Card-based cryptography realizes secure multiparty computation using physical cards. In 2018, Watanabe et al. proposed a card-based three-input majority voting protocol using three cards. In a card-based cryptographic protocol with n-bit inputs, it is known that a protocol using shuffles requires at least 2n cards. In contrast, as Watanabe et al.'s protocol, a protocol using private permutations can be constructed with fewer cards than the lower bounds above. Moreover, an n-input protocol using private permutations would not even require n cards in principle since a private permutation depending on an input can represent the input without using additional cards. However, there are only a few protocols with fewer than n cards. Recently, Abe et al. extended Watanabe et al.'s protocol and proposed an n-input majority voting protocol with n cards and n + ⌊n/2⌋ + 1 private permutations. This paper proposes an n-input majority voting protocol with ⌈n/2⌉ + 1 cards and 2n-1 private permutations, which is also obtained by extending Watanabe et al.'s protocol. Compared with Abe et al.'s protocol, although the number of private permutations increases by about n/2, the number of cards is reduced by about n/2. In addition, unlike Abe et al.'s protocol, our protocol includes Watanabe et al.'s protocol as a special case where n=3.

This paper presents the design, implementation, and verification of a blockchain-based online electronic voting system that ensures accuracy and reliability in electronic voting and its application to various types of voting using blockchain technologies, such as distributed ledgers and smart contracts. Specifically, in this study, the connection between the electronic voting system and blockchain nodes is simplified using the REST API design, and the voting opening and counting information is designed to store the latest values in the distributed ledger in JSON format, using a smart contract that cannot be falsified. The developed electronic voting system can provide blockchain authentication, secret voting, forgery prevention, ballot verification, and push notification functions, all of which are currently not supported in existing services. Furthermore, the developed system demonstrates excellence on all evaluation items, including 101 transactions per second (TPS) of blockchain online authentication, 57.6 TPS of secret voting services, 250 TPS of forgery prevention cases, 547 TPS of read transaction processing, and 149 TPS of write transaction processing, along with 100% ballot verification service, secret ballot authentication, and encryption accuracy. Functional and performance verifications were obtained through an external test certification agency in South Korea. Our design allows for blockchain authentication, non-forgery of ballot counting data, and secret voting through blockchain-based distributed ledger technology. In addition, we demonstrate how existing electronic voting systems can be easily converted to blockchain-based electronic voting systems by applying a blockchain-linked REST API. This study greatly contributes to enabling electronic voting using blockchain technology through cost reductions, information restoration, prevention of misrepresentation, and transparency enhancement for a variety of different forms of voting.

Blockchain-based voting, including liquid voting, has been extensively studied in recent years. However, it remains challenging to implement liquid voting on blockchain using Ethereum smart contract. The challenge comes from the gas limit, which is that the number of instructions for processing a ballot cannot exceed a certain amount. This restricts the application scenario with respect to algorithms whose time complexity is linear to the number of voters, i.e., O(n). As the blockchain technology can well share and reuse the resources, we study a model of liquid voting on blockchain and propose a fast algorithm, named Flash, to eliminate the restriction. The key idea behind our algorithm is to shift some on-chain process to off-chain. In detail, we first construct a Merkle tree off-chain which contains all voters' properties. Second, we use Merkle proof and interval tree to process each ballot with O(log n) on-chain time complexity. Theoretically, the algorithm can support up to 21000 voters with respect to the current gas limit on Ethereum. Experimentally, the result implies that the consumed gas fee remains at a very low level when the number of voters increases. This means our algorithm makes liquid voting on blockchain practical even for massive voters.

Many kinds of questionnaires, testing, and voting are performed in some completely electronic ways to do questions and answers on the Internet as Web applications, i.e. e-questionnaire systems, e-testing systems, and e-voting systems. Because there is no unified communication tool among the stakeholders of e-questionnaire, e-testing, and e-voting systems, until now, all the e-questionnaire, e-testing, and e-voting systems are designed, developed, used, and maintained in various ad hoc ways. As a result, the stakeholders are difficult to communicate to implement the systems, because there is neither an exhaustive requirement list to have a grasp of the overall e-questionnaire, e-testing, and e-voting systems nor a standardized terminology for these systems to avoid ambiguity. A general-purpose specification language to provide a unified description way for specifying various e-questionnaire, e-testing, and e-voting systems can solve the problems such that the stakeholders can refer to and use the complete requirements and standardized terminology for better communications, and can easily and unambiguously specify all the requirements of systems and services of e-questionnaire, e-testing, and e-voting, even can implement the systems. In this paper, we propose the first specification language, named “QSL,” with a standardized, consistent, and exhaustive list of requirements for specifying various e-questionnaire, e-testing, and e-voting systems such that the specifications can be used as the precondition of automatically generating e-questionnaire, e-testing, and e-voting systems. The paper presents our design addressing that QSL can specify all the requirements of various e-questionnaire, e-testing, and e-voting systems in a structured way, evaluates its effectiveness, performs real applications using QSL in case of e-questionnaire, e-testing, and e-voting systems, and shows various QSL applications for providing convenient QSL services to stakeholders.

