Reviewerly/exHarmony huggingface dataset:
“I just Google it” - Developing professional digital competence and preparing student teachers to exercise responsible ICT use

The rapid emergence of information and communication technology (ICT) has had implications for the education system and initial teacher education (ITE) in particular. This study investigated the extent to which teacher education assists student teachers in developing their professional digital competence (PDC) in general and, more specifically, their competence in using ICT responsibly. Responsible use of ICT is here taken to include privacy and copyright issues, ethical issues and the ability to evaluate digital information. To explore Norwegian student teachers’ perspectives, awareness and experience of the responsible use of ICT, in-depth interviews were conducted with 10 student teachers before their practice placements at local schools and with six students after their practice placements. Overall, the findings indicate that the student teachers mostly knew how to search for and evaluate digital information, but that they tended to choose the most convenient approach for search and evaluation. Further, it seems that the student teachers were aware to some extent of how to avoid advertisements, marketing or inappropriate content when using online resources in the classroom. However, they had limited competence in dealing with privacy and copyright issues in a teaching setting. One of the challenges identified through this study is that, during practice placements, the attention seems to be on the technical aspects of ICT rather than on pedagogical or responsible ways of using ICT. The study concludes that teacher education programmes need to include responsible use of ICT as an integral part of their programme, as well as during student teachers’ practice placements in schools, rather than providing stand-alone activities or courses of limited duration.
EDUCATIONAL AND DEVELOPMENTAL ENVIRONMENT AS A FACTOR OF SKILLS FORMATION OF SELF-ORGANIZATION OF PRIMARY SCHOOL PUPILS’ EDUCATIONAL ACTIVITY

Modernization of educational activities determines the necessity for the formation of educational and developmental environment. Today, the development of students' self-organization in an educational and developmental environment is one of the main demands that modern society puts forward to the system of primary education. The purpose is to study the features of the educational and developmental environment, its classification; development of didactic games at the lessons of Writing and Mathematics that promote the development of pupils’ self-organization in an educational and developmental environment. Research methods: a logical method; methods of system analysis; comparative analysis has made it possible to compare phenomena in order to establish similarities or differences between them. The academic paper has highlighted the essence of the concepts “educational and developmental environment”, “self-organization of educational activities”. The components of the educational and developmental environment of primary school and its classification have been formed. The basic types of primary pupils’ self-organization under the influence of the educational and developmental environment have been considered. The stages of the ability of self-organization of primary schoolchildren’s educational activity within the conditions of educational and developmental environment have been determined. Examples of Writing lessons in grades 1 and 4 have been given at the above stages, which contribute to the formation of skills of self-organization of pupils’ learning activities. Thus, types of didactic games at the lessons of Writing and Mathematics have been offered, which make it possible to provide material in accessible, interesting form, form skills of self-organization of schoolchildren in educational environment.
Bifurcations and the Emergence of L2 Syntactic Structures in a Complex Dynamic System

We report on a complex dynamic systems study of an untutored adult French learner's development of English syntax, specifically two non-finite adverbial constructions. The study was conducted over one academic year of 30 weeks. From an analysis of L2 speech samples collected weekly, certain patterns in the flux emerged. The learner's ensuing second language development is characterized by a series of bifurcations, stemming from forms competing for the same functional terrain. Each bifurcation is accompanied by turbulence as the system moves from one attractor state to another. The transition is characterized by loss of stability, an increase in variability, and a period of dysfluency. It is in the dynamic relationship of accuracy and fluency that novel syntactic forms emerge, both convergent with and divergent from dominant contextual patterns, with dominance established by consulting a well-known corpus of contemporary English. Non-linear development occurs with continuous and iterative exposure to and interaction in English-from relexification to adaptation and synchronization, animated by the learner's perception and memory of regular sequential associations, to pruning of divergent forms. What results over time is a branching hierarchy, connecting online processing with over time development. Multiple competing forms continue to co-exist in the learner's repertoire, which is likely more typical of adult L2 development than of L1 acquisition.
CP-GAN: A Cross-Pose Profile Face Frontalization Boosting Pose-Invariant Face Recognition

