Reviewerly/exHarmony huggingface dataset:
An Ensemble Approach for Question-Level Knowledge Tracing

Knowledge tracing—where a machine models the students’ knowledge as they interact with coursework—is a well-established area in the field of Artificial Intelligence in Education. In this paper, an ensemble approach is proposed that addresses existing limitations in question-centric knowledge tracing and achieves the goal of predicting future question correctness. The proposed approach consists of two models; one is Light Gradient Boosting Machine (LightGBM) built by incorporating all relevant key features engineered from the data. The second model is a Multiheaded-Self-Attention Knowledge Tracing model (MSAKT) that extracts historical student knowledge of future question by calculating their contextual similarity with previously attempted questions. The proposed model’s effectiveness is evaluated by conducting experiments on a big Kaggle dataset achieving an Area Under ROC Curve (AUC) score of 0.84 with 84% accuracy using 10fold cross-validation.
Unsystematic Pattern Mosaic Image Watermarking

Mosaic image methodologies have been effectively suggested should tackle different issues in the image transforming for example, image division. Similarly as it gets to be a great referred to art, there are many mosaic image images accessible in the web galleries. On addition, there will be truly an enormous amount about spare mosaic image making product accessible in the business sectors. In this paper we will consider the Characteristics of the mosaic image which could assistance on execute imperceptible information concealed methodology (that is Steganography approach). On a significant number correspondences we transmit advanced images so, there need aid an amount for strategies accessible to accomplishing this privacy, a standout amongst them being Steganography. This makes utilization of mosaic image formation which needs two unique techniques, DWT (Discrete wavelet Transform) and DCT (Discrete cosine the senior Transform). Those disguise image may be subjectively chosen Also employments for this image will hidey naught of the mystery image. In this paper we need aid portray the utilization from claiming DCT Furthermore DWT strategy with their points of interest. With the utilization about this system we hiddenite’s the mystery information under the mosaic images.
Calculation and Analysis of Induced Current in Optical Fiber Composite Overhead Ground Wire

Abstract In this paper, the mechanism of electromagnetic induction current of overhead ground wire is analyzed, the models of overhead ground wire with different voltage levels are established on ATP-EMTP, the electromagnetic induction current of optical fiber composite overhead ground wire (OPGW) is calculated, and the factors affecting the induced current on OPGW are analyzed, including the change in loads of transmission lines, the value of grounding resistance and the location of lines. The results of this paper provide a reference for the actual measurement and the use of this induced current for induction power supply.
Face Recognition for Criminal Identification

In this paper, we propose an alert system to identify suspects on the scene using mobile devices. This project's objective is to give the police officers a mobile application to receive alerts in real-time; it will help improve the current identification time by using facial recognition. This proposal is due to the increase in Peru's criminal acts that affects 27.3% of the population who has suffered more than one criminal act. Also, the current identification time is between 5 and 10 min. This alert system consists of 1. The collection and training of images of criminals; 2. IP video device configuration; 3. Sending data from the Python service to the application; 4. The process of receiving results in the application. The alert system was validated in a public area of the Rímac district in Lima, Peru. Preliminary results showed that the alert system reduces the current identification time by 91.46% and has an efficiency of 90% based on the tests performed.
Design of Cascaded CNN for Medical Image Super Resolution

The images captured through a camera usually belong to over or under exposed conditions. The reason may be inappropriate lighting conditions or camera resolution. Hence, it is of utmost importance to have a few enhancement techniques that could make these artefacts look better. Hence, the primary objective pertaining to the adjustment and enhancement techniques is to enhance the characteristics of an image. The initial numeric values related to an image get distorted when an image is enhanced. Therefore, enhancement techniques should be designed in such a way that the image quality isn’t compromised. This research work is focused on proposed a network design for deep convolution neural networks for application of super resolution techniques. To improve the complexity of existing techniques this work is intended towards network designs, different filter size and CNN architecture. The CNN model is most effective model for detection and segmentation in image. This model will improve the efficiency of medical image reconstruction from LR to HR. The proposed model showed its efficiency not only PET medical images but also on retinal database and achieved advance results as compared to existing works.
APPLICATION OF FIREFLY ALGORITHM IN BREAST CANCER DETECTİON IN MAMMOGRAPHIC IMAGES

