Reviewerly/exHarmony huggingface dataset:
Aggregating Electronic Health Record Data for COVID-19 Research—Caveat Emptor

Tellen D. Bennett, MD; Richard A. Moffitt, PhD; Janos G. Hajagos, PhD; Benjamin Amor, PhD; Adit Anand; Mark M. Bissell; Katie Rebecca Bradwell, PhD; Carolyn Bremer, BS; James Brian Byrd, MD; Alina Denham, PhD; Peter E. DeWitt, PhD; Davera Gabriel, RN; Brian T. Garibaldi, MD; Andrew T. Girvin, PhD; Justin Guinney, PhD; Elaine L. Hill, PhD; Stephanie S. Hong, BS; Hunter Jimenez, BS; Ramakanth Kavuluru, PhD; Kristin Kostka, MPH; Harold P. Lehmann, MD; Eli Levitt, MS; Sandeep K. Mallipattu, MD; Amin Manna, MEng; Julie A. McMurry, MPH; Michele Morris, BA; John Muschelli, PhD; Andrew J. Neumann, MBA; Matvey B. Palchuk, MD; Emily R. Pfaff, PhD; Zhenglong Qian, BS; Nabeel Qureshi, BA; Seth Russell, MS; Heidi Spratt, PhD; Anita Walden, MS; Andrew E. Williams, PhD; Jacob T. Wooldridge, MD; Yun Jae Yoo; Xiaohan Tanner Zhang, MD; Richard L. Zhu, MD; Christopher P. Austin, MD; Joel H. Saltz, MD; Ken R. Gersing, MD; Melissa A. Haendel, PhD; Christopher G. Chute, MD; National COVID Cohort Collaborative (N3C) Consortium; Joel Gagnier; Siqing Hu; Kanchan Lota; Sarah Maidlow; David A. Hanauer; Kevin Weatherwax; Nikhila Gandrakota; Rishikesan Kamaleswaran; Greg Martin; Jingjing Qian; Jason E. Farley; Patricia A. Francis; Dazhi Jiao; Hadi Kharrazi; Justin T. Reese; Mariam Deacy; Usman Sheikh; Jake Chen; Michael Patton; Thomas Ramsey; Jasvinder Singh; James J. Cimino; Jing Su; William G Adams; Tim Duong; John B. Buse; Jessica Yasmine Islam; Jihad S. Obeid; Stephane Meystre; Steve Patterson; Misha Zemmel; Ron Grider; Ashley Martinez; Carlos Santos; Julian Solway; Ryan Chiu; Gerald B. Brown; Jiafeng Cui; Sharon Liang; Kamil Khanipov; Jeremy Richard Harper; Peter J. Embi; David A. Eichmann; Boyd M. Knosp; William Hillegass; Chunlei Wu; James Aaron; Darren Henderson; Muhammad Gul; Tamela Harper; Daniel Harris; Jeff Talbert; Neil Bahroos; Steven Dubinett; Jomol Mathew; Gabriel McMahan; Hongfang Liu; Claudia Lucchinetti; David L Schwartz; Ralph Sacco; Peyman Taghioff; Diane Harper; Denise Angst; Andrew Marek; Carlos E. Figueroa Castro; Bruce Blazar; Steven G. Johnson; Melissa Basford; Laura Jones; Gordon Bernard; Rosalind Wright; Joseph Finkelstein; Thomas R. Campion Jr.; Christopher E. Mason; Xiaobo Fuld; Alfred Anzalone; James C. McClay; Shyam Visweswaran; Connor Cook; Alexandra Dest; David Ellison; Rose Relevo; Andrea M. Volz; Chengda Zhang; Martha Tenzer; David S. Bowers; Francis Farrell; Qiuyuan Qin; Martin Zand; Jeanne Holden-Wiltse; Ramkiran Gouripeddi; Julio Facelli; Robert Clark; Benjamin Becerra; Yao Yan; Jimmy Phuong; Yooree Chae; Rena C. Patel; Christine Suver; Elizabeth Zampino; Ahmed S. Said; Philip R.O. Payne; Randeep Jawa; Peter L. Elkin; Farrukh M Koraishy; George Golovko; Vignesh Subbian; Daniel Weisdorf; Lawrence Sinoway; Hiroki Morizono; Keith A. Crandall; Ali Rahnavard; Nawar Shara; Alysha Taxter; Brian Ostasiewski; Qianqian Song; Uma Maheswara Reddy Vangala; Katherine Ruiz De Luzuriaga; Rasha Khatib; John Kirwan; James von Oehsen; Jason Moore; Ankit Sakhuja; Joni Rutter
A bibliometric approach to quantitatively assess current research trends in 5G security

