Reviewerly/exHarmony huggingface dataset:
Proof of the possibility for a public audit of a secret internet voting system

The aim of this study is to prove the possibility of building a system of secret Internet voting, in which a full-fledged audit is available to all voters and their proxies. A full-fledged audit should be understood as such an audit, in which everything that may be in doubt is checked. The open block of servers was created using Raspberry Pi 3 Model B type minicomputers, which are widely known and well-established. On the basis of an open block of servers, a full-scale model of the system for conducting experimental voting was created and a detailed methodology for a full-fledged audit was developed. This methodology provides for two stages of the audit. In the first stage, voters or their proxies must be present near the server unit. In the second stage, they continue the audit remotely through a dedicated server without losing information about the security of their data. For practical acquaintance with this research, the possibility of experimental voting is given. The experiment can be conducted by anyone at any time through a link on the Internet. Thus, it is shown that not only with traditional secret voting technologies, a full-fledged audit is possible, thanks to which voters have no doubts about maintaining the secrecy of their votes and the honesty of the results. To conduct a full-fledged audit according to the described methodology, it is not require to involve highly qualified specialists, but school education, which is mandatory in many countries, is quite enough. The importance of the results is that the lack of a full-fledged audit of Internet voting systems is the main factor hindering the development of e-democracy. The lack of public auditing of Internet voting systems causes distrust in the possibility of using the Internet to conduct fair elections
Optimally Band-Limited Noise Filtering for Single-Qubit Gates

We introduce a quantum control protocol that produces smooth, experimentally implementable control sequences optimized to combat temporally correlated noise for single-qubit systems. The control ansatz is specifically chosen to be a functional expansion of discrete prolate spheroidal sequences, a discrete time basis known to be optimally concentrated in time and frequency, and quite attractive when faced with experimental control hardware constraints. We leverage the filter function formalism to transform the control problem into a filter design problem, and show that the frequency response of a quantum system can be carefully tailored to avoid the most relevant dynamical contributions of noise processes. Using gradient ascent, we obtain optimized filter functions and exploit them to elucidate important details about the relationship between filter function design, control bandwidth, and noise characteristics. In particular, we identify regimes of optimal noise suppression and, in turn, optimal control bandwidth directly proportional to the size of the frequency bands where the noise power is large. In addition to providing guiding principles for filter design, our approach enables the development of controls that simultaneously yield robust noise filtering and high-fidelity single-qubit logic operations in a wide variety of complex noise environments.
When quantum annealing meets multitasking: Potentials, challenges and opportunities

Quantum computers have provided a promising tool for tackling NP hard problems. However, most of the existing work on quantum annealers assumes exclusive access to all resources available in a quantum annealer. This is not resource efficient if a task consumes only a small part of an annealer and leaves the rest wasted. We ask if we can run multiple tasks in parallel or concurrently on an annealer, just like the multitasking capability of a classical general-purpose processor. By far, multitasking is not natively supported by any of the existing annealers. In this paper, we explore Multitasking in Quantum Annealer (QAMT) by identifying the parallelism in a quantum annealer from the aspect of space and time. Based on commercialised quantum annealers from D-Wave, we propose a realisation scheme for QAMT, which packs multiple tasks into a quantum machine instruction (QMI) and uses predefined sampling time to emulate task preemption. We enumerate a few scheduling algorithms that match well with QAMT and discuss the challenges in QAMT. To demonstrate the potential of QAMT, we simulate a quantum annealing system, implement a demo QAMT scheduling algorithm, and evaluate the algorithm. Experimental results suggest that there is great potential in multitasking in quantum annealing.
A Simple Algorithm for Drawing Apportionment Diagrams

This article gives an algorithm that will build an apportionment diagram for any apportionment method when there are three states. We then provide several images that are produced by the algorithm and discuss what these images can reveal about the given apportionment method.
Processing of Analyzed Textual Information to a Meaningful Structure

The results of studies of the morphological analysis of the text are demonstrated. Application of the technology of automatic processing of Russian-language texts to determine the parts of speech presented in the digital image. Definition of significant and non-essential information in the text. Identification and construction of a concept for implementation into an intelligent automated text recognition and analysis system. The correspondence of each word of the text to a certain part of speech according to a predetermined structure. The automatic selection of the necessary algorithms for bringing the text to the final form is carried out to get a real-time response. The designed system uses a certain structure, according to which, at the same time, text analysis is carried out to identify the main idea of a group of proposals, and presents the information received in an ordered form. The system can be upgraded and receive constant support in the form of updates to the relevance databases and calculation of criteria for weight characteristics. It can be implemented in the systems of Internet resources, used as an independent project supporting autonomous operation, or for implementation in intelligent systems.
Can Learned Models Replace Hash Functions?

