Reviewerly/exHarmony huggingface dataset:
A Comparative Study on the Application of Text Mining in Cybersecurity

Aims: This paper aims to conduct a Systematic Literature Review (SLR) of the relative applications of text mining in cybersecurity. Objectives: worldwide has been attributed to a change in the different activities associated with cyber security and demands a high automation level. Methods: In the cyber security domain, text mining is an alternative for improving the usefulness of various activities that entail unstructured data. This study searched databases of 516 papers from 2015 to 21. Out of which 75 papers are selected for analysis. A detailed evaluation of the selected studies employs source, techniques, and information extraction on cyber security applications. Results: This study extends gaps for future study such as text processing, availability of datasets, innovative methods, intelligent text mining. Conclusion: This study concludes with interesting findings of employing text mining in cybersecurity applications; the researchers need to exploit all related techniques and algorithms in text mining to detect and protect the organization from Cybersecurity applications.
Underestimation estimators to Q-learning

Q-learning (QL) is a popular method for control problems, which approximates the maximum expected action value using the maximum estimated action value, thus it suffers from positive overestimation bias. Various algorithms have been proposed to reduce overestimation bias. But some of these methods cause underestimation bias. Furtherly, about underestimation, which kind of estimators causes underestimation is less understood. In this paper, instead of a certain specific method, we focus on underestimation estimators, especially on K estimates of the action values. We generalize these estimators to propose an Underestimation Estimator Set (UES) and theoretically prove all of the estimators in this set suffer from underestimation bias. We further study the bias properties of these estimators and conclude that their biases are different from each other's and depend on the specific conditions they meet. Thus, our set provides various estimators for QL in different settings. Finally, to better illustrate the properties of estimators, we test the performance of several estimators in our set. Empirical results show that Median estimator (Me) underestimates less than double Q-learning (DQL) and doesn't suffer overestimation as QL, and Min estimator (M1E) underestimates more than DQL. Besides, Me and M1E perform as well as or better than other estimators on some benchmark environments.
Quality Criteria and Method of Synthesis for Adversarial Attack-Resistant Classifiers

The actual problem of adversarial attacks on classifiers, mainly implemented using deep neural networks, is considered. This problem is analyzed with a generalization to the case of any classifiers synthesized by machine learning methods. The imperfection of generally accepted criteria for assessing the quality of classifiers, including those used to confirm the effectiveness of protection measures against adversarial attacks, is noted. The reason for the appearance of adversarial examples and other errors of classifiers based on machine learning is investigated. A method for modeling adversarial attacks with a demonstration of the main effects observed during the attack is proposed. It is noted that it is necessary to develop quality criteria for classifiers in terms of potential susceptibility to adversarial attacks. To assess resistance to adversarial attacks, it is proposed to use the multidimensional EDCAP criterion (Excess, Deficit, Coating, Approx, Pref). We also propose a method for synthesizing a new EnAE (Ensemble of Auto-Encoders) multiclass classifier based on an ensemble of quality-controlled one-class classifiers according to EDCAP criteria. The EnAE classification algorithm implements a hard voting approach and can detect anomalous inputs. The proposed criterion, synthesis method and classifier are tested on several data sets with a medium dimension of the feature space.
Copy-Move Geometric Tampering Estimation Through Enhanced SIFT Detector Method

Digital picture forgery detection has recently become a popular and significant topic in image processing.Due to advancements in image processing and the availability of sophisticated software, picture fabrication may hide evidence and hinder the detection of such criminal cases.The practice of modifying original photographic images to generate a forged image is known as digital image forging.A section of an image is copied and pasted into another part of the same image to hide an item or duplicate particular image elements in copy-move forgery.In order to make the forgeries real and inconspicuous, geometric or postprocessing techniques are frequently performed on tampered regions during the tampering process.In Copy-Move forgery detection, the high similarity between the tampered regions and the source regions has become crucial evidence.The most frequent way for detecting copy-move forgeries is to partition the images into overlapping square blocks and utilize Discrete cosine transform (DCT) components as block representations.Due to the high dimensionality of the feature space, Gaussian Radial basis function (RBF) kernel based Principal component analysis (PCA) is used to minimize the dimensionality of the feature vector representation, which improves feature matching efficiency.In this paper, we propose to use a novel enhanced Scale-invariant feature transform (SIFT) detector method called as RootSIFT, combined with the similarity measures to mark the tampered areas in the image.The proposed method outperforms existing state-of-the-art methods in terms of matching time complexity, detection reliability, and forgery location accuracy, according to the experimental results.The F1 score of the proposed method is 92.3% while the literature methods are around 90% on an average.
A Bhattacharyya Coefficient-Based Framework for Noise Model-Aware Random Walker Image Segmentation

