Reviewerly/exHarmony huggingface dataset:
Expert System for Classification of Nutrition in Young Children

As an expert system, it is a computer program that is supposed to represent the ability to solve problems (a human expert). The forward sequencing approach, which is a logical method that uses logical reasoning to move from a problem to a solution, is the expert system method used. The goal of this research is to create an expert system that can identify acute malnutrition in young children (aged 0–5 years). The information comes from the nutritionist's response to the query, and the information is collected through the doctors' responses to the questions posed by the researcher in order to collect the target will appear, and the user's nutritional status will be displayed. This data will be used exclusively for this article and to attain the most dependable variables. The user will be able to diagnose and treat a child's eating problem or illness with this system application. This system may be used by any sort of user due to its ease of use. Parents may better understand the significance of preventing acute malnutrition thanks to the system's inclusion of crucial information on acute malnutrition as well as the most recent news regarding children's health, after training and evaluating the data, accuracy results show that the NV approach is more reliable than others. Additionally, it is exceptional in its capabilities, has the benefit of quick training and testing execution, and has the lowest error rate compared to other algorithms. The total number of fixed tries for all algorithms, with SVC having the highest accuracy rate (90%), followed by (KNN).
Research on 3D Product Service System Based on Spherical Model

This paper focuses on the intuitive, three-dimensional and convenient marine geological data service requirements of various applications. Based on the accumulation of 3D seabed visual modeling technology in the past, this paper realizes the uneven columnar sampling geological data processing, the rapid optimization processing of gravity and magnetic data, the extraction of key features of marine data field and the optimization of visual display can quickly and intuitively meet the service requirements for marine geological and geophysical data products, to realize related data analysis and simulation.
Obstacle Free Robot Motion Planning and Intelligent Maneuvering Controller

Autonomously controlled wheeled mobile robots (WMRs) are often instructed to navigate on a previously planned path through predefined control points within a clumsy obstacle-prone environment. In most path planning techniques, the planned path does not pass through all intermediate control points. Although, if the path passes through the intermediate control points in some cases, the overall path is constructed by many segments with the fulfillment of continuity criteria. Due to multiple segments, the existing path planning and control methods may sometimes fail to restore the WMR to its original planned path after negotiating obstacles. Considering the afore-mentioned drawback, a single segmented polynomial function-based motion planning cum path-following scheme suitable in confined and restricted places with the capability of negotiating unexpected obstacles is proposed in this article. The polynomial function is formulated based on the least square method encap-sulating all the predefined control points to the closest range. The proposed function is used as the position output function for the input-output feedback linearization controller (FBC) to maneuver the WMR through the planned path in a path-following paradigm. Further, considering a static and dynamic obstacles-prone working envelope, a fuzzy logic controller (FLC) is embedded with the FBC to inculcate intelligent behavior. Due to a single segment-based polynomial path, the intelligent controller ensures reinstating the WMR on the original path after avoiding obstacles. Finally, the effectiveness of the unique robot motion planning framework in avoiding collisions is illustrated in a simulated environment using robot parameters, and the relevance of the current research work is established over the piece-wise cubic spline-based path planning framework.
Work-in-Progress: BloCirNN: An Efficient Software/hardware Codesign Approach for Neural Network Accelerators with Block-Circulant Matrix

Nowadays, the scale of deep neural networks is getting larger and larger. These large-scale deep neural networks are both compute and memory intensive. To overcome these problems, we use block-circulant weight matrices and Fast Fourier Transform (FFT) to compress model and optimize computation. Compared to weight pruning, this method does not suffer from irregular networks. The main contributions of this paper include the implementation of a convolution module and a fully-connected module with High-Level Synthesis (HLS), deployment and performance test on FPGA platform. We use AlexNet as a case study, which demonstrates our design is more efficient than the FPGA2016.
Research on the Security System of Dangerous Goods Container Yard Based on Complex Network

Compared with the general yard, the prevention responsibility of dangerous goods container yard is heavier, safety control is greater, the consequences of safety accidents are more serious, so the safety management of dangerous goods container yard is particularly important. Complex network, as a kind of network of self-similar, self-organized or all nature, is widely used in various security systems. Therefore, this paper builds a security system of dangerous container yard based on complex network technology, monitors dangerous goods using Internet technology, acquires environmental information and location information of dangerous goods preservation and transportation, queries and stores various information, so as to improve the security of dangerous goods management.
Bayesian network-based importance analysis of hydraulic winches

