Reviewerly/exHarmony huggingface dataset:
Research on Offline Classification and Counting Algorithm of Long Fitness Video

With the holding of sports events such as the Winter Olympics, "fitness" has once again become a key topic of widespread concern. Scientific fitness has become a new requirement when people engage in fitness activities today. Scientific and efficient fitness guidance is often inseparable from the support of personal exercise data. At present, the motion data quantification method for motion recognition and counting through wearable devices such as sensors has problems such as poor convenience and poor visualization. This paper uses deep learning methods to study motion classification and counting, which makes up for the shortcomings of traditional quantification methods. The purpose of this paper is to identify and count five common types of exercises in long videos: squats, sit-ups, push-ups, pull-ups, and jumping jacks. The main work and innovations are as follows: (1) This paper proposes an algorithm for motion classification and counting based on long videos. The algorithm first uses the temporal action detection model MPGCN proposed in this paper to locate and classify valid segments of long videos, and then uses the ATRepNet motion counting model proposed in this paper to count the cropped valid segments. (2) The large difference between the action duration and the action amplitude in the long video leads to the deterioration of the effect of the time-series action detection model. In view of this, this paper designs a MS multi-scale graph convolution layer which is integrated into the PGCN model for graph convolution multi-scale information fusion, and the MPGCN model is proposed. Finally, the mean Average Precision (mAP) of the localization and classification of dual-stream features in the public dataset is improved by 1.2%. (3) The complex video background in this scene leads to poor counting accuracy. In response to this, this paper improves the hybrid attention mechanism and integrates it into the counting model RepNet, and proposes the ATRepNet model. Finally, on the self-made test dataset, the counting accuracy is improved by 1.5%.
CAU: A Consensus Model of Augmented Unlabeled Data for Medical Image Segmentation

Medical image segmentation plays an important role in medical diagnosis and treatment. However, medical image data are more expensive and time-consuming to obtain than ordinary image data. In this paper, we propose a novel semi-supervised method named CAU for medical image segmentation, which can easily use Convolutional Neural Networks (CNNs) to segment 2D images. The network learns through a combination of common supervision losses for labeled data and losses for unlabeled data. Specifically, we augment the unlabeled data strongly and weakly and send them to the student model and the teacher model respectively. We take full advantage of unlabeled data learning through a novel combination of minimizing the difference between network predictions for different data augmentation processing scenarios and using an unsupervised loss of min-entropy on the outputs of the two networks. In order to improve the regularization effect, we use the teacher-student model to optimize the teacher model by averaging the student model weights. Experiments show that our method in labeled data experiments with 5%, 10%,35% and 50% labeled data on Automated Cardiac Diagnosis Challeng(ACDC) dataset exceeds the fully supervised algorithm using the same amount of data and the existing popular semi-supervised learning algorithms 1.922% ~ 4.451%(Dice),0.841 ~ 5.031(Asd) and 0.06 ~ 1.116(HD <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">95</inf> ), respectively, and the Dice index exceeds the fully supervised algorithm 0.019% with 50% labeled data, which verifies its effectiveness in medical image segmentation.
Ancient Book Seals Segmentation Based on Automatic Region Growing

Seal is one of the components of ancient Chinese books, paintings or documents. Sealing is usually the last step before the final completion of works such as calligraphy or painting. The seal is of great significance in the study of ancient books, it indicates the copyright, ownership and other information. In this paper, due to the color characteristics of the seal, an improved region growing algorithm and contour detection method have been proposed that used for the seal segmentation. Firstly, the image that includes seals has been converted to Lab color space and obtain specific pixels as the initial seeds for region growing method. After that, the contour detection operation has been performed on the result image of the region growing algorithm to get the candidate position of the seals. Finally, the division of the seal extracted has been realized. The results of experiments showed that the segmentation accuracy of up to 98%.
Low Illumination Image Enhancement Algorithm Based on HSV-RNET

Aiming at the problems of color distortion, blurred edge and large noise in the RetinexNet image enhancement algorithm, a low-illumination image enhancement algorithm based on HSV-RNET is proposed. First, convert the image to HSV color space image, and enhance the illuminance image V separately; then, add denoising loss to control the noise of reflected image in the decomposition network, and add color loss function to the enhancement network to improve the color distortion of the image; secondly, after denoising the decomposed reflection image R, add Canny image sharpening and then combine with the enhanced illumination image I^; finally, convert the HSV image to RGB image. The experimental results show that, compared with image enhancement algorithms such as LIME and SRIE, the algorithm proposed in this paper can effectively reduce the color distortion and edge blur, and improve the visual effect of the image. There are obvious advantages in the quality evaluation index.
Compression and Rendering of Time-Varying Space Environment Volumes

