Reviewerly/exHarmony huggingface dataset:
PEDAGOGIC CONDITIONS FOR FORMING A CULTURAL IDENTITY OF RURAL SCHOOLCHILDREN OF LENINGRAD REGION WITH THE EYES OF TEACHERS

The article discusses the pedagogic conditions for the formation of the cultural identity of rural schoolchildren in Leningrad Region (using the example of three schools), describes the most common methods of museum pedagogy, according to teachers, creating positive conditions for the formation of the cultural identity of pupils on the basis of school museums of local history and historical profi les; three components of pedagogic conditions were revealed, which, according to teachers, «work» in the fi eld of formation of a cultured identity of rural schoolchildren; the author’s model of cultural identity of rural schoolchildren of Leningrad Region is described. An analysis of the narratives of schoolteachers and pupils, which were the resulting methods of focus groups, showed that pedagogic conditions are not only content and methods of educational practices. What plays a major role in the system of pedagogic conditions for the formation of cultural identity of rural schoolchildren is the personality of the teacher-handler, and often of the creator of the school museum of local history and historical profi les. Pedagogic conditions of formation of cultural identity of rural schoolchildren bring parents and pupils of school closer together, satisfy deep humanitarian interests of pupils. As for teachers, they contribute to broadcasting positive traditional norms of the rural hostel from older generations to younger ones, namely: strengthen neighbourhood ties and mutual assistance, interest in the history of native heath and objects of its sacred landscape, preservation of objects of intangible cultural heritage (local patois, holidays, songs, folklore). This is done most successfully by teachers in primary and middle-level classes.
Weight Quantization Retraining for Sparse and Compressed Spatial Domain Correlation Filters

Using Spatial Domain Correlation Pattern Recognition (CPR) in Internet-of-Things (IoT)-based applications often faces constraints, like inadequate computational resources and limited memory. To reduce the computation workload of inference due to large spatial-domain CPR filters and convert filter weights into hardware-friendly data-types, this paper introduces the power-of-two (Po2) and dynamic-fixed-point (DFP) quantization techniques for weight compression and the sparsity induction in filters. Weight quantization re-training (WQR), the log-polar, and the inverse log-polar geometric transformations are introduced to reduce quantization error. WQR is a method of retraining the CPR filter, which is presented to recover the accuracy loss. It forces the given quantization scheme by adding the quantization error in the training sample and then re-quantizes the filter to the desired quantization levels which reduce quantization noise. Further, Particle Swarm Optimization (PSO) is used to fine-tune parameters during WQR. Both geometric transforms are applied as pre-processing steps. The Po2 quantization scheme showed better performance close to the performance of full precision, while the DFP quantization showed further closeness to the Receiver Operator Characteristic of full precision for the same bit-length. Overall, spatial-trained filters showed a better compression ratio for Po2 quantization after retraining of the CPR filter. The direct quantization approach achieved a compression ratio of 8 at 4.37× speedup with no accuracy degradation. In contrast, quantization with a log-polar transform is accomplished at a compression ratio of 4 at 1.12× speedup, but, in this case, 16% accuracy of degradation is noticed. Inverse log-polar transform showed a compression ratio of 16 at 8.90× speedup and 6% accuracy degradation. All the mentioned accuracies are reported for a common database.
Language and Innovation: The Real Effect of Informal Institutions

Strong future-time reference (FTR) languages require speakers to grammatically mark future events, while weak-FTR languages do not. Using data from 33 countries, we find that firms in countries where strong-FTR languages are spoken are less likely to engage in innovative activities than those in countries where weak-FTR languages are spoken. Further evidence shows that strong-FTR languages affect inventors’ perceptions and beliefs around the future rewards from innovation. Moreover, due to interactions between people speaking different languages, globalization attenuates the negative impact of language FTR on innovation. To further support these findings, we provide evidence from a single country with a multilingual environment to control for omitted country-level characteristics. Our study emphasizes the impact of language on corporate innovation and sheds light on the importance of informal institutions on economic outcomes.
Use of “Internal Knowledge”: Biomedical Literature Search Liberated From External Resources

