Reviewerly/exHarmony huggingface dataset:
Detection of Mental Health Issues and Solution to Online Toxicity using Machine Learning

In modern times, the use of the internet and social media has greatly increased, leading to the emergence of online toxicity, which can have serious consequences for individuals' mental health. Machine learning techniques can be used to detect mental health issues and address online toxicity. One approach to detecting mental health issues in its early stages is to use machine learning to analyze social media posts and other online content. By training a model on large datasets of labeled text, it is possible to identify patterns and indicators of mental health issues such as depression, anxiety, and stress. To address online toxicity, machine learning can be used to identify and classify toxic content, such as hate speech or cyberbullying. This can be done by training a model on a dataset of labeled toxic and non-toxic content Once the model is trained, it can be used to identify and flag the toxic content in real-time, allowing it to be moderated or removed completely. Overall, the use of machine learning for the detection of mental health issues and the solution to online toxicity can be a powerful tool for promoting mental wellbeing and creating a safer and more positive online environment.
Hardware-friendly Activation Functions for HybridViT Models

In recent years, CNN+ ViT hybrid models have shown promising performance in computer vision tasks. To implement the CNN+ViT Hybrid model in resource-limited devices, various studies have been ongoing to address issues of parameter size and computational complexity through quantization, aiming to enable hardware-friendly low-bit integer operations. However, commonly used ViT activation functions (e.g., GeLU, Swish) inevitably require floating-point operations. To address this problem, some studies have been conducted to approximate these functions with alternatives that allow integer operations. Inspired by the Shift-GeLU approach, which approximates the GeLU function to enable integer operations, we propose and evaluate the Shift-Swish function on the MobileViT model at both software and hardware levels. Experimental results show that the hardware-level RTL design of the proposed method can reduce LUT by 63.25 %, FF usage by 87.69 %, and power consumption by 46.57 % with a minimum accuracy drop of 0.6 % compared to the baseline.
Momentum Iterative Fast Gradient Sign Algorithm for Adversarial Attacks and Defenses

Deep neural networks (DNNs) are particularly vulnerable to adversarial samples when used as machine learning (ML) models. These kinds of samples are typically created by combining real-world samples with low-level sounds so they can mimic and deceive the target models. Since adversarial samples may switch between many models, black-box type attacks can be used in a variety of real-world scenarios. The main goal of this project is to produce an adversarial assault (white box) using PyTorch and then offer a defense strategy as a countermeasure. We developed a powerful offensive strategy known as the MI-FGSM (Momentum Iterative Fast Gradient Sign Method). It can perform better than the I-FGSM because to its adaptation (Iterative Fast Gradient Sign Method). The usage of MI-FGSM will greatly enhance transferability. The other objective of this project is to combine machine learning algorithms with quantum annealing solvers for the execution of adversarial attack and defense. Here, we'll take model-based actions based on the existence of attacks. Finally, we provide the experimental findings to show the validity of the developed attacking method by assessing the strengths of various models as well as the defensive strategies.
Obstacle Avoidance Motion Using Elliptical Potential Field for Autonomous Robot

One method for obstacle avoidance motion is the potential field method, which determines a robot's movement speed and direction by assigning virtual potential fields to the target location and obstacles. Studies on the potential field method have been conducted extensively, with many defining the attractive and repulsive potential fields as spreading in a circular pattern. However, in our sequentially generated path using a circular potential field, when an obstacle appeared near the robot's side unrelated to the direction of travel, the robot made a large avoidance maneuver, resulting in unstable movement in narrow passages, for example. In this study, we developed a system for sequentially generating paths using an elliptical potential field spreading, with the aim of obstacle avoidance for autonomous mobile robots with preset waypoints. We also compared the obstacle avoidance motion in sequential path generation using a circular potential field and an elliptical potential field.
Website-Based Digital Wedding Invitation Generator Application

This research aims to answer the problems in making conventional digital invitations at Our invitation. Users can create digital invitations according to their own wishes and designs and can preview the appearance of the invitations instantly. And Admin can add new themes that Users can use. This application is made in the form of a website. The research starts from problem identification and literature study to application development using the waterfall method. The application has passed testing through the user acceptance test, and evaluation is carried out by distributing questionnaires to users with the conclusion that the application meets user needs.
Explainable Artificial Intelligence: A Study of Current State-of-the-Art Techniques for Making ML Models Interpretable and Transparent

