Reviewerly/exHarmony huggingface dataset:
PRESENTATION OF A TEACHER’S BLOG INTEGRATED THROUGH THE „BLOGGING MODULE“ METHOD IN THE BULGARIAN LANGUAGE LESSONS AND LITERATURE IN THE PRIMARY SCHOOL

This article describes an author’s methodology „Module Blogging“ that actively integrates the use of word processing through a blogging platform. It is created author’s content and structure of a blogging platform with a methodological focus on the learning process of Bulgarian language and literature in the initial stage of education. Its aim is to combine traditional teaching methods with modern technologies in order to increase digital (word processing) and develop more effectively the linguistic and personal competencies of the students. Through this approach, students have the opportunity to actively and creatively participate in the learning process by writing posts, sharing ideas, and commenting on the blog. This encourages their interest in learning and strengthens the learning environment.
Soft skills or digital skills: Is This the Question? Genesis of a workshop

Demand for skills building is increasing more than ever and it has only been accelerated by the COVID-19 pandemic. Digitalisation and globalisation are the driving factors, thus requiring not only digital skills but also soft skills. New and ever-changing contexts require new and flexible training approaches. Performative teaching and learning has proven to be instrumental in helping to develop emotional and social skills, but it is best suited for face-to-face pedagogical contexts, or is it? This article will describe the elements of a workshop that aimed to train soft skills. It was inspired by an online training module in which the teachers applied a performative approach to train digital skills. The module was an integral part of an innovative, interdisciplinary, and interactive workshop, Active Learning Lab (ALL-EM), organised at Ca’ Foscari University. An abridged and adapted version of this workshop was presented at the 8th Scenario Forum Sore back, square eyes? Going performative in digital teaching and learning spaces (18/19 June 2021, online).
Learning to Learn from Corrupted Data for Few-Shot Learning

Few-shot learning which aims to generalize knowledge learned from annotated base training data to recognize unseen novel classes has attracted considerable attention. Existing few-shot methods rely on completely clean training data. However, in the real world, the training data are always corrupted and accompanied by noise due to the disturbance in data transmission and low-quality annotation, which severely degrades the performance and generalization capability of few-shot models. To address the problem, we propose a unified peer-collaboration learning (PCL) framework to extract valid knowledge from corrupted data for few-shot learning. PCL leverages two modules to mimic the peer collaboration process which cooperatively evaluates the importance of each sample. Specifically, each module first estimates the importance weights of different samples by encoding the information provided by the other module from both global and local perspectives. Then, both modules leverage the obtained importance weights to guide the reevaluation of the loss value of each sample. In this way, the peers can mutually absorb knowledge to improve the robustness of few-shot models. Experiments verify that our framework combined with different few-shot methods can significantly improve the performance and robustness of original models.
Learning Calibrated Uncertainties for Domain Shift: A Distributionally Robust Learning Approach

We propose a framework for learning calibrated uncertainties under domain shifts, considering the case where the source (training) distribution differs from the target (test) distribution. We detect such domain shifts through the use of a differentiable density ratio estimator and train it together with the task network, composing an adjusted softmax predictive form that concerns the domain shift. In particular, the density ratio estimator yields a density ratio that reflects the closeness of a target (test) sample to the source (training) distribution. We employ it to adjust the uncertainty of prediction in the task network. This idea of using the density ratio is based on the distributionally robust learning (DRL) framework, which accounts for the domain shift through adversarial risk minimization. We demonstrate that our proposed method generates calibrated uncertainties that benefit many downstream tasks, such as unsupervised domain adaptation (UDA) and semi-supervised learning (SSL). On these tasks, methods like self-training and FixMatch use uncertainties to select confident pseudo-labels for re-training. Our experiments show that the introduction of DRL leads to significant improvements in cross-domain performance. We also demonstrate that the estimated density ratios show an agreement with the human selection frequencies, suggesting a positive correlation with a proxy of human perceived uncertainties.
Image Composition with Depth Registration

Handling occlusions is still a challenging problem for image composition. It always requires the source contents to be completely in front of the target contents or needs manual interventions to adjust occlusions, which is very tedious. Though several methods have suggested exploiting priors or learning techniques for promoting occlusion determination, their potentials are much limited. This paper addresses the challenge by presenting a depth registration method for merging the source contents seamlessly into the 3D space that the target image represents. Thus, the occlusions between the source contents and target contents can be conveniently handled through pixel-wise depth comparisons, allowing the user to more efficiently focus on the designs for image composition. Experimental results show that we can conveniently handle occlusions in image composition and improve efficiency by about 4 times compared to Photoshop.
DFVSR: Directional Frequency Video Super-Resolution via Asymmetric and Enhancement Alignment Network

