Reviewerly/exHarmony huggingface dataset:
FORMATION OF INFORMATION AND DIGITAL COMPETENCE OF FUTURE PRIMARY TEACHERS IN HIGHER EDUCATION INSTITUTIONS

The article considers the problem of formation of information and digital competence of future primary school teachers; the essence of the concept of «information and digital competence» is revealed, its components are defined. The purpose of the study is to substantiate the theoretical foundations and practical possibilities of forming information and digital competence of future primary school teachers in higher education institutions. It was found that a contemporary primary school teacher should navigate in the information space, receive information and operate it in accordance with their own needs and requirements of professional activity (create text documents, tables, pictures, diagrams, presentations; use Internet technologies, local networks, databases develop their own electronic products (electronic lesson development; electronic textbooks, training programs, demonstration material); must acquire new knowledge and skills that will help to work comfortably and be competitive in the educational space of the information society. At the stage of theoretical research the curricula of training specialists in the field of knowledge 01 Education / Pedagogy in the specialty 013 Primary Education of Precarpathian National University named after Vasyl Stafanyk are analyzed. The content of academic disciplines, which occupy a paramount place in the formation of information and digital competence, is substantiated. It is concluded that as a result of the formed information and digital competence future teachers will be able to work with computers and multimedia technologies, use different sources of information, use new information technologies and software that will make the educational process flexible, mobile, differentiated and individual.
Recurrent Autoencoder with Sequence-Aware Encoding

Recurrent Neural Networks (RNN) received a vast amount of attention last decade. Recently, the architectures of Recurrent AutoEncoders (RAE) found many applications in practice. RAE can extract the semantically valuable information, called context that represents a latent space useful for further processing. Nevertheless, recurrent autoencoders are hard to train, and the training process takes much time. In this paper, we propose an autoencoder architecture with sequence-aware encoding, which employs 1D convolutional layer to improve its performance in terms of model training time. We prove that the recurrent autoencoder with sequence-aware encoding outperforms a standard RAE in terms of training speed in most cases. The preliminary results show that the proposed solution dominates over the standard RAE, and the training process is order of magnitude faster.
Continuous Language Generative Flow

Recent years have witnessed various types of generative models for natural language generation (NLG), especially RNNs or transformer based sequence-to-sequence models, as well as variational autoencoder (VAE) and generative adversarial network (GAN) based models.However, flow-based generative models, which achieve strong performance in image generation due to their invertibility and exact density estimation properties, have been less explored for NLG.In this paper, we propose a flow-based language generation model by adapting previous flow generative models to language generation via continuous input embeddings, adapted affine coupling structures, and a novel architecture for autoregressive text generation.We also apply our framework to Sequence-to-Sequence generation, including text-and video-based Question Generation (QG) and Neural Machine Translation (NMT), and data augmentation for Question Answering (QA).We use our language flow model to provide extra input features for QG and NMT, which achieves improvements over the strong QG baselines on SQuAD and TVQA and NMT baseline on WMT16.We also augment QA data with new context by injecting noise to the latent features of the language flow and show this augmentation leads to a large performance improvement from strong baselines on SQuAD and TVQA. 1
Detecting Text Baselines in Historical Documents With Baseline Primitives

Previous deep learning based approaches to text baseline detection in historical documents usually take it as a semantic segmentation task. These methods adopt a fully convolutional neural network to predict baseline pixels first and then group them into lines by heuristic post-processing steps, which tends to suffer from a wrongly merged or wrongly split problem owing to limited context information provided by pixels. To address these issues, we introduce the concept of a baseline primitive, which is defined as a virtual bounding box centered at each baseline pixel. After baseline primitive detection, a relation network is used to predict a link relationship for each pair of primitives. Consequently, text baselines are generated by detecting baseline primitives and grouping them with the corresponding link relationships. Owing to the design of baseline primitives, wider context information can be leveraged to improve link prediction accuracy. Therefore, our approach can effectively detect text baselines with small inter-line or large inter-word spacing. Quantitative experimental results demonstrate the effectiveness of the proposed baseline primitive design. Our approach achieves state-of-the-art performance on two public benchmarks, namely cBAD 2017 and cBAD 2019.
Argumentation Frameworks with Strong and Weak Constraints: Semantics and Complexity

