Reviewerly/exHarmony huggingface dataset:
Spatial Interpolation Using Machine Learning: From Patterns and Regularities to Block Models

Abstract In geospatial data interpolation, as in mapping, mineral resource estimation, modeling and numerical modeling in geosciences, kriging has been a central technique since the advent of geostatistics. Here, we introduce a new method for spatial interpolation in 2D and 3D using a block discretization technique (i.e., microblocking) using purely machine-learning algorithms and workflow design. This paper addresses the challenges of modeling spatial patterns and regularities in nature, and how different approaches have been used to cope with these challenges. We specifically explore the advantages and drawbacks of kriging while highlighting the long and complex sequence of procedures associated with block kriging. We argue that machine-learning techniques offer opportunities to simplify and streamline the process of mapping and mineral resource estimation, especially in cases of strong spatial relationships between sample location and resource concentration. To test the new method, synthetic 2D and 3D data were used for both 2D block modeling and geometallurgical modeling of a synthetic porphyry Cu deposit. The synthetic porphyry Cu data were very useful in validating the performance of the proposed microblocking technique as we were able to reproduce known values at unsampled locations. Our proposed method delivers the benefits of a machine learning-based block modeling approach, which includes its simplicity (a minimum of 2 hyperparameters), speed and familiarity to data scientists. This enables data scientists working on spatial data to employ workflows familiar to their training, to tackle problems that were previously solely in the domain of geoscience. In exchange, we expect that our method will be a gateway to attract more data scientist to become geodata scientists, benefitting the modern data-driven mineral value chain.
Exploring pre-service teachers’ intention to use virtual reality: A mixed method approach

Despite the increasing integration of virtual reality (VR) into classrooms globally, there is a dearth of empirical evidence concerning perceptions and behavioural intentions of pre-service teachers to employ the technology in Nigerian classrooms, particularly within the Technology Acceptance Model (TAM) framework. Consequently, this study examines pre-service perceptions and behavioural intentions to use VR. To accomplish this objective, we engaged pre-service teachers who voluntarily participated in this study by filling out an online survey for data collection. Quantitative and qualitative data were collected and analyzed using variance-based structural equation modelling, SmartPLS, and Atlas.ti, respectively. This data triangulation provided a comprehensive understanding of pre-service teachers' perceptions and behavioural intentions to use VR. Our findings, among others, reveal that perceived usefulness strongly predicts pre-service teachers' readiness and behavioural intention to use VR. This finding contributes to the ongoing discourse on how teachers, particularly trainee teachers, make decisions to integrate emerging technologies like VR in their classrooms, thereby offering valuable insights for policy formulation to enhance teacher training programs, especially regarding technology integration in Nigerian classrooms. It also emphasizes the importance of equipping teachers to address challenges related to adopting innovative technologies.
Psychological insights into the research and practice of embodied conversational agents, chatbots and social assistive robots: a systematic meta-review

This study presents a systematic literature search and narrative meta-review of the current state of research on conversational agents (CAs), including embodied CAs, chatbots, and social assistive robots (SARs). The investigation identifies 1,830 academic articles, of which 315 articles satisfied the inclusion criteria for the review. Systematic reviews across various fields are reported, including mental disorders, neurodevelopmental disorders, dementia/cognitive impairment, other medical conditions, elderly support, health promotion, mental health, education, industrial applications, agent characteristics, and robot characteristics. The study highlights challenges in current CA research, such as the scarcity of high-quality comparative studies and the acceptance of CAs by users and caregivers, particularly in elderly support. The article also categorises ethical discussions into nine elements: privacy, safety, innovation, user acceptance, psychological attachment, care philosophy, evaluation, social systems compatibility, and rule development. It also offers insights into the development of future guidelines. The role of CAs in fostering human relationships through their conversational function is emphasised to provide guidance for subsequent CA research and social implementation. As advancements in CA technology and research continue to progress, there is an increasing demand for sophisticated psychological investigations addressing relationships, emotions, and the self.
Practical Implementation of Real-Time Waste Detection and Recycling based on Deep Learning for Delta Parallel Robot