Hashing is a fundamental operation in database management, playing a key role in the implementation of numerous core database data structures and algorithms. Traditional hash functions aim to mimic a function that maps a key to a random value, which can result in collisions, where multiple keys are mapped to the same value. There are many well-known schemes like chaining, probing, and cuckoo hashing to handle collisions. In this work, we aim to study if using learned models instead of traditional hash functions can reduce collisions and whether such a reduction translates to improved performance, particularly for indexing and joins. We show that learned models reduce collisions in some cases, which depend on how the data is distributed. To evaluate the effectiveness of learned models as hash function, we test them with bucket chaining, linear probing, and cuckoo hash tables. We find that learned models can (1) yield a 1.4x lower probe latency, and (2) reduce the non-partitioned hash join runtime with 28% over the next best baseline for certain datasets. On the other hand, if the data distribution is not suitable, we either do not see gains or see worse performance. In summary, we find that learned models can indeed outperform hash functions, but only for certain data distributions.
Integrative System of Deep Classifiers Certification: Case of Convolutional Attacks

Deep learning models robustness have been first studied under simple image attacks (2D rotation, brightness), and then, subsequently, under other perturbations such as filtering. These systems often evoke a single learning model on a single type of data. Here, we intend to introduce an integrative method to certify deep classifiers against convolutional attacks. We study the impact of combining several data sources on the strengthen of the verification process. Using the abstract interpretation theory, we propose a new verification routine dealing with curves as well as images. We formulate the lower and upper bounds with abstract intervals to support other classes of advanced attacks including image and 2D contours filtering. Experimentation are conducted on MNIST, CIFAR10 and MPEG7 databases. The obtained results prove the utility of combining different entries in the certification system.
TOD

Outlier detection (OD) is a key machine learning task for finding rare and deviant data samples, with many time-critical applications such as fraud detection and intrusion detection. In this work, we propose TOD, the first tensor-based system for efficient and scalable outlier detection on distributed multi-GPU machines. A key idea behind TOD is decomposing complex OD applications into a small collection of basic tensor algebra operators. This decomposition enables TOD to accelerate OD computations by leveraging recent advances in deep learning infrastructure in both hardware and software. Moreover, to deploy memory-intensive OD applications on modern GPUs with limited on-device memory, we introduce two key techniques. First, provable quantization speeds up OD computations and reduces its memory footprint by automatically performing specific floating-point operations in lower precision while provably guaranteeing no accuracy loss. Second, to exploit the aggregated compute resources and memory capacity of multiple GPUs, we introduce automatic batching , which decomposes OD computations into small batches for both sequential execution on a single GPU and parallel execution across multiple GPUs. TOD supports a diverse set of OD algorithms. Evaluation on 11 real-world and 3 synthetic OD datasets shows that TOD is on average 10.9X faster than the leading CPU-based OD system PyOD (with a maximum speedup of 38.9X), and can handle much larger datasets than existing GPU-based OD systems. In addition, TOD allows easy integration of new OD operators, enabling fast prototyping of emerging and yet-to-discovered OD algorithms.
Man-made target detection method based on the red-edge spectral information in natural background

Man-made target detection in the natural background is the core problem of high-resolution optical remote sensing image interpretation, and it is an important application direction in the target recognition, situation awareness, and other fields. This paper proposes a man-made target detection method based on red-edge spectral information in the natural background. In this method, the red-edge spectrum information of vegetation is used to suppress background interference and narrow the search area in large remote sensing images, to improve the probability of target detection. The automatic extraction model of man-made targets is trained by the public dataset COD10K and self-constructed remote sensing dataset, and detection experiments are carried out by using the multispectral images from the WorldView-3 satellite and the Sentinel-2 A/B satellites. The experimental results show that the proposed algorithm has high detection accuracy and fast detection speed for the green runway, green buildings, and another man-made targets in the forest or grassland. Compared with the traditional man-made target detection algorithm, this method is more suitable for the rapid search of large area remote sensing images.
Reverse engineering of high-fidelity nonadiabatic holonomic quantum gates in the ensemble-ions system

