Reviewerly/exHarmony huggingface dataset:
Synergetic use of the Sentinel-2, ASTER, and Landsat-8 data for hydrothermal alteration and iron oxide minerals mapping in a mine scale

Many of the known Tertiary porphyry Cu deposits (PCDs) are situated in the Central Iranian Volcanic Belt (CIVB).The study area is located in the southern part of the CIVB and southern part of the Kerman Cenozoic magmatic arc.This research highlights the significance of the synergetic use of operational land imager aboard the Landsat-8 (OLI), Sentinel-2, and advanced space-borne thermal emission and reflection radiometer (ASTER) data for exploration of copper mineralization in a mine scale.Multispectral images in the visible and near infrared bands of (0.45-1.0 µm) the OLI and Sentinel-2 were used to identify the gossan zones.ASTER short wave infrared (SWIR) data with the wavelength between 1.65 and 2.43 µm were used for mapping hydrothermal alteration zones.Laboratory spectra obtained from minerals such as muscovite and illite (phyllic alteration), kaolinite and montmorillonite (argillic alteration), epidote and chlorite (propylitic alteration) were applied in mixture tuned matched filtering (MTMF) algorithm on the ASTER data to enhance the existence of these minerals.The spectra of the index minerals from each alteration type were used in this algorithm and the abundances of minerals in the MTMF image were rescaled to be within the ranges of 34-54 %, 54-74 % and 74-100 %.The studied mineralized zone is associated with the iron rich phyllic and argillic hydrothermal alteration types which can be best detected by using integrated ASTER, OLI, and Sentinel-2 images.The diamond drill cores data also indicate that copper is more enriched below the gossan zones.The gossan index minerals (GIM) including goethite, jarosite, and hematite were identified by X-ray diffraction (XRD) method, spectral analyses, and petrographic examinations.The Sentinel -2 data proved to provide remarkably better mapping result for iron oxide minerals than the OLI and ASTER data.The directed principal component analysis (DPCA) method, In the data of the Landsat-8 and Sentinel-2 were used for mapping of the gossan index minerals such as goethite, jarosite and hematite and the argillic, phyllic and propylitic hydrothermal alteration types were enhanced using the MTMF methods.The hydrothermal alteration pattern and the distribution of the gossans in the study area show a porphyry-type mineralization.This study showed that the synergetic use of different satellite images with different spatial and spectral resolutions can be used for mineral exploration in a large scale.The use of laboratory spectra obtained from hydrothermal alteration minerals in MTMF method depicts that the alteration minerals were mapped more accurately relative to those MTMF images that are derived from standard spectral libraries.The samples collected from the surficial hydrothermal alteration zones as well as diamond drill cores showed that the copper mineralization occurres mainly along the border of the phyllic and argillic alteration zones, and the magnetite mineralization in the propylitic zone.
hMatcher: Matching Schemas Holistically

Schema matching is critical for applications that manipulate data across heterogeneous, autonomous and scattered data sources.We pick the schema matching approach based on the total number of data sources we wish to integrate: holistic matching approaches are ideally used for a big to a huge total number of data sources, while pairwise matching approaches are ideally used for a small to a medium total number of data sources.Nonetheless, the state of the art matching approaches obtain a very moderate (sometimes poor) matching accuracy.Furthermore, the state of the art holistic schema matching approaches proceed in a series of two-way matching steps.In this paper, we present hMatcher, an effective approach to holistic schema matching.To perform collective schema matching, hMatcher generates frequent schema elements before proceeding with the matching.To reach high matching accuracy, hMatcher employs a context-based semantic similarity measure.Experimental results on a real-world domain dataset show that hMatcher performs holistic schema matching properly, reaches a high matching accuracy (Precision=0.89;Recall=0.66;Overall=0.57), and outperforms the state of the art matching approaches in terms of matching accuracy.
A New Distributed Type-2 Fuzzy Logic Method for Efficient Data Science Models of Medical Informatics

The paper aims to propose a distributed method for machine learning models and its application for medical data analysis. The great challenge in the medicine field is to provide a scalable image processing model, which integrates the computing processing requirements and computing-aided medical decision making. The proposed Fuzzy logic method is based on a distributed approach of type-2 Fuzzy logic algorithm and merges the HPC (High Performance Computing) and cognitive aspect on one model. Accordingly, the method is assigned to be implemented on big data analysis and data science prediction models for healthcare applications. The paper focuses on the proposed distributed Type-2 Fuzzy Logic (DT2FL) method and its application for MRI data analysis under a massively parallel and distributed virtual mobile agent architecture. Indeed, the paper presents some experimental results which highlight the accuracy and efficiency of the proposed method.
Thermal transient stepping: a powerful thermal-based approach for evaluating the stress field by using digital photoelasticity