Intelligent robots play an essential role in waste management and recycling due to their high speed and a wide variety of applications. In this paper, two methods for waste detection and accurate pick-and-place based on computer vision and neural networks are presented. The suggested methods have been put into practical application on a 3-DOF Delta parallel robot to show the accuracy and fastness of the foregoing method for real intelligence systems. The first method, Multi-Stage Detection, consists of two stages to detect the waste objects, namely, object localization and segmentation, and classification. The second method, known as One-Stage object detectors, such as YOLOv5, has the capability to simultaneously localize and classify the waste objects. The dataset utilized in this paper relies on the TrashNet dataset as its foundation. In order to improve the classification capabilities in the multi-stage method, a larger dataset was created by utilizing data augmentation. Also, for the one-stage method, a new multi-label dataset is constructed based on the TrashNet dataset. Additionally, the results of the experimental implementation were compared based on time and evaluation metrics for detection and classification. The ResNet50 model achieved the highest accuracy in the multi-stage method, with 99.31% accuracy. In the one-stage detection method, the YOLOv5x model achieved the best mAP (@IoU = 0.75) of 97.4%, which outperformed the YOLOv5s model by 0.8 percent; however, the inference speed of the YOLOv5x in comparison with the YOLOv5s models was six times as slow. Therefore, the YOLOv5s model was employed in real-time online waste detection, which resulted in 82.1% mAP (@IoU = 0.5) after being trained on real images from the waste-sorting platform.
Single‐Photon Based Three‐Party Quantum Secure Direct Communication with Identity Authentication

Abstract Multipartite quantum secure direct communication (MQSDC) enables multiple message senders to simultaneously and independently transmit secret messages to a message receiver through quantum channels without sharing keys. Existing MQSDC protocols all assume that all the communication parties are legal, which is difficult to guarantee in practical applications. In this study, a single‐photon based three‐party QSDC protocol with identity authentication is proposed. In the protocol, the message receiver first authenticates the identity of two practical message senders. Only when the identity authentication is passed, the legal message senders can encode their messages by the hyper‐encoding technology. In theory, two bits of messages can be transmitted to the message receiver in a communication round. The protocol can resist the external attack and internal attack, and guarantee the security of the transmitted messages and the identity codes of each legal message sender. The secret message capacity of the protocol is simulated with two‐decoy‐state method. The maximal communication distance between any two communication parties can reach ≈31.75 km with weak signal and decoy state pulses. The three‐party QSDC protocol can be extended to a general MQSDC protocol and has important application in the further practical MQSDC field.
The Implicative Conditional

Abstract This paper investigates the implicative conditional, a connective intended to describe the logical behavior of an empirically defined class of natural language conditionals, also named implicative conditionals , which excludes concessive and some other conditionals. The implicative conditional strengthens the strict conditional with the possibility of the antecedent and of the contradictory of the consequent. $${p\Rightarrow q}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>⇒</mml:mo> <mml:mi>q</mml:mi> </mml:mrow> </mml:math> is thus defined as $${\lnot } \Diamond {(p \wedge \lnot q) \wedge } \Diamond {p \wedge } \Diamond {\lnot q}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>¬</mml:mo> <mml:mo>◊</mml:mo> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>p</mml:mi> <mml:mo>∧</mml:mo> <mml:mo>¬</mml:mo> <mml:mi>q</mml:mi> <mml:mo>)</mml:mo> <mml:mo>∧</mml:mo> </mml:mrow> <mml:mo>◊</mml:mo> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>∧</mml:mo> </mml:mrow> <mml:mo>◊</mml:mo> <mml:mrow> <mml:mo>¬</mml:mo> <mml:mi>q</mml:mi> </mml:mrow> </mml:mrow> </mml:math> . We explore the logical properties of this conditional in a reflexive normal Kripke semantics, provide an axiomatic system and prove it to be sound and complete for our semantics. The implicative conditional validates transitivity and contraposition, which we take to be integral parts of reasoning and communication. But it only validates restricted versions of strengthening the antecedent, right weakening, simplification, and rational monotonicity. Apparent counterexamples to some of these properties are explained as due to contextual factors. Finally, the implicative conditional avoids the paradoxes of material and strict implication, and validates some connexive principles such as Aristotle’s theses and weak Boethius’ thesis, as well as some highly entrenched principles of conditionals, such as conjunction of consequents, disjunction of antecedents, modus ponens, cautious monotonicity and cut.
Predicting Intensive Care Unit Patients’ Discharge Date with a Hybrid Machine Learning Model That Combines Length of Stay and Days to Discharge

Background: Accurate planning of the duration of stays at intensive care units is of utmost importance for resource planning. Currently, the discharge date used for resource management is calculated only at admission time and is called length of stay. However, the evolution of the treatment may be different from one patient to another, so a recalculation of the date of discharge should be performed, called days to discharge. The prediction of days to discharge during the stay at the ICU with statistical and data analysis methods has been poorly studied with low-quality results. This study aims to improve the prediction of the discharge date for any patient in intensive care units using artificial intelligence techniques. Methods: The paper proposes a hybrid method based on group-conditioned models obtained with machine learning techniques. Patients are grouped into three clusters based on an initial length of stay estimation. On each group (grouped by first days of stay), we calculate the group-conditioned length of stay value to know the predicted date of discharge, then, after a given number of days, another group-conditioned prediction model must be used to calculate the days to discharge in order to obtain a more accurate prediction of the number of remaining days. The study is performed with the eICU database, a public dataset of USA patients admitted to intensive care units between 2014 and 2015. Three machine learning methods (i.e., Random Forest, XGBoost, and lightGBM) are used to generate length of stay and days to discharge predictive models for each group. Results: Random Forest is the algorithm that obtains the best days to discharge predictors. The proposed hybrid method achieves a root mean square error (RMSE) and mean average error (MAE) below one day on the eICU dataset for the last six days of stay. Conclusions: Machine learning models improve quality of predictions for the days to discharge and length of stay for intensive care unit patients. The results demonstrate that the hybrid model, based on Random Forest, improves the accuracy for predicting length of stay at the start and days to discharge at the end of the intensive care unit stay. Implementing these prediction models may help in the accurate estimation of bed occupancy at intensive care units, thus improving the planning for these limited and critical health-care resources.
Exploring quantumness in quantum reservoir computing

