Reviewerly/exHarmony huggingface dataset:
Named entity recognition of Chinese electronic medical records based on a hybrid neural network and medical MC-BERT

Named entity recognition (NER) of electronic medical records is an important task in clinical medical research. Although deep learning combined with pretraining models performs well in recognizing entities in clinical texts, because Chinese electronic medical records have a special text structure and vocabulary distribution, general pretraining models cannot effectively incorporate entities and medical domain knowledge into representation learning; separate deep network models lack the ability to fully extract rich features in complex texts, which negatively affects the named entity recognition of electronic medical records.To better represent electronic medical record text, we extract the text's local features and multilevel sequence interaction information to improve the effectiveness of electronic medical record named entity recognition. This paper proposes a hybrid neural network model based on medical MC-BERT, namely, the MC-BERT + BiLSTM + CNN + MHA + CRF model. First, MC-BERT is used as the word embedding model of the text to obtain the word vector, and then BiLSTM and CNN obtain the feature information of the forward and backward directions of the word vector and the local context to obtain the corresponding feature vector. After merging the two feature vectors, they are sent to multihead self-attention (MHA) to obtain multilevel semantic features, and finally, CRF is used to decode the features and predict the label sequence.The experiments show that the F1 values of our proposed hybrid neural network model based on MC-BERT reach 94.22%, 86.47%, and 92.28% on the CCKS-2017, CCKS-2019 and cEHRNER datasets, respectively. Compared with the general-domain BERT-based BiLSTM + CRF, our F1 values increased by 0.89%, 1.65% and 2.63%. Finally, we analyzed the effect of an unbalanced number of entities in the electronic medical records on the results of the NER experiment.
Error Compensation Framework for Flow-Guided Video Inpainting

The key to video inpainting is to use correlation information from as many reference frames as possible. Existing flow-based propagation methods split the video synthesis process into multiple steps: flow completion $$\rightarrow {}$$ pixel propagation $$\rightarrow {}$$ synthesis. However, there is a significant drawback that the errors in each step continue to accumulate and amplify in the next step. To this end, we propose an Error Compensation Framework for Flow-guided Video Inpainting (ECFVI), which takes advantage of the flow-based method and offsets its weaknesses. We address the weakness with the newly designed flow completion module and the error compensation network that exploits the error guidance map. Our approach greatly improves the temporal consistency and the visual quality of the completed videos. Experimental results show the superior performance of our proposed method with the speed up of $$\times {6}$$ , compared to the state-of-the-art methods. In addition, we present a new benchmark dataset for evaluation by supplementing the weaknesses of existing test datasets.
Digital Twins Enabled On-Demand Matching for Multi-Task Federated Learning in HetVNets

In the heterogeneous vehicular networks (HetVNets), the roadside units (RUs) can exploit the massive amounts of valuable data collected by vehicles to complete federated learning tasks. However, most of the existing studies consider the scenario of one task requester (TR) and ignore the fact that multiple TRs may concurrently request their model training tasks in the HetVNets. In this paper, we consider the scenario of multi-TR and multi-RU and propose a digital twins (DT) enabled on-demand matching scheme for multi-task federated learning to address the two-way selection problem between TRs and RUs. Specifically, by jointly considering the diversified requirements of the TRs and the differentiated training capabilities of the RUs, we first design a DT enabled on-demand matching architecture to facilitate the multi-task federated learning in the HetVNets. Then, based on the personalized requirement of the DT of each TR (DT-TR), a marginal utility based vehicle selection mechanism is proposed to enable the DT of each RU (DT-RU) to determine the customized model training strategy. With the determined strategies, the two-way selection problem between the DT-TRs and the DT-RUs is formulated as an on-demand matching game in DT networks, where a matching algorithm is designed to obtain their optimal strategies. Simulation results demonstrate that the proposed scheme outperforms the conventional schemes in terms of training accuracy, performance-cost ratio (PCR), and task completion rate (TCR).
Eliminating Gradient Conflict in Reference-based Line-Art Colorization

Reference-based line-art colorization is a challenging task in computer vision. The color, texture, and shading are rendered based on an abstract sketch, which heavily relies on the precise long-range dependency modeling between the sketch and reference. Popular techniques to bridge the cross-modal information and model the long-range dependency employ the attention mechanism. However, in the context of reference-based line-art colorization, several techniques would intensify the existing training difficulty of attention, for instance, self-supervised training protocol and GAN-based losses. To understand the instability in training, we detect the gradient flow of attention and observe gradient conflict among attention branches. This phenomenon motivates us to alleviate the gradient issue by preserving the dominant gradient branch while removing the conflict ones. We propose a novel attention mechanism using this training strategy, Stop-Gradient Attention (SGA), outperforming the attention baseline by a large margin with better training stability. Compared with state-of-the-art modules in line-art colorization, our approach demonstrates significant improvements in Fréchet Inception Distance (FID, up to 27.21%) and structural similarity index measure (SSIM, up to 25.67%) on several benchmarks. The code of SGA is available at https://github.com/kunkun0w0/SGA .
Deep Time-Delay Reservoir Computing With Cascading Injection-Locked Lasers

