Reviewerly/exHarmony huggingface dataset:
Engineering Intelligent Construction Technology Based on BIM Technology

Intelligent building technology is a term used to describe the use of sensors and other technologies that allow remote monitoring and control of buildings. Intelligent building system is also known as smart home automation or smart home control. The goal of these systems is to provide automated solutions for controlling the lighting, heating, ventilation, safety and energy management of buildings. “Intelligent construction” is a new construction concept, which requires the development of the construction industry to take the road of low consumption, low pollution and sustainable development. At the same time, it can apply information technology to reform the traditional production mode of the construction industry. The core technology of “intelligent construction” concept is building information modeling (BIM). BIM Technology can change the phenomenon of low efficiency of information transmission and sharing among various departments and participants of engineering projects. It is the direct application of information means in the construction industry and the technical support for the construction industry to change the traditional construction concept.
Power Prediction of Solar Photovoltaic Power Generation Based on Matrix Algorithm

Photovoltaic power generation is affected by complex and changeable weather conditions, which has the characteristics of volatility and intermittence, resulting in difficulties in photovoltaic grid connection. In order to ensure the stable operation of power system after large-scale photovoltaic grid connection, it is necessary to accurately predict the photovoltaic power in advance. Different weather conditions will lead to great differences in the irradiance actually received by photovoltaic arrays, so a single prediction model is not enough to cope with the prediction of photovoltaic power generation under various complex weather changes. The precision of photovoltaic data clustering is not the key to improve the precision of photovoltaic data classification. To solve the above problems, this paper proposes a matrix algorithm based on solar photovoltaic power prediction.
An Approach for Waste Classification Using Data Augmentation and Transfer Learning Models

Waste segregation has become a daunting problem in the twenty-first century, as careless waste disposal manifests significant ecological and health concerns. Existing approaches to waste disposal primarily rely on incineration or land filling, neither of which are sustainable. Hence, responsible recycling and then adequate disposal is the optimal solution promoting both environment-friendly practices and reuse. In this paper, a computer vision-based approach for automated waste classification across multiple classes of waste products is proposed. We focus on improving the quality of existing datasets using data augmentation and image processing techniques. We also experiment with transfer learning based models such as ResNet and VGG for fast and accurate classification. The models were trained, validated, and tested on the benchmark TrashNet and TACO datasets. During experimental evaluation, the proposed model achieved 93.13% accuracy on TrashNet and outperformed state-of-the-art models by a margin of 16% on TACO.
Construction of Computer-Aided Hierarchical Teaching Model for Higher Mathematics Courses

The basic idea of this model is to use the hierarchical structure of mathematics to develop teaching methods that can be used in all higher mathematics courses. The main goal is to provide students with good mathematical knowledge and skills, which is crucial to their future study. The project consists of two parts: (1) establish a preliminary computer-assisted learning environment; (2) Formulation and evaluation of new teaching methods based on hierarchical teaching model. This paper introduces the construction of such a model, which includes not only the content, but also the teaching method. In addition, it also describes how to use this new teaching model as the basis for further research on computer-assisted learning methods in higher mathematics courses.
Design and Implementation of Online Adaptive Teaching System Based on Data Clustering Algorithm

Based on the two adaptive modules of automatic grouping of students and automatic grading of subjective answers, the evolutionary grouping algorithm is used to keep the relative consistency between groups at different times. According to the automatic grouping function of students, based on the behavioral sequences of students in different periods, the implicit behavioral patterns in the sequences are mined, and the grouping results are generated once for each period, so that the same group of students have similar behavioral patterns, which can be used as a reference for teachers to make teaching plans for each group. The system performs better than the traditional clustering algorithm in English translation teaching test.
Hybrid Methods for Increasing Security of IoT and Cloud Data

There has been a boom in the amount of data that is being transferred over the Internet. With the growth of IoT and various cloud services, it can be said that the growth of this ever-increasing flow of data will be exponential in future. Several new high-speed communication media, like optical fiber cables, are being used for faster transfer of data with higher speeds. However, there is a need to secure all the data. Especially, data from IoT devices as they usually communicate wirelessly because of their ad hoc placements which varies per individual needs. The data collected can be sensitive and hence must be secured. Same can be said for the cloud services. The data from cloud server may be routed via various different types of networks. The security of these networks cannot be guaranteed. Instead, there is a need to make sure that the data remains secured even when it is traveling through unsecure networks. Here, a new hybrid encryption algorithm is proposed which uses AES, Blowfish, and ECC combined to enhance security while still remaining time and space efficient so that it can work on relatively low powered IoT devices too.
Research on the IoT and AI Under the Background of Blockchain

