Reviewerly/exHarmony huggingface dataset:
A Fast Globally Optimal Seamline Detection Method for High-Resolution Remote Sensing Images

Seamline detection is one of the most important issues in mosaicking high-resolution remote sensing images (HRRSIs). However, it is difficult to make a balance between efficiency and accuracy. On that account, a shortest matrix path-based dynamic programming (SMP-DP) algorithm is proposed to find the optimal seamline for HRRSI mosaicking. First, a pixel cost matrix defined by intensity difference, gradient similarity, and geometric difference is constructed in the overlapping area. Second, the least average path cost from the starting pixel to each pixel is calculated and the SMP-DP algorithm is applied to find the optimal path. Experimental results on HRRSI prove that the proposed method detects high-quality seamline and crosses much fewer objects with the highest computational efficiency, compared with the state-of-the-art method and commercial software.
Fuzzy Inference Based Quadcopter Flight Regulation Under Overland Monitoring

Some territories where economic activity is carried out are characterized by the presence of mountain and forest tracts. To provide information support for the development of infrastructure and agriculture in these areas, in some cases overland monitoring using unmanned technologies is required. In this regard, an algorithm for the formation of a 3D trajectory of a quadcopter during overland piloting in a mountainous and wooded landscape is proposed, which implies autonomous maneuvering to overcome possible obstacles. As a basic model, it is proposed to use a fuzzy inference system with input characteristics in the form of linguistic variables that reflect fuzzy sectors of space, within which the presence of obstacles and the distance to them are interpreted verbally, that is, in the form of terms of corresponding input linguistic variables. Overcoming obstacles is supposed to be performed based on fuzzy conclusions of the proposed system, formulated as terms of output linguistic variables, which reflect changes in the angle of rotation in the horizontal plane, flight altitude and traverse speed of the quadcopter. The given paper analyzes the results of the model behavior for different scenarios of the terms of the input linguistic variables.
Impacts of PR and AI on the Reputation Management: A Case Study of Banking Sector Customers in UAE

The banking sector organizations focus on improving their reputation through Public Relations and communication. However, today, when Emotional Intelligence accompanies technology, providing clients with intelligent PR communication is significantly helping in reputation management. Emotional Intelligence and Artificial Intelligence also focus this study on the PR practices widely accompanied by Reputation Management Purposes. The conceptual model is supported by a symmetric communication model that is further tested using Structural Equation Modelling. Results revealed that Public Relations significantly affect Artificial Intelligence, indicating Emirati banks have widely integrated Artificial Intelligent systems in their clients’ support systems (p > .006). Besides, the effect of Emotional Intelligence on Artificial also remained significant, indicating the integrated PR system as having Artificial Intelligence (p > .082). The effect of Artificial Intelligence on Reputation Management also remained significant (p > .010), indicating the role of AI in improving organizational reputation. Finally, the mediating effect of Symmetric Communication also remained significant (p > .000), affirming its role in indirectly affecting Reputation Management. The current study concluded that Artificial Intelligence is integrated into Public Relations Practices and Emotional Intelligence. As a result, these factors play a constructive role in Reputation Management. While Symmetric Communication is further adding more to the Reputation Management process as an indirect variable, also highlighting the importance of communication for an organization.
Natural Language-Guided Semantic Navigation Using Scene Graph

We expect legged robots to perform complex navigation tasks by having higher mobility and sensing capability. However, the mobility does not easily lead to the ability of performing complex user tasks due to restricted interfaces such as a joystick. To perform tasks that users want, the robots require an expressive and accessible interface that can deliver human intention with lower mental demands, even in complex environments. In this work, we propose a novel natural language-guided robotic navigation framework that can effectively ground natural-language commands in large space. Our framework consists of three modules: a scene-graph generator, a grounding network, and a semantic navigation system. The scene-graph generator incrementally stores the semantic information of object instances, properties, and relationships. Then, the proposed scene graph-based grounding network (SGGNet) predicts the desired goal robustly by associating instances in a scene graph with a user command. Finally, the navigation system enables the robot to reach the goal location. Our evaluation result shows SGGNet achieves a grounding accuracy of 77.8% given 3, 000 scene graphs and 9, 000 natural language commands. The model also achieves a grounding accuracy of $$52.4\%$$ given unforeseen objects. We demonstrate the robust performance of the proposed framework in three real-world scenarios with various speech commands.
Deep Video Super-Resolution Using Hybrid Imaging System

