Reviewerly/exHarmony huggingface dataset:
Exploring the Potential of Interplanetary File System for Secure and Transparent Social Media

A new social media platform will be developed that utilizes blockchain technology to ensure user privacy and security. The platform will use the user's meta-mask wallet ID, which cannot be hacked, to connect to their profile on the blockchain, providing an immutable and tamper-proof ledger for their data. The platform will also allow for cryptocurrency transactions, which are secured by cryptography and do not require a centralized intermediary, adding another layer of security. Smart contracts will be used to enable decentralized and automated crowd funding and giveaways, among other features. The smart contracts will be designed using Solidity, a programming language specific to the Ethereum blockchain. When a user creates an account on the platform, their meta-mask wallet ID will be used for user identification and to verify transactions, ensuring that every transaction is validated by the blockchain.
Cursor Movement Based on Object Detection Using Vision Transformers

The research paper titled “Cursor Movement on Object Detection Using Vision Transformers” focuses on using Vision Transformers (ViT) to detect objects, specifically cursor movement. Modern deep learning architecture called ViT can spot things in still and moving pictures. The proposed method utilizes the transformer architecture to capture the contextual information of an image, which can be used to accurately detect and locate objects in the scene. Additionally, it uses a novel cursor movement algorithm that can accurately move the cursor toward the target object in real time. On common object identification datasets, the effectiveness of our suggested strategy is assessed and contrasted with numerous cutting-edge approaches. Experimental results demonstrate that our methodology outperforms state-of-the-art methods by a substantial margin.
Optimized Implementation of S-box and Inverse S-box for PRESENT Lightweight Block Cipher

In the recent years, the exchange of information in the constrained environment like RFID and sensor networks uses several lightweight cryptographic techniques that are established for block ciphers implementation. The lightweight cryptographic algorithms are majorly constrained towards computational resources, flexibility, and execution time. In the literature several authors proposed various techniques in block cipher architecture to achieve the required constraints, in which PRESENT block cipher is the simplest one. The general PRESENT cipher comprises of Substitution layer (S-box), Permutation layer and key generation for the encryption process and Inverse Substitution layer (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> -box), Inverse Permutation layer and key generation for the decryption process. In this paper, novel designs of S-box and S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> -box are proposed to achieve better hardware optimization. In the first approach of S-box (PD-I), the Boolean expressions are optimized over the existing design by sharing the common logic gates and in the second approach (PD-II), the Boolean expressions are represented using two level logic and further optimized by sharing the common terms. Similarly, in the new approach on S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> -box design the Boolean expressions are represented using factorization. Further, all the proposed and existing design of S-box and S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> -box are implemented and synthesized using TSMC 180nm CMOS technology. It is observed from the implementation results, among all the existing designs the proposed designs perform better.
Design and Development of AI-Powered Healthcare WhatsApp Chatbot

Technology has been increased in massive part of today's healthcare scenario. The advancement and improvement of information and communication technologies had led to increase the usage of chatbots in almost all the healthcare sectors. And the technology has changed the way of communication between the patients and doctors in medical sectors. Nowadays, chatbots were used for computer medical communication in order replace the human agents due to high efficiency and cost effectiveness. Artificial Intelligence (AI) and Chatbots are the two base breaking technologies that have changed the patients and doctors perceive in healthcare. In the field of healthcare sector, the use of AI (Artificial Intelligence) has been explored for automating the applications and appointments for public users. A WhatsApp chatbot is program which can reply to messages automatically on WhatsApp. In this paper, to make the healthcare system more effective, and interactive a diagnostic chatbot is designed and developed using machine learning algorithms, to help the public users in booking, cancelling, and rescheduling the Doctor's appointment based on their timings. This proposed work is used to employ an artificial chatbot, to do a live chat with a patient and users regarding to the medical appointment with the concerned doctors. This application can well be extended from B2B to a B2C utilization, where service providers like doctors, ayurvedic, physiotherapist, psychologist as well as therapist, dietician, nutrition, trainer can cater to specific users. Thus, the indention to more focus on Natural Language Processing (NLP) techniques to improve the chatbot conservation aspects in future studies.
Detection and Identification of Pills using Machine Learning Models

