Reviewerly/exHarmony huggingface dataset:
Performance measures for evolving predictions under delayed labelling classification

For many streaming classification tasks, the ground truth labels become available with a non-negligible latency. Given this delayed labelling setting, after the instance data arrives and before its true label is known, the online classifier model may change. Hence, the initial prediction can be replaced with additional periodic predictions gradually produced before the true label becomes available. The quality of these predictions may largely vary. Thus, the question arises of how to summarise the performance of these models when multiple predictions for a single instance are made due to delayed labels.In this study, we aim to provide intuitive performance measures summarising the performance of multiple predictions made for individual instances before their true labels arrive. Particular attention is paid to the fact that under the delayed label setting, the emphasis placed on the quality of initial predictions can vary depending on problem needs. The intermediate performance measures we propose complement existing initial and test-then-train performance evaluation when verification latency is observed. Results provided for both real and synthetic datasets show that the new measures can be used to easily rank methods in terms of their ability to produce and refine predictions before the true labels arrive.
Semantic Hierarchy-based Convolutional Neural Networks for Image Classification

In this work, three variations of hierarchical topologies of Convolutional Neural Networks (CNNs), two of which being original proposals introduced by this work, were tested to assess their impact on image classification problems. The hierarchical structure groups the images based on the semantic meaning of the classes, from the coarsest classes to the finest classes, forming hierarchical levels. The hierarchical models made were compared to a baseline regular CNN on benchmark image classification datasets, the Fashion-MNIST and CIFAR-100 datasets. Another contribution of this work is a new training strategy for hierarchical CNNs, that aims to be simple to implement and to produce a smooth loss during training, increasing stability, while maintaining characteristics like the transitioning from coarse-to-fine level emphasis during training, learning first high-level details and then specific details that differentiate the fine level classes. The hierarchical models produce outputs for each hierarchical level, which can lead to more interpretable results. Results suggest that providing semantic hierarchies can improve fine level accuracy on CNNs, while bringing relevant hierarchical information from their other coarser level outputs.
Decentralized security and data integrity of blockchain using deep learning techniques

Since the introduction of blockchain, cryptocurrencies have become very attractive as an alternative digital payment method and a highly speculative investment. With the rise in computational power and the growth of available data, the artificial intelligence concept of deep neural networks had a surge of popularity over the last years as well. With the introduction of the long short-term memory (LSTM) architecture, neural networks became more efficient in understanding long-term dependencies in data such as time series. In this research paper, we combine these two topics, by using LSTM networks to make a prognosis of decentralized blockchain security. In particular, we test if LSTM based neural networks can produce profitable trading signals for different blockchains. We experiment with different preprocessing techniques and different targets, both for security regression and trading signal classification. We evaluate LSTM based networks. As data for training we use historical security data in one-minute intervals from August 2019 to August 2020. We measure the performance of the models via back testing, where we simulate trading on historic data not used for training based on the model’s predictions. We analyze that performance and compare it with the buy and hold strategy. The simulation is carried out on bullish, bearish and stagnating time periods. In the evaluation, we find the best performing target and pinpoint two preprocessing combinations that are most suitable for this task. We conclude that the CNN LSTM hybrid is capable of profitably forecasting trading signals for securing blockchain, outperforming the buy and hold strategy by roughly 30%, while the performance was better. The LTSM method used by current system for encrypting passwords is efficient enough to mitigate modern attacks like man in the middle attack (MITM) and DDOS attack with 95.85% accuracy
Dimensions of teachers’ transdisciplinary competence based on a systematic review of three transdisciplinary areas

This study contributes to the conceptualisation of teachers’ competence through a systematic review (a concept synthesis) of three key transdisciplinary competencies—teachers’ diversity competence, teachers’ research and development competence, and teachers’ digital competence. Based on our analysis, we propose a set of dimensions to consider—within which there were important tensions in the research literature reviewed—when discussing teachers’ professional competence. These dimensions are: the 1) beneficiary, 2) teachers’ role, 3) attitudes, knowledge and skills, 4) sources of competence, 5) relationship to disciplinary content and 6) assessment. We discuss the three areas in light of these dimensions. We suggest that the complexity of being and becoming a teacher is a blind spot in research on teachers’ transdisciplinary competence and that many articles show deficit thinking about teachers.
51.15 SCREEN TIME IN THE CONTEXT OF COVID-19: THE GOOD, THE BAD, AND THE UGLY

In response to the novel coronavirus (COVID-19), most countries have instituted school closures and stay-at-home orders. As a result, there has been a significant increase in the use of technology. This literature review examines COVID-19–related changes in the role of electronic devices and the impact of screen time for children and adolescents worldwide.
A Novel Image Registration Algorithm Using Wavelet Transform and Matrix-Multiply Discrete Fourier Transform