Reservoir computing is a simplified recurrent neural network, which requires training only at the output layer and hence significantly reduces the training cost. Time-delay reservoir computing (TDRC) introduces a large number of virtual neurons based on a single physical neuron and a feedback loop, which is friendly for hardware implementations. This work proposes a scheme for implementing the deep TDRC architecture based on cascading injection-locked semiconductor lasers. In each layer, the reservoir consists of a quantum dot laser and an optical feedback loop. The output of each reservoir layer is fed into the subsequent one through the optical injection-locking technique. This all-optical approach (for reservoir layers) has the merit of high scalability without any depth limitation. Theoretical analysis shows that the deep TDRC improves the performance on multiple benchmark tasks, including the memory capacity, the prediction of chaos, the nonlinear channel equalization, and the recognition of spoken digits.
An Efficient Transformer Based on Global and Local Self-Attention for Face Photo-Sketch Synthesis

Face photo-sketch synthesis tasks have been dominated by convolutional neural networks (CNNs), especially CNN-based generative adversarial networks (GANs), because of their strong texture modeling capabilities and thus their ability to generate more realistic face photos/sketches beyond traditional methods. However, due to CNNs’ locality and spatial invariance properties, there have weaknesses in capturing the global and structural information which are extremely important for face images. Inspired by the recent phenomenal success of the Transformer in vision tasks, we propose replacing CNNs with Transformers that are able to model long-range dependencies to synthesize more structured and realistic face images. However, the existing vision Transformers are mainly designed for high-level vision tasks and lack the dense prediction ability to generate high resolution images due to the quadratic computational complexity of their self-attention mechanism. In addition, the original Transformer is not capable of modeling local correlations which is an important skill for image generation. To address these challenges, we propose two types of memory-friendly Transformer encoders, one for processing local correlations via local self-attention and another for modeling global information via global self-attention. By integrating the two proposed Transformer encoders, we present an efficient GL-Transformer for face photo-sketch synthesis, which can synthesize realistic face photo/sketch images from coarse to fine. Extensive experiments demonstrate that our model achieves a comparable or better performance beyond the state-of-the-art CNN-based methods both qualitatively and quantitatively.
Implementing robotics in library services

Purpose The main purpose of this paper is to explore how robots are being used in the library to transform library services and what are the future possibilities of application of robots in libraries. Design/methodology/approach Analyzing various library websites and consulting literature relating to the use of robots in libraries, the current application of robots in libraries has been enumerated. Findings With the practical examples of libraries using different types of robots, this study summarizes diverse activities of artificial intelligence-mediated robots: managing the shelf, controlling circulation workflow, assisting in document retrieval, transporting articles, acting as subject guides, attending reference queries, etc. Originality/value This paper highlights how the introduction of robots in libraries improves the service productivity and creates a more engaging environment with the user group. The benefits and challenges of using robots in the library and the future possibilities are also discussed.
IBM's Quantum Leap: The Company Will Take Quantum Tech Past the 1,000-Qubit Mark in 2023

IBM's Condor, the world's first universal quantum computer with more than 1,000 qubits, is set to debut in 2023. The year is also expected to see IBM launch Heron, the first of a new flock of modular quantum processors that the company says may help it produce quantum computers with more than 4,000 qubits by 2025.
Building Fake Review Detection Model Based on Sentiment Intensity and PU Learning

Fake review detection has the characteristics of huge stream data processing scale, unlimited data increment, dynamic change, and so on. However, the existing fake review detection methods mainly target limited and static review data. In addition, deceptive fake reviews have always been a difficult point in fake review detection due to their hidden and diverse characteristics. To solve the above problems, this article proposes a fake review detection model based on sentiment intensity and PU learning (SIPUL), which can continuously learn the prediction model from the constantly arriving streaming data. First, when the streaming data arrive, the sentiment intensity is introduced to divide the reviews into different subsets (i.e., strong sentiment set and weak sentiment set). Then, the initial positive and negative samples are extracted from the subset using the marking mechanism of selection completely at random (SCAR) and Spy technology. Second, building a semi-supervised positive-unlabeled (PU) learning detector based on the initial sample to detect fake reviews in the data stream iteratively. According to the detection results, the data of initial samples and the PU learning detector are continuously updated. Finally, the old data are continually deleted according to the historical record points, so that the training sample data are within a manageable size and prevent overfitting. Experimental results show that the model can effectively detect fake reviews, especially deceptive reviews.
Image Intensity Variation Information for Interest Point Detection

