Reviewerly/exHarmony huggingface dataset:
An Efficient Quantum cryptanalysis of lightweight hash function - PHOTON-Beetle

Quantum cryptanalysis methods are used to crack the majority of symmetric key ciphers. With this knowledge, cryptologists should be able to create post-quantum symmetric ciphers by incorporating these new quantum cryptanalysis techniques into their toolkit. Secure communication is crucial for applications related to the Internet of Things (IoT) to avoid cyberattacks. Cryptographic hash functions can be used to protect data integrity. The development of large-scale, fault-tolerant quantum computers is the most significant challenge. Recent techniques to attack symmetric key schemes can be achieved by using the Pre-image attack method, Simon’s algorithm, or by combining Simon’s and Grover’s algorithms. In this study, we explored some of the most recent methods for quantum cryptanalysis of symmetric key ciphers. Firstly, we discussed the design of quantum cryptanalysis methods, along with the advantages over classical procedures. In this paper, we illustrate the implementation of the NIST-standard lightweight hash function PHOTON-Beetle on a Quantum Computer (using IBM Qiskit), which is mostly used in IoT devices. Efficient quantum implementation of PHOTON-Beetle is proposed in this paper. This paper calculates the quantum cost of PHOTON-Beetle for every layer and compares it with [1].
Face Recognition using CNN and Amazon S3: An Expandable and Safe Integration for Various Uses

Lately, there has been notable interest in utilizing Convolutional Neural Networks (CNN) for face recognition, underlining its significance and relevance., finding utility in diverse areas such as security systems, personal device authentication, and social media tagging. This abstract presents an implementation of a CNN-based face recognition algorithm that effectively leverages data stored within an Amazon S3 bucket. The CNN model’s primary objective is the precise identification and categorization of human faces within the stored dataset, harnessing the inherent capabilities of CNNs for extracting and classifying facial features. The system’s efficiency is greatly enhanced by the scalability and accessibility provided by Amazon S3 storage, underscoring the seamless integration of cutting-edge machine learning techniques with a cloud-based storage infrastructure. This solution demonstrates a powerful and adaptable framework for face recognition applications, encompassing various potential use cases ranging from security and authentication to personalized user experiences.
Study and Analysis of various Privacy Preserved Data Sharing Framework in E-Government System based on Consortium Blockchain: A Challenging Overview

With the advanced improvement in E-government information sharing mechanism, the secure information sharing is beneficial and challenging. The consortium blockchain offers a new idea and ways to share the government information in secure and effective manner. Hence, this research study utilized various techniques corresponding to privacy preserved data sharing based on execution time, transactions per second, validation time, data loss, computational overhead, computational cost and so on. This survey utilized 25 research papers focussed on various techniques for privacy preserved data sharing techniques, and the review of researches technique-wise are provided. Finally, the analysis is promoted in the survey based on the research technique, year of publication, performance measures and achievement of the research methodologies towards privacy preserved data sharing techniques. Moreover, the research gaps and issues of the utilized techniques are explained, such that the motivation for developing an effective method for privacy preserved data sharing technique based on evaluation metrics is revealed.
Smart Attendance Registration and Management System through AI

Automatic Facial Recognition (AFR) stands as a widely employed technology in today’s rapidly advancing world. Within the context of a smart attendance system, real-time facial recognition is employed to track student attendance by capturing and processing their facial features in real-time, comparing them to a stored database. This facial recognition process swiftly identifies and verifies students, providing precise results when analyzing video inputs. There are several existing frameworks for facial recognition, and to enhance both the speed and accuracy of recognition, a new framework is proposed. This novel approach leverages deep learning techniques for video processing, modifying the existing Convolutional Neural Network (CNN) architecture to incorporate specialized features into a versatile neural network design. The proposed model considers artificial intelligence enabled Split Invariant Artificial Neural Network (SIANN) to make efficient attendance registration and management system. The proposed model performance is measured through accuracy, loss function etc.
PQC Secure: Strategies for Defending Against Quantum Threats

