Reviewerly/exHarmony huggingface dataset:
AUTOMATED PENETRATION TESTING METHOD USING DEEP MACHINE LEARNING TECHNOLOGY

The article developed a method for automated penetration testing using deep machine learning technology. The main purpose of the development is to improve the security of computer systems. To achieve this goal, the analysis of existing penetration testing methods was carried out and their main disadvantages were identified. They are mainly related to the subjectivity of assessments in the case of manual testing. In cases of automated testing, most authors confirm the fact that there is no unified effective solution for the procedures used. This contradiction is resolved using intelligent methods of analysis. It is proposed that the developed method be based on deep reinforcement learning technology. To achieve the main goal, a study was carried out of the Shadov system's ability to collect factual data for designing attack trees, as well as the Mulval platform for generating attack trees. A method for forming a matrix of cyber intrusions using the Mulval tool has been developed. The Deep Q - Lerning Network method has been improved for analyzing the cyber intrusion matrix and finding the optimal attack trajectory. In the study, according to the deep reinforcement learning method, the reward scores assigned to each node, according to the CVSS rating, were used. This made it possible to shrink the attack trees and identify an attack with a greater likelihood of occurring. A comparative study of the automated penetration testing method was carried out. The practical possibility of using the developed method to improve the security of a computer system has been revealed.
Photon-emitter dressed states in a closed waveguide

We study a system made up of one or two two-level quantum emitters, coupled to a single transverse mode of a closed waveguide, in which photon wavenumbers and frequencies are discretized, and characterize the states in which one excitation is steadily shared between the field and the emitters. We unearth finite-size effects in the field-emitter interactions and identify a family of dressed bound states that represent the forerunners of bound states in the continuum in the limit of an infinite waveguide. We finally consider the potential interest of such states for applications in the field of quantum information.
Tide-Inspired Path Planning Algorithm for Autonomous Vehicles

With the extensive developments in autonomous vehicles (AV) and the increase of interest in artificial intelligence (AI), path planning is becoming a focal area of research. However, path planning is an NP-hard problem and its execution time and complexity are major concerns when searching for optimal solutions. Thus, the optimal trade-off between the shortest path and computing resources must be found. This paper introduces a path planning algorithm, tide path planning (TPP), which is inspired by the natural tide phenomenon. The idea of the gravitational attraction between the Earth and the Moon is adopted to avoid searching blocked routes and to find a shortest path. Benchmarking the performance of the proposed algorithm against rival path planning algorithms, such as A*, breadth-first search (BFS), Dijkstra, and genetic algorithms (GA), revealed that the proposed TPP algorithm succeeded in finding a shortest path while visiting the least number of cells and showed the fastest execution time under different settings of environment size and obstacle ratios.
Design and Implementation of Intelligent Learning System Based on Big Data and Artificial Intelligence

In order to improve the level of education development in remote areas of China, help minority schools to better play the advantages of multimedia network teaching resources provided by the state, and further improve the level of multimedia network management, a multimedia network teaching management system is studied and developed. The teaching information and business process of the school is analyzed, the division of the structure of the film and television multimedia information management system is completed, and the overall structure, functional structure, and database of the system are designed; Tornado technology, NoSQL database technology, jQuery technology, and Ajax technology are used to complete the development of the system. The results show that the real system can provide a highly automated platform for the school multimedia network-teaching management. The system makes the teaching staff work more efficiently and accurately. The results show that the system studied has a significant effect on the multimedia teaching of minority films and television. Using the characteristics of the combination of pictures, text, sound, image, and shadow, the knowledge can be shown to students intuitively, which is helpful to relaxed and happy learning of students and stimulate their desire to learn. Multimedia teaching has become an important teaching assistant tool. This exploration can provide theoretical support for the design of the network teaching information management system based on artificial intelligence, decentralize the complex network environment, and carry out decentralized management.
Event and Entity Coreference using Trees to Encode Uncertainty in Joint Decisions

Coreference decisions among event mentions and among co-occurring entity mentions are highly interdependent, thus motivating joint inference.Capturing the uncertainty over each variable can be crucial for inference among multiple dependent variables.Previous work on joint coreference employs heuristic approaches, lacking well-defined objectives, and lacking modeling of uncertainty on each side of the joint problem.We present a new approach of joint coreference, including (1) a formal cost function inspired by Dasgupta's cost for hierarchical clustering, and (2) a representation for uncertainty of clustering of event and entity mentions, again based on a hierarchical structure.We describe an alternating optimization method for inference that when clustering event mentions, considers the uncertainty of the clustering of entity mentions and viceversa.We show that our proposed joint model provides empirical advantages over state-ofthe-art independent and joint models.
Subgraph Federated Learning with Missing Neighbor Generation

