Reviewerly/exHarmony huggingface dataset:
A novel technique to solve class imbalance problem

The introduction of Big Data has proclaimed the beginning of a new age of scientific advances. One of the most often encountered problems with raw data is a class imbalance, which refers to an imbalanced distribution of response variable values. Modern machine learning approaches struggle to cope with imbalanced data by concentrating only on the majority class's misclassification rate and neglecting the minority class. This study introduces a unique oversampling strategy for obtaining a balanced dataset. In this method, new samples of the minority class are increased in such a way that the misclassification rate of the majority class remains low. The reason behind this is, the data of the dominant class is authentic, but the data of the minority class is synthesized. So our goal was not to misclassify genuine data as a result of fabricated data. Using the decision tree as a base classifier, we conducted a largescale experiment using 5-fold cross-validation on three datasets from the UCI machine learning repository, with low, medium, and high-level imbalanced ratios, respectively. For validation purposes, the datasets are classified by the decision tree classifier utilizing 5-fold cross-validation. Finally, on three UCI datasets, our approach outperformed SMOTE, with the average values of the Decision tree classifier's Sensitivity and Specificity measures for each dataset.
Application of Internet of Things Technology in Student Management Evaluation System

In modern society, the rapid development of the knowledge economy makes education become the core resource of a country’s economic and social modernization development. Under this circumstance, student management has been paid more and more attention by people. This paper aims to study how to use the internet of things technology to study the student management evaluation system. This paper proposes a data mining algorithm based on the internet of things technology and proposes a decision tree method and an association rule algorithm based on the data mining algorithm. The experimental results of this paper show that the student growth rate in 2018 increased from 10% to 33% in 2019 and then from 21% in 2020 to 35% in 2021. It can be seen that with the development of the economy, the enrollment rate is also increasing, and the number of people who can go to college has increased rapidly. This also increases the difficulty of student management, making teachers’ work more arduous, and the complexity of the amount of information leads to a drop in the efficiency of student management. However, the student management evaluation system based on the internet of things technology is beneficial to solving this problem.
Constructive Outlook of Cafeteria Using Autodesk 3ds Max 3D Modelling and Rendering

In this modern world, we intend to modernize everything and try to get everything at the tip of our fingers. It has been changed with technology based on individual creativity. In this chapter, we see a cafeteria that has been rendered to a new version with the help of Autodesk 3ds max. Autodesk 3ds Max is a professional 3D computer graphics program for making 3D animations, models, games, and images. As we all know, eye-catching visuals, ambiance attracts all kinds of crowds. The structure of the architecture, menu format, lighting effects, seating arrangement, serving ideas have been rendered here. Keeping the world crisis in mind, an approach to the same issue is shown. This chapter aims at designing a cafeteria with enhanced models with material effects. The process of rendering the cafeteria is shown in detail.
Human-Machine Interactions

Research on human-computer interaction (HCI) has been widely developed since the 1960s as a result of the rapid growth of information systems. Designing ergonomic human-computer interfaces is the purpose of human-computer interaction (HCI). Nonetheless, the user had complete control of the computer, but they were mostly static. The management of increasingly complex and coupled systems has become indispensable to both the public and private sectors over the past few years. Therefore, humans and machines must be able to adapt to unforeseen circumstances by maintaining degrees of freedom. Though modern machine manages all the activities that humans have done in the past, processing information as well as dealing with dynamic situations but uncertain factors play a crucial role in these dynamic situations, which are difficult to control. The trend the industry is experiencing demands a human-machine cooperative (HMC) approach to handle the new challenges such as cyber security. The chapter explains how HMC might improve HCI to address problems emerging from this change.
Design of robust algorithm for machine learning based on deep search of outliers

Design of robust algorithm for machine learning based on deep search of outliers is implemented in this study. The depth first search is a graph traversal method. The traversal process is essentially the process of finding the leading point of each vertex. It starts from a vertex of the graph, and the visited vertices are marked with seven visited marks. The statistical inference rules under this new theoretical system not only take into account the requirements for asymptotic performance, but also seek to obtain optimal results under the condition of existing limited information. By transforming the original problem into a dual problem, the computational complexity of the support vector machine no longer depends on the spatial dimension, but on the number of support vectors in the sample. For the robust system, the abnormal detection is considered for the application scenario. Through the Hadoop testing, the ideas are validated.
Bangla Sign Language Recognition from Hand Gestures using Convolutional Neural Network

