Reviewerly/exHarmony huggingface dataset:
Adoption and continued use of mobile contact tracing technology: multilevel explanations from a three-wave panel survey and linked data

Objective To identify the key individual-level (demographics, attitudes, mobility) and contextual (COVID-19 case numbers, tiers of mobility restrictions, urban districts) determinants of adopting the NHS COVID-19 contact tracing app and continued use overtime. Design and setting A three-wave panel survey conducted in England in July 2020 (background survey), November 2020 (first measure of app adoption) and March 2021 (continued use of app and new adopters) linked with official data. Participants N=2500 adults living in England, representative of England’s population in terms of regional distribution, age and gender (2011 census). Primary outcome Repeated measures of self-reported app usage. Analytical approach Multilevel logistic regression linking a range of individual level (from survey) and contextual (from linked data) determinants to app usage. Results We observe initial app uptake at 41%, 95% CI (0.39% to 0.43%), and a 12% drop-out rate by March 2021, 95% CI (0.10% to 0.14%). We also found that 7% of nonusers as of wave 2 became new adopters by wave 3, 95% CI (0.05% to 0.08%). Initial uptake (or failure to use) of the app associated with social norms, privacy concerns and misinformation about third-party data access, with those living in postal districts with restrictions on mobility less likely to use the app. Perceived lack of transparent evidence of effectiveness was associated with drop-out of use. In addition, those who trusted the government were more likely to adopt in wave 3 as new adopters. Conclusions Successful uptake of the contact tracing app should be evaluated within the wider context of the UK Government’s response to the crisis. Trust in government is key to adoption of the app in wave 3 while continued use is linked to perceptions of transparent evidence. Providing clear information to address privacy concerns could increase uptake, however, the disparities in continued use among ethnic minority participants needs further investigation.
Hyperspectral Image Super-Resolution with RGB Image Super-Resolution as an Auxiliary Task

This work studies Hyperspectral image (HSI) super-resolution (SR). HSI SR is characterized by high-dimensional data and a limited amount of training examples. This raises challenges for training deep neural networks that are known to be data hungry. This work addresses this issue with two contributions. First, we observe that HSI SR and RGB image SR are correlated and develop a novel multi-tasking network to train them jointly so that the auxiliary task RGB image SR can provide additional supervision and regulate the network training. Second, we extend the network to a semi-supervised setting so that it can learn from datasets containing only low-resolution HSIs. With these contributions, our method is able to learn hyperspectral image super-resolution from heterogeneous datasets and lifts the requirement for having a large amount of high resolution (HR) HSI training samples. Extensive experiments on three standard datasets show that our method outperforms existing methods significantly and underpin the relevance of our contributions. Our code can be found at https://github.com/kli8996/HSISR.git.
MDPFuzz: testing models solving Markov decision processes

The Markov decision process (MDP) provides a mathematical frame- work for modeling sequential decision-making problems, many of which are crucial to security and safety, such as autonomous driving and robot control. The rapid development of artificial intelligence research has created efficient methods for solving MDPs, such as deep neural networks (DNNs), reinforcement learning (RL), and imitation learning (IL). However, these popular models solving MDPs are neither thoroughly tested nor rigorously reliable.
Designing a Framework for Collaborative Mixed Reality Training

Training simulations in Virtual Reality (VR) benefit from the technology’s interactive and intuitive nature. Typically, the VR user is isolated in the virtual environment and an external instructor or spectators cannot support the learning progress. Previous research work presents solutions for isolated use cases, but lacks specific guidelines for the implementation of collaborative multi-device systems. We propose a method for expanding collaborative extended reality (XR) applications to multiple platforms to support collaborative learning, but also other types of applications such as remote collaboration and maintenance. In order to test our approach, we expanded an existing application to four different platforms - Steam VR, Microsoft Mixed Reality, smartphone/tablet and desktop computer. Based on lessons learned from our prototype, we propose a solution for multi-device networking and interaction on each platform. The main strengths of our approach are in the decoupling of local and networked objects and the common interface for interaction, which accommodates multiple platforms. We believe our approach can provide useful insights into collaborative training and serve as a good starting point for future projects.
Deep Interactive Image Matting With Feature Propagation

