Reviewerly/exHarmony huggingface dataset:
Spatial Regularities of Localization of Gold Ore Occurrences in the Yana–Kolyma Province

Abstract —To solve the problems of regional forecast within the Yana–Kolyma gold ore province, analysis of the spatial distribution of 2140 orogenic ore objects and their gold grades was carried out. Ore objects with &amp;gt;1 ppm Au are mapped on a scale of 1: 2,500,000, and areas of high mineralization density points are outlined in the sequence: general contour–contours around clusters of points with high Au grade–contours around proximal clusters of points–long axes of anisotropic contours. The curves obtained after the interpolation between the axes, with regard to the actual position of the ore objects, are interpreted as intersections of the recent topographic surface with the planes of faults that were active at the time of ore formation (ore-hosting faults). We propose to call the curves intersecting the known deposits “trends” (arched and linear). If no deposits have been revealed, the curves should be called “ore lineaments”, regardless of their curvature. The shape of the general contour around the gold ore occurrences and the distribution of ore objects within this contour permit outlining the Upper Indigirka (UID) and Central Kolyma (CKD) megadistricts. The geometry and spatial position of trends and ore lineaments are compared with the recent structural plan of the area, with geophysical fields, and with the existing ideas of the kinematic types of faults arising under certain geodynamic regimes of evolution of structures in the Yana–Kolyma province. It is suggested that the ore-hosting faults formed successively during the collisional interaction of the passive margin of the Siberian continent with the Kolyma–Omolon superterrane and during the formation of the volcanic arcs of the Okhotsk–Chukchi volcanic belt. Trends and ore lineaments can be correlated with the groups of structures of ore fields formed at the late collisional and subduction (postcollisional) stages of the area evolution. The trends and ore lineaments of NW orientation are attributed to the folded reverse faults and thrusts and to shears of different kinematics. At the postcollisional stage, the ore lineaments and trends of NE orientation formed in the zones of tectonomagmatic activity, subparallel to the strike of volcanic arcs of the Okhotsk–Chukchi volcanogenic belt. The distribution of clusters of gold ore occurrences and “empty” intervals between them along the strike of the recognized structures probably corresponds to the distribution of areas of extension and compression in the plane of the ore-hosting faults. The correlation among the trends, ore lineaments, and ore-hosting faults permits forecasting for the position and approximate size of new ore bodies within the Yana–Kolyma province. Prospective areas for a gold ores prospecting and exploration have been outlined on the extrapolated and interpolated extensions of the trends and at the sites of their intersection.
ClustPTF: Clustering-based parallel tensor factorization for the diverse multi-criteria recommendation

In the recommender system field, diversity as the measure of recommendation quality has gained much attention recently. However, many pieces of research have shown that it has a trade-off relation with predictive performance. To improve recommendation diversity and predictive performance in multi-criteria recommender systems, we propose a clustering-based parallel tensor factorization (ClustPTF). In the ClustPTF, sentiment analysis alleviates model sparsity, and the K-means clustering considering rating behaviors groups similar user preferences into sub-models and leads to improve recommendation diversity. The sub-models are then factorized in parallel to predict ratings in near real-time. With one dataset gathered from TripAdvisor, experiments showed that the ClustPTF considerably improve recommendation diversity (13.44x of a conventional tensor factorization (TF0)) and response time (23.13x of the TF0). Even its predictive performance is superior to the TF0 (41.06% improvement in MAE). Furthermore, the ClustPTF outperformed recent techniques in recommendation diversity and predictive performance (i.e., MAE and precision).
Single Shot Multibox Detector With Deconvolutional Region Magnification Procedure

In this paper, we make an effort to improve the accuracy of small and medium object detections of SSD (Single Shot Multibox Detector). To this end, we introduce a deconvolutional region magnification procedure in which the existing layers in SSD play a role in the region proposal network and the proposed regions are magnified for recognition. Moreover, features are also extracted from a shallow layer and a new feature pyramid is constructed on top of these structures. Then, features are contacted and fed into classification and regression modules as in SSD. The weights of the present model are obtained via a pre-training-re-training strategy. By evaluating the model performance on a test set assembled by the samples in the PASCAL VOC and MS COCO datasets, the present model shows that the mAPs (mean average precisions) of small and medium object detections are 42.4% and 74.7% respectively, which are 27.1% and 15.6% better than SSD. This proves the effectiveness of our proposed method.
The potential of eye tracking for visual literacy research

