Reviewerly/exHarmony huggingface dataset:
Intelligent Cybersecurity Awareness and Assessment System (ICAAS)

With increasing use of sophisticated technologies, attack surface has widened leading to many known and unknown cybersecurity threats. Interestingly, lack of cybersecurity awareness among product manufacturers continues to be the major challenge. Cybersecurity is considered as non-functional requirement and even today, the criticality of cybersecurity is undermined. Several researches have been made to improve cybersecurity models and awareness among developers, however, there is limited to no research on engaging interventions that can help in preliminary education of product manufacturers regarding security-by-design. Besides, poor cybersecurity practices followed by suppliers and ignorance of the same among product manufacturers leads to additional cybersecurity challenges. This study suggests an innovative and convenient approach to help product manufacturers become more holistically aware of cybersecurity issues and help them make more successful and cost-effective decisions on cybersecurity plans for their products.
A Machine Learning-Based Traditional and Ensemble Technique for Predicting Breast Cancer

Breast cancer is a physical disease and increasing in recent years. The topic is known widely in the recent world. Most women are suffering from problem of breast cancer. The disease is measured by the differences between normal and affected area ratio and the rate of uncontrolled increase of the tissue. Many studies have been conducted in the past to predict and recognize breast cancer. We have found some good opportunities to improve the technique. We propose predicting the risks and making early awareness using effective algorithm models. Our proposed method can be easily implemented in real life and is suitable for easy breast cancer predictions. The dataset was collected from Kaggle. In our model, we have implemented some different classifiers named Random Forest (RF), Logistic Regression (LR), Gradient Boosting (GB), and K-Nearest Classifier algorithms. Logistic Regression and Random Forest Classifier were performed well with 98.245% testing accuracy. Other algorithms like Gradient Boosting 91.228%, and K-Nearest 92.105% testing accuracy. We also used some different ensemble models to justify the performances. We have used Bagging LRB 94.736%, RFB 94.736%, GBB 95.614%, and KNB 92.105% accuracy, Boosting LRBO 96.491%, RFBO 99.122%, and GBBO 98.218% accuracy, and Voting algorithm LRGK with 95.614% accuracy. We have used hyper-parameter tuning in each classifier to assign the best parameters. The experimental study indicates breast cancer predictions with a higher degree of accuracy and evaluated the findings of other current studies, RFBO with 99.122% accuracy being the best performance.
Incremental Cluster Interpretation with Fuzzy ART in Web Analytics

Clustering in web analytics extracts information from data based on similarity measurement on the data patterns, where similar data patterns are grouped as a cluster. However, the typical clustering methods used in web analytics suffer from three major shortcomings, viz., (1) the predefined number of clusters is hard to determined when new data are generated over time; (2) new data might not be adopted into the existing clusters; and (3) the information given by a cluster (centroid) is vague. In this study, an incremental learning method using the Fuzzy Adaptive Resonance Theory (Fuzzy ART) algorithm is adopted (1) to analyze the underlying structure (hidden message) of the data, and (2) to interpret cluster into an understandable and useful knowledge about user activity on a webpage. An experimental case study was conducted by capturing the integrated data from Google Analytics on the University of Technology Sarawak (UTS), Malaysia, website to analyze user activity on the webpage. The results were analyzed and discussed, and it shown that the information obtained at each cluster can be interpreted in term of cluster boundary at each feature space (dimension), whereas the user activity are explained from the cluster boundary without revisiting the trained data.
Losses and gains: An investigation of Graphic Design students’ perspectives on remote learning throughout Covid-19

In March 2020, the rapid spread of Covid-19 forced educational facilities to predominantly move to remote delivery. Unfortunately, there is little evidence that visualises the struggles of teaching and learning Graphic Design from a digital perspective. This study aimed to investigate the benefits and limitations of remote operation at a Further Education (FE) College throughout the pandemic, considering the subject’s reliance on a practical classroom setting.The mixed method case study collected qualitative and quantitative data from 35 learners that were encouraged to disclose their lived experiences through an anonymous online questionnaire and focus group. Participants were given the opportunity to uncover their mental and physical emotions and experiences of the remote learning environment in comparison to that in a classroom.Thematic analysis revealed that online delivery severely devalued, demotivated and isolated students who were able to disclose their desire for personal interaction and physical instruction. This study has discovered that students now require more resources, time and support in order to thrive in the classroom environment as face-to-face learning resumes. Findings have highlighted that recorded delivery and digital resources proved to be advantageous and enrich student learning in innovative ways. Further research could explore what strategies and implementations are required after the months of Covid-19, to enhance students’ subject knowledge, advance their understanding and progress underdeveloped skills.
Online Exam Portal Using Machine Learning and Face Detection