Pill color, pill size and shape are few important characteristics for automatic pill detection. However, the environmental factors may cause an effect such that a change is produced in the above factors. Often medication errors occur that may cause complications to patients and all these are caused due to damage in labels and mismatches in medicine intake, etc. In this report, a trained system is proposed using Keras and Tensor Flow mainly, for easy and quick identification of varieties of pills. The detected pill (object detection) connects to the pill database wherein the pill name is detected. After the process of detection, the pre-trained dataset is used to identify the pill. Moreover, the dataset would also include the use cases and required detailed information of the respective pill. The project involves collecting datasets for automated medicine detecting technology. Effectiveness of the proposed method can be verified in the experimental results.
Gesture-Based Control of Smart Devices for People with Vocal Disorders using YOLOv7

Using a physical controller for human-computer interaction, such as a mouse or keyboard establishes a strong barrier between the user and the computer. In this research, we developed a hand gesture detection system to effectively monitor dynamic hand gestures. The current methods for recognizing hand gestures are designed by CNN architecture. Real-time recognition of hand gestures from video streams is a challenging task because there is no indication in the video when a gesture begins and ends, performed gestures should only be recognized once, and the entire architecture should be designed with memory and power budgets in mind. In this work, YOLOv7 is a proposed method to address the difficulties above. In terms of speed and accuracy, the suggested YOLOv7 outperformed transformer-based detectors such as the SWINL Cascade-Mask R-CNN R-CNN. YOLOv7 fared better than YOLOX, YOLOR, Scaled-YOLOv4, YOLOv5, DETR, Deformable DETR, DINO-5scale-R50, and VitAdapter-B. After identifying the hand gesture, a corresponding action will be taken based on the recognized hand gesture. In this work, YOLOv7 is compared with YOLOv5 and it is proved that YOLOv7 outperforms YOLOv5 under performance metrics such as precision, recall, and mean average precision.
Classification of Intrusion Affected Stego Images Over a Channel Using Deep Learning Techniques

Image Steganography is a widely used technique to hide secret information in the cover image for secure transmission. A stego image is subject to many intrusions such as noise and packet loss during transmission, which may alter the characteristics of the transmitted image. Hence, it is necessary to classify the intrusion type to determine the mitigation strategy. This paper proposes a deep learning-based approach to organise the stego images based on the intrusion type. Three different deep learning techniques, as Convolutional Neural Networks (CNNs), Inception V3, and autoencoders, are trained and comparatively analysed to determine the optimal classifier. The proposed approach is evaluated using the standard BOSSBASE dataset, and the experimental results showed the highest classification accuracy of 84.533 % for Inception V3.
Automated Pathology Diagnosis Using Artifical Intelligence with Histopathological Images of Lungs

Lung cancer is of significant concern in India. Histopathological images are critical in the diagnosis of cancer and is usually done manually. Deep learning has a huge impact in the health care sector due to its high capabilities for accurate data analysis and early disease prediction. ResNet with predefined layers is used for diagnosis and 40 custom layers are added. The network is trained with images taken from the Kaggle data base. Both benign and malignant images are used for training. A Webpage is developed which serves as a graphical user interface for feeding the test images into the network. The network works on the test image and displays the results along with the type and stage. The network is robust with an accuracy of 98%.
An Interactive Puzzle Pattern-based CAPTCHA Scheme for Security

The Internet nowadays is playing a significant role in our everyday life. Many web services have registration and require user input. These involve completely automated public Turing tests to tell computers and humans apart (CAPTCHA). The web services like Gmail registration, bank transactions, and ticket booking often face attacks from bots which are computer programs. The CAPTCHA is used to protect the sites from these dangerous bots. This technique offers good security for websites as analysis proves that humans can easily solve the CAPTCHA but bots can rarely do that. This is because computers do not have the visual capacity and real understanding like humans. There are four types of methods in the development of CAPTCHA. They are CAPTCHA based on images, audio, video, and puzzles. Here the paper is focused on the development of Puzzle-based CAPTCHA. This implies the user needs to solve the puzzle to access the website. If the puzzle is not solved, the user can't go forward as the site treats it as a bot and denies access. This is one of the efficient methods which made it easily possible for humans but hard for bots. It involves human intelligence and simple testing. Various puzzles are used as a CAPTCHA. This paper is based on the pattern recognition puzzle CAPTCHA. The basic idea is that the user needs to identify the next pattern based on existing patterns. This involves simple logic which humans can solve. The pattern recognition puzzle challenges human intelligence and is much more difficult for the bots to solve so that it could be more secure.
Real Time Object Detection Using Deep Learning

