Reviewerly/exHarmony huggingface dataset:
Taking Myopic Best Response Against The Hedge Algorithm

With the rapid development of artificial intelligence (AI), the game between the human and machine/AI becomes more and more common. The related theoretical analysis becomes significant and necessary, which however is still rare. This problem involves evolution analysis and optimal control of dynamic game systems driven by learning algorithms. In this paper, we consider a finitely repeated two-player zero-sum game. We study the dynamic evolution process of the game system, in which one player employs the Hedge algorithm and the other player takes the myopic best response. By the updating formula of the Hedge algorithm, we define a quantity called the state and construct the State Transition Triangle Graph (STTG). Then, we prove that the game system is periodic after o(T) stages.
Consistency-based Active Learning for UAV Object Detection

In recent years, with the development of artificial intelligence, many new technologies have emerged in computer vision, such as object detection. At the same time, unmanned aerial vehicle (UAV) as a strategic emerging industry has demon-strated significant value and has been widely used. Therefore, using deep neural networks for object detection of UAV images has become one of the current research hotspots. Currently, mainstream object detection networks are divided into two-stage and one-stage networks. Their task is to identify all the target objects to be recognized in the measured image and determine their position and category. However, due to the specific characteristics of UAV images, such as small target size, high density, overlapping areas, and large angle changes, object detection tasks based on UAV platforms still face considerable challenges. This paper proposes to use an active learning method named consistency-based active learning for UAV object detection (CALD- UAV) based on a deep convolutional neural network. The method uses image consistency and mutual information as indicators to search for unlabeled images with high information content for labeling. It obtains a more effective model with fewer training samples. To verify the effectiveness of our method, we conducted relevant experiments on the VisDrone2019 dataset and compared it with other active learning methods. The results show that our method, CALD- UAV, achieves good results in UAV data.
MSAT: Multi-scale Sparse Attention Target Tracking

Current object tracking task have seen significant performance improvements is due to the huge expansion of model parameters. The Transformer architecture with global features and modality fusion capabilities is a typical example. However, the complete attention mechanism used in this architecture has a quadratic complexity dependence on the length of the input sequence, making it impractical for many practical engineering scenarios. In this paper, we propose an MSAT tracker based on multi-scale sparse Transformer, which includes a multi-scale feature interaction backbone network, that performs multi-stage feature fusion to enhance anti-interference ability and robustness, and alleviate the interference accumulation effect of shallow features in the backbone feature extraction. Secondly, we design a feature fusion module based on the deformable attention mechanism and the bilateral sparse attention mechanism, achieves the effect of reducing the memory consumption of Transformer, improving the focus on target features, and weakening the interference of background information. Additionally, we propose a multi-stage template feature update strategy to ensure that reliable template information is obtained during tracking. Extensive experiments show that our tracker achieved efficient performance that surpassed the most advanced trackers on GOT-10k, OTB2015, TrackingNet and LaSOT, while runs a stable real-time tracking at about 45FPS.
FAClue: Exploring Frequency Clues by Adaptive Frequency-Attention for Deepfake Detection

Detecting fake faces produced by face forgery technologies attracts intensive attention in recent years. Deep learning approaches have shown their effectiveness in deepfake detection task. Some previous deep learning-based methods exploit forgery artifacts in spatial domain but easily overfit the specific forgery patterns. Therefore, some works utilize additional frequency domain information to obtain generalized features. We consider to improve the frequency-based methods in two aspects: 1) extracting discriminative frequency features comprehensively; 2) mining complementary features in different domains sufficiently. In this paper, we propose a dual-stream network named FAClue for deepfake detection, which extracts comprehensive frequency information to complement spatial domain features. Specifically, the FAClue consists of three main components. A Frequency-Attention Extractor (FAE) is proposed to adaptively highlight prominent frequency bands from both global and local perspectives. A RGB-Frequency Complementary Enhancement (RFCE) module is developed to mine complementary information between RGB and frequency domains in an explicit manner. A Frequency Guided Attention (FGA) module is designed to fuse different domain features and generate discriminative features for detection. Extensive experiments on three benchmark datasets demonstrate the FAClue achieves competitive performance compared with state-of-the-art methods.
Nonlinear Noise Adaptive Kalman Filtering By Stochastic Search Variational Inference

