Reviewerly/exHarmony huggingface dataset:
Effects of COVID-19 on Education and Schools’ Reopening in Latin America

Lockdowns around the world have forced governments to close schools for most of 2020 and they will probably remain closed in several areas and for some time until the end of 2021. Millions of children around the world will not have experienced classes in classrooms for an entire year or longer, which has disrupted their routines and educational cycles. In this chapter we discuss the impact of the pandemic and school closures on the schooling system in Latin America. A rapid shift to digital learning became necessary, but the basic infrastructure to support it was mostly absent, so school closures have exacerbated the digital gap both within and between countries. In Latin America, after decades of expansion of the public schooling system, the pandemic has offset the important progress in terms of poverty alleviation.
CRB-Net: A Sign Language Recognition Deep Learning Strategy Based on Multi-modal Fusion with Attention Mechanism

At present, sign language recognition (SLR) researchers are mainly committed to establishing a sign language recognition model based on single-mode data. Nevertheless, this manipulation often leads to a defective understanding of the sign language semantics and ignoring some visual information. In a nutshell, the challenges locate redundancy removing and the alignment of the sign language data with the given tag. To solve the conundrum, this paper proposes a deep learning strategy called CRB-Net, which has used a kind of multimodal fusion attention mechanism. We first extract the features from RGB video and depth video, respectively, then conduct multi-modal fusion. Finally, the fused feature information is fed into an encoder-decoder network to achieve the goal of end-to-end continuous SLR. We verify the effectiveness of our method on three datasets, including the German dataset RWTH-Phoenix-Weather-2014, the Chinese dataset USTC-CSL and the Chinese dataset TJUT-SLRT. As shown by experimental results, the accuracy of 98.5% of our framework CRB-Net has outperformed the state-of-the-art works in the comparison, both in accuracy and algorithm execution efficiency.
Stabilization Analysis and Impulsive Controller Design for Positive Interval Type-2 Polynomial Fuzzy Systems

This article investigates the polynomial fuzzy impulsive control problem for positive nonlinear systems subject to parameter uncertainties. The positive nonlinear systems are represented as positive interval type-2 (IT2) polynomial fuzzy-model-based systems, while the parameter uncertainties are captured by the IT2 membership functions (MFs). Considering that the controller being designed needs to be implementable and practical, in addition to ensuring the positivity and stability of the system, there are still some potential requirements for the controller, such as lower implementation cost and control cost. To reduce the implementation cost, the IT2 polynomial fuzzy impulsive controller is designed under the imperfect premise matching concept that the premise MFs and the number of rules of fuzzy controller are different from those of fuzzy model. To reduce the control cost, the impulsive controller that can tolerate larger impulse control interval (ICI) is designed by the following two novel methods. The first one is the proposed impulse-time-dependent discretized polynomial copositive Lyapunov function whose Lyapunov variable is designed as the interpolation polynomial function of time. The second one is the advanced IT2 membership-function-dependent (IT2MFD) analysis method which is improved by a novel footprint of uncertainty partitioning method proposed by this paper, so that this advanced IT2MFD method can alleviate the negative impact of parameter uncertainties on the ICI. Finally, a simulation example using lipoprotein metabolism and potassium ion transfer model as the nonlinear system to be controlled is used to verify the effectiveness of the proposed impulsive control methods.
Information Seeking Behaviors, Attitudes, and Choices of Academic Physicists

Physicists in academic institutions utilize a variety of resources and strategies to seek, find, and use scholarly information and news. Using a sample of physicists, researchers surveyed 182 students and faculty at seven Canadian university institutions to explore self-perceived success rates, resources consulted, databases used, and use of social media and citation management systems. To complement the survey, 11 follow up interviews/focus groups were completed with participants to further uncover information-seeking behaviors, choices, strategies, and feelings around keeping up to date with information needs. According to survey results, a minority of physicists (15.4%) acknowledged that they were successfully keeping up to date. However, a significant number of physicists (28.6%) indicated that they were unsuccessful and could do better in remaining current with information needs. Co-investigators, using qualitative analyses, identified four emergent themes: (1) There are “too many papers – and not enough time” to effectively search, evaluate and read scholarly papers of interest; (2) Staying up to date is important especially in competitive research areas; (3) Graduate students seek information differently than faculty and experienced researchers; and (4) The arXiv database is important to many physicists. Additional minor themes included physics-related publishing is constantly evolving; physicists use a variety of information-seeking behaviors; and, information-seeking methods can differ between physics subdisciplines. This study aims to shed light on opportunities for academic librarians to identify and meet physicists’ evolving information behaviors, attitudes, choices, and needs.
Learning in Compressed Domain for Faster Machine Vision Tasks

