Reviewerly/exHarmony huggingface dataset:
A Novel Social Media-Based Adaptable Approach for Sentiment Analysis Data

Due to its interactive and real-time character, gathering public opinion through the analysis of massive social data has received considerable interest. Recent research has used sentiment analysis and social media to do this to follow major events by monitoring people's behaviour. In this article, we provide a flexible approach to sentiment analysis that instantly pulls user opinions from social media postings and evaluates them. The suggested method entails first creating a dynamic dictionary of words' polarity based on a chosen collection of hashtags associated with a certain topic, then categorizing the tweets into many classifications by adding additional characteristics that sharply fine-tune the polarity degree of a post. We categorized the tweets on the 2022 French presidential election to verify our methodology. The prototype tests' findings demonstrated high accuracy in identifying classes as well classes' subclasses.
Real-time-based Object Recognition using SIFT algorithm

In this paper, we propose a real-time object recognition system that makes use of local image features using Scale Invariant Feature Transform (SIFT). The features are partial changes in illumination and affine, and they are invariant to rotation, image scaling, and translation. Similar characteristics are shared by these features and the inferior temporal cortex neurons involved in object recognition. Features can be efficiently detected using a phased filtering technique in scale space. Image keys generally support the local geometric information representing the blurred image gradient at different scales and orientation planes. An indexing technique called nearest-neighbor search (NSS) uses the keys as input to determine the object matches. Based on the number of descriptors we categorize the object is recognized or not, and the final output is displayed in the microcontroller-based display unit.
Outsourcing the Computation of Plaintext Encryption for Homomorphic Encryption

Homomorphic encryption is a promising technology for private enhanced computing. It provides a data-centric privacy-preserving method that allows computing on encrypted data by a third proxy. However, lightweight terminals lack sufficient computing resources to perform a large amount of encryption operations in stream data applications, e.g., smart devices in blockchain network, sensors and cameras in urban intelligent transport. This paper presents a blinded encryption paradigm that outsources most computations to a third proxy, e.g., cloud computing and edge computing. It helps to eliminate the encryption overhead for lightweight terminals while preserving data privacy. The proposed paradigm is composed of plaintext blinding, blinded encryption, ciphertext recovery and one round of communication. Lightweight terminals only execute efficient plaintext blinding and ciphertext recovery to guarantee data privacy. The expensive computing for plaintext encryption is delegated to a third proxy through blinded encryption. In addition, the key analysis to achieve time consuming improvement is to overlap the communication latency with computation time. Furthermore, the security analysis demonstrates the outsourced encryption is secure under the assumption of secure blinding and recovery algorithms. Finally, the experiment shows our method outperforms the original version by 2x improvement from the prospective of resource consumption.
A Convenient Deep Learning Model Attack and Defense Evaluation Analysis Platform

Researchers have been studying in recent years how to improve the security of deep learning models to resist adversarial attacks. However, attack and defense algorithms are generally targeted, a unified, comprehensive, efficient, and convenient analysis platform is needed for deep learning model security evaluation. Deep learning model security evaluation faces the following challenges:(i) The difference of deep learning frameworks used to generate models causes inconvenience in model security evaluation. (ii) Most of the attack and defense algorithms are not universally applicable. A comprehensive measurement baseline is needed to comprehensively evaluate various attack and defense algorithms. (iii) A set of quantitative metric system needs to be proposed. In this paper, a convenient deep learning model attack and defense evaluation analysis platform is designed to automate the evaluate process. Users need only a small amount of configuration to complete the evaluation of various attack and defense algorithms. The platform integrates a variety of black-box attack algorithms and white-box attack algorithms and 6 defense algorithms. Besides, a system of metrics is constructed, covering imperceptibility, robustness, attack efficiency, etc. Based on this platform, we evaluate the performance of various algorithms on common image classification models. Compared with other platforms, this platform is efficient and convenient for evaluation, and has a wide range of evaluation. It has the ability of comparing and analyzing models generated by various frameworks and is useful for research on the security of deep learning models.
Exposing Deepfake Frames through Spectral Analysis of Color Channels in Frequency Domain

Highly realistic deepfakes are generated by employing generative neural networks, even to the point that it is difficult for humans to tell them apart from the real ones. Nowadays they are one of the causes of misrepresentation or misinformation regarding different subjects. The detection of deepfake content is very important. It can be analyzed in different domains, such as spatial domain and frequency domain, or by employing combinations of them. In this work, we first took inspiration from traditional image forensics and performed a comprehensive frequency spectrum analysis on the deepfake frames and their context color channels to detect spectral anomalies and statistical features. We then use the frequency spectrum statistical features to distinguish between pristine and deepfake content using both unsupervised and supervised learning approaches. Finally, we scrutinize the trained deepfake detection models’ generalization capability from the perspective of suggested statistical features across different deepfake datasets and methods. Our analysis demonstrated the effectiveness of statistical features by identifying real and deepfake content with high accuracy, surpassing the performance of several state-of-the-art methods.
A Study on Diffusion Modelling For Sensor-based Human Activity Recognition

