Reviewerly/exHarmony huggingface dataset:
A hierarchical federated learning incentive mechanism in UAV-assisted edge computing environment

Federated learning can utilize the local resources of user devices for model training while protecting their private security. Combining edge computing with federation learning can migrate computational tasks to the network edge, reducing communication latency and overhead. However, traditional edge stations are fixed in location and costly to deploy, making it difficult to cope with traffic surges, infrastructure failures, and device migration. Therefore, we introduce flexible and low-cost UAVs into federated learning, which can establish line-of-sight transmission paths with user devices and receive data for local aggregation. In federated learning, user devices need to contribute their own resources, and without sufficient rewards they may not be willing to participate, so we proposes a hierarchical federated learning incentive mechanism, which is designed based on contract theory considering data volume, data quality and cost input in an information asymmetry scenario. The experimental results compared with other benchmark schemes verify that the contractual design of this paper satisfies incentive compatibility and maximizes the utility of model owners.
Leadership and crisis management and their link to improvement of hotel performance: A study of the Jordanian hotel sector

The current environment of volatility, uncertainty, complexity and ambiguity has created a prolonged state of uncertainty for the Jordanian hotel industry. Crisis management leadership is one of the most important attributes for a hotel. The main aim of this study is to evaluate the mediating role of crisis management, the moderating role of a leader's experience, their relationship to styles of leadership (transformational and transactional) and the resultant performance of Jordanian hotels. Research was based on a self-distributed questionnaire survey of 119 respondents currently holding managerial positions in Jordanian 3 to 5 star hotels. Partial Least Square Structural Equation Modelling was then employed. The findings suggest a transformational leadership style and crisis management experience are the most important attributes for a leader to sustain hotel performance during a crisis. Leaders with a transactional leadership style need crisis management skills to sustain hotel performance rather than experience which is not as important in their case. This paper proves that different leadership styles have a different influence on a hotel's survivability during a crisis. Therefore, a hotel's management group must ensure that a leader with an appropriate leadership style takes control during these situations. By combining leadership attributes, experience, and crisis management in a comprehensive framework to ensure sustainable hotel performance in the face of a crisis, this study adds to the body of knowledge on leadership and crisis management practices.
Computational issues in parameter estimation for hidden Markov models with template model builder

A popular way to estimate the parameters of a hidden Markov model (HMM) is direct numerical maximization (DNM) of the (log-)likelihood function. The advantages of employing the TMB [Kristensen K, Nielsen A, Berg C, et al. TMB: automatic differentiation and Laplace approximation. J Stat Softw Articles. 2016;70(5):1–21] framework in R for this purpose were illustrated recently [Bacri T, Berentsen GD, Bulla J, et al. A gentle tutorial on accelerated parameter and confidence interval estimation for hidden Markov models using template model builder. Biom J. 2022 Oct;64(7):1260–1288]. In this paper, we present extensions of these results in two directions. First, we present a practical way to obtain uncertainty estimates in form of confidence intervals (CIs) for the so-called smoothing probabilities at moderate computational and programming effort via TMB. Our approach thus permits to avoid computer-intensive bootstrap methods. By means of several examples, we illustrate patterns present for the derived CIs. Secondly, we investigate the performance of popular optimizers available in R when estimating HMMs via DNM. Hereby, our focus lies on the potential benefits of employing TMB. Investigated criteria via a number of simulation studies are convergence speed, accuracy, and the impact of (poor) initial values. Our findings suggest that all optimizers considered benefit in terms of speed from using the gradient supplied by TMB. When supplying both gradient and Hessian from TMB, the number of iterations reduces, suggesting a more efficient convergence to the maximum of the log-likelihood. Last, we briefly point out potential advantages of a hybrid approach.
One Shot Learning with class partitioning and cross validation voting (CP-CVV)

One Shot Learning includes all those techniques that make it possible to classify images using a single image per category. One of its possible applications is the identification of food products. For a grocery store, it is interesting to record a single image of each product and be able to recognise it again from other images, such as photos taken by customers. Within deep learning, Siamese neural networks are able to verify whether two images belong to the same category or not. In this paper, a new Siamese network training technique, called CP-CVV, is presented. It uses the combination of different models trained with different classes. The separation of validation classes has been done in such a way that each of the combined models is different in order to avoid overfitting with respect to the validation. Unlike normal training, the test images belong to classes that have not previously been used in training, allowing the model to work on new categories, of which only one image exists. Different backbones have been evaluated in the Siamese composition, but also the integration of multiple models with different backbones. The results show that the model improves on previous works and allows the classification problem to be solved, an additional step towards the use of Siamese networks. To the best of our knowledge, there is no existing work that has proposed integrating Siamese neural networks using a class-based validation set separation technique so as to be better at generalising for unknown classes. Additionally, we have applied Cross-Validation-Voting with ConvNeXt to improve the existing classification results of a well-known Grocery Store Dataset.
Autonomous Navigation of the UAV through Deep Reinforcement Learning with Sensor Perception Enhancement

