Reviewerly/exHarmony huggingface dataset:
Improving Localization Accuracy in Connected Vehicle Networks Using Rao–Blackwellized Particle Filters: Theory, Simulations, and Experiments

A crucial function for automated vehicle technologies is accurate localization. Lane-level accuracy is not readily available from low-cost global navigation satellite system (GNSS) receivers because of factors such as multipath error and atmospheric bias. Approaches such as differential GNSS can improve localization accuracy, but usually require investment in expensive base stations. Connected vehicle technologies provide an alternative approach in improving the localization accuracy. It will be shown in this paper that localization accuracy can be enhanced using crude GNSS measurements from a group of connected vehicles, by matching their locations to a digital map. A Rao-Blackwellized particle filter is used to jointly estimate the common biases of the pseudo-ranges and the vehicle positions. Multipath biases, which introduce receiver-specific (non-common) error, are mitigated by a multi-hypothesis detection-rejection approach. The temporal correlation of the estimations is exploited through the prediction-update process. The proposed approach is compared with existing methods using both simulations and experimental results. It was found that the proposed algorithm can eliminate the common biases and reduce the localization error to below 1 m under open sky conditions.
New semifields and new MRD codes from skew polynomial rings

In this article we construct a new family of semifields, containing and extending two well-known families, namely Albert's generalised twisted fields and Petit's cyclic semifields (also known as Johnson-Jha semifields). The construction also gives examples of semifields with parameters for which no examples were previously known. In the case of semifields two dimensions over a nucleus and four-dimensional over their centre, the construction gives all possible examples. Furthermore we embed these semifields in a new family of maximum rank-distance codes, encompassing most known current constructions, including the (twisted) Delsarte-Gabidulin codes, and containing new examples for most parameters.
On the minimum weights of binary linear complementary dual codes

Linear complementary dual codes (or codes with complementary duals) are codes whose intersections with their dual codes are trivial. We study the largest minimum weight $d(n,k)$ among all binary linear complementary dual $[n,k]$ codes. We determine $d(n,4)$ for $n \equiv 2,3,4,5,6,9,10,13 \pmod{15}$, and $d(n,5)$ for $n \equiv 3,4,5,7,11,19,20,22,26 \pmod{31}$. Combined with known results, the values $d(n,k)$ are also determined for $n \le 24$.
Continuous-variable quantum digital signatures over insecure channels

Digital signatures ensure the integrity of a classical message and the authenticity of its sender. Despite their far-reaching use in modern communication, currently used signature schemes rely on computational assumptions and will be rendered insecure by a quantum computer. We present a quantum digital signatures (QDS) scheme whose security is instead based on the impossibility of perfectly and deterministically distinguishing between quantum states. Our continuous-variable (CV) scheme relies on phase measurement of a distributed alphabet of coherent states and allows for secure message authentication against a quantum adversary performing collective beamsplitter and entangling-cloner attacks. Crucially, in the CV setting we allow for an eavesdropper on the quantum channels and yet retain shorter signature lengths than previous protocols with no eavesdropper. This opens up the possibility to implement CV QDS alongside existing CV quantum key distribution platforms with minimal modification.
Quantum storage in quantum ferromagnets

We must protect inherently fragile quantum data to unlock the potential of quantum technologies. A pertinent concern in schemes for quantum storage is their potential for near-term implementation. Since Heisenberg ferromagnets are readily available, we investigate their potential for robust quantum storage. We propose to use permutation-invariant quantum codes to store quantum data in Heisenberg ferromagnets, because the ground space of any Heisenberg ferromagnet must be symmetric under any permutation of the underlying qubits. By exploiting an area law on the expected energy of Pauli errors, we show that increasing the effective dimension of Heisenberg ferromagnets can improve the storage lifetime. When the effective dimension of Heisenberg ferromagnets is maximal, we also obtain an upper bound for the storage error. This result relies on perturbation theory, where we use Davis' divided difference representation for Fr\'echet derivatives along with the recursive structure of these divided differences. Our numerical bounds allow us to better understand how quantum memory lifetimes can be enhanced in Heisenberg ferromagnets.
Morphology-Based Noise Reduction: Structural Variation and Thresholding in the Bitonic Filter