Interest point detection methods are gaining more attention and are widely applied in computer vision tasks such as image retrieval and 3D reconstruction. However, there still exist two main problems to be solved: (1) from the perspective of mathematical representations, the differences among edges, corners, and blobs have not been convincingly explained and the relationships among the amplitude response, scale factor, and filtering orientation for interest points have not been thoroughly explained; (2) the existing design mechanism for interest point detection does not show how to accurately obtain intensity variation information on corners and blobs. In this paper, the first- and second-order Gaussian directional derivative representations of a step edge, four common genres of corners, an anisotropic-type blob, and an isotropic-type blob are analyzed and derived. Multiple interest point characteristics are discovered. The characteristics for interest points that we obtained help us describe the differences among edges, corners, and blobs, explain why the existing interest point detection methods with multiple scales cannot properly obtain interest points from images, and present novel corner and blob detection methods. Extensive experiments demonstrate the superiority of our proposed methods in terms of detection performance, robustness to affine transformations, noise, image matching, and 3D reconstruction.
CCIO: A Cross-Chain Interoperability Approach for Consortium Blockchains Based on Oracle

Cross-chain interoperability can expand the ability of data interaction and value circulation between different blockchains, especially the value interaction and information sharing between industry consortium blockchains. However, some current public blockchain cross-chain technologies or data migration schemes between consortium blockchains need help to meet the consortium blockchain requirements for efficient two-way data interaction. The critical issue to solve in cross-chain technology is improving the efficiency of cross-chain exchange while ensuring the security of data transmission outside the consortium blockchain. In this article, we design a cross-chain architecture based on blockchain oracle technology. Then, we propose a bidirectional information cross-chain interaction approach (CCIO) based on the former architecture, we novelly improve three traditional blockchain oracle patterns, and we combine a mixture of symmetric and asymmetric keys to encrypt private information to ensure cross-chain data security. The experimental results demonstrate that the proposed CCIO approach can achieve efficient and secure two-way cross-chain data interactions and better meet the application needs of large-scale consortium blockchains.
OCR-IDL: OCR Annotations for Industry Document Library Dataset

Pretraining has proven successful in Document Intelligence tasks where deluge of documents are used to pretrain the models only later to be finetuned on downstream tasks. One of the problems of the pretraining approaches is the inconsistent usage of pretraining data with different OCR engines leading to incomparable results between models. In other words, it is not obvious whether the performance gain is coming from diverse usage of amount of data and distinct OCR engines or from the proposed models. To remedy the problem, we make public the OCR annotations for IDL documents using commercial OCR engine given their superior performance over open source OCR models. It is our hope that OCR-IDL can be a starting point for future works on Document Intelligence. All of our data and its collection process with the annotations can be found in https://github.com/furkanbiten/idl_data .
Creating large Fock states and massively squeezed states in optics using systems with nonlinear bound states in the continuum

The quantization of the electromagnetic field leads directly to the existence of quantum mechanical states, called Fock states, with an exact integer number of photons. Despite these fundamental states being long-understood, and despite their many potential applications, generating them is largely an open problem. For example, at optical frequencies, it is challenging to deterministically generate Fock states of order two and beyond. Here, we predict the existence of an effect in nonlinear optics, which enables the deterministic generation of large Fock states at arbitrary frequencies. The effect, which we call an n-photon bound state in the continuum, is one in which a photonic resonance (such as a cavity mode) becomes lossless when a precise number of photons n is inside the resonance. Based on analytical theory and numerical simulations, we show that these bound states enable a remarkable phenomenon in which a coherent state of light, when injected into a system supporting this bound state, can spontaneously evolve into a Fock state of a controllable photon number. This effect is also directly applicable for creating (highly) squeezed states of light, whose photon number fluctuations are (far) below the value expected from classical physics (i.e., shot noise). We suggest several examples of systems to experimentally realize the effects predicted here in nonlinear nanophotonic systems, showing examples of generating both optical Fock states with large n (n > 10), as well as more macroscopic photonic states with very large squeezing, with over 90% less noise (10 dB) than the classical value associated with shot noise.
Blockchainembedded supply chain finance solutions for Indian MSMEs: a TISM and MICMAC approach

