Reviewerly/exHarmony huggingface dataset:
Bayesian variable selection and estimation in binary quantile regression using global-local shrinkage priors

In this paper, we construct a Bayesian hierarchical model with global-local shrinkage priors for the regression coefficients, which includes the horseshoe prior and normal-gamma prior. This model is used for high-dimensional quantile regression models with dichotomous response data. We have developed an efficient sampling algorithm to generate posterior samplings for making posterior inference. We use a location-scale mixture representation of the asymmetric Laplace distribution. We assess the performance of the proposed methods through Monte Carlo simulations and two real-data applications in terms of parameter estimation and variable selection. Numerical results demonstrate that the proposed methods perform comparably with existing Bayesian methods under a variety of scenarios.
Vulnerability Detection and Response: Current Status and New Approaches

The rapid evolution of industrial components, the paradigm of Industry 4.0, and the new connectivity features introduced by 5G technology all increase the likelihood of cybersecurity incidents. These incidents have to be managed to limit or mitigate their impact, and in most cases, they are a consequence of existing vulnerabilities. This scenario raises the need for a tool that enables a faster (tracking the vulnerability state over time) and more precise (detect root cause) response. The defined Extended Dependency Graph (EDG) model is capable to respond to this need, being able to analyze known vulnerabilities for a given device over time. The EDG model can be applied throughout the entire lifespan of a device to track vulnerabilities, identify new requirements, root causes, and test cases. It also helps prioritize patching activities. This chapter defines the key terms used in vulnerability analysis, as well as the current state of the art of vulnerability analysis in both scientific literature and standards. The EDG model is described in more depth together with its fundamental elements: (1) the directed graph representation of the internal structure of the device, (2) the set of quantitative metrics based on the Common Vulnerability Scoring System (CVSS), and (3) the algorithm to build the EDG for a given device.
Some kernel estimators for varextropy function under length-biased sampling

In this article, we propose nonparametric estimators for varextropy function under length-biased sampling. Asymptotic properties of the estimators are established under suitable regularity conditions. Moreover, a simulation study is accomplished to compare the performance of the proposed estimators. Furthermore, two real datasets are used to evaluate the performance of the varextropy estimators.
The route to immersion: a conceptual framework for cross-disciplinary immersive theatre and experiences

Abstract The trend of immersive theatre, which began in the first decade of the twenty-first century, and has quickly established itself in contemporary mainstream theatre, is a subset of the larger phenomenon of emerging immersive experiences, which has expanded into a variety of fields, including healthcare, marketing, and education. This paper aims to provide a revised conceptual framework from the context of design through technologies with an addition of bodily immersion, or a sensation of bodily transference, creating illusory ownership over a virtual body, and activating praesence , a lived experience of the physical body responding within an imaginative, sensual environment, produced with the help of immersive technology. This concept, taken from the field of theatre and performance, is combined with four other components (systems, spatial, social/empathic, and narrative/sequential) to produce an immersive experience that realises the fullest potential of immersion. The proposed framework serves as a valuable guideline for creating immersive experiences across multiple disciplines, benefiting both creators and participants by enhancing comprehension and setting expectations. Additionally, it can be utilised by academics and students as a criteria for evaluation and analysis.
Reconstructing Turbulent Flows Using Spatio-Temporal Physical Dynamics

Accurate simulation of turbulent flows is of crucial importance in many branches of science and engineering. Direct numerical simulation (DNS) provides the highest fidelity means of capturing all intricate physics of turbulent transport. However, the method is computationally expensive because of the wide range of turbulence scales that must be accounted for in such simulations. Large eddy simulation (LES) provides an alternative. In such simulations, the large scales of the flow are resolved and the effects of small scales are modelled. Reconstruction of the DNS field from the low-resolution LES is needed for a wide variety of applications. Thus the construction of super-resolution (SR) methodologies that can provide this reconstruction has become an area of active research. In this work, a new physics-guided neural network is developed for such a reconstruction. The method leverages the partial differential equation that underlies the flow dynamics in the design of spatio-temporal model architecture. A degradation-based refinement method is also developed to enforce physical constraints and to further reduce the accumulated reconstruction errors over long periods. Detailed DNS data on two turbulent flow configurations are used to assess the performance of the model.
Blockchain-based Execution of BPMN Choreographies with Multiple Instances

