Reviewerly/exHarmony huggingface dataset:
Detecting Phishing Websites Using Machine Learning

The entire world is digitizing at a rapid pace. However, the ever-evolving transformation comes with its fair share of vulnerabilities, opening the doors wider for cybercriminals. One of the common types of attacks criminals indulge in these days is phishing. It involves creation of websites to dupe unsuspecting users into thinking that they are on a legitimate site, making them disclose confidential information like bank account details, usernames, and passwords. This paper aims to find out accurately if a URL is reliable or not, in other words, if it is a phished one or otherwise. Machine Learning (ML) based models provide an efficient way to detect these phishing attacks. This research paper focuses on using three different ML algorithms—Logistic Regression, Support Vector Machine (SVM), and Random Forest Classifier in order to find the most accurate model to predict whether a given URL is safe or not. To achieve this, the respective models are trained using a pre-existing data set and then tested as to whether they can accurately classify the websites or not. The algorithms are also compared based on performance measures like Precision, Accuracy, F1 Score, and Recall to deduce which one of the three is most efficient and reliable for classification and prediction.
Online and offline integrated teaching model

As the 2020 new crown pneumonia epidemic swept the world, during the epidemic prevention and control period, students learning Chinese could not be taught offline, but the teaching progress could not be stopped, so many schools adopted online teaching mode to teach. It is not only a test of teachers' teaching technology, but also a test of science and technology information technology. Online teaching requires both teachers and students to have an electronic facility, to find areas where the network is more fluid for teaching, and to be able to use an online teaching software and so on. In this paper, from the point of view of using Ding TALK in teaching process, the function of Ding TALK software in teaching process is analyzed to promote the reform and innovation of traditional Chinese as a foreign language model, to promote the formation of a fixed teaching model of online and offline integrated teaching, and to improve the quality of Chinese as a foreign language.
Review on Deep Learning Methodologies in Medical Image Restoration and Segmentation

This paper comprehensively reviews two major image processing tasks, such as restoration and segmentation in the medical field, from a deep learning perspective. These processes are essential because restoration removes noise and segmentation extracts the specific region of interest of an image, both of which are necessary for accurate diagnosis and therapy. This paper mainly focuses on deep learning techniques. It plays a prominent role over other conventional techniques in handling a large number of datasets in the medical field and provides accurate results. This paper reviewed the application of different convolutional neural network architectures in the restoration and segmentation processes. Based on the results in the case of image restoration, TLR-CNN and Stat-CNN are promising in achieving better PSNR, noise suppression, artifact suppression and improving the overall image quality. For the segmentation process, LCP net achieves the Dice score of 98.12% and sensitivity of 98.95% in the cell contour segmentation; the 3D FCNN model is found to be the best method for the segmentation of brain tumors. This review shows that deep learning methodologies can be a better alternative for medical image restoration and segmentation tasks, as data size is an important concern today.
Joint Multi-Objective Request Scheduling and Traffic Path Computation in 5g-Mec Systems

The paper considers the joint solution of the request scheduling problem and the traffic path computation problem in the Multi-access Edge Computing (MEC) scenario. The first problem aims to select the MEC Host (MEH) supporting the user request, while the second problem aim to find a path for steering the traffic from the user to the selected MEH. The presented technique is based on the definition of a model that allows representing MEH conditions in the graph modeling the link and the node of the transport network. In this model, the Multi-objective Dijkstra Algorithm (MDA) is applied to find solutions to the multi-objective optimization problem that considers simultaneously different performance metrics and constraints.The performance analysis considers both general theoretical graphs and experimental MEC scenarios. In the first case, the analysis shows how the proposed strategy works. In the second case, the strategy has been implemented in the AdvantEDGE platform to study its performance when integrated with the MEC architecture. The experimental results obtained in a scenario with a single MEH and the VideoLan application show the performance enhancements given by the proposed strategy.
Efficient and compact representations of some non-canonical prefix-free codes

