Reviewerly/exHarmony huggingface dataset:
The Potential of Blockchain Technology in Dental Healthcare: A Literature Review

Blockchain technology in the healthcare industry has potential to enable enhanced privacy, increased security, and an interoperable data record. Blockchain technology is being implemented in dental care systems to store and share medical information, improve insurance claims, and provide innovative dental data ledgers. Because the healthcare sector is a large and ever-growing industry, the use of blockchain technology would have many benefits. To improve dental care delivery, researchers advocate using blockchain technology and smart contracts due to their numerous advantages. In this research, we concentrate on blockchain-based dental care systems. In particular, we examine the current research literature, pinpoint issues with existing dental care systems, and consider how blockchain technology may be used to address these issues. Finally, the limitations of the proposed blockchain-based dental care systems are discussed which may be regarded as open issues.
Real-Time Obstacle Detection Method in the Driving Process of Driverless Rail Locomotives Based on DeblurGANv2 and Improved YOLOv4

In order to improve the detection accuracy of an algorithm in the complex environment of a coal mine, including low-illumination, motion-blur, occlusions, small-targets, and background-interference conditions; reduce the number of model parameters; improve the detection speed of the algorithm; and make it meet the real-time detection requirements of edge equipment, a real-time obstacle detection method in the driving of driverless rail locomotives based on DeblurGANv2 and improved YOLOv4 is proposed in this study. A blurred image was deblurred using DeblurGANv2. The improved design was based on YOLOv4, and the lightweight feature extraction network MobileNetv2 was used to replace the original CSPDarknet53 network to improve the detection speed of the algorithm. There was a high amount of background interference in the target detection of the coal mine scene. In order to strengthen the attention paid to the target, the SANet attention module was embedded in the Neck network to improve the detection accuracy of the algorithm under low-illumination, target-occlusion, small-target, and other conditions. To further improve the detection accuracy of the algorithm, the K-means++ algorithm was adopted to cluster prior frames, and the focal loss function was introduced to increase the weight loss of small-target samples. The experimental results show that the deblurring of the motion-blurred image can effectively improve the detection accuracy of obstacles and reduce missed detections. Compared with the original YOLOv4 algorithm, the improved YOLOv4 algorithm increases the detection speed by 65.85% to 68 FPS and the detection accuracy by 0.68% to 98.02%.
Multiscale feature cross‐layer fusion remote sensing target detection method

Abstract Target detection based on remotely sensed images, which has recently attracted much attention, is a fundamental but challenging task. In remote sensing images, the problem of difficult recognition of small targets or targets with a large aspect ratio arises because the targets have the characteristics of small proportion, dense distribution, and multidirectionality. To address the above problems, this article proposes an improved multiscale feature cross‐layer fusion remote sensing target detector based on YOLOv5. First, this method introduces the circular smooth label technique, using YOLOv5 as a rotation detector to solve the angular boundary condition and angle prediction problem for large aspect ratio targets. Second, the explicit visual centre module is introduced to solve the problem of missed detection in target‐dense distribution tasks. Finally, a multiscale feature cross‐layer fusion structure (S‐160) is proposed based on YOLOv5, which improves the detection accuracy of each scale target by fusing shallow and deep feature information and introduces new large‐scale features for small target detection to solve the problem that ultrasmall targets in remote sensing images cannot be recognised. Our experiments were conducted on three public remote sensing datasets, DOTA, DIOR‐R, and HRSC2016, and the average accuracy (mAP) on the datasets was 76.50%, 70.34%, and 97.68%, respectively, demonstrating the substantial detection performance of the proposed method.
Resilience and Resilient Systems of Artificial Intelligence: Taxonomy, Models and Methods

Artificial intelligence systems are increasingly being used in industrial applications, security and military contexts, disaster response complexes, policing and justice practices, finance, and healthcare systems. However, disruptions to these systems can have negative impacts on health, mortality, human rights, and asset values. The protection of such systems from various types of destructive influences is thus a relevant area of research. The vast majority of previously published works are aimed at reducing vulnerability to certain types of disturbances or implementing certain resilience properties. At the same time, the authors either do not consider the concept of resilience as such, or their understanding varies greatly. The aim of this study is to present a systematic approach to analyzing the resilience of artificial intelligence systems, along with an analysis of relevant scientific publications. Our methodology involves the formation of a set of resilience factors, organizing and defining taxonomic and ontological relationships for resilience factors of artificial intelligence systems, and analyzing relevant resilience solutions and challenges. This study analyzes the sources of threats and methods to ensure each resilience properties for artificial intelligence systems. As a result, the potential to create a resilient artificial intelligence system by configuring the architecture and learning scenarios is confirmed. The results can serve as a roadmap for establishing technical requirements for forthcoming artificial intelligence systems, as well as a framework for assessing the resilience of already developed artificial intelligence systems.
Blockchain Data Scalability and Retrieval Scheme Based on On-Chain Storage Medium for Internet of Things Data