The multitude of disciplines, perspectives, and practices that inform the study of visual literacy is incredibly valuable, but at the same time it means our research lacks a shared set of methodologies and methods. Unfamiliar research approaches may lead to mistrust of research findings and, even more problematic, to missed opportunities for knowledge development, opportunities that are vital to progress in the field. Eye tracking is one such method that is likely to be outside the traditional disciplinary approaches of most visual literacy scholars, yet it has the potential to be productive for many of the types of questions that the field seeks to answer. Following an introduction to eye-tracking methods, terminology, and data types, the article provides an overview of past eye-tracking research that can inform visual literacy studies. The article then turns to a discussion of visual literacy research that could potentially be addressed by eye-tracking methods.
AlignVis: Semi-automatic Alignment and Visualization of Parallel Translations

Digital humanities and translation scholars utilize off-the-shelf tools to align multiple related translations. These tools generally rely solely on domain expert knowledge and do not exploit the recent advancements in computational linguistics and text mining. This paper presents AlignVis, a visual tool that provides a semi-automatic alignment framework to align multiple translations. It presents the results of using text similarity measurements and enables the user to create, verify, and edit alignments using a novel visual interface. The design consists of three main components: the alignment editor canvas, the post-edit area, and the user options panel. AlignVis exploits both close and distant reading and is designed to help digital humanities and translation scholars enhance the process of text alignment for multiple translations. The design of AlignVis is driven by iterative discussions with the domain expert which resulted in five benefits: presenting an overview of the aligned translations, support for multiple alignments, enhancement and acceleration of the alignment process, alignment refinement, and testing different similarity measurements. We evaluate AlignVis with domain expert feedback and a comparison with a standard alignment tool and computational and visual alignment tools.
Mathematical collaborative learning in 21 <sup>st</sup> century based on national science olympiad in junior high school

Abstract Learning in 21 st century is no longer interpreted as process of transfer of knowledge from teachers to students, but rather emphasizes the efforts of teachers to facilitate students in constructing concepts and solving problems independently. The purpose of this study was to describe mathematical collaborative learning in 21 st century. It was qualitative research. The research was conducted at SMP Negeri 1 Colomadu Karanganyar in 2019/2020 academic year. The research instruments included lesson plans, problem solving tasks, and observation sheets. Data validity used time triangulation. Data analyses were done through data collection, data reduction, data presentation, and drawing conclusion. Based on results, 1) The ability of critical thinking: students were skilled in concluding discussion result that had been carried out with logical assumptions and were able to determine the right actions to solve the problems; 2) The ability of creativity: students were skilled at formulating complex problem solving strategies; 3) The ability of collaboration: there was a positive dependence among students so that an academic support system was embodied, in which each group member was commited to achieve mutual success; 4) The ability of communication: there was dynamic interaction between group members and teachers, so that learning occurs effectively.
Highlights Analysis System (HAnS) for Low Dynamic Range to High Dynamic Range Conversion of Cinematic Low Dynamic Range Content

We propose a novel and efficient algorithm for detection of specular reflections and light sources (highlights) in cinematic content. The detection of highlights is important for reconstructing them properly in the conversion of the low dynamic range (LDR) to high dynamic range (HDR) content. Highlights are often difficult to be distinguished from bright diffuse surfaces, due to their brightness being reduced in the conventional LDR content production. Moreover, the cinematic LDR content is subject to the artistic use of effects that change the apparent brightness of certain image regions (e.g. limiting depth of field, grading, complex multi-lighting setup, etc.). To ensure the robustness of highlights detection to these effects, the proposed algorithm goes beyond considering only absolute brightness and considers five different features. These features are: the size of the highlight relative to the size of the surrounding image structures, the relative contrast in the surrounding of the highlight, its absolute brightness expressed through the luminance (luma feature), through the saturation in the color space (maxRGB feature) and through the saturation in white (minRGB feature). We evaluate the algorithm on two different image data-sets. The first one is a publicly available LDR image data-set without cinematic content, which allows comparison to the broader State of the art. Additionally, for the evaluation on cinematic content, we create an image data-set consisted of manually annotated cinematic frames and real-world images. For the purpose of demonstrating the proposed highlights detection algorithm in a complete LDR-to-HDR conversion pipeline, we additionally propose a simple inverse-tone-mapping algorithm. The experimental analysis shows that the proposed approach outperforms conventional highlights detection algorithms on both image data-sets, achieves high quality reconstruction of the HDR content and is suited for use in LDR-to-HDR conversion.
Blended Learning as a Way of Teaching in the Pandemic Period

