Reviewerly/exHarmony huggingface dataset:
Significance of knowledge graph in SMART learning

The information available today is scattered everywhere. The traditional method of learning which is completely text-based takes more time to understand. Students must learn more in less time and also have good knowledge of the concepts which are not only present in the textbooks, but also from external sources. Visual learning techniques are the most well-known effective way to learn more information in less time. When information is presented visually, it is easy to understand the concepts and remember them for a long time. The knowledge graph is used to represent the information with the relationship between the different topics in a visually comprehensive manner. Our project aims in helping students to learn visually with the help of knowledge graphs (or concept maps). The Graphs for various subjects will be available separately and can be used by students to learn interactively. Our project is mainly focused on subjects from the computer science domain. For the knowledge graph construction, the required data is scraped from the internet using Wikipedia and Google Knowledge APIs. Natural language processing (NLP) is used to process scrapped data from the internet which is then processed using a graph database to display the knowledge graph with the data and the relationship connecting the data. All graphs are made in such a way to make a visual impact on learners such that it is easy to read and interpret.
An Hybrid Approach to Detect Malicious URL

The web site is a platform where people for their business transaction, banking, education, entertainment, and network with each other and sharing valuable information. But the huge crowd in cyberspaces are facing challenges and threats due to malicious attack. So, in this research work we proposed a HMUD (Hybrid Malicious URL Detector) an enhanced approach to detect the malicious URLs. The proposed approach is a two-phase classifier to detect the malevolent URLs. In first phase a rule-based classifier Social Media Attack Detector (SMAD) is used for classification and Enhanced Support Vector Machine (ESVM) is used in second phase to ensure the correctness of the classification. The two-phase approach improves the accuracy of the classification. The proposed approach is compared with Navie Bayes and logistic regression algorithms, the experimental research shows the efficiency of the proposed approach.
Blockchain application in preserving authenticity and enabling transparency in the approval of loans for the clients in banking system

The increase in the development of new technologies made transactions in the field of banking easy and customers can rely on quick transactions of money anywhere in the world. But when we see the process of availing loan by a customer, there are a large number of processes, and the customer has to look for the banks in person to expose his needs. Even when he is convenient in approaching the bank, there will be no transparency in sanctioning of the loan to the customer. In this model, we incorporate blockchain technology in sanctioning the loan to the eligible customer in a transparent way which takes place in a peer-to-peer network in the bank. We ensure confidentiality in preserving the customer’s data at the same time we provide transparency in providing the loan to the eligible customer as there are many false proofs increasing in the banking sector. Our model is carried out using the features of a consortium blockchain with the effective use of the consensus such as Proof of Work (PoW) and Proof of Authority (PoA) in authenticating the customers and loan sanctioning
Automatic Detection of Cyberbullying on Social Media Using Machine Learning

The prevalence of digital media is expanding every day as technology advances. Individuals in the 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century are growing up in an internet-equipped society. Digital media presents a lot of opportunities, but people frequently abuse it. Social media, for instance, is used to spread hatred for a person. Individuals are impacted by cyberbullying in various ways. It has an impact on more than just health; there are numerous other factors that put the victim’s life at risk. Cyber-bullying is a widespread modern event that an individual cannot completely avoid but can prevent. The method discussed in this study uses supervised-machine learning and neural networks to identify and stop cyber-bullying while considering its important attributes, such as the desire to harass a target time after time and the use of hate speech or abusive language. The models used in this study include 1-D Convolutional Neural Networks, Support Vector Machine (SVM), Logistic Regression (LR), and a Logistic Regression and Support Vector Machine (SVM) ensemble. The feature extraction methods used are Term Frequency-Inverse Document Frequency (TF-IDF) and N-grams. The optimizers used to fine-tune the models are also discussed. The method focuses on the recognition of text that is considered to be cyberbullying as well as the themes or categories that are considered to be cyberbullying, such as racism, sexual, physical mean, profanity, and many others. Results indicate that the Logistic Regression (LR) model with TF-IDF feature extraction and Stochastic Average Gradient (SAG) optimizer, outperforms existing models and methods used in the experiment, for identifying cyberbullying in text data.
Low to High Dynamic Image Reconstruction

