Reviewerly/exHarmony huggingface dataset:
Gold metallogeny of the northern Capricorn Orogen: The relationship between crustal architecture, fault reactivation and hydrothermal fluid flow

The timing and distribution of gold mineralization in Proterozoic orogens is influenced by crustal architecture which is often established long before gold mineralization occurs. Gold occurrences in such settings are commonly associated with crustal-scale faults formed at cratonic margins. Once established, these faults provide critical pathways for hydrothermal and mineralizing fluids which during repeated fault reactivations can result in remobilization or introduction of new auriferous fluids resulting in overprinting gold events. Recently published geochronological data for the northern part of the Proterozoic Capricorn Orogen in Western Australia show it has experienced at least three episodes of gold mineralization occurring at c. 2400, 1770 and 1680 Ma. This information, combined with new data directly linking the timing of hydrothermal activity, gold mineralization and fault reactivation show that the crustal-scale Nanjilgardy and Baring Downs Faults, and their subsidiary structures, were (re)activated during discrete orogenic events. Many of the gold deposits are associated with intracratonic reworking during the 1820–1770 Ma Capricorn Orogeny and 1680–1620 Ma Mangaroon Orogeny. Intracratonic settings are not normally considered prospective for gold mineralization due to a lack of input of juvenile material. However, it appears that repeated hydrothermal fluid flow during intracratonic events, has the potential to upgrade gold mineralization or increase gold endowment throughout the orogen, either through gold remobilization or through introduction of new gold, increasing the potential for economic gold deposits.
Performance Analysis of K-Means Seeding Algorithms

K-Means is one of the most used cluster algorithms. However, because of its optimization process is based on a greedy iterated gradient descent, K-Means is sensitive to the initial set of centers. It has been proved that a bad initial set of centroids can reduce clusters' quality. Therefore, numerous initialization methods have been developed to prevent a lousy performance of K-Means clustering. Nonetheless, we may notice that all of these initialization methods are usually validated by using the Sum of Squared Errors (SSE), as quality measurement. In this study, we evaluate three state-of-the-art initialization methods with three different quality measures, i.e., SSE, the Silhouette Coefficient, and the Adjusted Rand Index. The analysis is carried out with seventeen benchmarks. We provide new insight into the performance of initialization methods that traditionally are left behind; our results describe the high correlation between different initialization methods and fitness functions. These results may help to optimize K-Means for other topological structures beyond those covered by optimizing SSE with low effort.
DEDUCING THE LATERAL GROWTH OF HANDREEN, ZOZIK AND TANOUN ANTICLINES IN KURDISTAN REGION USING GEOMORPHOLOGICAL FEATURES

Handreen, Zozik and Tanoun are three major anticlines in the northern part of Iraqi Kurdistan Region located NE of Rawandouz town. All anticlines are oriented NW-SE with a steep southwestern limb. The length and width of Handreen, Zozik and Tanoun anticlines are 20.2 km, 23 km, 27 km, and 10 km, 4.7 km and 2.5 km, respectively. The anticlines are separated by very narrow synclines. However, Tanoun anticline is thrusted over Zozik anticline causing the disappearance of the synclinal axis between the two folds. The oldest exposed rocks in Handreen anticline are the Cretaceous age, in Zozik anticline are the Upper Jurassic rocks, whereas in Tanoun anticline, the Lower Jurassic rocks are exposed. The carapace of Handreen and Zozik anticlines is built up by Bekhme and Qamchuqa formations and that of Tanoun anticline by the Qamchuqa Formation. Satellite images and geological maps were used to recognize the geomorphological and structural features which were checked and confirmed in the field. Handreen, Zozik and Tanoun anticlines show clear geomorphological and structural features which indicate their lateral growth. Among those features are water gaps, wind gaps, forked-shaped valleys, curved valleys, inclined (curved) valleys, radial valleys, abandoned alluvial fans, wale-back shape, en-echelon folds and folds with malty domes.
A Comparative Study of the Interpretations of Emojis in Between U.S. and Chinese Users

In recent years, Emojis have permeated online communications.They constantly show up in text messages, chats and emails, playing a significant role in business and daily interaction.Although designed to replace language text and to convert some universal emotions through icons, there are no rules governing people how to correctly define the sentiments behind them.Therefore, users with different cultural background may interpret emojis differently.This have often led to misinterpretation and miscommunication from different user groups.This study examines the correlation of the sentiments felt from a group of U.S. users versus a group of Chinese users by having them rate their sentiments from positive to negative for the top 15 most popularly used emojis.The survey was circulated in U.S. and China respectively and received 402 responses.The study weighted the scores of both user groups and analyzed the reasons behind the misalignment, including the structure of the icon, the local culture background etc.It founded that of the 15 emojis tested, over 60% of them had major or minor interpretation differences between the U.S. users and the Chinese users, and only 40% were aligned.The final conclusion was that the sentiment that the user intended to send with an emoji may not be received to the degree that it was intended among U.S. and Chinese users.
Local-Global Feature for Video-Based One-Shot Person Re-Identification

