Reviewerly/exHarmony huggingface dataset:
A Proposed Method for Achieving the Confidentiality of Arabic Texts

Due to the rapid development of distributed computing systems, the use of local networks, and the huge expansion of the Internet, the process of maintaining the confidentiality of information becomes important and necessary.Encryption is one of the most important ways to keep information confidential and to prevent unauthorized people from disclosing this information.Many cryptographic algorithms have appeared in different ways and methods.But most of these algorithms have been devoted for encrypting texts in the English language.Because of the increasing important and sensitive information exchange in the Arabic language by users on the Internet, an urgent need has appeared to build dedicated encryption systems for the Arabic language.The aim of this research is to provide a proposed encryption method that uses the idea of combining the methods of transposition and substitution cipher for the purpose of Arabic texts confidentiality.This research depends on using diacritics in the Arabic language to perform the process of substitution cipher.The efficiency of the proposed method has been tested on different texts; the method provides high speed execution, the length of the encrypted texts is almost close to the clear text length, and the confidentiality of the resulting encrypted texts is high.
FDFST: facial structure constrained landmark detection using visual transformer

Facial landmark detection is challenging with occlusion, pose or inadequate training samples. We proposed a two-branch facial landmark detection network (Facial Detection with Face Structure and Transformers: FDFST) considering face structure constraints. Existing regression-based facial landmark detection models have not fully considered the general facial structure for landmark detection, that usually lead a unstable prediction. In contrast to facial landmarks, facial structure is more likely to be accurately estimated in the real scenario. Therefore, we try to provide a facial structure guidance for the facial landmark detection by a facial structure estimation sub-network. In this way, two targets are predefined to supervise our model, one is the facial structure described by five landmarks, the other is facial landmark denoted by 96 points. To address the lack of occlusion samples, we proposed a novel data augmentation to boost the training process on the public data sets WFLW. Experiments have revealed that our FDFST network on the WFLW dataset achieved significant improvement.
FORMS AND METHODS OF TEACHING LISTENERS TO THE SYSTEM OF IMPROVEMENT OF QUALIFICATION IN TOURISM ON THE EXAMPLE OF FOREIGN SCHOOLS

In this article, the authors determine that it is an integrative approach to the organization of training students, allows to determine the choice of forms of education, in which the attention of teachers and students focuses on deeper study of each subject included in the integrative module. It is also noted in the paper that the organization of the educational process on an integrative basis involves improving the quality of professional tourism training through the convergence of theoretical training with the natural conditions of professional tourism training of students. One of the options for such training, the authors consider the convergence in the educational process of theoretical learning with the natural psychological features of the formation of professional skills and abilities of students. Based on an integrative approach, the authors identified different forms of learning organization, which are presented in this article.
The power of algorithms and keys of participation

The use of some technologies of the digital age present great challenges for our collective autonomy: the robots that take our work, the algorithms that direct our attention and social media that undermine our democracy. We live in the era of algorithms, which are the kings of algorithms usage, namely for content curation, user data collectionand content creation. This chapter describes the use of algorithms in social media. Social media services are the kings of algorithms usage, namely for content curation, user data collection, and content creation. First, we describe these main applications of social media algorithms. Then, we outline challenges of using algorithms in social media, in particular transparency and objectivity.
Item Equivalence Verification according to Test Information Media of the Optician National Licensing Examination: Focused on the Smart Device Based and Paper Based Tests Including Multimedia Items

This study conducts the validity of the pen-and-paper and smart-device-based tests on optician’s examination. The developed questions for each media were based on the national optician’s simulation test. The subjects of this study were 60 students enrolled in E University. The data analysis was performed to verify the equivalence of the two evaluation methods, specifically, through split-plot factorial design of the evaluation method as a partition variable. As a result of the statistical significance test for the difference in achievement for each type of test information medium, indicating that there was no difference in achievement according to the type of test information medium at the significance level of .05. Although the validity of the smart device-based test and the paper-and-pencil test was verified through this study. To develop and set multimedia items in the optician national licensing examination, it is necessary to establish guidelines for how to develop the items.
Grayscale and color images encryption in a DRPE assisted by SPHIT, RLE, QR code and two chaos logistic maps

