Reviewerly/exHarmony huggingface dataset:
Comprehensive study on image forgery techniques using deep learning

Image forgery detection using deep learning is a technique that utilizes artificial neural networks to identify whether an image has been morphed or manipulated. This is an important task in the digital age, as the proliferation of easily accessible photo editing software has made it easy for individuals to alter images for various purposes, such as spreading misinformation or altering evidence. Deep learning algorithms work by training on large datasets of images and learning to recognize patterns and features that are indicative of forgery. These algorithms can then be used to analyze new images and determine whether they have been manipulated. One approach to image forgery detection using deep learning is to train a convolutional neural network (CNN) on a dataset of both genuine and forged images. The CNN can then be used to classify new images as either genuine or forged based on the features it has learned to recognize. An additional strategy is to make use of a generative adversarial network, often known as a GAN. This system is made up of two neural networks that collaborate with one another to generate and categorise images. By training, the GAN on a dataset of genuine and forged images, the discriminator network can learn to recognize the features of forged images and classify them accurately. There are several challenges to overcome when using deep learning for image forgery detection. Despite these challenges, deep learning has shown promising results for image forgery detection and is an active area of research. In the future, it is likely that deep learning algorithms will play an important role in detecting and preventing image forgery, helping to maintain the integrity and trustworthiness of digital images.
Stack and Queue Based Parser Approach for Parsing Expression Grammar

Parsing Expression Grammar (PEG) and Packrat Parser are the two recent developments in the field of Formal Languages and Automata Theory. The syntax of PEG is similar to the syntax of Context free grammar with ordered choice. It is based on backtracking recursive-descent parser. Packrat parsing technique is used for handling backtracking in recursive decent parser. Backtracking operation in parsing Expression Grammar ensure in polynomial time at huge memory cost. Recursive Descent parsers (RD) are top-down parsing technique in which derivation of string follows the structure of the grammar. It is very easy to write and debug. Recursive Descent parsers admit very limited class of grammar. It is extended by using recursive decent parser with backtracking which takes explosive runtimes. It cannot used for the grammar with left recursion. It also cannot deal with ambiguous grammar. We develop stack based parsing technique which is recursive descent-like. It takes cubic time in worst case. It may be parse even for left recursion grammar.
Label Correlation in Deep Learning-Based Side-Channel Analysis

The efficiency of the profiling side-channel analysis can be significantly improved with machine learning techniques. Although powerful, a fundamental machine learning limitation of being data-hungry received little attention in the side-channel community. In practice, the maximum number of leakage traces that evaluators/attackers can obtain is constrained by the scheme requirements or the limited accessibility of the target. Even worse, various countermeasures in modern devices increase the conditions on the profiling size to break the target. This work demonstrates a practical approach to dealing with the lack of profiling traces. Instead of learning from a one-hot encoded label, transferring the labels to their distribution can significantly speed up the convergence of guessing entropy. By studying the relationship between all possible key candidates, we propose a new metric, denoted Label Correlation (LC), to evaluate the generalization ability of the profiling model. We validate LC with two common use cases: early stopping and network architecture search, and the results indicate its superior performance.
A Nonlinear PID-Incorporated Adaptive Stochastic Gradient Descent Algorithm for Latent Factor Analysis

High-dimensional and incomplete (HDI) data are commonly encountered in various big data-related applications concerning the complex interactions among numerous nodes, such as the user-item iterations in a recommender system. A stochastic gradient descent (SGD)-based latent factor analysis (LFA) model can perform efficient representation learning to such HDI data, thereby extracting useful knowledge from them. However, a standard SGD algorithm updates a latent factor based on the current stochastic gradient only, without the considerations on the past information, making a resultant model suffer from slow convergence. To address this critical issue, this paper proposes an Adaptive Non-linear PID-incorporated SGD (ANPS) algorithm with two-fold ideas: 1) rebuilding the instant learning error when computing the stochastic gradient following the principle of a nonlinear PID controller to incorporate past update information into the learning scheme efficiently, and 2) implementing gain parameter adaptation following the principle of particle swarm optimization (PSO). Experiments on six widely-adopted HDI datasets demonstrate that compared with state-of-the-art LFA models, an ANPS-based LFA model achieves significant advantage in both efficiency and accuracy. Moreover, its flexible gain parameter adaptation mechanism greatly boosts its practicability for real issues. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In many industrial applications like recommender systems, social network systems, and cloud service systems, people usually encounter numerous nodes and their highly-incomplete relationships. An HDI matrix is commonly adopted to describe such specific relationships. One of the major challenges is to acquire useful knowledge from an HDI matrix efficiently and accurately for various data analysis tasks, e.g., accurate recommendation, community detection, and web service selection. An SGD-based LFA model has been widely adopted to tackle this issue. However, it suffers from slow convergence that leads to considerable time cost on large-scale datasets. This study proposes an ANPS algorithm following the principle of a nonlinear PID controller. With it, an ANPS-based LFA model is achieved, which possesses fast convergence rate on an industrial HDI matrix. The proposed ANPS algorithm can be leveraged for different types of various machine learning models, thereby improving their utility and scalability in practice.
Computational model for breast cancer diagnosis using HFSE framework

