Reviewerly/exHarmony huggingface dataset:
Improved Particle Swarm Optimization Algorithm in Multi-Objective Optimization System of Engineering Management

Multi-objective optimization is an important part of project management, which plays a vital role in the process of project construction. Multi-objective optimization algorithm is a dynamic programming method. It can simplify complex problems and realize the description and quantitative analysis of the relationship between different objects and variables in the system through mathematical modeling. This paper mainly uses the method of analytic hierarchy process and algorithm comparison. Through the performance comparison of different aspects of the system, the relevant conclusions of multi-objective optimization are obtained. The experimental survey data shows that only 20.5% of the projects can be completed in time, and most of the projects cannot be completed in time, so it is necessary to study the multi-objective optimization of project management.
Intelligent Scheduling Algorithm of Enterprise Human Resources Based on Data Analysis

Human resource management is an important part of enterprise management. Strengthening human resource management is an important measure to improve the efficiency of personnel use and reduce personnel costs. Because the traditional intelligent planning algorithm dynamic planning is not perfect and the calculation time is too long, this paper mainly discusses the intelligent enterprise human resource planning algorithm based on big data. This research realizes the dynamic planning of enterprise human resources by creating a dynamic planning model based on big data, developing the initial priority table of intelligent planning algorithm, and representing the intelligent planning algorithm based on the priority table. The experimental results show that compared with the traditional algorithm, the algorithm proposed in this study has a greater computational speed advantage, and the average running time is reduced by 6.96 seconds. Therefore, the intelligent scheduling algorithm of enterprise human resources based on data analysis can help reduce many burdens of enterprise human resources scheduling problems based on big data.
Application of VR Virtual Reality Technology in 3D Image Interior Design System

VR virtual reality technology uses a computer simulation system to make users interact with a computer-generated 3D simulation environment. The experience seem to be immersed in the design space and have a good sense of experience in visual, tactile, auditory and other aspects of perception. Along with the rapid development of VR virtual reality technology and interior design, more and more industries are connecting to VR virtual reality technology, leading to new developments such as industry innovation and technological reform. VR technology is applied to 3D image interior design system to optimize computer graphics image processing, 3D space modeling and rendering, and binocular stereo vision technology. From the perspective of multi-user real-time tracking and virtual scene rendering, it can provide users with richer, interactive and more immersive experience. This paper starts with the real-time computer graphics technology, real-time multi-user tracking and virtual scene drawing, and enhances the practical application of VR technology in interior design through technical improvement and method optimization.
Decryption of Encrypted Fractional Wavelet Transformed Image Using Its Inverse

There are various encryption processes to secure a signal or an image. This encryption is much more effective and feasible with the usage of wavelet transform. Here the emphasis is given to fractional wavelet transform because even the restrictions of wavelet transform can be rectified by fractional wavelet transform (FrWT), also the signals can be featured in time domain and in particular fractional frequency domain. In this article we would wish to concentrate on the decryption of the encrypted image. The encryption is performed by FrWT as the security level is much higher when compared.
Design of Matlab/Simulink-based Edge Detection operators and hardware implementation on ZYNQ FPGA

Edge detection is an algorithm that uses specialized kernels to capture boundary features of the image in the domains of vision research. Edge-detection algorithms can be accomplished to a large extent at the software extraction level, yet implementing it via FPGA can be significant for local edge recognition in images. Edge detection is an interaction to recognize boundaries from digital images by distinguishing brightness disparities. The paper presents diverse edge-detection techniques. This strategy incorporates morphological local edge detection and contrast enhancement. The paper presents a SIMULINK-based edge-detection model for operators including Prewitt, Sobel, and Robert and their corresponding simulation results. The boundaries of the region of interest are extracted as features from chest radiographs. The assessment of processed standards is significant in imaging applications. Image standard assessment is allied to similarity assessment where the standard is assessed on the distinction between a primary and a processed image. The edge detection models are implemented on EDGE ZYNQ SoC FPGA Development Board. Using an IP core built on Verilog, the input images are read from storage and the edge-detected images are written to storage. All tasks have been effectively accomplished via Xilinx Verilog code that is compiled using the Vivado and SDK programming tools. An EDGE ZYNQ SoC FPGA Development Board is then used to execute the algorithm, and computational results are derived. The findings of the Simulink implementation are used to substantiate the FPGA simulation results. According to the findings, the edge-detection algorithm is designed and implemented successfully on the EDGE ZYNQ SoC FPGA Development Board.
Detection Analysis of Abnormality in Kidney using Deep Learning Techniques and its Optimization

