Reviewerly/exHarmony huggingface dataset:
Determination of heavy and essential elements in tap water using Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) in Kembata Tembaro Zone: SNNPR, Ethiopia

An appropriate level of concentration of elements in the human body is vital for its proper functioning. The measurement of both heavy and essential elements determines the quality of the water that tremendously correlated with the health of the consumers. In this study, the concentration levels of five essential metals (Na, K, Ca, Mg and Zn) and three heavy metals (Pb, Hg and Cd) were determined in the tap water by Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES) technique after pre-concentration and acid digestion. The result of the study had revealed that the concentrations of metals in the tap water samples (in mg/L) were in the ranges of: Ca (16.52-24.74), K (8.43-11.44), Mg (5.10-11.30), Na (18.89-28.62), Pb (0.02- 0.03), Zn (1.22-2.44), Cd (0.01) and Hg ( 0.04-0.05). The concentrations of essential elements were very small as compared to their recommended value by USEPA/WHO while the concentration of heavy metals were above the maximum levels recommended by EPA, WHO and USEPA. Thus, it can be concluded that this water requires treatment for heavy metals beforeused by the community.
Samoučinkovitost odgojitelja i pedagoške kompetencije ravnatelja predškolskih ustanova

Self-efficacy is the belief in one's ability to organize and perform specific actions which are necessary to accomplish desired goals.It does not refer to objective abilities to carry out a specific action, but to subjective estimate if the action can be performed successfully.Self-efficacy is the key factor of human functioning, it is a determinant and a predictor of the degree of success that will eventually be achieved.Self-efficacy is largely affected by qualitative leadership in work environment.The precondition of qualitative principal's leadership are his optimally developed pedagogical competencies.On the basis of theoretical hypotheses and empirical research this paper seeks to examine the way preschool teachers evaluate their self-efficacy and their principals' pedagogical competencies.The relationship between preschool teachers' evaluation of self-efficacy and their evaluation of principals' pedagogical competencies is also studied in the paper.The study included 75 preschool teachers in Vukovar-Srijem County.The results have shown that the teachers highly evaluate their self-efficacy as well as the principals' pedagogical competencies.The statistically significant positive relationship between preschool teachers' evaluation of self-efficacy and their evaluation of principals' pedagogical competencies is found.The paper elaborates reasons of importance of self-efficacy in preschool teachers' work as well as the key roles of principals' pedagogical competencies and their quality management of the educational institution.
Reading Literation Strategies Applied by Lecturers in Learning at the Faculty of Education Universitas Pendidikan Ganesha

This research is survey research.The population of this study were 57 civil servant lecturers and 490 students of semester 3 and 5 of the Faculty of Education, Ganesha University of Education.The research sample selected by purposive random sampling was 32 lecturers and 200 students who were spread over four study programs.The data collection techniques used were questionnaires and interviews.The data analysis used quantitative descriptive analysis and qualitative descriptive analysis.The results showed that (1) there were eleven strategies applied by lecturers in reading literacy, (2) there were various problems faced by lecturers in implementing reading literacy, (3) the lecturers had implemented various solutions in solving reading literacy problems, (4) the Students of the Faculty of Education are most interested in reading in printed form (66.82%), then those who are interested in reading in softcopy (28.81%), and finally those who are interested in other types of reading (3.37%), and (5) There are 20 problems faced by students in reading literacy activities.
Identifying Named Entities as they are Typed

Identifying named entities in written text is an essential component of the text processing pipeline used in applications such as text editors to gain a better understanding of the semantics of the text. However, the typical experimental setup for evaluating Named Entity Recognition (NER) systems is not directly applicable to systems that process text in real time as the text is being typed. Evaluation is performed on a sentence level assuming the end-user is willing to wait until the entire sentence is typed for entities to be identified and further linked to identifiers or co-referenced. We introduce a novel experimental setup for NER systems for applications where decisions about named entity boundaries need to be performed in an online fashion. We study how state-of-the-art methods perform under this setup in multiple languages and propose adaptations to these models to suit this new experimental setup. Experimental results show that the best systems that are evaluated on each token after its typed, reach performance within 1–5 F1 points of systems that are evaluated at the end of the sentence. These show that entity recognition can be performed in this setup and open up the development of other NLP tools in a similar setup.
SOCIAL AND PHYSICAL PROBLEMS OF THE BLIND FEMALES AND THE ROLE OF NGOS - UNIVERSITY EDUCATION STAGE

