Reviewerly/exHarmony huggingface dataset:
MINDeSEA: exploring seabed mineral deposits in European seas, metallogeny and geological potential for strategic and critical raw materials

Abstract This study summarizes a compilation of studies and cartographical work on seabed mineral deposit types in pan-European seas developed under the GeoERA-MINDeSEA project. In total, 692 occurrences and 1194 individual mineral samples of volcanogenic massive sulfides and hydrothermal mineralization, ferromanganese crusts, phosphorites, marine placer deposits, polymetallic nodules, and their associated strategic and critical raw material (CRM) elements have been characterized. The GeoERA-MINDeSEA project has been built based on extensive studies carried out previously, which include geophysical surveys, sampling stations, underwater photography and remotely operated vehicle (ROV) surveys, and mineralogical, geochemical and isotopic studies. This study develops pan-European and national databases, and expands strategic and CRM knowledge through a compilation of mineral potential and metallogenic studies of CRM resources in European seas. For the first time, the GeoERA-MINDeSEA portal publishes harmonized marine mineral resource information, case studies and maps, and identifies potential areas for responsible resource exploration and extraction, strategic management, and marine spatial planning. This study also provides recommendations for future target areas, studies and standards to be used across Europe as part of this project.
Performance of a PV field's discontinuous two-position sun tracker systems supplying a water pumping system: Concept, theoretical and experimental studies – A case study of the Adrar area in Algeria's Sahara

A discontinuous East-West two-position tracking system (DT-2P), in which the control only sends two orders a day, is a good solution for balancing the benefits and drawbacks of continuous tracking and fixed systems. A theoretical study applied of Adrar, an area in Algeria's south, will be conducted initially, regarding the different parameters of the DT-2P, i.e. azimuth angle for one-axis DT-2P is called 1ADT-2P, when the DT-2P tilted angle equal to latitude, and both azimuth and tilt angles for two-axis DT-2P, called 2ADT-2P, when the DT-2P tilted at the monthly optimum angles. Secondly, a pumping system experimental bench with 1ADT-2P PV and fixed PV was done to verify its performance in terms of changing weather. As for 1ADT-2P test outdoor, the increase in the solar radiation with respect to fixed PV is between 28% and 41.5% for five particular days in summer. The PV energy output and water discharge are increased by 40% and 35%, respectively. The similar gains are between 11.48% and 19%, 10% and 13%, respectively, for four particular days in winter. Also, the use of this solar tracking system does not produce much more overheating of the PV panel than the optimally tilted fixed system. Annual irradiation increases for the 1ADT-2P and 2ADT-2P are predicted to be 19% and 37%, respectively. The result shows that, despite its simplicity, the DT-2P concept approximates the continuous tracker's behavior.
3D Mineral Prospectivity Mapping of Zaozigou Gold Deposit, West Qinling, China: Machine Learning-Based Mineral Prediction

This paper focuses on researching the scientific problem of deep extraction and inference of favorable geological and geochemical information about mineralization at depth, based on which a deep mineral resources prediction model is established and machine learning approaches are used to carry out deep quantitative mineral resources prediction. The main contents include: (i) discussing the method of 3D geochemical anomaly extraction under the multi-fractal content-volume (C-V) models, extracting the 12 element anomalies and constructing a 3D geochemical anomaly data volume model for laying the data foundation for researching geochemical element distribution and association; (ii) extracting the element association characteristics of primary geochemical halos and inferring deep metallogenic factors based on compositional data analysis (CoDA), including quantitatively extracting the geochemical element associations corresponding to ore-bearing structures (Sb-Hg) based on a data-driven CoDA framework, quantitatively identifying the front halo element association (As-Sb-Hg), near-ore halo element association (Au-Ag-Cu-Pb-Zn) and tail halo element association (W-Mo-Co-Bi), which provide quantitative indicators for the primary haloes’ structural analysis at depth; (iii) establishing a deep geological and geochemical mineral resources prediction model, which is constructed by five quantitative mineralization indicators as input variables: fracture buffer zone, element association (Sb-Hg) of ore-bearing structures, metallogenic element Au anomaly, near-ore halo element association Au-Ag-Cu-Pb-Zn and the ratio of front halo to tail halo (As-Sb-Hg)/(W-Mo-Bi); and (iv) three-dimensional MPM based on the maximum entropy model (MaxEnt) and Gaussian mixture model (GMM), and delineating exploration targets at depth. The results show that the C-V model can identify the geological element distribution and the CoDA method can extract geochemical element associations in 3D space reliably, and the machine learning methods of MaxEnt and GMM have high performance in 3D MPM.
An improved path planning algorithm based on artificial potential field and primal-dual neural network for surgical robot

