Reviewerly/exHarmony huggingface dataset:
Intelligent Internet of Things Attack Detection: Novel Approaches and Technologies

The Internet of Things (IoT) influences numerous facets of our daily lives, and artificial intelligence (AI)-based programs and applications are rapidly gaining popularity. Due to the vast adaptability of the IoT and the escalating data privacy concerns, classical AI algorithms may not be applicable in certain practical application domains. Federated learning (FL) has emerged as a viable technique for training distributed machine learning (ML) models while maintaining data privacy and security. In recent years, it has gained attention in the context of IoT security, particularly for detecting attacks on IoT devices and networks. In this article, we examine ML and DL in the context of the IoT and IoT attack detection. Then, we evaluate the most recent FL applications in IoT networks and IoT attack detection, investigating their potential to deliver a variety of IoT services, including security and privacy. Our work on detecting FL-IoT attacks has taught us a lot, and at the end, we suggest some directions for future research in this quickly growing field.
A Novel Approach for Building Domain-Specific Chatbots by Exploring Sentence Transformers-based Encoding

Chatbots have gained significant attention in recent years due to their potential to automate various conversational tasks. In this research paper, we explore the effectiveness of two different models, the Term Frequency-Inverse Document Frequency (TF-IDF), and the Sentence Transformers model for developing a restricted domain chatbot. TF-IDF captures the importance of words within a domain-specific knowledge base, aiding in query understanding and information extraction. However, sentence transformers encode sentence meaning and semantic similarity, enabling the generation of contextually coherent and relevant responses. The primary objective of this research study is to develop a chatbot architecture that can deliver accurate and pertinent answers to users’ queries in a conversational manner. To evaluate the effectiveness of our approach, we conducted experiments on a domain-specific dataset containing question-answer pairs. The results demonstrate that the sentence transformer model outperforms the traditional TF-IDF model in terms of different evaluation measures. The sentence transformer encoding exhibits superior capability in capturing semantic context and generating contextually relevant responses, leading to a more natural and engaging conversational experience.
Task-Oriented Semantic Communications for Speech Transmission

Semantic communications execute intelligent tasks at the receiver by only transmitting necessary information. In this paper, we introduce TOS-ST, a task-oriented semantic communication system for speech transmission, which efficiently serves the semantic tasks at the receiver, including speech-to-text translation and speech-to-speech translation. Particularly, TOS-ST condenses the input speech in the source language and extracts the task-related semantics features prior to transmission. At the receiver, these features are recovered and utilized by the neural network-based semantic preserver and machine translation module to generate the uncorrupted text in the target language. To perform the speech-to-speech translation task, the translated text passes through a sophisticated neural network to obtain speech in the target language. According to the simulation results, the TOS-ST outperforms conventional speech transmission systems and exhibits higher robustness against channel impairment.
Application of Blockchain Technology for Power System Security using Condition Number

Block chain technology secures electricity generation, transmission, and distribution. Contingency analysis helps assess and improve power system security. Contingency analysis predicts transmission line failure, equipment failure, and other disturbances to ensure energy system reliability. The power system security study, including assessment and augmentation, will use a lot of data from contingency analysis. Contingency analysis predicts transmission line failure, equipment failure, and other disturbances to ensure energy system reliability. The system's operation and maintenance depend on contingency analysis data. To make good decisions, public officials require these facts. This article proposes using blackchain technology for contingency analysis and data sharing. By modifying gearbox system characteristics, the condition number will improve power system controllability, stability, and transfer. Blockchain can protect power system data and ensure an uninterrupted power supply. The Python programmed developed and MATLAB software is used to generate the data under contingency condition. In this case UPSEB 75 bus system is considered for the study.
Blockchain-based Health Insurance Model Using IPFS: A Solution for Improved Optimization, Trustability, and User Control

