Reviewerly/exHarmony huggingface dataset:
Classification of Polytime Codes of LPI Radar Signals Using XGBOOST Algorithm

Low probability of intercept (LPI) radars are one of the most significant radars in radar technology because of their functionality. It has certain features such as low power, wide bandwidth, etc. To accumulate this, the LPI radars use a particular form of changed wave bureaucracy, which might be tough to intercept. The precept obligations of intercept receivers are to discover, estimate, and classify LPI signs even in the presence of excessive noise. Additionally, the estimation of the modulation parameters additionally offers statistics approximately the danger to radar so that essential counter measures may be initiated towards the enemy radar. In this mission, we initially describe the simulation/contour plots of poly time code indicators (T1, T2, T3, T4) and then the main intention of the task, i.e., the class of poly time codes are effectively labelled. All the four types of Poly time codes are classified using python in anaconda navigator and jupyter notebook. The algorithm used here involves image pre-processing, segmentation, and feature extraction, whose output is fed to an XGBOOST classifier used to classify polytime code images into T1, T2, T3, and T4. In this proposed method, the outcome of testing is very high, which is 89% (accuracy) and the AVC score is 92%, which is an appreciable value.
Model for capturing noise free traces for Side Channel Power Cryptanalysis based on SAKURA-G FPGA and Case study of AES

Side Channel Cryptanalysis is one of the minimum requirments for design of new cryptosystem. The main research is focused on power attack of Side Channel Cryptanalysis in which capturing of noise free traces is most important aspects.This paper presents methodology for side channel power attack based on SAKURA-G FPGA, Mixed signal oscillator, hamming distance model and statistical analysis. This method is tested with stranded cipher: Advanced Encryption Standard (AES) and recovered secret information of it. In this methodology, using SAKURA-G FPGA, captured clearly visible & noise free power traces.
CONVOLUTION MERGING TECHNIQUE FOR IMAGE ENCRYPTION APPLICATION

Multimedia apps on the Internet have grown in popularity in recent years. Because of the rapid development in the usage of multimedia information, data storage and transmission security has become increasingly crucial. Encryption is a useful tool for keeping multimedia data private. Various approaches for encrypting photos to make them more secure are discovered on a regular basis. Here developed a method to merge two types of images which is grayscale and RGB image. Initially, the image is converted into a digital format with the help of MATLAB. It is then fed to the top image module for merging and a digital output file is generated. This file is then reverted back to analog form for visualization purposes with the help of MATLAB. For computing RGB image, the image is split into 3 classes containing the reds, greens and blues of the image. These classes are separately convolved using the image multiplier module designed in Xilinx ISE. This results in the generation of 3 digital output files which are Output_red, Output_green and Output_blue. These files are then combined to form a single-color image and visualized using MATLAB. The structure is shown below. For a grayscale image, a single image multiplier is sufficient to compute or perform image merging. Here, it's no need to split the image into RGB because the image consists different shades of black. Upon processing, a single digital file is generated and this file is visualized using MATLAB.
A Journal on Cryptocurrency Analysis and Price Prediction Model using LSTM Neural Networks

Cryptocurrency is a kind of virtual currency that came into existence with the recent advancement of technology in finance. It is used to complete transactions in a secure way by using the techniques of cryptography. This virtual currency is created with the help of block chain technology. In many countries, the transactions using cryptocurrency are not legalised by the banks. Some of the most popular cryptocurrencies are Bitcoin, Dogecoin, Litecoin, etc. The value of each cryptocurrency keeps varying from time to time. In this research paper, we build a data analytics model of the various cryptocurrency and also a machine learning model using the LSTM (Long Short-Term Memory) algorithm to forecast the value of a certain cryptocurrency on a particular day. This paper uses a web application called Yahoo Finance(yfinance) which has all the details of the live stock market and this acts as a source of dataset for this paper. Also, various python packages such as numpy, pandas, tensorflow, seaborn, matplotlib, etc. are used for building a model. The LSTM algorithm makes use of RNN (Recurrent Neural Network) which is powerful to model data sequencer because it has an internal memory state to store the past seen data. The proposed LSTM algorithm has a 98% accuracy which outperforms the accuracy of other existing models.
FPGA BASED HARDWARE ACCELERATOR FOR CONVOLUTION NEURAL NETWORK

