Reviewerly/exHarmony huggingface dataset:
Feature Interaction Learning Network for Cross-Spectral Image Patch Matching

Recently, feature relation learning has attracted extensive attention in cross-spectral image patch matching. However, most feature relation learning methods can only extract shallow feature relations and are accompanied by the loss of useful discriminative features or the introduction of disturbing features. Although the latest multi-branch feature difference learning network can relatively sufficiently extract useful discriminative features, the multi-branch network structure it adopts has a large number of parameters. Therefore, we propose a novel two-branch feature interaction learning network (FIL-Net). Specifically, a novel feature interaction learning idea for cross-spectral image patch matching is proposed, and a new feature interaction learning module is constructed, which can effectively mine common and private features between cross-spectral image patches, and extract richer and deeper feature relations with invariance and discriminability. At the same time, we re-explore the feature extraction network for the cross-spectral image patch matching task, and a new two-branch residual feature extraction network with stronger feature extraction capabilities is constructed. In addition, we propose a new multi-loss strong-constrained optimization strategy, which can facilitate reasonable network optimization and efficient extraction of invariant and discriminative features. Furthermore, a public VIS-LWIR patch dataset and a public SEN1-2 patch dataset are constructed. At the same time, the corresponding experimental benchmarks are established, which are convenient for future research while solving few existing cross-spectral image patch matching datasets. Extensive experiments show that the proposed FIL-Net achieves state-of-the-art performance in three different cross-spectral image patch matching scenarios.
A perspective on potential psychological risks and solutions of using virtual reality in palliative care

Initial evidence suggests that virtual reality (VR) can effectively reduce palliative symptoms. While such findings shed a positive light on the ability of VR exposure to improve patients’ physical and emotional symptoms, VR could have downsides with adverse effects. As most of the reported adverse effects are related to physical risks or technical challenges, there is a scarcity of reports on possible psychological risks posed by VR exposure in palliative care settings, an area with considerable concerns. This is an area that is likely to have a significant impact on the future of clinical practice and research on the use of VR in palliative care. Based on the clinical experience of a registered clinical psychologist who has delivered VR in a palliative care unit for 3 years, we put forward a perspective on the potential psychological complications of using VR in palliative care. Our clinical experiences show that exposure to a desirable virtual environment that is beneficial to patients might not always align with realistic expectations, and that should the use of VR be considered, special precautions are needed to minimize possible psychological harms. This perspective article further proposes three approaches aiming to minimize possible psychological hazards: incorporation of psychological assessment prior to VR administration, psychological interventions right after VR, and professional training of the VR facilitators. We hope that our personally witnessed concerns and perspectives can alert future VR facilitators to the potential psychological hazards of using VR for patients receiving palliative care and inspire future research to minimize psychological harms.
Virtual reality public speaking training: effectiveness and user technology acceptance

Public speaking is a fundamental task in many professional or personal situations. At the same time, there is widespread fear of it, and it takes practice to present well. Previous studies suggest that Virtual Reality Public Speaking Training (VRPST) offers a promising opportunity for this. However, studies evaluating objective and subjective indicators are lacking so far, and valid control conditions are missing in previous studies. We aimed to overcome these drawbacks. In our experiment, participants (N = 42) had the task of presenting a card game to a four-person audience using five provided PowerPoint slides within a time limit of 5 minutes. They prepared either using VRPST or using common self-directed preparation (control condition), being randomly assigned to a condition. Both groups were instructed to prepare for the task at home and given 30 min to learn the rules of the game and present them using the slides. The control group was given an additional 30 min to prepare individually for the presentation task at home. The experimental group received an additional 30-min VRPST session. This training session was done without specific feedback and the presentation was repeated three times. The quality of the rule explanation, the audience-assessed presentation quality, and the subjects’ self-assessed presentation quality were measured. Our results indicate that the VRPST is effective. Subjects who completed the VRPST did a better job of explaining the rules and were better rated by the audience. In addition, the experimental subjects also tended to rate their presentation better in the VRPST condition. Further analyses of those participants who completed the VRPST show high technology acceptance. Our results show the VR training had a significant performance-enhancing effect and that participants would use the VRPST if it were available to them. It seems that practicing a presentation in VR is useful and even better than a conventional preparation.
An effective digital audio watermarking using a deep convolutional neural network with a search location optimization algorithm for improvement in Robustness and Imperceptibility

