Reviewerly/exHarmony huggingface dataset:
A Lightweight Subgraph-Based Deep Learning Approach for Fall Recognition

Falls pose a great danger to social development, especially to the elderly population. When a fall occurs, the body's center of gravity moves from a high position to a low position, and the magnitude of change varies among body parts. Most existing fall recognition methods based on deep learning have not yet considered the differences between the movement and the change in amplitude of each body part. Besides, some problems exist such as complicated design, slow detection speed, and lack of timeliness. To alleviate these problems, a lightweight subgraph-based deep learning method utilizing skeleton information for fall recognition is proposed in this paper. The skeleton information of the human body is extracted by OpenPose, and an end-to-end lightweight subgraph-based network is designed. Sub-graph division and sub-graph attention modules are introduced to add a larger perceptual field while maintaining its lightweight characteristics. A multi-scale temporal convolution module is also designed to extract and fuse multi-scale temporal features, which enriches the feature representation. The proposed method is evaluated on a partial fall dataset collected in NTU and on two public datasets, and outperforms existing methods. It indicates that the proposed method is accurate and lightweight, which means it is suitable for real-time detection and rapid response to falls.
GLM: General Language Model Pretraining with Autoregressive Blank Infilling

There have been various types of pretraining architectures including autoencoding models (e.g., BERT), autoregressive models (e.g., GPT), and encoder-decoder models (e.g., T5). However, none of the pretraining frameworks performs the best for all tasks of three main categories including natural language understanding (NLU), unconditional generation, and conditional generation. We propose a General Language Model (GLM) based on autoregressive blank infilling to address this challenge. GLM improves blank filling pretraining by adding 2D positional encodings and allowing an arbitrary order to predict spans, which results in performance gains over BERT and T5 on NLU tasks. Meanwhile, GLM can be pretrained for different types of tasks by varying the number and lengths of blanks. On a wide range of tasks across NLU, conditional and unconditional generation, GLM outperforms BERT, T5, and GPT given the same model sizes and data, and achieves the best performance from a single pretrained model with 1.25x parameters of BERT Large , demonstrating its generalizability to different downstream tasks.
Quantum algorithm for nonlinear differential equations

Quantum computers are known to provide an exponential advantage over classical computers for the solution of linear differential equations in high-dimensional spaces. Here, we present a quantum algorithm for the solution of nonlinear differential equations. The quantum algorithm provides an exponential advantage over classical algorithms for solving nonlinear differential equations. Potential applications include the Navier-Stokes equation, plasma hydrodynamics, epidemiology, and more.
BioADAPT-MRC: adversarial learning-based domain adaptation improves biomedical machine reading comprehension task

Biomedical machine reading comprehension (biomedical-MRC) aims to comprehend complex biomedical narratives and assist healthcare professionals in retrieving information from them. The high performance of modern neural network-based MRC systems depends on high-quality, large-scale, human-annotated training datasets. In the biomedical domain, a crucial challenge in creating such datasets is the requirement for domain knowledge, inducing the scarcity of labeled data and the need for transfer learning from the labeled general-purpose (source) domain to the biomedical (target) domain. However, there is a discrepancy in marginal distributions between the general-purpose and biomedical domains due to the variances in topics. Therefore, direct-transferring of learned representations from a model trained on a general-purpose domain to the biomedical domain can hurt the model's performance. We present an adversarial learning-based domain adaptation framework for the biomedical machine reading comprehension task (BioADAPT-MRC), a neural network-based method to address the discrepancies in the marginal distributions between the general and biomedical domain datasets. BioADAPT-MRC relaxes the need for generating pseudo labels for training a well-performing biomedical-MRC model. We extensively evaluate the performance of BioADAPT-MRC by comparing it with the best existing methods on three widely used benchmark biomedical-MRC datasets -- BioASQ-7b, BioASQ-8b, and BioASQ-9b. Our results suggest that without using any synthetic or human-annotated data from the biomedical domain, BioADAPT-MRC can achieve state-of-the-art performance on these datasets. Availability: BioADAPT-MRC is freely available as an open-source project at \url{https://github.com/mmahbub/BioADAPT-MRC}.
Federated one-class collaborative filtering via privacy-aware non-sampling matrix factorization

