Reviewerly/exHarmony huggingface dataset:
An Adversarial Patch Attack for Vehicle Detectors in the Physical World

Compared with global pixel-level adversarial samples, adversarial patches do not pursue visual concealment and are easier to achieve attack effects in the physical world. However, most of the existing adversarial patch attacks on vehicles have overlooked the influence of patch coverage position in the physical world. Some methods have not conducted real physical patch attack experiments, and the test of attack effect has been limited to the digital world. At the same time, during the patch optimization process, the attack effect when transferred to other models has not been considered. In this paper, we propose a vehicle adversarial patch attack method based on model perception and attention. Based on the model perception to high-frequency information, we use such high-frequency information of the image that the model cannot correctly classify as the initial adversarial patch to enhance the transferability. Based on the model attention patterns, the attention region of the model is obtained, and cover this region with patches as much as possible to achieve the attack more easily. Experiments demonstrate that our patch can remarkably reduce the detection accuracy of model in the digital world, while ensuring the attack effect of the adversarial patch in the physical world.
A Novel Hyper Chaotic-Cellular Automata Based Medical Image Encryption Technique

The security of healthcare and telemedicine systems stands as a matter of paramount importance, necessitating extensive research. In the upcoming years, the medical industry envisions widespread adoption of advanced telemedicine applications. With healthcare smart devices becoming increasingly interconnected through the Internet, enabling convenient access from any location, the protection of sensitive and private information, such as personal medical images, emerges as a critical focal point. As a result, the encryption of medical images assumes a pivotal role within the domain of telemedicine and healthcare applications. This article introduces a highly efficient cryptographic system designed to enhance the protection of medical images by harnessing the power of cellular automata techniques in conjunction with hyper-chaotic procedures. In the proposed medical image encryption technique, a layered cellular automata approach is embraced, and a pseudorandom sequence derived from a four-dimensional hyper-chaotic system is employed as the cryptographic key. The study encompasses both grayscale and color images. Several assessment criteria, including the Number of Changing Pixel Rate (NPCR), Unified Averaged Changed Intensity (UACI), average entropy, correlation coefficients, Structural Similarity (SSIM), Feature Similarity (FSIM), and Peak Signal-to-Noise Ratio (PSNR), are employed to evaluate the suggested cryptosystem. These evaluations vividly underscore the robust security performance exhibited by the proposed approach.
Algorithm of Air-Ground Localizing and Orienting Based on Semantic Information of Road Marking

The use of air-ground coordination system in urban environment can significantly increase the perception ability of the system, meanwhile, conversion of air-ground perspective is the key problem that restricts the realization of autonomous cooperation between UAV (Unmanned Aerial Vehicle) and UGV (unmanned ground vehicle). Considering the characteristics of road traffic signs in the urban environment, this paper proposes an air-ground view conversion method based on the semantic information of road traffic signs. Firstly, we preprocess the aerial view images and ground view images, establish semantic segment layout descriptors from the semantically segmented images according to the encoding rules. Secondly, the descriptors can be matched with each other to solve the homography matrix and calculate the relative position and attitude between the UAV and UGV by decomposing the matrix. Finally, we conduct experiments in the Gazebo simulation environment. The experimental results verify the accuracy and practicability of the method proposed in this paper and the problems and limitations are analyzed.
Photovoltaic Power Prediction Considering Direct and Scattered Radiation Effect

Photovoltaic (PV) power prediction is of great significance in grid optimization and dispatch. Aiming at the situation that most prediction models only use one type of irradiance as input, this paper discusses the influence of different types of irradiances on the accuracy of PV power prediction. In this paper, four types of irradiances are input into BP neural network, Elman neural network, and RF neural network for training and testing, using the real irradiance of a PV power plant in Gansu Province, China, and other meteorological data. The results show that all three models have higher accuracy when the irradiance is a combination of total, direct, and scattered radiation, and the Elman model has the lowest prediction error. The method can be widely applied to most models to achieve higher accuracy in PV power prediction.
Dynamic risk assessment of thermal fault in distribution station by online analysis of cloud model

Power distribution station plays a vital role in the entire power system. How to assess the risk level of an unattended distribution station is an important topic. This study proposes a dynamic risk assessment method of thermal fault based on the cloud model, order relationship analysis, and entropy weight method. Three indexes: temperature, temperature rise, and interphase temperature difference are selected to make a reasonable judgment of equipment thermal fault. The order relationship analysis method and entropy weight method are utilized to calculate the weight of the indexes, and then the calculated data is sent to the cloud model to generate a qualitative concept of the risk level. The numerical test shows that the proposed method can accurately reflect the risk level of the equipment. The cloud model can convert the quantitative value of equipment to a qualitative concept. Moreover, the cloud model can deal with abnormal condition when the data acquisition contains faulty data, avoiding the fault alarm.
Trajectory Data Publishing Method with Local Differential Privacy