Quantum reservoir computing is an emerging field in machine learning with quantum systems. While classical reservoir computing has proven to be a capable concept for enabling machine learning on real, complex dynamical systems with many degrees of freedom, the advantage of its quantum analog has yet to be fully explored. Here, we establish a link between quantum properties of a quantum reservoir, namely, entanglement and its occupied phase-space dimension, and its linear short-term memory performance. We find that a high degree of entanglement in the reservoir is a prerequisite for a more complex reservoir dynamics, which is key to unlocking the exponential phase space and higher short-term memory capacity. We quantify these relations and discuss the effect of dephasing in the performance of physical quantum reservoirs.
Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy

Probabilistic (Bayesian) modeling has experienced a surge of applications in almost all quantitative sciences and industrial areas. This development is driven by a combination of several factors, including better probabilistic estimation algorithms, flexible software, increased computing power, and a growing awareness of the benefits of probabilistic learning. However, a principled Bayesian model building workflow is far from complete and many challenges remain. To aid future research and applications of a principled Bayesian workflow, we ask and provide answers for what we perceive as two fundamental questions of Bayesian modeling, namely (a) “What actually is a Bayesian model?” and (b) “What makes a good Bayesian model?”. As an answer to the first question, we propose the PAD model taxonomy that defines four basic kinds of Bayesian models, each representing some combination of the assumed joint distribution of all observable and unobservable variables (P), a posterior approximator (A), and training data (D). As an answer to the second question, we propose and discuss ten utility dimensions according to which we can evaluate Bayesian models holistically, namely, (1) causal consistency, (2) parameter recoverability, (3) predictive performance, (4) fairness, (5) structural faithfulness, (6) parsimony, (7) interpretability, (8) convergence, (9) estimation speed, and (10) robustness. Finally, we propose two example utility decision trees that describe hierarchies and trade-offs between utilities depending on the inferential goals that drive model building and testing.
Deep reinforcement learning for multi-class imbalanced training: applications in healthcare

Abstract With the rapid growth of memory and computing power, datasets are becoming increasingly complex and imbalanced. This is especially severe in the context of clinical data, where there may be one rare event for many cases in the majority class. We introduce an imbalanced classification framework, based on reinforcement learning, for training extremely imbalanced data sets, and extend it for use in multi-class settings. We combine dueling and double deep Q-learning architectures, and formulate a custom reward function and episode-training procedure, specifically with the capability of handling multi-class imbalanced training. Using real-world clinical case studies, we demonstrate that our proposed framework outperforms current state-of-the-art imbalanced learning methods, achieving more fair and balanced classification, while also significantly improving the prediction of minority classes.
AE-Net: Novel Autoencoder-Based Deep Features for SQL Injection Attack Detection

Structured Query Language (SQL) injection attacks represent a critical threat to database-driven applications and systems, exploiting vulnerabilities in input fields to inject malicious SQL code into database queries. This unauthorized access enables attackers to manipulate, retrieve, or even delete sensitive data. The unauthorized access through SQL injection attacks underscores the critical importance of robust Artificial Intelligence (AI) based security measures to safeguard against SQL injection attacks. This study’s primary objective is the automated and timely detection of SQL injection attacks through AI without human intervention. Utilizing a preprocessed database of 46,392 SQL queries, we introduce a novel optimized approach, the Autoencoder network (AE-Net), for automatic feature engineering. The proposed AE-Net extracts new high-level deep features from SQL textual data, subsequently input into machine learning models for performance evaluations. Extensive experimental evaluation reveals that the extreme gradient boosting classifier outperforms existing studies with an impressive k-fold accuracy score of 0.99 for SQL injection detection. Each applied learning approach’s performance is further enhanced through hyperparameter tuning and validated via k-fold cross-validation. Additionally, statistical t-test analysis is applied to assess performance variations. Our innovative research has the potential to revolutionize the timely detection of SQL injection attacks, benefiting security specialists and organizations.
Modular Data Plane Verification for Compositional Networks