The combination of blockchain and internet of things (IoT) technology realizes reliable storage of IoT data. However, the data stored on the blockchain (on-chain) face the problem of poor scalability and inefficient retrieval. In this paper, the on-chain data scalability schemes based on transactions and smart contracts are first proposed. Subsequently, on the basis of the above on-chain data scalability scheme based on transactions, an on-chain data index based on skip lists is proposed to improve the retrieval efficiency. The experimental results show that both the on-chain data scalability schemes achieve on-chain data scalability while reducing storage overhead. Meanwhile, the on-chain data index based on skip lists has significantly improved dynamic range retrieval efficiency and reduced the time complexity of single data retrieval to O(log(n)).
Improvement of PBFT Algorithm Based on CART

In response to the problems of the practical Byzantine fault-tolerant algorithm (PBFT), such as random selection of master nodes and poor scalability, a CART-based PBFT optimization algorithm is proposed, namely the C-PBFT algorithm. First, the introduction of weighted impurity variables improves the CART algorithm, overcomes the mutual influence of attributes between nodes, and improves the classification accuracy. Secondly, through the point grouping mechanism, the nodes are divided into three types: consensus nodes, candidate nodes, and alternate nodes, which are dynamically adjusted based on node behavior to ensure the reliability of consensus nodes. Finally, the voting weight is introduced into the consensus node, and the consensus is reached by using the voting weight exceeding the threshold, which reduces the message transmission in the blockchain network and improves the efficiency of the algorithm operation. The experimental results show that the improved consensus algorithm has better performance in terms of delay, throughput, and fault tolerance and reduces the frequent switching of views caused by the failure of the master node.
Unsupervised structure subdomain adaptation based the Contrastive Cluster Center for bearing fault diagnosis

Recently, Unsupervised Domain Adaptation (UDA) as one of the transfer learning can handle the different data distributions and has been utilized in mechanical fault diagnosis under various working conditions successfully. However, most of them have only regarded two distributions as a global domain adaptation and ignored the subdomain adaptation issue, i.e., there is a subdomain distribution discrepancy between the two same categories. Additionally, most marking pseudo label approaches do not consider the influences of noise in pseudo labels. To circumvent the aforementioned challenges, firstly, a dropout trick has been developed and explored to filter the noisy pseudo label for obtaining the higher confident pseudo labels. Furthermore, a novel subdomain alignment method named Contrastive Cluster Center (CCC) has been proposed for pushing away the different domain cluster centers and bringing closer the same domain cluster centers for bridging the subdomain gap. Finally, the findings of the comparative experiments have demonstrated that the proposed method could boost the average accuracy of 2.2% and 3% on PU and LZUT bearing datasets than the state-of-art methods, respectively. Moreover, convergence analysis also suggests that the proposed method has superior robustness.
Model transfer from 2D to 3D study for boxing pose estimation

Boxing as a sport is growing on Chinese campuses, resulting in a coaching shortage. The human pose estimation technology can be employed to estimate boxing poses and teach interns to relieve the shortage. Currently, 3D cameras can provide more depth information than 2D cameras. It can potentially improve the estimation. However, the input channels are inconsistent between 2D and 3D images, and there is a lack of detailed analysis about the key point location, which indicates the network design for improving the human pose estimation technology.Therefore, a model transfer with channel patching was implemented to solve the problems of channel inconsistency. The differences between the key points were analyzed. Three popular and highly structured 2D models of OpenPose (OP), stacked Hourglass (HG), and High Resolution (HR) networks were employed. Ways of reusing RGB channels were investigated to fill up the depth channel. Then, their performances were investigated to find out the limitations of each network structure.The results show that model transfer learning by the mean way of RGB channels patching the lacking channel can improve the average accuracies of pose key points from 1 to 20% than without transfer. 3D accuracies are 0.3 to 0.5% higher than 2D baselines. The stacked structure of the network shows better on hip and knee points than the parallel structure, although the parallel design shows much better on the residue points. As a result, the model transfer can practically fulfill boxing pose estimation from 2D to 3D.
A Visually Meaningful Image Encryption Scheme Based on Lossless Compression SPIHT Coding