Concerns about privacy and security of data have increased alongside the digitization of healthcare services. Blockchain technology Due to its decentralized and unchangeable nature, may be able to solve these problems. To improve transparency and user control in health insurance, this study offers a blockchain-embedded architecture that makes use of the InterPlanetary File System (IPFS). The suggested system stores and manages health insurance data using the distributed ledger of the blockchain. This guarantees the integrity of the data and its accessibility to all stakeholders involved. Integrating IPFS paves the way for decentralized, efficient data storage and retrieval, which can increase data accessibility and availability. One of the most important features of this strategy is giving people more control over their personal health insurance information. Users can securely store their personal health information and grant permission-based access to insurers and relevant stakeholders. This user-centric approach allows individuals to actively manage their data, ensuring privacy and fostering trust within the health insurance ecosystem. The study facilitates optimization in health insurance operations. Through blockchain’s consensus mechanisms and smart contracts, The Proposed model enables automated and streamlined processes, reducing inefficiencies, fraud, and error rates. The immutability of the blockchain ensures data integrity, minimizing the risk of unauthorized modifications or tampering the proposed blockchain-embedded model offers several benefits, including improved trust, transparency, privacy, and operational efficiency within the health insurance domain. By integrating blockchain and IPFS, this study presents a robust and secure framework that empowers users, instills trust among stakeholders, and optimizes the health insurance ecosystem. This study contributes to a more secure and user-centric healthcare landscape.
Incentivizing Private Data Sharing in Vehicular Networks: A Game-Theoretic Approach

In the context of evolving smart cities and autonomous transportation systems, Vehicular Ad-hoc Networks (VANETs) and the Internet of Vehicles (IoV) are growing in significance. Vehicles are becoming more than just a means of transportation; they are collecting, processing, and transmitting massive amounts of data to make driving safer and more convenient. However, this advancement ushers in complex issues concerning the centralized structure of traditional vehicular networks and the privacy and security concerns around vehicular data. This paper offers a novel, game-theoretic network architecture to address these challenges. Our approach decentralizes data collection through distributed servers across the network, aggregating vehicular data into spatio-temporal maps via secure multi-party computation (SMPC). This strategy effectively reduces the chances of adversaries reconstructing a vehicle’s complete path, increasing privacy. We also introduce an economic model grounded in game theory that incentivizes vehicle owners to participate in the network, balancing the owners’ privacy concerns with the monetary benefits of data sharing. This model aims to maximize the data consumer’s utility from the gathered sensor data by determining the most suitable payment to participating vehicles, the frequency in which these vehicles share their data, and the total number of servers in the network. We explore the interdependencies among these parameters and present our findings accordingly. To define meaningful utility and loss functions for our study, we utilize a real dataset of vehicular movement traces.
YOLOv8 for Product Brand Recognition as Inter-Class Similarities

Smart retail has been increasing in interest due to societal shifts along with technological advancements. The fast-paced integration of artificial intelligence (AI) brings the opportunity to develop potential technology in the scope of daily activities. Smart retail implements a field of artificial intelligence that involves computer vision to detect and recognize products. This paper aims to analyze the performance of YOLOv8 for brand recognition on grocery products. However, the inter-class similarities problems are present among certain grocery products included in the dataset to further represent real-life conditions of smart retail. Firstly, an adequate dataset is internally developed with common grocery products from 20 classes that represent brands. In the dataset developed, 3095 images were used and split into training, validation, and testing in 70%, 20%, and 10%, respectively. Then, an experiment is conducted with the YOLOv8 model where the performance result showed by precision, recall, mean average precision (mAP), and confusion matrix. Results of the experiment show that the YOLOv8 model gives a mAP50 value of 0.96303. This result indicates that the YOLOv8 model's performance for object recognition opened new insights into its potential in supporting the automatic checkout process in smart retail.
A Quantum Safe Authentication Protocol for Remote Keyless Entry Systems in Cars

The keyless entry systems in cars enable users to lock or unlock cars remotely. One of the popular types of keyless entry systems used in cars is the Remote Keyless Entry (RKE) system. With the advent of quantum computers, quantum computing-enabled cyber-attacks are an imminent threat. The security offered by current cryptographic techniques is inadequate to protect systems such as RKE from such future quantum attacks. In this paper, we propose a quantum-safe authentication protocol leveraging Quantum Key Distribution (QKD) that authenticates a legitimate key fob before unlocking the car. We present a formal security proof and an informal analysis to show that the protocol is secure against several attacks. To the best of our knowledge, this is the first protocol that protects RKE systems from future quantum computing-enabled cyber-attacks, in addition to the existing replay and RollJam attacks.
Big Data Analysis of Social Media: A Case Study of Citizens Comments of Integrated Dynamic Record Information System Application (SRIKANDI)

