Reviewerly/exHarmony huggingface dataset:
Simple Image Features for Remote Sensing Strange Images Identification

The identification of the remote sensing images with non-monotonic dependence of the rate-distortion curve to the parameter that controls compression (strange images) is addressed in this research. Twenty-two simple features are extracted from the publicly available dataset with remote sensing images, and these features are compared using correlation between features and strange image membership function, as well as the correlations between analyzed features. Obtained results show that gray level entropy provides highest correlation with strange image membership function.
Applying Embedded Virtualization Technologies for the Next Generation Industrial Edge Applications

With the increasing availability of computing, storage, and network resources, there is a growing interest in efficiently utilizing these resources. Virtualization technology plays a crucial role in cloud computing as it provides flexibility and reliability in managing resources. In this paper, we conducted experiments with several virtualization technologies in the context of industrial edge computing. These technologies aim to reduce overall hardware costs. In addition to explaining the software architecture and describing various open-source solutions, we have also designed an architecture that incorporates embedded virtualization technology. This architecture simplifies and reconstructs the traditional industrial system architecture, enabling efficient utilization and maximizing the allocation of resources for edge-end devices. Finally, we implemented ACRN virtualization technologies in industrial edge-end devices and conducted experiments to validate their effectiveness.
A Breast Mass Image Segmentation Method Based on Improved UNet 3+ Network

In order to solve the problems of low signal-to-noise ratio, uncertain position, and blurred edges of mammography images, and to achieve full-view breast mass segmentation, a breast mass segmentation network based on improved UNet 3+ is proposed. The network provides prior knowledge of edge information for model segmentation by adding an edge-aware module and utilizing shallow and deep features in the network. At the same time, Hybrid loss function (HAM) is added to the network to obtain rich multi-scale information for handling lumps of different sizes and shapes. In addition, we add Dice loss and bce loss to the hybrid loss function to address the problem of pixel class imbalance in mammography images. The experimental results show that the improved UNet 3+ segmentation model has reached the Dice scores of 85.89% and 82.55% on the CBIS-DDSM and INbreast datasets, has improved aa and bb compared with the previous ones, and has a higher performance in the breast mass segmentation task. The accuracy rate is better than other classical models.
Path Planning Algorithm for IB-RRT* Robotic Arms Based on Optimal Sampling and Sparse Nodes

In this paper, we propose an improved OSSN-IBRRT* algorithm for path planning of robotic arms to address issues such as uneven sampling, low planning efficiency, and node redundancy encountered in the IB-RRT* algorithm in complex environments. To enhance the effectiveness of exploration in the spatial domain, we utilize low-discrepancy sequence sampling instead of pseudo-random sequence sampling generated by computers for the selection of sampling points. To improve the efficiency of path planning, we introduce path iterations within an ellipsoid space. Additionally, we tackle challenges arising from strong bias leading to node clustering and local minima in narrow spaces by incorporating a node filtering mechanism during the generation of new nodes. For obstacle avoidance in complex environments, we employ an envelope-based approach to transform obstacles into regular objects. By employing a laser radar for environment scanning and generating a dense point cloud, we convert it into an octree grid map for obstacle avoidance. Simulation results show that under the same number of iterations, the improved algorithm can get a better path, and the time is shorter.
A Lightweight Multi-Scale Based Attention Network for Image Super-Resolution

In this paper, we propose a lightweight multi-scale based attention network (MBAN) for single-image super-resolution (SISR). First, a deep feature transform block (DFTB) is designed for multi-scale feature extraction; this block combines group convolution and improved channel attention (ICA) for performance purposes while remaining sufficiently lightweight. Second, a dual multi-scale attention block (DMAB) is proposed for long-range information interaction; this block employs different window sizes for self-attention (SA) and short connections between different branches to achieve multiscale attention interaction. Finally, our MBAN is constructed by cascaded multi-scale based attention blocks (MBABs) that perform detail restoration; these blocks simultaneously extract multi-scale local features and integrate multi-scale global features with the DFTBs and DMABs. Extensive experiments suggest the superiority of our MBAN over the state-of-the-art (SOTA) lightweight SR methods in terms of both quantitative metrics and visual quality.
Improving Stability of Gaze Target Detection in Videos

Obtaining accurate and stable results in gaze target detection is vital for the subsequent analysis of gaze meaning. However, existing image-based methods, which focus solely on enhancing accuracy, demonstrate poor stability when directly applied on videos. Especially when the video frame rate is low, even though the actual gaze target positions do not differ significantly between adjacent frames, the detected positions vary considerably. This inconsistency, stemming from the lack of temporal information, makes dynamic detection challenging and can lead to jarring outcomes. To reduce the jitter in gaze target detection in videos, we introduce an approach that integrates spatial and temporal modules to combine spatial with temporal information. Additionally, we propose a Jitter loss function to capture significant jitter and impose a strong penalty during training, which empowers our model with increased stability for dynamic detection. Based on a self-collected dataset, experiments demonstrate that our approach exhibits superior stability without compromising accuracy.
Super-Resolution of Underwater Sonar Image Based on Generative Adversarial Network