As quantum computing emerges, the security domain is set to encounter unparalleled challenges, necessitating a pivotal shift to Post-Quantum Cryptography (PQC) for safeguarding against quantum adversities. Current cryptographic mechanisms, especially RSA and ECC, are susceptible to quantum breaches due to their reliance on factorization and discrete logarithm problems. The emergence of quantum computers endangers traditional cryptographic infrastructures, exposing them to swift decryption by algorithms like Shor’s, $N=p \cdot q$, and Grover’s, $r \equiv a^{\frac{N}{2}} \bmod N$, where N is the product of two significant primes, p and q, and r denotes the remainder after applying Grover’s algorithm to a. Conventional cryptographic methods, which rest on problems solvable by quantum computers, emphasize the need to transition to PQC, substituting prevalent methods with quantum-resilient alternatives, represented by $E=m^{e} \bmod n$, where E is the encrypted message, m the plaintext, e the public key exponent, and n the product of two prime numbers. This research evaluates the efficacy of PQC approaches, encompassing lattice-based, code-based, and isogeny-based cryptography, with assessments based on metrics like encryption duration, $T_{e}=\frac{1}{f_{e}}$, and key length, $\log _{2}(N)$, where $T_{e}$ represents encryption time and $f_{e}$ the encryption speed. Employing standardized datasets, we examine encryption and decryption speeds, key sizes, and success metrics against NIST’s endorsed security standards. The findings underscore PQC’s potential in delivering robust security, albeit with variations in performance metrics, guiding secure communication choices. In summation, the study highlights the essential shift to PQC in addressing the vulnerabilities ushered in by quantum computing, presenting a spectrum of fortified strategies for the future of information security.
Qualitative insight into the internet frauds within` the context of consumers in Bosnia and Herzegovina

Abstract Online shopping helps consumers search for information about products and services, makes it easier to compare prices and product features, and enables a quick and easy exchange of positive and negative experiences. However, through numerous positive advantages, there is inevitably a constant increase in the negative consequences of online shopping, especially from the consumer's perspective.
Using a Virtual Reality Tool for Experimental Learning in Engineering. A Survey of Learner Preferences

Engineering laboratories are designed to provide ‘real’ hands-on experience to the students. However, building laboratories that are suitable to serve a large student population is challenging due to the limited resources. We propose that unsupervised laboratory activities be conducted with virtual reality (VR) tools to support existing laboratories infrastructure or aid lecture delivery with VR experiments. The VR based laboratory permits students to interact virtually with the equipment and connect theoretical concepts with virtual experiments. We have designed a sample of undergrad VR laboratory lessons for students in engineering programs at the University of Glasgow Singapore and the Singapore Institute of Technology (SIT). In this paper, our study aims to obtain student feedback through structured questionnaires to identify what characteristics of the VR technology implementation enhance student engagement in an engineering laboratory provision. We present findings from the questionnaire on the learners' preferences. In particular, our findings identify that although the VR tool's characteristics are engaging, students find the implementation a cumbersome process to access VR lessons. Finally, we provide some recommendations for future implementations.
Automated Essay Grading of Constructive Response Test Responses for Mechanical Engineering Students

Machine learning education related applications have increased with the appearance of large language models. While automatic essay grading (AEG) has been studied extensively in the past, most of these studies have focused on evaluating English competence instead of assessing knowledge competence in an engineering field. This study aimed to develop an AEG model to evaluate student’s mechanical engineering Constructive Response Test (CRT) question responses which were instructor graded. Because of the small number of student responses (45), a synthesized set of responses was also generated by using text-to-text paraphrasing models. A neural network grading engine was built and trained to assess comprehension utilizing the Bidirectional Encoder Representation Transformer (BERT) and related models on student and synthesized responses. This study showed that the AEG based Natural Language Processing (NLP) model showed high accuracy and a higher degree of consistency in grading student responses compared to instructor-graded responses.
The Effects of Kindergarten Principals' Digital Leadership on Teachers' Technology Integration during the COVID-19 Pandemic in Western China