The present study examines both the social and physical problems of blind women and the role of NGOs in confronting them and to address the roles of NGOs.The main problem of the research was the social and physical problems facing the blind and the role of NGOs in facing them.The current study used the descriptive method and statistical method on a sample of 100 blind students from public universities who are attending NGOs. The study lasted for 6 months from January 2019 to June 2019 using a questionnaire as the main tool for collecting study data.The study reached several results, the most important of which are:-       The current problem can be seen as the inability of NGOs to address the social problems of blind women.-       The inability of NGOs to face the physical problems of blind women.-       Lack of books in the library for the visually impaired in Braille.-       Lack of procedures for blind women in Egyptian universities.
DebIE: A Platform for Implicit and Explicit Debiasing of Word Embedding Spaces

Recent research efforts in NLP have demonstrated that distributional word vector spaces often encode stereotypical human biases, such as racism and sexism.With word representations ubiquitously used in NLP models and pipelines, this raises ethical issues and jeopardizes the fairness of language technologies.While there exists a large body of work on bias measures and debiasing methods, to date, there is no platform that would unify these research efforts and make bias measuring and debiasing of representation spaces widely accessible.In this work, we present DEBIE, the first integrated platform for (1) measuring and (2) mitigating bias in word embeddings.Given an (i) embedding space (users can choose between the predefined spaces or upload their own) and (ii) a bias specification (users can choose between existing bias specifications or create their own), DEBIE can (1) compute several measures of implicit and explicit bias and modify the embedding space by executing two (mutually composable) debiasing models.DE-BIE's functionality can be accessed through four different interfaces: (a) a web application, (b) a desktop application, (c) a REST-ful API, and (d) as a command-line application.
Penggunaan Majas dan Citraan dalam Puisi Karya Siswa Kelas X di SMA Negeri 2 Sukoharjo

This study relates to the Majas and imagery. Researchers will analyze the use of the Majas and imagery used by the students in making poetry works. How the Majas and imagery contained in poetry work students, whether the work of poetry created by the students already there are majas and imagery or not. Researchers want to capitalize on students ' work to see how the employer and imagery are used in poetry. This time, researchers will focus on the Majas and imagery found in poetry by grade X students at SMA Negeri 2 Sukoharjo. The purpose of this study is to identify the use of Majas and imagery on student poetry. The research method used is a qualitative descriptive method. The design of this research belongs to context analysis. The data sources used in this study were poetry by students. The Data used in the form of poetry arrays are found in students ' work. The data collection techniques used are documentation techniques, see, and record. The validity of the data used in this study is the trianggulation of the source and method. The results of this research (1) There is a magistview of personification, metaphor, hyperbola, association, simile, parallelism Anafora, epidemic parallelism in the poetry of student work. However, most students use the personification Majas in their poetry work. (2) There are imagery of vision, hearing, feel, and imagery of motion in poetry by students. However, most of the students use imagery motion in their poetry work.
A New Model of Artificial Intelligence

In this article, I'll present a new model of artificial intelligence rooted in information theory that makes use of tractable, low-degree polynomial algorithms that nonetheless allow for the analysis of the same types of extremely high-dimensional datasets typically used in machine learning and deep learning techniques. Specifically, I'll show how these algorithms can be used to identify objects in images, predict complex random paths, predict projectile paths in three-dimensions, and classify three-dimensional objects, in each case making use of inferences drawn from millions of underlying data points, all using low-degree polynomial run time algorithms that can be executed quickly on an ordinary consumer device. In short, the purpose of these algorithms is to commoditize the building blocks of artificial intelligence. All of the code necessary to run these algorithms, and generate the training data, is available on my researchgate homepage, under the project heading, Information Theory. Note that I retain all rights, copyright and otherwise, to all of the algorithms, and other information presented in this paper. In particular, the information contained in this paper may not be used for any commercial purpose whatsoever without my prior written consent.
Measuring robustness and resilience against counters on autonomous platforms

Autonomous platforms are becoming ubiquitous in society, including UAVs, Roombas, and self-driving cars. With the increase in prevalence of autonomous platforms comes an increase in the threat of attacks against these platforms. These attacks can range from direct hacking to remotely take control of the platforms themselves [1], to attacks involving manipulation or deception such as spoofing or fooling sensor inputs [2, 3]. Ensuring autonomous systems are robust and resilient (R2) against these attacks will become an important challenge to overcome if they are to be trusted and widely adopted. This paper addresses the need to quantitatively define robustness and resilience against manipulation and deceptive attacks which are inherently harder to detect. We define a set of robust estimation metrics that are mathematically rigorous, can be applied to multiple algorithm use cases, and are easy to interpret. Since many of these functions are processed over time, the primary focus will be on process-based metrics. These metrics can be adapted over time by responding and reconfiguring at system runtime. This paper will: 1) provide background information on previous work in this area, including adversarial machine learning, robotics control, and engineering design. 2) Present the metrics and explain how to address our unique problem. 3) Apply these metrics to three different autonomy applications: target tracking, autonomous control, and automatic target recognition. 4) Discuss some additional caveats and potential areas for future work.
International Scientific Linguistic Conference LESEWA

