Reviewerly/exHarmony huggingface dataset:
Uncertainty teacher with dense focal loss for semi-supervised medical image segmentation

In medical scenarios, obtaining pixel-level annotations for medical images is expensive and time-consuming, even if considering its importance for automating segmentation tasks. Due to the scarcity of labels in the training phase, semi-supervised methods are widely applied for various medical tasks. To better utilize the unlabeled data, several works have explored the method of uncertainty estimation and exhibited huge success. Despite their impressive performance, we believe that the underlying information of the unlabeled data has been largely unexplored. Meanwhile, there is an extreme foreground-background class imbalance during the training phase of semantic segmentation, which may cause a vast number of easily classified samples to overwhelm the loss during training and lead to a model collapse. In this paper, we proposed uncertainty teacher with dense focal loss, a method that can take good advantage of unlabeled data simultaneously and address the class imbalance problem, based on Deep Co-Training. On one hand, the uncertainty teacher framework is presented to better utilize the unlabeled data by introducing a novel method to regularize uncertainty in the right direction, and the uncertainty is estimated by Monte Carlo Sampling. On the other hand, the dense focal loss is proposed to help solve the class imbalance problem between different classes of samples in medical image segmentation and effectively convert the multi-variate entropy into a multiple binary entropy. We implemented our method on three challenging public medical datasets and experimental results have shown desirable improvements to state-of-the-art.
Improved YOLOv5-S object detection method for optical remote sensing images based on contextual transformer

To address the problems of error and omission detection in remote sensing image detection caused by the diverse scale changes of remote sensing object scales and the abundant proportion of small-scale objects, as well as the global and dense distribution of remote sensing objects, a remote sensing image detection improvement method based on YOLOv5-S is proposed. First, according to the characteristics of remote sensing objects, the data enhancement strategy is adopted to expand the dataset samples for the characteristics of remote sensing objects to improve the generalization ability of the model. Second, the contextual transformer module is introduced to the backbone feature extraction network and the feature fusion network to ensure the local feature extraction capability while improving the global information acquisition capability of the model, making full use of the input contextual information and guiding the dynamic attention matrix learning to improve the visual representation ability. Third, based on the original model, a shallow detection scale is added, and then a multiscale complex fusion structure is adopted. Meanwhile, the K-means++ algorithm replaces the original K-means algorithm and then clusters 12 anchor box sizes. Fourth, the efficient intersection over union loss is used to improve the accuracy of the remote sensing object recognition prediction. In the experiment on the on two optical remote sensing image datasets, a comparison with several object detection algorithms based on convolutional neural network is made, the results show that the mAP@0.5 tested on the remote sensing datasets is higher than the original YOLOv5-S. Compared with other models, the detection efficiency is higher, and the problems of small-scale object detection in remote sensing image have been significantly improved.
Uncovering dimensions of the impact of blockchain technology in supply chain management

Abstract Supply chains around the globe are faced with difficulties and disruptions due to the worldwide pandemic situation and digital solutions are needed. There is significant research interest in the implementation of blockchain technology (BCT) for supply chain management (SCM). A challenge that remains is analyzing the interactions of BCT in different areas of SCM. This study aims to identify the influential dimensions of the impact of BCT adoption in SCM and to discuss the synergetic and counter-synergetic effects between these dimensions. Advantages, disadvantages, and constraints of adopting BCT in the SCM context are explored through a systematic literature review, which provides the foundation for identifying the dimensions of impact. The interactions between these dimensions are conceptually discussed. This study introduces three dimensions of the impact of implementing BCT in SCM: ‘operations and processes’, ‘supply chain relationships’, and ‘innovation and data access’. These dimensions are interrelated and have overlapping areas within them, which leads to synergetic and counter-synergetic effects. The overlaps and synergies of the three dimensions of impact are illustrated, and the virtuous and vicious cycles of BCT adoption in SCM cases are highlighted. This study assists scholars and practitioners by clarifying the synergetic relationships within the dimensions of the impact of BCT in SCM and by providing considerations to prevent undesirable effects and expand desired ones.
Securing Facial Bioinformation by Eliminating Adversarial Perturbations

Falsified faces generated by DeepFake are severe threats to our community. Many smart systems in Industry 4.0, such as electronic payments and identity verification, rely on bioinformation authentication. These applications may compromise with forgeries generated by DeepFake. Notwithstanding many promising results on DeepFake forensics have been reported recently, we are now facing new security challenges brought by antiforensics attacks. With adversarial perturbations injected by antiforensics algorithms, falsified faces could masquerade themselves to disrupt forensics detectors as well as industrial applications. Therefore, to secure biometric data, in particular facial information, we propose a countermeasure against the attacks of DeepFake antiforensics. The proposed model features dual channels and multiple supervisors to capture biological attributes from manifold aspects. After training, the proposed method can purify antiforensics images by eliminating adversarial perturbations. With experimental evaluations, we show that purified faces are highly distinguishable from real ones. The proposed method is justified as a reliable defense tool for protecting facial bioinformation against antiforensics amid Industry 4.0.
Learning with desktop virtual reality: changes and interrelationship of self-efficacy, goal orientation, technology acceptance and learning behavior