Nearly all existing image registration algorithms are based on the full information (e.g., intensity and/or features) carried by the images being registered. Such a full-image-based strategy can achieve high-precision registration (e.g., at the subpixel level) but at the expense of long computation times, especially for high spatial resolution remote-sensing (RS) images. This letter presents a novel subimage-based registration method that is expected to dramatically reduce the time consumption but still meet subpixel accuracy requirements. The core idea behind this method is that the transform pattern of the reference and sensed images under a global transformation is theoretically similar to that of subimages derived from the full image, and vice versa. Hence, this method first divides the original reference image into subimages with nearly equal sizes, and extracts the subimage with the maximum wavelet coefficient summation to serve as a new reference image using the wavelet transform technique. Then, the two-step methods based on the phase correlation and matrix-multiply discrete Fourier transform (DFT) algorithms are applied to register the sensed image to the reference subimage. Finally, the proposed method is tested with simulated data sets. The results indicate that this method can perform subpixel registration as achieved by full-image-based methods but with the benefit of much shorter computation times (e.g., approximately 89% compared with a widely used rapid registration algorithm named enhanced phase correlation).
MetRec: A dataset for meter classification of arabic poetry

In this data article, we report a dataset related to the research titled “Meter Classification of Arabic Poems Using Deep Bidirectional Recurrent Neural Networks” [2]. The dataset was collected from a large repository of Arabic poems, Aldiwan website [1]. The data collection was done using a Python script that scrapes the website to find the poems and their associated meters. The dataset contains the verses and their corresponding meter classes. Meter classes are represented as numbers from 0 to 13. The dataset can be highly useful for further research in order to improve the field of Arabic poems’ meter classification.
The Biblical Hebrew Beth Essentiae: Predicate Marker

The Biblical Hebrew preposition b has many functions, including one traditionally called the beth essentiae. The standard example is Exod 18:4: ki ’elohe avi b‘ezri, “for the God of my father (is) my help.” Most scholars agree that this usage of beth marks an equivalence or predication, the notable exception being Whitley (1972). The goal of this paper is to provide a generative syntactic analysis that supports the majority view and to respond to Whitley’s two most important counterarguments, namely that beth is unnecessary as marker of predication, since Biblical Hebrew allows null copula clauses, and that the occurrence of beth with the verb hayah shows that beth must have some other function or else be pleonastic. I propose that the beth essentiae is an (optional) overt marker of predication and that it is the overt realisation of the functional predication head Pr. This syntactic argument is supported by cross?linguistic data.
Fuzzy-Relation-Based Lexicographic Minimum Solution to the P2P Network System

In the existing work in the literature, the addition-min fuzzy relation inequalities was applied to describe a peer-to-peer (P2P) network system. In such a model, the total download traffic of a terminal was considered. However, it is more reasonable to consider the largest download traffic in some cases. It is found that max-min fuzzy relation inequalities are more suitable for the P2P network system in such cases. The objective of this work is to reduce the network congestion under some fixed priority grade of the terminals. The concept of a lexicographic minimum solution was first introduced to the max-min fuzzy relation inequalities system to achieve such an objective. Detailed and effective algorithms are proposed for solving the lexicographic minimum solution. Moreover, the validity of our proposed algorithm is formally proved in theory and is demonstrated by some experimental examples.
Coresets for Near-Convex Functions

Coreset is usually a small weighted subset of $n$ input points in $\mathbb{R}^d$, that provably approximates their loss function for a given set of queries (models, classifiers, etc.). Coresets become increasingly common in machine learning since existing heuristics or inefficient algorithms may be improved by running them possibly many times on the small coreset that can be maintained for streaming distributed data. Coresets can be obtained by sensitivity (importance) sampling, where its size is proportional to the total sum of sensitivities. Unfortunately, computing the sensitivity of each point is problem dependent and may be harder to compute than the original optimization problem at hand. We suggest a generic framework for computing sensitivities (and thus coresets) for wide family of loss functions which we call near-convex functions. This is by suggesting the $f$-SVD factorization that generalizes the SVD factorization of matrices to functions. Example applications include coresets that are either new or significantly improves previous results, such as SVM, Logistic regression, M-estimators, and $\ell_z$-regression. Experimental results and open source are also provided.
Automatic Extraction of Rules Governing Morphological Agreement

