Reviewerly/exHarmony huggingface dataset:
Golden eagle optimizer with double learning strategies for 3D path planning of UAV in power inspection

Unmanned aerial vehicle (UAV) inspection is an indispensable part of power inspection. In the process of power inspection, the UAV needs to obtain an efficient and feasible path in the complex environment. To solve this problem, a Golden eagle optimizer with double learning strategies (GEO-DLS) is proposed. The double learning strategies consist of personal example learning and mirror reflection learning. The personal example learning can enhance the search ability of the Golden eagle optimizer (GEO) and reduce the possibility of the GEO falling into the local optimum. The mirror reflection learning can improve the optimization accuracy of the GEO and accelerate the convergence speed of the GEO. To verify the optimization performance of the algorithm, the proposed GEO-DLS and several other algorithms were tested under the CEC2013 test suite. At the same time, the proposed GEO-DLS and the GEO were analyzed for population diversity and exploration–exploitation​ ratio. Finally, the proposed GEO-DLS is applied to the UAV path planning to generate the initial path, and the cubic B-spline curve is used to smooth the path. These experimental results show that the GEO-DLS has a good performance. The code can be publicly available at: https://www.mathworks.com/matlabcentral/fileexchange/98799-golden-eagle-optimizer-with-double-learning-strategies
DE-GAN: Domain Embedded GAN for High Quality Face Image Inpainting

Domain knowledge of face shapes and structures plays an important role in face inpainting. However, general inpainting methods focus mainly on the resolution of generated images without considering the particular structure of human faces and generally produce inharmonious facial parts. Existing face-inpainting methods incorporate only one type of facial feature for face completion, and their results are still undesirable. To improve face inpainting quality, we propose a Domain Embedded Generative Adversarial Network (DE-GAN) for face inpainting. DE-GAN embeds three types of face domain knowledge (i.e., face mask, face part, and landmark image) via a hierarchical variational auto-encoder (HVAE) into a latent variable space to guide face completion. Two adversarial discriminators, a global discriminator and a patch discriminator, are used to judge whether the generated distribution is close to the real distribution or not. Experiments on two public face datasets demonstrate that our proposed method generates higher quality inpainting results with consistent and harmonious facial structures and appearance than existing methods and achieves the state-of-the-art performance, esp. for inpainting under-pose variations.
Optimal Scheme for Quantum Metrology

Abstract Quantum metrology can achieve far better precision than classical metrology, and is one of the most important applications of quantum technologies in the real world. To attain the highest precision promised by quantum metrology, all steps of the schemes need to be optimized, which include the state preparation, parametrization, and measurement. Here the recent progresses on the optimization of these steps, which are essential for the identification and achievement of the ultimate precision limit in quantum metrology, are reviewed. It is hoped this provides a useful reference for the researchers in quantum metrology and related fields.
Self-Supervised monocular depth and ego-Motion estimation in endoscopy: Appearance flow to the rescue

Recently, self-supervised learning technology has been applied to calculate depth and ego-motion from monocular videos, achieving remarkable performance in autonomous driving scenarios. One widely adopted assumption of depth and ego-motion self-supervised learning is that the image brightness remains constant within nearby frames. Unfortunately, the endoscopic scene does not meet this assumption because there are severe brightness fluctuations induced by illumination variations, non-Lambertian reflections and interreflections during data collection, and these brightness fluctuations inevitably deteriorate the depth and ego-motion estimation accuracy. In this work, we introduce a novel concept referred to as appearance flow to address the brightness inconsistency problem. The appearance flow takes into consideration any variations in the brightness pattern and enables us to develop a generalized dynamic image constraint. Furthermore, we build a unified self-supervised framework to estimate monocular depth and ego-motion simultaneously in endoscopic scenes, which comprises a structure module, a motion module, an appearance module and a correspondence module, to accurately reconstruct the appearance and calibrate the image brightness. Extensive experiments are conducted on the SCARED dataset and EndoSLAM dataset, and the proposed unified framework exceeds other self-supervised approaches by a large margin. To validate our framework's generalization ability on different patients and cameras, we train our model on SCARED but test it on the SERV-CT and Hamlyn datasets without any fine-tuning, and the superior results reveal its strong generalization ability. Code is available at: https://github.com/ShuweiShao/AF-SfMLearner.
Patrol robot path planning in nuclear power plant using an interval multi-objective particle swarm optimization algorithm

