Reviewerly/exHarmony huggingface dataset:
Applicability of continuous-variable quantum key distribution over satellite links

We address the applicability of quantum key distribution (QKD) with continuous-variable (CV) coherent and squeezed signal states of light over long-distance satellite-based links, considering low Earth orbits in the advantageous downlink scenario and taking into account strong varying channel attenuation (referred to as fading), atmospheric turbulence and finite data ensemble size effects.The study is motivated by the possibility to use highly efficient homodyne detection for CV protocols, which can serve as filter of background radiation due to matching of the quantum signal to a narrowband intense local oscillator beam.Considering feasible Gaussian modulation, compatible with the most advanced security proofs, we obtain tight security bounds on the untrusted excess noise at the channel output, which suggest that the protocols are very sensitive to the channel noise at the respective loss levels.In this regime, either a set-up stabilization resulting in drastic decrease of noise or relaxation of security assumptions to individual or passive collective attacks is required for implementation over the low Earth orbit satellites.Relaxation of security assumptions can be particularly motivated by the possibility of line-of-sight control of the absence of equipment capable of active collective eavesdropping attacks using quantum memories.Furthermore, splitting the satellite pass into discrete segments and extracting the keys from each rather than from the overall single pass allows one to effectively improve robustness against the untrusted channel noise and establish a secure key even under active collective attacks.Such satellite pass segmentation provides another viable option to reduce channel fading thus improving the secure key rate and allowing to establish the secure link with satellites at higher altitudes.We show that the use of signal squeezing can make the protocols more applicable in the satellite links, allowing for higher attenuation and levels of noise at the given security assumptions, and it has to be optimized in the collective attacks scenario.On the other hand, in the case of trusted noise assumption and at high repetition rates, squeezed-state CV QKD can tolerate attenuation levels up to 42 dB, allowing for higher orbit altitudes or smaller receiving telescope apertures.The obtained results are promising for satellite-based QKD, potentially applicable in daylight conditions.
Gaussian hierarchical latent Dirichlet allocation: Bringing polysemy back

Topic models are widely used to discover the latent representation of a set of documents. The two canonical models are latent Dirichlet allocation, and Gaussian latent Dirichlet allocation, where the former uses multinomial distributions over words, and the latter uses multivariate Gaussian distributions over pre-trained word embedding vectors as the latent topic representations, respectively. Compared with latent Dirichlet allocation, Gaussian latent Dirichlet allocation is limited in the sense that it does not capture the polysemy of a word such as "bank." In this paper, we show that Gaussian latent Dirichlet allocation could recover the ability to capture polysemy by introducing a hierarchical structure in the set of topics that the model can use to represent a given document. Our Gaussian hierarchical latent Dirichlet allocation significantly improves polysemy detection compared with Gaussian-based models and provides more parsimonious topic representations compared with hierarchical latent Dirichlet allocation. Our extensive quantitative experiments show that our model also achieves better topic coherence and held-out document predictive accuracy over a wide range of corpus and word embedding vectors which significantly improves the capture of polysemy compared with GLDA and CGTM. Our model learns the underlying topic distribution and hierarchical structure among topics simultaneously, which can be further used to understand the correlation among topics. Moreover, the added flexibility of our model does not necessarily increase the time complexity compared with GLDA and CGTM, which makes our model a good competitor to GLDA.
Interferometric imaging as an alternative to telescopes

A concept of optical interferometric imager was proposed by Lockheed-Martin in the early 2010s.In this concept, the aperture is paved by lenses.The optical signals collected by these lenses are combined in photonic integrated circuits, allowing the simultaneous measurement of the Fourier components of the observed object at a number of spatial frequencies.This concept allows one to consider a very significant reduction of the size of optical telescopes, e.g., for Earth observation.Indeed, the transverse dimension of a device based on this concept remains close to that of a telescope of the same resolution since the transverse size determines the spatial resolution.On the other hand, its size along the optical axis is much smaller than that of a conventional telescope.After a quick description of this concept, its intrinsic performance will be analyzed.The field of this device, its spatial resolution, the spectral constraints imposed by the interferometric measurement will be presented.Based on these preliminary considerations, the noise on the measurement will be evaluated.The measurement noise will be compared to that obtained with a focal plane imaging instrument.The geometry of combination of the signals collected by the lenses, or aperture configuration, is a key issue to minimize the size of the device and optimize the quality of the reconstructed image at a given spatial resolution.The literature proposes solutions leading to a partial frequency coverage.In this communication, solutions will be presented leading to a frequency coverage that is more suitable for applications such as Earth observation.
Distributed fusion robust filter for multisensor networked singular control system with uncertain variances and missing measurement

