Reviewerly/exHarmony huggingface dataset:
A Bit-level Selective Image Encryption Algorithm based on Chaos

This paper presents a bit-level image encryption algorithm, which can protect the important bit-planes with respect to the content of plain-image. The plain-image is initially transform to a binary image and decomposed into 8 bitplanes. Then, the similarities among the binary image and each bitplane are measured, and we get an important index array of the 8 bitplanes. Next, the top k important bitplanes are encrypted. Finally, all the bitplanes are combined into the cipher-image. The experimental results of sufficient security analyses demonstrate that proposed algorithm can offer efficient image protection.
Hybrid Inverse Mapping Algorithm Based on Residual Iteration

The inversion of Generative Adversarial Networks (GANs) refers to mapping a target image to the latent space vector of a pre-trained GAN model, and then inputting the vector into the pre-trained generator to achieve image reconstruction. GAN inversion is the foundation for achieving semantic manipulation of real images. In this work, we combine learning and optimization methods to propose a new image inversion approach. Image features are encoded using the Feature Pyramid Network (FPN) encoder, and the encoding is continuously optimized through residual iteration. Extensive experimental results demonstrate that our approach surpasses current mainstream methods in terms of inversion accuracy and reconstruction precision, while also exhibiting excellent editability.
Multiple-Person Tracking with Criss-Cross Attention and Deep Matching

Multi-pedestrian tracking is one of the important application fields of computer vision, among which the real-time tracking of pedestrians and the ability to solve occlusion problems are two big challenges to improve the recognition accuracy. In this paper, we propose a new standardized tracking-by-detection framework that can effectively alleviate the above two problems. We first introduce the Criss-Cross Attention (CCA) mechanism in the detector. The CCA module can reduce the computational complexity of the detector and improve the detection accuracy. Then a pedestrian deep feature extractor fused with cosine metric learning for two-layer convolution is added to further improve the tracking accuracy and real-time performance. Finally, Kalman Filter prediction combined with pedestrian deep features effectively reduces identity switching under the occlusion. We conduct experiments on the MOT16 datasets and evaluate on HOTA. Experimental results demonstrate the competitiveness of our method against some selected state-of-the-art methods.
A Robust Multi-source Image Matching Method for Power Equipment Based on Improved PIIFD

Robust feature matching for multi-source images of power equipment is critical for automatic diagnosis of power grid. However, classical image matching methods may not be suitable for this challenging task due to different resolution, spectrum and viewpoint between multi-source images. To solve this problem, a robust multi-source image matching algorithm is presented. First, the maximum moment map of phase congruency of input image is computed to enhance the raw image. Then, Canny operator and contour tracking method are employed to detect contour map of enhanced image. A set of feature points on contours is extracted by the curvature scale space corner detector. Second, a robust method is presented to assign main orientation to each feature point based on the local contour information, and a scale-invariant PIIFD descriptor is computed for feature point description. Finally, the bidirectional matching procedure is implemented to achieve feature correspondence and matching results are refined by both geometric and photometric information. We conduct the experiments on pairs of visible and infrared images of power equipment, and the results can demonstrate the effectiveness of our methods.
Sparse Object Detector With Faster Convergence and Simpler Structure

Mainstream object detection methods are commonly categorized as dense object detection and sparse object detection. Sparse object detectors face a significant challenge in slow convergence due to the limitations of transformer encoders or feature pyramid networks in processing image feature maps. In this paper, a sparse object detector that does not rely on encoders or feature pyramid networks is proposed to mitigate this issue. Firstly, a 4D sampling module is designed to focus on regions where potential objects are located, rather than all regions. This design significantly enhances the convergence speed. Additionally, a relative position encoding is presented to enhance the position-aware ability of the sampling module. Lastly, the dynamic instance interactive head and multi-layer perception mixer are used to decode sparse queries. Experimental results demonstrate that our proposed detector surpasses the performance of the majority of existing sparse object detectors, reaching 46.9 AP with ResNet-50 backbone. Moreover, the proposed method achieves comparable performance with only 80% of training epochs.
Improved YOLOv4-Based Object Detection Method for UAVs

An improved UAV object detection method based on YOLOv4 is proposed in this paper for the problems faced by UAV vision detection, such as small targets, multiple scales, and complex backgrounds. First, in order to speed up the detection speed of the network and meet the actual detection demand, the backbone network is replaced with MobileNetv3 lightweight network, and the k-means++ is improved using a linear scale scaling method to improve the false detection rate by reclustering the prior frame; in addition, in order to reduce the loss of target information during downsampling, the stride convolution in PANet is replaced with non-stride convolution SPD-Conv, while further reducing the number of parameters and computational effort of the network model; for the small target of UAVs in the dataset, copy-pasting, a data enhancement strategy, is used to the UAVs to expand the dataset of small targets; finally, considering the problem that the complex background contributes significantly to the loss of the model, the Focal loss function is introduced, which interacts with the above methods to improve the accuracy and speed of the UAV detection model in complex backgrounds. The experimental results show that compared with the original YOLOv4, the proposed method improves the detection accuracy by 4.6%, the detection speed by 71%, and the missed detection rate by 17.9%, improving the UAV leakage problem in complex backgrounds while significantly improving the performance in terms of detection accuracy and detection speed.
MADA: Multi-Level Alignment in Domain Adaptation Network for Nighttime Semantic Segmentation

