Reviewerly/exHarmony huggingface dataset:
Real-time Online Learning Attention Tracker

As a result of the coronavirus pandemic, online learning has become an essential tool for institutions. Attention-aware systems monitor learners’ attention to ensure an effective e-learning experience, from multi-metric facial analysis tools to electroencephalogram hardware. This paper proposes a low-cost systematic approach that builds on existing web application interfaces to assess students’ attention levels in real-time during online classes. The solution uses an end-to-end approach that estimates average attention scores based on head pose features. Then, this aggregated data can be used to improve teacher efficacy. Test lessons show that the learner attention levels can be effectively captured effectively using a browser-based application hosted on the instructor’s end, with an average of 15 frames per second (FPS).
“PeriHack”: Designing a Serious Game for Cybersecurity Awareness

This paper describes the design process for the cybersecurity serious game “PeriHack” and, in doing so, it reviews the game by exemplifying its objectives and core game mechanics. Publicly released under a CC (BY-NC-SA) license, PeriHack is a board and card game for two players or teams that simulates the struggle between a red team (attackers) and a blue team (defenders). The game requires players to explore a sample network looking for vulnerabilities and then chain different attacks to exploit possible weaknesses of different nature, which may include both technical and social engineering exploits. At the same time, it also simulates budget level constraints for the blue team by providing limited resources to evaluate and prioritize different critical vulnerabilities. The game is discussed via the lenses of the AGE and 6-11 Frameworks and was primarily designed as a learning tool for college students in the cybersecurity and technology related fields.
Two Methods for Underwater Images Color Correction: Gamma Correction and Image Sharpening Algorithms

The underwater images suffer from diverse degradation because of the light scattering, absorption phenomenon in underwater medium and the depth at which the image is taken. So, underwater images color correction has been gained a vital traction in marine biology and surveillance and help marine archaeologists in locating objects. In this work, gamma correction and image sharpening algorithms are applied after color compensation to compensate the red color and correct colors in underwater images. Three sets of tested images are used (bluish, greenish and fogy) to evaluate the proposed methods. Three metrics are used Information Entropy IE, Underwater Color Image Quality Metric UCIQM and Underwater Image Quality Measure UIQM in statistical measurement and the results indicate the image sharpening algorithm get better results than gamma correction.
Order of Fourier Descriptor Effect on Geometrical Shapes

Shape descriptors are one of the tools commonly used in image processing applications. To enable a computer to be able to tell what the camera sees, the objects in the image need to be described in a systematic computer description. In this paper, the methodology proposed is based on Fourier Descriptor to study features for different geometric shapes (triangle, square, pentagon, and hexagon) with different sizes and different degrees of Fourier transformation. To do that, the shape image is converted to a binary form. The complement image is found for that binary image. Finally, the Fourier descriptor is got for the complemented image. Our work focuses on studying the effect of changing the order of Fourier descriptors when analyzing geometric shapes to study the extent of convergence of the edges of the same shape at different sizes and different order (number of coefficients) of Fourier descriptors. Experiments have proved the stability of the algorithm and its stability with the change in the size of the geometric shape. It is also proved to be stable and unaffected by the order adopted for the Fourier transform. The numerical results have been achieved using MATLAB software.
Collaborative Training of Acoustic Encoder for Recognizing the Impaired Children Speech

Encoder-decoder models have become an effective approach and are increasingly popular for sequence learning tasks like automatic speech recognition (ASR) due to their simplified processing stages. The traditional decoder-based approach usually learns a sequence-to-sequence mapping function from the source speech to target units (character-level, word-level, or phoneme-level). However, without sufficient and undefective training data it still has degraded performance. In a situation like impaired children's speech recognition, the low and disordered information within the audio signal due to impaired pronunciation requires processing with different levels to improve the model's ability. Therefore, this work adopts a particular encoder's structure that can improve the encoder's ability for encodes the input sequence into a sequence of hidden representations. This paper proposes a collaborative training of Sequence predictive models as an acoustic encoder for ASR systems for Arabic impaired children's speech. Each sequence model maps the input sequence to only one phoneme from the output sequence. Then, the encoder alignment the output phonemes within one output sequence. Experimental results on the Impaired Children's Speech of Arabic dataset show the collaboratively trained acoustic encoder to align phonemes sequence can provide up to a 10% relative improvement in accuracy compared to traditional methods. Especially, since this paper is the first in the area of recognizing impaired children's speech at the level of Arabic texts.
Robust Watermarking in Video using Discrete Cosine Transform and Facial Landmarks