In this paper, we study an emerging and important recommendation problem called federated one-class collaborative filtering (FOCCF). Specifically, we aim to build a recommendation model by exploiting each user’s one-class or implicit feedback in a distributed and privacy-aware manner rather than collecting and learning from data in a central server. For the studied problem, there are three important issues, i.e., recommendation accuracy, privacy and efficiency. As a response, we start from the state-of-the-art one-class collaborative filtering (OCCF) method, i.e., non-sampling matrix factorization (NSMF), and propose a novel federated recommendation framework called privacy-aware NSMF (P-NSMF). Our P-NSMF protects user privacy well without sacrificing recommendation accuracy and contains two variants, i.e., P-NSMF(ALS) and P-NSMF(BGD), which are based on alternating least squares (ALS) and batch gradient descent (BGD), respectively. Moreover, we design an improved strategy called group-wise concealing and adopt a secure aggregation technique in our framework for privacy and efficiency. We then analyze the security and complexity of our P-NSMF, and conduct extensive experiments on four public datasets. In particular, compared with the existing methods, our P-NSMF outperforms them in terms of recommendation accuracy, privacy and efficiency, which are important merits for real deployment.
SwahBERT: Language Model of Swahili

The rapid development of social networks, electronic commerce, mobile Internet, and other technologies has influenced the growth of Web data.Social media and Internet forums are valuable sources of citizens' opinions, which can be analyzed for community development and user behavior analysis.Unfortunately, the scarcity of resources (i.e., datasets or language models) has become a barrier to the development of natural language processing applications in low-resource languages.Thanks to the recent growth of online forums and news platforms of Swahili, we introduce two datasets of Swahili in this paper: a pre-training dataset of approximately 105MB with 16M words and an annotated dataset of 13K instances for the emotion classification task.The emotion classification dataset is manually annotated by two native Swahili speakers.We pre-trained a new monolingual language model for Swahili, namely SwahBERT, using our collected pre-training data, and tested it with four downstream tasks including emotion classification.We found that SwahBERT outperforms multilingual BERT, a well-known existing language model, in almost all downstream tasks.
Few-shot fine-tuning SOTA summarization models for medical dialogues

Abstractive summarization of medical dialogues presents a challenge for standard training approaches given the paucity of suitable datasets.We explore the performance of state-of-the-art models with zero-shot and few-shot learning strategies and measure the impact of pretraining with general domain and dialogue specific text on the summarization performance.
PropBank Comes of Age—Larger, Smarter, and more Diverse

This paper describes the evolution of the Prop-Bank approach to semantic role labeling over the last two decades.During this time the Prop-Bank frame files have been expanded to include non-verbal predicates such as adjectives, prepositions and multi-word expressions.The number of domains, genres and languages that have been PropBanked has also expanded greatly, creating an opportunity for much more challenging and robust testing of the generalization capabilities of PropBank semantic role labeling systems.We also describe the substantial effort that has gone into ensuring the consistency and reliability of the various annotated datasets and resources, to better support the training and evaluation of such systems.
Level of Digital Teaching Competence on the Verge of the Post Pandemic

With the arrival of Covid-19, education at all levels had a 180° turn so our research has as its main objective to describe the level of digital competence in teachers after having experienced two years with digital competencies which will be evaluated by students, this research is descriptive, cross-sectional and correlational with a quantitative approach, with a sample of 1045 students from state and private universities who participated in the online survey, an evaluation instrument was used with 21 questions divided into 6 parts, resulting in Cronbach's Alpha optimal for its application in the Peruvian context, analyzing with different statistical tests resulting in significant values such as Bartlett's sphericity with Chi-square approximation (35573, 691) with gl. 210 and value of Sig. .000, in the Kaiser-Meyer-Olkin test (0.975) and with the goodness of fit test with the Chi-square 5922,091 with gl 189 and value of Sig. .000. Concluding that they should continue with the training to be updated and be prepared for any future event.
Attributes of Expert Anticipation Should Inform the Design of Virtual Reality Simulators to Accelerate Learning and Transfer of Skill

Abstract Expert sport performers cope with a multitude of visual information to achieve precise skill goals under time stress and pressure. For example, a major league baseball or cricket batter must read opponent variations in actions and ball flight paths to strike the ball in less than a second. Crowded playing schedules and training load restrictions to minimise injury have limited opportunity for field-based practice in sports. As a result, many sports organisations are exploring the use of virtual reality (VR) simulators. Whilst VR synthetic experiences can allow greater control of visual stimuli, immersion to create presence in an environment, and interaction with stimuli, compared to traditional video simulation, the underpinning mechanisms of how experts use visual information for anticipation have not been properly incorporated into its content design. In themes, this opinion article briefly explains the mechanisms underpinning expert visual anticipation, as well as its learning and transfer, with a view that this knowledge can better inform VR simulator content design. In each theme, examples are discussed for improved content design of VR simulators taking into consideration its advantages and limitations relative to video simulation techniques. Whilst sport is used as the exemplar, the points discussed have implications for skill learning in other domains, such as military and law enforcement. It is hoped that our paper will stimulate improved content design of VR simulators for future research and skill enhancement across several domains.
Investigating Attention Mechanism for Page Object Detection in Document Images

