Reviewerly/exHarmony huggingface dataset:
Information-communications Competencies of Teachers as a Requirement of Contemporary Teaching and the Lifelong Learning Concept

This article aimed to make a theoretical review of the importance of ICT education of teachers in service of preparation for more efficient performances required by their profession in the context of general globalization changes they are exposed to. In that sense, a review of the advantages of ICT utilization and the requirements of lifelong learning which sets informatical skills as an imperative had been presented, emphasizing the role of teachers, since it requires working on self-improvement, as well as directing others towards competent, critically observed and purposeful usage of modern technological achievements. Some of the priorities for the development of higher education in B&amp;H have been presented, where in the context of scientific research work for the 2016-2026 period, providing all of the public higher education institutions with ICT equipment is planned, which indicates that the teachers working in these institutions must possess the competencies for their utilization, especially because institutions of higher education should play the role of one of the main promoters of lifelong learning.
QUIZLET APPLICATION EFFECT ON SENIOR HIGH SCHOOL STUDENTS VOCABULARY ACQUISITION

Vocabulary is one of the language features that should be acquired by the students to support them learning other language skills. The students will be easier to receive or produce the English language if they remember the considerable words. After being observed in the actual classroom, however, most students seem difficult to acquire words. Moreover, the influence of using media such as Quizlet Application on vocabulary acquisition needed to be proved by involving large participants since that application was not widely used by EFL teachers in Indonesian educational context. The study aims to find out if there is any significant difference in the grade ten students’ vocabulary acquisition between those who use Quizlet Application (QA) and those who do not use it in learning vocabulary. The participants were 110 tenth-grade students in senior high school. Their vocabulary knowledge was assessed through pre-test and post-test conducted before and after the treatment was given. The research finding reveals that there was a significant difference in students’ vocabulary acquisition between those who used Quizlet Application and those who were taught using the conventional method in learning vocabulary. That finding was indicated by the value of significant which was below 0.05. Moreover, it is also found that QA was less effective on students’ vocabulary acquisition while the conventional method was not effective to students in obtaining words.
Improvement of Random Undersampling to Avoid Excessive Removal of Points from a Given Area of the Majority Class

In this paper we focus on class imbalance issue which often leads to sub-optimal performance of classifiers. Despite many attempts to solve this problem, there is still a need to look for better ones, which can overcome the limitations of known methods. For this reason we developed a new algorithm that in contrast to traditional random undersampling removes maximum k nearest neighbors of the samples which belong to the majority class. In such a way, there has been achieved not only the effect of reduction in size of the majority set but also the excessive removal of too many points from the given area has been successfully prevented. The conducted experiments are provided for eighteen imbalanced datasets, and confirm the usefulness of the proposed method to improve the results of the classification task, as compared to other undersampling methods. Non-parametric statistical tests show that these differences are usually statistically significant.
Does Smartphone-Based Quizlet Affect Students Learning Interest?

This study aimed to provide empirical evidence of the effect of smartphone-based Quizlet on studentsâ€™ learning interest in Universitas PGRI Madiun, Indonesia. This study was pre-experimental with a one-shot case study that is a group that got help, and the results were observed next. The population were accounting education students in Universitas PGRI Madiun. 54 students received smartphone-based Quizlet assistance in learning activities. We used statistical testing to test comparisons formulated in research hypotheses using simple linear regression tests. The results proved empirically that the use of smartphone-based Quizlet affected the students' learning interest in Universitas PGRI Madiun, Indonesia. The menu provided in the Quizlet application could activate students in learning and give a positive effect to students. Students became active in learning activities, did assignments on time, and learnt independently. The Quizlet application can be used as material for educators to consider increasing student interest in learning social science.
A Survey on Application of English Learning APPs to English Listening and Speaking Course in English Major

This paper discusses the application of English learning Apps to English listening and speaking course in English majors at Baoding University.Using students' and teachers' questionnaires, the conclusion is drawn.With the help of English learning APPs, students in English majors can increase their interest and strengthen their confidences in studying English listening and speaking in class.At the same time, English teachers are willing to use English learning Apps to help students improve students' English listening and speaking abilities.The teaching model can help teachers of English listening and speaking course to adjust teaching strategies actively and it is also the orientation of the reform for English teaching at Baoding University.
A Method for Improving Word Representation Using Synonym Information