One-shot video-based re-identification, which uses only one labeled tracklet for each identity, is challenging since the framework usually suffers misalignment and inefficient utilizing of unlabeled data. In this paper we propose a novel local-global progressive learning framework to overcome the limitations. To obtain robust features in a tracklet, we first design sub-networks to learn four discriminative part-based feature maps and one global feature map which is insensitive to misalignment. Then a novel adaptive loss is proposed to balance the part-based and global feature properly. To utilize unlabeled data, our framework gradually select most reliable pseudo-labeled tracklets to the training set for iterative training. Extensive experiments are conducted on two video-based Re-ID datasets, MARS and DukeMTMC-VideoReID. The mAP of our model outperforms the state-of-the-art methods by 20.8% on the DukeMTMC-VideoReID dataset.
PERFORMANCE EVALUATION OF NOVEL SELECTION PROCESSES THROUGH HYBRIDIZATION OF K-MEANS CLUSTERING AND GENETIC ALGORITHM

K-means clustering combined with genetic algorithm (GA) techniques are used to improve the accuracy of estimation process and to minimize computational effort for solving nonlinear optimization problems.The main purpose of K-means clustering is to exhibit faster convergence which turns into quick evolution.This paper focuses on newly proposed cluster based GA selection techniques for solving unconstrained optimization problems.The K-means cluster based genetic algorithm (GKA) selection techniques comprise of four major stages: clustering, membership probability indexing, fitness evaluation and selection.The hybridization of genetic algorithm and clustering will effectively cater the problem of population diversity and selection pressure.There are two types of GKA selection techniques that are examined, the first selection technique (GKAF) includes two proposed selection operators which are linked with a fixed number of clusters while the second technique (GKAopt) is based on the optimum number of clusters.The main focus of these new selection techniques is to preserve population diversity as well as to avoid local optima.The performance of each technique is evaluated through eleven well known benchmark functions.On the whole, the novel cluster based selection techniques are demonstrated to be extremely efficient and effective for achieving optimum solutions which are verified by simulated results.
TransRev: Modeling Reviews as Translations from Users to Items

The text of a review expresses the sentiment a customer has towards a particular product. This is exploited in sentiment analysis where machine learning models are used to predict the review score from the text of the review. Furthermore, the products costumers have purchased in the past are indicative of the products they will purchase in the future. This is what recommender systems exploit by learning models from purchase information to predict the items a customer might be interested in. The underlying structure of this problem setting is a bipartite graph, wherein customer nodes are connected to product nodes via 'review' links. This is reminiscent of knowledge bases, with 'review' links replacing relation types. We propose TransRev, an approach to the product recommendation problem that integrates ideas from recommender systems, sentiment analysis, and multi-relational learning into a joint learning objective. TransRev learns vector representations for users, items, and reviews. The embedding of a review is learned such that (a) it performs well as input feature of a regression model for sentiment prediction; and (b) it always translates the reviewer embedding to the embedding of the reviewed item. This is reminiscent of TransE [5], a popular embedding method for link prediction in knowledge bases. This allows TransRev to approximate a review embedding at test time as the difference of the embedding of each item and the user embedding. The approximated review embedding is then used with the regression model to predict the review score for each item. TransRev outperforms state of the art recommender systems on a large number of benchmark data sets. Moreover, it is able to retrieve, for each user and item, the review text from the training set whose embedding is most similar to the approximated review embedding.
Big Data Sentimental Analysis Using Document to Vector and Optimized Support Vector Machine

