Reviewerly/exHarmony huggingface dataset:
Arabic Sign Language Recognition and Generating Arabic Speech Using Convolutional Neural Network

Sign language encompasses the movement of the arms and hands as a means of communication for people with hearing disabilities. An automated sign recognition system requires two main courses of action: the detection of particular features and the categorization of particular input data. In the past, many approaches for classifying and detecting sign languages have been put forward for improving system performance. However, the recent progress in the computer vision field has geared us towards the further exploration of hand signs/gestures’ recognition with the aid of deep neural networks. The Arabic sign language has witnessed unprecedented research activities to recognize hand signs and gestures using the deep learning model. A vision-based system by applying CNN for the recognition of Arabic hand sign-based letters and translating them into Arabic speech is proposed in this paper. The proposed system will automatically detect hand sign letters and speaks out the result with the Arabic language with a deep learning model. This system gives 90% accuracy to recognize the Arabic hand sign-based letters which assures it as a highly dependable system. The accuracy can be further improved by using more advanced hand gestures recognizing devices such as Leap Motion or Xbox Kinect. After recognizing the Arabic hand sign-based letters, the outcome will be fed to the text into the speech engine which produces the audio of the Arabic language as an output.
Everything is a Race and Nakamoto Always Wins

Nakamoto invented the longest chain protocol, and claimed its security by analyzing the private double-spend attack, a race between the adversary and the honest nodes to grow a longer chain. But is it the worst attack? We answer the question in the affirmative for three classes of longest chain protocols, designed for different consensus models: 1) Nakamoto's original Proof-of-Work protocol; 2) Ouroboros and SnowWhite Proof-of-Stake protocols; 3) Chia Proof-of-Space protocol. As a consequence, exact characterization of the maximum tolerable adversary power is obtained for each protocol as a function of the average block time normalized by the network delay. The security analysis of these protocols is performed in a unified manner by a novel method of reducing all attacks to a race between the adversary and the honest nodes.
The perils of premature phenotyping in COVID-19: a call for caution

<b>By prematurely phenotyping patients with COVID-19, we and our patients are exposed to considerable and preventable risk. If data-driven phenotypes are not insisted upon, our cognitive biases guarantee that we9ll end up with phenotype-driven data.</b>https://bit.ly/2ZM8wZV
A case-based ensemble learning system for explainable breast cancer recurrence prediction

Significant progress has been achieved in recent years in the application of artificial intelligence (AI) for medical decision support. However, many AI-based systems often only provide a final prediction to the doctor without an explanation of its underlying decision-making process. In scenarios concerning deadly diseases, such as breast cancer, a doctor adopting an auxiliary prediction is taking big risks, as a bad decision can have very harmful consequences for the patient. We propose an auxiliary decision support system that combines ensemble learning with case-based reasoning to help doctors improve the accuracy of breast cancer recurrence prediction. The system provides a case-based interpretation of its prediction, which is easier for doctors to understand, helping them assess the reliability of the system’s prediction and make their decisions accordingly. Our application and evaluation in a case study focusing on breast cancer recurrence prediction shows that the proposed system not only provides reasonably accurate predictions but is also well-received by oncologists.
Motivating information security policy compliance: The critical role of supervisor-subordinate guanxi and organizational commitment

Employees’ non-compliance with organizational information security policy (ISP) when using informational resources has become the main reason for continuous security incidents. Drawing upon technology threat avoidance theory (TTAT) and social exchange theory (SET), our study investigates the influence of supervisor-subordinate guanxi (SSG) and organizational commitment in the information security management. Our hypotheses were tested using survey data from 235 Chinese government employees. Results not only confirm the direct effect of SSG on government employees’ ISP compliance but also suggest that SSG indirectly influences compliance behavior via the mediation of organizational commitment. Organizational commitment weakens the negative influence of perceived costs on compliance behavior and also weakens the positive effect of self-efficacy on employees’ ISP compliance. For low-commitment employees, the negative influence of perceived costs on compliance behavior is more significant than that of those with strong organizational commitment, and self-efficacy exerts a stronger effect on ISP compliance for low-commitment employees than it does for high-commitment employees. This study contributes to current literature on information systems (IS) by confirming the critical roles of SSG and organizational commitment in motivating employees’ compliance behavior.
Private stochastic convex optimization: optimal rates in linear time

