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

Phishing is an online crime in which a criminal tries to persuade unsuspecting users to reveal sensitive (and valuable) personal information to the miscreant, such as usernames, passwords, financial account details, personal addresses, SSNs, and social contacts, for harmful purposes. Phishing is usually carried out by impersonating a reliable entity in Internet communication, which is accomplished through a combination of social engineering and technical trickery. Attackers regularly employ spoofing emails and deceptive websites to persuade users to provide personal information. Spoofing emails frequently pretend to be from legitimate companies and direct consumers to fake websites where they can enter important information. Phishing is one of the most common forms of online crime in today's world. Checking URLs against blacklists of known phishing websites, which are generally built based on manual verification, is a frequent countermeasure that is inefficient. As the Internet develops in size, automatic URL recognition becomes more necessary to offer end users with timely protection. This thesis explains how to use machine learning to detect dangerous phishing websites, with an emphasis on attributes retrieved just from the URL. It starts with a description of the available data and the feature engineering process, then moves on to choosing acceptable machine learning approaches. It compares algorithm performance and assesses the outcomes obtained.
Optimizing Continuous Prompts for Visual Relationship Detection by Affix-Tuning

Visual relationship detection is crucial for understanding visual scenes and is widely used in many areas, including visual navigation, visual question answering, and machine trouble detection.Traditional detection methods often fuse multiple region modules, which takes considerable time and resources to train every module with extensive samples.As every module is independent, the computation process has difficulty achieving unity and lacks a higher level of logical reasonability.In response to the above problems, we propose a novel method of affix-tuning transformers for visual relationship detection tasks, which keeps transformer model parameters frozen and optimizes a small continuous task-specific vector.It not only makes the model unified and reduces the training cost but also maintains the commonsense reasonability without multiscale training.In addition, we design a vision-and-language sentence expression prompt template and train a few transformer model parameters for downstream tasks.Our method, Prompt Template and Affix-Tuning Transformers (PTAT), is evaluated on visual relationship detection and Visual Genome datasets.Finally, the results of the proposed method are close to or even higher than those of the state-of-the-art methods on some evaluation metrics.
Comparing the Pathfinding Algorithms A*, Dijkstra’s, Bellman-Ford, Floyd-Warshall, and Best First Search for the Paparazzi Problem

This paper aims to compare A*, Dijkstra, Bellmann-Ford, Floyd-Warshall, and best first search algorithms to solve a particular variant of the pathfinding problem based on the so-called paparazzi problem. This problem consists of a grid with different non-moving obstacles that lead to different traversing costs which are considered as minimization objective in a specific model. The performance of the algorithms that solve the paparazzi problem is compared in terms of computation time, the number of visited nodes, shortest path cost, and accuracy of finding the shortest path. The comparison shows that heuristic algorithms mostly provide the optimal path but with a shorter computation time.
Measuring Motion Sickness Through Racing Simulator Based on Virtual Reality

Nowadays, we are experiencing a new boom in virtual reality (VR) technologies led by big video game companies. When these renewed forms of entertainment caught everyone’s attention, their most common issues began to be noticed. Motion sickness is a feeling of discomfort triggered by a misalignment between a person’s eyes and body when in motion. This condition occurs regularly in VR environments. Although it is a common problem in this context, very few studies have been conducted on this subject. This research work is the first step in a barely explored field that will hopefully help to develop new proposals. The present study aims to find techniques that reduce the negative effects of motion sickness that appear during the use of VR devices and try to identify the main factors that cause them. To this end, a driving simulator with multiple experimental countermeasures has been developed and tested with 13 volunteers to determine the key elements that allow a normative participant to tolerate and overcome the symptoms of this condition. The results will establish a series of recommendations and best practices for further work with virtual reality technologies.
Virtual Reality Interaction Toward the Replacement of Real Clinical Nursing Education

