Reviewerly/exHarmony huggingface dataset:
Solving Geometry Problems via Feature Learning and Contrastive Learning of Multimodal Data

This paper presents an end-to-end deep learning method to solve geometry problems via feature learning and contrastive learning of multimodal data.A key challenge in solving geometry problems using deep learning is to automatically adapt to the task of understanding single-modal and multimodal problems.Existing methods either focus on single-modal or multimodal problems, and they cannot fit each other.A general geometry problem solver should obviously be able to process various modal problems at the same time.In this paper, a shared feature-learning model of multimodal data is adopted to learn the unified feature representation of text and image, which can solve the heterogeneity issue between multimodal geometry problems.A contrastive learning model of multimodal data enhances the semantic relevance between multimodal features and maps them into a unified semantic space, which can effectively adapt to both single-modal and multimodal downstream tasks.Based on the feature extraction and fusion of multimodal data, a proposed geometry problem solver uses relation extraction, theorem reasoning, and problem solving to present solutions in a readable way.Experimental results show the effectiveness of the method.
Adaptive Consistent Management to Prevent System Collapse on Shared Object Manipulation in Mixed Reality

A concurrency control mechanism for collaborative work is a key element in a mixed reality environment.However, conventional locking mechanisms restrict potential tasks or the support of non-owners, thus increasing the working time because of waiting to avoid conflicts.Herein, we propose an adaptive concurrency control approach that can reduce conflicts and work time.We classify shared object manipulation in mixed reality into detailed goals and tasks.Then, we model the relationships among goal, task, and ownership.As the collaborative work progresses, the proposed system adapts the different concurrency control mechanisms of shared object manipulation according to the modeling of goal-task-ownership.With the proposed concurrency control scheme, users can hold shared objects and move and rotate together in a mixed reality environment similar to real industrial sites.Additionally, this system provides MS Hololens and Myo sensors to recognize inputs from a user and provides results in a mixed reality environment.The proposed method is applied to install an air conditioner as a case study.Experimental results and user studies show that, compared with the conventional approach, the proposed method reduced the number of conflicts, waiting time, and total working time.
Machine Learning Based Classifiers for QoE Prediction Framework in Video Streaming over 5G Wireless Networks

Recently, the combination of video services and 5G networks have been gaining attention in the wireless communication realm.With the brisk advancement in 5G network usage and the massive popularity of threedimensional video streaming, the quality of experience (QoE) of video in 5G systems has been receiving overwhelming significance from both customers and service provider ends.Therefore, effectively categorizing QoE-aware video streaming is imperative for achieving greater client satisfaction.This work makes the following contribution: First, a simulation platform based on NS-3 is introduced to analyze and improve the performance of video services.The simulation is formulated to offer real-time measurements, saving the expensive expenses associated with real-world equipment.Second, A valuable framework for QoE-aware video streaming categorization is introduced in 5G networks based on machine learning (ML) by incorporating the hyperparameter tuning (HPT) principle.It implements an enhanced hyperparameter tuning (EHPT) ensemble and decision tree (DT) classifier for video streaming categorization.The performance of the ML approach is assessed by considering precision, accuracy, recall, and computation time metrics for manifesting the superiority of these classifiers regarding video streaming categorization.This paper demonstrates that our ML classifiers achieve QoE prediction accuracy of 92.59% for (EHPT) ensemble and 87.037% for decision tree (DT) classifiers.
: Emoji and Religion in the Twitter Discourses on the Notre Dame Cathedral Fire

Abstract The fire that destroyed a large part of the world-famous Notre Dame Cathedral in France in April 2019 shocked the world. A lively expression of thoughts and feelings during and after the fire arose on Twitter. In this article, we will analyze the discourses about the Notre Dame fire on Twitter, with a specific focus on emoji, focusing on the thoughts and feelings emoji express and how they convey the meanings religious buildings have for people. Based on a dataset of almost 2 million tweets collected in the week following the incident, this paper leverages a variety of computational and qualitative methods to explore the topic from different angles. Temporal analysis and topic modelling show the dynamics of emoji usage, which drastically changes after a few days from expressing sorrow to expressing skepticism. Semantic analysis using the word2vec model reveals the implicit meaning of potentially ambiguous emoji characters.
A Novel Lightweight Image Encryption Scheme

