Reviewerly/exHarmony huggingface dataset:
Great expectations for earthquake early warnings on the United States West Coast

In October 2019, California became the first state in the United States to fully activate a public earthquake early warning system—ShakeAlert®—managed by the U.S. Geological Survey. The system was subsequently rolled out in March 2021 in Oregon and May 2021 in Washington. Earthquake early warning (EEW) systems can provide seconds of notice to people and technological systems that shaking is imminent, but their effectiveness depends on recipients’ expectations and actions as well as technical performance. To better understand these dependencies, we surveyed representative samples of adults in California (N = 1219), Oregon (N = 1020), and Washington (N = 1037) in February 2021. Most respondents had experienced earthquakes, but few had lived through violent shaking; most had not followed protective action guidance to Drop, Cover, and Hold On (DCHO) in earthquakes; and most reported no personal or social harm from prior earthquakes. Nevertheless, expectations and perceived usefulness of EEW were high, and higher still for those who expected alerts to be accurate and easy to use, expressed tolerance of missed and erroneous warnings, and expected to be affected by a damaging earthquake in their lifetime. Results suggest opportunities to better align public preferences and expectations with ShakeAlert operations. For example, some respondents preferred lower alerting thresholds than those proposed by government and scientists. Moreover, reported tolerance of warning errors was widespread, but respondents wanted explanations quickly, suggesting a need to further develop post-alert messaging. Findings from this study should be informative for future research on the co-evolution of experiences and expectations with EEW systems.
Convolutional Neural Network Algorithms for Semantic Segmentation of Volcanic Ash Plumes Using Visible Camera Imagery

In the last decade, video surveillance cameras have experienced a great technological advance, making capturing and processing of digital images and videos more reliable in many fields of application. Hence, video-camera-based systems appear as one of the techniques most widely used in the world for monitoring volcanoes, providing a low cost and handy tool in emergency phases, although the processing of large data volumes from continuous acquisition still represents a challenge. To make these systems more effective in cases of emergency, each pixel of the acquired images must be assigned to class labels to categorise them and to locate and segment the observable eruptive activity. This paper is focused on the detection and segmentation of volcanic ash plumes using convolutional neural networks. Two well-established architectures, the segNet and the U-Net, have been used for the processing of in situ images to validate their usability in the field of volcanology. The dataset fed into the two CNN models was acquired from in situ visible video cameras from a ground-based network (Etna_NETVIS) located on Mount Etna (Italy) during the eruptive episode of 24th December 2018, when 560 images were captured from three different stations: CATANIA-CUAD, BRONTE, and Mt. CAGLIATO. In the preprocessing phase, data labelling for computer vision was used, adding one meaningful and informative label to provide eruptive context and the appropriate input for the training of the machine-learning neural network. Methods presented in this work offer a generalised toolset for volcano monitoring to detect, segment, and track ash plume emissions. The automatic detection of plumes helps to significantly reduce the storage of useless data, starting to register and save eruptive events at the time of unrest when a volcano leaves the rest status, and the semantic segmentation allows volcanic plumes to be tracked automatically and allows geometric parameters to be calculated.
A New Method to Evaluate Gold Mineralisation-Potential Mapping Using Deep Learning and an Explainable Artificial Intelligence (XAI) Model

Geoscientists have extensively used machine learning for geological mapping and exploring the mineral prospect of a province. However, the interpretation of results becomes challenging due to the complexity of machine learning models. This study uses a convolutional neural network (CNN) and Shapley additive explanation (SHAP) to estimate potential locations for gold mineralisation in Rengali Province, a tectonised mosaic of volcano-sedimentary sequences juxtaposed at the interface of the Archaean cratonic segment in the north and the Proterozoic granulite provinces of the Eastern Ghats Belt in Eastern India. The objective is to integrate multi-thematic data involving geological, geophysical, mineralogical and geochemical surveys on a 1:50 K scale with the aim of prognosticating gold mineralisation. The available data utilised during the integration include aero-geophysical (aeromagnetic and aerospectrometric), geochemical (national geochemical mapping), ground geophysical (gravity), satellite gravity, remote sensing (multispectral) and National Geomorphology and Lineament Project structural lineament maps obtained from the Geological Survey of India Database. The CNN model has an overall accuracy of 90%. The SHAP values demonstrate that the major contributing factors are, in sequential order, antimony, clay, lead, arsenic content and a magnetic anomaly in CNN modelling. Geochemical pathfinders, including geophysical factors, have high importance, followed by the shear zones in mineralisation mapping. According to the results, the central parts of the study area, including the river valley, have higher gold prospects than the surrounding areas. Gold mineralisation is possibly associated with intermediate metavolcanics along the shear zone, which is later intruded by quartz veins in the northern part of the Rengali Province. This work intends to model known occurrences with respect to multiple themes so that the results can be replicated in surrounding areas.
An omni-scale global–local aware network for shadow extraction in remote sensing imagery