Semantic segmentation can provide spatial information about urban landscapes for autonomous driving. For safety consideration, segmentation methods should adapt to all day conditions including day and night. In this paper, a multilevel alignment in unsupervised domain adaptation network (MADA) is proposed for nighttime semantic segmentation. MADA aligns data distribution at multiple levels, utilizing the information of daytime source domain labels to improve the accuracy of nighttime semantic segmentation. Specifically, MADA includes two steps: 1) style-level alignment: twilight is chosen as intermediate domain and daytime and nighttime images are both transferred to twilight style; 2) feature-level and prediction-level alignment: discriminators are introduced after feature extractor and classifier in the segmentation network to align the distribution of features and predictions between the source and target domain images. The proposed MADA has achieved excellent nighttime segmentation results and minimized the training time. We conducted experiments on nighttime semantic segmentation datasets including Dark Zurich test set, ACDC nighttime validation set and Nighttime Driving test set. The mIoU reaches 44.3%, 37.59% and 44.92% respectively.
Schmitt Non-Gate Chaotic Circuits: Phase Diagram, Complex Walking, and Spring Testing

Small is beautiful. The highlights are to focus on the effective new applications of Schmitt non-gate, forming two novel chaotic circuits with simple structures and clear principles. RC Wien-bridge oscillator chaotic circuit uses series Schmitt non-gate and diode as nonlinear feedback block. Schmitt non-gate multi-vibrator chaotic circuit relies on RCR unit for differential action and current protection at the gate output end. The complexity measures of new 3D chaotic signals are modeling in phase diagram, complex walk and spring test. The mini demonstration of bifurcation diagrams of two simplest 555 timer Schmitt chaotic circuits be reported firstly.
SCGRU: A Model for Ethereum Smart Contract Vulnerability Detection Combining CNN and BiGRU-Attention

Smart contracts have driven the development of blockchain technology, but their security vulnerabilities pose numerous problems for blockchain applications.The existing smart contract vulnerability detection methods based on deep learning are not comprehensive in detecting vulnerability categories, have low accuracy, and are limited to binary classification tasks. This paper proposes a smart contract vulnerability detection model called SCGRU combining CNN and bidirectional gating recurrent unit-attention mechanism (BiGRU-Attention) for Ethereum smart contracts to solve these problems. The model transforms the standardized data into word vectors representation of smart contracts with semantic information through the Word2Vec word embedding module. The feature extraction module takes the word vector as input. It uses CNN and BiGRU training to extract high-level abstract features and sequence features of the smart contract respectively. Following the BiGRU feature extraction, an attention mechanism is introduced to highlight the key features related to vulnerabilities of the smart contract code. The features extracted by CNN are concatenated with the features extracted by BiGRU-Attention as the input to the vulnerability classification module. The softmax function normalizes the vulnerability classification module to complete the detection of vulnerabilities in Ethereum smart contracts. The experiments in this paper show that SCGRU has a high accuracy rate of vulnerability detection on the current public dataset. Moreover, SCGRU can identify all categories of smart contract vulnerabilities in the dataset with an average accuracy rate of 92.64%, among which the precision of Infinite Loop vulnerability detection is 99.06%.
Solution to the Problem of Passing Over the Given Targets by an Unmanned Aerial Vehicle in an Unstable Environment

The paper gives a statement and considers the solution to an urgent problem of flying over the given targets by an unmanned aerial vehicle (UAV) in unstable conditions. A criterion is formulated for constructing efficient routes and passing arbitrary located sites according to a given formal description of the terrain map with the presence of interferences. Heuristic rules are proposed aimed at minimizing the graph of the initial map and reducing the number of alternative flight routes, given the time constraints for completing the task. The search of the solution in this case can be carried out according to the base tree of routes. A heuristic algorithm for searching efficient routes in a limited time is considered, which has the complexity that ensures its implementation on the UAV onboard computer. It is substantiated that the proposed approach can significantly reduce the number of comparing alternatives for flying around given arbitrary located targets in comparison with the well-known methods for solving this problem. The paper describes a successful example of solving the UAV route planning problem with the proposed approach, which is complicated by the presence of wind flows affecting the speed and trajectory.
A Fine-Grained Data Encryption Scheme Based on Shortest Vector Problem in the Cloud Computing Environment

