Reviewerly/exHarmony huggingface dataset:
HTTP header based phishing attack detection using machine learning

Abstract In the past, many techniques like blacklisting/whitelisting, third‐party, search engine, visual similarity, heuristic, URL features, and website content were used for anti‐phishing. Search engine‐based, third‐party assisted tools and blacklist/whitelist fail to identify new phishing attacks resulting in high FPR. Heuristic and visual similarity approaches are slow, whereas URL and web content‐based techniques do not mimic the dynamic content of current websites and hence cannot stop zero‐day attacks. A study was conducted to understand the critical features used in the past for anti‐phishing, and we found 16 HTTP header features that were novel. In this paper, we have developed a real‐time, highly scalable, feature‐rich anti‐phishing detection technique based on ML that extracts the HTTP headers (predominantly security headers) from web pages to identify them as legitimate or phished. It is observed that phishing sites are short‐lived and are created to achieve a specific objective, like stealing the credential of a user. Once the goal is met, the sites are pulled down immediately. Hence these sites do not take pain to use the security features of web technology and only focus on making the site as similar as possible to the original website. Test results based on our novel features show high accuracy of 97.8% with an average response time of 1.57 s. We have created multiple datasets for different scenarios, like a dataset for website creation through phishing tools and a new dataset for testing unseen phishing attacks. The results thus obtained show detection accuracy of 99% and 95%, respectively.
What Factors Impact Visitors’ Intentions to Use Location-Based AR Games? An Empirical Study from Chinese Cultural Heritage Sites

Location-based AR games have been discussed as a promising means of providing visitors with meaningful experiences at cultural heritage sites, with great potential to enhance users’ positive emotions and well-being. This research created a conceptual model to empirically evaluate the impact of location-based AR games on the experiential intentions of cultural heritage sites based on the broaden-and-build theory of positive emotions. The model was empirically validated using confirmatory factor analysis (CFA) and tested using structural equation modeling (SEM) on 260 visitors who had played location-based AR games at several Chinese cultural heritage sites. The results suggested that visitors’ positive emotions (fulfillment, enjoyment, and self-existence) in location-based AR games positively impact the intention to use location-based AR games. Further, this study demonstrated that competence, relatedness, immersion, presence, interaction existence, emotional existence, and social existence significantly impact tourists’ positive emotional experience. The findings of this study help to complement the application of positive-affect expansion and building theory to the field of cultural heritage research while providing practical insights into the experience of cultural heritage sites.
Mineral Prospectivity Mapping for Orogenic Gold Mineralization in the Rainy River Area, Wabigoon Subprovince

Random Forest classification was applied to create mineral prospectivity maps (MPM) for orogenic gold in the Rainy River area of Ontario, Canada. Geological and geophysical data were used to create 36 predictive maps as RF algorithm input. Eighty-three (83) orogenic gold prospects/occurrences were used to train the classifier, and 33 occurrences were used to validate the model. The non-Au (negative) points were randomly selected with or without spatial restriction. The prospectivity mapping results show high performance for the training and test data in area-frequency curves. The F1 accuracy is high and moderate when assessed with the training and test data, respectively. The mean decrease accuracy was applied to calculate the variable importance. Density, proximity to lithological contacts, mafic to intermediate volcanics, analytic signal, and proximity to the Cameron-Pipestone deformation zone exhibit the highest variable importance in both models. The main difference between the models is in the uncertainty maps, in which the high-potential areas show lower uncertainty in the maps created with spatial restriction when selecting the negative points.
A neural network expert system that allows assessing the quality of the algorithm for choosing the parameters of optimal removal of low-frequency noise from seismograms using the fingerprint method

The article summarizes the results of research on the application of the fingerprint method in seismology. This method can be successfully used in solving various practical problems. We have used it as an effective tool for a thorough analysis of noisy seismograms of regional explosions and earthquakes in order to select the optimal filtering frequency. The cut-off frequency selected with the help of fingerprints will allow you to design an optimal filter that not only reliably suppresses low-frequency noise, but also carefully preserves the signal it hides. Moreover, the method works well when the noise intensity exceeds the signal intensity many times over and under conditions of partial overlap of their frequency ranges. Fundamental is the initial orientation of the method towards automatic implementation with minimal use of additional information. In conclusion, this article proposes to use a neural network expert system that allows you to evaluate the quality of the fingerprint algorithm. To create such a system, we needed to explore current trends in seismology regarding the use of artificial neural networks. Based on extensive worldwide experience, it has been shown that there is a clear renewed interest in the use of small, lowcost networks. The principle on which they are based lies in their perception as training and input data of compact, complexly structured parameters of a high degree of transformation of primary seismograms. It is these parameters that include the characteristics taken from fingerprints using the algorithms we propose. So our experience in creating a neural network expert system naturally turned out to be successful. The reliability of the estimates obtained turned out to be very close to the practical limit of the fingerprint method.
Application of Adaptive Robust Kalman Filter Base on MCC for SINS/GPS Integrated Navigation

