Reviewerly/exHarmony huggingface dataset:
The Teaching Dimension of Digital Education due to COVID-19 in the Light of a Survey in Hungary

Abstract Introduction: In the information society, the stimulus threshold for learners has changed, which requires a novel education strategy. Today, it is no longer what attracts students’ attention that it was 20 years ago. In addition to the rapid development of ICT, public education cannot go either. We believe that advanced teacher digital competence can be one of the keys of finding common ground with students and doing effective teaching work. Methods: In our research we examine the level of digital competence of public education teachers before the pandemic and during the 2nd wave in Hungary (2020 autumn). We examine the development of digital competence, student performance, and the effectiveness of education outside the classroom in the spring period based on teacher experience. Data from the completed forms were evaluated by using basic statistical indicators. Results: According to the data received, the pandemic revealed serious shortcomings in the level of development of the digital competence of both teachers and students. However, it also opened the way for catching up on both sides. Teachers were already better prepared during the second wave, so they were able to solve education more efficiently. Regional development differences and a lack of digital tools have widened the gap between students. Discussion: The COVID-19 pandemic has exposed these shortcomings in education. For here the process of teaching had to be placed in digital space from one moment to the next. Limitations: The research shows data valid only for Hungary. Conclusions: Expected result is the demonstrable development of competences.
Digital Teaching Competence of University Teachers: Levels and Teaching Typologies

Today, university teachers need to have not only basic digital skills, but to be able to use technologies in teaching-learning processes, in their professional development and that of their students. This article focuses on analysing digital teaching competence (DTC), and exploring its dimensions based on the self-perception of a sample of 558 teachers from a Spanish university, following the European DigCompEdu framework. According to the results, university teachers perceive themselves to have an intermediate level of DTC. Technical and professional aspects were higher than the pedagogical ones and those that refer to the effect on student’s digital competence. The ANOVA test did not show significant differences in DTC according to the academic position, but it did according to the scientific area. One of the most significant findings is that the DTC would not be a unitary construct; after the factorial analysis of the items, three categories were obtained that can constitute different teaching typologies: the inspiring teacher, the creator and the tutor. This study provides a new instrument to explore university teachers’ level of DTC and contributes to the debate on this competence by exploring the categories that underlie it, providing data that can be useful both at a scientific level and in the development of practices and policies for teaching improvement.
What do quantum computing students need to know about quantum physics?

Abstract Quantum computing is a rapidly developing technology, and we are experiencing an urgent need for experts in the field. However, there is a lack of traditions, research, and development on to what extent, and how, information technology students without a background in physics should be taught quantum physics. This paper contributes to the field by suggesting key topics in a course for information technology students on the master’s level. We have identified a core content of eight key topics, ranging from content purely from quantum physics and the formalism of quantum mechanics to its use in quantum algorithms and coding. The proposed content forms part of a master’s course developed for information technology students at Oslo Metropolitan University, Norway. By means of group interviews and questionnaires to students, the students’ experiences of the course are investigated. Results indicate that information technology students are capable, and interested in, learning quantum physics for the purpose of education in quantum computing. An integrated approach, where students learn quantum physics and quantum computing in the same course is found to work well for most students. However, as the challenge is extensive for some, it is important to make the purpose of each component of the content clear.
Empowering Participation Within Structures of Dependency

Participatory Design (PD) seeks political change to support people's democratic control over processes, solutions, and, in general, matters of concern to them. A particular challenge remains in supporting vulnerable groups to gain power and control when they are dependent on organizations and external structures. We reflect on our five-year engagement with survivors of sex trafficking in Nepal and an anti-trafficking organization that supports the survivors. Arguing that the prevalence of deficit perspective in the setting promotes dependency and robs the survivors' agency, we sought to bring change by exploring possibilities based on the survivors' existing assets. Three configurations illuminate how our design decisions and collective exploration operate to empower participation while attending to the substantial power implicitly and explicitly manifest in existing structures. We highlight the challenges we faced, uncovering actions that PD practitioners can take, including an emphasis on collaborative entanglements, attending to contingent factors, and encouraging provisional collectives.
CoSIm: Commonsense Reasoning for Counterfactual Scene Imagination

