Reviewerly/exHarmony huggingface dataset:
Reactive search-MST optimized clustering-based feature selection

Data clustering is a technique for analyzing the data that is incurred in various fields such as data processing, pattern recognition, knowledge discovery and machine learning. Feature clustering is an important paradigm for different types of feature selection techniques that aims to reduce redundant and irrelevant features from a given set of features in order to maintain load balance on the classification algorithm. The work proposed a PSO–GSO–MST, a hybrid approach that combines Particle Swarm Optimization (PSO) and Glowworm Swarm Optimization (GSO). The work performs efficient feature selection with improved classification accuracy. Clustering analysis plays an important role in knowledge discovery and data mining. It adopts the unsupervised learning method, and the results of clustering are similar within the class and are different between the classes. Aiming at some shortcomings of traditional clustering algorithms, some techniques for clustering using natural heuristic algorithms have emerged. The proposed work performs cluster using optimized Minimum Spanning Tree (MST). The work aims to perform optimization of MST with the help of two renowned techniques such as PSO and GSO. The proposed PSO–GSO–MST is compared with state-of-the-art algorithms such as Clustering-based Feature Selection (CFS) and PSO–MST. The results show that the classification accuracy for the proposed PSO–GSO–MST performs better by 16.9% than CFS and by 4.7% than PSO–MST optimized CFS, respectively. The outcome of the work proves that the proposed algorithm achieves improved performance than the currently available algorithms and can be used for clustering applications.
Multi-Exposure Image Fusion Using Cross-Attention Mechanism

Multi-exposure fusion (MEF) is a popular method for obtaining high dynamic range (HDR) image from multiple low dynamic range (LDR) images. Even though recent works have employed the convolutional neural networks (CNNs) for solving the MEF problem, there still remain various challenges, such as color distortion and detail loss, due to a limited receptive field. In this paper, we present a cross-attention module for multi-exposure image fusion. Different from existing CNN-based methods that capture the contexts of the local region in the target image, our method adaptively aggregates local features with global dependencies at all positions. Furthermore, we propose a detail compensation module as the feature fusion for restoring the loss (color and detail) in the saturation region. Our proposed network performs a feature extraction with an encoder, a fusion of a cross-attention module and a detail compensation module, and the fused image is reconstructed by a decoder. Experimental results show that compared with the state-of-the-art methods, the proposed method can obtain better performance in both the subjective and objective evaluation, particularly in terms of color expression and detail-preserving.
European Cybersecurity Certification Schemes and cybersecurity in the EU internal market

Abstract The principal question addressed by this paper is: how adequate are the minimum security objectives of the European Union Cybersecurity Act (Regulation (EU) 2019/881) in assisting organisations in the European Union internal market with resisting and recovering from cyber threats? The question is answered by first identifying the scope of the minimum security objectives. Scope identification, performed through legislative interpretation, reveals an integrated system of security objectives with significant gaps. Second, the minimum security objectives are evaluated within a model of cyber attacks from attack reconnaissance to legal proceedings to reveal further significant gaps. Finally, the minimum security objectives are evaluated within five cyber attack scenarios, reflecting the highest ranking cyber threats to the internal market. The simulation analysis accentuates the findings of the model analysis and identifies further significant gaps. In conclusion, the minimum security objectives are found to be largely inadequate in assisting organisations in the European Union internal market with resisting and recovering from cyber threats. The analysis of the adequacy of the minimum security objectives is timely, as the first European cybersecurity certification schemes are currently being designed.
MaskVO: Self-Supervised Visual Odometry with a Learnable Dynamic Mask

Visual odometry, one of the main research topics in robotics, is capable of offering pose information for mobile robotic systems. Recent advances in deep learning allow mobile robots to learn ego-motion and depth maps jointly in a self-supervised manner. However, the existing approaches are confounded by the problem of scale ambiguity and by environmental issues, preventing real-world applications. Our work aims to tackle these two problems by proposing a self-supervised visual odometry model that exploits the temporal dependencies of image sequences and produces scale-consistent motion transformations from a monocular camera. Our proposed framework is integrated with a novel mask network to provide learnable dynamic masks that reduce the influences of scene dynamics and illumination changes. We evaluated our framework against public benchmarks, showing that our MaskVO outperforms existing baselines. Moreover, we also investigated the effectiveness of our proposed dynamic mask network via a detailed ablation study.
PConv: simple yet effective convolutional layer for generative adversarial network