As multimedia has developed, digital video watermarking technology has become widely acknowledged as a crucial tool for copyright enforcement. In this paper, a new video watermarking algorithm is presented based on landmarks of faces; the landmark of a face is localized to certain characteristic points on the face, which made these marks an optimal host. Embedding the secret message in video frames used the Discrete Cosine Transform (DCT) technique, which slightly changes video. Confidentiality is improved by encrypting the watermark before embedding it in the video frames with the light SALSA20 algorithm, which increases the watermark's security. Results of experiments show that the suggested algorithm achieved significant imperceptibility and excellent resistance to different attacks.
An Efficient Bandwidth Based on the Cryptographic Technique of the RSA Accumulator in Block Chain Networks

Blockchain technology is a cryptographic technique that enables users to maintain their data in a decentralized way in a non-editable form without an arbitrator. Data integrity is a significant challenge in blockchain systems. Increasing connectivity will substantially increase the bandwidth and time complexity used by the transaction dissemination or authentication protocol in the blockchain using the Merkle tree, making it prohibitively expensive to operate a full node. The primary goal of this paper is to propose and compare a new transaction authentication technique based on the cryptographic RSA accumulator (ACC) model to the Merkle tree. A proposed ACC model for transaction authentication and proofing has effectively delivered all simulation tests and provided better performance than Merkle tree-SHA256 and Merkle tree-BLAKE256 in blockchain networks.
Video Data Authentication Using a Smart Contract Published on the Blockchain

With the significant advancement in digital photo and video editing software, it is becoming very easy to modify digital video content. However, because of the increased misuse of such editing software, detecting the modification of digital content and verifying the authenticity of such content has become a significant challenge. The paper aims to address the main problem of tampering with digital video content by verifying that digital video received via digital media is tamper-free and identical to what was sent. To solve and control such a problem, blockchain features are used by creating a decentralized application on the Ethereum platform. A watermark is also used to access the data stored on the blockchain. Blockchain-based digital video content authentication technology is safe and reliable, with lower recording time and lower cost. Since the process of watermark embedding occurs only for the first and last frame, the content of the digital video does not change and, as a result, the PSNR value of the video is unaffected. This system allows a user receiving digital video to verify the authenticity of the content received without referring to the sender.
A Survey on Semantic Recommendation System based on Linked Open Data(LOD)

A globally accessible Linked Open Data known as LOD is used to manage, reuse, share, exchange, and integrate information from different domains of knowledge. This is accomplished by offering tools that characterize, catalog, organize, and retrieve information, making them readily available for quick consumption, and publishing. Many approaches employed LOD to suggest data to the user that was close to what he was looking for within a vast amount of data. Each of them attested to the fact that the usage of LOD improves the process of making suggestions and results in accurate recommendations. In this paper, a survey is presented to show a recent overview of Linked Open Data methods in recommendation systems and their limitations such as cold start, sparsity, new user issue, and limited content analysis. Also, in this survey, the contributions and results of employing LOD in recommendations are presented to show their impact on recommendation systems. Different datasets were introduced to show the effectiveness of LOD methods on data.
A Multi-Weapon Detection Using Synthetic Dataset and Yolov5

With increasing terrorist and criminal activities around the world, detecting several types of weapons at the same time has become crucial. Especially most of these groups are used lightweight weapons which are easily lifted and run with them. For that reason, finding an intelligent system capable of discovering these types of weapons with high quality and speed becomes of utmost necessity. This paper focuses on utilizing the deep learning networks technique (namely YOLOV5) to detect various weapons with high accuracy and speed. YOLOV5 is a member of compound-scaled object detection, which is commonly trained on the Microsoft COCO dataset. However, the proposed system was trained on the self-collected dataset, which consists of sixty thousand images of different types of guns used in Iraq. Thus, the proposed system has the ability to detect different types of weapons at the same time. Furthermore, the system can achieve high performance by increasing the number of trained images for every kind of weapon. The results showed a high mAP reach of 97% of some types of weapons.
A Survey on Fruit Fly Optimization Algorithm (FOA) in Robust Secure Color Image Watermarking

recent improvement of Fruit Fly Optimization Algorithm (FOA) is attracting attention of many researchers' studies especially in watermarking and securing the color images. This review focuses on the recent improvements and uses of FOA within the last ten years. Inspired by the behavior of fruit flies, FOA was been originally proposed by Pan for the first time. The major contribution of this algorithm is finding the maximal and minimal values of a function. By using FOA, the solutions can be found correctly and easily. FOA is not only easy to understand, but also it is easy to be write into a program code. Compared to other algorithms, FOA code is not too long; hence, it is easy to use with all kinds of optimization problems, fast convergence and fewer control variables. Because of these advantages, FOA became a good robustness and applicability. However, and during FOA implementations, some deficiencies raised, thus numbers of improvements has applied in order to overcome its deficiencies as the literature review shows. In addition, FOA has utilized in different fields such as cyber security. FOA become very popular so in this field many numerous studies have been reviewed that related to FOA algorithm in watermarking.
Improving Low Resources Arabic Speech Recognition using Data Augmentation

