Reviewerly/exHarmony huggingface dataset:
Multiple Embeddings Enhanced Multi-Graph Neural Networks for Chinese Healthcare Named Entity Recognition

Named Entity Recognition (NER) is a natural language processing task for recognizing named entities in a given sentence. Chinese NER is difficult due to the lack of delimited spaces and conventional features for determining named entity boundaries and categories. This study proposes the ME-MGNN (Multiple Embeddings enhanced Multi-Graph Neural Networks) model for Chinese NER in the healthcare domain. We integrate multiple embeddings at different granularities from the radical, character to word levels for an extended character representation, and this is fed into multiple gated graph sequence neural networks to identify named entities and classify their types. The experimental datasets were collected from health-related news, digital health magazines and medical question/answer forums. Manual annotation was conducted for a total of 68,460 named entities across 10 entity types (body, symptom, instrument, examination, chemical, disease, drug, supplement, treatment and time) in 30,692 sentences. Experimental results indicated our ME-MGNN model achieved an F1-score result of 75.69, outperforming previous methods. In practice, a series of model analysis implied that our method is effective and efficient for Chinese healthcare NER.
SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization

With the increasing popularity of graph-based learning, Graph Neural Networks (GNNs) win lots of attention from research and industry field because of their high accuracy. However, existing GNNs suffer from high memory footprints (e.g., node embedding features). This high memory footprint hurdles the potential applications towards memory-constrained devices, such as the widely-deployed IoT devices. To this end, we propose a specialized GNN quantization scheme, SGQuant, to systematically reduce the GNN memory consumption. Specifically, we first propose a GNN-tailored quantization algorithm design and a GNN quantization fine-tuning scheme to reduce memory consumption while maintaining accuracy. Then, we investigate the multi-granularity quantization strategy that operates at different levels (components, graph topology, and layers) of GNN computation. Moreover, we offer an automatic bit-selecting (ABS) to pinpoint the most appropriate quantization bits for the above multi-granularity quantizations. Intensive experiments show that SGQuant can effectively reduce the memory footprint from 4.25× to 31.9× compared with the original full-precision GNNs while limiting the accuracy drop to 0.4% on average.
Radar-based 2D Car Detection Using Deep Neural Networks

A crucial part of safe navigation of autonomous vehicles is the robust detection of surrounding objects. While there are numerous approaches covering object detection in images or LiDAR point clouds, this paper addresses the problem of object detection in radar data. For this purpose, the fully convolutional neural network YOLOv3 is adapted to operate on sparse radar point clouds. In order to apply convolutions, the point cloud is transformed into a grid-like structure. The impact of this representation transformation is shown by comparison with a network based on Frustum PointNets, which directly processes point cloud data. The presented networks are trained and evaluated on the public nuScenes dataset. While experiments show that the point cloud-based network outperforms the grid-based approach in detection accuracy, the latter has a significantly faster inference time neglecting the grid conversion which is crucial for applications like autonomous driving.
Robust visual servoing control for quadrotors landing on a moving target

In this paper, a robust visual servoing control approach is proposed to address the landing problem for quadrotors on a moving platform. A vision system is implemented to estimate the position and velocity of the quadrotor. A robust cascade controller is proposed by following backstepping-like fundamentals and robust compensating theory. The effects of time-varying uncertainties, including parameter uncertainties and external disturbances, and time-varying delays resulted from image acquisition, image processing, and sensor measurement delays can be restrained. Experimental results using a quadrotor to land on a ground moving target illustrate the effectiveness of the proposed approach.
ROpenPose: A Rapider OpenPose Model for Astronaut Operation Attitude Detection

This article proposes a rapider OpenPose model (ROpenPose) to solve the posture detection problem of astronauts in a space capsule in a weightless environment. The ROpenPose model has three innovations as follows: 1) It uses MobileNets instead of VGG-19 to achieve lighter calculations while ensuring the accuracy of model recognition. 2) Three small convolution kernels replace the large convolution kernel of the original OpenPose, which significantly reduces the computational complexity of the model. 3) Through the parameter sharing of a convolution process, the original two-branch structure is changed to a single-branch structure, which obviously improves the calculation speed of the model. A residual network is proposed to suppress the hidden danger of gradient disappearance. The deployment of ROpenPose greatly improves astronauts’ detection efficiency while ensuring their high detection performance, and thereby realizing the real-time monitoring of their operation attitude. Experimental results show that ROpenPose runs at speed higher than and detection performance comparable to a number of the existing state-of-the-art models.
maskedFaceNet: A Progressive Semi-Supervised Masked Face Detector

