Reviewerly/exHarmony huggingface dataset:
Extreme sulfur isotope fractionation of hydrothermal auriferous pyrites from the SW fringe of the Taupo Volcanic Zone, New Zealand: Implications for epithermal gold exploration

The Taupo Volcanic Zone and its environs, New Zealand, represent an ideal place for epithermal gold exploration. We have documented structurally-bound gold in low temperature hydrothermal pyrite veins from the Erua Basin area on the outside fringe of the Taupo Volcanic Zone. Extreme 34S/32S fractionation of 122.2‰ from auriferous pyrite is documented in hydrothermal veins hosted in alluvial deposits from the Erua Basin. We document three generations of pyrite based on petrographic and Nano-SIMS microstructural analysis. First generation pyrite (Py assemblage -1) has extremely high δ34S values of +70.3 to +100.0‰ probably resulting from sequential-cyclic bacterial reduction and Rayleigh fractionation of limited sulfate. Second generation pyrite (Py assemblage -2) has relatively lower δ34S values of -39.8 to -3.3‰, interpreted to reflect the influx of magmatic-related oxidized fluids mixed with remobilized, reduced sedimentary sulfides that then formed the host calcite-pyrite veins. Third generation pyrite (Py assemblage -3) incorporated much heavier 34S resulting in positive δ34S values ranging from +6.8 to +72.2‰, indicating greater reduction of sulfate to H2S in an anoxic (reducing) environment. One single composite ~ 10 um pyrite grain encompassing all three types of subgrains has a large δ34S range from -22.2‰ along its rim and +100.0‰ from its core (122.2‰ for a single grain, and 139.8‰ for the system). This is the largest known variation in δ34S ever reported in a single pyrite grain from any system on Earth, but is comparable to δ34S ranges in pyrite systems of up to 186‰ reported from microbial sulfate reduction in deep bedrock fracture systems in Sweden. The growth stages from Py-2 to Py-3 indicate a transformation of the environment from an open to a closed (highly reducing) system, and it is these late-stage vein pyrites that are slightly auriferous. This process is interpreted to be induced by the precipitation of sulfides perhaps near the margin of the hydrothermal system or closed growth of pyrite in hydrothermal veins, which both restrict circulation and convection of sulfate within the veins. Our results suggest that the Erua Basin may sit on the fringe of a low-temperature epithermal gold system; other “low-temperature” areas around the TVZ may also preserve similar, potentially enriched zones on the edge of an epithermal Au belt.
Digital Convex + Unimodular Mapping = 8-Connected (All Points but One 4-Connected)

In two dimensional digital geometry, two lattice points are 4-connected (resp. 8-connected) if their Euclidean distance is at most one (resp. \(\sqrt{2}\)). A set \(S \subset \mathbb {Z}^2\) is 4-connected (resp. 8-connected) if for all pair of points \(p_1, p_2\) in S there is a path connecting \(p_1\) to \(p_2\) such that every edge consists of a 4-connected (resp. 8-connected) pair of points. The original definition of digital convexity which states that a set \(S \subset \mathbb {Z}^d\) is digital convex if \(\mathrm {conv}(S) \cap \mathbb {Z}^d= S\), where \(\mathrm {conv}(S)\) denotes the convex hull of S does not guarantee connectivity. However, multiple algorithms assume connectivity. In this paper, we show that in two dimensional space, any digital convex set S of n points is unimodularly equivalent to a 8-connected digital convex set C. In fact, the resulting digital convex set C is 4-connected except for at most one point which is 8-connected to the rest of the set. The matrix of \(SL_2(\mathbb {Z})\) defining the affine isomorphism of \(\mathbb {Z}^2\) between the two unimodularly equivalent lattice polytopes S and C can be computed in roughly O(n) time. We also show that no similar result is possible in higher dimension.
Unveiling the dynamics of social credit determinants using fuzzy cognitive mapping and system dynamics

Abstract Fighting poverty and social inequality are baseline principles of social credit. This type of microcredit has thus increasingly attracted academics and practitioners’ attention, particularly after Muhammad Yunus was awarded the 2006 Nobel Peace Prize. Researchers acknowledge that social credit is extremely important and that it should be taken seriously. However, studies examining the dynamics of its determinants are still extremely rare. This gap needs to be filled because the objectives of social credit are quite different from those of other types of credit, going beyond profit and embracing social concerns. Adopting a constructivist, process‐oriented stance that combined fuzzy cognitive mapping and the system dynamics (SD) approach, the present research sought to develop and analyze a cognitive structure that elucidates the dynamics of social credit determinants over time. Face‐to‐face group work sessions were held with a panel of professional credit analysts, and the results reveal that the combined use of fuzzy cognitive mapping and SD provides a clearer understanding of the dynamics of these determinants over time. The advantages, limitations, and implications of the proposed methodology are discussed.
Eye Tracking Analysis of Code Layout, Crowding and Dyslexia - An Open Data Set