Abstract A distributed fusion time‐varying robust filtering algorithm for multisensor networked singular control system is studied in this paper, mainly aiming at the system with missing measurement and uncertain variances in practical application. First, the singular value decomposition (SVD) method and de‐randomization method are used to transform the singular control system into a standard system only with uncertain variances. Based on the new standard system, a minimax robust local Kalman filter is proposed by using the minimax robust estimation principle, which ensures that the actual filtering error variance has the corresponding minimum upper bound for all allowable uncertainties. Further, the robust local and distributed fusion filters for original singular control systems are proposed, and the actual and conservative filtering error variances are obtained. Their robustness is proved by Lyapunov equation method and nonnegative definite matrix decomposition method. A simulation example of two‐loop system verifies the effectiveness of the proposed results.
DEV-PIM: Dynamic Execution Validation with Processing-in-Memory

Instruction injections or soft errors during execution on the CPU can cause serious system vulnerabilities. During the standard program flow of the processor, the injection of unauthorized instruction or the occurrence of an error in the expected instruction are the main conditions for potentially serious such vulnerabilities. With the execution of these unauthorized instructions, adversaries could exploit SoC and execute their own malicious program or get higher-level privileges on the system. On the other hand, non-intentional errors can potentially corrupt programs causing unintended executions or the cause of program crashes. Modern trusted architectures propose solutions for unauthorized execution on SoC with additional software mechanisms or extra hardware logic on the same untrusted SoC. Nevertheless, these SoCs can still be vulnerable, as long as deployed security detection mechanisms are embedded within the same SoC’s fabric. Furthermore, validation mechanisms on the SoC increase the complexity and power consumption of the SoC. This paper presents DEV-PIM, a new, high-performance, and low-cost execution validation mechanism in SoCs with external DRAM memory. The proposed approach uses processing-in-memory (PIM) method to detect instruction injections or corrupted instructions by utilising basic computing resources on a standard DRAM device. DEV-PIM transfers instructions scheduled for execution on the CPU to the DRAM and validates them by comparing content with the trusted program record on the DRAM using PIM operations. By optimising the DRAM scheduling process validation tasks are only executed when memory access is idle. The CPU retains uninterrupted memory access and can continue its normal program flow without penalty. We evaluate DEV-PIM in an end-to-end DRAM-compatible environment and run a set of software benchmarks. On average, the proposed architecture is able to detect 98.46% of instruction injections for different validation. We also measured on average only 0.346% CPU execution overhead with DEV-PIM enabled.
Error Resilient Transformers: A Novel Soft Error Vulnerability Guided Approach to Error Checking and Suppression

Transformer networks have achieved remarkable success in Natural Language Processing (NLP) and Computer Vision applications. However, the underlying large volumes of Transformer computations demand high reliability and resilience to soft errors in processor hardware. The objective of this research is to develop efficient techniques for design of error resilient Transformer architectures. To enable this, we first perform a soft error vulnerability analysis of every fully connected layers in Transformer computations. Based on this study, error detection and suppression modules are selectively introduced into datapaths to restore Transformer performance under anticipated error rate conditions. Memory access errors and neuron output errors are detected using checksums of linear Transformer computations. Correction consists of determining output neurons with out-of-range values and suppressing the same to zero. For a Transformer with nominal BLEU score of 52.7, such vulnerability guided selective error suppression can recover language translation performance from a BLEU score of 0 to 50.774 with as much as 0.001 probability of activation error, incurring negligible memory and computation overheads.
Correction Unwrapping Algorithm Based on Phase Estimation for Undersampled Homodyne Interferometer

In this paper, a correction undersampling unwrapping algorithm based on phase estimation is proposed. On the basis of direct unwrapping algorithm and the compensation period, the algorithm compensates the phase jump caused by undersampling. The calculation of compensation period is based on motion continuity and phase estimation. Meanwhile, integer ambiguity correction module avoids the accumulated phase error. The proposed algorithm is verified by simulations, which shows that the algorithm has high feasibility and accuracy, and can be applied to realize the displacement measurement of the undersampled homodyne interferometer.
RHSOA: Receding Horizon Optimization-Based Swift Obstacle Avoidance Approach for UAVs in Unknown Environments

