Reviewerly/exHarmony huggingface dataset:
Fixed Pattern Noise Removal Based on a Semi-Calibration Method

Due to the manufacturing imperfections, nonuniformities are ubiquitous in digital sensors, causing the notorious Fixed Pattern Noise (FPN). The ability of modern digital cameras to take images under low-light environments is severely limited by the FPN. This paper proposes a novel semi-calibration-based method for the FPN removal that utilizes a pre-calibrated Noise Pattern. The key observation of this work is that the FPN in each shot is actually a scaled Noise Pattern with an unknown scale parameter, since each pixel in the array generates a characteristic amount of dark current which is fundamentally determined by its physical properties. Given a noised image and the corresponding Noise Pattern, the scale parameter is automatically estimated, and then the FPN is removed by subtracting the scaled Noise Pattern from the noised image. The estimation of the scale parameter is based on an entropy minimization estimator, which is derived from the Maximum Likelihood principle and is further justified by subsequent analysis that minimizing the entropy uniquely identifies the true parameter. Convergence issues, as well as the optimality of the proposed estimator, are also theoretically discussed. Finally, some applications are given, illustrating the performance of the proposed FPN removal method in real-world tasks.
2.5D MFFAU-Net: a convolutional neural network for kidney segmentation

Kidney tumors have become increasingly prevalent among adults and are now considered one of the most common types of tumors. Accurate segmentation of kidney tumors can help physicians assess tumor complexity and aggressiveness before surgery. However, segmenting kidney tumors manually can be difficult because of their heterogeneity.This paper proposes a 2.5D MFFAU-Net (multi-level Feature Fusion Attention U-Net) to segment kidneys, tumors and cysts. First, we propose a 2.5D model for learning to combine and represent a given slice in 2D slices, thereby introducing 3D information to balance memory consumption and model complexity. Then, we propose a ResConv architecture in MFFAU-Net and use the high-level and low-level feature in the model. Finally, we use multi-level information to analyze the spatial features between slices to segment kidneys and tumors.The 2.5D MFFAU-Net was evaluated on KiTS19 and KiTS21 kidney datasets and demonstrated an average dice score of 0.924 and 0.875, respectively, and an average Surface dice (SD) score of 0.794 in KiTS21.The 2.5D MFFAU-Net model can effectively segment kidney tumors, and the results are comparable to those obtained with high-performance 3D CNN models, and have the potential to serve as a point of reference in clinical practice.
On the Dynamics behind Profit-Driven Cybercrime: From Contextual Factors to Perceived Group Structures, and the Workforce at the Periphery

Through an inductive thematic analysis of semi-structured interviews with experts, this study corroborates key findings on contextual and organisational dynamics behind profit-driven cybercrime. The findings pinpoint three contextual factors influencing individuals to participate in profit-driven cybercrime: lack of legal economic opportunities, lack of deterrents, and drifting means. The findings also highlight how experts perceive group structures of those behind profit-driven cybercrime: as organised, enterprise-like, loose networks, or communities. Experts’ narratives, moreover, emphasise the presence of a workforce at the periphery of cybercrime groups. Such a workforce is not actively involved in developing criminal schemes, yet it helps their orchestration by achieving necessary tasks such as writing texts or developing software. The study results confirm key insights on crime participation related to both cyber and non-cybercrime literature while also raising new research avenues, including questions concerning to what extent those forming the peripheral workforce are willing to participate in cybercrime.
Design of Spoken English Teaching Based on Artificial Intelligence Educational Robots and Wireless Network Technology

Introduction: The use of AI in education can give students a more engaging learning environment and boost their motivation, and it also represents a continuation of research into the problem of human individuality in the modern era.Objectives: This paper examines the challenge of human individuality in Artificial intelligence with the Capsule network (CapsNet) scheme from two vantage points: the practical need to address issues that have arisen with the latest wave of AI advancements and a philosophical examination of how AI has already been put to use in a variety of industries.Methodology: This article investigates the new Internet spoken English teaching method, describing its benefits and providing solutions to its drawbacks, and it describes in detail how wireless technology will be implemented into online spoken English teaching. The technology provides visual representations of each stage of the gesture recognition process to aid learning. The interactive interface guides students through the gesture recognition system using computer vision applications, allowing them to encounter it firsthand; then, the sophisticated and abstract action recognition method is described with a representational illustration, which is helpful for students in elementary and secondary school to gain a more thorough understanding of and develop their capacity for logical reasoning. This will benefit students at elementary and secondary levels because it will help them think more critically and thoroughly. As a final step, we devise an experiment to compare the results of using our CapsNet method to acquire AI knowledge with those of more conventional learning strategies. Results: Experimental findings were analyzed to demonstrate that this approach is useful for acquiring CapsNet and AI and that it increases users' motivation to study and their practical competence.
Blockchain-Based Secure Firmware Update Using an UAV