The local differential privacy algorithm has strong privacy protection ability, which can effectively prevent privacy attacks by untrusted third parties and provide comprehensive protection for sensitive information. However, it also has some disadvantages such as the huge demand for data quantity and the low accuracy of published data. We consider that the attacker has all the background knowledge, and propose a permanent random response to the finite number of digits to solve the accuracy problem. At the same time, we also propose an instant random response to specific disturbance bits to resist vertical attacks and violent decryption. In terms of parameter adjustment, our work adopts the method of information theory and redefines the pri-vacy protection degree with mutual information by constructing the function of privacy protection degree and data availability. The utility is improved under the precondition of the expected privacy protection degree. And the average error of the data is controlled to about 2 meters, which improves the availability of the data by about 5 % compared to a method with the same level of protection. The utility is improved by about 25 % compared to the conventional randomized response.
Human-Machine Collaborative Path Planning Based on Eye Movement Data

Human-machine collaborative driving system can effectively improve driving safety and traffic capability. However, how to implement safety-enhanced path planning in complicated environments with potentially risk obstacles remains a challenge. In this paper, the eye tracker outputs the driver's viewpoints on the environment, so as to be used to identify the danger degree of the obstacles and make local path planning. The visual field of driver is divided into five parts, and the distribution characteristics is analyzed, which helps to determine whether an area is dangerous or not. Meantime, the bounding box is used to approximate the outline of the risk obstacle, and an expansion coefficient is set to indicate the risk degree. The expanded box forms the risk obstacle layer, which can be added to the multilayer map. For the planning modules, the global path after deformation is used as reference, and the local planning path is then obtained by multistage state space sampling. Finally, the experiment verifies that the path planning module of the human-machine collaborative driving system based on eye movement data can output safer planning results in the environment with risk obstacles, which can effectively improve driving safety.
An Image Registration Method Based on Shape Features of Regional Groups

Aiming at the problem that local features such as midpoints, lines and so on in images before and after damage are severely changed, an image registration method based on shape features of regional building groups is proposed. Firstly, the building area is extracted by segmentation. Then a grouping strategy is designed to group building areas. The feature descriptor of regional group is constructed by using invariant moments, so that the feature descriptor is invariant with translation, rotation and scaling. Finally, the registration is completed by matching the invariant moments. In this paper, the images from the Russia-Ukraine war are used for registration experiments. The experimental results show that compared with the existing methods, the proposed method has a higher feature matching rate and can complete the registration of pre-damage images and post-damage images.
Multi-Objective Mission planning for UAV Swarm Based on Deep Reinforcement Learning

This research presents an innovative application of deep reinforcement learning (DRL) for multi-objective mission planning in UAV swarms, a subject at the forefront of emerging technologies. An in-depth exploration of existing models, strategies, and primary principles of DRL regarding UAV swarm mission planning is undertaken. The paper then introduces a cutting-edge approach to the multi-objective mission planning of UAV swarms, which includes rigorous problem definition, state and action space design, reward function configuration, DRL model formulation, and the creation of multi-objective optimization strategies. The performance and reliability of the proposed model were extensively evaluated using various simulation environments and diverse datasets. The outcomes show that our model surpasses traditional methods in planning UAV swarm missions, providing a groundbreaking perspective on integrating DRL into swarm robotics. Despite its strengths, the model also presents areas that warrant further investigation and enhancement, outlined in future research directions. This work contributes significantly to the understanding and application of efficient UAV swarm mission planning, with potentially far-reaching implications across numerous fields, including defense, agriculture, and environmental surveillance.
An Ensemble Method for Accurate Image Matching

Feature matching is the basis of many computer vision tasks, such as target tracking, visual localization, etc. Recently, methods based on deep learning have achieved significant results. However, for different application, such as UAV aerial scenes, the pre-trained models of these methods may not be suitable. As there are large visual differences between the application scenes and the training dataset. Accordingly, we propose an ensemble method for accurate image-based feature matching. Our model has good generalization for different datasets, and only the pre-trained models of existing methods are adopted. We firstly use the existing image matching algorithms contain feature matching and optical flow to produce huge amounts of bilateral matches, and then filter the matches generated by feature matching through epipolar geometry constraints. For matches produced by optical flow, we keep the points where the cycle consistency is below a certain threshold. Lastly, cross-validation is applied to filter correspondences, only the matches obtained by feature matching supported by optical flow are retained. Our ensemble model consistently outperforms the state of the art on both Megadepth, a public benchmark of visual localization, and VisDrone-MOT2019, a common dataset for UAVs.
A Robust Technique for Steganography of Enhanced Audio Signals