We propose a compression and rendering method for time-varying space environment volumes, by incorporating compression in temporal dimension into HVQ, based on the temporal coherence and cyclical change characteristics of space environment. We incorporate partitions and weights into vector quantization to give more consideration to concerned period data. We take advantage of the codebook-retraining algorithm to speed up the quantization of sequences with similar space environment condition. Furthermore, we employ the progressive rendering based on GPU for real-time interactive visualization. The results of our experiments prove that the method proposed in this paper can improve the compression rate, reduce the cost of reconstruction quality for compression significantly, and also increase rendering speed in space environment domain.
Full-Reference Image/Video Quality Assessment Algorithms Based on Contrastive Principal Component Analysis

With the rapid development of communication network, a large number of high resolution and high quality images and videos are transmitted in communication network. At the same time, the explosive growth of image and video content requires efficient management of image and video collection, compression, storage and transmission processes. However, during these processes, images and videos can be distorted, resulting in a decrease in perceived quality. Therefore, it is necessary to put forward an efficient and reliable image/video quality assessment (I/VQA)method to guide the process of image and video processing. This paper presents a series of simple and effective Full-Reference I/VQA algorithms based on contrastive principal component analysis(CPCA). Firstly, the CPCA algorithm is used to extract the Contrastive principal components(CPCs) from the reference image and the distorted image, and the features are calculated. Then, BP neural network is trained to make the features fit the image’s mean Opinion Score (MOS) or Difference Mean Opinion Score (DMOS). Finally, it is extended to VQA through different temporal pooling and temporal feature extraction. The proposed algorithms perform well on three image quality assessment datasets and two video quality assessment datasets, and in particular, beats all competitors on MCL-V.
Image Restoration Based on Improved Generative Adversarial Networks

Image restoration aims to recover image information from broken or missing regions, which plays an important role in the field of computer vision. There has been a major breakthrough in image restoration by using deep neural network models, but it is not effective in restoring above high-resolution image details. In order to solve this problem, this paper proposes a generative adversarial network with embedded channels and spatial attention, simultaneously, makes the network obtain a larger perceptual field by increasing the size of the expanded convolution, which leads to induce the network to learn the image details and broken edges better, so as to improve the restoration effect on details. The network proposed in this paper is trained and learned on a public datasets, and it is shown in the experimental results that the effect of the method on image restoration is improved to a certain extent.
An Unsupervised Domain Adaption Framework for Aerial Image Semantic Segmentation Based on Curriculum Learning

With the development of deep learning, semantic segmentation has made breakthrough progress, but supervised learning requires a large amount of data with pixel-level annotation. However, for remote sensing data, it is difficult to obtain large-scale pixel-level datasets. There is visual differences between the data of different geospatial regions inevitably. In particular, this difference is often referred to as a "domain gap" and can lead to significant performance degradation. The unsupervised domain adaptive method can effectively solve the above problems, by making the most of existing source domain annotated data, without re-annotating the target dataset, better semantic segmentation results can be obtained on the target dataset. In this paper, we propose a novel unsupervised domain adaptive framework based on curriculum learning (UDA-CL), and a class-aware pseudo-label filtering strategy to dynamically learn the class information during training. Comprehensive experiments show that this method achieves the encouraging semantic segmentation performance on aerial image datasets.
A Named Entity Recognition Method Based on Deep Learning For Chinese Legal Documents

As a fundamental task in information extraction, named entity recognition (NER) has received constant research attention over the recent years. Aiming at the NER of specific cause of action in the legal field, we summarize the key points in current legal documents, establish the corpus dataset of specific cause of action. Based on this dataset, we propose RoBERTa-BiLSTM-CRF legal case entity recognition model to improve the performance of Chinese legal NER. We compare the final experimental results and analyze the advantages and disadvantages of the model. Experimental results shows that our model achieves significant effect on legal NER.
Road Traffic Vehicle Detection Method Using Lightweight YOLOv5 and Attention Mechanism