Image matting has attracted growing interest in recent years for its wide applications in numerous vision tasks. Most previous image matting methods rely on trimaps as auxiliary input to define the foreground, background and unknown region. However, trimaps involve fussy manual annotation efforts and are expensive to be obtained in practice. Thus, it is hard and inflexible to update user's input or achieve real-time interaction with trimaps. Although some automatic matting approaches discard trimaps, they can only be applied to some certain scenarios, like human matting, which limits their versatility. In this work, we employ clicks as interactive behaviours for image matting, to indicate the user-defined foreground, background and unknown region, and propose a click-based deep interactive image matting (DIIM) approach. Compared with trimaps, clicks provide sparse information and are much easier and more flexible, especially for novice users. Based on clicks, users can perform interactive operations and gradually correct the errors until they are satisfied with the prediction. What's more, we propose a recurrent alpha feature propagation and a full-resolution extraction module to enhance the alpha matte estimation from high-level and low-level respectively. Experimental results show that the proposed click-based deep interactive image matting approach achieves promising performance on image matting datasets.
Look Outside the Room: Synthesizing A Consistent Long-Term 3D Scene Video from A Single Image

Novel view synthesis from a single image has recently attracted a lot of attention, and it has been primarily advanced by 3D deep learning and rendering techniques. However, most work is still limited by synthesizing new views within relatively small camera motions. In this paper, we propose a novel approach to synthesize a consistent long-term video given a single scene image and a trajectory of large camera motions. Our approach utilizes an autoregressive Transformer to perform sequential modeling of multiple frames, which reasons the relations between multiple frames and the corresponding cameras to predict the next frame. To facilitate learning and ensure consistency among generated frames, we introduce a locality constraint based on the input cameras to guide self-attention among a large number of patches across space and time. Our method outperforms state-of-the-art view synthesis approaches by a large margin, especially when synthesizing long-term future in indoor 3D scenes. Project page at https://xrenaa.github.io/look-outside-room/.
Multi-task nonparallel support vector machine for classification

Direct multi-task twin support vector machine (DMTSVM) explores the shared information between multiple correlated tasks, then it produces better generalization performance. However, it contains matrix inversion operation when solving the dual problems, so it costs much running time. Moreover, kernel trick cannot be directly utilized in the nonlinear case. To effectively avoid above problems, a novel multi-task nonparallel support vector machine (MTNPSVM) including linear and nonlinear cases is proposed in this paper. By introducing ε-insensitive loss instead of square loss in DMTSVM, MTNPSVM effectively avoids matrix inversion operation and takes full advantage of the kernel trick. Theoretical implication of the model is further discussed. To further improve the computational efficiency, the alternating direction method of multipliers (ADMM) is employed when solving the dual problem. The computational complexity and convergence of the algorithm are provided. In addition, the property and sensitivity of the parameter in model are further explored. The experimental results on fifteen benchmark datasets and twelve image datasets demonstrate the validity of MTNPSVM in comparison with the state-of-the-art algorithms. Finally, it is applied to real Chinese Wine dataset, and also verifies its effectiveness.
Self-Supervised Bi-Channel Transformer Networks for Computer-Aided Diagnosis

Self-supervised learning (SSL) can alleviate the issue of small sample size, which has shown its effectiveness for the computer-aided diagnosis (CAD) models. However, since the conventional SSL methods share the identical backbone in both the pretext and downstream tasks, the pretext network generally cannot be well trained in the pre-training stage, if the pretext task is totally different from the downstream one. In this work, we propose a novel task-driven SSL method, namely Self-Supervised Bi-channel Transformer Networks (SSBTN), to improve the diagnostic accuracy of a CAD model by enhancing SSL flexibility. In SSBTN, we innovatively integrate two different networks for the pretext and downstream tasks, respectively, into a unified framework. Consequently, the pretext task can be flexibly designed based on the data characteristics, and the corresponding designed pretext network thus learns more effective feature representation to be transferred to the downstream network. Furthermore, a transformer-based transfer module is developed to efficiently enhance knowledge transfer by conducting feature alignment between two different networks. The proposed SSBTN is evaluated on two publicly available datasets, namely the full-field digital mammography INbreast dataset and the wireless video capsule CrohnIPI dataset. The experimental results indicate that the proposed SSBTN outperforms all the compared algorithms.
Artificial Intelligence for AKI!Now: Let’s Not Await Plato’s Utopian Republic

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Interference and Consultation in Virtual Public Space: The Practice of Intermedia Art in Metaverse

Intermedia art is an art form that spans many fields such as digital art, biological art, interactive machinery, artificial intelligence, etc. Because of its multi-domain collaborative creation has become an inevitable choice for intermedia art creators to spread in public spaces with more complicated social relations. The public spaces can be recognized as the ones in the physical world involving the flesh of human bodies and those in the virtual world called “metaverse” in recent years. In this paper, based on the traditional intervention methods of intermedia art in physical public spaces, “interference” and “consultation,” we conclude the challenges when intermedia reveals its tendency to migrate from the physical world to the virtual world as the changing of social background. After that, we discuss the past, present and the future of metaverse serving as a new form of public space for the expression of intermedia art by analyzing the data collected from the two mainstream metaverse platforms called “CryptoVoxels” and “Decentraland.”
A Multi-Criteria Collaborative Filtering Approach Using Deep Learning and Dempster-Shafer Theory for Hotel Recommendations

