Reviewerly/exHarmony huggingface dataset:
Accounting for hydrological controls when clustering groundwater quality data

&amp;lt;p&amp;gt;Groundwater salinization occurs when high concentrations of water-soluble salts are present in groundwater systems and is regarded as one of the most worldwide, severe, and complex phenomena affecting coastal aquifers. Salinization might occur, for example, from: (i) from marine sources via seawater intrusion, seawater ingression, (ii) underground or terrestrial sources (e.g., natural soils and rocks through the dissolution of soluble minerals), and (iii) salt and saline fluids from anthropogenic activities.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The analysis of salinization-related data by multivariate statistical methods is often undertaken in the context of efficient groundwater management. For example, clustering algorithms are used to delineate hydrogeohemically distinct water classes, whereas dimensionality-reduction algorithms are being used to decipher underlying natural and anthropogenic influences responsible for these distinct water classes. However, most of these algorithms do not explicitly account for spatial information and/or constraints, which can often have a significant impact on the classification of groundwater quality samples.&amp;amp;#160; In the context of the MEDSAL Project (www.medsal.net), such spatial effects are incorporated in the clustering procedure via the inclusion of pertinent hydrological attributes, namely, hydraulic head and conductivity data, along with pair-wise distances between sample locations.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The application of the proposed spatially explicit clustering approach is illustrated using groundwater quality samples collected from the Rhodope coastal aquifer, located at north-eastern Greece. Sampling locations were grouped into four hydrogeochemically distinct water classes using k-means clustering with and without accounting explicitly for spatial information. Principal component analysis (PCA) was used to decipher underlying natural and anthropogenic influences responsible for these distinct water classes. The first four principal components (PCs) explained more than 83% of the total variance in water quality variables, from which the major component was found to be associated with salinization processes.&amp;lt;/p&amp;gt;
Texture Analysis Technique for Document Age Identification

In this paper, age identification of printed documents is proposed based on texture analysis methods. Age identification is one of the significant factors to authenticate the originality of the document. The experimental study carried out on life magazine cover pages published from 1930, 1940, 1950, 1960 to 1970. Total 200 life magazines of five decades are considered and computed their texture properties using HOG (Histogram of Oriented Gradients) and LPQ (Local Phase Quantization) methods. SVM (Support Vector Machine) is used for determining the age of the printed document images and achieved an accuracy of 86.2%.
A major update to the Exotic Seismic Events Catalog: A compilation of seismogenic mass movements

&amp;lt;p&amp;gt;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160; We present an update to the collection of seismogenic mass movements that forms the basis of the Exotic Seismic Events Catalog (ESEC), doubling the number of events in the catalog from 121 to 242 while broadening the geographic distribution and range of event types. The ESEC is available online through the Incorporated Research Institutions for Seismology (IRIS) Searchable Product Depository (http://ds.iris.edu/spud/esec) or as a downloadable SQLite database from USGS ScienceBase. This update adds more instances of seismogenic landslides, debris flows, snow avalanches, outburst floods, and lahars as well as some new event types: two mine collapses, a submarine landslide, and a volcanic flank collapse. We also now incorporate infrasound detection. Whereas the first version of the catalog focused on mass movements located primarily in the Western United States and Canada, this update includes events from Europe and Pacific Islands. We only include events for which seismic data are openly available.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160; We provide both basic seismic information (e.g., station detections on different frequency bands, seismic data location, etc.) and ancillary data such as geometric measurements, references, photographs, and satellite imagery. When available we use published values such as source location, drop height, runout distance, and volume, and when not documented, we estimate values from satellite imagery or photographs. Events are categorized in terms of the quality of the ancillary data, and we provide estimates of uncertainty on parameters such as location and volume. This update increases the availability of seismogenic mass movement data to the community to promote research that betters our understanding of event dynamics and improves methods for exotic event detection, classification, and characterization. Future updates could allow for the incorporation of other exotic event types like blasts and glacial events. The ESEC has a mechanism for community members to contribute events to the collection, so we encourage other researchers to join in the effort.&amp;lt;/p&amp;gt;
Learning Graphical Models

We will present how graphical models can be learned from given data. Until now, we were given the graphical structure, now we will introduce heuristics that allow us to induce it. We assume that we are given a database of samples over an attribute set and some prior information, which can be used as a bias towards certain graphical structures.
INVESTIGATING HOW TO IMPROVE TEACHER FEEDBACK: A QUALITATIVE STUDY

