Reviewerly/exHarmony huggingface dataset:
Based on GPS/SINS Combined Navigation data Fusion Algorithm Based on ISSUKF

Aiming at the robustness of the data fusion algorithm of GPS/SINS combined navigation on Unm anned Aerial Vehicle(UAV) and the requirements of real-time solution, the initial sampling value is selecte d based on the unscented Kalman filter to solve the se nsitive problem and 2n+ 1 Sigma sampling points are required for the solution, which will make the solutio n efficiency low. The Iterative Spherical Simplex Unt racked Kalman Filter (ISSUKF) is proposed, and the supersphere distribution sampling strategy is adopte d, and the sampling point Sigma is reduced to n+2 by changing the Sigma point sampling strategy, and the state estimation obtained by the measurement equati on is used to iteratively update to reduce the influenc e of the initial state value on the system, thereby redu cing the calculation amount and improving the stabili ty and accuracy of the algorithm. The simulation resu Its show that the ISSUKF algorithm has the character istics of high accuracy, high computational efficiency and good robustness.
Moving object Detection Based on Farneback Optical Flow

Moving object detection in an environment with changing illumination has been an interesting research topic recently. The optical flow method has been widely used for moving object detection as an effective method. However, the conventional optical flow method is constrained by illumination conditions. It cannot eliminate the negative impacts of illumination changes. In order to solve the negative impacts of illumination changes to the optical flow method, this paper proposes a moving object detection algorithm using the optical flow method combined with feature matching. The algorithm uses the farneback optical flow method to obtain the optical flow vector with noises. It uses the Canny operator to extract the moving object and the noisy contours from the optical flow. Then, feature extraction works to obtain multidimensional features. By weighted matching calculations of multidimensional features in the two optical flows, the extraction of the moving object from the noisy optical stream is completed. Experiment examples verify that the proposed method can detect moving objects accurately in the environment with changing light.
Power-law spectrum-based objective function to train a generative adversarial network with transfer learning for the synthetic breast CT image

Objective.This paper proposes a new objective function to improve the quality of synthesized breast CT images generated by the GAN and compares the GAN performances on transfer learning datasets from different image domains.Approach.The proposed objective function, named beta loss function, is based on the fact that x-ray-based breast images follow the power-law spectrum. Accordingly, the exponent of the power-law spectrum (beta value) for breast CT images is approximately two. The beta loss function is defined in terms of L1 distance between the beta value of synthetic images and validation samples. To compare the GAN performances for transfer learning datasets from different image domains, ImageNet and anatomical noise images are used in the transfer learning dataset. We employ styleGAN2 as the backbone network and add the proposed beta loss function. The patient-derived breast CT dataset is used as the training and validation dataset; 7355 and 212 images are used for network training and validation, respectively. We use the beta value evaluation and Fréchet inception distance (FID) score for quantitative evaluation.Main results.For qualitative assessment, we attempt to replicate the images from the validation dataset using the trained GAN. Our results show that the proposed beta loss function achieves a more similar beta value to real images and a lower FID score. Moreover, we observe that the GAN pretrained with anatomical noise images achieves better equality than ImageNet for beta value evaluation and FID score. Finally, the beta loss function with anatomical noise as the transfer learning dataset achieves the lowest FID score.Significance.Overall, the GAN using the proposed beta loss function with anatomical noise images as the transfer learning dataset provides the lowest FID score among all tested cases. Hence, this work has implications for developing GAN-based breast image synthesis methods for medical imaging applications.
Quantum walk-based protocol for secure communication between any two directly connected nodes on a network

Abstract The utilization of quantum entanglement as a cryptographic resource has superseded conventional approaches to secure communication. Security and fidelity of intranetwork communication between quantum devices is the backbone of a quantum network. This work presents an protocol that generates entanglement between any two directly connected nodes of a quantum network to be used as a resource to enable quantum communication across that pair in the network. The protocol is based on a directed discrete-time quantum walk and paves the way for private inter-node quantum communication channels in the network. We also present the simulation results of this protocol on random networks generated from various models. We show that after implementation, the probability of the walker being at all nodes other than the source and target is negligible and this holds independent of the random graph generation model. This constitutes a viable method for the practical realisation of secure communication over any random network topology.
Multi-agent blind quantum computation without universal cluster states