This paper presents an interval multi-objective path planning (PP) scheme for patrol robot in nuclear power plant. The purpose of this PP scheme is to find collision-free paths with the shortest length and smallest risk degree. Firstly, a novel workspace modeling method is proposed to describe the static PP environment of patrol robot in nuclear power plant. Then considering the conflicts of the shortest length and smallest risk degree, an interval multi-objective particle swarm optimization (IMOPSO) method is used. In the IMOPSO, an ingenious interval update law for the particle’s global best position and local best position based on the crowding distance of each risk degree interval is used to increase the diversity of population, and an iterative procedure is adopted to update the particle’s position when the found paths are collided with some existing obstacles. Finally, three representative simulation tests are used to verify the validity of proposed IMOPSO method. Results show that comparing with other three well-known multi-objective evolutionary algorithms, our proposed method has the advantages of finding a better Pareto optimal paths.
TBE-Net: A Three-Branch Embedding Network With Part-Aware Ability and Feature Complementary Learning for Vehicle Re-Identification

Vehicle re-identification (Re-ID) is one of the promising applications in the field of computer vision. Existing vehicle Re-ID methods mainly focus on global appearance features or pre-defined local region features, which have difficulties in handling inter-class similarities and intra-class differences among vehicles in various traffic scenarios. This paper proposes a novel end-to-end three-branch embedding network (TBE-Net) with feature complementary learning and part-aware ability. The proposed TBE-Net integrates complementary features, global appearance, and local region features into a unified framework for subtle feature learning, thereby obtaining more integral and diverse vehicle features to re-identify the vehicle from similar ones. The local region feature branch in the proposed TBE-Net contains an attention module that highlights the major differences among local regions by adaptively assigning large weights to the critical local regions and small weights to insignificant local regions, thereby enhancing the perception sensitivity of the network to subtle discrepancies. The complementary branch in the proposed TBE-Net exploits different pooling operations to obtain more comprehensive structural features and multi-granularity features as a supplement to the global appearance and local region features. The abundant features help accommodate the ever-changing critical local regions in vehicles’ images due to the sensors’ settings, such as the position and shooting angle of surveillance cameras. The extensive experiments on VehicleID and VeRi-776 datasets show that the proposed TBE-Net outperforms the state-of-the-art methods.
Radio Frequency Fingerprint Identification Based on Slice Integration Cooperation and Heat Constellation Trace Figure

Radio frequency fingerprint (RFF) identification is a popular topic in the field of physical layer security. However, machine learning based RFF identification methods require complicated feature extraction manually while deep learning based methods are hard to achieve robust identification performance. To solve these problems, we propose a novel RFF identification method based on heat constellation trace figure (HCTF) and slice integration cooperation (SIC). HCTF is utilized to avoid the manual feature extraction and SIC is adopted to extract more features automatically in RF signals. Experimental results show that our proposed HCTF-SIC identification method can achieve higher accuracy than the existing RFF methods. The identification accuracy achieves 91.07% when SNR <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pmb {=}$ </tex-math></inline-formula> 0 dB and it is even higher than 99.64% when the SNR <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pmb {\ge }$ </tex-math></inline-formula> 5 dB.
Enhancing sparrow search algorithm via multi-strategies for continuous optimization problems

As a recent swarm intelligence optimization algorithm, sparrow search algorithm (SSA) is widely adopted in many real-world problems. However, the solutions to the limitations of SSA (such as low accuracy of convergence and tendency of trapping into local optimum) are still not available. To address these issues, we propose an enhanced multi-strategies sparrow search algorithm (EMSSA) based on three strategies specifically addressing the limitations of SSA: 1) in the uniformity-diversification orientation strategy, we propose an adaptive-tent chaos theory to allow more diversity and greater randomness in the initial population; 2) in the hazard-aware transfer strategy, we construct a weighted sine and cosine algorithm based on the growth function to avoid trapping into the state of local optima stagnation; 3) in the dynamic evolutionary strategy, we design the similar perturbation function and introduce the triangle similarity theory to improve the exploration capability. The performance of EMSSA in solving the continuous optimization problems about the 23 benchmark functions, CEC2014, and CEC2017 problems is much improved than that of SSA and other state-of-the-art algorithms. Furthermore, the results of the density peak clustering optimization show that the EMSSA outperforms SSA.
Modelling of Destinations for Data-driven Pedestrian Trajectory Prediction in Public Buildings

Public buildings such as shopping arcades and railway stations are environments in which pedestrian movement is of significance to many smart building applications. The data-driven approach of pedestrian trajectory prediction is effective in learning a reliable model that can represent complex human movement. Pedestrian trajectories are highly linked to the locations of facilities and services inside a building as pedestrians move towards these destinations for engagement. This paper suggests that the notion of destination is a strong predictor of pedestrian trajectories and proposes a novel enhancement of the data-driven approach for pedestrian tracking in public buildings. The method of destination-driven pedestrian trajectory prediction (DDPTP) first evaluates the most likely destinations of the pedestrian using the destination classifier (DC) and then predicts the future trajectories with the destination-specific trajectory model (DTM). The proposed solution has been evaluated on the NYGC and the ATC datasets and found to outperform state-of-the-art models. The notion of destination can be further developed into a region of interest of which the within-region and out-of-region features can be factored out for more effective learning.
Region-Based Hybrid Medical Image Watermarking Scheme for Robust and Secured Transmission in IoMT