A thermal approach for measuring the stress field was developed by using digital photoelasticity. The approach relies on applying a thermal stimulation to the examined model, in conjunction with a computational hybrid algorithm of load stepping, to determine the isochromatic phase value from only three experimental images. The proposal was validated by using a PMMA disk under compressive load and exposed to thermal variations. This experiment was conducted in reflection photoelasticity where a face of the disk was used to observe the fringe patterns, and the back face to capture thermal variations. The results obtained in synthetic and experimental images, indicate that the approach is effective, easy to reproduce, and could enhance the capabilities of existing approaches to analyze stress fields.
Non-Monotonic Ontology-based Abstractions of Data Services

In Ontology-Based Data Access (OBDA), a domain ontology is linked to the data sources of an organization in order to query, integrate and manage data through the concepts and relations of the domain of interest, thus abstracting from the technical details of the data layer implementation. While the great majority of contributions in OBDA in the last decade have been concerned with the issue of computing the answers of queries expressed over the ontology, recent papers address a different problem, namely the one of providing suitable abstractions of data services, i.e., characterizing or explaining the semantics of queries over the sources in terms of queries over the domain ontology. Current works on this subject are based on expressing abstractions in terms of unions of conjunctive queries (UCQs) over the ontology. In this paper we advocate the use of a non-monotonic language for this task. As a first contribution, we present a simple extension of UCQs with non-monotonic features, and show that non-monotonicity provides more expressive power in characterizing the semantics of data services. A second contribution is to prove that, similarly to the case of monotonic abstractions, depending on the expressive power of the languages used to specify the various components of the OBDA system, there are cases where neither perfect nor approximated abstractions exist for a given data service. As a third contribution, we single out interesting special cases where the existence of abstractions is guaranteed, and we present algorithms for computing such abstractions in these cases.
Evaluating Deep Learning Biases Based on Grey-Box Testing Results

The very exciting and promising approaches of deep learning are immensely successful in processing large real world data sets, such as image recognition, speech recognition, and language translation. However, much research discovered that it has biases that arise in the design, production, deployment, and use of AI/ML technologies. In this paper, we first explain mathematically the causes of biases and then propose a way to evaluate biases based on testing results of neurons and auto-encoders in deep learning. Our interpretation views each neuron or autoencoder as an approximation of similarity measurement, of which grey-box testing results can be used to measure biases and finding ways to reduce them. We argue that monitoring deep learning network structures and parameters is an effective way to catch the sources of biases in deep learning.
Retrieving Sparser Fuzzy Cognitive Maps Directly from Categorical Ordinal Dataset using the Graphical Lasso Models and the MAX-threshold Algorithm

Learning FCM models from data without any a priori knowledge and expert intervention remains a considerable problem. This research study utilizes a fully data-based learning method (the glassoFCM) for automatic design of Fuzzy Cognitive Maps (FCM) using large ordinal dataset based on the efficient capabilities of graphical lasso (glasso) models. Therefore, glasso represents its structure as a sparser graph, while maintaining a high likelihood, by producing an adjacent weighted matrix, where relationships are expressed by conditional independences. By minimizing the negative log-likelihood indicates that the model fits better to the data under the assumption that the observed data are the most likely data. The principle questioning is which of the observed concepts is the appropriate to trigger the remaining concepts in the map in order to create the glassoFCMs and obtain reasonable results. The answer derives from the FCM structure analysis based on the strength centrality indices. Moreover, the MAX-threshold algorithm based on the FCM scenario analysis is proposed in order to prune edges and retrieve sparser graphs. This algorithm shrinks the meaningless weights of the FCM, without affecting significantly the outcomes in scenario analysis. The whole approach was implemented in a business intelligence problem of evaluating the attractiveness of Belgian companies.
Improving Performance of Colour-Histogram-Based CBIR Using Bin Matching for Similarity Measure

Content-Based Image Retrieval (CBIR) systems work by searching huge databases for similar images that match a query image. The CBIR systems depend on computing similarity between two images to retrieve images of interest. The choice of suitable similarity measuring tool is key for effective and efficient retrieval of images. Predominantly similarity metrics such as Euclidean, Manhattan, City block distances among others are used extensively to compute the images similarity measure when the conventional colour histogram is used for image indexing. However, each of these similarity metrics suffers from issues of non-similar images having the same histogram and outliers in the distribution of colour content in images. In this paper, a proposed bin-by-bin inspections and classification for the measurement of similarity is presented. The approach distinguishes between the queried image and the target image, to obtain a more robust outcome. The outcomes stood superior to other state-of-the-art similarity metrics in respect to retrieval accuracy.
Sentiment analysis of client reviews of the Russian Agricultural Bank service and predicted rating reviews

