Reviewerly/exHarmony huggingface dataset:
Relationships among Healthcare Digitalization, Social Capital, and Supply Chain Performance in the Healthcare Manufacturing Industry

Due to the impact of coronavirus disease 2019 (COVID-19), automation and artificial intelligence (AI) have attracted renewed interest in multiple industrial fields. Global manufacturing bases were affected strongly by workforce shortages associated with the spread of COVID-19, and are working to increase productivity by embracing digital manufacturing technologies that take advantage of artificial intelligence and the Internet of Things (IoT) that offer the promise of improved connectivity among supply chains. This trend can increase and smooth the flow of social capital, which is a potential resource in supply chains and can affect supply chain performance in healthcare industry. However, such an issue has not been properly recognized as the best practice in healthcare industry. Thus, this study investigates empirically the relationship between digitalization and supply chain performance in healthcare manufacturing companies based on previous research that proposed a role for social capital. We surveyed the staff of domestic small and medium-sized healthcare manufacturing companies in South Korea currently operating or planning to deploy digital manufacturing technologies. Online and email surveys were utilized to collect the data. Invalid responses were excluded and the remaining 130 responses were analyzed using a structural equation model in SPSS with the AMOS module. We found that digitalization has a positive effect on the formation of social capital, which in turn has a positive effect on supply chain performance. The direct effect of digitalization on supply chain performance is small, and relatively large portions are mediated and influenced by social capital. The establishment of strategic relationships in the healthcare manufacturing industry is significant, as supply chain networks and production processes can influence the intended use of factory output. Companies should, therefore, secure timely and accurate information to manage the flow of products and services. The formation of social capital in the supply chain can help visualize entire supply chains and has a positive effect on real-time information-sharing among key elements of those chains.
Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification

Deep convolutional neural networks (CNNs) have shown a strong ability in mining discriminative object pose and parts information for image recognition. For fine-grained recognition, context-aware rich feature representation of object/scene plays a key role since it exhibits a significant variance in the same subcategory and subtle variance among different subcategories. Finding the subtle variance that fully characterizes the object/scene is not straightforward. To address this, we propose a novel context-aware attentional pooling (CAP) that effectively captures subtle changes via sub-pixel gradients, and learns to attend informative integral regions and their importance in discriminating different subcategories without requiring the bounding-box and/or distinguishable part annotations. We also introduce a novel feature encoding by considering the intrinsic consistency between the informativeness of the integral regions and their spatial structures to capture the semantic correlation among them. Our approach is simple yet extremely effective and can be easily applied on top of a standard classification backbone network. We evaluate our approach using six state-of-the-art (SotA) backbone networks and eight benchmark datasets. Our method significantly outperforms the SotA approaches on six datasets and is very competitive with the remaining two.
Blockchain-Based Secured Access Control in an IoT System

The distributed nature of Internet of Things (IoT) and its rapid increase on a large scale raises many security and privacy issues. Access control is one of the major challenges currently addressed through centralized approaches that may rely on a third party and they are constrained by availability and scalability, which may result in a performance bottleneck. Therefore, this paper proposes a novel solution to manage the delivery of lightweight and decentralized secure access control of an IoT system based on a multi-agent system and a blockchain. The main objective of the proposed solution is to build Blockchain Managers (BCMs) for securing IoT access control, as well as allowing for secure communication between local IoT devices. Moreover, the solution also enables secure communication between IoT devices, fog nodes and cloud computing.
Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation

Despite the successes of deep neural networks on many challenging vision tasks, they often fail to generalize to new test domains that are not distributed identically to the training data. The domain adaptation becomes more challenging for cross-modality medical data with a notable domain shift. Given that specific annotated imaging modalities may not be accessible nor complete. Our proposed solution is based on the cross-modality synthesis of medical images to reduce the costly annotation burden by radiologists and bridge the domain gap in radiological images. We present a novel approach for image-to-image translation in medical images, capable of supervised or unsupervised (unpaired image data) setups. Built upon adversarial training, we propose a learnable self-attentive spatial normalization of the deep convolutional generator network's intermediate activations. Unlike previous attention-based image-to-image translation approaches, which are either domain-specific or require distortion of the source domain's structures, we unearth the importance of the auxiliary semantic information to handle the geometric changes and preserve anatomical structures during image translation. We achieve superior results for cross-modality segmentation between unpaired MRI and CT data for multi-modality whole heart and multi-modal brain tumor MRI (T1/T2) datasets compared to the state-of-the-art methods. We also observe encouraging results in cross-modality conversion for paired MRI and CT images on a brain dataset. Furthermore, a detailed analysis of the cross-modality image translation, thorough ablation studies confirm our proposed method's efficacy.
MTI-YOLO: A Light-Weight and Real-Time Deep Neural Network for Insulator Detection in Complex Aerial Images

