Reviewerly/exHarmony huggingface dataset:
Augmented Reality (AR) as a New Educational Medium for Children: Using Road Safety Content

Road accidents, often involving one or more vehicles, can result in property damage, injuries, or even fatalities. These incidents can occur due to a range of factors, including human errors, adverse weather conditions, or mechanical failures, despite precautions taken by road users. Consequently, early education on road safety is crucial, especially for children. This research focuses on assessing children's knowledge of road accidents in Malaysia and evaluating the impact of augmented reality (AR) experiences on their understanding. To support this project, we developed an AR application using the Pendidikan Keselamatan Jalan Raya (PKJR) Year 2 Guidebook as a medium for students to learn about road safety. Selected pages in the book serve as markers, enabling students to engage with AR content while using the book. The study aims to determine the effectiveness of the AR application as an educational tool for children. A group of school children participated in testing this AR application, and their experiences were assessed using an adapted Game Experience Questionnaire (GEQ) model. The results indicate that after using the AR application, the group of 8-year-old children exhibited improved awareness of the road environment. This paper also explores the emotional aspects of participant experiences as emphasized by the GEQ. These findings highlight the potential of AR as an innovative tool for educating children about road safety, challenging traditional approaches, and promoting safer road behaviors among young individuals. Furthermore, this project aligns with the strategies outlined in the Malaysian Road Safety Plan 2020-2030, particularly in reducing risks to pedestrians. In a broader context, this study contributes to the evolving landscape of technology-driven safety initiatives, underscoring the importance of adapting contemporary educational mediums to address complex societal issues like road accidents. In conclusion, we discuss the potential for future studies involving road safety education and interactive technology.
Data Privacy Practices of Private Higher Education Institutions in Malaysia: A Preliminary Study

Private higher education institutions as data users are subjected to the requirements of the Personal Data Protection Act 2010 (PDPA). These institutions process employee data as well as data of potential students, active students and alumni. They also deal with data of third parties such as vendors, visitors and contractors. Ten years after the coming into effect of the PDPA in 2013, the education sector has yet to develop their personal data protection code of practice as required by the Act. The General Code of Practice (CoP) of Personal Data Protection was introduced in December 2022 with the objective to provide guidelines to the Class of Data Users who have not prepared a Code of Practice and there is no data user forum to develop the relevant Code of Practice for the Class of Data Users. As the General CoP is legally binding, it is an offence punishable under the Act for any data user for failure to comply with any provision of this General CoP. As data users, private higher education institutions need to introduce certain mechanisms to adhere to the requirements such as privacy policy and procedure. This paper aims to compare the data privacy practices of private higher education institutions in Malaysia in order to determine to what extent the law has been complied with. Being a qualitative study, this paper applies content analysis technique. Data privacy policies of four private higher education institutions in Malaysia were examined to attain the objective. The four private higher education institutions are Universiti Tenaga Nasional (UNITEN), Universiti Teknologi PETRONAS (UTP), Taylor’s University and University of Nottingham Malaysia. The data privacy policies of the four institutions are accessible on the official website of the institutions. The study indicates that in the absence of a personal data protection code of practice for the education sector as a guideline, the data privacy practices of the institutions vary from one to another. While some of the privacy policies contain provisions which are general in nature which may lead to confusion to the data subjects, the data privacy policies show that the four institutions have, to a certain extent, complied with the requirements of the PDPA in general.
Aspect Based Sentiment Analysis: Feature Extraction using Latent Dirichlet Allocation (LDA) and Term Frequency - Inverse Document Frequency (TF-IDF) in Machine Learning (ML)

The growth and development of social networks, blogs, forums, and e-commerce websites has produced a number of data, notably textual data, which has increased tremendously. Twitter is one of the most popular media social platforms; during the COVID-19 pandemic, people all around the world use social media to share their opinions or concerns about the pandemic that has changed their lives. It revealed a significant rise in tweets on coronavirus, including positive, negative, and neutral tweets about the virus's impact. Sentiment analysis faces challenges: sparse data limits understanding, while topic coherence and interpretability demand improvement for clearer insights. The primary goal of this paper is to improve the accuracy and effectiveness of sentiment analysis during the COVID-19 pandemic through the application of advanced techniques and classifiers. In this article, we experiment with such Support Vector Machines (SVM) and Naive Bayes (NB) on Twitter data for high-accuracy machine learning models. Using Latent Dirichlet Allocation (LDA)for feature extraction, we aim to capture comprehensive aspects and topics for sentiment analysis. Additionally, we explore Count Vectorizer and Term Frequency - Inverse Document Frequency (TF-IDF) as word embedding techniques. The main objectives are to extract topics, understand public concerns about Covid-19, and compare classifier performance in Aspect-Based Sentiment Analysis on Covid-19 tweets. This paper introduces advanced sentiment analysis techniques, such as LDA, Count Vectorizer, and SVM, enhancing nuanced sentiment analysis during the COVID-19 pandemic with notable 85% accuracy in SVM classification.
A Robotic Approach for Detecting Alive Human Beings during Conflict Situation

