Reviewerly/exHarmony huggingface dataset:
Topology Aligned Least Cost Routing Model for Canals

Topography plays an important role in the development of infrastructural facilities such as irrigation canals and road networks. To determine the least-cost flow path between two geo-locations, given the grid-based Digital Elevation Models (DEMs) and a unit cost of construction per length, cost of lift to raise water up to a height of 10 meters, set of co-ordinates the resultant flow needs to pass-through. This work develops a generic model namely (i) Highly Scalable Gravitational Flow Model (HSGFM) and (ii) Lift Based Flow Model (LBFM) where anti-gravitational force or pumping is used to lift the water along the surface of the terrain. The algorithm correctness values for the 1KM resolution stand at 82.10%, whereas for 90 meters resolution stands at 82.08%. The average value stands out to be 82.09% proving that both the HSGFM and LBFM algorithms are very effective in practice.
Multi-Task Attentional U-Net for Hyperspectral Image Denoising

Hyperspectral image (HSI) denoising is a critical preprocessing step for ensuring the usability of HSIs. However, current deep learning-based approaches still struggle with modeling the global spectral correlation among bands, which is crucial for high-quality denoising. To address this issue, we introduce a multitask attention module and embed it into a U-Net architecture to yield a multitask attentional U-Net (MTA-Net) for HSI denoising. The module enforces all bands to focus on the same region by sharing the same attention map across all bands. This ensures that all bands capture the same image structure, effectively modeling the global spectral correlation. Experimental results demonstrate that the proposed MTA-Net achieves state-of-the-art performance on both synthetic and real-world data.
Self-Supervised Dense Depth Estimation with Panoramic Image and Sparse Lidar

The 360-depth estimation with spherical images and LiDAR data has recently become increasingly popular in autonomous driving and scene reconstruction. Compared with perspective images, spherical images have omnidirectional FoV, which exceedingly matches LiDAR data. However, the spherical distortion makes the 360-depth estimation a great challenge. To address this problem, we propose a self-supervised 360 depth estimation network in this paper. The network consists of a spherical convolution branch to extract panoramic image features and a ResNet branch to extract LiDAR features. Then an attention-based decoder is designed to estimate the depth. The reprojection error is used to self-supervise the network training. Experiments on the KITTI-360 dataset demonstrate the effectiveness of the proposed method.
Computer-Aided Breast Cancer Diagnosis Using Deep Learning: Malignancy Detection and HER2 Scoring

Breast cancer is the leading cause of cancer related deaths in women worldwide. Proper diagnosis is highly essential to determine the appropriate patient treatment based on their specific tumor type. Human epidermal growth factor receptor 2 (HER2) gene amplification is considered a poor prognostic factor in breast cancer and an excellent predictive marker. Fortunately, breast cancer survival rates were shown to significantly improve upon following anti-HER2 targeted therapies. Diagnosis of breast cancer has many phases: clinical, radiological, and pathological diagnosis. The role of artificial intelligence (AI) within radiological diagnosis is now well established and accepted by medical practitioners. In this study, a two-phase deep learning-based diagnostic tool is presented to aid in the pathological diagnosis of breast cancer. In the first phase, breast cancer classification (benign vs. malignant) was performed using hematoxylin & eosin (HE) images. In the second phase, HER2 classification (positive vs. negative) was considered using immunohistochemistry (IHC) images. Four pretrained networks with different architectures were compared in both phases which are EfficentNetB0, InceptionV3, ResNet34, and VGG19. For cancer detection, InceptionV3 and VGG19 gave the highest accuracy of 97% using the public BreakHis dataset. As for HER2 classification, an accuracy of 100 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> was achieved by the four considered networks for a private dataset. This work lays the foundation for creating a reliable and comprehensive automated screening tool that would assist in the early detection and proper treatment of breast cancer.
Probability-Based Binary Attribute Weighted Prediction Network for SAR Image Classification

The problem of insufficient samples has been limiting the performance of intelligent interpretation in Synthetic Aperture Radar (SAR) images. Humans have the ability to recognize new instances with only a few samples, indicating that attributes play a crucial role in recognition. Attributes can be shared across categories and provide a distinctive representation. Motivated by this fact, this paper proposes an attribute-guided network consisting of a base classifier (BC) and an attribute classifier (AC). Firstly, we design binary attributes for SAR objects to enable more distinct feature representations. Secondly, the images are mapped into a semantic embedding space by embedding the attribute vectors. Finally, the performance of few-shot classification in SAR images is improved by jointly optimizing the loss in both attribute space and deep feature space. Our attribute-based framework is validated through ablation experiments on the MSTAR dataset and a self-built SAR aircraft dataset.
Are Classic Forensic Tools Effective on Satellite Imagery?

