Reviewerly/exHarmony huggingface dataset:
A Multi-Objective Approach for Optimizing the Layout of Additional Boreholes in Mineral Exploration

Accurate subsurface exploration requires an optimal network of boreholes. This paper proposes a multi-objective approach to optimize the layout of additional exploratory boreholes. In order to illustrate this approach, geochemical analyses of core samples at the eastern part of the Kahang copper deposit, Central Iran, were used. A measure of the grade uncertainty (kriging standard deviation) and a confidence measure on the ore/waste classification were first calculated by implementing ordinary and indicator kriging. An ore value function was then determined to measure the total value of each block by considering the grades of all the effective variables and their ore membership degree derived from a fuzzy treatment of the grades. Finally, a misclassification cost is defined for each block based on the expected economic effects of ore loss and waste dilution. As a result, an index for the selection of additional boreholes was introduced in order to maximize the kriging standard deviation, the ore misclassification cost, and the ore value and to minimize the confidence measure. Applied to the Kahang copper deposit, this index allowed the prioritization of areas for infill sampling, leading to the recommendation for eight vertical and two directional additional boreholes.
Fuel Consumption Investigation for Quran Disposal Incinerator System

In this study, the fuel consumption characteristic of a Quran incineration system was conducted. The experiment examines the relation between fuel usage and Quran loads as well as how the usage of blower affects the fuel utilization. The result proved that the blower unit shortens the combustion time and uses less fuel. Nevertheless, the electrical source to power the blower will increase the operating costs, especially during full loading operation. Therefore, this study will provide insight in determining the balance between the combustion duration over the operational costing of such a system.
Does Digital Literacy Provide Evidence for Iranian EFL Learners’ Cooperative Learning in Online Classrooms

Introduction: Technology can revolutionize how members of cooperative groups interact and work with others. Since digital literacy and cooperative skills are necessary for teachers’ success and students’ achievement, the current research aimed to find the relationship between EFL learners’ digital literacy and their attitude towards cooperative learning in online classes. Methodology: The present research followed a quantitative study using a correlational research design to investigate the relationship between Iranian EFL learners’ digital literacy and their attitudes toward cooperative learning in online classrooms. In this study, 210 participants, who were all Iranian EFL students in different fields of study and from different cities, were invited to participate. They were from both genders and of different ages. For data collection, the researcher used a Google form link sent via social media and students were asked to complete the questionnaires online. The responses from participants were collected and analyzed using SPSS. Mean, standard deviation, multiple linear regression, and independent sample t-tests were run to analyze the obtained data and check whether the results were significant. Results: The findings of the study indicated a significant relationship between the two variables, meaning that learners could learn and collaborate better with technology. Besides, the study results showed that the second component of digital literacy (technical dimension), as the best predictor, significantly predicts a 34% variance in learners’ attitudes toward cooperative learning. Moreover, there was no significant difference between men and women in digital literacy and their attitudes toward cooperative learning. Conclusion: The results of this study indicated that the participants had a favorable view of using digital literacy in their attitude toward cooperative learning. Therefore, there is a need to change the English classroom curriculum and employ a proper syllabus to integrate digital literacy.
Effects of collaboration on teachers’ job satisfaction and self-efficacy: personal and structural characteristics as moderators

ABSTRACTTeacher collaboration (TC) is an effective means of enhancing teachers’ psychological factors towards their jobs. Yet research on the extent to which the personal and structural characteristics and their synergistic effects of collaboration on teachers’ psychological factors remain ambiguous. This study examines the effects of TC on multidimensional measures of teacher satisfaction and self-efficacy. The study also explores the moderated effects of personal and structural characteristics on the relationship of TC with job satisfaction and self-efficacy. The study employed latent moderated structural equation modelling on the Korean portion of the Programme of International Study Assessment 2018 data. The results show that TC positively predicted all job satisfaction and self-efficacy dimensions. However, not all personal and structural factors predicted job satisfaction and self-efficacy subscales. Gender had a moderating effect on the relationship between TC and job satisfaction. Additionally, participation in professional learning communities moderated the relationship between TC and work environment.KEYWORDS: Teacher collaborationteacher self-efficacyteacher satisfactionlatent moderated structural equation modellingPISA Disclosure statementWe have no known conflict of interest to disclosure. This work does not receive any financial support.Additional informationNotes on contributorsJaehong JangJaehong Jang received his doctoral degree in Educational Technology from Korea University. He has been an elementary school teacher since 2011. His major research interests include teacher education, teaching and learning methods, and longitudinal studies.Hawon YooHawon Yoo is currently a Ph.D. candidate in Educational Technology in the Department of Education at Korea University. Her major research interests include adaptive learning systems design and development and technology-integrated instructional design.Pey-Yan LiouPey-Yan Liou is a Professor in the Department of Education at Korea University. Her research agenda has focused on examining the contribution of multiple psychological constructs to people’s behaviours and intentions using international large-scale assessment datasets.
Mono-dysplasia score based on automated cell counter (Sysmex) – A novel parameter for differentiating reactive monocytosis from hematological malignancies

