Reviewerly/exHarmony huggingface dataset:
Mineralogical Variability of the Whabouchi Pegmatite and its Effect on the Li Concentrations

ABSTRACT The Whabouchi pegmatite, located in the James Bay area of Québec, is a lithium-cesium-tantalum pegmatite of albite-spodumene type. In order to evaluate the mineralogical and geochemical variability of the pegmatite, 168 samples were collected from drill core and analyzed for their whole rock geochemistry and mineralogy. The pegmatite is composed of quartz, albite, K-feldspar, spodumene, and muscovite, with trace amounts of spessartine garnet, apatite, beryl, tourmaline, and oxides. It is mostly homogenous, showing greatest variability with respect to the minerals albite, K-feldspar, and spodumene. The Li2O concentration varies between 0.03 and 4.46 wt.%, for an average of 1.53 wt.% and an estimated variability of 53%. Modal mineralogical data showed an inverse correlation between spodumene and the feldspars (albite + K-feldspar), which could also be observed when comparing the Li2O content with the sum of Na2O and K2O. To improve on this relationship, correlation matrices comparing all geochemical components were constructed and allowed the development of an equation able to estimate the Li2O content of the samples within 0.5 wt.% of the measured value. The applicability of the equation to other albite-spodumene type pegmatites was verified with samples collected from the Georgia Lake area of northwestern Ontario. The calculation provided a good approximation of the Li2O content of the samples, with 92% of the data showing a difference of 0.5 wt.% Li2O or less; however, it has an impact on the statistical mean of the data set, the data being recalculated to return an average closer to 1.5 wt.% Li2O. Considering the difficulty in analyzing lithium content of an in situ sample, this relationship could facilitate estimation in the field using portable X-ray fluorescence with the capability of analyzing all required components.
Google Places Enricher: A tool that Makes It Easy to Get and Enrich Google Places API Data

The growing number of available APIs means that more developers and interested users need to learn to use unfamiliar interfaces, requiring a learning curve that can compromise productivity. Thus, it becomes important to find ways to facilitate their usability. This work presents a tool that facilitates using the Google Places API, simplifying multiple API calls to cover a region of interest. In addition, the proposed tool also provides features for the enrichment of these data, extending the PoI data from that region with categories from other sources. It is hoped that developers and users without much computer knowledge can benefit from Google Places Enricher, helping to ease the development of new sophisticated urban applications and services.
Analysis of the (Mis)information Dissemination Network in WhatsApp

In this thesis, we aim at investigating the spread of information, notably misinformation, on WhatsApp. We show how WhatsApp enables the formation of a rich underlying network that, while crossing the boundaries of existing groups, have structural properties that favor information dissemination at large. Next, we delve deeper into this network to identify user communities, i.e., groupings of users who, intentionally or not, share the same content disproportionately often and characterize how such components could affect information dissemination. By analyzing content sharing and network topological properties focusing on three perspectives (individuals, WhatsApp groups, user communities), our study offers valuable insights into how WhatsApp users leverage the underlying network connecting different groups to gain large reach in the spread of misinformation in the platform.
Best Practices and Biblical Worldview for Technology Integration

This research focused on a study conducted across three different teacher education programs, focusing on equipping teacher candidates with strategies for technology integration while encouraging them to examine how to best glorify God with appropriate technology use in the classroom.Students were instructed using common research-based practices and assignments.These included instruction on the SAMR model, exploration of a variety of technology tools, and a reflection on what it means to glorify God through teaching with technology.Pre-and post-survey data was collected to measure the effectiveness of both the instruction and changes in students' attitudes and perceptions of technology use in teaching.Pre-service teachers read and discussed research addressing Christian wisdom on technology, pursuing excellence in Christian education, technology and computational thinking in the classroom, and digital citizenship as they engaged in these assignments.This article includes main themes, preliminary findings, and implications for future research based on results across all three institutions.
THE IMPLEMENTATION OF READ, ANSWER, DISCUSS, EXPLAIN, AND CREATE (RADEC) LEARNING MODEL TO IMPROVE STUDENTS’ READING ABILITY AT THE ELEVENTH GRADE OF SMA PESANTREN PUTRI YATAMA MANDIRI

This research aims to find out whether or not Read, Answer, Discuss, Explain, and Create (RADEC) learning model is effective to improve students’ reading ability in understanding a text at the eleventh grade of SMA Pesantren Putri Yatama Mandiri. The instrument of this research was a reading test about an explanation text consisted of 15 questions of multiple choice. This research used pre-experimental research that consisted of pre-test, treatment, and post-test. The population of this research was class XI A Regular that consisted of 20 students. The sample was selected by using a purposive sampling. The result of this research showed that the score of pre-test was 43.55 and post-test was 66.80. This research also showed the value of t-test was higher than the value of t-table (T-test &gt; T-table). The value of t-test was 8.906 and the value of t-table was 2.093. It means that the null hypothesis (H0) was rejected and the alternative hypothesis (H1) was accepted. Based on the research result, the researcher concluded that the implementation of Read, Answer, Discuss, Explain, and Create (RADEC) learning model was effective to improve students’ reading ability.
Integrity Protection for Data Aggregation in Smart Grid

