Reviewerly/exHarmony huggingface dataset:
Robust Password Encryption Technique with an Extra Security Layer

People are comfortable with e-banking services, but they are exposed to a great deal of danger these days due to fraudulent acts such as password hacking and personal information theft. Everything individuals do online relies heavily on passwords. Using a password protects one's identity online, in forums, and through email. Online transactions are vulnerable to identity theft if they do not have a secure password. Internet users with critical statements are vulnerable to various assaults, including the theft and exploitation of user IDs and passwords. This paper introduces novel password encryption by fingerprint and a random number to make each password unique and robust against attacks, with a magnificent time elapsed of under 40 milliseconds. This paper uses global password datasets with different stages of password complexity. This method protects the bank’s clients' password accounts inside the bank database.
Comparative Analysis of Pix2Pix and CycleGAN for Image-to-Image Translation

Image-to-Image translation technology is nowadays a prevailing research orientation in computer-vision domain, which aims to translate the styles and features in images from one image domain to another. With the rapid development of the convolutional neural networks, especially generative adversarial network technology, breakthroughs have been made in the performance of image translation, which has been widely used in many fields such as labeling photos to synthesize photos, reconstructing objects from line drawings, and coloring pictures. The Image-to-Image Translation problem is essentially a pixel-to-pixel mapping. Limited by specific task settings, the performance of different general frameworks often fluctuates greatly when dealing with different translation tasks. In this paper, focusing on the above problems, the advanced and commonly used Image-to-Image Translation frameworks such as Pix2Pix and CycleGAN, are selected to compare and analyze the advantages in detail from different dimensions including network structure, loss function, applications and model accuracy. Furthermore, for different practical application scenarios, we discuss solutions based on these two representative frameworks and show the results after image translation processing. Finally, the existing problems and future research directions are discussed and summarized.
Nursing Students' Perception regarding Online Learning and its Relation to their Academic Satisfaction

: Online learning provides many opportunities for student to learn and achive their goal so its important to study the relation between it and their academic satisfaction.
Simulating noisy quantum channels via quantum state preparation algorithms

Abstract In Xin et al (2017 Phys. Rev. A 96 062303) and Wei et al (2018 Sci. China Phys. Mech. Astron. 61 70311), the authors reported an algorithm to simulate, in a circuit-based quantum computer, a general quantum channel (QC). However, the application of their algorithm is limited because it entails the solution of intricate non-linear systems of equations in order to obtain the quantum circuit to be implemented for the simulation. Motivated by this issue, in this article we identify and discuss a simple way to implement the simulation of QCs on any d -level quantum system through quantum state preparation algorithms, that have received much attention in the quantum information science literature lately. We exemplify the versatility of our protocol applying it to most well known qubit QCs, to some qudit QCs, and to simulate the effect of Lorentz transformations on spin states. We also regard the application of our protocol for initial mixed states. Most of the given application examples are demonstrated using IBM’s quantum computers.
Artificial Potential Field Path Planning Algorithm in Differential Drive Mobile Robot Platform for Dynamic Environment

Mobile robots need path-planning abilities to achieve a collision-free trajectory. Obstacles between the robot and the goal position must be passed without crashing into them. The Artificial Potential Field (APF) algorithm is a method for robot path planning that is usually used to control the robot for avoiding obstacles in front of the robot. The APF algorithm consists of an attractive potential field and a repulsive potential field. The attractive potential fields work based on the predetermined goals that are generated to attract the robot to achieve the goal position. Apart from it, the obstacle generates a repulsive potential field to push the robot away from the obstacle. The robot's localization in producing the robot's position is generated by the differential drive kinematic equations of the mobile robot based on encoder and gyroscope data. In addition, the mapping of the robot's work environment is embedded in the robot's memory. According to the experiment's results, the mobile robot's differential drive can pass through existing obstacles. In this research, four test environments represent different obstacles in each environment. The track length is 1.5 meters. The robot's tolerance to the goal is 0.1 m, so when the robot is in the 1.41 m position, the robot's speed is 0 rpm. The safe distance between the robot and the obstacle is 0.2 m, so the robot will find a route to get away from the obstacle when the robot reaches that safe distance. The speed of the resulting robot decreases as the distance between the robot and the destination gets closer according to the differential drive kinematics equation of the mobile robot.
Synthesize High-dimensional Longitudinal Electronic Health Records via Hierarchical Autoregressive Language Model