A non-linear encryption approach for compressed grayscale and color images is proposed in Double Random Phase Encoding (DRPE) setup using two compression algorithms: Set Partitioning in Hierarchical Trees (SPIHT) and Run-length encoding (RLE). The compressed image is then segmented and embedded in multiple Quick Response (QR) codes, which are combined again into a single image. Further, this single image is scrambled by two-coupled chaos logistic maps, at which the scrambled image is now ready for DRPE encryption. The use of two-coupled chaos logistic increases the security level of this proposed DRPE encryption from various attacks. The proposed DRPE encryption is extended to handed color images, which are segregated into three color-channels (Red, Green and Blue), where each channel is individually encrypted. For grayscale images, the proposed system will have three security key codes while for color images it has nine security key codes. The robustness and invulnerability of the proposed scheme against various attacks is demonstrated through cryptoanalysis and computer simulation results.
Adolescent Identity Search Algorithm Based on Fast Search and Balance Optimization for Numerical and Engineering Design Problems

This paper proposed a fast convergence and balanced adolescent identity search algorithm (FCBAISA) for numerical and engineering design problems. The main contributions are as follows. Firstly, a hierarchical optimization strategy is proposed to balance the exploration and exploitation better. Secondly, a fast search strategy is proposed to avoid the local optimization and improve the accuracy of the algorithm; that is, the current optimal solution combines with the random disturbance of Brownian motion to guide other adolescents. Thirdly, the Chebyshev functional-link network (CFLN) is improved by recursive least squares estimation (RSLE), so as to find the optimal solution more effectively. Fourthly, the terminal bounce strategy is designed to avoid the algorithm falling into local optimization in the later stage of iteration. Fifthly, FCBAISA and comparison algorithms are tested by CEC2017 and CEC2022 benchmark functions, and the practical engineering problems are solved by algorithms above. The results show that FCBAISA is superior to other algorithms in all aspects and has high precision, fast convergence speed, and excellent performance.
Two stage real facial image super resolution with geometric knowledge infusion

Super-resolution(SR) is a classic issue in computer vision. Existing SR methods mainly rely on artificially downsampling to prepare the pair-wised training data set without considering the blur kernel inconsistency and often result in an unstable performance in the real scenario. Especially for facial images, unacceptable reconstruction seriously affects its downstream applications. In order to improve the quality in real facial image SR, we proposed an end-to-end deep neural network to mimic a realistic process of image degradation and reconstruct a high-resolution (HR) facial image from a low-resolution (LR) one. At the first stage, a generative adversarial network (GAN) is employed to sample more realistic LR images by jointly considering facial landmark and composite blur kernels. At the second stage, pairwised training samples are fed into a decoder to fit the mapping from LR to HR. Experiments were performed on real LR test set from Widerface and LS3D-W dataset. Results demonstrate that our model outperforms existing competing methods. Furthermore, the ablation studies valid the effectiveness of the proposed components.
Performance Evaluation of Machine Learning Algorithms on Textual Datasets for Spam Email Classification

Abstract: Email is one of the most popular modes of communication we have today. Billions of emails are sent every day in our world but not every one of them is relevant or of importance. The irrelevant and unwanted emails are termed email spam. These spam emails are sent with many different targets that range from advertisement to data theft. Filtering these spam emails is very essential in order to keep the email space fluent in its functioning. Machine Learning algorithms are being extensively used in the classification of spam emails. This paper showcases the performance evaluation of some selected supervised Machine Learning algorithms namely Naive Bayes Classifier, Support Vector Machine, Random Forest, &amp; XG-Boost for spam email classification on a combination of three different datasets. For feature extraction, both Bag of Words &amp; TF-IDF models were used separately and performance with both of these approaches was also compared. The results showed that SVM performed better than all the other algorithms when trained with TF-IDF feature vectors. The performance metrics used were accuracy, precision, recall, and f1-score, along with the ROC curve.
A Study on Curriculum Maturity Component Exploration and Design of Curriculum Consulting Programs