One well established method of interactive image segmentation is the random walker algorithm. Considerable research on this family of segmentation methods has been continuously conducted in recent years with numerous applications. These methods are common in using a simple Gaussian weight function which depends on a parameter that strongly influences the segmentation performance. In this work we propose a general framework of deriving weight functions based on probabilistic modeling. This framework can be concretized to cope with virtually any parametric noise model. It eliminates the critical parameter and thus avoids time-consuming parameter search. We derive the specific weight functions for common noise types and show their superior performance on synthetic data as well as different biomedical image data (MRI images from the NYU fastMRI dataset, larvae images acquired with the FIM technique). Our framework could also be used in multiple other applications, e.g., the graph cut algorithm and its extensions.
Comparing the Reasoning Capabilities of Equilibrium Theories and Answer Set Programs

Answer Set Programming (ASP) is a well established logical approach in artificial intelligence that is widely used for knowledge representation and problem solving. Equilibrium logic extends answer set semantics to more general classes of programs and theories. When intertheory relations are studied in ASP, or in the more general form of equilibrium logic, they are usually understood in the form of comparisons of the answer sets or equilibrium models of theories or programs. This is the case for strong and uniform equivalence and their relativised and projective versions. However, there are many potential areas of application of ASP for which query answering is relevant and a comparison of programs in terms of what can be inferred from them may be important. We formulate and study some natural equivalence and entailment concepts for programs and theories that are couched in terms of inference and query answering. We show that, for the most part, these new intertheory relations coincide with their model-theoretic counterparts. We also extend some previous results on projective entailment for theories and for the new connective called fork.
Pre-Trained Deep Neural Network-Based Computer-Aided Breast Tumor Diagnosis Using ROI Structures

Deep neural network (DNN) based computer-aided breast tumor diagnosis (CABTD) method plays a vital role in the early detection and diagnosis of breast tumors.However, a Brightness mode (B-mode) ultrasound image derives training feature samples that make closer isolation toward the infection part.Hence, it is expensive due to a meta-heuristic search of features occupying the global region of interest (ROI) structures of input images.Thus, it may lead to the high computational complexity of the pre-trained DNN-based CABTD method.This paper proposes a novel ensemble pre-trained DNN-based CABTD method using global-and local-ROI-structures of B-mode ultrasound images.It conveys the additional consideration of a local-ROI-structures for further enhancing the pre-trained DNN-based CABTD method's breast tumor diagnostic performance without degrading its visual quality.The features are extracted at various depths (18, 50, and 101) from the global and local ROI structures and feed to support vector machine for better classification.From the experimental results, it has been observed that the combined local and global ROI structure of small depth residual network ResNet18 (0.8 in %) has produced significant improvement in pixel ratio as compared to ResNet50 (0.5 in %) and ResNet101 (0.3 in %), respectively.Subsequently, the pre-trained DNN-based CABTD methods have been tested by influencing local and global ROI structures to diagnose two specific breast tumors (Benign and Malignant) and improve the diagnostic accuracy (86%) compared to Dense Net, Alex Net, VGG Net, and Google Net.Moreover, it reduces the computational complexity due to the small depth residual network ResNet18, respectively.
Voice Response Questionnaire System for Speaker Recognition Using Biometric Authentication Interface

The use of voice to perform biometric authentication is an important technological development, because it is a non-invasive identification method and does not require special hardware, so it is less likely to arouse user disgust.This study tries to apply the voice recognition technology to the speech-driven interactive voice response questionnaire system aiming to upgrade the traditional speech system to an intelligent voice response questionnaire network so that the new device may offer enterprises more precise data for customer relationship management (CRM).The intelligence-type voice response gadget is becoming a new mobile channel at the current time, with functions of the questionnaire to be built in for the convenience of collecting information on local preferences that can be used for localized promotion and publicity.Authors of this study propose a framework using voice recognition and intelligent analysis models to identify target customers through voice messages gathered in the voice response questionnaire system; that is, transforming the traditional speech system to an intelligent voice complex.The speaker recognition system discussed here employs volume as the acoustic feature in endpoint detection as the computation load is usually low in this method.To correct two types of errors found in the endpoint detection practice because of ambient noise, this study suggests ways to improve the situation.First, to reach high accuracy, this study follows a dynamic time warping (DTW) based method to gain speaker identification.Second, it is devoted to avoiding any errors in endpoint detection by filtering noise from voice signals before getting recognition and deleting any test utterances that might negatively affect the results of recognition.It is hoped that by so doing the recognition rate is improved.According to the experimental results, the method proposed in this research has a high recognition rate, whether it is on personal-level or industrial-level computers, and can reach the practical application standard.Therefore, the voice management system in this research can be regarded as Virtual customer service staff to use.
A modified variant of coyote optimization algorithm for solving ordinary differential equations and oscillatory mechanical problems