Breast cancer is one of the most common cancers in women. In many cases, there are no obvious symptoms in patients with it. Accurate diagnosis of this type of cancer in the early stages is essential to reduce mortality. Mammography is a standard method used to diagnose breast diseases. To prevent mental analysis of mammographic images by radiologists and increase the accuracy of breast cancer detection, artificial intelligence systems have received much attention in recent years. In this paper, a method based on combining firefly algorithm and applying appropriate pre-processing on the image to detect breast cancer in mammographic images is presented. To do this, mammographic images in the mammography screening (DDSM) digital data set are used. Three performance criteria of accuracy, sensitivity and precision (93.4%, 91%, 95%) indicate the proper performance of the proposed method compared to existing studies. This work shows better performance when compared to existing work in literature.
A Novel Image Restoration Method based on Iterative Algorithm

In the paper, we proposed a new iterative algorithm and use a entirely new iterative factor. Firstly, we adopt the Exp function in the iterative factor, because we want each iterative result preserves the nonnegative constraint; Secondly, we make the iterative factor in a reciprocal form ,this way can produce two advantages, one is we can get a more stable and continuous results after each iteration; the other is we can achieve this algorithm in hardware more convenient. Thirdly, we add a low-pass filter and the edge of the scale in the iterative factor, this way we can get a better result, the image SNR is higher and the MSE is lower. Meanwhile for the image sequence, we adopt the two-step iterative algorithm. The result shows the algorithm own the faster convergence speed and the better convergence result. Different from the other algorithm for blind restoration, although we should select the parameter in the starting of the algorithm, the algorithm doesn’t sensitive for the parameter. So the algorithm possesses very strong adaptability for the blind image deblurring. So a novel algorithm based on an iterative and nonnegative algorithm was proposed to perform blind deconvolution.
ERP System for College Automation Using Quick Response Code

An enterprise resource planning system (ERP) is a method used by almost all businesses today due to its ability to incorporate various domains and fractions of work. The ERP method atomizes the majority of the manual work performed internally in each of these units, as well as combining the total work output, allowing for a better overall view of the company. The education field, which also has several fractions that need integration, uses the same high-quality ERP framework. Paperwork in the education sector has long been a concern, owing to the ease with which errors can be made when work is performed manually. This proposed framework entails using Quick Response Code to introduce an ERP system for college automation. The use of a QR Code tag allows each student to be assigned a unique ID, which is then used for automated college process labeling. This framework consists of a series of integrated software modules that provide real-time support for the college. From the director level to the student level, several modules are built to serve each fraction of the institution. When this method is implemented, there is no paper work and no data inconsistency. It has many advantages for administrators, teachers, and students. In terms of deployment and maintenance, this is a low-cost system.
Testing independence between discrete random variables

In this paper we develop a likelihood ratio test for independence between multivariate discrete random vectors whose components may be dependent or may have different marginal distributions. A special case that tests independence between two discrete random variables is also discussed. The model under the null hypothesis is constructed based on the marginal distributions of the random variables, while the model under the alternative is built by adding a common random effect which is not restricted to follow the same distribution as those of the other variables. The test statistic is shown to admit an asymptotic distribution of a weighted sum of chi-square random variables, each with one degree of freedom. In order to avoid the high computational cost of finding the parameters of the asymptotic distribution, a permutation-based procedure is introduced. It is shown in simulations that the permutation-based realization yields empirical level close to the nominal level, while achieving power comparable to or higher than that of existing competitors in the literature. The application of the test is illustrated with a real data set.
Realization of Target Tracking Technology for Generated Infrared Images

Target tracking is to determine the motion parameters of the target in each infrared image in continuous sequence images. In this paper, the target tracking refers to getting the coordinate information in the infrared image. In the infrared image tracking system, there are many methods to determine the target position. In this paper, the traditional infrared tracking methods was applied including weighted centroid tracking, centroid tracking, matching tracking, and Kalman tracking. The tracking performance of the infrared detector system was tested under different signal-to-noise ratios.
Dynamic Learning of Cheerleading Rehearsals Based on Moodle2.0 Module

Based on the traditional cheerleading teaching foundation, combined with the development of computer technology, cheerleading combined with moodle2.0 modular dynamic learning research, taking modern education concept as the guide and promoting the two-way principal position of teachers and students as the starting point, focuses on the new breakthrough of traditional education and teaching mode under the guidance of the new curriculum concept, Carrying out the education and teaching activities of carrying out quality education and cultivating people's all-round development.
Visual analysis system for association-based outlier detection for data streams

