Reviewerly/exHarmony huggingface dataset:
Detecting Phishing Websites Using Neural Network and Bayes Classifier

Phishing is a social engineering attack that is based on a cyberattack and it focuses on naïve online users by spoofing them to provide their sensitive credentials such as password, username, security number, or debit card number, etc. Phishing can be performed by masking a webpage as a legitimate page to pull the personal credentials of the user. Nonetheless, there are a lot of methodologies that have been introduced as a solution for detecting the phishing websites such as the whitelist approach or blacklist approach, visual similarity-based approach, and meta-heuristic approach however still the online users are getting scammed into revealing sensitive credentials in phishing websites. In this research paper, a novel hybrid methodology PB-cup learner was proposed, which is based on integration dimensional and neural learning that is pulled from the source code, uniform resource locator, and representative state transfer API to overcome the drawbacks of the existing phishing techniques. This model gives the accuracy analysis of the Naïve Bayes Classifier, Genetic Algorithm, Multi-Layer Perceptron, Multiple Linear Regression, and PB-CUP neural learner and out of which, the Multi-Layer Perceptron algorithm has been performed the best with an accuracy of 99.17%. The experiments were iteratively analyzed with different orthogonal algorithms for finding the best classifier accuracy for phishing website detection.
Automatic Detection of Breast Calcification in Ultrasound Imaging with Convolutional Neural Network

Abstract Breast cancer is a common type of cancer that leading death causes of female in the worldwide. Breast calcification can be one of indicator that can be used to detect the breast cancer early. One of the preferred methods used by radiologist to detect breast cancer is ultrasound imaging. Ultrasound imaging is much safer than mammography that followed by radiological effect. However, ultrasound imaging contaminated with speckle noise that looks similar to breast calcification. It can be the cause of the long time diagnosis process. It encourages so many methods of computed aided diagnosis (CADx) that can detect abnormalities automatically. One of them is Convolutional Neural Network (CNN). CNN can be used to classify the normal breast and breast with abnormalities. In this paper, CNN has been proposed for the classification of the ultrasound images into normal breasts and breasts with calcification. Experimental results classification accuracy was 76 % and a sensitivity of 84.61%.
Grapheme to Phoneme Mapping for Tamil Language

In this paper, a Deep learning-based approach is analyzed to improve the accuracy of the Language Modelling of a system that is a part of ASR. The main intent is to build an End to End Neural Network Language Model. Generally, ASR works in three different modes. The modes are Acoustic Modeling, Lexicons, and Language Modeling. In ASR, G2P models are vital in the case of analyzing the Out Of Vocabulary words. Mostly these G2P models were fabricated using the traditional approaches such as Bi-gram, n-gram language models. The traditional approaches are deceptive in aiding the meanings, long term dependencies of the words and are also impotent in rendering the phonemes of new words. Thus Seq2Seq G2P models exceed the state of the art of the traditional approach. As we all know, the ASR (Speech to Text) system is widely used in AI applications such as voice dialing, system control, navigation, etc. The method which is applied here is the Statistical based one. A method other than the Rule-based approach is trialed out. A suitable neural network of LSTM and BI-LSTM is proposed which enhances the accuracy of the system.
MP-RW-LSH

Approximate Nearest Neighbor Search (ANNS) is a fundamental algorithmic problem, with numerous applications in many areas of computer science. Locality-Sensitive Hashing (LSH) is one of the most popular solution approaches for ANNS. A common shortcoming of many LSH schemes is that since they probe only a single bucket in a hash table, they need to use a large number of hash tables to achieve a high query accuracy. For ANNS- L 2 , a multi-probe scheme was proposed to overcome this drawback by strategically probing multiple buckets in a hash table. In this work, we propose MP-RW-LSH, the first and so far only multi-probe LSH solution to ANNS in L 1 distance, and show that it achieves a better tradeoff between scalability and query efficiency than all existing LSH-based solutions. We also explain why a state-of-the-art ANNS -L 1 solution called Cauchy projection LSH (CP-LSH) is fundamentally not suitable for multi-probe extension. Finally, as a use case, we construct, using MP-RW-LSH as the underlying "ANNS- L 1 engine", a new ANNS-E (E for edit distance) solution that beats the state of the art.
Extension of Contracts for Variability Modeling and Incremental Update Checks of Cyber Physical Systems