Watermarking is the advanced technology utilized to secure digital data by integrating ownership or copyright protection. Most of the traditional extracting processes in audio watermarking have some restrictions due to low reliability to various attacks. Hence, a deep learning-based audio watermarking system is proposed in this research to overcome the restriction in the traditional methods. The implication of the research relies on enhancing the performance of the watermarking system using the Discrete Wavelet Transform (DWT) and the optimized deep learning technique. The selection of optimal embedding location is the research contribution that is carried out by the deep convolutional neural network (DCNN). The hyperparameter tuning is performed by the so-called search location optimization, which minimizes the errors in the classifier. The experimental result reveals that the proposed digital audio watermarking system provides better robustness and performance in terms of Bit Error Rate (BER), Mean Square Error (MSE), and Signal-to-noise ratio. The BER, MSE, and SNR of the proposed audio watermarking model without the noise are 0.082, 0.099, and 45.363 respectively, which is found to be better performance than the existing watermarking models.
Tracking phishing on Ethereum: Transaction network embedding approach for accounts representation learning

The transaction volume of Ethereum has been witnessing a year-on-year increase, which has unfortunately been accompanied by significant losses due to phishing scams. To enhance the ability of downstream classifiers to distinguish phishing accounts more effectively, we produce dense representations of Ethereum accounts in latent space, leveraging the transaction network topology and associated statistical features. However, the task of learning representations from sparse yet voluminous transaction records presents a significant challenge. To address this, we introduce the Temporal-based Sequences Generator (TSG) and the Heterogeneous-based Sequences Generator (HSG). These generators create sequences from the transaction network, optimizing the use of transaction temporal constraints, diverse account types, and transaction amounts. Our method aims to capture latent higher-order information and generate dense vectors using a network embedding technique. Furthermore, we propose a novel Statistics-Based Sampling (SBS) method to mitigate label leakage. We validate our approach through experiments with various classic downstream classifiers, demonstrating that Phish2vec surpasses other comparative methods in performance and exhibits robustness and stability.
Joint localization and classification of breast masses on ultrasound images using an auxiliary attention-based framework

Multi-task learning (MTL) methods have been extensively employed for joint localization and classification of breast lesions on ultrasound images to assist in cancer diagnosis and personalized treatment. One typical paradigm in MTL is a shared trunk network architecture. However, such a model design may suffer information-sharing conflicts and only achieve suboptimal performance for individual tasks. Additionally, the model relies on fully-supervised learning methodologies, imposing heavy burdens on data annotation. In this study, we propose a novel joint localization and classification model based on attention mechanisms and a sequential semi-supervised learning strategy to address these challenges. Our proposed framework offers three primary advantages. First, a lesion-aware network with multiple attention modules is designed to improve model performance on lesion localization. An attention-based classifier explicitly establishes correlations between the two tasks, alleviating information-sharing conflicts while leveraging location information to assist in classification. Second, a two-stage sequential semi-supervised learning strategy is designed for model training to achieve optimal performance on both tasks and substantially reduces the need for data annotation. Third, the asymmetric and modular model architecture allows for the flexible interchangeability of individual components, rendering the model adaptable to various applications. Experimental results from two different breast ultrasound image datasets under varied conditions have demonstrated the effectiveness of the proposed method. Furthermore, we conduct comprehensive investigations into the impacts of various factors on model performance, gaining in-depth insights into the mechanism of our proposed framework. The code is available at https://github.com/comp-imaging-sci/lanet-bus.git.
Microlearning and Mobile Learning as a Response to the Challenges of Transformation of the Communication Environment of Modern Education

In modern society, communication on the Internet has completely changed the scenarios of human communication and education. Social networks provide access to information and global exchange of it, which poses new challenges to pedagogy as a science about the purpose and content of education. Internet pedagogy as a field of study and research activity explores the problems of the importance of the Internet for education and self-education, identifying the main forms of online learning, the relationship between the educational potential of the Internet and its actual use, the advantages and disadvantages of what users share online and what they learn with it. &#x0D; Aim. To identify the attitude of students to education in social networks to confirm the relevance of microlearning and mobile learning as challenges to the transformation of the communication environment of modern education.&#x0D; Methodology and research methods. To conduct the study, a questionnaire was developed, distributed using Google Forms. 466 university students from Chelyabinsk took part in the study. The sample type is spontaneous. The sampling contour is formed by registered users who have shown interest in this Internet survey. The mechanism for selecting respondents is self-selection.&#x0D; Results. The communication environment of modern education is undergoing transformation, education with social networks is becoming more and more popular. The Russian market of educational services is not yet so acutely reacting to these transformations. We can predict that microlearning and mobile learning will become a serious alternative to the traditional educational environment in the coming years. &#x0D; Scientific novelty. Analysis of the transformation of the communication environment of modern education allowed us to identify new trends - microlearning and mobile learning. &#x0D; Practical significance. The identified trends will help the educational environment to prepare adequate responses to the identified challenges.
APFed: Anti-Poisoning Attacks in Privacy-Preserving Heterogeneous Federated Learning

