Reviewerly/exHarmony huggingface dataset:
Quality assurance of integrative big data for medical research within a multihospital system

The need is growing to create medical big data based on the electronic health records collected from different hospitals. Errors for sure occur and how to correct them should be explored.Electronic health records of 9,197,817 patients and 53,081,148 visits, totaling about 500 million records for 2006-2016, were transmitted from eight hospitals into an integrated database. We randomly selected 10% of patients, accumulated the primary keys for their tabulated data, and compared the key numbers in the transmitted data with those of the raw data. Errors were identified based on statistical testing and clinical reasoning.Data were recorded in 1573 tables. Among these, 58 (3.7%) had different key numbers, with the maximum of 16.34/1000. Statistical differences (P < 0.05) were found in 34 (58.6%), of which 15 were caused by changes in diagnostic codes, wrong accounts, or modified orders. For the rest, the differences were related to accumulation of hospital visits over time. In the remaining 24 tables (41.4%) without significant differences, three were revised because of incorrect computer programming or wrong accounts. For the rest, the programming was correct and absolute differences were negligible. The applicability was confirmed using the data of 2,730,883 patients and 15,647,468 patient-visits transmitted during 2017-2018, in which 10 (3.5%) tables were corrected.Significant magnitude of inconsistent data does exist during the transmission of big data from diverse sources. Systematic validation is essential. Comparing the number of data tabulated using the primary keys allow us to rapidly identify and correct these scattered errors.
Blockchain Bridges Critical National Infrastructures: E-Healthcare Data Migration Perspective

Secure management of Critical National Infrastructures (CNI) is a burning challenge to any state. As a CNI, Electronic Healthcare System (EHS) infrastructure records citizens&#x2019; medical records, raising security and privacy concerns. Traditional EHS functions independently where patients&#x2019; records are recorded and maintained in centralized systems that produce massive redundant data. Due to the non-coherence of these systems, data atomicity is not maintained; hence research results based on these data create questioning. Moreover, medical records are valuable for research but cannot be public due to security and privacy. Blockchain (BC) is currently considered a potential solution for the challenges. Blockchain can integrate every independent EHS as a bridging platform. The solution can ensure data uniqueness and overcome security issues. The prime difficulties for the integration are data synchronization of the traditional EHS and BC-based EHS. Furthermore, the autonomous interoperability between <i>SQL</i> and <i>NoSQL</i> database used in typical EHS and BC-based EHS, respectively, is a prime challenge. Therefore, this research proposes a Blockchain-based framework that bridges Traditional E-Health Systems(TEHS) and allows uninterruptible data exchanges between two systems, even for archive medical records. Beyond that, the framework shows an elevated way to overcome a single point of failure, data security, access control, etc., issues in a centralized system. Finally, the testbed implementation justifies the proposed architecture.
DAMO-NLP at SemEval-2022 Task 11: A Knowledge-based System for Multilingual Named Entity Recognition

The MultiCoNER shared task aims at detecting semantically ambiguous and complex named entities in short and low-context settings for multiple languages.The lack of contexts makes the recognition of ambiguous named entities challenging.To alleviate this issue, our team DAMO-NLP proposes a knowledge-based system, where we build a multilingual knowledge base based on Wikipedia to provide related context information to the named entity recognition (NER) model.Given an input sentence, our system effectively retrieves related contexts from the knowledge base.The original input sentences are then augmented with such context information, allowing significantly better contextualized token representations to be captured.Our system wins 10 out of 13 tracks in the MultiCoNER shared task.
Multi-Centroid Representation Network for Domain Adaptive Person Re-ID

Recently, many approaches tackle the Unsupervised Domain Adaptive person re-identification (UDA re-ID) problem through pseudo-label-based contrastive learning. During training, a uni-centroid representation is obtained by simply averaging all the instance features from a cluster with the same pseudo label. However, a cluster may contain images with different identities (label noises) due to the imperfect clustering results, which makes the uni-centroid representation inappropriate. In this paper, we present a novel Multi-Centroid Memory (MCM) to adaptively capture different identity information within the cluster. MCM can effectively alleviate the issue of label noises by selecting proper positive/negative centroids for the query image. Moreover, we further propose two strategies to improve the contrastive learning process. First, we present a Domain-Specific Contrastive Learning (DSCL) mechanism to fully explore intra-domain information by comparing samples only from the same domain. Second, we propose Second-Order Nearest Interpolation (SONI) to obtain abundant and informative negative samples. We integrate MCM, DSCL, and SONI into a unified framework named Multi-Centroid Representation Network (MCRN). Extensive experiments demonstrate the superiority of MCRN over state-of-the-art approaches on multiple UDA re-ID tasks and fully unsupervised re-ID tasks.
Text Gestalt: Stroke-Aware Scene Text Image Super-resolution