Satellite images are becoming an increasingly important part of our world. Such images are used to forecast the weather, track green house gas emissions, monitor agricultural crop health, and many other applications. Such advances are possible thanks to the free availability of a large number of satellite images. Satellite imagery now plays a key role in many areas, including external security. In this context, it is necessary to question the reliability of this data. Can the authenticity of a satellite image be guaranteed? How can one protect oneself against an entity wishing to hide illegal military material or, conversely, to incite action against another entity by falsely suggesting that it possess such material? If the forensic analysis of photographs has attracted a great deal of academic interest in recent years, this is not yet the case for satellite imagery. In this paper, we propose a methodology to create a very simple but interesting dataset to test the performance of state-of-the-art forensic methods on pristine and manipulated satellite images. Despite the strong performance of such algorithms, satellite images require special attention due to the nature of the images themselves.
Digital Twin Earth for Climate Change Adapation: An AI based Federated System

Despite the permanent efforts to reduce emissions and achieve carbon neutrality a warmer climate is no longer to be avoided. The European mission „Adaptation to Climate Change" aims to build resilience by 2030 in at least 150 European communities and regions. At the same time, the „Destination Earth" (DestinE) initiative promotes the use of digital twins of the Earth enabling a thorough assessment of climate change by leveraging an accurate digital model of the Earth that can be used to monitor, model, and predict natural and human activity, and to develop and test scenarios for a more sustainable growth. Climate models describe changes at scales of 50km to 150km. However, adaptation measures shall be applied at human activities scales, from 10m to 1km. We propose to achieve this by scale-out novel paradigms of Artificial Intelligence for Earth Observation (AI4EO) including the use of coupled models across domains and spatiotemporal scales. The envisaged R&D work will be carried out in the project Competence Center for Climate Change Digital Twin Earth for forecasts and societal redressement: DTEClimate, in the frame of Romania National Recovery and Resilience Plan.
Multi-Scale Transformer Network for Hyperspectral Image Denoising

Removing noise from hyperspectral images (HSIs) has been widely regarded as one of the most meaningful preprocessing tasks in remote sensing image interpretation. In this paper, we aim to extend the Transformer backbone to HSI denoising, and propose a Multi-scale Transformer Denoising Network (MTDNet). Specifically, we design a multi-head global attention module to alleviate the computational burden caused by self-attention. Furthermore, we propose a multi-scale feed-forward network in which three branches of multi-scale features are extracted through dilated convolution. It enriches the non-linear feature transformation in the Transformer block. Both the objective and subjective experiments on the ICVL dataset demonstrate the superiority of the proposed MTDNet over four closely related methods.
An Optimized Association Rules Mining Framework for Chinese Social Insurance Fund Data Auditing

Association rules mining with the Chinese social insurance fund dataset can effectively discover different kinds of errors, irregularities, and illegal acts by providing auditors with relationships among the items and therefore improve auditing quality and efficiency. However, traditional positive and negative association rules (PNARs) mining algorithms inevitably produce too many meaningless or contradictory rules when these two types of rules are mined simultaneously, which brings a huge challenge to auditors retrieving decision information. Aimed to reduce the quantity of low-reliability PNARs without missing interesting rules, this paper first proposes an improved PNARs mining algorithm with minimum correlation and triple confidence threshold to control the mined rules number by narrowing the range of confidence settings. Then, a novel pruning algorithm based on the inclusion relation of the rule’s antecedent and consequent is given to remove those redundant rules. After that, the proposed optimized PNARs mining approach is applied to the Chinese social insurance fund dataset starting with audit features influence factors mining using the Hash table. The experimental results with different datasets show that the proposed framework not only can ensure effective and interesting rules extraction but also has better performance than traditional approaches in both accuracy and efficiency, reducing the number of redundant PNARs by over 70.1% with experimental datasets and average 78.5% with auditing data mining, respectively.
Coherence dynamics in quantum algorithm for linear systems of equations