This paper addresses the problem of multi-criteria recommendation in the hotel industry. The main focus is to analyze user preferences from different aspects based on multi-criteria ratings and develop a new multi-criteria collaborative filtering method for hotel recommendations. Particularly, the proposed recommendation system integrates matrix factorization into a deep learning model to predict the multi-criteria ratings, and then the evidential reasoning approach is adopted to model the uncertainty of those ratings represented as mass functions in Dempster-Shafer theory of evidence. Finally, Dempster’s rule of combination is utilized to aggregate those multi-criteria ratings to obtain the overall rating for recommendation. Extensive experiments conducted on a real-world dataset demonstrate the effectiveness and efficiency of the proposed method compared with other multi-criteria collaborative filtering methods.
Machine learning for continuous quantum error correction on superconducting qubits

Abstract Continuous quantum error correction has been found to have certain advantages over discrete quantum error correction, such as a reduction in hardware resources and the elimination of error mechanisms introduced by having entangling gates and ancilla qubits. We propose a machine learning algorithm for continuous quantum error correction that is based on the use of a recurrent neural network to identify bit-flip errors from continuous noisy syndrome measurements. The algorithm is designed to operate on measurement signals deviating from the ideal behavior in which the mean value corresponds to a code syndrome value and the measurement has white noise. We analyze continuous measurements taken from a superconducting architecture using three transmon qubits to identify three significant practical examples of non-ideal behavior, namely auto-correlation at temporal short lags, transient syndrome dynamics after each bit-flip, and drift in the steady-state syndrome values over the course of many experiments. Based on these real-world imperfections, we generate synthetic measurement signals from which to train the recurrent neural network, and then test its proficiency when implementing active error correction, comparing this with a traditional double threshold scheme and a discrete Bayesian classifier. The results show that our machine learning protocol is able to outperform the double threshold protocol across all tests, achieving a final state fidelity comparable to the discrete Bayesian classifier.
A Pairing-Free Heterogeneous Signcryption Scheme for Unmanned Aerial Vehicles

During the past few years, as Internet of Things develops rapidly, the applications of unmanned aerial vehicle (UAV) technology have been increasingly widely used. Meanwhile, the efficiency and security issues of UAV communication have brought about widespread attention of many researchers in different fields. In an open wireless network, the attacks on UAV communication mainly include eavesdropping, tampering, interrupting, forgery, etc. Signcryption is an appropriate method to address these security problems. However, the existing signcryption schemes are not suitable for UAVs with limited resources and ground station (GS) that needs to process a large number of messages from UAVs. Moreover, in a certain area, UAVs and a GS may belong to different cryptosystems. Therefore, in this article, we came up with a heterogeneous signcryption scheme for UAVs to communicate to a GS (HSC-U2G) without bilinear pairing operation, where UAVs belong to identity-based cryptography (IBC) while the GS belongs to public-key infrastructure (PKI). Moreover, our scheme provides identity privacy for UAVs and supports batch verification for the GS. Using the random oracle model, we demonstrate HSC-U2G can achieve insider security for confidentiality as well as unforgeability. After that, HSC-U2G is compared with several important related works and we provide all experimental results to show the efficiency.
Model Compression Based on Differentiable Network Channel Pruning

Although neural networks have achieved great success in various fields, applications on mobile devices are limited by the computational and storage costs required for large models. The model compression (neural network pruning) technology can significantly reduce network parameters and improve computational efficiency. In this article, we propose a differentiable network channel pruning (DNCP) method for model compression. Unlike existing methods that require sampling and evaluation of a large number of substructures, our method can efficiently search for optimal substructure that meets resource constraints (e.g., FLOPs) through gradient descent. Specifically, we assign a learnable probability to each possible number of channels in each layer of the network, relax the selection of a particular number of channels to a softmax over all possible numbers of channels, and optimize the learnable probability in an end-to-end manner through gradient descent. After the network parameters are optimized, we prune the network according to the learnable probability to obtain the optimal substructure. To demonstrate the effectiveness and efficiency of DNCP, experiments are conducted with ResNet and MobileNet V2 on CIFAR, Tiny ImageNet, and ImageNet datasets.
DetPoseNet: Improving Multi-Person Pose Estimation via Coarse-Pose Filtering

