Reviewerly/exHarmony huggingface dataset:
Remote Sensing Image Retrieval Based on 3D-Local Ternary Pattern (LTP) Features and Non-subsampled Shearlet Transform (NSST) Domain Statistical Features

With the increasing popularity of high-resolution remote sensing images, the remote sensing image retrieval (RSIR) has always been a topic of major issue.A combined, global non-subsampled shearlet transform (NSST)-domain statistical features (NSSTds) and local three dimensional local ternary pattern (3D-LTP) features, is proposed for high-resolution remote sensing images.We model the NSST image coefficients of detail subbands using 2state laplacian mixture (LM) distribution and its three parameters are estimated using Expectation-Maximization (EM) algorithm.We also calculate the statistical parameters such as subband kurtosis and skewness from detail subbands along with mean and standard deviation calculated from approximation subband, and concatenate all of them with the 2-state LM parameters to describe the global features of the image.The various properties of NSST such as multiscale, localization and flexible directional sensitivity make it a suitable choice to provide an effective approximation of an image.In order to extract the dense local features, a new 3D-LTP is proposed where dimension reduction is performed via selection of 'uniform' patterns.The 3D-LTP is calculated from spatial RGB planes of the input image.The proposed inter-channel 3D-LTP not only exploits the local texture information but the color information is captured too.Finally, a fused feature representation (NSSTds-3DLTP) is proposed using new global (NSSTds) and local (3D-LTP) features to enhance the discriminativeness of features.The retrieval performance of proposed NSSTds-3DLTP features are tested on three challenging remote sensing image datasets such as WHU-RS19, Aerial Image Dataset (AID) and PatternNet in terms of mean average precision (MAP), average normalized modified retrieval rank (ANMRR) and precision-recall (P-R) graph.The experimental results are encouraging and the NSSTds-3DLTP features leads to superior retrieval performance compared to many well known existing descriptors such as Gabor RGB, Granulometry, local binary pattern (LBP), Fisher vector (FV), vector of locally aggregated descriptors (VLAD) and median robust extended local binary pattern (MRELBP).For WHU-RS19 dataset, in terms of {MAP,ANMRR}, the NSSTds-3DLTP improves upon Gabor RGB, Granulometry, LBP, FV, VLAD and MRELBP descriptors by {41.93%,20.87%},{92.30%,32.68%},{86.14%,31.97%},{18.18%,15.22%},{8.96%,19.60%}and {15.60%,13.26%},respectively.
Practical Training for RF Fingerprinting of Commercial Transmitters at the Edge

We present a new RF fingerprinting technique for wireless emitters, which quickly adapts to the changing environment at the Edge because it is both real-time retrainable and power efficient. The quick and low-power training makes it amenable to the deployments at the tactical Edge, 5G Multi-Access Edge Computing, and portable spectrum sensors. While the existing work demonstrated high accuracy for hundreds of devices, flexible and robust Edge solutions do not exist. Efficient in-situ retrainability in the Wild is needed for RF fingerprinting to be used as an authentication mechanism in the Internet of Things. Our technique is based on augmented Ridge Regression (RR) classifiers, easily and efficiently retrainable on delay-loop outputs. The RR learns to identify emitters using bursts of emission samples, transformed and preprocessed by delay-loop reservoirs. Deep delay Loop Reservoir Computing (DLR) is processing architecture that supports general machine learning algorithms on resource-constrained devices by leveraging delay-loop reservoir computing (RC) and innovative loop trees, as demonstrated on our prototype platform. In prior work, we trained and evaluated DLR using high SNR device emissions in clean channels. We here demonstrate how to use DLR for IoT authentication by performing RF-based Specific Emitter Identification (SEI), even in the presence of fading channels and heavy in-band jamming by leveraging a matched filter (MF) extension, dubbed MF-DLR. We show that the MF processing improves the SEI performance of RR without the RC transformation (MF-RR), but the MF-DLR is more robust and applicable for addressing fingerprinting signatures beyond waveform transients (e.g. emission turn-on).
Discriminative Additive Scale Loss for Deep Imbalanced Classification and Embedding

Real-world data in emerging applications may suffer from highly-skewed class imbalanced distribution, however how to deal with this kind of problem appropriately through deep learning needs further investigation. In this paper, we mainly propose a novel cross-entropy based loss function, referred to as Additive Scale Loss (ASL), for deep representation learning and imbalanced classification. To deal with the class imbalanced problem, ASL aims at increasing the loss in case of misclassification, which can avoid the superimposed loss values caused by the large amount of easily classified data in the unbalanced database to dominate the loss value of misclassified data. Moreover, in real-world applications, one data source may be used for multiple scenarios, such as classification and embedding learning, however training two separable models to handle these problems is costly, especially in deep learning area. To tackle this issue, we present and integrate a discriminative inter-class separation term into ASL, and propose a discriminative ASL (D-ASL), which can not only improve the classification performance, but also obtain discriminative representations simultaneously. The discriminative inter-class separation term is general, and can be easily integrated to other loss functions, such as CE and FL, as the byproducts. Finally, a new deep convolutional neural network equipped with D-ASL and a fully-connected (FC) layer is proposed, which can classify the imbalanced image data and obtain the discriminative representations at the same time. Extensive experimental results verified the superior performance of our method.
Few-Shot Partial Multi-Label Learning