With the rapid evolution of the internet, using social media networks such as Twitter, Facebook, and Tumblr, is becoming so common that they have made a great impact on every aspect of human life. Twitter is one of the most popular micro-blogging social media that allow people to share their emotions in short text about variety of topics such as company’s products, people, politics, and services. Analyzing sentiment could be possible as emotions and reviews on different topics are shared every second, which makes social media to become a useful source of information in different fields such as business, politics, applications, and services. Twitter Application Programming Interface (Twitter-API), which is an interface between developers and Twitter, allows them to search for tweets based on the desired keyword using some secret keys and tokens. In this work, Twitter-API used to download the most recent tweets about four keywords, namely, (Trump, Bitcoin, IoT, and Toyota) with a different number of tweets. “Vader” that is a lexicon rule-based method used to categorize downloaded tweets into “Positive” and “Negative” based on their polarity, then the tweets were protected in Mongo database for the next processes. After pre-processing, the hold-out technique was used to split each dataset to 80% as “training-set” and rest 20% “testing-set.” After that, a deep learning-based Document to Vector model was used for feature extraction. To perform the classification task, Radial Bias Function kernel-based support vector machine (SVM) has been used. The accuracy of (RBF-SVM) mainly depends on the value of hyperplane “Soft Margin” penalty “C” and γ “gamma” parameters. The main goal of this work is to select best values for those parameters in order to improve the accuracy of RBF-SVM classifier. The objective of this study is to show the impacts of using four meta-heuristic optimizer algorithms, namely, particle swarm optimizer (PSO), modified PSO (MPSO), grey wolf optimizer (GWO), and hybrid of PSO-GWO in improving SVM classification accuracy by selecting the best values for those parameters. To the best of our knowledge, hybrid PSO-GWO has never been used in SVM optimization. The results show that these optimizers have a significant impact on increasing SVM accuracy. The best accuracy of the model with traditional SVM was 87.885%. After optimization, the highest accuracy obtained with GWO is 91.053% while PSO, hybrid PSO-GWO, and MPSO best accuracies are 90.736%, 90.657%, and 90.557%, respectively.
DeepMnemonic: Password Mnemonic Generation via Deep Attentive Encoder-Decoder Model

Strong passwords are fundamental to the security of password-based user authentication systems. In the recent years, much effort has been made to evaluate the password strength or to generate strong passwords. Unfortunately, the usability or memorability of the strong passwords has been largely neglected. In this article, we aim to bridge the gap between strong password generation and the usability of strong passwords. We propose to automatically generate textual password mnemonics, i.e., natural language sentences, which are intended to help users better memorize passwords. We introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepMnemonic</i> , a deep attentive encoder-decoder framework which takes a password as input and then automatically generates a mnemonic sentence for the password. We conduct extensive experiments to evaluate DeepMnemonic on the real-world data sets. The experimental results demonstrate that DeepMnemonic outperforms a well-known baseline for generating semantically meaningful mnemonic sentences. Moreover, the user study further validates that the generated mnemonic sentences by DeepMnemonic are useful in helping users memorize strong passwords.
Multi-Partitions Subspace Clustering

In model based clustering, it is often supposed that only one clustering latent variable explains the heterogeneity of the whole dataset. However, in many cases several latent variables could explain the heterogeneity of the data at hand. Finding such class variables could result in a richer interpretation of the data. In the continuous data setting, a multi-partition model based clustering is proposed. It assumes the existence of several latent clustering variables, each one explaining the heterogeneity of the data with respect to some clustering subspace. It allows to simultaneously find the multi-partitions and the related subspaces. Parameters of the model are estimated through an EM algorithm relying on a probabilistic reinterpretation of the factorial discriminant analysis. A model choice strategy relying on the BIC criterion is proposed to select to number of subspaces and the number of clusters by subspace. The obtained results are thus several projections of the data, each one conveying its own clustering of the data. Model’s behavior is illustrated on simulated and real data.
Classification of Compressed Remote Sensing Multispectral Images via Convolutional Neural Networks

Multispectral sensors constitute a core Earth observation image technology generating massive high-dimensional observations. To address the communication and storage constraints of remote sensing platforms, lossy data compression becomes necessary, but it unavoidably introduces unwanted artifacts. In this work, we consider the encoding of multispectral observations into high-order tensor structures which can naturally capture multi-dimensional dependencies and correlations, and we propose a resource-efficient compression scheme based on quantized low-rank tensor completion. The proposed method is also applicable to the case of missing observations due to environmental conditions, such as cloud cover. To quantify the performance of compression, we consider both typical image quality metrics as well as the impact on state-of-the-art deep learning-based land-cover classification schemes. Experimental analysis on observations from the ESA Sentinel-2 satellite reveals that even minimal compression can have negative effects on classification performance which can be efficiently addressed by our proposed recovery scheme.
The Effect of 360-Degree Video Authentic Materials on EFL Learners' Listening Comprehension

Although the increased use of authentic materials in listening comprehension teaching has become general practice, it is crucial to find out whether or not the new technology-supported authentic material could be beneficial to English as a Foreign Language (EFL) learners' listening comprehension. The present study examined the effect of authentic material presentation modes (the traditional presentation mode versus virtual reality mode) on EFL learners' English listening comprehension and cognitive load. Subjects were 53 English major sophomores of a university in Wuhan. The control group received the common traditional video playback (the traditional presentation mode) and the experimental group were provided material on Head-Mounted Display (HMD) by 360-degree video playback (the virtual reality mode). The same authentic video material-360-degree video journalism was used in the two groups. The results demonstrated that the EFL learners who watched 360-degree video journalism encountered higher cognitive load and did not outperform in English listening comprehension test than those who watched the material in traditional presentation mode. Pedagogical implications for English listening teaching were provided at the end of the study.
Interactive Trimap Generation for Digital Matting Based on Single-Sample Learning

