Reviewerly/exHarmony huggingface dataset:
Fine-Grained Ship Detection in High-Resolution Satellite Images With Shape-Aware Feature Learning

Fine-grained ship detection is an important task in high-resolution satellite remote sensing applications. However, large aspect ratios and severe category imbalance make fine-grained ship detection a challenging problem. Current methods usually extract square-like features that do not work well to detect ships with large aspect ratios, and the misalignments in feature representation will severely degrade the performance of ship localization and classification. To tackle this, we propose a shape-aware feature learning method to mitigate the misalignments during feature extraction. Furthermore, for the issue of category imbalance, we design a shape-aware instance switching to balance the quantity distribution of ships in different categories, which can greatly improve the network's learning ability for rare instances. To verify the effectiveness of the proposed method, we contribute a multi-category ship detection dataset (MCSD) that contains 4000 images carefully labeled with oriented bounding boxes, including 16 types of ship objects and nearly 18,000 instances. We conduct experiments on our MCSD and ShipRSImageNet, and extensive experimental results demonstrate the superiority of the proposed method over several state-of-the-art methods. Dataset and code will be available at https://guobo98.github.io/shape-aware-shipdet.
An enhanced traceable CP-ABE scheme against various types of privilege leakage in cloud storage

Cloud storage can save not only local storage costs but also provide data-sharing services. Ciphertext-policy attribute-based encryption (CP-ABE) can work as the underlying engine for cloud storage since it supports one-to-many encryption and fine-grained access control. To prevent users from disclosing their access permissions, we can apply traceable CP-ABE systems. There are two kinds of traceability: white-box traceability and black-box traceability. White-box traceability is simple and efficient, but it cannot support black-box tracing; black-box traceability supports white-box tracing, but its operations are usually cumbersome. So, these two kinds of traceability are unable (or unsuitable) to solve each other’s problems. However, in cloud storage applications, users may leak their privileges in various ways, which means the underlying CP-ABE system should have multiple traceability. Unfortunately, the current traceable CP-ABE schemes only support single traceability. Therefore, we propose a novel CP-ABE scheme with enhanced traceability. It intertwines white-box and black-box traceability together securely and efficiently, which makes it more targeted and simpler to solve distinct tracing problems than previous schemes. Moreover, the proposed scheme has scalability, flexible policy expressiveness, and certain advantages in computing performance.
BalanceHRNet: An effective network for bottom-up human pose estimation

In the study of human pose estimation, which is widely used in safety and sports scenes, the performance of deep learning methods is greatly reduced in high overlap rate and crowded scenes. Therefore, we propose a bottom-up model, called BalanceHRNet, which is based on balanced high-resolution module and a new branch attention module. BalanceHRNet draws on the multi-branch structure and fusion method of a popular model HigherHRNet. And our model overcomes the shortcoming of HigherHRNet that cannot obtain a large enough receptive field. Specifically, through the connecting structure in balanced high-resolution module, we can connect almost all convolutional layers and obtain a sufficiently large receptive field. At the same time, the multi-resolution representation can be maintained due to the use of balanced high-resolution module, which enable our model to recognize objects with richer scales and obtain more complex semantics information. And for branch fusion method, we design branch attention to obtain the importance of different branches at different stages. Finally, our model improves the accuracy while ensuring a smaller amount of computation than HigherHRNet. The CrowdPose dataset is used as test dataset, and HigherHRNet, AlphaPose, OpenPose and so on are taken as comparison models. The AP measured by BalanceHRNet is 63.0%, increased by 3.1% compared to best model - HigherHRNet. We also demonstrate the effectiveness of our network through the COCO(2017) keypoint detection dataset. Compared with HigherHRNet-w32, the AP of our model is improved by 1.6%.
Defense Against Adversarial Attacks with Efficient Frequency-Adaptive Compression and Reconstruction

