Reviewerly/exHarmony huggingface dataset:
Extract Analytical Indicators for Covid 19 Disease Database

In this paper, real data was collected on people infected with the Covid-19 epidemic in the city of Mosul for the period from 1/11/2020 to 1/4/2021. Where the items of the patient's form (resident/output) were approved in the city's hospitals, which contain a current reality of the pathological condition, in addition to the historical situation of the injured person in terms of health. This data was analyzed and build an SQL-Server database system to store patient’s data that was collected for analysis through Microsoft Excel. The main objective of the paper is to diagnose the main factors that control the issue of the spread of the Covid-19 epidemic by building a model that simulates all the social components of the city of Mosul in terms of the standard of living, cultural, class decline, and the rest of the factors that describe Mosul society, and then the data collected from health centers are reflected on that model. Through the study and analysis of the data, it was found that the relationships between the incidence of the epidemic and the factors that were identified in the data collection are directly proportional in some characteristics and inversely in other characteristics
Detection of secret information in the processing of multimedia information

This chapter describes an overview of steganography and the need for JPEG compression resistance. Moreover, we analyze the JPEG compression resistance of steganography using the correlation properties of the Discrete Cosine Transform (DCT) domain that we have proposed. Surprisingly, we found that our proposed steganography has the potential to minimize the file size of stego images while maintaining the secret information recovery performance by setting the quality factor slightly above a certain region for all embedding strengths.
The Influence of Leri Water Supplementation on the Growth and Production of White Oyster Mushrooms (Pleurotus ostreatus)

This study aimed to investigate the influence of leri water supplementation on mycelium growth, primordial appearance, fruiting body count, fruit body diameter, and wet weight, as well as the production of white oyster mushrooms (Pleurotus ostreatus). The research employed a Completely Randomized Design (CRD) with four treatments: A0 (control without leri water), A1 (20 ml of leri water), A2 (40 ml of leri water), and A3 (60 ml of leri water), with a 95% confidence level F-test. The results revealed that leri water supplementation significantly affected all observed variables. The treatment with 40 ml of leri water (A2) produced the highest mycelium growth with an average weight of 10.45 kg, the earliest primordial appearance, the highest fruiting body count (15.67), and the highest wet weight (372 kg). Meanwhile, the treatment with 60 ml of leri water (A3) resulted in the largest fruit body diameter (17 cm). Among the treatments, A2 with a 40% leri water nutrient supplement in the growing medium outperformed others, as it provided the necessary nutrients for mycelium growth and overall mushroom development.
Confidential Computing: Elevating Cloud Security and Privacy

Working toward a more secure and innovative future.
Hardware VM Isolation in the Cloud

Enabling confidential computing with AMD SEV-SNP technology.
FHSI and QRCPE-Based Low-Light Enhancement With Application to Night Traffic Monitoring Images

This paper proposes a fast HSI (hue, saturation, intensity) color space and an orthogonal triangular with column pivoting (QRCP) enhancement model to tackle the large size RGB (red, green, blue) night traffic monitoring (NTM) image. Firstly, the fast HSI (FHSI) is proposed to decompose the light and color information of the RGB image, whose hue is defined as the cosine value of the included angle, instead of the included angle in HSI. The saturation of FHSI is defined as the ratio of the projection vector length and the side length of the projection equilateral triangle, and a saturation correction model is further proposed to correct color distortion of the low-light image by adjusting the saturation of FHSI. FHSI is more concise and faster than HSI. Secondly, a novel QRCP enhancement (QRCPE) model is proposed to improve the light of the low-light image by enhancing the intensity of FHSI, which first strengthens diagonal elements of QRCP, and followed by controlling the normalization of strengthened diagonal elements of QRCP. Finally, the FHSI-QRCPE based RGB image can be obtained by transforming the processed FHSI to RGB. The experimental results on NTM, SICE, ExDark, and BDD 100K databases, indicate that the proposed FHSI-QRCPE is fast and efficient to tackle low-light image enhancement.
Enhancing Online Learning Monitoring with Novel Image Recognition Method Using Dlib for Eye Feature Detection

