Reviewerly/exHarmony huggingface dataset:
Trace Elements and REY in the Muschelkalk Limestones of the Opole Silesia in Poland

Abstract The results of researches of the selected trace elements: Ti, Sr, Ba, Zn, and also Cr, Ni, Cu, Zr, Mo, Pb, Rb, Mn, Na, K, P, Y and REE (REY) content were presented in this article. The elements were measured in the carbonate minerals of Muschelkalk (Middle Triassic) limestones of the Opole Silesia in Poland, using two methods: ICP MS spectrometry and X-ray fluorescence. These methods are characterized by very high precision and sensitivity of measurements. The results of researches show that the content of analyzed trace elements varies in from value below 1 ppm up to some hundreds ppm. However, the highest contents were measured for strontium and barium, elements characteristic for aragonite carbonate phase which is transformed such as high-Mg calcite into low magnesium calcite during diagenesis. So as Sr and Ba indicate the presence of aragonite in the primary carbonate material. The other trace elements probably form substitutions in clay or carbonate minerals. Some of trace elements such as Zn, Pb, Cu, Mo, Ni may also be associated with sulfide minerals and Ti, Cr, Mn, with oxides. Some measured REY elements- Ce, Nd, Sm, Gd, Dy, Er and Y are found in the rocks in very small quantities. So their content is really very low. It varies in from value below 1 ppm up to 6 ppm. REY usually substitute Ca ions in calcite.
The Nature of Organized Cybercrime Groups

If, we ignore the stereotype and repetitive discussions, regarding to cybercrimes and their importance in the third millennium, the issue of checking the matter of involved groups in cybercrimes, including important problems and less discussed cybercrimes field. Checking the matter of involved groups in cybercrimes, theoretical and experimental challenges regarding to cyber criminals, the role of organized groups in cybercrimes, presentation of known evidences and samples which represents individual and social manner, and also checking the motivation of cyber criminals. For instance we can enumerate official government as research objective. In this article trying to describe different Kinds of cybercrimes and different species of organized formations of criminals according to different typological like typology presented by Mack Gayre and Chabinski. It's obvious that wide range of formations and organizations are involved in cybercrimes. In the field of Partnerships and profitable activities, specially committed cyber Crimes by government officials, is need more leadership organization and expertise. However it seems that actions in practice with weak leadership and organization are done in this field.
Technological Fix: Students in Mobile Phone Repairs

The digital world is still evolving with many opportunities and challenges.The mobile phone is one of the devices that has transformed the telecommunication industry.Repair of mobile phones places itself within the idea of circular economy in an urban setting, and the allied process like refurbishing, reengineering, and recycling are advantageous to the environment, contributing to sustainable development goals.This study sought to access the performance and skill gained by some students in selected second cycle institutions in Southern Ghana who undergone training to technologically fix mobile phones.The study found out that about 90% of the students were able to install, uninstall and repair mobile systems as well as becoming conversant with the use of various tools & instruments in mobile phone repairing.
Answer Script Evaluation using Machine Learning

The current way of checking answer scripts is hectic for the college. They need to manually check the answers and allocate the marks to the students. Our proposed system uses Machine Learning and Natural Language Processing techniques to beat this. Machine learning algorithms use computational methods to find out directly from data without hopping on predetermined rules. NLP algorithms identify specific entities within the text, explore for key elements during a document, run a contextual search for synonyms and detect misspelled words or similar entries, and more. Our algorithm performs similarity checking and also the number of words associated with the question exactly matched between two documents. It also checks whether the grammar is correctly used or not within the student's answer. Our proposed system performs text extraction and evaluation of marks by applying Machine Learning and Natural Language Processing techniques.
FORMATION OF SELF-KNOWLEDGE AND SELF-DEVELOPMENT OF THE FUTURE PRIMARY SCHOOL TEACHER

The article describes the methods of formation of self-knowledge and self-development of students in the course of psycho-pedagogicalworkshop. The article describes the structure and content of the workshop aimed at the formation of self-knowledge and self-development ofthe future elementaryschool teacher, and presented methods implemented during operation: Self-diagnosis, self-examination, self-observation, role play, game exercises. The dynamics of growth in the level of formation of cognitive and affective components ofprofessional consciousness of the future teachers of primary school
Stemtech model in education 4.0: Designing height measuring instruments in grade 10