To evaluate the working state of the smart grid, users need to count the status of devices collected by multiple smart meters. To avoid network congestion during data collection processes in the smart grid, an aggregation protocol is required to aggregate messages from multiple smart meters into one short message. However, since these messages may contain sensitive information of smart meters, privacy is a fundamental requirement for such a data aggregation protocol. At the same time, to avoid tampering of transmitted messages, data integrity is another important requirement during data aggregation. Currently, the privacy issue has been well addressed by using homomorphic encryption algorithms such as the Paillier cryptographic system, where multiple smart meters use the same encryption key for encrypting different messages. However, the integrity issue is much harder than the privacy issue since multiple smart meters use different private keys for signing different messages. To address the integrity issue, we propose a novel data aggregation protocol for the smart grid, called DASG. In DASG, we shall show how to aggregate signatures from multiple smart meters using the Chinese Remainder theorem and the Lagrange interpolation techniques, respectively. Since these two techniques can aggregate multiple messages into one, DASG is quite light-weight. Therefore, our newly designed data aggregation protocol for the smart grid can achieve both security and efficiency goals in the smart grid environment. Experimental results show that DASG is feasible for real world applications.
Uncovering the Full Variant Continuum with Pioneering Solutions from Bionano

Inside Precision MedicineVol. 9, No. 5 Sponsored ContentUncovering the Full Variant Continuum with Pioneering Solutions from BionanoA Q&A with Erik Holmlin, PhD, President & CEO, Bionano Genomics, Inc.Published Online:22 Oct 2022https://doi.org/10.1089/ipm.09.05.06AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 9Issue 5Oct 2022 InformationCopyright © GEN PublishingTo cite this article:Uncovering the Full Variant Continuum with Pioneering Solutions from Bionano.Inside Precision Medicine.Oct 2022.27-27.http://doi.org/10.1089/ipm.09.05.06Published in Volume: 9 Issue 5: October 22, 2022PDF download
Warranty Data Prediction and Research for Automobile Based on Weibull Distribution Model

According to the automobile warranty requirement, the warranty data predicting method of automobile by using warranty records was proposed. Firstly, the Weibull distribution was established based on the preprocessing of warranty data; secondly, the reliability was analyzed and predicted by using parameter characteristics and probability diagram, at last, some vehicle warranty data was taken an example to verify the practicability and effectiveness of proposed model and analysis method according to errors of forecast method, which could provide analysis and improving bases of further improvement.
Continuous-Curvature Path Planner Based on RS Algorithm

Path planning algorithms directly affect the performance of the automatic parking assist system (APA). RS algorithm is very suitable for embedded code transplantation and can quickly generate the shortest parking path due to its low computational complexity, so it is widely used in the parking path planning algorithms. However, because the RS algorithm does not consider the constraint of curvature mutation, the phenomenon of turning the wheel on the spot often occurs during the actual parking process, which will not only accelerate tire wear, but also affect the driver’s driving experience. In order to solve the curvature instantaneous problem of the RS algorithm, this paper proposes a continuous curvature parking path planning algorithm, that is, to optimize and upgrade the RS algorithm with clothoid and straight line, and change the connection of arcs and straight lines used by the RS algorithm to clothoid arcs and straight lines, and so on the basis of retaining the advantages of RS algorithm, it can effectively solve the problem of curvature mutation problem during parking process. Finally, the effectiveness of the proposed algorithm is tested and verified by simulation and real vehicle experiments in two common parking scenarios. The experimental results show that the proposed algorithm is not only simple for embedded transplantation, but also can effectively solve the curvature mutation problem during parking process.
Generalizations of Topological Decomposition and Zeno Sequence in Fibered n-Spaces

The space-time geometry is rooted in the Minkowski 4-manifold. Minkowski and Euclidean topological 4-manifolds behave differently in view of compactness and local homogeneity. As a result, Zeno sequences are selectively admitted in such topological spaces. In this paper, the generalizations of topologically fibered n-spaces are proposed to formulate topological decomposition and the formation of projective fibered n-subspaces. The concept of quasi-compact fibering is introduced to analyze the formation of Zeno sequences in topological n-spaces (i.e., n-manifolds), where a quasi-compact fiber relaxes the Minkowski-type (algebraically) strict ordering relation under topological projections. The topological analyses of fibered Minkowski as well as Euclidean 4-spaces are presented under quasi-compact fibering and topological projections. The topological n-spaces endowed with quasi-compact fibers facilitated the detection of local as well as global compactness and the non-analytic behavior of a continuous function. It is illustrated that the 5-manifold with boundary embedding Minkowski 4-space transformed a quasi-compact fiber into a compact fiber maintaining generality.
LaBelle: A Deep Learning APP that Helps You Learn Ballet

