Reviewerly/exHarmony huggingface dataset:
Optimizing image-based deep learning for energy geoscience via an effortless end-to-end approach

The rapid growth of artificial intelligence (AI) technology and its applications in recent years has transformed the process of data analytics in many scientific fields, including geoscience. Geoscience has traditionally been a descriptive science and fundamentally relies upon visual recognition and identification of different geological features, from satellite images to subsurface seismic, to study Earth's history. Geological image data provides immense potential to apply advanced AI methods, such as deep learning to improve and optimize different geological and geophysical characterization workflows. Despite the increasing efforts and interest toward using AI in geosciences, its actual potential remains untapped, and further exploration is required. The prospect of AI application in geosciences is primarily hindered by the following: (i) limited availability of high-quality labeled datasets and (ii) inherited imbalance dataset distribution. These limitations are compounded by overexploitation of the transfer learning method to mitigate such issues, discarding the interpretability of the AI black-box problems. In this study, a robust and effortless strategy is proposed to overcome the limitations and simultaneously reduce our dependency on to the transfer learning method. Among the various methods available to mitigate these issues, only traditional data augmentation is heavily used in geosciences. This study, therefore, explored and developed a workflow by combining three readily available methods to maximize the performance of machine learning algorithms when dealing with a limited and imbalanced geoscience dataset. Here, the proposed method follows three robust and straightforward end-to-end steps: (i) combining traditional and advanced data augmentation (e.g., CutOut and CutMix) techniques to enhance localization and generalization performance; (ii) employing an algorithm-level class weight method to minimize detrimental impact and performance bias due to class imbalance; and (iii) applying a regularization label smoothing technique to improve the generalization and avoid overconfident prediction. Across the study datasets, the overall accuracy is typically improved up to 12% when comparing CNN without and with the proposed strategy. In addition, when combined with transfer learning, these methods could minimize model overfitting, and optimize generalization and model performance. This study further highlights that the proposed method should apply to different applications of AI in geosciences and could provide an alternative approach to the transfer learning method in analyzing limited and imbalanced geoscience datasets and improve the interpretability of how AI models work when combined with subject matter expertise.
Cyberbullying detection using deep transfer learning

Abstract Social networking platforms like Facebook, Twitter, and others have numerous advantages, but they have many dark sides also. One of the issues on these social platforms is cyberbullying. The impact of cyberbullying is immeasurable on the life of victims as it’s very subjective to how the person would tackle this. The message may be a bully for victims, but it may be normal for others. The ambiguities in cyberbullying messages create a big challenge to find the bully content. Some research has been reported to address this issue with textual posts. However, image-based cyberbullying detection is received less attention. This research aims to develop a model that helps to prevent image-based cyberbullying issues on social platforms. The deep learning-based convolutional neural network is initially used for model development. Later, transfer learning models are utilized in this research. The experimental outcomes of various settings of the hyper-parameters confirmed that the transfer learning-based model is the better choice for this problem. The proposed model achieved a satisfactory accuracy of 89% for the best case, indicating that the system detects most cyberbullying posts.
Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation

Learning vectorized embeddings is at the core of various recommender systems for user-item matching. To perform efficient online inference, representation quantization, aiming to embed the latent features by a compact sequence of discrete numbers, recently shows the promising potentiality in optimizing both memory and computation overheads. However, existing work merely focuses on numerical quantization whilst ignoring the concomitant information loss issue, which, consequently, leads to conspicuous performance degradation. In this paper, we propose a novel quantization framework to learn Binarized Graph Representations for Top-K Recommendation (BiGeaR). We introduce multi-faceted quantization reinforcement at the pre-, mid-, and post-stage of binarized representation learning, which substantially retains the informativeness against embedding binarization. In addition to saving the memory footprint, it further develops solid online inference acceleration with bitwise operations, providing alternative flexibility for the realistic deployment. The empirical results over five large real-world benchmarks show that BiGeaR achieves about 22%~40% performance improvement over the state-of-the-art quantization-based recommender system, and recovers about 95%~102% of the performance capability of the best full-precision counterpart with over 8× time and space reduction.
VALSE: A Task-Independent Benchmark for Vision and Language Models Centered on Linguistic Phenomena