The accuracy of autonomous navigation and obstacle avoidance of unmanned aerial vehicles (UAVs) in complex environments has become one challenging task. In this paper, an autonomous navigation and obstacle avoidance of the UAV (ANOAU) algorithm based on deep reinforcement learning (DRL) has been proposed to achieve accurate path planning in complex environments. In our work, we use an actor–critic-based DRL framework to achieve autonomous UAV control from sensor input to the output of the UAV’s action and design a set of reward functions that can be adapted to autonomous navigation and obstacle avoidance for the UAV in the complex environment. Meanwhile, to alleviate the decision-making bias caused by the incomplete observables of the UAV, we use a gate recurrent unit network to enhance the ability to perceive the uncertain environment, enhance the perception representation and improve the accuracy of UAV real-time decision-making. Experimental simulation results verify that the ANOAU algorithm achieves good UAV flight attitude adaptive adjustment in navigation and obstacle avoidance tasks and significantly improves the generalization ability and training efficiency of the UAV navigation controller in a complex environment.
PRISMA Archetype-Based Systematic Literature Review of Security Algorithms in the Cloud

Industries have embraced cloud computing for their daily operations due to the volume of data they create. As data generation and consumption have increased, the challenges and opportunities have also increased. Researchers have proposed various cryptographic schemes to secure data on the cloud. Regardless of the multiple cryptographic schemes proposed, security remains an obstacle to cloud computing’s widespread adoption. Also, these cryptographic schemes’ run times are proportional to data sizes, motivating excessive CPU engagement during execution of huge data, which has consequences for the need for high bandwidth to transfer data to the cloud. This systematic review tries to uncover the most often used cryptographic schemes and their run time trends to attain confidentiality and privacy of cloud data. The study considered published articles from well-known databases such as Taylor &amp; Francis, Scopus, Research Gate, Web of Science, IEEE Xplore, Science Direct, Hindawi, Google Scholar, Sage, Emerald, Wiley Online Library, and ACM from 2016 to 2022. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used to select 73 published works for this study using keyword searching. Data security and cloud security were the security challenges that received the greatest attention in the study with encryption techniques as the most common solution. From the study, 90% of the schemes used to secure data on the cloud produced linear run times. The investigation discovered that nonlinear symmetric stream cipher methods were infrequently employed to protect the secrecy and privacy of cloud data.
Local-and-Nonlocal Spectral Prior Regularized Tensor Recovery for Cauchy Noise Removal

Cauchy noise removal in multidimension visual data is a fundamental problem in image processing and data science communities. Recently, this problem can be effectively solved by resorting to an appropriate tensor recovery approach and has attracted increasing attention. However, it is not extensively reported whether tensor recovery approaches with multiple priors can achieve even better denosing performance. To address this issue, we propose a novel tensor recovery approach in this paper based on the maximum a posteriori estimation (MAP). Our approach is a nonconvex optimization model that includes a composite regularization term and a data fidelity term. The composite regularization term incorporates nonlocal self-similarity in the spatial and gradient domains of multidimensional visual data, preserving both local image details and non-local structures. The data fidelity term is obtained by computing the negative log-likelihood of Cauchy noise distribution. To solve this proposed tensor recovery model, we develop an effective denoising algorithm with a global convergence guarantee via the well-known nonconvex alternating direction method of multipliers (ADMM). Finally, we conduct extensive numerical experiments to demonstrate that our proposed recovery approach can achieve superior denoising performance, both quantitatively and qualitatively, compared to existing common methods.
A multi-level sorting prediction enhancement-based two-dimensional reversible data hiding algorithm for JPEG images