Classroom interaction has been a hot topic in recent decades. Most of the previous research, however, analyzed teacher feedback from a broad classification standard including positive or negative ones, direct or indirect ones, etc., and just offered suggestions to enhance one aspect of students’ second language proficiency such as writing. (Nakazawa, 2006; Purnawarman, 2011; Silver & Lee, 2007) This research aims to investigate how to improve teacher feedback based on a more detailed division and give more targeted strategies according to classroom records. This study began by selecting six classroom records totaling about four minutes in different China university English classrooms. Then the records were transcribed and annotated with pause time, tone changes, gestures to research teacher feedback. The researcher proposed an annotation scheme for teacher feedback classification combining mood and form such as directive pursuing feedback. Moods include assertive, directive, commissive, expressive, and declarative which are proposed by Searle. Forms of feedback include evaluative feedback giving an opinion on or explain students’ answers, pursuing feedback making a detailed inquiry about students’ answers and corrective feedback correcting students’ errors in their answers. After that, the results were qualitatively analyzed and the discussion for analysis followed. This research found that: 1) L1 (First Language) appropriate usage is good for students' second language acquisition; 2) Teachers with high Classroom Interactional Competence is easier to build harmonious interaction with students; 3) Peer feedback is very effective in assisting teacher feedback with students’ error correction; 4) Grice’s Cooperative Principles can instruct teachers to give better feedback; 5) Specific-general-specific feedback sandwich model is innovatively proposed in this research, which might save teachers’ time and satisfy students; 6) Teachers' questioning that lacks in skills will make students’ interaction difficult; 7) Recast can help students learn more second-language knowledge by adding new information. The key contribution in this research is to provide some specific strategies for the improvement in teacher feedback to promote teacher-student interaction and reduce power imbalance between them. This not only has guiding significance for university second language classes, but also enlightens traditional teacher-centered teaching.
3D Human Body Action Identification of Sports Based on Artificial Intelligence

With the continuous emplacement of modern life, people will be busy working and ignore physical exercise, which leads to a decline in the corners of physical health. The best way to solve this problem is to develop an easy-to-install, low-cost physical education system, which is very important for people to achieve better exercises in the project. The quality of this system is very important. A complete set of large-scale teaching systems include human motion identification, human movement 3D reconstruction and human activity analysis. Through a large number of literature reading and experimental investigations, the status quo of human body action identification research is summarized, introducing the connotation of artificial intelligence and human motion recognition; in order to in depth, the human body recognition of three-dimensional body action based on artificial intelligence Action recognition, this paper uses the artificial intelligent adaptation algorithm related data, and then the flowchart of the human body action identification algorithm identified by artificial intelligence sports teaching, and the flow of different modules in this process is accurately tested. The experimental results show that the accuracy of all module data operation in the process of the human body action identification algorithm for sports teaching is high, and it is suitable for the three-dimensional body action identification of sports teaching.
High-level multi-threaded programming with the Qt library

The Qt library is a cross-platform library that offer among other things high-level support for multi-threaded programming. Qt allows programmers to maximize productivity and performance without delving into the intricate details of launching or managing threads. Qt's support comes in the form of data filtering, mapping, and reduction capabilities, as well as their combinations, such as map-reduce or filter-reduce. In this chapter we explore how different computational problems can be formulated as instances of these operations. A number of case studies and performace tests show the potential of the Qt platform.
Syntactic and Semantic Features between ‘N-hada’ in Korean and ‘Be Adjective(N-hada)’ in English

This paper attempts to examine syntactic and semantic properties of ‘N-hada’ in Korean and apply them to ‘Be Adjectives(N-hada)’ in English similar to their Korean meaning, based on their semantic features. Some Korean researchers analyzed that ‘hada’ in ‘N-hada’ takes a grammatical role of Verb or Adjective depending on its preceding noun. However, I tried to explain this based on the agreement of feature between preceding nouns and ‘-ha/da’ unlike their explanations. In addition, there are syntactic and semantic feature differences between the same ‘Be Adjectives(N-hada)’ structures in meaning as English. As we can explain syntactic properties between the same ‘N-hada’ structures in Korean, I tried to show that we can explain syntactic and semantic differences between the same ‘N-hada’in the semantic structures as English on the basis of the agreement of features between ‘Be Adjectives.’
Principles of Low-Dimensional Models