Within computer science there is increasing recognition of the need for research data sets to be openly available to facilitate transparency and reproducibility of studies. In this short paper an open data set is described which contains the eye tracking recordings from an experiment in which programmers with and without dyslexia reviewed and described Java code. The aim of the experiment was to investigate if crowding in code layout affected the gaze behaviour and program comprehension of programmers with dyslexia. The data set provides data from 30 participants (14 dyslexia, 16 control) and their eye gaze behaviour in reviewing three small Java programs in various combinations of crowded and spaced configurations. The key features of the data set are described and observations made on the effect of alternative area of interest configurations. The paper concludes with some observations on enhancing access to data sets through metadata, data provenance and visualizations.
On Ovoids of the Generalized Quadrangle $$H(3,q^2)$$

We construct examples and families of locally Hermitian ovoids of the generalized quadrangle $$H(3,q^2)$$ . We also obtain a computer classification of all locally Hermitian ovoids of $$H(3,q^2)$$ for $$q \le 4$$ , and compare the obtained classification for $$q=3$$ with the classification of all ovoids of H(3, 9) which is also obtained by computer.
Data Hiding Based on Mini Program Code

A mini program code (also known as sunflower code) comes with WeChat mini program APPs. With the popularization of mini program APPs, mini program codes have become more and more widely used. As a new carrier, the study of information hiding technology based on mini program code is of great significance for the expansion of covert communication carriers and can also deal with security problems in advance. At present, to the best of our knowledge, there is no steganographic research based on mini program codes. In this paper, we propose a scheme to embed secret information into mini program codes for the first time. After studying the construction of mini program codes, a coordinate system is constructed to represent its module coordinates. Then, a binary stream of the secret message is embedded into the encoding region or the edge patch. Experiments show that the proposed data hiding scheme is effective and feasible. The embedded secret message could be extracted while keeping the readability of the mini program code. Moreover, the secret payloads of the encoding region and the edge patch for the V-36 mini program code are 72 bits and 29 bits, respectively.
ACEA: A Queueing Model-Based Elastic Scaling Algorithm for Container Cluster

Elastic scaling is one of the techniques to deal with the sudden change of the number of tasks and the long average waiting time of tasks in the container cluster. The unreasonable resource supply may lead to the low comprehensive resource utilization rate of the cluster. Therefore, balancing the relationship between the average waiting time of tasks and the comprehensive resource utilization rate of the cluster based on the number of tasks is the key to elastic scaling. In this paper, an adaptive scaling algorithm based on the queuing model called ACEA is proposed. This algorithm uses the hybrid multiserver queuing model (M/M/s/K) to quantitatively describe the relationship among number of tasks, average waiting time of tasks, and comprehensive resource utilization rate of cluster and builds the cluster performance model, evaluation function, and quality of service (QoS) constraints. Particle swarm optimization (PSO) is used to search feasible solution space determined by the constraint relation of ACEA quickly, so as to improve the dynamic optimization performance and convergence timeliness of ACEA. The experimental results show that the algorithm can ensure the comprehensive resource utilization rate of the cluster while the average waiting time of tasks meets the requirement.
A Business Merger and Acquisition Knowledge Management System Using Artificial Intelligence Techniques

Knowledge is recognized as a strategic resource, with major key drivers being the need to cut time to market and gain the business opportunities in a global market with new products and services. This chapter presents a knowledge management system known as guidance for business merger and acquisition (GBMA) process. This application uses a hybrid knowledge-based system to place bidding on the target company, formulating a strategy, and modification of the initial strategy if necessary, for the business acquisition processes. Legal knowledge for GBMA is represented in two forms, as rules and cases. Besides distinguishing the two different forms of knowledge representation, the chapter outlines the actual use of these forms in a computational architecture that is designed to generate a suitable solution, for a given new business scenario, using different reasoning mechanisms (e.g., rule-based reasoning, case-based reasoning). Business scenarios are used to show the functionalities of the presented architecture.
Assessment experience through a virtual classroom in course of Digital Signal Processing: a case study

This article describes the experience in the evaluation of students of the Digital Signal Processing course, using synchronous and non-synchronous modalities as a case study. This experience has been manifested in the academic semesters 2020-1 and 2020-2, of the Electronic Engineering career at the Ricardo Palma University, as a consequence of the abrupt change in the mode of delivery of classes due to the national confinement. Likewise, the Moodle and Blackboard Collaborate Ultra platforms were used as virtual classrooms. As for the evaluation, questionnaires, forums, simulation works, graded practices and oral interventions were used, all of them developed through a continuous evaluation for each academic unit, and without saturating the student with constant tasks and evaluations. And, as a result, the final averages were 12.26 and 12.69 for semesters 2020-1 and 2020-2, respectively; with a positive trend of 0.2353 with respect to previous years.
An Adaptive and Proactive Interface Agent for Interactivity and Decision-Making Improvement in a Collaborative Virtual Learning Environment