Abstract Quantum coherence is a fundamental issue in quantum mechanics and quantum information processing. We explore the coherence dynamics of the evolved states in HHL quantum algorithm for solving the linear system of equation <?CDATA $A\overrightarrow{x}=\overrightarrow{b}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>A</mml:mi> <mml:mover accent="true"> <mml:mrow> <mml:mi>x</mml:mi> </mml:mrow> <mml:mrow> <mml:mo stretchy="true">→</mml:mo> </mml:mrow> </mml:mover> <mml:mo>=</mml:mo> <mml:mover accent="true"> <mml:mrow> <mml:mi>b</mml:mi> </mml:mrow> <mml:mrow> <mml:mo stretchy="true">→</mml:mo> </mml:mrow> </mml:mover> </mml:math> . By using the Tsallis relative α entropy of coherence and the l 1, p norm of coherence, we show that the operator coherence of the phase estimation P relies on the coefficients β i obtained by decomposing ∣ b 〉 in the eigenbasis of A . We prove that the operator coherence of the inverse phase estimation <?CDATA $\widetilde{P}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mover accent="true"> <mml:mrow> <mml:mi>P</mml:mi> </mml:mrow> <mml:mrow> <mml:mo stretchy="true">̃</mml:mo> </mml:mrow> </mml:mover> </mml:math> relies on the coefficients β i , eigenvalues of A and the success probability P s , and it decreases with the increase of the probability when α ∈ (1, 2]. Moreover, the variations of coherence deplete with the increase of the success probability and rely on the eigenvalues of A as well as the success probability.
INSAR Phase Filtering By Attention-Based Reservoir Computing For High Reliability

In this paper, we propose the attention-based reservoir computing and conduct an experiment of InSAR phase filtering. We show that the proposed method can estimate the wrapped phase values more accurately compared with a conventional method using only reservoir computing networks.
Blockchain Based Future Banking by Decentralized Exchanges

Decentralized Finance (DeFi) is a blockchain-based financial system that offers open and transparent financial services. This paradigm will enhance traditional banking security, transparency, inclusivity, and efficiency. Decentralized financial systems employ blockchain decentralized exchanges (DEX)'s without intermediaries and their transactions are protected by cryptography. Public ledger users may verify blockchain transactions. Without ID or address, traditional banks decline customers. Anyone with internet may trade finance on DEXs’. This inclusion may aid unbanked and underbanked individuals, enhancing global prosperity. Future banks will employ DEX for efficiency and intermediaries and complex processes of conventional banking are removed. DEX speeds up transactions by eliminating intermediaries. Scalability issues must be addressed as platforms grow. Future banking on DEX-based blockchain requires study into the ecosystem's multiple applications and advancements.
Improved Semantic Segmentation for Identification of Flooded Regions in UAV Aerial Images: A Transformer-Based Approach

The Earth Observation data provides an effective tool to assess post-disaster damage for better relief and rescue efforts management. However, the longer satellite revisit time might delay the rescue efforts. In contrast, Unmanned Aerial Vehicles (UAV) can be rapidly deployed with a customized flight plan. This work focuses on analyzing images from UAV to identify flooded regions. Specifically, a Transformer based semantic segmentation method is proposed for flooded region identification. The proposed encoder-decoder model integrates the features of UNet (ResNet18 backbone) with that of Vision in Transformer (ViT). These fused features are fed to a decoder module to obtain the final segmentation map. The proposed work is evaluated on the FloodNet dataset containing post-disaster UAV images after Hurricane Harvey. A mIoU of 86.84% is obtained using the proposed approach compared to a mIoU of 74.95% using the traditional UNet model. The significant improvement in mIoU demonstrates the robustness of ViT in learning discriminant features for post-disaster scene understanding.
A Multi-Intermediate Domain Adversarial Defense Method for SAR Land Cover Classification

Convolutional neural networks (CNNs) have achieved greate success in a variety of computer vision tasks. However, they are susceptible to elaborate, human-imperceptible adversarial noise patterns, which limit their deployment in safety-critical systems. In this paper, we propose an adversarial training method for synthetic aperture radar (SAR) image segmentation, which can effectively suppress the effects of adversarial perturbations. The proposed method introduces a multiple intermediate domain mechanism to enhance the robustness of the network to adversarial attacks, by dynamically adjusting the distribution of input data during the training process, without modifying the network structure or adding a separate mechanism to detect adversarial images. Experiments validate that our approach not only improves the segmentation accuracy of the network, but also effectively enhances the robustness of the network when facing white-box adversarial attacks.
Assisted Multi-Frame Approach for Super-Resolution in UAV Photogrammetric Images

Improvement in spatial resolution of remote sensing images will always be a hot-topic since the sharpening of image objects is mandatory for many appications. Even thought image instruments have received great improvements in the recent years, super-resolution techniques are welcome to increase even more the level of quality of the data for further interpretations. In this paper we present a multi-frame super-resolution approach that uses a set of UAV images acquired at the same spot, but with slight different perspectives caused by random fluctuations. The small off-sets between consecutive pixels of the low spatial resolution images are considered at subpixel level to feed an arithmetic system of equations able to produce high resolution pixels, which are the unknowns of the system. The results are soundness and could show visual enhancement. However, further developments is need to in deep undertanding and possible advancement of the designed approach.
Process4eo: Super-Resolution Algorithms for Novel Satellite Sensors

process4EO <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> , the software solution for ground processing of Earth Observation imagery that Deimos Space has successfully deployed in several EO missions, has been recently upgraded with a super-resolution algorithm that substantially increases the spatial resolution of sensor-limited Earth Observation satellites by exploiting the overlapped sampling concept.The process4EO <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> super-resolution algorithm is not powered by AI techniques, but it relies on classical Bayesian estimation. While AI-based super-resolution techniques are actually trained predictors that cannot completely guarantee the truthfulness of details on any scene type, process4EO <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> implements an estimator able to recover spatial frequencies that have not been absorbed by the optics.
Risce- An Explainable ML Chain for Practical Sustainable Agriculture

