Reviewerly/exHarmony huggingface dataset:
Optimization of the path planning model through integration of improved RRT<sup>*</sup> algorithm and ACO algorithm

Path planning is an important part of AUV (Autonomous Underwater Vehicle) exploration system. The path planning of AUV should consider the optimization goal of the path, the path environment, the terrain and obstacles, and the performance constraints of the robot itself. This path planning problem can be solved by various algorithms, but the commonly used algorithms often have many problems. Therefore, aiming at the problems of long sampling time, large amount of calculation and too random path planning results of RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm, a multi-strategy improved NTDRRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm is proposed. Aiming at the problem that ACO algorithm is easy to fall into local optimum and slow convergence speed, a multi-strategy improved HPACO algorithm is proposed. The NTDRRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm and the HPACO algorithm are combined to form a hybrid improved algorithm, and an AUV path planning model is proposed based on this. Through experimental verification, the hybrid path planning algorithm proposed in this paper has achieved good results in search efficiency, global convergence and solution quality. The research content and results of this paper have certain reference value for improving the efficiency and accuracy of AUV path planning, and have made certain contributions to the development of AUV technology, and can also provide reference and inspiration for research in related fields.
Application of Robotic Arm Path Planning Based on TQC Algorithm

For the slow training of the Panda robotic arm grasping and placing task in a third-party environment in the Gym simulation environment, it is proposed to use the TQC algorithm for training. Compared with DDPG algorithm and SAC algorithm, the training speed is significantly improved. This algorithm has high performance when faced with a rudimentary reward function, in this experiment the reward function is only set to give 0 for grasping an object and gives a negative reward otherwise. This rudimentary reward function makes all actions equally negatively rewarded, and when the strategy is updated, there is no information about which action is better, and therefore the strategy is not improved, thus making it more difficult to explore the rewards for success. When it comes to solving the Gym-Panda robotic arm grasping task using the TQC (Truncated Quantile Critics) algorithm, it is first necessary to understand the TQC algorithm as well as the fundamentals of the Panda-Gym robotic arm simulation environment. After understanding the algorithms, the TQC, DDPG, and SAC algorithms are used in the same environment to train on the same task environment, and the superiority of the TQC algorithm is demonstrated by comparing the data curves.
Algorithm Design for Kubernetes Load Saturation Scheduling in Deep Learning

Kubernetes, the basic technology for building deep learning cloud platform, uses load balancing strategy in the scheduling process, which is easy to generate resource fragmentation and increase the waiting time of tasks. Kubernetes does not consider the scoring of external expansion resources such as GPU, which can not be well adapted to the business scenarios of deep learning cloud platform. Aiming at the above problems, a load-saturated scheduling strategy was proposed to improve the Kubernetes scheduling process, which could reduce resource fragmentation and improve resource utilization. This strategy supported scoring of user-specified external expansion resources, which could be better adapted to the business scenarios of deep learning cloud platform. The experimental results showed that the load-saturated scheduling strategy could reduce the task waiting time by 23.4%, and improve the GPU utilization by 14.15% and GPU memory utilization by 6.85%, respectively.
TOINet: Transfer Learning from Overt Speech- to Imagined Speech-Based EEG Signals with Convolutional Autoencoder

Brain-computer interface (BCI) enables the communication between humans and devices by reflecting humans' intentions and status. Endogenous BCI is the imagined-based BCI and it has the advantage that the fatigue level of the body, especially the eyes, is relatively low and no additional equipment for offering stimulation is required. When conducting imagined speech, one of the endogenous BCI paradigms, the users imagine the pronunciation as if actually speaking. In contrast, overt speech is that the users directly pronounce the words. We proposed the transfer learning-based method from overt speech- to imagined speech-based electroencephalogram (EEG) signals (TOINet). The proposed method utilizes an encoder to extract the feature vector of imagined speech from EEG signals, which is subsequently reconstructed into overt speech signals using the decoder. Through this process, the model can identify the significant and common features present in EEG signals for both overt and imagined speech, facilitating the classification of EEG signals associated with imagined speech. Eight subjects participated in the experiment. The average accuracy of the TOINet was 0.4841 for classifying four words and the EEG features of overt speech improved the performance by 0.0742. Hence, we demonstrated that EEG features of overt speech could improve the decoding performance of imagined speech.
Dynamic Facial Expression Recognition Based on Vision Transformer with Deformable Module