We proposed a theoretical protocol to generate robust pulses for executing an arbitrary nonadiabatic holonomic quantum gate operations via reverse engineering. Compared with the traditional scheme that the pulse area needs to satisfy the condition 0 &Omega; dt = π, the reverse engineering scheme no longer requires this constraint, which greatly improves the flexibility of pulse designing. By optimizing the envelope of the pulses, we show that the gate operations are more robust against the frequency detuning than the traditional scheme. In the meanwhile, we also improve the robustness of the pulse against Rabi frequency fluctuations by utilizing the perturbation theory. The robust pulses can be applied to other ensemble qubit systems to realize quantum error correction, qubit initialization, and quantum gate operations, such as ensemble nitrogen-vacancy center systems, superconducting qubit systems, and other systems where qubits are addressed in frequency.
Image haze removal algorithm based on nonsubsampled contourlet transform

Although the traditional dehazing algorithm can improve the clarity of hazy weather images, it may lead to the loss of many details and the distortion of color saturation in the process of processing. In order to overcome this defect and enhance the details of the image, a single image dehazing algorithm based on non-subsampling contourlet transform was proposed. First, the captured fog images are mapped from RGB space to HSI space, and the luminance channel map and saturation channel map are processed separately. The luminance channel image is decomposed by non-subsampling contourlet. The obtained high-frequency components are filtered by a guided filter, which can smooth the image while maintaining the edge. The obtained low-frequency components are processed by the single-scale Retinex algorithm to enhance the details of hazy areas in the image. A new luminance channel image is obtained through certain fusion rules and the inverse transformation. Then, the degradation model of the saturation component map is established. The parameters are estimated using the dark channel prior principle, and the estimated saturation map is obtained. Finally, the new luminance channel image, the estimated saturation image and the original hue channel image are inversely transformed to RGB space, resulting in a dehazed image. Experiments show that the method in this paper can solve the problem of color distortion in bright areas to a certain extent and the color saturation of the image, while keeping the overall outline structure of the image clearly and the edge details prominent. The visibility of the whole image is also improved, which is prior to the traditional detection algorithms.
Space target docking ring recognition and center point positioning based on Tiny Darknet YOLOv3 fusion CenterNet

Aiming at the visual measurement requirements of space manipulators for grasping non-cooperative targets, a space target docking ring recognition and center point positioning method based on Tiny Darknet YOLOv3 fusion CenterNet is proposed. First, training network model based on open source ImageNet VOC 2007 and self-built spatial non-cooperative target data set and use optimized Tiny Darknet YOLOv3 fusion CenterNet deep learning algorithm to identify space target docking and obtain two-dimensional pixel coordinates of the docking center point; secondly, using the EnsensoN10-408-18 depth camera to obtain the 3*3 neighborhood data of the depth value corresponding to the center point and calculate the neighborhood weighted optimal value to get docking center spatial coordinates in the camera coordinate system. Combined with the hand-eye calibration relationship, the docking center spatial coordinates are converted to the UR5 manipulator base coordinate system. A ground verification system for manipulator to capture the target was built to test the target docking ring identification and center point positioning, and the accuracy error evaluation is completed based on the OptiTrack motion capture global measurement benchmark system. The experimental results show that target positioning accuracy is better than 10mm, and real-time data update rate is better than 2Hz in the dynamic approximation process from 1.5m to 0.2m, which can effectively solve the slow speed and poor accuracy caused by the influence of environmental lighting, target surface material, target attitude scale changes and other factors in traditional feature extraction methods. It lays a foundation for the safe arrival, capture and other manipulator fine operations.
Design of a spaceborne high-performance and real-time image processing platform

This paper designs an image processing platform suitable for spaceborne equipment with FPGA and DSP as the core. In this paper, the hardware architecture, the selection of key components, and the interaction design of the interface between FPGA and DSP are introduced in detail. During the experiment, six high-definition cameras were used to capture multiple images of the visual target fixed in the motion mechanism, and the function and performance of the system were fully tested. The results show that the platform has the ability to receive image data from six high-definition cameras at the same time while only consuming 18w of power. And within 0.5s, based on the image data of the visual target captured by the six cameras, after image preprocessing, image noise reduction, threshold segmentation and other related processing, the relevant pose information is solved, the position error is less than 1mm, and the attitude error is less than 0.1°, the platform has high computing precision, fast computing speed, good stability and low power consumption.
Switching the iteration in alternating optimization for blind super-resolution