Human activity recognition (HAR) is a core research topic in mobile and wearable computing, and has been applied in many applications including biometrics, health monitoring and sports coaching. In recent years, researchers have focused more attention on sensor-based HAR due to the popularity of sensor devices. However, sensor-based HAR faces the challenge of limited data size caused by the high cost of data collection and labelling work, resulting in low performance for HAR tasks. Data transformation and generative adversarial network (GAN) have been proposed as data augmentation approaches to enrich sensor data, thereby addressing the problem of data size limitations. In this paper, we studied the effectiveness of diffusion-based generative models for generating synthetic sensor data as compared to the other data augmentation approaches in sensor-based HAR. In addition, UNet has been redesigned in order to improve the efficiency and practicality of diffusion modelling. Experiments on two public datasets showed the performance of diffusion modelling compared with different data augmentation methods, indicating the feasibility of synthetic sensor data generated using diffusion modelling.
Security Key-Based Steganography for Images

Data hiding is a type of data concealment for different purposes, to preserve personal information, to protect confidential information, and so on. The important thing is to exchange information securely over the Internet. There are various methods for safely exchanging or transferring data to the destination, such as cryptography and steganography. In this project, we propose a steganographic method based on LSB to mask data or information using the least significant bit algorithm (LSB), for embedding the data into the images. Image steganography is the process of hiding information, where information can be text, image, or video, hidden in a cover image. The information is hidden so that it is not visible in the eyes of man. The purpose of this project is to hide the message or information in an image and then decode that image. We have used Tkinter and Stegano modules. We have used functions in Python also. This project report intends to give an overview of security key-based steganography for images, using algorithms and techniques. It also describes the main features: Encoding and Decoding messages in images. Also, this project report shows the implementation and working of the actual image steganography algorithm and its enhanced security method using methods of cryptography.
An Improved Signature Forgery Detection using Modified CNN in Siamese Network

Signature forgery is a major security threat in many important applications, such as banking and legal systems. In this work, an enhanced technique for detecting signature forgeries utilizing convolutional neural networks in Siamese Network is proposed. An improved Convolutional Neural Network architecture is built by incorporating additional features, including feature maps from intermediate layers and additional convolutional layers. The proposed approach is trained and tested with a publicly accessible collection of genuine and forged signature images. The experimental results show that the proposed methodology outperforms the various state-of-art approaches considered in the experiment and attains considerable gain in terms of accuracy, false rejection rate and false acceptance rate. The proposed approach also shows high robustness for various types of forgeries, including skilled forgeries and simple forgeries. The findings also have important implications for the development of more effective and efficient signature forgery detection systems.
Cyber and Electronic Warfare in Context of Defence Forces in Present Scenario

The future of warfare is likely to focus less on firepower and more on Cyber and Electronic Warfare amongst other emerging technologies. This research aims to analyse the Cyber and Electromagnetic Activities (CEMA) capabilities of leading armies as well as well as bring out the challenges of the same for Indian Defence Forces in present context.
Transformer Models for Recognizing Abusive Language An investigation and review on Tweeteval and SOLID dataset

Social engineering communities have become very popular among the kids and elderly alike. In this era of social media, the streaming of comments, opinions, reviews and communications is done via most common social media messaging communities like Twitter, Meta owned WhatsApp, FB and Instagram, Snapchat, telegram and YouTube comments. In this paper we perform a review on the different methods and models used to identify the offensive language using different datasets. Offensive language detection is a tedious task as it is country and language specific. The corpus used to identify the offensiveness and abusiveness is not covering all the word usages. We have done a comparison study of different methods on text to detect the post is offensive or not. The detection of abusive language is an unsolved and challenging problem to researchers in Natural Language Processing (NLP). This has led to be one of the reasons for increased level of mental instability among teenagers to elderly. The crime via social media has increased to a large value than older days. The study and surveys show that to recognize the structure and context of the language is the best way to solve this problem to an extent. The paper aims to four recent transformer models pretrained and fine-tuned for offensive language detection on the tweeteval dataset viz; DistilBERT, RoBERTa, DistilRoBERTa and DeBERTa. All the model had limitation in the performance based on the training data size used but are optimized by tuning hyper parameters during training. The models are limited to English language offensive words and recent works are going on in the area of multilingual tweets on both text and speech processing.
Based on Discrete Diagonal Scan: A New Robust Image Encryption Using Confusion and Diffusion