Abstract With advantages such as ease of use and low cost, desktop virtual reality (VR) technologies are increasingly being used in practical learning. This study aims to clarify the relationship among students' self-efficacy, goal orientation, technology acceptance [e.g., perceived usefulness (PU) and perceived ease of use (PEOU)] and learning behavior, and the changes of these variables as well as gender difference in the early and late stages of course study when desktop VR technology is applied to business simulation learning. A pretest–posttest group design with two repeated measures is employed for this study. During a 10-week period, students' self-efficacy, goal orientation, technology acceptance and learning behavior are measured among junior and senior students majoring in Business Administration from a four-year undergraduate university who used desktop VR technology for practical learning. Course scores for these students are also collected and used to measure whether desktop VR is helping to improve their learning outcomes. Findings indicate that there is a significant correlation between self-efficacy, PEOU, PU and goal orientation, which further affects learners' learning behavior and learning outcomes when desktop VR is used for practical learning. After learning with desktop VR, self-efficacy, perceived ease of use and usefulness, and surface learning behaviors increased, while mastery goal orientation decreased. Furthermore, self-efficacy, PEOU and PU are found to be significantly higher in males than in females.
A PLS-based pruning algorithm for simplified long–short term memory neural network in time series prediction

As an extensively used model for time series prediction, the Long–Short Term Memory (LSTM) neural network suffers from shortcomings such as high computational cost and large memory requirement, due to its complex structure. To address these problems, a PLS-based pruning algorithm is hereby proposed for a simplified LSTM (PSLSTM). First, a hybrid strategy is designed to simplify the internal structure of LSTM, which combines the structure simplification and parameter reduction for gates. Second, partial least squares (PLS) regression coefficients are used as the metric to evaluate the importance of the memory blocks, and the redundant hidden layer size is pruned by merging unimportant blocks with their most correlated ones. The Backpropagation Through Time (BPTT) algorithm is utilized as the learning algorithm to update the network parameters. Finally, several benchmark and practical datasets for time series prediction are used to evaluate the performance of the proposed PSLSTM. The experimental results demonstrate that the PLS-based pruning algorithm can achieve the trade-off between a good generalization ability and a compact network structure. The computational complexity is improved by the simple internal structure as well as the compact hidden layer size, without sacrificing prediction accuracy.
Grouping-based Oversampling in Kernel Space for Imbalanced Data Classification

The class-imbalanced classification is a difficult problem because not only traditional classifiers are more biased towards the majority classes and inclined to generate incorrect predictions, but also the existing algorithms often have difficulty tackling this kind of problem with the class overlapping. Oversampling is a widely used and effective method to obtain balanced samples for imbalanced data, but the existing oversampling methods usually result in more serious class overlapping due to improper choice of the reference samples. To circumvent this shortcoming, according to the different possibilities of minority class samples appearing in the overlapping regions in the feature space, a grouping scheme for the minority class samples is first designed to identify the overlapping region samples. Then, a new oversampling method based on this grouping scheme is proposed to make the new samples far away from the overlapping region and rectify the decision boundary properly. Subsequently, a new effective classification algorithm is developed for imbalanced data. Extensive experiments show that the proposed algorithm is superior to the seventeen benchmark algorithms in terms of three performance metrics, especially on high imbalance ratio data sets.
An Image Security Model Based on Chaos and DNA Cryptography for IIoT Images

The images generated by smart cameras and sensors in an Industrial Internet of Things (IIoT) ecosystem are at significant risk when transmitted over a public network due to the dynamicity and open nature of the IIoT environment. Encryption is a viable method for safeguarding IIoT digital images. This article addresses an image security framework using chaotic maps and DNA cryptography. The proposed algorithm uses a tent, circle, Chebyshev, and 3-D logistic map in a multilevel fashion to generate three keys. These keys are used for row-column rotation of subblocks, determine the rule to perform DNA encoding–decoding on the subblocks, and generate a key image on which DNA XOR operation is performed to get the encrypted image. The proposed scheme’s result analysis illustrates that the average NPCR (99.6566%), UACI (33.4588%), Entropy (7.9971), and larger key-space of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ 10^{195}$</tex-math></inline-formula> are better than the existing schemes and are resistant to different attacks.
The Origin and Discrimination of High-Ti Magnetite in Magmatic-Hydrothermal Systems: Insight from Machine Learning Analysis