Purpose To stabilise the financial flow and ensure the smooth operations of the micro, small and medium enterprises (MSMEs), supply chain finance (SCF) is one of the efficient alternative solutions suggested by various researchers and practitioners. The application of blockchain technology (BT) could maximize the capability and performance of the SCF throughout the supply chain. Therefore, this study aims to identify and analyse the factors of implementing BT-embedded SCF solutions for Indian MSMEs. Design/methodology/approach The total interpretative structural modeling (TISM) methodology is applied to explore the hierarchical as well as non-hierarchical relationships among the factors of implementing BT-embedded SCF solutions in Indian MSMEs. Thereafter, the fuzzy-cross-impact matrix-multiplication (F-MICMAC) approach is opted to classify all the factors into four groups depending upon their driving and dependence powers in the overall TISM hierarchy. Findings The result of this study revealed that “real-time exchange of information” and “transparent platform” are the most influential factors and play important role in the application of blockchain in SCF in Indian MSMEs. The real-time exchange of information ensures the easy and quick access to data by any partner across the supply chain and, also the data and information remain secure. Practical implications The findings of this study help the MSMEs and supply chain managers to implement the blockchain-embedded solutions to overcome the traditional SCF challenges and obstructions by setting up short-term as well as long-term policies and will open the door for future research work. Originality/value To the best of the author’s/authors’ knowledge, this study is the first of its kind, where the data is collected from field experts to identify the factors influencing the blockchain-embedded SCF adoption in Indian MSMEs’ perspectives, to tackle the flaws of SCF and strengthen the MSMEs.
Three Pathways for Standardisation and Ethical Disclosure by Default under the European Union Artificial Intelligence Act

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A novel neural network model fusion approach for improving medical named entity recognition in online health expert question-answering services

Because of the frequent occurrence of chronic diseases, the COVID-19 pandemic, etc., online health expert question-answering (HQA) services have been unable to cope with the rapidly increasing demand for online consultations. Building a virtual health assistant based on medical named entity recognition (NER) can effectively assist with the consultation process, but the unstandardized expressions within HQA text pose a serious challenge for medical NER tasks. The main goal of this study is to propose a novel deep medical NER approach based on a collaborative decision strategy (CDS), i.e., co_decision_NER (CDN), that can identify standard and nonstandard medical entities in the HQA context. We collected 10,000 question–answer pairs from HaoDF, extracted medical entities from 15 entity categories, and used a CDS to fuse the advantages of different NER models. Ultimately, CDN achieved a performance (precision = 84.50%, recall = 84.30%, F1 = 84.40%) that was significantly better than that of the state-of-the-art (SOTA) method. Our empirical analysis suggests that the entity types Disease (DIS), Sign (SIG), Test (TES), Drug (DRU), Surgery (SUR), Precaution (PRE), and Region (REG) can be most easily expressed arbitrarily in the doctor–patient interaction scenario of HQA services. In addition, CDN can identify not only standard but also nonstandard medical entities, effectively alleviating the severe out-of-vocabulary (OOV) problem faced by HQA services when performing medical NER tasks. The core contribution of this study is the development of a novel neural network model fusion algorithm that can improve the performance of entity recognition in medical domain-specific tasks.
DDOD: Dive Deeper into the Disentanglement of Object Detector

Compared to many other dense prediction tasks, object detection plays a fundamental role in visual perception and scene understanding. Dense object detection, aiming at localizing objects directly from the feature map, has drawn great attention due to its low cost and high efficiency. Though it has been developed for a long time, the training pipeline of dense object detectors is still compromised to lots of conjunctions. In this paper, we demonstrate the existence of three conjunctions lying in the current paradigm of one-stage detectors: 1) only samples assigned as positive in classification head are used to train the regression head; 2) classification and regression share the same input feature and computational fields defined by the parallel head architecture; and 3) samples distributed in different feature pyramid layers are treated equally when computing the loss. Based on this, we propose Disentangled Dense Object Detector (DDOD), a simple, direct, and efficient framework for 2D detection with strong performance. We derive two DDOD variants (i.e., DR-CNN, and DDETR) following the basic one-stage/two-stage and recently developed transformer-based pipelines. Specifically, we develop three effective disentanglement mechanisms and integrate them into the current state-of-the-art object detectors. Extensive experiments on MS COCO benchmark show that our approach obtains significant enhancements with negligible extra overhead on various detectors. Notably, our best model reaches 55.4 mAP on the COCO <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">test-dev</i> set, achieving new state-of-the-art performance on this competitive benchmark. Additionally, we validate our model on several challenging tasks including small object detection and crowded object detection. The experimental results further prove the superiority of disentanglement on these conjunctions. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zehuichen123/DDOD</uri> .