Transport layer of IoT ecosystem relies on optics that work as frame relays in diverse areas. Real-Time images being captured find advantages in terms of latency and decision outcome when the edge computing is preferred keeping in consideration restriction on mobility and increased privacy concerns. These objectives can be achieved by Convolution Neural Networks (CNN) at specialized speed and precisely the right amount of adaptability to handle various computer vision algorithms. It is challenging to design a control accelerator for a CNN that requires a lot of recurrent feature extraction process. Driver assistance systems' real-time, safety-critical face emotion detection classification task is accomplished using a deep learning approach. In this study, an effective overlay for LeNet-5-based face emotion detection and classification utilizing the ZYNQ architecture is proposed. Using the Facial Expression Recognition dataset (FERD) and a pre-trained LeNet-5 Model, a multimodal ZYNQ evaluation system assesses the Overlay's capacity towards inference. Combining Hardware and Software by co-design reduces bandwidth requirement by 30%. Efficiency of per image inference reduces from 2.9 seconds to 30 milli seconds, an uplift of almost 98%. Impact on throughput is extensively visible from 0.5 GOP/sec to 3.6 GOP/sec. Experimental results show similar output in terms of classification accuracy by reducing the amount of time taken to complete classification, thereby making resources available for more data loads. Classification accuracy in both the methods are approximately 98%, thereby making the hypothesis of using Convolution Neural Networks (CNN) with specialized speed a better solution for real-time traffic sign classification.
Investigating Online Dating Fraud: An Extensive Review and Analysis

The growth of the online social networking (OSN) platform has been huge in recent decades. Apart from OSN platforms, online dating websites are gaining attention day by day. People can now meet their potential life partners more easily due to the rise of online dating platforms. However, it has made it easier for fraudsters to take advantage of this growing market. Online social networking fraud ranges from spamming to human-targeted fraud, in which the fraudsters build fake relationships with the victims and demand money. This paper provides an extensive review of online dating fraud methodology, global impact on users, different dating site features, and recent statistics about the rise of online dating users and online dating fraud. In addition to this, we have analyzed fraud prevention methodologies, taxonomies of scam detection, and scammer detection techniques using machine learning. Finally, this survey discusses open research questions, challenges, and important online dating safety guidelines.
Identity Based Encryption and Broadcast using Hybrid Cryptographic Techniques

The quick advancement of cloud computing has brought in an increment in the number of businesses and people who are using the public cloud to store and exchange data. In order to maintain the privacy of the data stored, the owner of the data encrypts the data. This enables the data to be accessible to only specific users who can decrypt it and are authorized to do so. A severe difficulty occurs when this encrypted data has to be shared in a vehicular ad-hoc network with people. To address this problem, we have introduced a hybrid protocol which includes identity based encryption which uses public key cryptography and for broadcast using LCM which uses symmetric key cryptography. We have designed a more efficient and lightweight protocol based on Bilinear maps and also proved its correctness.
An Investigation of the Bibliometric Landscape of the Many Facets of Machine Learning From the Years 2000 to 2022

Machine Learning as an emerging and applicable learning plays a vital role in the world of Computer Science. MachineLearning has attracted much attention in the last decade. With the development of Machine Learning, huge researchers and publications opted for this as the path. This paper is about the bibliometric analysis that is deployed from the year 2000 to 2022. The main three fundamental factors that guide the progress of machine learning are Data, computing, and algorithmic advances. Mainly there are four different types of machine learning which accomplish Supervised ML, Unsupervised ML, Semi-Supervised ML, and Reinforcement Machine Learning which is used in different areas and applications. This paper is mainly focused on what are the major areas where machine learning has used and what are the minimal area where there is still a requirement of using Machine learning is to be used.
Improved Interactive Multiple Model Kalman Filter Design for Tracking Complex Moving Vehicles under Concealed Conditions

In order to adapt to the possible discontinuity and discontinuity in the transmitting signals of special vehicles in the complex combat environment, the Improved Interacting Multiple Model Kalman Filter (I-IMM-KF) method is proposed in this paper. In order to improve the performance of maneuvering target tracking algorithm, this paper adopts four interactive algorithms: constant velocity (CV) model, constant acceleration (CA) model, constant left turn (CT-left) model and constant right turn (CT-right) model, the window function is used to improve the probability density function. Finally, the improved algorithm and the single filter Kalman filter (KF) algorithm are simulated in the case of low noise and large noise and continuous signal characteristics. Simulation results show that the improved IMM algorithm has a higher tracking accuracy than the ordinary KF algorithm, and the advantage is highlighted in the complex environment.
Adversarial Attacks on Deep Temporal Point Process