Creating a descriptive grammar of a language is an indispensable step for language documentation and preservation.However, at the same time it is a tedious, time-consuming task.In this paper, we take steps towards automating this process by devising an automated framework for extracting a first-pass grammatical specification from raw text in a concise, human-and machine-readable format.We focus on extracting rules describing agreement, a morphosyntactic phenomenon at the core of the grammars of many of the world's languages.We apply our framework to all languages included in the Universal Dependencies project, with promising results.Using cross-lingual transfer, even with no expert annotations in the language of interest, our framework extracts a grammatical specification which is nearly equivalent to those created with large amounts of gold-standard annotated data.We confirm this finding with human expert evaluations of the rules that our framework produces, which have an average accuracy of 78%.We release an interface demonstrating the extracted rules at https: //neulab.github.io/lase/.
Dual-Resolution Correspondence Networks

We tackle the problem of establishing dense pixel-wise correspondences between a pair of images. In this work, we introduce Dual-Resolution Correspondence Networks (DualRC-Net), to obtain pixel-wise correspondences in a coarse-to-fine manner. DualRC-Net extracts both coarse- and fine- resolution feature maps. The coarse maps are used to produce a full but coarse 4D correlation tensor, which is then refined by a learnable neighbourhood consensus module. The fine-resolution feature maps are used to obtain the final dense correspondences guided by the refined coarse 4D correlation tensor. The selected coarse-resolution matching scores allow the fine-resolution features to focus only on a limited number of possible matches with high confidence. In this way, DualRC-Net dramatically increases matching reliability and localisation accuracy, while avoiding to apply the expensive 4D convolution kernels on fine-resolution feature maps. We comprehensively evaluate our method on large-scale public benchmarks including HPatches, InLoc, and Aachen Day-Night. It achieves the state-of-the-art results on all of them.
Assisted Learning: A Framework for Multi-Organization Learning

Large-scale multimodal data are being rapidly generated from the interaction between humans and machines. Sharing heterogeneous data among multiple organizations for collaborative learning typically faces tradeoffs between learning efficiency and data privacy. In general, for a method to achieve the optimal performance on one aspect will sacrifice another one. To tackle this challenge, we introduce the Assisted Learning framework where a service provider Bob assists a user Alice with supervised learning tasks without transmitting Bob's private algorithm or data. Bob assists Alice either by building a predictive model using Alice's labels or by improving Alice's learning through iterative transmissions of task-specific statistics. Theoretical analysis shows that the proposed method can achieve lossless learning performance for certain models. We also demonstrate the wide applicability of the proposed approach by various experiments, including real-world medical benchmarks.
Learning to Plan and Realize Separately for Open-Ended Dialogue Systems

Achieving true human-like ability to conduct a conversation remains an elusive goal for open-ended dialogue systems.We posit this is because extant approaches towards natural language generation (NLG) are typically construed as end-to-end architectures that do not adequately model human generation processes.To investigate, we decouple generation into two separate phases: planning and realization.In the planning phase, we train two planners to generate plans for response utterances.The realization phase uses response plans to produce an appropriate response.Through rigorous evaluations, both automated and human, we demonstrate that decoupling the process into planning and realization performs better than an end-to-end approach.
Constraining Variational Inference with Geometric Jensen-Shannon Divergence

We examine the problem of controlling divergences for latent space regularisation in variational autoencoders. Specifically, when aiming to reconstruct example $x\in\mathbb{R}^{m}$ via latent space $z\in\mathbb{R}^{n}$ ($n\leq m$), while balancing this against the need for generalisable latent representations. We present a regularisation mechanism based on the skew-geometric Jensen-Shannon divergence $\left(\textrm{JS}^{\textrm{G}_{\alpha}}\right)$. We find a variation in $\textrm{JS}^{\textrm{G}_{\alpha}}$, motivated by limiting cases, which leads to an intuitive interpolation between forward and reverse KL in the space of both distributions and divergences. We motivate its potential benefits for VAEs through low-dimensional examples, before presenting quantitative and qualitative results. Our experiments demonstrate that skewing our variant of $\textrm{JS}^{\textrm{G}_{\alpha}}$, in the context of $\textrm{JS}^{\textrm{G}_{\alpha}}$-VAEs, leads to better reconstruction and generation when compared to several baseline VAEs. Our approach is entirely unsupervised and utilises only one hyperparameter which can be easily interpreted in latent space.
Perspectives of Environmental Education and the Formation of Ecological Culture Among School Children

ABSTRACT The democratic transformation in the Russian Federation is accompanied by the formation of a qualitatively new structure which is an updated civil society. This approach, of course, requires significant adjustments in the relationship between man and his environment. This study focuses on key aspects of environmental education and the formation of ecological culture among schoolchildren. It must be recognized that in most educational organizations, the solution to the problems of educating ecological culture still remains at the level of declarations. To solve the given problem, the researchers used a complex of theoretical and empirical methods. It was concluded that education makes sense as learning for life, and not for a set of “knowledge, skills.” The presence of a socially active nature in various methods of environmental education allows the researchers to steadily form an active civic position and implement the main tasks of the civic upbringing of students.
NF-Crowd: Nearly-free Blockchain-based Crowdsourcing

