Reviewerly/exHarmony huggingface dataset:
An approximation lagrangian-based algorithm for the maximum clique problem via deterministic annealing neural network

The maximum clique problem is an NP hard combinatorial optimization problem, which is widely used in industrial and management processes. In order to approach the solution of the maximum clique problem, a deterministic annealing algorithm is proposed. When the barrier parameter is reduced from a large positive number to zero, the algorithm can track the minimum path of the obstacle problem to obtain a high-quality solution, which is a continuous method. The global convergence iteration of the Lagrange multiplier can be performed to obtain the minimum point of the obstacle problem in the feasible descent direction for any given positive value of the barrier parameter and has a desired property that it automatically satisfies the upper and lower bounds of variables if the steplength is a number that is between 0 and 1. Numerical results are provided to illustrate the efficiency of the proposed algorithm.
Numerical Sensitivity Analysis and Hardware Verification of a Transiently-Chaotic Attractor

We introduce a new chaotic system with nonhyperbolic equilibrium and study its sensitivity to different numerical integration techniques prior to implementing it on an FPGA. We show that the discretization method used in numerically integrating the set of differential equations in MATLAB and Mathematica does not yield chaotic behavior except when a low accuracy Euler method is used. More accurate higher-order numerical algorithms (such as midpoint and fourth-order Runge–Kutta) result in divergence in both MATLAB and Mathematica (but not Python), which agrees with the divergence observed in an analog circuit implementation of the system. However, a fixed-point digital FPGA implementation confirms the chaotic behavior of the system using Euler and fourth-order Runge–Kutta realizations. Therefore, the increased sensitivity of chaotic systems with nonhyperbolic equilibrium should be carefully considered for reproducibility.
The effect of virtual reality media characteristics on flow and learning transfer in job training: The moderating effect of presence

Abstract Background This study aims to establish the mechanism by which virtual reality (VR) media characteristics affect learning transfer in a VR‐based job‐training environment. The two main determinants of learning transfer are the technology and the learner, but not many studies have considered both factors simultaneously. Objectives This study aims to ascertain the mechanism by which VR media characteristics affect learning transfer with flow as a mediator, and to explore whether presence moderates the effect of VR media characteristics on flow. Methods A survey of semiconductor facilities engineers who received VR‐based job training was conducted. A total of 106 responses were used in the statistical analysis. A path analysis was conducted to determine the promotional mechanism that connects VR media characteristics, flow, and learning transfer. Results and Conclusions The results were as follows. First, VR media characteristics had a positive impact on learning transfer. Second, flow acted as a mediator in the relationship between VR media characteristics and learning transfer. Third, presence played a moderating role in the relationship between VR media characteristics and flow. Key takeaways This study is significant as it identifies elements to consider in relation to both the technological and learner aspects of VR while designing a VR‐based job‐training programme. A corporation should consider VR media characteristics from a technological perspective, and flow and presence from a learner's perspective.
A survey of image encryption for healthcare applications

Recently, medical image encryption has attracted many researchers because of security issues in the communication process. The recent COVID-19 has highlighted the fact that medical images are consistently created and disseminated online, leading to a need for protection from unauthorised utilisation. This paper intends to review the various medical image encryption approaches along with their merits and limitations. It includes a survey, a brief introduction, and the most utilised interesting applications of image encryption. Then, the contributions of reviewed approaches are summarised and compared regarding different technical perspectives. Lastly, we highlight the recent challenges along with several directions of potential research that could fill the gaps in these domains for researchers and developers.
Fusing frequent sub-sequences in the session-based recommender system

A session-based recommender system (RS) forecasts the item to be clicked next by taking advantage of the item sequence in the current session. The deep learning models, such as the recurrent neural network (RNN) and the graph neural network (GNN), are recently applied in the session-based recommendation. However, to the best of our knowledge, the existing methods ignore the fusion of the frequent sub-sequences which refer to those item sub-sequences that appear frequently in different sessions. Intuitively, the more frequently an item sequence appears, the more important it could be. In this paper, we propose the frequent sub-sequence modeling (FSM) method, by utilizing both the local and global information of item sequences. More specifically, we use the item sequence in the current session to build a local session graph and the frequent sub-sequences to construct a global session graph. In addition, we design a gated layer to control the contributions of these two session graphs. Our method is capable of learning both the session-level and global-level item embedding, and thus supports accurate predictions. It is tested on four benchmark datasets and the results show that our method is superior to state-of-the-art approaches consistently.
Virtual Machine Migration for Green Cloud Computing