INTRODUCTION: In India, there is a high burden of infections such as tuberculosis, dengue, and malaria which are common causes of monocytosis. This increases the workload of smear examination by the pathologist. “Mono-dysplasia score” is obtained with a simple complete blood count on an automated cell counter and includes the parameters neutrophil (Ne) and monocyte (Mo) count and Ne-WX which is a Ne dispersion parameter. It is operator independent, objective, and does not require a high level of expertise.AIMS: The aims of the study were to assess the utility of Monoscore/mono-dysplasia score calculated using research parameters of Sysmex XN automated cell counter, as a screening tool for differentiating reactive monocytosis from hematological malignancies associated with monocytosis.MATERIALS AND METHODS: Samples sent in EDTA vacutainer for routine hemogram fulling the criteria for monocytosis (WHO criteria – absolute monocyte count ≥1 × 109/L and accounting for ≥10% of the total white blood cell count) were included in the study. Monoscore was calculated using the formula established by Schillinger et al. Flow cytometry, bone marrow examination, etc., were done as and when needed as standard-of-care tests to establish a final diagnosis.RESULTS: One thousand two hundred and fifty-seven samples were analyzed out of which 41 samples were chronic myelomonocytic leukemia and 126 were other hematological malignancies (HD) including acute leukemias, myelodysplastic syndrome, myeloproliferative neoplasm, etc. Using receiver operating characteristics curves, we established the cutoff 0.212 which showed a sensitivity of 97.6% and specificity of 96.4% to differentiated reactive monocytosis form HD.CONCLUSIONS: A sample showing monocytosis and Monoscore <0.212 and without any other flags can be safely auto-authorized without peripheral blood smears examination, reducing the burden of slides to be reviewed.
The secret life of garnets: a comprehensive, standardized dataset of garnet geochemical analyses integrating localities and petrogenesis

Abstract. Integrating mineralogy with data science is critical to modernizing Earth materials research and its applications to geosciences. Data were compiled on 95 650 garnet sample analyses from a variety of sources, ranging from large repositories (EarthChem, RRUFF, MetPetDB) to individual peer-reviewed literature. An important feature is the inclusion of mineralogical “dark data” from papers published prior to 1990. Garnets are commonly used as indicators of formation environments, which directly correlate with their geochemical properties; thus, they are an ideal subject for the creation of an extensive data resource that incorporates composition, locality information, paragenetic mode, age, temperature, pressure, and geochemistry. For the data extracted from existing databases and literature, we increased the resolution of several key aspects, including petrogenetic and paragenetic attributes, which we extended from generic material type (e.g., igneous, metamorphic) to more specific rock-type names (e.g., diorite, eclogite, skarn) and locality information, increasing specificity by examining the continent, country, area, geological context, longitude, and latitude. Likewise, we utilized end-member and quality index calculations to help assess the garnet sample analysis quality. This comprehensive dataset of garnet information is an open-access resource available in the Evolutionary System of Mineralogy Database (ESMD) for future mineralogical studies, paving the way for characterizing correlations between chemical composition and paragenesis through natural kind clustering (Chiama et al., 2022; https://doi.org/10.48484/camh-xy98). We encourage scientists to contribute their own unpublished and unarchived analyses to the growing data repositories of mineralogical information that are increasingly valuable for advancing scientific discovery.
Beyond my Real Body: Characterization, Impacts, Applications and Perspectives of “Dissimilar” Avatars in Virtual Reality

In virtual reality, the avatar - the user's digital representation - is an important element which can drastically influence the immersive experience. In this paper, we especially focus on the use of "dissimilar" avatars i.e., avatars diverging from the real appearance of the user, whether they preserve an anthropomorphic aspect or not. Previous studies reported that dissimilar avatars can positively impact the user experience, in terms for example of interaction, perception or behaviour. However, given the sparsity and multi-disciplinary character of research related to dissimilar avatars, it tends to lack common understanding and methodology, hampering the establishment of novel knowledge on this topic. In this paper, we propose to address these limitations by discussing: (i) a methodology for dissimilar avatars characterization, (ii) their impacts on the user experience, (iii) their different fields of application, and finally, (iv) future research direction on this topic. Taken together, we believe that this paper can support future research related to dissimilar avatars, and help designers of VR applications to leverage dissimilar avatars appropriately.
Texture representation via analysis and synthesis with generative adversarial networks