With the popularity of social networks and the increase of cloud platform applications, service computing has also developed. Therefore, the protection of information even privacy uploaded to the cloud server has become critical. Recently, some researchers have proposed encryption schemes of visual meaningful image by using compressive sensing. However, these schemes generally cannot hide the large-size of plain image into the small-size of cover image and cannot recover the original plain image lossless. To solve above problems, this paper proposed a visually meaningful image encryption scheme based on lossless compression set partitioning in hierarchical trees (SPIHT) coding. The sender encrypts the plain image into the cipher image through the proposed encryption scheme, and then uploads the cipher image to the cloud server which is assumed as semi-honest trusted. Authorized receiver can completely decrypt the plain image after downloading the cipher image. In addition, even if the cipher image is attacked by attacker in the cloud server, the final decrypted image is still readable. Experimental results show that the proposed scheme is not only completely reversible and can hide the large-size of plain image into the small-size of cover image, but also superior to other schemes in visual quality and anti-attack performance.
Multiview Clustering via Hypergraph Induced Semi-Supervised Symmetric Nonnegative Matrix Factorization

Nonnegative matrix factorization (NMF) based multiview technique has been commonly used in multiview data clustering tasks. However, previous NMF based multiview clustering approaches fail to take advantage of a small amount of supervisory information to effectively improve the clustering performance, and are easily affected by the additional post-processing method in clustering tasks. To cope with these issues, a novel framework named multiview clustering via hypergraph induced semi-supervised symmetric NMF (MVCHSS) is proposed in this paper for multiview data clustering applications. Specifically, the proposed method has the following features: 1) a new multiview based hypergraph pairwise constraints propagation (MHPCP) algorithm is developed in MVCHSS to construct a set of informative similarity matrices, revealing the high-order relationships effectively and fully utilizing the limited pairwise constraint supervisory information among samples of each view data; 2) the obtained similarity matrices with much supervisory information are not only enforced into the symmetric NMF (SNMF) model, but also incorporated into the graph regularization for each view data; 3) the optimization problem of MVCHSS is formulated for multiview data clustering tasks to acquire a more discriminative clustering indicator matrix (or called consensus assignment matrix) without additional post-processing method. Moreover, the proof of convergence and the computational complexity for MVCHSS are presented. Extensive experiments on five multiview datasets demonstrate that the proposed MVCHSS framework outperforms several state-of-the-art multiview clustering methods.
GRU-CNN-Based Prediction of LOCA Accident Condition in Nuclear Power Plants

In view of the development of Deep Learning and the emergence of the demand for intelligent reactor control, the GRU (Gated Recurrent Unit), a kind of cyclist neural network, is used in this paper to predict the working condition parameters of LOCA (Loss of Coolant Accident). The experiments show, the excellent ability of GRU to capture the information of long time series is suitable for the prediction of accident condition in nuclear power plants. Besides, for accident predictions with few data set, two methods of accident trend prediction are proposed in this paper to better show the short-term development trend of accident condition. In addition, considering the feature extraction ability of CNN (Convolutional Neural Networks), we try to fuse CNN and GRU to predict parameters under different break sizes, the results show that it improves the generalization of the model.
Adap DP-FL: Differentially Private Federated Learning with Adaptive Noise

Federated learning seeks to address the issue of isolated data islands by making clients disclose only their local training models. However, it was demonstrated that private information could still be inferred by analyzing local model parameters, such as deep neural network model weights. Recently, differential privacy has been applied to federated learning to protect data privacy, but the noise added may degrade the learning performance much. Typically, in previous work, training parameters were clipped equally and noises were added uniformly. The heterogeneity and convergence of training parameters were simply not considered. In this paper, we propose a differentially private scheme for federated learning with adaptive noise (Adap DP-FL). Specifically, due to the gradient heterogeneity, we conduct adaptive gradient clipping for different clients and different rounds; due to the gradient convergence, we add decreasing noises accordingly. Extensive experiments on real-world datasets demonstrate that our Adap DP-FL outperforms previous methods significantly.
CodeBERT-nt: Code Naturalness via CodeBERT