Recently, techniques utilizing frequency-based methods have gained significant attention, as they exhibit exceptional restoration capabilities for detail and structure in video super-resolution tasks. However, most of these frequency-based methods mainly have three major limitations: 1) insufficient exploration of object motion information, 2) inadequate enhancement for high-fidelity regions, and 3) loss of spatial information during convolution. In this paper, we propose a novel network, Directional Frequency Video Super-Resolution (DFVSR), to address these limitations. Specifically, we reconsider object motion from a new perspective and propose Directional Frequency Representation (DFR), which not only borrows the property of frequency representation of detail and structure information but also contains the direction information of the object motion that is extremely significant in videos. Based on this representation, we propose a Directional Frequency-Enhanced Alignment (DFEA) to use double enhancements of task-related information for ensuring the retention of high-fidelity frequency regions to generate the high-quality alignment feature. Furthermore, we design a novel Asymmetrical U-shaped network architecture to progressively fuse these alignment features and output the final output. This architecture enables the intercommunication of the same level of resolution in the encoder and decoder to achieve the supplement of spatial information. Powered by the above designs, our method achieves superior performance over state-of-the-art models on both quantitative and qualitative evaluations.
Expanding the Hyperbolic Kernels: A Curvature-aware Isometric Embedding View

Modeling data relation as a hierarchical structure has proven beneficial for many learning scenarios, and the hyperbolic space, with negative curvature, can encode such data hierarchy without distortion. Several recent studies also show that the representation power of the hyperbolic space can be further improved by endowing the kernel methods. Unfortunately, the known kernel methods, developed in hyperbolic space, are limited by the adaptation capacity or distortion issues. This paper addresses the issues through a novel embedding function. To this end, we propose a curvature-aware isometric embedding, which establishes an isometry from the Poincar\'e model to a special reproducing kernel Hilbert space (RKHS). Then we can further define a series of kernels on this RKHS, including several positive definite kernels and an indefinite kernel. Thorough experiments are conducted to demonstrate the superiority of our proposals over existing-known hyperbolic and Euclidean kernels in various learning tasks, e.g., graph learning and zero-shot learning.
Enhancing Efficient Continual Learning with Dynamic Structure Development of Spiking Neural Networks

Children possess the ability to learn multiple cognitive tasks sequentially, which is a major challenge toward the long-term goal of artificial general intelligence. Existing continual learning frameworks are usually applicable to Deep Neural Networks (DNNs) and lack the exploration on more brain-inspired, energy-efficient Spiking Neural Networks (SNNs). Drawing on continual learning mechanisms during child growth and development, we propose Dynamic Structure Development of Spiking Neural Networks (DSD-SNN) for efficient and adaptive continual learning. When learning a sequence of tasks, the DSD-SNN dynamically assigns and grows new neurons to new tasks and prunes redundant neurons, thereby increasing memory capacity and reducing computational overhead. In addition, the overlapping shared structure helps to quickly leverage all acquired knowledge to new tasks, empowering a single network capable of supporting multiple incremental tasks (without the separate sub-network mask for each task). We validate the effectiveness of the proposed model on multiple class incremental learning and task incremental learning benchmarks. Extensive experiments demonstrated that our model could significantly improve performance, learning speed and memory capacity, and reduce computational overhead. Besides, our DSD-SNN model achieves comparable performance with the DNNs-based methods, and significantly outperforms the state-of-the-art (SOTA) performance for existing SNNs-based continual learning methods.
Generative Flow Networks for Precise Reward-Oriented Active Learning on Graphs

Many score-based active learning methods have been successfully applied to graph-structured data, aiming to reduce the number of labels and achieve better performance of graph neural networks based on predefined score functions. However, these algorithms struggle to learn policy distributions that are proportional to rewards and have limited exploration capabilities. In this paper, we innovatively formulate the graph active learning problem as a generative process, named GFlowGNN, which generates various samples through sequential actions with probabilities precisely proportional to a predefined reward function. Furthermore, we propose the concept of flow nodes and flow features to efficiently model graphs as flows based on generative flow networks, where the policy network is trained with specially designed rewards. Extensive experiments on real datasets show that the proposed approach has good exploration capability and transferability, outperforming various state-of-the-art methods.
CONTEMPORARY EDUCATIONAL TECHNOLOGIES FOR CREATING NON-STANDARD TEACHING PRACTICES