Dung's abstract Argumentation Framework (AF) has emerged as a central formalism in formal argumentation. Key aspects of the success and popularity of Dung's framework include its simplicity and expressiveness. Integrity constraints help to express domain knowledge in a compact and natural way, thus keeping easy the modeling task even for problems that otherwise would be hard to encode within an AF. In this paper, after providing an intuitive semantics based on Lukasiewicz's logic for AFs with (strong) constraints, called Constrained AFs (CAFs), we propose Weak constrained AFs (WAFs) that enhance CAFs with weak constraints. Intuitively, these constraints can be used to find ``optimal'' solutions to problems defined through CAFs. We provide a detailed complexity analysis of CAFs and WAFs, showing that strong constraints do not increase the expressive power of AFs in most cases, while weak constraints systematically increase the expressive power of CAFs under several well-known argumentation semantics.
How to Train Your Agent to Read and Write

Reading and writing research papers is one of the most privileged abilities that a qualified researcher should master. However, it is difficult for new researchers (e.g., students) to fully grasp this ability. It would be fascinating if we could train an intelligent agent to help people read and summarize papers, and perhaps even discover and exploit the potential knowledge clues to write novel papers. Although there have been existing works focusing on summarizing (i.e., reading) the knowledge in a given text or generating (i.e., writing) a text based on the given knowledge, the ability of simultaneously reading and writing is still under development. Typically, this requires an agent to fully understand the knowledge from the given text materials and generate correct and fluent novel paragraphs, which is very challenging in practice. In this paper, we propose a Deep ReAder-Writer (DRAW) network, which consists of a Reader that can extract knowledge graphs (KGs) from input paragraphs and discover potential knowledge, a graph-to-text Writer that generates a novel paragraph, and a Reviewer that reviews the generated paragraph from three different aspects. Extensive experiments show that our DRAW network outperforms considered baselines and several state-of-the-art methods on AGENDA and M-AGENDA datasets. Our code and supplementary are released at https://github.com/menggehe/DRAW.
Transition Based Discount Factor for Model Free Algorithms in Reinforcement Learning

Reinforcement Learning (RL) enables an agent to learn control policies for achieving its long-term goals. One key parameter of RL algorithms is a discount factor that scales down future cost in the state’s current value estimate. This study introduces and analyses a transition-based discount factor in two model-free reinforcement learning algorithms: Q-learning and SARSA, and shows their convergence using the theory of stochastic approximation for finite state and action spaces. This causes an asymmetric discounting, favouring some transitions over others, which allows (1) faster convergence than constant discount factor variant of these algorithms, which is demonstrated by experiments on the Taxi domain and MountainCar environments; (2) provides better control over the RL agents to learn risk-averse or risk-taking policy, as demonstrated in a Cliff Walking experiment.
BIG DATA IN CLOUD COMPUTING: A LITERATURE REVIEW

—Big Data and Cloud Computing are two of the most important technologies of the day. Since data is being generated exponentially every day, Big Data has gained a lot of significance in any technology. The daily explosion of data means that it’s better to have big data included in the applications. Whereas cloud computing is allowing users to use platforms according to their time, convenience and affordability. It is providing users ability to collaborate and work efficiently more than ever. Combining these two technologies can give a hands down advantage to the users in terms of knowledge and efficiency. Big Data, when used in cloud computing has applications in different fields such as Finance, Management, supply chain, planning, data storage, warehouses and many more. In this paper, we have discussed Big Data implementation and application in Cloud Computing. 4 V’s in big data can be applied in Cloud computing to get better performance, higher input details, better insights, reliable and secure platforms at comparatively lower costs. Different analytics, technology involved in coupling of big data with cloud computing, the challenges involved in this process, trends applications of the domain and security factors involved are discussed in this paper
Tensions that Hinder the Implementation of Digital Security Governance

Today’s organizations are exposed to high risk because the established digital technologies are vulnerable to security attacks. The increased impact of security on business demands a strategic approach to information security, commonly referred to as digital security governance. While there is a growing understanding that digital security is one of the leading risks and challenges of today’s organizations, organizations still find it difficult to implement security governance as part of their regular organizing change activities. This study focuses on providing more empirical insight into “tensions that are present during the implementation of digital security governance”. We conducted an inductive study and interviewed 42 CISOs and CIOs of large organizations in the Netherlands. The study reveals the tensions that hinder the implementation of digital security governance. We draw from management theories to provide a fresh understanding of and guidance for how to unravel the tensions.
How Helpful is Inverse Reinforcement Learning for Table-to-Text Generation?

