Reviewerly/exHarmony huggingface dataset:
Prediction of Load Capacity in Microgrid by Multiple Regression Method

The article presents the results of load power forecasting in Microgrid systems by multiple regression with a forecast range of one day. energy sources, as well as tools for storage, redundancy and load management. The design and construction of such systems is cost-effective, as these systems are powered by renewable energy sources, which is attractive due to subsidies and discounts on energy distribution - the so-called "green tariff". depends on weather conditions, such as temperature, pressure, humidity, wind speed and direction, cloudiness, etc., the task of predicting the load capacity depending on environmental parameters is relevant. Therefore, a forecast model of load capacity based on environmental data is developed and its software implementation is given. The daily curves of changes in load power with a discreteness of one hour are presented. Daily curves of load capacity changes on weekdays and weekends are also provided. A free resource has been selected to download the environmental database. A specific day is set for load forecasting. Hourly values ​​of environmental data (temperature, pressure, humidity) for a given day are given. The criteria for finding such days according to the environmental data are selected and the allowable percentage difference of mathematical expectation and variance of the relevant data is established. The parameters of mathematical expectation and variance of a given day are calculated. The statistical dependence between load data and environmental data is calculated. Regressive equations of the found similar days are constructed, on the basis of which the regressive forecast equation of loading capacity for days ahead is received. The daily curve of the forecasted load is presented and the comparative schedule of the forecasted with the real value of the load is constructed. The accuracy of the prediction is estimated using the average absolute error of MAPE. The algorithm and results of work of the developed program on which search of a similar day and calculation of forecast value for forecasting of power of loading for days ahead are represented are resulted.
Multi-class Sentiment Analysis Over Social Networking Applications Using Text and Emoji-based Features

In social network, text, audio, video, and emoticons/emojis are often utilized to express a user's perspective. It includes everything from health applications, counseling, and business to community population studies. Many studies have been conducted to analyze reviews and determine the correct opinion regarding the application. It is highly required to analyze user reviews by studying the input data to improve prediction accuracy. This research work has proposed a multi-class sentiment analysis by using Enhanced Convolutional Neural Network (ECNN) by including text and emoji-based features. This paper describes how to use a multi-way emotions classification to extend the existing binary sentiment classification. The proposed work mainly focuses on analyzing product reviews to facilitate the faster discovery of opinion contents for classification by using Information Gain(IG), Gain Ratio(GR) and CHI-Squared(CHI) feature selection techniques. Multi-class sentiment analysis, in contrast to standard sentiment analysis, which evaluates if a text is positive or negative, employs categories or clusters such as excited, happy, bored, an d angry to better grasp the emotions portrayed in the text. The proposed ECNN outperforms as compare to the existing Convolutional Neural Network(CNN), Naive Bayes (NB), Support Vector Machine (SVM) and Decision Tree (DT) classifiers.
Internet of Things Task Migration Algorithm under Edge Computing in the Design of English Translation Theory and Teaching Practice Courses

This study aims to improve the quality of English teaching in contemporary colleges and universities, so as to cultivate more English translation talents. Taking corpus, English translation, and teaching practice as the main research horizons, this study analyzes the application of master of translation and interpreting (MTI) course design, English translation, and task migration by combining the task migration algorithm under Internet of Things (IoT) with the self-built corpus, so as to realize the cultivation of translation talents and design of teaching practice. A translation corpus is constructed, a two-way interactive online course is designed, and the experimental results of the complete local migration algorithm (CLM algorithm), random migration algorithm (RM algorithm), and greedy heuristic migration algorithm (GHM algorithm) used in English teaching practice courses are analyzed and compared. The experimental simulation results reveal that the GHM algorithm proposed in this study shows good system stability, its duration is 60% better than that of the CLM algorithm, and its system throughput is increased by 50% compared with the CLM algorithm. In addition, the maximum delay time has little effect on the system throughput. When the system time slot length is fixed at 20 ms, the user migration rate of the genetic algorithm is the highest under the different total numbers of users. In addition, in view of the wide application of neural network in English translation teaching, this study establishes an English translation evaluation model based on the combination of particle swarm optimization (PSO) algorithm and neural network and compares it with the traditional neural network model in simulation experiments. The results show that the addition of PSO algorithm can effectively improve the convergence speed of artificial neural network (ANN), reduce the training time of the model, and improve the accuracy of the ANN network. Using the PSO algorithm to train the neural network, the optimal solutions of different particle swarms can be obtained, and the error is small. The PSO-ANNs model can promote the quality of English translation teaching and improve the English translation ability of the students. Therefore, applying the task transfer algorithm and PSO algorithm to the practice of English translation teaching has greatly improved the efficiency of the English classroom. To sum up, this study provides new ideas for the curriculum design of contemporary college English teaching and has reference value for the cultivation of college English translation talents.
Study and Comparison of Plagiarism Detection Model for Online Studies