To reduce the risk of infecting or being infected by the recent COVID-19 virus, wearing mask is enforced or recommended by many countries. AI based system for automatically detecting whether individuals are wearing face mask becomes an urgent requirement in high risk facilities and crowded public places. Due to lacking of existing masked face datasets and the urgent low-cost application requirement, we propose a progressive semi-supervised learning method – called maskedFaceNet to minimize the efforts on data annotation and letting deep models to learn by using less annotated training data. With this method, the detection accuracy is further improved progressively while adapting to various application scenarios. Experimental results show that our maskedFaceNet is more efficient and accurate compared to other methods. Furthermore, we also contribute two masked face datasets for benchmarking and for the benefit of future research.
Nocal-Siam: Refining Visual Features and Response With Advanced Non-Local Blocks for Real-Time Siamese Tracking

Siamese trackers contain two core stages, i.e., learning the features of both target and search inputs at first and then calculating response maps via the cross-correlation operation, which can also be used for regression and classification to construct typical one-shot detection tracking framework. Although they have drawn continuous interest from the visual tracking community due to the proper trade-off between accuracy and speed, both stages are easily sensitive to the distracters in search branch, thereby inducing unreliable response positions. To fill this gap, we advance Siamese trackers with two novel non-local blocks named Nocal-Siam, which leverages the long-range dependency property of the non-local attention in a supervised fashion from two aspects. First, a target-aware non-local block (T-Nocal) is proposed for learning the target-guided feature weights, which serve to refine visual features of both target and search branches, and thus effectively suppress noisy distracters. This block reinforces the interplay between both target and search branches in the first stage. Second, we further develop a location-aware non-local block (L-Nocal) to associate multiple response maps, which prevents them inducing diverse candidate target positions in the future coming frame. Experiments on five popular benchmarks show that Nocal-Siam performs favorably against well-behaved counterparts both in quantity and quality.
Visual Analytics of Multivariate Event Sequence Data in Racquet Sports

In this work, we propose a generic visual analytics framework to support tactic analysis based on data collected from racquet sports (such as tennis and badminton). The proposed approach models each rally in a game as a sequence of hits (i.e., events) until one athlete scores a point. Each hit can be described with a set of attributes, such as the positions of the ball and the techniques used to hit the ball (such as drive and volley in tennis). Thus, the mentioned sequence of hits can be viewed as a multivariate event sequence. By detecting and analyzing the multivariate subsequences that frequently occur in the rallies (namely, tactical patterns), athletes can gain insights into the playing styles adopted by their opponents, and therefore help them identify systematic weaknesses of the opponents and develop counter strategies in matches. To support such analysis effectively, we propose a steerable multivariate sequential pattern mining algorithm with adjustable weights over event attributes, such that the domain expert can obtain frequent tactical patterns according to the attributes specified by himself. We also propose a re-configurable glyph design to help users simultaneously analyze multiple attributes of the hits. The framework further supports comparative analysis of the tactical patterns, e.g., for different athletes or the same athlete playing under different conditions. By applying the framework on two datasets collected in tennis and badminton matches, we demonstrate that the system is generic and effective for tactic analysis in sports and can help identify signature techniques used by individual athletes. Finally, we discuss the strengths and limitations of the proposed approach based on the feedback from the domain experts.
Perceptual Quality Assessment of Omnidirectional Images as Moving Camera Videos

Omnidirectional images (also referred to as static 360 ° panoramas) impose viewing conditions much different from those of regular 2D images. How do humans perceive image distortions in immersive virtual reality (VR) environments is an important problem which receives less attention. We argue that, apart from the distorted panorama itself, two types of VR viewing conditions are crucial in determining the viewing behaviors of users and the perceived quality of the panorama: the starting point and the exploration time. We first carry out a psychophysical experiment to investigate the interplay among the VR viewing conditions, the user viewing behaviors, and the perceived quality of 360 ° images. Then, we provide a thorough analysis of the collected human data, leading to several interesting findings. Moreover, we propose a computational framework for objective quality assessment of 360 ° images, embodying viewing conditions and behaviors in a delightful way. Specifically, we first transform an omnidirectional image to several video representations using different user viewing behaviors under different viewing conditions. We then leverage advanced 2D full-reference video quality models to compute the perceived quality. We construct a set of specific quality measures within the proposed framework, and demonstrate their promises on three VR quality databases.
The Psychological and Financial Impact of Cybercrime Victimization: A Novel Application of the Shattered Assumptions Theory