Artificial intelligence and Machine learning are becoming more prevalent in a variety of industries, necessitating an increased the demand for systems that are capable of explaining their decision-making processes to human users. The idea behind of “Explainable AI” is to create AI systems that can offer clear and reasoned arguments for their activities they perform. This research looks on new approaches for increasing transparency and interpretability in machine learning models. Our focus is on the wide range of different XAI approaches that have been put forth, with a new focus on how broadly applicable they are. Apart from that, we will look into the significant challenges that must be overcome to support the development of ML models that are both transparent and accessible.
Development of interactive multimedia for Arabic vocabulary learning through Android applications

This study aims to develop Android -based interactive multimedia in learning Arabic vocabulary so that students can use it as an independent learning resource that can be accessed anytime and anywhere and can be used by teachers to deliver Arabic vocabulary material effectively and efficiently. This research uses research and development methods, with ADDIE development model. The result of this research is to produce Android-based Arabic vocabulary learning media products developed with construct 2 software containing Arabic language learning for classes V MIN 2 South Lampung and V MI Ma'arif Nu South Lampung. To test the feasibility of Android-based interactive multimedia by validating products made to a team of experts and obtained an average percentage score of 80.56% by media experts, 83.89% by material experts. Then the product attractiveness test was assessed by students and obtained an assessment of 90.72% with the criteria of "very feasible". So the update that researchers made with previous researchers is a product packaged in the form of an Android-based interactive multimedia application in learning Arabic vocabulary so that students can learn it anywhere and anytime, both independently and in class.
Detection of Counterfeit Currency Notes Through Machine Learning Algorithms and Image Processing

In the modern financial sector, identifying fake money is a constant struggle. In order to address this issue, this research introduces a novel technique that correctly and quickly detects fake certifications by fusing machine learning methods with image processing. The suggested method trains a machine learning model with information from photos of actual and fake banknotes so that it can recognize the distinctive characteristics and patterns that separate real and fake banknotes. Additionally, different image processing techniques are employed to enhance image quality, lessen noise, and boost the clarity of crucial features. The efficiency of the procedure and accuracy in identifying fake goods were tested using the results. The proliferation of counterfeit certifications can be stopped, safeguarding the economy and boosting public confidence in the system, with the help of machine learning algorithms and machine images.elimination of fake credentials, safeguarding the economy and boosting public confidence in the system.
Investigation of Partial Discharge Characteristics as Electric Treeing Prognostic Markers

The purpose of this paper is to pinpoint encouraging signs for electrical treeing forecast. Both pulsed sequence analytics (PSA) and phase-resolved fractional discharges analyses (PRPDA) are used. The monotonicity, prognosability, & trend ability of the feature's prognosis qualities are assessed. According to the analysis, PRPDA and PSA have different prognostic appropriateness indices. The features with the maximum appropriateness index are fitted exponentially to show how it can be used for predictive modelling and predicting the time before failure.
Generative AI-Driven Approach to Converting Numerical Code into Mathematical Functions

In the era of Artificial Intelligence (AI), the role of Generative AI models has witnessed a paradigm shift in addressing programming challenges. This research delves into the evolving landscape of Generative AI-driven code generation for programming problems, with a focus on the convergence behavior, algorithmic efficiency, learning dynamics, and code-length-performance dynamics of AI models, exemplified by ChatGPT and BARD. Through meticulous experimentation and analysis, this study elucidates key findings that illuminate the strengths and nuances of these models in code generation contexts. ChatGPT demonstrates rapid initial convergence, making it advantageous in time-sensitive scenarios, while BARD exhibits potential for precision-critical tasks. Algorithmic efficiency comparisons reveal subtle differences, emphasizing the need for further investigation into runtime influences. Learning curve analysis underscores the significance of early iterations in training, while code length-performance dynamics present intriguing correlations that warrant in-depth exploration. These insights provide a foundation for informed model selection and training strategies, while highlighting the ethical considerations and future avenues in AI-driven code generation.
Carrying out Encryption and Decryption in a Multi-Tenant Cloud Environment

The proliferation of information is increasing on a daily basis; however, it is often accompanied by issues of irrelevance and inadequate security measures. It is imperative to minimize time complexity by furnishing pertinent outcomes to users, thereby enabling them to access cloud services directly in accordance with the pay-per-use policy. The study presents an algorithm that utilizes ontology to execute encryption and decryption of input data within a cloud environment that caters to multiple tenants. The process entails the categorization of categories, attributes, and examples of unstructured language through the creation of an ontology that pertains to a particular field. The ontology has been developed utilizing the Protégé ontology editor and facilitates the retrieval of pertinent information from cloud service providers through a pay-per-use model.
Conventional Neural Network for Maths Handwritten Digit Categorization