Knowledge plays an essential role in biomedical literature search (BLS) systems, filling the semantic gap between queries and documents. Knowledge bases, constructed by human experts or machine learning methods, are generally regarded as the main sources serving external knowledge. However, a good knowledge base must balances its particularity and generalization, resulting limited knowledge coverage and utilization to BLS systems. Considering massive documents in a BLS system, and recently developing Open IE techniques by which we can automatically extract structured knowledge from documents, how about harnessing distilled internal knowledge rather than external knowledge to conduct BLS systems? Internal knowledge, providing tailored particular knowledge to BLS systems, is supposed to lead better knowledge utilization and much more competitive performance on literature search. In this paper, we design an novel internal knowledge driven BLS system upon a Multi-layered Encoders incorporating Multi-layered internal Knowledge graph, called MEMK. MEMK harnesses distilled internal structural knowledge, empowering interactive representations learning of query and documents. The experiments show that MEMK outperforms strong baselines on a public benchmark, and internal knowledge based query expansion can further improve the performance to a new state of the art.
The professional and personal development of teachers in the context of continuing education

This article is devoted to the issue of professional and personal development of teachers in the context of continuing education, taking into account the needs of the developing society in teaching staff ready to build a successful career, which is characterized by mobility, dynamism, constructiveness, cooperation, and creativity.The research objective: to determine the impact of professional and personal development of teachers related to the success of their professional activity and level of personal maturity as a teacher.The research tasks: To determine subjective and objective factors of developing the personal maturity of teachers, to describe pedagogical conditions for developing the personal maturity of teachers in the process of continuing education, and to determine and characterize development levels of the personal maturity of teachers as a condition of professional and personal development.The research methods: 1) Theoretical ones: The analysis of research results on the issue, comparison and generalization; 2) Empirical ones: The test of D.A. Leontyev, the methodology of E.E. Rukavishnikova, the methodology of M.N. Mironova, and the “Questionnaire of Mastering Methods”; and 3) The correlation coefficient of Spearman.The authors determine that it is reasonable to consider the personal maturity of teachers not only as a condition of but also as a criterion for professional growth. Subjective and objective factors in the development of the personal maturity of teachers include competitive environment, hierarchy of human needs, age, individual craving for recognition, status, respect, and self-actualization as well as the norms and values of the social and cultural environment.Further, the authors determine three levels of teachers’ personal maturity (low, sufficient, and optimal) and present generalized characteristics of teachers demonstrating the different levels in the context of actual and potential professional and personal growth.Materials provided in the article can be useful in designing models and programs for additional professional education, in developing individual programs for teacher development, and in certifying teaching staff.
Cascaded channel pruning using hierarchical self-distillation.

In this paper, we propose an approach for filter-level pruning with hierarchical knowledge distillation based on the teacher, teaching-assistant, and student framework. Our method makes use of teaching assistants at intermediate pruning levels that share the same architecture and weights as the target student. We propose to prune each model independently using the gradient information from its corresponding teacher. By considering the relative sizes of each student-teacher pair, this formulation provides a natural trade-off between the capacity gap for knowledge distillation and the bias of the filter saliency updates. Our results show improvements in the attainable accuracy and model compression across the CIFAR10 and ImageNet classification tasks using the VGG16and ResNet50 architectures. We provide an extensive evaluation that demonstrates the benefits of using a varying number of teaching assistant models at different sizes.
The Teaching Design of Comprehensive English for English Majors in Higher Vocational Colleges Based on "Production Oriented Method"

As the core basic course for English majors, Comprehensive English is faced with the difficulties of time consuming and effort with little effect, serious separation between learning and application, and poor English output of students. The Production-oriented Approach (POA) provides a theoretical basis for the reform of comprehensive English. According to the POA theoretical system of "motivate-enable-assess teaching process", this paper carries out teaching design and teaching practice for unit 3 "My First Job" in college Comprehensive English. The practice shows that the Production-oriented Approach has a positive effect on the separation of learning and application and the unsatisfactory output effect of the comprehensive English course.
Critical and prospective analysis of online education in pandemic and post-pandemic contexts: Digital tools and resources to support teaching in synchronous and asynchronous learning modalities