The purpose of this study is to propose a design and operation plan for a curriculum consulting program by exploring the components of the curriculum maturity of elementary school teachers participating in the curriculum conference. To this end, prior research related to curriculum consulting was analyzed and a teacher-learning community centered on the same grade was formed to reconstruct the teacher-level curriculum. In addition, elementary school teachers participating in the curriculum conference were interviewed and a basic survey on the maturity of the curriculum was conducted through an open questionnaire. After that, 24 component factors needed for elementary school teachers were derived through a Delphi survey on the expert group and the selection of component factors of curriculum maturity. However, through the selection meeting of the curriculum expert group, the final 20 component factors were selected by excluding the component factors that were difficult to reinforce the reconfiguration capacity through curriculum consulting.&#x0D; Specifically, in order to compose a systematic curriculum consulting program, the curriculum maturity that elementary school teachers should have was classified into four core scales: curriculum diversity, curriculum systems, curriculum specificity, and curriculum effectiveness. In addition, the curriculum maturity reflected in the curriculum consulting program was refined into the competency and execution level of the curriculum for each core scale, and the curriculum consulting program was devised by modularizing it. In other words, in order to effectively operate the curriculum consulting program, each module consists of 4 stages: Diagnosis, Deliberation, Development, and Advancement. In conclusion, it could be said that the reconstruction of the teacher-level curriculum of the teacher learning community through curriculum consulting became a useful scaffolding strategy for improving curriculum literacy and strengthening the professionalism of participating teachers.
Enhanced Semantic Information Transfer of Multi-Domain Samples: An Adversarial Edge Detection Method Using Few High-Resolution Remote Sensing Images

Edge detection of ground objects is a typical task in the field of remote sensing and has advantages in accomplishing many complex ground object extraction tasks. Although recent mainstream edge detection methods based on deep learning have significant effects, these methods have a very high dependence on the quantity and quality of samples. Moreover, using datasets from other domains in detection tasks often leads to degraded network performance due to variations in the ground objects in different regions. If this problem can be solved to allow datasets from other domains to be reused, the number of labeled samples required in the new task domain can be reduced, thereby shortening the task cycle and reducing task costs. In this paper, we propose a weakly supervised domain adaptation method to address the high dependence of edge extraction networks on samples. The domain adaptation is performed on the edge level and the semantic level, which prevents deviations in the semantic features that are caused by the overgeneralization of edge features. Additionally, the effectiveness of our proposed domain adaptation module is verified. Finally, we demonstrate the superior edge extraction performance of our method in the SEGOS edge extraction network in contrast to other edge extraction methods.
use of data mining of to create of a fraud prevention and detection system in credit card

After defining the problem, we'll examine the various techniques (pre-and post-dynamic) for managing installment card fraud recognition, as well as collect all the data from various elements of card-based monetary fraud (regions, fraudulent activity, etc.). When all of the information has been gathered, it's time to put it into practice. It is best to avoid using data that has been gleaned from several sources because it has little practical use until it can be combined. For this collaboration, we're looking into novel data-combination approaches that can help us find the location of financial fraud. The Fraud Prevention and Detection systems were developed utilizing a variety of data mining techniques, including rule learning, rule generation, and rule selection.
UNIVERSAL APPROACH TO THE IDENTIFICATION OF STEGANOGRAPHIC TRANSFORMATION OF THE SPATIAL DOMAIN OF DIGITAL IMAGES

The article proposes a universal approach to detect the presence of additional information attachments in the spatial domain of digital images. The approach is based on the use of the steganalytic method developed by the author earlier and based on the analysis of sequential triads of triplets in the matrix of unique colors of image. The steganoanalytic method Color Triads allows to detect with high accuracy the additional information attachments embedded by various steganographic methods into the spatial domain of images. The perturbations in the matrix of unique colors of images as a result of steganographic transformation are illustrated which concludes about the sensitivity of the blue color component even to small modifications of the brightness values of this matrix. The efficiency of detecting stegoes formed at small values of payload (0.4 bpp and less) based on LSB, S-UNIWARD, MiPOD and WOW steganographic methods is shown. The obtained results of computational experiments allow detect the filled color components of digital images with high accuracy even at payload of 0.1 and 0.05 bpp that is much higher than the results of modern analogues. The steganalytic method analyzes the spatial domain of images, which avoids the accumulation of computational errors that affect the detection result.
The role of specific physical activitiesin archive work

The effects of physical activity are beneficial on the physical condition which aims to obtain a very good physical shape, as well as motor, functional and mental benefits.This article aims to draw attention to the fact that, despite the technology development, the electronic archiving law, the activity that the archivist carries out, for most of the work schedule, involves physical activity.Archivists must not only be professionals in the field of archiving, but they also must juggle records in archives, so that their health is not affected.Performing constant exercises inside the office and in free time / leisure will contribute to the physical condition development, have positive effects both personally and professionally, bringing benefits in order to carry out the daily activities with the highest efficiency.
Issues of formation, education and development of research capacity in students

