Reviewerly/exHarmony huggingface dataset:
A Study on Academic Achievement according to the Stress Level of Adolescents in Face-to-face and Non-face-to-face Education after COVID-19

The purpose of this study is to improve academic achievement through individual stress level management by finding the difference in the stress level perceived by students in 373 high school boys and girls in Seoul-based high school in face-to-face education and non-face-toface education. As a research method, t-test verification and distribution analysis were conducted to find out the correlation between variables using Pearson correlation coefficient, and to find out the difference between variables, and spss 21 was used. The research results derived statistically significant results in which students' learning environment changes before and after COVID-19, that is, face-to-face classes and non-face-to-face classes, affect academic achievement Korean, English, mathematics, society, and science. In addition, learning plan showed significant results in sub-factors of English subject efficacy and self-directed learning ability according to mock education. In future studies, a study on the level of stress according to learners’ academic achievement in a mixed education method of education in a post-pandemic learning environment is suggested.
Introduction to Deep Learning

This chapter systematically summarizes the principles, methods and applications of deep learning. First, the basic principles such as deep feedforward network, back propagation, regularization, optimization methods, hyperparameters, loss functions, parameter initialization strategy, and overfitting and underfitting, and the paradigms of small and zero sample, transfer, multi-task, multi-label, and multi-output learning were presented. Then, a detailed introduction to mainstream deep learning algorithms was shown, such as deep belief network, auto-encoder, convolutional neural network (CNN), deep CNN, fully convolutional network, and Recurrent neural network. Finally, the applications of deep learning, such as classification, detection, and segmentation, and some related public datasets were described.
Teacher-student interaction in the digital environment of the higher educational institution

The article analyzes the teacher-student interaction in the digital environment of the higher educational institution. A comparison between the pre-digital stage of education and the new educational principles that can be called digital pedagogy is made. It is shown that digital pedagogy refers mainly to higher education, and it is justified that the information environment of the modern educational process should be able to adapt individually to the educational tasks of each participant - these possibilities of modern digital technology and the tasks of humanistic pedagogy are combined in the manifesto of the digital educational environment.
Digital modernization of Russian education in modern conditions

In modern conditions, education cannot remain the same as it was twenty years ago. It's not just that the formats for presenting information have changed, but the semiotic environment itself has transformed. Visual communication has become the main system of signs, and network communications have become the main communication channel. As a consequence, there was a fundamental change in the system of thinking, first of all, among young people, for whom such a communication mode is the most natural. The article is devoted to the consideration of these features, as well as the system of digital education itself.
Drones Deliver Safety, Efficiency

Sometimes risk is unavoidable in water system operations. But in some cases, drones may be able to do the same job as effectively as utility employees, eliminating danger and possibly saving time and money.
Energy in Music: An Inventory of Observations and Ideas

The contemporary field of musical analysis and theory features an overwhelming pre ponderance of studies related to the pitch domain. However, very few studies have explored the question of energy—that force without which there would be no sound in the first place, no creative drive in the second place, no directed motion, no articulation, no climax, and so on. After summarizing some important work already conducted in the field of energy in music, I discuss various ideas and observations I have made, both in my efforts as a composer and through my work with aural analysis (aural sonology) of music-as-heard, since here the multiple roles of energy are far more evident than in the musical score. I present an inventory of ideas that, by implication, challenge the consensus of what the limits of music theory and analysis should be. I argue that to understand the craft of musical composition in greater depth, it is necessary to study the synergies between a greater number of musical "parameters," and I therefore propose the need for a multidimensional approach to music analysis.
Development of a Machine Translation System for Promoting the Use of a Low Resource Language in the Clinical Domain: The Case of Basque