Due to the increasing electronic and software portions in Cyber Physical Systems (CPSs), such as highly automated cars, and their safety-criticality, thorough verification and validation activities are an essential part of their development process. Meanwhile, software life cycles are getting shorter, and Over The Air updates are becoming state of the art, even in safetycritical domains. However, validating each software update for a wide range of system variants and versions is a challenging task, which requires efficient verification methods to reduce time, effort and costs. Contract-based Design (CBD) is a design paradigm to handle the complexity of safety-critical CPSs. Though, due to the multitude of maintained variants and versions, using CBD for continuous products improvement in form of updates may rapidly become challenging. In this paper, we integrate CBD into a product line development approach by mapping variant and fine-grained contracts to reusable components. Applying our approach to a case study from the automotive domain showed a significant reduction of the effort for contractifying the systems. Besides, it offers a basis for conducting incremental analysis and verification of modular updates.
Bayesian Optimization of Distributed Neurodynamical Controller Models for Spatial Navigation

Dynamical systems models for controlling multi-agent swarms have demonstrated advances toward resilient, decentralized navigation algorithms. We previously introduced the NeuroSwarms controller, in which agent-based interactions were modeled by analogy to neuronal network interactions, including attractor dynamics and phase synchrony, that have been theorized to operate within hippocampal place-cell circuits in navigating rodents. This complexity precludes linear analyses of stability, controllability, and performance typically used to study conventional swarm models. Further, tuning dynamical controllers by hand or grid search is often inadequate due to the complexity of objectives, dimensionality of model parameters, and computational costs of simulation-based sampling. Here, we present a framework for tuning dynamical controller models of autonomous multi-agent systems based on Bayesian Optimization (BayesOpt). Our approach utilizes a task-dependent objective function to train Gaussian Processes (GPs) as surrogate models to achieve adaptive and efficient exploration of a dynamical controller model's parameter space. We demonstrate this approach by studying an objective function selecting for NeuroSwarms behaviors that cooperatively localize and capture spatially distributed rewards under time pressure. We generalized task performance across environments by combining scores for simulations in distinct geometries. To validate search performance, we compared high-dimensional clustering for high- vs. low-likelihood parameter points by visualizing sample trajectories in Uniform Manifold Approximation and Projection (UMAP) embeddings. Our findings show that adaptive, sample-efficient evaluation of the self-organizing behavioral capacities of complex systems, including dynamical swarm controllers, can accelerate the translation of neuroscientific theory to applied domains.
Noise Map Guided Inpainting Network for Low-Light Image Enhancement

Capturing images in a low-light environment are usually bothered with problems such as serious noise, color degradation, and images underexposure. Most of the low-light image enhancement approaches cannot solve the problem of the loss of the details of the result caused by noise. Inspired by the image inpainting task, we propose a novel Noise-map Guided Inpainting Network (NGI-Net) that introduces inpainting modules to restore missing information. Specifically, the algorithm is divided into two stages. Stage I decomposes input images into a reflection map, an illumination map, and a noise map inspired by the Retinex theory. These maps are passed through Stage II to fine-tune the color and details of the images based on a designed feature enhance group and a selective kernel enhance module. Experiments on real-world and synthesized datasets demonstrate the advantages and robustness of our method. The source code of our method is public in https://github.com/JaChouSSS/NGI-Net.
Analysis of Block Chain Technology Applying in Digital Archives

After archives are digitized, the efficiency of their information value mining depends on the information technology used in the open sharing of digital archives. The article discusses the risks and technical requirements of open sharing of digital archives, and analyses the fit between block-chain technology and the open sharing needs of digital archives from seven dimensions, and proposes that block chain technology should be selected in digital archives based on the seven-dimensional needs. The application model of open sharing points out that the needs of “subject rights” and the “impossible triangle paradox” are the evolution direction of open sharing of digital archives based on block chain.
Secured File Storage in Cloud Computing Application: Secura-Drive

In the current digitizing world, storing our important documents or records on a cloud storage service has become an efficient method but storage drives can be vulnerable if not secured properly. This persuaded us to implement a secure drive that will value the highly rated motto of furtiveness which is the most desired principle in today's era. In this paper, we outlined a solution to overcome the problem of unauthorized user's access to the contents of the database by storing the files in the database in an encrypted format that will maintain the integrity of the data. A comparative study was constructed by analyzing three different cryptographic algorithms based on the security provided and the time required of encrypting-decrypting the file. Based on the comparison, the best technique was used in the cloud storage application. The drive will be immune to attacks and penetrations using encryption and two-step authentication using Face ID Authorization. To use the drive economically, the solution was to compress the files before storing them. The files selected for upload by the user will be compressed and encrypted at the client-side before uploading and decrypted and decompressed back in the original size after the user requests to view it.
Automatic Myelofibrosis Grading from Silver-Stained Images

