Reviewerly/exHarmony huggingface dataset:
Research and Development of Intelligent Recognition system for pull-up Action Norms Based on OpenPose

Pull-up is one of the common sports in national fitness and a compulsory item in the upper limb strength test for youth group required by the National Physical Exercise Standard Work Guidance Manual (2020 version). Currently, there is a lack of professional pull-up action specification recognition system, and there are problems such as unified counting standard and unadjustable difficulty level. The study uses computer vision technology based on deep learning to develop an intelligent recognition system for action specification, and proposes a difficulty-adjustable recognition algorithm model, which helps to improve the standardization level of pull-ups. The system adopts dual cameras to detect the key points of human joints by OpenPose, a human posture recognition framework, to judge the validity and normality of pull-up movements based on the key point positions and joint angle change characteristics, and to switch between different difficulty levels by adjusting the judgment conditions of movement norms.
Implementation of Cluster Server Monitoring System Based on Microservice

Nowadays, cloud computing technology and applications have become more and more popular. By integrating various types of inexpensive computing resources, cloud computing can form a resource pool with huge computing power, providing people with on-demand, self-service and scalable services. Effectively reduce the investment cost of IT infrastructure and improve the efficiency of enterprise IT work. Cluster server is an important part of cloud computing system. This paper aims to monitor the physical cluster servers of and provide help for the resource scheduling of the cloud platform. This paper introduces a cluster server monitoring system based on microservices. The purpose is to monitor various resource information of physical servers, change equipment information, and monitor alarms. In order to verify the performance of the proposed monitoring system, we built a clustered cloud platform is built in the data center and conducted tests. The test results verified the feasibility and performance of the system.
CCSAE-Based Un-Cooperative Communication Behavior Recognition Scheme

The task of non-cooperative communication behavior recognition (CBR) usually faces a complex electromagnetic environment, and the interfered monitoring data will affect the accuracy of communication behavior recognition. A convolutional conditional staked auto-encoder (CCSAE) based un-cooperative communication behavior recognition scheme is proposed in this paper. In particular, the proposed CCSAE denoising module can filter out the noise caused by complex electromagnetic interference by adding conditional constraint to the auto-encoder (AE) structure, and the deep convolutional AE structure can better extracts high-dimensional features related to communication behavior. By comparative experiments, it can be found that the CCSAE-based CBR scheme can stably and effectively improve the accuracy of un-cooperative communication behavior recognition task under complex electromagnetic environment.
Anti-Collusion Fingerprint Scheme of Digital Image

In this paper, a novel fingerprint scheme for digital images is proposed. The fingerprint code is inserted into the host image based on the local intrinsic dimension estimation. In this way, the proposed fingerprint scheme is robust to common image processing methods. If the collusion attacks occur afterwards, the proposed fingerprinting code can trace the colluders as well as estimate the number of the malicious users. Experimental results demonstrate the effectiveness of the algorithm.
Subtype Classification of Renal Parenchymal Tumors on MLP-Based Methods

Renal parenchymal tumors are among the most common tumors in humans. With the development of deep learning, it has become possible to use deep neural networks to distinguish renal parenchymal tumor subtypes. This paper aims to investigate the role of the Multilayer Perceptron (MLP) structure in the classification of renal parenchymal tumor subtypes on magnetic resonance (MR) images. We design a classification model based on ConvMLP. In addition, we introduce Convolutional Block Attention Modules (CBAMs) on the basis of ConvMLP to further improve the classification precision. In order to find where adding CBAMs improves the performance the most, we design four variant networks. We conduct extensive comparative experiments on these four variant networks and other convolutional neural networks. The experimental results show that the addition of CBAM improves the classification precision of renal parenchymal tumor subtypes by 3%, and compared with other CNNs, our classifier has the highest precision.
Video Frame Interpolation via Using GAN to Improve CNN Performance

Video frame interpolation generates intermediate frames between two consecutive ones. Convolutional neural networks (CNNs) achieved good results on computer vision and were frequently used in video frame interpolation. However, in large motion regions, it is challenging to collect enough motion information. The aim of our experiments is to apply generative adversarial networks (GANs) to improve frames generated by CNNs. In this paper, we propose a network that combines CNNs with GANs. We first use CNN as the generator, and then design a discriminator for adversarial learning. Experiments on Vimeo90K and UCF101 demonstrate that our model can attain outstanding performance than the state-of-the-art methods.
Study of a Complete Traversal Path Planning Algorithm<sup>1</sup>