This paper presents a novel approach, called RHSOA, for real-time obstacle avoidance control of nonlinear systems in dynamic environments, considering safety and speed for UAVs. The approach relies on the integration of nonlinear receding horizon optimization (RHO), joint control Lyapunov functions (CLFs), and control barrier functions (CBFs). The proposed method is aimed at achieving close-to-optimal obstacle avoidance control for the demand of real-time obstacle avoidance in unknown dynamic environments of UAVs. The method is divided into two main parts: firstly, the classification of obstacles is pre-processed, and a safety-enveloping ellipse is proposed to associate obstacles, which reduces the computational workload. Secondly, the RHO is improved under the limited time, taking into account both rapidity and safety priority. The effectiveness of the proposed method is demonstrated by comparing simulation results and performance metrics. The results show that the proposed method achieves superior performance and outperforms some existing approaches.
Research on Path Planning of Unmanned Vehicle Based on Improved Artificial Potential Field Method

An improved method is presented to solve the problems of local optimum and poor real-time performance of conventional artificial potential field method on route planning of unmanned surface vehicles. This method introduces a new repulsive potential field function and adds a regulatory factor. In order to limit the active area, the boundary repulsion field is introduced. By adjusting the boundary repulsion factor and the adjustment factor, the algorithm can be better prevented from falling into local optimum and the optimization efficiency can be improved. This method is simulated in Matlab and compared with traditional algorithms. The test results show that compared to traditional algorithms, using this method for path planning can shorten the range by 3.1%, have a relatively small curvature, reduce the turning point by 33.3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , and have a smoother path, which has broad application prospects.
An Improved Sand Cat Swarm Optimization and Its Application in Aluminum Melting Process

To optimize the furnace temperature prediction model for an aluminum melting process, an improved sand cat swarm optimization (PSCSO) is proposed. Firstly, a chaotic convergence factor is introduced in the process of each iteration, which changes the strategy selection mechanism of the algorithm and improves its speed and accuracy. Secondly, a population redistribution strategy is designed at the completion of each iteration to increase the algorithm's space utilization of the solution space. Finally, an optimal value jumping strategy is proposed to avoid the algorithm from falling into local optimum at the later stage of iteration. The proposed method is validated on some common benchmark functions and an example of its application used in aluminum smelting. The results show that PSCSO has better performance compared with other optimization algorithms.
An Improved KNNG Driven Graph Transfer Diagnosis Method Via Edge Predictor

By establishing relationships between cross-domain samples, graph data-driven transfer diagnosis methods have achieved a great success. However, there are still some limitations: 1) Cross-domain relationships cannot be correctly established due to data distribution discrepancy, thereby degenerating graph quality; 2) Calculation of distance matrix consumes much computational cost. To overcome above limitations, an improved KNNG driven graph transfer diagnosis method via edge predictor is proposed. Firstly, few source-domain samples are used to construct initial K-nearest neighbor graph (IKNNG) for edge predictor training. Then, a pre-trained edge predictor, learning the graph construction strategy and restoring the graph structure of the input graph, directly outputs distance matrix without calculation of distance matrix. All the source-domain and target-domain samples are input to the pre-trained edge predictor, obtaining corresponding distance matrix. Based on the generated distance matrix, source-domain and target-domain samples are converted into source-domain and target-domain graphs, respectively. Both two kinds of graphs are used to train the Chebyshev graph convolutional network, achieving transfer diagnosis tasks. Comparative experimental results show that the proposed method achieves a competitive result.
Weak Maneuvering Target Detection Using Cost-Reference Particle Filter Bank

In this paper, a novel cost-reference particle filter bank (CRPFB) based method is proposed for detecting a low signal to noise ratio (SNR) nonlinear frequency modulated signal. The proposed method has two stages. First, the designed CRPFB is used for state estimation of a possible target. Second, a test metric (i.e., generalized likelihood ratio) based on the state estimation of the first stage is compared with an analyzable threshold for determining the presence of a target. In first stage, the CRPFB consists of many parallel cost-reference particle filters (CRPFs). The output of the CRPF with the minimum cumulated cost is considered as the estimation results of the CRPFB. In second stage, the threshold can be analytically obtained from several noisy-only measurements by using generalized extreme value distribution. Simulation results illustrate that, compared with the state-of-the-art methods, the proposed method has low computational complexity and its detection threshold can be analytically estimated.
An Algorithm on Monocular 3D Object Detection Based on Depth Estimation

The traditional 2D convolution approaches are inappropriate for capturing information about local objects and their scale changes. To better depict 3D structures, the fundamental concept behind it is to mix depth information with RGB data to understand 3D space via depth estimations for creating depth maps of 2D images. However, it places a great deal of reliance on the precision of depth estimate, which results in subpar performance. In this paper, a monocular 3D object detection algorithm is proposed to resolve the issues mentioned earlier. The algorithm depends on the estimation of depth, which is essential for accurately detecting objects using a single camera. To begin with, a Normalization-based Attention Module has been deployed to a backbone network with two branches to extract RGB and depth maps separately. To merge the RGB and depth map features, a depth-guided dynamic local convolutional module is subsequently implemented. Depth map learns exemplar kernels from RGB images using a weight learning submodule and dilation convolution. Then it acts locally on the corresponding RGB images to solve the scale-sensitive problem of objects. By utilizing this approach, it becomes possible to extract feature representations from 2D images and map them onto 3D space, minimizing the disparity between the 2D and 3D representations. Experiments indicate that under the moderate setting of the class car - which is the most significant level of the KITTI dataset - our method outperforms other cutting-edge algorithms.
Particle Filter Based on Improved Gravitation Field Algorithm for 3D Target Tracking