The emergence of word embeddings has created good conditions for natural language processing used in an increasing number of applications related to machine translation and language understanding. Several word-embedding models have been developed and applied, achieving considerably good performance. In addition, several enriching word embedding methods have been provided by handling various information such as polysemous, subwords, temporal, and spatial. However, prior popular vector representations of words ignored the knowledge of synonyms. This is a drawback, particularly for languages with large vocabularies and numerous synonym words. In this study, we introduce an approach to enrich the vector representation of words by considering the synonym information based on the vectors’ extraction and presentation from their context words. Our proposal includes three main steps: First, the context words of the synonym candidates are extracted using a context window to scan the entire corpus; second, these context words are grouped into small clusters using the latent Dirichlet allocation method; and finally, synonyms are extracted and converted into vectors from the synonym candidates based on their context words. In comparison to recent word representation methods, we demonstrate that our proposal achieves considerably good performance in terms of word similarity.
Sample Complexity and Overparameterization Bounds for Temporal-Difference Learning With Neural Network Approximation

In this paper, we study the dynamics of temporal difference learning with neural network-based value function approximation over a general state space, namely, \emph{Neural TD learning}. We consider two practically used algorithms, projection-free and max-norm regularized Neural TD learning, and establish the first convergence bounds for these algorithms. An interesting observation from our results is that max-norm regularization can dramatically improve the performance of TD learning algorithms, both in terms of sample complexity and overparameterization. In particular, we prove that max-norm regularization improves state-of-the-art sample complexity and overparameterization bounds. The results in this work rely on a novel Lyapunov drift analysis of the network parameters as a stopped and controlled random process.
Object Sorting Robotic Arm Based on Colour Sensing

An Object Sorting Robotic Arm Based on Colour Sensing is a technique used to sort and analyse different colours of objects, after that picking up and place on desired area or location. This application improved efficiency as well as reduce the manual workload. In the project, dc servo motors are used which rotate as per the program to complete the task. Total six dc servo motors for different parts of the robotic arm like waist, shoulder, elbow, wrist roll, Wrist, pitch gripper. All motors are connected to Arduino. Also used one colour sensor (TCS 3200)that is also connected to Arduino. The colour sensor used here is to sense red, green, blue colours.
Efforts toward automatic aftershock sequences processing at the International Data Centre

&amp;lt;div&amp;gt; &amp;lt;p&amp;gt;The number of aftershocks after a large main shock may increase the daily number of seismic events by an order of magnitude for a few days or even weeks. The large number of incoming arrivals reduces the effectiveness of automatic bulletin generation and significantly increases the work of the analysts. In the verification context such aftershocks may delay the production of the CTBTO Reviewed Event Bulletin, as well as mask clandestine nuclear tests. Consequently, the CTBTO has been investigating ways to improve the performance of the automatic processing during aftershock sequences.&amp;amp;#160;&amp;amp;#160;&amp;lt;/p&amp;gt; &amp;lt;/div&amp;gt;&amp;lt;div&amp;gt; &amp;lt;p&amp;gt;In line with this investigation, the PTS launched a project with the objective to evaluate three algorithms that could address this issue, namely the Empirical Matched Field developed at NORSAR, the SeisCorr developed at Sandia National Labs and XSEL developed at the IDC. In this abstract we present comparisons on the performance of the three methods on the aftershock sequences of four very strong earthquakes: the Tohoku earthquake in Japan (March 2011), the Gorkha earthquake in Nepal (April 2015), the&amp;amp;#160; Illapel earthquake off the coast of Chile (September 2015) and the devastating earthquake in Papua New Guinea (February 2018).&amp;lt;/p&amp;gt; &amp;lt;/div&amp;gt;
Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings

Sparse regression has recently been applied to enable transfer learning from very limited data. We study an extension of this approach to unsupervised learning -- in particular, learning word embeddings from unstructured text corpora using low-rank matrix factorization. Intuitively, when transferring word embeddings to a new domain, we expect that the embeddings change for only a small number of words -- e.g., the ones with novel meanings in that domain. We propose a novel group-sparse penalty that exploits this sparsity to perform transfer learning when there is very little text data available in the target domain -- e.g., a single article of text. We prove generalization bounds for our algorithm. Furthermore, we empirically evaluate its effectiveness, both in terms of prediction accuracy in downstream tasks as well as the interpretability of the results.
Differentially Private Neural Networks with Bounded Activation Function

Deep learning has shown outstanding performance in various fields, and it is increasingly deployed in privacy-critical domains. If sensitive data in the deep learning model are exposed, it can cause serious privacy threats. To protect individual privacy, we propose a novel activation function and stochastic gradient descent for applying differential privacy to deep learning. Through experiments, we show that the proposed method can effectively protect the privacy and the performance of proposed method is better than the previous approaches.
The dawn of the quantum internet

