Reviewerly/exHarmony huggingface dataset:
The discovery and geology of Sinclair, Australia’s first caesium deposit

SummaryThe Sinclair Caesium Deposit, discovered in 2016 by Pioneer Resources Limited, is Australia’s first mining operation to commercially extract the caesium-rich mineral pollucite. Economic caesium deposits are extremely rare, with only three mining operations having produced commercial quantities of pollucite: the Bernic Lake Mine (or Tanco Mine, Manitoba, Canada), the Bikita Mine (Zimbabwe) and now the Sinclair Mine (Western Australia).The formation of pollucite only occurs in extremely differentiated lithium-caesium-tantalum (LCT) pegmatites and given their size and rarity, it can be assumed that caesium-rich deposits globally are either challenging to explore for or form surpassingly rarely during the emplacement of LCT pegmatites.Common with the Bernic Lake and Bikita Deposits, the Sinclair Deposit’s host LCT pegmatite consists of an outer pegmatite wall zone that is coarse grained, and dominated by plagioclase feldspar, muscovite and quartz with other accessory minerals; and an inner core zone composed of quartz, albite (cleavelandite), lepidolite, pollucite, petalite, zinnwaldite, eucryptite, beryl and amblygonite. The core zone is ‘capped’ by a thick (~35-40 m) monomineralic potassium feldspar zone.This paper will present details on the discovery and geology of the Sinclair Caesium Deposit.
SOS Boosting for Image Deblurring Algorithms

The work reported by Romano and Elad presents the SOS boosting - a generic recursive method for improving image denoising algorithms. Appealing properties of the SOS scheme are its flexibility to work with any denoising method, its simplicity, and its robustness. In this article we aim to generalize this to the image deblurring task. The proposed SOS procedure for deblurring is given by the following iterative steps: [S]trengthen the signal by blurring the output of the previous iteration and adding it to the blurred input image, [O]perate the deblurring algorithm on the strengthened image, and [S]ubtract the previous output from the current one. We demonstrate this procedure for several state-of-the-art methods (BM3D, EPLL and NCSR), showing the potential gain in output quality for each. As in the original SOS, we manipulate the iterative algorithm by two parameters, better controlling its steady state and rate of convergence.
A Graph-Based Approach to Explore Relationship Between Hashtags and Images

Online social networks are playing a great role in our daily life by providing a platform for users to present themselves, articulate their social circles, and interact with each other. Posting image is one of the most popular online activities, through which people could share experiences and express their emotions. Intuitively, there must exist a connection between images and their associated hashtags. In this paper, we focus on systematically describing this relationship and using it to improve downstream tasks. First, we use a two-sample Kolmogorov-Smirnov test on an Instagram dataset to show the existence of the relationship at a significance level of $$\alpha =0.001$$. Second, in order to comprehensively explore the relationship and quantitatively analyse it, we adopt a graph-based approach, utilising the semantic information of hashtags and graph structure among images, to mine meaningful features for both hashtags and images. At last, we apply the extracted features about the relationship to improve an image multi-label classification task. Compared to a state-of-the-art method, we achieve a $$12.0\%$$ overall precision gain.
A Reference Based Secure and Robust Zero Watermarking System

In this paper, a new approach for zero-watermarking has been proposed based on log-polar mapping, all phase biorthogonal sine transform and singular value decomposition. The core idea is to produce a zero watermark to protect the copyright of the image. For this purpose, the host image is transformed into the log-polar domain followed by the all phase biorthogonal sine transform (APBST). The transformed coefficients are then divided into nonoverlapping blocks and some blocks are selected based on secret key. These blocks are finally used to formulate a reference matrix which is utilized to generate a zero watermark for the host image. A detailed experimental analysis is conducted to demonstrate the feasibility of the proposed algorithm against various image/signal processing distortions.
Visual Analysis of Retailing Store Location Selection

An appropriate location of the retailing store is vital for achieving business success. However, a huge amount of complex information needs to be considered in location selection, such as customer flow, the business environment, and current business performance. Unlike traditional location recommendation method on account of statistical sampling, we establish a model of business-district attractiveness based on customer flow and provide a method of data-driven visual comparisons. In addition, we build an interactive visual analysis system with a user-friendly interface for an interactive visual query about complex business and environment information. Our system can help users select retailing store location, support interactive visual queries, and display rich information to facilitate manager in decision-making.
Analysis of Question Types in Second Foreign Language SNULT as a Language Testing Tool: Focusing on German and Japanese Tests