The existence of outliers will reduce the reliability of original data streams, thus adversely affecting data-based operations. Through the interpretation and analysis of outliers, users can clarify the difference between the normal data and outliers, thus clearing outliers to improve the reliability of original data streams. However, there are few explanations and analyses on the reasons of outliers. The contribution of this paper is that we have designed a visual analysis system for outlier detection of data streams based on association, VooddS, which can reasonably explain and analyze outliers from two aspects of algorithm operation process and data distribution, and better show users why data instances are judged as outliers. VooddS system starts from the running process of the algorithm and designs a visual forms according to the running process of outlier detection algorithm, so as to reasonably explain the reason for determining outliers to users; From the point of view of data distribution, a combination of box graph, broken line and histogram is designed, and the correlation between data and outlier degree is analyzed from two levels of data attribute and data tuple, which helps users to explore the influence of context data on outlier judgment. In addition, the effectiveness of the system is verified by real data sets, and the results show that VooddS can reasonably explain abnormal values, and the interactive mode of the system can bring better experience to users.
An Algorithm for Satellite Power Anomaly Detection Based on Time Series Prediction

With the popularity of mobile terminals and various location-based service applications, people’s demands for location information are increasing day by day. As an infrastructure, GNSS plays a pivotal role in both military and civilian usage. Real-time monitoring of GNSS services and early warning of abnormalities are essential. In this paper, by processing the GNSS satellite observation data monitored by the IGS tracking stations, a method for judging satellite power anomalies based on time series prediction is proposed. This method can effectively solve the problems of high false and false negative alarming rate in the traditional alarming mechanisms based on the ratio comparing and the sliding window methods, and it is difficult to deal with unknown abnormalities due to excessive reliance on manual experience to set the thresholds. Experiments show that this method can effectively find GPS satellite power abnormalities, and it can be applied to other scenarios.
AN EFFICIENT PATIENT HEALTH MONITORING SYSTEM USING IoT

The health monitoring system based on Internet of Things affords a lots of benefits for doctors as well as patients.This had led to high demands for a more robust healthcare monitoring and treatments facilities. Nowadays lot ofdevices were introduced, in order to monitor the patients. Because the care takers or attenders or nurses or doctorswere unable to monitor the health condition of the patient at all the times. These kind of devices can be helpful forall to monitor and do follow the process regularly. Various sensors such as level, temperature, blood pressure andgyro sensor has been used[1-3]. A level sensor has been used in order to monitor the fluid level of the fluid bottle aswell as it will give indication whenever it reaches the critical stage. Likewise temperature sensor will gaveinformation about current temperature of the patient. Blood pressure monitoring sensor was used to indicate theblood pressure range of the patient. Gyro sensor was used to notice the position of the nerves as well as otherinternal organs when it undergone fracture and some other problems. This device can be efficiently utilized inhospitals, homes as well as in orphanages
Chapman Kolmogorov and Jensen Shannon Ant Colony Optimization-Based Resource Efficient Task Scheduling in Cloud

Cloud computing is the most significant technology in recent days, and it facilitates the users to access computing resources as services anywhere through the Internet. Task scheduling is a major concern since traditional scheduling algorithms are not suitable for achieving higher efficiency. To increase the task scheduling efficiency, a novel technique called Chapman Kolmogorov Markov Stochastic Jensen–Shannon Divergence-based Multi-objective Ant Colony Optimization (CKMSJSD-MACO) technique is introduced. The proposed CKMSJSD-MACO technique finds the resource-efficient virtual machine from the population in the search space. For each virtual machine, the fitness is computed based on the multiple objective functions such as memory, bandwidth, energy, and CPU time. By applying the Chapman Kolmogorov Markov Stochastic transition probability, the better convergence rate of the proposed optimization technique is obtained. This helps to reduce the task completion time. Finally, Jensen–Shannon Divergence is applied for measuring the strength of pheromone update to discover the global optimum solution in search space. Finally, the scheduler allocates the incoming tasks to the resource-efficient virtual machine with higher efficiency. The multiple heterogeneous tasks are correctly scheduled for the resource optimized virtual machine and minimize the response time. The observed results indicate that the CKMSJSD-MACO technique achieves higher task scheduling efficiency with lesser makespan as well as lesser false-positive rate. Here, the information of this nature is given in the addresses, which will be deleted by typesetters.
A Crowd Flow Segmentation Method based on Deep Motion Transformation Network

The crowd motion in public places is generally disorderly but locally orderly. Therefore, dividing the crowd flow into regions with basically consistent motion states can help us better understand and analyze the crowd's motion states. For this reason, a deep motion transformation network is proposed to segment the crowd flow into different motion states, which avoids the problem of parameter selection based on the clustering method. We test the method in different crowd density scenarios, and the experimental results show that the proposed method can achieve a better segmentation effect than the previous methods.
Development of Information Technology Telecom Chatbot: An Artificial Intelligence and Machine Learning Approach