Shadows not only reduce image quality but also interfere with image interpretation, and accurate shadow extraction is the key to improving remote sensing image utilization. However, complex features lead to shadow extraction difficulties in remote sensing imagery. In this paper, an omni-scale global–local aware network (OGLANet) is proposed by analyzing the typical characteristics of shadows in remote sensing images. First, we establish a global–local aware module (GLAM) for fully extracting shadow features to solve the problem regarding the insufficient ability to control global and local network features. Second, the detailed and semantic information of shadows exists on different scales. We propose a dense feature fusion module (DFFM) between the encoder and decoder so that the detailed information can be restored in the decoding stage while retaining the semantic information. Finally, to solve the extreme scale differences of shadows, an omni-scale aggregation module (OAM) is established; this module can obtain more refined results in the prediction stage. To prove the effectiveness of our method, we compare it with state-of-the-art (SOTA) deep learning models proposed in recent studies on the same dataset. The results show that our method achieves higher accuracy and that the proposed OGLANet exhibits higher robustness and transferability than other methods.
Do financial inclusion and income inequality matter for human capital? Evidence from sub-Saharan economies

Sub-Saharan African (SSA) countries are struggling as they have a lower level of human capital than developed countries. These economies are also trying to develop their financial sector with improved products and services. The main cause of their slow development in this region is income disparity. In view of this potential problem, this study examines the impact of financial inclusion and income inequality on human capital in SSA countries. To test this, we use panel data from 36 SSA from 2004 to 2019. Controlling for other economic indicators, we find that a high level of financial inclusion and a reduction in income inequality improve human capital. Then, we test this association separately for the dimension of financial inclusion. Afterward, our study uses the GMM technique to address endogeneity concerns. Additional tests confirm that our results are robust when SSA economies have a high level of education.
Comparison of Different Machine Learning Methods for Predicting Cation Exchange Capacity Using Environmental and Remote Sensing Data

This study was conducted to examine the capability of topographic features and remote sensing data in combination with other auxiliary environmental variables (geology and geomorphology) to predict CEC by using different machine learning models ((random forest (RF), k-nearest neighbors (kNNs), Cubist model (Cu), and support vector machines (SVMs)) in the west of Iran. Accordingly, the collection of ninety-seven soil samples was performed from the surface layer (0-20 cm), and a number of soil properties and X-ray analyses, as well as CEC, were determined in the laboratory. The X-ray analysis showed that the clay types as the main dominant factor on CEC varied from illite to smectite. The results of modeling also displayed that in the training dataset based on 10-fold cross-validation, RF was identified as the best model for predicting CEC (R2 = 0.86; root mean square error: RMSE = 2.76; ratio of performance to deviation: RPD = 2.67), whereas the Cu model outperformed in the validation dataset (R2 = 0.49; RMSE = 4.51; RPD = 1.43)). RF, the best and most accurate model, was thus used to prepare the CEC map. The results confirm higher CEC in the early Quaternary deposits along with higher soil development and enrichment with smectite and vermiculite. On the other hand, lower CEC was observed in mountainous and coarse-textured soils (silt loam and sandy loam). The important variable analysis also showed that some topographic attributes (valley depth, elevation, slope, terrain ruggedness index-TRI) and remotely sensed data (ferric oxides, normalized difference moisture index-NDMI, and salinity index) could be considered as the most imperative variables explaining the variability of CEC by the best model in the study area.
A novel multi-view deep learning approach for BI-RADS and density assessment of mammograms

Advanced deep learning (DL) algorithms may predict the patient's risk of developing breast cancer based on the Breast Imaging Reporting and Data System (BI-RADS) and density standards. Recent studies have suggested that the combination of multi-view analysis improved the overall breast exam classification. In this paper, we propose a novel multi-view DL approach for BI-RADS and density assessment of mammograms. The proposed approach first deploys deep convolutional networks for feature extraction on each view separately. The extracted features are then stacked and fed into a Light Gradient Boosting Machine (LightGBM) classifier to predict BI-RADS and density scores. We conduct extensive experiments on both the internal mammography dataset and the public dataset Digital Database for Screening Mammogra-phy (DDSM). The experimental results demonstrate that the proposed approach outperforms the single-view classification approach on two benchmark datasets by huge F1-score margins (+5% on the internal dataset and +10% on the DDSM dataset). These results highlight the vital role of combining multi-view information to improve the performance of breast cancer risk prediction.
Addressing the relation between transparency and supply chain finance schemes