This paper presents a novel convolutional layer, called perturbed convolution (PConv), which performs not only a convolution operation but also a dropout one. The PConv focuses on achieving two goals simultaneously: improving the generative adversarial network (GAN) performance and alleviating the memorization problem in which the discriminator memorizes all images from a given dataset as training progresses. In PConv, perturbed features are generated by randomly disturbing an input tensor before performing the convolution operation. This approach is simple but surprisingly effective. First, to produce a similar output even with the perturbed tensor, each layer in the discriminator should learn robust features having a small local Lipschitz value. Second, since the input tensor is randomly perturbed during the training procedure like the dropout in neural networks, the memorization problem could be alleviated. To show the generalization ability of the proposed method, we conducted extensive experiments with various loss functions and datasets including CIFAR-10, CelebA, CelebA-HQ, LSUN, and tiny-ImageNet. The quantitative evaluations demonstrate that PConv effectively boosts the performance of GAN and conditional GAN in terms of Frechet inception distance (FID).
Design Patterns for Mobile Augmented Reality User Interfaces—An Incremental Review

The virtual enhancement of the physical world through Augmented Reality (AR) has an enormous potential in its application, but faces challenges in its development. The lack of standards and the increased complexity of interaction opportunities complicate the definition of suitable User Interfaces (UIs). Several principles and patterns have been formulated to simplify UI design for AR applications, but their joint contribution to a positive usability as well as the influence of individual patterns remain unclear. In this paper, AR design principles from selected research were reviewed and merged into a comprehensive pattern model within an incremental process. Based on an initial model, we developed ARScribble, a mobile AR application which imitates a physical spray can to virtually sketch within a real environment. In a user-based study, we evaluated the usability of ARScribble as well as the role of individual patterns for the overall usability. We found promising indications that the pattern model implementation is related to a positive usability. The individual pattern analysis showed that AR users particularly desire a consistent and structured UI. A consistent appealing design and multimodal interaction concepts were also found to positively correlate with the overall usability. Based on these results, we included additional related work to refine the initial model into a final pattern model. To evaluate this refinement, the colAR application was developed, which allows real-world colors to be assigned to virtual objects. As a result, we found the consideration of the final pattern model to be related to a positive usability, which was confirmed in an A/B test, in which an application neglecting the pattern model showed a significantly poorer usability.
The creation of a large set of realistic synthetic microcalcification clusters for simulation in (contrast-enhanced) mammography images

Characterization of microcalcification clusters in the breast and differentiation between benign and malignant structures on (contrast-enhanced) mammography (CEM) images is of great importance to determine cancerous lesions. Computer algorithms may help performing these tasks, but typically need large sets of data for model training. Therefore this paper develops a method to create synthetic microcalcification clusters that can later be used to overcome data sparsity problems. Starting from descriptors of the shape and size, both benign and malignant microcalcifications were created and then combined into 3-dimensional cluster models given realistic geometric properties. The distributions of the largest diameter and the number of microcalcifications per cluster in a set of 500 simulated clusters were set such that they agreed with those of real clusters. An existing simulation tool was then extended to insert the clusters into processed, low-energy CEM background images with appropriate contrast values. In a validation study comprised of 40 real and 40 synthetic cases, radiologists were asked to evaluate realism and malignancy. It was found that the shape and the structure of the individual microcalcifications as well as the complete clusters were realistic. Thus the descriptors were chosen correctly and enabled a good classification between benign and malignant cases. The realistic brightness and boundary smoothness proved the simulation tool can correctly insert the 3D clusters into real background images and is suitable of creating a large set of realistic microcalcification clusters simulated in existing (contrast-enhanced) mammography images. With improvements on the correspondence of insertion location in craniocaudal and mediolateral oblique view, which proved more challenging to simulate realistically, this promising method is expected to be applicable for modeling complete synthetic cases. Such a dataset can be used for data enrichment where data sources are limited and for development and training purposes.
A Comprehensive Study of Regression Analysis and the Existing Techniques

In many different sciences, including medicine, engineering, and observational studies, the investigation of the relationship between variables, i.e., dependents, and independents, is defined as research objectives. Employing statistical methods to achieve the relationship between variables is very time-consuming or costly in many scenarios and does not provide practical application. Therefore, numerous machine learning algorithms have been introduced with the advancement of science and inspiration from nature to perform regression and modeling. Machine learning models have been able to have an excellent position in this field and provide admirable results. This paper examines and compares various regression models and machine learning algorithms. The selected techniques include multiple linear regression (MLR), ridge regression (RR), least absolute shrinkage and selection operator (LASSO) regression, multilayer perceptron (MLP), radial basis function (RBF), decision tree (DT), support vector regression (SVR), and k-nearest neighbors (KNN). In addition, many evaluation metrics are employed to further investigate and compare the performances of the selected algorithms on a couple of datasets. Our simulation results further demonstrate the significant superiority and accuracy of the presented work.
A Hybrid Deep Learning Based Character Identification Model Using CNN, LSTM, And CTC To Recognize Handwritten English Characters And Numerals