For many kinds of prefix-free codes there are efficient and compact alternatives to the traditional tree-based representation. Since these put the codes into canonical form, however, they can only be used when we can choose the order in which codewords are assigned to symbols. In this paper we first show how, given a probability distribution over an alphabet of σ symbols, we can store an optimal alphabetic prefix-free code in O(σlg⁡L) bits such that we can encode and decode any codeword of length ℓ in O(min⁡(ℓ,lg⁡L)) time, where L is the maximum codeword length. With O(2Lϵ) further bits, for any constant ϵ>0, we can encode and decode O(lg⁡ℓ) time. We then show how to store a nearly optimal alphabetic prefix-free code in o(σ) bits such that we can encode and decode in constant time. We also consider a kind of optimal prefix-free code introduced recently where the codewords' lengths are non-decreasing if arranged in lexicographic order of their reverses. We reduce their storage space to O(σlg⁡L) while maintaining encoding and decoding times in O(ℓ). We also show how, with O(2ϵL) further bits, we can encode and decode in constant time. All of our results hold in the word-RAM model.
A Study of Unsupervised Networks Based on the Network Prior for the Image Inpainting

Image inpainting is a hot problem in the field of image processing, and the use of neural networks for image in painting is a hot research direction at present. Neural networks in this direction aim to let computers learn to repair a missing image quickly and automatically through machine learning. However, such learning often requires a large data set as a prerequisite for training. This paper focuses on image repair in an unsupervised manner based on a deep network prior. In the paper, the optimal model of this unsupervised network is explored using the control variables method, and the performance of the network on image repair is explored by ssim, psnr and other metrics.
Performance Improvement of Single-stage Target Detection by Tuning Parameters in CNN

Single-stage Target detection can find almost all the objects in the images and determine the category and location of the objects, which includes two key points: object positioning and object classifying. Deep learning methods have been widely used to target detection. However, parameters are tuned to find relations between parameters and performance of CNN, including training time needed, detecting time, and detection precision. This paper studies the influence of the parameters in CNN on the accuracy of single-stage target recognition and improves the performance of single-stage target detection. First, the data comes from PASCAL VOC 2012. Then, a series of related parameters are analyzed in our CNN model, including image size and batch size, the Strides, the Anchor size and number, etc. Finally, by changing the single parameter and keeping other parameters the same, we study how these parameters influence the performance of our model. The results show that the image size affects the time and accuracy of the training process, and there is an antagonistic relationship between them. Besides, in the training process, we find that with the training time increasing the training loss decreased. And if reasonably increasing the Strides, the Input shape and the Non-Maximum Suppression (NMS) threshold can improve the recognition rate. However, if decreasing the Anchor size and number and the Strides greatly can increase the running time. Besides the Anchor size and number is having a certain connection related to the Mean Average Precision (mAP), and the NMS has a certain positive impact on the MAP. Our analysis demonstrates that setting reasonable NMS, strides, image size and the number of anchors which belong to Convolutional Neural Network (CNN) can make the precision rate higher. Simultaneously, it can accelerate the training process.
A novel material fabrication to detect breast cancer using deep novel classification mechanism

Now-a-days, Breast Cancer is a serious cause in world and it raises a death ratio in drastic manner. Most of the women are suffered due to this kind of breast cancer disease, in which several literatures are available regarding this breast cancer identification, but all are strucked over certain level. Lots of researchers in industry providing different digital image processing schemes to identify the Breast Cancer in clear manner to save lives of women but all are strucked with real world prediction scenarios. For Breast Cancer identification mammography is a well-known technique, in which it screens the breast cancer cells in clear manner. However, based on the workload and manual expertise failures, a numerous failures and wrong classification procedures reported as well. So, that a novel computer assisted digital image processing technique and smart tool is required to provide intelligent prediction logics to identify the breast cancer on earlier conditions instead of detecting that in complex stages. In this paper, a novel Deep Learning enabled Image Processing tool is designed, called as Digital Image Processing Scheme enabled SmartKit (DIPSK) to identify the breast cancer disease effectively on earlier stages without any human intervention. The prop osed DIPSB enables the system to acquire the input scanning image and process that with DIP techniques with respect to pre-processing feature extraction and classification. The detailed features are cross-validated with the trained samples, in which the Deep Learning procedure called Improved Convolutional Neural Network (iCNN) is utilized by the SmartKitto provide classification process in accurate manner. The final resulting of this SmartKitis more efficient as compare to the traditional image processing schemes and provides accurate prediction on earlier stages. The resulting section provides the proper proof for the classification accuracy and prediction logics.
Facenet: A Cloud-Based Application Framework for Social Network