In the post-pandemic era, online learning has become a prevalent mode of education. To ensure effective monitoring of learners' engagement in online courses, this study proposes a novel method for real-time monitoring of learners' learning focus. It is divided into three main components: The first component is eye detection. We utilize Dlib technology and leverage pre-trained facial landmark detection to accurately identify the positions of the eyes. Through image processing and the application of appropriate thresholds (thresholds set above 190), this enables precise outlining of eye contours and recognition of eye movement characteristics, even during rapid eye movements or from varying angles. The second component is gaze estimation. By using adaptive thresholds for pupil segmentation and applying erosion and dilation to reduce image noise and enhance essential features, we can effectively determine pupil positions within images taken under varying photographic resolutions and lighting conditions. Angle calculation is subsequently employed to determine whether learners are concentrating on online learning courses. The third component involves an anti-cheating mechanism. By utilizing pupil-centered corneal reflex (PCCR) technology to address potential cheating behaviors of learners, an anti-cheating mechanism is proposed to detect and counter fraudulent tactics such as photos and makeup. Experimental results demonstrate that this study is capable of effectively monitoring learners' learning status and accurately determining whether learners exhibit appropriate learning attitudes and behaviors. This empowers a comprehensive assessment of learners' learning outcomes during the period of online learning.
An O(log<sub>2</sub> N) SMC<sup>2</sup> Algorithm on Distributed Memory with an Approx. Optimal L-Kernel

Calibrating statistical models using Bayesian inference often requires both accurate and timely estimates of parameters of interest. Particle Markov Chain Monte Carlo (p-MCMC) and Sequential Monte Carlo Squared (SMC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) are two methods that use an unbiased estimate of the log-likelihood obtained from a particle filter (PF) to evaluate the target distribution. P-MCMC constructs a single Markov chain which is sequential by nature so cannot be readily parallelized using Distributed Memory (DM) architectures. This is in contrast to SMC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> which includes processes, such as importance sampling, that are described as embarrassingly parallel. However, difficulties arise when attempting to parallelize resampling. None-the-less, the choice of backward kernel, recycling scheme and compatibility with DM architectures makes SMC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> an attractive option when compared with p-MCMC. In this paper, we present an SMC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> framework that includes the following features: an optimal (in terms of time complexity) $\mathcal{O}(\log_2 N)$ parallelization for DM architectures, an approximately optimal (in terms of accuracy) backward kernel, and an efficient recycling scheme. On a cluster of 128 DM processors, the results on a biomedical application show that SMC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> achieves up to a 70× speed-up vs its sequential implementation. It is also more accurate and roughly 54× faster than p-MCMC. A GitHub link is given which provides access to the code.
Comparing different deep learning models for cryptocurrency forecasting

In recent years, cryptocurrencies have become more and more popular and recognized, and they have the characteristics of financial products and are involved in many financial transactions. Therefore, cryptocurrency trading is generally considered one of the most popular and promising types of profitability. However, this growing financial market is unstable, so this paper aims to develop a relatively accurate and reliable prediction model. This paper compares and analyzes various neural networks and traditional analytical methods based on the need to forecast cryptocurrency prices. Finally, this paper uses a variety of deep learning neural networks, including Recurrent neural network (RNN), Long Short-Term Memory (LSTM) network, Gate Recurrent Unit (GRU), Bidirectional Long Short-Term Memory (BiLSTM), and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM). The data of three cryptocurrencies with the highest market capitalization, largest size, and best known were selected, namely Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB). We collect the three-cryptocurrency data for five consecutive years. The critical data collected includes daily (Eastern Standard Time) open, close, low, and high prices and the market capacity. The findings show that the CNN-LSTM neural network model has some limitations, such as the time lag in prediction. However, it can predict prices more accurately than other deep learning methods and have an overwhelming advantage over traditional time series methods.
Design of Community Education E-learning Platform Based on Data Network Technology

With the continuous development and popularization of information technology, online education has gradually become a new way of learning. Especially during the epidemic, E-learning has gradually become the mainstream. As an important part of basic education, community education also needs to adapt to the requirements of the development of the times and introduce E-learning into community education. Therefore, this paper aimed to explore the construction of community education E-learning platform based on data network technology, so as to improve the teaching quality and learning effect of community education and promote the development of community education. The research results indicated that under the same other conditions, the average scores (P value greater than 0.05) of Group X and Group Y before the experiment indicated that there was no significant difference between the two. The average scores (P value less than 0.05) of the two groups after the experiment indicated a significant difference between the two groups. This showed that data network technology could significantly improve the teaching effect of community education E-learning platform.
High-Capacity Reversible Data Hiding in Encrypted Images Based on MSB Bit-Plane Serpentine Rearrangement and Compression