Image matting refers to the task of estimating the foreground of images, which is an important problem in image processing. Recently, trimap generation has attracted considerable attention because designing a trimap for every image is labor-intensive. In this paper, a two-step algorithm is proposed to generate trimaps. To use the proposed algorithm, users must only provide some clicks (foreground clicks and background clicks), which are employed as the input to generate a binary mask. One-shot learning technique achieves remarkable progress on semantic segmentation, we extend this technique to perform the binary mask prediction task. The mask is further used to predict the trimap using image dilation. Extensive experiments were performed to evaluate the proposed algorithm. Experimental results show that the trimaps generated using the proposed algorithm are visually similar to the user-annotated ones. Comparing with the interactive matting algorithms, the proposed algoritm is less labor-intensive than trimap-based matting algorithm and achieved more accuate results than scribble-based matting algorithm.
Cryptocurrency Perception Within Countries: A Comparative Analysis

Purpose: The paper explores the differences between countries concerning perception and use of traditional and virtual money. We try to answer the question who uses virtual money for investment and building assets and who uses it just for Internet payments. The background of the analysis are significant changes that have taken place in the virtual money market in recent years in relation to changes in the global financial market. Design/methodology/approach: A pilot study was conducted in Poland, the Russian Federation, and China, which is supposed to be an introduction to the bigger and wider survey. It was conducted within December 2019 and January 2020 with 81 surveyed persons. These were students of financial studies in the chosen countries. The paper questionnaire used in the survey consisted of 26 questions connected to virtual money plus 5 demographic questions. It was provided personally by teachers in class. Findings: The findings indicated that there are differences between countries in perception and the use of traditional and virtual money. These discrepancies can have cultural or historical background. Practical Implications: The practical usefulness of the whole study is that gathered information will permit to examine the economic and financial literacy of the respondents and their preferences for the use of innovative financial instruments. Originality/value: The study is related to the very current issue – virtual money as alternative to the currently functioning fiduciary money. The result of the research as one of the first indicated that a different perception of traditional and virtual money among different countries exist. This statement might be a huge contribution to the analysis of the current and further financial system.
A Method for Generating Variable-Scale Maps for Small Displays

With the rapid development of the internet and information technology, visualization techniques for mobile and interactive web maps have developed different requirements. Small screens make it difficult to simultaneously present information details and the surrounding context. Aiming at this problem, this paper proposes a novel variable-scale method that can allow users to properly specify the size, shape, and number of the focus area(s). Our method first establishes a hierarchical data structure for representing geographic data and then the client-side can request and represent the information according to only the operational command input by users. Experimental results show that this method can realize the variable-scale representation of real geographic data on a single screen. It can effectively solve the contradiction between a small-screen display and a large quantity of information.
Improved k-means Algorithm using Density Estimation

Unsupervised learning approach, clustering, performs a set of groups where similar types of data points are placed in a group. K-means is a popular clustering algorithm, which is commonly used in different application because of its simplicity and efficiency. This main drawback of k-means algorithm is random selection of initial cluster centers. This paper proposes an improved k-means, namely, IK-means algorithm to overcome the main pitfall of k-means. The IK-means algorithm uses Kd-tree data structure to present and store data objects, and applies kernel density estimation technique to locate the densest areas of data points. Initial cluster centers are assigned from the densest areas. The IK-means algorithm produces the clustering results with better accuracy that improves the performance of the k-means algorithm on artificially generated and real datasets.
Person Re-Identification by Discriminative Local Features of Overlapping Stripes

The human visual system can recognize a person based on his physical appearance, even if extreme spatio-temporal variations exist. However, the surveillance system deployed so far fails to re-identify the individual when it travels through the non-overlapping camera’s field-of-view. Person re-identification (Re-ID) is the task of associating individuals across disjoint camera views. In this paper, we propose a robust feature extraction model named Discriminative Local Features of Overlapping Stripes (DLFOS) that can associate corresponding actual individuals in the disjoint visual surveillance system. The proposed DLFOS model accumulates the discriminative features from the local patch of each overlapping strip of the pedestrian appearance. The concatenation of histogram of oriented gradients, Gaussian of color, and the magnitude operator of CJLBP bring robustness in the final feature vector. The experimental results show that our proposed feature extraction model achieves rank@1 matching rate of 47.18% on VIPeR, 64.4% on CAVIAR4REID, and 62.68% on Market1501, outperforming the recently reported models from the literature and validating the advantage of the proposed model.