Abstract High-titanium (high-Ti, more than 1 wt % Ti) magnetite, commonly containing ilmenite exsolution, has long been attributed to an igneous origin and has been used as the most critical factor in previously developed discriminant diagrams. However, recent studies have shown that high-Ti magnetite can be present in high-temperature hydrothermal deposits, suggesting a probable hydrothermal origin. This also calls for reconsideration and necessary modification of the currently available discriminant diagrams. This high-Ti magnetite issue is particularly acute in iron oxide-apatite (IOA) deposits and raises controversy in the discussion of the origin of the high-Ti magnetite. With statistical analysis and machine learning techniques, this study applies two unsupervised dimensionality reduction methods—principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE)—on a compiled data set consisting of 876 laser ablation-inductively coupled plasma-mass spectrometry analyses of primary high-Ti magnetite from high-temperature ore-forming systems worldwide. Three models are built with different element combinations to identify magnetite of different origins. The models were further evaluated by the support vectors machine (SVM) and receiver operating characteristic (ROC) curves and proved to be able to describe the characteristics of trace element compositions of high-Ti magnetite of different origins. Our models suggest that Mg, Mn, Al, Ti, V, and Co from 59 analyzed trace elements show promising properties as effective discriminators, and on this basis, a new discrimination diagram of lg(Al) + lg(Ti) + lg(V) versus lg(Mn)/[lg(Co) + lg(Mg)] is developed for distinguishing high-Ti magnetite of igneous and hydrothermal origin. Our results also show that the high-Ti magnetite in the IOA deposits has chemical compositions similar to those of high-temperature hydrothermal deposits, including the iron oxide copper-gold and porphyry deposits, but significantly distinct from the igneous magnetite. Our study, hence, implies a magmatic-hydrothermal origin for the magnetite in IOA deposits.
On-Phone CNN Model-Based Implicit Authentication to Secure IoT Wearables

The connectivity of smart technologies, such as smartphones and smart wearables, is ever-increasing with the emergence of the internet of things (IoT). This technological advancement makes it possible to serve emerging applications, such as financial transactions, healthcare check-ups, and property access, easily through smart wearables, such as Apple Watch. This also presents a new vulnerability as hackers have more opportunities to attack users via the wearables. As the current knowledge-based wearable authentication schemes, such as passwords, PINs, or pattern locks, are overwhelming for users, we need an authentication system that can validate a user implicitly, i.e., without the need for active user interaction. In this work, we present an authentication system for the wearables leveraging the sensing and computation power of smartphones and IoT connectivity. We develop a smartphone application (TFL Auth app) using the TensorFlow Lite framework and an on-phone convolutional neural network (CNN) model that listens to a user’s breathing patterns through the microphone and verifies the user’s identity in real-time before sending the acceptance/rejection notification to a paired wearable that we want to secure. From a detailed analysis, we are able to achieve an average accuracy of 0.92 ± 0.01 using the Mel-frequency cepstral coefficients.
A Novel GAN-Based Anomaly Detection and Localization Method for Aerial Video Surveillance at Low Altitude

The last two decades have seen an incessant growth in the use of Unmanned Aerial Vehicles (UAVs) equipped with HD cameras for developing aerial vision-based systems to support civilian and military tasks, including land monitoring, change detection, and object classification. To perform most of these tasks, the artificial intelligence algorithms usually need to know, a priori, what to look for, identify. or recognize. Actually, in most operational scenarios, such as war zones or post-disaster situations, areas and objects of interest are not decidable a priori since their shape and visual features may have been altered by events or even intentionally disguised (e.g., improvised explosive devices (IEDs)). For these reasons, in recent years, more and more research groups are investigating the design of original anomaly detection methods, which, in short, are focused on detecting samples that differ from the others in terms of visual appearance and occurrences with respect to a given environment. In this paper, we present a novel two-branch Generative Adversarial Network (GAN)-based method for low-altitude RGB aerial video surveillance to detect and localize anomalies. We have chosen to focus on the low-altitude sequences as we are interested in complex operational scenarios where even a small object or device can represent a reason for danger or attention. The proposed model was tested on the UAV Mosaicking and Change Detection (UMCD) dataset, a one-of-a-kind collection of challenging videos whose sequences were acquired between 6 and 15 m above sea level on three types of ground (i.e., urban, dirt, and countryside). Results demonstrated the effectiveness of the model in terms of Area Under the Receiving Operating Curve (AUROC) and Structural Similarity Index (SSIM), achieving an average of 97.2% and 95.7%, respectively, thus suggesting that the system can be deployed in real-world applications.
Colour adaptive generative networks for stain normalisation of histopathology images