As an indispensable sensor for obtaining ocean resources, sonar can provide rich underwater observation information. However, due to the limitations of sonar equipment, the resolution of underwater sonar images collected is always low, resulting in some inexplicit underwater targets. To obtain high-resolution underwater information, image super-resolution reconstruction, as an efficient method, can significantly reduce hardware requirements. Super-resolution refers to the technology of restoring image details based on known image information. To achieve high-quality super-resolution for underwater sonar images, we propose a ConvGAN image super-resolution reconstruction model. We design the generator model with the improved ConvNeXt Block which enables the generator to generate rich details. At the same time, we use a VGG-Style discriminator and make its output a patch score map, which is used to guide the generator to recover complex texture details for sonar images. In addition, we compare with other state-of-the-art super-resolution networks on two different datasets. Quantitative and visual comparisons show the effectiveness of our proposed model.
Deep Multimodal Fusion with Corrupted Spatio-Temporal Data Using Fuzzy Regularization

Deep networks have been successfully applied to unsupervised feature learning and supervised classification and regression for unimodal data (e.g., sensors, images, or audio). Multimodal data is often used to improve the performance of networks according to the slogan: the more, the better. Limited research is available to compensate for corrupted signals from multimodal approaches. In this work, we propose a novel regularization method for deep networks to learn features over multiple modalities designed to compensate for relative sensor weaknesses, such as sensor malfunction, inaccuracy, restricted spatial coverage, and uncertainty. We have used a special augmentation strategy for image and time series modalities to enhance the dataset of underrepresented industrial failure cases. The primary objective is to prevent these cases from negatively impacting the model's predictions. Our approach involves incorporating a fuzzy regularizer that can modify the intensity of activations depending on the quality of the signal, enabling disturbances from various modalities to be identified based on the activations. Our experiments on a simulated Universal Robots UR5 dataset demonstrate the effectiveness of our proposed regularization in increasing the model's stability, accuracy, and generalization to uncertainties and failures in the input signals. By incorporating fuzzy regularization in deep fusion models, their efficiency on complex tasks can be improved while reducing the complexity of the architectures.
Analysis of Real time Seismic Signal Using Machine Learning

The development of seismic data acquisition has been driven to keep track of large volume of high sample frequency signal continuously recorded at the seismic station. The signals received at the seismic station show closeness between seismic signals and non-seismic signals. Conventional and Machine learning techniques are widely used to differentiate between seismic and non-seismic signals based on amplitude and further abnormalities in the seismic signals. Authors have prepared a dataset from seed to csv format. The features are detected for frequency, amplitude, and time duration of seismic signal dataset in csv format. These features are stable, strong, and significant. The size of the feature vector has been reduced using dimension reduction technique to improve time and space complexity. Precision, recall and F1 score are used to validate the performance. Performance parameter matrices show good results for all machine learning models; however, the logistic regression and decision tree models meet the best fit model's characteristics. Finally, a presentation using a hybrid dataset (captured by different sensors) demonstrates how this model be employed in a real-time setting while lowering false alarm rates. The experimentation is carried out on seismic signals obtained from individual sensors and sensor networks (SN). A wideband seismic signal from BSVK and CUKG station located near Basavakalyan, Karnataka, and the Central University of Karnataka, makes up the experimental dataset.
Cross-Domain Spatial Matching for Monocular 3D Object Detection

In this article, we explore the domain of 3D object detection, in particular its usage in autonomous driving systems. To replace expensive LiDAR-based perception systems, the monocular camera object detection methods have been successfully adapted to the 3D detection problem. On the other hand, they tend to be very complex and require lots of resources. Our novel Cross-Domain Spatial Matching (CDSM) method poses a simple yet effective alternative to achieve the same goal. We present the idea of reusing 2D object detection network structure and applying our feature domain adaptation layer that transforms learned representation from 2D image space to 3D. We show how we trained and tested it on popular open automotive datasets and present a comparison of obtained results with respect to current state-of-the-art solutions.
Adaptive Information Fusion Network for Arbitrary Style Transfer