With the rise in the number of vehicles on the streets, urban road problems are becoming more and more prominent. As the vehicle of the road subject, it is the subject of the problem of intelligent transportation system. In this paper, we discuss the vehicle detection problem in intelligent transportation system, and propose a lightweight YOLOv5 model combining SENet attention mechanism and depthwise separable convolution for the complex situation of vehicles in street video, such as vehicle occlusion, vehicle misdetection and vehicle omission. The feasibility and effectiveness of the method are verified by testing on UA-DETRAC and self-built vehicle dataset.
Primate Recognition System Design Based on Deep Learning Model VGG16

The study of primate recognition is of great significance to the survival of primates. Nowadays, animal classification system plays an indispensable role in primate classification and species research. China has a vast territory, there are a variety of rare primates, such as: golden monkey, white-headed langurs, macaques, etc., the primate recognition system mentioned in this paper provides convenience for the classification and recognition of primates. Artificial visual recognition not only has a high error rate, but also fails to recognize primate photos that have lost most of their details. Primate recognition systems can also accurately recognize and classify images that have lost most of their details. This paper is based on VGG16 deep learning network development, detailed neural network training and call process, and neural network recognition accuracy and loss function are analyzed, and use PyQt5 and QT Designer for visual interface design, to better realize human-computer interaction. The design is completed and the system is tested and the results are analyzed.
Camouflaged Object Detection with Discriminative Information Attention and Cross-level Feature Fusion

Camouflaged object detection (COD) aims to accurately locate and segment objects that blend in with surroundings, which is a challenging task due to the inconspicuous appearance and boundary. In this paper, we propose a novel Discriminative Information Attention and Cross-level Feature Fusion Network (DACF-Net) for camouflaged object detection. Specifically, we introduce a Discriminative Information Attention Module (DIAM) and a Context Enriched Module(CEM) to exploit and extract representative and contextual information. A Cross-level Feature Aggregation Module (CFAM) is utilized to suppress feature redundancies and exploit more complementary information. Extensive quantitative and qualitative experiments indicate that the proposed method has superior performance in explaining the camouflage prediction.
Improvement of PSNR by Using Shannon-Fano Compression Technique in AES-LSB StegoCrypto

One way to write information data is by using cryptography and steganography. Cryptography converts information messages into a form that can no longer be understood. In comparison, steganography is used to insert a secret message into the container media such as images or sounds so that someone will not be aware of the existence of the secret message. The combination of cryptography and steganography methods aims to provide a high level of security for information data. In this paper, we propose the use of the Shannon-Fano compression technique to increase the PSNR (Peak Signal-to-Noise Ratio) value of the steganography image. The StegoCrypto algorithm used is the AES (Advanced Encryption Standard) cryptographic algorithm which is the current standard for symmetric key encryption and the LSB (Least Significant Bit) steganography method. The use of the compression technique with the Shannon-Fano method aims to reduce the amount of embedded data so that it increases the value of the PSNR steganography image. From the test results, the resulting performance for the PSNR value from the combination of methods produces a value above 40, which means it is still above the criteria for a good steganography image and up to 2.785 dB or 89% better than a system that does not use a compression process.
BlockVac: A Universally Acceptable and Ideal Vaccination System on Blockchain

Vaccines are essential in protecting individuals and communities from the risk of harmful and fatal diseases. The world is migrating towards the digitization of vaccination documentation. Still, the current digital vaccination is a centralized model, lacking a universal acceptance. A Universally Acceptable and Ideal Vaccination System (UAIVS) should possess (a) a standard vaccination certificate verification algorithm, (b) privacy to personal information, (c) scalability, (d) support for all vaccinations, (e) accountability for vaccination information access, and (f) a scope for scientific research and development. Blockchain technology intruduces decentralization, immutability, accountability, persistence, and liveliness. These properties make blockchain an ideal candidate to solve the existing problems in digital vaccination. In this paper, we propose a novel block-chain-based system, namely BlockVac, to implement a UAIVS. Furthermore, our work provides a way to track an individual's and family's vaccination history, which is essential for scientific research and development. BlockVac is highly modular, sealable, and cost-efficient, providing enough room for future feature additions. The current works lack cost and scalability analysis. We show the scalability of our system through a thorough analysis. Our implementation shows that adding one vaccination record costs <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$7.16828\times 10^{-6}$</tex> ETH <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\sim 0.026\text{USD})$</tex> for registration, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.5089\times 10^{-5}$</tex> ETH <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\sim 0.078\text{USD})$</tex> for vaccination, and the system can easily handle up to 20K vaccinations per hour.
Fall Detection Scheme based on Deep Learning Model for High-Quality Life