The bitonic filter was recently developed to embody the novel concept of signal bitonicity (one local extremum within a set range) to differentiate from noise, by use of data ranking and linear operators. For processing images, the spatial extent was locally constrained to a fixed circular mask. Since structure in natural images varies, a novel structurally varying bitonic filter is presented, which locally adapts the mask, without following patterns in the noise. This new filter includes novel robust structurally varying morphological operations, with efficient implementations, and a novel formulation of non-iterative directional Gaussian filtering. Data thresholds are also integrated with the morphological operations, increasing noise reduction for low noise, and enabling a multi-resolution framework for high noise levels. The structurally varying bitonic filter is presented without presuming prior knowledge of morphological filtering, and compared to high-performance linear noise-reduction filters, to set this novel concept in context. These are tested over a wide range of noise levels, on a fairly broad set of images. The new filter is a considerable improvement on the fixed-mask bitonic, outperforms anisotropic diffusion and image-guided filtering in all but extremely low noise, non-local means at all noise levels, but not the block-matching 3D filter, though results are promising for very high noise. The structurally varying bitonic tends to have less characteristic residual noise in regions of smooth signal, and very good preservation of signal edges, though with some loss of small scale detail when compared to the block-matching 3D filter. The efficient implementation means that processing time, though slower than the fixed-mask bitonic filter, remains competitive.
On the complexity of an augmented Lagrangian method for nonconvex optimization

In this paper we study the worst-case complexity of an inexact Augmented Lagrangian method for nonconvex constrained problems. Assuming that the penalty parameters are bounded, we prove a complexity bound of $\mathcal{O}(|\log(\epsilon)|)$ outer iterations for the referred algorithm to generate an $\epsilon$-approximate KKT point, for $\epsilon\in (0,1)$. When the penalty parameters are unbounded, we prove an outer iteration complexity bound of $\mathcal{O}\left(\epsilon^{-2/(\alpha-1)}\right)$, where $\alpha>1$ controls the rate of increase of the penalty parameters. For linearly constrained problems, these bounds yield to evaluation complexity bounds of $\mathcal{O}(|\log(\epsilon)|^{2}\epsilon^{-2})$ and $\mathcal{O}\left(\epsilon^{-\left(\frac{2(2+\alpha)}{\alpha-1}+2\right)}\right)$, respectively, when appropriate first-order methods ($p=1$) are used to approximately solve the unconstrained subproblems at each iteration. In the case of problems having only linear equality constraints, the latter bounds are improved to $\mathcal{O}(|\log(\epsilon)|^{2}\epsilon^{-(p+1)/p})$ and $\mathcal{O}\left(\epsilon^{-\left(\frac{4}{\alpha-1}+\frac{p+1}{p}\right)}\right)$, respectively, when appropriate $p$-order methods ($p\geq 2$) are used as inner solvers.
An Expectation Conditional Maximization Approach for Gaussian Graphical Models

Bayesian graphical models are a useful tool for understanding dependence relationships among many variables, particularly in situations with external prior information. In high-dimensional settings, the space of possible graphs becomes enormous, rendering even state-of-the-art Bayesian stochastic search computationally infeasible. We propose a deterministic alternative to estimate Gaussian and Gaussian copula graphical models using an expectation conditional maximization (ECM) algorithm, extending the EM approach from Bayesian variable selection to graphical model estimation. We show that the ECM approach enables fast posterior exploration under a sequence of mixture priors, and can incorporate multiple sources of information. Supplementary materials for this article are available online.
Multilayer complex network descriptors for color–texture characterization

