Reviewerly/exHarmony huggingface dataset:
Primary mineralogy of Dalichai carbonate formation and separation of upper boundary with Lar formation based on geochemical studies in northeast of Semnan

The Dalichai Formation is the first rock unit of the Middle Jurassic in the Alborz sedimentary basin that has occurred in a marine environment. The boundary of the Dalichai formation with Shemshak Formation is erosional unconformable while it is concordant with Lar formation. Due to lithological similarity, the boundary of the Dalichai formation with the Lar formation is unclear. It is also very difficult to identify the primary carbonates mineralogy, due to the effects of diagenetic processes solely on the basis of petrographic studies. We studied a section of Sharaf mountain with a thickness of 1420m and 149 samples was studied. The study of the major and minor elements, the Oxygen and Carbon isotopes, and plotting of these values ​​against each other represents that the Dalichai formation have had original aragonite mineralogy, and the nonmarine diagenesis have had the highest impact on them. Plotting the Sr/Ca ratio versus Mn value, shows that these carbonates are affected in a semi-closed to open diagenetic system. Using Sr, Sr/Na, Sr/Ca, Oxygen and Carbon isotopes, the boundary of this formation with the Lar Formation is recognized in thickness of 1200m. The diagenetic environment temperature for this section limestones is 32.9oC.
Differentially Private Quantiles

Quantiles are often used for summarizing and understanding data. If that data is sensitive, it may be necessary to compute quantiles in a way that is differentially private, providing theoretical guarantees that the result does not reveal private information. However, in the common case where multiple quantiles are needed, existing differentially private algorithms scale poorly: they compute each quantile individually, splitting their privacy budget and thus decreasing accuracy. In this work we propose an instance of the exponential mechanism that simultaneously estimates $m$ quantiles from $n$ data points while guaranteeing differential privacy. The utility function is carefully structured to allow for an efficient implementation that avoids exponential dependence on $m$ and returns estimates of all $m$ quantiles in time $O(mn^2 + m^2n)$. Experiments show that our method significantly outperforms the current state of the art on both real and synthetic data while remaining efficient enough to be practical.
Extended JSSL for Multi-Feature Face Recognition via Intra-Class Variant Dictionary

This paper focuses on how to represent the testing face images for multi-feature face recognition. The choice of feature is critical for face recognition. The different features of the sample contribute differently to face recognition. The joint similar and specific learning (JSSL) has been effectively applied in multi-feature face recognition. In the JSSL, although the representation coefficient is divided into the similar coefficient and the specific coefficient, there is the disadvantage that the training images cannot represent the testing images well, because there are probable expressions, illuminations and disguises in the testing images. We think that the intra-class variations of one person can be linearly represented by those of other people. In order to solve well the disadvantage of JSSL, in the paper, we extend JSSL and propose the extended joint similar and specific learning (EJSSL) for multi-feature face recognition. EJSSL constructs the intra-class variant dictionary to represent the probable variation between the training images and the testing images. EJSSL uses the training images and the intra-class variant dictionary to effectively represent the testing images. The proposed EJSSL method is perfectly experimented on some available face databases, and its performance is superior to many current face recognition methods.
Partially Observed Exchangeable Modeling

Modeling dependencies among features is fundamental for many machine learning tasks. Although there are often multiple related instances that may be leveraged to inform conditional dependencies, typical approaches only model conditional dependencies over individual instances. In this work, we propose a novel framework, partially observed exchangeable modeling (POEx) that takes in a set of related partially observed instances and infers the conditional distribution for the unobserved dimensions over multiple elements. Our approach jointly models the intra-instance (among features in a point) and inter-instance (among multiple points in a set) dependencies in data. POEx is a general framework that encompasses many existing tasks such as point cloud expansion and few-shot generation, as well as new tasks like few-shot imputation. Despite its generality, extensive empirical evaluations show that our model achieves state-of-the-art performance across a range of applications.
Spatial variability in Karoo dolerites

