Reviewerly/exHarmony huggingface dataset:
An Improved Image Steganography Framework Based on Y Channel Information for Neural Style Transfer

Neural style transfer has effectively assisted artistic design in recent years, but it has also accelerated the tampering, synthesis, and dissemination of a large number of digital image resources without permission, resulting in a large number of copyright disputes. Image steganography can hide secret information in cover images to realize copyright protection, but the existing methods have poor robustness, which is hard to extract the original secret information from stylized steganographic (stego) images. To solve the above problem, we propose an improved image steganography framework for neural style transfer based on Y channel information and a novel structural loss, composed of an encoder, a style transfer network, and a decoder. By introducing a structural loss to restrain the process of network training, the encoder can embed the gray-scale secret image into Y channel of the cover image and then generate steganographic image, while the decoder can directly extract the above secret image from a stylized stego image output by the style transfer network. The experimental results demonstrate that the proposed method can effectively recover the original secret information from the stylized stego image, and the PSNR of the extracted secret image and the original secret image can reach 23.4 and 27.29 for the gray-scale secret image and binary image with the size of 256×256, respectively, maintaining most of the details and semantics. Therefore, the proposed method can not only preserve most of the secret information embedded in a stego image during the stylization process, but also help to further hide secret information and avoid steganographic attacks to a certain extent due to the stylization of a stego image, thus protecting secret information like copyright.
Linear Regression Analysis Using Log Transformation Model for Rainfall Data in Water Resources Management Krueng Pase, Aceh, Indonesia

Climate changes are one crucial factor that influenced water availability at one location since they affected the environmental, social, and agricultural systems.The study observed the agent factors that influenced the rainfall changes at Krueng Pasee Aceh watershed, Indonesia.The method used in this research is a linear regression with a log transformation approach on predictor variables.The data used in this study consisted of rainfall, a total of rainy days, temperature, humidity, duration of irradiation, and wind speed in the period ranging from 1992 to 2020.Results showed that the agent factors had not distributed normally.The regression model produced after log transformation had met the classical assumptions and can be used to predict the rainfall at R-quare 24.61% with an RMSE value of 57.676.From all factors studied, the wind speed should be excluded.Further study is recommended to use the nonlinear method to improve the model for rainfall prediction.
Analysis of the Effect of Martial Arts on Students' Physical and Mental Health Based on Internet of Things Technology

In order to explore the effect of martial arts on the improvement of students' physical and mental health, this article combines the Internet of Things technology to construct a martial arts learning platform. Moreover, this study improves the IoT node technology and energy consumption algorithm to reduce the system energy consumption of the IoT system, improve the operating efficiency of the IoT technology, and improve the accuracy of martial arts movement recognition and the effect of data transmission. In addition, this study combines the skeleton model to construct the martial arts action 3D human body model. The platform can be used as an intelligent system to improve the physical and mental health of students. In order to study the effect of martial arts on the improvement of students' physical and mental health, this study uses the system constructed as a test platform to allow college students to learn martial arts through this system platform and combines the questionnaire method to count the improvement of physical and mental health of students after learning martial arts. The experimental research results show that the martial arts learning platform based on the Internet of Things technology can play an important role in improving the physical and mental health of students.
A broad learning-based state estimation method for power system

With the fast growth of developing smart grid technology in the last several years, traditional state estimation methods have proven insufficient for the real-time and precise study of emerging complex power systems. The state assessment of electric grid depending on deep learning and other related intelligent algorithms often encounter problems such as time-consuming model training and easy to fall into local optimum. In this background, this research provides a broad learning-based state estimation approach for power system, which is quite different from the existing artificial intelligence algorithms. Due to the operation theory of matrix pseudo-inverse, the broad learning system can not only swiftly compute the connection weights between various network layers, but also has the advantage of incremental learning. Finally, the proposed method is verified based on IEEE 39-node system combined with actual load data.
A Machine Learning Approach to Identify Personality Traits from Social Media

