Reviewerly/exHarmony huggingface dataset:
DEEP LEARNING: NEW APPROACH FOR DETECTING SCHOLAR EXAMS FRAUD

Abstract. Cheating in exams is a worldwide phenomenon that hinders efforts to assess the skills and growth of students. With scientific and technological progress, it has become possible to develop detection systems in particular a system to monitor the movements and gestures of the candidates during the exam. Individually or collectively. Deep learning (DL) concepts are widely used to investigate image processing and machine learning applications. Our system is based on the advances in artificial intelligence, particularly 3D Convolutional Neural Network (3D CNN), object detector methods, OpenCV and especially Google Tensor Flow, to provides a real-time optimized Computer Vision. The proposal approach, we provide a detection system able to predict fraud during exams. Using the 3D CNN to generate a model from 7,638 selected images and objects detector to identify prohibited things. These experimental studies provide a detection performance with 95% accuracy of correlation between the training and validation data set.
Hybrid multistage framework for data manipulation by combining cryptography and steganography

In today's rapidly growing communication and internet technologies, such as 5G, cloud computing, and blockchain, information security has become a critical component. When data is transmitted in its raw form, it is vulnerable to a variety of cybersecurity assaults. In this hybrid multi-stage data encryption architecture, which builds sequential and pseudo-random encoding/decoding algorithms with pre-stage text encryption discovered that image resolution and attributes were unaffected by the change in image size after testing several text sizes with the cover image and various image formats, it is suitable that the text size should be 15% smaller than the cover image. Furthermore, when compared to sequential encoding/decoding, the hybrid cryptography and steganography-pseudo-random encoding/decoding procedure is more efficient and time consuming.
Research on an Anomaly Detection Method for Physical Condition Change of Elderly People in Care Facilities

Currently, the shortage of care workers for the elderly has become a big problem, and more streamlined care operations are needed. In care facilities, care workers are required to use their subjective experience to detect anomalies in physical condition of care receivers, including serious or insignificant deterioration or behavioral and psychological symptoms of dementia, which can decrease the work efficiency. Therefore, we aim to create a model using objective data for detecting anomalies in physical condition. In this study, data from 13 subjects in a care facility were collected, and isolation forest models were constructed for each subject. The subject's anomalies in physical condition were documented in a care record by a nurse and used as reference for model evaluation. Recall and specificity were used to evaluate the model, expressed as the percentage of detection success for abnormal or normal conditions. Data collected for 1 to 60 days were used to train the isolation models, and the relationship between the amount of training data and model performance was simulated. Heart rate, respiratory rate, and time of getting out of bed were collected from a sensor placed on the subject's bed and used as the model features. In addition, dietary intake information was collected from the care record. Analysis of the evaluation results showed recall and specificity of 45.6 ± 46.7% and 83.88 ± 6.06%, respectively, for the model constructed using training data of 60 days. For future studies, we will continue to collect data and increase the number of participants to improve the robustness and accuracy of the proposed anomaly detection system.
Towards Effective Mitigation of the Digital Transformation and COVID-19 Risk on Unemployment in Mobile Operators in Egypt

The new era of Digital transformation will Destroy many tasks and will create new owns, only certain tasks will depend on human skills which was defined by Carl Benedikt Frey and Michael A. Osborne, 2013 in which they called it, the &amp;ldquo;Engineering-Bottlenecks. Other tasks also defined by Katharina Dengler, Britta Matthes and Wiebke Paulus, 2014 in which they use the assignment of activities explained by Alexandra Spitz-Oener for non-automatable tasks and categorize them as: analytical non-routine tasks, interactive non-routine tasks. To mitigate the Risk of Automation and AI, there is a need for Upskilling and Reskilling the current workforce to perform the un-automatable tasks, created from the development of new products and services as well as performing new complementary tasks created to support the new automated systems. &#x0D; &#x0D; The purpose of this paper is to examine whether the current Reskilling and Upskilling activities are organized in the right track to fill in the new created jobs, and whether they are making use of COVID-19, which gave a surplus of time by WFH and cutting the transportation time.&#x0D; &#x0D; The conclusion of this research is 38% of the existing tasks, is under the risk of automation and 51% of the employees more than 40% of their current tasks are subject to automation. COVID-19 through WFH can provide more time for learning and development.&amp;nbsp; Reskilling and Upskilling of the current workforce will not be effective unless it is correlated with the new created jobs from introducing new products and services or complementary jobs for newly introduced automated systems as the new created jobs are mainly SW developers.&#x0D; &#x0D; The quantitative method used in this research is based on survey through questionnaires to a probability sample for 100 Engineers working in a technical department in Mobile Operator, and the Qualitative method is based on Structured interviews with a non-probability sample of Technology Managers, as Judgmental Sampling.
DIGITAL TOURISM MUSEUM NASIONAL INDONESIA MELALUI VIRTUAL TOUR DI MASA PANDEMI COVID-19