The recent growth of blockchain has opened the use of technology for supporting the creation of new kinds of trustable systems. Model-driven engineering methodologies have been conceived to facilitate the automatic generation and deployment of software applications starting from the definition and refinement of abstract specification. BPMN choreography diagrams permit the representation of inter-organisational systems from a high-level perspective, just focusing on message exchange. However, the usage of such models in a blockchain-based setting has been limited to scenarios in which parties are involved in single interactions. This aspect becomes significantly relevant when considering complex applications, and in particular those in the realm of the Internet of Things. In these cases, the multiplicity of parties and their actions is crucial and requires novel solutions. In this work, we propose a novel approach for modelling, refining, deploying, and executing a Choreography on the blockchain, taking into account those scenarios in which the model includes multiple-instances. In particular, the considered models are translated into smart contracts able to correctly manage multiplicity. To demonstrate the approach’s feasibility, we designed and presented a Smart Thermostat application, that is executed on the Polygon blockchain.
Enhancing Coherence and Diversity in Multi-Class Slogan Generation Systems

Many problems related to natural language processing are solved by neural networks and big data. Researchers have previously focused on single-task supervised goals with limited data management to train slogan classification. A multi-task learning framework is used to learn jointly across several tasks related to generating multi-class slogan types. This study proposes a multi-task model named slogan generative adversarial network systems (Slo-GAN) to enhance coherence and diversity in slogan generation, utilizing generative adversarial networks and recurrent neural networks (RNN). Slo-GAN generates a new text slogan-type corpus, and the training generalization process is improved. We explored active learning (AL) and meta-learning (ML) for dataset labeling efficiency. AL reduced annotations by 10% compared to ML but still needed about 70% of the full dataset for baseline performance. The whole framework of Slo-GAN is supervised and trained together on all of these tasks. The text with the higher reporting score level is filtered by Slo-GAN, and a classification accuracy of 87.2% is achieved. We leveraged relevant datasets to perform a cross-domain experiment, reinforcing our assertions regarding both the distinctiveness of our dataset and the challenges of adapting bilingual dialects to one another.
An Artificial-Intelligence-Driven Spanish Poetry Classification Framework

Spain possesses a vast number of poems. Most have features that mean they present significantly different styles. A superficial reading of these poems may confuse readers due to their complexity. Therefore, it is of vital importance to classify the style of the poems in advance. Currently, poetry classification studies are mostly carried out manually, which creates extremely high requirements for the professional quality of classifiers and consumes a large amount of time. Furthermore, the objectivity of the classification cannot be guaranteed because of the influence of the classifier’s subjectivity. To solve these problems, a Spanish poetry classification framework was designed using artificial intelligence technology, which improves the accuracy, efficiency, and objectivity of classification. First, an artificial-intelligence-driven Spanish poetry classification framework is described in detail, and is illustrated by a framework diagram to clearly represent each step in the process. The framework includes many algorithms and models, such as the Term Frequency–Inverse Document Frequency (TF_IDF), Bagging, Support Vector Machines (SVMs), Adaptive Boosting (AdaBoost), logistic regression (LR), Gradient Boosting Decision Trees (GBDT), LightGBM (LGB), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). The roles of each algorithm in the framework are clearly defined. Finally, experiments were performed for model selection, comparing the results of these algorithms.The Bagging model stood out for its high accuracy, and the experimental results showed that the proposed framework can help researchers carry out poetry research work more efficiently, accurately, and objectively.
On the propagation of light and the existence of physical distances

We will show that the prepositions: Propagation of light and existence of physical distances, together form a circular reasoning fallacy. Thereby, we will claim that we do not have an implicit proof for the propagation of light and the existence of physical distances.
Educational Technology: A Postdigital Social Tradition

Abstract The dissolution of the campus during the early stages of the current pandemic provides a compelling case study of how Higher Education responds to emergencies. Initially, digital education was perceived as a lifeline and educators spoke of a ‘pivot online’. What followed was an undignified stumble into remote teaching. Social theories of innovation suggest that rapid technological change is simply a matter of choice and organisations can make rapid and strategic changes when required. I argue that talk of pivots and innovation is misleading and deflects enquiries from an important reason for academic resilience: teaching as tradition. Drawing on early forays into the importance of imitation by Gabriel Tarde, I contribute to efforts to rediscover his microsociological approach and link this to modern evolutionary research. By using a sociocultural evolutionary lens to analyse 102 courses in a business school, I demonstrate the persistence and resilience of teaching traditions and the use of imitation, rather than top-down direction. Using a phylogenetic analysis, I show how flexible imitation of traditional practice created a resilient, but unplanned, response to crisis. I explain how these results can inform resilience-building in Higher Education, by supporting the flexible imitation of teaching traditions. The research also advocates the use of cultural evolution, drawing on the inspiration of Tarde, in postdigital educational research.
Improved Phishing Attack Detection with Machine Learning: A Comprehensive Evaluation of Classifiers and Features