Due to time and space constraints and limited resources, clinical nursing education in real-life environments has limitations for sufficient learning. In this paper, we propose a novel interaction method for immersive virtual simulations in clinical nursing that considers the characteristics and process of each to enable learners to learn and perform realistic nursing skills. First, (1) hand gestures and speech modalities can be used as input modalities according to each type of skill, and nursing skills can be performed while the degree of behavioral freedom is ensured. (2) The timing and method of evaluating the interaction of the skills to be learned vary depending on how they are performed through the analysis of hand gestures and speech input. (3) In addition, depending on the evaluation, this method can provide immediate feedback (e.g., audio-visual feedback) as well as feedback about accumulated nursing skills interaction, which can maximize the learning effects (e.g., additional quizzes and final clinical nursing results). With the actual nursing skill implemented by the proposed method, the feasibility of immersive VR nursing practice based on the pediatric pneumonia scenario was verified through a pilot study. As a result, high scores were obtained in a simple questionnaire evaluation, and the feasibility was confirmed through an interview evaluation of both learners and instructors. With the proposed method, it is expected that the quality of clinical nursing education can be improved while learning the details and the overall flow of nursing skills through realistic interactions.
Automatic Classification of Felsic, Mafic, and Ultramafic Rocks in Satellite Images from Palmira and La Victoria, Colombia

Manually inspecting and analyzing satellite images can lead to numerous errors and is quite time consuming. Our geological contribution is to offer a means for the automatic classification of areas with felsic, mafic, and ultramafic rocks via machine learning using satellite images from Palmira and La Victoria, Colombia. Specifically, this study focuses on two types of satellite images taken from the Earth Observation System (EOS), namely natural color (bands B04 B03 B02) and infrared color vegetation (B08 B04 B03). The following machine learning algorithms were used in this study: Random Forest, K-Nearest Neighbors, Support Vector Machines, Logistic Regression, and Multilayer Perceptron. The model generated with K-Nearest Neighbors performed best for classifying natural color images with an accuracy of 91%, a precision of 87%, and a recall of 88%. Random Forest was the best model for classifying infrared images with an overall accuracy of 83%, a precision of 31%, and a recall of 31%.
ResNet-Based Multiscale U-Net for Human Parsing

Human parsing is a subtask of semantic segmentation, which only segments the characters in the picture and ignores the background information. This technology has many application scenarios: such as pedestrian re-identification, smart home, human–computer interaction, etc. Due to the complexity of human semantic segmentation tasks, the existing network is not accurate enough. In view of this situation, this paper proposes a human parsing model based on deep learning to improve the accuracy of human body image semantic segmentation. The model includes three modules. In the human body feature extraction stage, this paper proposes an encoder module based on an improved residual network, which uses the residual network to continuously downsample the human body image; the decoder module uses bilinear interpolation and channel compression to continuously upsample the human body feature map; the encoder module and the decoder module merge the low-level and high-level features of the human body through the feature fusion module. The experimental results show that the details of the model proposed in this paper can process details better and effectively improve the accuracy.
Using Telegram Bots for Personalized Financial Advice for Staff of Manufacturing Engineering Enterprises

This paper covers the research on using instant messengers’ chatbots to provide automated financial advice to manufacturing engineering enterprises employees in the scope of a chatbot application, which helps to calculate personal savings programs to ensure a constant level of consumption. The paper aims to develop telegram bots for personalized financial advice of manufacturing engineering enterprises staff. The application uses a model of life-cycle hypothesis and generates a customized saving plan based on the information provided by a user. The factors affecting decisions regarding advice include previous satisfaction with decision-making, investors’ preferences, perceived difficulty, the relationship between financial literacy, expertise, and confidence. Different approaches to developing messengers’ bots have been compared. The paper also gives a brief overview of the life-cycle approach, which suggests that finance managers plan consumption and savings behavior over the whole life cycle of their personnel. The research is focused on the Telegram platform and includes an overview of Telegram Bot API and the process of chatbot development using Java programming language.
Intelligent Smart Waste Management Using Regression Analysis: An Empirical Study