We propose VALSE (Vision And Language Structured Evaluation), a novel benchmark designed for testing general-purpose pretrained vision and language (V&L) models for their visio-linguistic grounding capabilities on specific linguistic phenomena.VALSE offers a suite of six tests covering various linguistic constructs.Solving these requires models to ground linguistic phenomena in the visual modality, allowing more fine-grained evaluations than hitherto possible.We build VALSE using methods that support the construction of valid foils, and report results from evaluating five widely-used V&L models.Our experiments suggest that current models have considerable difficulty addressing most phenomena.Hence, we expect VALSE to serve as an important benchmark to measure future progress of pretrained V&L models from a linguistic perspective, complementing the canonical taskcentred V&L evaluations.
Supply chain management based on volatility clustering: The effect of CBDC volatility

A Central Bank Digital Currency (CBDC) launched by the Bank of England could enable businesses to directly make electronic payments. It can be argued that digital payment is helpful in supply chain management applications. However, the adoption of CBDC in the supply chain could bring new turbulence since the CBDC value may fluctuate. Therefore, this paper intends to optimize the production plan of manufacturing supply chain based on a volatility clustering model by reducing CBDC value uncertainty. We apply both GARCH model and machine learning model to depict the CBDC volatility clustering. Empirically, we employed Baltic Dry Index, Bitcoin and exchange rate as main variables with sample period from 2015 to 2021 to evaluate the performance of the two models. On this basis, we reveal that our machine learning model overwhelmingly outperforms the GARCH model. Consequently, our result implies that manufacturing companies’ performance can be strengthened through CBDC uncertainty reduction.
Assessment of Virtual Reality among University Professors: Influence of the Digital Generation

This paper conducts quantitative research on the assessment made by a group of 623 Spanish and Latin American university professors about the use of virtual reality technologies in the classroom and their own digital skills in this respect. The main objective is to analyze the differences that exist in this regard due to the digital generation of the professors (immigrants or digital natives). As an instrument, a survey designed for this purpose was used, the validity of which has been tested in the study. It was found that digital natives say they are more competent in the use of virtual reality and value its technical and didactic aspects more highly, although they also identify more disadvantages in its use than digital immigrants. Differences in responses were found by gender and areas of knowledge of the professors with respect to the opinions expressed. It is suggested that universities design training plans on teaching digital competence and include in them the didactic use of virtual reality technologies in higher education.
Transfer language selection for zero-shot cross-lingual abusive language detection

We study the selection of transfer languages for automatic abusive language detection. Instead of preparing a dataset for every language, we demonstrate the effectiveness of cross-lingual transfer learning for zero-shot abusive language detection. This way we can use existing data from higher-resource languages to build better detection systems for low-resource languages. Our datasets are from seven different languages from three language families. We measure the distance between the languages using several language similarity measures, especially by quantifying the World Atlas of Language Structures. We show that there is a correlation between linguistic similarity and classifier performance. This discovery allows us to choose an optimal transfer language for zero shot abusive language detection.
Deep Learning-Based Privacy-Preserving Data Transmission Scheme for Clustered IIoT Environment

The Industrial Internet of Things (IIoT) has received significant attention from several leading industries like agriculture, mining, transport, energy, and healthcare. IIoT acts as a vital part of Industry 4.0 that mainly employs machine learning (ML) to investigate the interconnection and massive quantity of the IIoT data. As the data are generally saved at the cloud server, security and privacy of the collected data from numerous distributed and heterogeneous devices remain a challenging issue. This article develops a novel multi-agent system (MAS) with deep learning-based privacy preserving data transmission (BDL-PPDT) scheme for clustered IIoT environment. The goal of the BDL-PPDT technique is to accomplish secure data transmission in clustered IIoT environment. The BDL-PPDT technique involves a two-stage process. Initially, an enhanced moth swarm algorithm-based clustering (EMSA-C) technique is derived to choose a proper set of clusters in the IIoT system and construct clusters. Besides, multi-agent system is used to enable secure inter-cluster communication. Moreover, multi-head attention with bidirectional long short-term memory (MHA-BLSTM) model is applied for intrusion detection process. Furthermore, the hyperparameter tuning process of the MHA-BLSTM model can be carried out by the stochastic gradient descent with momentum (SGDM) model to improve the detection rate. For examining the promising performance of the BDL-PPDT technique, an extensive comparison study takes place and the results are assessed under varying measures. A significant amount of capital is required. It goes without saying that one of the most obvious industrial IoT concerns is the high cost of adoption. Secure data storage and management connectivity failures are common among IoT devices due to the massive amount of data they create. The simulation results demonstrate the enhanced outcomes of the BDL-PPDT technique over the recent methods. Despite the fact that the offered BDL-PPDT technique has an accuracy of just 98.15 percent, it produces the best feasible outcome. Because of the data analysis conducted as detailed above, it was determined that the BDL-PPDT technique outperformed the other current techniques on a range of different criteria and was thus recommended.
K-Anonymization approach for privacy preservation using data perturbation techniques in data mining