The modern education system poses a number of challenges to the teacher – documentation, students, parents. Society expects the teacher to be able to teach students to learn, to want to know, to form in them a number of competencies they will need in their future life. In order to meet the new needs, the teacher must use a variety of modern technologies. Through them, he can shape his unique relationship with his students, or be unconventional. What are the incentives that motivate teachers to seek unconventionality? What is the non-standardity of the teacher? What modern educational technologies do teachers use in their practice? These are some of the questions that this paper seeks to answer. A survey was conducted among teachers from different stages of the education system. The results are described. Opinions and recommendations for practice are presented. The teachers’ attitudes towards the use of modern educational technologies and their desire to be non-standard in their pedagogical practice were studied.
APPROXIMATION OF A FUNCTION HAVING BOUNDED DERIVATIVES UPTO THE SECOND ORDER BY SINE-COSINE WAVELET EXPANSION AND ITS APPLICATIONS

In this paper, sine-cosine wavelet has been introduced and the approximation errors of the function f (t) whose first and second derivatives are bounded have been estimated using this wavelet and it is used to solve some linear differential equations.Solution obtained by this method is compared with Euler's method and with exact solution.We observe that the solution obtained by this method is better than the solution given by the Euler's method which shows the usefulness of this method.
PENGARUH MODEL PEMBELAJARAN TEAMS GAMES TOURNAMENT (TGT) BERBANTUAN MEDIA QUESTION BOX TERHADAP HASIL BELAJAR IPA KELAS V SD NEGERI 4 PEDAMARAN

This study intends to determine the effect of the media-assisted teams games tournament (TGT) learning model on the results of learning science in class V SD Negeri 4 Pedamaran and can be expected to increase knowledge about science learning using the media-assisted teams games tournament (TGT) learning model. questionbox. This research is an experimental research. This type of research is quantitative. In this study, researchers used a quasi-experimental design, namely the posttest only control design. The subjects in this study were class V.A and class V.B, which consisted of 36 students. Data collection is done by testing techniques. The data analysis technique uses test questions which are tested for validity and reliability. Based on the results of testing the hypothesis obtained t_count 60.208&gt; t_table 1.698 and a significance value &lt;0.05 (0.000&lt;0.05). So, it can be concluded that the use of the Media Question Box Assisted Teams Games Tournaments (TGT) Learning Model is appropriate for use in learning and has a significant influence.
From little acorns . . . A retrospective on OpenCitations

&lt;strong&gt;The initial vision&lt;/strong&gt; Now that OpenCitations is hosting over one billion freely available scholarly bibliographic citations, this is perhaps an opportune moment to look back to the start of this initiative.
Naturalistic Turn in Modern Philosophy: Philosophy and Neuroscience

The development of biology stimulates a naturalistic turn within the framework of philosophy, actualizes the problem of the dialogue between philosophical and scientific knowledge. In modern philosophy there is a tendency to imitate the empirical sciences. An attempt to conceptualize the empirical knowledge accumulated in modern brain sciences finds its expression within the neurophilosophy project. In classical philosophy, there was a skeptical assessment of the idea of a total reduction of the life of consciousness to the forms of motion of matter, which contributed to the preservation of dualism. The basis of modern neurobiology is the idea of the identity of physical and mental processes, the statement according to which information in our brain is encoded by the activity of neurons, neural networks. As a result, on the border of philosophy and science, corresponding ontological and epistemological programs are formed, designed to comprehend the classical philosophies of the opposition on naturalistic grounds. The author proposes an understanding of the naturalistic trend in modern epistemology, a reflection of the problem of the relationship between physical and mental processes on the example of the conceptualization of the achievements of modern neurosciences within the framework of the neurophilosophy project. The paper notes the diversity of interpretations of the concept of “neurophilosophy”, fixes the prerequisites for its occurrence, development problems and features of interpretation. In the definitions of the concept of “neurophilosophy” one can see a specific form of being of philosophy, the philosophy of neurosciences, a variant of biophilosophy, a form of the philosophy of consciousness. The paper highlights the problems that complicate the implementation of the neurophilosophy project: constant rethinking and supplementing the baggage of scientific knowledge about the brain, the lack of a unified theory of neurosciences, an interdisciplinary language, one-sided reductionism. To build neurophilosophy in the language of neuroscience means to liken it to a special scientific discipline. The project of neurophilosophy initiates, but does not offer models of a constructive dialogue between philosophy and neurosciences, reducing philosophical problems to the achievements of special scientific knowledge, which allows us to consider neurophilosophy, at this stage of its development, as a ‘scientific manifesto’, another attempt of science to do without philosophy. Against the background of the active appeal of philosophy to the special sciences, there is a need to build an empirical-ontological approach, which is based on the current achievements of the sciences, and makes it possible to bring empirical knowledge to the level of philosophical generalizations.
Diffusart: Enhancing Line Art Colorization with Conditional Diffusion Models