Insulator detection is an essential task for the safety and reliable operation of intelligent grids. Owing to insulator images including various background interferences, most traditional image-processing methods cannot achieve good performance. Some You Only Look Once (YOLO) networks are employed to meet the requirements of actual applications for insulator detection. To achieve a good trade-off among accuracy, running time, and memory storage, this work proposes the modified YOLO-tiny for insulator (MTI-YOLO) network for insulator detection in complex aerial images. First of all, composite insulator images are collected in common scenes and the “CCIN_detection” (Chinese Composite INsulator) dataset is constructed. Secondly, to improve the detection accuracy of different sizes of insulator, multi-scale feature detection headers, a structure of multi-scale feature fusion, and the spatial pyramid pooling (SPP) model are adopted to the MTI-YOLO network. Finally, the proposed MTI-YOLO network and the compared networks are trained and tested on the “CCIN_detection” dataset. The average precision (AP) of our proposed network is 17% and 9% higher than YOLO-tiny and YOLO-v2. Compared with YOLO-tiny and YOLO-v2, the running time of the proposed network is slightly higher. Furthermore, the memory usage of the proposed network is 25.6% and 38.9% lower than YOLO-v2 and YOLO-v3, respectively. Experimental results and analysis validate that the proposed network achieves good performance in both complex backgrounds and bright illumination conditions.
A Two-Stage Attentive Network for Single Image Super-Resolution

Recently, deep convolutional neural networks (CNNs) have been widely explored in single image super-resolution (SISR) and contribute remarkable progress. However, most of the existing CNNs-based SISR methods do not adequately explore contextual information in the feature extraction stage and pay little attention to the final high-resolution (HR) image reconstruction step, hence hindering the desired SR performance. To address the above two issues, in this paper, we propose a two-stage attentive network (TSAN) for accurate SISR in a coarse-to-fine manner. Specifically, we design a novel multi-context attentive block (MCAB) to make the network focus on more informative contextual features. Moreover, we present an essential refined attention block (RAB) which could explore useful cues in HR space for reconstructing fine-detailed HR image. Extensive evaluations on four benchmark datasets demonstrate the efficacy of our proposed TSAN in terms of quantitative metrics and visual effects. Code is available at https://github.com/Jee-King/TSAN.
Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment

Few-shot object detection (FSOD) aims to detect objects using only a few examples. How to adapt state-of-the-art object detectors to the few-shot domain remains challenging. Object proposal is a key ingredient in modern object detectors. However, the quality of proposals generated for few-shot classes using existing methods is far worse than that of many-shot classes, e.g., missing boxes for few-shot classes due to misclassification or inaccurate spatial locations with respect to true objects. To address the noisy proposal problem, we propose a novel meta-learning based FSOD model by jointly optimizing the few-shot proposal generation and fine-grained few-shot proposal classification. To improve proposal generation for few-shot classes, we propose to learn a lightweight metric-learning based prototype matching network, instead of the conventional simple linear object/nonobject classifier, e.g., used in RPN. Our non-linear classifier with the feature fusion network could improve the discriminative prototype matching and the proposal recall for few-shot classes. To improve the fine-grained few-shot proposal classification, we propose a novel attentive feature alignment method to address the spatial misalignment between the noisy proposals and few-shot classes, thus improving the performance of few-shot object detection. Meanwhile we learn a separate Faster R-CNN detection head for many-shot base classes and show strong performance of maintaining base-classes knowledge. Our model achieves state-of-the-art performance on multiple FSOD benchmarks over most of the shots and metrics.
How (Non-)Optimal is the Lexicon?