Graphs have been widely used in data mining and machine learning due to their unique representation of real-world objects and their interactions. As graphs are getting bigger and bigger nowadays, it is common to see their subgraphs separately collected and stored in multiple local systems. Therefore, it is natural to consider the subgraph federated learning setting, where each local system holding a small subgraph that may be biased from the distribution of the whole graph. Hence, the subgraph federated learning aims to collaboratively train a powerful and generalizable graph mining model without directly sharing their graph data. In this work, towards the novel yet realistic setting of subgraph federated learning, we propose two major techniques: (1) FedSage, which trains a GraphSage model based on FedAvg to integrate node features, link structures, and task labels on multiple local subgraphs; (2) FedSage+, which trains a missing neighbor generator along FedSage to deal with missing links across local subgraphs. Empirical results on four real-world graph datasets with synthesized subgraph federated learning settings demonstrate the effectiveness and efficiency of our proposed techniques. At the same time, consistent theoretical implications are made towards their generalization ability on the global graphs.
When Will Immersive Virtual Reality Have Its Day? Challenges to IVR Adoption in the Home as Exposed in Studies with Teenagers, Parents, and Experts

Abstract In response to the pandemic, many countries have had multiple lockdowns punctuated by partial freedoms limiting physically being together. In 2020–2021, during the COVID-19 pandemic, parents were stressed and exhausted by the challenges of work, home schooling, and barriers to typical childcare arrangements. Children were missing one another, their social lives, and the variety of experiences that the world beyond the home brings. Immersive Virtual Reality (IVR) offers tried and tested ways to enable children to maintain beyond-household family activities and dynamics. However, it is not viewed as a solution. Instead, as demonstrated through a multiple method study involving a Rapid Evidence Assessment, workshops with 91 teenagers, interviews with 15 experts, a Delphi study with 21 experts, 402 parent questionnaires pre-pandemic, 232 parent questionnaires during the pandemic, and longitudinal interviews with 13 parents during the first UK lockdown in 2020, IVR is not viewed as having value in the home beyond gaming. Results highlight limited consideration of IVR as a way to enhance family life or the home, with a lack of evidence and direction from current research, innovation, and policy. The article empirically demonstrates that experts, teenagers, and parents have limited expectations for VR. Further, with parental resistance to adoption and a lack of ideas or innovations in how IVR could be used, the likelihood of VR-headset adoption remains low as does its potential as a means of educating, entertaining, and socially engaging children and teenagers.
On the use of single non-uniform mutation in lightweight metaheuristics

This paper introduces two novel lightweight algorithms based on a single non-uniform mutation (SNUM) operator: a single solution algorithm and a SNUM-based compact Genetic Algorithm. The first algorithm, called also SNUM with reference to the operator, performs the search by an iterative process that perturbs one design variable selected randomly from a single solution. The latter, called compact SNUM (cSNUM), incorporates the SNUM mechanism into the compact Genetic Algorithm scheme, that replaces a population of solutions with a probabilistic model. Both approaches are characterised by a purposely simple and highly generic algorithmic structure. These two attractive features make it possible to readily employ the core part of each algorithm and combine it with other techniques for extended complexity. The results obtained from applying the two proposed algorithms on the BBOB and CEC-2017 benchmarks reveal that the use of SNUM is largely beneficial. Not only the two algorithms (in particular cSNUM) are able to deal with separable functions, especially when the problem dimensionality increases, but they also prove to be competitive on other classes of functions, displaying very good performances compared to other methods from the literature, also on non-separable functions
Direct implementation of a perceptron in superconducting circuit quantum hardware

The utility of classical neural networks as universal approximators suggests that their quantum analogues could play an important role in quantum generalizations of machine-learning methods. Inspired by the proposal in [Torrontegui and Garc\'ia-Ripoll 2019 EPL 125 30004], we demonstrate a superconducting qubit implementation of an adiabatic controlled gate, which generalizes the action of a classical perceptron as the basic building block of a quantum neural network. We show full control over the steepness of the perceptron activation function, the input weight and the bias by tuning the adiabatic gate length, the coupling between the qubits and the frequency of the applied drive, respectively. In its general form, the gate realizes a multi-qubit entangling operation in a single step, whose decomposition into single- and two-qubit gates would require a number of gates that is exponential in the number of qubits. Its demonstrated direct implementation as perceptron in quantum hardware may therefore lead to more powerful quantum neural networks when combined with suitable additional standard gates.
Mining sequences with exceptional transition behaviour of varying order using quality measures based on information-theoretic scoring functions

