Reviewerly/exHarmony huggingface dataset:
A novel trajectory tracking algorithm based on soft actor-critic-ResNet model

In order to effectively accomplish the control of robot in unknown environments, we propose a soft actor-critic- ResNet(SAC-ResNet) model based on multi-sensor information. First of all, comprehensively consider issues such as obstacle avoidance and robot walking efficiency, we design a state space based on various sensors and a continuous action space. Secondly, SAC combined with ResNet, multilayer perceptron(MLP) and multi-head-attention realize the organic integration of multi-sensor information, effectively solving the problems of low efficiency and environmental dependence caused by insufficient environmental information. The simulation results show that the SAC-ResNet model based on multi-sensor information can continuously control the robot to avoid unknown obstacles effectively in different environments, improving the accuracy, safety and robustness of local obstacle avoidance.
Application of improved YOLOv5 model on object detection across multiple datasets

This paper presents a series of enhancements made to the YOLOv5 model, which belongs to the well-established YOLO (You Only Look Once) series of object detection models. The proposed modifications yield a significantly advanced object detector exhibiting exceptional performance across diverse datasets. The primary focus of our improvements lies in the replacement of select convolutions within the model using an enhanced reparameterization technique tailored for convolutional models. In conjunction with other effective enhancement strategies, the augmented YOLOv5n model achieves a mean average precision (mAP) of 77.8 on the VOC2007 dataset, showcasing an impressive 18% performance gain over the original model (version 6.0). This notable improvement positions YOLOv5n ahead of the state-of-the-art YOLOv8 model, while concurrently attaining further enhancements in frames per second (FPS) compared to its predecessor. A comprehensive set of experimental results substantiates the efficacy of our approach towards enhancing the YOLOv5 model, rendering it more amenable to the requirements posed by various application domains within the field of object detection.
Explain To Me: Salience-Based Explainability for Synthetic Face Detection Models

The performance of convolutional neural networks has continued to improve over the last decade. At the same time, as model complexity grows, it becomes increasingly more difficult to explain model decisions. Such explanations may be of critical importance for reliable operation of human-machine pairing setups, or for model selection when the ”best’” model among many equally-accurate models must be established. Saliency maps represent one popular way of explaining model decisions by highlighting image regions models deem important when making a prediction. However, examining salience maps at scale is not practical. In this paper, we propose five novel methods of leveraging model salience to explain a model behavior at scale. These methods ask: (a) what is the average entropy for a model’s salience maps, (b) how does model salience change when fed out-of-set samples, (c) how closely does model salience follow geometrical transformations, (d) what is the stability of model salience across independent training runs, and (e) how does model salience react to salience-guided image degradations. To assess the proposed measures on a concrete and topical problem, we conducted a series of experiments for the task of synthetic face detection with two types of models: those trained traditionally with cross-entropy loss, and those guided by human salience when training to increase model generalizability. These two types of models are characterized by different, interpretable properties of their salience maps, which allows for the evaluation of the correctness of the proposed measures. We offer source codes for each measure along with this paper.
Path planning based on fuzzy-control dynamic window approach

A method integrating rapidly-exploring random tree (RRT) and improved dynamic window approach (DWA) is proposed to realize obstacle avoidance of mobile robots in complex dynamic obstacle environments. Based on the known environment information, the global optimal safe path is generated using the improved RRT algorithm. To address the problem of too many invalid nodes in the expansion of the RRT algorithm, the efficiency of the algorithm is improved by introducing a bidirectional alternating search (BAS) strategy; the forward search tree and the backward search tree alternate searching paths with new nodes as the target nodes until the paths meet; and the redundant nodes in the paths are removed using path node filtering to shorten the path. Secondly, the DWA algorithm is used to track the improved RRT algorithm to plan the path. When unknown obstacles appear in the environment, the weights of the DWA algorithm evaluation function are dynamically adjusted by fuzzy control to avoid the obstacles and return to the original route in time. Finally, the simulation experiments verify that the proposed improved fusion algorithm in complex dynamic environment has short running time, small path cost, and always keeps a safe distance from the obstacles to ensure the optimal path is tracked while safely avoiding the dynamic obstacles.
Rapid trajectory planning method for hypersonic penetration based on sequential convex optimization and deep learning