The increasing use of deep neural networks exposes themselves to adversarial attacks in the real world drawn from closed-set and open-set, which poses great threats to their application in safety-critical systems. Since adversarial attacks tend to mislead an original model by adding small perturbations into clean images, an intuitive idea of defensing adversarial attacks is eliminating perturbations as much as possible to mitigate attacking effects. However, such elimination-based strategies unfortunately fail to achieve satisfactory robustness. Aiming to investigate the intrinsic reasons for this phenomenon, systematic experiments are carried out in this paper to indicate that even a 20% residual perturbation can still preserve and exhibit attacking effects as strong as a full one. Our study also indicates that there are strong correlations between perturbations and legitimate images. Thus, breaking the correlation across multiple bands is more effective in mitigating attacking effects. Based on these findings, this paper proposes an efficient defense strategy called “Frequency-Adaptive Compression and rEconstruction (FACE)” to improve the robustness of the model to adversarial attacks. Specifically, low-frequency bands containing semantic information are compressed by a down-sampling operation, while the channel width of high-frequency bands is squeezed and further compressed by adding noise before the Tanh activating function. Meanwhile, attachment spaces of perturbations are also squeezed to the extent as much as possible. Finally, a clean output is obtained by upsampling together with expanded reconstruction. Experiments are extensively conducted on widely used datasets to demonstrate the effectiveness of the proposed method. For closed-set attacks, FACE outperforms the STOA elimination-based methods on ImageNet, achieving a 27.9% improvement. For the MNIST open-set attacks, it not only reduces the success rate of targeted attack by a large margin (from 100% to 24.7%), but also mitigates attacking effects with an FPR-95 value of 0.3.
Interval type-2 fuzzy neural networks with asymmetric MFs based on the twice optimization algorithm for nonlinear system identification

This paper proposes a novel algorithm twice optimization for interval type-2 fuzzy neural networks with asymmetric membership functions (TOIT2FNN-AMF), for nonlinear system identification problems. The proposed TOIT2FNN-AMF uses an asymmetric Gaussian interval type-2 membership function to enhance the network’s ability to describe and solve nonlinear and uncertain problems. The twice optimization algorithm consists of structure learning and parameter learning. Firstly, this paper proposes a multi-strategy adaptive differential evolution (MSADE) algorithm as the first optimization algorithm, which is used to determine the structure and the initial values of the parameters of the TOIT2FNN-AMF. It applies the root mean square error (RMSE) of the TOIT2FNN-AMF as the fitness function to determine the structure (number of rules) and initial parameters of the IT2FNN by searching for the RMSE values under different structures. When the fitness value reaches the minimum, that is, the RMSE value of the TOIT2FNN-AMF, the corresponding number will become the optimal one of fuzzy rules of the TOIT2FNN-AMF. Then, the second optimization algorithm of the TOIT2FNN-AMF turns into a hybrid optimization algorithm composed of an adaptive moment estimation (Adam) algorithm and recursive least squares (RLS) algorithm. Adam is used to optimize the antecedent parameters of TOIT2FNN-AMF rules, so as to maintain rapid convergence without generating oscillation during the training process; RLS is used to optimize the consequent parameters of TOIT2FNN-AMF rules, so that the network parameters can be optimized rapidly. In this way, the problems of excessive parameters to be adjusted and excessive slow convergence of the network can be solved. Finally, this paper evaluates the proposed TOIT2FNN-AMF by testing on problems of nonlinear system identification and chaotic time-series prediction. The simulation results are compared with those of similar methods in the existing literatures, which demonstrates that the proposed TOIT2FNN-AMF model yields a lower RMSE value and a simpler network structure than the other type-2 fuzzy neural networks (T2FNNs).
Application of Digital Image Correlation to Evaluate Strain, Stiffness and Ductility of Full-Scale LVL Beams Strengthened by CFRP

Due to limitations of traditional measuring methods, a necessity of verification of applicability of optical measuring systems in different fields of science is required. The paper presents the application of a non-contact, non-destructive ARAMIS optical system in the analysis of static work of unstrengthened and strengthened laminated veneer lumber beams (LVL) with composite materials, subjected to a four-point bending test. The beams were strengthened with Carbon Fiber Reinforced Polymer (CFRP) sheets and laminates. The sheets were bonded to the external surfaces in three configurations differing in the number of layers applied and the degree of coverage of the side surface. The CFRP laminates were glued into predrilled grooves and applied to the underside of the beams. An adhesive based on epoxy resin was used. The scope of the work includes analysis of the strain distribution, stiffness and ductility. The analysis was performed on the basis of measurements made with an optical measurement system. The strain analysis indicated a change of the distribution of the strain in the compressive zone from linear for the unstrengthened to bilinear for the strengthened beams. The stiffness increase was equal from 14% up to 45% for the application of the CFRP laminates in the grooves and CFRP sheets bonded externally, respectively. Similar improvement was obtained for the ductility.
Investigating the effectiveness of introducing virtual reality to elementary school students' moral education

This study was conducted to investigate whether a virtual reality (VR)-based morality education program is effective in cultivating morality among elementary school students. After conducting VR-based activities and classroom discussions, 162 elementary school students in South Korea were surveyed. According to Rest's theory, we measured moral sensitivity and moral judgment for the evaluation of moral development of students. The use of this model resulted in a significant improvement in moral sensitivity but no significant difference in moral judgment. This study revealed that, although moral sensitivity can be improved relatively easily through our educational program with VR, a separate educational effort is required to improve moral judgment. In addition, this study is meaningful in that it sheds light on the potential of VR, which is closely related to the technology used in the metaverse.
The Threat of Adversarial Attack on a COVID-19 CT Image-Based Deep Learning System