Histopathology studies the tissues to provide evidence of a disease, type and grade. Usually, the interpretation of these tissue specimens is performed under a microscope by human experts, but since the advent of digital pathology, the slides are digitised, shared and viewed remotely, facilitating diagnosis, prognosis and treatment planning. Furthermore, digital slides can be analysed automatically with computer vision methods to provide diagnostic support, reduce subjectivity and improve efficiency. This field has attracted many researchers in recent years who mainly focused on the analysis of cells morphology on Hematoxylin & Eosin stained samples. In this work, instead, we focused on the analysis of reticulin fibres from silver stained images. This task has been addressed rarely in the literature, mainly due to the total absence of public data sets, but it is beneficial to assess the presence of fibrotic degeneration. One of them is myelofibrosis, characterised by an excess of fibrous tissue. Here we propose an automated method to grade myelofibrosis from image patches. We evaluated different Convolutional Neural Networks for this purpose, and the obtained results demonstrate that myelofibrosis can be identified and graded automatically.
Airspace Object Detection Above the Guarded Area Using Segmentation Neural Network

With the increasing number of drones and unmanned aerial vehicles (UAVs), more emphasis is placed on guarding of the airspace around private and also public buildings. In this contribution authors are introducing a complex multi-step approach for aerial objects detection. Introduced process is composed of a few consecutive steps, where objects are cropped from original input with use of cropping pattern provided by task of image segmentation. These objects are then classified and evaluated as a threat or not. However, the emphasis here is placed on the segmentation part only. Neural network topology, adopted from U-Net architecture, was proposed. Case study was made and discussed in an effort to cover a large number of possible states. The results of a proposed convolutional neural network architecture were compared with the U-Net architecture. Applying of the convolutional neural network to the task of airspace object detection lead to sufficiently precise results, thanks to which it is possible to assume the possibility of its use in the proposed multi-step detection system in further work.
Reducing depth ambiguity in 3D human pose and body shape estimation

The previous 3D pose and shape estimation methods often suffer from the problem of depth ambiguity. Hence, we present a novel method to reduce the depth ambiguity by explicitly considering the depth of a person’s body surface. The key idea is minimizing the difference between the depth estimated from an input image and the projected depth of a reconstructed 3D mesh. This allows the proposed method to estimate 3D pose and body shape with plausible 3D joint locations. Evaluations show that the proposed method produces more appropriate 3D meshes and reduces both 3D pose and shape estimation errors.
The Influence of Social Media, Emotional Intelligence Parents Support on Learning Motivation During the Covid-19 Pandemic

The pandemic caused by the coronavirus or better known as COVID-19 has become a worldwide problem and Indonesia is no exception. This study aims to reveal the influence of social media, parental support, and emotional intelligence on students' learning motivation during the COVID-19 pandemic. This research is quantitative research with a survey approach. The population in this study were all students of Madrasah Aliyah Negeri 2 East Jakarta, totaling 840 students. The research sample was 205 students taken randomly. Research data is obtained through valid and reliable instruments. The results of the study can be concluded 1). Social media has a positive effect on learning motivation; 2). Parental support has a positive effect on learning motivation; 3). Emotional intelligence has a positive effect on learning motivation; 4). Social media, parental support, and emotional intelligence stimulants have a positive influence on learning motivation; The influence of social media variables, parental support, emotional intelligence on learning motivation is 67.9%.
Research on the application of immersive media in new retail cyberspace

While the new pneumonia epidemic (CVD-19) has had an unprecedented impact on the traditional economic model, it has also given a huge boost to the new retail industry immersive technologies such as Virtual Reality (VR) and Augmented Reality (AR). Mixed Reality (MR)will achieve breakthrough development of even more 5G technology, focusing on digital media with multi-sensory interaction features such as visual, auditory, and kinesthetic-immersive media-to build a new paradigm for commercial information dissemination.Therefore, it is of obvious relevance to understand the value of immersive media in the digital retail space an to find the right way to apply it to the way coastal cities grow economically.
Meta-networks: Reconfigurable cable network topologies for interference control

The importance of filters in signal processing abound in real-life applications such as audio electronics and power distribution networks. We introduce an efficient concept of constructing reconfigurable cable networks for interference control. The method utilises quantum-graph formalisms on composite nodes to implement reconfigurable, compact, wideband filters. By using a cascade of loop networks as building blocks, we show that such meta-networks can provide a flexible way of suppressing unwanted signals thereby increasing the efficiency of the underlying networks.Numerical results show that microwave interference can be filtered to allow only specific narrow (or wide) band frequencies to be fully transmitted while suppressing the other frequency bands. For example, a wave of frequency 0≤ f≤ 6 GHz can be filtered using a cascade of three-loop networks with cable lengths 0.0001 ≤ l ≤ 0.05 m. By tuning one of the constituent cables, we are able to achieve maximum power transmission on a specific set of frequencies while completely suppressing signals of unwanted frequencies. The present paper shows how to implement narrow-, medium- and wide-band bandpass filters by adopting a simple and easy-to-design cable topology for the reconfigurable filter. The proposed solution can be easily integrated with solutions, including varactor diode and phase-shifter based architectures, to achieve practical implementations.
Implementing An Improved Image Enhancement Algorithm On FPGA