The robotic car is designed for high-risk environments, employing a dual sensor system with Passive Infrared (PIR) and ultrasonic sensors. It distinguishes between alive and non-alive entities, triggering alerts and a 30-second pause for any instant actions, on detecting alive human beings. While simply acting as an obstacle detector if a non-living entity is detected, where it utilizes its ultrasonic sensors to navigate, turning right if space is available, left if not, and backwards if not even in the front. In cases of no available space, the robot continuously rotates 360 degrees. This versatile design finds applications in search and rescue operations, military reconnaissance, border patrol and security, offering precise and timely responses in dynamic environments.
Recommendation System to Predict Best Academic Program

Schools in India have multiple educational paths however it is a hassle to choose the proper path because here the performance of the students determines which education path they should join in the future and not their interests. This paper aims to propose a recommendation system that would predict the most suitable path for a student in their future ventures by utilizing the collaborative filtering approach. The system is designed to be easy to use and can adapt to different needs. By combining helpful suggestions with learning support, in this paper we aim to improve the overall learning experience for everyone. Our paper also deals with how we took care of major challenges including limited data, environment stability, and function implementation struggles with smart solutions. In addition to this approach, we also explored significant emerging solutions including advanced feature engineering and Natural Language Processing which has the potential to enhance our system positively in the future. Our system prioritizes the students’ choices carving a new road in the future of educational history.
Optimizing Cloud Data Centers for Environment Sustainability through Performance-Aware Virtual Machine Allocation

Cloud computing faces challenging issues concerning high-energy consumption and service-level agreement (SLA) violations, particularly as the demand for computational power continues to grow, necessitating large-scale cloud data centers. Existing energy-aware approaches tend to focus on performance optimization during VM migration phases but it can be effectively maintained at VM allocation level as preprocessing. Thereby, this paper proposed Energy Efficient SLA-aware virtual machine Allocation approach using a metaheuristic Firefly algorithm (FFO-EESA). The experimental findings showcase promising outcomes in the context of dynamic workloads and varying numbers of VMs (ranging from 1 to 290) allocated on individual hosts. The proposed method’s experimental results were obtained through computer-based simulations conducted in MATLAB version 2017(a). The outcomes showed that, there was a reduction of 12.34% in migrations and a saving of 4.36% in idle hosts with the FFO-EESA approach. proposed algorithm resulted in an average energy savings of 4.39% compared to the FF and MBFD techniques.
Analyzing Customer Sentiment in Drug Reviews Using Natural Language Processing

The continuous expansion of the internet has led to a surge in user-generated content. Patients frequently share feedback about medications they have taken to express their experiences and raise public awareness. The application of sentiment analysis plays a crucial role in the healthcare sector, as it allows for examining these public reviews to assess the efficacy and popularity of various medications. This research presents a comprehensive sentiment analysis of customer reviews from DrugRev, a dataset of drug reviews. The study evaluates customer sentiment regarding drug effectiveness, side effects, and ease of use. We employ natural language processing using multiple machine learning and deep learning algorithms to obtain valuable insights into customer satisfaction and preferences, aiding consumers and healthcare professionals in making informed medication choices. The findings demonstrate that Bidirectional Gated Recurrent Unit and BERT outperformed all other models with an F1 Score of 92.17. The outcomes of our experiments illustrate that our system can deliver precise, effective, and scalable drug suggestions. By understanding customer sentiments, we contribute to improving healthcare decision-making and enhancing the quality of healthcare services.
Hand Gesture Based Recognition System

Hand gesture recognition procedure is a computer vision technique that allows computers to recognize human hand gestures. This can be used for a variety of applications, such as controlling devices, providing feedback, and interacting with virtual reality environments. This paper proposes a novel approach to hand gesture recognition methodology using Python scripts and cv2 (OpenCV), media pipe, and time libraries. The proposed approach uses the Mediapipe library in order to detect as well as track hand landmarks in real time. The number of fingers raised is then calculated based on the detected hand landmarks. A message is then displayed on the screen based on the number of fingers detected and even a message is sent to a registered phone number. The proposed approach was evaluated using a set of data of hand gesture images. The results showed that the proposed approach was able to accurately recognize the number of fingers raised with an accuracy of 90%. The proposed approach exhibits novelty to hand gesture recognition that is simple to implement and can be used for a variety of applications. The approach is relatively accurate, making it a promising technique for hand gesture recognition.
Recommendation Systems in Education