Remote learning was widely adopted in kindergartens during the COVID-19 pandemic, although some people were skeptical about adopting information technology. To accommodate this shift, kindergarten principals faced unprecedented challenges requiring them to demonstrate digital leadership and guide teachers in integrating technology to ensure regular learning and development of children during the outbreak. This study aimed to investigate the status of digital leadership exhibited by kindergarten principals, the level of integration technology among kindergarten teachers in Western China, and the relationship between them. A mixed-method approach was employed, involving two surveys and two semi-structured interviews. A random sample of 106 principals and 415 teachers from kindergartens in four provinces in Western China was selected. The data analysis revealed that the level of digital leadership kindergarten principals demonstrated and the level of technology integration teachers exhibited are generally high. Teachers from different provinces showed statistically significant differences regarding the ability to integrate technology. The Pearson correlation analysis showed a significant positive correlation between principals' digital leadership and teachers' technology integration. Multiple regression showed that principals' digital leadership can somewhat predict teachers' technology integration, and interview results with principals and teachers also confirmed this point. These findings seem revealing that principals' digital leadership positively impact teachers' technology integration in Western China. Notably, excellence in professional practice is a reliable indicator of the extent to which teachers incorporate technological approaches into their instruction. The demonstration of digital leadership by principals can support teachers in enhancing their technology integration into distance learning activities, and the outcomes of interviews with both principals and teachers further corroborate this.
Exploring the Performance of Generative AI Tools in Electrical Engineering Education

This paper explores the use cases and performance of generative artificial intelligence (AI) tools in the context of Electrical Engineering education. A summary of commonly used generative AI tools in Engineering education is provided along with a review of their use cases from recent literature. The performances of the generative AI tools GPT-3.5, GPT-4 and Google Bard are examined using custom-made question sets in two representative courses: Digital Logic Design and Digital Signal Processing. The paper highlights that GPT4 achieves over 50% accuracy in both courses and generative AI tools are more effective at solving standalone concept questions than analytical or numerical questions.
Comparing Attitudes Towards Mobile App Development between International Students and Domestic Japanese Students

Mobile app development courses are popular among university students. However, learning design in app development education is primarily instructor-centered, and little effort has been made to understand students’ beliefs and opinions of the subject. Meanwhile, the on-going changes in the higher education landscape in Japan also demand investigations into potential differences between international students and domestic students. In this paper, we present our findings comparing the attitudes of international and domestic Japanese students towards mobile app development. We adapted a web development attitude survey to probe students’ confidence, motivation, belief in gender equality, perceived usefulness, perceived knowledge structure, learning style, self-reliance, and contextualization of app development. In total 162 responses (31% was international student) were collected during 2020-2023. We found that international students overall had more positive attitudes. Statistically significant differences were identified in the constructs of confidence, gender, and context. Surprisingly, we found that domestic students may benefit from a collaborative learning environment more than expected. Future course design should focus on helping students build their confidence in learning the subject, as well as fostering knowledge transfer from classroom to real-world problem-solving.
Cultivating Inclusivity: A Journey Towards Democratizing Cybersecurity Education

In today’s increasingly digital landscape, organizations and institutions are tasked with carrying out an array of risk-based activities, such as threat modeling, risk assessment, and microdata publishing. At the heart of these efforts lies the critical yet under-discussed skill of risk calculation in cybersecurity. This research aims to bridge the skill gap in cybersecurity and make the field more inclusive by exploring alternative mathematical approaches to risk calculation. We examine the applicability of Bayesian probability and set theory in portraying core cybersecurity concepts: Confidentiality, Integrity, and Authentication (CIA). Our work introduces a design study that leverages these mathematical fields to offer an accessible and intuitive alternative to traditional definition-based cybersecurity education. To enhance engagement and comprehension, we have also developed an interactive, browser-based dashboard that visually represents the CIA concepts in the context of risk assessment. This student-centric tool aspires to democratize cybersecurity learning by lowering mathematical barriers, making the field more accessible to a broader, more diverse audience.
A Novel Approach to Detect Phishing Webpages Hosted on Compromised Domain

Nowadays cybercriminals host malicious webpages on the legitimate domains to bypass existing anti-phishing methods. Therefore, this paper presents a new hyperlink-based mechanism to identify the phishing webpages hosted on a authenticate domains. The proposed approach is fast, reliable, third party independent, and able to filter the webpages written in any textual language. Moreover, the proposed approach can also identify the legitimate source of the phishing webpage. The results outcomes demonstrate that the proposed method is very accurate in the detection of phishing and legitimate webpages as it has 97.92 % of true negative rate and 97.34 % overall detection accuracy.
Hand Detection and Morse Code Translation for Alternative Communication