The mapping of lexical meanings to wordforms is a major feature of natural languages.While usage pressures might assign short words to frequent meanings (Zipf's law of abbreviation), the need for a productive and open-ended vocabulary, local constraints on sequences of symbols, and various other factors all shape the lexicons of the world's languages.Despite their importance in shaping lexical structure, the relative contributions of these factors have not been fully quantified.Taking a coding-theoretic view of the lexicon and making use of a novel generative statistical model, we define upper bounds for the compressibility of the lexicon under various constraints.Examining corpora from 7 typologically diverse languages, we use those upper bounds to quantify the lexicon's optimality and to explore the relative costs of major constraints on natural codes.We find that (compositional) morphology and graphotactics can sufficiently account for most of the complexity of natural codes-as measured by code length.
Self-Attention Attribution: Interpreting Information Interactions Inside Transformer

The great success of Transformer-based models benefits from the powerful multi-head self-attention mechanism, which learns token dependencies and encodes contextual information from the input. Prior work strives to attribute model decisions to individual input features with different saliency measures, but they fail to explain how these input features interact with each other to reach predictions. In this paper, we propose a self-attention attribution method to interpret the information interactions inside Transformer. We take BERT as an example to conduct extensive studies. Firstly, we apply self-attention attribution to identify the important attention heads, while others can be pruned with marginal performance degradation. Furthermore, we extract the most salient dependencies in each layer to construct an attribution tree, which reveals the hierarchical interactions inside Transformer. Finally, we show that the attribution results can be used as adversarial patterns to implement non-targeted attacks towards BERT.
Shallow-UWnet: Compressed Model for Underwater Image Enhancement (Student Abstract)

Over the past few decades, underwater image enhancement has attracted an increasing amount of research effort due to its significance in underwater robotics and ocean engineering. Research has evolved from implementing physics-based solutions to using very deep CNNs and GANs. However, these state-of-art algorithms are computationally expensive and memory intensive. This hinders their deployment on portable devices for underwater exploration tasks. These models are trained on either synthetic or limited real-world datasets making them less practical in real-world scenarios. In this paper, we propose a shallow neural network architecture, Shallow-UWnet which maintains performance and has fewer parameters than the state-of-art models. We also demonstrated the generalization of our model by benchmarking its performance on a combination of synthetic and real-world datasets.
Federated Model Distillation with Noise-Free Differential Privacy

Conventional federated learning directly averages model weights, which is only possible for collaboration between models with homogeneous architectures. Sharing prediction instead of weight removes this obstacle and eliminates the risk of white-box inference attacks in conventional federated learning. However, the predictions from local models are sensitive and would leak training data privacy to the public. To address this issue, one naive approach is adding the differentially private random noise to the predictions, which however brings a substantial trade-off between privacy budget and model performance. In this paper, we propose a novel framework called FEDMD-NFDP, which applies a Noise-FreeDifferential Privacy (NFDP) mechanism into a federated model distillation framework. Our extensive experimental results on various datasets validate that FEDMD-NFDP can deliver not only comparable utility and communication efficiency but also provide a noise-free differential privacy guarantee. We also demonstrate the feasibility of our FEDMD-NFDP by considering both IID and Non-IID settings, heterogeneous model architectures, and unlabelled public datasets from a different distribution.
Multi-Robot Space Exploration: An Augmented Arithmetic Approach

Space exploration refers to constructing a map with the aid of sensor data.This exploration is achieved utilizing a group of robots in an obstacle cluttered environment and distributing tasks amongst these robot(s).The robotic configuration is equipped with sensors to acquire data from the surroundings and to ensure collision-free motion.This paper presents a framework for the design of a Hybrid Stochastic Optimizer (HSO) for multi-robot space exploration.The proposed algorithm augments deterministic Coordinated Multi-Robot Exploration (CME) and stochastic Arithmetic Optimization (AO) techniques for maximizing the utility.The framework initially utilizes deterministic CME to ascertain the cost and utility values of adjacent cells around robot(s).The overall solution accuracy is then improved utilizing the Arithmetic Optimization algorithm.The proposed utilization of hybrid is interpreted that the algorithm starts with deterministic technique and continues off with stochastic method until the required improved solution with the desired accuracy is achieved.The effectiveness of the proposed Hybrid Stochastic Optimizer is ascertained by training the multi-robotic framework in various complexity maps.The results efficacy is then demonstrated by comparing the results of the HSO algorithm with those achieved from two contemporary techniques namely conventional CME and hybrid CME with whale optimizer.Results demonstrate that the proposed HSO algorithm significantly improved the exploration parameters by enhancing the explored area and reducing the search time.
Fairness-aware Agnostic Federated Learning