The use of virtual machines increases as more people switch to intelligent gadgets that require a lot of computing power to function correctly. With virtualization, different resources from existing physical infrastructure can be created. Virtualization strategies effectively address the increasing need for computers, storage, and communication resources in large Cloud Data Centers (CDC). It also achieves various resource management objectives such as load balancing, Internet system maintenance, speed, and tolerance with Virtual Machine migration. VM migration techniques would be helpful for autonomic resource management. So, in this paper, VM migration techniques, applications and their open challenges have been discussed.
Deep reinforcement learning-based model-free path planning and collision avoidance for UAVs: A soft actor–critic with hindsight experience replay approach

In this paper, we propose a soft actor–critic (SAC) algorithm with hindsight experience replay (HER), called SACHER, which is a class of deep reinforcement learning (DRL) algorithm. SAC is an off-policy model-free DRL algorithm that outperforms earlier DRL algorithms in terms of exploration and robustness. However, in SAC, maximizing the entropy-augmented objective degrades the optimality of learning outcomes. We propose SACHER to improve the learning performance of SAC. We apply SACHER to the path planning and collision avoidance control of unmanned aerial vehicles (UAVs). We demonstrate the effectiveness of SACHER in terms of the success rate, learning speed, and collision avoidance performance of UAV operation.
The construction of discrete additive generators of t-conorms based on the sum of ranges of discrete additive generators of t-conorms

The construction of ranges of discrete additive generators of t-conorms in the form of the sum of ranges of discrete additive generators of t-conorms is introduced. It is shown that for arbitrary ranges A and B of two discrete additive generators of t-conorms there exist positive integers α and β such that the set αA+βB is the range of a discrete additive generator of a t-conorm.
On the multi-level embedding of crypto-image reversible data hiding

Crypto-space reversible image steganography has attracted increasing attention, given its ability to embed authentication information without revealing the image content. This paper presents an efficient reversible data hiding scheme for crypto-images: a block predictor is applied to compute prediction errors, then an adaptive block mapping algorithm is utilized to compress them whose amplitudes are within a small threshold, finally, this strategy can be applied in a multi-level manner to achieve a higher embedding capacity. Due to the correlations among adjacent pixels in the block, images can be sufficiently compressed to reserve abundant space for additional data embedding. Different from the prior arts, the compression code of the image is fully encrypted. Experimental results verify that the embedded data and original image can be perfectly recovered, the security is higher compared with the state-of-the-arts, and a significant improvement in the average embedding rate is achieved on two large-scale image datasets.
Identification and Analysis of Phishing Website based on Machine Learning Methods

People are increasingly sharing their details online as internet usage grows. Therefore, fraudsters have access to a massive amount of information and financial activities. The attackers create web pages that seem like reputable sites and transmit the malevolent content to victims to get them to provide subtle information. Prevailing phishing security measures are inadequate for detecting new phishing assaults. To accomplish this aim, objective to meet for this research is to analyses and compare phishing website and legitimate by analyzing the data collected from open-source platforms through a survey. Another objective for this research is to propose a method to detect fake sites using Decision Tree and Random Forest approaches. Microsoft Form has been utilized to carry out the survey with 30 participants. Majority of the participants have poor awareness and phishing attack and does not obverse the features of interface before accessing the search browser. With the data collection, this survey supports the purpose of identifying the best phishing website detection where Decision Tree and Random Forest were trained and tested. In achieving high number of feature importance detection and accuracy rate, the result demonstrates that Random Forest has the best performance in phishing website detection compared to Decision Tree.
An Imbalanced Dataset and Class Overlapping Classification Model for Big Data