Safety and accuracy are essential for path planning in a surgical navigation system. In this paper, an improved path planning algorithm is proposed to increase the autonomous level of spine surgery robots for higher safety and accuracy. Firstly, the dynamic gravitational constant and piecewise repulsion function are adopted to improve the traditional Artificial Potential Field algorithm to solve the common issues of path planning, including local minimum, unable to reach the target near obstacles. To better control the pose of the end-effector in an operation space, the positions of the two endpoints of the end-effector are further constrained. Secondly, an improved Primal-Dual Neural Network with multiple constraints is proposed to minimize the joint angular velocity norm. The multiple constraints are formulated according to the planned path, the obstacle avoidance of the robot and the joint limits. Moreover, a real-time planned velocity scheme is applied to prevent the accumulation of position errors. The simulation results of the pedicle screw implantation demonstrate that the robot can find the collision-free trajectory and arrive at the target position in various complicated situations. More specifically, the error between two endpoints of the end-effector and the target pose is below 0.1 mm in reaching the surgical tool pose, while the maximum position error is around 0.05 mm when performing the planned path. Moreover, two experiments are conducted in the real-world to verify the proposed algorithm is effective in practice.
CroReLU: Cross-Crossing Space-Based Visual Activation Function for Lung Cancer Pathology Image Recognition

Lung cancer is one of the most common malignant tumors in human beings. It is highly fatal, as its early symptoms are not obvious. In clinical medicine, physicians rely on the information provided by pathology tests as an important reference for the final diagnosis of many diseases. Therefore, pathology diagnosis is known as the gold standard for disease diagnosis. However, the complexity of the information contained in pathology images and the increase in the number of patients far outpace the number of pathologists, especially for the treatment of lung cancer in less developed countries. To address this problem, we propose a plug-and-play visual activation function (AF), CroReLU, based on a priori knowledge of pathology, which makes it possible to use deep learning models for precision medicine. To the best of our knowledge, this work is the first to optimize deep learning models for pathology image diagnosis from the perspective of AFs. By adopting a unique crossover window design for the activation layer of the neural network, CroReLU is equipped with the ability to model spatial information and capture histological morphological features of lung cancer such as papillary, micropapillary, and tubular alveoli. To test the effectiveness of this design, 776 lung cancer pathology images were collected as experimental data. When CroReLU was inserted into the SeNet network (SeNet_CroReLU), the diagnostic accuracy reached 98.33%, which was significantly better than that of common neural network models at this stage. The generalization ability of the proposed method was validated on the LC25000 dataset with completely different data distribution and recognition tasks in the face of practical clinical needs. The experimental results show that CroReLU has the ability to recognize inter- and intra-class differences in cancer pathology images, and that the recognition accuracy exceeds the extant research work on the complex design of network layers.
Augmented reality material management system based on post-processing of aero-engine blade code recognition

The efficient management of aero-engine blades plays a vital role in improving material turnover and productivity, while the automatic recognition of blade code provides support for digital production. However, there is a lack of discussion about intelligent recognition, result correction, and effective solutions to material management methods for aero-engine blades. There is still a lot of manual work. Therefore, we developed an augmented reality (AR) material management system based on the post-processing of aero-engine blade code recognition, which not only supports the semi-automatic recognition of blade codes but also provides users with guidance on efficient storage and selection. Firstly, we proposed a line classification method of code Optical Character Recognition (OCR) recognition results to determine the structural relationship of discrete characters. Secondly, we constructed a Bayes error correction model which fuses the noise channel model to achieve the post-processing correction of wrong codes. Lastly, we developed the AR auxiliary guidance module to guide users on warehousing. The results show that the proposed line classification algorithm outperforms the baseline method, achieving a high success rate in terms of line classification. Post-processing error correction is effective in improving the accuracy of OCR results. In addition, AR visualization guidance can lead to a significant improvement in efficiency in material entry and out for aero-engine blades. In conclusion, the system proposed in this paper provides an effective solution to material management for the aero-engine blade.
Data-driven identification of heart failure disease states and progression pathways using electronic health records

Heart failure (HF) is a leading cause of morbidity, healthcare costs, and mortality. Guideline based segmentation of HF into distinct subtypes is coarse and unlikely to reflect the heterogeneity of etiologies and disease trajectories of patients. While analyses of electronic health records show promise in expanding our understanding of complex syndromes like HF in an evidence-driven way, limitations in data quality have presented challenges for large-scale EHR-based insight generation and decision-making. We present a hypothesis-free approach to generating real-world characteristics and progression patterns of HF. Patient disease state snapshots are extracted from the complaints mentioned in unstructured clinical notes. Typical disease states are generated by clustering and characterized in terms of their distinguishing features, temporal relationships, and risk of important clinical events. Our analysis generates a comprehensive "disease phenome" of real-world patients computed from large, noisy, secondary-use EHR datasets created in a routine clinical setting.
Spiral Search Grasshopper Features Selection with VGG19-ResNet50 for Remote Sensing Object Detection