In this paper, we develop a recursive solution of joint nonlinear state estimation and model parameters identification based on approximation Bayesian inference principle. The stochastic search variational inference, combining natural gradients and stochastic gradients, is adopted to offer a flexible, accurate, and effective approximation of the required posterior distributions. We make two contributions compared with the existing variational inference based noise adaptive filtering. First, we introduce an auxiliary latent variable to decouple the latent variables of state and model parameters, which improves the posterior flexibility and the tightness of the variational lower bound. Second, we split the evidence lower bound optimization into intractable and tractable parts, where stochastic gradients and natural gradients are derived, respectively. The performance of the proposed method is verified on radar target tracking applications by simulated data.
A Novel Blockchain-Based Distributed Power System Control Design

The large-scale penetration of distributed power systems in the power grid makes the safe and stable operation of the power grid a difficult problem. The distributed control strategy is more suitable for distributed power management than centralized control. However, distributed control that relies on network communication is vulnerable to network attacks, so research on security control strategies is very urgent. Blockchain technology is widely used in the field of energy trading due to its special security mechanism, distributed ledger, and decentralization. It is difficult to develop in the control field due to performance and latency issues. In this paper, we propose a blockchain-based distributed control architecture for island microgrids. Through ingenious structural design, the communication security of the blockchain is fully utilized, and troublesome on-chain calculations are avoided. Simulation results demonstrate the superiority of this architecture.
Symmetric Joint Detection and Re-Identification for UAV-Based Multiple Object Tracking

With the development of multi-tasking learning, multi-object tracking (MOT) has been dominated by the model of Joint Detection and Re-Identification (JDR). FairMOT is a milestone work under such a paradigm. It contends that the anchor-free methods are more suitable than the anchor-based ones for building JDR models. However, the anchor-based detectors are widely used in unmanned aerial vehicle (UAV) applications since they tend to achieve higher performance for detecting small targets which are universal in drone videos. In this paper, we propose an anchor-based JDR model for the MOT task related to UAV vision. Firstly, to balance the detection task and Re-Identification (Re-ID) task, a symmetric two-branch architecture is proposed. Secondly, to deal with the correspondence problem between the two tasks, we learn Re-ID features for every anchor box. Thirdly, to alleviate the conflicts between the detection problem and the Re-ID problem, we train the model in two steps, where the detection branch is trained first, then is the Re- Id branch. Our model based on the popular anchor-based detector YOLOv7 has achieved great performance on the Visdrone- MOT20 19 dataset. The experimental results demonstrate that our model can validly complete the MOT task related to UAV visions.
MA-RRT* Algorithm for Path Planning with Maximum Rotation Angle Constraint and Adaptive Crossing Threat Zone

With the gradual diversification of modern application scenes, the demand for UAV path planning is becoming more and more abundant. This paper first introduces a possible battle scenario, and the problems that must be considered in path planning in this scene are analyzed: the maximum rotation angle constraint and the demand that the UAV can cross the threat zone flexibly. Maximum rotation angle and Adaptive crossing threat zone RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> (MA-RRT*) algorithm is proposed to solve the above problems. The algorithm improves the reconnection process of neighbor set in RRT*, and adds the maximum rotation angle constraint to the RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> framework, so that the generated path strictly meets this constraint. In addition, the above process also reduces the time consumption of RRT*, and the speed advantage of MA-RRT* becomes more and more obvious with the increase of iteration times. Then, the obstacle information is regarded as one of the components of the node cost of the path, and the original obstacle is regarded as a soft constraint. This enables the path to adaptively cross the threat zone, which is more in line with the actual situation. We compare our algorithm with RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> in numerical simulation, which verifies the effectiveness and time consumption advantages of the algorithm.
Adversarial Example Generation Method Based on Probability Histogram Equalization

CNNs (Convolutional Neural Networks) have a good performance on most classification tasks, but they are vulnerable when meeting adversarial examples. Research and design of highly aggressive adversarial examples can help enhance the security and robustness of CNNs. The transferability of adversarial examples is still low in black-box settings. Therefore, an adversarial example method based on probability histogram equalization, namely HE-MI-FGSM (Histogram Equalization Momentum Iterative Fast Gradient Sign Method) is proposed. In each iteration of the adversarial example generation process, the original input image is randomly histogram equalized, and then the gradient is calculated to generate adversarial perturbations to mitigate overfitting in the adversarial example. The effectiveness of the method is verified on the ImageNet dataset. Compared with the advanced method I-FGSM (Iterative Fast Gradient Sign Method) and MI-FGSM (Momentum I-FGSM), the attack success rate in the adversarial training network increased by 27.9% and 7.7% on average, respectively.
Mixed Distorted Image Restoration Based on Residual Double Deep Q Network