We study differentially private (DP) algorithms for stochastic convex optimization: the problem of minimizing the population loss given i.i.d. samples from a distribution over convex loss functions. A recent work of Bassily et al. (2019) has established the optimal bound on the excess population loss achievable given n samples. Unfortunately, their algorithm achieving this bound is relatively inefficient: it requires O(min{n 3/2, n 5/2/d}) gradient computations, where d is the dimension of the optimization problem.
A Shared Multi-Attention Framework for Multi-Label Zero-Shot Learning

In this work, we develop a shared multi-attention model for multi-label zero-shot learning. We argue that designing attention mechanism for recognizing multiple seen and unseen labels in an image is a non-trivial task as there is no training signal to localize unseen labels and an image only contains a few present labels that need attentions out of thousands of possible labels. Therefore, instead of generating attentions for unseen labels which have unknown behaviors and could focus on irrelevant regions due to the lack of any training sample, we let the unseen labels select among a set of shared attentions which are trained to be label-agnostic and to focus on only relevant/foreground regions through our novel loss. Finally, we learn a compatibility function to distinguish labels based on the selected attention. We further propose a novel loss function that consists of three components guiding the attention to focus on diverse and relevant image regions while utilizing all attention features. By extensive experiments, we show that our method improves the state of the art by 2.9% and 1.4% F1 score on the NUS-WIDE and the large scale Open Images datasets, respectively.
Hierarchical Pyramid Diverse Attention Networks for Face Recognition

Deep learning has achieved a great success in face recognition (FR), however, few existing models take hierarchical multi-scale local features into consideration. In this work, we propose a hierarchical pyramid diverse attention (HPDA) network. First, it is observed that local patches would play important roles in FR when the global face appearance changes dramatically. Some recent works apply attention modules to locate local patches automatically without relying on face landmarks. Unfortunately, without considering diversity, some learned attentions tend to have redundant responses around some similar local patches, while neglecting other potential discriminative facial parts. Meanwhile, local patches may appear at different scales due to pose variations or large expression changes. To alleviate these challenges, we propose a pyramid diverse attention (PDA) to learn multi-scale diverse local representations automatically and adaptively. More specifically, a pyramid attention is developed to capture multi-scale features. Meanwhile, a diverse learning is developed to encourage models to focus on different local patches and generate diverse local features. Second, almost all existing models focus on extracting features from the last convolutional layer, lacking of local details or small-scale face parts in lower layers. Instead of simple concatenation or addition, we propose to use a hierarchical bilinear pooling (HBP) to fuse information from multiple layers effectively. Thus, the HPDA is developed by integrating the PDA into the HBP. Experimental results on several datasets show the effectiveness of the HPDA, compared to the state-of-the-art methods.
Iteratively-Refined Interactive 3D Medical Image Segmentation With Multi-Agent Reinforcement Learning

Existing automatic 3D image segmentation methods usually fail to meet the clinic use. Many studies have explored an interactive strategy to improve the image segmentation performance by iteratively incorporating user hints. However, the dynamic process for successive interactions is largely ignored. We here propose to model the dynamic process of iterative interactive image segmentation as a Markov decision process (MDP) and solve it with reinforcement learning (RL). Unfortunately, it is intractable to use single-agent RL for voxel-wise prediction due to the large exploration space. To reduce the exploration space to a tractable size, we treat each voxel as an agent with a shared voxel-level behavior strategy so that it can be solved with multi-agent reinforcement learning. An additional advantage of this multi-agent model is to capture the dependency among voxels for segmentation task. Meanwhile, to enrich the information of previous segmentations, we reserve the prediction uncertainty in the state space of MDP and derive an adjustment action space leading to a more precise and finer segmentation. In addition, to improve the efficiency of exploration, we design a relative cross-entropy gain-based reward to update the policy in a constrained direction. Experimental results on various medical datasets have shown that our method significantly outperforms existing state-of-the-art methods, with the advantage of less interactions and a faster convergence.
Making Better Mistakes: Leveraging Class Hierarchies With Deep Networks

Deep neural networks have improved image classification dramatically over the past decade, but have done so by focusing on performance measures that treat all classes other than the ground truth as equally wrong. This has led to a situation in which mistakes are less likely to be made than before, but are equally likely to be absurd or catastrophic when they do occur. Past works have recognised and tried to address this issue of mistake severity, often by using graph distances in class hierarchies, but this has largely been neglected since the advent of the current deep learning era in computer vision. In this paper, we aim to renew interest in this problem by reviewing past approaches and proposing two simple methods which outperform the prior art under several metrics on two large datasets with complex class hierarchies: tieredImageNet and iNaturalist'19.
Dirichlet Variational Autoencoder