As a new technology applied in computers, blockchain can encrypt computing, store data, transfer and consensus mechanism. Blockchain is a technology underlying Bitcoin with high security performance. The key of blockchain technology is to prevent the database information from being modified and destroyed. This technology can be divided into two types, mainly block and chain structure, which can reduce the probability of information theft or modification in the database and monitor the emergence of new blocks. Blockchain has a good effect in decentralizing the system, data traceability and tamper-proof, while the IoT is a platform for sharing resources and exchanging information between objects, and the value of data on it is constantly mined and revealed. Recently, a number of studies have explored the use of blockchain in the IoT. Under the background of blockchain, IoT technology and AI technology show an unstoppable development trend. First, the basic concepts of blockchain technology are introduced, and secondly, based on the discussion of blockchain technology in the context of blockchain, the IoT is centered on the characteristics of IoT technology and the core of blockchain technology. Communication between the analysis of the problem of the network. Finally, the function of the blockchain is explained from the background of the blockchain. With regard to AI technology, the opportunities and challenges brought about by the integrated application of IoT and AI technologies and new technologies will be elaborated. At this stage, the combination of blockchain technology and the IoT has achieved initial results, showing its application and research value, but its further expansion still needs to rely on mutual integration with new research technologies.
Construction of User Profile for Teaching Archive Knowledge Service Domain

With the arrival of the 5G era, the traditional archival service mode has changed. As an important support for schools to carry out education and teaching work, it is indeed necessary to promote the teaching archives of colleges and universities from information service to knowledge service. This paper firstly explains the current situation of the development of knowledge services for university teaching archives and defines the basic concept of user portraits. Then constructs a teaching archives user portrait set through K-means clustering algorithm, and builds a teaching archives knowledge service model on this basis, aiming at providing better service for the users of teaching archives and improving the awareness of related groups to the teaching archives in colleges and universities.
Ant Colony Algorithm Theory and Its Application in Control Engineering

Ant colony algorithm theory is an algorithm that helps to find a path or route for a given task. It is also used in control engineering to find the best way to control the traffic flow on the road. This method can be applied to various problems, such as routing, scheduling and so on. The main advantage of this algorithm is that compared with other algorithms (such as Dijkstra algorithm and a* search algorithm), it requires very little computing time. This process involves several steps. These steps are explained. This paper will introduce some basic concepts of ant colony algorithm (ACA) theory and its application in control engineering. We will also outline some existing work on ant colony algorithm (ACA) for various control system applications, including real-time scheduling and traffic flow.
Experimental Result Analysis of Extreme Learning Machine with Various Activation Functions: An Application in Credit Scoring

Credit scoring is widely employed method for measuring the risk accompanying with a credit applicant. Credit risk estimation focuses to estimate the risk in areas such as creditworthiness, default risk analysis, fraudulent transactions, though credit scoring has the potential to boost the economy for that industry. So, AI and ML can be leveraged to bring a significant impact to harvest the applicants’ historical data. Extreme Learning Machine (ELM) is a classification tool with single-hidden layer feedforward neural networks, and it analytically determines the output. Moreover, ELM performance depends firstly on number of neurons in different layers and secondly on activation function, weights and biases assigned to those neurons. So, this study presents experimental result analysis of ELMs with various set of neurons in hidden layer and various activation functions, as an appropriate activation function may improve the performance of ELMs.
Hellinger Metrics for Validating High Fidelity Simulators Using Target Tracking

To achieve autonomy at sea, it is believed simulations will be essential in testing, analysing and verifying autonomous systems due to the scarcity and high cost of obtaining real data for all relevant scenarios. This reliance on simulation raises the question on how much synthetic data can be trusted, especially from sensor data such as lidars, radars and cameras. Methods for validating specific sensor models exists, however these are often focusing on perceptional differences without considering the sensors impact on the autonomy’s situational awareness. In this paper we make an attempt to analyse this using a JIPDA target tracker, by comparing its performance on real and synthetic lidar data with various Hellinger metrics for Bernoulli and multi-Bernoulli multi-target densities. Our work showcases a method that quantifies sensor fidelity of synthetic data based on tracker performance, a step towards building trust to simulators targeted at validating autonomy systems.
Recommendation Method and System for Fitness of Children and Adolescents Based on Ant Colony Algorithm