As humans, we can modify our assumptions about a scene by imagining alternative objects or concepts in our minds.For example, we can easily anticipate the implications of the sun being overcast by rain clouds (e.g., the street will get wet) and accordingly prepare for that.In this paper, we introduce a new dataset called Commonsense Reasoning for Counterfactual Scene Imagination (COSIM) which is designed to evaluate the ability of AI systems to reason about scene change imagination.To be specific, in this multimodal task/dataset, models are given an image and an initial questionresponse pair about the image.Next, a counterfactual imagined scene change (in textual form) is applied, and the model has to predict the new response to the initial question based on this scene change.We collect 3.5K high-quality and challenging data instances, with each instance consisting of an image, a commonsense question with a response, a description of a counterfactual change, a new response to the question, and three distractor responses.Our dataset contains various complex scene change types (such as object addition/removal/state change, event description, environment change, etc.) that require models to imagine many different scenarios and reason about the changed scenes.We present a baseline model based on a vision-language Transformer (i.e., LXMERT) and ablation studies.Through human evaluation, we demonstrate a large human-model performance gap, suggesting room for promising future work on this challenging, counterfactual multimodal task.
Feel the Quantum Functioning: Instantiating Generic Multi-Input Functional Encryption from Learning with Errors

Functional Encryption (FE) allows users who hold a specific decryption key, to learn a specific function of encrypted data while the actual plaintexts remain private. While FE is still in its infancy, it is our strong belief that in the years to come, this remarkable cryptograhic primitive will have matured to a degree that will make it an integral part of access-control systems, especially cloud-based ones. To this end, we believe it is of great importance to not only provide theoretical and generic constructions but also concrete instantiations of FE schemes from well-studied cryptographic assumptions. Therefore, in this paper, we undertake the task of presenting two instantiations of the generic work presented in [5] from the Decisional Diffie-Hellman (DDH) problem that also satisfy the property of verifiable decryption. Moreover, we present a novel multi-input FE (MIFE) scheme, that can be instantiated from Regev’s cryptosystem, and thus remains secure even against quantum adversaries. Finally, we provide a multi-party computation (MPC) protocol that allows our MIFE construction to be deployed in the multi-client model.
Framework to Deploy Containers using Kubernetes and CI/CD Pipeline

Containers are continuously replacing the usage of virtual machines and gaining popularity in terms of scalability and agility in IT Industry.The key concept behind containers is Operating system based virtualization.In cloud, computing containers are getting deployed in terms of computing instances whereas in premises they are getting deployed using Docker as a part of CI/CD pipelines using Jenkin Server.When containers are going to be increased in number, its deployment and resource management is always a concern which is managed using the Kubernetes.Kubernetes is used to deploy and manage the containers in an autonomous manner and Rancher is used to manage the Kubernetes Cluster in an efficient manner.First Analysis is done for the scheduler, resource management which is used by Kubernetes to deploy the containers and proposed a framework which will automate the whole process using the helm-charts, ansible scripts from container deployment to the management of Kubernetes Cluster in a scalable manner.It is fully automated framework and can be used to deploy the scalable applications in form of containers as Docker images.CI/CD pipeline is also considered using Jenkin Server.
Ancestor-to-Creole Transfer is Not a Walk in the Park

We aim to learn language models for Creole languages for which large volumes of data are not readily available, and therefore explore the potential transfer from ancestor languages (the 'Ancestry Transfer Hypothesis').We find that standard transfer methods do not facilitate ancestry transfer.Surprisingly, different from other non-Creole languages, a very distinct twophase pattern emerges for Creoles: As our training losses plateau, and language models begin to overfit on their source languages, perplexity on the Creoles drop.We explore if this compression phase can lead to practically useful language models (the 'Ancestry Bottleneck Hypothesis'), but also falsify this.Moreover, we show that Creoles even exhibit this two-phase pattern even when training on random, unrelated languages.Thus Creoles seem to be typological outliers and we speculate whether there is a link between the two observations.
Modeling Coreference Relations in Visual Dialog