Abstract Blind quantum computation (BQC) protocols enable quantum algorithms to be executed on third-party quantum agents while keeping the data and algorithm confidential. The previous proposals for measurement-based BQC require preparing a highly entangled cluster state. In this paper, we show that such a requirement is not necessary. Our protocol only requires pre-shared Bell pairs between delegated quantum agents, and there is no requirement for any classical or quantum information exchange between agents during the execution. Our proposal requires fewer quantum resources than previous proposals by eliminating the need for a universal cluster state.
Efficient parallelization of quantum basis state shift

Abstract Basis state shift is central to many quantum algorithms, most notably the quantum walk. Efficient implementations are of major importance for achieving a quantum speedup for computational applications. We optimize the state shift algorithm by incorporating the shift in different directions in parallel. This provides a significant reduction in the depth of the quantum circuit in comparison to the currently known methods, giving a linear scaling in the number of gates versus working qubits in contrast to the quadratic scaling of the state-of-the-art method based on the quantum Fourier transform. For a one-dimensional array of size 2 n for n &gt; 4, we derive the total number of <?CDATA $15n + 74$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>15</mml:mn> <mml:mi>n</mml:mi> <mml:mo>+</mml:mo> <mml:mn>74</mml:mn> </mml:math> two-qubit CX gates in the parallel circuit, using a total of <?CDATA $2n-2$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>2</mml:mn> <mml:mi>n</mml:mi> <mml:mo>−</mml:mo> <mml:mn>2</mml:mn> </mml:math> qubits including an ancilla register for the decomposition of multi-controlled gates. We focus on the one-dimensional and periodic shift, but note that the method can be extended to more complex cases.
Post-mortem digital forensic analysis of the Garmin Connect application for Android

The Garmin Vivosmart 4 smartband can monitor various health metrics, including heart rate, oxygen saturation, body composition, and stress levels. It is a quite popular fitness tracking device, as its Android companion application – Garmin Connect – has been downloaded more than 10 million times and can provide critical forensic artifacts such as timestamped GPS-based locations. In this work, we analyze the Garmin Connect application to identify i) relevant digital forensic artifacts, and ii) assess methods to retrieve cloud-based data relevant to a digital forensic examination. For this purpose, we first establish a test scenario where the paired device/application collects data in regular real-world situations using a rooted smartphone running Android 11. The smartphone is then examined to gain insights into the data stored by the application and identify meaningful digital artifacts. To ease and automate the task of digital forensic practitioners, we have developed the Garmin Connect for Android Analyzer (GC4AA) set of Python 3 modules tailored for the digital forensic framework Android Logs Events And Protobuf Parser (ALEAPP). These open-source modules parse dumps of a Vivosmart 4 data directory and create reports displaying several digital artifacts, such as health metrics, GPS data and routes, and phone notifications. They automate the information-gathering process and produce a report specially tailored for Garmin Connect data, highlighting the most relevant artifacts. Our results show that the analysis of paired Garmin Collect/Vivosmart 4 with GC4AA can yield more digital forensic artifacts than existing open-source tools, including the following new artifacts: i) Daily Summary data; ii) GPS data; iii) Response Cache data; iv) Network Logs; v) Facebook API tokens; vi) Device Synchronization cache; vii) SpO2 reading charts. Our contributions include a graphical presentation of the collected data, greatly improving its readability and analysis.
Hardening RGB-D object recognition systems against adversarial patch attacks

RGB-D object recognition systems improve their predictive performances by fusing color and depth information, outperforming neural network architectures that rely solely on colors. While RGB-D systems are expected to be more robust to adversarial examples than RGB-only systems, they have also been proven to be highly vulnerable. Their robustness is similar even when the adversarial examples are generated by altering only the original images' colors. Different works highlighted the vulnerability of RGB-D systems; however, there is a lacking of technical explanations for this weakness. Hence, in our work, we bridge this gap by investigating the learned deep representation of RGB-D systems, discovering that color features make the function learned by the network more complex and, thus, more sensitive to small perturbations. To mitigate this problem, we propose a defense based on a detection mechanism that makes RGB-D systems more robust against adversarial examples. We empirically show that this defense improves the performances of RGB-D systems against adversarial examples even when they are computed ad-hoc to circumvent this detection mechanism, and that is also more effective than adversarial training.
A Robust Kalman Filter via Gamma Student’s t-Mixture Distribution Under Heavy-Tailed Measurement Noise