Facial expressions convey a great deal of information during human emotional interaction. However, due to the potential for various types of in-the-wild interferences, such as occlusions and variant head poses, dynamic facial expression recognition (DFER) has been a desperately complicated task. Previous methods focus on applying more robust models to extract the spatial-temporal features but ignore the impact of the key features of the regions of interest (ROIs). This inhibits further improvement of recognition accuracy. In this paper, we propose a 3D vision Transformer with a deformable module termed 3D-DSwin Transformer to guide our model to capture more discriminative features. The deformable module can gradually shift the deformable points to guide our model to pay more attention to the ROIs. A simple yet effective video augmentation method is proposed to expand the number of training samples and avoid overfitting. Visualizations and extensive experimental results demonstrate that our proposed 3D-DSwin Transformer has the ability to obtain the key feature maps, and outperforms the previous state-of-the-art methods on both the FERV39k and DFEW benchmarks.
Color image dehazing enhancement based on wavelet transform

Outdoor image acquisition is directly damaged by foggy weather, leading to image details being covered by fog, contrast and saturation being greatly reduced, and the difficulty of subsequent object recognition and image processing in the image being significantly increased. Due to the above reasons, it is proposed in this paper to use wavelet transform to de-fog the image. Firstly, histogram equalization is applied to images affected by fog in the RGB three-channel, followed by wavelet decomposition. The decomposed approximate value is subjected to the application of the Improved Retinex algorithm. At the same time, de-shadowing can be performed on the image to enhance the details and contrast that are obscured by fog. The images obtained after fog removal can more truly restore the authenticity of the scene and improve the visibility of human eyes. The results indicate that the algorithm can be employed for fog removal in pictures containing fog, showcasing a pronounced enhancement effect on images affected by fog.
Research on Algorithm for Multi Object Tracking Based on YOLOv5 and DeepSORT

Deep learning based object detection technology and multi object tracking technology are the main research branches in the field of machine vision. The research results in this field have a wide range of application scenarios in intelligent driving, video surveillance, and intelligent transportation. This article uses the DeepSORT multi-target tracking algorithm based on YOLOv5 detection to conduct research on pedestrian multi-target tracking and crowd monitoring mask detection. The aim is to provide an efficient automatic detection system to replace epidemic prevention and control in densely populated areas.
Assessment of crutch-assisted walking with sensorized crutches in a 6-Minute Walk Test

This paper presents a possible application of mCrutch, a mobile health system consisting of a pair of sensorized crutches and a mobile Android application, with the purpose of recording the applied force and crutch orientation during a 6-minute walking test with a single crutch. We developed a MATLAB-based threshold algorithm for the segmentation of stance crutch events in analogy with gait analysis. A morphological analysis was performed to quantitatively and visually depict various stance features. The threshold algorithm achieved a segmentation accuracy of 94%. The whole application helps in gaining a comprehensive quantification of stance-related features across various phases of the test.Clinical Relevance: This approach facilitates the design and development of additional algorithms aimed at gathering clinically relevant parameters pertaining to crutch movement within the context of the human-crutch system.
Fitness Landscape Approximation with Dimensionality Reduction Using Multi-dimensional Scaling

We propose a method that combines multi-dimensional scaling (MDS) with a regression model to approximate fitness landscapes and enhance the efficiency of evolutionary search algorithms. The approach involves projecting the original population onto a low-dimensional space using MDS, thereby preserving pairwise distances between data points. The optimal individual is determined using the approximated fitness landscape in the low-dimensional space and subsequently mapped back to the original space using our designed rules. Furthermore, several prediction points are generated in the proximity of the optimal individual to replace certain individuals. This iterative process is repeated multiple times to accelerate the search speed of evolutionary computation (EC). Experimental results demonstrate the superiority of our proposed method over the other two algorithms across most tested functions.
Fully Automated Detection and Segmentation Pipeline for the Bone Marrow of the Lytic Bone of Multiple Myeloma Patients

Monitoring the changes in bone marrow during therapy for multiple myeloma patients is a crucial task. Osteolytic lesions can cause deformation of the bones, affecting the robustness of traditional segmentation tools. A two-model deep learning analysis is explored in this study. A detection model reduces pixel imbalances between the background and the bone marrow pixels, achieving a mAP of 0.878±0.005. A residual U-Net segments the bone marrow, yielding a DSC of 0.856±0.003. The proposed deep learning-based segmentation pipeline allows accurate and fast annotation of the bone marrow in multiple myeloma patients.Clinical Relevance: The proposed deep learning-based pipeline for segmentation has the potential to fully automate the time-consuming process of delineating bone marrow in multiple myeloma patients, significantly improving patient monitoring.
Understanding Approaching Behavior for a Wheelchair with a Robotic Arm: A Human Study for Improving Autonomous Navigation