Nowadays, privacy is a big issue for everyone because the internet generates and shares massive amounts of data every day. Because traditional privacy-preserving techniques do not protect sensitive data well enough, malicious attacks on sensitive data are possible. Traditional methods may have many drawbacks due to various attacks on sensitive data. Thus, data must be preserved before being shared with others. K-Anonymity protects privacy. By definition, anonymization means that a person is not identifiable, traceable, or reachable. Anonymization using k-anonymity protects data privacy and protects entire datasets. The traditional data perturbation method only works with numerical data, but the proposed method is designed to work with categorical data. The Proposed Framework perturbs data for multiple columns at once. The proposed framework's performance is evaluated in two ways. Initially, it keeps the data mining model accurate. Second, it protects the original data privacy while minimizing data loss. The main motivation for using k-anonymity is to remove or transfer personally identifiable information.
Developments in Quantitative Assessment and Modeling of Mineral Resource Potential: An Overview

The special issue entitled “Developments in Quantitative Assessment and Modeling of Mineral Resource Potential” is composed of 17 papers that cover a diverse range of approaches to mineral resource assessment, including mainly multivariate statistical analysis, fractal and multifractal modeling, geostatistical modeling, machine learning, mathematical morphology and 3D mineral exploration. This introductory article first provides an overview of the developments and existing methods in quantitative assessment of mineral resources. Then, brief introductions of each of the 17 papers are provided. These papers can be grouped into three themes: (1) multifractal theory for mineral resource assessment; (2) machine learning for mineral prospectivity mapping; and (3) GIS-based 3D modeling for mineral exploration. The 17 papers either proposed novel methods or demonstrated innovative applications of existing methods.
Grid-optimized UAV indoor path planning algorithms in a complex environment

Path planning has become a predominant issue for unmanned aerial vehicles (UAVs), especially in complex indoor environments. Existing solutions for UAV indoor path planning are strongly limited due to their high computational complexity, slow convergence speed and poor flight paths. In this study, a set of grid-optimized UAV indoor path planning algorithms targeting complex environments is proposed to solve the abovementioned issues. First, the grid-optimized indoor airspace modeling (GO-IAM) algorithm based on the Geo-graphical coordinates Subdividing grid with One-dimension integral coding on 2n-Tree in 3 Dimensions (GeoSOT-3D) is proposed to reduce the computational complexity of modeling the indoor 3D airspace by means of spatial subdivisions. For regular indoor scenarios and a complex "dead zone" airspace, the grid-optimized A* path planning (GO-APP) algorithm and the grid-optimized local backtracking path planning (GO-LBPP) algorithm are established to address the efficiency and flyability of path planning. Comparison experiments of multiple UAV indoor path planning algorithms reveal that the GO-APP algorithm has the shortest computation time and planning path. Specifically, with the use of the GO-LBPP algorithm, the indoor local dead zone issue can be solved, and the deadlock of the planning path can be avoided, which cannot be achieved by other algorithms. Hence, the proposed grid-optimized UAV indoor path planning algorithms can realize state-of-the-art path planning through spatial subdivision in complex 3D indoor airspace.
Graphical Image Region Extraction with K-Means Clustering and Watershed