In modern society, sign language has become the primary language of the deaf and dumb community. To communicate with others, they employ a variety of signs. Sign language recognition is a new field of study that aims to improve communication with the deaf and dumb. Many studies on the identification of Bangla sign language have been reported in the literature. However, we found very little research with high accuracy for the identification of Bangla sign language. In Bangladesh, there is also a sizable deaf and dumb population. In this research, we build a modified convolutional neural network for recognizing Bangla sign language. We have 37970 data for 59 classes in our dataset. Finally, we looked at our model’s performance separately. We got 100% accuracy for digits, 99.84% for alphabets, and 99.5% for combined use.
Editing Shapes With Meshes for 3D Modelling in Autodesk Platform

Setting up the sources, working with editable poly, information in the inside of the kitchen design, and applying turbo-smooth and symmetry modifiers are all detailed in the process for generating a 3D model. In addition to lighting the scene and setting up the renderer, the method materials are introduced to the model. Methods and techniques for rendering are also defined. The final rendering was created by combining several pictures. The research aims to create a design that incorporates materials to enhance models. The shapes used were cylinder, sphere, box, plane, and spline. Modifiers include editable poly, editable spline, and UVW map. Finally, the authors used a material editor and target lighting to improve the model. The method of using meshes to create model is also used in this research. A mesh is a type of three-dimensional geometric model.
A Trusted Energy Data Sharing System Based on Blockchain

Sharing the statistic results of energy data on the blockchain has huge practical significance. However, the ability to store and analysis large data set is limited by the throughput of the blockchain. Introducing a data center as a third party to do the statistical task brings new issues of trust. In this paper, we put forward three different solutions to guarantee on-chain statistic availability, according to different scenarios. All our solution has on-chain time complexity sublinear to the number of pieces of data, which supports large size data set. Data availability are guaranteed by cryptographic techniques with high security level.
CVAS: An Interactive Visual Analytics System for Exploring COVID-19 Information on the Web

The outbreak of COVID-19 has been a critical social event in the past two years. The pandemic has seriously affected the world. Meanwhile, various forms of data about COVD-19 emerge on the Web endlessly, such as SNS discussions, Press releases, WHO statistics, etc. It is valuable work for government departments, news media, and health organizations to integrate and analyze these pandemics-related multi-source data on the web. In this work, we propose an interactive visual analytics system as CVAS that aims at mining and analyzing multi-source data concerned with COVD-19. Having been inspired by the Sankey diagram, we developed a view elaborately. Through appropriate interactions, massive patients’ mobility data can be visualized, thus showing the spread features of the pandemic in time and space more specifically. In addition, we collected more than 10,000 trending topics and nearly 10 million related comments on the SNS as Sina Weibo. We performed NLP to analyze their sentiment, identifying key events since the outbreak and the impact of the pandemic on public sentiment. Part of our work was awarded at the China visualization and visual analysis conference (ChinaVis2020) and recognized by peers.
Image Translator: An Unsupervised Image-to-Image Translation Approach using GAN

Unpaired image-to-image translation uses unpaired training data to predict domain-to-domain mapping. Unpaired Image-to-Image is a versatile Generative Adversarial Network (GAN) model for image-to-image translation. Using the GAN architecture, you may train a generator model, which is often used to create graphics. To train the discriminator model, the generator is trained to fool the discriminator model into believing that the images are real. Generative adversarial networks (GAN) are a general-purpose solution for image-to-image translation challenges. These networks are concurrently learning a loss function that can be used to train the mapping they have learnt. This invention allows the use of the same fundamental strategy to solve problems that would otherwise need completely separate loss equations. We show that this approach may be used to synthesize photos from label maps, rebuild objects from edge maps, and colorize images.
Hybrid Variational Autoencoder for Collaborative Filtering