An intelligent wheelchair is a system that evolves its functions for the well-being of humankind in the present status of robotics. Due to the active evolution of these wheelchair systems wheelchair-mounted robotic arms can be caught as the next level of development. However, the current state of intelligence is far behind the expansion and this lack causes to add extra cognitive load on handicapped users. Most of the available hindrances to autonomous operation could certainly be achieved by replicating natural human behaviors. Under these circumstances understanding human cognition on positioning a wheelchair around a table will drastically ease the autonomous object manipulation tasks by these systems. Hence to understand natural human behavior, a human study with three sub-studies was designed and reveal the prominent factors behind human cognition. Results were analyzed statistically to identify the significance of the considered factors. In lite of the study, approaching and positioning of the wheelchair mainly depend on the object position, obstacle position, and obstacles configuration within the considered workspace. Further orientation mainly depends on the object's position and approaching direction. Besides, these outcomes would be extremely beneficial in synthesizing human cognition to build human-friendly mobile robots for uplifting users' lives as well.
A Novel No-Reference HD Video Quality Metric Based on Perceptual Temporal Pooling

Impressive advancements in capturing, display, and broadcasting technologies significantly elevate image and video quality, and with that the need for designing new reference and no-reference image and video quality metrics. One of the latest and perceptually accurate video quality metrics is the Video Multi-Method Assessment Fusion (VMAF) method. However, VMAF considers the temporal nature of video using basic average temporal pooling, an approach that falls short from human perception. In this paper, we introduce a new no-reference video quality metric that uses deep learning to extract spatial features and a unique temporal pooling approach to accurately predict the visual quality score. To this end, first we created a video quality dataset that consists of high-resolution, 20s-long test video clips compressed at several different bitrates. These videos were labeled based on subjective evaluations and were used to determine the perceptual importance of frames in our temporal pooling scheme. Evaluations showed that our proposed approach achieved correlation of 90.55% with human perception and outperformed the state-of-the-art VMAF approach by 15.63% accuracy.
Deep Positional-Representation-Based Local Information Retention Networks for Mammography Classification

Early diagnosis of breast cancer is challenging because in the most common mammogram images, the tumor usually occupies only a very small part of the entire image, which often makes deep learning models lose attention to the tumor area. In previous work, most models solved this problem by using ROI labeling to train models, which was expensive and difficult to widely apply. Some recent ROI-free methods use multi-scale features or multi-stage training, which gets rid of the model's dependence on ROI but greatly increases the computational complexity and deployment difficulty, limiting the potential of deep neural networks. Therefore, a deep positional-representation-based local information retention networks (PR-LIR) was proposed. PR-LIR is a lightweight, end-to-end mammogram classification model, which uses positional representation (PR) and multi-scale regional pooling (MRP) modules to locate tumor regions and retain regional semantic information of small target tumors at different scales, without ROI labeling and multi-stage training, and almost no increasement in parameters and computational complexity. In particular, the proposed PR and MRP modules have good generalization performance, which can be applied to most CNN models and improve the classification accuracy of mammography images. Experimental results on two publicly available datasets show that PR-LIR achieves the best AUC and satisfactory accuracy compared to the previous state-of-the-art mammogram classification method.
Exploring the Optimal Bit Pair for a Quantized Generator and Discriminator

Generative Adversarial Networks (GANs) are hindered from real-world applications due to their high computational cost and memory requirements. Model compression techniques, such as quantization, pruning, and knowledge distillation, can compress neural networks, lower memory requirements, and model size. However, quantizing generators often leads to a suboptimal solution. In this paper, we propose a novel method to stabilize GAN quantization by quantizing the generator and discriminator with different bit precision. Our method maximizes the quantization efficiency by jointly quantizing the generator and discriminator, which we found to be dependent on each other’s quantization. Specifically, quantizing the discriminator enhances the performance of the quantized generator, while the discriminator’s optimal quantization bit depends on the generator’s quantization bit and architectural type. We conducted extensive experiments on various GAN models, including BigGAN, SAGAN, and SNGAN, using different quantization methods, such as LSQ, PACT, and DoReFa, on benchmark dataset (CIFAR10). The experimental results demonstrate that our joint quantization method achieves higher compression rates while offering better performance in Frechet Inception Distance (FID) and Inception Score (IS).
Removing Scattered Light in Biomedical Images via an Unsupervised Deep Neural Network