In multilingual environments wherein there is a strong language spoken by a majority and a low resource language spoken by a minority, Machine Translation (MT) can be useful for allowing clinical practitioners to write their reports in the minority language, which then can be automatically translated into the majority language. Current state-of-the-art approaches for MT require large quantities of parallel sentences of the desired languages and the specific domain, so MT systems developed for translating clinical text from/into a low resource language will usually need to go through a domain adaptation process. When there are enough in-domain resources for the target language, back-translation is commonly used for domain adaptation. In our case, since we have access to many Electronic Health Records (EHR) in Spanish, we make use of this and similar techniques that leverage monolingual data for translating clinical text from Basque into Spanish. Moreover, one of the main characteristics of clinical domain text is its rich terminology, which is not always available for any given language, so before developing an MT system for the clinical domain, it is beneficial to make a special effort to translate the clinical terminology into the low resource language. If the final objective is to implement a system that can be useful for clinical practitioners, it is important to work with them for defining the terminology and any other aspect that can affect the final performance of the systems. Needless to say, given the special relevance of the content to be translated, users of the MT systems should be aware of possible errors made; and whenever it is possible, a human translator should review the generated translations to guarantee their accuracy. In this chapter, we describe the approach we have followed to develop an MT system for translating clinical text from Basque into Spanish. In the first section, we introduce the Basque language and give some details about the sociolinguistic situation in the Basque Country. In the second section we present Itzulbide, the project carried out together with the Basque public health service for compiling clinical domain corpora to be used for developing an MT tool for the healthcare domain. In the third section, we overview the diverse corpora we have used in our systems, and specify how the training and evaluation are performed. The fourth section discusses the results obtained in the defined settings. Finally, the fifth section presents some conclusions and points to possible future directions.
Cross-collection Dataset of Public Domain Portuguese-language Works

Many datasets are published in English to get more engagement, popularity and reach within a research community. Indeed, most sciences are language-agnostic and thrive on publicly available data. However, such a claim is not always valid for Arts, where Literature and Music are two examples of fields that heavily rely on the language of the work. Especially in Literature, combining human expertise with book consumers’ data may generate what is needed to sustain constant changes experienced in the book publishing market. Therefore, we introduce PPORTAL, the first public domain Portuguese-language literature dataset that is composed of a wide variety of book-related metadata. Afterintroducing its building process and content, we present an exploratory data analysis with a quantitative description of its main features. We also show its usability as a resource on different research domains through examples of real-world applications, as well as pointing out other potential applications.
Emerging Optical Materials, Devices and Systems for Photonic Neuromorphic Computing: introduction to special issue

This is an introduction to the feature issue of Optical Materials Express on Emerging Optical Materials, Devices and Systems for Photonic Neuromorphic Computing.
A Design Space for Surfacing Content Recommendations in Visual Analytic Platforms

Recommendation algorithms have been leveraged in various ways within visualization systems to assist users as they perform of a range of information tasks. One common focus for these techniques has been the recommendation of content, rather than visual form, as a means to assist users in the identification of information that is relevant to their task context. A wide variety of techniques have been proposed to address this general problem, with a range of design choices in how these solutions surface relevant information to users. This paper reviews the state-of-the-art in how visualization systems surface recommended content to users during users' visual analysis; introduces a four-dimensional design space for visual content recommendation based on a characterization of prior work; and discusses key observations regarding common patterns and future research opportunities.
Simulating the automation of sorting crates – a stepwise approach

This paper presents an initial study on the task of automating the sorting of crates. It was paramount in the study that the technology used for the automation process is already existing and available to the client in Norway; therefore an existing pallet sorting system was used as state of the art. The simulation was performed via a stepwise approach, that is, first we simulated the process as it is now. Based on this manual case we simulated the same system under a new automated task at time, until the final case (close to) fully automated. A set of KPIs was defined and used in the assessment of all cases, including the manual one. These are Opex [NOK], Capex [NOK], Reliability [Stops over time], Robustness [%, Probability of consequence], and Efficiency [crates per hour]. Our finding overall is that the time it took to sort a given number of crates decreased with increasing implementation of automation through the various change cases. With reduced time consumption the efficiency and number of crates per hour increased, from 300 in the manual case, to 1200 in the fully automated case. The increase in automation also resulted in increased costs, both capital- and operating costs. If we look at the cost increase in relation to the capacity increase, the cost per sorted crate decreases by about 50%. The manual process sorts 300 crates per hour, and if we look at the cost per sorted crate, the fully automated case must sort 600 crates per hour to breakeven and justify the automation.
Learned Parameterized Convolutonal Approximation of Image Filters

Multilayer neural networks are considered universal&lt;br&gt;approximators applicable to a wide range of problems. There are quite detailed theoretical and applied studies for fully connected networks, while for convolutional networks the results are more scarce. In this paper, we tested the approximating capability of deep neural networks with typical architectures like ConvNet, ResNet, and UNet as applied to classical image processing algorithms. Canny edge detector and grayscale morphological dilation with the disk structuring element were selected as target algorithms. As we have seen, even relatively lightweight neural models are able to approximate a filter with fixed parameters. Since classical algorithms are parameterized, we considered different approaches to the parameterization of the neural networks and found out that even the simplest of them, adding parameters in the input images channels, works well for low parameter count. Also, we measured an inference time of a neural network approximation and a classical implementation of the grayscale dilation with the disk structuring element. Starting from a certain radius, a neural network works faster than an algorithm even on one core of the CPU without fine-tuning the architecture for performance, thus confirming the viability of ConvNets as a differentiable approximation technique for optimization of classical-based methods.
Dynamic trajectory for landing an aerial vehicle on a mobile platform.