Learned image compression (LIC) has illustrated good ability for reconstruction quality driven tasks (e.g. PSNR, MS-SSIM) and machine vision tasks such as image understanding. However, most LIC frameworks are based on pixel domain, which requires the decoding process. In this paper, we develop a learned compressed domain framework for machine vision tasks. 1) By sending the compressed latent representation directly to the task network, the decoding computation can be eliminated to reduce the complexity. 2) By sorting the latent channels by entropy, only selective channels will be transmitted to the task network, which can reduce the bitrate. As a result, compared with the traditional pixel domain methods, we can reduce about 1/3 multiply-add operations (MACs) and 1/5 inference time while keeping the same accuracy. Moreover, proposed channel selection can contribute to at most 6.8% bitrate saving.
SolarWinds Software Supply Chain Security: Better Protection with Enforced Policies and Technologies

A recent cybersecurity attack took place on US governments and companies utilizing a popular network performance monitoring tool, SolarWinds. The attack appears to be not only extensive but also comprehensive in the scope of the common security tools that have been breached. This attack targeted the complex software supply chain. The wave of those attacks was mainly focused on the critical departments of the U.S. government and of many other leading corporations. Even if the attackers did not actively exploit their systems, the comprehensive nature of these breaches seems to indicate that there are fundamental flaws with existing security infrastructure. This paper investigates what caused this significant attack and what solutions we might have to prevent similar attacks in the future. The paper concludes that a combined set of actions of the government and industries on better policies and technologies is needed to develop a unified strategy in each organization.
DropDim: A Regularization Method for Transformer Networks

We introduceDropDim, a structured dropout method designed for regularizing the self-attention mechanism, which is a key component of the transformer. In contrast to the general dropout method, which randomly drops neurons, DropDim drops part of the embedding dimensions. In this way, the semantic information can be completely discarded. Thus, the excessive coadapting between different embedding dimensions can be broken, and the self-attention is forced to encode meaningful featureswith a certain number of embedding dimensions erased. Experiments on a wide range of tasks executed on the MUST-C English-Germany dataset show that DropDim can effectively improve model performance, reduce over-fitting, and show complementary effects with other regularization methods. When combined with label smoothing, the WER can be reduced from 19.1% to 15.1% on the ASR task, and the BLEU value can be increased from26.90 to 28.38 on the MT task. On the ST task, the model can reach a BLEU score of 22.99, an increase by 1.86 BLEU points compared to the strong baseline.
Federated Learning for Privacy-Preserved Medical Internet of Things

Healthcare is one of the notable areas where the integration of the Internet of Things (IoT) is highly adopted, also known as the Medical IoT (MIoT).So far, MIoT is revolutionizing healthcare because it provides many advantages for the benefit of patients and healthcare personnel.The use of MIoT is becoming a booming trend, generating a large amount of IoT data, which requires proper analysis to infer meaningful information.This has led to the rise of deploying artificial intelligence (AI) technologies, such as machine learning (ML) and deep learning (DL) algorithms, to learn the meaning of this underlying medical data, where the learning process usually occurs in the cloud or telemedicine servers.Due to the exponential growth of MIoT devices and widely distributed private MIoT data sets, it is becoming a challenge to use centralized learning AI algorithms for such tasks.In this connection, federated learning (FL) is gaining traction as a possible method of learning on devices that do not need to migrate private and sensitive data to a central cloud.The terminal equipment and the central server in FL only share learning model updates to ensure that sensitive data is always kept secret.Even though this has recently become a promising research area, no other research has been conducted on this topic recently.In this paper, we synthesize recent literature and FL improvements to support FL-driven MIoT applications and services in healthcare.The findings of this research help stakeholders in academia and industry to realize the competitive advantage of the most advanced privacy preserved MIoT systems based on federal learning.
A zero trust method based on BLP and BIBA model

Zero trust is a novel type of access control security architecture that adopts the fundamental principle of “never trust, always verify”. The Bell-LaPadula and BIBA Model follow the access control principles of “no read up, no write down” and “no write up, no read down” to meet the confidentiality and integrity goals respectively. This paper aims to further expand the concepts of subject, object and security label in the Bell-LaPadula and BIBA Model, propose a novel zero trust method. This method performs comprehensive trust scores on system components such as users, terminals, channels, files and applications, and sets different weights for confidentiality and integrity requirements, thereby achieving better confidentiality and integrity protection for objects.
Detecting TensorFlow Program Bugs in Real-World Industrial Environment