Chronic kidney disease (CKD) is a global health burden that affects approximately 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> of the adult populationin the world. It is also recognized as the top 20 causes of death worldwide. Unfortunately, there is no cure for CKD houserver, it is possible to slow down its progression and mollify the damage by early diagnosis of the disease. Therefore, the use of modern computer-aided methods is necessary to aid the traditional CKD diagnosis system to be more efficient and accurate. Our proposed model is RCNN to classify the Tumour Area in the X-ray Kidney Image. Compare the Deep Learning Techniques of Mask RCNN Model with other models. Evaluated model is compared with other models by metrics of the Mask R-CNN model and Tuned Hyper parameter CNN Model. It gives Training accuracy of 0.9861 and testing accuracy of 0.9389 in the 5th Epochs of Mask RCNN Algorithm. And also, method uses More Metrics of PrecisionRecall, and F1-Score by comparing the RCNN Model and Hyper tuned CNN Model.
Enhanced Oversampling Framework in Conjunction with Nearest Neighbors

Many of the real-world data sets that are readily accessible are unbalanced. Balancing has a superior part in classifier performance and outcome. To increase the performance of machine learning algorithms on unbalanced data sets, the class imbalance issue must be fixed. The majority of machine learning algorithms are built to maximise measures like accuracy, which in unbalanced data sets can be deceptive. Furthermore, since noise may introduce unpredictability into the predictions of the model, the performance of machine learning algorithms on noisy data can be quite varied. It may be challenging to gauge the algorithm's actual performance and to compare the performance of several algorithms due to this fluctuation. Thus comes the importance of an enhanced synthetic minority oversampling strategy in conjunction with reverse k-nearest neighbours, proposed in this paper as it helps to overcome both unbalancing and noise issues. The kernel density estimation method, which identifies noise based on probability density but not local neighbourhood information, is used to adaptively distribute the number of synthetic samples to each cluster in the minority class. Particularly, RkNN, a well-known KNN version, provides the data on each instance's probability density. According to their pertinent probability density, noisy instances are located and removed. The results showed that the performance of the classifiers were improved with this enhanced synthetic minority oversampling strategy when used in conjunction with reverse k-nearest neighbours and outperformed the performance with original data and SMOTE based on the classifier, dataset, and evaluation metric.
Sentiment Analysis Using Deep Neural Network 1D Convolutional with Long Short Term Memory

Both speech recognition and picture recognition have tremendously benefited by deep learning's (DL). Without a doubt, deep learning neural nets have had a significant impact on machine learning (NLP). Due to the enormous amount of information and ideas that are currently produced, shared, and moved via the internet as well as other media, sentiment analysis (SA) is among the most active areas of NLP. This essay's main topic is SA's prevailing NLP problem. Text mining techniques like sentiment analysis are used to find and extract subjective information from text. Sentiment analysis allows businesses to understand how people feel about their brand, product, & service while monitoring online interactions. For sentiment categorization, this research introduces a Hybrid model with Deep Neural Network 1D Convolutional with Long Short Term Memory (1DLSTM). When applied to sentiment analysis, the movie review and amazon review datasets results suggest that the network model can obtain a good classification impact. The preprocessing is applied for text mining, filtering punctuation, and for creating vocabulary. The proposed results were compared with other base models like SVM, KNN, MNB, which shows that the hybrid model outperforms other models.
Building Trusted Artificial Intelligence with Cross-view: Cases Study

With the widespread application of artificial intelligence, the safety of artificial intelligence has also attracted people’s attention. In this paper, we propose to construct cross-views on three levels including parameter diversification, sample diversification and algorithm diversification which is able to improve the credibility of artificial intelligence. This paper discusses the difficulties and feasible solutions of the three methods, and illustrates the specific implementation of the three diversification with three cases. In our opinion, artificial intelligence security problems, in a short time, can not be completely solved. Taking diverse approaches and constructing cross-views may be a feasible way to mitigate AI security issues.
An Autonomous Landing Simulation System for UAV Tracking Mobile Carriers Based on Visual Servo