Encryption algorithms are one of the methods to protect data during its transmission through an unsafe transmission medium.But encryption methods need a lot of time during encryption and decryption, so it is necessary to find encryption algorithms that consume little time while preserving the security of the data.In this paper, more than one algorithm was combined to obtain high security with a short implementation time.A chaotic system, DNA computing, and Salsa20 were combined.A proposed 5D chaos system was used to generate more robust keys in a Salsa algorithm and DNA computing.Also, the confusion is performed using a new S-Box.The proposed chaos system achieves three positive Lyapunov values.So results demonstrate of the proposed scheme has a sufficient peak signalto-noise ratio, a low correlation, and a large key space.These factors make it more efficient than its classical counterpart and can resist statistical and differential attacks.The number of changing pixel rates (NPCR) and the unified averaged changed intensity (UACI) values were 0.99710 and UACI 33.68.The entropy oscillates from 7.9965 to 7.9982 for the tested encrypted images.The suggested approach is resistant to heavy attacks and takes less time to execute than previously discussed methods, making it an efficient, lightweight image encryption scheme.The method provides lower correlation coefficients than other methods, another indicator of an efficient image encryption system.Even though the proposed scheme has useful applications in image transmission, it still requires profound improvement in implementing the high-intelligence scheme and verifying its feasibility on devices with the Internet of Things (IoT) enabled.
An Active Image Forgery Detection Approach Based on Edge Detection

Recently, digital images have become the most used data, thanks to high internet speed and high resolution, cheap and easily accessible digital cameras.We generate, transmit and store millions of images every second.Most of these images are insignificant images containing only personal information.However, in many fields such as banking, finance, public institutions, and educational institutions, the images of many valuable objects like ID cards, photographs, credit cards, and transaction receipts are stored and transmitted to the digital environment.These images are very significant and must be secured.A valuable image can be maliciously modified by an attacker.The modification of an image is sometimes imperceptible even by the person who stored the image.In this paper, an active image forgery detection method that encodes and decodes image edge information is proposed.The proposed method is implemented by designing an interface and applied on a test image which is frequently used in the literature.Various tampering attacks are simulated to test the fidelity of the method.The method not only notifies whether the image is forged or not but also marks the tampered region of the image.Also, the proposed method successfully detected tampered regions after geometric attacks, even on self-copy attacks.Also, it didn't fail on JPEG compression.
Machine Learning Techniques for Detecting Phishing URL Attacks

Cyber Attacks are critical and destructive to all industry sectors.They affect social engineering by allowing unapproved access to a Personal Computer (PC) that breaks the corrupted system and threatens humans.The defense of security requires understanding the nature of Cyber Attacks, so prevention becomes easy and accurate by acquiring sufficient knowledge about various features of Cyber Attacks.Cyber-Security proposes appropriate actions that can handle and block attacks.A phishing attack is one of the cybercrimes in which users follow a link to illegal websites that will persuade them to divulge their private information.One of the online security challenges is the enormous number of daily transactions done via phishing sites.As Cyber-Security have a priority for all organizations, Cyber-Security risks are considered part of an organization's risk management process.This paper presents a survey of different modern machine-learning approaches that handle phishing problems and detect with high-quality accuracy different phishing attacks.A dataset consisting of more than 11000 websites from the Kaggle dataset was utilized and studying the effect of 30 website features and the resulting class label indicating whether or not it is a phishing website (1 or -1).Furthermore, we determined the confusion matrices of Machine Learning models: Neural Networks (NN), Naïve Bayes, and Adaboost, and the results indicated that the accuracies achieved were 90.23%, 92.97%, and 95.43%, respectively.
Teamwork Optimization with Deep Learning Based Fall Detection for營oT-Enabled Smart Healthcare System

The current advancement in cloud computing, Artificial Intelligence (AI), and the Internet of Things (IoT) transformed the traditional healthcare system into smart healthcare.Healthcare services could be enhanced by incorporating key techniques like AI and IoT.The convergence of AI and IoT provides distinct opportunities in the medical field.Fall is regarded as a primary cause of death or post-traumatic complication for the ageing population.Therefore, earlier detection of older person falls in smart homes is required to improve the survival rate of an individual or provide the necessary support.Lately, the emergence of IoT, AI, smartphones, wearables, and so on making it possible to design fall detection (FD) systems for smart home care.This article introduces a new Teamwork Optimization with Deep Learning based Fall Detection for IoT Enabled Smart Healthcare Systems (TWODL-FDSHS).The TWODL-FDSHS technique's goal is to detect fall events for a smart healthcare system.Initially, the presented TWODL-FDSHS technique exploits IoT devices for the data collection process.Next, the TWODL-FDSHS technique applies the TWO with Capsule Network (CapsNet) model for feature extraction.At last, a deep random vector functional link network (DRVFLN) with an Adam optimizer is exploited for fall event detection.A wide range of simulations took place to exhibit the enhanced performance of the presented TWODL-FDSHS technique.The experimental outcomes stated the enhancements of the TWODL-FDSHS method over other models with increased accuracy of 98.30% on the URFD dataset.
REGULATORY AND LEGAL BASIS OF THE DIGITALIZATION OF THE PROFESSIONAL TRAINING OF TEACHERS IN HUNGARY