In the presented work, a trajectory is designed allowing an Unmanned Aerial Vehicle (UAV) to perform landing on a mobile platform. The proposed trajectory behaves in such a way that grants a seamless integration of information about the target landing platform into its design. This allows any generic tracking controller to follow the trajectory regardless of the target's dynamics or movement, as they are indirectly applied to the trajectory's architecture. The focus of this work is for fixed-wing aircraft to land on water surface vehicles but can be adjusted for different scenarios. One of which is used for the experimental corroboration of the proposed strategy in a real-world environment.
Experimental Demonstration of A Spatial Mode Quantum Gate Assisted by Diffractive Deep Neural Networks

We demonstrate a quantum controlled-NOT (CNOT) gate operating on spatial modes of single photons. The CNOT gate is implemented by a series of spatially separated phase plates generated by diffractive deep neural networks (D 2 NNs).
Computation with degenerate optical parametric oscillator networks

We describe recent progress of computation using degenerate optical parametric oscillator (DOPO) networks, including the development of a 100,000-spin coherent Ising machine and realization of a photonic spiking neuron using a pair of DOPOs.
АЗЫРКЫ МЕКТЕПТИН МУГАЛИМИНИН ПРОФЕССИОГРАММАСЫ ТУУРАЛУУ

The article updates future teachers about the dignity and importance of their teaching profession, their place in society as a teacher and the level of usefulness of their professional profile as the source of motivation and guidance for professional growth. The article analyzes the work of domestic and foreign scholars on the professional profile of а teacher. Besides various requirements of teachers’ professional features, there are also his/her personal qualities as kindness, sensivity and love for children are discussed.
The Best of Both Worlds: Using Qualitative and Quantitative Feedback to Improve the Design of Connected Medical Devices

The medical device industry has a long legacy of product development processes that are heavily influenced by the design of the device hardware. As devices have become connected, often to smartphones via connections such as Bluetooth, software is playing an increasing role in the overall system and in particular the user interface. As a result, iterative agile design practices that are prevalent in software are starting to influence the connected device development process. These modern agile practices put the user at the center of the design problem. But what is the best way in this new world to get feedback from users such that the overall product can be improved? In this paper, we share two techniques, one qualitative and one quantitative, that can answer this question. We’ll also explore the synergy of using both techniques together.
Mini-transformer with pooling for unsupervised domain adaptation person reidentification

Person reidentification (Re-ID) aims to match specific pedestrians across nonoverlapping camera views. Due to the dramatic disparities between different datasets, transferring a Re-ID model trained on the source domain to the target domain is challenging. Some outstanding unsupervised domain adaptation (UDA) Re-ID methods use clustering to generate pseudolabels, optimizing the model on the target domain, but the pseudolabels are inevitably noisy. To address the above issues, we propose a framework named mini-transformer with pooling (MTP) to facilitate the generation of superior quality pseudolabels by improving the model’s feature representation capability. First, we introduce an effective mini-transformer (MT) that can be placed directly behind the CNNs as a feature extractor to capture long-range dependency. Then, we design two delicate pooling methods named global hybrid pooling (GHP) and global subvalue pooling (GSVP) to suit mini-transformer’s tremendous capability without increasing computational complexity. Specifically, GHP can keep more global information and GSVP can keep more discriminative information. Finally, experiments on four mainstream UDA Re-ID tasks demonstrate that MTP achieves competitive mAP and rank-1 accuracy to the current state-of-the-art methods, suggesting that our technique is simple but effective. In addition to UDA Re-ID, our MTP can be extended to other supervised retrieval tasks.
KONSEP MEDIA BELAJAR DAN TEORI PEMBELAJARAN BERBASIS TEKNOLOGI