With the development of information technology, the fields of aerospace, electronic information, navigation and positioning are constantly developing in the direction of intelligence. The UAV, as an important part of modern military equipment, has been rapidly developed and widely used, but currently UAVs are flying according to the way the vehicle is designed to be structured. However, with the development of technology, the way in which vehicle structures used to be designed, manufactured and assembled has changed considerably and is also playing an increasingly important role in different fields. The main objective of this paper is to investigate the design of a simulation system for UAV tracking and autonomous landing based on visual servoing. This paper addresses the shortcomings of the current UAV autonomous landing simulation technology and proposes a visual servo-based UAV tracking mobile carrier autonomous landing simulation system, which carries out motion simulation calculations such as motion trajectory measurement at the landing point and autonomous landing through the mobile carrier carried by the UAV autonomously. The simulation system does not affect the aircraft landing process, but also provides a new idea and method for the study of the autonomous landing mechanism of mobile carriers. The simulation system mainly consists of three basic structures: the UAV body, the mobile carrier and the visual servo; the simulation system mainly completes the autonomous landing process of the UAV carrier through two control methods: one is real-time motion control; the other is the tracking path parameter optimisation algorithm; the target attitude information is obtained for the landing simulation calculation under the two control methods. The system will provide a new way of thinking and a new method for the research of the UAV autonomous landing mechanism.
Design and Implementation of Financial Accounting System Based on Cloud Computing Technology

The financial system is the soul of the company. A stable and well-functioning financial system is of great significance to the handling of financial affairs, the accounting and payment of employees’ salaries, the management of company assets and even the smooth operation of the company. Based on the saa S model, this paper understands the existing problems and shortcomings in the field of enterprise management accounting information based on investigation and literature reading, introduces cloud computing, a new Internet technology, into the implementation of enterprise management accounting informatization, and puts forward the research topic of management accounting informatization in small and medium-sized enterprises. The system is developed in the SaaS mode, and data management is carried out by sharing data. At the same time, the role-based access control method is used to ensure the security of data. Practice shows that the application of information technology to enterprise financial module realizes the integration of resources within the financial system and even enterprises.
Research on Multi-Objective Optimization System of Construction Project Management Based on K-Means Algorithm

The system in construction project management is constantly improving and increasing, and the traditional method is no longer applicable to the nature of construction engineering management in the current industry, and it is of great practical significance to optimize its multi-objective system. In order to realize the multi-objective system optimization of construction project management, a multi-objective optimization system of construction project management based on K-means algorithm is designed and used, and multi-objective system optimization is carried out on this basis, and the K-means algorithm provided by NET is used for calculation on the basis of the framework system system, in order to open up the research of K-means algorithm in construction project management and provide a new reference basis and practice path for the multi-objective optimization system of construction project management.
Human Motion Pattern Perception Algorithm Based on Heterogeneous Multiprocessor and Deep Learning

With the continuous progress of mankind, the level of sports is particularly important in today’s society. Mobile communication system is one of the key parts. This paper proposes a method of human biological signal processing based on heterogeneous multiprocessor and deep learning. The algorithm utilizes the characteristics of real-time, scalability and high reliability, and can adapt to the needs of users with good performance, easy to use, and can realize human-computer interactive perception. After that, this paper describes the concept of heterogeneous multiprocessor and studies the application of deep learning algorithm. In this paper, the MATLAB programming language is used to verify its effectiveness, and the simulation results prove the accuracy of this scheme. The experimental results show that the correct rate of recognition features is basically above 90%.
Image Dehazing based on Multi-scale Feature Fusion under Attention Mechanism

To solve the problems of insufficient feature extraction and the loss of too much image information in existing methods, a dehazing network based on multi-scale feature fusion under attention mechanism is proposed. Firstly, the base convolutional layer in U-Net is built using improved fully connected residual blocks to reduce the amount of computation. Secondly, the self-convolution block based on the self-attention mechanism is added to extract more delicate feature information of the image. Finally, to increase feature reuse and reduce feature information loss, the feature maps of different levels are fused using various scale gated units. In order to improve the capacity of the restored image to be recognized subjectively, the mixed loss function of multi-scale structural similarity and minimal absolute error is introduced. Experiments are carried out with synthetic haze data sets. Compared with other neural networks, the multi-scale structural similarity and peak signal-to-noise of the dehazed image of the proposed network are increased by 4.31% and 18.33% on average, respectively. The experiment results demonstrate that the network can efficiently avoid color distortion, halo and strong edge effect around the object, and the image has high subjective recognition after haze removal.
Research on Application and Development of Intelligent Video Recognition Technology in Power Transmission, Transformer and Distribution System