Federated learning is an emerging framework that builds centralized machine learning models with training data distributed across multiple devices.Most of the previous works about federated learning focus on the privacy protection and communication cost reduction.However, how to achieve fairness in federated learning is underexplored and challenging especially when testing data distribution is different from training distribution or even unknown.Introducing simple fairness constraints on the centralized model cannot achieve model fairness on unknown testing data.In this paper, we develop a fairness-aware agnostic federated learning framework (Agnostic-Fair) to deal with the challenge of unknown testing distribution.We use kernel reweighing functions to assign a reweighing value on each training sample in both loss function and fairness constraint.Therefore, the centralized model built from AgnosticFair can achieve high accuracy and fairness guarantee on unknown testing data.Moreover, the built model can be directly applied to local sites as it guarantees fairness on local data distributions.To our best knowledge, this is the first work to achieve fairness in federated learning.Experimental results on two real datasets demonstrate the effectiveness in terms of both utility and fairness under data shift scenarios.
TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework Using Self-Supervised Multi-Task Learning

In this paper, we propose TransMEF, a transformer-based multi-exposure image fusion framework that uses self-supervised multi-task learning. The framework is based on an encoder-decoder network, which can be trained on large natural image datasets and does not require ground truth fusion images. We design three self-supervised reconstruction tasks according to the characteristics of multi-exposure images and conduct these tasks simultaneously using multi-task learning; through this process, the network can learn the characteristics of multi-exposure images and extract more generalized features. In addition, to compensate for the defect in establishing long-range dependencies in CNN-based architectures, we design an encoder that combines a CNN module with a transformer module. This combination enables the network to focus on both local and global information. We evaluated our method and compared it to 11 competitive traditional and deep learning-based methods on the latest released multi-exposure image fusion benchmark dataset, and our method achieved the best performance in both subjective and objective evaluations. Code will be available at https://github.com/miccaiif/TransMEF.
Adaptive rectification based adversarial network with spectrum constraint for high-quality PET image synthesis

Positron emission tomography (PET) is a typical nuclear imaging technique, which can provide crucial functional information for early brain disease diagnosis. Generally, clinically acceptable PET images are obtained by injecting a standard-dose radioactive tracer into human body, while on the other hand the cumulative radiation exposure inevitably raises concerns about potential health risks. However, reducing the tracer dose will increase the noise and artifacts of the reconstructed PET image. For the purpose of acquiring high-quality PET images while reducing radiation exposure, in this paper, we innovatively present an adaptive rectification based generative adversarial network with spectrum constraint, named AR-GAN, which uses low-dose PET (LPET) image to synthesize standard-dose PET (SPET) image of high-quality. Specifically, considering the existing differences between the synthesized SPET image by traditional GAN and the real SPET image, an adaptive rectification network (AR-Net) is devised to estimate the residual between the preliminarily predicted image and the real SPET image, based on the hypothesis that a more realistic rectified image can be obtained by incorporating both the residual and the preliminarily predicted PET image. Moreover, to address the issue of high-frequency distortions in the output image, we employ a spectral regularization term in the training optimization objective to constrain the consistency of the synthesized image and the real image in the frequency domain, which further preserves the high-frequency detailed information and improves synthesis performance. Validations on both the phantom dataset and the clinical dataset show that the proposed AR-GAN can estimate SPET images from LPET images effectively and outperform other state-of-the-art image synthesis approaches.
SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-trained Language Models

Knowledge graph completion (KGC) aims to reason over known facts and infer the missing links.Text-based methods such as KG-BERT (Yao et al., 2019) learn entity representations from natural language descriptions, and have the potential for inductive KGC.However, the performance of text-based methods still largely lag behind graph embedding-based methods like TransE (Bordes et al., 2013) and RotatE (Sun et al., 2019b).In this paper, we identify that the key issue is efficient contrastive learning.To improve the learning efficiency, we introduce three types of negatives: in-batch negatives, pre-batch negatives, and self-negatives which act as a simple form of hard negatives.Combined with InfoNCE loss, our proposed model SimKGC can substantially outperform embedding-based methods on several benchmark datasets.In terms of mean reciprocal rank (MRR), we advance the state-of-the-art by +19% on WN18RR, +6.8% on the Wikidata5M transductive setting, and +22% on the Wikidata5M inductive setting.Thorough analyses are conducted to gain insights into each component.Our code is available at https://github.com/intfloat/SimKGC.