Reviewerly/exHarmony huggingface dataset:
Integrated geochemical and magnetic potentially toxic elements assessment: a statistical solution discriminating anthropogenic and lithogenic magnetic signals in a complex area of the southeast Nile Delta

Magnetic proxy approaches proved to be efficient for potentially toxic elements (PTEs) pollution assessment when targeting forests or areas with a homogenous background where anthropogenic magnetic signals could be easily distinguished. Here, we present a multidisciplinary approach for magnetic susceptibility ([Formula: see text]) and HM assessment in a complex area in the Nile Delta, where geogenic input, land use, and various industries with different fly ash and surface water emissions interfere. Statistical analysis discriminates between the effects of lithologic elements and the concentrations of toxic anthropogenic elements. The studied elements are classified into lithogenic and anthropogenic-related (HMs, Au industry, and fertilizers industry) groups with maximum contamination levels of eight anthropogenic-related and highly toxic PTEs (Cu, Zn, Mo, Cd, Sb, Pb, Hg, and As) in the Akrasha industrial area (pollution load index = 15.84). Considering the whole data set, the numerical correlation of [Formula: see text] with most PTE concentrations and the pollution load index (PLI) is weak, while it is moderate to strong with lithogenic elements. However, a comparison of lithogenic elements and PTE concentrations along with x-values in two separate clusters supports the correspondence of lithology with elevated x-values in silt and clay-rich soil samples as well as HM concentration in industrial sandy soils. Correspondence between magnetic maps and chemistry data with land use reflects the potential of magnetic proxy methods for qualitative PTE pollution pre-delineation of the polluted spots, provided that lithological conditions are carefully considered.
Cooperative task allocation method for multi‐unmanned aerial vehicles based on the modified genetic algorithm

Abstract Unmanned aerial vehicles (UAVs) play a crucial role in various domains such as military, civil, industrial, and so on. However, the coordination and task allocation of multiple UAVs in engineering practices face numerous challenges. As the scale of battlefields expands, and the diversity of UAV missions and constraints increases, the existing task allocation methods suffer from issues such as a mismatch between theoretical models and real‐world applications, low task execution efficiency, and poor responsiveness in dynamic environments. To address these challenges, this paper proposes an improved genetic algorithm (GA)‐based approach for multi‐UAV cooperative task allocation. By collecting battlefield information, decomposing tasks, and considering UAV resource types, an optimization model for multi‐UAV cooperative task allocation is constructed. The proposed method, using an improved GA, generates a set of Pareto‐optimal task solutions for decision‐makers. Case studies demonstrate that this approach effectively enhances task execution efficiency and reduces the total flight distance cost of UAVs.
Cross-linguistic Dependency Length Minimization in scientific language

Abstract We use Universal Dependencies (UD) for the study of cross-linguistic diachronic syntactic complexity reduction. Specifically, we look at whether and how scientific English and German minimize the length of syntactic dependency relations in the Late Modern period (ca. 1650–1900). Our linguistic analysis follows the assumption that over time, scientific discourse cross-linguistically develops towards an increasingly efficient syntactic code by minimizing Dependency Length (DL) as a factor of syntactic complexity. For each language, we analyse a large UD-annotated scientific and general language corpus for comparison. While on a macro level, our analysis suggests that there is an overall diachronic cross-linguistic and cross-register reduction in Average Dependency Length (ADL), on the micro level we find that only scientific language shows a sentence length independent reduction of ADL, while general language shows an overall decrease of ADL due to sentence length reduction. We further analyse the syntactic constructions responsible for this reduction in both languages, showing that both scientific English and German increasingly make use of short, intra-phrasal dependency relations while long dependency relations such as clausal embeddings become rather disfavoured over time.
Relational sensitivity in participatory design

Relational sensitivity involves attuning to subtle and complex collaborative dynamics and plays an important role in participatory design (PD) processes. PD practitioners, including researchers, designers, and facilitators, need abilities to navigate group dynamics while responsibly considering their own impact on the process. This article focuses on the term ‘relational sensitivity’ to develop perspectives and a vocabulary for discussing the competence and readiness needed in PD. This knowledge can contribute to design research by addressing the lack of understanding regarding how practitioners are trained in design facilitation and sensitivity to relationships. The article articulates relational sensitivity through the perspectives: sensitivity to self, to intersubjectivity, and to materiality and process. It makes a valuable contribution to the PD field by bringing together and exemplifying concepts and aspects connected to relational sensitivity, as well as applying Wilber’s four-quadrant model and Ofman’s adapted model in a new research context.
Consumer attitude toward using artificial intelligence (AI) devices in hospitality services