Pose variant or self-occlusion is one of the open issues which severely degrades the performance of pose-invariant face recognition (PIFR). Existing solutions to PIFR either have undesirable generalization based on challenging pose normalization or are complicated for implement on account of deep neural network. To relieve the impact of ill-pose on PIFR, we have proposed Cross-Pose Generative Adversarial Networks(CP-GAN) to frontalize the profile face with unaltered identity by learning the mapping between the profile and frontal faces in image space. The generator is an encoder-decoder U-net, and generate frontal face image by fusing multiple profile images to achieve a better performance in PIFR. The siamese discriminative network attends to extract the deep representations of the generated frontal face and the ground truth without introducing extra networks in verification and recognition. Besides the implementable architecture, this problem is well alleviated by introducing a combination of adversarial loss for both the generator and the discriminator, symmetry loss, patch-wise loss, and identity loss guiding an identity reserving property of the generated frontal view. Quantitative and qualitative evaluation on both controlled and in-the-wild datasets attest that the solution we proposed to PIFR presents satisfactory perceptual results and transcends state-of-the-art methods on ill-pose face recognition.
Cuttlefish: Neural Configuration Adaptation for Video Analysis in Live Augmented Reality

Instead of relying on remote clouds, today's Augmented Reality (AR) applications usually send videos to nearby edge servers for analysis (such as objection detection) so as to optimize the user's quality of experience (QoE), which is often determined by not only detection latency but also detection accuracy, playback fluency, etc. Therefore, many studies have been conducted to help adaptively choose best video configuration, e.g., resolution and frame per second (fps), based on network bandwidth to further improve QoE. However, we notice that the video content itself has significant impacts on the configuration selection, e.g., the videos with high-speed objects must be encoded with a high fps to meet the user's fluency requirement. In this article, we aim to adaptively select configurations that match the time-varying network condition as well as the video content. We design Cuttlefish, a system that generates video configuration decisions using reinforcement learning (RL). Cuttlefish trains a neural network model that picks a configuration for the next encoding slot based on observations collected by AR devices. Cuttlefish does not rely on any pre-programmed models or specific assumptions on the environments. Instead, it learns to make configuration decisions solely through observations of the resulting performance of historical decisions. Cuttlefish automatically learns the adaptive configuration policy for diverse AR video streams and obtains a gratifying QoE. We compared Cuttlefish to several state-of-the-art bandwidth-based and velocity-based methods using trace-driven and real world experiments. The results show that Cuttlefish achieves a 18.4-25.8 percent higher QoE than the others.
ASR-Free Pronunciation Assessment

Most of the pronunciation assessment methods are based on local features derived from automatic speech recognition (ASR), e.g., the Goodness of Pronunciation (GOP) score. In this paper, we investigate an ASR-free scoring approach that is derived from the marginal distribution of raw speech signals. The hypothesis is that even if we have no knowledge of the language (so cannot recognize the phones/words), we can still tell how good a pronunciation is, by comparatively listening to some speech data from the target language. Our analysis shows that this new scoring approach provides an interesting correction for the phone-competition problem of GOP. Experimental results on the ERJ dataset demonstrated that combining the ASR-free score and GOP can achieve better performance than the GOP baseline.
Breaking out of your comfort zone: an archival research on epistemology in inclusive education pedagogy for Industry 4.0

Purpose As skills need to be changed in a dynamic learning environment, employability depends not just on what people already know but on how well they learn, apply and adapt breaking out their comfort zone. This study explores how students from all backgrounds and teachers can engage with inclusive education without discrimination through pedagogy. The research provides a platform through implication for other international readers of developing countries to implement pedagogies of the Indian context. Design/methodology/approach This archival research focuses on the topical literature to scrutinize efficient ways to elevate the realization of all learners in inclusive settings. What inclusive pedagogy teaching approaches, focusing on the key competences and sustains learning which are effectual in elevating the academic success of all novices. Findings Educators need to develop their skills and competency by breaking their comfort zone, and individual recital of every faculty affiliate is a decisive feature in accomplishing quality for inclusive education. An education institution also needs to provide passable facilities to academicians and students in order to adapt and utilize technology efficiently without any discrimination. This is an important method of assisting educators to recognize and investigate using this epistemology in new innovative inclusive teaching pedagogy with technologies in industry 4.0. Research limitations/implications The study momentarily suggests an innovative pedagogy approach for stakeholders and users to be adapted in current digital arena. Originality/value Review of the concepts can provide valuable pointers for policy makers in other jurisdictions contemplating inclusive education. The issues that are dealt with relate to how all students with and without disability can be engaged in a classroom without discrimination, and development is incentivized using Information and Communication Technology (ICT) in teaching pedagogy.
Embedding group and obstacle information in LSTM networks for human trajectory prediction in crowded scenes

