Voting Mechanism Research Articles

Part-in-whole point cloud registration for aircraft partial scan automated localization

Skin deformation measurement is quite important for the aerodynamic performance of aircrafts. To achieve this, 3D scanning is usually adopted to capture the 3D point cloud, which reflects the geometric information of partial skin surface. The scanned point cloud is then compared with the designed CAD model to quantify the shape deformation. However, accurate localization of the partial scanned point cloud, which is the prerequisite for the shape comparison, still remains an open problem. This can be formulated as a part-in-whole registration problem in the free-form surface, which is of significant difficulty due to the featurelessness and local similarity problems on the aircraft skin surface. In this paper, we propose a “coarse-to-fine” registration framework to address these two challenges. First, a Multi-Descriptor Voting (MDV) scheme is presented to roughly locate the partial scanned point cloud in the whole aircraft skin model. A voting mechanism using multiple 3D descriptors leads to the high possibility of correct localization under the featurelessness situation on the free-form surface. Then, we observed in the aircraft skin that there are seam structures which can assist in localization, we accordingly design a Seam Structure Aware ICP (SSA-ICP) for fine registration to eliminate the localization ambiguity in local regions, based on the detected seam points. The proposed algorithm is implemented using Point Cloud Library (PCL), and the results show that the proposed MDV and SSA-ICP achieve promising performance on both synthetic and real scanned point clouds.

A deep learning based multiscale approach to segment the areas of interest in whole slide images.

This paper addresses the problem of liver cancer segmentation in Whole Slide Images (WSIs). We propose a multi-scale image processing method based on an automatic end-to-end deep neural network algorithm for the segmentation of cancerous areas. A seven-level gaussian pyramid representation of the histopathological image was built to provide the texture information at different scales. In this work, several neural architectures were compared using the original image level for the training procedure. The proposed method is based on U-Net applied to seven levels of various resolutions (pyramidal subsampling). The predictions in different levels are combined through a voting mechanism. The final segmentation result is generated at the original image level. Partial color normalization and the weighted overlapping method were applied in preprocessing and prediction separately. The results show the effectiveness of the proposed multi-scale approach which achieved better scores than state-of-the-art methods.

"I can show what I really like."

Surveys are a common instrument to gauge self-reported opinions from the crowd for scholars in the CSCW community, the social sciences, and many other research areas. Researchers often use surveys to prioritize a subset of given options when there are resource constraints. Over the past century, researchers have developed a wide range of surveying techniques, including one of the most popular instruments, the Likert ordinal scale, to elicit individual preferences. However, the challenge to elicit accurate and rich self-reported responses with surveys in a resource-constrained context still persists today. In this study, we examine Quadratic Voting (QV), a voting mechanism powered by the affordances of a modern computer and straddles ratings and rankings approaches, as an alternative online survey technique. We argue that QV could elicit more accurate self-reported responses compared to the Likert scale when the goal is to understand relative preferences under resource constraints. We conducted two randomized controlled experiments on Amazon Mechanical Turk, one in the context of public opinion polling and the other in a human-computer interaction user study. Based on our Bayesian analysis results, a QV survey with a sufficient amount of voice credits, aligned significantly closer to participants' incentive-compatible behaviors than a Likert scale survey, with a medium to high effect size. In addition, we extended QV's application scenario from typical public policy and education research to a problem setting familiar to the CSCW community: a prototypical HCI user study. Our experiment results, QV survey design, and QV interface serve as a stepping stone for CSCW researchers to further explore this surveying methodology in their studies and encourage decision-makers from other communities to consider QV as a promising alternative.

Do Majority‐of‐Minority Shareholder Voting Rights Reduce Expropriation? Evidence from Related Party Transactions

ABSTRACTIn the presence of business groups, the expropriation through related party transactions (RPTs) is common and costly to minority shareholders. Using the setting of India's RPT voting rule, I find that a majority‐of‐minority shareholder voting mechanism helps mitigate expropriation. Minority shareholders actively raise their voice against RPT resolutions. A difference‐in‐differences analysis reveals that shareholder voting has a significant deterrence effect on RPT volume. I also find that stock markets react positively to the voting rule's passage, and that the rule makes Indian firms more attractive to foreign institutional investors.