Reversible data hiding techniques in encrypted images (RDH-EI) have been extensively researched to safeguard the privacy of images. To enhance the embedding capability of RDH-EI algorithms and security, this work leverages the topological coherence within the input plaintext image to create more additional space for embedding confidential information prior to image encryption. Initially, the prediction error of the input image is computed using the pixel prediction technique. Subsequently, the bit-plane serpentine rearrangement mechanism and the bit-stream compression method are employed to further enhance the process. This compression technique efficiently compresses the most significant bit-planes, resulting in a high-capacity space for embedding. Finally, the end-user successfully extracts the data and restores the input image using the possessed keys. The results clearly show that the proposed approach achieves not only attains a superior embedding capacity which achieves the maximum average embedding rate with a value of 3.624 (bpp), but also obtains a commendable reconstructed image quality that is PSNR=+∞ and SSIM=1.
Exploring the Trends of Bitcoin Research in COVID-19 Era: A Bibliometric Study

Bitcoin has attracted significant attention from academia and industry and has emerged as a new and rapidly growing research field over the past decade. However, there has been no previous bibliometric analysis of Bitcoin-related publications within business, management, finance, and economics. Consequently, the study conducts a bibliometric analysis of 1109 articles written in English language in the Scopus database, published between years 2020 to 2022, focusing on Bitcoin research within the domains of business, management, finance, and economics. This bibliometric analysis was performed using R and VOSviewer. The analysis explores publication trends, influential authors/institutions/countries, and significant articles in the field. The results provide valuable insights into the evolution and trends of Bitcoin research and offer guidance for researchers aiming to publish in reputable journals. The future research agenda includes investigating price volatility and risk analysis, exploring market inefficiency and price discovery mechanisms, understanding acceptance factors, studying governance and regulations, and examining business applications. Addressing these areas will contribute to a better understanding of Bitcoin's dynamics, its impact on financial markets, and its integration into traditional financial systems.
Recent Recommender Systems and Analysis of the Machine Learning Public Instances

Recommendation systems have become increasingly important in various online applications. One of the significant types of research in machine learning (ML) is recommendation systems. A recommender system, alternatively referred to as a recommendation system, is an information filtering system that supports users in identifying their “rating” or “preference” for an object and makes predictions based on that rating or preference. This research analyzes current recommendation systems applied in ML public instances to explore knowledge discovery. The suggestions systems are developed for musical, online dating, and restaurant applications. The recommendation system serves as a tool to assist users in discovering what they are interested in by providing them with appropriate ideas. To provide personalized recommendations to users, primarily employ collaborative, content-based, session-based, demographic, and hybrid filtering. Classification, clustering, and association rule discovery are the critical data mining and ML techniques most widely employed in recommendation systems. In general, the accuracy of the recent models lies at (75%, 99 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ) and the error rate occurs at (5%, 25 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ) for the ML public instances.
Impact of Trivial Solutions on Classification Tasks Using CNNs

This paper investigated the impact on the training process of convolutional neural networks (CNN) caused by unconscious perturbations during data collection in the form of unintentional encoding the ground truth label. For this purpose, three possible scenarios (background, dead pixel and illumination gradient) were created and their influence on the classification accuracy were evaluated using the BloodMNIST dataset. In practice, especially in sensitive areas such as medical contexts, the generation of appropriately large and diverse datasets for training CNNs is often challenging due to limited access, lack of financial resources, but also due to a lack of awareness of the need for diverse data. This contribution showed that transfer learning was less vulnerable to label encoded perturbations than models learned from scratch, even when scratch models performed better on the collected dataset. Simulation results support the assumption, that transfer learning provides a better choice to address challenging data acquisition.
Environmental Education Based on The Potential of Kuala Selangor Nature Park Through Experiential and Joyful Learning-Marine Edutourism

It is very urgent that environmental education is carried out to increase students' environmental literacy. Effective methods need to be developed to provide environmental experiences while increasing students' contribution to the environment. Malaysia has natural potential as a learning resource for environmental education, one of which is the Kuala Selangor Nature Park. This research aims to 1) conduct a study of the potential of the Kuala Selangor National Park mangrove ecosystem for the development of environmental education learning tools and 2) implement the experiential and joyfull learning-marine edutourism (EJoy-ME) learning model based on the potential of Kuala Selangor National Park to increase students' environmental literacy. The method used was observation of the natural resource potential of Kuala Selangor Nature Park and quasi-experimentation of the EJoy-ME learning model. The results of the study show that Kuala Selangor Nature Park has the potential to be developed as a learning resource for environmental education through EJoy-ME. The implementation of EJoy-ME has been proven to be able to increase students' environmental literacy. The EJoy-ME learning model also received positive responses from students, namely providing satisfaction with environmental materials, satisfaction with the learning process, and providing a pleasant learning experience
NVIDIA at the Center of the Generative AI Ecosystem---For Now