Advancements in distributed ledger technologies are rapidly driving the rise of decentralized crowdsourcing systems on top of open smart contract platforms like Ethereum. While decentralized blockchain-based crowdsourcing provides numerous benefits compared to centralized solutions, current implementations of decentralized crowdsourcing suffer from fundamental scalability limitations by requiring all participants to pay a small transaction fee every time they interact with the blockchain. This increases the cost of using decentralized crowdsourcing solutions, resulting in a total payment that could be even higher than the price charged by centralized crowdsourcing platforms. This paper proposes a novel suite of protocols called NF-Crowd that resolves the scalability issue by reducing the lower bound of the total cost of a decentralized crowdsourcing project to O(1). NF-Crowd is a highly reliable solution for scaling decentralized crowdsourcing. We prove that as long as participants of a project powered by NF-Crowd are rational, the O(1) lower bound of cost could be reached regardless of the scale of the crowd. We also demonstrate that as long as at least one participant of a project powered by NF-Crowd is honest, the project cannot be aborted and the results are guaranteed to be correct. We design NF-Crowd protocols for a representative type of project named crowdsourcing contest with open community review (CC-OCR). We implement the protocols over the Ethereum official test network. Our results demonstrate that NF-Crowd protocols can reduce the cost of running a CC-OCR project to less than $2 regardless of the scale of the crowd, providing a significant cost benefit in adopting decentralized crowdsourcing solutions.
From Dataset Recycling to Multi-Property Extraction and Beyond

This paper investigates various Transformer architectures on the WikiReading Information Extraction and Machine Reading Comprehension dataset.The proposed dual-source model outperforms the current state-of-theart by a large margin.Next, we introduce WikiReading Recycled-a newly developed public dataset, and the task of multipleproperty extraction.It uses the same data as WikiReading but does not inherit its predecessor's identified disadvantages.In addition, we provide a human-annotated test set with diagnostic subsets for a detailed analysis of model performance.
Towards Scalable Bayesian Learning of Causal DAGs

We give methods for Bayesian inference of directed acyclic graphs, DAGs, and the induced causal effects from passively observed complete data. Our methods build on a recent Markov chain Monte Carlo scheme for learning Bayesian networks, which enables efficient approximate sampling from the graph posterior, provided that each node is assigned a small number $K$ of candidate parents. We present algorithmic techniques to significantly reduce the space and time requirements, which make the use of substantially larger values of $K$ feasible. Furthermore, we investigate the problem of selecting the candidate parents per node so as to maximize the covered posterior mass. Finally, we combine our sampling method with a novel Bayesian approach for estimating causal effects in linear Gaussian DAG models. Numerical experiments demonstrate the performance of our methods in detecting ancestor-descendant relations, and in causal effect estimation our Bayesian method is shown to outperform previous approaches.
Creating Digital Musical Instruments With and for Children: Including Vocal Sketching as a Method for Engaging in Codesign

A class of master of science students and a group of preschool children codesigned new digital musical instruments based on workshop interviews involving vocal sketching, a method for imitating and portraying sounds. The aim of the study was to explore how the students and children would approach vocal sketching as one of several design methods. The children described musical instruments to the students using vocal sketching and other modalities (verbal, drawing, gestures). The resulting instruments built by the students were showcased at the Swedish Museum of Performing Arts in Stockholm. Although all the children tried vocal sketching during preparatory tasks, few employed the method during the workshop. However, the instruments seemed to meet the children’s expectations. Consequently, even though the vocal sketching method alone provided few design directives in the given context, we suggest that vocal sketching, under favorable circumstances, can be an engaging component that complements other modalities in codesign involving children.
LIFE-WORLD ORIENTED EDUCATION IN TIMES OF THE COVID-19 LOCKDOWN. TWITCH.TV AS PEDAGOGICAL TOOL

1 Massachusetts Institute of Technology (UNITED STATES)2 Ministry for Education and Employment (MALTA)3 Donau-University Krems (AUSTRIA)4 AIT Austrian Institute of Technology GmbH (AUSTRIA)5 Danube-University Krems (AUSTRIA)
Transformation of the values of Generation Z - residents of the digital society of sustainable development

The transition to a society of sustainable development is possible only when the priorities and values of the active part of society are changing. From the values of individualism, the weak ties of corporatism, the extensive growth of an industrial society to the values of networked solidarity, social justice, the strong ties of the information/digital society. The transition to a new level of civilizational development not only contributed to the transformation of the economy, politics and the social sphere under the influence of digital technologies, but played a significant role in the formation of Generation Z (born in the late 20th and early 21st centuries). The article contains study material that was carried out at the Ufa State Petroleum Technological University in the summer of 2020.