Temporal point processes have many applications, from crime forecasting to modeling earthquake aftershocks sequences. Due to the flexibility and expressiveness of deep learning, neural network-based approaches have recently shown promise for modeling point process intensities. However, there is a lack of research on the robustness of such models in regards to adversarial attacks and natural shocks to systems. Precisely, while neural point processes may outperform simpler parametric models on in-sample tests, how these models perform when encountering adversarial examples or sharp non-stationary trends remains unknown. Current work proposes several white-box and blackbox adversarial attacks against temporal point processes modeled by deep neural networks. Extensive experiments confirm that predictive performance and parametric modeling of neural point processes are vulnerable to adversarial attacks. Additionally, we evaluate the vulnerability and performance of these models in the presence of non-stationary abrupt changes, using the crimes dataset, during the Covid-19 pandemic, as an example.
Application of Hybrid Encryption Methodsin Digital Steganography Technique for Secure Communication

In this research article, the four main challenges of steganography viz. payload capacity, imperceptibility, robustness and security have been addressed. Four different algorithms viz. Discrete Wavelet Transformation (DWT), Bald Eagle Search Optimization (BES), Huffman Compression and 2-dimensional logistic chaotic map have been hybridized to construct a novel algorithm. The proposed methodology is validated by calculating the values of PSNR, MSE, SSIM, and BPP and compared with the results achieved by different researchers. Further Gaussian noise and crop attacks have been considered to investigate the effect on the secret image. Our methodology is able to thwart the Gaussian Noise attack but could not recover from the cropping attack with optimum quality. This can become an interesting topic for further research in the future. The proposed method has been compared to others, and it performs better overall. For future work, the pixels of the secret image that has been losing can be regenerated by applying any suitable technique.
Cloud Computing Virtual Machine Migration Methodology

In a traditional cloud-based datacentre, several physical machines (PM) host large numbers of virtual machines (VM), which in turn run a wide range of applications and services. VM load changes depending on the various programmes and traffic that users use, and occasionally this traffic may be too much for VMs to handle. Physical machines that host numerous VMs have a limited capacity, and if they become overloaded, their performance could suffer or they could stop functioning altogether. To take advantage of the resources' availability and balance the load, a VM machine that runs multiple apps on a severely loaded PM can be moved to a PM that isn't as busy. If VM migration occurs over a network link that is overloaded with user traffic in data centres where VMs are exposed to a lot of user traffic, it will cause a bottleneck if there isn't enough bandwidth on the link to support the VM migration. To guarantee the bandwidth required for VM migration, there is an obvious need for tools to restrict user traffic on both the source and destination PMs.
Automated Interoperability based on Decision Tree for Schema Matching

The idea of data integration is to provide standardized access to a simultaneous set of independent and possibly heterogeneous data sources within a given property. In these complexions, schema matching is referred to as the task of discovering semantic correspondences between elements of two determined data source schemas. These tasks are important to allow the integration of data and the interoperability of systems in different domains. This task is currently done manually, and prior research has uncovered the difficulty of automation. This article shows the importance of machine learning to create an automatic mapping facility for matching patterns with smarter models and integrating symbiotic methods to improve matching results. This is a new approach to reduce the time needed for schema matching tasks. Our contribution is based on a reference architecture and a prototype for smarter interoperability using a combination of machine learning based on smarter schema matching for mediation systems.
Performance Comparison of AI Models for Digital Image captioning

Digital Image captioning field deals with the concept of interpreting a given image by the application of different Artificial Intelligence (AI) based algorithms. These kind of systems always considered the input in the form of image and replying with an output representing some text or speech samples. This output explains the content present in the image. These kind of automated systems proven good in the areas like medical image processing, online review systems, speech conversion for visually impaired persons etc. This paper represents a comparative study among four different AI models: Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM) and kNN (k-Nearest Neighbor) in context of image captioning activity. Random scenes in the form of input images has been consider to show the part of diverse nature of these algorithms. It has been observed that SVM classifier outshine as compared to rest of the models with the overall accuracy of 98%.
Toward Dynamic Enterprise Architecture Model using MAPE-K, Control theory and Machine Learning to achieve autonomic adaptiveness

Nowadays, it is no longer a secret that organizations must be able to accommodate changes to survive the turbulent and stormy changes we are going through. Enterprise architecture is a tool used to guarantee the alignment of strategy, business, and IT. In this study, we focus on giving the organization, through a dynamic model of Enterprise architecture, an adaptive tool to respond to external and internal constraints keeping the alignment and stability of the enterprise during the response to change. With the prospect of allowing autonomic adaptiveness of our EA model, we propose applying an integrated approach based on the well-known loop MAPE-K in the domain of autonomic computing combined with the Control Theory (CT). We use Machine Learning techniques to give the self-learning ability to our model. This defined pattern, inspired from Dynamico and enriched by ML techniques was applied to our theme of study, Enterprise architecture.
A thorough analysis of Microsoft HoloLens applications in the medical and healthcare fields