In this paper, we investigate an image restoration method based on a deep reinforcement learning framework, aiming to recover low quality images to high quality images. The general deep learning-based approach trains a single large network to accomplish a specific task, which is difficult to handle when facing mixed distorted images. To address this problem, we propose the residual Double DQN algorithm, which introduces the idea of residuals into the deep reinforcement learning framework. The agent learns a policy to select appropriate actions from the action set to gradually restore the quality of mixed distorted images. The framework uses the residual blocks to improve the feature extraction ability of the agent, so as to guide it to adaptively select the appropriate action for image recovery. In addition, based on the the new reward function which is designed based on human-eye inspiration, the framework can handle the mixed distortion of images containing noise, blur, and JPEG compression at the same time. The experimental results show that our proposed model has low complexity and is superior to the existing methods in processing mixed distortion images.
Solving Time-Varying Vector-Valued Nonlinear Equations Including Scalar-Valued Ones As Special Cases by Gradient-Zhang Neural Net

Solving nonlinear equations plays a fundamental role in science and engineering. In this paper, gradient neural net (GNN) and Zhang neural net (ZNN), two classes of recurrent neural net, are utilized to construct a new neural net, i.e., gradient-Zhang neural net (GZNN), to solve time-varying vector-valued nonlinear equations including scalar-valued ones as special cases. Note that GNN and ZNN are proved efficient when applied in dynamic systems, such as time-varying linear equations and time-varying matrix inverses. Moreover, with the development of digital circuits, some Zhang time discretization (ZTD) formulas are presented to solve the discrete time-varying problems. In this paper, four ZTD formulas are utilized to construct discrete GZNN algorithms. Furthermore, we perform some simulation experiments to compare the performances of ZNN, GNN and GZNN, and show the feasibility of the four discrete GZNN algorithms.
Self-Detection and Comprehensive Learning-Based BRO for Cloud Workflow Scheduling Under Budget Constraints

To guarantee the diversified user QoS requirements, workflow scheduling in the cloud data centers still face challenges. In this paper, a Self-detection and Comprehensive Learning-based Battle Royale Optimization algorithm (SCLBRO) is proposed for scheduling workflows to optimize the makespan under budget constraints. Firstly, a Comprehensive Learning Strategy-based re-spawn mechanism is incorporated into the original Battle Royale Optimization (BRO) algorithm to improve the global search ability. Second, a local optimum detection method is designed by counting and evaluating the similar soldiers to reduce the possibility of falling into local optima. Third, an elite enhancement strategy is adopted to increase the search diversity for better balancing between exploration and exploitation. Extensive experiments are conducted on four well-known scientific workflows with different scales, and the results demonstrate that SCLBRO outperforms its peers in the success rate, convergence and solution quality.
NG-RC Based Multi-Target Data Association Algorithm

The data association problem is the most challenging task in the field of multi-target tracking at sea. Traditional data association methods often fail to balance tracking accuracy and computing speed, while deep learning-based data association methods require a lot of training data. In this paper, we propose a multi-target data association algorithm based on Next Generation Reservoir Computing (NG-RC). Firstly, virtual measurement and distance matrices are introduced to define data correlation models for false alarms and missed detections. Subsequently, the NG-RC network is used to learn the association probability between measurements and existing targets, and the association of multiple targets and multiple measurements is completed in the tracking process. Finally, the simulation results denote that the method can accomplish the multi-target tracking task quickly and accurately in a cluttered environment.
An improved Convolutional Neural Network model for human posture detection

The classic OpenPose convolutional neural network model has excellent performance in the field of human posture detection. But it also has some shortcomings, such as poor accuracy and low accuracy of human posture detection partly. An improved OpenPose network model is proposed to solve these problems. First of all, the feature extraction network VGG -19 of OpenPose model is replaced by the residual network (ResN et) with residual learning structure to improve the training accuracy of the network. Secondly, at the same stage, the dual-branch parallel structure is changed to the single-branch serial structure to predict the part confidence maps (PCMs) and the part affinity fields (PAFs) of human joint points. Finally, the structure of the convolution kernel is optimized and the residual network is introduced. The accuracy of the algorithm is improved and the computational speed of the model is guaranteed as much as possible. Experimental verification on MPII data sets shows that the detection accuracy of the improved algorithm model in this paper can reach 79.5%, which is 3.9% higher than the original model. At the same time, it has a higher accuracy of detection compared with other human posture detection algorithms. It can be applied to human posture detection with a high pursuit of the accuracy of detection of the model, such as the field of intelligent driving and rehabilitation training and so on.
A Robust Model Based on Context Attention and Multi-Scale Feature Fusion for Vehicle Detection