Purpose Alongside the remarkable evolution of cellular communication to 5G networks, significant security and privacy challenges have risen which can affect the widespread adoption of advanced communication technologies. In this context, the purpose of this paper is to examine research within security and privacy for 5G-based systems highlighting contributions made by the research community and identify research trends within different subdomains of 5G security where open issues still exist. Design/methodology/approach This paper uses a bibliographic approach to review the state-of-the-art in the field of 5G security and is the pioneering effort to investigate 5G security research using this methodology. Specifically, the paper presents a quantitative description of the existing contributions in terms of authors, organizations, and countries. It then presents detailed keyword and co-citation analysis that shows the quantity and pattern of research work in different subfields. Finally, 5G security areas are identified having open challenges for future research work. Findings The study shows that China leads the world in terms of published research in the field of 5G security with USA and India ranked second and third respectively. Xidian University, China is ranked highest for number of publications and h-index followed by University Oulu and AALTO University Finland. IEEE Access , Sensors and IEEE Internet of Things Journal are the top publication venues in the field of 5G security. Using VOSViewer aided analysis with respect to productivity, research areas and keywords, the authors have identified research trends in 5G security among scientific community whilst highlighting specific challenges which require further efforts. Originality/value Existing studies have focused on surveys covering state-of-the art research in secure 5G network (Zhang et al. 2019), physical layer security (Wu et al. , 2018), security and privacy of 5G technologies (Khan et al. , 2020) and security and privacy challenges when 5G is used in IoT (Sicari et al. 2020). However, our research has revealed no existing bibliometric studies in this area and therefore, to our best knowledge, this paper represents pioneering such effort for security within 5G.
WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis

This paper introduces WaveGrad 2, a non-autoregressive generative model for text-to-speech synthesis. WaveGrad 2 is trained to estimate the gradient of the log conditional density of the waveform given a phoneme sequence. The model takes an input phoneme sequence, and through an iterative refinement process, generates an audio waveform. This contrasts to the original WaveGrad vocoder which conditions on mel-spectrogram features, generated by a separate model. The iterative refinement process starts from Gaussian noise, and through a series of refinement steps (e.g., 50 steps), progressively recovers the audio sequence. WaveGrad 2 offers a natural way to trade-off between inference speed and sample quality, through adjusting the number of refinement steps. Experiments show that the model can generate high fidelity audio, approaching the performance of a state-of-the-art neural TTS system. We also report various ablation studies over different model configurations. Audio samples are available at this https URL.
Changing the landscape of cybersecurity education in the EU: Will the new approach produce the required cybersecurity skills?

Abstract Recruiting, retaining, and maintaining sufficient numbers of cybersecurity professionals in the workplace is a constant battle, not only for the technical side of cybersecurity, but also for the overlooked area of non-technical, managerial-related jobs in the cyber sector. The problem is the lack of cybersecurity skills in the European labour force. This paper presents the results of a study carried out with the aim to identify how much the cybersecurity education system within the high-level educational institutions and the industrial sector meets the needs for graduate students to gain the required cybersecurity skills. The method applied in the study is based on data collected from surveys carried out by the European competence centres on cybersecurity and the European Cybersecurity organisation. The problem of common educational program accreditation in Europe is highlighted and discussed. The actions undertaken to improve the education in both sectors are described and the emerging educational landscape is commented. The main cybersecurity knowledge specified by the industrial needs is presented in the form of five knowledge pillars. The study’s findings show that there are missing topics in high-level institution’s cybersecurity programs and that that there is a need to re-shape the content of the courses provided by the professional education providers.
Towards Robust General Medical Image Segmentation

