Reviewerly/exHarmony huggingface dataset:
La oferta editorial para Educación Infantil: una oportunidad en tiempos de pandemia

Digital teaching materials offer new prisms from which to learn, taking advantage of the potential of technology to make learning processes ubiquitous. In this way, formal learning is extended to spaces beyond the school, consolidating alternatives so as not to halt training processes in times of pandemic. From this perspective, this paper analyses the characteristics of a selection of digital teaching materials for the Early Childhood Education stage from three publishers that can be used in the context of the home. Through their link with the curriculum, the impact on learning and other didactic characteristics. The results show that, in general, the publishers are committed to providing the digital materials with a playful purpose as opposed to the physical format of the school environment, leaving the pedagogical potential in the background. Thus, although the materials are attractive, interactive and coherent with the curriculum, there is room for improvement in terms of their adaptability for inclusion or attention to diversity. This helps to identify that, although they are useful resources in times of pandemic, it is still open as a future line of work to provide materials with greater pedagogical potential.
On the Evaluation of Answer-Agnostic Paragraph-level Multi-Question Generation

We study the task of predicting a set of salient questions from a given paragraph without any prior knowledge of the precise answer. We make two main contributions. First, we propose a new method to evaluate a set of predicted questions against the set of references by using the Hungarian algorithm to assign predicted questions to references before scoring the assigned pairs. We show that our proposed evaluation strategy has better theoretical and practical properties compared to prior methods because it can properly account for the coverage of references. Second, we compare different strategies to utilize a pre-trained seq2seq model to generate and select a set of questions related to a given paragraph. The code is available.
Film Review Sentiment Classification based on BiGRU and Attention

Aiming at the problem of insufficient features extracted from film review data in traditional neural network models, combined with deep learning methods, this paper proposes a movie review sentiment classification model Roberta-BiGRU-ATT based on BiGRU and Attention. Firstly, Roberta model is used to extract feature representation of film review text to improve the ability of word vector representation. Secondly, the acqu32ired word features are inputed into BiGRU network to extract deeper features. Finally, the attention mechanism is introduced to highlight the target sentences and achieve the classification of film review texts. Experiments have proved that on the IMDB and SST data sets, the movie review sentiment classification model based on BiGRU and attention model has achieved better accuracy than other models.
Optimized Generative Adversarial Networks for Adversarial Sample Generation

Detecting the anomalous entity in real-time network traffic is a popular area of research in recent times.Very few researches have focused on creating malware that fools the intrusion detection system and this paper focuses on this topic.We are using Deep Convolutional Generative Adversarial Networks (DCGAN) to trick the malware classifier to believe it is a normal entity.In this work, a new dataset is created to fool the Artificial Intelligence (AI) based malware detectors, and it consists of different types of attacks such as Denial of Service (DoS), scan 11, scan 44, botnet, spam, User Datagram Portal (UDP) scan, and ssh scan.The discriminator used in the DCGAN discriminates two different attack classes (anomaly and synthetic) and one normal class.The model collapse, instability, and vanishing gradient issues associated with the DCGAN are overcome using the proposed hybrid Aquila optimizer-based Mine blast harmony search algorithm (AO-MBHS).This algorithm helps the generator to create realistic malware samples to be undetected by the discriminator.The performance of the proposed methodology is evaluated using different performance metrics such as training time, detection rate, F-Score, loss function, Accuracy, False alarm rate, etc.The superiority of the hybrid AO-MBHS based DCGAN model is noticed when the detection rate is changed to 0 after the retraining method to make the defensive technique hard to be noticed by the malware detection system.The support vector machines (SVM) is used as the malicious traffic detection application and its True positive rate (TPR) goes from 80% to 0% after retraining the proposed model which shows the efficiency of the proposed model in hiding the samples.
Equivalence of coupled parametric oscillator dynamics to Lagrange multiplier primal-dual optimization

There has been a recent surge of interest in physics-based solvers for combinatorial optimization problems. We present a dynamical solver for the Ising problem that is comprised of a network of coupled parametric oscillators and show that it implements Lagrange multiplier constrained optimization. We show that the pump depletion effect, which is intrinsic to parametric oscillators, enforces binary constraints and enables the system's continuous analog variables to converge to the optimal binary solutions to the optimization problem. Moreover, there is an exact correspondence between the equations of motion for the coupled oscillators and the update rules in the primal-dual method of Lagrange multipliers. Though our analysis is performed using electrical LC oscillators, it can be generalized to any system of coupled parametric oscillators. We simulate the dynamics of the coupled oscillator system and demonstrate that the performance of the solver on a set of benchmark problems is comparable to the best-known results obtained by digital algorithms in the literature.
DocAMR: Multi-Sentence AMR Representation and Evaluation