Detection and recognition of handwritten English language characters and numerals is facing challenges due to the huge variation and haziness of strokes from one individual to the other, variation in the handwriting style, poor quality of the original document as the paper deteriorate over time, and text in printed documents sit in a straight line whereas humans need not write a line of text in a straight line on white paper. To address these challenges, a deep learning based handwritten text model is proposed that is capable of identifying both characters and numerals from an input image consisting of English language characters and numbers. The proposed model makes use of Convolutional Neural Network (CNN) with Long Short Term Memory (LSTM) method to detect and identify the text. The proposed model has been trained and tested using MNIST dataset consisting of full page images from 3600 writers and 800,000 images. Experimented with different number of epochs, it is found that the proposed model is able to identify the text with an accuracy of about 88.8&#x0025;.
Evaluating the impact of MR image contrast on whole brain segmentation

The development of automatic whole brain segmentation algorithms has greatly facilitated large-scale multi cohort magnetic resonance (MR) image analyses in recent years. However, the performance of these segmentation algorithms is often affected by image contrast due to the variations in pulse sequences, acquisitions parameters, and manufacturers. Quantitatively evaluating segmentation algorithms on different image contrasts is challenging because manual delineations of the human brain are usually limited. In this study, we tackle the problem by synthesizing new contrast MR images from a small set of images with manual delineations. We quantitatively evaluate the current state-of-the-art whole brain segmentation algorithm, SLANT, on various MR image contrasts. Based on 50 manually delineated T1-weighted MR images acquired from a single site, we synthesize new contrast images using a deep learning-based harmonization algorithm. Two types of contrast synthesis were conducted to simulate both intra- and inter-site contrast variability in MR imaging. SLANT performance is measured using the Dice similarity coefficient (DSC). Experiments show that the average DSC of SLANT varies with image contrast. We also demonstrate the preferred and the least preferred contrast of SLANT based on 11 real MR imaging sites.
Customized deep neural network model for autonomous and efficient surveillance of wildlife in national parks

As National parks are one of the best places of attraction to the tourists, the so-called abode of the animals has to be protected due to the frequent intervention of human beings. On the other hand, the lives of human beings have also to be insulated from the sudden and vulnerable attack of the animals. When it comes to ‘life’, it becomes a vital factor for humans and animals. Therefore, here arises a dire need to create a system which can protect the life and safety of human beings as well as animals. However, when it comes to mutual safety of both, there has not been an effective solution till date that can practically be implemented in a national park or a sanctuary. In this scenario, ‘Video Object Identifying’ using Machine Learning Technique has been proven to render promising results in terms of accuracy and performance. Classifying and identifying objects using Customized Convolutional Neural-Network Model in order to detect the multi-class objects (vehicle and animal) become the aim of this present research. In this research, five categories of data sets namely lion, elephant, tiger, car and motorcycle have been used for training and testing. The custom model for video object detection renders with an accuracy of 82% which proves its efficiency in detecting the multi class datasets.
Identification of Bots and Cyborgs in the #FeesMustFall Campaign

Bots (social robots) are computer programs that replicate human behavior in online social networks. They are either fully automated or semi-automated, and their use makes online activism vulnerable to manipulation. This study examines the existence of social robots in the #FeesMustFall movement by conducting a scientific investigation into whether social bots were present in the form of Twitter bots and cyborgs. A total of 576,823 tweets posted between 15 October 2015 and 10 April 2017 were cleaned, with 490,449 tweets analyzed for 90,783 unique persons. Three separate approaches were used to screen out suspicious bot and cyborg activity, supplemented by the DeBot team’s methodology. User 1 and User 2, two of the 90,783 individuals, were recognized as bots or cyborgs in the study and contributed 22,413 (4.57 percent) of the 490,449 tweets. This confirms the existence of bots throughout the campaign, which aided in the #FeesMustFall’s amplification on Twitter, complicating sentiment analysis and invariably making it the most popular and lengthiest hashtag campaign in Africa, particularly at the time of data collection.
CheckScan: a reference hashing for identity document quality detection