With the continuous development of the world's oceans, hydraulic winches have been widely used, in order to quickly and accurately determine the weak link of hydraulic winches to improve the reliability of hydraulic winches, this paper proposes a bayesian network-based hydraulic winch importance analysis method. Firstly, the bayesian network of hydraulic winches is established according to the working principle and reliability analysis of hydraulic winches, and then the probability of node occurrence is calculated by using the common life distribution of electromechanical products. In order to verify the accuracy of this method, the results obtained using the proposed method in this paper were compared with those obtained from Griffith importance analysis, and the method was proved to be feasible.
Research on Intelligent Abnormal Body Weight Monitoring Algorithm Based on Fat Depth Learning

At present, body fat and body weight measurement is only a single measurement, which can not achieve long-term and continuous health monitoring. There are some limitations in obesity prevention. The intelligent monitoring algorithm of body fat and body weight is designed based on deep learning. Human intracellular fluid and extracellular fluid belong to electrolytes. The cell membrane is equivalent to a capacitor to establish the equivalent model of human impedance. The impedance signal is collected, and the bioelectrical impedance of the human body is obtained by using the four electrode method. Combined with the variables such as height, age and gender, the content of human body components in the whole body of the subject is predicted. Calculate the body fat content and establish a complete body fat and weight data set. Input various human parameters related to body fat and body weight into the deep learning model for training, and the output prediction result is the intelligent monitoring result of body fat and body weight. The experimental results show that the intelligent monitoring algorithm of body fat and body weight based on deep learning can reduce the prediction error and effectively improve the monitoring performance. It has great credibility in the quantitative study of body fat rate.
Personalized and Stable Recommendation Algorithm of E-commerce Commodity Information Based on Collaborative Filtering

In order to improve the personalized recommendation effect of e-commerce commodity information and accurately capture the user's interest under different quantities of related topics, this paper designs a personalized recommendation algorithm of e-commerce commodity information based on collaborative filtering. Firstly, the framework of collaborative filtering recommendation algorithm is established, and the scoring relationship between user e-commerce commodity information models is obtained. Reuse the user rating data table to evaluate resources at different levels. Select the nearest neighbor, calculate the similarity of e-commerce commodity information based on collaborative filtering algorithm, and generate the recommendation list based on the uneven resource scoring matrix. The experimental results prove that the personalized recommendation of commodity information has good effect, high accuracy and certain application value.
Edge Computing Node Robust Deployment Method Based on Improved Particle Swarm Algorithm

In order to better meet the requirements of node deployment method, a node deployment method of edge computing based on improved particle swarm optimization algorithm is proposed. Based on the improved particle swarm optimization algorithm, the node distribution model of edge computing is constructed, and the node distribution structure is optimized. The node distribution algorithm of edge computing is designed, which simplifies the deployment process of edge computing nodes. The experiment proves that the node deployment method of edge computing based on improved particle swarm optimization algorithm has high practicability and fully meets the research requirements.
Research on Measurement Certification Technology for Trusted Computing of On-board Operating system

Based on the complexity of space environment and the limitation of space resources, a dynamic measurement authentication method for spaceborne operating system with the trusted chip support is proposed, and the configurable key measurement object identification scheme and verification strategy are given. In this method, Key measurement objects and measurement strategies can be set according to the level of spaceborne application. The critical application processes in operation can be measured and verified periodically, then a system security protection verification mechanism for spaceborne software is obtained. The experimental results show that the dynamic measurement method improves the reliability of the system, and the performance meets the requirements of spaceborne applications, which has great practical value.
UAV-to-Ground Target Robust Tracking Algorithm Based on Two-Way CNN

In order to make the UAV equipment track the ground target accurately in the flight process, this paper proposes a UAV-To-Ground target robust tracking algorithm based on Two-Way CNN. The algorithm is based on two-way CNN architecture, and detects the visual target of UAV after solving the surf feature of the ground target image. According to the linear correction expression of the tracking parameters, the algorithm determines the performance intensity of the non maximum suppression effect of the tracking coefficient on UAV ground target parameters, and then combines the known tracking coefficient and the loss function to realize the tracking of the UAV ground target. The experimental results show that under the effect of the dual CNN network architecture, the tracking accuracy of the UAV equipment for the established ground target during flight is significantly improved, which can meet the actual application requirements.
Query Decomposition Algorithm of Non-Relational Database Based on Semantic Clustering