The Covid-19 pandemic that has hit Indonesia has succeeded in forming new adaptations of people's habits, one of which is social distancing. The government has appealed to the public to carry out activities from home. Museum Nasional Indonesia as a tourist destination was also affected because it had to close direct visit services. However, Museum Nasional Indonesia provides innovation by utilizing digital tourism, namely through virtual museum tours. Later, the public can visit the Museum Nasional Indonesia virtually. The purpose of this study is to find out Digital Tourism at Museum Nasional Indonesia through Virtual Tours during the Covid-19 Pandemic Period. This research uses a case study method with a qualitative approach and constructivism paradigm. This research uses diffusion of innovation theory from Everett M. Rogers, which explains how to introduce and adapt an innovation by a community or group to society. The results of this study indicate that virtual museum tours are a way for Museum Nasional Indonesia to continue to reach and provide museum services to the public, of course, so that they remain interested and can visit museums during the Covid-19 pandemic.
White-Light Endoscopic Colorectal Lesion Detection Based on Improved YOLOv5

As an effective tool for colorectal lesion detection, it is still difficult to avoid the phenomenon of missed and false detection when using white-light endoscopy. In order to improve the lesion detection rate of colorectal cancer patients, this paper proposes a real-time lesion diagnosis model (YOLOv5x-CG) based on YOLOv5 improvement. In this diagnostic model, colorectal lesions were subdivided into three categories: micropolyps, adenomas, and cancer. In the course of convolutional network training, Mosaic data enhancement strategy was used to improve the detection rate of small target polyps. At the same time, coordinate attention (CA) mechanism was introduced to take into account channel and location information in the network, so as to realize the effective extraction of three kinds of pathological features. The Ghost module was also used to generate more feature maps through linear processing, which reduces the stress of learning model parameters and speeds up detection. The experimental results show that the lesion diagnosis model proposed in this paper has a more rapid and accurate lesion detection ability, and the AP value of polyps, adenomas, and cancer is 0.923, 0.955, and 0.87, and mAP@50 is 0.916.
The impact of social commerce on online purchase intention: The mediation role of trust in social network sites

This research aimed to investigate the impact of social commerce on online purchase intention. The mediation role of trust in social network sites between social commerce and online purchase intention was explored. Dataset obtained from 467 Jordanian users of social network sites. PLS-SEM was conducted for analysis purposes. The findings reveal that social commerce and trust in social network sites directly affect online purchase intention. The findings have additionally shown that trust in social network sites has a mediating role between social commerce and online purchase intention. The main recommendation to companies is to derive significant benefits from social commerce and consider them to maximize the potential level of online purchases. Suggestions for further studies are mentioned at the end of the study.
Tangible and Personalized DS Application Approach in Cultural Heritage: The CHATS Project

Storytelling is widely used to project cultural elements and engage people emotionally. Digital storytelling enhances the process by integrating images, music, narrative, and voice along with traditional storytelling methods. Newer visualization technologies such as Augmented Reality allow more vivid representations and further influence the way museums present their narratives. Cultural institutions aim towards integrating such technologies in order to provide a more engaging experience, which is also tailored to the user by exploiting personalization and context-awareness. This paper presents CHATS, a system for personalized digital storytelling in cultural heritage sites. Storytelling is based on a tangible interface, which adds a gamification aspect and improves interactivity for people with visual impairment. Technologies of AR and Smart Glasses are used to enhance visitors’ experience. To test CHATS, a case study was implemented and evaluated.
The Effect of Animation-Based Worked Examples Supported with Augmented Reality on the Academic Achievement, Attitude and Motivation of Students towards Learning Programming