In the wake of technological innovation, supply chain transparency has become a concrete avenue for long-awaited improvements to supply chain processes and practices. Transparency is of particular interest to Supply Chain Finance; however, it is yet empirically unknown whether and how benefits would materialise. Being at the interface of finance and operations, Supply Chain Finance aims at improving the inter-company management of financial flows. Nowadays, different financing schemes are offered foremost by cooperation between financial institutions and fintechs. Uncertainty remains on whether and how supply chain transparency would benefit these schemes to allow the adoption of more sophisticated, higher-value schemes. By applying the Gioia method, we analysed a series of interviews with experts from the Supply Chain Finance industry regarding transaction cost economics, agency theory, and dynamic capability. The resulting series of pragmatic, theory-grounded but practice-inspired propositions untangle the relationship and barriers between supply chain transparency and Supply Chain Finance. The propositions provide advances in the theoretical understanding of Supply Chain Finance and uncover relevant directions for future research. This poses the base for actionable results in future research and leads to an impact in research by addressing the concerns and tensions raised by Supply Chain Finance experts.
Energy Efficient User Scheduling for Hybrid Split and Federated Learning in Wireless UAV Networks

The use of unmanned aerial vehicles (UAVs) as flying users provides various applications by exploiting machine learning (ML) algorithms. Recently, distributed learning algorithms, federated learning (FL) and split learning (SL), have been exploited to train ML models distributedly via sharing model parameters rather than large raw datasets in the conventional centralized learning algorithms. Considering the diversity of users with heterogeneous resources, computation capabilities, and data distributions, we propose a hybrid split and federated learning (HSFL) framework that allows users to select either split training (ST) or federated training (FT) method based on the characteristics of the users in wireless UAV networks. Due to unreliable wireless channels and the limited energy of the users, we further formulate a user scheduling and training method selection problem within HSFL framework as a Multiple-Choice Knapsack Problem (MCKP) and propose an energy-efficient user scheduling algorithm to select a subset of users in each round and schedule each user with either ST or FT method. The simulations demonstrate that our proposed HSFL framework consumes less energy while having the same good test accuracy performance compared to the currently distributed learning algorithms, and the proposed user scheduling algorithm achieves energy-efficient selection of ST or FT method under different distributions.
A novel flexible feature extraction algorithm for Spanish tweet sentiment analysis based on the context of words

A tweet polarity classifier is presented with four categories: positive, neutral, negative and no opinion. A grouping genetic algorithm performs feature extraction on the reviews. The feature definition is based on entropy and semantic context described as the relative positions between words. The feature selection is flexible because it is customized to each word studied in the reviews. The algorithm has been applied with two corpuses written in Spanish, of 3,413 tweets and more than 63,000 tweets, to classify an evaluation set of 1,899 reviews. The results were evaluated by the metrics M−F1 and accuracy. The algorithm has improved the results of both metrics and on both corpuses compared to the previous literature works, achieving a M−F1 of 0.640 and an accuracy of 0.689. The flexibility property in feature extraction has been the major qualitative improvement of the classifier.
Performance benchmark on semantic web repositories for spatially explicit knowledge graph applications

Knowledge graph has become a cutting-edge technology for linking and integrating heterogeneous, cross-domain datasets to address critical scientific questions. As big data has become prevalent in today's scientific analysis, semantic data repositories that can store and manage large knowledge graph data have become critical in successfully deploying spatially explicit knowledge graph applications. This paper provides a comprehensive evaluation of the popular semantic data repositories and their computational performance in managing and providing semantic support for spatial queries. There are three types of semantic data repositories: (1) triple store solutions (RDF4j, Fuseki, GraphDB, Virtuoso), (2) property graph databases (Neo4j), and (3) an Ontology-Based Data Access (OBDA) approach (Ontop). Experiments were conducted to compare each repository's efficiency (e.g., query response time) in handling geometric, topological, and spatial-semantic related queries. The results show that Virtuoso achieves the overall best performance in both non-spatial and spatial-semantic queries. The OBDA solution, Ontop, has the second-best query performance in spatial and complex queries and the best storage efficiency, requiring the least data-to-RDF conversion efforts. Other triple store solutions suffer from various issues that cause performance bottlenecks in handling spatial queries, such as inefficient memory management and lack of proper query optimization.
Deep Graph Memory Networks for Forgetting-Robust Knowledge Tracing