In this paper, an adaptive and robust Kalman filter algorithm based on the maximum correntropy criterion (MCC) is proposed to solve the problem of integrated navigation accuracy reduction, which is caused by the non-Gaussian noise and time-varying noise of GPS measurement in complex environment. Firstly, the Grubbs criterion was used to remove outliers, which are contained in the GPS measurement. Then, a fixed-length sliding window was used to estimate the decay factor adaptively. Based on the fixed-length sliding window method, the time-varying noises, which are considered in integrated navigation system, are addressed. Moreover, a MCC method is used to suppress the non-Gaussian noises, which are generated with external corruption. Finally, the method, which is proposed in this paper, is verified by the designed simulation and field tests. The results show that the influence of the non-Gaussian noise and time-varying noise of the GPS measurement is detected and isolated by the proposed algorithm, effectively. The navigation accuracy and stability are improved.
nEGXAI: a negation-based explainable AI through feature learning in Fourier domain

The explainable artificial intelligence (AI) techniques can help us explain the predictive behavior of a machine learning model without using the information of the model architecture, model parameters, or the training strategies but using the human (users) interpretable features and the predictions. This paper presents a technique, namely nEGXAI, to develop explainable AI models. The current explainable AI approaches are generally built on the affirmation-based logic. In other words, when a model predicts a class label, the explainable AI may explain the prediction results by the features that are associated with the predicted class and interpretable by the users. In contrast to the use of affirmation-based logic, the proposed approach uses the negation-based mathematical logic. In other words, when a model predicts a class label, the proposed nEGXAI can explain why the predicted class is not one of the other classes by using the features that are not associated with the predicted class. The proposed nEGXAI is also a model-agnostic technique in the sense that it does not assume any knowledge of model-related information but uses only the features from the data and the predictions, the same way the Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) techniques use. The nEGXAI utilizes the mathematical concept of constructive negation and its effect on the spectral density function of the features in the Fourier domain. It allows nEGXAI to capture the possible prediction failures that are human interpretable and explainable; hence, it delivers a trustworthy explainable AI for decision making.
A GAN-Based Augmentation Scheme for SAR Deceptive Jamming Templates with Shadows

To realize fast and effective synthetic aperture radar (SAR) deception jamming, a high-quality SAR deception jamming template library can be generated by performing sample augmentation on SAR deception jamming templates. However, the current sample augmentation schemes of SAR deception jamming templates face certain problems. First, the authenticity of the templates is low due to the lack of speckle noise. Second, the generated templates have a low similarity to the target and shadow areas of the input templates. To solve these problems, this study proposed a sample augmentation scheme based on generative adversarial networks, which can generate a high-quality library of SAR deception jamming templates with shadows. The proposed scheme solved the two aforementioned problems from the following aspects. First, the influence of the speckle noise was considered in the network to avoid the problem of reduced authenticity in the generated images. Second, a channel attention mechanism module was used to improve the network’s learning ability of the shadow features, which improved the similarity between the generated template and the shadow area in the input template. Finally, the single generative adversarial network (SinGAN) scheme, which is a generative adversarial network capable of image sample augmentation for a single SAR image, and the proposed scheme were compared regarding the equivalent number of looks and the structural similarity between the target and shadow in the sample augmentation results. The comparison results demonstrated that, compared to the templates generated by the SinGAN scheme, those generated by the proposed scheme had targets and shadow features similar to those of the original image and could incorporate speckle noise characteristics, resulting in a higher authenticity, which helps to achieve fast and effective SAR deception jamming.
IDAF: Iterative Dual-Scale Attentional Fusion Network for Automatic Modulation Recognition