espanolLa humanidad, previo a la pandemia del COVID-19, experimentaba una gradual transicion cultural entre la modernidad y la postmodernidad. La profundizacion del avance cientifico y tecnologico, condujo al hom-bre hacia una progresiva adaptacion, desarrollando nuevas formas de enfrentar y resolver sus problemas. En este contexto excepcional, las personas y las instituciones enfrentan la necesidad de readaptar bruscamente esos modos de vida y sus formas de comunicacion. En educacion, la necesidad de migrar desde aulas tradicionales hacia sis-temas de modalidad en linea, requiere herramientas y recursos digitales de apoyo a la docencia. Este articulo teorico tiene como objetivo analizar criticamente esta disrupcion, detallando adaptaciones pedagogicas, metodo-logicas, didacticas y evaluativas que aporten a la eficacia de estos procesos educativos. Se consideran orientaciones para el aprendizaje en plataformas sincronicas y asincronicas. Son analizadas las tendencias asociadas a estos cambios, aportando una prospectiva sobre la educacion en contextos de post-pandemia. EnglishHumanity, prior to the COVID-19 pandemic, was already in the process of a gradual cultural transi-tion between modernity and postmodernity. The rapid pace of scientific and technological advances has led humans towards a progressive adaptation, as they develop new ways to confront and to solve their problems. In the excep-tional context of the pandemic, people and institutions have faced a sudden need to readapt their ways of life and their means of communication. In education, the need to migrate from traditional classrooms to online systems was urgent, and the shift required digital tools and resources to support teaching. This theoretical article aims to analyze the disruption in the educational context and to detail the adaptations that may contribute to the effective-ness of educational processes. Moreover, it discusses the pedagogical, methodological, didactic, and evaluative aspects of the change in education. The article also proposes some guidelines for learning on both synchronous and asynchronous platforms. The trends associated with these changes are analyzed in order to offer a perspective on education in the post-pandemic world.
Iterative convergent computation is not a useful inductive bias for ResNets

Recent work has suggested that feedforward residual neural networks (ResNets) approximate iterative recurrent computations. Iterative computations are useful in many domains, so they might provide good solutions for neural networks to learn. Here we quantify the degree to which ResNets learn iterative solutions and introduce a regularization approach that encourages learning of iterative solutions. Iterative methods are characterized by two properties: iteration and convergence. To quantify these properties, we define three indices of iterative convergence. Consistent with previous work, we show that, even though ResNets can express iterative solutions, they do not learn them when trained conventionally on computer vision tasks. We then introduce regularizations to encourage iterative convergent computation and test whether this provides a useful inductive bias. To make the networks more iterative, we manipulate the degree of weight sharing across layers using soft gradient coupling. This new method provides a form of recurrence regularization and can interpolate smoothly between an ordinary ResNet and a ``recurrent ResNet (i.e., one that uses identical weights across layers and thus could be physically implemented with a recurrent network computing the successive stages iteratively across time). To make the networks more convergent we impose a Lipschitz constraint on the residual functions using spectral normalization. The three indices of iterative convergence reveal that the gradient coupling and the Lipschitz constraint succeed at making the networks iterative and convergent, respectively. However, neither recurrence regularization nor spectral normalization improve classification accuracy on standard visual recognition tasks (MNIST, CIFAR-10, CIFAR-100) or on challenging recognition tasks with partial occlusions (Digitclutter). Iterative convergent computation, in these tasks, does not provide a useful inductive bias for ResNets.
Integrating Pre-trained Model into Rule-based Dialogue Management

Rule-based dialogue management is still the most popular solution for industrial task-oriented dialogue systems for their interpretablility. However, it is hard for developers to maintain the dialogue logic when the scenarios get more and more complex. On the other hand, data-driven dialogue systems, usually with end-to-end structures, are popular in academic research and easier to deal with complex conversations, but such methods require plenty of training data and the behaviors are less interpretable. In this paper, we propose a method to leverages the strength of both rule-based and data-driven dialogue managers (DM). We firstly introduce the DM of Carina Dialog System (CDS, an advanced industrial dialogue system built by Microsoft). Then we propose the "model-trigger" design to make the DM trainable thus scalable to scenario changes. Furthermore, we integrate pre-trained models and empower the DM with few-shot capability. The experimental results demonstrate the effectiveness and strong few-shot capability of our method.
PhraseTransformer: Self-Attention using Local Context for Semantic Parsing

Semantic parsing is a challenging task whose purpose is to convert a natural language utterance to machine-understandable information representation. Recently, solutions using Neural Machine Translation have achieved many promising results, especially Transformer because of the ability to learn long-range word dependencies. However, the one drawback of adapting the original Transformer to the semantic parsing is the lack of detail in expressing the information of sentences. Therefore, this work proposes a PhraseTransformer architecture that is capable of a more detailed meaning representation by learning the phrase dependencies in the sentence. The main idea is to incorporate Long Short-Term Memory (LSTM) into the Self-Attention mechanism of the original Transformer to capture more local context of phrases. Experimental results show that the proposed model captures the detailed meaning better than Transformer, raises local context awareness and achieves strong competitive performance on Geo, MSParS datasets, and leads to SOTA performance on Atis dataset in methods using Neural Network.
Nonparametric frequency polygon estimation for modeling input data