With a wide range of applications, image segmentation is a complex and difficult preprocessing step that plays an important role in automatic visual systems, which accuracy impacts, not only on segmentation results, but directly affects the effectiveness of the follow-up tasks. Despite the many advances achieved in the last decades, image segmentation remains a challenging problem, particularly, the segmenting of color images due to the diverse inhomogeneities of color, textures and shapes present in the descriptive features of the images. In trademark graphic images segmentation, beyond these difficulties, we must also take into account the high noise and low resolution, which are often present. Trademark graphic images can also be very heterogeneous with regard to the elements that make them up, which can be overlapping and with varying lighting conditions. Due to the immense variation encountered in corporate logos and trademark graphic images, it is often difficult to select a single method for extracting relevant image regions in a way that produces satisfactory results. Many of the hybrid approaches that integrate the Watershed and K-Means algorithms involve processing very high quality and visually similar images, such as medical images, meaning that either approach can be tweaked to work on images that follow a certain pattern. Trademark images are totally different from each other and are usually fully colored. Our system solves this difficulty given it is a generalized implementation designed to work in most scenarios, through the use of customizable parameters and completely unbiased for an image type. In this paper, we propose a hybrid approach to Image Region Extraction that focuses on automated region proposal and segmentation techniques. In particular, we analyze popular techniques such as K-Means Clustering and Watershedding and their effectiveness when deployed in a hybrid environment to be applied to a highly variable dataset. The proposed system consists of a multi-stage algorithm that takes as input an RGB image and produces multiple outputs, corresponding to the extracted regions. After preprocessing steps, a K-Means function with random initial centroids and a user-defined value for k is executed over the RGB image, generating a gray-scale segmented image, to which a threshold method is applied to generate a binary mask, containing the necessary information to generate a distance map. Then, the Watershed function is performed over the distance map, using the markers defined by the Connected Component Analysis function that labels regions on 8-way pixel connectivity, ensuring that all regions are correctly found. Finally, individual objects are labelled for extraction through a contour method, based on border following. The achieved results show adequate region extraction capabilities when processing graphical images from different datasets, where the system correctly distinguishes the most relevant visual elements of images with minimal tweaking.
Semi-supervised partial multi-label classification via consistency learning

Partial multi-label learning refers to the problem that each instance is associated with a candidate label set involving both relevant and noisy labels. Existing solutions mainly focus on label disambiguation, while ignoring the negative effect of the inconsistency between feature information and label information. Specifically, the existence of completely unlabeled instances makes the estimation of label co-occurrence difficult. To tackle these problems, we propose a novel framework for partial multi-label learning in semi-supervised scenarios by solving the inconsistency between features and labels. In the first stage, the label-level correlation matrix on both labeled and unlabeled instances is derived via Hilbert-Schmidt Independence Criterion (HSIC). The correlation matrix can characterize the label correlation of labeled instances and can propagate the label correlation of unlabeled instances. In the second stage, the proposed framework achieves the training of feature mapping, the recovery of ground-truth labels, and the alleviation of noisy labels in a mutually beneficial manner, and develops an alternative optimization procedure to optimize them. In addition, a nonlinear version is extended by using kernel trick. Experimental studies demonstrate that the proposed methods can achieve competitive superiority against existing well-established methods.
Buckling identification in composite cylindrical shells with measured imperfections using a Multi-DIC method and finite element analysis

In this study, we investigated the global–local buckling phenomenon with the aid of a multi-Digital Image Correlation (DIC) technique. First, the multi-DIC technique was introduced to conduct 360-degree full-field deformation measurement of shell structures. Second, we performed the axial compression on cylindrical shells made of carbon fiber reinforced polymer (CFRP) to investigate the global–local buckling phenomenon. To confirm our experimental results, we conducted a finite element analysis of CFRP cylindrical shells with measured imperfections. In the case of a perfect shell under perfect loading, theoretically only global buckling occurs. However, manufactured cylindrical shells cannot be perfect and tend to have a few imperfections, which cause local buckling of the shell. To better confirm the experimental results, an extensive numerical analysis was performed with actual measured imperfections.
Tweet topics and sentiments relating to distance learning among Italian Twitter users

The outbreak of COVID-19 forced a dramatic shift in education, from in-person learning to an increased use of distance learning over the past 2 years. Opinions and sentiments regarding this switch from traditional to remote classes can be tracked in real time in microblog messages promptly shared by Twitter users, who constitute a large and ever-increasing number of individuals today. Given this framework, the present study aims to investigate sentiments and topics related to distance learning in Italy from March 2020 to November 2021. A two-step sentiment analysis was performed using the VADER model and the syuzhet package to understand the overall sentiments and emotions. A dynamic latent Dirichlet allocation model (DLDA) was built to identify commonly discussed topics in tweets and their evolution over time. The results show a modest majority of negative opinions, which shifted over time until the trend reversed. Among the eight emotions of the syuzhet package, 'trust' was the most positive emotion observed in the tweets, while 'fear' and 'sadness' were the top negative emotions. Our analysis also identified three topics: (1) requests for support measures for distance learning, (2) concerns about distance learning and its application, and (3) anxiety about the government decrees introducing the red zones and the corresponding restrictions. People's attitudes changed over time. The concerns about distance learning and its future applications (topic 2) gained importance in the latter stages of 2021, while the first and third topics, which were ranked highly at first, started a steep descent in the last part of the period. The results indicate that even if current distance learning ends, the Italian people are concerned that any new emergency will bring distance learning back into use again.