Style transfer techniques have found wide-ranging applications in diverse domains, including image enhancement, film and animation production, augmented reality, and social media, garnering significant attention across various disciplines. However, prevailing approaches mainly rely on data-driven and adaptive normalization techniques for learning transformation matrices, while overlooking critical aspects such as multi-scale feature extraction, local-global distributions, and spatial-channel dimensions information. In this paper, we present a novel Adaptive Information Fusion Network (AIFN), comprising an encoder, an information fusion module, and a decoder symmetric to the encoder. Specifically, the information fusion module receives multi-scale feature maps extracted from a pre-trained encoder, consisting of three parallel sub-modules: Adaptive Attention Normalization (AdaAttN), Spatial-channel correlation, and a Linear submodule. Through adaptive learning of sub-module weights, we seamlessly integrate style features to achieve a harmonious fusion. Furthermore, we introduce illumination loss, ink wash loss, and identity loss to enhance stylization performance concerning lighting variations, global hue and diffusion mode, while retaining accurate and rich content features. Through comparison and ablation experiments, the proposed method produces high-quality stylized images, demonstrating excellent performance in arbitrary style transfer tasks.
Characteristic Analysis and Optimal Design of Building Photovoltaic Array Based on Model Analysis

Aiming at the problem of the array design and low energy yield of the photovoltaic (PV) system, the PV system of an actual building is taken as a practical application case to optimize the energy yield of the system from two aspects of the parameters of the PV panel and PV array design. Firstly, an equivalent model of the single-phase PV panel was established. The influence of internal and external parameters on the output characteristics of the PV system is analyzed in detail based on simulations of the model. The system model is further optimized according to the simulation results to enhance the broad application of the system model. Then, based on the actual electricity demand of an existing building, the maximum power output point limit of multiphase PV panels is considered. The array laying of PV panels is optimized in combination with the limitation of the head space of the building to meet the PV power generation needs. The PV array model is further established based on the PV array analysis.
Task Switching Model for Acceleration Control of Multi-DOF Manipulator Using Behavior Trees

In recent years, there has been a growing interest in developing robots capable of replacing human labor, driven by factors such as the decline in the working-age population. One crucial aspect of developing such robots is the ability to decompose complex tasks into manageable subtasks and establish models that govern the switching between these subtasks. Finite state machines (FSMs) and behavior trees (BTs) are two commonly used models for task-switching in robotics. FSMs are mathematical models that describe the behavior of a system with a finite number of states. They have been extensively employed in various robotic applications, including gait pattern, trajectory, and motion generation of robots. However, these transition models primarily focus on the relationships at the higher level. Additionally, FSMs are rarely utilized in acceleration control systems, which offer precise position and velocity control, as well as flexible force control and hybrid control capabilities. BTs, on the other hand, are graphical models that represent an agent's behavior as a graph composed of nodes and edges. BTs also enable modelling of switching between multiple tasks and have been explored for automatically generating behavior trees through machine learning techniques. This approach is well-suited for motion control using acceleration control. In this research, by combining BT-based task-switching with acceleration control architecture, we enable autonomous switching between target object approaching and obstacle avoidance. The proposed method is validated through simulations and experiments using a 6-DOF manipulator.
Transfer Learning Algorithm for Image Classification Task and its Convergence Analysis

Theoretical analysis of transfer learning of the deep neural networks (DNN) is crucial in ensuring stability or convergence and gaining a better understanding of the networks for further development.However, most current transfer learning methods are black-box approaches that are more focused on empirical studies.This paper develops a transfer learning algorithm for deep convolutional neural networks (CNN) with batch normalization layers.A convergence-guaranteed transfer learning algorithm is proposed to train the classifier of a deep CNN with pretrained convolutional layers.Two classification case studies based on VGG11 with the MNIST dataset and CIFAR10 dataset are presented to demonstrate the performance of the proposed approach and explore the effect of batch normalization layers on transfer learning.
Research on Safety Helmet Wearing Detection Based on YOLO

In certain industries such as construction, high risks are often associated with the construction process, and safety helmets are crucial protective gear for workers on construction sites. To address the issues of missed and false detections of safety helmets in complex environments with current helmet detection methods, we propose an improved YOLOv5 object detection algorithm to detect the wearing of safety helmets. The proposed improvements include the addition of an attention mechanism and replacement of the loss function. The proposed method adds an efficient channel attention (ECA) mechanism to the YOLOv5 head network to enhance the model's ability to extract features related to safety helmets, thereby increasing precision without adding too much computational complexity. The loss function is replaced with NWD to effectively improve the detection progress of small safety helmets in YOLOv5.
Multiview Deformation for Dynamic Human Modeling