The purpose of this article is to analyze major attributes and features of different question (or item) types used in Seoul National University Language Tests (SNULT) as a way to verify the validity of these tests.A total of 50 reading and listening items each were selected from SNULT German vol. 2. (in press) and SNULT Japanese vol. 2. (2019) and then analyzed by using Bachman's (1990) Test Method Facets framework.Results of analysis have shown that the reading and listening sections of both tests are so constructed that each question has not only various degrees of difficulties, but also focuses on measuring test taker's ability to grasp main ideas, to understand details, and to make plausible inferences.These findings suggest that both SNULT German and Japanese tests are equipped with appropriate formats and contents for testing test-takers' foreign language competence and therefore seen to be valid foreign language testing tools.
Passivity-based Pose Control for a Ground Vehicle through Drone's Visual Feedback Control

This paper presents passivity-based visual feedback control for an endpoint closed-loop system with a movable camera. In particular, we discuss a control scheme for the vehicle system that consists of two ground vehicles and a camera-mounted aerial vehicle. The objective of the system is that a controlled ground vehicle tracks a target ground vehicle by using only aerial vehicle's visual information. First, a brief summary of the estimation and pose error systems for the camera-mounted aerial vehicle is presented. Second, we design a novel nonlinear observer to estimate the pose of the controlled ground vehicle. Next, we propose a visual feedback control law for the constructed visual motion error system based on the passivity. After discussing stability analysis of the closed-loop system through Lyapunov stability theorem, we present L2-gain performance analysis that deals with the disturbance attenuation problem via dissipation systems theory. Finally, we provide simulation results and actual nonlinear experimental results using two ground vehicles and a camera-mounted aerial vehicle to illustrate the performance of the proposed control method.
Imitating Popular Photos to Select Views for an Indoor Scene

Abstract Selecting informative and visually appealing views for 3D indoor scenes is beneficial for the housing, decoration, and entertainment industries. A set of views that exhibit comfort, aesthetics, and functionality of a particular scene can attract customers and facilitate business transactions. However, selecting views for an indoor scene is challenging because the system has to consider not only the need to reveal as much information as possible, but also object arrangements, occlusions, and characteristics. Since there can be many principles utilized to guide the view selection, and various principles to follow under different circumstances, we achieve the goal by imitating popular photos on the Internet. Specifically, we select the view that can optimize the contour similarity of corresponding objects to the photo. Because the selected view can be inadequate if object arrangements in the 3D scene and the photo are different, our system imitates many popular photos and selects a certain number of views. After that, it clusters the selected views and determines the view/cluster centers by the weighted average to finally exhibit the scene. Experimental results demonstrate that the views selected by our method are visually appealing.
Privacy by design in statistics: Should it become a default/standard?

Privacy by design (PbD) as an approach to systems engineering has been conceptualized for more than a decade. It has inspired the legal norm of managing personal data in EU and incorporated into the GDPR. In practice PbD is far from being the standard in systems engineering in both private and publ ic sector procurement. We illustrate why privacy needs to be engineered into statistical systems and explain risk when privacy is patched into a solution. To show how the statistical system could proceed in adopting PbD we bring an example from a high level self assessment of a statistical organization. We walk through future proofing existing services by redesigning an official tourism statistics application that uses mobile location big data. Finally we envision how statistical organizations can cooperate in supporting each other and learn together how to build trust required in utilising new data sources.
Space Weather Data Management System and Monitoring in Decentralized Storage Environment

Indonesian National Institute of Aeronautics and Space (LAPAN) concerns to develop a system that provides actual information and prediction related to space weather activities called Space Weather Information and Forecast Services (SWIFtS). SWIFtS is supported by a data storage system that serves data near real-time. Because the data is collected on one single server and is served by a centralized model, problems emerge when the researchers need the server for data processing and making forecasts to update the content on the SWIFtS website. The system is incapable of providing the latest data when the server is down. Therefore, we propose a new system that utilizes the decentralized model for storing data using the Inter Planetary File System (IPFS). Our proposed method focuses on the background process, and its scheme will increase the data availability and throughput by spreading it into nodes through a peer-to-peer connection. Other unused resources would be useful and no single point of failure. For monitoring, we develop a real-time data flow from each node and information of status nodes. As our expected, performance shown that our proposed system has better throughput than the existing system.
Intelligent system for monitoring the air pollution of the city in real time