The race is on to create a super secure online space that channels the eerie power of the quantum world. Stephen Battersby logs on
Performant barcode decoding for herbarium specimen images using vector‐assisted region proposals (VARP)

Premise The scale and associated costs of herbarium digitization make process automation appealing. One such process for many workflows is the association of specimen image files with barcode values stored with the specimen. Here, an innovation is presented that improves the speed and accuracy of decoding barcodes from specimen images. Methods and Results Geometric features common in barcodes are used to identify the regions of specimen images that are likely to contain a barcode. The proposed regions are then combined into a significantly reduced composite image that is decoded using traditional barcode reading libraries. Tested against existing solutions, this method demonstrated the highest success rate (96.5%) and the second fastest processing time (617 ms). Conclusions This method was developed to support a larger effort to automate specimen image post‐processing in real‐time, highlighting the importance of execution time. Although initially designed for herbarium digitization, this method may be useful for other high‐resolution applications.
SEOVER: Sentence-Level Emotion Orientation Vector Based Conversation Emotion Recognition Model

For the task of conversation emotion recognition, recent works focus on speaker relationship modeling but ignore the role of utterance's emotional tendency.In this paper, we propose a new expression paradigm of sentence-level emotion orientation vector to model the potential correlation of emotions between sentence vectors. Based on it, we design an emotion recognition model, which extracts the sentence-level emotion orientation vectors from the language model and jointly learns from the dialogue sentiment analysis model and extracted sentence-level emotion orientation vectors to identify the speaker's emotional orientation during the conversation. We conduct experiments on two benchmark datasets and compare them with the five baseline models.The experimental results show that our model has better performance on all data sets.
DEVELOPMENT OF DIGITAL SKILLS FOR FUTURE TEACHERS BASED ON CROSS-SUBJECT INTEGRATION

Today, the study of the problem of interdisciplinary integration in professional education is a historically formed direction and has been studied by scientists deeply enough. However, within the framework of the implementation of the competence-based approach in teaching, which is the focus of state compulsory education standards, the problem of interdisciplinary integration acquires a new meaning. The essence of the new meaning of interdisciplinary integration is substantiated by the modern socio-cultural conditions of globalization, informatization, interdisciplinarity, and digitalization. Interdisciplinary integration is the applied focus of the components of the educational process of the university (goal, result, content, form and methodology of teaching) and manifests itself in the relationship of academic disciplines with the content and technologies of professional activity of future teachers, as well as in the consistency of the processes of teaching and upbringing at the university. The globalization of education refers to the process of creating a worldwide unified education system, which erases the differences between the educational systems included in it. Informatization of education is a process when the sphere of education is provided with the methodology and practice of the development and optimal use of modern information technologies, focused on the implementation of the psychological and pedagogical goals of training and education. Digital skills in the modern world are necessary and must be complemented by cross-cutting interpersonal skills, as the ability to effectively interact in the current environment of distance learning. In the process of distance learning, video conferences using computer communication, virtual environment, online lectures and seminars are actively used. A distinctive feature of distance learning is computer-based learning, which contributes to the development of digital skills, since it is based on the student's independent cognitive activity, is active and personality-oriented. The above requires the development of digital skills in future teachers based on cross-subject integration. This article examines the problem of developing digital skills in future teachers based on interdisciplinary integration, and also provides a meaningful characteristic of digital skills.
Challenging the image of the altruistic and flexible household in the smart grid using design fiction

Power shortages and fluctuations in electricity supply are expected to become more frequent in the future, as renewable energy increases in the electricity mix.This poses a problem in that electricity might not always be available at the time of demand for industries as well as for households.Demand flexibility in households has been brought forward as part of a solution to this problem.How to engage households in this, however, is still unclear.Actors have tried to liken demand flexibility with waste sorting and other altruistic activities with connotations of contributing to the benefit of society, rather than being an activity springing from economic motives.In this article, we use design fiction to critically explore what this analogy would mean in the context of household electricity consumption.We describe fictive user scenarios for cooking, charging the electric car, laundry & dishes and heating to draw the demand flexibility analogies to the forefront.By exemplifying and concretizing these scenarios, it becomes evident that using waste sorting as an analogy for demand flexibility is not realistic.We discuss the implications of the scenarios in relation to the current visions that inform the development of the smart grid, and the emerging services and service providers.We conclude that there is a need to challenge the current images of flexible households in the smart grid in order to design systems that support thriving within limits.
"Early" and "definitive" taking charge of subjects positive to SARS-CoV2: the experience of an Italian Local Health Authority.