This paper proposes Dirichlet Variational Autoencoder (DirVAE) using a Dirichlet prior. To infer the parameters of DirVAE, we utilize the stochastic gradient method by approximating the inverse cumulative distribution function of the Gamma distribution, which is a component of the Dirichlet distribution. This approximation on a new prior led an investigation on the component collapsing, and DirVAE revealed that the component collapsing originates from two problem sources: decoder weight collapsing and latent value collapsing. The experimental results show that 1) DirVAE generates the result with the best log-likelihood compared to the baselines; 2) DirVAE produces more interpretable latent values with no collapsing issues which the baselines suffer from; 3) the latent representation from DirVAE achieves the best classification accuracy in the (semi-)supervised classification tasks on MNIST, OMNIGLOT, COIL-20, SVHN, and CIFAR-10 compared to the baseline VAEs; and 4) the DirVAE augmented topic models show better performances in most cases.
Particle Swarm Optimization Based Highly Nonlinear Substitution-Boxes Generation for Security Applications

Symmetric encryption has been considered as one of the essential means of ensuring security of end to end communication. The robustness and strength of modern day block encryption systems are based on the cryptographic features of substitution-boxes which are used to inject confusion ability during substitution-phases. In this paper, as an alternative to random, chaos or algebraic based construction methods, we propose to present an efficient method for the generation of cryptographic highly nonlinear substitution-boxes. The nature-inspired particle swarm optimization is reconnoitered to develop proposed method wherein the initial population is generated through a simple but with rich dynamics chaotic Renyi map. The anticipated method is analyzed for different scenarios such as change in population size, number of iterations, and linear increase in inertial weight. The performance assessment of generated S-boxes under standard criterions corroborate that the proposed method has excellent cryptographic features and found grander than many recent optimization based S-box methods. Moreover, an image encryption application of proposed S-boxes is also suggested to determine their suitability and applicability for image based security applications.
PMBANet: Progressive Multi-Branch Aggregation Network for Scene Depth Super-Resolution

Depth map super-resolution is an ill-posed inverse problem with many challenges. First, depth boundaries are generally hard to reconstruct particularly at large magnification factors. Second, depth regions on fine structures and tiny objects in the scene are destroyed seriously by downsampling degradation. To tackle these difficulties, we propose a progressive multi-branch aggregation network (PMBANet), which consists of stacked MBA blocks to fully address the above problems and progressively recover the degraded depth map. Specifically, each MBA block has multiple parallel branches: 1) The reconstruction branch is proposed based on the designed attention-based error feed-forward/-back modules, which iteratively exploits and compensates the downsampling errors to refine the depth map by imposing the attention mechanism on the module to gradually highlight the informative features at depth boundaries. 2) We formulate a separate guidance branch as prior knowledge to help to recover the depth details, in which the multi-scale branch is to learn a multi-scale representation that pays close attention at objects of different scales, while the color branch regularizes the depth map by using auxiliary color information. Then, a fusion block is introduced to adaptively fuse and select the discriminative features from all the branches. The design methodology of our whole network is well-founded, and extensive experiments on benchmark datasets demonstrate that our method achieves superior performance in comparison with the state-of-the-art methods. Our code and models are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Sunbaoli/PMBANet_DSR/</uri> .
Understanding digitalization and educational change in school by means of activity theory and the levels of learning concept

Abstract As shown in research and practice digitalization processes are many times limited to implementation of digital technologies without pedagogical and organizational change. In this study it is argued for a broader perspective on the concept of digitalization, viewing it as a process involving change and transformation in different stages and several organizational levels. Based on cultural–historical activity theory and the concept of levels of learning, this study will elaborate on the concept of digitalization as well as how schools are dealing with digital and educational change. Two schools known for their large-scale digitalization processes are analyzed. In the analysis, it is indicated that the object of digitalization harbors an idea that influence how digitalization is planned for and enacted within the school organization. How schools conceptualized—what is theoretically and practically meant by digitalization—influence how they plan their budget, professional development, and organizational change. With this backdrop, it is a concluded need for explicit discussions and conceptual clarifications on what digitalization is and what it involves in different school contexts.
Blockchain-Enabled Certificate-Based Authentication for Vehicle Accident Detection and Notification in Intelligent Transportation Systems