Image encryption technique has emerged as a remarkable solution to shield the image against adversaries. Despite much advancement, a robust image encryption system is needed to achieve excellent encryption quality. Therefore, a highly-secured encryption algorithm needs impressive random key with excellent expansion method, small initial size and regeneratable capabilities, improved confusion and diffusion techniques. To achieve these goals a method for image encryption containing three major phases was developed. Firstly, an encryption and decryption key were generated and expanded with high level of randomness via Knight Tour (KT) algorithm with small initial size. Secondly, a new method of confusion was developed using switching technique and Discrete Diagonal-SCAN (DD-SCAN) approach for enhancing the level of pixel dispersion. Finally, an innovative method of diffusion was introduced to change the image pixels' value using bit wise operator and pixel circular shifting method. These involved steps such as generating a Ginger bread man image using the initial key, applying Arnold Cat Map (ACM) transform to dissolve flat regions in the ginger bread man image, and performing a bit-wise operation between the outcome from ACM transform, using results from DD-SCAN method, and utilizing the pixels circular shifting based on the generated key. The encryption key was proven to withstand against several types of attacks and tested using National Institute of Standards and Technology (NIST) randomness test. Thus, the pixel's value was altered and the cipher image was produced. The performance of the scheme was validated against forty-four images acquired from Signal and Image Processing Institute (SIPI) for standard image dataset. Shortly, the proposed image encryption scheme has been affirmed to be a superior image encryption scheme development.
Improved Likelihood Ratios for Surveillance Video Face Recognition with Multimodal Feature Pairing

The accuracy of face recognition in real-world surveillance videos plays a crucial role in forensic investigation and security monitoring systems. Despite advancements in technology, face recognition methods can be influenced by variations in pose, illumination, and facial expression that often occur in these videos. To address this issue, we propose a new method for images-to-video face recognition that pairs face images with multiple attributes (soft labels) and face image quality (FIQ). This is followed by the application of three calibration methods to estimate the likelihood ratio, which is a statistical measure commonly used in forensic investigations. To validate the results, we test our method on the ENFSI proficiency test 2015 dataset, using SCFace and ForenFace as calibration datasets and three embedding models: ArcFace, FaceNet, and QMagFace. Our results indicate that using only high quality frames can improve face recognition performance for forensic purposes compared to using all frames. The best results were achieved when using the highest number of common attributes between the reference image and selected frames, or by creating a single common embedding from the selected frames, weighted by the quality of each frame’s face image.
Enhancing Dehaze Method in Real hill Based Images using Gaussian Filter Over Gabor Filter for Better Accuracy

Enhancing dehaze method in real hill based images using Gaussian filter over gabor filter for better exactness. The Gaussian filter (N=10) and gabor filter method (N=10) these two algorithms are calculated by using 2 Groups and taking 20 samples for both algorithm and accuracy in this work. According to the Results In terms of accuracy, the Gaussian filter outperformed the Gabor filter method (91.2%) with a result of 93.9%. Statistical significance difference between Gaussian filter algorithm and gabor filter Algorithm was found to be 0.002 (p<0.05). The Prediction of enhancing dehaze method in real time hill based images Gaussian filter when compared with gabor filter.
Image Inpainting Based on Improved Criminisi Algorithm

To solve the problem of unreasonable priority repairing order of traditional Criminisi algorithm, this paper proposes an image repairing algorithm with improved priority Criminisi. Firstly, the information entropy, which measures the complexity of the image structure, is introduced into the calculation of the priority to integrate the structural information of the known part of the block to be repaired, to enhance the influence of the structural information of the image on the priority. Then, the priority function is redefined as the product of the confidence term plus data term and information entropy, which makes the restoration order of the image more reasonable. The experimental results show that the improved Criminisi algorithm can improve the repair quality of images with different complexity.
A Multi-Tasked Approach Towards Face Detection and Face Quality Assessment