Mammography is one of the imaging modalities used in diagnosing breast cancer at an earlier stage. Misdiagnosis leads to risks for the patients. Better feature extraction and selection techniques can reduce misdiagnoses as they are essential in better-performing classifiers. The proposed approach in this paper introduces a novel Hybrid Feature Extraction and Hybrid Feature Selection (HFSE) framework. It uses mammograms from Digital Database for Screening Mammography (DDSM) datasets to classify mammograms into benign and malignant images. This paper presents a novel hybrid feature extraction approach from the Gray level co-occurrence matrix (GLCM), Linear local binary pattern (LBP), Gabor, and Tamura. Combinedly, it extracts 104 features to train the advanced classifiers such as Logistic Regression, Linear Perceptron, Support Vector Machine, Decision Tree, and Artificial Neural Network. The proposed Hybrid feature extraction method vigorously compares combinations of existing single feature extraction methods. The paper also presents a novel hybrid feature selection approach to choose a subset of the most relevant feature. It compares the Intrinsic feature with the proposed hybrid feature selection method. Hyperparameter tuning and Pipeline optimization techniques applied to the classifiers improve their performance metrics. The experimental results show that the proposed framework performs better using Hybrid feature extraction and feature selection on Artificial Neural Networks. This paper makes a comparative analysis of the related works. It outperforms by achieving a classifier accuracy of 94.57%, specificity of 91.80%, the sensitivity of 95.59 %, and an F1-score of 94.89% on Artificial Neural Networks.
Domain adaptation assisted automatic real-time human-based video summarization

The video summarization plays a momentous role for real-time surveillance for detection of suspicious human activities in public scenes. However, efficient, and accurate human-centric video summarization in real-time videos is still a challenging issue due to several inconsistencies such as noise, illumination effects, scale, or rotational variance, etc. Though, several prominent research studies are available related to human activities detection but a robust human-centric video summarization approach is a major issue. In this research, we expound an efficient video summary generation model (ReHuSum) that creates a short summary with human. Our approach employs the conception of domain adaptation for efficient summarization via transferring the knowledge of a potent pre-trained benchmark Inception-v3 model. Additionally, a frame differencing redundancy removal technique is adopted that aids in generating a precise and accurate video summary with human. Our model generates efficient short summaries from real-time videos with an overall human detection accuracy of 99.97%. Besides, we present two novel qualitative metrics namely; summarization rate (Summrate) and summarization loss (Summloss) for assessing the efficacy of overall summarization. The approach generates a real-time human-based summary of 46s with a (Summrate) of 98.7% and (Summloss) of 0.013 for an outdoor CCTV with a duration of 25.2 min. Similarly, an indoor CCTV of 40.10 min duration is summarized to 44s at a (Summrate) of 96.5% and (Summloss) of 0.035. The model can be utilized in real-time security applications (i.e., border or home surveillance, critical infrastructure, smart cities, etc.) to generate accurate video summaries with human.
Dark-Center Based Insulator Detection Method in Foggy Environment

In foggy environments, outdoor insulator detection is always with low visibility and unclear targets. Meanwhile, the scale of haze simulation insulator datasets is insufficient. Aiming to solve these problems, this paper proposes a novel Dark-Center algorithm, which is a joint learning framework based on image defogging and target detection. Firstly, the dark channel prior algorithm is used to calculate the foggy sky image transmittance and then transpose it to the original image to generate a foggy-simulated insulator dataset; secondly, the defogging and restoration modules and an optimized defogging module are combined to improve the robustness of the defogging algorithm; then, for small insulator detection, the CenterNet network structure is improved to enhance the feature extraction capability for small targets; finally, the target detection accuracy in foggy environments is improved by jointly learning the structure details and color features recovered in image defogging via the defogging model and the target detection model, which effectively learn the structure details and color features recovered in image defogging. The experimental results on the CPILD dataset show that the proposed Dark-Center algorithm based on image defogging and target detection can effectively improve the performance of the target detector in foggy scenes, with a detection accuracy of 96.76%.
Iterative cleaning and learning of big highly-imbalanced fraud data using unsupervised learning