Synthetic electronic health records (EHRs) that are both realistic and preserve privacy can serve as an alternative to real EHRs for machine learning (ML) modeling and statistical analysis. However, generating high-fidelity and granular electronic health record (EHR) data in its original, highly-dimensional form poses challenges for existing methods due to the complexities inherent in high-dimensional data. In this paper, we propose Hierarchical Autoregressive Language mOdel (HALO) for generating longitudinal high-dimensional EHR, which preserve the statistical properties of real EHR and can be used to train accurate ML models without privacy concerns. Our HALO method, designed as a hierarchical autoregressive model, generates a probability density function of medical codes, clinical visits, and patient records, allowing for the generation of realistic EHR data in its original, unaggregated form without the need for variable selection or aggregation. Additionally, our model also produces high-quality continuous variables in a longitudinal and probabilistic manner. We conducted extensive experiments and demonstrate that HALO can generate high-fidelity EHR data with high-dimensional disease code probabilities (d > 10,000), disease co-occurrence probabilities within visits (d > 1,000,000), and conditional probabilities across consecutive visits (d > 5,000,000) and achieve above 0.9 R2 correlation in comparison to real EHR data. This performance then enables downstream ML models trained on its synthetic data to achieve comparable accuracy to models trained on real data (0.938 AUROC with HALO data vs. 0.943 with real data). Finally, using a combination of real and synthetic data enhances the accuracy of ML models beyond that achieved by using only real EHR data.
Comparative Analysis of Models of Gene and Neural Networks

In the language of mathematics, the method of cognition of the surrounding world in which the description of the object is carried out the name is mathematical modeling. The study of the model is carried out using certain mathematical methods. The systems of the ordinary differential equations modeling artificial neuronal networks and the systems modeling the gene regulatory networks are considered. The one system consists of a sigmoidal function which depends on linear combinations of the arguments minus the linear part. The other system consists of a sigmoidal function which depends on the hyperbolic tangent function. The linear combinations and hyperbolic tangent functions of the arguments are described by one regulatory matrix. For the three-dimensional cases, two types of matrices are considered and the behavior of the solutions of the system is analyzed. The attracting sets are constructed for several cases. Illustrative examples are provided. The list of references consists of 19 items.
Training future educators to develop ecological knowledge of preschool and school children

In the article, the basic principles of training future educators and teachers in the field of environmental education are discussed. Such a process requires mastering a set of innovative pedagogical knowledge on ecology to be aware of them in the future and integrate such knowledge into the profession of a teacher. This determined the relevance of the topic of this study. The article aims to determine the results of the effectiveness of the introduction of disciplines that increase the level of environmental knowledge and introduce innovative pedagogical methods in this area. It is equally important to determine the assessment of the usefulness of introducing new disciplines on methods of teaching environmental knowledge among students (attitude to innovations, anxious use of innovations, external barriers to the integration of innovations into pedagogical practice, readiness to use innovative pedagogical knowledge). A comprehensive approach to methodology was used in the study. The main method in the work is the method of pedagogical experiment. Moreover, descriptive, statistical, and theoretical methods are also applied. The methods of questionnaires and observation were employed to diagnose the effectiveness of innovative research, according to preschool teachers. The main hypothesis is the assumption that innovative environmental knowledge courses and self-education activities contribute to the competence and quality of teacher training significantly and provide for their integration into the work of school and preschool education. The result of this study is evidence that teacher training programs and new pedagogical methods related to innovations in ecological education affect the ability to operate with new methods and modern knowledge in future professional practice. A further prospect is the development and implementation of effective methods for introducing innovative pedagogical knowledge into the training of teachers in the field of preschool and school education.
Multistage Encryption for Text Using Steganography and Cryptography

Recently, the movement of data between smart devices has piqued the world's curiosity because it transmits important and unimportant data via the Internet. Thus, important data must be encrypted during passing over a network so that information can only access by an intended receiver and processed by it. As a result, information security has become even more critical than before. Our proposal suggests a method to secure data in three stages using cryptography and steganography. The important message will divide into two parts a part will encrypt by Caesar Cipher and another by Vigenere Cipher. The ciphertext will process by Morse code and will then hide in a cover image using Least Significant Bits (LSB) technique. According to the value the of peak signal-to-noise ratio (PSNR) obtained in this work our proposal has an extra security level and robustness. Finally, our research provides more security because of the mixture between cryptography and steganography.
Forming ecological culture in educational applicants within the context of modern education