Real-time object detection using deep learning has emerged as a burgeoning field of study due to its potential for a wide range of applications, including autonomous driving, robotics, and surveillance systems. The primary goal of this method is to identify interesting objects in real-world situations quickly and accurately. By utilizing convolutional brain organizations (CNNs) for highlight extraction and article identification, advanced learning-based strategies have demonstrated exceptional outcomes in this area. CNNs are trained on large-scale image datasets to learn discriminative features that capture object appearance and context effectively. The features extracted by the CNN are then used to detect objects using a detection algorithm. The Region-based Convolutional Neural Network (R-CNN) framework is one popular approach, which first proposes a set of candidate regions and then applies a CNN to each region to extract features for classification and localization. Faster CNN architectures such as Single Shot Detector (SSD) and You Only Look Once (YOLO), as well as hardware acceleration strategies such as graphics processing units (GPUs) and field-programmable gate arrays (FPGAs), have been proposed as ways to improve real-time performance. These methods allow for high frame rates and real-time object detection, making them suitable for a wide range of real-world applications.
Secure health data transmission on IOT

Data security is essential to the e-healthcare industry for protecting patients' confidential information. It is necessary to continuously monitor the patient's health to give them the proper medical care. The proposed work develops a Raspberry Pi cryptosystem by sending real-time patient health data to the physician via the Thing Speak cloud environment. In order to create the highly random keys for encrypting the color medical images and sensor data (temperature, heartbeat, medical images), this research uses numerous chaotic maps in a single procedure. Firstly, the shuffling algorithm based on a logistic map is designed to shuffle the image pixels randomly. Then, the diffusion algorithm based on circle map is developed to diffuse the shuffled image pixels. The effectiveness of the proposed algorithm is analysed based on MSE (Mean Squared Error), PSNR (Peak Signal-to-Noise), Entropy, Histogram, and correlation is calculated analyses.
Building Customer Support Chatbots With Intent Recognition

In recent years, chatbots have emerged as a promising tool for delivering healthcare services. Chatbots can provide 24/7 access to healthcare information, improve patient engagement, and reduce the workload of healthcare professionals. However, building effective chatbots for healthcare requires expertise in natural language processing (NLP), machine learning, and healthcare domain knowledge. Chatbots have become increasingly popular in recent years, thanks to advancements in natural language processing (NLP) and machine learning (ML) technologies. One critical component of chatbots is intent recognition, which enables them to understand the user's purpose and respond appropriately. In this paper, we present a comparative study of different machine learning algorithms for intent classification in chatbots. We evaluate the performance of four ML algorithms: logistic regression, random forest, support vector machines (SVM), and deep learning (DL) neural networks, using a publicly available dataset. Our results show that SVM model outperform the other two algorithms in terms of accuracy and F1 score.
Bypassing Deep Learning based Sentiment Analysis from Business Reviews

In recent years, online reviews of businesses have grown increasingly significant, as customers and even competitors use them to evaluate a company's quality. Yelp is one of the most popular review websites, and it would be advantageous for them to be capable of predicting the sentiment or even the star rating of a review. Current deep-learning algorithms excel at sentiment classification. With the tremendous performance of models based on deep learning in text-related problems, they are susceptible to adversarial manipulations that result in inaccurate sentiment classification. An adversarial text is created by manipulating just few letters or words in such a manner so that general meaning of the text remains unchanged for humans but fooling a system into making false predictions. This study highlights the shortcomings of sentiment categorization by employing a range of cutting-edge attack techniques to generate perturbed text. We examined the performance of several models, including BERT, an advanced transformer model, and the extensively used LSTM and Word-CNN classifiers trained on the Yelp polarity dataset. For each model, Attack Success Rates (ASR) are calculated as the evaluation metric. Based on the experimental results, we determined which sentiment classifier is more vulnerable to adversarial perturbations and which is more resistant. The results demonstrate that automatic sentiment classification techniques can be circumvented, which has implications for present policy approaches.
Research on the Identification Method of Cells with Deteriorating Wireless Network Indicators

The severe degradation of wireless network performance indicators in cells seriously affects user perception. Rapidly identifying small cell performance degradation based on fixed thresholds set by network optimization personnel through business experience can lead to misjudgment and omission. An anomaly detection algorithm is used to remove abnormal indicator values to eliminate sudden fluctuations caused by sudden situations. Then, the DBSCAN clustering algorithm is used to calculate the dynamic threshold of the indicators, and a method for quantifying the degree of indicator degradation is proposed. Finally, combined with historical poor quality cell label data, the Lightgbm classification algorithm is used to identify cells with degraded wireless network performance indicators. To verify the effectiveness of this method, the experimental results show that the accuracy of identifying cells with degraded wireless network performance indicators using this method is higher than that of empirical rules and traditional classification algorithms.
MDMA: Multimodal Data and Multi-attention Based Deep Learning Model for Alzheimer’s Disease Diagnosis