Most modern technologies, such as social media, smart cities, and the internet of things (IoT), rely on big data.When big data is used in the real-world applications, two data challenges such as class overlap and class imbalance arises.When dealing with large datasets, most traditional classifiers are stuck in the local optimum problem.As a result, it's necessary to look into new methods for dealing with large data collections.Several solutions have been proposed for overcoming this issue.The rapid growth of the available data threatens to limit the usefulness of many traditional methods.Methods such as oversampling and undersampling have shown great promises in addressing the issues of class imbalance.Among all of these techniques, Synthetic Minority Oversampling TechniquE (SMOTE) has produced the best results by generating synthetic samples for the minority class in creating a balanced dataset.The issue is that their practical applicability is restricted to problems involving tens of thousands or lower instances of each.In this paper, we have proposed a parallel mode method using SMOTE and MapReduce strategy, this distributes the operation of the algorithm among a group of computational nodes for addressing the aforementioned problem.Our proposed solution has been divided into three stages.The first stage involves the process of splitting the data into different blocks using a mapping function, followed by a pre-processing step for each mapping block that employs a hybrid SMOTE algorithm for solving the class imbalanced problem.On each map block, a decision tree model would be constructed.Finally, the decision tree blocks would be combined for creating a classification model.We have used numerous datasets with up to 4 million instances in our experiments for testing the proposed scheme's capabilities.As a result, the Hybrid SMOTE appears to have good scalability within the framework proposed, and it also cuts down the processing time.
Could simplified stimuli change how the brain performs visual search tasks? A deep neural network study

Visual search is a complex behavior influenced by many factors. To control for these factors, many studies use highly simplified stimuli. However, the statistics of these stimuli are very different from the statistics of the natural images that the human visual system is optimized by evolution and experience to perceive. Could this difference change search behavior? If so, simplified stimuli may contribute to effects typically attributed to cognitive processes, such as selective attention. Here we use deep neural networks to test how optimizing models for the statistics of one distribution of images constrains performance on a task using images from a different distribution. We train four deep neural network architectures on one of three source datasets-natural images, faces, and x-ray images-and then adapt them to a visual search task using simplified stimuli. This adaptation produces models that exhibit performance limitations similar to humans, whereas models trained on the search task alone exhibit no such limitations. However, we also find that deep neural networks trained to classify natural images exhibit similar limitations when adapted to a search task that uses a different set of natural images. Therefore, the distribution of data alone cannot explain this effect. We discuss how future work might integrate an optimization-based approach into existing models of visual search behavior.
Re-thinking model robustness from stability: a new insight to defend adversarial examples

We study the model robustness against adversarial examples, referred to as small perturbed input data that may however fool many state-of-the-art deep learning models. Unlike previous research, we establish a novel theory addressing the robustness issue from the perspective of stability of the loss function in the small neighborhood of natural examples. We propose to exploit an energy function to describe the total variation in a small neighborhood and prove that reducing such energy guarantees the robustness against adversarial examples. We also show that the traditional training methods including adversarial training and virtual adversarial training tend to minimize the lower bound of our proposed energy function. Importantly, we prove that minimizing the energy function can obtain a better generalization bound than traditional adversarial training approaches. Through a series of experiments, we demonstrate the superiority of our model on different datasets for defending adversarial attacks. In particular, our proposed adversarial framework achieves the best performance compared with previous adversarial training methods on benchmark datasets CIFAR-10, CIFAR-100 and SVHN and they demonstrate much better robustness against adversarial examples than all the other comparison methods.
HollowBox: An anchor‐free UAV detection method

Unmanned aerial vehicles (UAV) “black flight” incidents cause serious security risks and economic losses to airports. Additionally, existing UAV detection methods are mainly radar technology at airports. Whereas, it is unable to correctly identify the number of UAVs and visualise their size, which undoubtedly poses a serious security risk. Accordingly, an anchor-free UAV detection method HollowBox is proposed to supplement radar detection equipment for the airport “black flight” problem. It is inspired by the FoveaBox object detection method, the object detection feature layers are reset and the allocation ratio of positive and negative samples in the training phase are redefined, the HollowBox UAV detection is proposed according to the multi-size characteristics. Extensive experiments show that, the approach achieves 90.1% AP, 6% false detection rate and 17.2 FPS inference speed, which accomplished a satisfactory performance, as verified by the real-shot data collected of an airport in Tianjin. This work is of great significance for the application in airport UAV detection.
Cat Swarm with Fuzzy Cognitive Maps for Automated Soil Classification