Purpose The study investigates the consumer’s attitude to using artificial intelligence (AI) devices in hospitality service settings considering social influence, hedonic motivation, anthropomorphism, effort expectancy, performance expectancy and emotions. Design/methodology/approach This study employed a quantitative methodology to collect data from Bangladeshi consumers who utilized AI-enabled technologies in the hospitality sector. A total of 343 data were collected using a purposive sampling method. The SmartPLS 4.0 software was used to determine the constructs' internal consistency, reliability and validity. This study also applied the partial least squares structural equation modeling (PLS-SEM) to test the research model and hypotheses. Findings The finding shows that consumer attitude toward AI is influenced by social influence, hedonic motivation, anthropomorphism, performance and effort expectancy and emotions. Specifically, hedonic motivation, social influence and anthropomorphism affect performance and effort expectations, affecting consumer emotion. Moreover, emotions ultimately influenced the perceptions of hotel customers' willingness to use AI devices. Practical implications This study provides a practical understanding of issues when adopting more stringent AI-enabled devices in the hospitality sector. Managers, practitioners and decision-makers will get helpful information discussed in this article. Originality/value This study investigates the perceptions of guests' attitudes toward the use of AI devices in hospitality services. This study emphasizes the cultural context of the hospitality industry in Bangladesh, but its findings may be reflected in other areas and regions.
Generating Independent RDCs Using Paramagnetic Tags

A metal ion with magnetic anisotropy in a biomolecule exerts strong paramagnetic effects on the nucleus spins of biomolecules in the NMR spectrum. The paramagnetic effects including paramagnetic relaxation enhancement (PRE), pseudocontact shift (PCS) and residual dipolar coupling (RDC) are valuable structural and dynamic restraints of biomolecules, and generation of these paramagnetic effects is a prerequisite. The susceptibility of magnetic anisotropy plays a similar role in the alignment of biomolecules to the commonly used alignment media in an external magnetic field. We summarize the strategies available for generating RDCs in proteins by paramagnetic labeling, selection of paramagnetic ions, and the factors influencing the magnitude of RDCs.
Innovating the humanities, reimagining the future: empirical evidence from virtual reality practice in China

The influence of digital technologies on culture and the liberal arts is a global phenomenon. To contribute a non-Western perspective to such debates, this article considers the uptake of virtual reality technology in Chinese humanities education via the policy lens of a higher education reform called New Humanities Education, which was issued by the Ministry of Education of the People’s Republic of China in 2018. The paper draws on the concept of a ‘techno-cultural imaginary’ to show how the humanities and STEM fields have merged and have contributed to the task of national rejuvenation in the past decade. Drawing on interviews with virtual reality practitioners in universities and industry, the results indicate two ideological shifts in humanities education in China: The first is a shift from praising scholar-officials (shi) to praising academic entrepreneurs; and the second is from valuing knowledge to valuing power. The article argues that those being cultivated and nurtured in the contemporary Chinese humanities education system are expected to become pragmatic problem-solvers rather than critical thinkers.
Simultaneous Trajectory and Speed Planning for Autonomous Vehicles Considering Maneuver Variants

The paper presents a technique of motion planning for autonomous vehicles (AV) based on simultaneous trajectory and speed optimization. The method includes representing the trajectory by a finite element (FE), determining trajectory parameters in Frenet coordinates, composing a model of vehicle kinematics, defining optimization criteria and a cost function, forming a set of constraints, and adapting the Gaussian N-point scheme for quadrature numerical integration. The study also defines a set of minimum optimization parameters sufficient for making motion predictions with smooth functions of the trajectory and speed. For this, piecewise functions with three degrees of freedom (DOF) in FE’s nodes are implemented. Therefore, the high differentiability of the trajectory and speed functions is ensured to obtain motion criteria such as linear and angular speeds, acceleration, and jerks used in the cost function and constraints. To form the AV roadway position, the Frenet coordinate system and two variable parameters are used: the reference path length and the lateral displacement perpendicular to reference line’s tangent. The trajectory shape, then, depends only on the final position of the AV’s mass center and the final reference’s curvature. The method uses geometric, kinematic, dynamic, and physical constraints, some of which are related to hard restrictions and some to soft restrictions. The planning technique involves parallel forecasting for several variants of the AV maneuver followed by selecting the one corresponding to a specified criterion. The sequential quadratic programming (SQP) technique is used to find the optimal solution. Graphs of trajectories, speeds, accelerations, jerks, and other parameters are presented based on the simulation results. Finally, the efficiency, rapidity, and prognosis quality are evaluated.
A Comparison of Head Movement Classification Methods

