Reviewerly/exHarmony huggingface dataset:
On the Performance of Machine Learning at the Network Edge to Detect Industrial IoT Faults

Industrial Internet-of-Things (IoT) massively deploys intelligent computing in industrial production and manufacturing environments seeking automation, reliability, and control. Machine Learning models provide intelligent decisions to drive manufacturing systems to the next level of productivity, efficiency, and safety. One of the critical challenges that must be faced is the deployment of Machine Learning models at the network edge to detect data anomalies caused by Industrial IoT hardware failures, since industrial IoT devices are prone to errors and failures. These anomalies can harm the industrial IoT system by producing false alarms, consuming network resources, and affecting productivity. Because of that, it is critical to rely on low latency and high precision detection systems to verify the data received from industrial IoT devices. In light of this, we assessed key performance indicators of five machine learning models running at edge computing, to provide in-depth discussions. The performance results were obtained from an oil refinery scenario using a real industrial IoT dataset. The performance was measured in terms of (a) Accuracy, (b) Precision, (c) Recall, (d) F1 score, (e) Training time, and (f) Response time.
Day-Ahead and Week-Ahead Solar PV Power Forecasting Using Deep Learning Neural Networks

This paper presents the development of a deep learning model using Long Short-Term Memory Neural Network (LSTMNN) to perform short-term forecasts for solar PV power generation for multiple seasons of the year. Other artificial neural network (ANN) models such as feed-forward Extreme Learning Machine (ELM) and the shallow learning Elman Neural Network (ENN) are compared with the proposed LSTMNN model to perform day-ahead and week-ahead PV power forecasts. The LSTMNN is the preferred tool for solving time-series forecasting problems as it can predict future values based on previous sequential data. All three machine learning models (LSTMNN, ELM, and ENN) are trained using historical meteorological data, i.e., Solar Radiation (SR), and temperature, as well as using solar PV power generation data. To validate the performance of the forecasting accuracy of the models, accuracy measures are calculated. Test results obtained from various cases for multiple seasons of the year demonstrate the higher degree of accuracy of the proposed LSTMNN model.
Bi-objective optimization of availability and cost for cloud services

Cloud-based services are a current approach for developing large-scale applications with advantages such as flexibility, access to on-demand resources, and business agility. The overall application functionality results from complex interactions of many decoupled services, each having its operational specificity. Due to this complexity, the manual configuration of these systems is very arduous, error-prone and likely to impair the quality of service, leading to malfunctioning services, lowering availability and accruing costs. Identifying the optimal solution to simultaneously optimize availability and costs, whilst meeting service level objectives remains a challenge for professionals developing solutions using cloud services. This paper proposes a mathematical formulation of a bi-objective problem to identify the optimal set of solutions for the system configuration. Empirical evaluation of the proposed approach in a case study of a real industrial scenario results in an R-Squared of 0.85, an MSE of 0.021 and an optimization accuracy of 0.928. These methods can help practitioners to keep services at an optimum configuration enabling autonomic service operation, whilst improving availability and cost.
Specific Emitter Identification Based on Feature Diagram Superposition

Specific emitter identification (SEI) refers to individual emitter identification by analyzing hardware defect characteristics carried by received signals. Because distortion characteristics of components inside transmitter are complex and their differences are subtle, it is difficult to extract a single Radio frequency fingerprint (RFF) feature to improve recognition performance. Therefore, this paper proposes a novel SEI method based on feature diagram superposition. By constructing emitter distortion model and analyzing visual representation of different types of distortion, superimposed feature diagram can better reflect fingerprint features of different emitters compared to a single feature. Combining feature diagram superposition with neural network model, experimental simulation shows that the proposed method can enhance performance of individual emitter identification without increasing model complexity.
Distinguishing Computer-Generated Images from Photographic Images: a Texture-Aware Deep Learning-Based Method

With the rapid development of computer graphics and generative models, computers are capable of generating images containing non-existent objects and scenes. Moreover, the computer-generated (CG) images may be indistinguishable from photographic (PG) images due to the strong representation ability of neural network and huge advancement of 3D rendering technologies. The abuse of such CG images may bring potential risks for personal property and social stability. Therefore, in this paper, we propose a dual-stream neural network to extract features enhanced by texture information to deal with the CG and PG image classification task. First, the input images are first converted to texture maps using the rotation-invariant uniform local binary patterns. Then we employ an attention-based texture-aware feature enhancement module to fuse the features extracted from each stage of the dual-stream neural network. Finally, the features are pooled and regressed into the predicted results by fully connected layers. The experimental results show that the proposed method achieves the best performance among all three popular CG and PG classification databases. The ablation study and cross-database validation experiments further confirm the effectiveness and generalization ability of the proposed algorithm.
Swarming with (Visual) Secret (Shared) Mission