Vehicle detection is a significant part of autonomous vehicles. Although many vehicle detection approaches have achieved impressive performance, achieving robust vehicle detection is still challenging due to the problem caused by occlusion, truncation, small-size vehicles and vehicles with a large variance of scales. In this paper, we propose a vehicle detection model based on the context attention module (CAM) and the multi-scale feature fusion module (MSFFM). Firstly, we construct the backbone of the vehicle detection network with ELAN and MAP. Secondly, we propose the context attention module (CAM) to establish long-range dependencies between pixels, which help the vehicle detection model capture useful context information to promote the robustness of model in challenging scenarios. Finally, we propose the multi-scale feature fusion module (MSFFM) to enhance the model's capability to adapt vehicles with a large variance of scales by collecting multi-scale features. Sufficient qualitative and quantitative experimental results on the KITTI dataset verify that our model achieves the impressive detection accuracy and speed, and outperforming the state-of-the-art methods.
Region to Global Vision Transformer for Baggage Re-Identification

Baggage re-identification(ReID) is a challenging and important task in airport intelligent surveillance, facing the problems of occlusion, background changes, pose differences and highly similar baggage appearance in complex scenes that are difficult to solve in current related research. In order to effectively improve the performance of baggage re-identification, a region-to-global Transformer network(RGViT) is proposed. Three improvement modules including regional self-attention module, global spatial-relation attention module and quadruplet loss function are used to enhance the re-identification performance of the Vision Transformer, which has made outstanding achievements in the field of image processing in recent years. The regional self-attention module further condenses local features into regional features to intensify feature extraction while reducing memory complexity. The global spatial-relation attention module captures the spatial structure of relationships between regional features, stacking them according to their spatial importance distribution to form distinct global contextual information. The quadruplet loss function reduces the intra-class distance of the same baggage identity under different views and increases the inter-class distance, effectively solving the problem of re-identification of high similarity baggage. The proposed method achieves 88.7% Rank-1 accuracy and 89.6% mAP on the baggage re-identification dataset MVB, which has remarkably excellent performance compared with other state-of-the-art methods.
Distributed Online Sparse Gaussian Process Regression for Multi-Agent Coverage Control

Multi-agent systems have been employed to perform optimal coverage control in unknown environment by predicting the unknown density function in an online fashion. However, traditional Gaussian process (GP) learning and prediction methods are not suitable for big data processing because of its high computational complexity. In this work, we propose two classes of distributed online sparse Gaussian process regression (GPR) methods to enable optimal coverage control of multi-agent systems in unknown environment. Firstly, a distributed online hyperparameter learning method based on proximal alternating direction method of multipliers (pxADMM) is employed for each agent to estimate the optimal hyperparameters. Then, two classes of distributed GP prediction methods are used to obtain local estimation of the density function based on the trained hyperparameters. Finally, distributed coverage controller is designed for each agent to achieve optimal coverage in the unknown environment. Simulation examples are provided which verify the effectiveness of the proposed control strategies.
An Improved Grey Wolf Algorithm for Scheduling Agricultural Harvesting Robots to Minimize Energy Consumption and Time Cost

Aimed at improving the efficiency of agricultural harvesting robots to perform picking tasks, this paper takes the weighted sum of energy consumption and time cost as the objective function and designs an improved grey wolf optimization (GWO) algorithm. According to the characteristics of the problem, a discrete integer coding method is adopted first, and the Clarke & Wright's algorithm is used to generate a high-quality and high-diversity initial population. Secondly, a nonlinear convergence factor and destruction reconstruction strategy based on correlation are introduced to balance the global search and local search capabilities. The experimental results show that the proposed algorithm has higher optimization accuracy and faster optimization speed than the well-known genetic algorithm and tabu search in the literature.
Indoor UAV Formation System Based on UWB Positioning

With the speedy development of UAV technology, multi-aircraft collaborative mission accomplishment has shown many advantages, and the positioning of UAV clusters is a prerequisite for collaborative mission accomplishment, among which the accuracy of positioning will directly affect the efficiency of mission execution.Among the applications of UAV clusters, multi-aircraft formation flight is a major application, which is widely used in both agriculture, military and industry.At present, the main positioning technology for UAV clusters is still GPS, but the effect is not ideal indoors. UWB with low cost, high accuracy and wide range can be an alternative solution for indoor positioning. Thus, this paper designs a formation system based on UWB positioning, in which LSTM neural network combined with UWB raw data is used for positioning, which effectively utilizes historical information, and the optimal coordinates are solved using the least squares method and used as the training set after establishing the LSTM model.The optimal coordinates are solved using the least squares method and used as the training set. The target position is predicted using the neural network, and the output positioning values are fused with the IMU sensors in the UAV for EKF, so that the UAV in formation can obtain a stable and accurate positioning method with a positioning accuracy of 5 cm.