To understand human behavior, it is essential to study it in the context of natural movement in immersive, three-dimensional environments. Virtual reality (VR), with head-mounted displays, offers an unprecedented compromise between ecological validity and experimental control. However, such technological advancements mean that new data streams will become more widely available, and therefore, a need arises to standardize methodologies by which these streams are analyzed. One such data stream is that of head position and rotation tracking, now made easily available from head-mounted systems. The current study presents five candidate algorithms of varying complexity for classifying head movements. Each algorithm is compared against human rater classifications and graded based on the overall agreement as well as biases in metrics such as movement onset/offset time and movement amplitude. Finally, we conclude this article by offering recommendations for the best practices and considerations for VR researchers looking to incorporate head movement analysis in their future studies.
Machine Learning-Based Lithological Mapping from ASTER Remote-Sensing Imagery

Accurately mapping lithological features is essential for geological surveys and the exploration of mineral resources. Remote-sensing images have been widely used to extract information about mineralized alteration zones due to their cost-effectiveness and potential for being widely applied. Automated methods, such as machine-learning algorithms, for lithological mapping using satellite imagery have also received attention. This study aims to map lithologies and minerals indirectly through machine-learning algorithms using advanced spaceborne thermal emission and reflection radiometer (ASTER) remote-sensing data. The capabilities of several machine-learning (ML) algorithms were evaluated for lithological mapping, including random forest (RF), support vector machine (SVM), gradient boosting (GB), extreme gradient boosting (XGB), and a deep-learning artificial neural network (ANN). These methods were applied to ASTER imagery of the Sar-Cheshmeh copper mining region of Kerman Province, in southern Iran. First, several spectral features that were extracted from ASTER bands were used as input data. Second, correlation coefficients between the original spectral bands and features were extracted. The importance of the random forest features (RF’s feature importance) was subsequently computed, and features with less importance were removed. Finally, the remained features were given to the models as input data in the second scenario. Accuracy assessments were performed for lithological classes in the study region, including Sar-Cheshmeh porphyry, quartz eye, late fine porphyry, hornblende dike, granodiorite, feldspar dike, biotite dike, andesite, and alluvium. The overall accuracy results of lithological mapping showed that ML-based algorithms without feature extraction have the highest accuracy. The overall accuracy percentages for ML-based algorithms without conducting feature extraction were 84%, 85%, 80%, 82%, and 80% for RF, SVM, GB, XGB, and ANN, respectively. The results of this study would be of great interest to geologists for lithological mapping and mineral exploration, particularly for selecting appropriate ML-based techniques to be implemented in similar regions.
Real-Time Moving Object Tracking on Smartphone Using Cradle Head Servo Motor

The increasing demand for artificially intelligent smartphone cradles has prompted the need for real-time moving object detection. Real-time moving object tracking requires the development of algorithms for instant tracking analysis without delays. In particular, developing a system for smartphones should consider different operating systems and software development environments. Issues in current real-time moving object tracking systems arise when small and large objects coexist, causing the algorithm to prioritize larger objects or struggle with consistent tracking across varying scales. Fast object motion further complicates accurate tracking and leads to potential errors and misidentification. To address these issues, we propose a deep learning-based real-time moving object tracking system which provides an accuracy priority mode and a speed priority mode. The accuracy priority mode achieves a balance between the high accuracy and speed required in the smartphone environment. The speed priority mode optimizes the speed of inference to track fast-moving objects. The accuracy priority mode incorporates CSPNet with ResNet to maintain high accuracy, whereas the speed priority mode simplifies the complexity of the convolutional layer while maintaining accuracy. In our experiments, we evaluated both modes in terms of accuracy and speed.
Improved Differential Evolution Algorithm Guided by Best and Worst Positions Exploration Dynamics

The exploration of premium and new locations is regarded as a fundamental function of every evolutionary algorithm. This is achieved using the crossover and mutation stages of the differential evolution (DE) method. A best-and-worst position-guided novel exploration approach for the DE algorithm is provided in this study. The proposed version, known as "Improved DE with Best and Worst positions (IDEBW)", offers a more advantageous alternative for exploring new locations, either proceeding directly towards the best location or evacuating the worst location. The performance of the proposed IDEBW is investigated and compared with other DE variants and meta-heuristics algorithms based on 42 benchmark functions, including 13 classical and 29 non-traditional IEEE CEC-2017 test functions and 3 real-life applications of the IEEE CEC-2011 test suite. The results prove that the proposed approach successfully completes its task and makes the DE algorithm more efficient.
Hybrid Quantum-Classical Search Algorithms