State estimation is the key stage of target tracking. Particle Filter (PF) as a method to solve the state estimation problem has been applied to target tracking. However, PF has some problems such as particle degradation and algorithm redundancy. To solve these problems, an Improved Gravitation Field Algorithm-based Particle Filter (IGFA-PF) is proposed in this paper. IGFA-PF introduces the gravitation field algorithm after the sampling step of PF to guide the particles to move to the high likelihood region, so as to adjust the particle distribution, with the purpose of reducing particle degradation. Meanwhile, divide-and-conquer sampling method is used to divide the state space and independently sample particles in each subspace, so as to reduce algorithm redundancy and improve real-time performance. Finally, IGFA-PF is used to track a 3D moving target. The results of the simulation show that IGFA-PF can estimate the target state more accurately and has better tracking performance.
Short-Term Photovoltaic Power Prediction Based on ICHOA-GRU

Photovoltaic power generation is affected by many factors. In order to improve the prediction accuracy of short-term photovoltaic power, a prediction model based on Pearson coefficient method, improved Chimpanzee algorithm (ICHOA) and gate selected cycle unit (GRU) was proposed. Firstly, Pearson correlation coefficient was used to obtain the main factors affecting photovoltaic power generation, which reduced the computational complexity of the model. Then, the chimpanzee algorithm combined with elite reverse learning and adaptive mutation is used to optimize the relevant hyperparameters of GRU network, and the photovoltaic power generation is predicted under the condition of obtaining the optimal hyperparameters. The simulation results of a power station in Australia show that the MAE of the proposed model is reduced by 26.83%, under different weather conditions, which verifies the effectiveness of the proposed method
Traffic Sign Detection Based on Improved Yolov7

Traffic sign detection in the natural environment with small targets, weak light and other influencing factors often make the neural network has low accuracy in the detection process. In view of the above problems, this paper proposes an improved YOLOv7-WCN network based on YOLOv7. Firstly, the improvement of the YOLOv7 network is added to the backbone network to enrich the image information In terms of improvements to the YOLO v7 network, CHB module is added to the backbone network in order to enrich the image information. Then, the normalization-based attention module (NAM) is introduced to make the network pay more attention to the target region for the purpose of enhancement. Moreover, the CIOU loss function of YOLOv7 is replaced with the Wasserstein Distance Loss to optimize the algorithm training process. Experimental results show that the detection accuracy of YOLOv7-WCN improves from 85.5% to 89.0% compared with the original algorithm, which is 3.5 percentage points better.
Ground Penetrating Radar Image De-Noising Method Based on Multi-Noise and Self-Supervised Learning

The quality of ground penetrating radar (GPR) images is usually low due to unwanted noise interference such as antenna coupling, soil reflection, and industrial frequency interference, which often affects subsequent studies. Deep learning methods in the field of ground penetrating radar image denoising mainly include non-blind denoising studies based on a single additive Gaussian white noise, and blind denoising studies based on masks. Due to the presence of multiple composite noises in GPR images, methods based on such a single noise perform poorly in practical denoising work; and blind denoising deep learning methods based on image masking lose feature details of anomalous regions. To solve the above problems, this paper proposes a multi-noise self-supervised denoising model MNSSDN for GPR image denoising, including a noise generator, a denoising generation network and a label pre-denoising module; meanwhile a corresponding loss function is designed to train the whole network. Firstly, the GPR image is input to the noise generator to obtain the image with random horizontal noise, and then passed into the denoising generation network for denoising. Next, integrate them into one fusion image. Finally, the output of the label pre-denoising module is used as the label for backpropagation to constrain the evolution direction of the network.
The Secret Life of CVEs

The Common Vulnerabilities and Exposures (CVEs) system is a reference method for documenting publicly known information security weaknesses and exposures. This paper presents a study of the lifetime of CVEs in software projects and the risk factors affecting their existence. The study uses survival analysis to examine how features of programming languages, projects, and CVEs themselves impact the lifetime of CVEs. We suggest avenues for future research to investigate the effect of various factors on the resolution of vulnerabilities.