Humans are unique individuals with extremely complex patterns of thinking and perception, which is reflected in their behaviour directly, defined as their personality trait. Several attempts have been made to analyse an individual's personality based on these traits because it is quite difficult to map out the exact thinking pattern. Analysing one's personality helps to understand themselves better and deal with their psychological issues. These days people love to express themselves on social media, through pictures, songs, videos, etc. Although it might feel that these posts are seemingly random, a deeper analysis might reveal a correlation between such posts and the user's personality, and therefore, mapping such data to his/her personality becomes an interesting motivation for our paper. In this chapter, we have tried to predict a person's personality based on their textual social media posts. We have considered Myers Briggs Type Indicator to categorise the personalities. Four categories of the Myers Briggs Types are: Extraversion/Introversion, Sensing/Intuition, Thinking/Feeling, and Judging/Perceiving. We have used machine learning algorithms to classify the posts based on these categories. Based on the outcomes of the algorithm, we can further evaluate the result based on different characteristics of an individual and prepare an extensive personality profile of that person which might also help in counselling if needed.
Adaptation to Accelerated Digitalization in the Context of a Pandemic: Comparative Study of Higher Education Systems in Russia and Belarus

The article reflects the input of the first and second waves of the pandemic on the higher education system in Russia and Belarus. The purpose of the article is to identify the level of adaptation to the widespread use of information and communication technologies (ICT) in a pandemic on the basis of comparison of Belarus and Russia. Two research cases of adaptation are compared. Russian research materials covered two stages of functioning the higher education system: during the full transition to distance learning (DL) in Spring 2020, and in the end of 2020 when the institutions of higher education (HEIs) used DL selectively. In Belarus, DL was used partially. Belarusian case is presented by the data of the representative republican online survey conducted in Spring 2021. Survey materials at the Belarusian State University (December 2020 and February 2021) were also used to compare the opinions of students and employees. The Russian and Belarusian research data were compared by issues related to adaptation to DL and its future perspective. It was found that the level of adaptation of the Russian higher education system steadily increased, although differentiations between the HEIs remained. In Belarus, students’ assessment of the quality of DL was generally positive, and adaptation strategies were successful. The results demonstrate an increase in the level of adaptation to the challenges of the pandemic. They revealed the educational potential of DL and confirmed the possibility of successful digitalization of higher education systems. This approach will help maintain competitiveness, the quality of higher education, increase the level of digital literacy and competence of students and teachers.
Radar Emitter Signal Identification Based on Intra-pulse Features

Intra-pulse feature extraction is important to apply for intercepting complex modulation radar signal with low probability. Based on intra-pulse minute feature analysis for several typical radar signals, a fast recognition approach is obtained for intra-pulse complex-modulated radar signals. This approach can recognize four kinds of signals with three characteristic parameters, which are extracted from signal continuous wavelet transform eigenvalue, frequency domain moment kurtosis coefficient and frequency domain moment skewness coefficient. When the signal-noise ratio(SNR) is higher than 0 dB, the probability of signal recognition is greater than 98&#x0025;. The simulation experiments can further verify the validity of this approach.
The Teaching Evaluation Index System and Intelligent Evaluation Methods of Vocational Undergraduate Pilot Colleges

In recent years, with the popularization of higher education, quality problems have become increasingly prominent, and government documents often propose that the main task of education reform and development is to improve the quality of higher education. As a result, teaching evaluations have appeared one after another. Although various evaluation activities are carried out dynamically, expert evaluation has proven to be one of the most effective methods to ensure professional quality. This will help improve the quality of human resource development, promote regional economic development, improve the quality assurance system of higher education, and promote the formation of professional skills development mechanisms. This article studies the teaching evaluation index system and intelligent evaluation methods of vocational undergraduate pilot colleges, understands the relevant knowledge of the teaching evaluation index system on the basis of literature data, and then, constructs the teaching evaluation system of vocational undergraduate pilot colleges. The constructed system is tested, and the test results show that the error of the results of teacher self-evaluation and student evaluation is controlled within the two, which also verifies that the construction of the teaching ability evaluation index system this time is reasonable and scientific.
Clustering by Hybrid K-Means-Based Rider Sunflower Optimization Algorithm for Medical Data

Currently, medical data clustering is a very active and effective part of the research area to take proper decisions at the medical field from medical data sets. But medical data clustering is a very challenging issue due to limitless receiving data, vast size, and high frequencies. To achieve this and improve the performance with fast and effective clustering, this paper proposes a hybrid optimization technique, namely, the K-means-based rider sunflower optimization (RSFO) algorithm for medical data. In this research, initially, the data preprocessing phase has been carried out to clean the current input medical data, and then in the second phase, important features are chosen with the help of the Tversky index with holoentropy. Finally, medical data clustering has been carried out by using hybrid K-means-based rider sunflower optimization (RSFO) algorithm. RSFO is designed to produce optimum clustering centroid, which is the combination of two optimization techniques, such as rider optimization algorithm (ROA) and sunflower optimization (SFO). This hybrid algorithm can get the advantages of both K-means and RSFO technique and avoid premature convergence of K-means algorithm and high computation cost of optimization technique. K-Means clustering algorithm is used to cluster the medical data by using an optimum centroid. The efficiency of the proposed K-means-based rider sunflower optimization algorithm is examined with a heart disease data set and analyzed based on three different performance metrics.
The Use of Internet of Things and Cloud Computing Technology in the Performance Appraisal Management of Innovation Capability of University Scientific Research Team