To acquire a wide dynamic range, High Dynamic Range (HDR) photographs are difficult for the majority of people to access since they need to be created with pricey tools that can function outside the parameters of conventional camera sensor technology. Multiple exposure fusion (MEF) methods that create multi-exposure Low Dynamic Range (LDR) images have been developed to eliminate these limitations. An HDR image is produced by combining the photographs. The HDR picture is created by combining features with wider brightness ranges with the suggested multi-stage deep learning network, which brightens bright regions and darkens dark ones. Use the deep learning technique presented in this research study to separate the bright and dark regions of a large number of LDR photos and reconstruct them into an HDR image. When a certain detail is absent from the images that were taken at the crime scene, this strategy might be employed in a criminal investigation. The proposed approach complies with the tone mapping method. The proposed method produces an image with properties of High Dynamic Range photo with increased exposure and brightness of the highly dense pixels.
Sentiment Analysis and Exploratory Data Analysis on the Impact of COVID-19 Using Tweets

Sentimental analysis is an important concept for any occasion, administration or item delivered. To comprehend the feelings of individuals dependent on the effect of COVID-19 occurring across the globe utilizing tweets, this paper predominantly centres around sentiment analysis and text mining of twitter information utilizing R programming, which assists with extracting tweets where conclusions are profoundly unstructured, heterogeneous and impartial at times
Gesture based Sign Language Recognition System

The use of hand gestures in sign language has been a crucial method of non-verbal communication, particularly for individuals with hearing or speech impairments. Despite numerous sign language systems developed by various developers worldwide, most of them are not flexible or cost-effective for end-users. This project proposes a hand gesture recognition system that can automatically detect sign language, allowing for more effective communication between deaf and dumb people and normal individuals. Gesture and pattern recognition are rapidly evolving fields that play a crucial role in nonverbal communication. Hand gestures have become an integral part of human’s daily lives, and the Hand Gesture recognition system offers a natural and approachable way of communicating with computers, which people are more familiar with. The proposed system combines several cutting-edge technologies such as MobileNet, Xception, ResNet 101, and DenseNet 121. Among the four algorithms, DenseNet 121 achieved the highest overall accuracy, with an accuracy of 98.9%. Real-time sign language detection with text-to-speech technology and the development of a Telegram bot for sign language detection are additional features of this project. The integration of these technologies has resulted in a novel and effective method of sign language communication that can enhance the lives of individuals with hearing or speech impairments.
Certificate Storage and Verification using Blockchain.

Utilizing blockchain technology can potentially affect a significant transformation in the way that certificates are verified and stored. Utilizing a decentralized and transparent database can prove a reliable and secure technique for verifying and storing records immune to fraudulent activities. Our manuscript proposes a blockchain-based system for verifying and storing certificates, offering a reliable and secure solution immune to fraud. It aims to streamline administrative procedures by removing intermediaries, expediting verification, and reducing bureaucracy. The system is adaptable and scalable, applicable to various certificate types and applications. Through experiments, its effectiveness is demonstrated, highlighting the potential of blockchain for certificate verification in education. However, the extensive adoption of blockchain expertise in edification requires increased education and awareness due to its early-stage acceptance.
Intelligent Security System with Haptic Device and Random Number Generator on Touch Enabled Devices

Among the most common means of communication between haptic devices and humans nowadays is vibrating response. Important vibrational considerations must be looked into and used to haptic devices in order to precisely and effectively communicate a greater variety of data. In this article, we present a cutting-edge and reliable defense against side-channel attacks that aim to steal the PINs and other locker credentials. To strengthen the resistance to surveillance assaults, some PIN-entry mechanisms for portable devices based on audios and/or haptics have been created. Unfortunately, none of the PIN-entry systems currently in use can successfully combine high usability with strong protection against surveillance threats. In this article, we suggest Loc-Hap PIN, a new PIN-entry system that is immune to observation assaults and works with touchscreen devices that offer localised haptic feedback. The usability and resilience to observation attacks are enhanced by the localised haptic feedback technology. By including a haptic feedback mechanism into the proposed system, we will increase resistance against observation attacks. In order to inform the user of the internal status of the Keypad, our proposed solution makes use of the fact that the effect of haptic feedback can be easily concealed, making direct observation techniques and indirect sensing techniques ineffective.
Session Recording in Android

Session recording is a valuable tool for developers to gain insights into user behavior in mobile applications. Android, as the most commonly used mobile operating system, offers developers several options for implementing session recording. This paper provides an overview of session recording on Android, outlining its benefits, implementation, and potential privacy concerns. By recording and analyzing user sessions, developers can gain a better understanding of which features are most frequently used and where users may be experiencing difficulties. However, privacy concerns can arise with session recording, and developers must be transparent about their use of it and comply with relevant privacy regulations. This study suggests a native implementation of session recording without the use of third-party services, giving organizations complete control over their data and helping to address privacy concerns. Additionally, the study covers performance metrics that are most appropriate for user-friendly session recording.
Product similarity detection in E-Commerce website