Recently, deep learning models have been widely applied to modulation recognition, and they have become a hot topic due to their excellent end-to-end learning capabilities. However, current methods are mostly based on uni-modal inputs, which suffer from incomplete information and local optimization. To complement the advantages of different modalities, we focus on the multi-modal fusion method. Therefore, we introduce an iterative dual-scale attentional fusion (iDAF) method to integrate multimodal data. Firstly, two feature maps with different receptive field sizes are constructed using local and global embedding layers. Secondly, the feature inputs are iterated into the iterative dual-channel attention module (iDCAM), where the two branches capture the details of high-level features and the global weights of each modal channel, respectively. The iDAF not only extracts the recognition characteristics of each of the specific domains, but also complements the strengths of different modalities to obtain a fruitful view. Our iDAF achieves a recognition accuracy of 93.5% at 10 dB and 0.6232 at full signal-to-noise ratio (SNR). The comparative experiments and ablation studies effectively demonstrate the effectiveness and superiority of the iDAF.
A Scoping Review on the Progress, Applicability, and Future of Explainable Artificial Intelligence in Medicine

Due to the success of artificial intelligence (AI) applications in the medical field over the past decade, concerns about the explainability of these systems have increased. The reliability requirements of black-box algorithms for making decisions affecting patients pose a challenge even beyond their accuracy. Recent advances in AI increasingly emphasize the necessity of integrating explainability into these systems. While most traditional AI methods and expert systems are inherently interpretable, the recent literature has focused primarily on explainability techniques for more complex models such as deep learning. This scoping review critically analyzes the existing literature regarding the explainability and interpretability of AI methods within the clinical domain. It offers a comprehensive overview of past and current research trends with the objective of identifying limitations that hinder the advancement of Explainable Artificial Intelligence (XAI) in the field of medicine. Such constraints encompass the diverse requirements of key stakeholders, including clinicians, patients, and developers, as well as cognitive barriers to knowledge acquisition, the absence of standardised evaluation criteria, the potential for mistaking explanations for causal relationships, and the apparent trade-off between model accuracy and interpretability. Furthermore, this review discusses possible research directions aimed at surmounting these challenges. These include alternative approaches to leveraging medical expertise to enhance interpretability within clinical settings, such as data fusion techniques and interdisciplinary assessments throughout the development process, emphasizing the relevance of taking into account the needs of final users to design trustable explainability methods.
Photonic decision making using optical spatiotemporal chaos generated from spatial light modulator

Photonic decision making for solving the multi-armed bandit problem has been reported recently. However, experimental studies have been limited owing to technological difficulties. In this study, we experimentally demonstrate photonic decision making for solving a large-scale multi-armed bandit problem using optical spatiotemporal chaos generated by a semiconductor laser, spatial light modulator, and camera. We implement a parallel configuration of decision making with up to 512 slot machines, which is much larger than the previous experiment by two orders of magnitude. We found better scaling characteristics between decision-making performance and the number of slot machines than those obtained from software-based algorithms.
Physics-aware-trained diffractive deep neural networks

We have applied physics aware training (PAT) to diffractive deep neural networks (D2NN) consisting of multiple spatial light modulators (SLMs) to close the reality gap between the simulation model and the physical system. Compared to conventional training methods using only simulation models, PAT improves classification accuracy in the experiment. In this method, an analytic expression for backpropagation is based on Rayleigh-Sommerfeld diffraction integral as conventional, but the backpropagated error values are replaced by the measured values.
An Advisor-Based Architecture for a Sample-Efficient Training of Autonomous Navigation Agents with Reinforcement Learning

Recent advancements in artificial intelligence have enabled reinforcement learning (RL) agents to exceed human-level performance in various gaming tasks. However, despite the state-of-the-art performance demonstrated by model-free RL algorithms, they suffer from high sample complexity. Hence, it is uncommon to find their applications in robotics, autonomous navigation, and self-driving, as gathering many samples is impractical in real-world hardware systems. Therefore, developing sample-efficient learning algorithms for RL agents is crucial in deploying them in real-world tasks without sacrificing performance. This paper presents an advisor-based learning algorithm, incorporating prior knowledge into the training by modifying the deep deterministic policy gradient algorithm to reduce the sample complexity. Also, we propose an effective method of employing an advisor in data collection to train autonomous navigation agents to maneuver physical platforms, minimizing the risk of collision. We analyze the performance of our methods with the support of simulation and physical experimental setups. Experiments reveal that incorporating an advisor into the training phase significantly reduces the sample complexity without compromising the agent’s performance compared to various benchmark approaches. Also, they show that the advisor’s constant involvement in the data collection process diminishes the agent’s performance, while the limited involvement makes training more effective.
Integration of the methods of algebra of algorithms and computational intelligence for automation of program systems design