To improve the performance of reversible data hiding on JPEG images, a multi-level sorting prediction enhancement-based two-dimensional reversible data hiding algorithm for JPEG images is proposed in this paper. Firstly, the smoothness of each DCT block is evaluated by counting the location of the last non-zero coefficient in each DCT block. Then, the gradient of the object block is calculated with the DC coefficients of its surrounding blocks, and the lowest gradient direction blocks are employed to achieve the prediction of object block AC coefficients. Secondly, these gradient of adjacent blocks of the object block are compared to found the lowest gradient direction. The AC coefficient of the object block is accurately predicted by coefficients at the same position of its lowest gradient-direction adjacent blocks. Thirdly, the obtained DCT coefficient prediction errors are paired with a similar prediction-error pairing strategy to construct a two-dimensional prediction-error plane, and the secret data is imperceptibly embedded into the AC coefficients by using the two-dimensional histogram shifting algorithm. The performance of the proposed algorithm is evaluated on different datasets, and six representative images are involved in demonstrating its superiority. Extensive experimental results show that, compared to other state-of-the-art reversible data hiding algorithms, the proposed algorithm can achieve higher visual quality of the data-embedded image while maintaining low JPEG image file size increment.
Multi-target Cooperative Path Planning for Air-Sea Heterogeneous Unmanned System

This paper focuses on the cooperative path planning of multi-target heterogeneous unmanned systems in air-sea. In the face of multiple targets, Unmanned Aerial Vehicles (UAV) flies fast, but its range and load capacity are limited; Unmanned Surface Vehicles (USV) has strong load capacity and long endurance, but its navigation speed is slow. This paper studies the path planning problem of the air-sea heterogeneous unmanned system composed of UAVs and USVs for a large range and multiple targets, taking the time for the unmanned system to complete this task as the optimization objective. Firstly, distinguish targets with ReConDensity-Ratio based clustering algorithm, and find appropriate refueling points for UAVs and USVs in each classification. Secondly, the Double-deck Travel Modified Particle Swarm Optimization algorithm (DT-MPSO) is used to plan the path of targets and find the shortest route. Finally, the effectiveness of the proposed method is verified by simulation experiments.
A Recommendation System Based on Adaptive Genetic Algorithm for Enclosed Spaces

Most of the management systems used in common enclosed spaces only have basic functions such as booking and payment, and do not have epidemic prevention and traceability functions. In the COVID-19 pandemic, enclosed scenes, without an epidemic prevention system, clearly increase the risk of cross-contamination of the population, making it a black box that will amplify the severity of the outbreak. In this paper, we develop an intelligent recommendation system designed to coordinate the operation of enclosed spaces and decentralize the new customers to a low-infection risk box. An adaptive genetic algorithm is proposed to achieve optimal allocation of personnel and boxes, which can avoid contact between customers during low peak hours and minimize cross-contact during peak hours. On the one hand, it guarantees the user experience, and on the other hand, it guarantees these enclosed spaces have a high decentralization of crowd density when an epidemic occurs. This greatly reduces the risk of exposure to infection and is of great significance in preventing the spread of the epidemic.
Lotus effect optimization algorithm (LEA): a lotus nature-inspired algorithm for engineering design optimization

Abstract Here we introduce a new evolutionary algorithm called the Lotus Effect Algorithm, which combines efficient operators from the dragonfly algorithm, such as the movement of dragonflies in flower pollination for exploration, with the self-cleaning feature of water on flower leaves known as the lotus effect, for extraction and local search operations. The authors compared this method to other improved versions of the dragonfly algorithm using standard benchmark functions, and it outperformed all other methods according to Fredman's test on 29 benchmark functions. The article also highlights the practical application of LEA in reducing energy consumption in IoT nodes through clustering, resulting in increased packet delivery ratio and network lifetime. Additionally, the performance of the proposed method was tested on real-world problems with multiple constraints, such as the welded beam design optimization problem and the speed-reducer problem applied in a gearbox, and the results showed that LEA performs better than other methods in terms of accuracy.
FedDNA: Federated learning using dynamic node alignment

Federated Learning (FL), as a new computing framework, has received significant attentions recently due to its advantageous in preserving data privacy in training models with superb performance. During FL learning, distributed sites first learn respective parameters. A central site will consolidate learned parameters, using average or other approaches, and disseminate new weights across all sites to carryout next round of learning. The distributed parameter learning and consolidation repeat in an iterative fashion until the algorithm converges or terminates. Many FL methods exist to aggregate weights from distributed sites, but most approaches use a static node alignment approach, where nodes of distributed networks are statically assigned, in advance, to match nodes and aggregate their weights. In reality, neural networks, especially dense networks, have nontransparent roles with respect to individual nodes. Combined with random nature of the networks, static node matching often does not result in best matching between nodes across sites. In this paper, we propose, FedDNA, a dynamic node alignment federated learning algorithm. Our theme is to find best matching nodes between different sites, and then aggregate weights of matching nodes for federated learning. For each node in a neural network, we represent its weight values as a vector, and use a distance function to find most similar nodes, i.e., nodes with the smallest distance from other sides. Because finding best matching across all sites are computationally expensive, we further design a minimum spanning tree based approach to ensure that a node from each site will have matched peers from other sites, such that the total pairwise distances across all sites are minimized. Experiments and comparisons demonstrate that FedDNA outperforms commonly used baseline, such as FedAvg, for federated learning.
Cooperative Passive Location of Multi-UAVs Based on TDOA-DOA Fusion Algorithm