To construct valid probability distributions solely from input data, this paper compares three nonparametric density estimators: (1) histograms, (2) Kernel Density Estimation, and (3) Frequency Polygon Estimation. A pseudocode is implemented, a practical example is illustrated, and the Simphony.NET simulation environment is used to fit the nonparametric frequency polygon to a set of data to recreate it as a posterior distribution via the Metropolis-Hastings algorithm.
Noncyclic and Nonadiabatic Geometric Quantum Gates with Smooth Paths

Nonadiabatic geometric quantum computation is dedicated to the realization of high-fidelity and robust quantum gates, which is the necessity of fault-tolerant quantum computation. However, it is limited by cyclic and mutative evolution path, which usually requires longer gate-time and abrupt pulse control, and thus weaken the gate performance. Here, we propose a scheme to realize geometric quantum gates with noncyclic and nonadiabatic evolutions, which effectively shorten the gate duration, and universal quantum gates can be realized in one step without path mutation. Our numerical simulation shows that, comparing with the conventional dynamical gates, the constructed geometric gates have stronger resistant not only to noise but also to decoherence effect. In addition, our scheme can also be implemented on a superconducting circuit platform, with the fidelities of single-qubit and two-qubit gates higher than 99.97$\%$ and 99.84$\%$, respectively. Therefore, our scheme provides a promising way to realize high-fidelity fault-tolerant quantum gates for scalable quantum computation.
Frame Aggregation and Multi-modal Fusion Framework for Video-Based Person Recognition

Video-based person recognition is challenging due to persons being blocked and blurred, and the variation of shooting angle. Previous research always focused on person recognition on still images, ignoring similarity and continuity between video frames. To tackle the challenges above, we propose a novel Frame Aggregation and Multi-Modal Fusion (FAMF) framework for video-based person recognition, which aggregates face features and incorporates them with multi-modal information to identify persons in videos. For frame aggregation, we propose a novel trainable layer based on NetVLAD (named AttentionVLAD), which takes arbitrary number of features as input and computes a fixed-length aggregated feature based on the feature quality. We show that introducing an attention mechanism into NetVLAD effectively decreases the impact of low-quality frames. For the multi-model information of videos, we propose a Multi-Layer Multi-Modal Attention (MLMA) module to learn the correlation of multi-modality by adaptively updating correlation Gram matrix. Experimental results on iQIYI-VID-2019 dataset show that our framework outperforms other state-of-the-art methods.
Evaluation of the Effect of Preprocessing Data on Network Traffic Classifier Based on ML Methods for Qos Predication in Real-Time

The paper proposes a traffic classification model based on Machine Learning methods for definition of QoS, which contains a new element - data preprocessing block. In earlier works data preprocessing is understood only as forming a feature matrix for classification. This paper explores nine main methods based on normalization, standardization, scaling, generating additional features and working with outliers. Our experiment on a data set obtained from a real network showed that the most important procedure was to replace outliers with median values. In General, the most successful preprocessing methods for classifying traffic were Quantile and Power transformation, since they transform different scales and distributions similar to Gaussian distribution and it is more convenient to work with many ML methods. Using of preprocessing methods can increase the classification accuracy by more than 50%.
Segmentation of Satellite Images of the Earth's surface using Neural Network Technologies

The authors present an algorithm for segmentation of satellite images based on the U-net convolutional neural network. It includes the following stages: preparation of input data, modification of the structure of a convolutional neural network (CNN) taking into account the parameters of the studied images, training CNN on the formed training sample, and segmentation of test images. The authors suggest procedures for optimizing the CNN training time and increasing the accuracy of the selected classes of objects with a limited training sample of images and computing resources. The results of experimental studies that confirm the effectiveness of the method are presented.
Translation Memory Guided Neural Machine Translation

Many studies have proven that Translation Memory (TM) can help improve the translation quality of neural machine translation (NMT). Existing ways either employ extra encoder to encode information from TM or concatenate source sentence and TM sentences as encoder's input. These previous methods don't model the semantic relationship between the source sentence and TM sentences. Meanwhile, the training corpus related to TM is limited, and the sentence level retrieval approach further limits its scale. In this paper, we propose a novel method to combine the strengths of both TM and NMT. We treat the matched sentence pair of TM as the additional signal and apply one encoder enhanced by the pre-trained language model (PLM) to encode the TM information and source sentence together. Additionally, we extend the sentence level retrieval method to the n-gram retrieval method that we don't need to calculate the similarity score. Further, we explore new methods to manipulate the information flow from TM to the NMT decoder. We validate our proposed methods on a mixed test set of multiple domains. Experiment results demonstrate that the proposed methods can significantly improve the translation quality and show strong adaptation for an unknown or new domain.