Audio steganography is a tool for concealing data (a secret message) inside an audio signal (a carrier). It is regarded as an essential approach for information security. This paper presents a proposed technique for audio signal steganography, which is implemented in the wavelet domain, with a pre-processing enhancement step. First, adaptive Wiener filtering is implemented to reduce the noise on audio signals. Then, the Inverse Short-Time Fourier Transform (ISTFT) is applied to convert the target image from the spectrogram-like nature to the 1-D signal-like nature. The embedding process is performed on high-frequency coefficients in the wavelet domain to keep the audio signals unchanged for human perception. The reverse process is implemented at the receiver. Hence, the embedded coefficients are extracted to recover the embedded image through Short-Time Fourier Transform (STFT). The recovered audio signal is also obtained after the removal of embedded coefficients. The major goal is to enhance the performance of the audio steganography technique, while retaining the audio signal quality. The outcomes show that the suggested technique successfully allows audio steganography and enhances the resultant audio signal quality at different Signal-to-Noise Ratio (SNR) levels.
Enhancement of Electronic Medical Record (EMR) Security in Private Networks with Blockchains

In this paper, we propose a system for improving the security of Electronic Medical Records (EMRs) in private networks using Blockchain technology. There are currently problems with data integrity in database systems that contain health records. Application Programming Interfaces (APIs) are frequently used by amateur organizations to create internal frameworks, which might result in data manipulation in an improper way and unauthorized access. We put up a practical solution for this issue that makes use of Blockchain immutability. Our system depends on HTML forms and NodeJS to store patient-related records in a MySQL database. However, using the secure hashing SHA-256, we store the hash values of the records in blocks rather than the actual records themselves. This method ensures that any unauthorized alterations in the records may be quickly identified by comparing the stored hash value with the actual hash of the data that is obtained from the database. Our system performance analysis, which shows acceptable latency, was done. The Blockchain runs in the background without having a substantial influence on speed, thus the database continues to run quickly. Our system also saves money, because it is designed to be used with a user-programmed Blockchain within private networks. It is offered at no additional cost, and is primarily made to verify data accuracy. For private networks depending on EMR systems, our suggested solution provides improved security, effective performance, and affordability as compared to existing approaches and frameworks.
A Software Design and FPGA Implementation of Telemedicine Security System

Secure data transfers in communication applications for telemedicine play a significant role in security. In this paper, a secure telemedicine corneal data concealment algorithm based on the pixel segmentation strategy (PSS) and generalized exploiting modification direction (GEMD) is presented. The suggested algorithm is implemented using the Xilinx System Generator (XSG) and hardware/software co-simulation is carried out using the Spartan 3E FPGA board, which offers the least embedding time and offers a quicker, programmable, and commercial hardware solution for secure communication. The proposed approach can be applied in low-cost embedded system applications since it has high embedding speed and low computational complexity. The outcomes of the practical tests of the suggested algorithm are the same as the simulation results of the same algorithm. The suggested algorithm has a high payload capacity while maintaining good stego-image quality in comparison to the earlier data concealment algorithms. The proposed algorithm presents a practical results as the same as that of simulated results.
An Experimental Evaluation Based on New Air-to-Air Multi-UAV Tracking Dataset

Visual-based multi-object tracking (MOT) of micro unmanned aerial vehicles (UAV s) is a crucial technology that plays a significant role in advancing the development of UAV s. It can be applied in cooperative UAV formation, UAV countermeasure systems, multi-UAV logistics and other fields. However, the performance of existing visual-based MOT algorithms in UAVs has yet to be evaluated. To alleviate this situation, we provide a comprehensive air-to-air multi-UAV tracking dataset, MOT-FLY, which includes more than 11 000 images of three types of UAVs. The dataset encompasses various backgrounds, viewing angles, lighting conditions, object sizes, target movement patterns, and challenging scenarios. Additionally, this paper designs evaluation experiments on eight representative MOT algorithms using the proposed dataset. The results indicate that dataset composition, network structure, and image characteristics all have an impact on the algorithm's performance. Based on these findings, we provide recommendations to address the challenges faced by air-to-air multi-UAV tracking algorithms. The MOT-FLY dataset is published at https://github.com/CZC-123IMOT-FLY.
Path Planning of Cross-Domain Unmanned System Based on Improved Artificial Potential Field Method