This study examines the effect of using animation-based worked examples (ARAWEs) that are prepared using Augmented Reality (AR) technology instead of using traditional paper-based worked examples (TWEs) on the achievement, motivation, and attitude of high school students during their programming education. The research was designed through the “Nonequivalent Control Group Model”, one of the quasi-experimental models. The participants consisted of second year students (N=94) who were taking the “Basics of Programming” course in the Department of Information Technologies a vocational and technical Anatolian high school in Turkey. An achievement test was applied to the participants as pre-test and final test. Motivation of the students was measured by means of the “Instructional Materials Motivation Survey” developed by Kutu and Sözbilir (2011), and participant attitudes towards using AR was measured by means of the “AR Attitude Scale” developed by Küçük, Yılmaz, Baydaş and Göktaş (2014). According to the results, both the achievement and motivation levels of the students studying on ARAWEs increased significantly compared to those studying on TWEs. There was a high level of correlation between the final test scores and the attitude levels of the students studying on ARAWEs. Considering that instructional materials containing animation-based worked examples prepared using AR technology increase students' interest and motivation in the subject, their use in lessons that include teaching problem solving skills, such as mathematics, physics, and chemistry, can be implemented in future studies.
Application of Artificial Intelligence Recognition Technology in Digital Image Processing

Synthetic Artificial Intelligence technique is a science and technique derived and developed on the basis of calculator application technology. Image recognition is a special image processing step that plays an important role. Only after image recognition can it enter the stage of picture analysis and understanding. With the development of various computer technologies, images have gradually become and have become an important source of information for people. The use of calculator artificial intelligence is becoming increasingly widespread; therefore, understanding its application and related research is more conducive to pointing out the direction of research and learning for us. The goal of this paper is to discuss the emergence and development of synthetic intelligence identification technology and analyze the application bottlenecks of various types of synthetic intelligence identification technology, so as to increase our understanding of Synthetic Artificial Intelligence technique and provide reference for the research in related fields. This article simply introduces the technology of artificial intelligence type and its new development trend, and by combining concrete images of public facilities, the application of different computer artificial recognition methods of image recognition processing on the basis of the traditional method is improved, and through the corresponding simulation software of processing and identification methods for the analysis and comparison, the main application of two methods, the image processing recognition error rate is less than 0.5; improving computer artificial intelligence identification technique for the analysis of its application in image processing has certain help. The preprocessing process generally includes image digitization, grayscale, binarization, noise removal, and character segmentation. In terms of image recognition, algorithms mainly include statistical recognition, syntax recognition, and template matching. In recent years, with the development of neural networks and support vector machine technology, image recognition technology has a new and higher level of development.
Considerations for University Pedagogy: Distance Learning One Year After the Covid-19 Pandemic Outbreak

Due to the recent outbreak of the COVID-19 pandemic, the institutions of higher education had to transform their educational function from face-to-face to e-learning. The purpose of this study is to examine the effects of e-learning and the consequent disruption of the traditional educational function in higher education in Greece with special reference to the views of undergraduates compared to those of their peers collected via the same method and tool one year earlier in two regional Greek Universities, the University of Patras and the Democritus University of Thrace. The present research was conducted in May 2021 in order to explore students’ views and feelings one year after the sudden and total transition to online teaching during the pandemic. The results suggest that students seem to prefer face-to-face courses, but they provide interesting aspects regarding e-teaching and learning -thus valuable guidelines for higher educational institutions in developing didactic approaches, to motivate students on their academic pathway, emphasizing the necessity of University Pedagogy.
Rule extraction with guarantees from regression models