Colorization of line art drawings is an important task in illustration and animation workflows. However, this highly laborious process is mainly done manually, limiting the creative productivity. This paper presents a novel interactive approach for line art colorization using conditional Diffusion Probabilistic Models (DPMs). In our proposed approach, the user provides initial color strokes for colorizing the line art. The strokes are then integrated into the conditional DPM-based colorization process by means of a coupled implicit and explicit conditioning strategy to generates diverse and high-quality colorized images. We evaluate our proposal and show it outperforms existing state-of-the-art approaches using the FID, LPIPS and SSIM metrics.
Development of Intelligent Assistant for Automated Generation of Department Documents

The article is devoted to the development of formal mathematical methods for modeling business processes and the application of these methods to create intelligent assistants. The theory of partial models is used to formalize the workflow at the university department. The problem of creating an intelligent assistant for automated generation of university department documents is considered. To solve this problem, an ontological model of the subject area is developed and document templates are built. The focus is on the problem of automated generation of assuredly correct documents. The generation of documents occurs in three stages: a) creation of templates; b) automatic partial filling of documents with fixed information contained in the knowledge base; c) the final meaningful filling of documents by participants in the business process. Relationships between templates, prepopulated documents, and fully populated documents are formally described in terms of homomorphisms of partial models and submodel-supermodel relationships between partial models. Semantic Web technologies are used to search for and eliminate contradictions in the information presented in the knowledge base. Thanks to the automated filling of documents, the efficiency and quality of the produced documents are increased, as well as the time for working with them is reduced. The ability to flexibly change the developed ontological model allows you to optimize the work with documents in accordance with changing regulations
Long-Range Transformer Architectures for Document Understanding

Since their release, Transformers have revolutionized many fields from Natural Language Understanding to Computer Vision. Document Understanding (DU) was not left behind with first Transformer based models for DU dating from late 2019. However, the computational complexity of the self-attention operation limits their capabilities to small sequences. In this paper we explore multiple strategies to apply Transformer based models to long multi-page documents. We introduce 2 new multi-modal (text + layout) long-range models for DU. They are based on efficient implementations of Transformers for long sequences. Long-range models can process whole documents at once effectively and are less impaired by the document’s length. We compare them to LayoutLM, a classical Transformer adapted for DU and pre-trained on millions of documents. We further propose 2D relative attention bias to guide self-attention towards relevant tokens without harming model efficiency. We observe improvements on multi-page business documents on Information Retrieval for a small performance cost on smaller sequences. Relative 2D attention revealed to be effective on dense text for both normal and long-range models.
Research on Grey Wolf optimization algorithm based on dynamic weighting

In response to the Grey Wolf Optimization (GWO) algorithm, the issue of unbalanced global and local search capabilities, as well as convergence speed and search mechanism. This article proposes a grey wolf optimization algorithm based on dynamic weights. By introducing a nonlinear convergence factor and initializes the population using a logistic map. The search technique is enhanced to achieve more varied location choices and more international individuals by using |A| to reselect the location calculation algorithm for gray wolves. Finally, combined with a dynamic weight strategy, the search mechanism of the GWO algorithm is improved. Standard function testing revealed that the revised technique presented in this study outperformed the baseline GWO algorithm in terms of accuracy and convergence speed, as well as stability and generalizability.
The Influence of Think, Predict, Read, and Connect Strategy Towards Students’ Reading Comprehension

The research aimed to know the influence of Think, Predict, Read, and Connect strategy on students’ reading comprehension. This strategy helped the students comprehend main idea, supporting idea, inference, reference and vocabularies of the text. In this research the writer used quantitative method and categorized the research as quasi experimental method. The sample was taken by using Cluster Random Sampling technique. There were two classes for the sample. X PM 1 was an experimental class consisted of 35 students and X PM 2 was a control class consisted of 34 students. Based on the data analysis and testing of hypotheses, it showed that the average score of students who were taught through Think, Predict, Read, and Connect (TPRC) strategy was higher than which was taught through direct instruction. It was got 75.24 &gt; 63.97 and in the end of result, it showed that was higher than . In the calculation of two averages score, it was obtained 3.99 &gt; 2.00 &lt; 2.66 and in the calculation of the difference of two averages score, it was obtained 3.99 &gt; 1.67 &lt; 2.39. Therefore, it can be concluded that there was a significance influence of using Think, Predict, Read, and Connect strategy towards students’ reading comprehension at the tenth grade of SMK Negeri 4 Bandar Lampung in 2022/2023.