Lattice-based encryption is a promising public key cryptography to resist quantum computing attacks. Simultaneously, due to the need for fine-grained access control in cloud environments, it is crucial to do the research on attribute-based encryption (ABE). However, researches on ABE algorithm have some problems such as not having mechanisms secure enough to defend against quantum computing attacks with high decryption accuracy, having vulnerabilities to collusion attacks and lacking of attribute revocation mechanism. In order to solve the above problems, we propose a new data encryption scheme with fine-grained access control based on the shortest vector problem in the cloud computing environment (DES-SVP). We formally prove that our DES-SVP scheme can resist quantum computing attacks and can resist collusion attacks between users. We reduce our DES-SVP scheme to the problem of finding a short vector in a lattice so that our scheme can withstand quantum computing attacks. Finally, the performance analysis shows that our DES-SVP scheme can achieve efficient results comparing with other related works.
An Improved Flyod-Weighted Jump Point Search Algorithm in Mobile Path Planning

The search-based path planning algorithm encounters several issues, such as time-consuming calculations and redundant path nodes during mobile robot path finding. This paper aims to optimize the heuristic function by utilizing the jump search algorithm and an adaptive dynamic weight. This optimization significantly accelerates the search speed of the algorithm. Additionally, the Floyd algorithm is employed to p rune redundant intermediate nodes, effectively reducing inflection points. Comparing the newly proposed F-WJPS algorithm with the A* and JPS algorithms, we observe a reduction in solution time by 95.29% and 46.82% respectively. Furthermore, the F-WJPS algorithm achieves a path length reduction of 6.2% and 6.1% respectively. Moreover, the number of inflection points in the path is decreased, making it more suitable for path planning in mobile robots. Additionally, this paper addresses the issue of collision between the F-WJPS algorithm-generated path and obstacles. By considering the constraint of a safe corridor, we solve the final Bessel curve trajectory, effectively avoiding collision risks and ensuring a smoother trajectory.
Improving Graph Convolutional Neural Networks for q-Consistent Summary-Explanations with Application to Credit Evaluation Systems

Decision support systems (DSS) are increasingly applied in critical domains like healthcare and criminal justice, where trust is of paramount importance. Trust relies on system transparency and explainability. A notable approach is the globally-consistent summary-explanation (SE) method, providing rule-based local explanations with valuable global insights while ensuring 100% dataset consistency. However, simpler globally-consistent SEs suffer from reduced support, undermining their persuasiveness. To address this, we apply q-consistent SEs, striking a subtle balance between consistency and support. Resolving the maximizing support with q-consistency (MSqC) problem poses a challenge more intricate than global-consistency. To enhance solution efficiency, our paper introduces an improved graph convolutional neural network (GCNN) model with a Sample graph attention network (GAT) layer for learning branch and bound (B&B) variable selection strategies. Experimental results on credit evaluation scenarios confirm the effectiveness of the GAT-GCNN model in improving accuracy and efficiency in solving MSqC problems.
Efficient Low-light Image Enhancement Using Radar–Camera Fusion Framework

Low-light image enhancement (LLIE) is often used to improve the quality of video images in dark light conditions. For LLIE, global image enhancement is performed on the entire image which often leads to a waste of computational resources and ineffectiveness. In this paper, we proposed a radar-camera fusion framework-based approach to enhance the region of interest (ROI). The radar was utilized to locate target areas in video data for local enhancement. Target location was then projected onto the image based on the spatio-temporal calibration and a bounding box centered on the target was got. The corresponding part of the image within the bounding box was the ROI, and only this part of the image was considered for LLIE. An obvious benefit for image local enhancement is the computational efficiency. Experimental results showed that the local image enhancement scheme improved the visual effect while reducing the computation time. One more inspiring benefit is the improvement of object detection performance. It can be seen that the accuracy and the metric of mAP@0.5:0.95 trained by the network based on locally enhanced images were better than the training results using unprocessed images or globally enhanced images.
Algorithm for Interaction of UAVs and Ground-Based Robotic Systems for Collaboration Operations over Vast Territory

This work proposes group interaction algorithms that take into account not only the different functionality of the robotic systems, but also their state and sensor data, which rebuild paths in real time in order to avoid collisions with obstacles. The scenario for maintaining communication over a vast area by delivering communication devices for the interaction of a heterogeneous group is considered. To simulate the collaborative interaction of a group of heterogeneous robotic systems, the algorithms were developed. These algorithms include the algorithm for the distribution of target points and the algorithm for planning trajectories. In this work, the algorithm for the optimal distribution of clusters of a heterogeneous group of robotic system and the determination of the composition of clusters in order to ensure the connection of robotic systems was proposed. The results of experiments on modeling the interaction of a heterogeneous group using the proposed algorithms showed the operability of the proposed solution for the problem of simultaneous operation of a heterogeneous group over a vast area with communication by ground and air robotic systems, as well as the potential applicability of the proposed algorithms in real conditions.
Elemental Images Labelling and Grouping to Minimise Disparity Error in Texture-less Regions of Holoscopic Images