The reliability of Deep Learning systems depends on their accuracy but also on their robustness against adversarial perturbations to the input data. Several attacks and defenses have been proposed to improve the performance of Deep Neural Networks under the presence of adversarial noise in the natural image domain. However, robustness in computer-aided diagnosis for volumetric data has only been explored for specific tasks and with limited attacks. We propose a new framework to assess the robustness of general medical image segmentation systems. Our contributions are two-fold: (i) we propose a new benchmark to evaluate robustness in the context of the Medical Segmentation Decathlon (MSD) by extending the recent AutoAttack natural image classification framework to the domain of volumetric data segmentation, and (ii) we present a novel lattice architecture for RObust Generic medical image segmentation (ROG). Our results show that ROG is capable of generalizing across different tasks of the MSD and largely surpasses the state-of-the-art under sophisticated adversarial attacks.
Pre-training Methods in Information Retrieval

The core of information retrieval (IR) is to identify relevant information from large-scale resources and return it as a ranked list to respond to the user's information need. Recently, the resurgence of deep learning has greatly advanced this field and leads to a hot topic named NeuIR (i.e., neural information retrieval), especially the paradigm of pre-training methods (PTMs). Owing to sophisticated pre-training objectives and huge model size, pre-trained models can learn universal language representations from massive textual data, which are beneficial to the ranking task of IR. Since there have been a large number of works dedicating to the application of PTMs in IR, we believe it is the right time to summarize the current status, learn from existing researches, and gain some insights for future development. In this survey, we present an overview of PTMs applied in different components of an IR system, including the retrieval component, the re-ranking component, and other components. In addition, we also introduce PTMs specifically designed for IR, and summarize available datasets as well as benchmark leaderboards. Moreover, we discuss some open challenges and envision some promising directions, with the hope of inspiring more works on these topics for future research.
Quantum Reservoir Computing Using Arrays of Rydberg Atoms

An analogy between coupled Rydberg atoms and coupled biological neurons is derived and used to advance the field of quantum recurrent neural networks, presenting exciting opportunities in the NISQ era.
Performance Evaluation Among ID3, C4.5, and CART Decision Tree Algorithm

Data is the most valuable resource in the present. Classifying the data and using the classified data to make a decision holds the highest priority. Computers are trained to manage the data automatically using machine learning algorithms and making judgments as outputs. Several data mining algorithms can be obtained for Artificial Neural Network classification, Nearest Neighbor Law and Baysen classifiers, but the decision tree mining is most commonly used. Data can be classified easily using the decision tree classification learning process. It’s trained on a training dataset and then implemented on a test set from which a result is expected. There are three decision trees (ID3 C4.5 and CART) that are extensively used. The algorithms are all based on Hut’s algorithm. This paper focuses on the difference between the working processes, significance, and accuracy of the three (ID3 C4.5 and CART) algorithms. Comparative analysis among the algorithms is illustrated as well.
Unified Adaptive Relevance Distinguishable Attention Network for Image-Text Matching

Image-text matching, as a fundamental cross-modal task, bridges the gap between vision and language. The core is to accurately learn semantic alignment to find relevant shared semantics in image and text. Existing methods typically attend to all fragments with word-region similarity greater than empirical threshold zero as relevant shared semantics, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , via a ReLU operation that forces the negative to zero and maintains the positive. However, this fixed threshold is totally isolated with feature learning, which cannot adaptively and accurately distinguish the varying distributions of relevant and irrelevant word-region similarity in training, inevitably limiting the semantic alignment learning. To solve this issue, we propose a novel Unified Adaptive Relevance Distinguishable Attention (UARDA) mechanism, incorporating the relevance threshold into a unified learning framework, to maximally distinguish the relevant and irrelevant distributions to obtain better semantic alignment. Specifically, our method adaptively learns the optimal relevance boundary between these two distributions to improve the model to learn more discriminative features. The explicit relevance threshold is well integrated into similarity matching, which kills two birds with one stone as: (1) excluding the disturbances of irrelevant fragment contents to aggregate precisely relevant shared semantics for boosting matching accuracy, and (2) avoiding the calculation of irrelevant fragment queries for reducing retrieval time. Experimental results on benchmarks show that UARDA can substantially and consistently outperform state-of-the-arts, with relative rSum improvements of 2%−4% (16.9%−35.3% for baseline SCAN), and reducing the retrieval time by 50%−73%.
Quaternion Generative Adversarial Networks