This paper proposes a blockchain-based firmware update method using unmanned aerial vehicles (UAVs) to solve one of the security issues arising in the Internet of Things (IoT) environment, which is the firmware security problem. It has high scalability and transaction speed using private blockchains and solves the limitations of internet connections by updating the firmware using an UAV. The proposed firmware update system safely manages the IoT device and firmware information through four processes: participant registration, firmware registration/update, firmware update request, and firmware update. The verification of IoT devices and UAVs is performed using the IoT device’s public key and Bloom filter, and firmware updates can be safely performed using public-key encryption communication. To prove the security of the proposed method, a security analysis based on the STRIDE model was conducted, and the performance of the blockchain network was analyzed by simulation on the Hyperledger.
A Few-Shot Defect Detection Method for Transmission Lines Based on Meta-Attention and Feature Reconstruction

In tasks of transmission line defect detection, traditional object detection algorithms are ineffective, with few training samples of defective components. Meta-learning uses multi-task learning as well as fine-tuning to learn common features in different tasks, which has the ability to adapt to new tasks quickly, shows good performance in few-shot object detection, and has good generalization in new tasks. For this reason, we proposed a few-shot defect detection method (Meta PowerNet) with a Meta-attention RPN and Feature Reconstruction Module for transmission lines based on meta-learning. First, in the stage of region proposal, a new region proposal network (Meta-Attention Region Proposal Network, MA-RPN) is designed to fuse the support set features and the query set features to filter the noise in anchor boxes. In addition, it has the ability to focus on the subtle texture features of smaller-sized objects by fusing low-level features from the query set. Second, in the meta-feature construction stage, we designed a meta-learner with the defect feature reconstruction module as the core to capture and focus on the defect-related feature channels. The experimental results show that under the condition, there are only 30 training objects for various types of component defects. The method achieves 72.5% detection accuracy for component defects, which is a significant improvement compared with other mainstream few-shot object detection. Meanwhile, the MA-RPN designed in this paper can be used in other meta-learning object detection models universally.
ROV State Estimation Using Mixture of Gaussian Based on Expectation-Maximization Cubature Particle Filter

The underwater motion of the ROV is affected by various environmental factors, such as wind, waves, and currents. The complex relationship between these disturbance variables results in non-Gaussian noise distribution, which cannot be handled by the classical Kalman filter. For the accurate and real-time observation of ROV climbing, and, at the same time, to reduce the influence of the uncertainty of the noise distribution, the ROV state filter is designed based on the mixture of Gaussian model theory with the expectation-maximization cubature particle filter (EM-MOGCPF). The EM-MOGCPF considers different sensor measurement noises, and the addition of mixture of Gaussian (MOG) improves the fineness and real-time properties, while the expectation-maximization (EM) reduces the complexity of the algorithm. To estimate the ROV xyz-axis and yaw angular states, we establish a four-degree-of-freedom (4-DOF) ROV kinetics model, which uses a simulation platform for multiple sea state degrees. The results show that the EM-MOGCPF effectively improves the estimation accuracy and exhibits strong adaptability to nonlinear and non-Gaussian environments. We believe that this algorithm holds promise in solving the state estimation challenge in these difficult environments.
Gaze Estimation via Strip Pooling and Multi-Criss-Cross Attention Networks

Deep learning techniques for gaze estimation usually determine gaze direction directly from images of the face. These algorithms achieve good performance because face images contain more feature information than eye images. However, these image classes contain a substantial amount of redundant information that may interfere with gaze prediction and may represent a bottleneck for performance improvement. To address these issues, we model long-distance dependencies between the eyes via Strip Pooling and Multi-Criss-Cross Attention Networks (SPMCCA-Net), which consist of two newly designed network modules. One module is represented by a feature enhancement bottleneck block based on fringe pooling. By incorporating strip pooling, this residual module not only enlarges its receptive fields to capture long-distance dependence between the eyes but also increases weights on important features and reduces the interference of redundant information unrelated to gaze. The other module is a multi-criss-cross attention network. This module exploits a cross-attention mechanism to further enhance long-range dependence between the eyes by incorporating the distribution of eye-gaze features and providing more gaze cues for improving estimation accuracy. Network training relies on the multi-loss function, combined with smooth L1 loss and cross entropy loss. This approach speeds up training convergence while increasing gaze estimation precision. Extensive experiments demonstrate that SPMCCA-Net outperforms several state-of-the-art methods, achieving mean angular error values of 10.13° on the Gaze360 dataset and 6.61° on the RT-gene dataset.
Signature Split Method for a PQC-DSA Compliant with V2V Communication Standards