Abstract Discrete Markov chains are frequently used to analyse transition behaviour in sequential data. Here, the transition probabilities can be estimated using varying order Markov chains, where order k specifies the length of the sequence history that is used to model these probabilities. Generally, such a model is fitted to the entire dataset, but in practice it is likely that some heterogeneity in the data exists and that some sequences would be better modelled with alternative parameter values, or with a Markov chain of a different order. We use the framework of Exceptional Model Mining (EMM) to discover these exceptionally behaving sequences. In particular, we propose an EMM model class that allows for discovering subgroups with transition behaviour of varying order. To that end, we propose three new quality measures based on information-theoretic scoring functions. Our findings from controlled experiments show that all three quality measures find exceptional transition behaviour of varying order and are reasonably sensitive. The quality measure based on Akaike’s Information Criterion is most robust for the number of observations. We furthermore add to existing work by seeking for subgroups of sequences, as opposite to subgroups of transitions. Since we use sequence-level descriptive attributes, we form subgroups of entire sequences, which is practically relevant in situations where you want to identify the originators of exceptional sequences, such as patients. We show this relevance by analysing sequences of blood glucose values of adult persons with diabetes type 2. In the experiments, we find subgroups of patients based on age and glycated haemoglobin (HbA1c), a measure known to correlate with average blood glucose values. Clinicians and domain experts confirmed the transition behaviour as estimated by the fitted Markov chain models.
Contrast Enhancement of Images Using Meta-Heuristic Algorithm

One of the most important processes in image processing is image enhancement, which aims to enhance image contrast and quality of information. Due to the lack of adequate conventional image enhancement and the challenge of mean shift, intelligence-based image enhancement systems are becoming an essential requirement in image processing. This paper proposes a new approach for enhancing low contrast images utilizing a modified measure and integrating a new Chaotic Crow Search (CCS) and Krill Herd (KH) Optimization-based metaheuristic algorithm. Crow Search Algorithm is a cutting-edge meta-heuristic optimization technique. Chaotic maps are incorporated into the Crow Search Method in this work to improve its global optimization. The new Chaotic Crow Search Algorithm depends on chaotic sequences to replace a random location in the search space and the crow's recognition factor. Based on a new fitness function, Krill Herd optimization is utilized to optimize the tunable parameter. The fitness function requires different primary objective functions that use the image's edge, entropy, grey level co-occurrence matrix (GLCM) contrast, and GLCM energy for increased visual, contrast, and other descriptive information. The results proved that the suggested approach outperforms all-new methods in terms of contrast, edge details, and structural similarity, both subjectively and statistically.
Dialogic-interactive media: Alternative learning media to improve speaking skills

The COVID-19 pandemic has not ended, so the learning process is still being carried out online. This results in ambiguity about how to teach. Language learning must be able to improve language skills, but some of these skills are difficult to teach. Based on the survey, speaking skills are the most difficult skills to teach online. Efforts are needed to overcome this, because speaking is considered the ultimate goal of language learning and the most basic skill to achieve. The purpose of this study was to develop dialogic-interactive media to improve students' speaking skills. The type of research used is development research using the Plomp Model (Preliminary Research, Prototype Phase, &amp; Assessment Phase). Based on the research stages, the research objectives described in this article are to (1) describe the results of development research at the Preliminary Research stage and (2) describe the results of development research at the Prototyping Phase. The data analysis technique used in this research is descriptive data analysis technique. Based on the results of the study, it is shown that the use of learning methods and media in learning to speak has not helped students to improve their speaking skills. Therefore, interactive dialogical media were developed (introduction, interactive setting, everyday talk, learning talk, teaching talk, presenting; questioning; extending) and the teacher had a good response to the media model developed for use in learning to speak.
Design and Application of English Grammar Error Correction System Based on Deep Learning

In order to solve the problems of low correction accuracy and long correction time in the traditional English grammar error correction system, an English grammar error correction system based on deep learning is designed in this paper. This method analyzes the business requirements and functions of the English grammar error correction system and then designs the overall architecture of the system according to the analysis results, including English grammar error correction module, service access module, and feedback filtering module. The multilayer feedforward neural network is used to construct the language model to judge whether the language sequence is a normal sentence, so as to complete the correction of English grammatical errors. The experimental results show that the designed system has high accuracy and fast speed in correcting English grammatical errors.
Geochemical and Behavioral Modeling of Phosphorus and Sulfur as Deleterious Elements of Iron Ore to Be Used in Geometallurgical Studies, Sheytoor Iron Ore, Iran