Traversal path planning is widely applied in many technical or strategical spheres. The path planning algorithm plays an important role in designing an effective path for robot- sweepers or lawnmowers operations. Additionally, there are requirements of wide applications in agriculture for generating appropriate moving guides for some irregular paddy fields. However, it is still a problem how to achieve a complete path planning. This paper proposes an algorithm to achieve full coverage of the map with low repeat rates. In this paper, a composed strategy of local and global path planning algorithms is investigated. A global path planning algorithm named Improved Ant Colony Algorithm is studied to work on the graph. An algorithm named Vertical Wave-Front and Distance Transformation Algorithm is carried out to study local path planning issues. Simulations are carried out based on maps with different characteristics. Simulation results show that the proposed algorithm in this paper can perform very well in covering maps and generating a moving route at a low repeat rate.
A Research of Fall Detection Module Based on NB-IOT

It is of great significance for the health of the senior citizens to study the fall of the them. A timely and accurately detect the fall of the elderly and promptly inform the relatives and family members have a great influence of reducing the risk of more serious injury. To this, it&#x2019;s designs a fall detection module based on Narrow Band Internet of Things (NB-IOT) and Micro-Electro-Mechanical System (MEMS). First of all, this paper introduced the fall detection research background, then expounds the module design, focusing on data acquisition module, data processing module, the design of the data transmission module, the design of the power supply module, finally complete the module Printed Circuit Board (PCB) drawing, and tested data transmission, the experimental results show that the module achieves the expected goal.
Unsupervised Cross-Modal Retrieval Based on Deep Convolutional Neural Networks

With the introduction of deep learning in the field of cross-modal retrieval, the performance of cross-modal retrieval has been greatly improved. But the continuous deepening of network layers will lead to the loss of a lot of original information, and once the deepening to a certain extent, there will be a state of accuracy saturation. Based on this, an Unsupervised Cross-Modal Retrieval Based on Deep Convolutional Neural Networks (URCNN) is proposed. The structure of the model with residual Res2net50 convolution neural network structure model to extract the image features, this model can get multi-scale feature information. And its structure can effectively prevent the accuracy of the layer number is too deep and fell, this can make learning to binary codes contains more semantic information and more similar with the original neighborhood structure. At first, Res2net50 is compared with other network structures on three datasets respectively to verify the superiority of Res2net50. Then URCNN using Res2net50 has excellent performance compared with other unsupervised cross-modal retrieval baseline methods.
An Efficient Scene Text Spotter with a Feature-Level Super-Resolution Module

End-to-end scene text spotting, which aims to jointly detect and recognize text in an image, plays an essential role in numerous practical applications. However, low-resolution and arbitrary-shaped text are still two challenges to be addressed to-wards accurate text spotting. To solve these two problems, in this paper, we present an end-to-end accurate text spotting network, which adopts super-resolution from feature-level for text texture enhancement. In detection stage, we adopt a segmentation-based detection manner using anchor-free mechanism without the restriction by pre-designed anchors to generate accurate polygonal proposals of text instances, especially for the arbitrary-shaped scene text. In recognition stage, we design a feature-level super-resolution module, which uses masked pooled text feature as input to focus on reconstructing the text region efficiently and thus provides robust feature representation, to improve the accuracy of recognition results by enhancing text feature representation. Experiment results demonstrate that the proposed method not only outperforms existing methods on the accuracy of low-resolution text spotting, but also achieves a certain extent accuracy improvement when spotting arbitrary-shape scene text.
An Intrusion Detection Algorithm for Resource-Constrained IoT

Most of the existing intrusion detection algorithms are complex and consume a lot of energy, making them unsuitable for application in many resource-constrained IoT scenarios. According to the characteristics of the application scenarios of IoT intrusion detection (i.e. limited computing resources), this paper introduces the Genetic Algorithm into the Isolation Forest (iForest) algorithm, and proposes the Gen-iForest algorithm, which improves the detection accuracy of the algorithm, reduces the amount of calculation required by the algorithm, and reduces the memory space required by the algorithm, making it more suitable for deployment on IoT network devices with limited computing resources. The paper gives the realization of the Gen-iForest algorithm, and gives the experimental results and experimental analysis.
Estimate Occluded Human Pose Progressively by Graph Convolutional Network

The overlap and occlusion between people are an important factor that limits the accuracy of multi-person pose estimation. Although the previous method based on the heatmap-based approaches has achieved good results without occlusion, it is difficult to locate the coordinates of the overlap and occluded part. For this question, the direct learning of the key point coordinates based on the visible image features is ineffective and there is no reasonable use of scale information for reconstruction in the estimation process. In this article, we follow the bottom-up approach and introduce a GCN module to take advantage of this neglected information. The occlusion problem is regarded as a process of inferring implicit features from explicit features. We propose the PE-GCN module, which creatively combine the features of the key points and the connection between the key points in heat-map features and the scale features of the nodes in the pose generation process. Through self-designed evaluation metrics for invisible points in the data, our model improves the ability to locate key points of occlusion under existing benchmarks.
TransIEA: Transformer-Baseartd Image Emotion Analysis