The development of quantum computing systems poses a great threat to the security of existing public key-based systems. As a result, the National Institute of Standards and Technology (NIST) started a Post-Quantum Cryptography (PQC) standardization project in 2015, and currently active research is being conducted to apply PQC to various cryptographic protocols. Unlike elliptic curve cryptography (ECC)-based schemes, PQC requires a large memory footprint and key/signature size. Therefore, when migrating PQC to a protocol, depending on the PQC and protocol specifications, it can be hard to migrate PQC. In the case of the WAVE protocol, it is difficult to satisfy the accuracy of a specific PQC algorithm because segmentation of the signature occurs during transmission due to the limitation of the maximum packet size. Therefore, in this paper, we present two methodologies that can apply PQC while complying with IEEE 1609.2 standards to the WAVE protocol in the V2V environment. Whereas previous migration studies have focused on designing a hybrid mode of protocols, this paper explores solutions more intuitively at the application layer of protocols. We analyzed two postquantum digital signature algorithms (Crystals-Dilithium and Falcon) and the structure of basic-safety messages (BSMs) of the V2V protocol on the size side. Through this, we propose methods that can perform an independent signature verification process without waiting for all divided signatures in the WAVE protocol. Our methodology overcomes the limitation that schemes with large signature sizes cannot be mounted into the WAVE protocol. We also note that the architecture used as an on-board unit (OBU) in an autonomous driving environment is mainly a microprocessor. We investigated an optimized PQC implementation in the OBU environment and simulated our methodology with the V2Verifier. Finally, we measured the accurate latency through simulation in Jetson Xavier, which is mainly used as an OBU in the V2V communication network.
Challenges Faced by Agricultural Supply Chain Finance under Blockchain Application

With the rise of Internet finance and big data, blockchain technology is expected to propose solutions to the challenges faced by agricultural supply chain finance in recent years. This paper will study the problems of food and safety and the low level of technology in rural areas through literature research. There is a gap between China's grain production rate and that of developed countries. Because of its decentralization and precise traceability characteristics, blockchain technology helps to build a distinctive regulatory and accountability system for food and agricultural safety in China. At the same time, blockchain technology with intelligent contract can effectively simplify the business process of agricultural supply chain finance, and reduce the threshold and cost of rural technology promotion, and increase security because of its features that cannot be changed artificially. It can be seen that the blockchain has practical significance to the challenges faced agricultural supply chain finance.
Efficient and Secure Federated Learning for Financial Applications

Conventional machine learning (ML) and deep learning approaches require sharing customers’ sensitive information with an external credit bureau to generate a prediction model, thereby increasing the risk of privacy leakage. This poses a significant challenge for financial companies. To address this challenge, federated learning has emerged as a promising approach to protect data privacy. However, the high communication costs associated with federated systems, particularly for large neural networks, can be a bottleneck. To mitigate this issue, it is necessary to limit the number and size of communications for practical training of large neural structures. Gradient sparsification is a technique that has gained increasing attention as a method to reduce communication costs, as it updates only significant gradients and accumulates insignificant gradients locally. However, the secure aggregation framework cannot directly employ gradient sparsification. To overcome this limitation, this article proposes two sparsification methods for reducing the communication costs of federated learning. The first method is a time-varying hierarchical sparsification method for model parameter updates, which addresses the challenge of maintaining model accuracy after a high sparsity ratio. This method can significantly reduce the cost of a single communication. The second method is to apply sparsification to the secure aggregation framework. Specifically, the encryption mask matrix is sparsified to reduce communication costs while protecting privacy. Experiments demonstrate that our method can reduce the upload communication costs to approximately 2.9% to 18.9% of the conventional federated learning algorithm under different non-IID experiment settings when the sparsity rate is 0.01.
Optical and SAR Image Registration Based on the Phase Congruency Framework