In the last decade, the blossom of deep learning has witnessed the rapid development of scene text recognition. However, the recognition of low-resolution scene text images remains a challenge. Even though some super-resolution methods have been proposed to tackle this problem, they usually treat text images as general images while ignoring the fact that the visual quality of strokes (the atomic unit of text) plays an essential role for text recognition. According to Gestalt Psychology, humans are capable of composing parts of details into the most similar objects guided by prior knowledge. Likewise, when humans observe a low-resolution text image, they will inherently use partial stroke-level details to recover the appearance of holistic characters. Inspired by Gestalt Psychology, we put forward a Stroke-Aware Scene Text Image Super-Resolution method containing a Stroke-Focused Module (SFM) to concentrate on stroke-level internal structures of characters in text images. Specifically, we attempt to design rules for decomposing English characters and digits at stroke-level, then pre-train a text recognizer to provide stroke-level attention maps as positional clues with the purpose of controlling the consistency between the generated super-resolution image and high-resolution ground truth. The extensive experimental results validate that the proposed method can indeed generate more distinguishable images on TextZoom and manually constructed Chinese character dataset Degraded-IC13. Furthermore, since the proposed SFM is only used to provide stroke-level guidance when training, it will not bring any time overhead during the test phase. Code is available at https://github.com/FudanVI/FudanOCR/tree/main/text-gestalt.
Graph convolutional recurrent networks for reward shaping in reinforcement learning

In this paper, we consider the problem of low-speed convergence in Reinforcement Learning (RL). As a solution, various potential-based reward shaping techniques were proposed to form the potential function. Learning a potential function is still challenging and comparable to building a value function from scratch. In this work, our main contribution is proposing a new scheme for reward shaping, which combines (1) the Graph Convolutional Recurrent Networks (GCRN), (2) augmented Krylov, and (3) look-ahead advice to form the potential function. We propose an architecture for GCRN that combines Graph Convolutional Networks (GCN) to capture spatial dependencies and Bi-Directional Gated Recurrent Units (Bi-GRUs) to account for temporal dependencies. Our definition of the loss function of GCRN incorporates the message passing technique of the Hidden Markov Models (HMM). Since the transition matrix of the environment is hard to compute, we use the Krylov basis to estimate the transition matrix, which outperforms the existing approximation bases. Unlike existing potential functions that only rely on states to perform reward shaping, we use both the states and actions through the look-ahead advice mechanism to produce more precise advice. Our evaluations conducted on the Atari 2600 and MuJoCo games show that our solution outperforms the state-of-the-art that utilizes GCN as the potential function in most games in terms of the learning speed while reaching higher rewards.
Machine and Deep Learning Approaches for IoT Attack Classification

In recent years, Internet of Things (IoT) traffic has increased dramatically and is expected to grow further in the next future. Because of their vulnerabilities, IoT devices are often the target of cyber-attacks with dramatic consequences. For this reason, there is a strong need for powerful tools to guarantee a good level of security in IoT networks. Machine and deep learning approaches promise good performance for such a complex task. In this work, we employ state-of-art traffic classifiers based on deep learning and assess their effectiveness in accomplishing IoT attack classification. We aim to recognize different attack classes and distinguish them from benign network traffic. In more detail, we utilize effective and unbiased input data that allow fast (i.e. “early”) detection of anomalies and we compare performance with that of traditional (i.e. “postmortem”) machine learning classifiers. The experimental results highlight the need for advanced deep learning architectures fed with input data specifically tailored and designed for IoT attack classification. Furthermore, we perform an occlusion analysis to assess the influence on the performance of some network layer fields and the possible bias they may introduce.
Trust-Based Multi-Agent Imitation Learning for Green Edge Computing in Smart Cities

Green communications and networking technologies boost the interconnection and communication of Internet of Things (IoT) devices, so as to facilitate the task offloading. Artificial Intelligence (AI) based task offloading scheme is being widely studied. However, most of AI based task offloading schemes only reward the devices that process tasks locally, and do not consider the untrusted devices. To solve these issues, a Trust based Multi-Agent Imitation Learning (T-MAIL) scheme is proposed by us to improve task offloading for edge computing in smart cities. Firstly, we established a full task offloading incentive model, in which edge devices can get comprehensive reward from local processing and task re-offloading. Secondly, we proposed an active trust acquisition method, which can obtain the device trust efficiently and accurately. Finally, the new task offloading incentive scheme and trust acquisition method are introduced into multi-agent imitation learning. The experimental results show that, the proposed T-MAIL will effectively improve task offloading. Compared with MILP and DQN based task offloading solution, the average task completion time is reduced by 5.5% and 52.7% respectively. Compared with MILP scheme, the task offloading rate is increased by 19.2%. In addition, the trust difference ratio between trusted devices and untrusted devices can reach 56.1%.
A Real-Time CNN-Based Lightweight Mobile Masked Face Recognition System