This paper focuses on developing a real-time Morse code translation system using finger movements captured by a camera and processed with the MediaPipe framework. To enable this, the paper compares the performance of three hand detection algorithms: Yolov5, RetinaNet, and Detectron 2, and identifies MediaPipe as the most effective method for hand tracking. The proposed approach provides a practical and accessible communication method for individuals with speech disorders and opens up new opportunities for improved communication in various settings. The novelty of this work lies in the combination of hand detection algorithms and Morse code translation to develop a robust and efficient communication system for those with speech disorders. Overall, this study highlights the potential of Assistive Speech Technologies in improving communication and promoting inclusion for individuals with speech disorders.
Machine Learning Based Improved Phishing Detection using Adversarial Auto Encoder

Phishing scams are frequently used by malicious actors to get login credentials from unsuspecting users. One method for detecting phishing is machine learning. Despite the fact that the accuracy of machine learning algorithms is mostly determined by the training data, there isn't much easily accessible attack data. We present a method for enhancing the datasets used by machine learning algorithms. We create samples that resemble phishing websites using an Adversarial Auto Encoder (AAE) and offer metrics to judge the effectiveness of the samples. We compare these samples to models created from actual data. Some of the created samples could be impossible for the current model to recognize. After that, training is done with the remaining samples. Improved levels of accuracy and reliability are provided by the upgraded machine learning models. In other words, phishing detection is improved during model construction by merging data from real phishing sites with AAE fake data.
The Synergy of Advanced Language Models, Sequential Skeleton Features, and GAN-Enhanced Data Augmentation for Unprecedented Precision

The precision comparison between the proposed method and traditional methods is a crucial evaluation of their performance in machine learning and information retrieval tasks. Precision, a fundamental metric, measures the accuracy of positive predictions. This study presents the “Proposed Method,” an innovative approach, alongside “Traditional Methods,” representing conventional approaches. The comparison, visualized in a precision-focused diagram, demonstrates how effectively each method identifies relevant instances. A higher precision value in the Proposed Method implies its proficiency in minimizing false positives, particularly vital in precision-critical applications like medical diagnosis and spam email classification. This analysis aids stakeholders and researchers in assessing the precision-related trade-offs between innovation and convention, guiding informed decisions for specific contexts.
Neural Network-Based Handwritten Character Detection

Due to increased utilization of digital technologies in most day to day activities to keep and pass information, Hand- written characters recognition has become a well- liked subject of investigation because people still want to have Handwritten copies converted into electronic copies that can be kept electronically. Detection of hand- written character has got to become computer's ability to recognize and interpret accessible Hand- written input from the sources like Images, records, touchscreens. Understanding the Handwritten characters is complex because handwriting differs from person to person. The goal of this project is to create an artificial neural network-based model for handwritten character detection that can recognize certain typeface characters in part. Being a relatively young discipline, neural computing's style components are less fully defined than those of other architectures. Data parallelism was carried out via neural computers. Neural computers operate in a completely different manner than standard computers. Given a specific initial state (data input), a neural computer is prepared (not programmed) to either classify the input file into one of the many classes or to cause the original data to develop in such a manner that a specific desirable attribute is optimized. The experimental outcomes demonstrate that our suggested approach succeeds in classification accuracy on the test set of 99.3, exceeding the most recent techniques. This study offers a precise answer for real-world applications including postal automation, bank cheque processing, and signature verification while demonstrating the efficiency of utilizing neural networks for handwritten character detection. The field of computer vision and pattern recognition includes hand-written character recognition as a significant research subject. Due to its capacity to learn robust features directly from raw picture input, convolutional neural net-works (CNNs) have recently attained state-of-the-art performance in character recognition challenges.
Pharmacy Location and Recommendation Services

The medical sector has continually advanced in recent years. The integration of medical services with a centralized system, on the other hand, is effective in enhancing people's lives thanks to the technology that is currently available. This system will help people to make out their medicinal requirements in their nearby location. If the exact medicine is not available alternative medicines are recommended based on user searched medicine combination it is achieved by using the rank-based algorithm to find similar medicine. This approach will computerize all pharmacy records so that customers and pharmacists can access them whenever and wherever they choose. The proposed system is to lay out a plan of action to the consumers plus pharmacist and do the representation of web services between them. This system intensifies the engagement of unorganized pharmacies into the computerized system to lift medical assistance. In such a way, the system will become the forthcoming way of e-medicinal services and advances the approach of medicinal assistance that are given to the patients by hospitals and doctors. It also helps to get drug availability in every locality