Now a days, in college education, computer programming is becoming increasingly vital in the process of programme design. However, some students' homework include the trick of plagiarising with minor changes. Teachers have a tough time determining whether or not source code contains plagiarised information. This circumstance is unsuitable for traditional detection techniques. According to the common approach of students plagiarising, it is required for an effective and comprehensive method for detecting source code plagiarising. The paper studies, the deeper look at the problem of algorithm plagiarism, which arises when a plagiarist violates intellectual property rights by stealing and secretly implementing the algorithms of others.
Evaluation Method of Financial Internal Control in Colleges and Universities Based on Artificial Intelligence Technology

The internal financial control of colleges and universities is the main tool to restrain the behavior of financial managers. With the improvement of the economic operation efficiency of colleges and universities, the application effect of internal control management system is poor. Therefore, the evaluation method of the university financial internal control based on artificial intelligence technology is proposed. First put forward the artificial intelligence technology in the application of financial internal control in colleges and universities, and then construct the financial internal organizational structure, on this basis, to meet the enormous workload processing requirements, the creation of financial internal control evaluation in colleges and universities, the chain at the same time use of financial system of general function, the university financial internal control evaluation data output to the financial system. The data acquisition program is used to obtain the relevant data in the evaluation report so as to realize the evaluation of internal financial control in colleges and universities under the artificial intelligence technology. The experimental results show that the evaluation acceleration is faster with the increase of the evaluation time of the key elements. The evaluation effect is the best; the closest to the ideal value of 0.01, and the error detection rate curve is relatively stable, with good effect.
High-precision anamorphic lens calibration with 3D and 2D calibration targets

This paper presents a high-precision camera calibration approach for anamorphic lenses. The distortion model for anamorphic lenses is substantially more sophisticated than the distortion model for spherical lenses for the adaption of cylindrical lenses in anamorphic lens attachments. Based on the anamorphic aberration theory and numerical experiments, a polynomial type distortion model is provided for anamorphic lenses, with which high-precision camera calibrations can be achieved using 3D calibration targets. 3D calibration targets are typically limited in size, unable to encompass the whole imaging field of anamorphic lenses. The calibration results using 3D calibration targets tend to be unstable with the increased number of distortion coefficients in the anamorphic distortion model. Thus, after the anamorphic lenses have been calibrated with 3D calibration targets, they are recalibrated with 2D calibration targets that may fill the anamorphic lenses’ field of view, resulting in more dependable and exact calibration results. Two anamorphic lenses are calibrated with high precision at the end the paper, which proves the effectiveness of the proposed method.
Eye Tracking Analysis Using Convolutional Neural Network

Eye tracking plays a pivotal role in fixing the user interface allowing one to understand what a person is actually looking at while browsing through a webcam or external cam or using an infrared eye tracker etc., which depends on needs and conditions. With eye-tracking, one can easily test any video material, AD performance, package design concepts, product shelf placement, website performance, mobile websites, and apps. It can be the supreme technology in providing various insights into the processes which involve application into various fields of academics, science &#x0026; technology, marketing, and other researchers. The goal of eye tracking is to detect and measure the point of gaze (where one is looking) or the motion of eye(s) relative to the head. This study examines the current state-of-the-art in deep learning-based gaze estimation algorithms, with a particular focus on Convolutional Neural Networks (CNN). Several studies are focusing on various approaches for dealing with different head pose and gaze estimation. Large-scale gaze estimate datasets with various head poses and illumination conditions were reported in the current study. We are building a model detecting if the eyes captured are right or left and detecting the gazing point and the aim is to solve the problem if they are accurate. This defined problem requires a method with high learning capacity which is able to manage the complexity of the given dataset. For the present study Convolutional neural network(CNN) has proved effective to get better results for the defined problem.
Application of C4.5 Decision Tree Algorithm for Evaluating the College Music Education