Abstract The paper provides a sentimental analysis of reviews of the Russian Agriculture Bank service. An analysis of the sentiment of a text is a class of methods for the content analysis of texts in computer linguistics for the automated detection of emotional assessment - sentiment. As methods, we use XGBoost, neural networks. In work, we determine the tone of reviews with a rating of three, since we can not always attribute reviews to good or bad; we also determine the sentiment of reviews without a score. As a result, it was determined that the main array of reviews about the bank’s service has a negative sentiment.
GNOSTIC EMOTIONS OF STUDENTS IN SOLVING OF THINKING TASKS

The paper presents the results of an empirical study of the emotional characteristics of students when solving thinking problems. The study used an experiment that simulates a students’ collaborative solution to the puzzle-doing problem. To study emotional variables, we used the method of B.I. Dodonov for determination of the general emotional orientation. To investigate feelings experienced by students, we used the method of K. Isard. According to the authors, the following types of general emotional orientation (with decrease) are dominated among students: altruistic, communicative, and mixed types. All students showed the dominance of emotions of interest, joy and surprise under the decision processes, regardless of the emotional type of personali-ty. It was revealed that under the processes of solving thinking problems, students with the dominance of various types of emo-tional orientation showed a different ratio of emotions. Four strategies of solving thinking problems were identified, which are characterized by the representation of various emotions.
Assessment of Heavy Metals Concentration and Enrichment Levels in Soils around Quarries and Barite Mine Sites in Part of Akamkpa and Biase Area, Southeastern Nigeria

Geochemical investigation of the soils around quarry and barite mine sites in part of Akamkpa and Biase area of Southeastern Nigeria, was carried out to assess the concentration and enrichment levels of some heavy metals and trace elements in the soils.The investigation involved the collection of fifteen (15) soil samples between the depth of 15 -30 cm.The samples were dried at room temperature, prepared and analyzed for heavy metals using inductively couple plasma mass spectrometer.The result shows that the concentration of Co, Cr, Nb, Pb and Sn were higher than the average shale values around the quarries while Ba, Pb and Sn were higher around the barite mines.The contamination factor indicates low to minimal contamination and low to considerable contamination around the quarries and barite mines respectively.While the Enrichment factor indicates minimal to significant enrichment around the quarries and minimal to extremely high enrichments around the barite mines.The result of the factor analysis and spatial distribution of the heavy metals suggests that the metal concentration and enrichment were controlled by the rock types, weathering and anthropogenic activities around the mine sites.
On maximum additive Hermitian rank-metric codes

Inspired by the work of Zhou (Des Codes Cryptogr 88:841–850, 2020) based on the paper of Schmidt (J Algebraic Combin 42(2):635–670, 2015), we investigate the equivalence issue of maximum d-codes of Hermitian matrices. More precisely, in the space $${{H}}_n(q^2)$$ of Hermitian matrices over $${\mathbb {F}}_{q^2}$$ we have two possible equivalences: the classical one coming from the maps that preserve the rank in $${\mathbb {F}}_{q^2}^{n\times n}$$ , and the one that comes from restricting to those maps preserving both the rank and the space $${H}_n(q^2)$$ . We prove that when $$d<n$$ and the codes considered are maximum additive d-codes and $$(n-d)$$ -designs, these two equivalence relations coincide. As a consequence, we get that the idealisers of such codes are not distinguishers, unlike what usually happens for rank metric codes. Finally, we deal with the combinatorial properties of known maximum Hermitian codes and, by means of this investigation, we present a new family of maximum Hermitian 2-code, extending the construction presented by Longobardi et al. (Discrete Math 343(7):111871, 2020).
CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning

Unsupervised graph representation learning aims to learn low-dimensional node embeddings without supervision while preserving graph topological structures and node attributive features. Previous graph neural networks (GNN) require a large number of labeled nodes, which may not be accessible in real-world graph data. In this paper, we present a novel cluster-aware graph neural network (CAGNN) model for unsupervised graph representation learning using self-supervised techniques. In CAGNN, we perform clustering on the node embeddings and update the model parameters by predicting the cluster assignments. Moreover, we observe that graphs often contain inter-class edges, which mislead the GNN model to aggregate noisy information from neighborhood nodes. We further refine the graph topology by strengthening intra-class edges and reducing node connections between different classes based on cluster labels, which better preserves cluster structures in the embedding space. We conduct comprehensive experiments on two benchmark tasks using real-world datasets. The results demonstrate the superior performance of the proposed model over existing baseline methods. Notably, our model gains over 7% improvements in terms of accuracy on node clustering over state-of-the-arts.
PriFi: Low-Latency Anonymity for Organizational Networks

