Reviewerly/exHarmony huggingface dataset:
Causal attribution of agricultural expansion in a small island system using approximate Bayesian computation

The extent and arrangement of land cover types on our planet directly affects biodiversity, carbon storage, water quality, and many other critical social and ecological conditions at virtually all scales. Given the fundamental importance of land cover, a key mandate for land system scientists is to describe the mechanisms by which pertinent cover types spread and shrink. Identifying causal drivers of change is challenging however, because land systems, such as small-scale agricultural communities, do not lend themselves well to controlled experimentation for logistical and ethical reasons. Even natural experiments in these systems can produce only limited causal inference as they often contain unobserved confounding drivers of land cover change and complex feedbacks between drivers and outcomes. Land system scientists commonly grapple with this complexity by using computer simulations to explicitly delineate hypothesized causal pathways that could have resulted in observed land cover change. Yet, land system science lacks a systematic method for comparing multiple hypothesized pathways and quantifying the probability that a given simulated causal process was in fact responsible for the patterns observed. Here we use a case study of agricultural expansion in Pemba, Tanzania to demonstrate how approximate Bayesian computation (ABC) provides a straightforward solution to this methodological gap. Specifically, we pair an individual-based simulation of land cover change in Pemba with ABC to probabilistically estimate the likelihood that observed deforestation from 2018 to 2021 was driven by soil degradation rather than external market forces. Using this approach, we can show not only how well a specific hypothesized mechanism fits with empirical data on land cover change, but we can also quantify the range of other mechanisms that could have reasonably produced the same outcome (i.e. equifinality). While ABC was developed for use in population genetics, we argue that it is particularly promising as a tool for causal inference for land system science given the wealth of data available in the satellite record. Thus, this paper demonstrates a robust process for identifying the emergent landscape-level signatures of complex social-ecological mechanisms.
Defining and visualizing process execution variants from partially ordered event data

The execution of operational processes generates event data stored in enterprise information systems. Process mining techniques analyze such event data to obtain insights vital for decision-makers to improve the reviewed process. In this context, event data visualizations are essential. We focus on visualizing variants describing process executions that are control flow equivalent. Such variants are an integral concept for process mining and are used, e.g., for data exploration and filtering. We propose high-level and low-level variants covering different levels of abstraction and present corresponding visualizations. Compared to existing variant visualizations, we support partially ordered event data and allow for heterogeneous temporal information per event, i.e., we support both time intervals and time points. We evaluate our contributions using automated experiments showing practical applicability to real-life event data. Finally, we present a user study indicating significantly improved usefulness and ease of use of the proposed high-level variant visualization compared to existing variant visualizations for typical analysis tasks.
Augmented reality guided autonomous assembly system: A novel framework for assembly sequence input validations and creation of virtual content for AR instructions development

In recent years, industries are striving to adapt immersive technologies in the real-time assembly shop floor setting to achieve effective outcomes in terms of enabling the worker to handle the complex cognitive assembly task execution/training circumstances. The manufacturers are presently facing difficulties with the process of Augmented reality (AR) instruction creation for the required product assembly system. The existing AR instruction development process demands highly skilled experts and more time consumption. Hence, the manufacturers are presently relying on third parties to acquire the required AR instructions by providing the necessary assembly particulars as input. Where, the cost of developing AR instruction is another big challenging concern. Predominantly, the significance of providing exact assembly input is vital for the experts to develop the appropriate AR content without any errors. An automated approach is developed in this study to resolve the difficulties with assembly sequence input for the AR instructions development process. The automated approach affords four different provisions that include validation of an assembly sequence input prior to the AR instructions development stage, suggestions for assembly sequence input generation, simulation of validated assembly sequence in the Virtual reality (VR) environment for user perception, and generation of virtual content for AR visualizations. Subsequently, the generated digital contents from the automated approach are imparted into the AR Unity platform to develop the required AR instructions for the product assembly system. Notably, this study explores the AR customized marker technique to visualize the AR content in the user's view of the physical context. Finally, the AR guidelines are built as apps for the selected 11-part assembly and deployed on the shop floor for the user to perceive the AR content through visualization devices for execution of assembly operations in a real-time context. In addition, the effectiveness of the automated approach is analyzed with the existing approach, and the consequences are explored through the process of receiving feedback from the experts about the error rate, cognitive level, and creation time.
Brain-inspired GCN: Modularity-based Siamese simple graph convolutional networks