While criminality is digitizing, a theory-based understanding of the impact of cybercrime on victims is lacking. Therefore, this study addresses the psychological and financial impact of cybercrime on victims, applying the shattered assumptions theory (SAT) to predict that impact. A secondary analysis was performed on a representative data set of Dutch citizens ( N = 33,702), exploring the psychological and financial impact for different groups of cybercrime victims. The results showed a higher negative impact on emotional well-being for victims of person-centered cybercrime, victims for whom the offender was an acquaintance, and victims whose financial loss was not compensated and a lower negative impact on emotional well-being for victims with a higher income. The study led to novel scientific insights and showed the applicability of the SAT for developing hypotheses about cybercrime victimization impact. In this study, most hypotheses had to be rejected, leading to the conclusion that more work has to be done to test the applicability of the SAT in the field of cybercrime. Furthermore, policy implications were identified considering the prioritization of and approach to specific cybercrimes, treatment of victims, and financial loss compensation.
Integrating Human Gaze into Attention for Egocentric Activity Recognition

It is well known that human gaze carries significant information about visual attention. However, there are three main difficulties in incorporating the gaze data in an attention mechanism of deep neural networks: (i) the gaze fixation points are likely to have measurement errors due to blinking and rapid eye movements; (ii) it is unclear when and how much the gaze data is correlated with visual attention; and (iii) gaze data is not always available in many real-world situations. In this work, we introduce an effective probabilistic approach to integrate human gaze into spatiotemporal attention for egocentric activity recognition. Specifically, we represent the locations of gaze fixation points as structured discrete latent variables to model their uncertainties. In addition, we model the distribution of gaze fixations using a variational method. The gaze distribution is learned during the training process so that the ground-truth annotations of gaze locations are no longer needed in testing situations since they are predicted from the learned gaze distribution. The predicted gaze locations are used to provide informative attentional cues to improve the recognition performance. Our method outperforms all the previous state-of-the-art approaches on EGTEA, which is a large-scale dataset for egocentric activity recognition provided with gaze measurements. We also perform an ablation study and qualitative analysis to demonstrate that our attention mechanism is effective.
Dynamical-decoupling-protected nonadiabatic holonomic quantum computation

The main obstacles to the realization of high-fidelity quantum gates are the control errors arising from inaccurate manipulation of a quantum system and the decoherence caused by the interaction between the quantum system and its environment. Nonadiabatic holonomic quantum computation allows for high-speed implementation of whole-geometric quantum gates, making quantum computation robust against control errors. Dynamical decoupling provides an effective method to protect quantum gates against environment-induced decoherence, regardless of collective decoherence or independent decoherence. In this paper, we put forward a protocol of nonadiabatic holonomic quantum computation protected by dynamical decoupling. Due to the combination of nonadiabatic holonomic quantum computation and dynamical decoupling, our protocol not only possesses the intrinsic robustness against control errors but also protects quantum gates against environment-induced decoherence.
The c-differential behavior of the inverse function under the EA-equivalence

While the classical differential uniformity (c = 1) is invariant under the CCZ-equivalence, the newly defined (Ellingsen et al., IEEE Trans. Inf. Theory 66(9), 5781–5789, 2020) concept of c-differential uniformity (cDU), as was observed in Hasan et al. (2020), is not invariant under EA or CCZ-equivalence, for c≠ 1. In this paper, we find an intriguing behavior of the inverse function, namely, that adding some appropriate linearized monomials increases the c-differential uniformity significantly, for some c. For example, adding the linearized monomial $x^{2^{d}}$ to $x^{2^{n}-2}$ , where d is the largest nontrivial divisor of n, increases the mentioned c-differential uniformity from 2 or 3 (for c≠ 0,1) to ≥ 2d + 2, which in the case of the inverse function (as used in the AES) on ${\mathbb {F}}_{2^{8}}$ is a significant value of 18. We consider the case of perturbations via more general linearized polynomials and give bounds for the cDU based upon character sums. We further provide some computational results on other known Sboxes.
3-D Relation Network for visual relation recognition in videos

Video visual relation recognition aims at mining the dynamic relation instances between objects in the form of 〈subject,predicate,object〉, such as “person1-towards-person2” and “person-ride-bicycle”. Existing solutions treat the problem as several independent sub-tasks, i.e., image object detection, video object tracking and trajectory-based relation prediction. We argue that such separation results in the lack of information flow between different sub-models, which creates redundant representation while each sub-task cannot share a common set of task-specific features. Toward this end, we connect these three sub-tasks in an end-to-end manner by proposing the 3-D relation proposal that serves as a bridge for relation feature learning. Specifically, we put forward a novel deep neural network, named 3DRN, to fuse the spatio-temporal visual characteristics, object label features, and spatial interactive features for learning the relation instances with multi-modal cues. In addition, a three-staged training strategy is also provided to facilitate large-scale parameter optimization. We conduct extensive experiments on two public datasets with different emphasis to demonstrate the effectiveness of the proposed end-to-end feature learning method for visual relation recognition in videos. Furthermore, we verify the potential of our approach by tackling the video relation detection task.
Distance Distributions of Cyclic Orbit Codes