With the growth in Internet and digital technology, Internet of Medical Things (IoMT) and Telemedicine have become buzzwords in healthcare. A large number of medical images and information is shared through a public network in these applications. This paper proposes a region-based hybrid Medical Image Watermarking (MIW) scheme to ensure the authenticity, authorization, integrity, and confidentiality of the medical images transmitted through a public network in IoMT. In the proposed scheme, medical images are partitioned into Region of Interest (RoI) and Region of Non-Interest (RoNI). To ascertain integrity of RoI, tamper detection and recovery bits are embedded in RoI in the medical image. RoI is watermarked using adaptive Least Significant Bit (LSB) substitution with respect to the hiding capacity map for higher RoI imperceptibility and accuracy in tamper detection and recovery. Electronic Patient Record (EPR) is compressed using Huffman coding and encrypted using a pseudo-random key (secret key) to provide higher confidentiality and payload. QR code of hospital logo, Encrypted EPR, and RoI recovery bits are interleaved in RoNI using Discrete Wavelet Transform-Singular Value Decomposition (DWT-SVD) hybrid transforms to achieve a robust watermark. The proposed scheme is tested under various geometric and non-geometric attacks such as filtering, compression, rotation, salt and pepper noise and shearing. The evaluation results demonstrate that the proposed scheme has high imperceptibility, robustness, security, payload, tamper detection, and recovery accuracy under image processing attacks. Therefore, the proposed scheme can be used in the transmission of medical images and EPR in IoMT. Relevance of the proposed scheme is established by its superior performance in comparison to some of the popular existing schemes.
Vehicle and Pedestrian Detection Algorithm Based on Lightweight YOLOv3-Promote and Semi-Precision Acceleration

Aiming at the shortcomings of the current YOLOv3 model, such as large size, slow response speed, and difficulty in deploying to real devices, this paper reconstructs the target detection model YOLOv3, and proposes a new lightweight target detection network YOLOv3-promote: Firstly, the G-Module combined with the Depth-Wise convolution is used to construct the backbone network of the entire model, and the attention mechanism is introduced and added to perform weighting operations on each channel to get more key features and remove redundant features, thereby strengthening the identification ability of feature network model’s to distinguish target objects among background; Secondly, in order to delete some less important channels to achieve the effect of compressing the model size and improving the calculation speed, the size of the scaling factor gamma in the batch normalization layer is used; Finally, based on NVIDIA’s TensorRT framework model conversion and half-precision acceleration were carried out, and the accelerated model was successfully deployed on the embedded platform Jetson Nano. The performed KITTI experimental results show that the inference speed of our proposed method is about 5 times that of the original model, the parameter volume is reduced to one tenth, the mAP is increased from 86.1% of the original model to 93.1%, and the FPS reaches 25.5fps, realizing the requirements of real-time detection with high precision.
ExAID: A multimodal explanation framework for computer-aided diagnosis of skin lesions

One principal impediment in the successful deployment of Artificial Intelligence (AI) based Computer-Aided Diagnosis (CAD) systems in everyday clinical workflows is their lack of transparent decision-making. Although commonly used eXplainable AI (XAI) methods provide insights into these largely opaque algorithms, such explanations are usually convoluted and not readily comprehensible. The explanation of decisions regarding the malignancy of skin lesions from dermoscopic images demands particular clarity, as the underlying medical problem definition is ambiguous in itself. This work presents ExAID (Explainable AI for Dermatology), a novel XAI framework for biomedical image analysis that provides multi-modal concept-based explanations, consisting of easy-to-understand textual explanations and visual maps, to justify the predictions.Our framework relies on Concept Activation Vectors to map human-understandable concepts to those learned by an arbitrary Deep Learning (DL) based algorithm, and Concept Localisation Maps to highlight those concepts in the input space. This identification of relevant concepts is then used to construct fine-grained textual explanations supplemented by concept-wise location information to provide comprehensive and coherent multi-modal explanations. All decision-related information is presented in a diagnostic interface for use in clinical routines. Moreover, the framework includes an educational mode providing dataset-level explanation statistics as well as tools for data and model exploration to aid medical research and education processes.Through rigorous quantitative and qualitative evaluation of our framework on a range of publicly available dermoscopic image datasets, we show the utility of multi-modal explanations for CAD-assisted scenarios even in case of wrong disease predictions. We demonstrate that concept detectors for the explanation of pre-trained networks reach accuracies of up to 81.46%, which is comparable to supervised networks trained end-to-end.We present a new end-to-end framework for the multi-modal explanation of DL-based biomedical image analysis in Melanoma classification and evaluate its utility on an array of datasets. Since perspicuous explanation is one of the cornerstones of any CAD system, we believe that ExAID will accelerate the transition from AI research to practice by providing dermatologists and researchers with an effective tool that they can both understand and trust. ExAID can also serve as the basis for similar applications in other biomedical fields.
A survey on the application of path-planning algorithms for multi-rotor UAVs in precision agriculture

