Reviewerly/exHarmony huggingface dataset:
Mobile Learning- A New Methodology in Education System

Technology today is reversing old learning techniques, revealing many new dimensions of learning for students.There are smart devices and mobile phones occupied entire Entertainment and Communication Lives up to the way we learn first.Education in Mobile learning transforms for digital natives learning.Today 86 percent that college students have a smart device, nearly half of them and the use of mobile devices for daily college tasks a study shows, which is not significant.Moreover, of com's report Smart phones and Mobile devices including tablets to access the Internet now the most favorite gadgets are, Accounting for 50% of the global browsing process.These figures justify the beginning of an interesting trend right now -education mobile learning.M-learning or providing educational content, especially Smart phones and Such as tablets using mobile technology, fast in the field of education is becoming popular.In fact, it has been the subject of much debate in recent times the higher education community.Universities and Colleges around the world are exploring ways to use mobile learning to enhance student engagement.Here are the top 9 reasons for the growth of e-learning in education.Mobile learning, on the other hand, offers highly interactive forms and designed content tailored to individual learning styles.It increases the level of learning engagement to help students better Understand.M-Learning, Videos, Info graphics and Like audio from various instructional media Allows students to choose.helping them to easily learn a given lesson, improve engagement and thus retain it.
Hyperparameter optimization using custom genetic algorithm for classification of benign and malicious traffic on internet of things–23 dataset

Hyperparameter optimization is one of the main challenges in deep learning despite its successful exploration in many areas such as image classification, speech recognition, natural language processing, and fraud detections. Hyperparameters are critical as they control the learning rate of a model and should be tuned to improve performance. Tuning the hyperparameters manually with default values is a challenging and time-intensive task. Though the time and efforts spent on tuning the hyperparameters are decreasing, it is always a burden when it comes to a new dataset or solving a new task or improving the existing model. In our paper, we propose a custom genetic algorithm to auto-tune the hyperparameters of the deep learning sequential model to classify benign and malicious traffic from internet of things-23 dataset captured by Czech Technical University, Czech Republic. The dataset is a collection of 30.85 million records of malicious and benign traffic. The experimental results show a promising outcome of 98.9% accuracy.
Rapid Evaluation and Response to Impacts on Critical End-Use Loads Following Natural Hazard-Driven Power Outages: A Modular and Responsive Geospatial Technology

Abstract The disparate nature of data for electric power utilities complicates the emergency recovery and response process. The reduced efficiency of response to natural hazards and disasters can extend the time that electrical service is not available for critical end-use loads, and in extreme events, leave the public without power for extended periods. This article presents a methodology for the development of a semantic data model for power systems and the integration of electrical grid topology, population, and electric distribution line reliability indices into a unified, cloud-based, serverless framework that supports power system operations in response to extreme events. An iterative and pragmatic approach to working with large and disparate datasets of different formats and types resulted in improved application runtime and efficiency, which is important to consider in real time decision-making processes during hurricanes and similar catastrophic events. This technology was developed initially for Puerto Rico, following extreme hurricane and earthquake events in 2017 and 2020, but is applicable to utilities around the world. Given the highly abstract and modular design approach, this technology is equally applicable to any geographic region and similar natural hazard events. In addition to a review of the requirements, development, and deployment of this framework, technical aspects related to application performance and response time are highlighted.
A Novel Method Based on GAN Using a Segmentation Module for Oligodendroglioma Pathological Image Generation

Digital pathology analysis using deep learning has been the subject of several studies. As with other medical data, pathological data are not easily obtained. Because deep learning-based image analysis requires large amounts of data, augmentation techniques are used to increase the size of pathological datasets. This study proposes a novel method for synthesizing brain tumor pathology data using a generative model. For image synthesis, we used embedding features extracted from a segmentation module in a general generative model. We also introduce a simple solution for training a segmentation model in an environment in which the masked label of the training dataset is not supplied. As a result of this experiment, the proposed method did not make great progress in quantitative metrics but showed improved results in the confusion rate of more than 70 subjects and the quality of the visual output.
Personalized Federated Learning With Server-Side Information

Personalized Federated Learning (FL) is an emerging research field in FL that learns an easily adaptable global model in the presence of data heterogeneity among clients. However, one of the main challenges for personalized FL is the heavy reliance on clients’ computing resources to calculate higher-order gradients since client data is segregated from the server to ensure privacy. To resolve this, we focus on a problem setting where the server may possess data independent of clients’ data – a prevalent problem setting in various applications, yet relatively unexplored in the existing literature. Specifically, we propose FedSIM, a new method for personalized FL that actively utilizes such <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">server data</i> to improve meta-gradient calculation in the server for increased personalization performance. Experimentally, we demonstrate through various benchmarks and ablations that FedSIM is superior to existing methods in terms of accuracy, more computationally efficient by calculating the full meta-gradients in the server, and converges up to 34.2% faster.
Binary QUATRE Using Time-varying Transfer Functions