During the second covid-19 pandemic wave in November-December 2021 Prevention Departments had to face a hardly-sustainable workload of contact tracing and taking charge of the sars-cov2 positive case and of his or her close contacts. Also laboratories have been stressed in their ability to process timely the extraordinary load of swabs performed. In this context of hazardous delays, the Prevention Department of Belluno (Italy) tested its resilience: a simple and effective method of taking charge was implemented, by initially phoning to the positive case and imposing the isolation measure on him or her and later on proceeding with the conventional contact tracing.
Performance Improvement of Atmospheric Continuous-Variable Quantum Key Distribution with Untrusted Source

Atmospheric continuous-variable quantum key distribution (ACVQKD) has been proven to be secure theoretically with the assumption that the signal source is well protected by the sender so that it cannot be compromised. However, this assumption is quite unpractical in realistic quantum communication system. In this work, we investigate a practical situation in which the signal source is no longer protected by the legitimate parts, but is exposed to the untrusted atmospheric channel. We show that the performance of ACVQKD is reduced by removing the assumption, especially when putting the untrusted source at the middle of the channel. To improve the performance of the ACVQKD with the untrusted source, a non-Gaussian operation, called photon subtraction, is subsequently introduced. Numerical analysis shows that the performance of ACVQKD with an untrusted source can be improved by properly adopting the photon subtraction operation. Moreover, a special situation where the untrusted source is located in the middle of the atmospheric channel is also considered. Under direct reconciliation, we find that its performance can be significantly improved when the photon subtraction operation is manipulated by the sender.
Empowerment of Digital Technology to Improve the Level of Agricultural Economic Development based on Data Mining

Rural economic development is of the great significance to local regional economic development and Rural Revitalization Strategy. The main task of multi-scale data mining has two aspects, namely, multi-scale realization of data and multi-scale discovery of knowledge: the former belongs to data preprocessing, which can be realized by data scale division. The latter needs to improve the specific mining technology, excavate knowledge in the multi-scale representation of data, analyze and deduce the relationship between knowledge. How to find the valuable information in the mass data and the security risks in the network is one of the problems to be solved in the information processing technology. The clustering analysis method in data mining technology is to cluster the data with high similarity according to the similarity measurement standard. Therefore, the data objects in the same cluster are more similar to those in different clusters. With the prevalence of supply side reform and the implementation of Rural Revitalization Strategy, the agriculture of the primary industry takes advantage of the situation. From the perspective of urban-rural system, rural development plays an important role in the growth of cities and regions. This paper uses data mining technology to improve the level of agricultural economic development.
Entanglement concentration for six-particle cluster states

Multi-particle cluster states play a significant role in quantum information processing. However, due to the inevitable interaction with the environment in the transmission process, the fidelity of entanglement decreases. To distill the perfect cluster states, we present two schemes for arbitrary six-particle cluster states. POVM local unitary operators that are obtained by solving the equation according to the properties of cluster states are used in the first protocol. The second protocol is based on cross-Kerr nonlinearity which is exploited to check parity between original qubit with ancillary single qubit and it can achieve a higher probability of success through iteration. Furthermore, we can generalize the first method to concentrate entanglement on even number of particles. Our protocol will be useful in practical applications.
Aplikasi Media Pembelajaran pada Kelas 4 Tema 8 Subtema 3 Bangga Terhadap Daerah Tempat Tinggalku di MIN 2 Kebonagung Imogiri Bantul, Yogyakarta

The learning media used in the process of learning Theme 8 Subtema 3 Proud of the Region where I live in grade 4 at MIN 2 Kebon Agung Imogiri Bantul is internet and projector media. This media is very commonly used by teachers. In its learning activities the media is used to convey material about various production and professional activities that exist around students through pictures and videos. The obstacle in the learning media application is that teachers have difficulty in determining the media used because not all material can use interesting media. This is also due to the teacher's limitations in mastering learning media. But in the learning process, many students have understood the material by using learning media. Apart from all that, there are still students who do not understand the material because the teacher does not pay attention to all student activities in class. Many students play during learning. Classroom learning begins with showing pictures and videos related to the material to be explained, then the teacher will provide stimulus questions to students. The problem is that teachers have difficulty choosing a fixed learning media, this is also related to the teacher's limitations in mastering learning media