The improved phase congruency (PC) algorithms have been successfully applied to optical and synthetic aperture radar (SAR) image registration since they are insensitive to nonlinear radiometric and geometric differences. However, most of the algorithms are sensitive to large-scale differences and rotation differences between optical and SAR images. To tackle this, we propose a PC framework to register optical and SAR images. It is compatible with large-scale and rotation invariance. Firstly, a multi-scale Harris keypoint extraction method based on the maximum moment of PC (named PC-Harris) is proposed. The scale space is constructed by combining PC with the log-Gabor filter. Secondly, we propose a PC model to construct the feature descriptors. The orientation and amplitude responses are obtained based on the PC model. Meanwhile, the novel descriptor is constructed based on the polar coordinate system and thus can handle the scale and rotation differences between optical and SAR images. Finally, outliers are removed by the fast sample consensus (FSC). The experiments conducted on several optical and SAR images verify the effectiveness of the proposed framework.
Historical document image analysis using controlled data for pre-training

Abstract Using neural networks for semantic labeling has become a dominant technique for layout analysis of historical document images. However, to train or fine-tune appropriate models, large labeled datasets are needed. This paper addresses the case when only limited labeled data are available and promotes a novel approach using so-called controlled data to pre-train the networks. Two different strategies are proposed: The first addresses the real labeling task by using artificial data; the second uses real data to pre-train the networks with a pretext task. To assess these strategies, a large set of experiments has been carried out on a text line detection and classification task using different variants of U-Net. The observations, obtained from two different datasets, show that globally the approach reduces the training time while offering similar or better performance. Furthermore, the effect is bigger on lightweight network architectures.
Authentication of fog‐assisted <scp>IoT</scp> networks using <scp>Advanced Encryption</scp> credibility approach with modified <scp>D</scp>iffie–<scp>H</scp>ellman encryption

Summary Cloud storage in the network enables the users to access the data from anywhere irrespective of any devices, which prevents data loss and provides centralized management. The cloud server is a pooled, centrally located server resource that many users can access as needed. More efficient data storage and processing could be achieved through fog computing devices and, the fog node handles the task including the semantic transformation of the data before transmitting it to the back‐end cloud servers, managing the process of data from several edge devices simultaneously. The security of the cloud and fog nodes acts as a key component, and that must be ensured for effective computations. In this research, the security of the fog‐assisted Internet of Things (IoT) network is enhanced using the proposed advanced encryption credibility approach (Ad‐En credibility approach), which ensures the privacy and security of the networks by preventing malicious attempts of accessing the data. The proposed Ad‐En credibility approach is performed based on a modified Diffie‐Hellman algorithm that consists of different phases such as the setup phase, entry and identity phase, fog node entry validation phase, and authentication phase. These phases perform different functions and provide strong authentication mechanisms that secure the data with high confidentiality by detecting malicious attempts within a reduced time. The efficiency of the research is proved by measuring the computational time, memory usage, and detection rate. The proposed Ad‐En credibility approach consumed less time of 7 s and memory usage of 8.17 mb with a high detection rate of 85.34%.
A novel parallel unsupervised texture segmentation approach

Abstract This study presents a new idea for unsupervised texture segmentation from the angle of bionics. According to this idea, a novel approach called “side-by-side superposition unsupervised texture segmentation (SSUTS)” is designed. When doing texture segmentation in this paper, the traditional time–frequency analysis-based methods are improved by introducing the analytic phase theory and the Bi-dimensional Empirical mode decomposition (BEMD) theory. Experiments prove that the SSUTS approach is effective in texture segmentation of different styles. Article Highlights From the angle of bionics, an effective approach called “side-by-side superposition unsupervised texture segmentation (SSUTS)” is proposed in this paper. To get reasonable choice of texture segmentation tool for the approach of SSUTS, some classic texture segmentation methods are analyzed and compared in this paper. This paper shows and explains the improvements in texture segmentation, when the Bi-dimensional Empirical Mode Decomposition (BEMD) theory and the two-dimensional analytic phase theory are applied in some classic texture segmentation methods.
Applied Research Analysis of Game Theory and Markov chain

Since probability theory was put forward, it has been applied in all aspects of life and many science and technology. A good use of probability thinking can make a more rational, wise, and correct judgment and improve the probability of winning. Among them, game theory and Markov chain are the most influential. They have become one of the important theoretical foundations of various industries and fields, and gradually mathematize various fields, playing a vital role in their applied research field, promoting the development of society and science and technology. The prisoner's dilemma model of game theory was analyzed in various situations and explained how to apply it to economics, how to affect the social economy, and how to solve some social economic problems. By analyzing and utilizing the integrability, constant return, periodicity, and ergodicity of Markov chain, it is applied to signal transmission, traffic management, and bad debts of banks. Markov chain is used to model various transmission processes to facilitate the application field.