In phishing attack detection, machine learning-based approaches are more effective than simple blacklisting strategies, as they can adapt to new types of attacks and do not require manual updates. However, for these approaches, the choice of features and classifiers directly influences detection performance. Therefore, in this work, the contributions of various features and classifiers to detecting phishing attacks were thoroughly analyzed to find the best classifier and feature set in terms of different performance metrics including accuracy, precision, recall, F1-score, and classification time. For this purpose, a brand-new phishing dataset was prepared and made publicly available. Using an exhaustive strategy, every combination of the feature groups was fed into various classifiers to detect phishing websites. Two existing benchmark datasets were also used in addition to ours for further analysis. The experimental results revealed that the features based on the uniform resource locator (URL) and hypertext transfer protocol (HTTP), rather than all features, offered the best performance. Also, the decision tree classifier surpassed the others, achieving an F1-score of 0.99 and being one of the fastest classifiers overall.
Secure and Efficient Deduplication for Cloud Storage with Dynamic Ownership Management

Cloud storage services have become indispensable in resolving the constraints of local storage and ensuring data accessibility from anywhere at any time. Data deduplication technology is utilized to decrease storage space and bandwidth requirements. This technology has the potential to save up to 90% of space by eliminating redundant data in cloud storage. The secure data sharing in cloud (SeDaSC) protocol is an efficient data-sharing solution supporting secure deduplication. In the SeDaSC protocol, a cryptographic server (CS) encrypts clients’ data on behalf of clients to reduce their computational overhead, but this essentially requires complete trust in the CS. Moreover, the SeDaSC protocol does not consider data deduplication. To address these issues, we propose a secure deduplication protocol based on the SeDaSC protocol that minimizes the computational cost of clients while leveraging trust in the CS. Our protocol enhances data privacy and ensures computational efficiency for clients. Moreover, it dynamically manages client ownership, satisfying forward and backward secrecy.
Automated uniform recognition to enhance video surveillance at correctional services in South Africa

Injuries from inmate altercations are common in correctional service facilities. Monitoring incidents manually from video surveillance can be challenging. Computer vision has the potential to assist security personnel in securing facilities. This work compares two methods for recognising occupational uniforms with the aim of improving situational awareness and safety in prisons. The first method uses histograms of hue and saturation (HS) colour-space features and a shallow learning classifier. The second method uses convolutional neural network (CNN) models trained with either hand-engineered or automatically learned features. A training dataset with civilians, South African correctional service and police uniforms was created. The experimental results demonstrate comparable performance from shallow learning algorithms and CNN models. Machine learning algorithms evaluated on the proposed colour features achieved an average balanced performance (F1-score) of 0.85 and inference times range from 0.01 to 4.9 milliseconds.
The distributed trading platform design based on blockchain technology

At present, Chinese renewable energy generation is growing rapidly, distributed power trading as a market-oriented means to promote the consumption of renewable energy power has attracted wide attention. It is urgent to optimize the mechanism design and build a credible and efficient trading environment to meet the needs of credibility and timeliness of large-scale multi-agent transactions. This paper first constructs a distributed power trading mechanism considering multi-subject transactions, and then elaborates the design idea of the power distributed trading platform based on blockchain technology from three aspects: total volume architecture, platform core technology and platform deployment. Finally, based on the operation of the actual power trading center in a certain region, field application verification is carried out, which proves the effectiveness and superiority of the distributed trading platform designed in this paper based on blockchain technology. The next research will continue to expand the typical model application pilot in the applicable areas to further improve the platform functions.
Optimised path planning of a UAV for inventory management applications

Inventory management in warehouses is a crucial task in the logistics industry. Manual stocktaking in larger-scale warehouses can be time-consuming and labour-intensive. To automate this process, unmanned aerial vehicles (UAVs) have gained popularity due to their potential to offer a safer, timeous, and more efficient solution. However, deploying drone systems can face challenges and therefore requires planning tasks such as path planning. This study investigates two commonly used UAV flight paths to identify the optimal path within a warehouse: zigzag and up-down flight paths. A Gazebo simulation was considered with a six-rotor UAV model to analyse the different flight paths. The accuracy of both path types is measured for comparison, and flight times were considered as a means for optimisation. The results indicated that the zigzag flight path is the most optimal with the shortest flight time. The study found that the zigzag path resulted in a 27.25% shorter estimated flight time compared to the up-down path.
Autonomous navigation for multiple unmanned aerial vehicles (UAVs) in urban environments

