an integrated model of multi classifier fusion is established, and the weight of each classifier is optimally configured through pigeon swarm algorithm. In the experiment, three kinds of integration models are obtained according to the voting mechanism and pigeon swarm algorithm. The three integrated models are used to diagnose the fault of the heat meter, and the three indicators of precision, recall and F1 Core have achieved satisfactory results.

Voting Schemes in DAO Governance

This paper aims to provide a comprehensive overview of the different voting schemes used in DAO governance. We will examine the various features of these schemes and compare their differences. We propose a new hypothetical voting mechanism specifically designed for decentralized and permissionless DAO governance. This new scheme, which incorporates incentive designs, is intended to be more efficient than existing schemes and can be easily adapted to a permissioned scenario. Through this examination and proposal, we hope to contribute to the ongoing discourse on how to govern decentralized autonomous organizations effectively.