Partial multi-label learning (PML) aims at learning a robust multi-label classifier by training on ambiguous data, where each sample is associated with a set of candidate labels, among which only a subset are valid labels. A basic premise of existing PML solutions is to obtain enough partial multi-label samples for inducing the classification model. However, when dealing with new tasks, we may only have a few PML samples for those tasks. Furthermore, existing few-shot learning approaches assume the support (training) samples are precisely labeled; as such, irrelevant labels in the candidate label set may seriously mislead the meta-learner and thus result in a compromised performance. How to achieve PML with limited few-shot support samples is an important and practical problem, but not yet well studied. In this paper, we propose an approach called FsPML (Few-shot PML) to tackle this problem. Specifically, FsPML first performs adaptive distance metric learning via an embedding network using both sample features and label semantics in the embedding space. Next it rectifies the positive and negative prototypes of each new label of the target task in the embedding space. An unseen example can then be classified via its distances to the positive and to the negative prototypes. Experimental results on widely-used multi-label datasets (MS COCO and NUS-WIDE) demonstrate that our FsPML outperforms competitive baselines across different settings, and it can quickly generalize to new tasks with fewer training samples.
Adversarial Attacks on Deep Neural Networks for Time Series Prediction

Time series data is widespread in real-world scenarios. To recover and infer missing information in practical domains, such as stock price monitoring, electricity load forecasting, traffic flows analysis, climate trend prediction, etc., the problem of time series prediction has been widely studied as a classical research topic in data mining. Over the past decade, deep learning architectures are introduced as a vital part of the next generation of time series prediction models. However, recent studies showed that deep learning models are vulnerable to adversarial attacks. In this paper, we study the adversarial attacks on the time series prediction models prospectively. We propose an attack strategy to generate adversarial samples by adding imperceptible perturbed data to the original time series with the goal of reducing the accuracy of time series prediction models. Specifically, the perturbation-based adversarial example generation algorithm is proposed using gradient information of time series prediction model. Moreover, adversarial examples should be imperceptible to humans. To address the challenge, we craft adversarial samples based on importance measuring to perturb the original data locally. We evaluate our attacks on state-of-the-art time series prediction models using three time series datasets. Our results demonstrate that our attacks can effectively evade the time series prediction models, and the adversarial attacks mechanisms can be used as robustness metric for constructing robust time series prediction models.
Laryngeal Cancer Lesion Segmentation in P63 Immunohistochemically Stained Histology Images

In 2017, 1.09 million active laryngeal cancer cases were reported globally, increasing 23.8% in the last three decades. Hematoxylin and eosin (H&E) stained histological analysis is used as a gold standard for diagnosing and staging H&N cancers. Recent studies showed the effectiveness of P63 stain along with H&E stain. Histological analysis of H&E and P63 stained images is performed manually by a pathologist in current medical practices. This process is tiresome and requires specialized skills. The primary factor that the process is manual is that P63 staining is a new technique, which has been developed recently. The second factor is the lack of an available dataset in the recent past. We present an automatic system for laryngeal cancer lesion segmentation in H&E and p63 stained images. The proposed method is an encoder-decoder-based UNet++. We have used MEDISP HICL dataset for training and testing of the proposed network. To our knowledge, only one attempt for this purpose exists in the literature. The proposed network has shown superior accuracy, dice score, and MeanIoU, 95.37%, 87.82, and 0.7868, respectively.
Vedic Multiplier-based International Data Encryption Algorithm Crypto-Core for Efficient Hardware Multiphase Encryption Design