Due to the global spread of the Covid-19 virus and its variants, new needs and problems have emerged during the pandemic that deeply affects our lives.Wearing masks as the most effective measure to prevent the spread and transmission of the virus has brought various security vulnerabilities.Today we are going through times when wearing a mask is part of our lives, thus it is very important to identify individuals who violate this rule.Besides, this pandemic makes the traditional biometric authentication systems less effective in many cases such as facial security checks, gated community access control, and facial attendance.So far, in the area of masked face recognition, a small number of contributions have been accomplished.It is definitely imperative to enhance the recognition performance of the traditional face recognition methods on masked faces.Existing masked face recognition approaches are mostly performed based on deep learning models that require plenty of samples.Nevertheless, currently, there are not enough image datasets that contain a masked face.As such, the main objective of this study is to identify individuals who do not use masks or use them incorrectly and to verify their identity by building a masked face dataset.On this basis, a novel real-time masked detection service and face recognition mobile application were developed based on an ensemble of fine-tuned lightweight deep Convolutional Neural Networks (CNN).The proposed model achieves 90.40% validation accuracy using 12 individuals' 1849 face samples.Experiments on the five datasets built in this research demonstrate that the proposed system notably enhances the performance of masked face recognition compared to the other state-of-the-art approaches.
NTIRE 2022 Image Inpainting Challenge: Report

Image Inpainting has recently become an important research problem due to the rise of generative image synthesis models. While many solutions have been proposed for this problem, it is challenging to establish a testbed due to the different possible types of inpainting masks e.g., completion mask, expand mask, thick brushes mask, etc. Most inpainting solutions shine on object removal or texture synthesis, while semantic generation is still difficult to achieve. To address these issues, we introduce the first general Image Inpainting Challenge. The target is to develop solutions that can achieve a robust performance across different and challenging masks while generating compelling semantic images. The proposed challenge consists of two tracks: unsupervised image inpainting and semantically-guided image inpainting. For Track 1, the participants were provided with four datasets: FFHQ, Places, ImageNet, and WikiArt, and trained their models to perform a mask-agnostic image inpainting solution. For Track 2, FFHQ and Places only. This report gathers the description and discussion of all solutions that participated in the final stage of the challenge.
A Lightweight Hybrid Dilated Ghost Model-Based Approach for the Prognosis of Breast Cancer

Most approaches use interactive priors to find tumours and then segment them based on tumour-centric candidates. A fully convolutional network is demonstrated for end-to-end breast tumour segmentation. When confronted with such a variety of options, to enhance tumour detection in digital mammograms, one uses multiscale picture information. Enhanced segmentation precision. The sampling of convolution layers are carefully chosen without adding parameters to prevent overfitting. The loss function is tuned to the tumor pixel fraction during training. Several studies have shown that the recommended method is effective. Tumour segmentation is automated for a variety of tumour sizes and forms postprocessing. Due to an increase in malignant cases, fundamental IoT malignant detection and family categorisation methodologies have been put to the test. In this paper, a novel malignant detection and family categorisation model based on the improved stochastic channel attention of convolutional neural networks (CNNs) is presented. The lightweight deep learning model complies with tougher execution, training, and energy limits in practice. The improved stochastic channel attention and DenseNet models are employed to identify malignant cells, followed by family classification. On our datasets, the proposed model detects malignant cells with 99.3 percent accuracy and family categorisation with 98.5 percent accuracy. The model can detect and classify malignancy.
On the Integration of Blockchain and SDN: Overview, Applications, and Future Perspectives