Collaborative secure image matching is a problem that is applicable in various domains, for both – data in rest and data in motion. The problem is defined as follows. There is a secret image, and a set of n mobile agents. The set of mobile agents should match (compare) an observed image to the original secret image. In this paper we discuss some of the existing approaches, and present an alternative solution applied and analyzed for different applications. The first application is a swarm of Unmanned Aerial Vehicles (UAV) that search for a target specified by an image. The second application is a social network that serves as a smart storage device capable of performing distributed, secret image matching operations. Our solution is based on the well-known Visual Encryption Scheme (VES) and projections of visual bit maps rather than (quadratic complexity) messages exchange in implementing Secure Multi Party Computation (MPC) scheme. We present a perfect-information-theoretic secure solution for this problem. To keep the original image secrecy, at least k out of n mobile agents are required to retrieve any information about the original image.
Asynchronous Autoregressive Prediction for Satellite Anomaly Detection

This paper proposes an ASynchronous Autoregressive Prediction (ASAP) method for satellite anomaly detection. We empirically observe that a single classification model can hardly detect unknown anomalous situations and neglect the Markov nature of temporal satellite data. To address this, we adopt an autoregressive model to deal with the prediction of unknown anomaly for satellite data. We further propose a non-uniform temporal encoding method for asynchronous data and a median filtering method for more accurate detection. To reduce the effect of outliers, we employ an adaptive threshold selection method to achieve a more robust classification boundary. Experiments on real satellite data demonstrate that the proposed ASAP method outperforms the baseline classification method by 55.79%.
A Learning-based Approach for Martian Image Compression

For the scientific exploration and research on Mars, it is an indispensable step to transmit high-quality Martian images from distant Mars to Earth. Image compression is the key technique given the extremely limited Mars-Earth bandwidth. Recently, deep learning has demonstrated remarkable performance in natural image compression, which provides a possibility for efficient Martian image compression. However, deep learning usually requires large training data. In this paper, we establish the first large-scale high-resolution Martian image compression (MIC) dataset. Through analyzing this dataset, we observe an important non-local self-similarity prior for Marian images. Benefiting from this prior, we propose a deep Martian image compression network with the non-local block to explore both local and non-local dependencies among Martian image patches. Experimental results verify the effectiveness of the proposed network in Martian image compression, which outperforms both the deep learning based compression methods and HEVC codec.
SAD360: Spherical Viewport-Aware Dynamic Tiling for 360-Degree Video Streaming

As a kind of medium that provides strongly immersive experience, 360° videos suffer greatly from pixel inefficiency as the content will not be fully viewed by users, leading to a high-bandwidth requirement of streaming. Recently, Tile-based streaming systems have become popular to lower bandwidth usage. However, most of these systems inevitably treat non-viewport areas as viewport because the fixed tiling configuration fails to adapt to the viewport effectively. A finer-grained tiling configuration helps adapt to the viewport, but also introduces significant encoding overhead. Recently proposed dynamic tiling systems address the issue by tiling chunks dynamically based on the features of projected 360° videos. However, because projection inherently introduces serious distortion to image, the results can be misleading. To overcome the viewport adaption problem, we propose Spherical Viewport-Aware Dynamic Tiling for 360° Video Streaming (SAD360). Given that popularity of different areas can be reflected by viewers' behaviours on the whole, a dynamic tiling algorithm is proposed to find the optimal tiling configuration for each chunk by analysing head movement data in hand on a sphere. The algorithm tries its best to generate bigger tiles to reduce encoding overhead and still manages to adapt to the viewport effectively. We also use Reinforcement Learning (RL) to solve the problem of bitrate allocation of tiles varying in size. Experiments demonstrate that our system can get a 14% average QoE gain compared with fixed tiling configuration.
Image Inpainting with Frequency Domain Wavelet Convolution

This paper used Time-Frequency Analysis (TFA) techniques for signal processing on tasks of computer vision. Our main idea is as follows: To build a simple network architecture without two or more convolutional neural networks (CNNs), ana-lyze hidden features by Discrete Wavelet Transform (DWT), and send them into filters as weights by convolutions, transformers or other methods. And we do not need to build the network with 2 or more stages to accomplish this idea. Actually, we try to directly use TFA skills on CNN to build one-stage network. Networks which build by this way not only keep their outstanding performance, but also cost lower computing resources. In this paper, we mainly use DWT on CNN to solve image inpainting problems. And the results show that our model can work stably in frequency domain to realize free-form image inpainting.
MRIQA: Subjective Method and Objective Model for Magnetic Resonance Image Quality Assessment