Page object detection in scanned document images is a complex task due to varying document layouts and diverse page objects. In the past, traditional methods such as Optical Character Recognition (OCR)-based techniques have been employed to extract textual information. However, these methods fail to comprehend complex page objects such as tables and figures. This paper addresses the localization problem and classification of graphical objects that visually summarize vital information in documents. Furthermore, this work examines the benefit of incorporating attention mechanisms in different object detection networks to perform page object detection on scanned document images. The model is designed with a Pytorch-based framework called Detectron2. The proposed pipelines can be optimized end-to-end and exhaustively evaluated on publicly available datasets such as DocBank, PublayNet, and IIIT-AR-13K. The achieved results reflect the effectiveness of incorporating the attention mechanism for page object detection in documents.
Automated Feature-Topic Pairing

Automated characterization of spatial data is a kind of critical geographical intelligence. As an emerging technique for characterization, Spatial Representation Learning (SRL) uses deep neural networks (DNNs) to learn non-linear embedded features of spatial data for characterization. However, SRL extracts features by internal layers of DNNs, and thus suffers from lacking semantic labels. Texts of spatial entities, on the other hand, provide semantic understanding of latent feature labels, but is insensible to deep SRL models. How can we teach a SRL model to discover appropriate topic labels in texts and pair learned features with the labels? This paper formulates a new problem: feature-topic pairing, and proposes a novel Particle Swarm Optimization (PSO) based deep learning framework. Specifically, we formulate the feature-topic pairing problem into an automated alignment task between 1) a latent embedding feature space and 2) a textual semantic topic space. We decompose the alignment of the two spaces into: 1) point-wise alignment, denoting the correlation between a topic distribution and an embedding vector; 2) pair-wise alignment, denoting the consistency between a feature-feature similarity matrix and a topic-topic similarity matrix. We design a PSO based solver to simultaneously select an optimal set of topics and learn corresponding features based on the selected topics. We develop a closed loop algorithm to iterate between 1) minimizing losses of representation reconstruction and feature-topic alignment and 2) searching the best topics. Finally, we present extensive experiments to demonstrate the enhanced performance of our method.
Criteria for selecting open web-oriented technologies for teaching the basics of programming to future software engineers

The article presents the criteria for selecting open web-oriented technologies for teaching the basics of programming to future software engineers. An analysis of the available open web-oriented technologies for teaching the basics of programming to future software engineers made it possible to divide them into compilers, automated systems for checking programming tasks, mind maps, and massive open online courses. Criteria and corresponding indicators are defined for each of them: for web-oriented compilers and mind maps, design and functional criteria are allocated, for web-oriented automated systems for checking programming tasks, design, information-didactic, and communication criteria are allocated, for massive open online courses - informational didactic and functional. Comparative tables are given for all the listed web-oriented technologies for teaching the basics of programming to future software engineers according to certain criteria and indicators.
Reading In-Between the Lines

Efforts by content creators and social networks to enforce legal and policy-based norms, e.g. blocking hate speech and users, has driven the rise of unrestricted communication platforms. One such recent effort is Dissenter, a browser and web application that provides a conversational overlay for any web page. These conversations hide in plain sight - users of Dissenter can see and participate in this conversation, whereas visitors using other browsers are oblivious to their existence. Further, the website and content owners have no power over the conversation as it resides in an overlay outside their control. In this work, we obtain a history of Dissenter comments, users, and the websites being discussed, from the initial release of Dissenter in Feb. 2019 through Apr. 2020 (14 months). Our corpus consists of approximately 1.68M comments made by 101k users commenting on 588k distinct URLs. We first analyze macro characteristics of the network, including the user-base, comment distribution, and growth. We then use toxicity dictionaries, Perspective API, and a Natural Language Processing model to understand the nature of the comments and measure the propensity of particular websites and content to elicit hateful and offensive Dissenter comments. Using curated rankings of media bias, we examine the conditional probability of hateful comments given left and right-leaning content. Finally, we study Dissenter as a social network, and identify a core group of users with high comment toxicity.
SpanBERT: Improving Pre-training by Representing and Predicting Spans