Abstract Fraud datasets often times lack consistent and accurate labels, and are characterized by having high class imbalance where the number of fraudulent examples are far fewer than those of normal ones. Machine learning designed for effectively detecting fraud is an important task since fraudulent behavior can have significant financial or health consequences, but is presented with significant challenges due to the class imbalance and availability of reliable labels. This paper presents an unsupervised fraud detection method that uses an iterative cleaning process for effective fraud detection. We measure our method performance using a newly created Medicare fraud big dataset and a widely used credit card fraud dataset. Additionally, we detail the process of creating the highly-imbalanced Medicare dataset from multiple publicly available sources, how additional trainable features were added, and how fraudulent labels were assigned for final model performance measurements. The results are compared with two popular unsupervised learners and show that our method outperforms both models in both datasets. Our work achieves a higher AUPRC with relatively few iterations across both domains.
Being Bayesian about learning Gaussian Bayesian networks from incomplete data

We propose a Bayesian model averaging (BMA) approach for inferring the structure of Gaussian Bayesian networks (BNs) from incomplete data, i.e. from data with missing values. Our method builds on the ‘Bayesian metric for Gaussian networks having score equivalence’ (BGe score) and we make the assumption that the unobserved data points are ‘missing completely at random’. We present a Markov Chain Monte Carlo sampling algorithm that allows for simultaneously sampling directed acyclic graphs (DAGs) as well as the values of the unobserved data points. We empirically cross-compare the network reconstruction accuracy of the new BMA approach with two non-Bayesian approaches for dealing with incomplete BN data, namely the classical structural Expectation Maximisation (EM) approach and the more recently proposed node average likelihood (NAL) method. For the empirical evaluation we use synthetic data from a benchmark Gaussian BN and real wet-lab protein phosphorylation data from the RAF signalling pathway.
A Transformer-Based Cross-Window Aggregated Attentional Image Inpainting Model

To overcome the fault of convolutional networks, which can be over-smooth, blurred, or discontinuous, a novel transformer network with cross-window aggregated attention is proposed. Our network as a whole is constructed as a generative adversarial network model, and by embedding the Window Aggregation Transformer (WAT) module, we improve the information aggregation between windows without increasing the computational complexity and effectively obtain the image long-range dependencies to solve the problem that convolutional operations are limited by local feature extraction. First, the encoder extracts the multi-scale features of the image with convolution kernels of different scales; second, the feature maps of different scales are input into a WAT module to realize the aggregation between feature information and finally, these features are reconstructed by the decoder, and then, the generated image is input into the global discriminator, in which the discrimination between real and fake images is completed. It is experimentally verified that our designed Transformer window attention network is able to make the structured texture of the restored images richer and more natural when performing the restoration task of large broken or structurally complex images.
Mercury Prediction in Urban Soils by Remote Sensing and Relief Data Using Machine Learning Techniques

This article aims to explore the use of machine learning (ML) methods for mapping the distribution of mercury (Hg) content in topsoil, using the city of Ufa (Russia) and adjacent areas as an example. For this purpose, a soil dataset of 250 points sampled from a 0–20 cm depth on different land uses, including residential, industrial and undisturbed (forests and parks), was used. Random Forest (RF), Extreme Gradient Boosting (XGboost), Cubist and k-Nearest Neighbor (kNN) ML techniques were employed to model and map the Hg concentrations. We used remote sensing data (RSD) and topographic attributes as explanatory variables. ML models were calibrated and validated using the leave-one-out cross-validation approach. The Hg content varied from 0.005 to 0.58 mg/kg and was characterized by very high variability. According to the MAE and RMSE metrics, the RF method resulted in the most accurate spatial prediction for the Hg content (0.029 and 0.065 mg/kg, respectively), while the XGBoost approach showed the lowest prediction efficiency (0.032 and 0.073 mg/kg, respectively). The results showed that the slope map, spectral index MSI and Sentinel-2A band B11 were the key variables in explaining the variability of Hg content. We found that higher uncertainty values of soil Hg were found in croplands, urban residential and industrial areas, which supports the view that spatial modelling of HM in urban landscapes is challenging. The present study provides insights into the potential of digital soil mapping techniques in combination with RSD and terrain variables for identifying areas at risk of Hg contamination in urban areas, which can inform land-use planning and management strategies to protect human health and the environment.
Impact of DEMs for Improvement Sentinel 2 Lithological Mapping Utilizing Support Vector Machine: A Case Study of Mineralized Fe-Ti-Rich Gabbroic Rocks from the South Eastern Desert of Egypt