Tools for understanding and explaining complex predictive models are critical for user acceptance and trust. One such tool is rule extraction, i.e., approximating opaque models with less powerful but interpretable models. Pedagogical (or black-box) rule extraction, where the interpretable model is induced using the original training instances, but with the predictions from the opaque model as targets, has many advantages compared to the decompositional (white-box) approach. Most importantly, pedagogical methods are agnostic to the kind of opaque model used, and any learning algorithm producing interpretable models can be employed for the learning step. The pedagogical approach has, however, one main problem, clearly limiting its utility. Specifically, while the extracted models are trained to mimic the opaque, there are absolutely no guarantees that this will transfer to novel data. This potentially low test set fidelity must be considered a severe drawback, in particular when the extracted models are used for explanation and analysis. In this paper, a novel approach, solving the problem with test set fidelity by utilizing the conformal prediction framework, is suggested for extracting interpretable regression models from opaque models. The extracted models are standard regression trees, but augmented with valid prediction intervals in the leaves. Depending on the exact setup, the use of conformal prediction guarantees that either the test set fidelity or the test set accuracy will be equal to a preset confidence level, in the long run. In the extensive empirical investigation, using 20 publicly available data sets, the validity of the extracted models is demonstrated. In addition, it is shown how normalization can be used to provide individualized prediction intervals, thus providing highly informative extracted models.
Virtual Reality Medical Training for COVID-19 Swab Testing and Proper Handling of Personal Protective Equipment: Development and Usability

Efficient and riskless training of healthcare professionals is imperative as the battle against the Covid-19 pandemic still rages. Recent advances in the field of Virtual Reality (VR), both in software and hardware level, unlocked the true potential of VR medical education (Hooper et al., The Journal of Arthroplasty, 2019, 34 (10), 2,278–2,283; Almarzooq et al., Virtual learning during the COVID-19 pandemic: a disruptive technology in graduate medical education, 2020; Wayne et al., Medical education in the time of COVID-19, 2020; Birrenbach et al., JMIR Serious Games, 2021, 9 (4), e29586). The main objective of this work is to describe the algorithms, models and architecture of a medical virtual reality simulation aiming to train medical personnel and volunteers in properly performing Covid-19 swab testing and using protective measures, based on a world-standard hygiene protocol. The learning procedure is carried out in a novel and gamified way that facilitates skill transfer from virtual to real world, with performance that matches and even exceeds traditional methods, as shown in detail in (Birrenbach et al., JMIR Serious Games, 2021, 9 (4), e29586). In this work we are providing all computational science methods, models together with the necessary algorithms and architecture to realize this ambitions and complex task verified via an in-depth usability study with year 3–6 medical school students.
DA-GAN: Dual Attention Generative Adversarial Network for Cross-Modal Retrieval

Cross-modal retrieval aims to search samples of one modality via queries of other modalities, which is a hot issue in the community of multimedia. However, two main challenges, i.e., heterogeneity gap and semantic interaction across different modalities, have not been solved efficaciously. Reducing the heterogeneous gap can improve the cross-modal similarity measurement. Meanwhile, modeling cross-modal semantic interaction can capture the semantic correlations more accurately. To this end, this paper presents a novel end-to-end framework, called Dual Attention Generative Adversarial Network (DA-GAN). This technique is an adversarial semantic representation model with a dual attention mechanism, i.e., intra-modal attention and inter-modal attention. Intra-modal attention is used to focus on the important semantic feature within a modality, while inter-modal attention is to explore the semantic interaction between different modalities and then represent the high-level semantic correlation more precisely. A dual adversarial learning strategy is designed to generate modality-invariant representations, which can reduce the cross-modal heterogeneity efficiently. The experiments on three commonly used benchmarks show the better performance of DA-GAN than these competitors.
An Effective Approach for Human Activity Classification Using Feature Fusion and Machine Learning Methods

Recent advances in image processing and machine learning methods have greatly enhanced the ability of object classification from images and videos in different applications. Classification of human activities is one of the emerging research areas in the field of computer vision. It can be used in several applications including medical informatics, surveillance, human computer interaction, and task monitoring. In the medical and healthcare field, the classification of patients' activities is important for providing the required information to doctors and physicians for medication reactions and diagnosis. Nowadays, some research approaches to recognize human activity from videos and images have been proposed using machine learning (ML) and soft computational algorithms. However, advanced computer vision methods are still considered promising development directions for developing human activity classification approach from a sequence of video frames. This paper proposes an effective automated approach using feature fusion and ML methods. It consists of five steps, which are the preprocessing, feature extraction, feature selection, feature fusion, and classification steps. Two available public benchmark datasets are utilized to train, validate, and test ML classifiers of the developed approach. The experimental results of this research work show that the accuracies achieved are 99.5% and 99.9% on the first and second datasets, respectively. Compared with many existing related approaches, the proposed approach attained high performance results in terms of sensitivity, accuracy, precision, and specificity evaluation metric.