With the national attention to health and the public attention to their own health, fitness exercise has become one of the most concerned issues. As the future and hope of the country, teenagers’ physical health is not optimistic at present. How to improve teenagers’ interest in fitness exercise and provide complete and effective fitness exercise theory for teenagers’ fitness exercise should become the focus of our attention. The health recommendation method and system for children and adolescents based on ant colony algorithm is a new method to improve the health level of the body. In this case, it is a computer program that uses ants as a model. The main idea behind this method is that there are many methods to find the best solution to any problem, but in order to know which method is the most effective, we need some standard or rule (in our example: ant colony algorithm).
Research on Performance Enhancement of Parallel Computing in Intelligent Proofreading System

This paper describes the status quo of intelligent checking technology and the efficiency problems existing in the current checking system, and analyzes the reasons why the processing complexity of the checking system determines the time consuming of serial execution. Through the introduction of parallel computing technology, the paper finds out the tasks suitable for multi-process and multi-thread in the checking system. Through the reference of openMPI and Pthread, through multi-scene and multi-parameter experiments, the direction and method of parallel computing performance improvement and parameter value measures are obtained, and the parallel computing optimization strategy of the existing intelligent checking and checking system is proposed.
A Study of Teaching Resource Retrieval System Based on Semantic Web

This paper starts from the architecture of the Semantic Web, and studies the core technologies of the Semantic Web, such as RDF/RDFS, OWL and SPARQL, from the perspective of implementation. Secondly, the theoretical basis and related technologies of the Semantic Web are studied and analyzed from the aspects of the Semantic Web’s functions and hierarchical composition, grid technology, ontology technology, etc., especially its knowledge organization, knowledge management, knowledge Advantages in discovery and knowledge retrieval. Finally, the research analysis teaching resource management related technologies under the Semantic Web framework, it provides theoretical and technical guarantees for the next research work.
Overload Damage Detection Method of Motor Car Axle Based on Neural Network Algorithm

Neural network is a new theoretical model. It has the ability of parallel processing. It can classify, define and optimize information and knowledge by simulating biological neural system. In this paper, a typical nonlinear deformation and damage monitoring method is trained by BP algorithm, which is based on neural network to detect the axle overload strength. Firstly, the fatigue response characteristics of the corresponding working conditions (such as low speed) under different stress states on the axle when the method is running in the motor car are studied by experimental method. Secondly, the actual working environment is simulated as the process of high-speed driving through the design model, and the detection degree of axle overload damage in this scenario is tested by the model. Finally, the test results show that the running time of the motor car axle overload damage detection model based on neural network algorithm is relatively short, and the delay time is also relatively short. The probability of checking the overload damage is basically more than 90%, which shows that the motor car axle overload damage detection rate of this model is very high and can meet the needs of users.
Blockchain Based Certificate Deposit System for Judicial Departments

In the traditional judicial system, the public security, procuratorate, court and judicial bureau all involve private information, so the data current and sharing of each department will be greatly restricted. To solve these problems, a blockchain-based public security, procuratorial, and judicial evidence storage system is designed, which can, to a certain extent, solve the morass of data sharing among various departments of the judicial system, and can ensure the privacy of data. The proposed scheme uses the chain structure of the permissioned chain FISCO BCOS as the blockchain platform and combines the Merkle tree and hash function to access data. The structural storage of data can ensure that the uploaded data is traceable and without being tampered with. The public security, judicial and judicial evidence storage system designed with blockchain technology can realize database sharing among various departments of public security, procuratorate, court, and judicial bureau, eliminate the risk of leakage and tampering in the process of case evidence flow, and protect citizens' privacy and national information safety.
Construction of Network Teaching Platform of University Management Based on Internet

Information technology and network technology are increasingly widely used in the field of teaching, and the idea of open teaching has become the core of modern teaching development. The teaching mode corresponding to the education system of using the Internet to carry out education and teaching has changed from teacher centered to student-centered. The system is a network teaching platform system using b/s structure, and the system is selected Net and SQL Server2005 are development environments. Starting from the characteristics of network teaching, through the investigation of network teaching platform, understand the business needs of the system. Using UML modeling technology, the system analysis is further elaborated.
Detection of Physical Impairments on Solar Panel Using YOLOv5

The paper presents a detailed description of damages in panels such as cracks, PID effect, delamination, hotspot, and snail trail using neural network techniques. The proposed model uses LabelImg for image pre-processing and Roboflow for image augmentation. YOLOv5 algorithm includes the PyTorch 1.8 framework, which comprises maxpooling, bottleneck CSP, SPP, upsample, and concat. The evaluation matrices which are used in this paper are IoU, precision, recall, and mAP. The simulated results are correctly classified with high efficiency and a more significant number of datasets.
Construction of English Teaching Multimedia Resource Library Based on Cloud Computing Technology