We investigate data-driven texture modeling via analysis and synthesis with generative adversarial networks. For network training and testing, we have compiled a diverse set of spatially homogeneous textures, ranging from stochastic to regular. We adopt StyleGAN3 for synthesis and demonstrate that it produces diverse textures beyond those represented in the training data. For texture analysis, we propose GAN inversion using a novel latent domain reconstruction consistency criterion for synthesized textures, and iterative refinement with a sliced Wasserstein loss for real-world textures. We propose perceptual procedures for evaluating network capabilities, exploring the global and local behavior of latent space trajectories, and comparing with existing texture analysis-synthesis techniques.
GEOSPATIAL ANALYSIS OF INSECURITY AND ITS THREAT ON ROAD TRANSPORTATION IN KADUNA STATE NIGERIA

The escalating waves of criminal activities involving motorists and other road users on highways is alarming, consequently resulting in many travellers losing lives and properties. This paper is aimed at analyzing the spatial distribution of criminal activities and its threat on road transportation across the state. The study utilized both primary and secondary data sources namely: structured questionnaire (likert scale), FGD and printed material such as journals and records from security agency. The ArcGIS software was used to determine the spatial pattern of the criminal activities in the study area. The questionnaire was analyzed using Microsoft Excel Package and the results were presented in charts. The results indicated that the state is bedeviled with four major types of insecurities which are arm robbery, Kidnapping, Ethnic violence and religious violence of which armed robbery predominate with 32%. The results also indicated that more than 61% of the LGAs in state had faced at least one attacked for the period 2017 to 2021. The areas bothering thick forests such as Birnin Gwari, Chikun and Giwa Local Government Areas were heavily hit by criminal activities. It was recommended that security apparatus should be placed more on the problematic areas for proactive majors to minimize the incidences of the attacks.
LTACL: long-tail awareness contrastive learning for distantly supervised relation extraction

Abstract Distantly supervised relation extraction is an automatically annotating method for large corpora by classifying a bound of sentences with two same entities and the relation. Recent works exploit sound performance by adopting contrastive learning to efficiently obtain instance representations under the multi-instance learning framework. Though these methods weaken the impact of noisy labels, it ignores the long-tail distribution problem in distantly supervised sets and fails to capture the mutual information of different parts. We are thus motivated to tackle these issues and establishing a long-tail awareness contrastive learning method for efficiently utilizing the long-tail data. Our model treats major and tail parts differently by adopting hyper-augmentation strategies. Moreover, the model provides various views by constructing novel positive and negative pairs in contrastive learning for gaining a better representation between different parts. The experimental results on the NYT10 dataset demonstrate our model surpasses the existing SOTA by more than 2.61% AUC score on relation extraction. In manual evaluation datasets including NYT10m and Wiki20m, our method obtains competitive results by achieving 59.42% and 79.19% AUC scores on relation extraction, respectively. Extensive discussions further confirm the effectiveness of our approach.
Perceptual cue-guided adaptive image downscaling for enhanced semantic segmentation on large document images

Abstract Image downscaling is an essential operation to reduce spatial complexity for various applications and is becoming increasingly important due to the growing number of solutions that rely on memory-intensive approaches, such as applying deep convolutional neural networks to semantic segmentation tasks on large images. Although conventional content-independent image downscaling can efficiently reduce complexity, it is vulnerable to losing perceptual details, which are important to preserve. Alternatively, existing content-aware downscaling severely distorts spatial structure and is not effectively applicable for segmentation tasks involving document images. In this paper, we propose a novel image downscaling approach that combines the strengths of both content-independent and content-aware strategies. The approach limits the sampling space per the content-independent strategy, adaptively relocating such sampled pixel points, and amplifying their intensities based on the local gradient and texture via the content-aware strategy. To demonstrate its effectiveness, we plug our adaptive downscaling method into a deep learning-based document image segmentation pipeline and evaluate the performance improvement. We perform the evaluation on three publicly available historical newspaper digital collections with differences in quality and quantity, comparing our method with one widely used downscaling method, Lanczos. We further demonstrate the robustness of the proposed method by using three different training scenarios: stand-alone, image-pyramid, and augmentation. The results show that training a deep convolutional neural network using images generated by the proposed method outperforms Lanczos, which relies on only content-independent strategies.
Deeply integrating unsupervised semantics and syntax into heterogeneous graphs for inductive text classification