Automatic greenhouse pest recognition based on multiple color space features

Recognition and counting of greenhouse pests are important for monitoring and forecasting pest population dynamics. This study used image processing techniques to recognize and count whiteflies and thrips on a sticky trap located in a greenhouse environment. The digital images of sticky traps were collected using an image-acquisition system under different greenhouse conditions. If a single color space is used, it is difficult to segment the small pests correctly because of the detrimental effects of non-uniform illumination in complex scenarios. Therefore, a method that first segments object pests in two color spaces using the Prewitt operator in I component of the hue-saturation-intensity (HSI) color space and the Canny operator in the B component of the Lab color space was proposed. Then, the segmented results for the two-color spaces were summed and achieved 91.57% segmentation accuracy. Next, because different features of pests contribute differently to the classification of pest species, the study extracted multiple features (e.g., color and shape features) in different color spaces for each segmented pest region to improve the recognition performance. Twenty decision trees were used to form a strong ensemble learning classifier that used a majority voting mechanism and obtains 95.73% recognition accuracy. The proposed method is a feasible and effective way to process greenhouse pest images. The system accurately recognized and counted pests in sticky trap images captured under real greenhouse conditions. Keywords: ensemble learning classifier, greenhouse sticky trap, automated pest recognition and counting, HSI and Lab color spaces, multiple color space features DOI: 10.25165/j.ijabe.20211402.5098 Citation: Yang Z K, Li W Y, Li M, Yang X T. Automatic greenhouse pest recognition based on multiple color space features. Int J Agric & Biol Eng, 2021; 14(2): 188–195.

A Trust-Evaluation-Enhanced Blockchain-Secured Industrial IoT System

The Industrial Internet of Things (IIoT), allowing direct wireless communications between industrial machines and people, is the key component of Industry 4.0. By grouping several IIoT devices (IIoT-Ds) into IIoT groups (IIoT-Gs), the IIoT-Ds within one group can share information and deter malicious users from accessing the network by modifying the access control list (ACL) in IIoT-Ds. In an IIoT-G, several IIoT-Ds jointly manage the access control of IIoT sensors. The security of ACL is protected by blockchains deployed in IIoT-Ds. Even secured by blockchains, a blockchain-based IIoT-G (B-IIoT-G) still cannot secure correct access control in a system with more than half of the blockchain-based IIoT-Ds (B-IIoT-Ds) from spreading wrong authorization information. In this article, we propose a B-IIoT-G with trust evaluation for each B-IIoT-D to evaluate the weight in voting for final decisions of authorization. By designing a new voting mechanism with trust evaluation for the access control in B-IIoT-G, the system has a higher probability of making correct authorization even with malicious B-IIoT-Ds. Simulation results have demonstrated that the mechanism with trust evaluation can still make, with a high probability, correct authorization even with more than half of the B-IIoT-Ds in B-IIoT-G being malicious.

HER2 Molecular Marker Scoring Using Transfer Learning and Decision Level Fusion.

In prognostic evaluation of breast cancer, immunohistochemical (IHC) marker human epidermal growth factor receptor 2 (HER2) is used for prognostic evaluation. Accurate assessment of HER2-stained tissue sample is essential in therapeutic decision making for the patients. In regular clinical settings, expert pathologists assess the HER2-stained tissue slide under microscope for manual scoring based on prior experience. Manual scoring is time consuming, tedious, and often prone to inter-observer variation among group of pathologists. With the recent advancement in the area of computer vision and deep learning, medical image analysis has got significant attention. A number of deep learning architectures have been proposed for classification of different image groups. These networks are also used for transfer learning to classify other image classes. In the presented study, a number of transfer learning architectures are used for HER2 scoring. Five pre-trained architectures viz. VGG16, VGG19, ResNet50, MobileNetV2, and NASNetMobile with decimating the fully connected layers to get 3-class classification have been used for the comparative assessment of the networks as well as further scoring of stained tissue sample image based on statistical voting using mode operator. HER2 Challenge dataset from Warwick University is used in this study. A total of 2130 image patches were extracted to generate the training dataset from 300 training images corresponding to 30 training cases. The output model is then tested on 800 new test image patches from 100 test images acquired from 10 test cases (different from training cases) to report the outcome results. The transfer learning models have shown significant accuracy with VGG19 showing thebestaccuracy for the test images. The accuracy is found to be 93%, which increases to 98% on the image-based scoring using statistical voting mechanism. The output shows a capable quantification pipeline in automated HER2 score generation.