At present, there are several pieces of research on designing and implementing new cryptographic algorithms that are lightweight and resistant to several, if not major forms of security attacks. However, some algorithms such as the International Data Encryption Algorithm (IDEA), which has been around for some time is yet to record any real threat against its functionality. To ensure its continued usage, current implementations rely on multiphase encryption where it is combined with other algorithms such as ROTation (ROT) and Data Encryption Standard (DES) for maximum security strength. Multiphase encryption implies that there is a tendency for an increase in hardware area and a reduction in overall speed. In such cases, having fast and reduced area algorithms are much desired. This paper, therefore, proposes an efficient hardware implementation of the IDEA cipher that is based on arithmetic modulo multiplication—one of the main computations of the IDEA—on a novel Vedic multiplier architecture. The increase in efficiency of the IDEA crypto architecture and the reduction in resources utilization is achieved through an enhancement of its structural architecture to utilize a fixed set of resources for all eight identical rounds of computation and the use of a proposed fast and lightweight Vedic hardware multiplier. The proposed hardware modification and resulting architecture are designed using the Xilinx ISE and Vivado tools. The architecture is synthesized using Precision Synthesis Tool (PS) and simulated using Modelsim SE 10.6d and ISIM simulation tools. The proposed IDEA cipher is 100% more efficient when designed based on the Vedic multiplier compared to existing designs. The hardware architecture is implemented on Spartan-6-FGG484 Field Programmable Gate Array (FPGA) using Verilog HDL. Verified results show that the proposed Vedic-based IDEA occupied 212 Slices with the Vedic multiplier only occupying 28 Slices out of the total 212. The proposed architecture operates at a maximum frequency of 253.3 MHz.
A fine‐grained medical data sharing scheme based on federated learning

Abstract With the rapid development of smart health, the privacy problem of medical data has become more prominent. Aiming at the problem of mining the potential value of medical data and realizing secure sharing, a fine‐grained medical data sharing scheme based on federated learning is proposed. The scheme uses collaboration‐oriented attribute‐based encryption technologies to formulate fine‐grained access strategies, allowing medical institutions or doctors to decrypt individually or collaboratively with certain conditions to achieve the purpose of accurately screening the required medical data. In the proposed scheme, the model parameters are shared such that the screened medical data is modeled and analyzed based on federated learning, which allows more people to enjoy top medical resources. In addition, a blockchain‐based incentive mechanism is used to reward medical institutions which are either honest with high‐quality or helpful in decryption. Hence, the enthusiasm of various medical institutions to screen data and participate in federal learning is improved. Finally, security analysis shows that the scheme is secure, and theoretical analysis and simulation test show the practicability of the scheme.
Probabilistic Modeling for Image Registration Using Radial Basis Functions: Application to Cardiac Motion Estimation

Cardiovascular diseases (CVDs) are the leading cause of death, affecting the cardiac dynamics over the cardiac cycle. Estimation of cardiac motion plays an essential role in many medical clinical tasks. This article proposes a probabilistic framework for image registration using compact support radial basis functions (CSRBFs) to estimate cardiac motion. A variational inference-based generative model with convolutional neural networks (CNNs) is proposed to learn the probabilistic coefficients of CSRBFs used in image deformation. We designed two networks to estimate the deformation coefficients of CSRBFs: the first one solves the spatial transformation using given control points, and the second one models the transformation using drifting control points. The given-point-based network estimates the probabilistic coefficients of control points. In contrast, the drifting-point-based model predicts the probabilistic coefficients and spatial distribution of control points simultaneously. To regularize these coefficients, we derive the bending energy (BE) in the variational bound by defining the covariance of coefficients. The proposed framework has been evaluated on the cardiac motion estimation and the calculation of the myocardial strain. In the experiments, 1409 slice pairs of end-diastolic (ED) and end-systolic (ES) phase in 4-D cardiac magnetic resonance (MR) images selected from three public datasets are employed to evaluate our networks. The experimental results show that our framework outperforms the state-of-the-art registration methods concerning the deformation smoothness and registration accuracy.
FDA-SSD: Fast Depth-Assisted Single-Shot MultiBox Detector for 3D Tracking Based on Monocular Vision

In this study, a set of benchmarks for object tracking with motion parameters (OTMP) was first designed. The sample images were matched with the spatial depth of the camera, the pose of the camera, and other spatial parameters for the training of the detection model. Then, a Fast Depth-Assisted Single-Shot MultiBox Detector (FDA-SSD) algorithm suitable for 3D target tracking was proposed by combining the depth information of the sample into the original Single-Shot MultiBox Detector (SSD). Finally, an FDA-SSD-based monocular motion platform target detection and tracking algorithm framework were established. Specifically, the spatial geometric constraints of the target were adapted to solve the target depth information, which was fed back to the detection model. Then, the normalized depth information of the target was employed to select the feature window of the convolutional layer for the detector at a specific scale. This significantly reduces the computational power for simultaneously calculating detectors of all scales. This framework effectively combines the two-dimensional detection model and the three-dimensional positioning algorithm. Compared with the original SSD method, the network model designed in this study has fewer actual operating parameters; the measured detection operation speed was increased by about 18.1% on average; the recognition rate was maintained at a high level consistent with that of the original SSD. Furthermore, several groups of experiments were conducted on target detection and target space tracking based on monocular motion platforms indoors. The root mean square error (RMSE) of the spatial tracking trajectory was less than 4.72 cm. The experimental results verified that the algorithm framework in this study can effectively realize tasks such as visual detection, classification, and spatial tracking based on a monocular motion platform.
GraphANGEL: Adaptive aNd Structure-Aware Sampling on Graph NEuraL Networks