The use of data-augmentation on training data can significantly improve the robustness of the deep neural networks-based automatic speech recognition (ASR) system. We propose an approach to building and developing an ASR system for the low-resource Arabic language using an End-to-End model and demonstrates the impact of data augmentation and the suggested language model on the results. We aimed to develop a system that transcribes Arabic audio containing human speech into text since a few existing systems tailored for Arabic are developed compared to other languages. Our model is trained based on the DeepSpeech2 framework that uses End-to-End deep learning. When data augmentation is utilized during training, the word error rate (WER) is improved by around 13%, and when using the language model, the word error rate is reduced by around 48%. Our best model achieves a competitive word error rate when the system was evaluated on the Common Voice 8.0 dataset at 2.8 of WER.
Applied Augmented Reality in Entertaiment withBehavior and Geometry Consistency

This research proposes a revolutionary AR approach for inserting virtual movie objects into a real video environment in order to boost the reality by providing virtual information that was not included in real world environment video. During the inserting process there are three aspects challenges need to consider, which are geometry, behavior(time), and illumination. This study focused on the first and second issue and suggests a several techniques in order to interactively projecting the behavior and geometry consistency between real and virtual object. The proposed system can be dividing into three phases: Detection of moving objects, tracking moving object, and merging process. The Detection of moving objects, includes rebuild a background model in order to apply subtraction process between background and foreground of the video frames. The phase of tracking moving objects, includes features extraction such as border of shape and center point of each certain objects and then track these objects. The last phase is Merging process includes Behavior consistency by using collision detection algorithm in order to avoiding the collision or floating the virtual objects in the space of real objects in the augmented video.
A Survey on Speech Encryption Using Chaos Map

Due to the ease of compromising communication channels by attackers, Wireless communications confront considerable challenges in having high security. As a result, ongoing efforts are to provide a practical strategy to ensure robust wireless communication security. Network interferences like reflection regularly affect speech signals, making them vulnerable to security risks. Different techniques for encrypting speech signals have already been proposed. This paper, based on chaotic maps, demonstrates various techniques of speech encryption and decryption. Analog and digital audio algorithms were discussed, displayed, and compared, illustrating the drawbacks and essential features. Different audio and digital proposed methods of speech encryption utilizing chaotic maps have been discussed. These methods demonstrated conducting in quasi-random manner, unpredictability, and significant sensitivity to beginning conditions.
Classification Arabic and Dialect Iraqi Spoken to Sign- Language Based on Iraqi Dictionary

Sign language(SL) for the deaf or hard hearing is the best way to communicate between their community and other communities. Many researchers have dealt with this topic due to its utmost importance for dealing in creating an easy and accurate way to communicate with deaf groups. There are many computer technologies that have been used to deal with SL and have given the most importance to how find a high-tech and accurate way to convert text or audio into SL. In this study, the Arabic sign language (ArSL) was dealt with, especially the Iraqi sign dialect because it lacks a system or application to deal with SL like the other Arab countries, many studies deal with this problem and several techniques depending on the amount of data deal with this groups. The aim of this study deal with Iraqi -data Dictionary used in deaf schools. In this paper used the decision tree (DT) by machine learning (ML) algorithm where DT is the best technique use with voice recognition specially in Arabic language to classify the spoken Arabic word to SL as image, also the objective to reach this classification by using multi techniques (noise reduction, filtering,…) to achieve a high accurate deal with deaf human in easy communicate. This study applied on 25 person's cases, then analyzing their voice through use a set of features that help to convert audio into text and then into ArSL, indicating the language of Iraqi dialect, and adopting the data of the Iraqi signal dictionary, and Compared to two researches used Convolution Neural Network (CNN), Recurrent Neural Network RNN methods, found this proposed system result achieved the best an accuracy rate of about 94%.
Adapting the Teaching of Academic Writing to the Emergence of Natural Language Generators