Abstract: With the expansion of Internet and technology over the past decade, E-learning has grown exponentially day by day. Cheating in exams has been a widespread phenomenon all over the world regardless of the levels of development. Therefore, detection of traditional cheating methods may no longer be wholly successful to fully prevent cheating during examinations. Online examination is an integral and vital component of E-learning. Student’s exams in E- learning are remotely submitted without any monitoring from physical proctors. As a result of being able to easily cheat during e-exams, E-learning universities depend on an examination process in which students take a face-to-face examination in a physical place allocated at the institution premises under supervised conditions, however these conflicts with the concept of distant E-learning environment.
A Smart Walking Stick for Elderly People – PART 1 : Market Survey and Concept Generation

This paper is the first of two, which focuses on the use of walking sticks as the mobility aid tools that are used by the elderly or people with mobility limitations to maintain their balance and stability while they carry out their daily activities, enabling them to live a life of independence. With the advancement of technology, a modern walking sticks can be found aplenty on commercial platforms. However, choosing a suitable walking tool may not be easy. Ease of function, specific features and value for money are among important factors that influence potential users in choosing their walking aid. For this project, a Smart Walking Stick is developed. It is equipped with an auto fall alarm sensor and a Global Positioning System (GPS) is used to detect the location and later, send notifications to closely related people via mobile devices in the event of an emergency. This newly developed Smart Walking Stick aims to be a device that can improve mobility and accessibility. Notably, this device is also developed with the desire to contribute to Malaysia’s IR4.0 and Sustainable Development Goals (SDG) ambitions, specifically in helping people with physical difficulties that may have barriers to mobility. Along with the development of digitalization, this innovation also links the user with the Internet of Things (IoT) to increase their safety. This paper covers detailed results from the market survey conducted and concept generation for a novel walking stick. The second paper covers comprehensive design configuration and technical analysis that was performed on the prototype.
Innovative pedagogical technologies – the most important resource in modernizing the training of a modern specialist

The article analyzes the content lines of innovative pedagogical technologies, information technologies, interactive technologies, humanitarian technologies, knowledge design technologies. The purpose of the article is to consider innovative pedagogical technologies in education and to show their influence on the training of a modern specialist. The main research methodology is the need to organize the process of training specialists, ensure their social interaction in the professional environment of using innovative pedagogical technologies in education and show their impact on the training of a modern specialist based on theory. generations, the priority of project-problem-oriented learning methods associated with educational robotics. The author proved the necessity of virtual and augmented reality technology, which provides new opportunities for education and competitiveness of a specialist in the labor market, for self-expression, directing students to continuous independent work and forms self-organization, information culture in them, provides all opportunities for their own learning rhythm. The importance of telecommunication technologies in education is substantiated, the purpose of which is to increase the motivation of education seekers to study and use innovative pedagogical technologies in the workplace for the modernization of the country.
A Smart Walking Stick for Elderly People – Part 2 : Design Configuration, Technical Analysis and Prototyping

This paper is the second of two, which focuses on the use of walking sticks as the mobility aid tools that are used by the elderly or people with mobility limitations to maintain their balance and stability while they carry out their daily activities, enabling them to live a life of independence. With the advancement of technology, a modern walking sticks can be found aplenty on commercial platforms. However, choosing a suitable walking tool may not be easy. Ease of function, specific features and value for money are among important factors that influence potential users in choosing their walking aid. For this project, a Smart Walking Stick is developed. It is equipped with an auto fall alarm sensor and a Global Positioning System (GPS) is used to detect the location and later, send notifications to closely related people via mobile devices in the event of an emergency. This newly developed Smart Walking Stick aims to be a device that can improve mobility and accessibility. Notably, this device is also developed with the desire to contribute to Malaysia’s IR4.0 and Sustainable Development Goals (SDG) ambitions, specifically in helping people with physical difficulties that may have barriers to mobility. Along with the development of digitalization, this innovation also links the user with the Internet of Things (IoT) to increase their safety. This paper covers detail on design configurations and technical analysis conducted on the prototype, while the first paper highlights the results and outputs from the market survey and concept generation.
‘From Hygiene to Health’ in Modern Korea: A Narrative Review