Elderly individuals worldwide have a significant risk of injury or death due to falls. An aged person's mobility, independence, and quality of life can be significantly impacted if he or she falls and does not receive timely assistance. This paper proposes the creation of a low-power network, smart devices, and cloud computing-based fall detection system for senior individuals in indoor spaces as part of this study. 10 volunteers volunteered to have their fall and non-fall movement data collected using a wireless telemetry surface electromyography capture system. Generative Adversarial Network (GAN) models use the effective signal segments as input. Notifications are automatically sent to caregivers of the elderly if a fall is detected, and an alert is triggered. As the final step, the system offers cloud-based services. There is a service that allows healthcare professionals to obtain the fall data for further examination from a medical standpoint. In experiments, fall detection has been found to be highly accurate, precise, and recallable.
Mining Algorithm for Eliminating Redundant Data in WSN

In the Internet of Things, wireless sensor networks (WSNs) are being utilised in a number of ways, and their development is constantly developing. These data are often transformed in numerous formats, enormous quantities and safeguarded information. For modern applications in industry, home, Smart Cities and Transportation, the quality of the information obtained from the sensor data is vital. With all these limitations available, the enhanced mining algorithm needs to be identified so as to increase the quality of services supplied by the IoT based on the WSN. This article highlights the technological problems that need to be taken into account for constructing IoT-based wireless sensor networks to improve the quality of the application on this platform.
Hybrid Deep Convolutional Generative Adversarial Networks (DCGANS) and Style Generative Adversarial Network (STYLEGANS) Algorithms to Improve Image Quality

Generative Adversarial Networks or GANS, are another way of achieving generative modeling using various deep learning methods like convoluted neural networks. They have a wide range of applications like :- image to image translation, improving the resolution of the images, creating multiple images using a single image, checking if the image is real or fake and the list goes on. One of the implementations of GANS, i.e, DCGANS would be discussed in this paper. DCGANS stands for Deep Convolutional Generative Adversarial Networks, it uses convolutional and convolutional-transpose layers in the generator and discriminator, respectively. This DCGANS give prompt result while processing many real time images. In their study Unsupervised representation Learning with Deep Convolutional Generative Adversarial Networks, Radford et al. proposed it. This algorithm generates fake images after being trained on a dataset of human faces. However to create these fake images, the quality of these images should be compromised and this can also make them easily identifiable as fake image. So, here StyleGANS will be used as another implementation of GANS. StyleGANS is not only used for improving the quality of these images but also it helps us to increase fakeness of the images. This project uses the CelebA dataset from Kaggle to train the models.
Improving the Performance of Leukemia Detection using Machine Learning Techniques

Leukemia diagnosis is performed by blood tests and bone marrow diagnostic assay. In the health sector, blood cells counting plays a vital role. The technique used in hospital laboratories involves the manual numeration of blood cells victimization using a device referred to as Haemocytometer. Using this method can be monotonous, sometimes produces inaccurate results and also is a time consuming process. So as to beat the complications, this analysis presents an absolute automatic systemised system to associate with nursing platelets cells within the blood samples and to classify various forms of Leukaemia. Many issues and lost options in the existing cell classifiers are identified by implementing an effusively automatic technique employing a multi-class classifier. This approach identifies varied colour feature statistics with geographical measures in machine learning centred on supervised learning. This model has been trained and evaluated with few machine learning techniques, and offers an accuracy of 92%.
Stagchain – A Steganography based Application Working on a Blockchain Environment

Communication plays a vital role in human life. But in the case of sharing confidential information, there are many issues of concern, such as the lack of confidentiality, authenticity and integrity. Steganography is one of the methods used for conventional covert communication, but it sends the information via an insecure channel. Even though the hackers entering into the channel cannot decrypt the original data, information such as recipient details and communication time are exposed and the communication can be disrupted or hijacked. To address this problem, "Stag-chain," a blockchain-based design that combines an effective steganographic technique with the Advance Encryption Standard (AES) algorithm and the InterPlanetary File System (IPFS) Protocol (decentralized cloud storage), is introduced. Furthermore, the image file will be available on the cloud for retrieval only for a limited period after which it will be replaced by a normal image. Since the data is sent as a transaction in Ethereum, it is almost impossible to manipulate or retrieve that data, hence ensuring data integrity and data security. The aim of the said scheme is to develop an app that will ensure data confidentiality, establish a secure channel to transmit data, enhance data security and protect the data from unauthorized access or manipulation.