In graph representation learning, Graph Convolutional Networks (GCNs) and their variants have received much attention. However, GCNs encounter oversmoothing as the models get deeper, limiting their ability to aggregate node representations within high-order neighborhood. Inspired by the modular structure of the brain network, we propose Modularity-based Siamese Simple Graph Convolution (MS-SGC), a Siamese network architecture that incorporates the characteristics of brain modular structure into graph convolutional networks. Spectral clustering is leveraged to detect the modular structure in the graph, and then the weight of cross edges between modules is lowered. Siamese network is adopted to combine the modularity-preserved graph representation with the original graph representation, improving classification performance and reducing oversmoothing. Furthermore, a graph convolution method that functions as a linear low-pass graph filter is elaborated through spectral analysis to preserve the similarity of nodes within the same module and further alleviate the oversmoothing problem. We validate the effectiveness of MS-SGC on citation networks and extend our experimental analysis to various downstream tasks. Extensive experiments demonstrate that MS-SGC outperforms state-of-the-arts while reducing the time and computation complexity for node classification. Moreover, MS-SGC achieves competitive performance compared to other state-of-the-arts in node clustering, community prediction and text classification tasks.
Grassmannians of codes

Consider the point line-geometry Pt(n,k) having as points all the [n,k]-linear codes having minimum dual distance at least t+1 and where two points X and Y are collinear whenever X∩Y is a [n,k−1]-linear code having minimum dual distance at least t+1. We are interested in the collinearity graph Λt(n,k) of Pt(n,k). The graph Λt(n,k) is a subgraph of the Grassmann graph and also a subgraph of the graph Δt(n,k) of the linear codes having minimum dual distance at least t+1 introduced in [9]. We shall study the structure of Λt(n,k) in relation to that of Δt(n,k) and we will characterize the set of its isolated vertices. We will then focus on Λ1(n,k) and Λ2(n,k) providing necessary and sufficient conditions for them to be connected.
Mobile robot’s path-planning and path-tracking in static and dynamic environments: Dynamic programming approach

One of the main issues in robotics systems is planning and tracking a safe path in diverse environments. This paper addresses an optimal methodology for generating the desired path and thereafter forces the mobile robot to follow the designed reference path. The proposed technique has the potential to tackle the inherent challenges and intricacies of the environment, enabling the robot to navigate both static and dynamic workspaces. Several simulations are exploited to verify the captured theoretical results. Three cases were examined in a static environment, achieving average performance metrics for reference signals in the X- and Y-directions, and heading tracking of 99.62%, 99.64%, and 95.08%, respectively. For the dynamic environment, two cases were studied, with average performance in following the Y-direction and heading angle recorded as 97.58% and 86.79%, respectively. Simulation results show that the controller calculated the optimal signal with an average computational time of 7 ms per iteration for static environments and 21.3 ms per iteration for dynamic environments.
Are virtual reality applications effective for construction safety training and education? A systematic review and meta-analysis

Virtual reality (VR) gains attention in construction safety training because it allows users to simulate real activities without the risks of real activities. However, a literary work comprehensively describing the effectiveness of VR in construction safety training and education (CSTE) is lacking.This study provides a systematic review of the research related to VR applications for CSTE over the past decade using meta-analysis techniques. Standardized mean differences between traditional training methods and VR training were grouped by measurement. Potential moderators possibly affecting the effectiveness of VR in CSTE were analyzed.Results showed that VR is significantly more effective in construction training and education than traditional methods. The effectiveness of VR was 0.593, 0.432, and 0.777 higher than that of traditional methods for behaviors, skills, and experience measurements, respectively. The training context and mean work experience of trainees were two important moderators that significantly affected the effectiveness of VR in CSTE (p < 0.001).The presented results suggested the need for targeted development and management of VR technology in the construction industry and the early promotion of VR for general safety training among young, inexperienced construction workers.
Geothermal Play Fairway Analysis, Part 2: GIS methodology