Existing approaches for the Table-to-Text task suffer from issues such as missing information, hallucination and repetition.Many approaches to this problem use Reinforcement Learning (RL), which maximizes a single manually defined reward, such as BLEU.In this work, we instead pose the Table-to-Text task as Inverse Reinforcement Learning (IRL) problem.We explore using multiple interpretable unsupervised reward components that are combined linearly to form a composite reward function.The composite reward function and the description generator are learned jointly.We find that IRL outperforms strong RL baselines marginally.We further study the generalization of learned IRL rewards in scenarios involving domain adaptation.Our experiments reveal significant challenges in using IRL for this task.
High performance optical free-space links for quantum communications

Secure communication networks are the critical infrastructure of the information age. To ensure secure communication between governmental institutions and other high-security environments, the German Federal Ministry of Education and Research (BMBF) initiated an ambitious project { the QuNET initiative. In a joint effort, the Max Planck Institute for the Science of Light (MPL), the German Aerospace Center (DLR) and the Fraunhofer Society aim to develop the technological basis of a German quantum key distribution (QKD) infrastructure. This paper describes the infrastructure used in a first link demonstrator within this project and how we achieved the transition from initial quantum transceiver concepts to first link experiments.
An ADMM-based Optimal Transmission Frequency Management System for IoT Edge Intelligence

In this paper, we investigate a key problem of Internet of Things (IoT) applications in practice. Our research objective is to optimize the transmission frequencies for a group of IoT edge devices under practical constraints. Our key assumption is that different IoT devices may have different priority levels when transmitting data in a resource-constrained environment and that those priority levels may only be locally defined and accessible by edge devices for privacy concerns. To address this problem, we leverage the well-known Alternating Direction Method of Multipliers (ADMM) optimization method and demonstrate its applicability for effectively managing various IoT data streams in a decentralized framework. Our experimental results show that the transmission frequency of each edge device can converge to optimality with little delay using ADMM, and the proposed system can be adjusted dynamically when a new device connects to the system. In addition, we also introduce an anomaly detection mechanism to the system when a device’s transmission frequency may be compromised by external manipulation, showing that the proposed system is robust and secure for various IoT applications.
A Span-based Dynamic Local Attention Model for Sequential Sentence Classification

Sequential sentence classification aims to classify each sentence in the document based on the context in which sentences appear.Most existing work addresses this problem using a hierarchical sequence labeling network.However, they ignore considering the latent segment structure of the document, in which contiguous sentences often have coherent semantics.In this paper, we proposed a span-based dynamic local attention model that could explicitly capture the structural information by the proposed supervised dynamic local attention.We further introduce an auxiliary task called span-based classification to explore the span-level representations.Extensive experiments show that our model achieves better or competitive performance against state-of-theart baselines on two benchmark datasets.
Robustness Improvement Against a Non-Geometrical Attacks of Lifting SchemeBased Image Watermarking through Singular Value and Schur Decompositions

Image watermarking is a technique to embed a piece of information or data, which also can be an image, into the host or medium image for specific purposes, such as authentication, security, or copyright protection.A good image watermarking method should have a good perceptibility after adding the watermark.It should also robust against attacks and the watermark should be able to be recovered although the watermarked image has distorted.This paper presents the proposed lifting image watermarking scheme based on wavelet transform through singular value and Schur decomposition.This method is proposed to improve the perceptibility and the robustness of the image watermarking scheme, especially against non-geometrical attacks.The lifting scheme is performed by lifting wavelet transform to produce a better imperceptibility.The use of singular value decomposition is used to increase the intactness of the embedded watermark from the distorted image, which is performed on the triangular matrix of lowfrequency sub-band produced by Schur decomposition.In perceptibility performance, experimental results show that the proposed method could produce a watermarked image very close to the host image with more than 99% of similarity level.In robustness performance, the proposed method can recover the embedded watermark even though the watermarked image has been distorted by non-geometrical attacks with more than 90% of normalized correlation average.
RADAR-X: An Interactive Interface Pairing Contrastive Explanations with Revised Plan Suggestions