With the fast development of social media, the social relationship between people becomes more and more complex, and the social network becomes bloated. In this work, motivated by the six degrees of separation theory and with the help of cloud database, a social network model called facenet is built, which clearly represents the social relationship between people. First, a mathematical model is designed to estimate the diameter of one specific social network, leading to a random sparse graph. On this basis, a unique social platform is established. It has cutting-edge augmented reality (AR) functionality, allowing users to easily find the shortest social connection between themselves and any stranger in the world. Furthermore, an application framework is designed to illustrate the software architecture of the facenet and the relationship between various modules. Users can create their own cloud-based AR applications through this framework. Experimental results in the real world show that the proposed face recognition model achieves a recognition accuracy of 91.2&#x0025; and great potential and extensibility.
Social Media and Cyber Security

The proliferation of social media in Nigeria has birthed new paradigms of communication and two-way interactions among friends, family colleagues, and business associates. It has equally morphed to become a channel for money generation among enterprising youth across the nation. It cannot be denied that the merits associated with this phenomenon called social media are enviable; however, it is also arguable that cyber security is a key factor to the enjoyment of these attendant benefits. This study is anchored on the protection motivation theory and the technological determinism theory. The study identified some cyber security risks synonymous to the use of social media in Nigeria. To remedy the situation, the study recommends that education and practical training in technological best practices in formal and non-formal school settings will help mitigate the inherent risks discussed. Also, initiatives should be taken by the government and organisations to curb this serious issue.
Using solar panels on building exterior walls is not reasonable in dry-cold climates (experimental study)

PV panels can be used on the roof or exterior building walls. This paper is an hourly experimental study in a cold-dry climate (for 3 days in Tabriz City). In the first, the PV panel fixed on building exterior walls and efficiency have been investigated and compared with a fixed PV panel in south direction (facing south and tilted with 25 degrees from the horizon). In the next step it has been compared with a horizontal solar panel on the roof. The results show that, Fixed South case has more PV power compared others. Using PV panels on south wall is some better than west or east walls. Using it on south wall can be generate 59.02 percent less than Fixed South case and it is more. The worst wall is East walls and it has power as 72.25 percent less than Fixed South. In the cloudy days the horizontal PV panels has 1.39 percent better efficiency than Fixed South. The results show that using PV panels on South, West or East walls reduce the PV efficiency 60 to 70 percent compare to Fixed South. So, using it on exterior building walls is not reasonable. The PV efficiency difference between south and west walls is negligible. But using it on roof reduce the PV efficiency about 30 percent compare to Fixed South. Paper shows that to find real efficiency of a solar cell in a climate condition should be have an experimental study because climate change and pollution are important parameters. Simulation software (Like Energyplus) not use pollution as a weather parameter.
Robust and Imperceptible Multiple Watermarking Using Transform Domain Algorithm for Digital Media

This paper discussed a novel robust multiple watermarking method with three transform domain techniques, discrete wavelet transforms (DWT), discrete cosine transforms (DCT) and singular value decomposition (SVD) for color images. For security reasons, secret media is inserted into same multimedia items to offers an additional level of security and to achieve two important performance metrices of watermarking. Firstly, the original media (image) is disintegrated into first level DWT and generate DWT coefficients then select the lower frequency sub-band for applying DCT then SVD. The secret image is also altered by DCT decomposition then SVD transformation is applied on DCT coefficients in embedding process. Then after, a watermarked image is produced by applying converse of all transform domain SVD then DCT and DWT. The secret media can be extracted with recovery algorithm. This procedure has been generously tried and evaluated against various attacks of watermarking and it is found that it achieves better robustness and imperceptibility.
Sentiment Analysis using Different Machine Learning Techniques for Product Review

The World Wide Web (WWW) has turned into an immense wellspring of crude information produced by both the customers and the clients. Utilizing online media, web - based business sites, and film surveys, for example, Facebook, Twitter, Amazon, Flip kart and so forth, clients share their perspectives and emotions in an advantageous way. In WWW, where many individuals express their perspectives in their day by day connection, by the same token in the web-based media or in e-commerce which can be their opinions , what’s more, sentiments about a specific thing. Sentiment analysis is characterized as the way toward mining information, reviewing or anticipating the feelings or emotions behind the sentence via natural language processing. This paper includes two approaches, the first one is a lexicon-based approach, and the second one is machine learning technique logistic regression to extract the sentiments from text.
The Adversarial Attack on Domain Adaptation