Human Pose Estimation has proven versatility in improving real-world applications in healthcare, sports, etc. [1]. Proper stance, form and movement is instrumental to succeeding in these activities. This paper will explain the research process behind the deep learning mobile ballet app, LaBelle [2]. LaBelle takes in two short videos: one of a teacher, and one of a student. Utilizing MediaPipe Pose to identify, analyze, and store data about the poses and movements of both dancers, the app calculates the angles created between different joints and major body parts. The app’s AI Model uses a K-means clustering algorithm to create a group of clusters for both the student dataset and the teacher dataset [3]. Using the two sets of clusters, LaBelle identifies the key frames in the student-video and searches the teacher cluster set for a matching set of properties and frames. It evaluates the differences between the paired frames and produces a final score as well as feedback on the poses that need improving. We propose an unsupervised guided-learning approach with improved efficiency in video comparison, which is usually both time and resource consuming. This efficient model can be used not just in dance, but athletics and medicine (physical therapy like activities) as well, where stance, form, and movements are often hard to track with the naked eye.
Estimation of States Under Colored Measurement Noise (CMN) Using UFIR and Kalman Filters Modified

The estimate process of a moving target trajectory is a well-known problem, where the main objective is to improve the estimation of object position. During the tracking are presented errors or variations between the true position and the estimated. In this paper, we treat such variations as a Gauss-Markov colored measurement noise (CMN). The estimation process is performed in predict and update, where the prediction indicates the next position of the bounding box, and the update is a correction step, which includes the new measurement of the tracking model and helps to improve the estimation. Looking for this improvement we use Kalman and Unbiased Finite Impulse Response filters in the standard version and modified for CMN to demonstrate the filter with the best performance. To test the most robust filter we use a high coloredness factor. The tests were carried out with simulated data (ideal and no ideal conditions) and with benchmark data (no ideal conditions). The UFIR modified for the CMN algorithm showed favorable results with high precision and low RMSE in the object tracking process with benchmark data and under no ideal conditions. While KF CMN showed better results under ideal conditions.
21 Interjections and other emotional communicative acts

The chapter defines the notion of interjection: a holophrastic vocal signal, that is, a single unitary sound sequence that conveys a whole speech act, comprised of performative and propositional content, in a deictic way, i.e., by leaving a part of the content to be retrieved from contextual information. “Wow!” means “I inform you that I am pleasantly surprised by this event”, where what is the surprising event is to be understood from context. A taxonomy of Italian interjections is presented, distinguishing them according to their performatives: informative, interrogative, requestive, and optative. Among the informative ones, many interjections convey the Speaker’s positive and negative emotions, but others convey other kinds of mental states, like the acquisition of new, expected or unexpected beliefs. Finally, the chapter moves to other ways to convey negative emotions, different from interjections: bad words, curses, imprecations, and insults. They are defined and described in terms of their syntactic, semantic and pragmatic features, and in terms of the emotions that trigger them and those they express or communicate.
Explorative study on the Menzerath- Altmann law regarding style, text length, and distributions of data points

Explorative study on the Menzerath- Altmann law regarding style, text length, and distributions of data points was published in Quantitative Approaches to Universality and Individuality in Language on page 161.
Exploring complex lexemes cross-linguistically

Exploring complex lexemes cross-linguisticallyEditors' introduction 1 Towards a typology of complex lexemes Concept-naming is one of the most fundamental activities performed by speakers, who need either ready-made labels to talk about entities or devices to build new labels (be they rules or processes, schemas or analogical mechanisms).Knowing how languages perform the basic function of creating labels to name concepts, especially complex concepts, is crucial to understanding their creative potential in building new (potentially stable) categories and, more generally, to understanding how they (may) categorize reality, and refer to it.What are the strategies employed by languages for naming complex concepts?How do they differ cross-linguistically, and what are the limits of their variation?Are there strategies that are more widespread than others, or even universal? 1 These are questions for lexical typology and/or word-formation typology, but what we know about the typology of complex concept naming is very limited compared to what we know about domains like word order or inflectional morphology.There may be different reasons behind this state-of-affairs.Analysing all of them falls outside the scope of the present introduction: we will just discuss some factors that we deem relevant for our current purposes.Complex concept naming is definitely related to word-formation.The domain of word-formation can count on an extremely rich and ever-growing body of literature, which would be impossible to credit here (suffice it to mention the collections edited by Booij, Lehmann & Mugdan 2000;2004; Lieber & Štekauer 2009a;2014; Müller et al. 2015; Lieber et al. 2021).However, quite surprisingly, word-formation has rarely been the subject of large-scale, thorough typological 1 We wish to thank the many anonymous referees who generously agreed to review the chapters included in this volume, including this introduction: their insightful comments significantly improved the quality of the volume.Heartfelt thanks are also due to Jean-Christophe Verstraete for his guidance and constant support, which were essential to bring this project to conclusion.We are also grateful to the audience of the When "noun" meets "noun" workshop at the
Uncertain imputation for time-series forecasting: Application to COVID-19 daily mortality prediction