Deep learning has been widely adopted in industry and has achieved great success in a wide range of application areas. Bugs in deep learning programs can cause catastrophic failures, in addition to a serious waste of resources and time.This paper aims at detecting industrial TensorFlow program bugs. We report an extensive empirical study on 12,289 failed TensorFlow jobs, showing that existing static tools can effectively detect 72.55% of the top three types of Python bugs in industrial TensorFlow programs. In addition, we propose (for the first time) a constraint-based approach for detecting TensorFlow shape-related errors (one of the most common TensorFlow-specific bugs), together with an associated tool, ShapeTracer. Our evaluation on a set of 60 industrial TensorFlow programs shows that ShapeTracer is efficient and effective: it analyzes each program in at most 3 seconds and detects effectively 40 out of 60 industrial TensorFlow program bugs, with no false positives. ShapeTracer has been deployed in the platform-X platform and will be released soon.
Distributed Identity and Verifiable Claims Using Ethereum Standards

Identity management is an inevitable part of the proper delivery of schemes and services to individuals by the government and private organizations. Identity management encompasses the creation and maintenance of identity. It ensures that the right entity gains access to the right resources at the right time for verification. It also involves providing high security, privacy, productivity, and enhanced user experience. Rising incidents of data infringements and identity thefts in a centralized identity system are a growing concern. Blockchain-based identity solutions gained a competitive edge over the present centralized identity system due to the features of Self-Sovereign Identity (SSI) and Verifiable Claims (VC). In this paper, we investigate various blockchain-based identity solutions that are self-sovereign and can create VC. We have also analyzed and proposed to create a user-centric identity and claims using ERC-725 and ERC-780 Ethereum standards powered by IPFS for distributed data storage.
Type-2 fuzzy instrumental variable algorithm for evolving neural-fuzzy modeling of nonlinear dynamic systems in noisy environment

In neural-fuzzy modeling, dealing with dynamic systems in a noisy environment is a relevant issue due to uncertainties in the experimental data. In this sense, a type-2 fuzzy instrumental variable based learning algorithm for evolving neural-fuzzy modeling is proposed in this paper. For antecedent adaptation, an Extend Kalman Filter based evolving type-2 fuzzy algorithm is adopted, where the participatory concept is used to mitigate the effects of outliers in the data flow. For consequent estimation, a type-2 fuzzy instrumental variable based subspace identification method is proposed. From the data flow, the instrumental variables are computed via recursive singular spectral analysis and used to estimate the fuzzy Markov parameters. From Markov parameters, the linear state-space matrices are obtained, recursively. Results illustrate the efficiency of the proposed methodology compared to other prominent approaches in the literature for identifying a nonlinear dynamic system with discontinuous function corrupted by colored noise and online identification of a 2DoF helicopter with correlated noise.
Arg2P: an argumentation framework for explainable intelligent systems

Abstract In this paper we present the computational model of Arg2P, a logic-based argumentation framework for defeasible reasoning and agent conversation particularly suitable for explaining agent intelligent behaviours. The model is reified as the Arg2P technology, which is presented and discussed both from an architectural and a technological perspective so as to point out its potential in the engineering of intelligent systems. Finally, an illustrative application scenario is discussed in the domain of computable law for autonomous vehicles.
3D Hand Pose Estimation From Monocular RGB With Feature Interaction Module

3D hand pose estimation from a monocular RGB image is a highly challenging task due to self-occlusion, diverse appearances, and inherent depth ambiguities within monocular images. Most of the previous methods first employ deep neural networks to fit 2D joint location maps, then combines them with implicit or explicit pose-aware features to directly regress 3D hand joints positions using their designed network structure. However, the skeleton positions and corresponding skeleton-aware content information located in the latent space are invariably ignored. These skeleton-aware contents effectively bridge the gap between hand joint and hand skeleton information by associating the relationship between different hand joints features and the hand skeleton positions distribution in 2D space. To address this issue, we propose a simple yet efficient deep neural network to directly recover reliable 3D hand pose from monocular RGB images with faster estimation process. Our purpose is the reduction of the model computational complexity while maintaining high precision performance. Therefore, we design a novel Feature Chat Block (FCB) to complete feature boosting, which enables the intuitively enhanced interaction between joint and skeleton features. First, this FCB module updates joint features effectively based on semantic graph convolutional neural network and multi-head self-attention mechanism. The GCN-based structure focuses on the physical hand joints included in a binary adjacency matrix and the self-attention part pays attention to hand joints located in a complementary matrix. Then, the FCB module employs query and key mechanisms respectively representing joint and skeleton features to further implement feature interaction. After a set of FCB modules, our model updates the fused features in a coarse-to-fine manner and finally outputs a predicted 3D hand pose. We conducted a comprehensive set of ablation experiments on the InterHand2.6M dataset to validate the effectiveness and significance of the proposed method. Additionally, experimental results on Rendered Hand Dataset, Stereo Hand Datasets, First-Person Hand Action Dataset and FreiHAND Dataset show our model surpasses the state-of-the-art methods with faster inference speed.
Towards Robust Human Trajectory Prediction in Raw Videos