We present a novel multi-view dynamic 3D human reconstruction technique based on model-based shape deformation. Our approach specifically targets at handling challenging cases such as textureless appearance, heavy occlusions, and depth order ambiguity that are problematic to stereo-based techniques. We propose to pose match and shape deform a human template model to avoid meshing the point cloud. To robustly match the template pose with image observations, we present a novel Graph Convolutional Networks (GCN) to gradually filter out erroneous views and impose appropriate weights on the optimal subset for recovering the 3D skeleton and warping the template shape. Next, We use the warped human template to guide the cross-view consistent semantic segmentation. We set out to deform the warped 3D model so that the silhouette of the deformed model best matches the target in respective views while maintaining semantic consistency. Comprehensive experiments on publicly available and our newly generated complex motion datasets show our approach significantly outperforms the state-of-the-art on sparse cameras, textureless regions (e.g., under black clothing), complex motions, etc.
Noise-Robust Automatic Speech Recognition for Industrial and Urban Environments

Automatic Speech Recognition (ASR) models can achieve human parity, but their performance degrades significantly when used in noisy industrial and urban environments. In this paper, we present monolingual and multilingual ASR models, which can perform effectively even in extreme noise conditions. Specifically, we first generated a large synthetic noise-augmented dataset for Kazakh and English speech, and then used this data to train mono- and multilingual ASR models using state-of-the-art deep learning architectures. To evaluate our models, we have compared them to models trained on original data. The results showed that our models outperform the original ones in terms of word error rate (WER). For the monolingual case, the average WER of our model is 25.1%, while the original model yields 39.9% WER. For the multilingual case, we created a noise-robust customization of the Whisper model, which has a significantly improved performance on Kazakh (for both original and noisy data) and resulted on average 10% performance improvement on noisy English audios. Finally, we tested our models in an industrial setting, which has proven that our models are robust to unseen types of noise and can be used for real-life applications.
Sports Motion Correction Based on Domain Adaptation Using VAE

Recently, the scales of datasets have become a crucial factor directly affecting the accuracy of machine learning, and the use of large-scale datasets is essential. However, in reality, acquiring the amount of data necessary for learning is often difficult. For this problem, this study proposes a model that utilizes domain adaptation based on adversarial discriminative domain adaptation and introduces a variational autoencoder. In general neural networks, learning is accomplished using an unlimited latent space. In contrast, this study attempts to accommodate learning in small-scale datasets by imposing constraints on the latent space. We validated the effectiveness of this approach in a sports motion correction.
Structural Entropy Based Graph Node Classification

Node classification is to predict the labels of the unlabeled nodes in a graph, which is useful for various applications of social network and biological information analysis. To measure the uncertainty of structural information, we propose the structural entropy theory based method for graph node classification. First, we calculate the structural entropy of different graph structures, since the smaller the structural entropy, the less the uncertainty of the structural relationship in the graph. Then, we use the minimal structural entropy to determine the uncertainty of graphical structures. Finally, the nodes with similar structural relationships in the graph are classified. Experimental results show that our method outperforms some state-of-the-art competitors.
BigWavGAN: A Wave-To-Wave Generative Adversarial Network for Music Super-Resolution

Generally, Deep Neural Networks (DNNs) are expected to have high performance when their model size is large. However, large models failed to produce high-quality results commensurate with their scale in music Super-Resolution (SR). We attribute this to that DNNs cannot learn information commensurate with their size from standard mean square error losses. To unleash the potential of large DNN models in music SR, we propose BigWavGAN, which incorporates Demucs, a large-scale wave-to-wave model, with State-Of-The-Art (SOTA) discriminators and adversarial training strategies. Our discriminator consists of Multi-Scale Discriminator (MSD) and Multi-Resolution Discriminator (MRD). During inference, since only the generator is utilized, there are no additional parameters or computational resources required compared to the baseline model Demucs. Objective evaluation affirms the effectiveness of BigWavGAN in music SR. Subjective evaluations indicate that BigWavGAN can generate music with significantly high perceptual quality over the baseline model. Notably, BigWavGAN surpasses the SOTA music SR model in both simulated and real-world scenarios. Moreover, BigWavGAN represents its superior generalization ability to address out-of-distribution data. The conducted ablation study reveals the importance of our discriminators and training strategies. Samples are available on the demo page: https://mannmaruko.github.io/demopage/BigWavGAN/d.html.
Future Object Detection Using Frame Prediction

Future object detection is a task in computer vision that predicts bounding box coordinates, confidence score and class prediction of the objects in the future frames from the past few frames of a video. In this task, not only detecting objects, but also acquiring temporal information is required. To tackle this problem, we propose one-stage detection model based on YOLOV. We improve the detection performance of YOLOV by incorporating the image restoration technique, called Dual Attention Block. Experimental results show that our model achieves better performance than YOLOV (up to 6% gain in mAP) on several settings of future object detection.