Abstract The article reveals the theoretical foundations, types of pollutants, classification of monitoring and visualization systems of air pollution. A description of the design of the structure of the system is described, its components are described in detail, and the justification of the selected technologies is given. In general, the system consists of two parts: a device for collecting air pollution data and a site that displays these data in user-friendly form. Graphic images that display the appearance of a software product are also added.
Virtual Avatars as a tool for audience engagement

Modern motion capture tools can be used to animate sophisticated digital characters in real time. Through these virtual avatars human performers can communicate with live audience, creating a promising new area of application for public engagement. This study describes a social experiment where a real-time multimedia setup was used to facilitate an interaction between a digital character and visitors at a public venue. The technical implementation featured some innovative elements, such as using iPhone TrueDepth Camera as part of the performance capture pipeline. The study examined public reactions during the experiment in order to explore the empathic potential of virtual avatars and assess their ability to engage live audience.
Prediction of FDG uptake in Lung Tumors from CT Images Using Generative Adversarial Networks

In modern medicine, combined PET-CT is a commonly-used tool in clinical diagnostics, which is especially important in oncology for staging or treatment planning. Variations in FDG uptake visible in a PET image, which indicate variances in metabolic activity, are not visually recognizable within a CT scan from the same region, making both imaging modalities necessary for diagnosis and exposing the patient to a high amount of radiation. In this study, we investigate the possibility of using generative adversarial networks (GANs) to synthesize a PET image from a CT scan to predict metabolic activity.
Practices and Experiences in High Volumes of Satellite Data Management

High volumes of satellite data management within an organization is still challenging and daunting in the era of big data. The increasing information technology costs and limited budgets, growing satellite data needs, data availability across multiple teams and projects, strategic goals of organization, and expected project outcomes require better satellite data management mechanism and system to facilitate research and development activities. An organization level centralized satellite data repository is a practical solution to satisfy these requirements. This paper describes the best practices and experiences from building such a central satellite data repository within our organization, including data management strategy and policy, scalable and extensible system infrastructure, comprehensive data management system, and technical support and user assistance. These practices can be borrowed and applied in other organizations with similar requirements.
The influence of right and left brain intelligence on mathematics learning achievement

Abstract This research aims to determine the effects of right and left brain intelligence on mathematics learning achievement. This research used a quantitative approach with ex post facto research type involving learning achievement as the dependent variable and right brain acumen and left brain intelligence as an independent variable. The subjects in this research are seventh-grade students in junior high school in Madiun. The data collection instrument used is the mathematics achievement test and the right and left brain intelligence questionnaire. Data analysis in this research uses simple linear regression. The results of the research showed that there was an influence between the intelligence of the right and left brains on students’ mathematics learning achievement. The influence of right brain intelligence is 28%, and left brain intelligence is 46.7%.
Identification of Altered Mineral Using Hyperion Hyperspectral Image In South Of Tibet, China

The combination of hyperspectral data and atlas and high spectral resolution make it possible to identify altered minerals. In this paper, FLAASH atmospheric correction module and appropriate parameter settings are used to correct Hyperion hyperspectral data in Kelu deposit, Shannan area of Tibet, and then MNF conversion and anti-MNF conversion are carried out to smooth the spectrum. The ground spectrum acquired by SVC spectrometer is compared with the Hyperion result after atmospheric correction. It is considered that the Hyperion reflectance after atmospheric correction is basically consistent with the actual situation. The main altered minerals in the study area are calcite, chlorite, epidote, kaolinite and sericite by phase analysis. Then the altered minerals in the study area are identified by MTMF method, which are reliable. The result is very important meaning for mineral prospecting.
Linear complexity of binary sequences with optimal autocorrelation magnitude of length 4N

Abstract In this paper, we study the linear complexity of series of binary sequences with optimal autocorrelation magnitude of length 4 N . These sequences are obtained from almost-perfect binary sequences and binary sequences with optimal autocorrelation of length N . The construction of these sequences were presented E.I. Krengel and P.V. Ivanov. We show that considered sequences have the high linear complexity. Also we derive the 1-error linear complexity of these sequences.
Noise reduction for DIC measurements