Blended learning is a formal education program in which a student learns at least in part through online delivery of content and instruction with some element of student control over time, place, path, and/or pace and at least in part at a supervised brick-and-mortar location away from home (see [1]). In this paper we present an example of use of blended learning education for the university courses, specifically for the Fuzzy sets courses. These courses are taught on Matej Bel University, Faculty of Natural Sciences, Department of Computer Science. The presented example is being used in the teaching of external students. Since this period is affected by the COVID-19 disease it is useful to be prepared for teaching all of the students with the use of these methods.
A Symbolic-AI Approach for UAV Exploration Tasks

Performing autonomous exploration and exploitation is essential for un- manned aerial vehicles (UAVs) operating in unknown environments. Often, such missions involve first building a map of the environment via pure exploration and subsequently exploiting it for specific downstream tasks. But, conducting separate exploration and exploitation steps is not always feasible in practice. In this paper, we develop a novel exploration approach enabling exploration and exploitation in a single step for an area-of-interest (AoI) search task. The basic idea is to employ a probabilistic information gain map, called a belief map, as a prior to guide the exploration trajectory, while efficiently reducing false positive information in the process. The approach is composed of three layers. The first is an information potential layer to decide the exploration direction for the UAV. Next, the proximity layer exploits detected AoI by exploring their proximal areas. The last layer, a forced movement layer, is responsible for enabling the UAV to escape local maxima caused by the previous layers. We tested the performance of our approach in two different tasks relative to two exploration methods published in the literature. The results demonstrate that our proposed approach is capable of navigating through randomly generated environments and covering more AoI in fewer time steps compared to the baselines.
How important is motion in sign language translation?

More than 70 million people use at least one sign language (SL) as their main channel of communication. Nevertheless, the absence of effective mechanisms to translate massive information among sign, written and spoken languages is the main cause of a negligible inclusion of deaf people into society. Therefore, SL automatic recognition systems have widely proposed to support the characterisation of the sign structure. Today, natural and continuous SL recognition is an open research problem due to multiple spatio-temporal shape variations, challenging visual sign characterisation, as well as the non-linear correlation among signs to express a message. A compact sign is introduced to text architecture that explores motion as an alternative to support sign translation. Such characterisation results are robust to appearance variance with relative support to geometrical variations. The proposed representation focuses on the main spatio-temporal regions to each corresponding word. The proposed architecture was evaluated in a built SL data set (LSCDv1) dedicated to motion study and also in the state-of-the-art RWTH-Phoenix. From the LSCDv1 data set, the best configuration reports a BLEU-4 score of 63.04 in a testing set. Regarding RWTH-Phoenix, the proposed strategy achieved a BLEU-4 score in a test of 4.56, improving the results under similar reduced conditions.
Ranked List Loss for Deep Metric Learning

The objective of deep metric learning (DML) is to learn embeddings that can capture semantic similarity and dissimilarity information among data points. Existing pairwise or tripletwise loss functions used in DML are known to suffer from slow convergence due to a large proportion of trivial pairs or triplets as the model improves. To improve this, ranking-motivated structured losses are proposed recently to incorporate multiple examples and exploit the structured information among them. They converge faster and achieve state-of-the-art performance. In this work, we unveil two limitations of existing ranking-motivated structured losses and propose a novel ranked list loss to solve both of them. First, given a query, only a fraction of data points is incorporated to build the similarity structure. Consequently, some useful examples are ignored and the structure is less informative. To address this, we propose to build a set-based similarity structure by exploiting all instances in the gallery. The learning setting can be interpreted as few-shot retrieval: given a mini-batch, every example is iteratively used as a query, and the rest ones compose the gallery to search, i.e., the support set in few-shot setting. The rest examples are split into a positive set and a negative set. For every mini-batch, the learning objective of ranked list loss is to make the query closer to the positive set than to the negative set by a margin. Second, previous methods aim to pull positive pairs as close as possible in the embedding space. As a result, the intraclass data distribution tends to be extremely compressed. In contrast, we propose to learn a hypersphere for each class in order to preserve useful similarity structure inside it, which functions as regularisation. Extensive experiments demonstrate the superiority of our proposal by comparing with the state-of-the-art methods on the fine-grained image retrieval task. Our source code is available online: https://github.com/XinshaoAmosWang/Ranked-List-Loss-for-DML.
A Novel Switching State-Space Model for Post-ICU Mortality Prediction and Survival Analysis