According to a report published by the Brazilian Association of Maintainers of Higher Education, the number of places offered in distance learning courses (aka distance education) in 2018 exceeded the places offered in classroom courses. At the same year, there was an increase of 20.6% in enrollments in distance education, with a forecast that exceeds classroom attendance by 2023. The same report showed concerns about the high dropout rates and low graduation rates in distance learning. The research indicates that the possible reasons for this are the students' feelings of loneliness, isolation, and demotivation. This paper presents an Interface Agent in the context of collaborative agents which main responsibility is to make the Virtual Learning Environment (VLE) more interactive, proactive, and able to adapt to the student's profile. This research is expected to contribute for making VLE more attractive, improve the teaching process, motivate students and, consequently, reduce dropout rates.
Weighted Burrows–Wheeler Compression

A weight-based dynamic compression method has recently been proposed, which is especially suitable for the encoding of files with locally skewed distributions. Its main idea is to assign larger weights to closer to be encoded symbols by means of an increasing weight function, rather than considering each position in the text evenly. A well known transformation that tends to convert input files into files with a more skewed distribution is the Burrows–Wheeler Transform (BWT). This paper proposes to apply the weighted approach on Burrows–Wheeler transformed files. While it is shown that the compression performance is not altered for static and adaptive arithmetic coding by any permutation of the symbols, hence in particular for BWT, empirical evidence of the efficiency of the combination of BWT with the weighted approach is provided.
Ultrasound Image Preprocessing Method for Deep-Learning-Based Fatty Liver Diagnosis

An iterative image processing method is proposed to improve the performance of Convolutional Neural Network (CNN) model for diagnosing fatty liver. A dataset that contains 17,400 ultrasound images of liver are used in this paper. These liver image samples are preprocessed for noise reduction and texture enhancement firstly. Then the processed image samples are used for training CNN model LeNet-5. According to the accuracy of the trained LeNet-5, the optimal combination of noise reduction and enhancement is obtained. The experimental results show that the accuracy of LeNet-5 model based on original data set is 80.58% and area under the Receiver Operation Characteristic (ROC) Curve value (AUC) is 0.574. And the optimal image processing combination is Butterworth low pass (BLP) and Exposure based Sub Image Histogram Equalization (ESIHE). The model accuracy is 10.01% higher than original data set, and reaches 90.59%. The AUC value of the optimal model has reached 0.845, which is 0.271 higher than the AUC value of the original model. Therefore, iterative image processing is a feasible method to improve the performance of CNN model in diagnosing fatty liver.
Humanitarian reversing higher education in the Russian Federation in light of the transhumanist challenges

Abstract The aim of this paper is to study the methodological foundations of the humanities education for international students as an alternative to transhumanist ideology. The paradigmatic approach is used as a factor analysis of students' educational processes. Particular attention is paid to the substantiation of the methodological foundations of the education humanisation on the basis of modern ontologically anthropological and religious‐anthropological achievements of the philosophical field of knowledge. The novelty of the study is due to a discussion of the contradictions caused by all‐round technocratisation and informatisation, the fall of moral standards and the degradation of human behaviour. The practical significance of the study is determined by the need to develop a different pedagogy and model of education for modern students. It was concluded that the value‐oriented pedagogical cocreation is able to direct the learner's self‐development and strengthen it with effective means of self‐change.
Explaining Predictions of the X-Vector Speaker Age and Gender Classifier

In this paper, we assess the applicability of two Explainable Artificial Intelligence methods: model-agnostic Feature Ablation and Neural Networks-specific Integrated Gradients, to explain predictions of Deep Neural Network-based speech classification models. We use these techniques to explain predictions of two Deep Learning x-vector models trained for age and gender classification from speech. Our results show that both methods can be successfully used for speech classification related tasks, providing a deeper understanding of the model’s behaviour. In particular, we confirm that the features highlighted by the explored methods are fundamental to the performance of the models and their removal results in a rapid performance degradation compared to the random baseline. We also show that the highlighted characteristics align with the theoretical fundamentals regarding age- and gender-based changes in the speech production process.
The Role of Institutional Infrastructures in Financial Inclusion-Growth Relations: Evidence from SSA