The application of the so-called Transformer network for natural language sentiment recognition is well established. It contains a self-attentive mechanism that allows for better learning based on the context. This mechanism facilitates the analysis of emotional content. Similar to natural language sentiment analysis, image emotion analysis also needs to combine the context in the image, i.e., global and local features of the image to analyze must be combined. No previous studies validated the performance of Transformer in image emotion analysis. In this study, we applied a new approach. For the first time, we aimed at classifying emotion images on the basis of the Transformer network. A new module for convolutional-neural-network-based feature extraction was added in front of the network. The conducted experimental analysis show that our network model outperforms most of the deep-learning models on the commonly used emotion image classification dataset, i.e., the FI (Facebook and instagram) dataset. The model achieves a classification accuracy of 73.40% on this dataset.
A Chaotic Dynamic Congestion Prediction Method under the Space-Terrestrial Integrated Network

The space-terrestrial integrated network is an important research field of the future network. The dynamic and heterogeneous nature of the space-terrestrial integrated network brings challenges to the research of congestion prediction methods. In this paper, we propose a chaotic dynamic congestion prediction method under the space-terrestrial integrated network, which solves the problems of insufficient dynamics and low accuracy of the existing congestion prediction methods for the space-terrestrial integrated network. The chaotic dynamic congestion prediction method is to predict the time series by using the GRU neural network model of the improved particle swarm algorithm to optimize the parameters after wavelet analysis. The experimental results show that the prediction accuracy of the chaotic dynamic congestion prediction method is higher, and it is more suitable for the space-terrestrial integrated network.
Deepfakes Audio Detection Techniques Using Deep Convolutional Neural Network

This article will introduce and discuss a new tool for detecting AI-generated audio deepfake. The convolutional neural network (CNN), is a kind of black box to detect acoustic objects, is used to build the technique in deep learning. The proposed models could be used as trustable standard networks for audial categorization. XCeption is an abbreviation for “extreme inception,” It takes the principles of Inception to their logical conclusion. It is the deep convolutional neural network architecture composed of Depth Wise Separable Convolutions, which CNN employs for audio detection. To streamline the overall system and produce real-time results
Web Service Composition based on Meta-heuristics

Progress of communication and information technology is reinforcing the role of the internet in business. Information sharing, customer support, and marketing support are a few areas where use of internet increasing day by day. Internet impact on business is now affecting economic, social, and technical changes around the world, which are transforming their approach to interact with customers. Web Services are an essential component that helps the business in this regard. But it is very complex to discover the best one from the vast web services pool. The proliferation of services that deliver similar functionalities is rising exponentially. With the advent of meta-heuristic computing, several models have been developed based living on the behavior of many living organisms. These models have been used either alone or in association with other algorithms to solve many optimization problems. Web Service selection is one such application where these models have been used. In this approach, a grey wolf algorithm is proposed to solve the problem of web service selection which has been formulated as a constraint-based selection task. Experimental result shows that the suggested approach provides an improved solution in execution time.
Ethical Hacking: A Technique to Enhance Information Security

In today’s world, security has become the paramount truth. Every organization faces several challenges in securing their information, like master card info from online-looking websites and other crucial information. Every organization fears black hat hackers because they perform hacking to an intrusive extent to hack the data and information for their benefit. Hacking is an act of finding possible entry points or finding vulnerable flaws in a particular system or a network device so that a hacker can get into the system through that vulnerabilities or entry points. So, this research paper discusses ‘Ethical Hacking’ as a legal process by which one can secure data and protect it from malicious hackers by applying tools and techniques. Ethical hacking is also known as penetration testing, which uses its powers to hack data and information, steal passwords, credit card info, scan their e-mail id and other personal information for a good purpose in the world
Threshold based Dynamic Resource Balancing (TDRB) algorithm in Cloud Computing