Recently, multimodal data-based methods have shown excellent performance in Alzheimer’s Disease(AD) diagnosis. However, these methods commonly have the following two shortcomings: 1) the feature extraction processes of different modalities are independent and lack cooperation, which may lead to limited representation ability of the extracted features, and 2) the multimodal fusion operation is a simple concatenation, thus causing rough fusion features. To address these two issues, we propose a deep learning network based on multimodal data and multi-attention(MDMA), which consists of two key components, namely Cross-Modal Channel and Spatial attention (CMCS) and Cross-Modal Cross-Attention (CMCA). The CMCS module uses the interaction information from MRI and PET to recalibrate both channel-wise and spatial features for each modality. The CMCA module utilizes two multi-head cross attention to interactively fuse information from images and clinical data. In addition, Gradient-weighted Class Activation Mapping (Grad-CAM) method is used for visualizing the focused areas of the proposed model to make our model more transparent. Evaluated on the ADNI dataset with multimodal data collected from 113 AD, 146 mild cognitive impairment (MCI), and 135 normal controls (NC), we demonstrate that the proposed model achieves better results in terms of accuracy, sensitivity and specificity.
Layer Separation Network with Contrastive Loss for Robust Dehazing

The single image dehazing is a challenging problem because of the acute degradation of the hazing image. Traditional CNNs-based dehazing models suffer from two fundamental issues: one is that the convolutional layers of dehazing networks are often content-independent and cannot effectively learn remotely dependent feature information. The other is that transparent images are used to training the dehazing networks without utilizing hazing information. Therefore, we introduces a multi-layer fusion network based on contrast learning that integrates multiple models into a single layer centrally and improves the image dehazing performance. First, we utilize the MSBDN-DFF model as the original feature extraction of initial dehazed images; then, with the obtained features, we further predict the hazy-free results of the atmospheric scattering module and the four hazy layer separation modules; Finally, we add a contrast learning mechanism to make our model effective in generating highquality hazy-free images to fully use the information from fuzzy and clear images. An extensive evaluation shows that our proposed method is effective in the RESIDE dataset and outperforms current techniques. And our method improves the best-published PSNR value by 0.55 dB on the SOTS test dataset and the original model by 1.12dB/0.83dB on the SOTS indoor/outdoor test dataset, respectively. A comprehensive analysis of our method is also performed through additional experiments and detailed ablation studies.
Design and Implementation of Authentication for Remote Sensing Images in Traffic Congestion Control System using Hash Function in Graph Theory

Internet of Things (IoT) is a system that combines many technologies and devices, eliminating the need for human intervention. IoT envisions a world of networked, intelligent things that can exchange data digitally. The implementation of IoT paradigm in the urban setting has been one of the most fascinating application areas since the IoT paradigm's debut, creating the so-called Smart City idea. The transportation industry is using connected and smart devices more and more, which finally exposes security concerns from the external world without any constraints. Cryptography has become a crucial area of study and development as a result of the increased usage of telecommunications technology in recent years, especially when it comes to data transfer securely between two or more different entities when that data is deemed to be critical. Authenticity, confidentiality, security and privacy are crucial components of smart city applications and are prominent in the data management interface of the infrastructure. Authentication and Data integrity are made possible through the use of hashing, a method for providing a type of computer security. A hash function is a mathematical procedure that converts one input numeric value into the other. This paper provides a solution to secure traffic data problems and avoid identity theft by implementing message hashing and image hashing.
How Far is User Privacy Leakage: A Revisit of Client Certificate Usage

X.509 Certificates are an important medium for identity authentication when the client establishes a TLS connection with the server. As the security issues on TLS are more and more widely discussed, the TLS1.3 [1] protocol was officially released in 2018 for the sake of enhancing security. The TLS1.3 protocol shares the secret key in the ClientHello phase, so that all information except the hello message is encrypted. Although TLS1.3 is developing rapidly, in our observations, TLS1.2 [2] traffic is still the majority of TLS/SSL traffic, accounting for more than 60%. In this paper, we focus on the measurement of client certificates in the existing internet, by extracting useful information from a passive measurement framework deployed on an enterprise gateway. We perform a comprehensive analysis of the information and disclose some noteworthy flaws in the usage of client certificates that may possibly compromise user privacy.