Electronic warfare plays an essential role in modern warfare. In this background, the multiple UAVs cooperative passive positioning technology, which has the advantages of long operating distance and strong concealment, has received significant attention. Considering the scenario of three UAVs attacking enemy surface ships in a naval battle, this paper proposes a collaborative passive localization algorithm based on TDOA and DOA to solve the positioning problem in three-dimensional space. Firstly, we establish a passive location model according to the time delay and measurement errors of the ship target and UAV. Second, the nonlinear terms are linearized using the relative spatial position between the ship and the UAV. Third, a loss function is constructed for the error term, and the least square estimation algorithm obtains the ship coordinate position. Finally, we design two comparative experiments. One discusses the influence of acute angle, isosceles, right angle, equilateral and obtuse angle five UAV spatial structures on positioning accuracy; the other explores the impact of different delay and measurement errors on positioning accuracy. The numerical simulation results effectively verify the model’s rationality and the algorithm’s effectiveness.
UAV route planning based on improved whale optimization algorithm and dynamic artificial potential field method

Aiming at the problem that the global path optimization cannot be guaranteed in the dynamic path planning of rotorcraft formation, a static path planning and dynamic obstacle avoidance algorithm combining the improved whale optimization algorithm and dynamic artificial potential field method is proposed. The optimization results of whale optimization algorithm are greatly affected by the distribution of initial solutions. The paper proposes to combine the hierarchical system of grey wolf optimization algorithm with the standard whale algorithm, and incorporate the first three historical optimal solutions into the calculation range of potential optimal solutions to improve the ability of the population to escape from the value of local minimum. The commonly used artificial potential field method includes the problem of target reachability and local minima. This paper improves the classical exclusion function model. On the basis of the improved model, the dynamic potential field model is added to realize the avoidance of dynamic obstacles. The simulation results show that the path planning ability of rotorcraft UAVs has been improved through the improvement of whale optimization algorithm and artificial potential field model, and the formation UAVs have the static path planning and dynamic obstacle avoidance ability. At the same time, it has great advantages in convergence speed and solution accuracy.
Hilbert-Huang-Transform Based Features for Accent Classification of Non-Native English Speakers

The development of accurate accent-recognition technologies is important towards high-performance Automatic Speech Recognition (ASR) through the use of accent-specific models. Usually deep-learning models are employed to perform ASR and we intend to enhance such models by accurately capturing the accent-related idiosyncrasies of non-native English speakers. The typical features used for the deep-learning networks, in ASR, are Mel-Frequency Cepstral Coefficients (MFCCs) and Mel-Spectrogram of the audio samples. However, novel features based on the Hilbert-Huang Transform (HHT), an effective non-linear signal analysis technique can aid or even replace typical features used in Automatic Speech Recognition. In this paper, we seek to apply these features in the task of accent recognition for non-native English speakers.We propose a new novel input feature, the Hilbert Mel-Spectrogram, a logarithmic frequency representation of the signal derived using the HHT. The use of HHT creates an input feature free of the traditional limitations of Fourier analysis, namely limited frequency resolution, spectral energy leakage, and harmonic artifacts. A four-stage Convolution Neural Network (CNN) is used to model the features of accented speech samples spanning 5 native languages. Our results show that the models with the Hilbert-Mel-Spectrogram-based features outperform their Fourier-based counterparts for a wide range of datasets.
Breast masses detection on mammograms using recent one-shot deep object detectors

Breast cancer is a prevalent cancer that affects mostly women worldwide. The detection of breast masses is a crucial part of breast cancer diagnosis and treatment. However, breast masses detection on mammograms is challenging due to variations in breast tissue appearance and abnormalities’ complexity. This article presents a breast mass detection system that utilizes recent deep object detection techniques such as YOLOv7 and YOLOv8. The system’s first step involves extracting images from the VinDr-Mammo dataset and enhancing image quality using different techniques like contrast-limited adaptive histogram equalization, median filter, and bilateral filter. With a mean average precision of 0.65, YOLOv8 outperformed YOLOv7, demonstrating the system’s potential to detect masses on mammograms.