Despite extensive research on parsing of English sentences into Abstract Meaning Representation (AMR) graphs, full-document parsing into a unified graph representation lacks welldefined representation and evaluation.Taking advantage of a super-sentential level of AMR coreference annotation from previous work, we introduce a simple algorithm for deriving a unified graph representation, avoiding the pitfalls of information loss from over-merging and lack of coherence from under-merging.Next, we describe improvements to the Smatch metric to make it tractable for comparing document-level graphs, and use it to re-evaluate the best published document-level AMR parser.We also present a pipeline approach combining the top performing AMR parser and coreference resolution systems, providing a strong baseline for future research.
Artificial intelligence based learning for wireless application – A survey

The wireless networks of the future generation are evolved as complex systems due to broadening in service prerequisites, application heterogeneity and networking of gadgets. In recent times, the major step forward of machine learning techniques is deep learning. However, in wireless/heterogeneous networks, the deep learning application for network traffic control is relatively new. The key disputes in the wireless backbone networks since the advancement of wireless networks are resource allocation and efficient network traffic control such as routing. In larger scale of networks and tangled radio environments, the method of adding intelligence to wireless networks is achieved by Deep Learning (DL). The tangled wireless networks supported with several nodes and their quality of variable link can be investigated by Deep Learning. This paper is presented with a perception of harnessing the next generation communication networks with the artificial neural networks. This work helps the readers to explore the unsolved issues to pursue their research and deeply understand the wireless network design with DL based state of the art facilities. In this work, we integrate the deep learning and wireless networking research with a widespread survey. Finally, this paper summarizes the disputes and benefits of acquiring ML and AI for next generation wireless systems.
A constant lower bound for any quantum protocol for secure function evaluation

Secure function evaluation is a two-party cryptographic primitive where Bob computes a function of Alice's and his respective inputs, and both hope to keep their inputs private from the other party. It has been proven that perfect (or near perfect) security is impossible, even for quantum protocols. We generalize this no-go result by exhibiting a constant lower bound on the cheating probabilities for any quantum protocol for secure function evaluation, and present many applications from oblivious transfer to the millionaire's problem. Constant lower bounds are of practical interest since they imply the impossibility to arbitrarily amplify the security of quantum protocols by any means.
Behavioral biometric based personal authentication in feature phones

&lt;p&gt;&lt;span&gt;The usage of mobile phones has increased multifold in the recent decades mostly because of its utility in most of the aspects of daily life, such as communications, entertainment, and financial transactions. Feature phones are generally the keyboard based or lower version of touch based mobile phones, mostly targeted for efficient calling and messaging. In comparison to smart phones, feature phones have no provision of a biometrics system for the user access. The literature, have shown very less attempts in designing a biometrics system which could be most suitable to the low-cost feature phones. A biometric system utilizes the features and attributes based on the physiological or behavioral properties of the individual. In this research, we explore the usefulness of keystroke dynamics for feature phones which offers an efficient and versatile biometric framework. In our research, we have suggested an approach to incorporate the user’s typing patterns to enhance the security in the feature phone. We have applied k-nearest neighbors (k-NN) with fuzzy logic and achieved the equal error rate (EER) 1.88% to get the better accuracy. The experiments are performed with 25 users on Samsung On7 Pro C3590. On comparison, our proposed technique is competitive with almost all the other techniques available in the literature.&lt;/span&gt;&lt;/p&gt;
One size does not fit all: Investigating strategies for differentially-private learning across NLP tasks

Preserving privacy in contemporary NLP models allows us to work with sensitive data, but unfortunately comes at a price. We know that stricter privacy guarantees in differentially-private stochastic gradient descent (DP-SGD) generally degrade model performance. However, previous research on the efficiency of DP-SGD in NLP is inconclusive or even counter-intuitive. In this short paper, we provide an extensive analysis of different privacy preserving strategies on seven downstream datasets in five different `typical' NLP tasks with varying complexity using modern neural models based on BERT and XtremeDistil architectures. We show that unlike standard non-private approaches to solving NLP tasks, where bigger is usually better, privacy-preserving strategies do not exhibit a winning pattern, and each task and privacy regime requires a special treatment to achieve adequate performance.
LEAD1.0: A Large-scale Annotated Dataset for Energy Anomaly Detection in Commercial Buildings