Play Fairway Analysis (PFA) in geothermal exploration originates from a systematic methodology developed within the petroleum industry and is based on a geologic, geophysical, and hydrologic framework of identified geothermal systems. We tailored this methodology to study the geothermal resource potential of the Snake River Plain and surrounding region, but it can be adapted to other geothermal resource settings. We adapted the PFA approach to geothermal resource exploration by cataloging the critical elements controlling exploitable hydrothermal systems, establishing risk matrices that evaluate these elements in terms of both probability of success and level of knowledge, and building a code-based ‘processing model’ to process results. A geographic information system was used to compile a range of different data types, which we refer to as elements (e.g., faults, vents, heat flow, etc.), with distinct characteristics and measures of confidence. Discontinuous discrete data (points, lines, or polygons) for each element were transformed into continuous interpretive 2D grid surfaces called evidence layers. Because different data types have varying uncertainties, most evidence layers have an accompanying confidence layer which reflects spatial variations in these uncertainties. Risk layers, as defined here, are the product of evidence and confidence layers, and are the building blocks used to construct Common Risk Segment (CRS) maps for heat, permeability, and seal, using a weighted sum for permeability and heat, but a different approach with seal. CRS maps quantify the variable risk associated with each of these critical components. In a final step, the three CRS maps were combined into a Composite Common Risk Segment (CCRS) map, using a modified weighted sum, for results that reveal favorable areas for geothermal exploration. Additional maps are also presented that do not mix contributions from evidence and confidence (to allow an isolated view of evidence and confidence), as well as maps that calculate favorability using the product of components instead of a weighted sum (to highlight where all components are present). Our approach helped to identify areas of high geothermal favorability in the western and central Snake River Plain during the first phase of study and helped identify more precise local drilling targets during the second phase of work. By identifying favorable areas, this methodology can help to reduce uncertainty in geothermal energy exploration and development.
RepDehazeNet: Dual subnets image dehazing network based on structural re-parameterization

In recent times, there has been notable and swift advancement in the field of image dehazing. Several deep learning techniques have demonstrated remarkable proficiency in resolving homogeneous dehazing issues. Nonetheless, the current dehazing approaches are generally formulated to deal with homogeneous haze, which is often undermined in real-world scenarios due to the uncertain haze dispersion. In this paper, we propose a dehazing model named RepDehazeNet by combining a structurally Reparameterization Encoder-Decoder subnet and a Full-Resolution Attention subnet. To be specific, the structural reparameterization idea is introduced into the encoder–decoder subnet to strengthen the feature extraction of dehazed images and improve the feature extraction speed. RepDehazeNet is compared with seven SOTA models on different datasets in terms of PSNR, SSIM, parameter quantity, and inference time. Compared to the DW-GAN model, the proposed RepDehazeNet model reduces the number of parameters by 2.7 million, and improves the inference speed by 90.3%, while achieving a higher PSNR of 0.5 dB on the NH-Haze2021 dataset. The experimental results demonstrate that the proposed RepDehazeNet model can effectively improve the real-time performance, accuracy of dehazing synthesized and nonhomogeneous haze images.
A supervised parallel optimisation framework for metaheuristic algorithms

A Supervised Parallel Optimisation (SPO) is presented. The proposed framework couples different optimisation algorithms to solve single-objective optimisation problems. The supervision balances the exploration and exploitation capabilities of the distinct optimisers included, providing a general framework to solve problems with diverse characteristics. In this work, five optimisation algorithms are included in the ensemble: Particle Swarm Optimisation (PSO), Genetic Algorithm (GA), Covariance Matrix Adaption - Evolution Strategy (CMA-ES), Differential Evolution (DE), and Modified Cuckoo Search (MCS). A geometric path-finding problem with numerous local minima is used to demonstrate the advantage of SPO. The effectiveness of the approach is compared with that of stand-alone incidences of the integrated optimisation strategies and with state-of-the-art algorithms. In addition, a benchmark test suit composed of engineering applications is utilised to validate the general applicability of SPO with respect to a variety of problems. The good solutions generated by SPO are shown to be generally reproducible, while isolated algorithms, at best, render good solutions only occasionally.
A transition from porphyry Mo to vein-style Zn-Pb mineralization: Insights from hydrothermal fluid evolution of the Chalukou deposit, NE China

To reveal the hydrothermal evolution from porphyry Mo to vein-style Zn-Pb mineralization, the Chalukou deposit in NE China with these two typical mineralization overprinted is selected for detailed research. From early to late hydrothermal veins, the quartz cathodoluminescence intensities change from bright to dark and the textures also evolve. The Ti concentrations of quartz in these veins change from high to moderate and then to low values. The fluid inclusion petrography, microthermometry and stable isotope analyses indicate intermediate-density magma-derived fluids (δ18Owater = +4.7 to + 6.2‰, δD = −132 to −136‰) exsolved at high temperatures (560° to 600 °C) with low to moderate salinity (2.4 to 8.0 wt% NaCl equiv) containing minor CO2 (<5.7 mol %). Early minor molybdenite occurred under lithostatic conditions at high temperatures (540° to 580 °C) during fluids pH change and early magnetite precipitation. Later main molybdenite precipitation at moderate temperatures (350° to 430 °C), result from fluid cooling and involving of meteoric water under the transition from lithostatic to hydrostatic conditions. The precipitation of pyrite in transitional veins at moderate temperatures (330° to 420 °C), and the late sphalerite-galena from a lower-temperature fluid (180° to 300 °C) is consistent with the cooling of magmatic fluid, and gradually dilution with meteoric water under hydrostatic conditions. It is deduced the transition from porphyry Mo to vein-style Zn-Pb mineralization is likely driven by progressive decrease in temperature of the magmatic fluids and the increasing influx of meteoric water.
3D face reconstruction from a single image based on hybrid-level contextual information with weak supervision