Magnetic Resonance Imaging (MRI) is widely used for medical diagnosis, staging and follow-up of disease. However, MRI images may have artifacts due to various reasons such as patient movement or machine distortion, which may be unintentionally introduced during the procedure of medical image acquisition, processing, etc. These artifacts may affect the effectiveness of diagnosis or even cause false diagnosis. To solve this problem, we propose a general medical image quality assessment (MIQA) methodology, including subjective MIQA procedures and objective MIQA algorithms. We further apply this methodology to MRI images in this paper due to its widespread use in practical applications. We first establish a magnetic resonance imaging quality assessment (MRIQA) database, which contains 3809 MRI images. Then a subjective image quality assessment experiment is conducted by expert doctors according to the diagnostic value of these images, which split all MRI images into 1285 low quality images and 2524 high quality images. We then conduct a baseline deep learning experiment, and propose an attention based MIQANet model to automatically separate MRI images into high quality and low quality based on their diagnosis value. Our proposed method achieves a great quality assessment accuracy of 96.59%. The constructed MRIQA database and proposed MIQA model will be public available to further promote medical IQA research.
Summarizing the Experience of Using Educational Online Platforms in Ukrainian Universities

The paper is devoted to the analysis of the results of the practical use of educational online platforms on the example of Google Classroom, Microsoft 365, the virtual educational space (VES) of the university based on the Moodle platform by the universities of Ukraine in the process of online education during 2020–2022. Based on the practical experience of teachers and students who used these platforms, recommendations to Ukrainian universities to improve VES as the most promising online educational platform for Ukraine today were substantiated. The prospects of online education were also emphasized, the difficulties of using this form of education for teachers and students were clarified, as well as the fact that the level of absolute success and the quality of success of applicants is significantly affected not so much by the functionality of each of the studied online platforms, but, first after all, psychological factors and the level of digital competences of users.
BAM: A Bidirectional Attention Module for Masked Face Recognition

Masked Face Recognition (MFR) is a recent addition to the directory of existing challenges in facial biometrics. Due to the limited exposure of facial regions due to mask-occlusion, it is essential to exploit the available non-occluded regions as much as possible for identity feature learning. Aiming to address this issue, we propose a dual-branch bidirectional attention module (BAM), which consists of a spatial attention block (SAB) and a channel attention block (CAB) in each branch. In the first stage, the SAB performs bidirectional interactions between the original feature map and its augmented version to highlight informative spatial locations for feature learning. The learned bidirectional spatial attention maps are then passed through a channel attention block (CAB) to assign high weights to only informative feature channels. Finally, the channel-wise calibrated feature responses are fused to generate a final attention-aware feature representation for MFR. Extensive experiments indicate that our proposed BAM is superior to various state-of-the-art methods in terms of recognizing mask-occluded face images under complex facial variations.
A Comparative Study of Cross-Model Universal Adversarial Perturbation for Face Forgery

Although the rapid development of deep generative models (DGM) enables diverse applications of content creation, increasing illegal uses of the technologies also severely threaten the privacy and security of personal information, especially for faces. Several previous works have been proposed to leverage adversarial attacks to fight against these malicious manipulations by adding an imperceptible perturbation to each input image to disrupt the output. In addition, to improve its scalability, a sequential cross-model universal perturbation attack has been proposed to learn a common adversarial perturbation to defend the images from the manipulation of multiple DGMs. However, we find that the order of DGMs for the adversarial perturbation generation does matter and influence the final defense performance. To address this issue, we propose to generate the universal perturbation through joint optimization of multiple DGMs. From the extensive experimental results, we find that the universal perturbation generated by the proposed method can successfully disrupt the output faces of multiple DGMs at the same time and achieves higher attack success rates than the previous state-of-the-art method based on the sequential generation, even under the situations where the model robustness of DGMs are enhanced by random perturbations.
Multi-stage Locally and Long-range Correlated Feature Fusion for Learned In-loop Filter in VVC

Versatile Video Coding (VVC)/H.266 is currently the state-of-the-art video coding standard with significant improvement in coding efficiency over its predecessor High Efficiency Video Coding (HEVC)/H.26S. Nonetheless, VVC is also block-based video coding technology where decoded pictures contain compression artifacts. In VVC, in-loop filters serve to suppress these compression artifacts. In this paper, convolution neural network (CNN) is utilized to better facilitate the suppression of compression artifacts over VVC. Nonetheless, our approach has uniqueness in obtaining better features by exploiting locally correlated spatial features in the pixel domain as well as long-range correlated spectral features in the discrete cosine transform (DCT) domain. In particular, we utilized CNN-features from DCT transformed input to extract high-frequency components and induce long-range correlation into the spatial CNN-features by employing multi-stage feature fusion. Our experimental result shows that the proposed approach achieves significant coding improvements up to 9.70% on average Bjantegaard Delta (BD)-Bitrate savings under AI configurations for luma (Y) components.
Single Image Super-Resolution Using ConvNeXt