E-commerce websites are becoming increasingly popular, and with the abundance of products available, it can be challenging for customers to find what they are looking for. One approach to address this issue is by providing product recommendations based on their similarities. In this paper, we propose a product similarity detection system using Generative Adversarial Networks (GANs). Our system generates a latent representation of the product images, which are then used to compute a similarity score between two products. We trained our GAN network on a dataset of product images, and we evaluate our system's performance on a separate test set. Our results demonstrate that our proposed approach outperforms traditional similarity measures such as cosine similarity and Euclidean distance. The proposed system can be integrated into online business sites to give clients precise and customized item proposals, ultimately improving their shopping experience.
A Survey of Prominent Image Processing Research on Needle-type Instrument Reading on Aircraft

Image processing methods can be a useful tool for detecting and analyzing aircraft panel values in order to ensure the safe and efficient operation of the aircraft. This short literature review features the motivation and methods used in cockpit dashboard image processing and the unique setbacks of dynamic flight environments such as lighting and vibration. Another objective is to identify methodology from other needle-type instrument reading research to potentially transfer to this scarcely explored application area.
A Role-Based Access Control Management Model on Blockchain for Restricted Facilities: An Airport Example

Since numerous firms and governments are in dire need of safeguarding their premises, the confidentiality of data is of utmost importance in achieving this goal. The security of facility entry is crucial, but it's also crucial for future demands that the entrance logs are auditable. Due to these reasons, restricted locations require access control and round-the-clock management. A centralized approach for administering grants and monitoring access is a simple choice as it is among the most frequently employed ones. In this respect, airports are deemed as being one of the safest places since every airport provides certain public and security services and accession to these restricted facilities must be controlled and authorized by the airport management organization. Unchangeable, auditable and always-available access records and open management in accordance with its confidentiality necessities are all answers to the issues experienced in the sector with regard to access control. In this work, a role-based access control strategy for regulating airport entrances that are scattered and require government oversight is presented and implemented using the Ethereum blockchain. Our obtained results demonstrate the adaptability and also utility of our proposal for other restricted facilities that have similar requirements.
Recognizing and Tracking Person of Interest: A Real-Time Efficient Deep Learning based Method for Quadcopters

The recognition and tracking of a person of interest is a crucial task in many applications, including search and rescue, security, and surveillance. This paper presents a distributed system architecture that leverages the asynchronous threading and communication property of ROS2 to develop and implement a real-time efficient Deep Learning (DL) based method for recognizing and tracking a person of interest. The DL model receives snapshots from the quadcopter's camera and sends back an information vector, which includes all recognized persons and their corresponding position information within the camera frame of the quadcopter. The person of interest tracking control system receives face set information about the person of interest and generates reference velocity signals to be tracked by low-level controllers embedded within the drone. Experiments conducted in a cluttered and complex environment demonstrate the efficiency of the DL-based architecture for quadcopters. The presented real- world results validate the effectiveness of the proposed approach in recognizing and tracking a person of interest. The experimental video is available at https://youtu.be/i7bYXnRy8Vc.
An Immutable Navigation Database on Blockchain

The Flight Management System (FMS) minimizes the crew's workload while simultaneously lowering human-related errors. By merging aircraft navigation and sensor data to offer primary navigation, flight planning, and route determination, it also improves situational awareness. The navigation database (NavDB) is one of the key data sources and feeds the FMS with ARINC424 standard data format. Updating and tracking changes in the NavDB are issues, as is the demand that it be immutable and always available. Because of its intrinsic properties of resilience to change and single point failure (SPoF), blockchain is an emerging technology that can serve as storage for NavDB. In this study, a blockchain-based approach for managing and storing NAVDB is proposed and evaluated since it is the primary data source for the FMS. Our obtained results demonstrate that a navigation database can be stored on the blockchain efficiently and used in real-time in the aviation domain.
Analyzing the Effects of Visual Based Tracking Algorithms on Classical Guidance Methods