The distance distribution of a code is the vector whose ith entry is the number of pairs of codewords with distance i. We investigate the structure of the distance distribution for cyclic orbit codes, which are subspace codes generated by the action of $${\mathbb {F}}_{q^n}^*$$ on an $${\mathbb {F}}_q$$ -subspace U of $${\mathbb {F}}_{q^n}$$ . Note that $${\mathbb {F}}_{q^n}^*$$ is a Singer cycle in the general linear group of all $${\mathbb {F}}_q$$ -automorphisms of $${\mathbb {F}}_{q^n}$$ . We show that for full-length orbit codes with maximal possible distance the distance distribution depends only on $$q,\,n$$ , and the dimension of U. For full-length orbit codes with lower minimum distance, we provide partial results towards a characterization of the distance distribution, especially in the case that any two codewords intersect in a space of dimension at most 2. Finally, we briefly address the distance distribution of a union of full-length orbit codes with maximum distance.
Construction of a Medical Resource Sharing Mechanism Based on Blockchain Technology: Evidence from the Medical Resource Imbalance of China

Health equity is a very important part of social equity. The outbreak of the novel coronavirus pneumonia (COVID-19) in a short period of time exposed the problems existing in the allocation of medical resources and the response to major public health emergencies in China. By using Kernel density estimation and Data envelopment analysis (DEA), it is found that the allocation and imbalance of medical resources in China are greatly different among regions, and the polarization phenomenon is obvious. As an important part of the information technology system, blockchain technology is characterized by decentralization and non-tampering. It can realize sharing of medical resources through a mechanism of resource storage, circulation, supervision, and protection. The construction of a medical resource sharing mechanism under the condition of blockchain technology will greatly improve the degree of medical resource sharing, will narrow the differences in resource allocation between regions, and can effectively respond to an outbreak of major public health emergencies.
Deep Active Learning for Joint Classification &amp; Segmentation with Weak Annotator

CNN visualization and interpretation methods, like class-activation maps (CAMs), are typically used to highlight the image regions linked to class predictions. These models allow to simultaneously classify images and extract class-dependent saliency maps, without the need for costly pixel-level annotations. However, they typically yield segmentations with high false-positive rates and, therefore, coarse visualisations, more so when processing challenging images, as encountered in histology. To mitigate this issue, we propose an active learning (AL) framework, which progressively integrates pixel-level annotations during training. Given training data with global image-level labels, our deep weakly-supervised learning model jointly performs supervised image-level classification and active learning for segmentation, integrating pixel annotations by an oracle. Unlike standard AL methods that focus on sample selection, we also leverage large numbers of unlabeled images via pseudo-segmentations (i.e., self-learning at the pixel level), and integrate them with the oracle-annotated samples during training. We report extensive experiments over two challenging benchmarks - high-resolution medical images (histology GlaS data for colon cancer) and natural images (CUB-200-2011 for bird species). Our results indicate that, by simply using random sample selection, the proposed approach can significantly outperform state-of the-art CAMs and AL methods, with an identical oracle-supervision budget. Our code is publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Pyramid regional graph representation learning for content-based video retrieval

Conventionally, it is common that video retrieval methods aggregate the visual feature representations from every frame as the feature of the video, where each frame is treated as an isolated, static image. Such methods lack the power of modeling the intra-frame and inter-frame relationships for the local regions, and are often vulnerable to the visual redundancy and noise caused by various types of video transformation and editing, such as adding image patches, adding banner, etc. From the perspective of video retrieval, a video’s key information is more often than not convoyed by geometrically centered, dynamic visual content, and static areas often reside in regions that are farther from the center and often exhibit heavy visual redundancies temporally. This phenomenon is hardly investigated by conventional retrieval methods. In this article, we propose an unsupervised video retrieval method that simultaneously models intra-frame and inter-frame contextual information for video representation with a graph topology that is constructed on top of pyramid regional feature maps. By decomposing a frame into a pyramid regional sub-graph, and transforming a video into a regional graph, we use graph convolutional networks to extract features that incorporate information from multiple types of context. Our method is unsupervised and only uses the frame features extracted by pre-trained network. We have conducted extensive experiments and have demonstrated that the proposed method outperforms state-of-the-art video retrieval methods.