Efficient road-lane detection is expected to be achievable by application of the Hough transform (HT) which realizes high-accuracy straight-line extraction from images. The main challenge for HT-hardware implementation in actual applications is the trade-off optimization between accuracy maximization, power-dissipation reduction and real-time requirements. We report a HT-hardware architecture for road-lane detection with parallelized voting procedure, local maximum algorithm and FPGA-prototype implementation. Parallelization of the global design is realized on the basis of θ-value discretization in the Hough space. Four major hardware modules are developed for edge detection in the original video frames, computation of the characteristic edge-pixel values (ρ,θ) in Hough-space, voting procedure for each (ρ,θ) pair with parallel local-maximum-based peak voting-point extraction in Hough space to determine the detected straight lines. Implementation of a prototype system for real-time road-lane detection on a low-cost DE1 platform with a Cyclone II FPGA device was verified to be possible. An average detection speed of 135 frames/s for VGA (640x480)-frames was achieved at 50 MHz working frequency.

This letter presents a novel tensor voting mechanism — analytic tensor voting (ATV), to get rid of the difficulties in original tensor voting, especially the efficiency. One of the main advantages is its explicit voting formulations, which benefit the completion of tensor voting theory and computational efficiency. Firstly, new decaying function was designed following the basic spirit of decaying function in original tensor voting (OTV). Secondly, analytic stick tensor voting (ASTV) was formulated using the new decaying function. Thirdly, analytic plate and ball tensor voting (APTV, ABTV) were formulated through controllable stick tensor construction and tensorial integration. These make the each voting of tensor can be computed by several non-iterative matrix operations, improving the efficiency of tensor voting remarkably. Experimental results validate the effectiveness of proposed method.

Wireless Sensor Networks (WSNs) are randomly deployed in a hostile environment and left unattended. These networks are composed of small auto mouse sensor devices which can monitor target information and send it to the Base Station (BS) for action. The sensor nodes can easily be compromised by an adversary and the compromised nodes can be used to inject false vote or false report attacks. To counter these two kinds of attacks, the Probabilistic Voting-based Filtering Scheme (PVFS) was proposed by Li and Wu, which consists of three phases; 1) Key Initialization and assignment, 2) Report generation, and 3) En-route filtering. This scheme can be a successful countermeasure against these attacks, however, when one or more nodes are compromised, the re-distribution of keys is not handled. Therefore, after a sensor node or Cluster Head (CH) is compromised, the detection power and effectiveness of PVFS is reduced. This also results in adverse effects on the sensor network's lifetime. In this paper, we propose a Fuzzy Rule-based Key Redistribution Method (FRKM) to address the limitations of the PVFS. The experimental results confirm the effectiveness of the proposed method by improving the detection power by up to 13.75% when the key-redistribution period is not fixed. Moreover, the proposed method achieves an energy improvement of up to 9.2% over PVFS.

Seed detection or sometimes known as nuclei detection is a prerequisite step of nuclei segmentation which plays a critical role in quantitative cell analysis. The detection result is considered as accurate if each detected seed lies only in one nucleus and is close to the nucleus center. In previous works, voting methods are employed to detect nucleus center by extracting the nucleus saliency features. However, these methods still encounter the risk of false seeding, especially for the heterogeneous intensity images. To overcome the drawbacks of previous works, a novel detection method is proposed, which is called secant normal voting. Secant normal voting achieves good performance with the proposed skipping range. Skipping range avoids over-segmentation by preventing false seeding on the occlusion regions. Nucleus centers are obtained by mean-shift clustering from clouds of voting points. In the experiments, we show that our proposed method outperforms the comparison methods by achieving high detection accuracy without sacrificing the computational efficiency.

Word sense disambiguation (WSD) is to identify the right sense of ambiguous words via mining their context information. Previous studies show that classifier combination is an effective approach to enhance the performance of WSD. In this paper, we systematically review state-of-the-art methods for classifier combination based WSD, including probability-based and voting-based approaches. Furthermore, a new classifier combination based WSD, namely the probability weighted voting method with dynamic self-adaptation, is proposed in this paper. Compared with existing approaches, the new method can take into consideration both the differences of classifiers and ambiguous instances. Exhaustive experiments are performed on a real-world dataset, the results show the superiority of our method over state-of-the-art methods.