Modern networks are increasingly using layering and bridging to form a compositional architecture. Layering protocols like VXLAN create multiple overlay networks on top of a single underlay network infrastructure. This makes network configurations even more complex, and error-prone. To check the correctness of such compositional networks, one needs to model the dependency across multiple layers (underlay and overlay) and multiple domains (different VPNs/VPCs). Existing verifiers, which are optimized to scale in single-layer single-domain networks, exhibit scalability limitations when applied to compositional networks. This paper proposes MNV, a modular network verifier that scales to large compositional networks. At its core is a new verification method termed decompose-merge reasoning, which decomposes the network into self-contained modules, verifies each module independently, and merges the verification results. Our experiments show that for a typical data center network virtualized with VXLAN, to check reachability for more than 100 million pairs of subnets, MNV is at least 100x faster than state-of-the-art tools.
Political communication on TikTok: from the feminisation of discourse to incivility expressed in emoji form. An analysis of the Spanish political platform Sumar and reactions to its strategy

In a context of permanent electoral campaign, an increasing number of political communication specialists are trying to unravel the resources with which government officials and their parties seek to influence TikTok users. From a broad perspective, the theme is more current than novel, however, in the specific case of this research, an academic void is identified by combining the identification of idiosyncratic traits of the feminization of political discourse in TikTok with the study of reactions (text and emojis) that audiovisual content imbued with this trend provokes in users. The intention is to find out whether the inclusive tone of the feminized rhetorical style can be extrapolated to the Chinese-origin social network and, if so, whether its particular characteristics mitigate forms of incivility. To carry out the checks, the first seven months of activity on TikTok by the political platform Sumar with its leader, Yolanda Díaz, as the protagonist of most of the videos, are selected. The mixed methodology of analysis on audiovisual content and comments allows to verify that, although the new Spanish party does not apply a strategy perfectly adapted to the social network under study, the anti-polarization rhetoric and the storytelling techniques manage to neutralize extreme forms of flaming.
The Effect of Electronic Human Resource Management Systems on Sustainable Competitive Advantages: The Roles of Sustainable Innovation and Organizational Agility

This study attempts to examine the impact of electronic human resource management (E-HRM) systems on achieving a sustainable competitive advantage (SCA) in the tourism and hospitality industry by incorporating sustainable innovation (SI) and organizational agility (OA) as mediators. To survive, tourist and hospitality organizations must embrace digital transformation by using E-HRM practices such as digital recruiting, online training, and performance management tools. Organizations may improve their HR departments’ efficiency, effectiveness, and decision making, enhancing the overall organizational performance and preserving a sustainable competitive edge. Middle and first-line management of the five-star hotels and travel agencies in category A operating in Egypt were investigated. A PLS-SEM analysis was performed on 313 valid responses, gathered using WarpPLS 7.0. The findings revealed that E-HRM is positively associated with SCA, SI, and OA. Furthermore, there were positive associations between SI and OA and SCA. SI and OA were also shown to be mediators in the relationship between E-HRM and SCA. The study adds to the existing literature on how E-HRM impacts SI, OA, and SCA. The relationship between these components in hotels and travel agencies has not been the subject of any empirical research. The current study has addressed gaps by empirically analyzing these relationships in the context of Egyptian hotels and travel agencies. It integrates E-HRM, innovation, organizational agility, and competitive advantage within the context of sustainability in tourism and hospitality.
Day-Ahead Hourly Solar Photovoltaic Output Forecasting Using SARIMAX, Long Short-Term Memory, and Extreme Gradient Boosting: Case of the Philippines

This study explores the forecasting accuracy of SARIMAX, LSTM, and XGBoost models in predicting solar PV output using one-year data from three solar PV installations in the Philippines. The research aims to compare the performance of these models with their hybrid counterparts and investigate their performance. The study utilizes the adjusted shortwave radiation (SWR) product in the Advanced Himawari Imager 8 (AHI-8), as a proxy for in situ solar irradiance, and weather parameters, to improve the accuracy of the forecasting models. The results show that SARIMAX outperforms LSTM, XGBoost, and their combinations for Plants 1 and 2, while XGBoost performs best for Plant 3. Contrary to previous studies, the hybrid models did not provide more accurate forecasts than the individual methods. The performance of the models varied depending on the forecasted month and installation site. Using adjusted SWR and other weather parameters, as inputs in forecasting solar PV output, adds novelty to this research. Future research should consider comparing the accuracy of using adjusted SWR alone and combined with other weather parameters. This study contributes to solar PV output forecasting by utilizing adjusted satellite-derived solar radiation, and combining SARIMAX, LSTM, and XGBoost models, including their hybrid counterparts, in a single and comprehensive analysis.