Latest Generative Adversarial Networks (GANs) are gathering outstanding results through a large-scale training, thus employing models composed of millions of parameters requiring extensive computational capabilities. Building such huge models undermines their replicability and increases the training instability. Moreover, multi-channel data, such as images or audio, are usually processed by realvalued convolutional networks that flatten and concatenate the input, often losing intra-channel spatial relations. To address these issues related to complexity and information loss, we propose a family of quaternion-valued generative adversarial networks (QGANs). QGANs exploit the properties of quaternion algebra, e.g., the Hamilton product, that allows to process channels as a single entity and capture internal latent relations, while reducing by a factor of 4 the overall number of parameters. We show how to design QGANs and to extend the proposed approach even to advanced models.We compare the proposed QGANs with real-valued counterparts on several image generation benchmarks. Results show that QGANs are able to obtain better FID scores than real-valued GANs and to generate visually pleasing images. Furthermore, QGANs save up to 75% of the training parameters. We believe these results may pave the way to novel, more accessible, GANs capable of improving performance and saving computational resources.
Education After COVID-19

The year 2020 has brought life-changing events for many and affected numerous professional sectors. Education has been one of those fields heavily impacted, and institutions have almost worldwide switched to forms of online education, which has become a common practice. With a fourth industrial revolution happening in front of our eyes, some elements of the existing education system are showing themselves as out- dated. However, despite the realization that online teaching is here to stay, frontal classes are a millennia-old practice that cannot be entirely replaced without neglecting human nature. Instead, old and new can coexist, and humanity and machines can cooperate for societal development. In this paper, we present the past, present, and future of education, what we have learned by the experience of teaching online, and how we see and are getting ready for future developments in the field.
CausPref: Causal Preference Learning for Out-of-Distribution Recommendation

In spite of the tremendous development of recommender system owing to the progressive capability of machine learning recently, the current recommender system is still vulnerable to the distribution shift of users and items in realistic scenarios, leading to the sharp decline of performance in testing environments. It is even more severe in many common applications where only the implicit feedback from sparse data is available. Hence, it is crucial to promote the performance stability of recommendation method in different environments. In this work, we first make a thorough analysis of implicit recommendation problem from the viewpoint of out-of-distribution (OOD) generalization. Then under the guidance of our theoretical analysis, we propose to incorporate the recommendation-specific DAG learner into a novel causal preference-based recommendation framework named CausPref, mainly consisting of causal learning of invariant user preference and anti-preference negative sampling to deal with implicit feedback. Extensive experimental results from real-world datasets clearly demonstrate that our approach surpasses the benchmark models significantly under types of out-of-distribution settings, and show its impressive interpretability.
A TIR-Visible Automatic Registration and Geometric Correction Method for SDGSAT-1 Thermal Infrared Image Based on Modified RIFT