The modern education system requires the further improvement of the mechanisms for educating future specialists in research skills based on creative approaches and the innovative system of their implementation. From this point of view, education of the research ability of specialists based on a creative approach in the field of education is of particular importance in creating innovations, creating intellectual resources of socio-economic development and expanding competitive training opportunities by educating cognitive and divergent thinking skills based on modern pedagogical processes that create new knowledge.
FAKE NEWS DETECTION USING LINEAR REGRESSION MACHINE LEARNING ALGORITHIM

Fake news has become a significant part of social media and the political realm in recent years. Fake news detection is an important research project, but it comes with its own set of obstacles. Some difficulties may arise as a result of a scarcity of resources, such as a dataset and published literature. The majority of smartphone users prefer to read news on social media rather than on the internet. The news is published on news websites, which also serve as a source of authentication. The challenge is how to verify news and articles shared through social media platforms such as WhatsApp groups, Facebook Pages, Twitter, and other microblogging and social networking sites. Believing in rumours and pretending to be news is damaging to society. Stopping rumours, especially in emerging nations like India, and focusing on accurate, verified news items is the need of the hour.
An Automatic System to Enhance Videos for a Learning Experience, Using an Interlinking Technique

Basing on the emergent evolution of web technologies and the increasing number of video consumerson the one hand, and the immense role of this multimedia data in the information diffusion in the other hand, we have thought of designing a new system which makes connection between videosand Knowledge base connected to the semantic web technologies, inorder to enhance videos.We aim by this work to enhance information received by the video's viewer with extra-data. The added data must be related to the video topics and explains more the content of the video. In this paper, we present the proposed system with the different components and used techniques.
Transfer Learning in Deep Reinforcement Learning

Reinforcement learning has quickly risen in popularity because of its simple, intuitive nature, and its powerful results. In this paper, we study a number of reinforcement learning algorithms, ranging from asynchronous q-learning to deep reinforcement learning. We focus on the improvements they provide over standard reinforcement learning algorithms, as well as the impact of initial starting conditions on the performance of a reinforcement learning agent.
Open science tools: Supporting hands-on creation of the \"digital extended specimen\"

As a biodiversity informatics community, we have mobilised and interconnected a wide array of information, including specimen collections, published literature and metadata resources, which compile facts about collections and the people that work with them. We have defined data standards to facilitate data interoperability and tools development. Along with colleagues in allied research disciplines, we have helped to develop training resources, enabling researchers to automate routine tasks like data access and reference management. We have also started to explore how we could realise the vision of the digital extended specimen, which would integrate specimens and associated data across multiple research infrastructures, allowing the investigation of wider scale research questions. How this would be achieved is still the subject of discussion and experimentation: an open community will support a diverse range of approaches. In the construction of the digital extended specimen, we can envision useful activities operating at very different scales: from large scale computational processes run at (or between) research infrastructures, to an ecosystem of lightweight tools that support link construction in context, closer to researchers. A toolset enabling in-context link construction could play a similar role to Open Refine (which has been effective at democratizing data linking between different sources), and supply valuable training data for the development of machine learning approaches. We will review the use of Open Refine in the biodiversity informatics (and wider research) communities and examine the resources and working practices that facilitated the adoption of this tool. We will showcase work towards “an extensible notebook for open science”, and we aim to open a discussion on how a link-aware editor for semi-structured data plus standard open science tools (i.e., those covered by training resources such as software, data and author carpentry) could be viewed as a lightweight alternative to traditional document production—just as Open Refine is a viable alternative to many traditional spreadsheet use cases. Our aim is to enable researchers to develop the digital extended specimen at research time, but without being prescriptive about their workflow. We will conclude by discussing how this effort supports open science, showing how researchers are open to access the data needed to explore their area of study and to form their hypotheses, using well recognised entities (specimens, names, people, institutions, citations etc), represented in data standards and accessed via open APIs - but also open to choose how they organise their work. In the authors own domain (botany), such a tool will be fundamental to e-taxonomic undertakings to build an online reference system in which all known plant species are described, as well as to significant acceleration of parts of the taxonomic process to address the biodiversity crisis.