Remote sensing object detection plays a major role in satellite imaging and is required in various scenarios such as transportation, forestry, and the ocean. Deep learning techniques provide efficient performance in remote sensing object detection. The existing techniques have the limitations of data imbalance, overfitting, and lower efficiency in detecting small objects. This research proposes the spiral search grasshopper (SSG) optimization technique to increase the exploitation in feature selection. Augmentation is applied in input images to generate more images, and this helps to train the model and reduces data imbalance problems. The VGG-19 and ResNet50 model is applied for feature extraction, and this helps to extract deep features to represent objects. The SSG feature selection technique increases the exploitation and select unique features for object detection that helps to overcome the data imbalance and overfitting problem. The SSG feature selection model helps to balance the exploration and exploitation that escape from the local optima trap. The SSG model has 82.45% mAP, the SSD model has 52.6% mAP, and the MPFP-Net model has 80.43% mAP.
Association between stress and information security policy non-compliance behavior: A meta-analysis

Stress plays a critical role in information security policy (ISP) compliance behavior. Since there are multiple sources of stress, the current literature does not provide a consistent view of the association between stress and ISP compliance behavior. Understanding the significance of the association between stress and ISP compliance behavior is valuable for managers, especially in managing the tradeoff between stress and ISP compliance behavior among individuals. It is also important to understand why there has been so much inconsistency in past studies. Thus, we attempted a meta-analysis to provide a generalized interpretation of the association between stress and ISP non-compliance behavior. A systematic literature review and a meta-analysis are conducted to examine the underlying contradiction and aggregate the findings of relevant studies. The results of this meta-analysis confirmed a weak positive correlation between stress and ISP non-compliance behavior. Demographic characteristics like age, country, and the category of the respondents (employee vs non-employee) are significant predictors of the association between stress and ISP non-compliance behavior. The meta-analysis presented here is also pertinent to the recent COVID-19 pandemic, which forced organizations to engage with their employees in new working conditions, putting employees under more stress and strain.
Towards explainable artificial intelligence in optical networks: the use case of lightpath QoT estimation

Artificial intelligence (AI) and machine learning (ML) continue to demonstrate substantial capabilities in solving a wide range of optical-network-related tasks such as fault management, resource allocation, and lightpath quality of transmission (QoT) estimation. However, the focus of the research community has been centered on ML models’ predictive capabilities, neglecting aspects related to models’ understanding, i.e., to interpret how the model reasons and makes its predictions. This lack of transparency hinders the understanding of a model’s behavior and prevents operators from judging, and hence trusting, the model’s decisions. To mitigate the lack of transparency and trust in ML, explainable AI (XAI) frameworks can be leveraged to explain how a model correlates input features to its outputs. In this paper, we focus on the application of XAI to lightpath QoT estimation. In particular, we exploit Shapley additive explanations (SHAP) as the XAI framework. Before presenting our analysis, we provide a brief overview of XAI and SHAP, then discuss the benefits of the application of XAI in networking and survey studies that apply XAI to networking tasks. Then, we model the lightpath QoT estimation problem as a supervised binary classification task to predict whether the value of the bit error rate associated with a lightpath is below or above a reference acceptability threshold and train an ML extreme gradient boosting model as the classifier. Finally, we demonstrate how to apply SHAP to extract insights about the model and to inspect misclassifications.
Reconfigurable Elastic Metamaterials

We present a novel design for materials that are reconfigurable by end-users. Conceptually, we propose decomposing such reconfigurable materials into (1) a generic, complex material consisting of engineered microstructures (known as metamaterials) designed to be purchased and (2) a simple configuration geometry that can be fabricated by end-users to fit their individual use cases. Specifically, in this paper we investigate reconfiguring our material's elasticity, such that it can cover existing objects and thereby augment their material properties. Users can configure their materials by generating the configuration geometry using our interactive editor, 3D printing it using commonly available filaments (e. g., PLA), and pressing it onto the generic material for local coupling. We characterize the mechanical properties of our reconfigurable elastic metamaterial and showcase the material's applicability as, e.g., augmentation for haptic props in virtual reality, a reconfigurable shoe sole for different activities, or a battleship-like ball game.
Mixels: Fabricating Interfaces using Programmable Magnetic Pixels