With the development in the fourth industrial revolution, there have been urgent demands for new human resources in the world. The mission set for the education sector is to prepare students with skills and knowledge in line with global standards to keep up with the industrial age 4.0. Among the educational trends that serve the fourth industrial revolution, STEM education has been demonstrating its superiority through various studies. This study applies the STEMTech model, which connects the fields of Science, Technology, Engineering, and Mathematics with a central technological factor, to create STEM products in the context of high schools in Vietnam recently. Firstly, the research trains students about STEM education, STEMTech model, and some new technologies; then divides the class into multiple groups of students to conduct a project based on STEMTech model; finally, surveys students. Statistical analysis was used to evaluate STEMTech model, whose results show that STEMTech model can engage students in learning, develop their creativity, and promote other competencies.
Towards Equal Gender Representation in the Annotations of Toxic Language Detection

Classifiers tend to propagate biases present in the data on which they are trained. Hence, it is important to understand how the demographic identities of the annotators of comments affect the fairness of the resulting model. In this paper, we focus on the differences in the ways men and women annotate comments for toxicity, investigating how these differences result in models that amplify the opinions of male annotators. We find that the BERT model as-sociates toxic comments containing offensive words with male annotators, causing the model to predict 67.7% of toxic comments as having been annotated by men. We show that this disparity between gender predictions can be mitigated by removing offensive words and highly toxic comments from the training data. We then apply the learned associations between gender and language to toxic language classifiers, finding that models trained exclusively on female-annotated data perform 1.8% better than those trained solely on male-annotated data and that training models on data after removing all offensive words reduces bias in the model by 55.5% while increasing the sensitivity by 0.4%.
Individual differences in prosodic imitation

Speakers strongly vary in their imitation abilities, but the factors underlying this variation are still unclear. This study examined whether individual differences in working memory affect the accuracy of imitation of phonological and phonetic aspects of French prosody. Thirty-six French native speakers were asked to listen to twenty sentences extracted from a read and a spontaneous speech corpus, and to repeat the words and the way the utterances were said. Overall, obligatory phonological events (boundary tones and the H* tone of LH* rises) were more accurately reproduced than optional phonological ones (the Hi tone of LHi rises) and their speaker-specific phonetic details. Speakers with higher working memory capacities were more accurate in phonological imitation of both obligatory and optional phonological events, possibly because of their increased capacity in retaining the prosodic characteristics of the utterances. Imitating read speech, which was richer in terms of number of LHi rises, was slightly more difficult for speakers with low working memory capacities. There was no relation between working memory and imitation of phonetic aspects, which showed more idiosyncratic patterns of imitation. Our findings indicate that working memory constraints should be taken into account in modelling prosodic imitation, along with linguistic and task-specific factors.
Towards Analyzing Mobile App Characteristics for Mobile Software Development

A mobile application (i.e., mobile app) is a small software program, which is mainly developed for mobile phones. Mobile apps have some peculiar characteristics, which are concerned with particular aspects in the form of requirements, pertaining to hardware, software and network connectivity. Designing an appropriate mobile app according to users' perspective depends on the mobile app characteristics. In this paper, we present a review of various available mobile app characteristics concerning to present mobile apps. It is observed that the user experience is one of the most exciting and evolving feature that attracts the majority of mobile app users. In order to deal with user experience, we have further explored and found some essential factors that affect it. Furthermore, mobile app paradigms play an important role for mobile app development. Mobile app characteristics help to select a suitable paradigm for mobile apps development. We have analyzed that hybrid mobile app paradigm fits in most of the mobile app development situations.
Library Guides: Interlibrary Loan: Using Library Search

This guide will explain what Interlibrary loan (ILL) is and how you can use ILL to request materials not owned by Purdue Libraries
A Novel Machine Learning based Multiple-user Hand Gesture Recognition Approach

Machines are generally designed offering attributes, such as, of usability, accuracy, affordability, and scalability. This study aims to facilitate value addition Human-Computer Interaction employing hand gesture recognition,  introducing a user-friendly and natural interaction for conveying useful information. Initially, machine vision-based techniques are applied for hand segmentation and detection, subsequently, deep learning classifier is trained on various hand gestures. In this study, shape-based hand features, which offer less variation under various lighting conditions are presented in contrast to the other means of hand gesture recognition (such as texture and skin colour). Furthermore, experiments were conducted under varying light conditions and the recognition performance of the developed algorithm for multiple-user hand gesture is investigated. The developed method results in achieving a classification accuracy of  94.6%  and 93.% for single and multiple-user hand gestures respectively.  The developed hand gesture-based non-verbal communication would assist handicap and physically challenged personals for non-invasive machine interaction.
Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences

Approximate Policy Iteration (API) algorithms alternate between (approximate) policy evaluation and (approximate) greedification. Many different approaches have been explored for approximate policy evaluation, but less is understood about approximate greedification and what choices guarantee policy improvement. In this work, we investigate approximate greedification when reducing the KL divergence between the parameterized policy and the Boltzmann distribution over action values. In particular, we investigate the difference between the forward and reverse KL divergences, with varying degrees of entropy regularization. We show that the reverse KL has stronger policy improvement guarantees, but that reducing the forward KL can result in a worse policy. We also demonstrate, however, that a large enough reduction of the forward KL can induce improvement under additional assumptions. Empirically, we show on simple continuous-action environments that the forward KL can induce more exploration, but at the cost of a more suboptimal policy. No significant differences were observed in the discrete-action setting or on a suite of benchmark problems. Throughout, we highlight that many policy gradient methods can be seen as an instance of API, with either the forward or reverse KL for the policy update, and discuss next steps for understanding and improving our policy optimization algorithms.
REPAINT: Knowledge Transfer in Deep Reinforcement Learning

Accelerating learning processes for complex tasks by leveraging previously learned tasks has been one of the most challenging problems in reinforcement learning, especially when the similarity between source and target tasks is low. This work proposes REPresentation And INstance Transfer (REPAINT) algorithm for knowledge transfer in deep reinforcement learning. REPAINT not only transfers the representation of a pre-trained teacher policy in the on-policy learning, but also uses an advantage-based experience selection approach to transfer useful samples collected following the teacher policy in the off-policy learning. Our experimental results on several benchmark tasks show that REPAINT significantly reduces the total training time in generic cases of task similarity. In particular, when the source tasks are dissimilar to, or sub-tasks of, the target tasks, REPAINT outperforms other baselines in both training-time reduction and asymptotic performance of return scores.
Modelling and Forecasting of Solar Radiation Data: A Case Study

Renewable source of energy plays an important role in meeting the world’s power demands. The major factor that makes this source of energy to be important is due to its clean energy generation methodology. Hydro, wind and solar are effectively harnessed in almost every part of the world. However, solar energy, although has less environmental impacts, remains behind when compared to hydro and wind. For effective solar energy generation, it is necessary to know the amount of solar radiation that it receives in a month or a year at that location to harness and install photovoltaic (PV) system in that particular location. Solar radiation at any place can either be measured directly using instruments or empirically determined from global solar radiation. However, these methods are either expensive or not accurate. Artificial neural network (ANN) method provides a convenient approach to overcome the drawbacks of conventional instrumental or other empirical methods. ANN is analogous to human nervous system and are widely used for solving linear and nonlinear problems. This paper has developed neural network model for prediction of monthly solar radiation considering two places in Arunachal Pradesh, i.e., Itanagar and Pasighat. Multilayer feed forward network with back propagation has been proposed for the ANN model. Five-year meteorological data was collected and used for training the proposed network model. The network performance was checked by considering mean square error (MSE) and R (coefficient of correlation) which gave the value very close to zero above 92% for the considered location, respectively. Thus, the predicted output is almost in agreement with the actual output making this model applicable for prediction of monthly solar radiation for considered places. Apart for this prediction, performance of different combination of meteorological parameter were also evaluated and found that parameters such as temperature, humidity, sunshine hour and wind speed are found to be more influencing parameter for solar radiation estimation. This paper demonstrates that ANN has the potential to overcome many of the drawbacks and challenges in estimating the solar radiation, which is essential for designing solar energy generation panels. The findings are particularly useful in data sparse regions and highly applicable in regions where adequate energy supply is still a herculean task.
Fast Binary Embedding of Deep Learning Image Features Using Golay-Hadamard Matrices

Convolutional neural networks (CNNs) have emerged as powerful tools for image retrieval and classification. In this paper, we study binary embedding of CNN-based image descriptors for resource-constrained devices. We propose two classes of fast computable and memory-efficient dimension reduction operators using Golay-Hadamard matrices (GHMs), which are constructed by multiplying the columns of a Hadamard matrix with a Golay sequence. Simulation results on CNN-based instance image retrieval and classification show that GHM-based operators can offer competitive performance to those of full random Gaussian matrices and Gaussian circulant matrices at much lower computational cost and storage space. This implies the potential of proposed approaches on devices with low memory, bandwidth and power restrictions.
Hibernate Xdoclet Mapping transformation to JPA