In order to increase the accuracy of the state estimation with nonstationary heavy-tailed measurement noises by correcting the mean vector and covariance matrix of Student’s t distribution, the gamma Student’s t (GaST) distribution is proposed. In the proposed GaST, a shape parameter modeled as Gaussian distribution and an auxiliary variable modeled as gamma distribution are introduced into the Student’s t distribution, and the mean vector and covariance matrix of Student’s t distribution can be estimated by learning the shape parameter and the auxiliary variable. Based on the GaST distribution, which can be written as a hierarchical Gaussian structure and the variational Bayesian approach, a GaST distribution-based Kalman filter (KF) is proposed. The simulation results show that the proposed KF possesses better accuracy compared to the existing best KFs for a state-space model with nonstationary heavy-tailed measurement noise. The effectiveness of the proposed KF is also demonstrated in the field experiments for mobile robot localization.
Dense False Target Jamming Recognition Based on Fast–Slow Time Domain Joint Frequency Response Features

Dense false target jamming (DFTJ) is one of the most common and threatening jamming modes, seriously affecting a radar from detecting a target. This article counters DFTJ by proposing a fast–slow time domain joint frequency response feature-based jamming recognition method that utilizes the differences in the frequency response characteristics of the radar and digital radio frequency memory (DRFM) jammer. The core idea of this method is to model the influence of the radar's and DRFM jammer's frequency response characteristics on the frequency response features of the target echo and jamming signal. In addition, the developed strategy extends the influence from the fast-time domain to the fast–slow time domain through pulse compression and coherent accumulation processing. Then, the fast–slow time domain joint amplitude–frequency response features of both signals are combined to construct a training dataset. Finally, a dual-channel feature fusion network (1DCNN-LSTM) comprising a 1-D convolutional neural network (1DCNN) and a long short-term memory network (LSTM) is constructed for jamming recognition. The effectiveness of the proposed method is proven through simulated and measured experiments. The results show that the proposed method can achieve a recognition accuracy of 98.9$\%$ and 96$\%$ on the two measured data experiments, respectively.
Airborne SAR to Optical Image Registration Based on SAR Georeferencing and Deep Learning Approach

Synthetic aperture radar (SAR) to optical image registration is a crucial pre-processing step in remote sensing applications. As a multisource image registration problem, it has several challenges due to radiometric and geometric differences and the presence of speckle noise in SAR images. This article presents a coarse to fine registration approach based on georeferencing and a deep learning framework to deal with these problems. The purpose of the method is to combine georeferencing information and a deep learning registration approach to reduce the outliers and increase the ratio of correct correspondences (ROCC). In the georeferencing approach, using the geometry of the SAR payload, latitude and longitude are assigned to each pixel of the SAR image. It is, therefore, possible to make an initial match between the SAR and a georeferenced optical image. It should be noted that due to the inherent errors of georeferencing, the image-based approach as a fine registration step is inevitable. In the training phase, the SAR-SIFT and scale-invariant feature transform (SIFT) algorithms are applied to pairs of registered SAR and optical images, respectively. If the detected keypoints in the two images are spatially matched, the descriptors are applied to a deep neural network (DNN). The network is trained to create a binary output for the corresponding and noncorresponding descriptors. In the validation stage, using the trained network and the georeferencing, the number of incorrect correspondences can be effectively reduced. The experimental results on several pairs of SAR and optical modalities indicate the effectiveness of the proposed algorithm in terms of registration accuracy and robustness.
Feature Extraction Methods for Binary Code Similarity Detection Using Neural Machine Translation Models

Binary code similarity detection is an effective analysis technique for vulnerability, bug, and plagiarism detection in software for which the source code cannot be obtained. The recent proliferation of IoT devices has also increased the demand for similarity detection across different architectures. However, there are currently not many examples of feature extraction methods using neural machine translation models being applied to similarity detection in basic block units across different architectures. In this research, we propose new methods that extract features at a higher speed and detect similarities across different architectures with higher accuracy than existing methods for basic block feature extraction using neural machine translation models. We assume that the intermediate representation of the NMT model, which learned the translation of basic blocks across different architectures, includes the semantics of the instructions in the basic block. Hence we adopted the intermediate representation as the features of the basic blocks. Then, we applied the linear transformation used in bilingual word embedding to match the embedding space of basic blocks across different architectures. This enables the similarity detection in basic block units across different architectures with higher accuracy than the distance learning method used in existing research to match the embedding space. In the evaluation experiment, we compare the Precision at k (P@k) on the same dataset with existing research methods and our method achieved the highest accuracy of 92%.
EGDSR: Encoder-Generator-Decoder Network for Remote Sensing Super-Resolution Reconstruction