Abstract Digital image correlation (DIC) is well known as a contact‐less, full‐field strain and displacement measuring method. The use of the method is widely spread in experimental mechanics. Nevertheless, there are error sources such as light settings, speckle pattern and noisy images, which strongly affect measurement quality, but often stays uninvestigated. Therefore, this work outlines a simple routine to identify errors and enhance the accuracy of DIC measurements. A calibration sample adapted to the specifications of the setup using the beam theory to link machine displacement, DIC measurements and forces. All evaluations are done in the commercial software ISTRA4D. The noise in the measurements is reduced to an error of under two per cent. The routine allows estimating the quality of the images before performing the experiments. Furthermore, the routine is transferable to all experimental setups. The calibration of the setup allows getting reliable experimental results with a known error. With this estimated stochastic error, the advantages of DIC can be used to evaluate mechanical experiments such as three‐point bending tests on single struts of an open cell metal foam.
A Novel Scheme for Recruitment Text Categorization Based on KNN Algorithm

With the rapid development of the Internet, online recruitment has gradually become mainstream. However, job seekers need to spend a lot of time to find a suitable job when there are a large variety of job information, which will seriously affect their efficiency, so it is necessary to carry out more detailed and efficient classification of the recruitment documents. Currently, common text classification algorithms include KNN (k-Nearest Neighbor), SVM (Support Vector Machine) and Naïve Bayes. Particularly, KNN algorithm is widely used in text classification for its simple implementation and accurate classification. But KNN algorithm has been criticized for its inefficiency in the face of large-scale recruitment. This paper improves the original KNN algorithm and proposes RS-KNN algorithm to achieve rapid refinement and classification of recruitment information. Experiments show that the improved algorithm has higher efficiency and less time consumption than the original algorithm.
Interpolative self-training approach for link prediction

In this paper, learning social networks from incomplete relationship data is proposed. Link prediction is addressed as a semi-supervised learning problem where the task is to predict a larger part of networks using available knowledge of smaller parts. By this assumption, social network extraction is translated into a classification problem. While in real case scenarios majority of links are unknown, we hypothesis self-training as the most common semi-supervised learning method can provide an effective approach for learning from unlabelled data. We proposed an interpolative self-training technique that leverages node information to generate a set of examples in learning phase along with their connections as their associated labels. The approach generates data by interpolation of documents assigned to a pair of nodes. Documents as the implicit content shared between the individual nodes provide a scope for the estimation of their similarities. Then generated training data are employed in a link prediction model with two different scenarios. The first scenario interprets the link prediction as a conventional classification problem in which we have examples from both positive (link) and negative (no-link) classes. However, the second scenario addresses more realistic case where only some positive examples (links or connections) are known. Social networks are usually very sparse structures. The sparsity of social networks implies that in the classification framework of link prediction, we deal with an imbalance class distribution in which among all possible links there are a few connections (positive class) vs. many disconnections (negative class). In order to deal with class skew and enhance the performance of the classifier, a data selection method based on node similarity was proposed. To evaluate the merit of the proposed methods, a set of experiments were conducted on co-authorship networks of 18 different domains. The result implies the feasibility of achieving significantly high performance for most of the networks using the proposed self training approach.
WITHDRAWN: Application research of multimedia video system in water conservancy and hydropower engineering management

Abstract The development of water conservancy and hydropower projects is related to China's economic lifeline, strengthen the construction and management of water conservancy and hydropower projects, realize the information management of water conservancy and hydropower project construction, improve the efficiency of project construction management, save the management cost of project construction, and then improve Construction quality and investment efficiency of water conservancy and hydropower projects. However, traditional project management methods have not been able to achieve the purpose of improvement. With the development of science and technology, the emergence of multimedia provides a platform for the realization of efficient construction and management of water conservancy and hydropower projects. As a platform application of current technology, multimedia can be multi-user-oriented. Users do not need to consider network equipment and configuration issues. Just rent the functions you need according to your needs. You can use the software by entering the URL when you have a network. Convenient and intuitive. Through the analysis of the construction management of water conservancy and hydropower projects, this paper constructs the construction and management of water conservancy and hydropower projects based on the multimedia video system model, and realizes the efficient and low-cost management of project construction. This paper combines the characteristics of water conservancy and hydropower project construction management to build a water conservancy and hydropower project construction management system, realize multi-user multimedia mode water conservancy and hydropower project construction management system, and more meet the multimedia video system model water conservancy and hydropower project construction management system. Actual demand. Finally, the developed system is applied to a water conservancy investment company to realize multi-user collaborative management.
Computer viruses: how to inoculate your business

The world of the computer hacker is continually evolving, and systems that are connected to the internet are permanently at risk. Adam Bernstein offers advice on how to protect a network
Fifty years of morphological theory in the Netherlands

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