Cloud Computing is a relevant computing model which uses virtualization for the on-demand allocation of resources deliver to the users through the internet. It is the best epitome for ensuring security, privacy, reliability and productivity. It is a multifaceted and an adaptable technology. Some of the issues in cloud computing are to maintain efficient job allocation strategy, faster response time optimization, efficient load balancing and decreasing bandwidth wastage. One of the major issues is VM allocation to execute jobs in cloud environment. Hence, an efficient VM allocation algorithm is needed to provide optimal solutions for allocating jobs to the VM. In this paper, Threshold based dynamic resource balancing (TDRB) algorithm is proposed for efficient VM allocation with load balancing by comparing the standard deviation of all the available VMs with the threshold value. The jobs are allocated to the Vm, only when the standard deviation of that VM is less than or equal to the threshold value. The experimental results demonstrate that the proposed TDRB performs efficiently than the existing algorithms to allocate jobs to the VMs in cloud environment.
A Systematic Approach for the Detection of Software Bug Using Catboost

The amount of information on web is expanding as the client of the web develops. Presently, Software is sufficiently skilled to replace all that requires human intervention or presence. SDLC (Software Defect LifeCycle) is process that is continued in fostering the Software, which contains thorough testing. To try not to sit around and exertion on Bugs, Software Bug Prediction plays a pivotal part in the beginning stage of improvement in keeping up with and fostering the great nature of Software. Programming Bug Prediction worries with by and large accomplishment as early programming expectation can work on the quality, dependability, proficiency, decreased cost of Software. We have used different AI approaches on NASA dataset and contrasted them and as of late presented calculation called Catboost. The results achieved, when contrasted and other approaches, CatBoost method rout them for all dataset.
Global Warming Dataset and Analysis

Global warming is a major problem today. Climate change is changing and human activity seems to be one of the main causes. A combination of open scientific data, freely provided by reputable organizations such as NASA, and many open source tools that make climate data analysis accessible to everyone. Data scientists have the skills and expertise to convert raw data into information and data data In this paper, we go through three in-depth frameworks of neural network-based captions: CNN-RNN-based, CNN-CNN-based, and dynamic-based. Then show the completed work.
Efficient Hybrid Technique for Devanagari Numerals Recognition

As a subset of Pattern Recognition, Handwritten Devanagari Character Recognition (HDCR) is one of the exciting field of research from decades but Devanagari numerals don’t receive proper attention from the researchers. In India, Government offices provide preferences for filling the application form in Hindi or English & use the scanner for making the record of those applications. But the drawback of the scanned form is that they cannot be edited without using HDCR. This paper proposes a Handwritten Devanagari Numerals Recognition (HDNR) technique which is used to identify Devanagari numerals from given digital images, without human intervention. In this paper a novel technique is proposed which perform pre-processing on the inputted image where features are extracted & based on those features Devanagari numerals are classified.
Impact Of Social Media On Stock Market- A Case Of Sentiment Analysis Using Vader

With the evolution in the area of data intelligence technologies, today it is possible to analyze large amounts of online data depicting emotions in form of tweets and comments. As observed in different sectors and occasions that internet users are motivated by the data they see online in form of advertisements or videos, there is a huge possibility that such online data in form of tweets might also impact the stock market. Thus, present research has been focused on to determine the effect of sentiments on share market. For this, the present research analyzed the tweets of 5 years that is from 2015-2019. No target variable was available thus, the classification has been unsupervised. VADER model was used for classification of tweets into positive tweets and negative tweets, which were then compared to the company’s daily stock prices at the time.
Customized Object Proposal Algorithm Based Traffic Sign Detection

Convolutional neural networks help in extracting information from the camera images. In the present work, traffic signs were recognized by a deep neural network architecture from a camera image. The customized object proposal method is proposed for localizing the region of interest. The number of regions generated by the proposed method is five times fewer than selective search segmentation, this makes the method faster as compared to selective search segmentation as the time spent on false regions proposed by selective search is saved. The proposed method is computationally efficient and simple as compared to modern faster regional convolutional neural networks, where Convolutional layers (RPN) are embedded in the network to determine the region of interest. The proposed system can be utilized for developing a smart driver assistant system and self-driving cars.
Quick Analysis of the NIST Lightweight Cryptography Standardization Process Finalists

Following the final steps in the process of the National Institute of Standards and Technology (NIST) lightweight cryptography standardization, extremely helpful in constrained systems such as the continuously growing Internet of Things (IoT) with the 5G communications as a major actor, this paper aims to draw and summarize some key features and particularities of the final ten algorithms that remained in the competition of this standardization process. The ten final algorithms are analyzed in terms of parameters used, providing AEAD-only functionality or AEAD and hashing functionality and software and hardware performance. While software performance compares code size for the smallest implementations and timings for the fastest implementations on different microcontrollers versus NIST references (AES-GCM/SHA-256), hardware performance compares resources utilization, maximum frequency achieved and throughput versus NIST references (variants of AES-GCM and SHA family).