A new method based on complex networks is proposed for color–texture analysis. The proposal consists of modeling the image as a multilayer complex network where each color channel is a layer, and each pixel (in each color channel) is represented as a network vertex. The network dynamic evolution is accessed using a set of modeling parameters (radii and thresholds), and new characterization techniques are introduced to capt information regarding within and between color channel spatial interaction. An automatic and adaptive approach for threshold selection is also proposed. We conduct classification experiments on 5 well-known datasets: Vistex, Usptex, Outex13, CURet, and MBT. Results among various literature methods are compared, including deep convolutional neural networks. The proposed method presented the highest overall performance over the 5 datasets, with 97.7 of mean accuracy against 97.0 achieved by the ResNet convolutional neural network with 50 layers.
Domain Adaptation for Privacy-Preserving Pedestrian Detection in Thermal Imagery

Pedestrian detection is a core problem in computer vision, and is a problem that is gaining prominence due to its importance in assisted and autonomous driving applications. Many state-of-the-art approaches, especially those used for autonomous driving, combine thermal and visible spectrum imagery in order to robustly detect persons independent of time of day or weather conditions. In this paper we investigate two domain adaptation techniques for fine-tuning a YOLOv3 detector to perform accurate and robust pedestrian detection using thermal images. Our approaches are motivated by the fact that thermal imagery is privacy-preserving in the sense that person identification is difficult or impossible. Results on the KAIST dataset show that our approaches perform comparably to state-of-the-art approaches and outperform the state-of-the-art on nighttime pedestrian detection, even outperforming multimodal techniques that use both thermal and visible spectrum imagery at test time.
Receptive Field Size Versus Model Depth for Single Image Super-Resolution

The performance of single image super-resolution (SISR) has been largely improved by innovative designs of deep architectures. An important claim raised by these designs is that the deep models have large receptive field size and strong nonlinearity. However, we are concerned about the question that which factor, receptive field size or model depth, is more critical for SISR. Towards revealing the answers, in this paper, we propose a strategy based on dilated convolution to investigate how the two factors affect the performance of SISR. Our findings from exhaustive investigations suggest that SISR is more sensitive to the changes of receptive field size than to the model depth variations, and that the model depth must be congruent with the receptive field size to produce improved performance. These findings inspire us to design a shallower architecture which can save computational and memory cost while preserving comparable effectiveness with respect to a much deeper architecture.
Masking quantum information on hyperdisks

Masking information is a protocol that encodes quantum information into a bipartite entangled state while the information is completely unknown to local systems. This paper explicitly studies the structure of the set of maskable states and its relation to hyperdisks. We prove that, although the qubit states which can be masked must locate on a single hyperdisk, the set of maskable states can consist of two or more hyperdisks for high-dimensional cases. Our results may shed light on several research fields of quantum information theory, such as the structure of entangled states and the local discrimination of bipartite states.
Performance of a Deep Neural Network Algorithm Based on a Small Medical Image Dataset: Incremental Impact of 3D-to-2D Reformation Combined with Novel Data Augmentation, Photometric Conversion, or Transfer Learning

Collecting and curating large medical-image datasets for deep neural network (DNN) algorithm development is typically difficult and resource-intensive. While transfer learning (TL) decreases reliance on large data collections, current TL implementations are tailored to two-dimensional (2D) datasets, limiting applicability to volumetric imaging (e.g., computed tomography). Targeting performance enhancement of a DNN algorithm based on a small image dataset, we assessed incremental impact of 3D-to-2D projection methods, one supporting novel data augmentation (DA); photometric grayscale-to-color conversion (GCC); and/or TL on training of an algorithm from a small coronary computed tomography angiography (CCTA) dataset (200 examinations, 50% with atherosclerosis and 50% atherosclerosis-free) producing 245 diseased and 1127 normal coronary arteries/branches. Volumetric CCTA data was converted to a 2D format creating both an Aggregate Projection View (APV) and a Mosaic Projection View (MPV), supporting DA per vessel; both grayscale and color-mapped versions of each view were also obtained. Training was performed both without and with TL, and algorithm performance of all permutations was compared using area under the receiver operating characteristics curve. Without TL, APV performance was 0.74 and 0.87 on grayscale and color images, respectively, compared to 0.90 and 0.87 for MPV. With TL, APV performance was 0.78 and 0.88 on grayscale and color images, respectively, compared with 0.93 and 0.91 for MPV. In conclusion, TL enhances performance of a DNN algorithm from a small volumetric dataset after proposed 3D-to-2D reformatting, but additive gain is achieved with application of either GCC to APV or the proposed novel MPV technique for DA.
Detection and Tracking of Human Motion Targets in Video Images Based on Camshift Algorithms