High-resolution high-frame-rate videos can record motion scenes detailedly and smoothly, but usually only professional cameras have enough transmission bandwidth to meet the video capture requirement. The conventional solutions use video processing methods such as video super-resolution (VSR) and video frame interpolation (VFI), but their results suffer from unreal spatial-temporal details in complex dynamic cases. To address this problem, we reconstruct a more real high-resolution high-frame-rate video using a hybrid video input, including a low-resolution high-frame-rate video (main video) and a high-resolution low-frame-rate video (auxiliary video). We propose a deep learning model named HIS-VSR, which consists of three parts: super-resolution of the main video, detail feature extraction of the auxiliary video and hybrid video information aggregation. Among them, the first part processes the main video to generate preliminary high-resolution frames; the second part warps the auxiliary frames for alignment and extracts their high-resolution detail features; the last part uses a weighted aggregation method to fuse the results of the first and second part. We train our model on synthetic datasets and demonstrate its excellent performance of reconstructing dynamic scenes by comparing it with Deep-SloMo on synthetic and real videos.
Counterfactual Graph Learning for Anomaly Detection on Attributed Networks

Graph anomaly detection is attracting remarkable multidisciplinary research interests ranging from finance, healthcare, and social network analysis. Recent advances on graph neural networks have substantially improved the detection performance via semi-supervised representation learning. However, prior work suggests that deep graph-based methods tend to learn spurious correlations. As a result, they fail to generalize beyond training data distribution. In this article, we aim to identify structural and contextual anomaly nodes in an attributed graph. Based on our preliminary data analyses, spurious correlations can be eliminated with causal subgraph interventions. Therefore, we propose a new graph-based anomaly detection model that can learn causal relations for anomaly detection while generalizing to new environments. To handle situations with varying environments, we steer the generative model to manufacture synthetic environment features, which are exerted on realistic subgraphs to generate counterfactual subgraphs. Further, these counterfactual subgraphs help a few-shot anomaly detection model learn transferable and causal relations across different environments. The experiments on three real-world attributed graphs show that the proposed approach achieves the best performance compared to the state-of-the-art baselines and learns robust causal representations resistant to noises and spurious correlations.
Interpretable Deep Attention Prior for Image Restoration and Enhancement

An inductive bias induced by an untrained network architecture has been shown to be effective as a deep image prior (DIP) in solving inverse imaging problems. However, it is still unclear as to what kind of prior is encoded in the network architecture, and the early stopping for the overfitting problem of DIP still remains the challenge. To address this, we introduce an interpretable network that explores self-attention as a deep attention prior (DAP). Specifically, the proposed deep attention prior is formulated as an interpretable optimization problem. A nonlocal self-similarity prior is incorporated into the network architecture by a self-attention mechanism. Each attention map from our proposed DAP reveals how an output value is generated, which leads to a better understanding of the prior. Furthermore, compared to DIP, the proposed DAP regards the single input degraded image as input to reduce the instability, and introduces the mask operation to handle the early stopping problem. Experiments show that the proposed network works as an effective image prior for solving different inverse imaging problems, such as denoising, inpainting, or pansharpening, while also showing potential applications in higher-level processing such as interactive segmentation and selective colorization.
A Complexity-Efficient Quantum Architecture and Simulation for Eigen Spectrum Estimation of Vandermonde System in a Large Antenna Array

Vandermonde matrix finds a wide range of applications in the digital signal processing domain including antenna array processing. Eigenspectrum estimation remains a challenge in such a system when the matrix dimension increases. This precise problem can be well addressed in a quantum simulation framework based on quantum Hamiltonian simulation (QHS). However, the structural exploitation of the matrix is never considered, which can give benefits in terms of quantum gate complexity. In this work, we exploit the special structural property of a dense Vandermonde matrix by decomposing it into several sparse matrices and then use them for a complexity-efficient eigenvalue spectrum estimation in a quantum framework. In this context, we propose an iterative quantum simulation framework for a Vandermonde-structured Hamiltonian called quantum Vandermonde simulation (QVS). The proposed approach requires fewer quantum gate operations compared to the standard QHS of a dense Vandermonde matrix. In this work, we have considered a large antenna array system, that exploits a delay Vandermonde matrix (DVM) to create a multi-beam beamformer using the proposed low complex quantum architecture. Extensive theoretical and numerical analysis in terms of complexity and other quality parameters have been conducted to show the benefit of the proposed method.
Few-Shot Object Detection in Aerial Imagery Guided by Text-Modal Knowledge