Recurrent neural networks have shown good abilities in learning the spatio-temporal dependencies of moving agents in crowded scenes. Recently, they have been adopted to predict the motion of pedestrians by learning the relative motion of each individual in the crowd with respect to its neighbors. Crowded scenes present a wide variety of situations, which do not depend solely on the agents’ positions, but also relate to the structure of the environment, the density of the crowd, and the social relationships between pedestrians. In this work we propose a framework to improve the state-of-the-art models of crowd motion prediction by enriching the learning model with the social relationships between pedestrians walking in the crowd, as well as the layout of the environment. We observe that socially-related people tend to exhibit coherent motion patterns. Exploiting the motion coherency, we are able to cluster trajectories with similar motion properties and improve the trajectory prediction, especially at the group level. Furthermore, we incorporate into the model also the layout of the environment, to guarantee a more realistic and reliable learning framework. We evaluate our approach on standard crowd benchmark datasets, demonstrating its efficacy and applicability, improving the accuracy in trajectory prediction.
BEN: An Architecture for the Behavior of Social Agents

Over the last few years, the use of agent-based simulations to study social systems has spread to many domains (e.g., geography, ecology, sociology, economy). These simulations aim to reproduce real life situations involving human beings and thus need to integrate complex agents to match the behavior of the simulated people. Therefore, notions such as cognition, emotions, personality, social relationships or norms have to be taken into account, but there is currently no agent architecture that could incorporate all these features and be used by the majority of modelers, including those with low levels of skills in programming. In this paper, the BEN (Behavior with Emotions and Norms) architecture is introduced to tackle this issue. It is a modular architecture based on the BDI model of cognition and featuring modules to add emotions, emotional contagion, personality, social relationships and norms to agent behavior. This architecture is integrated into the GAMA simulation platform. An application of BEN to the simulation of the evacuation of a nightclub on fire is presented and shows the complexity of behaviors that may be developed with this architecture to create credible and expressive simulations.
Spectra of the Conjugate Kernel and Neural Tangent Kernel for linear-width neural networks

We study the eigenvalue distributions of the Conjugate Kernel and Neural Tangent Kernel associated to multi-layer feedforward neural networks. In an asymptotic regime where network width is increasing linearly in sample size, under random initialization of the weights, and for input samples satisfying a notion of approximate pairwise orthogonality, we show that the eigenvalue distributions of the CK and NTK converge to deterministic limits. The limit for the CK is described by iterating the Marcenko-Pastur map across the hidden layers. The limit for the NTK is equivalent to that of a linear combination of the CK matrices across layers, and may be described by recursive fixed-point equations that extend this Marcenko-Pastur map. We demonstrate the agreement of these asymptotic predictions with the observed spectra for both synthetic and CIFAR-10 training data, and we perform a small simulation to investigate the evolutions of these spectra over training.
FastFormers: Highly Efficient Transformer Models for Natural Language Understanding

Transformer-based models are the state-ofthe-art for Natural Language Understanding (NLU) applications.Models are getting bigger and better on various tasks.However, Transformer models remain computationally challenging since they are not efficient at inferencetime compared to traditional approaches.In this paper, we present FastFormers, a set of recipes to achieve efficient inference-time performance for Transformer-based models on various NLU tasks.We show how carefully utilizing knowledge distillation, structured pruning and numerical optimization can lead to drastic improvements on inference efficiency.We provide effective recipes that can guide practitioners to choose the best settings for various NLU tasks and pretrained models.Applying the proposed recipes to the Super-GLUE benchmark, we achieve from 9.8x up to 233.9x speed-up compared to out-of-the-box models on CPU.On GPU, we also achieve up to 12.4x speed-up with the presented methods.We show that FastFormers can drastically reduce cost of serving 100 million requests from 4,223 USD to just 18 USD on an Azure F16s v2 1 instance.This translates to a sustainable runtime by reducing energy consumption 6.9x -125.8xaccording to the metrics used in the SustaiNLP 2020 shared task.
Don’t Parse, Insert: Multilingual Semantic Parsing with Insertion Based Decoding

Semantic parsing is one of the key components of natural language understanding systems.A successful parse transforms an input utterance to an action that is easily understood by the system.Many algorithms have been proposed to solve this problem, from conventional rulebased or statistical slot-filling systems to shiftreduce based neural parsers.For complex parsing tasks, the state-of-the-art method is based on autoregressive sequence to sequence models to generate the parse directly.This model is slow at inference time, generating parses in O(n) decoding steps (n is the length of the target sequence).In addition, we demonstrate that this method performs poorly in zero-shot cross-lingual transfer learning settings.In this paper, we propose a non-autoregressive parser which is based on the insertion transformer to overcome these two issues.Our approach 1) speeds up decoding by 3x while outperforming the autoregressive model and 2) significantly improves cross-lingual transfer in the low-resource setting by 37% compared to autoregressive baseline.We test our approach on three well-known monolingual datasets: ATIS, SNIPS and TOP.For cross lingual semantic parsing, we use the MultiATIS++ and the multilingual TOP datasets.
Methods for Numeracy-Preserving Word Embeddings