One of important challenges in the document liveness detection process for identity document verification is quality verification. To tackle this challenge, this paper proposes a reference hashing approach to discriminate between the original template of the identity document image and the scan one, which is called CheckScan. Actually, the discrimination process takes place between two aligned identity document images. The proposed approach is made up of two steps: feature extraction based on Fast Fourier Transform (FFT) and hash construction. Feature extraction step involves partitioning the identity document image into set of non-overlapping blocks, and for each block the FFT magnitude spectrum is calculated. Hence, a specific number from the FFT magnitude peaks is selected as discriminative features. The hash construction step quantizes the selected peaks into binary codes by applying a new quantization approach that is based on the coordinates of the selected peaks. These two steps are combined together in this work to achieve good discriminate (well anti-collision) capability for distinct identity document images. Experiments were conducted in order to analyze and identify the most proper parameters to achieve higher discrimination performance. The experimental results were performed on the Mobile Identity Document Video dataset (MIDV-2020), and the results show that the proposed approach builds binary codes quite discriminative for distinct identity document images.
Machine Learning-based Improvement in Quality of Learning using 4Q &amp; S Approach

Learning concepts have been extensively used in the manufacturing education sector, but practitioners have shown less interest in transferring learning theory to the educational sector. This work aims to demonstrate the critical nature of incorporating learning principles into educational institutions via the 4Q & S Model. The learning process for getting good outcomes by improving the overall performance. Eliminating waste in the organization, preparation, and execution of teaching processes in the classroom and resultant environment by adding value to the student learning process via appropriate predication. This may increase student performance using the Machine learning (ML) approach. Researchers who wish to enhance their activities within the classroom/college/university and teaching process to improve student outcomes can follow the learning-based 4Q & S Model presented in this work, namely (Intelligence, Adversity, Spiritual, Emotional, Stress), and thus make research and educational work more efficient and effective in terms of student outcomes by predicting their view of learning. The suggested 4Q & S model may assist college/university staff and students when used in an educational setting.
An Efficient Deepfake Video Detection Approach with Combination of EfficientNet and Xception Models Using Deep Learning

Artificial intelligence is used in many areas and is constantly being developed. In recent years, videos made with deep fakes, which are often heard, have also developed. The use of videos made with deep fakes as blackmail in people&#x0027;s lives, manipulating the videos of important people to cause anxiety on people and etc. due to the fact that it poses a threat in many areas presents a big problem today. Efforts are being made to prevent this threat by detecting deep fake videos. Deep fake detection is still not fully resolved. For this reason, prominent technology companies provide support to researchers in this field and develop deep fraud detection by suggesting methods and organizing contests on most platforms such as Kaggle. In this article, a detection method is proposed to minimize the current concern of deep forgery. In the proposed method, the Xception model with high performance and speed and the EfficientNetB4 model with high accuracy were used. The proposed method aims to achieve better results and improvements in detecting fake videos.
DLCP: A Robust Deep Learning with Non-linear CA Mechanism for Lung Cancer Prediction

Lung cancer is identified as the most dangerous hazards faced by both female and male. Lung cancer may result various breathing problems during inhaling and exhaling. Many static and dynamic methods are invented to predict this disease and mitigate the effect. The death rate due to lung cancer is increasing in old and young people which is alarming. We have studied the existing literature and found that, there is a need for a comprehensive, adoptable and accurate versatile classifier for the lung cancer to predict the same at an early stage to mitigate the mortality rate. We have collected 1, 37, 896 datasets (CT images) from UCI machine learning repository to train and test our classifier. We have applied non-linear cellular automata augmented with convolution neural network (CNN) for predicting the lung cancer. The proposed classifier DLCP (Deep Learning Cancer Prediction) is compared with standard baseline methods with the parameters accuracy, error rate, specificity, sensitivity, ROC and precision. DLCP reports an average accuracy of 98.49% which is promising compared with the cited literature.
Prospects of Geoinformatics in Analyzing Spatial Heterogeneities of Microstructural Properties of a Tectonic Fault

The paper proposes a special technique for microstructural analysis (STMA) of rock samples based on two provisions. The first one is an algorithm for the automatic detection and digitalization of microstructures in images of oriented thin sections. The second one utilizes geographic information system (GIS) tools for an automatized analysis of objects at the micro scale. Using STMA allows the establishment of geometric features of fissure and pore space of rock samples to determine the parameters of stress–strain fields at different stages of rock massif deformation and to establish a relationship between microstructures and macrostructures. STMA makes it possible to evaluate the spatial heterogeneity of physical and structural properties of rocks at the micro scale. Verification of STMA was carried out using 15 rock samples collected across the core of the Primorsky Fault of the Baikal Rift Zone. Petrographic data were compared to the quantitative parameters of microfracture networks. The damage zone of the Primorsky Fault includes three clusters characterized by different porosity, permeability, and deformation type. Findings point to the efficiency of STMA in revealing the spatial heterogeneity of a tectonic fault.