Automated Planning techniques can be leveraged to build effective decision support systems that assist the human-in-the-loop. Such systems must provide intuitive explanations when the suggestions made by these systems seem inexplicable to the human. In this regard, we consider scenarios where the user questions the system's suggestion by providing alternatives (referred to as foils). In response, we empower existing decision support technologies to engage in an interactive explanatory dialogue with the user and provide contrastive explanations based on user-specified foils to reach a consensus on proposed decisions. To provide contrastive explanations, we adapt existing techniques in Explainable AI Planning (XAIP). Furthermore, we use this dialog to elicit the user's latent preferences and propose three modes of interaction that use these preferences to provide revised plan suggestions. Finally, we showcase a decision support system that provides all these capabilities.
TURING: an Accurate and Interpretable Multi-Hypothesis Cross-Domain Natural Language Database Interface

Peng Xu, Wenjie Zi, Hamidreza Shahidi, Ákos Kádár, Keyi Tang, Wei Yang, Jawad Ateeq, Harsh Barot, Meidan Alon, Yanshuai Cao. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations. 2021.
Elastic Consistency: A Practical Consistency Model for Distributed Stochastic Gradient Descent

One key element behind the recent progress of machine learning has been the ability to train machine learning models in large-scale distributed shared-memory and message-passing environments. Most of these models are trained employing variants of stochastic gradient descent (SGD) based optimization, but most methods involve some type of consistency relaxation relative to sequential SGD, to mitigate its large communication or synchronization costs at scale. In this paper, we introduce a general consistency condition covering communication-reduced and asynchronous distributed SGD implementations. Our framework, called elastic consistency, decouples the system-specific aspects of the implementation from the SGD convergence requirements, giving a general way to obtain convergence bounds for a wide variety of distributed SGD methods used in practice. Elastic consistency can be used to re-derive or improve several previous convergence bounds in message-passing and shared-memory settings, but also to analyze new models and distribution schemes. As a direct application, we propose and analyze a new synchronization-avoiding scheduling scheme for distributed SGD, and show that it can be used to efficiently train deep convolutional models for image classification.
LOA: Logical Optimal Actions for Text-based Interaction Games

We present Logical Optimal Actions (LOA), an action decision architecture of reinforcement learning applications with a neurosymbolic framework which is a combination of neural network and symbolic knowledge acquisition approach for natural language interaction games.The demonstration for LOA experiments consists of a web-based interactive platform for text-based games and visualization for acquired knowledge for improving interpretability for trained rules.This demonstration also provides a comparison module with other neuro-symbolic approaches as well as non-symbolic state-ofthe-art agent models on the same text-based games.Our LOA also provides open-sourced implementation in Python for the reinforcement learning environment to facilitate an experiment for studying neuro-symbolic agents.
Replicating and Extending “Because Their Treebanks Leak”: Graph Isomorphism, Covariants, and Parser Performance

Søgaard (2020) obtained results suggesting the fraction of trees occurring in the test data isomorphic to trees in the training set accounts for a non-trivial variation in parser performance.Similar to other statistical analyses in NLP, the results were based on evaluating linear regressions.However, the study had methodological issues and was undertaken using a small sample size leading to unreliable results.We present a replication study in which we also bin sentences by length and find that only a small subset of sentences vary in performance with respect to graph isomorphism.Further, the correlation observed between parser performance and graph isomorphism in the wild disappears when controlling for covariants.However, in a controlled experiment, where covariants are kept fixed, we do observe a strong correlation.We suggest that conclusions drawn from statistical analyses like this need to be tempered and that controlled experiments can complement them by more readily teasing factors apart.
Local Structural Aware Heterogeneous Information Network Embedding Based on Relational Self-Attention Graph Neural Network

Heterogeneous Information Networks (HINs) are ubiquitous in our daily life, as they can describe complex interactions among various types of objects.Heterogeneous information network embedding aims to project the network elements of HINs into low-dimensional node representation vectors, which can facilitate effective analyze of HINs.To learn appropriate HIN embeddings, most existing methods usually adopt meta-paths to capture the heterogeneous semantic information, which require domain knowledge and trial-and-error to find suitable meta-paths.Besides, most existing methods fail to preserve high-order local structural information, and pay little attention to the implicit semantics in HINs.In this paper, we propose a local structural aware heterogeneous information network embedding model named LSA-HNE.Specifically, we first design a relational self-attention graph neural network model to aggregate heterogeneous information and automatically extract semantic similarity without using meta-paths.In addition, we employ a biased random walk based sampling method to extract the local structural information and preserve the implicit semantics in HINs.The experiments conducted on four real-world datasets show that our proposed model is effective compared with the state-of-the-art methods.