&amp;lt;p&amp;gt;AD Kotze and RJ Roberts&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Department of Geology, University of Pretoria, Hatfield, Pretoria, South Africa; u04541686@tuks.co.za&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The Karoo Large Igneous Province (KLIP) in South Africa consists of both a spatially limited extrusive basalt suite (Drakensberg Group) and a spatially extensive dolerite suite, both generally considered to be remarkable homogenous and of a &amp;amp;#8220;low-Ti&amp;amp;#8221; character (Luttinen, 2018). The homogeneity of the rocks requires that statistical analysis is necessary to look for spatial and geochemical trends in the data, which may yield clues to the mantle processes producing the 60 000 km&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt; expanse of basaltic magma. In this project, data derived from several locations are used as proxies to check for lateral variability in the Karoo dolerites. A principal component analysis (PCA) on trace element data using a covariance matrix was performed, and comparisons based on variables that are 1) common to the Karoo dolerites and Lesotho basalts and, 2) responsible for the most amount of variation to the data set are made. Trace element modelling is then used to test different mantle melting scenarios possibly responsible for the variation seen in the dolerites.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Principal component analyses revealed several trace elements are responsible for most of the variability in the dolerites. Cr and Ni has the strongest positive loading on Component 1 whereas Cr and Ba has the strongest positive loading on Component 2. Ba has a strong negative loading on Component 1. Cu, Sr, V and Zr do impart an appreciable amount of variation to the data, but all four variables have weak negative loadings on both components. Interestingly, the activity of Cu and V seems to be the inverse of that of Cr and Ni.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Due to the nature of a PCA, this work is afforded an opportunity to place the geochemistry of the Karoo dolerites within a larger geodynamic context without bias. From the observed variation, the activity of Ba and Cr is interpreted as an assimilation-oxidation process, whereas the Ni signature reflects the mantle origin of the magmas. Further modelling of these processes will allow the testing of suggested mechanisms for the formation of the KLIP, especially whether the magmatism is plume-related or related to the foundering of crustal blocks.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Luttinen, A., 2018. Bilateral geochemical asymmetry in the Karoo large igneous province. Scientific Reports, 8(5223).&amp;lt;/p&amp;gt;
Classification of Pop and RnB (Rhythm and Blues) Songs with MFCC Feature Extraction and K-NN Classifier

Classification is a technique for designing functions based on observations of attributes in a data so that data can be mapped that do not have a class which in this study can be called genres, into data that has been classified according to the given rules. In this research, music classification is conducted to determine whether the class or genre of music is pop or RnB (Rhythm and Blues) by using MFCC as the feature extraction method and K-NN as the classification method. The test results in this study obtained an accuracy of 77.5% with an optimal value of k = 31 as a parameter in K-NN.
Galaxy Image Simulation Using Progressive GANs

In this work, we provide an efficient and realistic data-driven approach to simulate astronomical images using deep generative models from machine learning. Our solution is based on a variant of the generative adversarial network (GAN) with progressive training methodology and Wasserstein cost function. The proposed solution generates naturalistic images of galaxies that show complex structures and high diversity, which suggests that data-driven simulations using machine learning can replace many of the expensive model-driven methods used in astronomical data processing.
Craig, beatty and baird model of performance appraisal-A comprehensive overview

Performance appraisal is a tool used to assess employee performance in a scientific manner. It is an operational function carried out by human resource department to enhance the productivity of employees and thereby achieve organisational effectiveness. There are several steps involved in the process of performance appraisal. Most of the literate suggested that Standard fixation, were the major steps consisting in performance appraisal. But at same time there is only a single standard model is available from different literature for the better understanding of performance appraisal process, i.e., Craig, Beatty and Baird (1986) model of Performance Appraisal. This article tries to give a more comprehensive overview on Craig, Beatty and Baird (1986) model of Performance Appraisal.
Central Louisiana Technical Community College Library: Industrial Manufacturing Technology: Library Resources: Electronic Books (eBooks)

This LibGuide will provide you with basic research information for the Industrial Manufacturing Technology program.Use the tabs to the left to get information on finding books, finding articles from magazines and journals, and locating recommended web
LibGuides: PBSC Integrated Library System (ILS) Implementation: Alma and Primo VE: Home

This guide is to assist library staff and others with the transition to the new systems coming on July 13, 2021.
한국어교육에서의 회통합프로그램(KIIP) 관련 연구 동향 분석

The Korean Immigration & Integration Program (KIIP) was introduced by the Ministry of Justice in 2009. The purpose of this paper was to present a desirable direction for research so that the results of research and major issues in Korean language education can be reflected in the improvement and development of the KIIP in the future.As a result, compared to the early days of the operation of the KIIP the past, although there have been many quantitative achievements recently, there have been many areas where sufficient research has not yet to be done in order for social integration programs to achieve their operational purposes in a multifaceted manner.Teaching and content studies related to the KIIP, in particular, were found to be significantly lacking. In the future, the overall direction needs to be continuously checked to ensure that the KIIP meets the purpose of its operation and increases its effectiveness, and various studies on participants’ needs, achievements and satisfaction should be conducted together.
A combination classification method based on Ripper and Adaboost