The article examines the regulatory and legal basis of digitalization of professional training of teachers in Hungary. This study analyzes the legal framework for digitalization of teacher training in Hungary and the steps taken in the direction of digital transformation. Based on the analysis of regulatory documents and reports of the Hungarian government and the European Union, the works of Hungarian and domestic scientists, which directly highlight the topic of professional training of teachers, the digitalization of education, the necessary trends for further research into the digitalization of higher education in Hungary have been identified. To solve the tasks and achieve the goal of the research, a set of general scientific methods is used: theoretical: analysis and synthesis in order to determine the main directions of research into the problem of digitalization of professional training of teachers in Hungary; Empirical: study of the regulatory and legal framework regarding digitalization of professional training of teachers in Hungary; translation of authentic literature; description of factual information for the purpose of analytical interpretation and study of specific facts and phenomena; statistical processing of research results: for quantitative and qualitative data acquisition.As evidenced by the results of current studies of regulatory and legal documents, an urgent task for scientific research and educational policy in many European countries is to ensure the optimal use of the full potential of digital technologies for learning, while effective learning in the digital age becomes possible thanks to a systemic approach and holistic changes. Many European countries, including Hungary, are actively conducting research in the direction of full integration of digital technologies in education and vocational training.
Augmented Reality as an Innovative Tool for the Training of Bilingual Education Teachers in Primary Schools

Children know a language when they associate words with images and sounds—this facilitates the assimilation of knowledge. This research uses applications based on augmented reality, which are designed to help teachers who want to teach another language, particularly English. The set of useful terms for the student to learn is defined in various categories such as animals, colors, and things. These terms are stored in a database with different formats such as text, 3D image, audio, and video which are associated with items that contain, in turn, a vocabulary which represents abstract entities, which are necessary to complement the learning of a language. The words are associated with the images and with the corresponding audio so that the students learn to read, write, listen, and, consequently, pronounce the words correctly. This research is projected as an innovative technological support that helps primary school teachers in the process of teaching the English language, and it is expected that in the short term it will become an indispensable basis for this educational dynamic.
African Women Vulnerability Index: Focus on Rural Women

In this paper, we develop a new index labelled the African Women Vulnerability Index (AWVI) with a focus on rural women using Round 7 of the Afrobarometer Survey. The AWVI comprises 59 indicators in six dimensions, namely: safety, empowerment, health, education, economic prosperity, and digitalisation. Our findings show that: (i) Botswana performs best while women in Guinea and Sudan are the most vulnerable. Indeed, Mauritius appears as a good example in some dimensions such as health and digitalisation. (ii) Except for the dimension of digitalisation, rural women's vulnerabilities in other dimensions are very close to those at the national level. (iii) National vulnerability trends strongly explain rural women's vulnerability especially for the economic, empowerment, and health dimensions.
Applications of Chatbots in Education

This chapter mainly concerns the application of artificial intelligence (AI) based chatbots in education (in the general sense). Three general applications of such systems are studied: a) language learning, b) teaching a course, and c) assistive for educational purposes (in the narrow sense). There are advantages and disadvantages for using chatbots in education. Their interaction with the students, human-like conversation simulation, 24/7 availability, and easy accessibility are some of the key advantages of using AI-based chatbots in education. The main disadvantage of such systems is their knowledge-bases (KB) requirement. A KB plays as the brain of a chatbot. However, their development is labor intensive and expensive in terms of time and effort. In this chapter, the main research studies on chatbots for the educational domain are reviewed and general construction of a chatbot as well as the evaluation metrics of chatbots are explained; and the available chatbot tools and systems used in education, in the general sense, are collected.
Chatbots as Motivational Agents

Chatbots have offered numerous useful applications to a range of market industries due to their absolute benefit of saving costs and eliminating unneeded work for employees. This state had a greater impact on other features than on itself. With the aid of chatbots, the efficiency, competency, and productivity of labor and processes have been maximized on a scale that transcends human capabilities. As a result of such a profitable conclusion for firms and businesses, which stimulates additional skills of both humans and machines, it is argued that chatbots are not only a useful tool but also motivational agents.
KalaamBot and KalimaBot

Chatbot technology is a subfield of Artificial Intelligence (AI) that deals with text-based or speech-based conversational agents. In general terms, a chatbot enables a user to have a conversational interaction with a computer. Chatbots have applications in several fields including trade, tourism, customer care, health services, education, et cetera. This chapter describes two chatbot systems that we are developing for learning Arabic as a foreign language. KalaamBot is a speech-based chatbot that converses with learners and teaches them the language in a conversational setting. KalimaBot is a text-based personal vocabulary assistant that enables students to search for the meaning of words, synonyms, antonyms, and word usage in context. This chapter provides extensive discussion of the several challenges second language researchers and chatbot practitioners encounter when designing chatbots for language learning. Then, it concludes with recommendations and future research.
Multi-hop quantum teleportation of an arbitrary two-qubit state based on hierarchical simultaneous entanglement swapping