Adversarial attack and defense have been a hot topic recently. The subtle perturbations that are not recognized by the human eye can fool models and lead to misclassification. While most recent works focus on the attack for image classification models, there is no systematic research on the adversarial attack for unsupervised domain adaption(UDA) models. We empirically investigate the resilience of UDA models to adversarial perturbations in this work. And we propose to attack the target domain so that the UDA models can correctly classify the source domain images but classify the target domain images wrong. This will make the adversarial examples more confusing to humans. Experiments show that the UDA models are vulnerable to adversarial examples under both white-box and black-box manners. We hope that this work will attract the increasing attention to the robustness of the UDA models.
Design and Application of Edge Intelligent Recognition Device for Multiple Video Streams in Substations

Substation is an important part of the power grid. Its maintenance is related to the safety and stability of the substation. The contradiction between the increase in the number of substation equipment and the relative shortage of personnel led to the change from manual inspection to intelligent inspection. There are a large number of video cameras in substation, and the video data is generally transmitted to the back-end server for analysis and processing through optical fibers. The channel bandwidth is under pressure and the image recognition efficiency is low. Based on artificial intelligence and edge computing technology, this paper developed the edge intelligent recognition equipment for multiple video streams in substation, designed the intelligent identification algorithms for substation equipment defects. The test and application verification were carried out. The results showed that the real-time collection, online intelligent recognition, and optimized streaming of multiple video streams were realized at the edge. The intelligent level of substation maintenance was significantly improved.
An Empirical Study on Sense of English Classroom Security of English Majors Based on Internet Information Technology

With the rapid development of modern science and technology, combining Internet technology and education has become a new method of education. With the aid of Internet information technology and questionnaire survey, 68 English majors from School of Foreign Languages of Fuzhou University of International Studies and Trade were selected as the subjects, aiming to explore the current situation and reasons of sense of English classroom security of English majors in different grades from the four factors of sense of belonging, trust, certainty and control. The results of the research show that the overall sense of English classroom security of English majors is not satisfactory; there is no significant difference in the security index of English majors in English classrooms; among them, sophomores' sense of English classroom security is slightly higher than that of freshmen and juniors, which rises at first and then falls. Meanwhile, it is not significant among sense of belonging, trust, certainty, and control in different grades. To certain extent, these findings would cast some light on the college English instructions in China.
The Applicability of Machine Learning Algorithms in Predictive Modeling for Sustainable Energy Management

The world’s energy sector is having difficulties governing the best management synthesis because of challenges such as a request in production supply and demand design changes. Mapping data of the energy sector to machine learning (ML) can effectively alleviate the problem. ML algorithms can analyze equipment data, build predictive models and solve issues regarding sustainability. Innovative areas designed with ML algorithms can naturally react to fluctuations in power costs and control energy utilization. Frameworks dependent on ML can help energy providers to get ready to stay up with fluctuating sustainable power supplies through predicting energy demand, forecasting the maintenance period of pieces of equipment in energy plants such as sunlight based PVs, wind power and hydrogen sources enabling to eliminate the applicability limits of these renewable energy sources around the world. Designing smart grids in combination with advanced control techniques, such as model predictive control (MPC) enables to comfort satisfaction of consumers while handling constraints needed to meet sustainability. This paper is devoted to use of ML algorithms in different renewable energy sources and bridging ML with MPC to achieve sustainable energy management .
Design of Robotic Measuring System for Large Diameter Pipe Fittings Based on Line Laser

At present, the manual inspection efficiency of large-diameter pipe fittings is low. A high-efficiency automatic measurement system for industrial robots using line laser sensors was proposed. This system has realized the accurate measurement of the inner diameter of the pipe fittings. According to the geometric relationship among pipe fittings, line laser sensors, and industrial robots, a measurement mathematical model was constructed. Among them, a posture analysis method based on projection of three-dimensional contour data was proposed to solve the installation angle of the line laser sensor; the least square method was used to solve the exact solution of the six-axis rotation axis; the Levenberg-Marquardt algorithm was used to fit the ellipse to avoid the singular value problem in the solution process. The system error of the measurement principle was analyzed, and the incremental compensation method was used to construct the system error compensation model to solve the influence of the position and attitude error of the laser sensor on the measurement accuracy. A rigorous calibration and acquisition methods were used to ensure that the engineering system accurately implements the measurement design principle. The experimental results show that the measurement error of the line laser large-diameter pipe robot measurement system designed in this paper was less than 0.025%.