Data tampering attacks diagnosis in dynamic wireless sensor networks

Real-world wireless sensor networks (WSNs) usually have dynamic topologies and are vulnerable to data tampering attacks, which may possibly lead to serious consequences. This paper addresses the issue of defending against data tampering attacks to dynamic WSNs. First, a hybrid diagnosis algorithm is proposed, which consists of three phases: data comparison phase, distributed diagnosis phase, and global diagnosis phase. In the distributed diagnosis phase, a majority voting mechanism and a multi-round diagnosis mechanism are introduced. As the phase may lead to inconsistent diagnosis results among different sensor nodes, we introduce a global diagnosis phase to overcome the shortcoming. Next, we evaluate the performance of the diagnosis algorithm, accompanied with a few examples. Finally, to achieve near perfect performance, some experiments on how to choose the parameters of the algorithm are provided. This work provides guidance for dealing with data tampering attacks in dynamic WSNs.

A decision making framework for incorporating fairness in allocating slots at capacity-constrained airports

Fairness constitutes an important criterion for allocating scarce resources among self-interested parties. The capacity of congested airports is a scarce resource which is allocated in the form of slots to airlines to operate their flights. Traditionally, the optimisation of slot allocation efficiency has been considered as the primary criterion in making slot allocation decisions at capacity-constrained airports. The efficiency of slot allocation is expressed as a function of displacement, which is defined as the difference between the time requested by the airlines and the time allocated to airlines to operate their flights. Recent research work has considered fairness as an additional slot allocation objective. However, the literature has not adequately addressed the following two key research questions: i) in the presence of alternative fairness measures, which fairness measure should be used to investigate the slot allocation efficiency-fairness trade-off, and how the choice of the efficient frontier describing this trade-off should be made? and ii) given that the choice of the efficient frontier has been made, which slot allocation solution should be selected for implementation, and how this choice should be made? To address these questions, we investigate three inter-airline fairness objectives, and we introduce a decision-making framework to assist decision-makers in identifying the most preferable acceptable slot allocation outcome. The proposed decision-making framework is based on a voting mechanism which incorporates the preferences of stakeholders. We use slot request and capacity data to determine the most preferable slot allocation outcome at a capacity-constrained airport.

Neural guided visual slam system with Laplacian of Gaussian operator

Simultaneous localization and mapping (SLAM) addresses the problem of constructing the map from noisy sensor data and tracking the robot's path within the built map. After decades of development, a lot of mature systems achieve competent results in feature-based implementations. However, there are still problems when migrating the technology to practical applications. One typical example is the accuracy and robustness of SLAM in environment with illuminance and texture variations. To this end, two modules in the existing systems are improved here namely tracking and camera relocalization. In tracking module, image pyramid is processed with Laplacian of Gaussian (LoG) operator in feature extraction for enhanced edges and details. A majority voting mechanism is proposed to dynamically evaluate and redetermine the zero-mean sum of square difference threshold according to the matching error estimation in patch search. In camera relocalization module, full convolutional neural network which focuses on certain parts of the input data is utilized in guiding for accurate output predictions. The authors implement the two modules into OpenvSLAM and propose a neural guided visual SLAM system named LoG-SLAM. Experiments on publicly available datasets show that the accuracy and efficiency increase with LoG-SLAM when compared with other feature-based methods, and relocalization accuracy also improves compared with the recently proposed deep learning pipelines.

Optimization of PBFT Algorithm Based on Improved C4.5

Aiming at the problems of PBFT algorithm of consortium blockchain, such as high communication overhead, low consensus efficiency, and random selection of leader nodes, an optimized algorithm of PBFT is proposed. Firstly, the algorithm improves C4.5 and introduces weighted average information gain to overcome the mutual influence between conditional attributes and improve the classification accuracy. Then classify the nodes with improved C4.5, and select the ones with a high trust level to form the main consensus group. Finally, the integral voting mechanism is introduced to determine the leader node. Experimental results show that compared with traditional PBFT algorithm, the communication times of the improved PBFT algorithm are reduced greatly, which effectively alleviates the problem that the number of nodes in traditional PBFT algorithm increases and the traffic volume is too large, and significantly reduces the probability of the leader node doing evil and improves the consensus efficiency.

Data-driven tree structure for PIN models

Probability of informed trading (PIN) models characterize trading with certain types of information through a tree structure. Different tree structures with different numbers of groups for market participants have been proposed, with no clear, consistent tree used in the literature. One of the main causes of this inconsistency is that these trees are artificially proposed through a bottom-up approach rather than implied by actual market data. Therefore, in this paper, we propose a method that infers a tree structure directly from empirical data. More precisely, we use hierarchical clustering to construct a tree for each individual firm and then infer an aggregate tree through a voting mechanism. We test this method on US data from January 2002 for 7608 companies, which results in a tree with two layers and four groups. The characteristics of the resulting aggregate tree are between those of several proposed tree structures in the literature, demonstrating that these proposed trees all reflect only part of the market, and one should consider the proposed empirically driven method when seeking a tree representing the whole market.