The object of this study is to put forward uncertainty modeling associated with missing time series data imputation in a predictive context. We propose three imputation methods associated with uncertainty modeling. These methods are evaluated on a COVID-19 dataset out of which some values have been randomly removed. The dataset contains the numbers of daily COVID-19 confirmed diagnoses (“new cases”) and daily deaths (“new deaths”) recorded since the start of the pandemic up to July 2021. The considered task is to predict the number of new deaths 7 days in advance. The more values are missing, the higher the imputation impact is on the predictive performances. The Evidential K -Nearest Neighbors (E K NN) algorithm is used for its ability to take into account labels uncertainty. Experiments are provided to measure the benefits of the label uncertainty models. Results show the positive impact of uncertainty models on imputation performances, especially in a noisy context where the number of missing values is high.
Analysis of the Influence of Modeling, Data Format and Processing Tool on the Performance of Hadoop-Hive Based Data Warehouse

With the emergence of Big Data and the continuous growth of massive data produced by web applications, smartphones, social networks, and others, organizations began to invest in alternative solutions that would derive value from this amount of data. In this context, this article evaluates three factors that can significantly influence the performance of Big Data Hive queries: data modeling, data format and processing tool. The objective is to present a comparative analysis of the Hive platform performance with the snowflake model and the fully denormalized one. Moreover, the influence of two types of table storage file types (CSV and Parquet) and two types of data processing tools, Hadoop and Spark, were also comparatively analyzed. The data used for analysis is the open data of the Brazilian Army in the Google Cloud environment. Analysis was performed for different data volumes in Hive and cluster configuration scenarios. The results yielded that the Parquet storage format always performed better than when CSV storage formats were used, regardless of the model and processing tool selected for the test scenario.
Beyond Click-and-View: a Comparative Study of Data Management Approaches for Interactive Visualization

Visual analytics solutions have been growing in popularity in recent years, not only for showing final results but also for assisting in interactive analysis and decision-making. Analysis of a large amount of data requires flexible exploration and visualizations. However, queries that span geographical regions over time slices are expensive to compute, which turns it challenging to accomplish interactive speeds for huge data sets. Such systems require efficient data availability, so that response time does not interfere with the user’s ability to observe and analyze. Simultaneously, researches in the database domain have proposed solutions that can be used to support visualization systems. This article presents a comparative study of data management approaches to support interactive visualizations. The chosen data management solutions are (i) Apache Drill (a Polystore system), (ii) Apache Spark (a big data framework), (iii) Elasticsearch (a search engine), (iv) MonetDB (a column-oriented DBMS), and (v) PostgreSQL (a relational DBMS). To evaluate the performance of each solution, we selected a list of spatiotemporal queries among multiple queries submitted by users in a visual analytics system for rainfall data analysis named TEMPO. The results of this study show that Apache Spark and MonetDB present the best performance for the selected queries.
Sub-shot-noise-limited phase estimation via single-mode inputs

In 1981, Caves pointed out that the phase sensitivity of a Mach-Zehnder interferometer with single-mode inputs is bounded by the shot-noise limit. The quantum Fisher information analysis shows that this statement holds true for the scenario where two antisymmetric phase shifts occur in two arms, but it is invalid for the scenario where an unknown phase is embedded in one of two arms. In this paper, we focus on the phase sensitivity directed against the latter scenario. The optimal single-mode input is discussed by analyzing common states, including displaced squeezed states, displaced number states, squeezed number states, Schrödinger cat states and completely mixed states. We find that the best choice is a squeezed vacuum state and show the specific measurement scheme which is capable of saturating the corresponding phase sensitivity limit. In addition, we study the effects of several realistic factors-anti-squeezing noise, photon loss and dark counts-on the phase sensitivity. Our results suggest that sub-shot-noise-limited phase sensitivity is attainable with low noise or loss, which paves the way for practical metrology.