In recent years, Variational AutoEncoder (VAE) based methods have made many important achievements in the field of collaborative filtering recommendation system. VAE is a kind of Bayesian model which combines latent variable model with variational inference, but its optimization is often troubled by posterior collapse. By comparing the optimization process of VAE and ordinary autoencoder, we observe that the mismatch between poorly optimized encoder and decoder with too strong characterization capabilities makes it difficult to learn the mapping from the data manifold to the parameterized graph. Since the learning of a posteriori network corresponds to the encoder, we think that the problem of a posteriori collapse can be alleviated by balancing the encoder and decoder better. Therefore, we proposed Hy-VAE, which combines conventional VAE with deterministic autoencoder, and has the advantages of both VAE and deterministic autoencoder. Experiments on three real-world recommendation data sets show that our method alleviates the posterior crash problem in VAE and improves the recommendation performance.
Object Detection Methods for Improving Smart City Safety

The safety measures in a city are of major concern to its people. A smart surveillance system is necessary to make the safety measures robust and reliable. Currently, there are many object detection methods available for image analysis. Image analysis is only possible when we can detect the number of objects in the image, type of objects, as well as their location. Image analysis methods include Fast RCNN, YOLO, region-based convolution neural network (RCNN), and so on. These object detection methods are based on deep learning (DL) techniques. Video analysis requires the detection of moving objects. These methods involve background subtraction and extracting the foreground objects for motion analysis. There are several deep learning (DL)-based methods for video analysis such as the YOLO algorithm for detecting objects in a video frame and identifying their location. This chapter describes significant object detection methods in images and video that can be used in surveillance systems to improve the safety measures in a city.
Research and design of four axis aircraft based on TM4C123G

With the development of UAVs, more and more technologies are applied to UAVs. New wing shapes and light materials have greatly increased the endurance of UAVs. The use of advanced signal processing and communication technologies has improved UAVs&#x2019; performance. Image transmission speed and digital transmission speed; the application of these technologies has improved the technical performance of UAVs and enlarged the application range of UAVs. With the continuous innovation in the technical field and the strong support of the country, I believe that the update and development of drone technology is becoming more and more perfect, and the future prospects of drones will be infinitely great. With UAV obstacle avoidance and tracking technology becoming the focus of the industry, it has important application value in cruising, tracking and other fields. This project aims to take the interest of drones as the carrier, with the TI Cup competition problem circumventing aircraft as the goal, to explore the UAV obstacle avoidance system. The quadrotor has reasonable structure, small size, light weight, and peripheral hardware circuit requirements. The advantages of low height, relatively simple control procedures, and vertical take-off and landing have made quadrotors more widely used. This project focuses on UAV obstacle avoidance, with STM32 chip as the main control, and elaborates on UAV information reading, calculation and control. This design is based on reality and uses STM32 series single-chip microcomputer as the control core. The real drone of this project realizes the acquisition of information through a number of sensors, and then the drone&#x2019;s master STM32 chip reads its own sensor data, including the roll angle of the aircraft. , Pitch angle and yaw angle and other key information. After the UAV master control obtains the information, the cascade PID algorithm is used to calculate the speed of the four motors. Finally, the main controller sends the resolved information to the ESC through the interface to adjust the states of the four motors, thereby realizing the control of the flight attitude and ensuring the smooth operation of the aircraft.
Coal Mine Safety Risk Prediction Based on Incremental Extreme Learning Machine

Safety is the most important part of coal industry production. The existing safety monitoring for coal mines mainly adopts the hard division of the values collected by sensors, the analysis and processing means of related information are single, and the overall scheme of intelligent mining of risk situation is lack. Based on the temporal and spatial distribution law of coal mine production big data, this paper constructs a coal mine safety risk assessment model, and combines the incremental extreme learning machine to predict the coal mine production safety risk. This method fully selects the optimal time granularity of the time window characteristics before the accident, focuses on the impact of recent sample characteristics on the risk development trend, and dynamically selects more appropriate characteristics, which can effectively meet the parameter update of coal mine safety risk model under the condition of incremental data. Experiments on multi class monitoring data from several mines show that this method has better accuracy than other similar methods.
A Neuroevolution-Inspired Scheme for Generating Robust Internet of Things

Internet of Things (IoT) is growing with various applications linked in, and node failures are becoming more common as a result of malicious strikes and other issues. The cascading collapse induced by local node failures can be mitigated by robust network topology. Existing approaches for fixed topology enhance the robustness of IoT topology by reconstructing device connections. However, using existing techniques necessitates global topology optimization when new nodes are added, which takes time. To tackle this situation, this study introduces an evolutionary algorithm based on neuroevolution that generates robust IoT topology. It provides IoT topology with inherent robustness when adding extra nodes by utilizing unique mutation and crossover operators. What’s more, we establish an adaptive edge density management method to reduce the rise in energy consumption caused by redundant connections when nodes join. Experimental results indicate that the proposed scheme can effectively build robust topology than multiple existing topology optimization methods in less time for diverse network sizes.
Design and Implementation of Production Visual Report Subsystem Based on Echarts