Visual dialog is a vision-language task where an agent needs to answer a series of questions grounded in an image based on the understanding of the dialog history and the image.The occurrences of coreference relations in the dialog makes it a more challenging task than visual question-answering.Most previous works have focused on learning better multi-modal representations or on exploring different ways of fusing visual and language features, while the coreferences in the dialog are mainly ignored.In this paper, based on linguistic knowledge and discourse features of human dialog we propose two soft constraints that can improve the model's ability of resolving coreferences in dialog in an unsupervised way.Experimental results on the VisDial v1.0 dataset shows that our model, which integrates two novel and linguistically inspired soft constraints in a deep transformer neural architecture, obtains new state-of-the-art performance in terms of recall at 1 and other evaluation metrics compared to current existing models and this without pretraining on other visionlanguage datasets.Our qualitative results also demonstrate the effectiveness of the method that we propose. 1
CougaR

Despite their development for over a decade, a key problem block-chains are still facing is scalability in terms of throughput, typically limited to a few transactions per second. A fundamental factor limiting this metric is the propagation latency of blocks through the underlying peer-to-peer network, which is typically constructed by means of random connectivity. Disseminating blocks fast improves not only the transaction throughput, but also the security of the system as it reduces the probability of forks. In this paper we present CougaR: a simple yet efficient, eclipse-resistant, decentralized protocol that substantially reduces the block dissemination time in blockchain networks. CougaR's key advantages stem from its link selection policy, which combines a network latency criterion with randomness to offer fast and reliable block dissemination to the entire network. Moreover, CougaR is eclipse-resistant by design, as nodes are protected from having all their links directly or indirectly imposed on them by others, which is the typical vulnerability exploited to deploy eclipse attacks. We rigorously evaluate CougaR by an extensive set of experiments, both against a wide spectrum of parameter settings, and in comparison to the current state of the art.
Sparse and Smooth Functional Data Clustering

A new model-based procedure is developed for sparse clustering of functional data that aims to classify a sample of curves into homogeneous groups while jointly detecting the most informative portions of domain. The proposed method is referred to as sparse and smooth functional clustering (SaS-Funclust) and relies on a general functional Gaussian mixture model whose parameters are estimated by maximizing a log-likelihood function penalized with a functional adaptive pairwise penalty and a roughness penalty. The former allows identifying the noninformative portion of domain by shrinking the means of separated clusters to some common values, whereas the latter improves the interpretability by imposing some degree of smoothing to the estimated cluster means. The model is estimated via an expectation-conditional maximization algorithm paired with a cross-validation procedure. Through a Monte Carlo simulation study, the SaS-Funclust method is shown to outperform other methods already appeared in the literature, both in terms of clustering performance and interpretability. Finally, three real-data examples are presented to demonstrate the favourable performance of the proposed method. The SaS-Funclust method is implemented in the $\textsf{R}$ package $\textsf{sasfunclust}$, available online at https://github.com/unina-sfere/sasfunclust.
Domain Adaptation with Data Uncertainty Measure Based on Evidence Theory

Domain adaptation aims to learn a classifier for a target domain task by using related labeled data from the source domain. Because source domain data and target domain task may be mismatched, there is an uncertainty of source domain data with respect to the target domain task. Ignoring the uncertainty may lead to models with unreliable and suboptimal classification results for the target domain task. However, most previous works focus on reducing the gap in data distribution between the source and target domains. They do not consider the uncertainty of source domain data about the target domain task and cannot apply the uncertainty to learn an adaptive classifier. Aimed at this problem, we revisit the domain adaptation from source domain data uncertainty based on evidence theory and thereby devise an adaptive classifier with the uncertainty measure. Based on evidence theory, we first design an evidence net to estimate the uncertainty of source domain data about the target domain task. Second, we design a general loss function with the uncertainty measure for the adaptive classifier and extend the loss function to support vector machine. Finally, numerical experiments on simulation datasets and real-world applications are given to comprehensively demonstrate the effectiveness of the adaptive classifier with the uncertainty measure.
Digital Literacy from 2018-2021: A scientometric study of the literature