We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text. Our approach extends BERT by (1) masking contiguous random spans, rather than random tokens, and (2) training the span boundary representations to predict the entire content of the masked span, without relying on the individual token representations within it. SpanBERT consistently outperforms BERT and our better-tuned baselines, with substantial gains on span selection tasks such as question answering and coreference resolution. In particular, with the same training data and model size as BERT-large, our single model obtains 94.6% and 88.7% F1 on SQuAD 1.1 and 2.0, respectively. We also achieve a new state of the art on the OntoNotes coreference resolution task (79.6\% F1), strong performance on the TACRED relation extraction benchmark, and even show gains on GLUE.
Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches

In the NLP community, recent years have seen a surge of research activities that address machines' ability to perform deep language understanding which goes beyond what is explicitly stated in text, rather relying on reasoning and knowledge of the world. Many benchmark tasks and datasets have been created to support the development and evaluation of such natural language inference ability. As these benchmarks become instrumental and a driving force for the NLP research community, this paper aims to provide an overview of recent benchmarks, relevant knowledge resources, and state-of-the-art learning and inference approaches in order to support a better understanding of this growing field.
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents

We introduce a new approach to generative data-driven dialogue systems (e.g. chatbots) called TransferTransfo which is a combination of a Transfer learning based training scheme and a high-capacity Transformer model. Fine-tuning is performed by using a multi-task objective which combines several unsupervised prediction tasks. The resulting fine-tuned model shows strong improvements over the current state-of-the-art end-to-end conversational models like memory augmented seq2seq and information-retrieval models. On the privately held PERSONA-CHAT dataset of the Conversational Intelligence Challenge 2, this approach obtains a new state-of-the-art, with respective perplexity, Hits@1 and F1 metrics of 16.28 (45 % absolute improvement), 80.7 (46 % absolute improvement) and 19.5 (20 % absolute improvement).
Discrete Missing Data Imputation Using Multilayer Perceptron and Momentum Gradient Descent

Data are a strategic resource for industrial production, and an efficient data-mining process will increase productivity. However, there exist many missing values in data collected in real life due to various problems. Because the missing data may reduce productivity, missing value imputation is an important research topic in data mining. At present, most studies mainly focus on imputation methods for continuous missing data, while a few concentrate on discrete missing data. In this paper, a discrete missing value imputation method based on a multilayer perceptron (MLP) is proposed, which employs a momentum gradient descent algorithm, and some prefilling strategies are utilized to improve the convergence speed of the MLP. To verify the effectiveness of the method, experiments are conducted to compare the classification accuracy with eight common imputation methods, such as the mode, random, hot-deck, KNN, autoencoder, and MLP, under different missing mechanisms and missing proportions. Experimental results verify that the improved MLP model (IMLP) can effectively impute discrete missing values in most situations under three missing patterns.
Effectiveness of Virtual Reality on Attention Training for Elementary School Students

This study is aimed at investigating the effectiveness of virtual reality (VR) on attention training for elementary school students. A pre-test and post-test design of the quasi-experimental method was adopted and 66 third and fourth graders from an elementary school in Hsinchu, Taiwan were used as experimental subjects, divided into a control group and experimental group. The former used the computerized Attention Process Training (APT) system and the latter used the proposed VR system for attention training, both for two weeks. The attention scale for elementary school children was used to evaluate the participant’s attention before and after training, including the dimensions of focused attention, sustained attention, selective attention, alternating attention, and divided attention. A questionnaire survey was conducted to measure the learning anxiety and cognitive load during the training process. The experimental results indicated: (1) The overall attention was significantly improved after the training process for both groups, and the VR system was more effective than the computerized APT in improving children’s attention. (2) The questionnaire results showed that the experimental group had lower learning anxiety and cognitive load than the control group. According to the experimental results, VR training is more effective in improving the attention of participants while reducing their learning anxiety and cognitive load. Therefore, it is a useful tool for attention training in elementary schools.
Facial age estimation using tensor based subspace learning and deep random forests

Recently, the estimation of facial age has attracted much attention. This letter extends and improves a recently developed method (Guehairia et al., 2020) for fusing multiple deep facial features for age estimation. This method was based on deep random forests. We propose a new pipeline that integrates tensor-based subspace learning before applying DRFs. Deep face features of a training set are represented as a 3D tensor. Multi-linear Whitened Principal Component (MWPCA) and Tensor Exponential Discriminant (TEDA) are used to extract the most discriminative information. The tensor subspace features are then fed into DRFs to predict age. Experiments conducted on five public face databases show that our method can compete with many state-of-the-art methods.