&lt;p&gt;QUasi-Affine TRansformation Evolutionary (QUATRE) is an excellent algorithm that generalized differential evolution algorithm to matrix form. There are two approaches of Binary QUATRE (BQUATRE) algorithm that convert the continuous search space to the binary one. The convergence process of meta-heuristic algorithms can be regarded as two stages: exploration and exploitation. Generally speaking, transfer function plays an important role in the binary metaheuristic algorithms. Therefore, four families of transfer functions are converted into the time-varying form to balance the exploration and exploitation. In particular, a new timevarying formula for the V-shaped, U-shaped and Z-shaped transfer functions is designed in this manuscript. Then, two new approaches of BQUATRE algorithm using the timevarying transfer functions are proposed. In addition, a wrapper feature selection algorithm is designed based on the proposed time-varying BQUATRE. In order to verify the performance of the proposed algorithms, the UCI repository is used in the experiment and the results show that the proposed algorithms are superior to the original BQUATRE and the state-of-the-art algorithms.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Learning Coupled Oscillators System with Reservoir Computing

In this paper, we reconstruct the dynamic behavior of the ring-coupled Lorenz oscillators system by reservoir computing. Although the reconstruction of various complex chaotic attractors has been well studied by using various neural networks, little attention has been paid to whether the spatio-temporal structure of some special attractors can be maintained in long-term prediction. Reservoir computing has been shown to be effective for model-free prediction, so we want to investigate whether reservoir computing can restore the rotational symmetry of the original ring-coupled Lorenz system. We find that although the state prediction of the trained reservoir computer will gradually deviate from the actual trajectory of the original system, the associated spatio-temporal structure is maintained in the process of reconstruction. Specifically, we show that the rotational symmetric structure of periodic rotating waves, quasi-periodic torus, and chaotic rotating waves is well maintained.
Pentapartite Entanglement Measures of GHZ and W-Class State in the Noninertial Frame

We study both pentapartite GHZ and W-class states in the noninertial frame and explore their entanglement properties by carrying out the negativities including 1-4, 2-3, and 1-1 tangles, the whole entanglement measures such as algebraic and geometric averages π5 and Π5, and von Neumann entropy. We illustrate graphically the difference between the pentapartite GHZ and W-class states. We find that all 1-4, 2-3 tangles and the whole entanglements, which are observer dependent, degrade more quickly as the number of accelerated qubits increases. The entanglements of these quantities still exist even at the infinite acceleration limit. We also notice that all 1-1 tangles of pentapartite GHZ state Nαβ=NαIβ=NαIβI=0 where α,β∈(A,B,C,D,E), whereas all 1-1 tangles of the W-class state Nαβ,NαIβ and NαIβI are unequal to zero, e.g., Nαβ=0.12111 but NαIβ and NαIβI disappear at r>0.61548 and r>0.38671, respectively. We notice that the entanglement of the pentapartite GHZ and W-class quantum systems decays faster as the number of accelerated particles increases. Moreover, we also illustrate the difference of von Neumann entropy between them and find that the entropy in the pentapartite W-class state is greater than that of GHZ state. The von Neumann entropy in the pentapartite case is more unstable than those of tripartite and tetrapartite subsystems in the noninertial frame.
Secure Channel for Financial Transactions in Cloud Environment Using Blockchain Technology

Abstract The progression of digital technology impacts the users to migrate from traditional financial transactions to the online transaction system for performing their financial transactions. The existence of cloud computing and its major benefit of access from anywhere promotes financial transactions. The cloud infrastructure supporting performing financial transactions deals with the huge volume of data and it is quintessential to sustain a high level of security to those sensitive credentials in the cloud storage. The conventional cryptographic algorithms hold good in providing a better level of security to the sensitive data in cloud computing but still fall back to advanced attacks against the cloud data storage. Furthermore, the employment of conventional encryption algorithms in securing the cloud data consumes huge execution time, as it has to perform the authentication check for all types of user access including legitimate and non-legitimate accesses. This research article proposes a novel methodology of incorporating blockchain technology to perform online financial transactions in cloud computing with a high level of security, availability, and reduced execution time. This research work employs Blockchain technology integrated with Elliptical Curve Cryptography (ECC) and Chaotic Map Confusion and Diffusion algorithm to perform secured transactions over a cloud network. The interpretation of the proposed method contributes to better performance in the aforementioned parameters than the existing methodologies. The proposed framework is compared with the conventional method of online transactions using Multi-Factor Authentication (MFA) encompassing the Elliptical Curve Cryptography (ECC). The performance analysis proves that the proposed framework exhibits a high level of security along with reduced computational time, encryption, and decryption cost. The proposed work is interpreted with sample dataset and the computational time is reduced.
A more efficient microgrid operation through the integration of probabilistic solar forecasts