Assessing the ascent of NVIDIA.
Comparative Analysis and Evaluation of Stemming and Preprocessing Techniques for Arabic Text

Arabic information retrieval is challenging due to the language's complex morphology and syntax. Preprocessing and stemming improve the accuracy and efficiency of Arabic information retrieval. This paper provides a comprehensive analysis of the existing literature on Arabic preprocessing and stemming techniques. The paper identifies the limitations and challenges of these techniques. The paper emphasizes the importance of preprocessing and stemming and underscores the need for further research to improve Arabic information retrieval. This study evaluates ten stemmers on a public dataset. The results show that root-based stemmers: Lucene, and khoja got the highest reduction rate 90.9%, and 85% respectively. The results emphasize that root-based stemmers have good conflating ability for similar terms, while light-based stemmers under-stem similar terms.
Analysis Of Mining Algorithms for Patterns of Frequent Item sets and Hidden

Frequent set-item mining is a widely used technique in the field of data mining. The algorithm utilized in Association Rules constitutes the primary source of contention; this approach is memory-intensive and time-intensive. The extraction process for concealed patterns of frequent item sets, on the other hand, becomes more time-consuming as the volume of data grows. Therefore, the necessary algorithm for mining the patterns of frequently occurring concealed item sets must be memory-efficient and fast-running. By analyzing various algorithms for locating frequent item sets, this paper aims to contribute to the development of a more efficient algorithm in this domain. The paper will evaluate the efficiency of different algorithms by considering their memory usage and runtime. Additionally, it will explore potential improvements to existing algorithms to enhance their performance in mining frequently occurring concealed item sets.
Interactive Hand Gesture Control System for Augmented Reality-based Games

This research paper presents the development of an innovative 2D augmented reality (AR) car game utilizing Python, OpenCV, and Pygame. The primary objective of the game is to provide an interactive platform for individuals with motor skill challenges, such as Parkinson’s disease, to enhance their hand and gesture control abilities. The game involves controlling a virtual car superimposed onto the real-world environment using an AR marker. Hand gestures captured by a webcam control the car's movements, creating a novel and accessible gaming experience. The implementation incorporates computer vision techniques from the OpenCV library to detect an AR marker, interpret hand gestures, and map them to specific actions within the game. The Pygame library facilitates the creation of an engaging gaming environment, where users can employ intuitive hand movements to navigate the virtual car. The system's performance is evaluated in varying lighting conditions, and the results demonstrate a promising accuracy rate in gesture detection. The limitations and potential improvements are discussed, emphasizing the system's application in assisting individuals with motor impairments and its broader implications for human-computer interaction. This research provides insights into the intersection of augmented reality, computer vision, and assistive technology, offering a foundation for further advancements in accessible gaming experiences. Keywords: Augmented Reality, Hand Gesture Control, Computer Vision, Game Interaction, Assistive Technology
3DPL: A VIRTUAL REALITY BASED 3D PROGRAMMING LANGUAGE TO TEACH PRIMARY SCHOOL STUDENTS

This research paper introduces "3DPL," a Virtual Reality (VR) based 3D programming language designed to address the inherent challenges of teaching computer programming to primary school students. Traditional programming languages often create cognitive barriers for young learners, impeding their understanding of fundamental computational concepts. In response, 3DPL leverages VR technology to provide an immersive and intuitive coding environment. By seamlessly integrating VR with Zero Coded Chatbots and the MIT Scratch Application, 3DPL offers a solution that not only mitigates the learning curve for students but also enhances their overall pedagogical experience. The research employs rigorous quantitative and qualitative analyses to measure the computational and programming proficiency of primary school students using 3DPL. The findings highlight how this innovative approach fosters creativity and engagement among students. The results also contribute insights into the strengths and weaknesses of 3DPL, facilitating a comprehensive analysis of its potential shortcomings. Key Words: Virtual Reality, 3D Programming Language, Zero Coded Chatbots, Mixed Reality, Metaverse, Teaching Assistant, Drag and Drop MIT App