Abstract Multi-rotor Unmanned Aerial Vehicles (UAVs), although originally designed and developed for defence and military purposes, in the last ten years have gained momentum, especially for civilian applications, such as search and rescue, surveying and mapping, and agricultural crops and monitoring. Thanks to their hovering and Vertical Take-Off and Landing (VTOL) capabilities and the capacity to carry out tasks with complete autonomy, they are now a standard platform for both research and industrial uses. However, while the flight control architecture is well established in the literature, there are still many challenges in designing autonomous guidance and navigation systems to make the UAV able to work in constrained and cluttered environments or also indoors. Therefore, the main motivation of this work is to provide a comprehensive and exhaustive literature review on the numerous methods and approaches to address path-planning problems for multi-rotor UAVs. In particular, the inclusion of a review of the related research in the context of Precision Agriculture (PA) provides a unified and accessible presentation for researchers who are initiating their endeavours in this subject.
Artificial intelligence, digital transformation and cybersecurity in the banking sector: A multi-stakeholder cognition-driven framework

Traditional banking institutions are currently under great pressure from their stakeholders to adapt to new technologies. However, due to this sector's inherent nature, data security cannot be jeopardized. Users must place great trust in their bank branches—a necessary feature of relationships between banking institutions and their clients. Banks' reputation also directly affects their success, ability to attract new customers, and retention of existing ones. These issues mean that making decisions regarding how to deal with the challenges of incorporating artificial intelligence (AI), digital transformation and cybersecurity into the banking sector is quite complex. This study sought to develop a realistic decision-support model by combining cognitive mapping and the decision-making trial and evaluation laboratory (DEMATEL) method in order to address this topic. Group sessions with an expert panel were conducted to apply the methodological procedures, thereby allowing realism to be incorporated into the final results.
Source Inference Attacks in Federated Learning

Federated learning (FL) has emerged as a promising privacy-aware paradigm that allows multiple clients to jointly train a model without sharing their private data. Recently, many studies have shown that FL is vulnerable to membership inference attacks (MIAs) that can distinguish the training members of the given model from the non-members. However, existing MIAs ignore the source of a training member, i.e., the information of the client owning the training member, while it is essential to explore source privacy in FL beyond membership privacy of examples from all clients. The leakage of source information can lead to severe privacy issues. For example, identification of the hospital contributing to the training of an FL model for the COVID-19 pandemic can render the owner of a data record from this hospital more prone to discrimination if the hospital is in a high risk region. In this paper, we propose a new inference attack called source inference attack (SIA), which can derive an optimal estimation of the source of a training member. Specifically, we innovatively adopt the Bayesian perspective to demonstrate that an honest-but-curious server can launch an SIA to steal non-trivial source information of the training members without violating the FL protocol. The server leverages the prediction loss of local models on the training members to achieve the attack effectively and non-intrusively. We conduct extensive experiments on one synthetic and five real datasets to evaluate the key factors in an SIA, and the results show the efficacy of the proposed source inference attack.
Information technology, income inequality and economic growth in sub-Saharan African countries

In this paper, the dynamic relationship between ICT, income inequality and economic growth in sub-Saharan African (SSA) countries is examined during the period 2004–2014. Three ICT and three income inequality indicators were used to examine this linkage. The ICT indicators used include internet penetration, mobile phone penetration and fixed broadband subscription, while the income inequality indicators include the Gini coefficient, the Atkinson index and the Palma ratio. Using the Generalised Method of Moments (GMM) estimation technique, the study found that, on the whole, an increase in ICT development unconditionally leads to an increase in economic growth in the countries under study. The study also found that the threshold level of income inequality, which should not be exceeded in order for the positive impact of ICT on economic growth to be sustained, depends on the ICT proxy used and the income inequality indicator. Specifically, the study found that for ICT to have a sustained positive impact on economic growth, i) the Gini coefficient in the mobile penetration specification should not exceed 0.520; ii) the Gini coefficient and Atkinson index in the internet penetration specification should not exceed 0.531 and 0.560, respectively; and iii) the Gini coefficient, Atkinson index and Palma ratio in the fixed broadband subscriptions should not exceed 0.551, 0.633 and 4.664, respectively. Policy implications are discussed.