For the traditional moving target tracking algorithm, it is difficult to detect the target area accurately because of the influence of environmental factors. Therefore, manual selection of moving targets is often used, but this method is not intelligent enough, and has strong subjectivity and experience, so the data obtained is not convincing enough. For this reason, based on the multi-frame subtraction method, the connected region search is used to select the appropriate moving target and calibrate the target area, and then the moving target is determined and tracked. At the target tracking stage, the fusion feature vectors in the calibrated area are updated in real time to improve the accuracy and robustness of the follow-up tracking algorithm. Experiments show that the method is feasible. In the stage of target detection, the improved algorithm filters and calibrates the target area on the premise of increasing a small amount of computation. In the stage of target tracking, the computational area is reduced to the calibration and tracking range, which improves the efficiency of the algorithm and guarantees the stability and robustness of the algorithm.
Forecasting Damage Mechanics by Deep Learning

We in this paper exploit time series algorithm based deep learning in forecasting damage mechanics problems. The methodologies that are able to work accurately for less computational and resolving attempts are a significant d... | Find, read and cite all the research you need on Tech Science Press
DBGE: Employee Turnover Prediction Based on Dynamic Bipartite Graph Embedding

The issue of employee turnover is always critical for companies, and accurate predictions can help them prepare in time.Most past studies on employee turnover have focused on analyzing impact factors or using simple network centrality measures.In this paper, we study the problem from a completely new perspective by modeling users' historical job records as a dynamic bipartite graph.Specifically, we propose a bipartite graph embedding method with temporal information called dynamic bipartite graph embedding (DBGE) to learn the vector representation of employees and companies.Our approach not only considers the relations between employees and companies but also incorporates temporal information embedded in consecutive work records.We first define the Horary Random Walk on a bipartite graph to generate a sequence for each vertex in chronological order.Then, we employ the skip-gram model to obtain a temporal low-dimensional vector representation for each vertex and apply machine learning methods to predict employee turnover behavior by combining embedded features with employees' basic information.Experiments on a real-world dataset collected from one of China's largest online professional social networks show that the features learned through DBGE can significantly improve turnover prediction performance.Moreover, experiments on public Amazon and Taobao datasets show that our approach achieves better performance in the link prediction and visualization task than other graph embedding methods that do not consider temporal information.
Genuine tripartite Einstein-Podolsky-Rosen steering in the cascaded nonlinear processes of third-harmonic generation

Recently, Einstein-Podolski-Rosen (EPR) steering has important application in quantum information processing, and it has been received considerable attention because of its uniqueness. The properties of quantum steering among three output fields generated by cascaded nonlinear processes of quasi-phase-matching third-harmonic generation in an optical cavity are investigated. Based on the criteria for multipartite EPR steering which proposed by He and Reid [PRL, 111, 250403 (2013)], the genuine tripartite EPR steering among pump, second-harmonic, and third-harmonic is demonstrated. The parameters which affect the quantum property are also discussed.
Time-optimal control of a dissipative qubit