Modern buildings are densely equipped with smart energy meters, which periodically generate a massive amount of time-series data yielding few million data points every day. This data can be leveraged to discover the underlying loads, infer their energy consumption patterns, inter-dependencies on environmental factors, and the building's operational properties. Furthermore, it allows us to simultaneously identify anomalies present in the electricity consumption profiles, which is a big step towards saving energy and achieving global sustainability. However, to date, the lack of large-scale annotated energy consumption datasets hinders the ongoing research in anomaly detection. We contribute to this effort by releasing a well-annotated version of a publicly available ASHRAE Great Energy Predictor III data set containing 1,413 smart electricity meter time series spanning over one year. In addition, we benchmark the performance of eight state-of-the-art anomaly detection methods on our dataset and compare their performance.
A Preliminary Study for Literary Rhyme Generation based on Neuronal Representation, Semantics and Shallow Parsing

In recent years, researchers in the area of Computational Creativity have studied the human creative process proposing different approaches to reproduce it with a formal procedure. In this paper, we introduce a model for the generation of literary rhymes in Spanish, combining structures of language and neural network models %(\textit{Word2vec}).%, into a structure for semantic assimilation. The results obtained with a manual evaluation of the texts generated by our algorithm are encouraging.
Progressive image transmission based on image spatio-temporal decomposition by sigma-delta cellular neural network

In this paper, we propose a novel progressive image transmission framework based on spatio-temporal image decomposition and synthesis by the SD-CNN. In our method, we redesign the baseline SD-CNN and the weighted sum is introduced in the accumulator. This innovation enables lossless or near-lossless progressive image transmission. Experimental results in various test images support that the image reconstruction performance of the SD-CNN has dramatically improved by our method.
Almost indistinguishable single photons via multiplexing cascaded biphotons with cavity modulation and phase compensation

The cascade-emitted biphotons generated from alkali metal atomic ensembles are an excellent entanglement resource which enables long-distance quantum communication. The communication of quantum information between distant locations can be realized by utilizing the low-loss telecom bandwidth in the upper transition of the cascaded photons in a fiber-based quantum network. Meanwhile, the infrared photon from the lower transition of this highly directional and frequency-correlated biphoton can be created using the four-wave mixing process and can be stored locally as a collective spin wave. Here, we theoretically investigate the frequency entanglement of this biphoton and propose two approaches to remove the two photons' mutual correlations in frequency spaces. The first approach applies an optical cavity which modulates the biphoton spectrum into a more symmetric and narrow spectral function by multiplexing multiple atomic ensembles with phase compensation. The purity of the single photons reaches up to 0.999, and the entanglement entropy $S$ of the biphoton is reduced to 0.006, which is 200 times smaller than the one without multiplexing. The other approach employs a symmetric pumping of the laser fields in two atomic ensembles, which leads to a moderate reduction of $S\ensuremath{\sim}0.3$ when nondiscrimination detection devices are used for both photons. An extremely low frequency entanglement implies an almost indistinguishable single-photon source, which offers a potential resource for photonic quantum emulation and computation.
Comprehensive vulnerability evaluation of urban earthquake disaster based on RAGA-PPE

In view of the multi factor influence problem in the comprehensive vulnerability assessment of urban earthquake disasters combined with the complex internal characteristics of urban system based on the pressure state response model, the comprehensive vulnerability index system of urban earthquake disasters is constructed from seven aspects: natural factors, human factors, lifeline system, social status, economic status, engineering seismic capacity and recovery capacity. The urban vulnerability is evaluated by establishing the comprehensive vulnerability evaluation model of urban earthquake disaster, and the best projection direction of projection pursuit evaluation model is sought by using accelerated genetic algorithm. Combined with the evaluation index value, the projection characteristic index value of the evaluation sample is determined. Taking 12 cities in Gansu Province as an example, the comprehensive vulnerability state of cities is determined by the above model, and the main factors influencing the earthquake vulnerability of cities are analysed according to the results, which provides the basis for decision makers to make earthquake prevention and disaster reduction planning.
Mga Kwento ni Lola Basyang: An Augmented Reality On Selected Philippine Folklore

Children storybooks have gone far, from flat books to embossed to Audio-book to Pop-up book and now Augmented Reality books. Augmented Reality or AR is one of the innovative technologies that will be universally used given its potential and fascination.The goal of this study is to create a new way of learning with children storybooks with new technology. The innovation underpinning this research is the embedded Augmented Reality 2-Dimensional of children’s book on a mobile application. The research provides an insight into what was done using AR on children’s story books enabling the reader to place this example of AR in perspective and understand it more clearly. This paper specifically highlights an innovative development of the interfaces for providing an AR children storybook that enhances story reading and learning experience for preschool and young schoolers children via mobile AR application.
On Relaxed Greedy Randomized Iterative Methods for the Solution of Factorized Linear Systems