The coronavirus disease 2019 (COVID-19) rapidly spread around the world, and resulted in a global pandemic. Applying artificial intelligence to COVID-19 research can produce very exciting results. However, most research has focused on applying AI techniques in the study of COVID-19, but has ignored the security and reliability of AI systems. In this paper, we explore adversarial attacks on a deep learning system based on COVID-19 CT images with the aim of helping to address this problem. Firstly, we built a deep learning system that could identify COVID-19 CT images and non-COVID-19 CT images with an average accuracy of 76.27%. Secondly, we attacked the pretrained model with an adversarial attack algorithm, i.e., FGSM, to cause the COVID-19 deep learning system to misclassify the CT images, and the classification accuracy of non-COVID-19 CT images dropped from 80% to 0%. Finally, in response to this attack, we proposed how a more secure and reliable deep learning model based on COVID-19 medical images could be built. This research is based on a COVID-19 CT image recognition system, which studies the security of a COVID-19 CT image-based deep learning system. We hope to draw more researchers' attention to the security and reliability of medical deep learning systems.
Optimized Implementation of Quantum Binary Field Multiplication with Toffoli Depth One

Shor's algorithm models discrete logarithms on binary elliptic curves and provides polynomial-time solutions. One of major overheads in applying Shor's algorithm is implementing binary elliptic curve arithmetic in quantum circuits. Among operations of elliptic curves over binary fields, the multiplication is essential and cost-critical even in the quantum field. In this paper, we aim to optimize quantum binary field multiplication. Previous works on quantum multiplication focused on minimizing the number of Toffoli gates or qubits. In contrast, our work presents strategies for optimizing Toffoli depth and full depth, which are key factors in the Noisy Intermediate-Scale Quantum (NISQ) era. To achieve our goal, Karatsuba multiplication using divide-and-conquer approach is adopted. In a nutshell, we present an optimized quantum multiplication with Toffoli depth one. Furthermore, under the influence of the optimized Toffoli depth, the full depth is naturally reduced. In order to show the effectiveness of proposed method, the performance is evaluated by various metrics, such as, qubits, quantum gates, depth, and qubits-depth product. To the best of our knowledge, this is the first study on quantum multiplication that optimizes Toffoli depth and full depth.
Crypto currency and green investment impact on global environment: A time series analysis

Climate change has become a central theme in both national and international forums in recent decades. In this regard, the argument has quickly moved and centered on the role of crypto currencies, in addition to the fundamental culprits of ecological destruction, such as fossil fuels, agricultural, and industrial pollution. The aim of the present study is to assess the role of asymmetries in determining the relationship between blockchain and green investment with the environment using the Non-linear Autoregressive Distributive Lag (NARDL) technique. The data from the United States of America (USA) is used over the period from 2011 to 2020. The findings reveal that, contrary to common belief, there is asymmetric relation between crypto currencies and biofuel usage in both the short and long run. Similarly, asymmetry also exists between renewable energy use and consumption of biofuel. Further, there is a strong coherence among the concerned variables is also proved in this study. Therefore, the study implies that assuming symmetric and weak coherence relationships between blockchain technology and green investment in the global environment produce biased and misleading findings which are not a true representation of the real-world scenario. Based on this the study suggests that policymakers and environmentalists may strive to achieve low carbon emissions using environment-friendly technology and less energy use. Lastly, the negative nonlinear impacts of blockchain technology and green investment must be considered in the carbon emissions released in the USA economy.
Efficient Stereo Depth Estimation for Pseudo-LiDAR: A Self-Supervised Approach Based on Multi-Input ResNet Encoder

Perception and localization are essential for autonomous delivery vehicles, mostly estimated from 3D LiDAR sensors due to their precise distance measurement capability. This paper presents a strategy to obtain a real-time pseudo point cloud from image sensors (cameras) instead of laser-based sensors (LiDARs). Previous studies (such as PSMNet-based point cloud generation) built the algorithm based on accuracy but failed to operate in real time as LiDAR. We propose an approach to use different depth estimators to obtain pseudo point clouds similar to LiDAR to achieve better performance. Moreover, the depth estimator has used stereo imagery data to achieve more accurate depth estimation as well as point cloud results. Our approach to generating depth maps outperforms other existing approaches on KITTI depth prediction while yielding point clouds significantly faster than other approaches as well. Additionally, the proposed approach is evaluated on the KITTI stereo benchmark, where it shows effectiveness in runtime.
PCNet: A structure similarity enhancement method for multispectral and multimodal image registration