As image enhancement works with the initial raw inputs at the front end of the image processing applications, it can be considered as a backbone of image processing, and offers a wide range of area of applications, and also have started being used in medical field over the past decade. On the other hand, these image enhancement techniques are bit demanding in choice of processing units when it comes to implementation due to the demand of high resolution. In this paper we present an improved image enhancement algorithm in terms of performance and its implementation on FPGA to satiate the necessity of high speed, powerful, and cost-effective processing unit by providing flexibility, parallelization, pipelining and reconfigurability. The FPGA implementation was obtained using High Level Synthesis from MATLAB specifications and implemented an improved image enhancement algorithm on Cyclone V by using Intel Quartus Prime CAD tool suite. We have considered an X-ray image size of 1000x1920p for implementation and achieved a quantitative improvement in PSNR values and hardware resource utilization along with the better visual interpretability by our proposed improvements.
THE BASICS OF QUANTUM TECHNOLOGIES IN GENERAL SECONDARY EDUCATION INSTITUTIONS: CONDITIONALITY AND PROVISION

Information about curricula of quantum-oriented content disciplines and training of quantum specialists at different levels of higher education in Ukrainian institutions is systematized. The necessity of propaedeutics of quantum technologies training in institutions of general secondary education is substantiated. Examples of the world and Ukrainian practices of popularization of quantum technologies among students of high school age are given.
Learning Indistinguishable and Transferable Adversarial Examples

The fast gradient sign method series can attack deep neural networks (DNNs) with high black-box success rates but with low image fidelity. Although the Adam iterative fast gradient tanh method breaks this limitation, its performance is not good enough. In this paper, we propose a Mixed-input Adam Iterative Fast Gradient Piecewise Linear Method (MAI-FGPLM) to generate adversarial examples with more indistinguishability and transferability for image classification task. Our method utilizes the piecewise linear function and the gradient regularization term to reduce the perturbation size for better image fidelity, and improves the transferability of adversarial examples via the mixed-input strategy for higher attack success rates. Extensive experiments on an ImageNet-compatible dataset show that the adversarial examples generated by our attack method have smaller perturbation size while offering higher attack success rates. Our best attack, NI-TI-DI-MAILM, evades six black-box defenses with the average perturbation size decreased by 1.11 and the average success rate increased by 2.1% compared with the state-of-the-art gradient-based attacks.
Feature Model Configuration Reuse Scheme for Self-Adaptive Systems

Most large-scale systems including self-adaptive systems utilize feature models (FMs) to represent their complex architectures and benefit from the reuse of commonalities and variability information. Self-adaptive systems (SASs) are capable of reconfiguring themselves during the run time to satisfy the scenarios of the requisite contexts. However, reconfiguration of SASs corresponding to each adaptation of the system requires significant computational time and resources. The process of configuration reuse can be a better alternative to some contexts to reduce computational time, effort and error-prone. Nevertheless, systems’ complexity can be reduced while the development process of systems by reusing elements or components. FMs are considered one of the new ways of reuse process that are able to introduce new opportunities for the reuse process beyond the conventional system components. While current FM-based modelling techniques represent, manage, and reuse elementary features to model SASs concepts, modeling and reusing configurations have not yet been considered. In this context, this study presents an extension to FMs by introducing and managing configuration features and their reuse process. Evaluation results demonstrate that reusing configuration features reduces the effort and time required by a reconfiguration process during the run time to meet the required scenario according to the current context.
Vessel Image Enhancement Method Using Nonlinear Transformation Based on Guided Filtering

Aiming at the problems of blurred contrast and difficult diagnosis of vascular tissue in medical images, A nonlinear transform blood vessel enhancement method based on guided filtering is proposed. In this paper, a hybrid filter is obtained by fusing guided filtering and Laplacian filtering to complete the preprocessing of image vessel detail enhancement, then the green component is stretched by nonlinear mapping function in RGB color space, the red component and blue component are adjusted according to the stretching degree. Finally, the saturation component is nonlinear contrast stretched in HSV color space to achieve vascular image enhancement. The paper uses objective evaluation methods such as image DV-BV, information entropy and average gradient to compare the performance of existing image enhancement algorithms. The results show that the proposed method has excellent performance.
Generalized Net Model for Collecting, Evaluating and Including of Facts in the Educational Content

The research expounded in this paper is a continuation of previous investigations into the modelling of information flow with a typical university. A generalized net model which describes the process of creating and evaluation of educational content using a set of criteria is constructed. The example for education of medical students in diabetes mellitus and possibilities for venous thrombotic disease is given.