This study analyzes the comments made by netizens on social media regarding the Integrated Dynamic Record Information System (SRIKANDI) application, which was launched as part of the SPBE governance element. SRIKANDI is the main SPBE application related with the archive in Indonesia. The aim of this research is to identify the topics discussed by netizens on social media related to SRIKANDI and to understand their satisfaction levels and issues with the application. The study uses data from Twitter and YouTube comments related to SRIKANDI since 2020, when it first launched. Topic modeling using Latent Dirichlet Allocation is applied to analyze the data. The results reveal two main topics: 1) the difficulty of using the SRIKANDI application, and 2) the need for better user guidance and tutorials. The study provides insights for further development of the SRIKANDI application, including improving user guidance and addressing the issues that affect user satisfaction. This study demonstrates the potential of big data analysis in social media to inform software development and improve user experience.
Transformer-Based Unsupervised Image Registration Using SSIM and Homography Loss for Steady Camera and Aerial Videos

Image registration is an essential and initial block in the pipeline of computer vision tasks and systems. It is defined as transforming a moving image into a target image with the minimum difference when aligned. Unlike previous work for general-purpose datasets, aerial images suffer from mechanical shakes, leading to deformed distortion similar to medical volumetric image registration task. Inspired by medical approaches, we use a Transformer-based network with a semi-unlimited receptive field Swin block to produce a general output for each pixel named flow matrix. Flow matrix is utilized instead of regressing parameters of transformation matrix with a fixed degree of freedom that cannot handle the structural difference between images. This leads to introducing a new loss function based on the Structural Similarity Index Measure (SSIM) and embedding Homography transformation as a regularization term. Combining a generalized-designed network and loss function based on problem definition significantly enhanced results.
Exploring The Potential of HMMs in Linguistics for Part of Speech (POS) Tagging

The exponential growth in the adoption of information and communication technologies has sparked a notable surge in the demand for Natural Language Processing (NLP) tools. The tagging/identification of Part-of-Speech (POS) is of utmost importance in numerous natural language processing applications, including information extraction, parsing, and machine translation. It entails the assignment of a distinct part of speech to every word within a given corpus. The Hidden Markov Model (HMM) stands as the prevailing technique employed for Part-of-Speech tagging. The foundation of this approach lies in a probabilistic model that effectively captures the intricate relationships between words and their corresponding tags in a sequential manner. In this research, the Hidden Markov Model (HMM) is investigated for its potential application in POS tagging. It showcases the dependability and efficacy of this approach by attaining an impressive accuracy rate of 96.67%. Also, the precision, recall, and F-score metrics achieved stand at 96%, 96.5%, and 95% respectively, further solidifying the effectiveness of the HMM method.
Applying CNN to Classify Gender for Identity Verification System

Computers have difficulty recognizing and distinguishing between faces, especially when it comes to gender and age. Gender is important for emotional recognition and security reasons and can help identify individuals who look alike. This research proposes a binary gender recognition system that consists of two stages: face detection and gender classification. The face detection component is compared to three models: Python face recognition library, SSD MobileNet, and EfficientNet. The gender classification component uses two models: custom CNN and VGG Face models. Based on this research, the final prototype is accessible via a web application and is capable of performing image-based face detection and processing each known face to determine its gender (male or female). The face identification model yields varying results, with the EfficientNet model outperforming the others after the model is deployed. The VGG Face model also performs well, with an average accuracy of 99% for faces taken under optimal settings (training and testing datasets) as well as for specific images collected outside of the dataset. However, for masked faces and captures at low brightness levels, it still results in less accurate detection and classification for several types of images.
A Systematic Literature Review: Challenges of Security Optimization while Maintaining a Good Accessibility in Internet of Things

The Internet of Things (IoT), which has had a tremendous development in recent years, has altered many aspects of daily life by giving rise to commercial tools, home automation, wearable technology, and the framework for smart cities. The modern fantasy of having remote control over several parts of life has been realized because to this continuous connectivity and the ability of devices to acquire and interpret information. However, the various features and functionalities of IoT devices are a double-edged sword because they introduce a number of security flaws and dangers. Through a Systematic Literature Review (SLR), we explore vulnerabilities and their mitigation strategies based on prior research in the field. Notably, while the importance of security in IoT is paramount, especially considering the sensitivity of data involved, there exists a challenge in striking a balance between robust security and user convenience. It's imperative that while bolstering security, we do not compromise on user accessibility. This paper presents a comprehensive discourse addressing the dual challenges of security and accessibility in the IoT landscape. Our findings underscore the urgent need for layered security protocols, encryption, and user authentication to counteract the escalating cyber threats. We conclude by proposing a balanced solution that ensures both security and accessibility in the IoT ecosystem.
Classification of Handwritten Sundanese Script via Transfer Learning on CNN-Based Architectures