With the growing demand for data analysis, machine learning technology has been widely used in many applications, such as mass data summarising rules, predicting behaviours and dividing characteristics.The Ripper algorithm presents better pruning and stopping criteria than the traditional decision tree algorithm (C4.5), while its error rate less than or equal to C4.5 by O(nlog2n) time complexity.As a result of that, Ripper can maintain high efficiency even on the massive dataset which contains lots of noise.Adaboost is one of iterative algorithms, which combines a group of weak classifiers together to set up a strong classifier.In order to improve the accuracy of Ripper classification algorithm and reduce the computational complexity, this paper proposes a Ripper-Adaboost combined classification method (Ripper-ADB).The experiment result shows Ripper-ADB could improve the classifier and get higher classification accuracy than decision tree and SVM.
FEATURES OF THE DEVELOPMENT OF PROFESSIONAL AND PERSONAL QUALITIES OF UNIVERSITY TEACHERS

A modern university teacher is not only a teacher, educator, organizer of students ' activities, researcher of the pedagogical process, consultant, educator, who constantly improves the level of his professionalism and pedagogical skills, leads a creative search for new things. A comprehensive indicator that characterizes the quality of the performance of its functions is professionalism. The professionalism of the higher school teacher consists in the effective implementation of the system of professional knowledge and skills. The article discusses the features of the development of professional and personal qualities of University teachers.
A Novel Facial Image Deviation Estimation and Image Selection Algorithm (Fide-Isa) for Training Data Reduction in Facial Recognition System

Deep learning models have been successful and shown to perform better in terms of accuracy and efficiency for facial recognition applications. However, they require a huge amount of data samples that were well annotated to be successful. Their data requirements have led to some complications which include increased processing demands of the systems where such systems were to be deployed. Furthermore, the unavailability of well-annotated data has also affected the success of deep learning facial recognition models. Reducing the training sample sizes of deep learning models is still an open problem. This paper proses in reducing the number of samples required by the convolutional neural network used in training a facial recognition system. A Facial Image Deviation Estimation and Image Selection Algorithm (FIDE-ISA) was developed to select appropriate training samples incrementally based on their facial deviation. The model was tested using an online dataset called Face94 facial image dataset. The results indicated that training a CNN model with at least 3 images per individual produces a 100% accuracy of recognition. This is an indication of the effectiveness of the developed algorithm.
СТУДЕНТ- ПСИХОЛОГТАРДЫҢ ТҰЛҒАЛЫҚ ӨЗІНДІК ТИІМДІЛІГІНІҢ ДИНАМИКАЛЫҚ ЕРЕКШЕЛІКТЕРІ

The article deals with the problem of the dynamics of personal of psychology students during their studies at the University. Theoretical approaches to understanding the term self-efficacy and its relation to related concepts related to psychological confidence and personal potential are analyzed. The actual problem of personal of psychology students is discussed. The results of an empirical study of the dynamic characteristics of of a sample of 80 students are presented, and statistical indicators of subject and development of in communication, their relationship with the motivation for choosing a profession and the level of self-attitude are determined. Dynamism, integrity and multicomponent nature of self-efficacy; non-linear nature of its development at different stages of training; the relationship between the level of development and the experience of independent work.
PENERAPAN MODEL PEMBELAJARAN SCRAMBLE UNTUK MENINGKATKAN AKTIVITAS BELAJAR BIOLOGI

This study aims to see student learning activities and student learning outcomes during learning in Biology subjects with the Scramble learning model in class XII IPA MA Muhammadiyah Kabila. Student activity data according to observers' observations in Cycle I, including writing / reading (37%), working (20%), asking friends (15%), asking questions to the teacher (14%), and those that are not relevant to teaching and learning activities (15 %). Student activity data according to observations in Cycle II include writing / reading (29%), working (42%), asking fellow friends (14%), asking teachers (12%), and those that are not relevant to teaching and learning activities (15%). There is an increase in students' biology learning activities when applying the Scramble learning model.
NorDial: A Preliminary Corpus of Written Norwegian Dialect Use.

Norway has a large amount of dialectal variation, as well as a general tolerance to its use in the public sphere. There are, however, few available resources to study this variation and its change over time and in more informal areas, \eg on social media. In this paper, we propose a first step to creating a corpus of dialectal variation of written Norwegian. We collect a small corpus of tweets and manually annotate them as Bokmal, Nynorsk, any dialect, or a mix. We further perform preliminary experiments with state-of-the-art models, as well as an analysis of the data to expand this corpus in the future. Finally, we make the annotations and models available for future work.
Automatic Facial Expression Recognition using CNN and RNN Algorithm’s