Through the research of Web interactive database technology, this paper introduces the query decomposition algorithm of non-relational database based on semantic clustering in detail. Constructs the non-relational database model, divides the query data category characteristics of non-relational database. And forms a complete technical system by elaborating the application path and implementation method of this technology. By means of cost analysis of query methods, the quality of database query is improved, and finally the progressiveness of this technology is verified. The final research results show that the query decomposition algorithm of non-relational database based on semantic clustering can achieve satisfactory results in Web interactive database, which is worthy of further promotion. I hope it can be helpful to the work of relevant personnel.
JDet-based SAR Images Multi-directional Maritime Ship Target Detection

In complex remote sensing image detection, there are still many challenges in maritime ship detection. In this paper, we combine S2anet, Faster R-CNN, RoI Transformer, FCOS and RetinaNet algorithms from the latest JDet remote sensing image detection algorithm library with satellite remote sensing big data, and use the only SAR image dataset with rotated border annotation, SSDD+, to study the target detection of maritime ships. The experimental results show that the various algorithms in the JDet algorithm library are suitable for remote sensing maritime ship target detection, especially for small target detection, among which S2anet has the most superior results with a mAP of 89.8% In the results, this framework can achieve high accuracy in remote sensing image detection. In addition, we verify the effectiveness, stability and In addition, we verify the effectiveness, stability and accuracy of the above algorithms for rotating border target detection in complex remote sensing scenarios.
A Random Forest Based Encryption Algorithm for Privacy Data of E-Commerce Information

In order to improve the ability of data being stolen by attacks, a privacy data encryption algorithm for e-commerce information based on random forest is proposed. The big data distribution structure model of privacy data is built, random forest hyper chaotic sequence is used to restructure the structure of privacy big data, the fuzzy correlation feature of privacy big data is extracted, hyper chaotic sequence reconstruction method is used to encode and rearrange the extracted privacy data correlation feature, random forest nonlinear vector quantization coding method is used to fuse and encrypt privacy big data, and random forest linear encryption key and decryption key are designed, encrypted transmission and information protection of privacy big data are realized. The simulation results show that the encryption performance of this method is up to 1.31%, and the encryption anti-attack performance is up to 1.000. It has good hyperchaos automation of private data, strong privacy protection ability and anti-attack ability of data encryption.
Visual Communication Brand Design Prototype Structure Robust Optimization Algorithm Based on Virtual Simulation Technology

In order to solve the problems of many constraint variables and complex interference in the design process of the visual communication brand design prototype, and to improve the running quality of the brand design prototype, a structural optimization algorithm of the visual communication brand design prototype was proposed based on virtual simulation technology. This paper builds a prototype structure model based on virtual simulation technology, determines the visual communication form of the brand design prototype, and sets the optimization constraints of the brand design prototype structure. Then, by calculating the structural parameters of the prototype of the visual communication brand design, the components such as bearings, valves, and adjustment devices of the prototype were optimized. Through the virtual assembly of multiple components, the optimized results of the prototype structure of the visual communication brand design are obtained. Finally, the advanced nature of the method is proved by experiments. Compared with the traditional structural optimization algorithm, the design algorithm shows that the structural deformation of the prototype is reduced by 4.547mm, the structural stability coefficient is increased by 0.187, and the optimization speed is significantly improved.
A Recurrent and Meta-learned Model of Weakly Supervised Object Localization

The object localization and detection has improved greatly over the past decade, thanks to developments in deep learning based representations and localization models. However, a major bottleneck remains at the reliance on fully-supervised datasets, which can be difficult to gather in many real-world scenarios. In this work, we focus on the problem of weakly-supervised localization, where the goal is to localize instances of objects based on simple image-level class annotations. In particular, instead of engineering a specific weakly-supervised localization model, we aim to meta-learn a recurrent neural network based model that aims to take series of training images of a novel class, and progressively discover the foreground pattern over them. We experimentally explore the model over scenes composed of MNIST digits and noisy patches as distractors.
Correction Method of Key Movements in Basketball Training Based on Virtual Reality Technology