Predicting mortality risk in patients accurately during and after intensive care unit (ICU) stay is an essential component for supporting critical care decision-making. To date, various scoring systems have been designed for survival analysis and mortality prediction by providing risk scores based on patient's vital signs and lab results. However, it is challenging using these universal scores to represent the overall severity level of illness and to look into patient's deterioration leading to high mortality risk during ICU stay. Thus, a close monitoring of the severity level over time during ICU stay is more preferable. In this study, we design a new switching state-space model by correlating patient's condition dynamics in last hours of ICU stay to the risk probabilities in a short time period (1-6 days) after ICU discharge. More specifically, we propose to integrate a cumulative hazard function estimating survival probability into the autoregressive hidden Markov model using time-interval sequential SAPS II scores as features. We demonstrate the significant improvement of mortality prediction comparing to SAPS I, SAPS II, and SOFA scoring systems for the PhysioNet MIMIC II Challenge data.
Appropriation of skills in students who migrated from traditional education model to an online education model, derivated from COVID-19

As a result of Covid-19 at the beginning of the year 2020 the need to migrate teaching from face-to-face model to an online has brought as consequence the lack of skills in students from the University of Guadalajara to perform in an online educational environment. For this purpose, first we point out the differences between virtual, online, and distance education in order to establish the type of educational model that is operating in the current circumstances. Subsequently we identify and define those skills which are basic to carry out the teaching-learning process virtually and online. The selected skills were time management, responsibility, digital skills, and self-management. The methodology used in this research was a combination of the qualitative and quantitative approach. A survey of 21 items was apply to 197 students in calendar 2020 B. Later, in the next section we proceed to the analysis and discussion of the result obtained.Finally, we return to the most relevant points of each section, we relate them, we contrast them and we reflect on the information that the survey yielded and in this way we reach the conclusions.
SCN: A Novel Shape Classification Algorithm Based on Convolutional Neural Network

Shape classification and matching is an important branch of computer vision. It is widely used in image retrieval and target tracking. Shape context method, curvature scale space (CSS) operator and its improvement have been the main algorithms of shape matching and classification. The shape classification network (SCN) algorithm is proposed inspired by LeNet5 basic network structure. Then, the network structure of SCN is introduced and analyzed in detail, and the specific parameters of the network structure are explained. In the experimental part, SCN is used to perform classification tasks on three shape datasets, and the advantages and limitations of our algorithm are analyzed in detail according to the experimental results. SCN performs better than many traditional shape classification algorithms. Accordingly, a practical example is given to show that SCN can save computing resources.
Building outlier detection ensembles by selective parameterization of heterogeneous methods

We address the problem of selecting members of ensembles for unsupervised outlier detection. The challenge here is to identify individually accurate but diverse members due to unsupervised nature of the problem. For this, we herein propose AnD-SELECT: Accurate-and-Diverse Selector, which considers a set of heterogeneous outlier detection methods at input and systematically selects accurate parameter variants i.e. parameterization of each type. Outlier detection methods in this input set are chosen such that they usually exhibit the characteristics of either progressive or regressive performance behavior with increasing parameter values. We then consider a wide range of parameter variants of each of these methods. From such homogeneous set of a method type, the objective is to select the more accurate parameterization-end, while avoiding selection of both the ends together due to above mentioned characteristics. Therefore, either a single accurate variant or a set of two variants showing explicit trade-off between accuracy and diversity, get selected. Evaluation on benchmark datasets shows notable performance improvement over existing selectors.
Question Answering System Over Semantic Web

The Semantic Web contains a large amount of data in the form of knowledge bases.Question Answering system is the most promising way of retrieving data from the available knowledge base to the end-users, to get the appropriate result for their questions.Although many systems have been developed over the years, it remains a challenge that most systems yet to require improvements to increase the accuracy for correct interpretation of the question and provide an answer.Many Question Answering systems convert the questions into triples which are mapped to the Knowledge base from which answer is derived.However, these triples do not express the semantic representation of the question, due to which the answers cannot be located.To handle this, a template-based approach is proposed which classifies the question types and finds appropriate SPARQL query templates for each type including comparatives and superlatives.The SPARQL query built is executed in the DBpedia endpoint and results are obtained.Compared with other factoid question answering systems, the proposed approach has the potential to deal with a large number of question types, including comparatives and superlatives.Also, the experimental evaluations of the system performed on the QALD-8 dataset present good performance and can help users to find answers to their questions.