Cloud computing, as a new type of network service model, shares computer resources such as computing, data and storage in the all-round way, provides dynamic and scalable virtualization resources, and provides the good basic support platform for college education reform. Multimedia as an auxiliary means, the use of multimedia technology can make teaching methods rich and diverse. The construction of the multimedia resource library based on cloud computing technology is in line with the development direction of English teaching. Taking the development of mobile information technology and teaching reform as the opportunity, we have studied the core technology of cloud computing and cloud computing technology architecture, designed the structure of the multimedia resource library for English teaching, and proposed the practical application strategy of the multimedia resource library for English teaching, and actively promoted English teaching and research is undergoing the comprehensive transformation and upgrading towards modernization.
A Lightweight Network for Detecting Pedestrians in Hazy Weather

Most of the existing detection models fails to detect pedestrians in hazy weather conditions. Therefore, to improve the safety of the driver in semi-autonomous or autonomous vehicles, a lightweight network has been proposed which can detect pedestrians effectively in hazy weather. A lightweight network YOU-ONLY-LOOK-ONCE-v2 (YOLOv2) + MobileNetv2 + Convolutional Block Attention Module (CBAM) was proposed. To build more efficient and faster model, YOLOv2 is employed, and to reduce both number of parameters and computational complexity, we adopt MobileNetv2 as our backbone model. In the proposed model, bounding box loss error is optimized by applying normalization and we also introduced attention module (CBAM) to improve the detection accuracy. Prior to the model training, we applied K-means clustering algorithm to figure out optimal number of prior anchor boxes in our dataset. Experimental results show that the proposed network achieves 87.4% average precision and detection accuracy on hazy person dataset and runs with 173.6 frames per second (FPS).
Depth Completion Using Infinity Laplacian Based on Steering Positive Definite Metric Operator Plus Convolutional Stage

Depth completion is an important task for autonomous vehicles, video games, and many others applications. Frequently depth maps present holes or data with low confidence level. This paper is devoted to the guided depth completion problem using a scene color reference image to guide the completion of a sparse depth map. In this paper we propose a model to complete the depth map based on an interpolator model and convolution stages enforcing color features of the reference image. In one hand, the interpolator we used is the infinity Laplacian (which is the simplest interpolator that holds a set of axioms). In the other hand, The convolutional stage is constructed using either Gabor filters (GF) or steering filters (SF). We tested different configuration of our proposal to find the best performance: i) SF Stage-infinity Laplacian-GF Stage. ii) SF Stage-Steering kernel-GF Stage. iii) SF Stage-infinity Laplacian (based on Steering Positive Definite Operator)-GF Stage. These models were assessed in the publicly available KITTI Depth Completion Suite. Best performance was obtained by the model iii) outperforming our model previous version, and others contemporaneous models.
Multi-scale Channel Attention for Image Registration

In recent years, image registration methods based on convolutional neural networks have achieved good results and operational advantages in deformable image registration. However, due to the intrinsic property of convolution, namely the limited size of the convolution kernel, the learning of global contextual information is lacking. In order to solve the above problems, this paper proposes a multi-scale channel attention image registration model (MCAReg-Net), which can learn multi-scale contextual information and effectively fuse features of different scales for more accurate image registration. To demonstrate the effectiveness of the experiments, we conduct a series of experiments on the OASIS dataset. The experimental results show that our method can achieve better performance compared with other state-of-the-art methods, which proves the effectiveness of our proposed method.
Design of an IoT-Based Cross-Modality Pedestrian Monitoring System for Contact Tracing in COVID-19 Prevention

COVID-19 epidemic prevention and control has become a regular part of life, and tracking people’s trajectory (especially fever patients) in public areas can help curb the spread of the epidemic. IoT technology has dramatically improved the efficiency of epidemic prevention and control. In this paper, we propose an IoT-based cross-modality pedestrian surveillance system architecture with the following characteristics: (1) robust, the system accesses multiple modal information (visible light, infrared, temperature, mobile phone signals), and can achieve trajectory tracking in non-cooperative situations; (2) with flexibility, we develop a nodal visual artificial intelligence software platform for deep neural network training and optimization, using which the deployment can be iteratively optimized intuitively and quickly. Finally, we also discuss the research prospects of artificial intelligence and IoT technologies in the direction of epidemic prevention and control.