Handwritten digit recognition plays a crucial role in various sectors such as document processing, automatic form identification, and postal automation. This task involves accurately identifying and classifying handwritten digits into their respective numerical values. This presentation provides a summary of current developments in machine learning techniques for handwritten digit recognition. The primary objective of this research is to develop an advanced system for recognizing handwritten digits by harnessing state-of-the-art machine learning techniques. To train and test the suggested system, a sizable dataset of handwritten digits is used. We are utilizing the MNIST dataset here. To improve the quality of the input pictures and guarantee uniform feature representation, preprocessing techniques like image enhancement and normalization are used. Experimental results indicate that the CNN model surpasses all other investigated techniques in accuracy, boasting an impressive accuracy rate of over 98%. The capacity of the CNN model to automatically deduce pertinent characteristics from the input pictures, catching both local and global patterns, is credited with its better performance. Additionally, the model can successfully handle spatial relationships in the digit pictures because to CNNs' usage of convolutional layers. A number of practical applications, such as computerized form processing, digital document analysis, and intelligent character recognition, are possible with the suggested handwritten digit recognition system. The CNN model's high accuracy enables accurate digit categorization, enhancing efficiency and accuracy across a variety of applications. Future research can concentrate on investigating ensemble approaches, data augmentation methods, and fine-tuning techniques to improve the handwritten digit recognition system's performance even further.
Using In Death: Unchained as case to investigate embodiment-related elements in VR game design

The research focuses on the concept of embodiment in VR game design, which is a trending topic in VR theories and applications. Currently, other investigations focus on a more broad term of VR products or a specific category of VR games, which ignores the importance of embodiment in generic VR game design. This research focuses on various approaches towards embodiment and uses the VR game In Death: Unchained as an example to demonstrate those approaches' application, effect, and limitations. To begin with, the virtual environment on embodiment in VR games like In Death will be examined, the same as the usage of players’ actions and haptic feedback designed to increase the sense of presence. Next, the limitation of VR games will be listed after comparing with other VR projects from Meta or Google, which undermine the embodiment of VR games. finally, this research is looking forward to the future and trend of development in VR games' embodiment.
Digital Currency Fees Payment Portal Using Blockchain Technology

This is a blockchain based web application, used to pay student's fees in digital currency from various department in a university. In this application an admin can enter details for the students from various departments. The admin can also check the student's fees payment status along with their basic information. The admin also creates the students profile and generate their user password. In the student's fees portal, the students can view their fees receipt, see their due date for the fees payment and pay fees through crypto currencies. The students are redirected to a secure payment portal where a QR code is shown. The code can be scanned and payment can be made to the admins crypto currency wallet. The crypto currency from the student's wallet is transferred to the admins account through secure blockchain consensus mechanism. The admin stores all the data of the students and their payment status using smart contracts. These blocks cannot be modified or deleted, this system provides only add a new block with updated data, which makes the data secure and we can find when the data is modified and by whom the block has been altered.
IoT-Based Theft Detection For Women Handbags

In this paper, we are providing handbag security against theft by detecting suspicious and unauthorized activities using Passive infrared (PIR) sensors. Nowadays, the IoT is used in many fields and has a vital role in automating the application. PIR is an electronic sensor that helps in detecting motion, with the help of IR(Infrared) radiation. This principle is used to detect strange or suspicious access in our Handbag. The PIR sensor detects the motion and on successful detection, the integrated Camera gets switched on automatically. Then a set of five pictures are clicked and stored in the memory. Once any kind of suspicious activity is detected a loud alarm, which will also be integrated into our project, beeps. To maintain the practical use of this project we are installing an ON and OFF switch, which will be purely manual, and the user will have to switch it ON when he/she enters a crowded place. To make this project more efficient, GPS will be installed in the handbag to always receive location updates of the handbag. As a future development, further optimization can be done to make the smart handbag very portable and handier.
Prediction of Defective Artifacts by Removing Redundant Metrics in Software Development Life Cycle (SDLC)