Music courses in colleges and universities have undergone significant changes as the new curriculum reform has proceeded. As a result, student evaluations in the classroom are changing, and a diversified evaluation paradigm is gradually developing. Numerous new and more effective teaching concepts and teaching methods have been developed for revitalizing the state with science and education. This interrupts the standard instructional activities’ backward teaching pattern. Online teaching has become more significant in the area of education as technology, science, and Internet technologies have advanced. Music instructors at universities and colleges are continually updating their teaching methods and utilize several techniques to provide in-depth instruction in the classrooms. To expand students’ enthusiasm and involvement while also developing their musical creative talents, a web-based information educational administrations management system has been widely used in many universities and colleges. This study utilizes the C4.5 algorithm to create a decision tree model for establishing an evaluation system of classroom teaching to enhance the quality. The proposed algorithm evaluates the model’s accuracy and practicability using performance information from 125 teachers’ music classroom teaching. Finally, it identifies the decision-making attributes that affect the teachers’ evaluation. The quality of classroom teaching is evaluated, and some useful suggestions are provided based on the experimental results, which can support college and university decision-making by motivating teachers to improve their classroom teaching quality.
Development of EV battery swapping station using automated vision system

The paper presents the concept of computer vision and image processing applications. To locate and replace the battery under the electric vehicle from the reference symbol position using the AGV (Automated Guided Vehicle) robot, instead of using sensors and adjusting the electric vehicle's wheelbase to minimize the number of sensors. Due to the nature of this system, there are no restrictions on the wheelbase size of the battery switching stations. Consequently, it can apply to all kinds of electric vehicle sizes. The experiment's results indicated that the system had tolerances varying between 0.0989 to 0.2111 mm at a 95% Confidence Level.
Thermal and Visual Face Recognition using Eigenfaces and Transfer Learning

Images with dark backgrounds captured using thermal camera sensors are used for thermal face recognition, whereas a visual camera captures images in RGB format and can be used with good lighting conditions. The light conditions do not affect the thermal images as it entirely depends on the heat patterns which are emitted by the body. Human faces and bodies have nearly the same temperature, which varies from 35.5 °C to 37.5°C. The study on face recognition started in 1960. Individual faces are used for authentication into various systems throughout the world. This paper combines the traditional approach of face recognition using Eigenfaces and the transfer learning approach. In the process, first, the face is cropped out using a deep learning model. Then the features are extracted by using a pre-trained deep learning model, which essentially converts an image to a low dimensional array. Eigenfaces are formed using the array for each person and when new face images are given for recognition, the system calculates the Euclidean distance between the new image eigenface to all previously stored eigenfaces. The solution is tested on the IRIS Thermal and Visual face dataset and accuracy is found to be 62.9% and 91.93% respectively.
Privacy-preserving process mining: A microaggregation-based approach

The proper exploitation of vast amounts of event data by means of process mining techniques enables the discovery, monitoring and improvement of business processes, allowing organizations to develop more efficient business intelligence systems. However, event data often contain personal and/or confidential information that, unless properly managed, may jeopardize people’s privacy while conducting process mining analysis. Despite its relevance, privacy aspects have barely been considered within process mining, and the field of privacy-preserving process mining is still in an embryonic stage. With the aim to protect people’s privacy, this article presents a novel privacy-preserving process mining method based on microaggregation techniques, called k-PPPM, that increases privacy in process mining through k-anonymity. Contrary to current solutions, mostly based on pseudonyms and encryption, this method averts the re-identification of targeted individuals from attacks based on the analysis of process models in combination with location-oriented attacks, such as Restricted Space Identification and Object Identification attacks. The proposed method provides adjustable parameters to tune different anonymization aspects. Six real-life event logs have been employed to evaluate the method in terms of process models quality and information loss.
(Retracted) Improved encrypted AI robot for package recognition in IoT logistics environment