This paper examines the effects of target position and velocity estimation accuracy obtained by visual-based tracking algorithms on the performance of classical guidance laws. Present deep learning-based visual MOT (Multiple Object Tracking) algorithms use a linear Kalman Filter to estimate the motion of moving objects. Thus, these methods require high-frame rate videos to accurately track the objects and apply the prediction-update steps sequentially since they do not rely on prediction results if the measurement is unavailable. As a result, employing the linear Kalman Filter give rise to the objects being untracked or lost due to occlusion in a short time window. For this reason, we utilized Unequal Dimension Multiple Model (UDIMM) based motion estimation filter in deep learning-based visual MOT to increase the estimation accuracy of a moving target. By doing that, we keep providing highly accurate state estimations to our quadcopter while the target is occluded and maneuvering. The study presents the performance of guidance methods such as True Proportional Navigation (TPN), Pure Proportional Navigation (PPN), and Effective Pure Proportional Navigation (EPPN) as a result of Monte Carlo (MC) simulations when employing both linear and UDIMM filters.
Blockchain-Based Safety and Efficiency Approach to Air Traffic Management

The aviation sector has been leading the way in implementing new technologies and is currently adopting blockchain technology. Blockchain is a revolutionary technology that enables secure data encryption and storage through a distributed ledger system. This system ensures that multiple nodes can access the data hashes at any time. Any tampering or modifications result in a new hash, making it easy to verify the authenticity of data files and track the node where the changes occurred. The use of blockchain technology holds great promise for digital data security, particularly for digital transactions involving information or money. Like other industries that exchange information between multiple stakeholders, the aviation industry has found many applications for blockchain technology. Although the industry is progressing in adopting this technology, there is still a long way to go. This paper examines the possible application and adaptation the blockchain technology to air traffic management from a safety and efficiency perspective.
Deep Reinforcement Learning for Trajectory Generation and Optimisation of UAVs

In recent years, the rapid advancements in Machine Learning have led to substantial research in control systems for autonomous aerial vehicles. Particularly, Reinforcement Learning (RL) has attracted a lot of attention for the design and development of such control algorithms. This paper examines the control issues of autonomous flight and how these are addressed using RL approaches. The objective is to investigate how RL algorithms like Deep Deterministic Policy Gradient may be applied particularly for control actions in an unmanned aerial vehicle (UAV). This learning paradigm acts as a mechanism that continuously generates policies for tasks such as attitude and position control, which converges into an optimized trajectory. As an outlook, the application of Continual Reinforcement Learning is proposed. This is a novel RL methodology that holds the potential to advance the control system of a UAV operating in dynamic, unknown environments with the ability to reapply learnt behavior and flexibly adapt to new situations.
Agile Methods on Flight Tests

This study discusses the usage of agile methodologies in flight tests. Agile methods can provide certain advantages for flight tests. A literature review was done to evaluate the usage of agile methods on non-software projects and a flight test case study was examined. This study presents a scrum-based agile model to use on flight tests based on the research done.
Decision Probability based Immune Plasma Algorithm for Path Planning Problem of Unmanned Aerial Vehicles

The increasing operational capabilities of modern unmanned aerial vehicles and their military counterparts focused researchers' attentions for solving complex problems to do with them optimally. In this study, Immune Plasma algorithm (IP al-gorithm or IPA) that is a recent intelligent optimization technique was modified by integrating newly designed plasma transfer schema and treatment continuation procedure and then decision probability based IPA (decIPA) was introduced for solving path planning problem of the mentioned aerial vehicles. In order to investigate how the improvements made on the fundamental operations of IPA change the qualities of the discovered paths, a set of experiments and comparative studies were carried out. The comparative studies between decIPA and other path planners allowed to state that decIPA is capable of planning more robust and safe paths especially for the test cases with high number of segmentation points.
A Multi-Level Blockchain-based NodeAuthentication Approach for UAV-assisted WirelessSensor Networks

The majority of Wireless Sensor Networks (WSNs) are resource-constrained, hence several research have been done to make the most of these networks with a particular focus on the data collection requirements under challenging circum-stances. Since these networks are among the essential elements of Internet of Things (IoT) applications, energy-efficiency and security aspects of these networks have become the center of gravity for current research. While mobile agents have joined the scene to reduce energy consumption, Blockchain has recently made an appearance as an alternative to the centralized security solutions. This paper introduces a multi-level blockchain-based node authentication approach for Unmanned Aerial Vehicle (UAV)-assisted WSNs, where UAVs serve as mobile base stations for data collection. When utilized, the proposal will allow for secure data transmission from a WSN leaf node to the IoT. Furthermore, simulation results indicate that by employing this lightweight authentication method, duration for chain creation and chain size can be decreased significantly,