A new hybrid ensemble model with voting-based outlier detection and balanced sampling for credit scoring

The credit scoring system has been revolutionized with the development of the financial system and has received increasing attention from the academia and industry. Artificial intelligence technology has reshaped credit scoring through predictive classification. In this study, a new hybrid ensemble model with voting-based outlier detection and balanced sampling is proposed to achieve superior predictive power for credit scoring. To avoid noise-filled data from misleading the classifier training, a new voting-based outlier detection method is proposed to enhance the classic outlier detection algorithms with the weighted voting mechanism and boost the outlier scores into the training set to form an outlier-adapted training set. To reduce the information loss caused by under-sampling when dealing with imbalanced data, a new bagging-based balanced sampling method is proposed to enhance the traditional under-sampling methods with the bagging strategy to obtain a balanced training set. To further improve the performance of the proposed model, a stacking-based ensemble modeling method is proposed to first perform parametrical optimization and then construct the stacking-based multi-stage ensemble model. Five datasets from the UC Irvine machine learning repository and five evaluation indicators were adopted to evaluate the model performance. The experimental results indicate the superior performance of the proposed model and prove its robustness and effectiveness.

Leveraging semantic features for recommendation: Sentence-level emotion analysis

Personalized recommendation systems can help users to filter redundant information from a large amount of data. Previous relevant researches focused on learning user preferences by analyzing texts from comment communities without exploring the detailed sentiment polarity, which encountered the cold-start problem. To address this research gap, we propose a hybrid personalized recommendation model that extracts user preferences by analyzing user review content in different sentiment polarity at the sentence level, based on jointly applying user-item score matrices and dimension reduction methods. A novel voting mechanism is also designed based on positive preferences from the neighbors of the target user to directly generate the recommendation results. The experimental results of testing the proposed model with a real-world data set show that our proposed model can achieve better recommendation effects than the representative recommendation algorithms. In addition, we demonstrated that fine-grained emotion recognition has good adaptability to a sparse rating matrix with a reasonable and good performance.

Helpfulness of online consumer reviews: A multi-perspective approach

Helpful online reviews crave the attention of many researchers as it significantly affects purchase decision. However, consumers’ perception of helpfulness remains an open problem due to a lack of semantic analysis of review content and unreliable voting mechanism. In this work, we propose three qualitative perspectives considering both semantic and syntactic features of review content - lexical, sequential and structural to assess helpfulness. N-gram based semantic relation among words is explored with a d-CNN model, to predict helpfulness from lexical perspective. Sequential perspective is analysed with LSTM model, which predict helpfulness by comprehending sequence of words. Structural perspective is addressed with fourteen syntactic statistical features and predict helpfulness of review. These three models of qualitative perspective trained with “X of Y” ratio of helpfulness voting. Now, to decimate the unreliability of helpfulness voting mechanism and unveil the human perception of helpfulness, the manual scoring approach is implemented over a sample of reviews. With experimentation, we show that there exists a linear relationship among the perspectives with the human perceived helpfulness score. It is observed that all these perspectives have an impact on consumers’ perception of helpfulness of a review. Five different product category of a benchmark dataset has been used for experimentation. A sample of 2000 reviews from five different categories has been used for human scoring of helpfulness. Finally, we estimate the weights of each of the perspectives of consumers’ perception of helpfulness from online reviews and discuss the significant theoretical and practical implications.

On the Distortion Value of Elections with Abstention

In Spatial Voting Theory, distortion is a measure of how good the winner is. It has been proved that no deterministic voting mechanism can guarantee a distortion better than 3, even for simple metrics such as a line. In this study, we wish to answer the following question: how does the distortion value change if we allow less motivated agents to abstain from the election?
 We consider an election with two candidates and suggest an abstention model, which is a general form of the abstention model proposed by Kirchgässner. Our results characterize the distortion ¨ value and provide a rather complete picture of the model.