Natural Language Generators are already consistently impossible for humans to distinguish from human writing. They have been used to develop personalized tests, but also to plagiarize essays. This paper asks what are the issues and opportunities brought about by such algorithms and seeks to map potential directions for an education that is successful in overcoming plagiarism issues, while still teaching the principles of proper academic writing, but also in teaching students how to deploy NLG algorithms to their own use.
Formalizing the processes of evaluating the characteristics of scientific and educational information resources

Scientific and educational information today has become an important strategic asset and, like other important sources, requires protection from adverse impacts. The issue of protecting such resources is primarily related to the assessment of the importance of information, since measures and means of protecting sources of information should be closely related to the price of the protected source. This article classifies the criteria that characterize the importance of sources of information in science and education. Also, proposed a fuzzy correspondence model and a method of expert evaluation of sources of scientific and educational information. The purpose of the research is to develop methods for assessing scientific and educational resources and providing recommendations for their effective use, taking into account the subjective assessments of experts and the situations in which the user is.
Mathematical Models for Visualization of the Process of Dispersion of Active Aerosols in the Atmosphere

One of the actual issues in atmospheric dispersion modeling is the development of active aerosols propagation models. Thus, this work aimed to develop a computer model for estimating and forecasting the spatial-temporal evolution of active aerosol particles emission in the atmospheric boundary layer. The model is traditionally based on the semi-empirical equation of substance transfer and diffusion in the atmosphere coupled with initial and boundary conditions. We used well-known methods of computational mathematics in order to solve the obtained differential equations, such as finite differences, fractional steps and Thomas algorithm. The stated problem was approximated by an implicit difference scheme. Based on the proposed model and algorithm, there was developed software tool intended for predicting the levels of air pollution over industrial regions. As well as to optimal location of newly constructed facilities, taking into account local weather and climatic conditions in order to minimize negative environmental impact.
Co- evolutionary hybrid intelligence technologies are the next step in the development of personalized intelligent human assistants

In this article we show that the transfer to a fundamentally new level of development of intelligent systems is possible if they are personified. The optimal way of transition to personified AI systems is their construction on the basis of the concept of co-evolutionary hybrid intelligence. In this article the basic principles of coevolutionary hybrid intelligence and their importance for the construction of a new generation of intelligent systems are considered. An example of creating a personalized intelligent human assistant for patients with Parkinson’s disease is given.
Algorithms for highlighting the contours of images based on the theory of fuzzy sets

In the work, algorithms for isolating the contours of images based on the theory of fuzzy sets are carried out, In this section, the state of the issue of image processing using the concept of fuzzy sets is considered in relation to the following tasks: improving image quality, image segmentation and outlining on images.
Critical Analysis of Validation Methods for Machine Learning Models Used in E-health Applications

Different types of data sets are established using various sensor-based networks and wearable devices for performing human activity recognition that can be used effectively in the e-health domain. Various machine learning models and relevant algorithms are used to perform detection with high accuracy. Depending on the characteristics of the data set, validation methods used to compute the accuracy may vary. The correct validation of machine learning algorithms is essential to correctly assess the performance of the models especially when the data analysed are related to the health domain. Activity recognition algorithms based on sliding windows with different overlapping ratios are popularly used for validation together with popular cross-validation methods such as k-fold, leave-one-out and leave-one-subject-out. In this study, validation methods commonly used for windowing-based activity recognition systems using wearable devices are analyzed. The advantages and disadvantages of each method are discussed taking into account various parameters. A case study, using the well-known MHEALTH data set, is presented with state-of-the art machine learning approaches. Experimental testing with a second window size using 10-fold cross-validation, a five-second window size using leave one out cross-validation, and a second window size using 5 fold cross-validation gave the highest accuracy, 96.71%, 95.65% and 95% respectively while the window overlap is 50%.
Data Transfer Methods and Algorithms in Wireless Sensor Networks for IoT-based Remote Monitoring System of Hybrid Energy Supply Sources

The most important requirement in the provision of telecommunication services is its continuous provision. Continuity of services depends mainly on the uninterrupted power supply of telecommunications equipment. for this reason, today, scientists are conducting research on continuous monitoring of energy supply sources, prediction and early detection of possible failures and outages. In particular, wireless sensor networks are widely used in real-time monitoring of energy supply sources and have become the center of interest. In this article, the data transmission processes and transmission problems of wireless sensor networks used in monitoring systems are studied. Also, several algorithms aimed at improving the effectiveness of wireless sensor networks and improving the efficiency of data transmission are proposed in the article.