Federated learning (FL) is an emerging paradigm of privacy-preserving distributed machine learning that effectively deals with the privacy leakage problem by utilizing cryptographic primitives. However, how to prevent poisoning attacks in distributed situations has recently become a major FL concern. Indeed, an adversary can manipulate multiple edge nodes and submit malicious gradients to disturb the global model’s availability. Currently, most existing works rely on an Independently Identical Distribution (IID) situation and identify malicious gradients using plaintext. However, we demonstrates that current works cannot handle the data heterogeneity scenario challenges and that publishing unencrypted gradients imposes significant privacy leakage problems. Therefore, we develop APFed, a layered privacy-preserving defense mechanism that significantly mitigates the effects of poisoning attacks in data heterogeneity scenarios. Specifically, we exploit HE as the underlying technique and employ the median coordinate as the benchmark. Subsequently, we propose a secure cosine similarity scheme to identify poisonous gradients, and we innovatively use clustering as part of the defense mechanism and develop a hierarchical aggregation that enhances our scheme’s robustness in IID and non-IID scenarios. Extensive evaluations on two benchmark datasets demonstrate that APFed outperforms existing defense strategies while reducing the communication overhead by replacing the expensive remote communication method with inexpensive intra-cluster communication.
THE Benchmark: Transferable Representation Learning for Monocular Height Estimation

Generating 3D city models rapidly is crucial for many applications. Monocular height estimation is one of the most efficient and timely ways to obtain large-scale geometric information. However, existing works focus primarily on training and testing models using unbiased datasets, which does not align well with real-world applications. Therefore, we propose a new benchmark dataset to study the transferability of height estimation models in a cross-dataset setting. To this end, we first design and construct a large-scale benchmark dataset for cross-dataset transfer learning on the height estimation task. This benchmark dataset includes a newly proposed large-scale synthetic dataset, a newly collected real-world dataset, and four existing datasets from different cities. Next, a new experimental protocol, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">few-shot cross-dataset transfer</i> , is designed. Furthermore, in this paper, we propose a scale-deformable convolution module to enhance the window-based Transformer for handling the scale-variation problem in the height estimation task. Experimental results have demonstrated the effectiveness of the proposed methods in traditional and cross-dataset transfer settings. The datasets and codes are publicly available at https://mediatum.ub.tum.de/1662763 and https://thebenchmarkh.github.io/.
Asynchronous Online Federated Learning With Reduced Communication Requirements

Online federated learning (FL) enables geographically distributed devices to learn a global shared model from locally available streaming data. Most online FL literature considers a best case scenario regarding the participating clients and the communication channels. However, these assumptions are often not met in real-world applications. Asynchronous settings can reflect a more realistic environment, such as heterogeneous client participation due to available computational power and battery constraints, as well as delays caused by communication channels or straggler devices. Further, in most applications, energy efficiency must be taken into consideration. Using the principles of partial-sharing-based communications, we propose a communication-efficient asynchronous online FL (PAO-Fed) strategy. By reducing the communication load of the participants, the proposed method renders participation more accessible and efficient. In addition, the proposed aggregation mechanism accounts for random participation, handles delayed updates, and mitigates their effect on accuracy. We study the first- and second-order convergence of the proposed PAO-Fed method and obtain an expression for its steady-state mean square deviation. Finally, we conduct comprehensive simulations to study the performance of the proposed method on both synthetic and real-life data sets. The simulations reveal that in asynchronous settings, the proposed PAO-Fed is able to achieve the same convergence properties as that of the online federated stochastic gradient while reducing the communication by 98%.
Brea-Net: An Interpretable Dual-Attention Network for Imbalanced Breast Cancer Classification