Accurate soil prediction is a vital parameter involved to decide appropriate crop, which is commonly carried out by the farmers.Designing an automated soil prediction tool helps to considerably improve the efficacy of the farmers.At the same time, fuzzy logic (FL) approaches can be used for the design of predictive models, particularly, Fuzzy Cognitive Maps (FCMs) have involved the concept of uncertainty representation and cognitive mapping.In other words, the FCM is an integration of the recurrent neural network (RNN) and FL involved in the knowledge engineering phase.In this aspect, this paper introduces effective fuzzy cognitive maps with cat swarm optimization for automated soil classification (FCMCSO-ASC) technique.The goal of the FCMCSO-ASC technique is to identify and categorize seven different types of soil.To accomplish this, the FCMCSO-ASC technique incorporates local diagonal extrema pattern (LDEP) as a feature extractor for producing a collection of feature vectors.In addition, the FCMCSO model is applied for soil classification and the weight values of the FCM model are optimally adjusted by the use of CSO algorithm.For examining the enhanced soil classification outcomes of the FCMCSO-ASC technique, a series of simulations were carried out on benchmark dataset and the experimental outcomes reported the enhanced performance of the FCMCSO-ASC technique over the recent techniques with maximum accuracy of 96.84%.
A multi-dimension traceable privacy-preserving prevention and control scheme of the COVID-19 epidemic based on blockchain

The outbreak of COVID-19 has brought great pain to people around the world. As an epidemic prevention and control measure, the health QR code (HC) has been designed to trace the confirmed cases and close contacts quickly. Although some existing health code schemes preserve the privacy, but most of them are either unsupported for fine-grained auditability or centralised health code storage. Therefore, we propose a multi-dimension traceable privacy-preserving HC scheme based on blockchain. It prevents health code information being tampered with and supports the traceability of virus transmission chain. We utilise attribute-based encryption to protect residents' privacy information and achieve fine-grained access control. Furthermore, to support the multi-dimension traceability by the epidemic prevention and control departments, the searchable encryption has been introduced. Finally, we give the security analysis and performance evaluation to verify the feasibility and practical significance of our scheme.
ns3-fl: Simulating Federated Learning with ns-3

In recent years, there has been a spike in interest in the field of federated learning (FL). As a result, an increasing number of federated learning algorithms have been developed. Large-scale deployments to validate these algorithms are often not feasible, resulting in a need for simulation tools which closely emulate real deployment conditions. Existing federated learning simulators lack complex network settings, and instead focus on data and algorithmic development. ns-3 is a discrete event network simulator, which has a plethora of models to represent network components and can simulate complex networking scenarios. In this paper, we present ns3-fl, which is a tool that connects an existing FL simulator, flsim, with ns-3 to produce a federated learning simulator that considers data, algorithm, and network. We first discuss the learning, network and power models used to develop our tool. We then present an overview of our implementation, including the Client/Server ns-3 applications and interprocess communication protocols. A real Raspberry Pi-based deployment is setup to validate our tool. Finally, we perform a simulation emulating FL training on 40 clients throughout the UCSD campus and analyze the performance of our tool, in terms of real clock execution time for various FL rounds.
Design of Edge Computing Online Classroom Based on College English Teaching

This study expects to solve the network delay in college English online teaching, prevent disrupting the normal teaching order of college English classroom caused by network problems, increase college students' interest in learning English, and improve the time spent by college students in English learning. First, the study investigates and analyzes the current needs and situation of college English online learning and puts forward the improvement standards for college English online classroom. Second, the study applies edge computing (EC) technology to improve online teaching system and designs an online learning system for college specific English based on EC. Finally, the improvement effect is tested, and it is concluded that the effect of using EC to improve the online classroom system for college English is better than that of the unmodified online teaching system. The results reveal that the online classroom system for college English based on EC designed can effectively reduce the network delay of the traditional teaching system, ensure a high reliability, and increase the time for students to learn English, so it is helpful to improve the teaching quality. The results provide a new direction for applying emerging computer technology in online teaching in colleges and universities, a guarantee for improving college students' English level, and set an example for the relevant research in the future.
Trust Management for Artificial Intelligence: A Standardization Perspective

With the continuous increase in the development and use of artificial intelligence systems and applications, problems due to unexpected operations and errors of artificial intelligence systems have emerged. In particular, the importance of trust analysis and management technology for artificial intelligence systems is continuously growing so that users who desire to apply and use artificial intelligence systems can predict and safely use services. This study proposes trust management requirements for artificial intelligence and a trust management framework based on it. Furthermore, we present challenges for standardization so that trust management technology can be applied and spread to actual artificial intelligence systems. In this paper, we aim to stimulate related standardization activities to develop globally acceptable methodology in order to support trust management for artificial intelligence while emphasizing challenges to be addressed in the future from a standardization perspective.