VR technology refers to virtual reality technology, which can simulate all kinds of things in the real environment, make users integrate into the simulation world and personally experience the change laws and specific characteristics of things [1]. At present, the application of virtual reality technology in training has become a major development trend of physical education. This model can help students improve the effect of physical education learning and further deepen their thinking mode and understanding of knowledge [2]. Combined with virtual reality technology and computer technology, this paper establishes a key action correction method of basketball training based on virtual reality technology. This system is composed of three-dimensional simulation database, capture motion virtual simulation model, motion technology simulation and other parts. The three-dimensional simulation of basketball based on virtual reality technology focuses on the principle, implementation method and specific application of virtual reality technology in the three-dimensional simulation of basketball. Using this method can bring more superior learning environment for athletes.
Algorithm Design of Ideological And Political Teaching Resources Integration Under The Background of Curriculum Security Sharing and High-Quality Education

Based on the data mining technology, it identifies that the curriculum shares the quality educational resources, constructs the management model of ideological and political educational resources, constructs the evaluation and management algorithm of teaching resources, constructs the management platform of teaching resources integration. Build an integrated framework for the quality and utilization of teaching resources to ensure the rationality of basic teaching decisions.
The Implementation of Fuzzy logic controller for the obstacle avoidance in 3 Mecanum-wheeled Robot

mobile robots play an enormous role in different fields of daily life applications including military, safety, and logistic multi-tasking capabilities. A new approach is introduced to the market which is the 3 Mecanum wheeled mobile robot (3-MWMR) is being tested and validated. The main goal of the proposed design is to achieve all the desired directions of motion and to improve the performance of the mobile robot in avoiding obstacles using the fuzzy logic controller.
Evaluation Algorithm of Vocational Undergraduate Education Quality Based on Entropy Weight Method

The conventional evaluation method is to give weight according to the subjective feelings of experts and scholars, and often different people have different judgments or even different views on the weight of a certain index, and the evaluation results will lack recognition at this time. This paper designs an evaluation algorithm of vocational undergraduate education quality based on entropy weight method. According to the evaluation principles, the evaluation system is constructed and the evaluation data is collected. The entropy weight method is used to calculate the index weight, and the result is multiplied with the decision matrix to obtain the optimal solution and the worst solution of the index, calculate the closeness between the optimal solution and the evaluation value, and take it as the evaluation result. The experimental results show that the algorithm can accurately evaluate the quality of vocational undergraduate education and provide reference for educational decision-making.
Study of Swelling Potential Soil in Diwaniya City Based on Previous Empirical Equations

AI-Diwaniya city is within the Mesopotamia plain lands with clay and silt character, the high shallow groundwater level about (0.60-1.55) m from the ground level is one of its features. Revealed the properties of soil and groundwater serious problems that occur to buildings and roads in the city, as a result of soil expansion and contraction. To understand the problem, ten boreholes were drilled, field information was recorded and the examination was conducted. The results showed that the clay percentage (11 % −67 %), Plasticity Index (15 %-35 %), and Soil activity (Ac %) is low to moderately active. Four empirical equations from previous works were used to estimate the potential soil swelling, and evaluate the relationship of the physical properties of the soil with the swelling potential. All samples collected from Diwaniya city showed that the soil had low to medium swelling potential, it's possible that it'll be risky for lightweight structures at medium swelling potential. Also, the relationship between swelling potential (Sp %) and plasticity index (PI %) is strong and acceptable, converse the relationship between swelling potential (Sp %) and clay content (Cc %) was weak. This indicates that swelling soil depends on the quality of the mineral present in clay and does not depend on clay percentage, as the type of mineral that appeared in this study is Kaolinites, and Illites according to the plasticity chart of Diwaniya soil samples. Groundwater level is effective in deformations. For this, it is necessary to plan the construction according to groundwater level of the city, as it affects the swell and shrinkage soil.
Correction Method of Basketball Shooting Errors Based on Virtual Reality Technology

The initial angle error of basketball shooting wrong action correction is large, which leads to the low number of shots. A correction method for basketball shooting wrong action based on virtual reality technology is proposed. Pre-construct 3D scenes, sample basketball training shooting angle vision, establish shooting angle behavior parameter analysis model, estimate shooting angle, analyze shooting angle using joint feature analysis method and key technical action parameters, and obtain basketball training action under multi-view feature fusion Image rectification frame structure. On this basis, the scene model of motion attitude tracking is established, and the third-order autocorrelation function is used to track and analyze the motion trajectory, so as to correct the wrong action of basketball shooting. The experimental results show that the correction method studied has a good tracking effect in motion tracking, the maximum error of the initial angle is only 0.1 rad, and the average number of hits reaches 7.7 times. This method not only effectively controls the initial angle, but also effectively improves the The number of shots made to meet the design requirements of the method.