Few-shot object detection (FSOD) has received numerous attention due to the difficulty and time-consuming of labeling objects. Recent researches achieve excellent performance in a natural scene by only using a few instances of novel classes to fine-tune the last prediction layer of the model well-trained on plentiful base data. However, compared with natural scene objects with a single direction and small size variety, the direction and size of the objects in remote sensing images (RSIs) vary greatly. The methods proposed for the natural scene cannot be directly applied to RSIs. In this article, we first propose a strong baseline for RSIs. It fine-tunes all detector components acting on high-level features and effectively improves the performance of novel classes. Further analyzing the results of the baseline, we find that the error for novel classes is mainly concentrated in classification. It misclassifies novel classes as confusable base classes or backgrounds due to the difficulty in extracting generalized information from limited instances. As is well-known, text-modal knowledge can highly summarize the generalized and unique characteristics of categories. Thus, we introduce text-modal descriptions for each category and propose an FSOD method guided by TExt-MOdal knowledge, called TEMO. Specifically, a text-modal knowledge extractor and a cross-modal assembly module are proposed to extract text features and fuse the text-modal features into visual-modal features. The fused features greatly reduce the classification confusion of novel classes. Furthermore, we introduce a mask strategy and a separation loss to avoid over-fitting and ambiguity of text-modal features. Experimental results on detection in optical remote sensing images (DIOR), Northwestern Polytechnical University (NWPU), and fine-grained object recognition in high-resolution remote sensing imagery (FAIR1M) illustrate that our TEMO achieves state-of-the-art performance in all settings.
3-D HANet: A Flexible 3-D Heatmap Auxiliary Network for Object Detection

3D object detection is a vital part of outdoor scene perception. Learning the complete size and accurate positioning of objects from an incomplete point cloud spatial structure is essential to 3D object detection. We propose a novel flexible 3D heatmap auxiliary network for object detection (3D HANet). To obtain complete structure and location information from an incomplete point cloud structure, we propose a 3D heatmap to reflect object information. Also, we design a plug-and-play auxiliary network based on 3D heatmap, which improves the accuracy of the entire detection network without extra computation in the inference stage. We validate the 3D heatmap auxiliary network on the basis of three classic 3D object detection networks: PointPillars, SECOND and SASSD. Experimental results show that our auxiliary network augments the feature extraction ability of the backbone network, which is manifested in that the predicted boxes and the ground truth boxes are more suitable in size and more aligned in direction. Furthermore, we conducted verification experiments on the state-of-the-art detector, CasA, and made a further improvement on the official ranking of KITTI dataset.
Linear Feature-Based Image/LiDAR Integration for a Stockpile Monitoring and Reporting Technology

Stockpile monitoring has been recently conducted with the help of modern remote sensing techniquese.g., terrestrial/aerial photogrammetry/LiDARthat can efficiently produce accurate 3D models for the area of interest.However, monitoring of indoor stockpiles still requires more investigation due to unfavorable conditions in these environments such as lack of global navigation satellite system (GNSS) signals and/or homogenous texture.This study develops a fully-automated image/LiDAR integration framework that is capable of generating accurate 3D models with color information for stockpiles under challenging environmental conditions.The derived colorized 3D point cloud can be subsequently used for volume estimation and visual inspection of stockpiles.The main contribution of the developed strategy is using automatically derived conjugate image/LiDAR linear features for simultaneous registration and camera/LiDAR system calibration.Data for this study is acquired using a camera-assisted LiDAR mapping platformdenoted as stockpile monitoring and reporting technology (SMART) -which was recently designed as a time-efficient and cost-effective bulk material tracking.Experimental results on three datasets show that the developed framework outperforms a classical planar feature-based registration technique in terms of the alignment of acquired point cloud.Results also indicate that the proposed approach can lead to a high relative accuracy between image lines and their corresponding back-projected LiDAR features in the range of 4-7 pixels.
Deep Cleaner—A Few Shot Image Dataset Cleaner Using Supervised Contrastive Learning