Since 2016, Microsoft HoloLens has been utilized in a assortment of businesses, counting healthcare, instruction, preparing, engineering, and building. The objective of this review of the literature is to highlight the employments for HoloLens within the therapeutic industry. In order to comprehend the applications of Microsoft HoloLens within the therapeutic and healthcare segments, a intensive assessment of 44 distributions distributed between 2016 and 2022 was conducted. HoloLens is bringing almost a critical change within the healthcare industry, from the teaching and training of therapeutic students to executing troublesome surgery in real-time reenactments. The consider discoveries of this report will offer assistance analysts and healthcare experts understand how HoloLens is utilized and how it can move forward medical methods in the future.
The airborne antennas' test process and analysis method for lightning direct effect based on system function analysis

This paper is based on the verification requirements for lightning direct effect protection of civil aircraft airborne antennas, Using the method of combining test and big-data analysis, The airborne antennas' airworthiness compliance of lightning direct effect protection is evaluated more scientifically and comprehensively, And summarizing the test procedure, the test qualification criteria that meet the actual needs of the project are proposed. The verification methods and processes established by this project are universal and feasible.
Research on evaluation metric of cross device bypass attack based on deep learning

Based on the bypass analysis of SPECK encryption algorithm, this paper considers the feasibility of using commonly used in-depth learning evaluation index to evaluate the attack efficiency in real cross-device scenarios. We considered the impact of in-depth learning model evaluation indicators for side channel attacks in three different scenarios. We showed that using different devices and keys or changing the chip base during the analysis and attack phase would significantly affect the accuracy indicators compared with changing the probe measurement position. Our experimental results show that, although accuracy is the most commonly used indicator for monitoring and evaluating neural networks, it can result in a significant underestimation of attack efficiency under cross-device conditions.
Route Planning for a Tractor in an Agriculture Field with Obstacles

In this article, we propose a trajectory planning method for a tractor in a rural environment. Our method is based on the simultaneous use of offline and real-time acquired data. The offline data is the geographical map of the agricultural field, which is assumed to be known, as well as vehicle driving data prerecorded by several experienced drivers. The depth video and the location of the vehicle are acquired in real time and used with existing data for the detection and avoidance of obstacles. The planning is seen as a constrained optimization problem. The constraints are related to the presence of obstacles which are considered as spatiotemporal events recognized through their geometric structure represented in the depth frames. We show that the proposed approach validated by using real data is effective and fulfills the real-time requirement.
Keyphrases extraction: Approach Based on Document Paragraph Weights

In recent years, the exploitation of sentence embedding techniques in natural language processing field has encouraged the proposal of new methods for extracting keyphrases from documents based on these techniques. Most of these approaches select keyphrases from a set of candidate phrases based on their semantic proximity to the document. In general, most documents contain complementary paragraphs that are unrelated to the topics covered. This factor reduces the credibility of the semantic proximity of candidate keyphrases to the document. Exploitation of document paragraphs weights during the semantic similarity calculation will inevitably improve the performance of keyphrase extraction from document. In this paper, we propose a new method to extract keyphrases based on document paragraphs weights. Our method is based on sentence embedding techniques and semantic proximity of candidate key phrases from document paragraphs. We evaluated the proposed method on three datasets, Inspec, Semeval2010 and KPTimes, where our results showed that that using document paragraph weight improved the performance of keyphrases extraction.
State of the Art, Recent Developments, and Future Directions in Applying Deep Learning to Part of Speech Tagging in NLP

Rapid information and communicationtechnology advancements have prompted widespread interest in natural language processing (NLP) applications. This has led to the development of a plethora of NLP resources. However, several obstacles stand in the way of creating reliable NLP systems that can process natural languages effectively and efficiently. Part of speech (POS) tagging is one such technology;it assigns labels to sentences or phrases inside a paragraph based on where they appear. Researchers have made great strides in POS tagging, but there is always room for improvement, especially in decreasing false positives and correctly categorizing new words. It's also important to remember that there is sure to be some confusion if you tag phrases with manypossible interpretations depending on the surrounding material. In order to effectively identify words in a particular phrase across a paragraph, POS taggers based on “deep learning (DL) and machine learning (ML)” have recently been deployed as viable solutions.
GMM Evaluation for Speaker Identification

This work present the results obtained with an automatic speaker identification system, which we have developed and which is based on the GMM speaker modeling method, the identification task is assigned to the GMM-UBM. Several experiments of automatic speaker identification carried out in quiet and noisy environments, on TIMIT database is studied. We experimentally show that increasing the number of adaptation coefficients beyond 10 does not bring a significant improvement of the identification rate in quiet environment. and show degradation on performance when the environment where our identification system is operational becomes noisy.