In the dynamic landscape of online education, the quest for effective course discovery is a multifaceted challenge. This paper investigates a recommendation system tailored to enrich course discovery on Udemy, employing a context-based, collaborative, and hybrid approach. Our system excels with a multi-dimensional course recommendation strategy, seamlessly blending three core techniques: context-based recommendations that consider user profiles and learning contexts, collaborative filtering to identify behavioral patterns and hybridization for fine-tuned suggestions aligned with immediate learning needs and user community insights. The paper delves into evaluating recommendation systems using appropriate metrics and benchmark datasets, addressing challenges like limited data and initiation hurdles with practical solutions. Looking forward, we explore the potential of emerging technologies, including deep learning and reinforcement learning, to further enhance recommendation algorithms. Our vision prioritizes personalization, diversity, and transparency, shaping the future of course recommendations on Udemy.
Design Evaluation and Uses of Paraphraser Content Generation

Paraphrasing is a challenging and important job in NLP, which involves generating alternative versions of text that preserve the original meaning. Paraphrasing can benefit many NLP applications, such as summarization, translation, and sentiment analysis, by improving the diversity, quality, and readability of the generated text. However, paraphrasing is not a trivial task, as it requires a deep understanding of the semantics and syntax of the text, as well as the ability to produce fluent and natural language. Existing paraphrasing tools are often limited in their scope, accuracy, and flexibility, and they cannot handle complex and diverse texts. In this project, we present Paraphraser, a novel and comprehensive software tool that can perform paraphrasing on any text, using advanced natural language processing techniques, deep learning models, and rich language resources. Paraphraser can generate multiple paraphrased versions of text, ranging from subtle rewording to significant revisions, while maintaining the real meaning and style. Paraphraser can also optimize the paraphrased text for various purposes, such as avoiding plagiarism, ensuring message clarity, and reaching a wider audience. Paraphraser is a versatile and reliable tool that can be used in various domains, such as content marketing, academic writing, and SEO optimization, enhancing the originality and attractiveness of textual content. We demonstrate the effectiveness and usefulness of Paraphraser through various experiments and evaluations, and we show how Paraphraser can improve the performance of various NLP tasks and applications.
A Secure Text Steganography using Randomized Mathematical Functions and LSB

In the realm of information security, hiding data within ordinary images, known as text steganography, has become a crucial method for secret communication. The Least Significant Bit (LSB) technique is a popular way to embed text within images. It works by tweaking the least significant bits of image pixels to represent hidden text in binary form. This alteration is subtle, ensuring the image looks normal and enabling efficient sharing through various platforms. This research paper introduces a creative expansion of LSB steganography. It utilizes LSB steganography for replacing the Least Significant Bits of the carrier image with the binary of the encoded text after the text has been passed through various mathematical functions like polynomials, integrals, and differentials. These functions are called randomly to encode text. Thus, enabling randomicity in the encoding of the text allows a secure and efficient way of encrypting and decrypting the text needed.
Advancements in Fall Detection Technologies: A Comprehensive Review and Innovative Solutions

The aging population is facing an increasing risk of accidental falls, making fall detection technologies crucial for public health and the well-being of elderly individuals. This paper provides a comprehensive research of sensor-based fall detection and rescue systems, categorizing them into single sensor-based and multiple sensor-based solutions. It explores the integration of Machine Learning (ML) and the Internet of Things (IoT) to address the challenges of real-time monitoring, high accuracy, and minimizing false alarms in fall detection. In the quest to enhance fall detection accuracy, innovative solutions are proposed, including the use of Convolutional Neural Networks (CNNs) for efficient event classification. CNNs, originally designed for image analysis, exhibit impressive performance in processing time-series data from various sensors, such as accelerometers, gyroscopes, and ambient environmental sensors.These sensors, part of an IoT network, collect real-time data and transmit it to the CNN model, enabling rapid detection and response to fall incidents. The integration of contextual data from video and audio sources further aids in distinguishing real falls from other events generating similar vibrations. This innovation demonstrates remarkable mean sensitivities and specificities, addressing issues of computational demands and resilience. An accompanying mobile app complements the wearable system, enabling automatic fall detection and immediate alarms, which is vital for timely medical intervention. Continuous calibration and online learning mechanisms are also emphasized to ensure adaptability to changing environmental conditions and performance improvement over time. This comprehensive review and innovative solutions pave the way for more advanced, proactive, and accurate fall detection systems, offering a practical and promising approach to mitigate the consequences of accidental falls among the elderly.
Mitigating Bias in Image Classification - Pretraining on Diverse Data for Improved Generalization