Breast cancer is a prevalent disease worldwide, and early diagnosis plays a vital role in improving patient outcomes. Recent advancements in deep learning have shown great potential for accurate and efficient breast cancer classification. However, the existing methods still suffer from low accuracy and lack of interpretability. To overcome these limitations, we propose a novel and interpretable network to improve the performance of breast cancer classification tasks. By employing a dual-attention module called Convolutional Block Attention Module (CBAM) and a flexible and efficient classifier named Convolutional Multi-Layer Perceptron (ConvMLP), our model is able to effectively capture and exploit the discriminative spatial and channel features within histopathological images and learn complex patterns and relationships between features, leading to improved classification performance. The proposed model outperforms previous state-of-the-art works in terms of accuracy, precision, recall, and F1-score, yielding the highest accuracy of binary and eight-class classification on the BreaKHis dataset. Further, the generalization ability of our proposed network is tested on a different dataset called ICIAR 2018, scoring outstanding accuracy of 95.5%.
SMART: Self-Morphing Adaptive Replanning Tree

The paper presents an algorithm, called Self-Morphing Adaptive Replanning Tree (SMART), that facilitates fast replanning in dynamic environments.SMART performs riskbased tree-pruning if the current path is obstructed by nearby moving obstacle(s), resulting in multiple disjoint subtrees.Then, for speedy recovery, it exploits these subtrees and performs informed tree-repair at hot-spots that lie at the intersection of subtrees to find a new path.The performance of SMART is comparatively evaluated with eight existing algorithms through extensive simulations.Two scenarios are considered with: 1) dynamic obstacles and 2) both static and dynamic obstacles.The results show that SMART yields significant improvements in replanning time, success rate and travel time.Finally, the performance of SMART is validated by a real laboratory experiment.
Multi-Objective Cloud Task Scheduling Optimization Based on Evolutionary Multi-Factor Algorithm

Cloud platforms scheduling resources based on the demand of the tasks submitted by the users, is critical to the cloud provider's interest and customer satisfaction. In this paper, we propose a multi-objective cloud task scheduling algorithm based on an evolutionary multi-factorial optimization algorithm. First, we choose execution time, execution cost, and virtual machines load balancing as the objective functions to construct a multi-objective cloud task scheduling model. Second, the multi-factor optimization (MFO) technique is applied to the task scheduling problem, and the task scheduling characteristics are combined with the multi-objective multi-factor optimization (MO-MFO) algorithm to construct an assisted optimization task. Finally, a dynamic adaptive transfer strategy is designed to determine the similarity between tasks according to the degree of overlap of the MFO problem and to control the intensity of knowledge transfer. The results of simulation experiments on the cloud task test dataset show that our method significantly improves scheduling efficiency, compared with other evolutionary algorithms (EAs), the scheduling method simplifies the decomposition of complex problems by a multi-factor approach, while using knowledge transfer to share the convergence direction among sub-populations, which can find the optimal solution interval more quickly and achieve the best results among all objective functions.
Natural Weather-Style Black-Box Adversarial Attacks Against Optical Aerial Detectors

Most existing adversarial attack methods against detectors involve adding adversarial perturbations to benign images to synthesiz adversarial examples. However, directly applying these methods, originally designed for natural image detectors, to optical aerial image detectors can lead to perturbations that appear unnatural and suspicious to human eyes, owing to intrinsic dissimilarities between these two types of images. Inspired by the fact that the captured optical aerial images are heavily affected by weather conditions, this paper proposes a novel method for conducting adversarial attacks against optical aerial detectors by leveraging natural weather-style perturbations. Compared to existing methods, our scheme produces more natural and stealthy adversarial examples. To enhance the practicality of the proposed method in real-world scenarios, we implement the attacks in black-box settings where only the model’s predictions are accessible. Specifically, we formulate the generation of adversarial weather perturbations in black-box as an optimization problem and effectively solve it using the Differential Evolution (DE) algorithm. Through extensive experiments, we verify the effectiveness of our method and investigate the transferability of generated adversarial examples across different models. In light of the significant generalization and effectiveness of our method, we generate and release the first dataset with adversarial weather-style perturbations based on the DOTA dataset, which we abbreviate as DOTA-W. This dataset serves as a valuable resource for evaluating and improving the robustness of optical aerial detectors. The code and dataset have been released at https://github.com/tang-agui/attADs-AWP.