The term deep learning is seen as an important part of artificial intelligence that allows the system to understand and make decisions without special human intervention. In-depth learning uses a variety of statistical models and programs that allow different computational properties to reach the highest point. It is estimated that the market development of artificial intelligence and technology for deep learning will amount to USD 500 billion by 2026. The use of advanced technology, such as neural networks, enables better image recognition and the use of automated processes for deep operations. The main purpose of the study is to understand the critical determinants of Deep Learning in Creating a better City through Intelligent Smart Waste Management, the major determinants cover: System usability scale, Implementation of RFID sensors and Optimizing route selection. The proposed work is that implementation of advanced tools like deep learning methodologies and machine learning tools can support in managing the waste in a smart way, this will enable in creating better cities, enhance the environment and support sustainable living. Smart cities today need to use tools like deep learning and other artificial intelligence to effectively manage waste. Smart vessels are mainly controlled and implemented, which makes it easier for users to open vessels, it is also suitable for storing solid and dry waste, but provides information on the total degree of filling, can share data and information with central waste management service, you can collect waste quickly and avoid flooding. To achieve this, governments, administrators and communities are introducing sensors that transmit data and information to the waste management company in real-time and take appropriate action.
MStrans: Multiscale Vision Transformer for Aerial Objects Detection

Detecting objects in aerial images is a challenging task due to the large-scale variations and arbitrary orientations with tiny instances. A new multi-scale transformer-based aerial objects detector called MStrans is proposed in this paper to deal with the challenges in aerial detection. To detect remote instances, MStrans adopts a multi-scale patch embedding transformer (MViT) to extract the global features of the image effectively. Furthermore, to capture the different discriminant features for classification and regression branch tasks, the partial interactive fusion module (PIFM) is designed to enhance the semantic expression of the key features of classification and regression tasks by using the strategy of interactive modeling of adjacent layer features. In addition, considering that the transformer may worsen the local feature details while capturing long-distance feature dependencies, this paper designs a global to local interactive fusion module (GLIFM). It uses the advantage of convolution to extract local features to enrich the detailed information in the transformer. Experiments were carried out on DOTA and DIOR datasets, and the MStrans achieves superior detection performances compared with other approaches.
Confidence Estimation for Object Detection in Document Images

Deep neural networks are becoming increasingly powerful and large and require always more labelled data to be trained. However, since annotating data is time consuming, it is now necessary to develop systems that show good performance while learning on a limited amount of data. These data must be correctly chosen in order to obtain models that are still efficient. For this, the systems must be able to determine which data should be annotated in order to achieve the best results. In this paper, we propose three estimators to estimate the confidence of object detection predictions. The first two are based on Monte Carlo dropout and the third one on descriptive statistics. In active learning framework, the three estimators show a significant improvement in performance for the detection of document physical pages and text lines compared to a random selection of images. We also show that the proposed estimator based on descriptive statistics can replace MC dropout, reducing the computation cost without compromising the performances.
Chinese News Text Classification Based on Convolutional Neural Network

With the explosive growth of Internet text information, the task of text classification is more important.As a part of text classification, Chinese news text classification also plays an important role.In public security work, public opinion news classification is an important topic.Effective and accurate classification of public opinion news is a necessary prerequisite for relevant departments to grasp the situation of public opinion and control the trend of public opinion in time.This paper introduces a combinedconvolutional neural network text classification model based on word2vec and improved TF-IDF: firstly, the word vector is trained through word2vec model, then the weight of each word is calculated by using the improved TF-IDF algorithm based on class frequency variance, and the word vector and weight are combined to construct the text vector representation.Finally, the combined-convolutional neural network is used to train and test the Thucnews data set.The results show that the classification effect of this model is better than the traditional Text-RNN model, the traditional Text-CNN model and word2vec-CNN model.The test accuracy is 97.56%, the accuracy rate is 97%, the recall rate is 97%, and the F1-score is 97%.
Efficient Textual Explanations for Complex Road and Traffic Scenarios Based on Semantic Segmentation

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Augmented Books: Hybrid Electronics Bring Paper to Life

In today's digital world, paper's reason of being is challenged. Yet, studies suggest that books and paper-based objects have advantages ranging from the tactile sensation to information retention and indexing. We have developed a hybrid electronic device, the “a-book,” that offers access to up-to-date and pertinent multimedia content as part of the ordinary interaction with a typical hardcover book. The device maintains the look and feel of a conventional book and is connected to the web through an adjunct smart device. Here, we provide a technical project summary of the electronic system for book augmentation. We outline the system's functionality and discuss its manufacturability, prospects, and limitations in the context of current and emerging flexible electronics technologies.
Comparative Analysis Using Machine Learning Techniques for Fine Grain Sentiments