Fused remote sensing datasets have greatly contributed to enhancing lithological targets and providing significant information for mineral exploration. For instance, multispectral datasets can discriminate rock units through their unique spectral signatures. Digital Elevation Models (DEMs) could be an effective tool boosting lithological discrimination based mainly on their topographic variations. Consequently, the current study applied the power of the support vector machine (SVM) algorithm and the integrated Phased Array L-type band Synthetic Aperture Radar (PALSAR) DEM and multispectral Sentinel 2 data to: 1—Create an updated lithological map of a poorly mapped arid terrain (Khashir-Ras El-Kharit district, Eastern Desert of Egypt), and 2—Compare and assess the distribution of ferrogabbros with the aim of improving the localization of these rock bodies and investigating their mineral content. Our results enhanced the lithological mapping and revealed a better generalization of mineralized ferrogabbros when the input was a fused DEM with Sentinel 2 compared to the salt and pepper effect observed when adopting the Sentinel 2 solely as the input data. Additionally, the current research strongly recommends detailed exploration programs for Fe-Ti oxide ores within the gabbroic rocks delineated through the resultant thematic map. The ferrogabbros were subjected to a comprehensive analysis involving whole rock geochemistry, XRD, EDX, and light-reflecting investigations, which revealed the existence of magnetite, titanomagnetite, and hematite. The titanomagnetite crystals display subhedral morphology and exhibit fine- to medium-grained surfaces with irregular shapes. X-ray diffraction (XRD) analysis revealed the presence of magnetite and hematite in the concentrated Fe-Ti oxides. Additionally, the whole rock geochemistry highlighted the origin of the ferrogabbros and their proposed tectonic setting as an alkaline to calc-alkaline magma type that developed in a continental arc setting.
Bilingual education as a means of improving future natural sciences teachers quality of education

The article deals with the problem of introduction of the elements of bilingual education in professional training of future natural sciences teachers. It has been found out that Ukraine has some historical experience of introducing bilingual education in general secondary education institutions. It has been revealed that the main drawback, in this perspective, is the training of future teachers who would be able to provide the opportunity for school students to study natural sciences subjects in two languages. The content of the elective course "Science teachers training in US universities" which is taught in English, as one of the means of ensuring a "parity model" of bilingual education for future natural sciences teachers and a "displacing model" has been revealed. Learning of this course involves a dual purpose - the formation of students’ knowledge of the concept of natural sciences teachers training in the United States (structure, features of the content, forms and methods of natural sciences teachers training under conditions of continuous pedagogical education) and adaptation of this knowledge to the educational process in Ukraine; formation of intercultural interaction skills. The peculiarity of the training technology is that teaching is carried out in English depending on the level of students’ foreign language competence formation and educational tasks complexity. It has been established that the teaching methods which include the use of different doses of assistance during the performance of cognitive tasks and differentiating of their complexity level are effective while teaching this course. The use of such teaching methods allows to provide each student with the optimal conditions for the maximum satisfaction of his cognitive needs and formation of communicative skills. These methods involve a gradual transition from collective forms of work to partially independent and fully independent and performing of the tasks, the complexity of which is growing. The effectiveness of the content of the course "Science teachers training in US universities" according to such a criterion as didactic quality has been experimentally proved. The quality of its study by the criterion of formation of methods of mental activity and development of types of speech activity has been confirmed.
A prediction error based reversible data hiding scheme in encrypted image using block marking and cover image pre-processing

A drastic change in communication is happening with digitization. Technological advancements will escalate its pace further. The human health care systems have improved with technology, remodeling the traditional way of treatments. There has been a peak increase in the rate of telehealth and e-health care services during the coronavirus disease 2019 (COVID-19) pandemic. These implications make reversible data hiding (RDH) a hot topic in research, especially for medical image transmission. Recovering the transmitted medical image (MI) at the receiver side is challenging, as an incorrect MI can lead to the wrong diagnosis. Hence, in this paper, we propose a MSB prediction error-based RDH scheme in an encrypted image with high embedding capacity, which recovers the original image with a peak signal-to-noise ratio (PSNR) of ∞ dB and structural similarity index (SSIM) value of 1. We scan the MI from the first pixel on the top left corner using the snake scan approach in dual modes: i) performing a rightward direction scan and ii) performing a downward direction scan to identify the best optimal embedding rate for an image. Banking upon the prediction error strategy, multiple MSBs are utilized for embedding the encrypted PHR data. The experimental studies on test images project a high embedding rate with more than 3 bpp for 16-bit high-quality DICOM images and more than 1 bpp for most natural images. The outcomes are much more promising compared to other similar state-of-the-art RDH methods.