Organized learning system is a combination that includes elements of human, material, facilities, equipment, and procedures that interact to achieve a goal. Human element in the system of learning is students, teachers / faculty, librarians, laboratory, administrative personnel as well as those that support the success of the learning process. Information and communication technology is the result of human engineering to information process and the process of delivering a message (ideas, ideas) from one party to another resulting in faster, wider distribution, and longer storage. To be able to take advantage of ICT to improve the quality of learning, there are three things that must be realized that (1) students and teachers should have access to digital technology and the Internet in the classroom, school, and teacher education institutions, (2) must be available the material quality, meaningful and cultural support for students and teachers, and (3) the teacher should have knowledge and skills in using tools and digital resources to help students to mencaqpai academic standards. The demands of learning in the future should be open and two-way, diverse, multi-disciplinary and related to the productivity of work "at that moment" and competitive. Information and communication technology cheaply and easily will eliminate the limitations of space and time that had been limiting world of education. Today many impacts that occur as a result of the development of IT in the country. Indeed, the development of IT if addressed in a positive able to have a positive impact if addressed negative and may be adversely affected as well
Designing a Curriculum With Custom and Present Virtual Reality

The pandemic has created many challenges for education. Despite many schools now having already returned to instruction in-person as opposed to the challenge of doing so virtually, many have adapted to the new normal of having hybrid modes of instruction. One of these methods is through VR/AR technology. This paper will discuss how custom VR/AR
Revisiting simplification in corpus-based translation studies: Insights from readability research

Ever since the publication of Laviosa’s (1998a; 1998b) pioneering work, the study of lexico-syntactic simplification has held centre stage in corpus translation research concerned with the typical features of translated texts. The simplification hypothesis states that translated texts are simpler than non-translated texts. The convergence hypothesis, also discussed by Laviosa (1998a; 1998b), but less so in follow-up studies, is that translated texts are more homogeneous than original texts, that is they display less variance. To date, simplification has mostly been operationalised in CBTS as type-token ratio, lexical density, core vocabulary coverage, list head coverage and average sentence length. Relying on these parameters, previous research has produced mixed results, with simplification varying across translation modalities, language pairs and registers. The present article sets out to revisit the simplification and convergence hypotheses through the lens of NLP-informed readability research. In particular, we rely on a larger set of simplification indicators and make use of multivariate statistical techniques. We present a simplification study of Europarl corpus data in French translated from English and in non-translated French. The results show that translated French is simpler than original French, lexically and syntactically. We also find evidence of convergence that shows that translators smooth out cross-speaker lexical heterogeneity in translated parliamentary proceedings.
Learning Co-occurrence Features Across Spatial and Temporal Domains for Hand Gesture Recognition

Hand gesture is the most natural modality for human-machine interaction and its recognition can be considered one of the most complicated and interesting challenges for computer vision community. In recent years, there has been a noticeable advancement in the field of machine learning and computer vision. However, providing a hand gesture recognition system robust enough to work in real-time applications remains challenging. Dynamic hand gestures can be seen as variations in shape or movement during hand motion and often both together. To tackle these challenges, we propose a dynamic hand gesture recognition approach based on hand skeletal sequences. In particular, we introduce a simple but effective deep network architecture to deal with Spatio-temporal co-occurrence features computed on 3D coordinates of hand joints along the gesture sequence. Experimental results show that our approach outperforms state-of-the-art methods on two public datasets, First Person Hand Action and SHREC’2017, with an efficient time computational model compared to most existing approaches.
Shakespeare’s Rare Words and Chronology

This essay presents a “rare words” analysis of Shakespeare’s plays and poems with the goal of better establishing their chronology. The technique of examining Shakespeare’s rare words for what they suggest about the order of his works was proposed by Gregor Sarrazin over a century ago, and subsequently elaborated by such figures as, among others, Karolina Steinhäuser, Alfred Hart, Eliot Slater, M. W. A. Smith, Thomas Merriam, and MacDonald P. Jackson.1 As modeled by Sarrazin and refined by these and other scholars, the procedure examines the strength and weakness of associations in the overlap, across Shakespeare’s texts, of words he seldom used. Indeed, while called “rare” by tradition, “seldom-used” is more to the point: such words are less like “honorificabilitudinitatibus” in Love’s Labor’s Lost (5.1.41)—understandably used only once in the canon, and in that respect too rare to connect this comedy with other texts—and more like the same play’s “jangling” (2.1.225), which it shares only with A Midsummer Night’s Dream (3.2.353); “moderately” (1.1.197), used elsewhere just in Romeo and Juliet (2.6.14); and “specialties” (2.1.164), which it has in common only with The Taming of the Shrew (2.1.126).2 Because they link a target text with other texts, such words are referred to as “link words.” Texts written closer in time to each other tend to be linked by their word choices.