A vital subject of engineering structures is mechanical oscillations. If the mechanical oscillation is uncontrolled, it can lead to structural failure due to large dynamic stresses developed, as the collapse occurred on the Broughton Suspension bridge due to soldiers walking in step. This paper addresses mechanical oscillation problems employing a proposed modified variant of coyote optimizer. Coyote optimization algorithm (COA) is a new meta-heuristic which mimics the social behavior of coyotes. COA suffers with stagnation problems and immature convergence while solving optimization problems. In this paper, the COA is hybridized with Laplace Crossover operator and new culture tendency strategies are adapted, modified variants of coyote optimization algorithm (MvCOA). The proposed MvCOA is presented to approximately solve mechanical oscillation problems independently of their order, form, and stated conditions. With the fundamental concepts of ordinary differential equations and Fourier series expansion, mechanical oscillation problems can be modeled as a problem of optimization whereby the optimization task is achieved using the proposed MvCOA. Five different ordinary differential equations and four mechanical oscillation problems are solved approximately and then compared with their corresponding exact solutions. The statistical analysis validates that the presented MvCOA is an effective algorithm for different optimization problems. However, from the empirical results, it is visible that the suggested MvCOA approximate approach was able to reach a successful performance solving different mechanical oscillation problems.
An intelligent Surveillance System for Detecting Abnormal Behaviors on Campus using YOLO and CNN-LSTM Networks

Like many other surveillance systems, security and safety are mainly what concern people these days. It can be used in two methods, watching threats in real-time or searching for an event that already happened. Surveillance systems can be defined in many different ways like violence or any abnormal behaviour detection. In this paper, we propose a surveillance system for detecting abnormal behaviours on campus using deep learning models. It also includes how YOLO, CNN, and LSTM are used to apply concepts like feature extraction, object detection, action detection and identification. YOLO algorithm was used in the smoking detection and the playing cards achieving the accuracy of 90% and 93.8% respectively. As for the CNN-LSTM model, we achieved an accuracy of 93.5%.
Human–System Interaction Based on Eye Tracking for a Virtual Workshop

With the constant exploration and development of intelligent manufacturing, the concept of digital twins has been proposed and applied. In view of the complexity and intellectualization of virtual workshop systems, real workshops can link with virtual workshosp based on AR under the structure of digital twins, which allows users to interact with virtual information and perceive the virtual information superimposed on the real world with great immersion. However, the three-dimensionality of virtual workshops and interaction with complex workshop information can be challenging for users. Due to a shortage of input bandwidth and the nontraditional mode of interaction, a more natural interaction technique for virtual workshops is required. To solve such problems, this paper presents a technical framework for 3D eye movement interaction applied to a virtual workshop. An eye movement interaction technique, oriented to implicit interaction and explicit interaction, is developed by establishing behavior recognition and interaction intention understanding. An eye-movement experiment verifies the former’s accuracy is above 90% and had better recognition performance. A better feature vector group of the latter is selected to establish a model and verify its feasibility and effectiveness. Finally, the feasibility of the framework is verified through the development of an application example.
Neighbour feature attention-based pooling

In modern convolutional neural networks (CNNs), the pooling layer is seen as one of the primary layers for building the CNN model, which effectively downscales the spatial size of feature maps to reduce memory consumption. Several types of pooling operations, such as average pooling, max pooling, and strided convolution, fail to capture the spatial dependence between the pooling region feature and its neighbour features. In this paper, we propose a simple but effective attention-based pooling method called Neighbour Feature Attention-Based Pooling (NFP), which integrates neighbour features of the pooling region to keep semantic continuity across multiple layers. NFP adopts attention weights encoding with neighbour features by depthwise convolution, which effectively directs local spatial pooling for learning discriminative features. Compared to other pooling methods, the proposed method generates more discriminative features directed by neighbour information of the pooling region. The experiments results show that it consistently improves the performance across various backbone architectures on image classification tasks.
Guided-Pix2Pix+: End-to-end spatial and color refinement network for image dehazing