DRBFT: Delegated randomization Byzantine fault tolerance consensus protocol for blockchains

Blockchain, as a potentially revolutionary technology, has been used in cryptocurrency to record transactions chronologically among multiple parties. Due to the fast development of the blockchain and its de-centralization, blockchain technology has been applied in broader scenarios, such as smart factories, supply chains, and smart cities. Consensus protocol plays a vital role in the blockchain, which addresses the issue of reaching consensus on transaction results among involved participants. Nevertheless, with the complexity of the network environment and growing amount of network users, the advance of blockchain is gradually restricted by the efficiency, security and reliability of consensus protocols. In this paper, we propose a delegated randomization Byzantine fault tolerance consensus protocol named DRBFT based on Practical Byzantine Fault Tolerance (PBFT) to enhance the efficiency and reliability of the consensus procedure. Specifically, a random selection algorithm called RS is developed to cooperate with the voting mechanism, which can effectively reduce the number of nodes participating in the consensus process. Our proposed scheme is characterized by the unpredictability, randomicity and impartiality, which accelerate the system to reach consensus on the premise of ensuring the system activity. Furthermore, the feasibility of our proposed scheme is also proved by both theoretical analysis and experimental evaluations.

ESRE: handling repeated entities in distant supervised relation extraction

Distant supervised relation extraction has been widely used to find novel relational facts from unstructured text. As far as we know, nearly all existing relation extraction models assume that each sentence contains precisely one entity pair, i.e., two entities. However, in reality, the datasets constructed by distant supervision have lots of sentences which contain repeated entities. In other words, there may be more than two entities in a sentence. This phenomenon breaks the assumption of existing models and makes them inevitably encounter the attention bias problem; that is, the model focuses on the wrong entities during relation extraction. To alleviate this problem, in this paper, we utilize the idea of ensemble learning and propose a novel distant supervised relation extraction model. The proposed model follows the multi-instance multi-label learning mechanism and conducts relation extraction based on the sentence-bag representations. Specifically, it first tries to identify the most critical entity and keywords, and then it uses voting mechanism to determine the sentence-level and bag-level features. Experimental results show that our proposed model outperforms the state-of-the-art baselines in relation extraction on a popular benchmark dataset, and it also indicates that the proposed model can indeed alleviate the problem caused by repeated-entity phenomenon.

Robust and Efficient Graph Correspondence Transfer for Person Re-Identification.

Spatial misalignment caused by variations in poses and viewpoints is one of the most critical issues that hinder the performance improvement in existing person re-identification (Re-ID) algorithms. Although it is straightforward to explore correspondence learning algorithms for alignment, online learning is intractable for negative pairs due to the intrinsic visual difference between negative pairs and efficiency concern. To address this problem, in this paper, we present a robust and efficient graph correspondence transfer (REGCT) approach for explicit spatial alignment in Re-ID. Specifically, we propose the off-line correspondence learning and on-line correspondence transfer framework. During training, patch-wise correspondences between positive training pairs are established via graph matching. By exploiting both spatial and visual contexts of human appearance in graph matching, meaningful semantic correspondences can be obtained. During testing, the off-line learned patch-wise correspondence templates are transferred to test pairs with similar pose-pair configurations for local feature distance calculation. To enhance the robustness of correspondence transfer, we design a novel pose context descriptor to accurately model human body configurations, and present an approach to measure the similarity between a pair of pose context descriptors. Meanwhile, to improve testing efficiency, we propose a correspondence template ensemble method using the voting mechanism, which significantly reduces the amount of patch-wise matchings involved in distance calculation. With the aforementioned strategies, the REGCT model can effectively and efficiently handle the spatial misalignment problem in Re-ID. Extensive experiments on five challenging benchmarks, including VIPeR, Road, PRID450S, 3DPES, and CUHK01, evidence the superior performance of REGCT over other state-of-the-art approaches.

Multiscale Adaptive Edge Detector for Images Based on a Novel Standard Deviation Map

Edge detection plays an important role in many applications, such as industrial inspection and automatic driving. However, it is difficult to effectively distinguish between faint edges and noise, which may result in losing effective edges or generating spurious edges. This will reduce the accuracy of edge detection. In addition, some parameters need to be set artificially. In the case of the fixed parameters, the overall performance of edge detection on different images is not high. The adaptivity of edge detection needs to be improved further. To solve these problems, this article proposes a multiscale adaptive edge detector for images. First, multiscale pyramid images are constructed from an input image to provide multiscale features for edge detection. At each scale, a gradient map and a novel standard deviation map are calculated based on the gradients and the statistical characteristics of the local gradient differences, respectively, to accurately distinguish the edges from the background and noise. By using these two feature maps, candidate edges are adaptively identified from the image by using pixel-by-pixel detection. Then, candidate edges at different scales are thinned and fused together based on a novel voting mechanism. Finally, a binarized edge map is obtained by using adaptive hysteresis linking. These steps make the proposed edge detector accurate and adaptive. Experiments demonstrate that the proposed edge detector achieves good performance, which is beneficial to measurement applications.