Huge amount of data is being produced every second for microblogs, different content sharing sites, and social networking.Sentimental classification is a tool that is frequently used to identify underlying opinions and sentiments present in the text and classifying them.It is widely used for social media platforms to find user's sentiments about a particular topic or product.Capturing, assembling, and analyzing sentiments has been challenge for researchers.To handle these challenges, we present a comparative sentiment analysis study in which we used the fine-grained Stanford Sentiment Treebank (SST) dataset, based on 215,154 exclusive texts of different lengths that are manually labeled.We present comparative sentiment analysis to solve the fine-grained sentiment classification problem.The proposed approach takes start by pre-processing the data and then apply eight machine-learning algorithms for the sentiment classification namely Support Vector Machine (SVM), Logistic Regression (LR), Neural Networks (NN), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbor (KNN), Adaboost and Naïve Bayes (NB).On the basis of results obtained the accuracy, precision, recall and F1-score were calculated to draw a comparison between the classification approaches being used.
Analisis Kesalahan Berbahasa Dalam Penulisan Insya’ di Madrasah Tsanawiyah Badridduja

This research is related to the analysis of linguistic errors in writing insya' in class VII(B) Madrasah Tsanawiyah Badridduja. The purpose of this study is to identify the various language errors that occur, the causative factors and solutions that can be done to correct these problems. The research method used is an associative quantitative approach with data collection techniques using written media in the form of written tests, observations and interviews. Then the researcher classified the errors into 4 categories. Based on the results of the research, from the results of the written essay writing test (insya') found 38 errors with 4 categories: in the phonological order 10 errors, the morphological order 6 errors, the syntactic order 13 errors and in the semantic order 9 errors. Based on the data from the recapitulation of the first test before the evaluation, it obtained an average score of 40 with the data from the recapitulation of the second test after the evaluation, obtaining an average value of 71.5. So, this shows that the evaluation activities carried out by researchers on students' language errors went well and showed an increase in the ability of students' maharah kitabah.
FEATURES AND PROSPECTS OF BLENDED LEARNING IN MODERN EDUCATIONAL PROCESS

The article relevance consists in the analysis of trends in blended learning development in leading countries of the world and Ukraine. Main features, advantages and weaknesses of the chosen learning model are identified; further developing prospects of blended education technologies in Ukraine are confirmed. The article reveals prospects for the development and the implementation of blended learning technologies. The purpose of the article is to identify features, advantages and disadvantages of blended learning technologies. The authors used various research methods and techniques, namely, the method of a targeting selection, the comparative method, the method of generalization and systematization of theoretical material. It should be noted that Ukrainian and foreign scientists’ works focusing on the concept of “blended learning” were analyzed; ways of the distance learning implementation were outlined; the situations of appropriate and effective blended learning introduction were indicated; advantages and disadvantages of the blended learning were identified, the blended education development in Ukraine was characterized. The paper explains reasons of the appearance and development of blended learning; it specifies the current problems of blended learning implementation. The paper bases on scientific studies of blended learning carried out during 2016-2021. It reveals that a student needs to have a great willpower, significant motivation and time-management skills to participate in self-education or self-development such as blended learning. The practical significance of the results focuses on using blended technologies in the educational activities of higher education organizations and accelerating modernization and adaptation of the current Ukrainian educational system in accordance with the requirements and trends of the global educational sphere.
The Current State of Linked Data-based Recommender Systems

Linked Open Data (LOD) is a key Semantic Web technology that manages, reuses, shares, exchanges, and interoperates knowledge and information spaces from various knowledge domains. The use of LOD and its benefits in recommender systems have emerged a lot throughout the past few years. Many approaches made use of LOD to provide a recommendation to the user to help them find relevant data close to their needs out of a massive amount of data. All of them verified that using LOD enhances the recommendation task and produces an accurate recommendation. In this paper, a review is presented on the current state of LOD in the field of recommendation system, with a focus on reviewing the various recommendation techniques used, the problems facing the recommendation processes, the importance of using linked data, and the methods used in previous studies to generate a recommendation based on the Linked Open Data. Despite the recent trend to use linked data to improve recommendations, there are still many challenges, especially if more than one dataset is chosen, as heterogeneity and complexity will arise, aside from the issue of evaluating such systems and comparing them in a way that avoids bias and places each method in its proper comparative position.