High-resolution thermal infrared (TIR) remote sensing images can more accurately retrieve land surface temperature and describe the spatial pattern of urban thermal environment. The Thermal Infrared Spectrometer (TIS), which has high spatial resolution among spaceborne thermal infrared sensors at present, and global data acquisition capability, is one of the sensors equipped in the SDGSAT-1. It is an important complement to the existing international mainstream satellites. In order to produce standard data products, rapidly and accurately, the automatic registration and geometric correction method needs to be developed. Unlike visible–visible image registration, thermal infrared images are blurred in edge details and have obvious non-linear radiometric differences from visible images, which make it challenging for the TIR-visible image registration task. To address these problems, homomorphic filtering is employed to enhance TIR image details and the modified RIFT algorithm is proposed to achieve TIR-visible image registration. Different from using MIM for feature description in RIFT, the proposed modified RIFT uses the novel binary pattern string to descriptor construction. With sufficient and uniformly distributed ground control points, the two-step orthorectification framework, from SDGSAT-1 TIS L1A image to L4 orthoimage, are proposed in this study. The first experiment, with six TIR-visible image pairs, captured in different landforms, is performed to verify the registration performance, and the result indicates that the homomorphic filtering and modified RIFT greatly increase the number of corresponding points. The second experiment, with one scene of an SDGSAT-1 TIS image, is executed to test the proposed orthorectification framework. Subsequently, 52 GCPs are selected manually to evaluate the orthorectification accuracy. The result indicates that the proposed orthorectification framework is helpful to improve the geometric accuracy and guarantee for the subsequent thermal infrared applications.
LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT

Self-supervised speech representation learning has shown promising results in various speech processing tasks. However, the pre-trained models, e.g., HuBERT, are storage-intensive Transformers, limiting their scope of applications under low-resource settings. To this end, we propose LightHuBERT, a once-for-all Transformer compression framework, to find the desired architectures automatically by pruning structured parameters. More precisely, we create a Transformer-based supernet that is nested with thousands of weight-sharing subnets and design a two-stage distillation strategy to leverage the contextualized latent representations from HuBERT. Experiments on automatic speech recognition (ASR) and the SUPERB benchmark show the proposed LightHuBERT enables over $10^9$ architectures concerning the embedding dimension, attention dimension, head number, feed-forward network ratio, and network depth. LightHuBERT outperforms the original HuBERT on ASR and five SUPERB tasks with the HuBERT size, achieves comparable performance to the teacher model in most tasks with a reduction of 29% parameters, and obtains a $3.5\times$ compression ratio in three SUPERB tasks, e.g., automatic speaker verification, keyword spotting, and intent classification, with a slight accuracy loss. The code and pre-trained models are available at https://github.com/mechanicalsea/lighthubert.
A Multi-Agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning

Federated learning (FL) is a training technique that enables client devices to jointly learn a shared model by aggregating locally computed models without exposing their raw data. While most of the existing work focuses on improving the FL model accuracy, in this paper, we focus on the improving the training efficiency, which is often a hurdle for adopting FL in real world applications. Specifically, we design an efficient FL framework which jointly optimizes model accuracy, processing latency and communication efficiency, all of which are primary design considerations for real implementation of FL. Inspired by the recent success of Multi Agent Reinforcement Learning (MARL) in solving complex control problems, we present FedMarl, a federated learning framework that relies on trained MARL agents to perform efficient run-time client selection. Experiments show that FedMarl can significantly improve model accuracy with much lower processing latency and communication cost.
FedBalancer

Federated Learning (FL) trains a machine learning model on distributed clients without exposing individual data. Unlike centralized training that is usually based on carefully-organized data, FL deals with on-device data that are often unfiltered and imbalanced. As a result, conventional FL training protocol that treats all data equally leads to a waste of local computational resources and slows down the global learning process. To this end, we propose FedBalancer, a systematic FL framework that actively selects clients' training samples. Our sample selection strategy prioritizes more "informative" data while respecting privacy and computational capabilities of clients. To better utilize the sample selection to speed up global training, we further introduce an adaptive deadline control scheme that predicts the optimal deadline for each round with varying client training data. Compared with existing FL algorithms with deadline configuration methods, our evaluation on five datasets from three different domains shows that FedBalancer improves the time-to-accuracy performance by 1.20~4.48× while improving the model accuracy by 1.1~5.0%. We also show that FedBalancer is readily applicable to other FL approaches by demonstrating that FedBalancer improves the convergence speed and accuracy when operating jointly with three different FL algorithms.