Knowledge systems in sustainable agriculture see a big gap with end users due to lack of easy-to-use interfaces with existing knowledge. Adding to the problem, decisions coming from black-box models are not understandable for most users. We try to bridge the gap with an integrated chain of explainable ML models to address the most useful applications in the agri-food industry. To make the integrated model available to users and help them draw benefits out of it, we also propose a novel idea of an explainable ML framework for interaction with human users. This human-in-the-loop approach makes ML models more trustworthy. End-users understand the output from ML models and also improve models with feedback. The application interface is also proposed to have features for multilingual communication among users to build communities. Feedback from communities help further refine ML models. The proposed system is named as Reusable Intelligent solution for Cultivation Enhancement (RISCE). In this article, we provide a demonstration of our system with an intrinsically explainable model for crop vigor analysis.
Scribble-Supervised Target Extraction Method Based on Inner Structure-Constraint for Remote Sensing Images

Weakly supervised learning based on scribble annotations in target extraction of remote sensing images has drawn much interest due to scribbles’ flexibility in denoting winding objects and low cost of manually labeling. However, scribbles are too sparse to identify object structure and detailed information, bringing great challenges in target localization and boundary description. To alleviate these problems, in this paper, we construct two inner structure- constraints, a deformation consistency loss and a trainable active contour loss, together with a scribble-constraint to supervise the optimization of the encoder-decoder network without introducing any auxiliary module or extra operation based on prior cues. Comprehensive experiments demonstrate our method’s superiority over five state-of-the- art algorithms in this field. Source code is available at https://github.com/yitongli123/ISC-TE.
Improper Normalization on Hyperspectral Data Cause Cheating Result on Retrieval Task

We address the problems inherent in studying wrongful result coming from analyzing the hyperspectral data in Baiyangdian Lake. Let’s consider the following two cases: (i) Normalize on the entire set, then break into training/test sets and make predictions on the test set. (ii) Break into training/test sets, then normalize training/test sets separately, and make predictions on the test set. The main purpose of this paper is to present the case (i) yields a systematic overestimation of prediction capability that is triggered by the normalization. We provide theoretical analysis and use our example to demonstrate in some circumstances, the R-square criteria can reach to one for any data sets. This perfect R-square rate neither implies the data sets are well collected, nor does it give a good assessment on the algorithms it used, it happens when we apply (i) and combine Z-score transformation with leave-one-out cross validation together. Normalization is a fundamental block that most scientists will do before applying their algorithm, the paper indicates that the results of data analysis may be overestimated on a large scale.
Exponential Feature Extraction and Learning for Pixel-Wise Hyperspectral Image Compression

Hyperspectral images are captured over a wide range of the electromagnetic spectrum providing detailed information about the Earth’s surface. Hyperspectral imaging is widely used in agriculture, astronomy, molecular biology, physics, etc. Due to the very large size of information that the remotely-sensed hyperspectral data cube contains, transmission is a challenge. Various hyperspectral image compression techniques have been proposed in the last decades. We propose a new lossy compression technique that is based on the Fast Fourier Transform, negative exponential feature extraction, and machine learning. For the Pavia University data set, we obtained a compression rate of approximately 11, while preserving an important amount of the information in the original scene. Furthermore, we visualized the decompressed data starting from only two retained parameters and we evaluated the results by employing various metrics.
Incorporating Texture Features into Optical Flow for Atmospheric Wind Velocity Estimation

Wind velocity estimation is a critical component of weather prediction, but accurately estimating atmospheric winds with in-place instruments is expensive, inefficient, and provides only sparse coverage. Consequently, methods which can extract wind patterns from remote sensing data would offer significant benefits—satellites and satellite-derived data in particular, provide comprehensive coverage of global weather phenomena. In this paper, we develop an optical flow method which successfully extracts dense wind velocity estimates from water vapor data, a commonly estimated quantity by satellites equipped with infrared or microwave instruments. The test-data comes from direct numerical simulations of a mesoscale convective system over the eastern Pacific and provides accompanying ground truth wind velocities, allowing us to quantitatively measure the performance of our methods.