Scattered light is unavoidable during biomedical imaging, leading to downgraded images. Removing such scattered light, however, is challenging, because the imaging objects are complex and the light path is complicated. In this paper, we propose to use deep learning methods to computationally remove such scattered light. Inspired by the dehazing methods for natural images, we first developed a novel mathematical model for biomedical images. Then, we developed a deep neural network method to solve this model. Our network simultaneously estimates the clear image and the scattered light. Several experiments are conducted to confirm the network’s effectiveness and efficiency.
Trajectory Planning of Robot Terminal Pose by Interpolation Method

In order to improve the stability of the robot at work, this paper adds an insertion point between the end of the robot and the next target point, and changes the original linear trajectory to a new trajectory planned by interpolation method. The simulation experiment is carried out in MATLAB software. The interpolation method curve is compared with the linear curve. The optimization results show that the velocity and acceleration curves under the interpolation method become more gentle, which verifies the effectiveness of the method.
A Practical YOLOV5 Face Detector with Decoupled Swin Head

Face detection is a fundamental and practical problem in computer vision, which aims to indicate the face positions in a wild environment precisely. However, different from the generic object detection tasks, there are a large number of face samples that suffer from unconstrained poses, occlusion, extreame lights, or other harmful conditions. YOLOV5 is an incredible milestone in the object detection area, but still not powerful enough for the challenging samples. To ease these difficulties, in this paper, we customize DSH-YOLOV5, a practical face detector. Specifically, we integrate a decoupled head with swin transformer layers, whose self-attention mechanism has great potential to explore subtle interconnection details. Additionally, we use two context modules (CBAM and SSH) to enhance the performance of features. Furthermore, we design a novel copy-paste data augmentation to fit the above challenging scenes. Extensive experiments demonstrate that we achieve SOTA performance on WIDER FACE, FDDB, and PASCAL FACE with competitive computational costs. Moreover, under COVID-19, we add the breathing mask and gender classification branches based on the ROI Align to produce more practical face information.
Saliency and Detail Map Interactive Model for Salient Region Detection

Salient object detection (SOD) is a preprocessing step for several computer vision techniques, including visual tracking, image captioning, image segmentation, and so on. In this work, several techniques are adopted to improve the accuracy of SOD. Instead of directly using edge maps as guidance, we improve the adaptive two-stream encoder by employing a clever technique to generate body maps and detail maps, which can provide much information for the final predictions. Regarding body maps and detail maps, different parameter contrasts are provided for users to choose the desired results. Compared to state-of-the-art SOD algorithms, our method outperforms almost all other methods on twelve datasets under two evaluation metrics.
A Decentralized Importance-based Multi-UAV Path Planning Algorithm for Wildfire Monitoring

Wildfire monitoring is a highly important task in preventing the potential damages caused by this disastrous event. In wildfire monitoring, Unmanned Aerial Vehicles (UAVs) has gained significant attention from researchers and practitioners worldwide in recent years. While different UAV-based wildfire monitoring systems have been proposed in the past, a few of them consider decentralized computation and take into account the uneven spatiotemporal propagation nature of wildfires in their proposed solutions. This paper presents a decentralized and importance-based multi-UAV path planning algorithm for wildfire monitoring. We describe the algorithm design, implementation, and use simulation to evaluate its performance. Experiment results show the effectiveness of the proposed decentralized algorithm for importance-based wildfire monitoring.
Eye gaze estimation using iris segmentation trained by semi-automated annotation work

The pupil center and glints have been used in conventional eye-gaze estimations, and the optical axis and point-of-gaze are calculated using these as keys. However, the pupil center used as a basis gets shifted when the diameter of the pupil varies, and the variance in illumination condition leads to degradation of accuracy. Therefore, we propose a semi-automated annotation for iris detection and a model-based gaze estimation method that uses an iris center. The iris area was extracted using segmentation trained with annotated data. The optical axis is determined using the iris center, which does not shift, and is therefore implemented instead of the pupil center. We evaluated the robustness of the proposed gaze estimation through an experiment under changing illumination conditions and confirmed the effectiveness of the iris-based estimation.