Inevitable hazy contamination degrades the visibility of images, and the resulting haze removal is one of the essential prerequisites for image processing and computer vision tasks. We proposed an end-to-end dehazing network, referred to as Guided-Pix2Pix, to estimate spatially refined transmission maps and dehaze via the physical scattering equation. The remaining enhancement of color contrast, ill-posed adversarial training, and redundant backbone, however, should be thoroughly investigated, as required in the prospect of Guided-Pix2Pix. In this paper, we inherit the end-to-end structure of Guided-Pix2Pix and accordingly propose Guided-Pix2Pix+ as an update, which concatenates transmission estimation and refinement, physical scattering equation-based dehazing, together with color refinement, to achieve haze removal in a one-stage way. Specifically, we make use of the pretrained instance of EfficientNetB0 to estimate coarse-grained transmission maps and concatenate a guided filter layer to perform spatial refinement for the incoming transmission maps. Restored by the physical scattering equation, color refinement of dehazed proposals is finally performed via the standardization and clipping of pixel intensities. All the operations are differentiable, making it possible to achieve end-to-end, tight training. Furthermore, adversarial and perceptual losses are employed to regulate the performance of our model, giving rise to structurally similar but photo-realistic dehazed proposals. Extensive experiments confirm that our Guided-Pix2Pix+ yields dehazed proposals with fine-grained spatial refinement and relatively effective color contrast, compared to our previous Guided-Pix2Pix, the baseline, and advanced dehazing methods. The source code is currently available at https://github.com/92xianshen/guided-pix2pixplus . • Guided-Pix2Pix+ for spatially and color-refined haze removal. • Stable compound adversarial training using WGAN-GP. • Lightweight but visually effective dehazing generator.
Sentiment Analysis of COVID-19 Tweets

Sentiment analysis is the field of computer science that studies people's behaviour or attitude towards objects, events, organizations, and products. Organizations make use of this to improve their products. This paper aims to study people's behaviour towards COVID 19. We have taken data from Twitter to analyse the sentiments. The results show that people don't have positive sentiments towards this coronavirus.
Evaluation of the Patient Experience with the Mawid App during the COVID-19 Pandemic in Al Hassa, Saudi Arabia

(1) Introduction: The objective of this study was to evaluate the patient experience with the Mawid application during the COVID-19 pandemic in Al Hassa, Saudi Arabia. (2) Methodology: A quantitative cross-sectional survey was designed to evaluate the patient experience with the Mawid app during the COVID-19 pandemic in Al Hassa, Saudi Arabia. A total of 146 respondents completed the questionnaire. (3) Results: More than half of the participants (65.8%) opined that application was easy to use. Furthermore, 65.1% of the participants considered it to be very easy and easy to search for the required information; and 63.7% of the respondents reflected that it was easy to book an appointment. There was a statistically significant difference between the ease of searching for the required information (p-value = 0.006); the ease of undoing an unwanted move and gender (p-value = 0.049); the ease of searching for the required information and educational level (p-value = 0.048); the ease of booking an appointment and educational level (p = 0.049); and the ease of searching for the required information and the labor sector of the respondents (p value= 0.049) among the genders. No significant differences were identified among the age groups. (4) Conclusions: Overall, most participants suggested that the Mawid app was easy to use and had a potentially useful set of features to help mitigate and manage the COVID-19 pandemic in Al Hassa, Saudi Arabia.
VDN: Variant‐depth network for motion deblurring

Abstract Motion deblurring is a challenging task in vision and graphics. Recent researches aim to deblur by using multiple sub‐networks with multi‐scale or multi‐patch inputs. However, scaling or splitting operations on input images inevitably loses the spatial details of the images. Meanwhile, their models are usually complex and computationally expensive. To address these problems, we propose a novel variant‐depth scheme. In particular, we utilize the multiple variant‐depth sub‐networks with scale‐invariant inputs to combine into a variant‐depth network (VDN). In our design, different levels of sub‐networks accomplish progressive deblurring effects without transforming the inputs, thereby effectively reducing the computational complexity of the model. Extensive experiments have shown that our VDN outperforms the state‐of‐the‐art motion deblurring methods while maintaining a lower computational cost. The source code is publicly available at: https://github.com/CaiGuoHS/VDN .