Connecting theory with practice, this systematic and rigorous introduction covers the fundamental principles, algorithms and applications of key mathematical models for high-dimensional data analysis. Comprehensive in its approach, it provides unified coverage of many different low-dimensional models and analytical techniques, including sparse and low-rank models, and both convex and non-convex formulations. Readers will learn how to develop efficient and scalable algorithms for solving real-world problems, supported by numerous examples and exercises throughout, and how to use the computational tools learnt in several application contexts. Applications presented include scientific imaging, communication, face recognition, 3D vision, and deep networks for classification. With code available online, this is an ideal textbook for senior and graduate students in computer science, data science, and electrical engineering, as well as for those taking courses on sparsity, low-dimensional structures, and high-dimensional data. Foreword by Emmanuel Candès.
Human Motion Recognition Based on Kinect Sensor and Leap Motion Controller

Augmented reality (AR) is an interactive experience of a real-world environment where objects reside in the real world are enhanced by computer-generated perceptual information. In essence, emulating and altering reality that include, in relatively real-time and precision, position and motion tracking (sensors like Kinect and Leap Motion Controller), match moving (techniques allowing insertion of computer graphics into live-action footage with correct position, scale, orientation and motion), and finally motion capturing (process of recording movements of objects or people). This review is dedicated to the question of what object recognition (motion tracking, match moving and motion capture) is and how this technique can be identified, thus synthesizing knowledge in the field. The former is further clarified, elaborated, and compared using input sensors like Kinect and Leap Motion Controller.
UAV Data Analysis in the Cloud - A Case Study

&amp;lt;p&amp;gt;The mapping of fracture networks from aerial photographs, tracing of fault scarps in digital elevation models, and digitisation of boundaries from potential field data is fundamental to many geological applications (e.g. resource management, natural hazard assessment, geotechnical stability etc.). However, conventional approaches to digitising geological features are labour intensive and do not scale.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We describe how we designed an automated fracture detection workflow and implemented it in a cloud environment, using free and open-source software, as part of The Australian Scalable Drone Cloud (ASDC, https://asdc.io) national initiative. The ASDC aims to standardise and scale drone data, then analyse and translate it for users in academia, government, and industry.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this use case, we applied automatic ridge/edge detection techniques to generate trace maps of discontinuities (e.g. fractures or lineaments). The approach allows for internal classification based on statistical description and/or geometry and enhances the understanding of the internal structure of such networks. Further, photogrammetry and image analysis at scale can be limited by the available computing resources, but this issue was overcome through implementation in the cloud. The simple methods l serve as a basis for emerging techniques that utilise machine learning to fully automate the discontinuity identification and represents an important step in the cultural adoption of such tools in the Earth Science community.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We deployed Open Drone Map (ODM) onto a cloud infrastructure to produce orthophoto mosaics from aerial images taken by UAV to implement this case study. We ported a fracture detection and mapping algorithm from Matlab to Python for the image analysis. The image analysis workflow is orchestrated through a Jupyter Notebook on a Jupyter Hub. The resulting prototype workflow will be used to better scope the services needed to manage the ASDC platform, like user management and data logistics.&amp;lt;/p&amp;gt;
12 Commandments for a Successful SAP Project

We do not want to be presumptuous and claim that the complexity of a transformation project exceeds the complexity of divine instructions. Nevertheless, we did not quite manage with only ten commandments. In this chapter, you will find 12 commandments for a successful SAP project—admittedly an ambitious announcement. We would like to discuss the quintessence of our experience here—no more, but also no less.
General Neural Networks

In this chapter, we introduce a general model of (artificial) neural networks that capture (more or less) all special forms, which we consider in the following chapters. We start by defining the structure of an (artificial) neural network and then describe generally the operation and finally the training of an (artificial) neural network.
THE APPLICATION OF HIERARCHICAL CLUSTERING ON STUDENT ASSESSMENT DATA IN SECONDARY EDUCATION

The process of data mining to research student assessment data is very intriguing in the field of secondary education. In this paper, we have applied the data analytics methods of the Principal Component Analysis (PCA) and hierarchical and flat clustering algorithms to student assessment data to discover unknown patterns, trends, and characteristic associations for predicting and assessing success outcomes that would not otherwise be detected. Clustering of data allows student assessment data to be grouped and enables key policymakers, administrators, and school districts to make more informed, data-driven decisions within their schools. The student data clusters within this study is divided into 5 groups to gain the performance characteristics of students: advanced, proficient, passing, basic, and minimum. This study has utilized student data and four variables from State Assessment courses in English (English 2), History (U.S. History), Mathematics (Algebra 1), and Science (Biology 1) focusing on freshman, sophomore, and junior level high school students in a public school district. The analytics methodology implored here can be adopted by school systemsto assist in informing educators about the importance of using data in assessing student performances on the different metrics of measuring learning progressions and increasing graduation rates in secondary education. The importance of the analytics lies in the fact that they are amenable to Artificial Intelligence (AI) to automate and ease the process by analyzing large and higher dimensional sets of data, spotting of significant patterns and generation of single output that navigates educational policy makers towards a particular decision based on predicted assessment outcomes.
Bayesian Networks