Abstract Digital literacy has been receiving a lot of attention from the academic community. The aim of the study is to examine and analyse the current research status in the areas of digital literacy over the four years from 2018 to 2021. By using the keywords “digital literacy” “digital skills” and “digital competence”, this study retrieved 3057 articles from the Web of Science from 2018 to 2021. These journals are then analyzed using the Web of Science analytical search results service and the VOSviewer application based on scientometric methods. The study finds that there has been a largely upward trend in scholarly research in the field of digital literacy over these four years. Europe and the United States have contributed the most research in this field. Digital literacy is multidisciplinary, there are five main themes in this area of research: digital literacy, digital skills, higher education, education and the digital divide, with several sub-themes under each theme. The findings indicate that there does not seem to be a globally agreed definition of digital literacy and there are limitations to the existing tools for measuring digital literacy in the education field. Hence, future research could be done to determine a more inclusive measure of digital literacy that can be used in different contexts.
Bottom-up discovery of structure and variation in response tokens (‘backchannels’) across diverse languages

Response tokens (also known as backchannels, continuers, or feedback) are a frequent feature of human interaction, where they serve to display understanding and streamline turn-taking. We propose a bottom-up method to study responsive behaviour across 16 languages (8 language families). We use sequential context and recurrence of turns formats to identify candidate response tokens in a language-agnostic way across diverse conversational corpora. We then use UMAP clustering directly on speech signals to represent structure and variation. We find that (i) written orthographic annotations underrepresent the attested variation, (ii) distinctions between formats can be gradient rather than discrete, (iii) most languages appear to make available a broad distinction between a minimal nasal format ‘mm’ and a fuller ‘yeah’-like format. Charting this aspect of human interaction contributes to our understanding of interactional infrastructure across languages and can inform the design of speech technologies.
Standard symmetrized variance with applications to coherence, uncertainty, and entanglement

Variance is a ubiquitous quantity in quantum information theory. Given a basis, we consider the averaged variances of a fixed diagonal observable in a pure state under all possible permutations on the components of the pure state and call it symmetrized variance. Moreover, we work out the analytical expression of the symmetrized variance and find that such an expression is in the factorized form where two factors separately depend on the diagonal observable and quantum state. By shifting the factor corresponding to the diagonal observable, we introduce the notion referred to as standard symmetrized variance for the pure state which is independent of the diagonal observable. We then extend the standard symmetrized variance to mixed states in three different ways, which characterize the uncertainty, the coherence, and the coherence of assistance, respectively. These quantities are evaluated analytically and the relations among them are established. In addition, we show that standard symmetrized variance is also an entanglement measure for bipartite systems. In this way, these different quantumnesses of quantum states are unified by the variance.
Exploratory Research on the Practice of College English Classroom Teaching Based on Internet and Artificial Intelligence

With the rapid development of cloud computing and mobile Internet technology, the information of optional services in the network is exploding and the problem of information overload is becoming more and more serious. The recommendation system can recommend suitable English online classes for students according to their interests and needs, effectively reduce the data load, help students extract effective information from the mass of information, and make accurate recommendations. Aiming at the problems of data sparsity and cold start in the recommendation system, this paper proposes a recommendation method to improve the collaborative recommendation algorithm in college English online classroom teaching practice. Based on the extracted student tag feature information, this method uses spectral clustering algorithm to cluster similar students and transforms the original high-dimension scoring matrix into several lower-dimension subscoring matrices. Then, the implicit meaning model is used to locally predict the missing score in the subscore matrix. Finally, after obtaining the missing score, the improved neighborhood-based collaborative recommendation algorithm is used to predict the global score of the target student. Experiments are performed on commonly used public data sets. Compared with the traditional recommendation algorithm, the proposed algorithm has higher recommendation accuracy and better RMSE performance.
Intelligent and Robust Controller Tuned with WOA: Applied for the Inverted Pendulum

Inverted pendulum is a well-known problem in the control theory because several systems such as robot balancing, Segway, hover board riding and operation of a rocket propeller are inherently based on Inverted Pendulum, furthermore it possesses a height non-linear and unstable dynamics. The main objective of our study is to introduce a comparative analysis of fuzzy logic (FLC), radial basis function neural network (RBF) and integral sliding mode control (ISMC) tuned with whale optimizer algorithm (WOA) for the control of the angle position and velocity of the inverted pendulum system. The implemented controller schemas can adequately reflect and approximate a certain type of uncertainties, nevertheless their parameters should be fine-tuned in order to get height and efficient performance, therefore all the antecedents and consequences of those controllers were tuned with WOA. This later provide height accuracy and fast convergence with height dimensional cost function. Comparative results shows that ISMC-WOA outperforms other techniques in term of settling time and overshoot.