Abstract Combined with hierarchy simultaneous entanglement swapping, the scheme of the multi-hop quantum teleportation (QT) is proposed by using multi-pair Bell states. The scheme consists of two levels based on simultaneous entanglement swapping, namely Level-1 and Level-2. The former realizes simultaneous entanglement swapping of the inner segment, while the latter completes simultaneous entanglement swapping of the inter segment. Besides, the effect of four different types of noise (phase-damping, amplitude-damping, phase-flip, and bit-flip noise) on our scheme is analyzed by calculating the fidelity and concurrence. The results indicate that the fidelity and concurrence are related to the amplitude parameter, decoherence rate and number of nodes. Furthermore, compared with the scheme of the multi-hop QT adopting simultaneous entanglement swapping, our scheme can greatly reduce classical communication cost, and shows that the optimal classical communication cost can be obtained by selecting different segmentation methods.
How to do human evaluation: A brief introduction to user studies in NLP

Abstract Many research topics in natural language processing (NLP), such as explanation generation, dialog modeling, or machine translation, require evaluation that goes beyond standard metrics like accuracy or F 1 score toward a more human-centered approach. Therefore, understanding how to design user studies becomes increasingly important. However, few comprehensive resources exist on planning, conducting, and evaluating user studies for NLP, making it hard to get started for researchers without prior experience in the field of human evaluation. In this paper, we summarize the most important aspects of user studies and their design and evaluation, providing direct links to NLP tasks and NLP-specific challenges where appropriate. We (i) outline general study design, ethical considerations, and factors to consider for crowdsourcing, (ii) discuss the particularities of user studies in NLP, and provide starting points to select questionnaires, experimental designs, and evaluation methods that are tailored to the specific NLP tasks. Additionally, we offer examples with accompanying statistical evaluation code, to bridge the gap between theoretical guidelines and practical applications.
Review on Density Based Clustering Algorithms for Big Data

Big data is termed as huge volume of both structured and unstructured dataset. When dealing with these huge dataset several challenges are encountered by the users such as analysis, capture, storage, search, transfer, sharing, and visualization. To handle these complex data sets numerous technologies have been developed such as data mining, web mining, machine learning and optimization methods. From the above technologies data mining technology is considered in this paper. Data mining technology contains various techniques such as classification, prediction and clustering etc., This paper reviews the basic concepts and clustering approaches used for big data and also a comparison study is carried out in DBSCAN, DENCLUE, and OPTICS algorithms. Each of these methods has its own pros and cons. This paper reveals that DBSCAN is efficient than the other algorithms because of its simplicity.
Algorithms for Syllogistic Using RMMR

Syllogistic reasoning uses diagrams for decision-making, among other methods, as they are effective in visual cognition. Similarly, decision-making and problem-solving use algorithms extensively as they depict the rules to be followed and show us the line of reasoning. This paper develops algorithms to define a moving environment, which helps us efficaciously understand the state of affairs and line of reasoning concerning syllogistic using the retooled method of minimal representation (RMMR), an alternate diagrammatic technique to test the validity of syllogism using diagrams and algorithms.
Photo Restoration: A Sequential Pipeline Approach Involving Denoising and Deblurring

Photos capture moments that can be archived forever. They play an essential role in helping people relive events from the past, making them appreciate and view different scenarios from various perspectives. These frozen moments, however, can be lost with time due to natural or man-made causes. The work proposed shows how using a sequential pipeline involving independent, denoising and deblurring models paired with spatial filters can facilitate restoring the appearance of photos. The system takes in a digital copy of the image that requires restoration, which after passing through spatial filtering is fed to the denoising and deblurring models built using different convolutional auto-encoder architectures. The pipeline proposed consisting of independent models provides the additional benefits of using only the particular component needed for recovery of an image while also supporting the change of an individual model architecture without influencing the other components involved. This paper, thus presents a novel approach for restoring the condition of photos that have lost their quality due to the addition of noise and blur.
Space Fractionalized Lattice Boltzmann Model-Based Image Denoising

In this paper, the recently proposed lattice Boltzmann (LB) model for time sub-diffusion equation in Caputo sense has been Caputo sense space fractionalized. The proposed model has been applied to denoise images corrupted by Gaussian noise and salt and pepper noise, respectively. The comparison of the proposed method with some of the other state-of-the-art methods is done in terms of PSNR and SSIM values. The obtained results are considerably improved compared to some of the other recently reported state-of-the-art methods.