Holoscopic imaging is a promising technique that captures full-colour spatial images using a single aperture.It uses a micro-lens array to view the scene at different angles and record 4D information on a two-dimensional surface, making it useful for depth estimation.However, current disparity estimation methods suffer from poor performance in texture-less regions.This paper proposes a novel method to reduce the disparity error in these regions by directly labelling and grouping elemental images from a Holoscopic image.The proposed approach involves extracting a subset of viewpoint images from the Holoscopic image and subjecting them to conventional image segmentation.Labels are then applied to the elemental images corresponding to each segmented object using viewpoint images/elemental image pixels mapping.Content-based image retrieval is also employed to improve segmentation.The proposed technique has wide applications for 3D imaging, including augmented and virtual reality, inspection, robotics, security, and entertainment.
Dual Attention and Edge Refinement Network for Camouflaged Object Detection

Camouflaged object detection (COD) aims to identify and segment objects similar to the background. However, existing methods suffer from unsatisfactory camouflaged object identifying or segmenting. In this paper, we propose a novel Dual Attention and Edge Refinement Network (DAERNet) to boost the performance of COD. Specifically, a Dual Attention Mechanism (DAM) is proposed to capture the scale diversities of camouflaged objects, which mainly includes Spotlight Attention Module (SAM) and Modulation Attention Module (MAM). The SAM aims to obtain multi-scale refinement features which contain discriminative semantic information, while the MAM aims to obtain the refined features with specific semantic information. Then, we propose a Boundary Extraction Module (BEM) to obtain edge information. Finally, a Feature Aggregation Module (FAM) is designed to fuse the refined multi-scale features obtained by DAM with the help of edge information to achieve accurate camouflage object prediction. Extensive experiments on four datasets demonstrate that the proposed DAERNet performs comparably against other SOTA methods.
BUSI Classification Based on Singularity-Exponent-Domain Image Feature Transform and Deep Neural Network

A novel breast ultrasonic image classification is proposed based on singularity-exponent-domain image feature transform (SIFT) and deep neural networks. As an extension of the conventional singularity power spectrum (SPS), the SIFT of the ultrasonic image is deduced, to extract the statistical feature image of the power distribution in the singularity exponent domain. The point-wise SPS is derived from the pseudo Wigner-Ville distribution (PWVD) of the breast ultrasound images (BUSI). Furthermore, the feature images of the BUSI are extracted to discriminate the specificity of the tumor regions. Then, a novel BUSI classification method based on the SIFT and deep learning network is proposed and tested on the public BUSI datasets. The experiment results indicate that the classification accuracy of the original deep network is considerably improved by about 6% with the proposed method.
Universally Strict Black-Box Attacks Against Deep Neural Networks

Adversarial attacks have demonstrated the vulnerability of deep neural networks (DNNs), which raises considerable security concerns. The existing attack methods require either prior knowledge of the victim DNNs and labels or frequent model querying. However, these requirements are usually infeasible or time-consuming, leading to suspicions about whether the attacks can be launched in real-world scenarios. To this end, we propose a universally strict black-box attack, which generates adversarial samples only using unlabeled data. It reduces the reliance on external information such as victim models, training processes, and ground-truth labels. Specifically, we first learn a latent manifold using contrastive learning. Then a novel universally adversarial loss is proposed to obtain adversaries directly in the latent space. It utilizes the dissimilarity between samples to craft perturbations without accessing labels and decision boundaries. Moreover, we propose a cluster selection for negative samples to improve the effectiveness of our attack. By evaluating the universally strict black-box attack against baseline models, we find it reaches an average fooling rate of 57.93%, which is on par with transfer-based black-box attacks. Our method shows the threats of adversarial attacks under more practical conditions and could serve as a new benchmark for assessing the robustness of DNNs.
An External Denoising Framework for Magnetic Resonance Imaging: Leveraging Anatomical Similarities Across Subjects with Fast Searches

External denoising, also known as reference-based denoising, utilizes information from clean reference images, yielding more robust results than internal denoising, especially in situations with high noise levels. The effectiveness of reference-based denoising relies on the similarity between reference and noisy images. In this paper, we introduce a novel external denoising framework for magnetic resonance imaging (MRI) that takes advantage of the inherent anatomical structure similarities across subjects. Our framework employs noise-resistant neural networks to extract deep features, which facilitate searches for high-quality reference images from a large external database. This versatile framework is compatible with all reference-based denoising algorithms, obviating the need for acquiring additional reference images from the subject under examination.