The article reveals the foundations of theformation of the ecological culture of youth. Themost important function of culture is highlighted.Taking into account the latest global trends, it isclaimed that the formation of ecological cultureamong young people is gaining importance. Thearticle notes that culture consists of four main elements. Each of these components plays asignificant role in creating a cultural image andproviding environmental education for specialists. Digitization of education is anundeniable requirement of modern times. Thecurrent system of education and science must undergo major digital changes and meet theglobal trends of digital development so that everyperson can successfully realize his potential. Thearticle defines the main goal of open education.Features of open education are quality,excellence, accessibility, flexibility andcreativity. In this system, the learning processbecomes open and creative, free access toinformation resources, freedom of choice, anindividualized approach are ensured, conditions are created for joint creative mastering of the world in the formation of the ecological culture of the students of education. We are lighted up the principles of the educational policy of digital development.
Measuring perception towards AI-based chatbots in Insurance Sector

Recent developments in artificial intelligence (AI) technology, many industries, including banking, insurance, and e-commerce, have begun to adopt this cutting-edge technology to provide customers with timely information and service suitably. However, nothing is known about how Generation Z perception about chatbots using artificial intelligence in the insurance sector. In this paper, we surveyed 120 participants (Generation Z) about their perspectives on using chatbots that AI drives. Our study revealed that although participants in our study had little knowledge or experience with AI systems, they typically had a favourable opinion of chatbots. Most participants expressed high levels of comfort and willingness to use health AI technologies and felt that AI might be helpful as TPA (third-party administrator). Some inherent aspects include knowledge about AI applications and digital literacy playing an essential role in generation Z perceptions of using AI-based chatbots. People with high technical knowledge believe AI chatbots may help them better understand insurance. The study's findings also reveal a significant relationship between knowledge of AI applications, experience with chatbots, and chatbots potential to help respondents' understanding. This study contributes to both researchers and the industry by advancing our understanding of the perception of AI-based chatbots in the insurance industry, emphasizing the likelihood of AI.
Machine Learning Modeling for Radiofrequency Electromagnetic Fields (RF-EMF) Signals From mmWave 5G Signals

Vot No.: 9696300) for the financial assistance in the measurement campaign.This
Lifelong learning with Shared and Private Latent Representations learned through synaptic intelligence

This paper explores a novel lifelong learning method with Shared and Private Latent Representations (SPLR), which are learned through synaptic intelligence. To solve a sequence of tasks, by considering the entire parameter learning trajectory, SPLR can learn task-invariant representation which changes little, and task-specific features that change greatly along the entire parameter updating trajectory. Therefore, in the lifelong learning scenarios, our model can obtain a task-invariant structure shared by all tasks and also contain some private properties that are task-specific to each task. To reduce the parameter quantity, a ℓ1 regularization to promote sparsity is employed in the weights. We use multiple datasets under lifelong learning scenes to verify our SPLR, on these datasets it can get comparable performance compared with existing lifelong learning approaches, and learn a sparse network which means fewer parameters while requiring less model training time.
GAN-Driven Data Poisoning Attacks and Their Mitigation in Federated Learning Systems

Federated learning (FL) is an emerging machine learning technique where machine learning models are trained in a decentralized manner. The main advantage of this approach is the data privacy it provides because the data are not processed in a centralized device. Moreover, the local client models are aggregated on a server, resulting in a global model that has accumulated knowledge from all the different clients. This approach, however, is vulnerable to attacks because clients can be malicious or malicious actors may interfere within the network. In the first case, these types of attacks may refer to data or model poisoning attacks where the data or model parameters, respectively, may be altered. In this paper, we investigate the data poisoning attacks and, more specifically, the label-flipping case within a federated learning system. For an image classification task, we introduce two variants of data poisoning attacks, namely model degradation and targeted label attacks. These attacks are based on synthetic images generated by a generative adversarial network (GAN). This network is trained jointly by the malicious clients using a concatenated malicious dataset. Due to dataset sample limitations, the architecture and learning procedure of the GAN are adjusted accordingly. Through the experiments, we demonstrate that these types of attacks are effective in achieving their task and managing to fool common federated defenses (stealth). We also propose a mechanism to mitigate these attacks based on clean label training on the server side. In more detail, we see that the model degradation attack causes an accuracy degradation of up to 25%, while common defenses can only alleviate this for a percentage of ∼5%. Similarly, the targeted label attack results in a misclassification of 56% compared to 2.5% when no attack takes place. Moreover, our proposed defense mechanism is able to mitigate these attacks.
Decision Support System Design for Informatics Student Final Projects Using C4.5 Algorithm