Multispectral and multimodal images are of important usage in the field of multi-source visual information fusion. Due to the alternation or movement of image devices, the acquired multispectral and multimodal images are usually misaligned, and hence image registration is pre-requisite. Different from the registration of common images, the registration of multispectral or multimodal images is a challenging problem due to the nonlinear variation of intensity and gradient. To cope with this challenge, we propose the phase congruency network (PCNet) to enhance the structure similarity of multispectral or multimodal images. The images can then be aligned using the similarity-enhanced feature maps produced by the network. PCNet is constructed under the inspiration of the well-known phase congruency. The network embeds the phase congruency prior into two simple trainable layers and series of modified learnable Gabor kernels. Thanks to the prior knowledge, once trained, PCNet is applicable on a variety of multispectral and multimodal data such as flash/no-flash and RGB/NIR images without additional further tuning. The prior also makes the network lightweight. The trainable parameters of PCNet is 2400× and 1500×less than the deep-learning registration method deep homography network (DHN) and unsupervised deep homography network (UDHN), while its registration performance surpasses them. Experimental results validate that PCNet outperforms current state-of-the-art conventional multimodal registration algorithms. Besides, PCNet can act as a complementary part of the deep-learning registration methods, which significantly boosts their registration accuracy. On the Columbia imaging and vision laboratory (CAVE) multispectral dataset, the percentage of the number of images under 1 pixel average corner error (ACE) of UDHN is raised from 0.1% to 82.5% after the processing of PCNet.
Efficiency of automatic text generators for online review content generation

The evolution of Artificial Intelligence has led to the appearance of automatic text generators able to closely resemble human writing, endangering the development of e-commerce and the consumer confidence. Thus, it is critical to deeply understand how these text generators work to present the presence of deceptive reviews. This paper analyzes one of the most popular text generators, GPT2 (Generative Pre-trained Transformer 2), and studies its effectivity compared to human-generated reviews using previously published classifiers trained to distinguish between real and deceptive reviews. One parameter of the model is the so-called temperature, which determines how deterministic the model is. The temperature adjusts the probability distribution of the words in the model, so that a higher temperature translates into a higher degree of inventiveness in the generation of the texts. Findings reveal (i) that automatically-generated deceptive reviews worsen the accuracy of existing classifiers, this effect being accentuated by the degree of inventiveness; (ii) that their performance depends on the data used to train the generator; and (iii) that the sentiment polarity has no effect on the performance of detection classifiers.
An Empirical Study on Factors Impacting the Adoption of Digital Technologies in Supply Chain Management and What Blockchain Technology Could Do for the Manufacturing Sector of Bangladesh

This paper employed an integrated model for examining behavioral intention to adopt blockchain technology in the supply chain management of manufacturing industries in Bangladesh. The proposed conceptual model was empirically tested using data collected from 189 supply chain managers working in manufacturing organizations in Bangladesh. The findings suggest that perceived usefulness, trading partners’ pressure, and competitive pressure are the most important determinant of behavioral intention.
Few-shot incremental learning with continual prototype calibration for remote sensing image fine-grained classification

With the rapid acquisition of remote sensing (RS) data, new categories of objects continue to emerge, and some categories can only obtain a few training samples. Thus, few-shot class-incremental learning (FSCIL) has received intense attention in recent years. Nevertheless, the existing FSCIL methods are difficult to adapt to RS characteristics with high inter-class similarity, large intra-class differences, and complex backgrounds. In this paper, we propose the FSCIL method called continual prototype calibration (CPC), which applies the decoupled learning strategy to cope with the demand to retrain the model when new few-shot categories appear. It can also address the tedious problems in fine-grained RS images by calibrating inter-class and intra-class prototypes. Concretely, considering the severe issue of inter-class confusion, we introduce a prototype separability module (PSM) to update the distribution of inter-class prototypes. In this way, we can distinguish different categories more clearly by weakening their similarities, and the phenomenon of catastrophic forgetting can also be effectively mitigated. Furthermore, to generate more representative and accurate intra-class prototypes, we design a Meta-Network based on foreground enhancement mechanism (FEM). The Meta-Network can boost the learning ability of the model for few-shot data and suppress the occurrence of overfitting by training on task-based data. And the FEM enhances the object features by filtering out redundant background information, facilitating fine-grained classification. Our CPC method is the first FSCIL method applied to optical RS images, and we verify its effectiveness on three datasets. Extensive experiments illustrate that our method achieves state-of-the-art performance in FSCIL of fine-grained RS images. The code is available at https://github.com/ningerhhh/CPC.