Sheytoor Iron Ore deposit is located in Yazd province of Iran (Bafq).The most abundant ore is magnetite, which can be seen in the form of mass and granular tissue in various forms of self-shaped, semi-self-shaped and amorphous.The main purpose of this study is to identify the geochemical relationship of phosphorus and sulfur elements and also three-dimensional modeling of mineralization of these elements in iron ore.In order to achieve the research goal, methods such as k-mean clustering technique, concentration-volume fractal as well as block modeling with kriging estimator and Inverse Distance Weighting (IDW) interpolator were used.The model of geochemical behavior of phosphorus and sulfur elements compared to iron is of great importance because these two elements are known as deleterious elements in mineral processing and steelmaking processes, which are the post-mining stages.Existence of geochemical model and identification of elements' behavior towards each other play a key role in optimizing mining operations in order to achieve geometallurgical goals.The results of this study are the three-dimensional model of mineralization of iron, phosphorus and sulfur elements, separation of phosphorus and sulfur mineralization communities and also presenting the model of enrichment community of these two elements.All the results are in line with geometallurgical studies and can optimize the next steps by optimizing the mining process.
Quantum-Inspired Classification Algorithm from DBSCAN–Deutsch–Jozsa Support Vectors and Ising Prediction Model

Quantum computing is suggested as a new tool to deal with large data set for machine learning applications. However, many quantum algorithms are too expensive to fit into the small-scale quantum hardware available today and the loading of big classical data into small quantum memory is still an unsolved obstacle. These difficulties lead to the study of quantum-inspired techniques using classical computation. In this work, we propose a new classification method based on support vectors from a DBSCAN–Deutsch–Jozsa ranking and an Ising prediction model. The proposed algorithm has an advantage over standard classical SVM in the scaling with respect to the number of training data at the training phase. The method can be executed in a pure classical computer and can be accelerated in a hybrid quantum–classical computing environment. We demonstrate the applicability of the proposed algorithm with simulations and theory.
Long Distance Moving Vehicle Tracking with a Multirotor Based on IMM-Directional Track Association

The multirotor has the capability to capture distant objects. Because the computing resources of the multirotor are limited, efficiency is an important factor to consider. In this paper, multiple target tracking with a multirotor at a long distance (~400 m) is addressed; the interacting multiple model (IMM) estimator combined with the directional track-to-track association (abbreviated as track association) is proposed. The previous work of the Kalman estimator with the track association approach is extended to the IMM estimator with the directional track association. The IMM estimator can handle multiple targets with various maneuvers. The track association scheme is modified in consideration of the direction of the target movement. The overall system is composed of moving object detection for measurement generation and multiple target tracking for state estimation. The moving object detection consists of frame-to-frame subtraction of three-color layers and thresholding, morphological operation, and false alarm removing based on the object size and shape properties. The centroid of the detected object is input into the next tracking stage. The track is initialized using the difference between two nearest points measured in consecutive frames. The measurement nearest to the state prediction is used to update the state of the target for measurement-to-track association. The directional track association tests both the hypothesis and the maximum deviation between the displacement and directions of two tracks followed by track selection, fusion, and termination. In the experiment, a multirotor flying at an altitude of 400 m captured 55 moving vehicles around a highway interchange for about 20 s. The tracking performance is evaluated for the IMMs using constant velocity (CV) and constant acceleration (CA) motion models. The IMM-CA with the directional track association scheme outperforms other methods with an average total track life of 91.7% and an average mean track life of 84.2%.
Generalised Regression Neural Network (GRNN) Architecture-Based Motion Planning and Control of an E-Puck Robot in V-REP Software Platform

Abstract This article focuses on the motion planning and control of an automated differential-driven two-wheeled E-puck robot using Generalized Regression Neural Network (GRNN) architecture in the Virtual Robot Experimentation Platform (V-REP) software platform among scattered obstacles. The main advantage of this GRNN over the feedforward neural network is that it provides accurate results in a short period with minimal error. First, the designed GRNN architecture receives real-time obstacle information from the Infra-Red (IR) sensors of an E-puck robot. According to IR sensor data interpretation, this architecture sends the left and right wheel velocities command to the E-puck robot in the V-REP software platform. In the present study, the GRNN architecture includes the MIMO system, i.e., multiple inputs (IR sensors data) and multiple outputs (left and right wheel velocities). The three-dimensional (3D) motion and orientation results of the GRNN architecture-controlled E-puck robot are carried out in the V-REP software platform among scattered and wall-type obstacles. Further on, compared with the feedforward neural network, the proposed GRNN architecture obtains better navigation path length with minimum error results.