Firstly, the concept and basic research framework of video intelligent identification technology are proposed, and five common video identification technologies that may be commonly used in power transmission, transformation and distribution systems are summarized. The typical application research scenarios of video image intelligent identification technology in power transmission, transformation and distribution system are summarized from two perspectives: intelligent inspection for power grid equipment and environmental safety monitoring and intelligent inspection for personal safety monitoring. On this basis, the development problems faced by artificial intelligence and deep learning in the application of video image intelligent recognition in power transmission, transformation and distribution system are discussed, and a scientific solution is put forward for the future development.
Research on Target Detection of Regional Monitoring with Complex Background using CNN and Background Modelling

The regional monitoring systems aim to recognize and localize the target of interest in the region area. However, the target detection algorithm currently used in the regional monitoring system has problems such as low recognition probability under complex background conditions. The type of moving object recognized by the background modelling algorithm is difficult to judge. This paper proposes a monitoring area target detection method that fuses the detection results of the two YOLOv5 target detection algorithms and the Vibe background modelling method through Kalman filtering. Experiments show that the proposed method can improve the consistency and stability of target detection results in regional monitoring scenarios.
Design and Implementation of a Cloud-Based Software Platform for Dynamically Reconfigurable Wearable Computers

As a support platform for new Internet services, the cloud computing (CC) platform serves both end users and service providers, and also provides technical support for the development of some emerging technologies. This paper designs and implements a dynamic wearable computer software platform based on the CC platform, including writing a dynamic reconfiguration management module, developing a CPLD driver, an FPGA driver, a dynamic link module and a configuration file download module; building a graphical desktop manager, which provides a graphical management interface for the wearable computer and specifies the packaging form of the application; developing a GPS resolution module, which provides upper layer applications to provide GPS positioning information; build the communication subsystem on the software platform, do some research on heterogeneous network communication, and provide functions such as authentication, permission management, message sending and information encryption on top of network communication. The encryption and decryption module is accelerated by the hardware encryption function provided by the dynamic reconfigurable (DR) module, and the usability of the DR wearable computer software platform is verified.
Teaching Management System Based on.NET and Mobile Communication Technology

The teaching management system is based on the combination of advanced network communication technology and computer technology, and effectively provides solutions for various problems faced by colleges and universities. It effectively avoids the problem of resource islands with high data repetition. The purpose of this paper is to research teaching management system based on.NET and mobile communication technology. This paper analyzes the research background and importance, and analyzes the research status at home and abroad. Explore performance evaluation and learn to recommend key technology algorithms, and select design technologies before system design, including.NET platform and WAP technology. Finally, the system is implemented and tested. The realization part of the system includes the introduction of the realization function. After testing, the function and performance can reach the design goal.
An Automatic Solution in Security Inspection

In this paper, we present a brand new dataset named cellphone buttery defects in X-ray(CBDx). CBDx consists of 300 X-ray images and 250 of them are anomaly free. We name them ‘good’. Others have some defects in the area of buttery. We name them ‘anomaly’, as Fig. 1. It raises a new task of detecting anomaly defects of cellphone butteries. But the challenge is how to distinguish the anomaly in the case of only training the ‘good’ cellphone. We define this task as an anomaly detection task. We propose an approach to deal with the task from the perspective of unbalanced classification. Specifically, we propose a data augmentation strategy that creates an anomaly sample mimic to the real defects. It helps the classifier to learn self-supervised deep representations and then make it an one-class classifier based on the representations. The classifier is well designed to discriminate the defect samples from the good ones. We evaluate different data augmentation strategies on CBDx. Our approach is more significant in this scenario with no defect training samples, which can be applied in real-world security inspection someday. Also, it can be used on the industrial texture anomaly detection, such as MVTec_AD.