Physical security deals with package recognition for video surveillance. Robots play an important role in understanding the dynamic recognition of package images and their movement. Recognition of package images in robotic applications at high speed is a necessity. The in-built programming function in the onboard sources of robotics limits the computation speed of the recognition of the package images. By introducing the cloud environment into the package recognition, this limitation will be overcome. This cloud-based technology has an improved computation speed, high memory capacity, less cost, and flexibility in throughput. Therefore, we can store the package images in the cloud environment. Users can access the image from the cloud storage; however, the transmission of the image may create a lack of security and privacy attacks on the package image database. To overcome this security issue and maintain the privacy of the package, image database encryption is needed. We propose a secure encrypted robot secure package recognition using the encryption algorithm of Ridgelet transform with Rubik’s cube principle and DenseNet-CNN based on the robot secure package recognition (RRCP-DenseNet). The performance of this proposed work is evaluated in the aspects of security and accuracy in the recognition of package images. The percentages of improvement from LeNet to DenseNet including raw input images were 16.5%, 7.26%, 21.41%, 23.12%, 9.65%, and 287.89% for raw input images and encrypted images using the Hill cipher, RSA, bit slicing, AES, RCP, and RRCP encryption algorithms, respectively.
A Design Model of English Auxiliary Teaching System Using Artificial Neural Networks

At present, the development of artificial intelligence has entered a new stage. In the next few years, human life will be greatly covered by artificial intelligence. Artificial intelligence (AI) is an intelligent technology that simulates the human brain for thinking operation. English is very difficult for Chinese students to learn. Moreover, at present, there are still many areas that need to be improved or improved in the design of English assistant instruction system (EAIS) in China. This study aims to study an EAIS based on AI. This study presents an algorithm combined with neural network. This study designs a system that can enable students’ autonomous learning system, English teacher assisted teaching, AI expert intelligent diagnosis, and system administrator to manage and analyze data. And this study tests the actual effect of the system. By comparing the academic performance of two classes in the school, the results show that although the average score of students’ monthly test fluctuates after using the AI EAIS, it tends to rise as a whole. Moreover, its initial score of 81.5 was 1.1 lower than that of the class without the teaching system and finally increased to 90.11. It is 6.89 points higher than the class that does not use the teaching system. This shows that the auxiliary system designed in this study has a certain effect on the improvement of students’ English performance.
Interaction Design System for Artificial Intelligence User Interfaces Based on UML Extension Mechanisms

With the rapid development of computer network technology in recent years, more and more demands have been placed on the functionality and attributes of the user interface. In the development of many computer projects, the variability and flexibility of user interface requirements have greatly increased the complexity of program development for researchers. In addition, the poor reusability of page access control writing has created a pressing need for a highly standardized and flexible way of developing software. Thus, the development and design of user interfaces for application software systems occupy an important position and have been a hot topic of research in the field of human-computer interaction. The traditional methods of describing user interaction, such as state transitions and data flow diagrams, are not based on global and intuitive concepts. Moreover, there is little support for the design of user interface interaction behavior, resulting in user interfaces being ignored at design time and left to implementers to grasp at coding time. It is therefore an issue that needs to be addressed in order to integrate traditional methods and intuitive descriptions from the user’s perspective into a new interface development model and methodology. This research creates a user interface framework based on interaction behavior from the user’s perspective. Furthermore, UML extension mechanisms are used to enable the user interface framework to better support UML-based modelling environments. In addition, the UML is structured and extended to include structural elements that support interface generation, and a structured use case model is proposed, which drives the analysis and design of the individual submodels. The extracted abstract interface elements and their mapping to concrete interface elements are documented in a way that explores the generation of different target languages under different platforms. This study incorporates user requirements and provides a scientific reference for the development and design of user interfaces.
Sports Feedback System Based on Video Digital Processing

The current sports feedback system has high application value and is widely used in physical education courses, but it has problems such as large feedback error, long response time, and incomplete data, leading to poor effect of sports feedback system. In order to improve the effectiveness of sports feedback system, a sports feedback system based on video digital processing is designed. By constructing the overall architecture of video digital sports feedback system, the positioning module, interface module, power supply circuit module, sampling module, input module, signal extraction module, and video digital processing module are designed. Using information communication mechanism, TCP/IP network protocol, serial communication, and shared memory to improve video digital processing, experimental results show that the designed system has good error convergence of 0.1, short response time of 32 s, and high data integrity of 94%, which has strong practicability.