Academic consultation activities between students and academic supervisors are necessary to help students carry out academic activities. Based on the transcript of grades obtained, many students do not choose the appropriate final project/thesis specialization fields based on their academic abilities, resulting in a lot of inconsistencies between the course grades and the final project specialization fields. The purpose of this research is to minimize the subjectivity aspect of students in choosing their final project academic supervisors and minimize the inconsistencies between the course grades and the final project specialization fields. The method used in this research is classification data mining using the Decision Tree and C4.5 Algorithm methods, with the attributes involved being courses, course grades, and specialization courses. The C4.5 Decision Tree algorithm is used to transform data (tables) into a tree model and then convert the tree model into rules. The implementation of the C4.5 Decision Tree algorithm in the specialization field decision support system has been successfully carried out, with an accuracy rate of 70% from the total calculation data. The data used in this research is a sample data from several senior students in the Informatics program at Ubhara-Jaya. The results of the research decision support system can be used as a good recommendation for the Informatics program and senior students to direct their final project research. It is expected that further research will use more sample data so that the accuracy rate will be better and can be implemented in website or mobile-based applications.
Post-COVID-19 Education for a Sustainable Future: Challenges, Emerging Technologies and Trends

The COVID-19 pandemic, which has been destabilizing the world since the end of 2019, has significant repercussions on not only health but also the economy, finances, society, politics, the environments, culture and education [...]
Knowledge-Enhanced Semi-Supervised Federated Learning for Aggregating Heterogeneous Lightweight Clients in IoT

Federated learning (FL) enables multiple clients to train models collaboratively without sharing local data, which has achieved promising results in different areas, including the Internet of Things (IoT). However, end IoT devices do not have abilities to automatically annotate their collected data, which leads to the label shortage issue at the client side. To collaboratively train an FL model, we can only use a small number of labeled data stored on the server. This is a new yet practical scenario in federated learning, i.e., labels-at-server semi-supervised federated learning (SemiFL). Although several SemiFL approaches have been proposed recently, none of them can focus on the personalization issue in their model design. IoT environments make SemiFL more challenging, as we need to take device computational constraints and communication cost into consideration simultaneously. To tackle these new challenges together, we propose a novel SemiFL framework named pFedKnow. pFedKnow generates lightweight personalized client models via neural network pruning techniques to reduce communication cost. Moreover, it incorporates pretrained large models as prior knowledge to guide the aggregation of personalized client models and further enhance the framework performance. Experiment results on both image and text datasets show that the proposed pFedKnow outperforms state-of-the-art baselines as well as reducing considerable communication cost. The source code of the proposed pFedKnow is available at https://github.com/JackqqWang/pfedknow/tree/master.
Measuring and Evading Turkmenistan’s Internet Censorship

Since 2006, Turkmenistan has been listed as one of the few Internet enemies by Reporters without Borders due to its extensively censored Internet and strictly regulated information control policies. Existing reports of filtering in Turkmenistan rely on a handful of vantage points or test a small number of websites. Yet, the country’s poor Internet adoption rates and small population can make more comprehensive measurement challenging. With a population of only six million people and an Internet penetration rate of only 38%, it is challenging to either recruit in-country volunteers or obtain vantage points to conduct remote network measurements at scale.
A New Discretization Scheme for the Non-Isotropic Stockwell Transform

To avoid the undesired angular expansion of the sampling grid in the discrete non-isotropic Stockwell transform, in this communication we propose a scale-dependent discretization scheme that controls both the radial and angular expansions in unison. Based on the new discretization scheme, we derive a sufficient condition for the construction of Stockwell frames in L2(R2).
IMAGE ENCRYPTION AND DECRYPTION USING AES ALGORITHM

Security concerns have taken front stage in the rapidly expanding digital exchange of data storage and transmission. Since the use of images is expanding quickly across a wide range of industries, it is crucial to safeguard sensitive image data from hackers. The need for image protection has become critical. It is now essential to secure someone's privacy. The preservation of data and personal information has been studied and developed using a variety of ways. Image encryption is used to shield sensitive data from unauthorised users. One of the most popular methods for keeping data concealed from unauthorised access is encryption. For picture encryption, the Advanced Encryption Standard (AES) is utilised, which leverages the key stream generator to improve image encryption performance. Keywords- ➢ AES:- Advanced Encryption Standard ➢ DES:- Data Encryption Standard