Nowadays, educators can show case the technology of chatbots in various fields such as teaching and learning. Earlier the resources are almost negligible, in the field of education with the learning design integrated in it. But now with the advancement of technology, chatbot can fill the gap in the teaching landscape too. Using chatbot in the education domain is reassuring, since these bots can point out some of the logistics and diversified issues that a normal class might face. Besides this, with the further advancement of Artificial Intelligence, tech-giants like Apple, Google, and Amazon are providing platforms where building conversation is more focused rather than technicalities of computer programming i.e., to be more specific in natural language processing. In this paper, we have built a rule based chatbot on a platform named Discord, also showcased that how a chatbot can be integrated into other online platforms to counter the challenges faced in teaching. Q/A features are used to get in-depth knowledge about various preinstalled data in chatbot.
802.11ac Device Identification based on MAC Frame Analysis

In Wi-Fi networks, devices can be identified by physical features or MAC layer features, and the solutions of device identification can be used to enhance device authentication. Since 802.11ac Standard has been widely applied in Wi-Fi devices in recent years, the traditional identification methods designed for 802.11b/g/n devices will be no longer applicable. Therefore, it is necessary to design the corresponding 802.11ac device identification method. Compared with the physical feature-based method, the MAC layer-based method has advantages of low cost and easy deployment, so it has attracted more and more researchers' attention. In this paper, we use the fields from 802.11ac MAC frame as fingerprints. Through the analysis of 802.11ac MAC frame, a preprocessing method of the frame is proposed to mask strong and easy-to-modified identifiers. Then to overcome the difficulties caused by random changes in field values, we propose a device identification method based on the deep learning to select features automatically. Compared with the previous one using the transmitting rate as a feature, our method does not spend much time capturing packets in the device identification stage and has better performance whose average precision and recall exceed 99%.
Application Research of Classroom Teaching and Online Teaching in Advanced Mathematics

This article studies students’ attitude and adaptability to classroom teaching and online teaching before and after the COVID-19 outbreak. The questionnaire survey method was used to investigate the students of the University of Jinan. According to the students’ feedback, this study analyzes the problems caused by the full use of online teaching, and provides reliable information for future teaching research. Finally, the study discusses the self-worth of ordinary teachers and the trend of teaching method with the rapid development of information technology.
Automated Leadership Selection Educational Projects Based on Genetic Algorithms

Purpose or research. The purpose of the study is to conduct a research of the possibility to use automated methods of finding a solution for the task of forming a selection of candidates in solving a wide range of business problems with the possibility of the influence of such factors as: requirements for the quality of business processes, restrictions on the competence of candidates, the employment of candidates in other processes of the company, planned urgency of fulfilling tasks, the amount of tasks in the pool and expected tasks, tasks characteristics, private policy of the company when selecting candidates, company's policy on risks. Methods. A typology of innovative business tasks is given by the subject area, the range of target results, the duration of execution, etc. External and internal factors are considered to ensure the effective operation of the project team and the success of the project. There are offered sets of attributive characteristics for assessment of projects quality and potential projects. The structure of the algorithm for solving the problem of creating a selection of candidates for solving a set of problems is considered. Prerequisites for using principles of genetic programming in solving the problem under consideration are given. Search algorithm implementation parameters, criteria and constraints are defined. Algorithm was implemented as well as modeling in the Jupyter Lab v2 environment. The results are described in the text. A relative analysis of practical effectiveness of the algorithm was carried out depending on the modeling parameters to justify the selection of their values. Results. The task of creating a selection of candidates for the implementation of projects pool considering a number of factors was described during the study. An approach to solving the problem based on a genetic heuristic search algorithm has been developed. A numerical simulation was performed in Jupyter Lab v2. Simulation results were analyzed, and algorithm parameters were selected. Conclusion. The proposed approach allows not only automatize the selection of managers based on the accumulated data history, but also to adjust the established process to change the vector of organization development. The interaction of education and informatics (information technology) can enrich and expand the field of both sciences in the field of recruitment of innovative project teams. Their object analysis, supplemented by genetic programming capabilities, together allows you to achieve specified qualities of the head of innovative project teams that help maximize business benefits while minimizing material costs. As a result of computational experiments using mathematical apparatus and genetic algorithm technologies, it is necessary to emphasize the possibility of extrapolating such approaches to any level of implementation of innovative projects.