Face in Video Recognition (FiVR) commonly follows a sequential pipeline of face detection, face quality assessment, and face recognition. However, performing these often machine learning-based tasks sequentially in real-time is a challenge when considering the excessive overhead caused by convolution and other feature extraction operations typically seen in neural networks employed across these stages. To overcome this challenge, a process that can perform these operations in parallel is needed. In this paper, we propose a methodology that can alleviate the constraints of real-time processing found in the sequential pipeline of FiVR. We exploit the similarities in features used in face detection and face quality assessment, hence designing a multi-tasked face detection and quality assessment network which can perform our FiVR operations with less inference time without sparing prediction accuracy. We evaluated the face quality prediction performance of our proposed approach in comparison with a stand-alone face quality network. We also evaluated the reduction in inference time by comparing the prediction speed of our multi-tasked face detection and quality network against its sequential counterparts. Our experimental results show that our multi-tasked model can successfully meet real-time processing demand while performing at the same level of accuracy as the sequential stand-alone models.
Mersenne Weighted Graph for Increasing the Symmetric Encryption Schemes Security

The intersection of cryptography and number theory has been encapsulated by studying the graph theory. The graphs have an important role in building the cryptographic schemes. In this work, a new contribution has been proposed by using the Mersenne numbers. These numbers are used to define new weighted graphs which named by Mersenne weight graphs (MWG). An efficient symmetric encryption (SE) scheme using MWG has been formed. The implementation results of a proposed algorithm are presented in this study. The security analysis of the proposed MWG-SE algorithm is discussed as well.
Efficient Sentiment classification with Minimal parameters using Average Embedding Approach

Sentiment analysis is the area of research for analyzing customer opinions on services or products delivered by an entity. With the evaluation of deep learning, the recurrent neural network is picked as the preferred method for most of the sentiment analysis research. The goal of this paper is to build a model that uses minimum parameters without compromising too much on the performance. Three models are built on the publicly available dataset. The performance of these models is then evaluated. It is observed that the model using long-short term memory gives very good performance among all the models but uses too many parameters. The last model uses average of word embeddings which uses half of the parameters used in the previous model and its performance is very much near to the previous one.
BiGRU-DA: Based on Improved BiGRU Multi-Target Data Association Method

Trajectory data association algorithm is an important part of multi-target tracking method. Traditional association methods require priori information such as target motion model and clutter density to conduct association, and the association accuracy is low in complex scenes such as trajectory accompaniments and crosses. Based on the sensitivity of BiGRU to time series, this paper proposes an improved trajectory data association algorithm of BiGRU network: BiGRU-DA. In this paper, an output gate structure is added on the basis of the existing BiGRU model, which can optimize the output results of the model and extract the influence of trajectory timing sequence on trajectory data association. In order to verify the data association effect of BiGRU-DA method, the experiment conducted comparison experiments in four datasets under the condition of clutter interference. Experimental results show that BiGRU-DA is superior to other methods in all datasets.
L-CJADE: Differential Evolution Algorithm Based On A Linearly Decreasing Population Structure

Chaotic local search based differential evolutionary optimization algorithm (CJADE) is an improved algorithm based on JADE. It incorporates the ideas of chaotic mapping and local search to enhance the global and local search capabilities of differential evolutionary algorithms. Although the performance of this algorithm has been greatly improved compared with the original algorithm, there are still some shortcomings, such as insufficient exploitation ability, slow convergence, insufficient exploration ability, and easy to lead to local optimum. To solve these problems, we propose a linearly decreasing strategy named L-CJADE, which changes the original population structure and introduces a linearly decreasing population structure to balance the relationship between development ability and exploration ability, making the algorithm more stable and suitable for practical problems. The L-CJADE algorithm was compared with other state-of-the-art algorithms in terms of problem sets. The results show that it has better optimization ability and faster convergence speed along with relatively high stability compared to other algorithms.
A perfect hashing to enhance the performance of Apriori algorithm

Data mining is the process of discovering patterns, relationships, and insights from large datasets using various computational techniques. Association Rules is a data mining method to find various relations between items in an itemset. The Apriori algorithm is a popular and classical algorithm for association rule mining. It has been observed that an increase in the number of candidate sets decreases the efficiency of the Apriori algorithm rapidly. To overcome this issue, the hashing technique, which uses a hash function to reduce the size of the candidate set itemset, is often used. In this paper, a perfect hashing function for a d-itemset is proposed. The efficiency of the Apriori algorithm is enhanced by using the hash function.
Comparison of Reinforcement Learning controller with a classical controller for an UAV

Presently Reinforcement Learning (RL) poses a stiff challenge to analytical control techniques due to its model-free controller design using data and advanced statistical methods. This work attempts to compare the performance of Model Predictive Control (MPC) and a controller trained in RL for an Unmanned Aerial Vehicle (UAV). The RL agent is trained on a Soft Actor-Critic (SAC) algorithm while the PyTorch framework is used for building the Deep Neural Networks. The simulation results on the actual nonlinear UAV model indicate that the RL controller can match and even outperform MPC.