Human trajectory prediction has received increased attention lately due to its importance in applications such as autonomous vehicles and indoor robots. However, most existing methods make predictions based on human-labeled trajectories and ignore the errors and noises in detection and tracking. In this paper, we study the problem of human trajectory forecasting in raw videos, and show that the prediction accuracy can be severely affected by various types of tracking errors. Accordingly, we propose a simple yet effective strategy to correct the tracking failures by enforcing prediction consistency over time. The proposed "re-tracking" algorithm can be applied to any existing tracking and prediction pipelines. Experiments on public benchmark datasets demonstrate that the proposed method can improve both tracking and prediction performance in challenging real-world scenarios. The code and data are available at https://git.io/retracking-prediction.
Probabilistic online self-distillation

Deploying state-of-the-art deep learning models on devices with limited computational power imposes certain computation and storage restrictions. Knowledge Distillation, i.e. training compact models by transferring knowledge from more powerful models, constitutes a promising route to address this issue that has been followed during the recent years. A limitation of conventional knowledge distillation is that it is a long-lasting, computationally and memory demanding process, since it requires multiple stages of training process. To this end, a novel online probabilistic self-distillation method, namely Probabilistic Online Self-Distillation (POSD), aiming to improve the performance of any deep neural model in an online manner, is proposed in this paper. We argue that considering a classification problem, apart from the explicit concepts expressed with the hard labels, there are also implicit concepts expressed with the so-called latent labels. These implicit concepts reflect similarities among data, regardless of the classes. Then, our goal is to maximize the Mutual Information between the data samples and the latent labels. In this way, we are able to derive additional knowledge from the model itself, without the need of building multiple identical models or using multiple models to teach each other, like existing online distillation methods, rendering the POSD method more efficient. The experimental evaluation on six datasets validates that the proposed method improves the classification performance.
Dynamic embeddings for efficient parameter learning of Bayesian network with multiple latent variables

Latent variables (LVs), representing the unobservable abstract concepts, such as patient disease and customer credit, play an important role in the simplification of network structure and improving the interpretability of Bayesian network (BN). However, LVs incorporated into BN lead to missing probability parameters due to the missing observation. As the classic method for parameter estimation for the situation with LVs, the expectation–maximization (EM) suffers high complexity and slow convergence. To this end, we propose dynamic embeddings for parameter learning of BN with LVs. Firstly, we reconstruct the E-step of EM and propose to use the dynamic embeddings to calculate the weights of fractional samples that could reduce the computational complexity of parameter learning. Secondly, we propose to construct the point mutual information (PMI) matrix to represent directed weighted graphs (DWGs) transformed from the updated parameters. Thirdly, the incremental singular value decomposition (SVD) is adopted to generate dynamic embeddings while capturing the updated parameters and preserving BN’s graphical structure. Experimental results show that our proposed methods are efficient and effective. On real-world BNs, the efficiency, convergence and accuracy of our method outperform those of the state-of-the-art methods for parameter learning of BN with multiple LVs.
A study on the performance evaluation of wavelet decomposition in transient‐based radio frequency fingerprinting of Bluetooth devices

Abstract Radio frequency fingerprinting (RFF) is used as a physical‐layer security method to provide security in wireless networks. Basically, it exploits the distinctive features (fingerprints) extracted from the physical waveforms emitted from radio devices in the network. One of the major challenges in RFF is to create robust features forming the fingerprints of radio devices. Here, dual‐tree complex wavelet transform (DT‐CWT) provides an accurate way of extracting those robust features. However, its performance on the RFF of Bluetooth transients which fall into narrowband signaling has not been reported yet. Therefore, this study examines the performance of DT‐CWT features on the use of transient‐based RFF of Bluetooth devices. Initially, experimentally collected Bluetooth transients from different smartphones are decomposed by DT‐CWT. Then, the characteristics and statistics of the wavelet domain signal are exploited to create robust features. Next, the support vector machine (SVM) is used to classify the smartphones. The classification accuracy is demonstrated by varying channel signal‐to‐noise ratio (SNR) and the size of transient duration. Results show that reasonable accuracy can be achieved (lower bound of 88%) even with short transient duration (1024 samples) at low SNRs (0–5 dB).