Hibernateis an open source object relational mapping (ORM) framework for the java-based technologies. Hibernate was started in 2001 before Java language annotations (feature of Java 5) came into existence, this led to a lot of enterprise systems build using Xdoclet, whichprovided a way to specify metadata as comments for Hibernate ORM within the java entity classes. Ant (a build tool) was then used to convert these Xdoclet metadata to generate hibernate mapping files (hbm files) which were then processed by the Hibernate runtime engine. With the advent of Java annotations, Hibernate also created anannotations module to provide a way to use standard java annotations within the entity classes, making the Hibernate Xdoclet annotations obsolete. Based on the community feedback and popularity of ORM based engines, Java Persistence Architecture (JPA) was first released in 2006 to provide a standard way of utilizing ORM products and its annotations. Enterprise systems that might have thousands of entities which still utilize Xdoclet way of managing metadata, have a hard time of moving to JPA as manual transformation can be buggy and expensive and that's where an automated mechanism of transforming java source codebase to not only convert Xdoclet metadata mapping to standard JPA annotations but also move the proprietary use of Hibernate API's to JPA API's across the codebase. If Hibernate engine is used as the JPA provider, one can use Hibernate API where JPA doesn't provide that specific feature.
Adjacency List Oriented Relational Fact Extraction via Adaptive Multi-task Learning

Relational fact extraction aims to extract semantic triplets from unstructured text. In this work, we show that all of the relational fact extraction models can be organized according to a graph-oriented analytical perspective. An efficient model, aDjacency lIst oRiented rElational faCT (DIRECT), is proposed based on this analytical framework. To alleviate challenges of error propagation and sub-task loss equilibrium, DIRECT employs a novel adaptive multi-task learning strategy with dynamic sub-task loss balancing. Extensive experiments are conducted on two benchmark datasets, and results prove that the proposed model outperforms a series of state-of-the-art (SoTA) models for relational triplet extraction.
IMPLEMENTING SUGGESTOPEDIA LEARNING METHOD IN DEVELOPING WRITING ABILITY OF CIVIL ENGINEERING STUDENTS

ABSTRACT This present research conducted in Civil Engineering Department and reports the implementation of Suggestopedia Learning Method in developing students’ writing ability. The objective of the research was to find out whether or not the first semester of TKJJ Study Program in Civil Engineering Department of Politeknik Negeri Ambon can develop their writing ability through Suggestopedia Learning Method. A quasi-experimental design as a part a quantitative research employed in this study. The dates obtained through the Pretest and Posttest. The sample was using cluster random sampling. Based on the result of observation, it concluded that: The result of the data analysis showed that the teaching through suggestopedia improved significantly the writing ability of the first semester of TKJJ Study Program in Civil Engineering Department of Politeknik Negeri Ambon. It was proved by the mean score on pretest was 47.077 and the posttest was 79.35. In addition, after analyzing the data by using the test formula, the result of t-test value was 3.746 and t-table value 1,684. It means that the t-test value was greater than t-table value it indicated that H1 was accepted and H0 was rejecting. Therefore, the hypothesis of the research is the students’ writing ability that was taught through Suggestopedia Learning Method was better than the students who were taught through conversation method.
What do deep saliency models learn about where we look in scenes?

Abstract Deep saliency models represent the current state-of-the-art for predicting where humans look in real-world scenes. However, for deep saliency models to inform cognitive theories of attention, we need to know how deep saliency models predict where people look. Here we open the black box of deep saliency models using an approach that models the association between the output of 3 prominent deep saliency models (MSI-Net, DeepGaze II, and SAM-ResNet) and low-, mid-, and high-level scene features. Specifically, we measured the association between each deep saliency model and low-level image saliency, mid-level contour symmetry and junctions, and high-level meaning by applying a mixed effects modeling approach to a large eye movement dataset. We found that despite different architectures, training regimens, and loss functions, all three deep saliency models were most strongly associated with high-level meaning. These findings suggest that deep saliency models are primarily learning image features associated with scene meaning.
Speech BERT Embedding For Improving Prosody in Neural TTS

This paper presents a speech BERT model to extract embedded prosody information in speech segments for improving the prosody of synthesized speech in neural text-to-speech (TTS). As a pre-trained model, it can learn prosody attributes from a large amount of speech data, which can utilize more data than the original training data used by the target TTS. The embedding is extracted from the previous segment of a fixed length in the proposed BERT. The extracted embedding is then used together with the mel-spectrogram to predict the following segment in the TTS decoder. Experimental results obtained by the Transformer TTS show that the proposed BERT can extract fine-grained, segment-level prosody, which is complementary to utterance-level prosody to improve the final prosody of the TTS speech. The objective distortions measured on a single speaker TTS are reduced between the generated speech and original recordings. Subjective listening tests also show that the proposed approach is favorably preferred over the TTS without the BERT prosody embedding module, for both in-domain and out-of-domain applications. For Microsoft professional, single/multiple speakers and the LJ Speaker in the public database, subjective preference is similarly confirmed with the new BERT prosody embedding. TTS demo audio samples are in https://judy44chen.github.io/TTSSpeechBERT/.