The Sundanese script is a cultural heritage of the Sundanese people that deserves to be preserved. With the advances in computer vision and deep learning, the study of Indonesian local character recognition still has room for improvement and therefore needs to be encouraged. In this study, the Sundanese script used was Swara and Ngalagena script. The classification of the Sundanese script poses a significant challenge due to the complexity and variability of the script's visual patterns. Therefore, this study classifies Sundanese script using the deep learning method, namely the Transfer Learning method based on the Convolutional Neural Network. The architectures used in this research are ResNet-50, VGG-19, and MobileNet. Then to evaluate the performance of the three architectures, the evaluation metrics used in this study are Accuracy, Precision, Recall, and F1-scores will be used. The three CNN architectures show very satisfactory results in classifying Sundanese scripts. VGG-19 architecture Accuracy is 99% for training data and 95% for validation data, the ResNet-50 architecture is 97%, and for validation data is 91%. Meanwhile, the MobileNet architecture data train produces an Accuracy of 97% and data validation of 92%. Overall. The VGG-19 architecture is the best in this Sundanese script classification study.
A Computational Approach to Understand the Human Thought Process

The aim of this paper is to understand the human mind functions such that we can simulate them into a computational model for making cognitive systems more intelligent. We then present this model with formulae using a top-down approach. We propose that the human thought process, which is the fundamental unit for the human mind and all the different emotions coming from it, can be implemented as a mathematical formula to a reasonable extent. Furthermore, we argue that our model has the ability to derive mind functions such as dreams and thinking. The model utilizes a top-down approach for understanding the factors involved in the thought process while explaining the factors involved. The authors are intending to make continuous improvements in the formulae as more scientific advances happen in the understanding of the human cognitive functions.
Virtual Reality Based Interior Designing Using Amazon Web Services

This paper explores the integration of Virtual Reality and Cloud Computing to develop an innovative app for interior design. Virtual Reality represents a groundbreaking advancement in the digital realm, offering a transition from 2D to 3D environments that closely resemble real-life simulations. On the other hand, Cloud Computing revolutionizes traditional approaches to server management, storage, networking, and software by providing these services over the Internet. Our objective was to leverage the capabilities of both technologies to create an interior design app that allows users to conveniently renovate and modify architectural spaces from anywhere in the world, regardless of their physical location.
Recognizing Physiotherapy Exercises using Machine Learning

Physiotherapy, which is also referred to as physical therapy (PT) is a healthcare profession that is primarily concerned with correcting or physiotherapy aims to improve mobility and functionality while addressing impairments through the use of mechanical force and movements, manual therapy, exercise therapy, and electrotherapy. However, the effectiveness of the treatment relies on the patient's adherence to the recommended exercises. Failure to comply with the prescribed exercises may lead to unsuccessful outcomes. The examination of human posture finds various uses in sports and medical science, such as monitoring patients, analyzing lifestyle, and caring for the elderly. However, computer vision techniques, which have been predominantly utilized in this area, have their limitations when it comes to delivering real-time solutions. Thus, Machine Learning based solution are being planned and used for the human posture recognition and detection. For individuals who are new to specialized exercises, it can be challenging to identifying the areas where they are making mistakes without any external help or guidance. To address this, we propose a Physiotherapy exercise assessment system that utilizes pose detection to support self-learning of yoga. The model has an accuracy rate of 96% for pose detection. The system first detects a Physiotherapy exercise using multi-part detection solely through a PC camera. It then calculates the difference in specific body angles between the user's pose and that of the instructor. If the discrepancy surpasses a set threshold, the system differentiates between the feedback of the instructor and the user and offers a corrective suggestion.
A Rotating Server Scheme for Secure Federated Learning in Networked Autonomous Driving

Edge intelligence and federated learning (FL), as key enablers of 6G, is a promising solution for networked Autonomous Driving (NAD). However, traditional federated learning is a server-client architecture, which makes the model training overly dependent on a fixed single aggregation server and makes the FL process insecure and unreliable due to the vulnerability of the aggregation server to a single point of failure. In this paper, we propose a rotating server FL scheme (RSFL) to solve the problem of single point of failure and limited resources and improve environmental adaptability. Specifically, we consider multiple factors to measure the vehicle performance and find the vehicle with the highest performance score in this round as the server for the next round while setting weights that are randomized in each round, which reduces even more the likelihood that a malicious user will recognize the regularity of the chosen server. Finally, the performance of RSFL is evaluated through a large number of experiments, and the results show that compared with baseline FL, FL with randomly selected servers, and peer-to-peer decentralized FL, RSFL can effectively reduce the cases of servers being detected and attacked by malicious adversaries, and improve the accuracy of the model.