Human detection and pose estimation are essential for understanding human activities in images and videos. Mainstream multi-human pose estimation methods take a top-down approach, where human detection is first performed, then each detected person bounding box is fed into a pose estimation network. This top-down approach suffers from the early commitment of initial detections in crowded scenes and other cases with ambiguities or occlusions, leading to pose estimation failures. We propose the DetPoseNet, an end-to-end multi-human detection and pose estimation framework in a unified three-stage network. Our method consists of a coarse-pose proposal extraction sub-net, a coarse-pose based proposal filtering module, and a multi-scale pose refinement sub-net. The coarse-pose proposal sub-net extracts whole-body bounding boxes and body keypoint proposals in a single shot. The coarse-pose filtering step based on the person and keypoint proposals can effectively rule out unlikely detections, thus improving subsequent processing. The pose refinement sub-net performs cascaded pose estimation on each refined proposal region. Multi-scale supervision and multi-scale regression are used in the pose refinement sub-net to simultaneously strengthen context feature learning. Structure-aware loss and keypoint masking are applied to further improve the pose refinement robustness. Our framework is flexible to accept most existing top-down pose estimators as the role of the pose refinement sub-net in our approach. Experiments on COCO and OCHuman datasets demonstrate the effectiveness of the proposed framework. The proposed method is computationally efficient (5-6x speedup) in estimating multi-person poses with refined bounding boxes in sub-seconds.
Gesture Recognition System Using 24 GHz FMCW Radar Sensor Realized on Real-Time Edge Computing Platform

Affected by the global epidemic, non-contact unmanned control system provides people with safe human-computer interaction (HCI). This paper presents a radar hand gesture recognition system based on real-time edge computing platform. The system uses a low-cost 24 GHz commercial low bandwidth frequency modulated continuous wave (FMCW) radar as the detection source. For real-time gesture recognition, the background echo of radar is fitted by the exponential weighted average method which can reduce the hardware resource consumption when implementing on field programmable gate array (FPGA). A novel extracted feature named range-Doppler matrix focus (RDMF) is designed to reduce the data dimension and preserve the useful information. 3-D CNN + LSTM encoder classification framework is used to classify the features. A data set of 3200 samples for the basic 8 gestures and 1600 samples for the additional 4 gestures is designed and collected for training and testing the classification framework. The results show that the average accuracy of 3-D CNN + LSTM encoder on training and testing data set of 12 gesture classification exceeds 97.9%. The model size of this network is nearly 390 kB. In order to compress the size for recognition on edge-computing platform, a simplified framework for sending one antenna’s RDMF to the LSTM encoder is proposed. The model size is reduced to 20 kB, and the average accuracy of the simplified framework on basic 8 gestures is 95.9%. We deploy this gesture recognition framework on the FPGA platform to speed up computing.
Robust Attentive Deep Neural Network for Detecting GAN-Generated Faces

Generative Adversarial Network (GAN) based techniques can generate and synthesize realistic faces that cause profound social concerns and security problems.Existing methods for detecting GAN-generated faces can perform well on limited public datasets.However, images from existing datasets do not represent real-world scenarios well enough in terms of view variations and data distributions, where real faces largely outnumber synthetic ones.The state-of-the-art methods do not generalize well in real-world problems and lack the interpretability of detection results.Performance of existing GAN-face detection models degrades accordingly when facing data imbalance issues.To address these shortcomings, we propose a robust, attentive, end-to-end framework that spots GAN-generated faces by analyzing eye inconsistencies.Our model automatically learns to identify inconsistent eye components by localizing and comparing artifacts between eyes.After the iris regions are extracted by Mask-RCNN, we design a Residual Attention Network (RAN) to examine the consistency between the corneal specular highlights of the two eyes.Our method can effectively learn from imbalanced data using a joint loss function combining the traditional cross-entropy loss with a relaxation of the ROC-AUC loss via Wilcoxon-Mann-Whitney (WMW) statistics.Comprehensive evaluations on a newly created FFHQ-GAN dataset in both balanced and imbalanced scenarios demonstrate the superiority of our method.
Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters

Recent years have witnessed an exponential growth of model scale in deep learning-based recommender systems---from Google's 2016 model with 1 billion parameters to the latest Facebook's model with 12 trillion parameters. Significant quality boost has come with each jump of the model capacity, which makes us believe the era of 100 trillion parameters is around the corner. However, the training of such models is challenging even within industrial scale data centers. We resolve this challenge by careful co-design of both optimization algorithm and distributed system architecture. Specifically, to ensure both the training efficiency and the training accuracy, we design a novel hybrid training algorithm, where the embedding layer and the dense neural network are handled by different synchronization mechanisms; then we build a system called Persia (short for parallel recommendation training system with hybrid acceleration) to support this hybrid training algorithm. Both theoretical demonstrations and empirical studies with up to 100 trillion parameters have been conducted to justify the system design and implementation of Persia. We make Persia publicly available (at github.com/PersiaML/Persia) so that anyone can easily train a recommender model at the scale of 100 trillion parameters.