This work proposes a methodology based on the probabilistic dynamic programming (PDP) to integrate operational probabilistic forecasts of a photovoltaic (PV) plant into the optimization of the day-ahead schedule of an energy storage system (ESS). The proposed approach is tested on a microgrid based on a real educational building, a PV farm and Li-ion batteries. The objective is to minimize the operating cost of the microgrid. The operational day-ahead forecasts are derived from the Ensemble Prediction System (EPS) provided by a well-known Numerical Weather Prediction (NWP) model. Contrary to the classical use of deterministic forecasts, we demonstrate that the integration of the probabilistic forecasts in the optimization process leads to a more efficient microgrid management and to a reduction of up to 38% of the operating costs. Besides, it is shown that the non linearity resulting from the power dependency of the efficiency of the inverters must be taken into account in order to yield relevant optimization results.
Hybrid cryptosystem based on deterministic phase masks in Fresnel domain and Yang-Gu mixture amplitude-phase retrieval algorithm

Abstract In this paper, an encryption scheme based on double deterministic phase encoding and Yang-Gu mixture amplitude-phase retrieval algorithm is proposed. The deterministic phase masks are constructed using chaotic logistic and cubic maps. Firstly, an input image is encoded with double deterministic phase masks in Fresnel domain. The phase truncated part of the resulting image is used as an input to the Yang-Gu mixture amplitude-phase retrieval algorithm in the Fourier domain. The encrypted image is obtained by one-way binary modulation is real-valued which makes it more convenient for storage and transmission. The proposed cryptosystem is termed as hybrid as it combines a symmetric and an asymmetric scheme. The validation of the proposed cryptosystem is performed by numerical simulations on grayscale images of Man, DICOM, and a binary image. The proposed scheme is investigated for its robustness through statistical analysis using histogram and correlation plots. The scheme is also tested for its resistance against occlusion, noise, and cryptographic attacks. The proposed cryptosystem is found to be highly sensitive to its private key and the Fresnel parameters.
A High-Throughput Hardware Implementation of NAT Traversal For IPSEC VPN

In this paper, we present a high-throughput FPGA implementation of IPSec core. The core supports both NAT and non-NAT mode and can be used in high speed security gateway devices. Although IPSec ESP is very computing intensive for its cryptography process, our implementation shows that it can achieve high throughput and low lantency. The system is realized on the Zynq XC7Z045 from Xilinx and was verified and tested in practice. Results show that the design can gives a peak throughput of 5.721 Gbps for the IPSec ESP tunnel mode in NAT mode and 7.753 Gbps in non-NAT mode using one single AES encrypt core. We also compare the performance of the core when running in other mode of encryption.
Performance Evaluation of Sentiment Analysis on Balanced and Imbalanced Dataset Using Ensemble Approach

Background: Class imbalance is often discussed as a strenuous task in the realm of sentiment analysis.In an imbalanced classification, few minority class instances are unable to provide sufficient information, therefore direct learning from an unbalanced dataset can produce unsatisfactory results.This work aims to address the problem of class imbalance.Methods: At primary level this study uses a novel Synthetic Minority Oversampling Technique (SMOTE) for balancing the dataset and then proposes an ensemble model, named Ensemble Bagging Support Vector Machine (EBSVM) for opinion mining.To measure the performance of the particular approach, numerous analyses are conducted on both imbalanced and balanced datasets.Then the work compares the effectiveness of the suggested model with three base classifiers (Nave Bayes (NB), Decision Tree (DT), and Support Vector Machine (SVM)).The customer reviews for restaurants is chose as the dataset for this work.Accuracy, precision, recall and F-measure are used as metrics for evaluation.Findings: According to the results, the suggested EBSVM model excels all other individual classifiers with the imbalanced and SMOTE balanced dataset.The balanced EBSVM classifier improves the imbalanced EBSVM Classifier in terms of accuracy.Precision, recall and F-measure of the minority class in the imbalanced classifiers have been improved in balanced Classifiers.Novelty: The performance of opinion mining classifiers for imbalanced and balanced datasets is evaluated in this paper.The work examines not only general opinions, but also specific aspects such as food, service, ambiance, quality, and price.Comparing the suggested model with existing classification algorithms in the literature, it has found that it outperformed the other models.
The Relationship between Online Learning Acceptance and Emotional Well-Being among Undergraduates