Deep learning models have demonstrated remarkable abilities to learn complex patterns and concepts from the data they are trained on. However, recent studies have also revealed that these models can inherit and amplify the biases that exist in the data, such as those related to gender or race. These biases can pose serious ethical issues and need to be addressed. One possible solution to reduce these biases is to pretrain the models on large and diverse datasets that cover a broad spectrum of scenarios and features. In the current study,, we investigate the effect of pretraining and transfer learning on a biased dataset that we created using a subset of the celebA dataset. The dataset is biased towards people with black hair smiling and people with blonde hair not smiling. We use the new YOLO V8 model to classify the images based on whether the person in the image is smiling or not. The paper conducts a comparative analysis between a model that underwent no prior pretraining and a model pretrained on the ImageNet dataset. We find that the pretrained model has a lower bias and also a higher accuracy than the non-pretrained model. Our findings support that pretraining has the potential to reduce the bias in computer vision tasks while enhancing the model’s generalization capabilities.
MLing-Net: A computationally inexpensive deep neural framework designed to perform multilingual image document classification

Multilingual image document classification has become an emerging research topic in the present era because of immense social, economic, and cultural perspectives. It supports economic globalization by aiding multinational companies in streamlining operations, including documentation and marketing, across regions with distinct languages. It enhances market reach by catering to a global audience with diverse linguistic preferences. Despite real-world significance of this research area, it remains less explored, leaving numerous aspects yet to be investigated. To mitigate this drawback, in this work we have designed a computationally inexpensive light-weight deep neural network framework namely, MLing-Net to perform multilingual image document classification. Apart from designing this novel deep learning framework, another significant contribution of this work lies in the integration of five new activation functions as replacement of existing popular activation functions in the designed network. These new activation functions are designed by leveraging the advantages of existing activation functions. The effectiveness of the proposed activation functions as well as novel MLing-Net is validated using popular multilingual historic image document databases and the excellence of the newly designed activation functions and network over their existing counterparts is established using quantitative parameters like Test Accuracy, Precision, Recall,F1 score and Total parameter count.
A Temporal Action Detection Model Based on Deep Reinforcement Learning

Existing methods for temporal action detection typically follow a two-stage approach, generating numerous action proposals at various scales on untrimmed long videos. These proposals are then used with a pre-trained action classifier to estimate the action type and accuracy for each proposal. This method not only generates a large number of irrelevant proposals, leading to significant computational overhead, but also deviates from the human perception process. This paper solves the temporal action detection problem from a human cognition perspective, considering it as a process of observing and refining proposals to locate temporal actions. The paper introduces a deep reinforcement learning-based Temporal Action Detection model which leverages deep learning networks to understand the temporal information in video sequences. Through reinforcement learning, the agent learns strategies to change the position of proposals, aiming to locate different temporal actions. The Action Buffer module records the actions performed by the proposals, the Proposal Regression Network refines the position deviation between the predicted results and labels, ensuring more accurate results. Correct proposals are stored in the Proposal Buffer module to avoid redundant predictions. Experimental results on the THUMOS'14 dataset confirm the accuracy of the model, achieving excellent results in metrics such as AP values and recall.
Indian Sign Language Classification using Advancement of CNN

In our increasingly digital world, addressing the communication needs of Indian Sign Language (ISL) users is a pressing challenge. This research focuses on developing an advanced Convolutional Neural Network (CNN) model for real-time ISL gesture classification. The paper aims to design a CNN-based system capable of recognizing and classifying a wide range of ISL gestures, involving convolutional layers, pooling layers, and fully connected layers. The primary objective is to provide an innovative and efficient solution for real-time ISL gesture classification, empowering the hearing-impaired community. The novelty lies in leveraging CNNs to decode the complexity of ISL gestures in various media, a milestone in the field. Our analysis indicates that the proposed methodology outperforms existing techniques with an accuracy of 91%, a precision of 92%, a recall of 90%, and an F-measure of 91%, demonstrating the robustness and effectiveness of the CNN-based approach. In summary, this research presents a pioneering solution for real-time ISL gesture recognition, utilizing CNNs to offer efficient communication tools, enhancing accessibility and inclusivity for the hearing-impaired.