To improve the penetration capability for hypersonic vehicles, a rapid trajectory planning method for hypersonic penetration based on sequential convex optimization and deep learning is proposed in this paper. Firstly, to get a better evaluation of the hypersonic penetration problem under two interceptors, the penetration dynamics and its convex model are presented, which include the linearization, the discretization and the convexity processing. Meanwhile, to realize the rapid hypersonic penetration trajectory planning, the penetration problem is transformed into a second-order cone programming problem. Secondly, in order to further improve the real-time performance of hypersonic penetration trajectory planning, a penetration trajectory generation method based on deep neural networks is proposed. Finally, multiple scenarios are simulated to verify the proposed DNN based rapid trajectory planning method. Results indicate that the method can realized the effectively penetration which both realize the milliseconds calculating and the excellent robustness.
Path planning of manipulator based on variable probability RRT algorithm

The traditional fast rapidly exploring random tree(RRT) algorithm has the advantages of probabilistic integrity in the motion planning of robotic arm, but at the same time, it suffers from blind search, poor goal orientation, and slow planning speed, while the goal-oriented RRT algorithm based on probability P has good goal orientation but suffers from the problem that it is easy to fall into local minima that cannot be jumped out. In this paper, we propose an RRT algorithm with variable probability P based on the influence of historical nodes to address the above problems so that it has the advantages of both algorithms, relying on the ability of historical nodes to perceive the environment, allowing it to maintain a high goaldirected probability in an open environment and being able to automatically reduce the value of P in an environment with many obstacles, giving it the ability to jump out of the local minimum of traditional RRT.
Visual-language modal hybrid tracking algorithm

Visual and linguistic modalities provide complementary information for various computer vision tasks. This paper proposes a novel approach for improving the tracking algorithm SiamBAN by integrating visual and language modalities. We introduce the concept of a visual-language modal mixer, which combines visual features and language representations to enhance the tracking performance. Specifically, we leverage a language model to extract semantic features from language descriptions and align them with visual features using a linear layer. The VL Modal Mixer is implemented through the Hadamard product operator, preserving spatial information. The mixed features are then fused with visual features through a residual connection to retain fine-grained visual details. Extensive experiments on benchmark datasets demonstrate the effectiveness of our proposed method, achieving state-of-the-art performance in terms of accuracy and robustness. Our work contributes to the advancement of multimodal tracking algorithms and opens up new possibilities for integrating visual and linguistic cues in computer vision tasks.
An improved IMM-KF for UAV position prediction

Traditional Kalman filter algorithms used for predicting drone positions rely solely on the historical positioning information of the drone itself, which makes it challenging to accurately estimate the position of the drone over longer time periods. In this paper, an improved IMM-KF (Interacting Multiple Models-Kalman Filter) algorithm is proposed. This algorithm incorporates a priori information about the drone’s planned flight route and the limited states of the drone, such as position, velocity, and acceleration, to achieve real-time prediction of drone position information. The advantages of interacting multiple models are also leveraged in the algorithm. Mathematical simulation results validate that this improved algorithm outperforms traditional Kalman filters in predicting drone positions, with the improvement becoming more pronounced as the prediction time horizon increases.
A Hybrid AI-based Adaptive Path Planning for Intelligent Robot Arms

Intelligent robot arms are advanced robotic systems used in Industry 4.0 to perform complex tasks. Unlike conventional robot arms, which perform predefined tasks, intelligent robot arms have autonomy and can operate in changing environments, interact with other machines, and collaborate with humans. In this regard, adaptive path planning is crucial for intelligent robot arms, involving real-time environment monitoring and path generation to continuously update the robot’s trajectory based on changes in the surroundings. This paper presents an adaptive path planning method for intelligent robot arms to be used in dynamic environments. The proposed method is based on a hybrid active-passive approach and has been tested in a dynamic workspace simulation environment. The results indicate the ability of the proposed method to respond dynamically in a complex scenario where the target is fluctuating, and an obstacle is intentionally placed in the robot’s path. Additionally, real-time analysis results show that the method can be categorized as real-time path planning with less than 100 ms reaction time for grid sizes with less than 96×96×96 cells. This insight presents opportunities for the establishment of smart factories, smart homes, and smart cities, where the presence of intelligent robot arms in dynamic environments becomes essential.
Re-Aging GAN++: Temporally Consistent Transformation of Faces in Videos