Path planning is the key technology in the entire unmanned system cooperative combat system, but most of the current research can only produce better planning results in the two-dimensional environment. The path planning in the three-dimensional environment still has low planning efficiency and poor planning results. This paper proposes an improved artificial potential field method, which is applied to the 3D environment path planning between cross-domain unmanned platforms. At the same time, it solves the shortcomings of the traditional artificial potential field method in the path planning of unmanned systems. Aiming at the problem of local minimum and unreachable target point, a new repulsion function and an adaptive repulsion parameter adjustment method are introduced, so that the unmanned platform can quickly leave the local minimum point, and the algorithm can overcome the problem of unreachable target points in the artificial potential field method. Aiming at network communication problems in engineering practice, a method of eliminating jitter and necessity detection is proposed, which eliminates redundant path points in path planning results. The experimental results prove that the improved artificial potential field method can overcome the problem of narrow channel jitter, local minimum value and target point inaccessibility, generate a smoother path, and significantly shorten the algorithm operation time.
An Optimized Global Disturbance Adversarial Attack Method for Infrared Object Detection

Deep neural network (DNN) has various applications in various fields. It realizes the function of real-time object detection used in transportation, medical treatment, and industry. It is closely related to our daily life. During the epidemic, infrared object detection has also been widely used, but the vulnerability and security of neural networks should be paid attention. To understand the security of object detection networks and empha-size the urgent need to develop robust systems, a Faster-RCNN object detection attack method for infrared images is proposed in this paper. By controlling the gradient update direction and loss function optimization, an adversarial disturbance is customized for each input image so that the object detection network (Faster-RCNN) marks the target error or creates a new wrong target, thus deceiving the object detection network and makes it detect errors and invalidate the task. In this paper, we show how to generate negative infrared images by adding a tiny disturbance to the infrared image to deceive the object detection network at a significant level, making it visually imperceptible and impossible to detect in the network. Experiments are performed on FLIR dataset, and our methods are compared and verified.
Self and Cross Motion Extracted in Drone Video to Improve Object Detection

The abundant temporal information presented in videos can effectively address the issue of significant appearance deterioration observed in drone images. Learning temporal features in videos poses a significant challenge. Simple feature aggregation methods often result in the interference of background information into targets. To address this issue, we propose a simple yet effective Self and Cross Motion Extracted (SACME). The key idea is to leverage transformer layers and a novel temporal fusion network to learn and fuse temporal features. SACME serves as an addon module, enabling existing static object detectors to achieve high-performance video object detection without incurring significant additional computational costs. To validate the efficacy of our module, we select YOLOv7 as baseline and carried out comprehensive experiments on the VisDrone2019-VID dataset. Particularly, our SACME-YOLOv7 not only achieves a significant 5.1 % improvement in mean average precision (mAP) on the challenging VisDrone dataset, but also achieves a remarkable running speed of 31 frames per second (fps).
Sample-Efficient Imitation Learning via Reconstructed Intrinsic Reward

Sample inefficiency has remained a main challenge in existing imitation learning methods. We attribute this inefficiency to the use of random exploration strategies, which collect many repeated state samples that are dispensable in training the agent. In this paper, we propose a method termed reconstructed intrinsic reward (RIR), which encourages the agent to bypass explorations of repeated states by adding an exploration bonus to the reward function. RIR serves as an incentive mechanism that rewards the agent with a high bonus if it collects a sample rarely encountered in previous explorations; otherwise, it gives a low bonus if a state is repeatedly gathered many times. The bonus is computed as the relative quaternion error of input samples by solving a reconstruction problem of collected states. Experiments show that our method outperforms state-of-the-art imitation learning methods in sample efficiency. Ablation studies prove that this efficiency comes from a more advisable exploration strategy used by RIR.
Unmanned Aerial Vehicle Tactical Intention Recognition Method Based on Dynamic Series Bayesian Network

To solve the problem of dynamic and sequential air combat intention recognition in complex battlefield environment, a target tactical intention recognition method based on the extended multi-entities Bayesian network (EMEBN) is proposed in this paper. First the dynamic series Bayesian network (DSBN) model is established to describe the process of intention representation and reasoning, the deficiency of the multi-entities Bayesian network (MEBN) in expressing the probability inference knowledge of the planning process is analyzed. Then the probability transfer MEBN fragments (PT-MFrags) and the series relation MEBN fragments (SR-MFrags) are introduced to depict the probability transfer and series relationship of rule knowledge. Finally, the feasibility and effectiveness of this method is verified by an experimental example.