Although online learning promotes many advantages, especially in adapting to the spread of the COVID-19 pandemic, minimal studies have examined the influence of online learning on undergraduates' emotional well-being.This study is crucial to prevent the adverse effects on emotional well-being such as depression, low self-esteem, and lack of self-efficacy, contributing to suicidal thoughts.In attempting to examine the factors that affect students' emotional well-being, this study utilized and extended Technology Acceptance Model (TAM) by adding emotional well-being as a new variable.This study aims to 1) investigate the predicting factors that contribute to online learning acceptance among undergraduates, and 2) examine the relationship between the actual use of online learning and emotional wellbeing among undergraduates.Online survey research and cross-sectional data were employed to test the conceptual framework and developed hypotheses.The respondents consisted of undergraduates from Universiti Teknologi MARA Pahang who underwent online learning.Structural equation modeling (SEM) was performed in this analysis to test the measurement model and to evaluate the hypotheses.The study found that all the predicting factors had a significant relationship with online learning acceptance except for perceived usefulness.Furthermore, the use of online learning has a significant relationship with emotional well-being whereby the students are able to adapt and feel convenient with the new learning method.The findings of this study are expected to assist educators or instructors in understanding the factors that contribute to online learning acceptance among undergraduate students and conducting their online learning in a way that may not be harmful to students' emotional well-being.
New robot path planning optimization using hybrid GWO-PSO algorithm

Actually, path planning is one of the most crucial aspects of mobile robots study. The primary goal of this research is to develop a solution to the path planning issues that occur when a “mobile robot” operates in a static environment. The problem is handled by finding a collision-free path that meets the given criteria for the shortest distance with quite the smoothness of the path. Two nature-inspired metaheuristic algorithms are used in the computation. By leading a hybrid “gray wolf optimization” with the “particle swarm optimization” (HGWO-PSO) computation that restricts the distance and follows path perfection guidelines, the primary shape is improved. In addition, simulation findings reveal that the proposed HGWO-PSO method is deeply serious in terms of path optimality when compared to path planning approaches such as group search optimizer GSO, PSO, artificial bee colony ABC, and GWO.
MARKETING STRATEGIES OF BANKS IN THE PERIOD OF METAVERSE, BLOCKCHAIN, AND CRYPTOCURRENCY IN THE CONTEXT OF CONSUMER BEHAVIOR THEORIES

The banking sector is one of the sectors that follows and adapts to technology the most, using it to develop solutions for problems. With the development of information and communication technologies, telephone banking, internet banking, EFT, Call Centers, and ATMs can be given as examples of banks’ technologies. With the introduction of Web 2.0 into our lives, the banks’ advertisements, which have increased their service quality by using cloud solutions and mobile applications, are generally based on the theme of “the bank that solves the problems of consumers quickly and reliably with the help of technology”. Banks that work with data warehouses, in the background, use artificial intelligence-supported applications to be active 24/7 in call centers and offer voice assistant services which now have Web 3.0 technologies at their disposal. It is thought that with the development of new concepts and related technologies such as Metaverse, Blockchain, and Crypto money, which are mentioned together with the Web 3.0 infrastructure, the banking sector will make innovations both in its services and in the marketing of these services. This study aims to reveal new marketing activities and strategies waiting for the banking sector to focus on technologies such as Metaverse, Blockchain, and Cryptocurrency. In addition, in the light of consumer behavior models, predictions will be made about how consumers and banks will meet in the metaverse world and what awaits the parties. The study is unique both in terms of the relationship between the concepts it contains and the research question. It is thought that this situation will contribute to the literature and help possible future studies. In addition, in the conclusion part, a new concept is defined under the name of Metaverse Marketing.
Multispectral Facial Recognition in the Wild

This work proposes a multi-spectral face recognition system in an uncontrolled environment, aiming to identify or authenticate identities (people) through their facial images. Face recognition systems in uncontrolled environments have shown impressive performance improvements over recent decades. However, most are limited to the use of a single spectral band in the visible spectrum. The use of multi-spectral images makes it possible to collect information that is not obtainable in the visible spectrum when certain occlusions exist (e.g., fog or plastic materials) and in low- or no-light environments. The proposed work uses the scores obtained by face recognition systems in different spectral bands to make a joint final decision in identification. The evaluation of different methods for each of the components of a face recognition system allowed the most suitable ones for a multi-spectral face recognition system in an uncontrolled environment to be selected. The experimental results, expressed in Rank-1 scores, were 99.5% and 99.6% in the TUFTS multi-spectral database with pose variation and expression variation, respectively, and 100.0% in the CASIA NIR-VIS 2.0 database, indicating that the use of multi-spectral images in an uncontrolled environment is advantageous when compared with the use of single spectral band images.