This paper investigates the role of institutional infrastructures in the financial inclusion-growth nexus for a panel of twenty countries in sub-Sahara Africa (SSA).Employing the System Generalized Method of Moments (GMM), the following insightful outcomes are established. First, while there is an unrestricted positive impact of physical access to ATMs and ICT measures of financial inclusion on SSA’s growth but only the former was found significant. Second, the four institutional components via economic, political, institutional and general governances were also found to be growth-spurring. Lastly, countries with low levels of real per capita income are matching up with other countries with high levels of real income per capita. The empirical evidence of some negative net effects and insignificant marginal impacts are indication that imperfections in the financial markets are sometimes employed to the disadvantage of the poor. On the whole, we established positive effects on growth for the most part. The positive effects are evident because the governance indicators compliment financial inclusion in reducing pecuniary constraints hindering credit access and allocation to the poor that deteriorate growth.
Improving Association Rule Mining for Infrequent Items Using Direct Importance Estimation

This paper proposes a method to find association rules for infrequent items. Despite the long history of association rule mining, infrequent items have been usually ignored. Recently, owing to the online nature of most systems, tackling infrequent items has become increasingly important to find emerging information. The proposed method not only has a sound theoretical background but is an exact solution of error minimization. Although highly similar to the standard method, Apriori, the solution uses a different formula than Apriori. Moreover, it consistently outperforms Apriori.
The Sentimental Analysis of Social Media Data: A Survey

Nowadays, machine learning plays a very important role in every field. For recommendation systems, user feedback is relevant because they contain different forms of emotional details that may affect the reliability or consistency of the recommendation. Online reviews, comments are very helpful in selecting the said items and services as it gives real feedback about the quality of these items and services. The categorizations of these items based on feedback provided by actual users are known as sentimental analysis. In this study, we described various machine learning techniques and parameters used for the sentimental analysis of reviews, comments, and feedback available on health care, Facebook, Twitter, and other social media networking sites. The study reveals that the most commonly used approaches are machine learning and deep learning.
Object Detection with Auto-Learning Anchor Algorithm

As an effective auxiliary means for object detection task, region anchors are widely adopted in most of state-of-the art detectors. However, anchor's location and shape in those works are normally determined by experience or some preprocessing methods, i.e., clustering, which leads to time consumption and limits the flexibility of anchors. In this paper, we explore the possibility that networks can predict bounding boxes and simultaneously learn anchor's location and shape end to end. Specifically, we propose a new anchoring scheme, named Automatic Anchor Learning, which can be integrated into any object detectors and enable detectors to learn the location and size of anchors while training, without sampling anchors over any predefined set of scales and aspect ratios. The proposed method first predicts where the centers of objects of interest might exist and then predict the shape of anchor that should be placed in this location. By applying the proposed Automatic Anchoring Learning method to Yolov3 model, we achieve around 3.3% and 1.6% higher recall and mAP on MS COCO with 80% less anchors, and 10% more FPS than the original Yolov3. Additionally, we also integrate our method into other object algorithms, i.e., Fast R-CNN and RetinaNet, we respectively improve their detection mAP by 2.5% and 1.1%.
Modified Chaotic Bee Colony Optimization (MCBCO) algorithm for data clustering

Results of the heuristic search-based optimization algorithms largely depend on the initial guess. When the initial guess is closer to the optimal result, then the algorithm converges faster. But for large datasets, the probability of getting this closer guess is difficult. In this paper, a Modified Chaotic Bee Colony Optimization (MCBCO) algorithm is proposed for data clustering. It is capable to explore the solution space in all directions, despite of initial guesses. The chaotic bees that are created using chaotic sequences enable the algorithm to do this. It uses steady state selection tactic for better exploration. The algorithm also uses Gaussian mutation for further exploitations in the solution. The simulation results and analysis reflects that the algorithm is competent for the data clustering problem.
Delay-compensating visual positioning of the mobile robot

Abstract The paper considers the task of the automatic positioning of the mobile robot in front of the external visual marker in the case of the presence of the transport delay in the control channel. As a basic regulator without the delay compensation we use mixed approach based on the visual servoing method and a special multipurpose control structure which is capable of taking into account a set of requirements to the controlled dynamics. To compensate the delay we apply the modification of the basic regulator which keeps the original transfer matrix. We also demonstrate the efficiency of the proposed approach by the results of the experiments with the computer model.
The non-face-to-face assessment in basic mathematics in times of Covid: “we are prepared for this educational pandemic”

The first semester of the year 2020 was a changing period for Education in general due to the adaptation from face-to-face teaching to virtual teaching, both in conceptual and methodological aspects in the process of teaching-learning in all subjects, especially in math. In the process to get adopted from the face-to-face performance to a new virtual one, the assessment dimension has also taken a huge turn, mainly in those subjects that reflect the necessity to know how the students achieve their goals, for example, in problem-solving, in communication, interpretation, and representation, by using the appropriate symbols amid the virtuality, taking the risk that it could be an objective assessment instead of subjective assessment. In this research paper, you will find a descriptive analysis of these assessment processes.