RK-RK and REK-RK methods are the two latest while very effective randomized iteration solvers for factorized linear system $UV \beta = y$ by interlacing randomized Kaczmarz (RK) and randomized extended Kaczmarz (REK) updates. This paper considers two latest randomized iterative methods for solving large-scale linear systems and linear least-squares problems—greedy randomized Kaczmarz (GRK) method and greedy randomized Gauss–Seidel (GRGS) method. By introducing a relaxation parameter $\omega$ into the iterates of GRK and GRGS, we construct relaxed GRK and GRGS methods, respectively. In addition, by interlacing their updates, we propose relaxed GRK-GRK and GRGS-GRK methods to solve consistent and inconsistent factorized linear systems, respectively. We prove the exponential convergence of these two interlaced methods and show that relaxed GRK-GRK and GRGS-GRK can be more efficient than RK-RK and REK-RK, respectively, if the relaxation parameters are chosen appropriately.
Doctoral Education in Ukraine: the Application of Digital Tools and Services by Doctoral Students under COVID-19 Pandemic

The paper aims to explore the peculiarities of doctoral education under COVID-19 in Ukraine. The application of digital instruments and services by doctoral students has become a priority during distance training. However, this approach is mainly a new experience and, accordingly, undergoes theoretical substantiation and piloting. The focus of the study is two-fold. It theoretically discusses the digital support of dissertation research at various stages. Secondly, based on the findings of the survey on the doctoral students’ application of digital tools and services the challenges have been identified. We conclude that the priority of digital education in the EU countries, especially under COVID-19 pandemic highlights the task of restructuring the doctoral education in Ukraine towards its dynamic digitalization. The digitization should be comprehensive and systematic. Systematization should include technical, organizational, motivational and developmental components aimed at synergy of institutions, doctoral students and supervisors. The complexity should include both the digitization of the training component and independent work of the doctoral students while writing a dissertation. The groups of DT&amp;S for application by doctoral students are recommended for the inclusion into the doctoral students’ training curriculum both in the format of a separate course and by integration into the syllabi of other disciplines. Based on the research findings the targeted scientific/methodical events (seminars, master classes, doctoral schools, etc.) providing knowledge and skills on DT&amp;S application are recommended.
MODERN CHALLENGES IN THE PRACTICAL TRAINING OF FUTURE TEACHERS IN THE INSTITUTIONS OF HIGHER PEDAGOGICAL EDUCATION

The article describes the state of conducting various types of practice in higher education institutions based on the results of a survey of teachers, practice managers. The results of the survey of teachers, practice managers showed that teachers are aware of the need to implement qualitative changes in the conduct of practice, including the creation of high-quality scientific and methodological support for the conduct of practice; the establishment of a stable relationship with educational institutions; the need to develop virtual resources for the organization of practice; the use of practice results in the research work, in the creation of a database; the structuring of the content of practice on the interdisciplinary basis; the establishment of an effective partnership between the management of institutions of higher pedagogical or professional pre-higher education, educational institutions, and other stakeholders; the expediency of interaction with scientific institutions while improving the process of conducting practice at various levels; the need for decent payment for managing the practice, etc. We outlined the current challenges in organizing practical training of future teachers at bachelor's, master's and doctoral educational levels, including the need to preserve the traditions of conducting various types of practice formed in the institutions of higher or professional pre-higher education; ensuring the use of interactive forms, methods, technologies to form appropriate professional competencies within the practice; the expediency of establishing a partnership with educational institutions, organizations, branches; the need to use remote forms of organizing practice, which is especially important in the context of a pandemic (Google Moodle programs, Microsoft Classroom, Teams, Zoom, Hangouts, and Skype communication platforms); ensuring the pragmatism of the organization of various types of practice, which is focusing on the collection and systematization of empirical material for writing qualification papers, on mastering the real «tools» of future professional activities.
Human-Computer Interaction in Mobile Learning: A Review

Mobile learning mainly concerns mobility and high-quality education, regardless of location or time.Humancomputer interaction comprises the concepts and methods in which humans interact with computers, including designing, implementing, and evaluating computer systems that are accessible and provide an intuitive user interface.Some studies showed that mobile learning could help overcome multiple limitations and improve learning in educational systems.The study investigates the HCI design challenges, including the guidelines and methods in mobile HCI for education.An existing mobile learning tool was discussed on the current and future design enhancements of Udemy.Next is the further discussion on future mobile learning to provide the possible improvements for learners based on the challenges of mobile HCI in education.