Images are increasingly used for AI-based diagnosis and analysis of many diseases like cervical cancer, mouth cancer, glucose analysis from retina etc. In many cases, data collection is done by specialised camera modules which capture images of affected areas. As with any other sources of data, this process is also error-prone and may contain unwanted objects and regions that may require cleaning by removing them. Outliers in these kinds of dataset may adversely affect the performance of machine learning models. Manually cleaning would be a tedious task, especially when the data is collated from different sources. Hence, cleaning the data before training the model is of utmost importance. In this paper, we propose a Few-Shot learning based model pre-trained in supervised contrastive learning settings to automate the process of data cleaning. Our model learns the dataset distribution and distinguishes the accurate data points from noisy data points. We also show that scaling up the model can greatly improve the Few-Shot performance. On the noisy MobileODT cervical data, which was collected from Kaggle, our model obtained 52% accuracy without cleaning data using an EfficientNet architecture for the classification task. Whereas the same architecture with ROI cropping achieved an accuracy of 76.56% after cleaning through the proposed Deep Cleaner approach that requires only 100 clean images. The proposed approach performs 2.74% better than a denoising auto-encoder, which is considered a powerful anomaly detection technique.
Identifying Hot Information Security Topics Using LDA and Multivariate Mann-Kendall Test

Discovering promising research themes in a scientific domain by evaluating semantic information extracted from bibliometric databases represents a challenging task for Natural Language Processing (NLP). While existing NLP methods generally characterize the research topics using unique key terms, we take a step further by more accurately modeling the research themes as finite sets of key terms. The proposed approach involves two stages: identifying the research themes from paper metadata using LDA topic modeling; and, evaluation of research theme trends by employing a version of the Mann-Kendall test that is able to cope with multivariate time series of term occurrences. The results obtained by applying this general methodology to Information Security domain confirm its viability.
Intermediate results: how students assess distance learning and whether there are prospects for its application in interactive educational environments

The paper presents the results of the study of the use of services and technologies in distance learning for students. Particular attention is paid to the analysis of the advantages and disadvantages of using distance learning technologies that affect learning outcomes and students' satisfaction with the educational process. The work is relevant to improving the educational process by increasing the effectiveness of distance learning tools and technologies, taking into account their advantages, improving the quality characteristics of the educational process, its elements and the use of optimal technologies for both students and teachers. As part of the preparation of the work, a sociological study of 1,000 university students was conducted, in which the preferences of students in the use of distance learning services, the frequency of their use, the degree of application of such tools by teachers in the educational process were studied. The most popular digital services for distance learning were identified, including the most widely used interactive learning services. Identification of the main negative and positive aspects of distance learning in terms of students. Prospects for the use of digital services in virtual educational environments and meta-universes were shown, as well as the development of electronic didactic means for the implementation of the educational process. The results obtained during the study allow clarifying the attitude of students to distance education, assess the degree of influence of various factors on the effectiveness of distance education. The results can be used to make changes in the current educational process in terms of application or rejection of specific methods and technologies, their adaptation and improvement.
Computational intelligence for the internet of things

The paper considers the classification of computational intelligence methods and their further development with the use of hybrid technologies for managing the Internet of things. The new paradigm of the Internet of Things allows the use of various objects in a wider range of tasks. In healthcare, devices are being developed for many purposes, such as monitoring patients to help them cope with chronic illnesses, recover from injuries, or prevent strokes. Thus, in order to realize a dynamic new generation technology, corresponding to the new paradigm of the "Internet of Things", it is proposed to use machine-learning algorithms in hybrid technology, which will make it possible not only to protect confidential patient data, but also to analyze them and make assumptions.
One year of digital contact tracing: Who was more likely to install the NHS COVID-19 app? Results from a tracker survey in England and Wales

Objective To examine changes in the uptake of the National Health Service (NHS) COVID-19 proximity (contact) tracing application (‘app’) over one year, amongst smartphone users in England and Wales. Methods We conducted a longitudinal survey between October 2020 and September 2021, amongst an online panel representative of smartphone users aged 18–79 and a purposeful sample from six of the largest minority ethnic groups. We fitted pooled logistic regression models to examine factors associated with app installation and a longitudinal logistic regression model to estimate factors associated with installing/uninstalling the app over time. Results Around 50% of respondents had the app installed at each time point. The majority of installations took place soon after its launch. The key reason for installing at launch was ‘civic, public or social responsibility’. Amongst those who installed the app later, it was ‘needed to scan NHS QR code’. Uptake was higher amongst individuals who considered themselves vulnerable to COVID-19 or were concerned about the risk COVID-19 posed, were more highly educated, of White ethnicity, and who reported higher levels of trust in government information. Factors associated with installing the app over time included becoming more concerned about the risk COVID-19 poses to the country, or perceiving that the crisis in their local area had worsened. Conclusions Despite changes in pandemic response and case numbers, app installation in England and Wales remained relatively stable after launch. If governments wish to increase app installation and use rates in future pandemics, they need to highlight those app features that encourage engagement, and take related action to allay privacy concerns and improve trust in government information sharing.