A formalism based on Pontryagin's maximum principle is applied to determine the time-optimal protocol that drives a general initial state to a target state by a Hamiltonian with limited control, i.e., there is a single control field with bounded amplitude. The coupling between the bath and the qubit is modeled by a Lindblad master equation. Dissipation typically drives the system to the maximally mixed state; consequently, there generally exists an optimal evolution time beyond which the decoherence prevents the system from getting closer to the target state. For some specific dissipation channel, however, the optimal control can keep the system from the maximum entropy state for infinitely long. The conditions under which this specific situation arises are discussed in detail. The numerical procedure to construct the time-optimal protocol is described. In particular, the formalism adopted here can efficiently evaluate the time-dependent singular control which turns out to be crucial in controlling either an isolated or a dissipative qubit.
Data Hiding Method Based on 3D Magic Cube

Medical records are extremely sensitive information and may cause a violation of patients' rights if the data is captured or altered.Such data requires uncompromising security during transmission or when the data is stored in medical datacenters.The most commonly used procedure is to hide sensitive information inside a cover medium without incurring any increase in data size or computational overhead.Even though many steganography techniques have been proposed in recent years, only a few have addressed issues related to protecting medical data.In this paper, we proposed a data hiding method based on magic cube generated using magic matrix.In the proposed method, 9 bits of secret data were embedded in each group of 4 pixels using secret keys that scattered values in the square template to make it more unpredictable.Experimental results showed that the proposed method achieved an embedding capacity of 2.25 bpp and an average PSNR of 44 dB outperforming previous methods.
Duality of Session Types: The Final Cut

Duality is a central concept in the theory of session types.Since a flaw was found in the original definition of duality for recursive types, several other definitions have been published.As their connection is not obvious, we compare the competing definitions, discuss tradeoffs, and prove some equivalences.Some of the results are mechanized in Agda.
An Advanced Approach to Detect Edges of Digital Images for Image Segmentation

Image segmentation has been an active topic of research for many years. Edges characterize boundaries, and therefore, detection of edges is a problem of fundamental importance in image processing. Edge detection in images significantly reduces the amount of data and filters out useless information while preserving the important structural properties in an image. Edges carry significant information about the image structure and shape, which is useful in various applications related with computer vision. In many applications, the edge detection is used as a pre-processing step. Edge detection is highly beneficial in automated cell counting, structural analysis of the image, automated object detection, shape analysis, optical character recognition, etc. Different filters are developed to find the gradients and detect edges. In this chapter, a new filter (kernel) is proposed, and the compass operator is applied on it to detect edges more efficiently. The results are compared with some of the previously proposed filters both qualitatively and quantitatively.
The Antecedents of Poor Doctor-Patient Relationship in Mobile Consultation: A Perspective from Computer-Mediated Communication

This study aims to understand the underlying reasons for poor doctor-patient relationships (DPR). While extant studies on antecedents of poor DPR mainly focus on the offline context and often adopt the patients’ perspective, this work focuses on the mobile context and take both doctors’ and mobile consultation users’ perspectives into consideration. To fulfill this purpose, we first construct a theoretical framework based on the Computer-Mediated Communication (CMC) literature. Then we coded 592 doctor-user communication records to validate and elaborate the proposed theoretical model. This work reveals that characteristics of mobile technologies pose potential challenges on both doctors’ and patients’ information providing, informative interpreting, and relationship maintaining behaviors, resulting in 10 and 6 types of inappropriate behaviors of doctors and users, respectively, that trigger poor DPR in the mobile context. The findings enrich the research on online DPR and provide insights for improving DPR in the mobile context.
Effect of an interactive e-book on nursing students' electrocardiogram-related learning achievement: A quasi-experimental design

• The EKG interactive e-book help promote students' learning motivation, self-learning and learning achievements. • An interactive e-book can help students learn complicated concepts more efficiently. • Most students prefer the use of the interactive e-book for EKG learning. • For content retention, paper- and e-book-based study do not significantly differ. • Technical problems regarding e-book use can produce users' frustration.