Software defect prediction (SDP) models aid software testing teams in forecasting faulty artifacts prior to actual testing. Software Development Process (SDP) models aim to comprehend previous shortcomings based on software metrics data that resulted in faulty modules. Previous research has determined that defect prediction models constructed without eliminating superfluous metrics yield unforeseen outcomes. This study examines the effect of superfluous metrics on the defect prediction performance of different software development processes. Through the analysis of five publicly available datasets containing system defects, it was determined that significant portions of the metrics examined were found to be superfluous and had only a negligible impact on predictive capabilities. The aforementioned observation leads to the inference that the development of a proficient defect prediction model necessitates the elimination of superfluous metrics from the datasets.
Body Posture Detection and Comparison Between OpenPose, MoveNet and PoseNet

Human body posture recognition has piqued the interest of numerous academics because of its crucial issues and wide range of applications. Simple posture recognition to complex behavior comprehension in surveillance systems are among the uses. The results are significant in advancements of approaches linked to body posture representation and recognition. The use cases and general framework of posture of human body identification are discussed in this research work. In this research work, we offer a method for detecting body posture in Tensor Flow for tracking correct posture of persons performing various activities. The study also underlines the benefits and drawbacks and comparison between two widely used posture estimation models.
Public Library Services in Digital Environment: A study on Innovation activities with special reference to Public Libraries in Rajasthan

Public Libraries are an essential part of the society in educating people to strengthen them as the human capital of the community. But today, public libraries are at a turning point and are struggling to survive in the contemporary world. One of the causes of this is the advent of new technologies that have changed the reading habits and learning experiences of people completely. This presents major challenges and opportunities for public library systems across the world. To address the changing needs, community challenges, and to survive in the digital age, public libraries need to be innovative. The digital era is a huge opportunity for libraries to innovate and change. Innovation has a huge significance to revive the public libraries and restore their prime position in society as a knowledge hub. By considering the same, this paper provides an overview of public library and initiatives that portray an innovative idea, approach, or service with examples of innovations that have been identified through a literature review of various research papers in the field of library science. The innovative activities can be categorized into broad areas like- collections, customer services, technology, events, and library spaces/buildings, etc. The paper also describes the role of Librarians towards the transformation of public libraries and their challenges. The present status of Rajasthan State is also described in brief by considering the telephonic interview of library professionals working in Public libraries of Jaipur, Ajmer &amp; Kota District.
DEVELOPMENT OF SOCIAL INTELLIGENCE OF FUTURE PEDAGOGICAL-PSYCHOLOGISTS IN THE PROCESS OF ADAPTATION TO THE SYSTEM OF EDUCATION AT THE UNIVERSITY

The relevance of the article is due to the high requirements of the modern education system to the level of professional training of practical teachers-psychologists. Here, as an indicator of the successful professional development of a teacher-psychologist during the period of basic vocational education, one can name the indicators of the student's adaptation to the educational process of a higher educational institution. That is, the article deals with the problem of the development of social intelligence of future teachers-psychologists in the aspect of adaptation to the educational system of the university. The purpose of the article is a theoretical understanding of the essence of the scientific content of the concepts of «social intelligence» and «adaptation to the university education system», the solution of which is carried out through the following tasks: theoretical analysis taking into account various theoretical and applied approaches in Western and domestic pedagogy and psychology, determination of structural components of social intelligence of future teachers-psychologists; determination of indicators of social intelligence future teachers-psychologists and their adaptation to the learning process at the university The article clarifies the concepts of «social intelligence» and «adaptation to the university education system». The indicators of students' adaptation to the educational «group» and educational «activity» are determined. Correlations between indicators of social intelligence of future teachers-psychologists and indicators of their adaptation to the educational process of the university on the basis of the empirical method have been identified and verified.
Soft skills – from interviews to professional activities of an accountant

An overview and characteristics of personal competencies in the professional activities of an accountant is presented. Their influence on the effectiveness and efficiency of activities of both the accountant himself and the enterprise as a whole has been determined. The role of the use of flexible accountant skills, which in turn have a significant impact on the relationships between company employees, has been identified. The advantages of successful career advancement of an accountant thanks to soft skills are considered.
Investigación y aplicación de tecnologías inalámbricas de bajo consumo energético en la Internet de las cosas

This scientific article aims to analyze the impact of the implementation of recreational activities on the learning of the English language in primary school students. As indicated in the introduction, learning a foreign language can be challenging for young students, which affects their motivation and interest in learning English. Therefore, the implementation of entertainment activities in the classroom is proposed as a way to improve English learning and motivation. Entertainment activities involve engaging students in fun and interactive ways, such as role-playing, board games, songs, stories, and other interactive activities. The study will evaluate the students' knowledge of English before and after carrying out these activities, through standardized tests and surveys. The expected results are the improvement of English knowledge and increased motivation to learn it.