According to the actual business requirements of a production data statistics, this paper designs and implements a production visual report subsystem. The system mainly applies Echarts technology to realize the report visualization, and adopts Sringboot framework to separate the web page and the server. This system solves the problems of poor real-time and low flexibility which exist in original manual statistics and reporting. It can help administrative staffs to quickly obtain information from massive production data, and it is easier for managers to find the changing trend of manufacturing data and quality data.
Human-machine Collaboration Method Based on Key Nodes of Human Posture

Aiming at the personal safety problems in the complex working conditions and high-risk working environments such as high temperature and high radiation in the industrial field, a human-machine collaboration method based on the key nodes of human posture is proposed. First, compare the human pose detection performance of AlphaPose, OpenPose, and HRNet models in complex environments such as different lighting, different human poses, and occlusions, and select AlphaPose, which is suitable for real-time human-machine collaboration, as the basic model of human-machine collaboration; then, formulate standard human actions, establish the mapping relationship between human actions and robot actions; then, use the spatial position data of key human nodes to establish a human action recognition algorithm to calculate the similarity between the action to be detected and the standard human action to identify the human action; finally, real-time control according to the mapping relationship the movement of the robot realizes the collaboration between human and robot. Experimental results show that the average action recognition accuracy of the method proposed in this paper can reach 91.2%, the average action recognition speed can reach 67ms, the average action recognition accuracy under 0-60 Lux illumination intensity can reach 90.4%, and the average recognition accuracy under partial occlusion conditions up to 87.6%.
A Semantic Model for Pavement Management Data

Pavement management requires up-to-date data from several sources. For a network of roads, the necessary data include: (1) pavement condition survey data such as pavement distress and surface friction; (2) pavement attribute data such as the number of lanes, length, width, surface type, functional class, and shoulder type; (3) traffic data such as volume, vehicle type, and load; (4) pavement construction and rehabilitation history data; and (5) a digital model of the road network. Currently, these data are mostly kept in heterogeneous databases, which makes the integration of data from sources distributed across the Internet difficult. This paper presents the results of an ongoing research project on a semantic approach to pavement management data modeling. The semantic approach creates a vocabulary to define the pavement management data and ontologies that represent contextual relationships behind the vocabulary. To facilitate automatic data integration from various knowledge domains such as the pavement inventory and the pavement conditions knowledge bases, this study uses a shared ontology that defines the vocabulary common to all pavement management knowledge domains; each knowledge domain must extend the shared ontology for developing its own domain ontology. The scope of this paper will be limited to a discussion of the shared ontology and pavement management domain ontology.
Realize the Off-line Electronic Map in the Area of Electric Institute Based on C#.NET

In this paper, javascript is used to realize the offline electronic map of electric institute based on C#.NET. Considering in various cases, based on the platform of map information system, the 16th to 19th layer geographic information data as the main data source to make an offline electronic map. You can drag the map on the map, there are visual buttons to drag the map, for everyone to find the destination to provide convenience. Although the marking on the map was not realized in the production process, it may not be as intuitive as the site map in the search process, but it can basically achieve most of the functions of online electronic map, and this defect will be further improved in the future.
Thangka image caption method based on attention mechanism and encoder-decoder architecture

The task of image description is to automatically generate sentences describing the image based on the input image, which belongs to the intersection of computer vision and natural language processing. Aiming at the characteristics of Thangka image with complex background and numerous objects, this paper proposes a Thangka image description method based on attention mechanism and encoder-decoder architecture. To begin, a convolutional neural network encoder is built, and an attention mechanism is added to it to better extract the features of thangka images. Second, construct a decoder based on a long short-term memory network to collect semantic features between words inside a sentence and to understand the mapping relationship between picture features and word semantic features. On the Thangka dataset and the public dataset Flickr8K, the model is validated. The results of the studies show that the model considerably improves the accuracy of providing descriptive sentences.