Deep learning has shown its effectiveness in histopathology image analysis, such as pathology detection and classification. However, stain colour variation in Hematoxylin and Eosin (H&E) stained histopathology images poses challenges in effectively training deep learning-based algorithms. To alleviate this problem, stain normalisation methods have been proposed, with most of the recent methods utilising generative adversarial networks (GAN). However, these methods are either trained fully with paired images from the target domain (supervised) or with unpaired images (unsupervised), suffering from either large discrepancy between domains or risks of undertrained/overfitted models when only the target domain images are used for training. In this paper, we introduce a colour adaptive generative network (CAGAN) for stain normalisation which combines both supervised learning from target domain and unsupervised learning from source domain. Specifically, we propose a dual-decoder generator and force consistency between their outputs thus introducing extra supervision which benefits from extra training with source domain images. Moreover, our model is immutable to stain colour variations due to the use of stain colour augmentation. We further implement histogram loss to ensure the processed images are coloured with the target domain colours regardless of their content differences. Extensive experiments on four public histopathology image datasets including TCGA-IDH, CAMELYON16, CAMELYON17 and BreakHis demonstrate that our proposed method produces high quality stain normalised images which improve the performance of benchmark algorithms by 5% to 10% compared to baselines not using normalisation.
Evaluating the opportunities, challenges and risks of applying the blockchain technology in tourism: a Delphi study approach

Purpose The purpose of this study is to empirically evaluate the potential of the blockchain technology in tourism. The blockchain technology (BCT) holds potential to contribute significantly to tourism policy and practice. Academic interest in the BCT is rapidly growing with studies looking at the opportunities and challenges of its application. The shortcoming of research on the BCT in tourism has however been in its conceptual nature. The lack of empirical investigations hinders an understanding of how the BCT can be more broadly adopted in tourism, especially from the viewpoint of minimizing its risks. Design/methodology/approach The initial screening of the opportunities, challenges and risks is undertaken via a systematic literature review. The Delphi study is subsequently applied to empirically confirm what opportunities, challenges and risks can be attributed to the BCT use in tourism. Twelve industry and academic experts have contributed to the Delphi study. Findings The risks identified have been categorized as societal, technical, financial and legal. Propositions have been made on how these risks can, at least partially, be overcome. Originality/value To the best of the authors’ knowledge, it is the first known attempt to study the BCT from the perspective of academic and industry experts. This research is also one of the first to evaluate the risks of the BCT use in tourism. Most risks are identified as not critical and can be addressed as the BCT develops.
Modeling and simulation of a novel secure quantum key distribution (SQKD) for ensuring data security in cloud environment

In recent days, providing data security over cloud is a complicated process. There are many research works that are developed for authentication based data security over cloud, using cryptographic methods. In contrast, physical rules are used for encrypting data. When the quantum models are appeared, it is called quantum cryptography. And, the key distribution in such models is called, Quantum Key Distribution (QKD). Using QKD in securing data is more effective against several attacks. This paper develops a novel simulation model called Secure Quantum Key Distribution for Cloud Data Security (SQKD-CDS) Model. For encrypting the user data, the simulation model uses Non-Abelian Encryption (NAE) for providing secure data security and, further, the quantum key is used for accessing the stored data from cloud. Moreover, the keys are shared between nodes in secure manner using the quantum channel. This proposed simulation model is evaluated using cloud simulator. The results show that the proposed simulation model outperforms other classical security simulation model in terms of efficiency, time complexity and computational complexity.
SeTransformer: A Transformer-Based Code Semantic Parser for Code Comment Generation

Automated code comment generation technologies can help developers understand code intent, which can significantly reduce the cost of software maintenance and revision. The latest studies in this field mainly depend on deep neural networks, such as convolutional neural networks and recurrent neural network. However, these methods may not generate high-quality and readable code comments due to the long-term dependence problem, which means that the code blocks used to summarize information are far from each other. Owing to the long-term dependence problem, these methods forget the previous input data’s feature information during the training process. In this article, to solve the long-term dependence problem and extract both the text and structure information from the program code, we propose a novel improved-Transformer-based comment generation method, named SeTransformer. Specifically, the SeTransformer utilizes the code tokens and an abstract syntax tree (AST) of programs to extract information as the inputs, and then, it leverages the self-attention mechanism to analyze the text and structural features of code simultaneously. Experimental results based on public corpus gathered from large-scale open-source projects show that our method can significantly outperform five state-of-the-art baselines (such as Hybrid-DeepCom and AST-attendgru). Furthermore, we also conduct a questionnaire survey for developers, and the results show that the SeTransformer can generate higher quality comments than those of other baselines.