An approach to the creation of intelligent systems is considered, which combines the ideas of Glushkov’s algebra of algorithms with the well-known neuroevolutionary algorithms of computational intelligence, which can be used to automate the design and synthesis of programs. The method of neuroevolution of augmenting topologies is intended to reduce the dimensionality of the space for searching for neural network parameters in the form of gradual development of its structure in the process of evolution. A software toolkit has been developed to support the approach, which is based on the construction of high-level specifications of algorithms represented in systems of algorithmic algebras, and generation of corresponding programs based on implementation templates in a target programming language. Parameterized algorithms called hyperschemes are used to solve a certain class of problems. Setting specific values of parameters and subsequent interpretation of hyperschemes allows obtaining algorithms adapted to specific application conditions. The developed program design and synthesis toolkit provides step-by-step development of programs, starting from a high-level algebraic-algorithmic specification. At the output of the toolkit, a program is automatically generated in one of the target programming languages (С, С++, Java, Python), that also include a subject-oriented language for designing neural networks. The mentioned language includes operators and conditions for working with populations, configurations, genomes, and fitness function. The work of the toolkit is illustrated by the example of designing a parameterized evaluation algorithm for a binary multiplexer and generation of a program for the single-pole balancing problem
Optimal solutions in systems consisting of rational agents

Rational agents and systems consisting of such agents are considered. An agent is an autonomous object that has sources of information about the environment (for example, physical sensors) and affects this environment (for example, with the help of actuators). A rational agent is an agent who, in order to achieve a set goal, is able to act effectively, that is, to use behavioral strategies that are close to optimal. It is assumed that there is a utility function, which is defined on the set of possible sequences of actions of the agent (agents, if a system of agents is considered) and takes values in the set of real numbers. A rational agent acts in such a way as to maximize the utility function. A system of rational agents is a system consisting of rational agents that have a common goal and act in an optimal way to achieve it. Agents act using the optimal (or close to optimal) solution of the extreme problem formulated for the system. The work gives examples of systems consisting of rational agents. The main groups of problems and the corresponding mathematical methods of their solution related to the management of agents and systems of rational agents are determined: cooperation of agents (creation of a system of agents), planning and coordination of actions of agents, search of placement of the system of agents, recognition. Cooperation is necessary when no single agent has sufficient experience, resources, and information to solve a problem, but different agents have the expertise and capabilities to solve different parts of the problem. Planning is the development of a method of action of agents and the entire system in the future depending on the situations that may arise, the choice of an effective method of action, optimal distribution of resources. Coordination is such an organization of the actions of various agents that make up the system, which ensures the efficiency of this system. The tasks of finding the optimal placement of agent system and the task of recognizing the state of the environment are important. Examples of such problems are given and mathematical methods of their solution are indicated.
Evaluation of ChatGPT Applicability to Learning Quantum Physics

ChatGPT is an application that uses a large language model. Its purpose is to generate answers to various questions as well as provide information, help solve problems and participate in conversations on a wide range of topics. This application is also widely used by students for the purposes of learning or cheating (e.g., writing essays or programming codes). Therefore, in this contribution, we evaluate the ability of ChatGPT to answer questions in quantum physics. That is, we develop a benchmark consisting of ten questions, whose difficulty is measured on a ten-grade scale. Then ChatGPT answers are evaluated and discussed. In this way, we can measure how well quantum-physics information is processed by this application. Our results demonstrate that ChatGPT does not notice subtle differences between physical terms, and can provide wrong answers to quantum-physics-related questions. It can also provide false mathematical formulas, claim that they are correct and confirm its answers. Note that this AI application is not sure of its answers, and in seven cases it apologizes for the first answer when a user has negated it. To sum up, AI represented by ChatGPT is only able to support students in the process of learning quantum physics at the fundamental level. Moreover, during collective exams in the future, where cheating and the use of AI by students may occur, exam questions should not be descriptive, but should be focused on solving computational problems.
A Secure Peer-to-Peer Image Sharing Using Rubik’s Cube Algorithm and Key Distribution Centre

Abstract In this work, we build upon an implementation of a peer-to-peer image encryption algorithm: “Rubik’s cube algorithm”. The algorithm utilizes pixel-level scrambling and XOR-based diffusion, facilitated through the symmetric key. Empirical analysis has proven this algorithm to have the advantage of large key space, high-level security, high obscurity level, and high speed, aiding in secure image transmission over insecure channels. However, the base approach has drawbacks of key generation being handled client-side (at nodes) and the process is time-consuming due to dynamically generating keys. Our work solves these issues by introducing a Key Distribution Center (KDC) to distribute symmetric keys for transmission, increasing confidentiality, and reducing key-generation overhead on nodes. Three approaches utilizing the KDC are presented, communicating the dimensions with KDC to generate keys, standardizing any image to fixed dimensions to standardize key-generation, and lastly, using a single session key which is cyclically iterated over, emulating different dimensions.