Remote sensing single-image super-resolution reconstruction process detail information is easy to be lost and prone to defocus phenomenon, in order to solve these problems, we are based on the idea of potential spatial vector mapping, focusing on the attribute control of the feature recovery process, to improve the quality of remote sensing image reconstruction. This letter proposes an encoder-generator-decoder super-resolution reconstruction network for remote sensing named EGDSR. We design three modules: multiscale feature extraction and latent code generation module, multi-attribute control of resolution progression module (recovery of latent encoding, fusion of multi-scale features, and generation of high-resolution features), high-resolution image reconstruction module. The experimental results show that the high-resolution images generated by our proposed EGDSR network have a stronger sense of truth, richer texture, and more realistic details, and have a better perceptual effect compared with other state-of-the-art super-resolution networks. In addition, we also combine the semantic segmentation task to assist in verifying the quality of the high-resolution remote sensing images generated by EGDSR, and successfully verify the higher application value of our proposed method.
<i>CET2:</i> Modelling Topic Transitions for Coherent and Engaging Knowledge-Grounded Conversations

Knowledge-grounded dialogue systems aim to generate coherent and engaging responses based on the dialogue contexts and selected external knowledge. Previous knowledge selection methods tend to rely too heavily on the dialogue contexts or over-emphasize the new information in the selected knowledge, resulting in the selection of repetitious or incongruous knowledge and further generating repetitive or incoherent responses, as the generation of the response depends on the chosen knowledge. To address these shortcomings, we introduce a Coherent and Engaging Topic Transition (CET2) framework to model topic transitions for selecting knowledge that is coherent to the context of the conversations while providing adequate knowledge diversity for topic development. Our CET2 framework considers multiple factors for knowledge selection, including valid transition logic from dialogue contexts to the following topics and systematic comparisons between available knowledge candidates. Extensive experiments on two public benchmarks demonstrate the superiority and the better generalization ability of CET2 on knowledge selection. This is due to our well-designed transition features and comparative knowledge selection strategy, which are more transferable to conversations about unseen topics. Analysis of fine-grained knowledge selection accuracy also shows that CET2 can better balance topic entailment (contextual coherence) and development (knowledge diversity) in dialogue than existing approaches.
Discrete Wavelet Transform Meets Transformer: Unleashing the Full Potential of the Transformer for Visual Recognition

Traditionally, the success of the Transformer has been attributed to its token mixer, particularly the self-attention mechanism. However, recent studies suggest that replacing such attention-based token mixer with alternative techniques can yield comparable results in various vision tasks. This highlights the importance of the model’s overall architectural structure in achieving optimal performance, rather than relying exclusively on the specific choice of the token mixer. Building on this insight, we introduce Discrete Wavelet TransFormer, an innovative framework that incorporates Discrete Wavelet Transform to elevate all the building blocks of the Transformer to a higher standard. By exploiting distinct attributes of Discrete Wavelet Transform, Discrete Wavelet TransFormer not only strengthens the network’s ability to learn more intricate feature representations across different levels of abstraction, but also facilitates lossless down-sampling to promote a more resilient and efficient network. A comprehensive evaluation is conducted on diverse vision tasks, and the results conclusively demonstrate that Discrete Wavelet TransFormer outperforms all other state-of-the-art Transformer-based models across all tasks by a significant margin.
LSTM-DGMDH: High-Dimensional Index Tracking Based on LSTM and Adaptive Deep Evolutionary GMDH Neural Network

Stock index is an indicator that describes the changes in the total price level of the stock market, and it is susceptible to many dynamic factors, with such characteristics as high dimension, uncertainty, non-linearity, time delay, complexity, etc., resulting in abnormal and missing values in stock index data, which will lead to instability or unreliability of the stock index tracking model. In order to solve these problems, we take the historical stock index as the input, model the internal dynamic changes of features, and learn the change rule. Firstly, we introduce an attention mechanism, that is, to assign different weights to the implicit state of the long short term memory network (LSTM) through mapping weights and learning parameters. We further propose a stock index data preprocessing model of the LSTM based on the attention mechanism. Secondly, the group method of data handling type neural networks (GMDH-NN) is a self-organizing data mining technology, which is especially suitable for modeling complex systems. So we choose a discrete form of Kolmogorov-Gabor ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K - G</i> ) polynomial of the first-order as the reference function of GMDH-NN to establish the general relationship between input and output variables. We further present a deep evolutionary GMDH polynomial neural network (DGMDH) to perform stock index tracking. Moreover, for a high-dimensional stock index dataset, the traditional external criterion can no longer meet the needs of reality, so we propose a tracking error external criterion (TEEC) for stock indices, which is based on the difference between allocation yield and target yield. The TEEC provides better information for selecting the optimal complex DGMDH model. Our experiments clearly show the effectiveness of our methodology.