We motivate the efficient representation of expert knowledge, the notion of inference or evidence propagation, and the necessity of learning said representations from data.
Deep learning to identify breast disease in an 87-patient clinical study of breast core biopsies to provide rapid biopsy evaluation

Optical coherence tomography (OCT) is being studied to provide rapid biopsy evaluation. Here we developed a deep learning algorithm to rapidly identify disease in OCT images in an 87-patient IRB-approved clinical study. Pathologists labelled each biopsy into two categories: non-interest (no disease) and interest (for further pathological analysis). Our dataset was split by patients into training (n = 70) and validation (n = 17). The Resnet18 architecture used the Adam optimizer, had a learning rate of 0.01, batch size of 8, and ran for 30 epochs. The network achieved 97% training accuracy and 70% validation accuracy.
DIGITAL LEARNING PLATFORMS: ANALYSIS AND TRENDS

P. Rito, N.S. Morais, T. Gouveia, S. FerreiraCentre for Studies in Education and Innovation, Higher School of Education, Polytechnic of Viseu (PORTUGAL)
Simple and Memory-Efficient Signature Generation of $$\mathrm {XMSS^{MT}}$$

Stateful hash-based signature schemes are one of the most promising post-quantum signature schemes. Among them, XMSS and $$\mathrm {XMSS^{MT}}$$ have already been specified in RFC 8391 and NIST SP 800-208. The signing time is exponential if the schemes are naively implemented. To reduce the signing time, Merkle tree traversal algorithms are used and the most time/memory efficient one is the BDS algorithm. We focus on $$\mathrm {XMSS^{MT}}$$ (layered XMSS) with the BDS algorithm. Since $$\mathrm {XMSS^{MT}}$$ is vulnerable to incorrect state management, the algorithm and state structure must be simple. Also, the state size for the BDS algorithm must be reduced in order to implement the scheme in resource-constrained devices. To achieve these objectives, we propose a simple and memory-efficient signature-generation algorithm for $$\mathrm {XMSS^{MT}}$$ .
Upcoming meetings

Artificial OrgansVolume 46, Issue 4 p. 721-722 UPCOMING MEETING Upcoming meetings First published: 26 March 2022 https://doi.org/10.1111/aor.14204Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume46, Issue4April 2022Pages 721-722 RelatedInformation
From Field Application to Publication: An end-to-end Solution for FAIR Geoscience Data

&amp;lt;p&amp;gt;In this abstract we introduce a suite of free applications to produce FAIR consistent, clean and easily available geoscience data for research and industry alike.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Creation of data starts with sample collection in the field and the assigning of an unique global IGSN sample identifier to samples, these samples are stored along with any subsequent&amp;amp;#160; analytical data in our fine-grained and detailed geochemical data models allowing visualising and publishing acquired datasets. This unique solution has been developed by Lithodat Pty Ltd in conjunction with the AuScope Geochemical Network (AGN), Australian geochemical laboratories and can be accessed by the public on the AusGeochem web platform.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Using our fully integrated field application users can enter and store all sample details on-the-fly during field collection, the data will be stored in the user's private data collection. Once the researchers return from the field they can log into their account on the browser-based AusGeochem platform and view or edit all collected samples. After running subsequent geochemical analyses on the sample those results, including all metadata, can be stored in the database and attached to the sample. Once uploaded, data can be visualised within AusGeochem, using simple data analytics via technique-specific dashboards and graphs. The data can be shared with collaborators, downloaded in multiple formats and made public enabling FAIR data for the research community.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Here we show a complete sample workflow example, from collection in the field to the final result as a thermochronology study. Sample analysis using fission track and (U-Th)/He and all associated data will be uploaded and stored in the AusGeochem platform. Once all analyses are complete, the data will be shared with collaborators and made available to the public. An important step during this process is by having an integrated IGSN minting option which will give the sample a unique global sample identifier, making the sample globally discoverable.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Having all data stored in a clean and curated relational database with very detailed and fine-grained data models gives researchers free access to large amounts of structured and normalised data, helping them develop new technologies using machine learning and automated data integration in numerical models. Having all data in one place including all metadata such as ORCIDs from involved researchers, funding sources, grant numbers and laboratories enables the quantification and quality assessment of research projects over time.&amp;lt;/p&amp;gt;