The reputation-based majority-voting approach is a promising solution for detecting malicious workers in a cloud system. However, this approach has a drawback in that it can detect malicious workers only when the number of colluders make up no more than half of all workers. In this paper, we simulate the behavior of a reputation-based method and mathematically analyze its accuracy. Through the analysis, we observe that, regardless of the number of colluders and their collusion probability, if the reputation value of a group is significantly different from those of other groups, it is a completely honest group. Based on the analysis result, we propose a new method for distinguishing honest workers from colluders even when the colluders make up the majority group. The proposed method constructs groups based on their reputations. A group with the significantly highest or lowest reputation value is considered a completely honest group. Otherwise, honest workers are mixed together with colluders in a group. The proposed method accurately identifies honest workers even in a mixed group by comparing each voting result one by one. The results of a security analysis and an experiment show that our method can identify honest workers much more accurately than a traditional reputation-based approach with little additional computational overhead.

It is important to reduce the power consumption of complementary metal oxide semiconductor (CMOS) logic circuits, especially those used in mobile devices. A CMOS logic circuit consists of metal-oxide-semiconductor field-effect transistors (MOSFETs), which consume electrical power dynamically when they charge and discharge load capacitance that is connected to their output. Load capacitance mainly exists in wiring or buses, and transitions between logic 0 and logic 1 cause these charges and discharges. Many methods have been proposed to reduce these transitions. One novel method (called segmentation coding) has recently been proposed that reduces power consumption of CMOS buses carrying band-limited signals, such as audio data. It improves performance by employing dedicated encoders for the upper and lower bits of transmitted data, in which the transition characteristics of band-limited signals are utilized. However, it uses a conventional majority voting circuit in the encoder for lower bits, and the circuit uses many adders to count the number of 1s to calculate the Hamming distance between the transmitted data. This paper proposes segmentation coding with pseudo-majority voting. The proposed pseudo-majority voting circuit counts the number of 1s with fewer circuit resources than the conventional circuit by further utilizing the transition characteristics of band-limited signals. The effectiveness of the proposed method was demonstrated through computer simulations and experiments.

The complicated indoor environment such as obstacles causes the non-line of sight (NLOS) environment. In this paper, we propose a voting matrix based residual weighting (VM-Rwgh) algorithm to mitigate NLOS errors in indoor localization system. The voting matrix is employed to provide initial localization results. The residual weighting is used to improve the localization accuracy. The VM-Rwgh algorithm can overcome the effects of NLOS errors, even when more than half of the measurements contain NLOS errors. Simulation results show that the VM-Rwgh algorithm provides higher location accuracy with relatively lower computational complexity in comparison with other methods.

We present an algorithm for simultaneously recognizing and localizing planar textured objects in an image. The algorithm can scale efficiently with respect to a large number of objects added into the database. In contrast to the current state-of-the-art on large scale image search, our algorithm can accurately work with query images consisting of several specific objects and/or multiple instances of the same object. Our proposed algorithm consists of two major steps. The first step is to generate a set of hypotheses that provides information about the identities and the locations of objects in the image. To serve this purpose, we extend Bag-Of-Visual-Word (BOVW) image retrieval by incorporating a re-ranking scheme based on the Hough voting technique. Subsequently, in the second step, we propose a geometric verification algorithm based on A Contrario decision framework to draw out the final detection results from the generated hypotheses. We demonstrate the performance of the algorithm on the scenario of recognizing CD covers with a database consisting of more than ten thousand images of different CD covers. Our algorithm yield to the detection results of more than 90% precision and recall within a few seconds of processing time per image.

To handle the variety of scene characters, we propose a cooperative multiple-hypothesis framework which consists of an image operator set module, an Optical Character Recognition (OCR) module and an integration module. Multiple image operators activated by multiple parameters probe suspected character regions. The OCR module is then applied to each suspected region and returns multiple candidates with weight values for future integration. Without the aid of the heuristic rules which impose constraints on segmentation area, aspect ratio, color consistency, text line orientations, etc., the integration module automatically prunes the redundant detection/recognition and pads the missing detection/recognition. The proposed framework bridges the gap between scene character detection and recognition, in the sense that a practical OCR engine is effectively leveraged for result refinement. In addition, the proposed method achieves the detection and recognition at the character level, which enables dealing with special scenarios such as single character, text along arbitrary orientations or text along curves. We perform experiments on the benchmark ICDAR 2011 Robust Reading Competition dataset which includes a text localization task and a word recognition task. The quantitative results demonstrate that multiple hypotheses outperform a single hypothesis, and be comparable with state-of-the-art methods in terms of recall, precision, F-measure, character recognition rate, total edit distance and word recognition rate. Moreover, two additional experiments are conducted to confirm the simplicity of parameter setting in this proposal.