In recent years, a lot of deep convolution neural networks have been successfully applied in single image super-resolution (SISR). Even in the case of using small convolution kernel, those methods still require large number of parameters and computation. To tackle the problem above, we propose a novel framework to extract features more efficiently. Inspired by the idea of deep separable convolution, we improve the standard residual block and propose the inverted bottleneck block (IBNB). The IBNB replaces the small-sized convolution kernel with the large-sized convolution kernel without introducing additional computation. The proposed IBNB proves that large kernel size convolution is available for SISR. Comprehensive experiments demonstrate that our method surpasses most methods by up to 0.10 ~ 0.32dB in quantitative metrics with fewer parameters.
Blind Gaussian Deep Denoiser Network using Multi-Scale Pixel Attention

Many deep learning networks focus on the task of Gaussian denoising by processing images on a fixed scale or multiple scales using convolution and deconvolution. In certain cases, excessive scaling applied in the network results in the loss of image details. Sometimes, the usage of deeper convolutional networks results in the loss of network gradient. In this paper, to overcome both the problems, we propose a multi-scale pixel attention-based blind Gaussian denoiser network that utilizes a combination of important features at five different scales. The proposed network performs blind Gaussian denoising in the sense that it does not need any prior information about noise. It comprises a central multi-scale pixel attention block together with dilated convolutional layers and skip connections that help in utilizing the full receptive field of the first convolutional layer to the last convolutional layer and is based on residual architecture for propagating high-level information easily in the network. We have provided the code of the proposed technique at https://github.com/RTSIR/MSPABDN.
Wearable Solutions for the Sign Language

Effective interpersonal communication is of key importance in the professional environment, but above all in private life. People with hearing problems find it difficult to communicate their thoughts and feelings to the outside world. To enable them to communicate, sign language was created, to be a bridge between the world of deaf people and the world of people without hearing impairment. Every day it is difficult to find people who are not deaf, and who can use sign language. In this paper, the authors describe the methods used for communication between people hard of hearing and people with normal audible range. The article presents the solutions that use vision systems available at the world market as well as descriptions of gloves enabling the mentioned communication. Additionally, the authors describe a self-made glove that is used to translate letters from sign language into text or speech. For this purpose, as detectors of the degree of finger bending, the sensors built based on textile substrates with thin metallic layers, changing their resistance depending on the degree of finger bending are used. The effectiveness of repeating hand gestures in the proposed solution is 86.5%, with 520 hand gestures symbolizing the letters of the Polish alphabet.
Tire pattern classification based on ConvNeXt Network

Tire pattern classification is an important means to provide clues for traffic accident processing. With the rapid increase in the number of vehicles, it is in urgent need to develop efficient and automatic tire pattern image classification and recognition system. In this paper, we design a novel lightweight network model for tire pattern image classification dataset. The proposed method consists of two parts. On the one hand, the powerful ConvNeXt is used as the main baseline. On the other hand, the attention mechanism is introduced without sacrificing FLOPs, which improves the classification accuracy. Experimental results on the Tire pattern dataset demonstrate that, the proposed model performed satisfactorily when tested against low quality tire patterns taken from actual scenarios. In addition, experimental analysis of ablation demonstrates the good robustness of the proposed method.
3D object detection combined with inverse kinematics to achieve robotic arm grasping

In recent years, the research of autonomous robotic arms has received high attention from academia and industry, so this thesis aims to develop a visual detection system for robotic arms to grasp and place objects. Object detection technology The purpose of which is to locate objects of interest (target) in a given image, providing object location bounding boxes and categories. ROS is a set of open-source software libraries designed to simplify the creation of complex and robust on various robotic platforms. the task of the robot behavior. The proposed scheme uses a camera with depth information and combines target localization and edge detection algorithms to accurately measure the relative distance between the object and the robot arm. The process includes deep neural network construction and use, robotic arm positioning, object bounding box position information, and robotic arm movement control. Considering the visual scale, the relative distance between the target and the robotic arm is calculated so that the robotic arm can grasp the target and place the object in a specific position. This paper introduces how to combine the deep learning object detection model and ROS moveit! to complete the object identification and grasp and place the robotic arm.
Reduction of Numerical Arrays in Magnetometry Problems Calculations

the paper proposes moving from spatial problems to problems on the plane in similar problems. This will significantly reduce the machine memory requirements and shorten the calculation time.