The challenge of transforming the apparent age of human faces in videos has not been adequately addressed due to the complexities involved in preserving spatial and temporal consistency. This task is further complicated by the scarcity of video datasets featuring specific individuals across various age groups. To address these issues, we introduce Re-Aging GAN++ (RAGAN++), a unified framework designed to perform facial age transformation in videos utilizing an innovative GAN-based model trained on still image data. Initially, the modulation process acquires multi-scale personalized age features to depict the attributes of the target age group. Subsequently, the encoder applies Gaussian smoothing at each scale, ensuring a seamless frame-to-frame transition that accounts for inter-frame variations, such as facial motion within the camera’s field of view. Remarkably, the proposed model demonstrates the ability to perform facial age transformation in videos despite being trained exclusively on image data. Our proposed method exhibits exceptional spatio-temporal consistency concerning facial identity, expression, and pose while maintaining natural variations across diverse age groups.
Path Planning and Trajectory Optimization Based on Improved APF and Multi-target

Aiming at the shortcomings of the path generated by the artificial potential field (APF) method, such as local minimum, target unreachability, and low path smoothness, an improved artificial potential field method is proposed. First, to reduce the collision risk and planning difficulty, based on known environmental information such as the location of obstacles and targets, the area with fewer obstacles is selected as the priority area for path planning. Second, to improve the path smoothness and reduce the computation amount, an adaptive step-size adjustment method based on the distance and angle relationship with obstacles within the prediction range is proposed. Third, in view of the effect on each other between obstacle, local minimum, and unsmooth path, a multi-target model considering the size and influence range of obstacles and an improved potential field function are proposed on the basis of the identified planning priority area. Finally, in order that the path is smooth enough to be tracked by autonomous mobile robots, a safe driving corridor without collision with obstacles is constructed on the planned path, and a trajectory fully constrained to the safe driving corridor is generated using the quadratic programming method. Compared with the traditional APF algorithm by using matlab simulation software, the improved APF algorithm can effectively solve the problems of local minimum and target unreachability, generate collision-free trajectories with better smoothness, and have better real-time performance.
Software Vulnerability Detection using Informed Code Graph Pruning

Security vulnerabilities in source code are a growing problem which can lead to financial, reputation, physical, and even human damage when exploited by malicious actors. The use of deep learning to locate these vulnerabilities before they are deployed is currently a major area of security research. Code graphs such as Abstract Syntax Trees and Code Property Graphs are commonly used as input data to deep learning models as they are highly expressive of the code’s function. However, as the length of source code increases, so too does the computational cost of vulnerability detection models. Typically this cost is managed by using only a sample of the input graph, however this is often done randomly and with little consideration for the lost information. Little work has been done to explore informed heuristic-based pruning methods that can be used to reduce graph size to manageable levels by removing information irrelevant to vulnerabilities, while preserving relevant information. We present “Semantic-enhanced Code Embedding for Vulnerability Detection” (SCEVD), a deep learning model for vulnerability detection that seeks to fill these gaps by using more detailed information about code semantics to select vulnerability-relevant features from code graphs. We propose several heuristic-based pruning methods, implement them as part of SCEVD, and conduct experiments to verify their effectiveness. Our heuristic-based pruning improves on vulnerability detection results by up to 12% over the baseline pruning method.
Enhanced Unpaired Image-to-Image Translation via Transformation in Saliency Domain

Unpaired image to image translation is the task of converting images in unpaired datasets. The primary goal of the task is to translate a source image into the image aligned with the target domain while keeping the fundamental content. Existing researches have introduced effective techniques to translate images with unpaired datasets, focusing on preserving the fundamental content. However, these techniques have limitations in dealing with significant shape changes and preserving backgrounds that should not be transformed. The proposed method attempts to address these problems by utilizing the saliency domain for translation and simultaneously learning the translation in the saliency domain as well as in the image domain. The saliency domain represents the shape and position of the main object. The explicit learning of transformations within the saliency domain improves network’s ability to transform shapes while maintaining the background. Experimental results show that the proposed method successfully addresses the problems of unpaired image to image translation and achieves competitive metrics with existing methods.
The Potential of Metaverse Fundamentals, Technologies, and Applications: A Systematic Literature Review

Metaverse is a virtual world platform that enables users to create and explore 3D environments. Recently, this technology has gained traction due to its potential to revolutionize the way people interact and share information. This paper provides an overview of the current state of Metaverse technologies. It begins by introducing their core components and surveys recent research and development activities in the area. Next, the paper examines the potential applications of Metaverse technology in different domains, such as gaming, social networking, training, education, healthcare, and marketing. It then outlines the challenges and opportunities associated with the development and deployment of Metaverse technologies, including scalability, privacy, and security issues. Finally, the paper concludes with a discussion of the implications of Metaverse technologies for the future of digital communication and information sharing.
Wavelet transform-based fuzzy clustering microseismic first-arrival picking method