Background: Previous research dealt with the concept of ‘hygiene’ in Korea, mainly regarding the modern transition summarized as ‘from regimen to hygiene’, has been based on qualitative research methods. However, because modern Korean language material had been constructed as a corpus with a considerable size, a quantitative approach based on language material has become possible. Through this, the provision of a new implication point can be received.&#x0D; Methods: The Corpus of Modern Korean Magazines, which is a morph-tagged corpus constructed based on 20 kinds of original texts of modern magazines in Korea, was utilized. In this corpus, a list of the co-occurred words of ‘wisaeng [hygiene], yangsaeang [regimen], geongang [health], and cheonggyeol [cleanliness]’, importantly related to the concept of ‘modern hygiene’ and the strength of co-occurrence that was calculated with the t-score, were extracted and analyzed. &#x0D; Results: Through a quantitative analysis of the corpus base, it was confirmed that the number of the co-occurred words of ‘wisaeng [hygiene]’ decreases and the strength of the co-occurrence gets low. In contrast, the number of the co-occurred words of ‘geongang [health]’ increases and the strength of the co-occurred words becomes heightened. &#x0D; Conclusion: Regarding the change after ‘from regimen to hygiene’, an analysis based on the corpus has been attempted. Through the quantitative data, the conversion into ‘from hygiene to health’ was confirmed. Research with such a method proves the need and the feasibility of quantitative research that supplements the previous qualitative analysis.
Classifying Argument Component using Deep Learning on English Dataset

The argument component is a part of argumentation mining where the study focused on the type of argument like claim and premise. nowadays there are already various argumentation components datasets, and each dataset has different argument component classes. Our focus is on the performance of deep learning architectures, especially on using contextual embedding as the initial layer of the architecture. As a validation, we use six datasets that have different argument components. And also, this research provides a comprehensive comparison among all deep learning architectures by combining multiple layers of deep learning such as combining the Bidirectional Encoder Representations from Transformers (BERT) base model or word embedding as an embedding layer with LSTM, GRU, or CNN. Related to the results of the research, will be discussed at the end of the journal. After conducting several experiments, significant results were obtained in this study with the BERT-BiGRU-CRF architecture. Povzetek: Namen te študije je določiti učinkovitost več modelov na šestih različnih delih podatkov, ki uporabljajo več transformatorjev kot kombinirano ali nezdruženo vdelano plast, kot tudi druge modele globokega učenja.
Efficient Tracking of Missing Person Using AI

For a very long time, law enforcement and search and rescue organizations have struggled to locate missing people.However, recent developments in artificial intelligence (AI) have made it possible to create more effective strategies for locating the missing person.One possible tactic is to use AI algorithms to examine a variety of data sources, like as social media posts and surveillance camera footage, in order to build a detailed profile of the whereabouts and conduct of the missing person.By combining these numerous data sources, AI systems might help detectives uncover trends and anomalies that might be indicative of the missing person's travels or activities.Face recognition has emerged as a popular and difficult problem in the image processing field, which is currently one of the technology trends.Finally, depending on past information and other important factors such as facial features, AI-powered predictive models can be created to assist authorities in anticipating where missing persons may be located.These models can support search and rescue teams.The application of AI technologies has the potential to completely transform the way missing people are tracked and located, offering quicker and more accurate findings while requiring less time and money to conduct a search.
Diagnosing Progressive Face Recognition from Face Morphing Using ViT Technique Through DL Approach

The face-morphing attack, which occurs in private, public, and governmental institutions, is one of the most well-known in today’s world. Face recognition systems tend to be vulnerable if the face images are manipulated with duplicate images. Manipulated images are combined with the original image so that the images look like legitimate ones. Several face recognition studies are being conducted to determine whether the face images are manipulated. Using the DL algorithm, the face image is trained to attain the original and morphed face images by recognizing the face images. DL algorithms determine the images by classifying whether they are morphs or not recognizable to humans. In this paper, the foremost emphasis is on diagnosing the face recognition from those face-morphed images using the different DL techniques. Different DL techniques are effectively compared, where the ViT transformer attains improved accuracy when compared to Resnet, RNN, and CNN, respectively. This paper provides an overview of the various deep learning algorithms for detecting those face recognition images that focus on challenges and issues in the facial datasets from Face Recognition Kaggle dataset with training and testing image dataset. It determines the higher contrast in image efficiency and the evaluation of the face recognized images with an improved image.
Video Analytics with FPGA based smart cameras for Object Recognition in the game of field hockey