Currently, deep learning-based 3D face reconstruction methods have shown promising results. However, they ignore the contextual information of the face, which is a topologically unified entirety. This paper proposes a 3D face reconstruction approach based on hybrid-level contextual information. Firstly, we suggest a regression network with contextual modeling capability at the feature level, PPR-CNet, which adopts a preferential parameter regression to regress the 3DMM parameters dynamically based on their various impacts on the reconstructed 3D face. Furthermore, we design a contextual landmark loss to constrain the face geometry at the landmark level. We introduce a differentiable renderer combined with multiple loss functions for weakly-supervised training. Quantitative experiments on two benchmarks show our method outperforms several SOTA methods. Extensive qualitative experiments indicate that our method performs efficiently in realism, facial proportion, and occlusion.
Trust in a ‘trust-free’ system: Blockchain acceptance in the banking and finance sector

Blockchain is a disruptive technology that revolutionizes traditional business models and revamps relationships between financial transaction counterparties. While the volume of literature on blockchains has been rapidly growing in recent years, research on the relationship between trust and blockchain acceptance at the individual level is underexplored. Our study aims to fill this research gap by investigating the determinants of blockchain acceptance in the banking and finance sector from the affordance theory. We extend UTAUT model with trust-related constructs (trust in technology and trust among communities), advocate a research framework to examine the impact of trust on blockchain acceptance. Quantitative data were collected from 218 employees at Chinese banks, and structural equation modeling was applied to analyze the data. Our empirical analysis revealed that these trust relationships are positively associated with the intention to accept blockchain technology. The results of polynomial regressions and the response surface support the significance of the trust congruence effect. We make a theoretical contribution by extending the UTAUT with two trust relationships in a Fintech context. The results can also be used as a guideline for the practitioners, inspiring them to consider the critical determinants of blockchains acceptance in blockchain implementation.
Robust weapon detection in dark environments using Yolov7-DarkVision

Currently, weapon detection through video surveillance has been extensively studied using deep learning techniques by researchers. However, limited attention has been given to the detection of weapons in night time or dark scenarios. This paper aims to address this gap by proposing a novel approach for weapon detection specifically modified for low-light conditions. The authors demonstrate accurate and robust detection of weapons in challenging nighttime environments by modifying the deep learning model YOLOv7. The YOLOv7-DarkVision model has been developed by combining a brightening algorithm with the advanced image processing techniques and architecture of YOLOv7. A dataset of 15,367 images were collected for training of the model, along with five dark videos from various sources for performance evaluation. The derived detection model, which has a precision score of 95.50% and an F1-Score of 93.41%, performs absolutely well as a weapon detector.
SI-ViT: Shuffle instance-based Vision Transformer for pancreatic cancer ROSE image classification

The rapid on-site evaluation (ROSE) technique improves pancreatic cancer diagnosis by enabling immediate analysis of fast-stained cytopathological images. Automating ROSE classification could not only reduce the burden on pathologists but also broaden the application of this increasingly popular technique. However, this approach faces substantial challenges due to complex perturbations in color distribution, brightness, and contrast, which are influenced by various staining environments and devices. Additionally, the pronounced variability in cancerous patterns across samples further complicates classification, underscoring the difficulty in precisely identifying local cells and establishing their global relationships. To address these challenges, we propose an instance-aware approach that enhances the Vision Transformer with a novel shuffle instance strategy (SI-ViT). Our approach presents a shuffle step to generate bags of shuffled instances and corresponding bag-level soft-labels, allowing the model to understand relationships and distributions beyond the limited original distributions. Simultaneously, combined with an un-shuffle step, the traditional ViT can model the relationships corresponding to the sample labels. This dual-step approach helps the model to focus on inner-sample and cross-sample instance relationships, making it potent in extracting diverse image patterns and reducing complicated perturbations. Compared to state-of-the-art methods, significant improvements in ROSE classification have been achieved. Aiming for interpretability, equipped with instance shuffling, SI-ViT yields precise attention regions that identifying cancer and normal cells in various scenarios. Additionally, the approach shows excellent potential in pathological image analysis through generalization validation on other datasets. By proposing instance relationship modeling through shuffling, we introduce a new insight in pathological image analysis. The significant improvements in ROSE classification leads to protential AI-on-site applications in pancreatic cancer diagnosis. The code and results are publicly available at https://github.com/sagizty/MIL-SI.