In current days for identifying a person emotion takes a lot of time because of varying expressions and features present in human faces. There was a lot of study going on to detect the several types of expressions from human’s in different environments such as work,play,relax,break and so on. Home room correspondence includes instructor's conduct and understudy's reactions. There was a lot of research work done on the investigation of below average students and average students by the instructor to identify those students and take some special care.In the primitive day’s almost human emotions are identified manually based on the current expression and feelings of the human’s in day to day environment. But there was no accurate method designed in primitive days for identifying the emotions or expressions automatically. This emotion recognition is becoming an interesting aspect which is used mainly for diagnosis of human brain and psychological disorders. In a recent survey conducted by a team of experts, we came to know that deep learning has gained a lot of user’s attention in the field of image classification. These emotions is used for not only diagnosis of human brain but also used as a recommended systems to assist users in finding items that match their needs and preferences. Hence this motivated me to develop a system which can effectively and efficiently recognizes emotions from the facial expressions of the user. In this proposed article we try to develop an application which can be used for prediction of expressions of both still images. Here we try to develop a system by using two well-known models such as CNN and RNN and then check which model suits best for facial expression recognition. The highest probability value to the corresponding expression will be the predicted expression for the image. We have conducted experiments on FER_2013 Data set which we collected from KAGGLE website and try to train the system to detect the emotions accurately. Experimentation results show critical execution acquire on boundaries like exactness, F1-score, and review.
Trajetória pessoal e criação de acervos: a atuação da professora Magda Suely Pereira da Costa na institucionalização do Centro de Documentação e Memória de Arraias e Região (Cdmar)

This article presents the trajectory of professor Magda Suely Pereira da Costa in the creation process of the Centro de Documentacao e Memoria de Arraias e Regiao (Cdmar). We describe how this personage played an important role in records preservation in the state of Tocantins. For that, we report her trajectory, how Cdmar is thought and the actions developed in it.Keywords: documentation center; regional memory; preservation; professional performance.
Formal Model for Explicit Aware Knowledge

ThisExplicit Aware Knowledge (EAK) chapter presents a formal model that constitutes the logical correlation to the conceptual schemaConceptual schema for Explicit Aware KnowledgeExplicit Aware Knowledge (EAK) (EAK-Schema)EAK-Schema. Using neighbourhood semanticsNeighbourhood semantics this model depicts the informational attitudesInformational attitude of a non-omniscientAgent (epistemic) epistemic agentEpistemic agent. It represents the awareness ofAwareness of and awareness thatAwareness that of the agent and the different combinations of them that result in Explicit Aware KnowledgeExplicit Aware Knowledge (EAK), implicit knowledgeImplicit knowledge and other forms of knowledgeKnowledge. The model also includes the formalisation of epistemic actionsEpistemic action and discusses the main properties of the informational attitudesInformational attitude and the corresponding epistemic actionsEpistemic action that change these attitudes.
Highly secure Virtual Identity Approach in Cloud Computing Environment

In this technological era, cloud computing has become a buzz word for all the service sectors, be it banking, health or any other business sector. Primarily, it provides significant benefits in terms of storage and maintenance of data and services. However, data security has always remained one of the biggest challenge in cloud. Therefore it is very important to focus on the security measures and techniques on the cloud environment so that cloud usage can be increased. This paper proposes a novel approach for enhancing security in cloud environment using virtual identity by hiding actual user identity. For achieving high security, Diffie-Hellman algorithm with station to station protocol has been used by the system. Paper also analyses the performance of system by taking various scenarios and discussion of previous research works.
NAUČNO MIŠLjENjE I UMETNIKOVA IDEJA

Through the analysis of parts – from font to audio recordings, the paper tries to point out those important elements from which Dušan Matić, a poet, constructed his works, but which were sufficiently stimulating for authors who dealt with certain possibilities of a multimedia digital book.
LibGuides: Research Starter - COMM 353: What Makes a Good Resource?

This page will help explain the difference between Primary and Secondary sources. You will also learn how to determine the reliability of a resource and/or website
LibGuides: HISP 430 - Advanced Composition: Citing Sources & Getting Help

This guide is for Paige Andersson's Fall 2021 section of Hispanic Studies Advanced Composition course.
Designing a Public Experimental Terminal for Citizen Engagement

Experimental research involving human participants has received an increasing interest in the Information Systems (IS) discipline. Much of today's experimental research is done with studentparticipants, which comes with the advantage of easy availability and homogeneity of the sample. However, non-representative student samples are also often criticized due to limited generalizability. Thus, an important challenge is to engage citizens in experimental research following an open science paradigm. In this ongoing design science research project, we propose design requirements and design principles for public experimental terminals to increase engagement of citizens in experimental research. On this basis, we create an instantiation of a n operational experimental terminal that can be deployed in the public space. With our research project, we present a novel approach to raise awareness and actively engage citizens in experimental research.