Microseismic arrival time picking serves as the foundation for microseismic source localization and holds significant importance in the field of microseismic monitoring. Traditional methods, such as Short-Time Average/Long-Time Average (STA/LTA) and clustering methods based on STA/LTA as feature vectors, require manual adjustments of time window parameters to achieve accurate picking. Furthermore, they are susceptible to inaccuracies in high-background noise environments.In response to these challenges, this paper introduces a fuzzy clustering algorithm based on Continuous Wavelet Transform (CWT-FCM) for microseismic arrival time picking. This method begins by transforming the raw data into the wavelet domain and selects scales with relatively large standard deviations as input for the fuzzy clustering process. Ultimately, it identifies the initial arrivals of microseismic events within the resulting clusters.In this study, our proposed method is applied to microseismic datasets with low signal-to-noise ratios as well as real data, successfully and accurately picking microseismic arrivals. Compared to traditional methods, our approach demonstrates increased robustness and practical value in high-interference scenarios. Notably, it eliminates the need for manual parameter adjustments, thereby enhancing efficiency and precision in automated microseismic signal picking.As a result, it establishes a foundational dataset for subsequent automatic and high-precision microseismic arrival time localization.
Enhancing Facial Reconstruction Using Graph Attention Networks

Traditionally, research on three-dimensional (3D) facial reconstruction has focused heavily on methods that use 3D Morphable Models (3DMMs) based on principal component analysis (PCA). Because such methods are linear, they are robust to external noise. The PCA method has limitations when restoring faces that deviate from the training data distribution, particularly when recovering fine details. By contrast, restoration methods utilizing Graph Convolution Networks (GCN) offer the advantages of non-linearity and direct regression of vertex coordinates and colors. However, GCN-based approaches can be prone to overfitting, making them less stable. This study presents a face restoration approach that aims to regress the vertex coordinates and colors of a 3D face model directly from a single wilds 2D facial image. This method demonstrates greater stability and higher accuracy compared to conventional techniques. In addition, Graph Attention Networks (GAT) enhance the restoration performance while separating the networks responsible for facial shape and color, reducing noise caused by interference between different data attributes. Through experiments, we demonstrate the most optimized network structures and training methods and demonstrate improved performance compared to existing approaches.
Cell-based Refinement Processor utilizing Disparity Characteristics of Road Environment for SGM-based Stereo Vision Systems

Embedded stereo vision systems based on traditional approaches often require a disparity refinement process to enhance image quality. Weighted median filter (WMF)-based processors are commonly employed for their excellent refinement performance. However, when implemented on a field-programmable gate array (FPGA), WMF-based processors face a trade-off between hardware resource utilization and refinement performance. To address this trade-off, we previously proposed a new disparity refinement processor based on the hybrid max-median filter (HMMF). However, our earlier work did not guarantee flawless operation in large occluded and texture-less regions, particularly in areas with numerous holes. In order to overcome this limitation of conventional processors, we proposed a cell-based disparity refinement processor. This processor extends our previous HMMF-based disparity refinement processor. To evaluate its refinement performance, we conducted experiments using four types of publicly available stereo datasets. When comparing refinement performance, our proposed processor outperforms conventional processors when using the KITTI 2012 and 2015 stereo benchmark datasets. Additionally, the results demonstrate that our proposed processor exhibits superior refinement performance when applied to the Cityscapes and StereoDriving datasets in comparison to conventional processors. Furthermore, when considering hardware resource utilization, our proposed processor demonstrates lower resource requirements than conventional processors when implemented on an FPGA. Therefore, our proposed disparity refinement processor is well-suited for the disparity refinement process in stereo vision systems that require cost-effectiveness and high performance.
Research on automatic guided vehicle scheduling strategies based on artificial intelligent

Automatic guided vehicles have widely utilized in the transportation works and assist to move the objects into certain areas. Therefore, avoiding the conflict of these vehicles and determine the effective scheduling strategies are an essential component to improve the effectiveness and guarantee the safe of these vehicles. Existing scheduling strategies are concentrated to utilize the mathematical models or train a machine learning algorithm to dispose the scheduling for each vehicles. In this work, we utilize the artificial neural network to solve the scheduling issue and achieve the high effectiveness of moving vehicles. Initially, we order the vehicles with the principle of priority and get the minimum Euclid distance for these vehicles. Subsequently, an artificial neural network is trained to obtain the scheduling routes for each vehicles. From our extensive simulation results and comparison analysis, we can conclude our proposed model can effectively achieve the scheduling routes for each vehicles and avoid the conflict or congestion among guided vehicles with reasonable computation and response time.