This paper presents an autonomous navigation system for multiple unmanned aerial vehicles (UAVs) in urban environments. The navigation system comprises a long-term route planner and a short-term cooperative collision avoidance function. A 3D model of a real urban terrain is used to represent the environment within which the UAVs operate, and the system takes the motion constraints of the UAVs into account when performing planning. The navigation system enables multiple independent UAVs to travel from their departure points to their destinations while predicting and avoiding collisions with one another and with static terrain. Monte Carlo simulations on the system indicate a worst-case route planner replan rate of 11.65 %, route planner failure rate of 9.82×10 -4 %, and collision resolution failure rate of 0.12 % for the collision avoidance function.
Using a multi-robot system for improved path planning

Numerous terrestrial robotic platforms use computational power for path planning. These platforms typically use a vision system to identify obstacles and perform path planning. In cases where the vision systems are unable to function due to larger obstacles in the area, the paths are chosen as random functions of the given terrain to explore the environment and often results in missteps and moving away from the target location. In this research, we present a multi-robot system comprised of a terrestrial robot with a tethered aerial drone. By making use of the additive overhead view, the target location can be identified, and the exploration and path planning algorithms biased, subsequently reducing the computational cost, and creating a more efficient path planning approach. Focus has been placed on the control architecture of the system.
Geochemical Mapping and Reference Values of Potentially Toxic Elements in a Contaminated Mining Region: Upper Velhas River Basin Stream Sediments, Iron Quadrangle, Brazil

The Upper Velhas River Basin, in the mining region of the Iron Quadrangle, is one of the most polluted basins in Minas Gerais, Brazil. The region has been exploited for gold and iron, among other substances of interest. In addition to abandoned mines, active works and mineralized rocks contribute to the discharge of contaminated waters into the rivers and streams. Thus, high-density geochemical mapping with the determination of reference values has become very important, as it allows the spatial distribution of contaminant elements to be obtained, contributing to the recognition of areas with deviant values in the basin. Two hundred and eight sediment samples were collected from streams throughout the Velhas River Basin, with a density of one sample per 15 km2. Geochemical maps were compiled using the distance-weighted inverse interpolation method, and concentrations were distinguished from anomalies using the box plot Upper Inner Fence technique. It was found that 73–78% of the basin area does not present geogenic and anthropic anomalies, with values up to the third quartile for As, Cd, Cr, Ni, Cu, Pb, and Zn. However, anomalies related to human actions, mainly mining works and rock types, occupy 2 to 11% of the area. This first high-density mapping in the Upper Velhas River Basin found numerous streams with concentrations of the elements studied above the Probable Effect Level, allowing us to determine which locations, cities, and river sub-basins are exposed to environmental risks and should be monitored and protected.
Improved detector in orchard via top-to-down texture enhancement and adaptive region-aware feature fusion

Abstract Accurate target detection in complex orchard environments is the basis for automatic picking and pollination. The characteristics of small, clustered and complex interference greatly increase the difficulty of detection. Toward this end, we explore a detector in the orchard and improve the detection ability of complex targets. Our model includes two core designs to make it suitable for reducing the risk of error detection due to small and camouflaged object features. Multi-scale texture enhancement design focuses on extracting and enhancing more distinguishable features for each level with multiple parallel branches. Our adaptive region-aware feature fusion module extracts the dependencies between locations and channels, potential cross-relations among different levels and multi-types information to build distinctive representations. By combining enhancement and fusion, experiments on various real-world datasets show that the proposed network can outperform previous state-of-the-art methods, especially for detection in complex conditions.
Forest nighttime image enhancement based on improved SCI light estimation and reflection optimization

Abstract The images captured by forest nighttime imaging devices are prone to degradation and noise issues due to environmental constraints. This paper proposes a novel framework based on the self‐calibrating illumination framework to improve image quality. The framework consists of four specific steps: (1) addressing non‐uniform degradation by estimating illuminance components through non‐uniform MaxRGB, (2) extracting rich information using multi‐scale inflationary convolution, (3) analyzing the relationship between different input levels and the initial level and progressively adjusting the luminance components using a channel self‐calibration module to achieve continuous improvement in luminance, and (4) designing a reflectance optimization module that enhances illuminance while suppressing noise through a regularization model. To evaluate the proposed method, a dataset comprising 1000 forest night images is constructed using an image simulation strategy. The effectiveness of this method was validated on both synthetic and real datasets, demonstrating its ability to significantly enhance nighttime forest images. When compared to six other algorithms, this method outperformed them in eight evaluation metrics, including a peak signal‐to‐noise ratio metric of 22.839 and a structural similarity metric of 0.909. The experimental results clearly indicate that this method excels in enhancing image quality and fidelity.