As the communications among the vehicles, the Road-Side Units ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RSU</i> ) and the Edge Servers ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ES</i> ) take place via wireless communication and the Internet, an adversary may take the opportunity to tamper with the data communicated among various entities in an Internet of Vehicles (IoV) environment. Therefore, it demands secure communication among the involved entities in an IoV-based Intelligent Transportation System (ITS) deployment. In this work, we design a new blockchain-enabled certificate-based authentication scheme for vehicle accident detection and notification in ITS, called BCAS-VADN. In BCAS-VADN, through the authentication process, each vehicle can securely notify accident related transactions to its nearby Cluster Head ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CH</i> ), if an accident is detected on roads either by its own or neighbor vehicle(s). The <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CH</i> then securely sends the transactions received from the vehicles to its <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RSU</i> and subsequently, these transactions are also received securely by the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ES</i> s. The <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ES</i> is responsible for preparing partial block containing transactions and Merkle tree root, and a digital signature on those information, and then forwarding to its associated Cloud Server ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CS</i> ) in the Blockchain Center ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BC</i> ) for complete block creation, verification and addition of the block using the designed consensus process. Due to blockchain technology usage, it is shown that BCAS-VADN is not only secure against various potential attacks, but also maintains transparency, immutability and decentralization of the information. Furthermore, a comprehensive comparative analysis reveals that BCAS-VADN achieves better security and more functionality attributes, and has low communication and computational overheads as compared to other competitive authentication schemes in IoV. In addition, the practical demonstration using the blockchain technology has been also provided.
Optimizing Feature Selection for Efficient Encrypted Traffic Classification: A Systematic Approach

Traffic classification is a technology for classifying and identifying sensitive information from cluttered traffic. With the increasing use of encryption and other evasion technologies, traditional content- based network traffic classification becomes impossible, and traffic classification is increasingly related to security and privacy. Many studies have been conducted to investigate traffic classification in various scenarios. A major challenge to existing schemes is extending traffic classification technology to a broader space. In other words, most traffic classification work is not universal and can only show great performance on specific datasets. In this article, we present a systematic approach to optimizing feature selection for encrypted traffic classification. We summarize the optional encrypted traffic features and analyze the approaches of feature selection in detail for different datasets. The experimental result demonstrates that our scheme is more accurate and universal than other state-of-the-art approaches. More precisely, our mechanism provides a guideline for future research in the field of traffic classification.
A Mobile Robotic Measurement System for Large-scale Complex Components Based on Optical Scanning and Visual Tracking

Large-scale components with complex curved surfaces are the foundation of aerospace, energy, and transportation fields, while full-field 3D measurements along with accuracy analyses are critical to control manufacturing quality. Most of the existing measurement methodologies rely on manual inspection, and the accuracy and efficiency are unsatisfactory. This paper introduces an integrated mobile robotic measurement system for the accurate and automatic 3D measurement of large-scale components with complex curved surfaces. The measurement system is composed of a mobile manipulator, a fringe projection scanner and a stereo vision system, and it can provide accurate noncontact 3D measurements of large-scale complex components. By proposing a hand-eye calibration method and scanning pose tracking method based on a stereo vision system, the local point clouds obtained by scanning along with the movement of the mobile robot around the component can be accurately unified into a common reference frame. The proposed measuring system and method are verified by measuring and reconstructing the whole surface of a wind turbine blade model with a length of 2.8 m. The accuracy evaluation proves the effectiveness of the proposed system and method.
Enhancing Explainability of Neural Networks Through Architecture Constraints

Prediction accuracy and model explainability are the two most important objectives when developing machine learning algorithms to solve real-world problems. Neural networks are known to possess good prediction performance but suffer from a lack of model interpretability. In this article, we propose to enhance the explainability of neural networks through the following architecture constraints: 1) sparse additive subnetworks; 2) projection pursuit with orthogonality constraint; and 3) smooth function approximation. It leads to an enhanced explainable neural network (ExNN) with a superior balance between prediction performance and model interpretability. We derive sufficient identifiability conditions for the proposed ExNN model. The multiple parameters are simultaneously estimated by a modified minibatch gradient descent method based on the backpropagation algorithm for calculating the derivatives and the Cayley transform for preserving the projection orthogonality. Through simulation study under six different scenarios, we compare the proposed method to several benchmarks, including least absolute shrinkage and selection operator, support vector machine, random forest, extreme learning machine, and multilayer perceptron. It is shown that the proposed ExNN model keeps the flexibility of pursuing high prediction accuracy while attaining improved interpretability. Finally, a real data example is employed as a showcase application.