In this paper, we present Mixels, programmable magnetic pixels that can be rapidly fabricated using an electromagnetic printhead mounted on an off-the-shelve 3-axis CNC machine. The ability to program magnetic material pixel-wise with varying magnetic force enables Mixels to create new tangible, tactile, and haptic interfaces. To facilitate the creation of interactive objects with Mixels, we provide a user interface that lets users specify the high-level magnetic behavior and that then computes the underlying magnetic pixel assignments and fabrication instructions to program the magnetic surface. Our custom hardware add-on based on an electromagnetic printhead and hall effect sensor clips onto a standard 3-axis CNC machine and can both write and read magnetic pixel values from magnetic material. Our evaluation shows that our system can reliably program and read magnetic pixels of various strengths, that we can predict the behavior of two interacting magnetic surfaces before programming them, that our electromagnet is strong enough to create pixels that utilize the maximum magnetic strength of the material being programmed, and that this material remains magnetized when removed from the magnetic plotter.
Chatbots Facilitating Consensus-Building in Asynchronous Co-Design

Consensus-building is an essential process for the success of co-design projects. To build consensus, stakeholders need to discuss conflicting needs and viewpoints, converge their ideas toward shared interests, and grow their willingness to commit to group decisions. However, managing group discussions is challenging in large co-design projects with multiple stakeholders. In this paper, we investigate the interaction design of a chatbot that can mediate consensus-building conversationally. By interacting with individual stakeholders, the chatbot collects ideas to satisfy conflicting needs and engages stakeholders to consider others' viewpoints, without having stakeholders directly interact with each other. Results from an empirical study in an educational setting (N = 12) suggest that the approach can increase stakeholders' commitment to group decisions and maintain the effect even on the group decisions that conflict with personal interests. We conclude that chatbots can facilitate consensus-building in small-to-medium-sized projects, but more work is needed to scale up to larger projects.
Exploring Sensory Conflict Effect Due to Upright Redirection While Using VR in Reclining &amp; Lying Positions

When users use Virtual Reality (VR) in nontraditional postures, such as while reclining or lying in relaxed positions, their views lean upwards and need to be corrected, to make sure they see upright contents and perceive the interactions as if they were standing. Such upright redirection is excepted to cause visual-vestibular-proprioceptive conflict, affecting users' internal perceptions (e.g., body ownership, presence, simulator sickness) and external perceptions (e.g., egocentric space perception) in VR. Different body reclining angles may affect vestibular sensitivity and lead to the dynamic weighting of multi-sensory signals in the sensory integration. In the paper, we investigated the impact of upright redirection on users' perceptions, with users' physical bodies tilted at various angles backward and views upright redirected accordingly. The results showed that upright redirection led to simulator sickness, confused self-awareness, weak upright illusion, and increased space perception deviations to various extents when users are at different reclining positions, and the situations were the worst at the 45° conditions. Based on these results, we designed some illusion-based and sensory-based methods, that were shown effective in reducing the impact of sensory conflict through preliminary evaluations.
Combined YOLOv5 and HRNet for High Accuracy 2D Keypoint and Human Pose Estimation

Abstract Two-dimensional human pose estimation has been widely applied in real-world applications such as sports analysis, medical fall detection, human-robot interaction, with many positive results obtained utilizing Convolutional Neural Networks (CNNs). Li et al. at CVPR 2020 proposed a study in which they achieved high accuracy in estimating 2D keypoints estimation/2D human pose estimation. However, the study performed estimation only on the cropped human image data. In this research, we propose a method for automatically detecting and estimating human poses in photos using a combination of YOLOv5 + CC (Contextual Constraints) and HRNet. Our approach inherits the speed of the YOLOv5 for detecting humans and the efficiency of the HRNet for estimating 2D keypoints/2D human pose on the images. We also performed human marking on the images by bounding boxes of the Human 3.6M dataset (Protocol #1) for human detection evaluation. Our approach obtained high detection results in the image and the processing time is 55 FPS on the Human 3.6M dataset (Protocol #1). The mean error distance is 5.14 pixels on the full size of the image (1000 × 1002). In particular, the average results of 2D human pose estimation/2D keypoints estimation are 94.8% of PCK and 99.2% of PDJ@0.4 (head joint). The results are available.
Quantum Chaotic Honey Badger Algorithm for Feature Selection

Determining the most relevant features is a critical pre-processing step in various fields to enhance prediction. To address this issue, a set of feature selection (FS) techniques have been proposed; however, they still have certain limitations. For example, they may focus on nearby points, which lowers classification accuracy because the chosen features may include noisy features. To take advantage of the benefits of the quantum-based optimization technique and the 2D chaotic Hénon map, we provide a modified version of the honey badger algorithm (HBA) called QCHBA. The ability of such strategies to strike a balance between exploitation and exploration while identifying the workable subset of pertinent features is the basis for employing them to enhance HBA. The effectiveness of QCHBA was evaluated in a series of experiments conducted using eighteen datasets involving comparison with recognized FS techniques. The results indicate high efficiency of the QCHBA among the datasets using various performance criteria.