A report on the International Scientific Linguistic Conference LESEWA held under the aegis of the Institute of Countries of Asia and Africa of Moscow State University and St. Petersburg State University on 23–24 November in Moscow. Brief descriptions of the presented papers are given.
THE REIATIONSHIP BETWEEN EDUCATIONAL MOTIVATION AND STUDENT PERFORMANCE EDUCATIONAL ORGANIZATION OF THE MINISTRY OF INTERNAL AFFAIRS OF RUSSIA

The article reviews the study of the manifestation of educational motivation as a factor affecting the of educational activities. The relevance of the research in this area is determined by the context of training the psychoemotional stable and active employees of internal affairs units. The motivation of students formed during the learning process educational organizations of the Ministry of Internal Affairs of Russia is the key to the successful development of personal and professional characteristics of employees of the internal affairs units. In the conducted study were used: empirical methods studies Motivation for success (author - T. Ellers), Motivation for the avoidance of failures , a method of researching the motivation for and fear of failure, developed by A.A. Rean. The data were subjected to a comparative method of statistical analysis. As a result of the study, the relationship between motivation and educational activities aimed at and achievement in educational and service activities.
Developing Your Human Firewall

Increasingly, we see the insider threat as being the greatest cause of security incidents and data loss. From a benchmarked sample, across 13 industries (as depicted in Figure 12-2), the average cost of the insider threat was identified at $11.45 million (astonishingly, this equates to a 47% increase in the last 2 years).
Research on DCN Traffic Scheduling Based on Private Cloud Platform

In the virtual switching of multi-tenant network under private cloud, the traffic load in peak period is too large to ensure the balance and bandwidth quality of service, while in the low period, the traffic load is too low to waste computing resources. Based on the current SDN, using Openflow to send traffic table to the data plane to realize dispatching and classifying the data flow according to the granularity of the data flow, and considering the factors such as QoS and resource utilization. This paper studies the hybrid scheduling strategy and priority based dynamic scheduling strategy to ensure the load balance and service quality in the peak period, in the low period, the utilization and throughput of computing resources are maintained.
Detection of unknown galaxy types in large databases of galaxy images

Modern digital sky surveys utilize robotic telescopes that collect extremely large multi- PB astronomical databases. While these databases can contain billions of galaxies, most of the galaxies are “regular” galaxies of known galaxy types. However, a small portion of the galaxies is rare “peculiar” galaxies that are not yet known. These unknown galaxies are of paramount scientific interest, but due to the enormous size of astronomical databases they are practically impossible to find without automation. Since these novelty galaxies are, by definition, not known, machine learning models cannot be trained to detect them. In this paper, an unsupervised machine learning method for automatic detection of novelty galaxies in large databases is proposed. The method is based on a large and comprehensive set of numerical image content descriptors weighted by their entropy, and the farthest neighbors are ranked-ordered to handle self-similar peculiar galaxies that are expected in the very large datasets. Experimental results using data from the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) show that the ability of the method to detect novelty galaxies outperforms other shallow learning methods such as one-class SVM, Local Outlier Factor, and K-Means, and also newer deep learning-based methods such as auto-encoders. The dataset used to evaluate the method is publicly available and can be used as a benchmark to test future algorithms for automatic detection of peculiar galaxies.
Bitcoin in the Literature of Economics and Finance: A Survey

This paper provides a systematic review of the literature on key matters related to the popular cryptocurrency Bitcoin. The key motivation of this paper is to provide a review of existing literature and also to understand the underlying principle of this digital currency from the economic and finance point of view. For the survey to be comprehensive, the paper is categorized in varied themes: Price Dynamics, Volatility, and Bubble Dynamics, Mode of recognition in the financial market, Efficiency, and economics, social media and investor sentiment and lastly regulation and legality.
Quality Attributes for UX Design and Evaluation

This paperutilizeda literature survey method to find out the quality attributes (or design factors) that impact on or contribute to user experience in extant literature. The results indicate that there areten dimensions that model UX.Each of these dimensions has associated quality attributes that contribute to it and influence the UX of interactive applications. The study therefore proposes the use of these dimensions and the related quality attributes in the development and modeling of the UX of interactive systems. Furthermore, these dimensions and attributes can be utilized in the design and evaluation of interactive digital artifacts.
Rancang Bangun Aplikasi Penilaian Kinerja Guru Berbasis Website Berdasarkan Pedoman Pelaksanaan Penilaian Kinerja Guru Pada SMK Rajasa Surabaya