This study aims to speed up the progress of scientific research projects in colleges and universities, continuously improve the innovation ability of scientific research teams in colleges and universities, and optimize the current management methods of performance appraisal of college innovation ability. Firstly, the needs of the innovation performance evaluation system are analyzed, and the corresponding innovation performance evaluation index system of scientific research team is constructed. Secondly, the Internet of Things (IoT) combines the Field Programmable Gate Array (FPGA) to build an innovation capability performance appraisal management terminal. Thirdly, the lightweight deep network has been built into the innovation ability performance assessment management network of university scientific research teams, which relates to the innovation performance assessment index system of scientific research teams. Finally, the system performance is tested. The results show that the proposed method has different degrees of compression for MobileNet, which can significantly reduce the network computation and retain the original recognition ability. Models whose Floating-Point Operations (FLOPs) are reduced by 70% to 90% have 3.6 to 14.3 times fewer parameters. Under different pruning rates, the proposed model has higher model compression rate and recognition accuracy than other models. The results also show that the output of the results is closely related to the interests of the research team. The academic influence score of Team 1 is 0.17, which is the highest among the six groups in this experimental study, indicating that Team 1 has the most significant academic influence. These results provide certain data support and method reference for evaluating the innovation ability of scientific research teams in colleges and universities and contribute to the comprehensive development of efficient scientific research teams.
Domain Adaptation Using Convolutional Autoencoder and Gradient Boosting for Adverse Events Prediction in the Intensive Care Unit

More than 5 million patients have admitted annually to intensive care units (ICUs) in the United States. The leading causes of mortality are cardiovascular failures, multi-organ failures, and sepsis. Data-driven techniques have been used in the analysis of patient data to predict adverse events, such as ICU mortality and ICU readmission. These models often make use of temporal or static features from a single ICU database to make predictions on subsequent adverse events. To explore the potential of domain adaptation, we propose a method of data analysis using gradient boosting and convolutional autoencoder (CAE) to predict significant adverse events in the ICU, such as ICU mortality and ICU readmission. We demonstrate our results from a retrospective data analysis using patient records from a publicly available database called Multi-parameter Intelligent Monitoring in Intensive Care-II (MIMIC-II) and a local database from Children's Healthcare of Atlanta (CHOA). We demonstrate that after adopting novel data imputation on patient ICU data, gradient boosting is effective in both the mortality prediction task and the ICU readmission prediction task. In addition, we use gradient boosting to identify top-ranking temporal and non-temporal features in both prediction tasks. We discuss the relationship between these features and the specific prediction task. Lastly, we indicate that CAE might not be effective in feature extraction on one dataset, but domain adaptation with CAE feature extraction across two datasets shows promising results.
RETRACTED ARTICLE: Graph based CNN Algorithm to Detect Spammer Activity Over Social Media

A spammer sends many useless advertisements to recipients via social networking sites without their permission, posing a serious threat to the information security of regular users, as well as the credit system of these sites. A social network Spammer detection technology based on graph convolution networks (GCNs) is presented with the goal of addressing the shortcomings of existing social network Spammer detection technologies, such as their shallow feature extraction and excessive computational complexity. With the help of an introduction of a network representation learning algorithm, this method extracts the local structural features of the network and then combines the GCN algorithm with renormalization technology to obtain the global structural features of the network, which can be used to detect spam. Experiments conducted on data from the social networking site demonstrate that the proposed method has good accuracy and efficiency.
GazeBreath: Input Method Using Gaze Pointing and Breath Selection