Estimating a proper location of vanishing point from a single road image without any prior known camera parameters is a challenging problem due to limited information from the input image. Most edge-based methods for vanishing point detection only work well for structured roads with clear painted lines or distinct boundaries, while they usually fail in unstructured roads lacking sharply defined, smoothly curving edges. In order to overcome this limitation, texture-based methods for vanishing point detection have been widely published. Authors of these methods often calculate the texture orientation at every pixel of the road image by using directional filter banks such as Gabor wavelet filter, and seek the vanishing point by a voting scheme. A local adaptive soft voting method for obtaining the vanishing point was proposed in a previous study. Although this method is more effective and faster than prior texture-based methods, the associated computational cost is still high due to a large number of scanning pixels. On the other hand, this method leads to an estimation error in some images, in which the radius of the proposed half-disk voting region is not large enough. The goal of this paper is to reduce the computational cost and improve the performance of the algorithm. Therefore, we propose a novel local soft voting method, in which the number of scanning pixels is much reduced, and a new vanishing point candidate region is introduced to improve the estimation accuracy. The proposed method has been implemented and tested on 1000 road images which contain large variations in color, texture, lighting condition and surrounding environment. The experimental results demonstrate that this new voting method is both efficient and effective in detecting the vanishing point from a single road image and requires much less computational cost when compared to the previous voting method.

We present a fast voting scheme for localizing circular objects among clutter and occlusion. Typical solutions for the problem are based on Hough transform that evaluates an instance of circle by counting the number of edge points along its boundary. The evaluated value is proportional to radius, making the normalization with respect to the factor necessary for detecting circles with different radii. By representing circle with a number of sampled points, we get rid of the step. To evaluate an instance then involves obtaining the same number of edge points, each close to a sampled point in both spatial position and orientation. The closeness is measured by compatibility function, where a truncating operation is used to suppress noise and deal with occlusion. To evaluate all instances of circle is fulfilled by letting edge point vote in a maximizing way such that any instance possesses a set of maximally compatible edge points. The voting process is further separated into the radius-independent and -dependent parts. The time-consuming independent part can be shared by different radii and outputs the sparse matrices. The radius-dependent part shifts these sparse matrices according to the radius. We present precision-recall curves showing that the proposed approach outperforms the solutions based on Hough transform, at the same time, achieves the comparable time complexity as algorithm of Hough transform using 2D accumulator array.

With the development of COMPASS system, finding suitable and efficient multiplexing solutions have become important for the system signal design. In this paper, based on the alternative BOC (AltBOC) modulation technique, the multiplexing scheme for COMPASS Phase II B1 signals is proposed. Then, to combine all COMPASS Phase III (CP III) B1 components into a composite signal with constant envelope, the generalized majority voting (GMV) technique is employed based on the characteristics of CP III B1 signals. The proposed multiplexing schemes also provide potential opportunities for GNSS modernization and construction, such as GPS, Galileo, etc.

A novel grouping approach to segment text lines from handwritten documents is presented. In this text line segmentation algorithm, for each text line, a text string that connects the center points of the characters in this text line is built. The text lines are then segmented using the resulting text strings. Since the characters of the same text line are situated close together and aligned on a smooth curve, 2D tensor voting is used to reduce the conflicts when building these text strings. First, the text lines are represented by separate connected components. The center points of these connected components are then encoded by second order tensors. Finally, a voting process is applied to extract the curve saliency values and normal vectors, which are used to remove outliers and build the text strings. The experimental results obtained from the test dataset of the ICDAR 2009 Handwriting Segmentation Contest show that the proposed method generates high detection rate and recognition accuracy.

Previous studies of pattern recognition have shown that classifiers ensemble approaches can lead to better recognition results. In this paper, we apply the voting technique for the CoNLL-2010 shared task on detecting hedge cues and their scope in biomedical texts. Six machine learning-based systems are combined through three different voting schemes. We demonstrate the effectiveness of classifiers ensemble approaches and compare the performance of three different voting schemes for hedge cue and their scope detection. Experiments on the CoNLL-2010 evaluation data show that our best system achieves an F-score of 87.49% on hedge detection task and 60.87% on scope finding task respectively, which are significantly better than those of the previous systems.

In this letter, a novel technique for fast block matching using a new matching criterion is proposed. The matching speed and image quality are controlled by the one control parameter called matching region ratio. An efficient matching scheme with a gradual voting strategy is also proposed. This scheme can greatly boost the matching speed. The proposed technique is fast and applicable even in the presence of speckle noise or partial occlusion.