Much of recent software-engineering research has investigated the naturalness of code, the fact that code, in small code snippets, is repetitive and can be predicted using statistical language models like n-gram. Although powerful, training such models on large code corpus can be tedious, time consuming and sensitive to code patterns (and practices) encountered during training. Consequently, these models are often trained on a small corpus and thus only estimate the language naturalness relative to a specific style of programming or type of project. To overcome these issues, we investigate the use of pre-trained generative language models to infer code naturalness. Pre-trained models are often built on big data, are easy to use in an out-of-the-box way and include powerful learning associations mechanisms. Our key idea is to quantify code naturalness through its predictability, by using state-of-the-art generative pre-trained language models. Thus, we suggest to infer naturalness by masking (omitting) code tokens, one at a time, of code-sequences, and checking the models’ ability to predict them. We explore three different predictability metrics; a) measuring the number of exact matches of the predictions, b) computing the embedding similarity between the original and predicted code, i.e., similarity at the vector space, and c) computing the confidence of the model when doing the token completion task regardless of the outcome. We implement this workflow, named CODEBERT-NT, and evaluate its capability to prioritize buggy lines over non-buggy ones when ranking code based on its naturalness. Our results, on 2,510 buggy versions of 40 projects from the SmartShark dataset, show that CODEBERTNT outperforms both, random-uniform and complexity-based ranking techniques, and yields comparable results to the n-gram models.
DIV-SC: A Data Integrity Verification Scheme for Centralized Database Using Smart Contract

When faced with massive data volumes, many current companies and institutions usually choose centralized databases or distributed databases to meet their data storage needs. But untrusted centralized third-party auditor can pose serious security problems. Malicious database service providers may tamper with or delete users data to achieve certain benefits, while returning false data integrity verification results to users. The traditional solution is to introduce a third-party auditor to ensure the reliability of the data verification results, but this third-party auditor may also be untrustworthy and partner with the database service provider to forge false data verification results. The centralization of the database system makes the verification of data integrity a difficult but necessary task. Therefore, we propose a data integrity verification scheme using smart contract (DIV-SC) to ensure the reliability of data integrity verification results in a centralized database environment. We introduce the blockchain as a decentralized third-party auditor. The immutability of the blockchain can ensure that the information stored on the blockchain will not be maliciously tampered with. Meanwhile, the smart contract deployed on the blockchain can ensure that the procedure of storing verification information and the verification procedure are correct and will not be affected by any malicious parties.
A Survey on Backdoor Attack and Defense in Natural Language Processing

Deep learning is becoming increasingly popular in real-life applications, especially in natural language processing (NLP). Users often choose training outsourcing or adopt third-party data and models due to data and computation resources being limited. In such a situation, training data and models are exposed to the public. As a result, attackers can manipulate the training process to inject some triggers into the model, which is called backdoor attack. Backdoor attack is quite stealthy and difficult to be detected because it has little inferior influence on the model’s performance for the clean samples. To get a precise grasp and understanding of this problem, in this paper, we conduct a comprehensive review of backdoor attacks and defenses in the field of NLP. Besides, we summarize benchmark datasets and point out the open issues to design credible systems to defend against backdoor attacks.
Face Mask Detection Using Convolution Neural Network

Face recognition in the industry now is playing an important role in each sector. Each person has different type of features and face; therefore, each identity is unidentical. In this COVID outbreak, a major crisis has occurred due to which preventions are to be made. One such prevention is use of a face mask which is very much important. Nowadays, various firms and organizations are using facial recognition systems for their own general purpose. We all know that it has now been a crucial task to wear a mask every time, when we go somewhere. But as we know it is not possible to keep track of who wears a mask and who does not. We make the use of AI in our daily life. We achieve this with the help of a neural network system, which we train so that it can further describe people's features. Even though the original dataset was limited, the Convolutional Neural Network (CNN) model achieved exceptional accuracy utilizing the deep learning technique. With the use of a face mask detection dataset that contains both with and without face mask photographs, we are able to recognize faces in real-time from a live webcam stream using OpenCV. We will develop a COVID-19 face mask detection system using our dataset, along with Python, OpenCV, Tensor Flow, and Keras.
Scientific Documents Retrieval Based on Graph Convolutional Network and Hesitant Fuzzy Set

Previous scientific literature retrieval methods, which are based on mathematical expression, ignore the literature attributes and the association between the literature, and the retrieval accuracy was affected. In this study, literature retrieval model based on Graph Convolutional Network (GCN) is proposed. By extracting document attributes from a structured document dataset, an Attribute Relation Graph (ARG) is constructed. Using GCN to capture the dependencies among literature nodes and generate literature representations by information aggregation to realize graph-based literature modeling; Introducing the advantages of Hesitant Fuzzy Set (HFS) theory in multi-attribute decision-making to realize the similarity evaluation between mathematical query expressions and mathematical retrieval result expressions. Finally, the similarity between literature features and mathematical expressions is integrated to obtain the ordered output of scientific literature retrieval results. Experiments were conducted on the arXiv public dataset, and the average precision of the top 10 retrieval results was 0.892, and the average NDCG value of the top 10 rankings was 0.875.