Tracing a student's knowledge is vital for tailoring the learning experience. Recent knowledge tracing methods tend to respond to these challenges by modelling knowledge state dynamics across learning concepts. However, they still suffer from several inherent challenges including: modelling forgetting behaviors and identifying relationships among latent concepts. To address these challenges, in this paper, we propose a novel knowledge tracing model, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Deep Graph Memory Network</i> (DGMN). In this model, we incorporate a forget gating mechanism into an attention memory structure in order to capture forgetting behaviors dynamically during the knowledge tracing process. Particularly, this forget gating mechanism is built upon attention forgetting features over latent concepts considering their mutual dependencies. Further, this model has the capability of learning relationships between latent concepts from a dynamic latent concept graph in light of a student's evolving knowledge states. A comprehensive experimental evaluation has been conducted using four well-established benchmark datasets. The results show that DGMN consistently outperforms the state-of-the-art KT models over all the datasets. The effectiveness of modelling forgetting behaviors and learning latent concept graphs has also been analyzed in our experiments.
Open scholarship in applied linguistics: What, why, and how

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A Lightweight Specific Emitter Identification Model for IIoT Devices Based on Adaptive Broad Learning

Specific emitter identification (SEI) is a technology that extracts subtle features from signals sent by emitters to identify different individuals. It can effectively improve the security of the Industrial Internet of Things (IIoT) by acting on the physical layer of the internet. Recent research on SEI has focused on deep learning (DL) models that can automatically learn effective inherent emitter features from raw signals. Nevertheless, training popular DL models is computationally expensive because of the numerous hyperparameters and nonscalable structures. This limits the application of DL-based SEI models in certain practical IIoT scenarios. To address this concern, we propose an adaptive broad learning (ABL) method to build a lightweight SEI model. In the proposed model, the raw signal samples are mapped to feature nodes, and the emitters are denoted as the output nodes. The hidden nodes are directly connected to the output nodes by a broad network. Through this flat structure, the size and calculation amount of the model can be effectively reduced. To further economize the computational cost, we designed an adaptive node expansion strategy for rapidly obtaining the optimal hyperparameters of the models. The results of experiments on real-world data prove the superiority of ABL over popular state-of-the-art DL-based SEI models.
Adaptive guided salp swarm algorithm with velocity clamping mechanism for solving optimization problems

Abstract Salp swarm algorithm (SSA) is a well-established population-based optimizer that exhibits strong exploration ability, but slow convergence and poor exploitation capability. In this paper, an endeavour is made to enhance the performance of the basic SSA. The new upgraded version of SSA named as ‘adaptive strategy-based SSA (ABSSA) algorithm’ is proposed in this paper. First, the exploratory scope and food source navigating commands of SSA are enriched using the inertia weight and boosted global best-guided mechanism. Next, a novel velocity clamping strategy is designed to efficiently stabilize the balance between the exploration and exploitation operations. In addition, an adaptive conversion parameter tactic is designed to modify the position update equation to effectively intensify the local exploitation competency and solution accuracy. The effectiveness of the proposed ABSSA algorithm is verified by a series of problems, including 23 classical benchmark functions, 29 complex optimization problems from CEC 2017, and five engineering design tasks. The experimental results show that the developed ABSSA approach performs significantly better than the standard SSA and other competitors. Moreover, ABSSA is implemented to handle path planning and obstacle avoidance (PPOA) tasks in autonomous mobile robots and compared with some swarm intelligent approach-based path planners. The experimental results indicate that the ABSSA-based PPOA method is a reliable path planning algorithm.
Software Architecture for Quantum Computing Systems - A Systematic Review

Quantum computing systems rely on the principles of quantum mechanics to perform a multitude of computationally challenging tasks more efficiently than their classical counterparts. The architecture of software-intensive systems can empower architects who can leverage architecture centric processes, practices, description languages, etc., to model, develop, and evolve quantum computing software (quantum software for short) at higher abstraction levels. We conducted Systematic Literature Review (SLR) to investigate (i) architectural process, (ii) modeling notations, (iii) architecture design patterns, (iv) tool support, and (iv) challenging factors for quantum software architecture. Results of the SLR indicate that quantum software represents a new genre of software-intensive systems; however, existing processes and notations can be tailored to derive the architecting activities and develop modeling languages for quantum software. Quantum bits (Qubits) mapped to Quantum gates (Qugates) can be represented as architectural components and connectors that implement quantum software. Tool-chains can incorporate reusable knowledge and human roles (e.g., quantum domain engineers, quantum code developers) to automate and customize the architectural process. Results of this SLR can facilitate researchers and practitioners to develop new hypotheses to be tested, derive reference architectures, and leverage architecture-centric principles and practices to engineer emerging and next generations of quantum software.