Abstract Graph-based neural networks and unsupervised pre-trained models are both cutting-edge text representation methods, given their outstanding ability to capture global information and contextualized information, respectively. However, both representation methods meet obstacles to further performance improvements. On one hand, graph-based neural networks lack knowledge orientation to guide textual interpretation during global information interaction. On the other hand, unsupervised pre-trained models imply rich semantic and syntactic knowledge which lacks sufficient induction and expression. Therefore, how to effectively integrate graph-based global information and unsupervised contextualized semantic and syntactic information to achieve better text representation is an important issue pending for solution. In this paper, we propose a representation method that deeply integrates Unsupervised Semantics and Syntax into heterogeneous Graphs (USS-Graph) for inductive text classification. By constructing a heterogeneous graph whose edges and nodes are totally generated by knowledge from unsupervised pre-trained models, USS-Graph can harmonize the two perspectives of information under a bidirectionally weighted graph structure and thereby realizing the intra-fusion of graph-based global information and unsupervised contextualized semantic and syntactic information. Based on USS-Graph, we also propose a series of optimization measures to further improve the knowledge integration and representation performance. Extensive experiments conducted on benchmark datasets show that USS-Graph consistently achieves state-of-the-art performances on inductive text classification tasks. Additionally, extended experiments are conducted to deeply analyze the characteristics of USS-Graph and the effectiveness of our proposed optimization measures for further knowledge integration and information complementation.
Empirical study on using adapters for debiased Visual Question Answering

In this work, we empirically study debiased Visual Question Answering (VQA) works with Adapters. Most VQA debiasing works sacrifice in-distribution (ID) performance for the sake of out-of-distribution (OOD) performance. Hence, we explore and experiment with the use of adapters to preserve the ID performance by training only a simple adapter network to debias and recreate performance. We conduct an extensive empirical study on recent well-established VQA debiasing works and show that the entirety of the debiasing information from the proposed debiasing methods can be captured and modeled using a single fully connected layer while preserving original network performance by skipping the adapters. Through our exploration, we find that different placements of adapters are required for different debiasing techniques and show the different possibilities of using adapters for debiasing through our experiments. We believe our findings in this work open up more questions to be asked and explored for the VQA community.
A novel ensemble framework driven by diversity and cooperativity for non-stationary data stream classification

Data stream classification is of great significance to numerous real-world scenarios. Nevertheless, the prevalent data stream classification techniques are influenced by concept drift and demonstrate unreliability in non-stationary environments. Ensemble models are typically successful when they increase diversity among their members. Several ensembles that enhance diversity have been proposed in literatures. Regrettably, there is no established method to verify that cooperativity indeed improves performance. In response to this knowledge gap, we have developed an innovative ensemble learning framework driven by diversity and cooperativity, termed EDDC, to address the issue. EDDC first dynamically maintains multiple groups of classifiers, with primary classifier in each group chosen to enhance diversity. Next, cooperativity is employed to update groups and replace outdated members. Finally, when environment changes, EDDC adaptively selects either diversity or cooperativity as the strategy for predicting labeling of new instances, while also establishing an excellent performance guarantee. Through simulation experiments, we assessed the performance of EDDC and the benefits of cooperativity for enhancing prediction. The results demonstrated that EDDC is efficient and robust in most scenarios, particularly when dealing with gradual drift. Furthermore, EDDC maintains a competitive edge in terms of classification accuracy and other metrics.
On the behavior of parallel island models

Parallel island models (PIMs) are used to enhance the performance of evolutionary algorithms (EAs). In PIMs, each island executes an EA for evolving its local population, and periodically individuals migrate to neighborhoods synchronously or asynchronously. Neighborhoods are organized through a topology of communication, and migration policies guide individual exchange. This work explores migration policies over different communication topologies in synchronous and asynchronous PIMs to improve the speed-up and accuracy of genetic algorithms (GAs). The aim is to explain such models' adequacy from a general perspective, attempting to answer questions such as the best way to implement GAs in PIMs. To reach this goal, the quality of the solutions and the running time provided by proposed PIMs are evaluated over four NP-hard problems of different combinatorial nature: reversal and translocation evolutionary distance, task mapping and scheduling, and N-Queens. The experiments show that tuning the parameters of the breeding cycle and migration policies is vital to guarantee that all proposed PIMs reach good speed-ups and more accurate solutions than the sequential GA. In addition, experiments ratify that synchronous models provide the best solutions, while asynchronous models deliver the best speed-ups. Finally, the results show that no model provides either better speed-up or accuracy in general since, as for sequential models, the nature of the problem define which would be the best-adapted PIM.