To promote the super-resolution (SR) technology in real-world applications, the blind SR, involving kernel estimation and image restoration to super-resolve images with unknown degradation, has become one of the research focuses. Most existing methods either implement the above two tasks step-by-step so that do not well consider the compatibility between them, or repeatedly apply two modules over and over again to emphasize cooperation but limit the adaptive development of each one. Towards the above issues, based on the Deep Alternating Network (DAN), a novel training strategy named switching the iteration is proposed in this paper. In the first stage, an estimation module and a restoration module are optimized alternately to promote compatibility. In the second stage, duplicate the pre-trained modules and place them alternately to form a linear structure to promote adaptive development. Extensive experiments on isotropic Gaussian degradation datasets and irregular blur kernel degradation datasets show that the proposed method can achieve visually pleasing results and state-of-the-art performance in blind SR.
Overview of Digital Electrophoresis Analysis

Digital imaging is the method of choice for documentation and analysis of electrophoretic separations of protein and DNA. Digital images of gel electropherograms can be obtained rapidly using sCMOS and CCD-based cameras, and the images can be easily archived and analyzed using image analysis software. This overview defines important key terms and calculations for imaging, explains the capture process, reviews the range of technologies used for image capture, and provides an introduction to the software and methods used for one- and two-dimensional digital image analysis. © 2023 Wiley Periodicals LLC.
Exploring trends in blockchain publications with topic modeling: Implications for forecasting the emergence of industry applications

Abstract Technological innovation generates products, services, and processes that can disrupt existing industries and lead to the emergence of new fields. Distributed ledger technology, or blockchain, offers novel transparency, security, and anonymity characteristics in transaction data that may disrupt existing industries. However, research attention has largely examined its application to finance. Less is known of any broader applications, particularly in Industry 4.0. This study investigates academic research publications on blockchain and predicts emerging industries using academia‐industry dynamics. This study adopts latent Dirichlet allocation and dynamic topic models to analyze large text data with a high capacity for dimensionality reduction. Prior studies confirm that research contributes to technological innovation through spillover, including products, processes, and services. This study predicts emerging industries that will likely incorporate blockchain technology using insights from the knowledge structure of publications.
Discriminative analysis of wavelets for efficient medical image compression

Critical diagnostic information inferred using state of the artradiology techniques helps radiologists in determining the severity of diseases and hence suggest suitable treatment procedures. As a result, dealing with medical image compression necessitates a trade-off between good perceptual quality and high compression rate. The objective of this work is twofold, i) to investigate the effect of increasing the number of encoding loops on medical image compression parameters, and ii) to determine the most suitable wavelet for medical image compression. Haar, Daubechies, Biorthogonal Demeyer, Coifletand Symlet wavelets are used for comparison. Six different sets of medical images are used for testing and from the results obtained it is observed that increasing the number of encoding loops results in better compression parameters but increasing beyond 9 has no significant effect on compression parameters and thus the optimum choice for the number of encoding loops is 9. From the second analysis it is observed that changing the type of wavelets used has no significant effect on the compression parameters.
Generating the structural graph‐based model from a program source‐code using chaotic forrest optimization algorithm

Abstract One of the most important and costly stages in software development is maintenance. Understanding the structure of software will make it easier to maintain it more efficiently. Clustering software modules is thought to be an effective reverse engineering technique for deriving structural models of software from source code. In software module clustering, the most essential objectives are to minimize connections between produced clusters, maximize internal connections within created clusters, and maximize clustering quality. Finding the appropriate software system clustering model is considered an NP‐complete task. The previously proposed approaches' key limitations are their low success rate, low stability, and poor modularization quality. In this paper, for optimal clustering of software modules, Chaotic based heuristic method using a forest optimization algorithm is proposed. The impact of chaos theory on the performance of the other SFLA‐GA and PSO‐GA has also been investigated. The results show that using the logistic chaos approach improves the performance of these methods in the software‐module clustering problem. The performance of chaotic based FOA, SFLA‐GA and PSO‐GA is superior to the other heuristic methods in terms of modularization quality and stability of the results.
Study on the HDR fusion method based on dual gain grayscale image

For the scientific complementary metal-oxide-semiconductor detector with dual gain control function, two images can be output simultaneously at the any exposure time. The low gain image has small pixel brightness value, and most of low illumination regions are usually black pixel. However, the high illumination regions in the high gain image may be saturated and overexposed. To obtain a detailed image for different illumination, a high dynamic range image fusion method base on dual gain grayscale image was developed. First Laplacian pyramid for dual gain images was established; then fusion was performed according to the weight value of each layer and image reconstruction was carried out by the pyramid results. Finally, the high dynamic range image could be obtained through grayscale adjustment. Results showed that the high dynamic range image fusion had better performance than others in many regions, such as the buildings, the roof, the sky and cars with low illumination. The image still had high resolution under the condition of zoom. Therefore, the fusion method base on dual gain grayscale image is suitable to high and low gain image, and it provides reference for high dynamic range image generation.