Gaze input is a promising input method that allows intuitive and fast pointing. There are two phases in gaze input: pointing and selection. In the pointing phase, the cursor follows the eye movement to the target object, whereas in the selection phase, the user clicks a button to generate the next event. Switching between the two phases is a general problem in gaze input. In this study, we propose a method for selection by applying an additional modality, namely breathing. In the proposed system, we measured the time of inhalation and exhalation from the change in the nose and mouth temperature observed by a thermal camera. Using this time, we judge which phase the user is in. Through user studies, we found that our method enables selection at a speed not quite inferior to the conventional method. In addition, this method has the advantage of allowing multiple selection commands.
Modeling and Control of One-stage Inverted Pendulum Body Based on Matlab

Abstract In this paper, two mathematical models of the primary inverted pendulum are established based on the Simulink and the Simscape toolboxes of the MATLAB respectively. The inverted pendulum changes from the unstable state to the stable state after performing the state feedback control and the PID control method. Under these two control methods, the overshoot of the Simulink model is 57% and 80% smaller, and the adjustment time is 25% and 20% larger than that of the Simscape model. The control effect of the PID method in the two models is better than that of the state feedback method. The adjustment time of the pendulum rod is 40% and 37.5% smaller, and the overshoot is 82% and 61% smaller than that of state feedback control, respectively.
Impact of Parameter Disentanglement on Collaborative Alignment

The interactive alignment of real and virtual content in AR is often non-trivial. Positional errors along the users view direction frequently lead to the misjudgment of the objects depth. This work takes advantage of alternative users&#x0027; viewpoints in collaborative settings to mitigate these errors. Furthermore, we systematically restrict the parameters used to control the virtual contents pose and investigate the impact of sharing and disentangling such parameters. Results from this work show that alignment schemes that disentangle the control parameters improve overall alignment accuracy with a similar workload for the users and no significant increase in execution time.
Construction and Implementation of Procedural Nursing System for General Surgery Laparoscopic Surgery Based on Deep Learning

In order to explore the construction and implementation effect of a procedural nursing system for laparoscopic surgery in general surgery based on deep learning, this article selects 150 cases of laparoscopic surgery patients admitted to our hospital from January 2020 to January 2021 for research. According to the time of enrollment, the control set and the study set were included in order, with 75 cases in each set. The control set was given routine nursing methods, and the research set was given the management of programmed nursing system based on deep learning. The nursing quality, pain, postoperative recovery, and incidence of complications were compared between the two sets. Logistic regression multivariate analysis of the risk factors for postoperative complications in patients undergoing laparoscopic surgery in general surgery was performed. Based on deep learning, the construction of the procedural nursing system for laparoscopic surgery in general surgery is applied to the nursing management of general surgery laparoscopic surgery, which can improve the quality of care and the VAS score of the patient's pain level, and reduce the incidence of complications. Underlying diseases and routine nursing are risk factors for complications of general surgery laparoscopic surgery, suggesting that corresponding prevention and control work should be done in the procedural nursing of general surgery laparoscopic surgery based on deep learning.
Machine-Learning-Based Real-Time Multi-Camera Vehicle Tracking and Travel-Time Estimation

Travel-time estimation of traffic flow is an important problem with critical implications for traffic congestion analysis. We developed techniques for using intersection videos to identify vehicle trajectories across multiple cameras and analyze corridor travel time. Our approach consists of (1) multi-object single-camera tracking, (2) vehicle re-identification among different cameras, (3) multi-object multi-camera tracking, and (4) travel-time estimation. We evaluated the proposed framework on real intersections in Florida with pan and fisheye cameras. The experimental results demonstrate the viability and effectiveness of our method.
Path Planning of Unmanned Helicopter in Complex Dynamic Environment Based on State-Coded Deep Q-Network

Unmanned helicopters (UH) can avoid radar detection by flying at ultra-low altitudes; thus, they have been widely used in the battlefield. The flight safety of UH is seriously affected by moving obstacles such as flocks of birds in low airspace. Therefore, an algorithm that can plan a safe path to UH is urgently needed. Due to the strong randomness of the movement of bird flocks, the existing path planning algorithms are incompetent for this task. To solve this problem, a state-coded deep Q-network (SC-DQN) algorithm with symmetric properties is proposed, which can effectively avoid randomly moving obstacles and plan a safe path for UH. First, a dynamic reward function is designed to give UH appropriate rewards in real time, so as to improve the sparse reward problem. Then, a state-coding scheme is proposed, which uses binary Boolean expression to encode the environment state to compress environment state space. The encoded state is used as the input to the deep learning network, which is an important improvement to the traditional algorithm. Experimental results show that the SC-DQN algorithm can help UH avoid the moving obstacles to unknown motion status in the environment safely and effectively and successfully complete the raid task.