Word embedding models are typically able to capture the semantics of words via the distributional hypothesis, but fail to capture the numerical properties of numbers that appear in a text.This leads to problems with numerical reasoning involving tasks such as question answering.We propose a new methodology to assign and learn embeddings for numbers.Our approach creates Deterministic, Independentof-Corpus Embeddings (referred to as DICE) for numbers, such that their cosine similarity reflects the actual distance on the number line.DICE outperforms a wide range of pre-trained word embedding models across multiple examples of two tasks: (i) evaluating the ability to capture numeration and magnitude; and (ii) to perform list maximum, decoding, and addition.We further explore the utility of these embeddings in downstream applications by initializing numbers with our approach for the task of magnitude prediction.We also introduce a regularization approach to learn model-based embeddings of numbers in a contextual setting.
Modelling Lexical Ambiguity with Density Matrices

Words can have multiple senses.Compositional distributional models of meaning have been argued to deal well with finer shades of meaning variation known as polysemy, but are not so well equipped to handle word senses that are etymologically unrelated, or homonymy.Moving from vectors to density matrices allows us to encode a probability distribution over different senses of a word, and can also be accommodated within a compositional distributional model of meaning.In this paper we present three new neural models for learning density matrices from a corpus, and test their ability to discriminate between word senses on a range of compositional datasets.When paired with a particular composition method, our best model outperforms existing vector-based compositional models as well as strong sentence encoders.
HM-ANN: Efficient Billion-Point Nearest Neighbor Search on Heterogeneous Memory.

The state-of-the-art approximate nearest neighbor search (ANNS) algorithms face a fundamental tradeoff between query latency and accuracy, because of small main memory capacity: To store indices in main memory for fast query response, They have to limit the number of data points or store compressed vectors, which hurts search accuracy. The emergence of heterogeneous memory (HM) brings opportunities to largely increase memory capacity and break the above tradeoff: Using HM, billions of data points can be placed in main memory on a single machine without using any data compression. However, HM consists of both fast (but small) memory and slow (but large) memory, and using HM inappropriately slows down query time significantly. In this work, we present a novel graph-based similarity search algorithm called HM-ANN, which takes both memory and data heterogeneity into consideration and enables billion-scale similarity search on a single node without using compression. On two billion-sized datasets BIGANN and DEEP1B, HM-ANN outperforms state-of-the-art compression-based solutions such as L&C and IMI+OPQ in recall-vs-latency by a large margin, obtaining 46% higher recall under the same search latency. We also extend existing graph-based methods such as HNSW and NSG with two strong baseline implementations on HM. At billion-point scale, HM-ANN is 2X and 5.8X faster than our HNSW and NSG baselines respectively to reach the same accuracy.
Cross-Lingual Training of Neural Models for Document Ranking

We tackle the challenge of cross-lingual training of neural document ranking models for mono-lingual retrieval, specifically leveraging relevance judgments in English to improve search in non-English languages.Our work successfully applies multi-lingual BERT (mBERT) to document ranking and additionally compares against a number of alternatives: translating the training data, translating documents, multi-stage hybrids, and ensembles.Experiments on test collections in six different languages from diverse language families reveal many interesting findings: modelbased relevance transfer using mBERT can significantly improve search quality in (non-English) mono-lingual retrieval, but other "low resource" approaches are competitive as well.
Implementation a Various Types of Machine Learning Approaches for Biomedical Datasets based on Sickle Cell Disorder

This paper presents implementation a various kinds of machine learning models to classify the dataset of sickle cell patients. Artificial intelligence techniques have served to strengthen the medical field in solving its problems and providing rapid technical methods with high efficiency instead of traditional methods that can be subject to many problems in diagnosis and to determine the appropriate treatment. The main objective of this study to obtain a highly qualified classifier capable of determining the suitable dose of the SCD patients from 9 classes. Through examining the techniques used in our experiment based on performance evaluation metrics and making sure that each model performs. We applied numerous models of machine learning classifiers to examine the sickle cell dataset based on the performance evaluation metrics. The outcomes obtained from all classifiers, show that the Naïve Bayes Classifier obtained poor results compared to other classifiers. While Levenberg-Marquardt Neural Network during the training phase obtained the highest performance and accuracy of 0.935222, AUC 0.963889. The test phase obtained an accuracy of 0.846444, AUC 0.871889.