Abstract Organizational networks are vulnerable to trafficanalysis attacks that enable adversaries to infer sensitive information fromnetwork traffic—even if encryption is used. Typical anonymous communication networks are tailored to the Internet and are poorly suited for organizational networks.We present PriFi, an anonymous communication protocol for LANs, which protects users against eavesdroppers and provides high-performance traffic-analysis resistance. PriFi builds onDining Cryptographers networks (DC-nets), but reduces the high communication latency of prior designs via a new client/relay/server architecture, in which a client’s packets remain on their usual network path without additional hops, and in which a set of remote servers assist the anonymization process without adding latency. PriFi also solves the challenge of equivocation attacks, which are not addressed by related work, by encrypting traffic based on communication history. Our evaluation shows that PriFi introduces modest latency overhead (≈ 100ms for 100 clients) and is compatible with delay-sensitive applications such as Voice-over-IP.
Financial development and economic growth: Empirical evidence from <scp>Sub‐Saharan</scp> Africa

Abstract This paper evaluates the impact of financial development on economic growth in a sample of 32 Sub‐Saharan Africa (SSA) countries. The countries were grouped into four sub‐regions, and data were collected for the period 1990–2016 on finance and growth indicators on an annual basis. In the estimation procedure, panel estimation and dynamic panel techniques were used. When the disaggregated components of financial development variables were used, findings, among others, reveal the role of credit to the private sector by banks (CPB) even though mixed, to have more impact on growth followed by broad money (BM) and liquidity liability (LL). However, an aggregated index of the financial development indicators via principal component analysis and their simultaneous interaction with human capital improvement brought about a greater positive impact on growth throughout the sub‐regions and the entire SSA.
Analysis of K-means, DBSCAN and OPTICS Cluster Algorithms on Al-Quran Verses

Chapter Al-Baqarah is the longest chapter in the Holy Quran, and it covers various topics.Al-Quran is the primary text of Islamic faith and practice.Millions of Muslims worldwide use Al -Quran as their reference book, and it, therefore, helps Muslims and Islamic scholars as guidance of the law life.Text clustering (unsupervised learning) is a process of separation that has to be divided text into the same section of similar documents.There are many text clustering algorithms and techniques used to make clusters, such as partitioning and density-based methods.In this paper, k-means preferred as a partitioning method and DBSCAN, OPTICS as a density-based method.This study aims to investigate and find which algorithm produced as the best accurate performance cluster for Al-Baqarah's English Tafseer chapter.Data preprocessing and feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF) have applied for the dataset.The result shows k-means outperformed even has the smallest of Silhouette Coefficient (SC) score compared to others due to less implementation time with no noise production for seven clusters of Al-Baqarah chapter.OPTICS has no noise with the medium of SC score but has the longest implementation time due to its complexity.
Benchmarking 50-Photon Gaussian Boson Sampling on the Sunway TaihuLight

Boson sampling is expected to be one of an important milestones that will demonstrate quantum supremacy. The present work establishes the benchmarking of Gaussian boson sampling (GBS) with threshold detection based on the Sunway TaihuLight supercomputer. To achieve the best performance and provide a competitive scenario for future quantum computing studies, the selected simulation algorithm is fully optimized based on a set of innovative approaches, including a parallel scheme and instruction-level optimizing method. Furthermore, data precision and instruction scheduling are handled in a sophisticated manner by an adaptive precision optimization scheme and a DAG-based heuristic search algorithm, respectively. Based on these methods, a highly efficient and parallel quantum sampling algorithm is designed. The largest run enables us to obtain one Torontonian function of a 100 x 100 submatrix from 50-photon GBS within 20 hours in 128-bit precision and 2 days in 256-bit precision.
Online Teaching Platform Based on Big Data Recommendation System

The essence of big data education is the combination of network education and traditional education. The development of big data and Internet industry has brought new changes to the traditional learning model. The way to acquire knowledge is not limited to books and classrooms, and the Internet era is the era of information explosion, so the Internet has gradually become the most important channel for people to acquire knowledge. Therefore, the big data learning platform has gradually become a new mode of teaching adopted by schools. In the big data teaching mode, personalized recommendation system plays an indispensable role in online education.
An Improved Unscented Kalman Filter Algorithm for Radar Azimuth Mutation

An improved UKF (Unscented Kalman Filter) algorithm is proposed to solve the problem of radar azimuth mutation. Since the radar azimuth angle will restart to count after each revolution of the radar, and when the aircraft just passes the abrupt angle change, the radar observation measurement will have a sudden change, which has serious consequences and is solved by the proposed novel UKF based on SVD. In order to improve the tracking accuracy and stability of the radar tracking system further, the SVD-MUKF (Singular Value Decomposition-based Memory Unscented Kalman Filter) based on multiple memory fading is constructed. Furthermore, several simulation results show that the SVD-MUKF algorithm proposed in this paper is better than the SVD-UKF (Singular Value Decomposition of Unscented Kalman Filter) algorithm and classical UKF algorithm in accuracy and stability. Last but not the least, the SVD-MUKF can achieve stable tracking of targets even in the case of angle mutation.