Video Analytics is an image processing method that takes video as input and extracts information. It is the latest technology that analyzes and processes a digital video signal for real time monitoring. Some of the countries of south and eastern hemisphere are top at field hockey because they are very much developed in technology. Even though, field hockey is our national game we are not in the top because of lack of technology. By using video analytics, we can analyze the player’s video and from that they can improve their performance. Not only performance analysis there are many advantages like weapon detection, missing person in stadium, replay of players, real time instant videos, scores, reconstruct story of a match, violent detection can be analyzed. The experimental setup for this work is camera with SD-card, VGA interfacing with Field Programmable Gate Array (FPGA). The SD-card communicates with FPGA using SD-card interface. Image datas stored in SD-card are read by FPGA and stored in its memory. Then that image is processed using Convolutional Neural Network (CNN). This algorithm detects the objects in an image. Then object recognition is performed for videos to make decisions. This whole processing of image is performed inside FPGA. Finally, to display the processed image in monitor, FPGA communicates with VGA interface through VGA cable. Real time videos will be displayed by increasing the frame rate. Our work is implemented in Altera DE 1 FPGA board of device EP2C20F484C7N which comes under family of cyclone II and image displayed on monitor with resolution and frequency of 1366x768-60 Hz.
Perception-Driven Graphics and Animation for User Experience

The subject of perception-driven graphics and animation for user experience is rapidly increasing, and this is being done with the intention of better catering to the requirements and preferences of users.The most recent advancements in the field, such as the use of deep learning algorithms and the utilization of virtual reality, are also discussed in this article.In this article, the primary processing stages involved in producing graphics and animation that are optimized for the user experience are detailed.In addition, a system that proposes images and animation that are driven by the user's perception is shown.We address how perception-driven graphics and animation have the potential to dramatically enhance the quality of user experiences across a wide variety of situations in the final section of the study.
Implementing machine learning methods on QICK hardware for qubit readout &amp;amp; control

Quantum readout and control is a fundamental aspect of quantum computing that requires accurate measurement of qubit states. Errors emerge in all stages, from initialization to readout, and identifying errors in post-processing necessitates resource-intensive statistical analysis. In our work, we use a lightweight fully-connected neural network (NN) to classify states of a transmon system with no prior processing. Our NN accelerator yields higher fidelities (92%) than the classical matched filter method (84%). By exploiting the natural parallelism of NNs and their placement near the source of data on field-programmable gate arrays (FPGAs), we can achieve ultra-low latency on the Quantum Instrumentation Control Kit (QICK). Integrating machine learning methods on QICK opens several pathways for efficient real-time processing of quantum circuits.
Leveraging Advanced Convolutional Neural Networks and Transfer Learning for Vision-Based Human Activity Recognition

The goal of human activity recognition (HAR) is to characterize activities based on a series of observations on people’s actions and surroundings variables. The relevance of vision-based HAR research is growing, not just in terms of security and surveillance but also due to psychological interests in understanding human behavior responses. The purpose of our article is to identify a person’s movement and behaviors using modern deep learning models deployed on the UTKinect dataset. We are mostly interested in human positions such as pulling, waving hands, picking up, walking, pushing, throwing, sitting down, standing up, carrying, and clapping hands, and in this procesSs the image is treated as an object. The proposed work compares existing approaches such as YOLOv5, deep convolutional neural network (CNN), VGG-16, VGG-19, ResNet50, ResNet101, and ResNet152. The Mean Average Precision (mAP) is recorded at 96.6 % for YOLOv5 while 96.94 % accuracy is recorded for CNN.
Penyuluhan Bahaya Penipuan Online sebagai Bentuk Peningkatan Literasi Digital bagi Masyarakat

Fraud is a crime committed for the benefit of one party or oneself at the expense of another party. With the development of the digital age, fraud can be carried out online as fraud today knows no boundaries of time and place. Bago Village is one of the remote villages in Slagen District that needs to improve its digital literacy. This is evidenced by the continued online fraud in the Bago village area. To avoid these problems, a digital competence consultation can be conducted. The purpose of this advice is to avoid online scams, use electronic devices more cautiously, and improve your digital prowess when dealing with social media. As a method, after socialization, the participants are measured by a questionnaire, and the counseling method is used to evaluate the results. The survey revealed that almost everyone in Bagor Village, Miri and Slagen use social media in their daily lives. In addition, 93.94% of the counseling participants were satisfied with their counseling activities regarding online fraud and improved their digital literacy knowledge to always be cautious when using social media. Finally, 95.45% of the participants hoped that such activities would be held regularly due to the rapid development of technology. This indicates that the expansion measures have been successfully implemented and have had a positive effect.