Graph neural networks (GNNs) have gained increasing attention in their great success at graph representation learning. In GNNs, the neighborhood of the target node is aggregated iteratively to capture and learn its local structure and neighbor information. Observing that different nodes often require a distinct number of iterations to better learn the representation, we propose an adaptive and structure-aware graph sampling scheme GraphANGEL for GNNs. However, it is quite challenging because both the suitable range of exploration and the important substructure in the neighborhood are difficult to determine. Exploiting the unique feature of random walk mixing time and various node structural role importance measures, we first propose a lightweight component to flexibly estimate the proper neighborhood exploration depth for each target node. Then we investigate different importance metrics to identify and sample the most structurally critical subgraphs that carry a larger influence in messaging passing. Moreover, since different importance metrics unveil different aspects of the graph, we combine and ensemble various importance measures with attention to boost the final performance. In this manner, our method adaptively and explicitly embeds the structural importance information of a node and its critical neighborhood at the same time for finer structure-aware graph representation learning. Evaluation on the benchmark datasets suggests the competitive performance of GraphANGEL to the state-of-the-art approaches, demonstrating the effectiveness of our adaptive and structure-aware sampling approach.
Proposal of Self-generation of Reward for danger avoidance by disregarding specific situations

Reward design in reinforcement learning should be less burdensome on the designer and be able to respond to changes in the environment and task. Therefore, we are studying the Self-generation of Reward, a method of reward design that does not depend on changes in the environment or task. Self-generation of Reward is a method in which an agent generates its own reward by evaluating sensor information from the outside world from multiple perspectives, using the sense of danger avoidance of biological life as an index. The only desired indicator of danger avoidance is a negative evaluation when the sensor information is dangerous. However, when the state is not dangerous, the evaluation is positive. Positive evaluation means that the agent is desired to achieve the goal, but the non-dangerous state has nothing to do with achieving the goal. Therefore, there is a problem that the measure of risk avoidance includes the evaluation of the achievement of undesired goals. To solve this problem, we propose a method to control whether sensor information is evaluated or not. Improve the indicator of risk avoidance to generate only negative evaluations by not including positive evaluations in the sensor evaluation if they are indicated. In this study, we conducted a simulation experiment using path finding on a grid map to verify whether the proposed method can be used for learning. As a result, we found that learning was possible by adjusting the reward given to each action.
Quantum (hyper)computation through universal quantum gates in water coherent domains

Abstract According to quantum electrodynamics (QED), liquid water is composed by two phases, the first one characterized by a coherent state in which all the molecules oscillate in phase, the other by a non-coherent state in which they are all non-correlated. In the coherent state, the phase-correlated oscillations take place within macroscopic spatial regions called “coherent domains” (CD), admitting a spectrum of excited energy levels and generating, at their borders, an evanescent coherent e.m. field. When two or more excited CDs are sufficiently close to each other, the overlapping between their evanescent fields gives rise to a novel type of interaction due to the mutual exchange of virtual photons by quantum tunnel effect. Furthermore, when such water coherent domains are enclosed with waveguides consisting of suitable materials and design, these effects are stabilized and enhanced, allowing for the realization of an extended network of interacting coherent domains. In this paper, we’ll discuss how to exploit this dynamics to perform quantum computations by setting-up a set of universal quantum gates and calculate their operational time as a function the main parameters of the proposed physical model. We show this model can represent a basic architecture for a novel kind of quantum hyper-computer, characterized by a very high computational speed and able to overcame some of the main issues currently affecting the quantum computational frameworks so far proposed.
Apply Green Marketing Strategies to Improve Market Performance by Competitive Advantage as Mediating: An Analytical Study of Some Small Organizations in Iraq

Small organizations are an important part of each large economy, and research shows that they are more creative and effective in their management, project management, and marketing strategies. Many businesses use green marketing strategies to achieve a competitive advantage and enhanced their overall performance. This research’s goal is to evaluate the influence of Green marketing strategy actions on market performance and competitive advantage. Feedback from a questionnaire containing 41 items on a five-point Likert scale is collected using a quantitative methodology. Small organizations in Iraq are the analysis unit. Supervisors of divisions are the responders in this study. To evaluate the findings, Smart PLS 3.2.9 was utilised. The findings of a partial least squares (PLS) path analysis supported factors’ direct links with market performance. According to the findings, competitive advantage partly moderates the association between green marketing strategies and market performance to some extent. The study makes numerous recommendations that will assist both scholars and regulators in conducting additional work in this field and also improving organizations’ (MP) and (CA) in the long term.