Search is one of the most commonly used primitives in quantum algorithm design. It is known that quadratic speedups provided by Grover’s algorithm are optimal, and no faster quantum algorithms for Search exist. While it is known that at least some quantum computation is required to achieve these speedups, the existing bounds do not rule out the possibility of an equally fast hybrid quantum-classical algorithm where most of the computation is classical. In this work, we study such hybrid algorithms and we show that classical computation, unless it by itself can solve the Search problem, cannot assist quantum computation. In addition, we generalize this result to algorithms with subconstant success probabilities.
Understanding students’ adoption of the ChatGPT chatbot in higher education: the role of anthropomorphism, trust, design novelty and institutional policy

The present research aims to highlight the underlying factors that drive students’ adoption of the ChatGPT chatbot in higher education. This study extends the meta-UTAUT framework by including additional exogenous factors of anthropomorphism, trust, design novelty, and institutional policy. Empirical examination with Structural Equation Modelling among 355 students in Dutch higher education institutions revealed attitude and behavioural intention as significant positive predictors of students’ ChatGPT use behaviour. Institutional policy negatively moderated the effect of behavioural intention on use behaviour. Behavioural intention was significantly and positively influenced by attitude, performance expectancy, social influence, and facilitating conditions. Anthropomorphism, design novelty, trust, performance expectancy, and effort expectancy were unveiled as significant positive antecedents of attitude. The central theoretical contributions of this research include investigating students’ use behaviour instead of behavioural intention, establishing attitude as a core construct, underlining additional antecedents of attitude, and highlighting the importance of institutional policy. The present study contributes to prior research on technology adoption, especially in the area of artificial intelligence in education. The findings yield valuable insights for chatbot designers, product managers, and higher education policy writers.
GNNCL: A Graph Neural Network Recommendation Model Based on Contrastive Learning

Abstract In the field of recommendation algorithms, the representation learning for users and items has evolved from using single IDs or historical interactions to utilizing higher-order neighbors. This can be achieved by modeling the user–item interaction graph to capture user preferences for items. Despite the promising results achieved by these algorithms, they still suffer from the issue of data sparsity. In order to mitigate the impact of data sparsity, contrastive learning has been adopted in graph collaborative filtering to enhance performance. However, current recommendation algorithms using contrastive learning yield uneven representations after data augmentation and do not consider the potential relationships among users (or items). To address these challenges, we propose a graph neural network-based recommendation model that integrates contrastive learning (GNNCL). This model combines data augmentation with added noise and the exploration of semantic neighbors for nodes. For the structural neighbors on the interaction graph, we introduce a novel and straightforward contrastive learning approach, abandoning previous graph augmentation methods, and introducing uniform noise into the embedding space to create contrastive views. To unearth potential semantic neighbor relationships in the semantic space, we assume that users with similar representations possess semantic neighbor relationships and merge these semantic neighbors into the prototype contrastive learning. We utilize a clustering algorithm to obtain prototypes for users and items and employ the EM algorithm for prototype contrastive learning. Experimental results validate the effectiveness of our approach. Particularly, on the Yelp2018 and Amazon-book datasets, our method exhibits significant performance improvements compared to basic graph collaborative filtering models.
A Modular Framework for Domain-Specific Conversational Systems Powered by Never-Ending Learning

Complex and long interactions (e.g., a change of topic during a conversation) justify the use of dialog systems to develop task-oriented chatbots and intelligent virtual assistants. The development of dialog systems requires considerable effort and takes more time to deliver when compared to regular BotBuilder tools because of time-consuming tasks such as training machine learning models and low module reusability. We propose a framework for building scalable dialog systems for specific domains using the semi-automatic methods of corpus, ontology, and code development. By separating the dialog application logic from domain knowledge in the form of an ontology, we were able to create a dialog system for the banking domain in the Portuguese language and quickly change the domain of the conversation by changing the ontology. Moreover, by using the principles of never-ending learning, unsupported operations or unanswered questions create triggers for system knowledge demand that can be gathered from external sources and added to the ontology, augmenting the system’s ability to respond to more questions over time.