Rajasa Vocational High School (SMK) is one of the private schools in the city of Surabaya. SMK Rajasa is located at Jalan Genteng Kali no. 27 Surabaya. The results of the interview with the school found that performance appraisal was important to determine whether the teacher was successful or not in working during a predetermined working period which was useful as an objective consideration in teacher guidance. Performance appraisals are also used for bonuses and teacher salaries at SMK Rajasa Surabaya. At this time, several problems were found, namely teacher performance appraisal takes a long time, namely one month. This makes the process of reporting the performance appraisal to the Principal late. Another problem is the calculation of performance appraisal is still done manually by the assessment team so that sometimes mistakes occur. This led the assessment team to recalculate the performance appraisal. The next problem is that the teacher has not been able to see the results of the performance appraisal in the previous period. Therefore, in this study conducted to design and build teacher performance appraisal applications with a checklist method based on guidelines for implementing teacher performance appraisals. The resulting applications include master management, teacher assessment history, teacher performance appraisal, teacher performance appraisal reports, uploading documents to the system at SMK Rajasa Surabaya. Based on the results of testing with the black box contained in the implementation and evaluation chapter of the system, it was found that all functions were running well.
“Sharks are not the threat humans are”: Argument Component Segmentation in School Student Essays

Argument mining is often addressed by a pipeline method where segmentation of text into argumentative units is conducted first and proceeded by an argument component identification task. In this research, we apply a token-level classification to identify claim and premise tokens from a new corpus of argumentative essays written by middle school students. To this end, we compare a variety of state-of-the-art models such as discrete features and deep learning architectures (e.g., BiLSTM networks and BERT-based architectures) to identify the argument components. We demonstrate that a BERT-based multi-task learning architecture (i.e., token and sentence level classification) adaptively pretrained on a relevant unlabeled dataset obtains the best results.
LibGuides: Criminology: Search tips

A Library Guide to Criminology resources for students and staff of the University of Newcastle
Feature Selection Approach Based on a Novel Variant of Hybrid Differential Evolution and PSO for Sentiment Classification

Over the last few years, digital text data is increasing with the advancement in use of technologies. Therefore, sentiment analysis is an important step for improving the performance of any classifier. But with the increase in data, this problem has become more complex. To improve the overall performance of classifier and reducing the data-dimensionality, Feature selection is a key sub-stage for the sentiment analysis. In this paper, we propose a feature selection approach, which is based on a novel variant of hybrid differential evolution algorithm and PSO for sentiment classification. Experimental results show that our approach achieves 97.56% and 98.54% of average accuracy by using Naïve Bayes (NB) classifier for the two Twitter datasets, viz., TestData_2015 and twitter sanders, respectively. The results also show that our proposed approach is showing better results with state-of-art algorithms on Twitter datasets. Statistical results also showcase the effectiveness of our proposed model.
Penerapan Model Pembelajaran Cooperative Integrated Reading and Composition Pada Pembelajaran Matematika

This study aims to determine the effectiveness of the application of the model of integrated reading and composition cooperative in students of SMP 44 Palembang in terms of three aspects: student activity during learning, student attitudes to learning, and student learning completeness after being applied in an integrated cooperative reading and composition model. This research is applied research using Pre-Experimental design, One-Shot Case Study. The subjects in this study were class VIII.7 students at SMP Negeri 44 Palembang in the academic year 2018/2019. The instruments used in this study were observation sheets, description test sheets, namely student learning outcomes tests, and student attitude questionnaire sheets on the CIRC learning model. Based on the results of the research and discussion, it was concluded that the effectiveness of the application of the CIRC model on mathematics learning at SMP Negeri 44 Palembang in terms of three aspects of student activity, student attitudes, and student mastery was obtained by KPM (Effectiveness of Model Use) 80% with the category effective
Complete consistency and convergence rate of the nearest neighbor estimator of the density function based on WOD samples

By using the exponential inequality of widely orthant dependent (WOD, for short) random variables, we mainly investigate the complete consistency and convergence rate of the nearest neighbor estimator of the density function based on WOD samples.The results obtained in the paper generalize and improve some corresponding ones in the literature.In addition, the restriction on the dominating coefficients g(n) is much weak, even if the geometric growth of g(n) , the consistency result and convergence rate still hold by using the results that we obtained.