Abstract Blockchain (BC) and software-defined networking (SDN) are leading technologies which have recently found applications in several network-related scenarios and have consequently experienced a growing interest in the research community. Indeed, current networks connect a massive number of objects over the Internet and in this complex scenario, to ensure security, privacy, confidentiality, and programmability, the utilization of BC and SDN have been successfully proposed. In this work, we provide a comprehensive survey regarding these two recent research trends and review the related state-of-the-art literature. We first describe the main features of each technology and discuss their most common and used variants. Furthermore, we envision the integration of such technologies to jointly take advantage of these latter efficiently. Indeed, we consider their group-wise utilization—named BC–SDN—based on the need for stronger security and privacy. Additionally, we cover the application fields of these technologies both individually and combined. Finally, we discuss the open issues of reviewed research and describe potential directions for future avenues regarding the integration of BC and SDN. To summarize, the contribution of the present survey spans from an overview of the literature background on BC and SDN to the discussion of the benefits and limitations of BC–SDN integration in different fields, which also raises open challenges and possible future avenues examined herein. To the best of our knowledge, compared to existing surveys, this is the first work that analyzes the aforementioned aspects in light of a broad BC–SDN integration, with a specific focus on security and privacy issues in actual utilization scenarios.
The adoption of artificial intelligence applications in education

Artificial intelligence is user-friendly and possesses useful characteristics to share across the various services that are provided. By enhancing innovative contact, artificial intelligence applications (AIA) enable a more involved environment in governmental institutions. The goal of this study is to discover how users in the UAE feel about using AIA for educational reasons. Data collected from a survey of 387 university students were used to validate the model and hypotheses. The adoption features, such as perceived compatibility, trialability, relative advantage, ease of doing business, and technology export, are included in the conceptual model. The current study's practical implications are crucial in that they push the relevant educational authorities to comprehend the significance of each component and enable them to make plans and efforts in accordance with the order of the factors' relative importance. The managerial implications give educational sectors insight on how to apply AIA in their system to improve the growth of the provided service and to make the process easier for all users. The conceptual model of the paper, which links both traits of the individual and those of the technology, is what makes it new. The findings indicate that the diffusion theory variables outperform the other two variables of ease of doing business and technology export.
Learning-Aided Evolution for Optimization

Abstract-Learning and optimization are the two essential abilities of human beings for problem solving.Similarly, computer scientists have made great efforts to design artificial neural network (ANN) and evolutionary computation (EC) to simulate the learning ability and the optimization ability for solving real-world problems, respectively.These have been two essential branches in artificial intelligence (AI) and computer science.However, in humans, learning and optimization are usually integrated together for problem solving.Therefore, how to efficiently integrate these two abilities together to develop powerful AI remains a significant but challenging issue.Motivated by this, this paper proposes a novel learning-aided evolutionary optimization (LEO) framework that plus learning and evolution for solving optimization problems.The LEO is integrated with the evolution knowledge learned by ANN from the evolution process of EC to promote optimization efficiency.The LEO framework is applied to both classical EC algorithms and some state-of-the-art EC algorithms including a champion algorithm, with benchmarking against the IEEE Congress on Evolutionary Computation competition data.The experimental results show that the LEO can significantly enhance the existing EC algorithms to better solve both single-objective and multi-/many-objective global optimization problems, suggesting that learning plus evolution is more intelligent for problem solving.Moreover, the experimental results have also validated the time efficiency of the LEO, where the additional time cost for using LEO is greatly deserved.Therefore, the promising LEO can lead to a new and more efficient paradigm for EC algorithms to solve global optimization problems by plus learning and evolution.
Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather

Most object detection methods for autonomous driving usually assume a consistent feature distribution between training and testing data, which is not always the case when weathers differ significantly. The object detection model trained under clear weather might be not effective enough on the foggy weather because of the domain gap. This paper proposes a novel domain adaptive object detection framework for autonomous driving under foggy weather. Our method leverages both image-level and object-level adaptation to diminish the domain discrepancy in image style and object appearance. To further enhance the model’s capabilities under challenging samples, we also come up with a new adversarial gradient reversal layer to perform adversarial mining for the hard examples together with domain adaptation. Moreover, we propose to generate an auxiliary domain by data augmentation to enforce a new domain-level metric regularization. Experimental results on public benchmarks show the effectiveness and accuracy of the proposed method. The code is available at https://github.com/jinlong17/DA-Detect.
NTIRE 2023 Image Shadow Removal Challenge Report

This work reviews the results of the NTIRE 2023 Challenge on Image Shadow Removal. The described set of solutions were proposed for a novel dataset, which captures a wide range of object-light interactions. It consists of 1200 roughly pixel aligned pairs of real shadow free and shadow affected images, captured in a controlled environment. The data was captured in a white-box setup, using professional equipment for lights and data acquisition sensors. The challenge had a number of 144 participants registered, out of which 19 teams were compared in the final ranking. The proposed solutions extend the work on shadow removal, improving over the performance level describing state-of-the-art methods.