Reviewerly/exHarmony huggingface dataset:
Determining sampling spacing according to volume of investigation in geophysical surveys, application of air dose rate mapping

SUMMARY Geophysical measurements are a sort of averaging a physical property over a volume of investigation (VOI). Within the VOI, the spatial elements closer to the detector contribute more to the measured value compared to farther elements. In this study, a VOI-oriented algorithm is proposed which establishes a relation between the localization probability, the sampling spacing and the size of the exploration target. Hence, the probability of localizing a target could be calculated as a function of sampling spacing when the target dimension is known. The novelty of this work is the use of VOI in the sampling optimization, which is important for geophysical survey design in environmental sampling and mineral exploration. Although the published sampling optimization methods are often variogram-oriented, the one presented here is based on the spatial variability of the measured property. Finally, coupling the VOI-oriented algorithm with a variogram model could be recommended in order to consider both the measurement mechanism and the spatial variability in the sampling design.
Multi-agent collaboration environment simulation

Machine Learning (ML) and Artificial intelligence (AI) have increased automation potential within defense applications such as border protection, compound security, and surveillance applications. Advances in low-size weight and power (SWAP) computing platforms and unmanned aerial systems (UAS) have enabled autonomous systems to meet the critical needs of future defense systems. Recent academic advances in deep learning aided computer vision yielding impressive results on object detection and recognition, necessary capabilities to enable autonomy in defense applications. These advances, often open-sourced, enable the opportunistic integration of state-of-the-art (SOTA) algorithms. However, these systems require a large amount of object-relevant data to transfer from general academic domains to more relevant situations. Additionally, UAS systems require costly verification and validation of autonomy logic. These challenges can lead to high costs for both training data generation and costly field autonomy integration and testing activities. To address these challenges, in conjunction with partners, Elbit America has developed a multipurpose synthetic simulation environment capable of generating synthetic training data and prototyping, verifying, and validating autonomous distributed behaviors. We integrated a thermal modeling capability into Unreal Engine to create realistic training data by enabling the real-time simulation of SWIR, MWIR, and LWIR sensors. This radiometrically correct sensor model capability enables the simulation-based training data generation for our object recognition and classification pipeline, called Rapid Algorithm Development and Deployment (RADD). Several drones were instantiated using emulated flight controllers to enable end-to-end autonomy training and development before hardware availability. Herein, we describe an overview of the simulation environment and its relevance to detection, classification, and distributed autonomous decision-making.
Hamiltonian engineering using coupled cavity arrays

Analog quantum simulators rely on programmable quantum devices to emulate Hamiltonians describing various physical phenomenon. Photonic coupled cavity arrays are a promising platform for realizing such devices. Using a silicon photonic coupled cavity array made up of 8 high quality-factor resonators and equipped with specially designed thermo-optic island heaters for independent control of cavities, we demonstrate a programmable device implementing tight-binding Hamiltonians with access to the full eigen-energy spectrum. We report a ~50% reduction in the thermal crosstalk between neighboring sites of the cavity array compared to traditional heaters, and then present a control scheme to program the cavity array to a given tight-binding Hamiltonian.
Building blocks of scalable quantum information science

Quantum information technologies are expected to enable transformative technologies with wide-ranging global impact. Towards realizing this tremendous promise, efforts have emerged to pursue quantum architectures capable of supporting distributed quantum computing, networks and quantum sensors. Quantum architecture at scale would consist of interconnected physical systems, many operating at their individual classical or quantum limit. Such scalable quantum architecture requires modeling that accurately describes these mesoscopic hybrid phenomena. I will discuss predictive theoretical and computational approaches to study dynamics, decoherence, and correlations in such hybrid quantum technologies.
Synthetic data, measured data integrated learning experiments

This paper addresses the problem of adequately training deep learning networks to be operational on measured Synthetic Aperture Radar (SAR) data when the quantity of measured data alone is insufficient. In particular, this is a study in transfer learning utilizing synthetically generated SAR data and measured SAR data to train a deep learning algorithm to classify military tactical vehicles. The present study is motivated by sparsity of measured data for Air Force targets of interest. Specifically, this effort builds on an existing Convolution Neural Network (CNN) architecture, i.e. Understanding the Synthetic and Measured GAP from the CNN Classifier Perspective and aims to improve achievable performance by increasing the algorithm complexity and performing parameter analysis on MSTAR data.
Using image enhancement to improve automated analytics

Many tasks previously performed by human analysts watching videos (live or recorded) are now being done by machine learning and artificial intelligence-based tools. Image enhancement techniques have been developed over decades to improve the quality of video for human operators. However, little work has been done to determine if this same approach can improve the efficacy of automated tools. In this paper, we briefly look at the improvement image enhancement can provide to common tasks such as detection, classification, and identification.
Enhanced kinship verification analysis based on color and texture handcrafted techniques

Abstract Nowadays, kinship verification is an attractive research area within computer vision. It significantly affects applications in the real world, such as finding missing individuals and forensics. Despite the importance of this research topic, it still faces many challenges, such as low accuracy and illumination variations. Due to the existence of different classes of feature extraction techniques, different types of information can be extracted from the input data. Moreover, the fusion power produces complementary information that can address kinship verification problems. Therefore, this paper proposes a new approach for verifying kinship by fusing features from different perspectives, including color-texture and color features in different color spaces. Besides using promising methods in the field, such as local binary pattern (LBP) and scale-invariant feature transform (SIFT), the paper utilizes other feature extraction methods, which are heterogeneous auto-similarities of characteristics (HASC), color correlogram (CC), and dense color histogram (DCH). As far as we know, these features haven’t been employed before in this research area. Accordingly, the proposed approach goes into six stages: preprocessing, feature extraction, feature normalization, feature fusion, feature representation, and kinship verification. The proposed approach was evaluated on the KinFaceW-I and KinFaceW-II field standard datasets, achieving maximum accuracy of 79.54% and 90.65%, respectively. Compared with many state-of-the-art approaches, the results of the proposed approach reflect the promising achievements and encourage the authors to plan for future enhancement.
A few shots at few shot learning

Deep learning models are currently the models of choice for image classification tasks. But large scale models require large quantities of data. For many tasks, acquiring a sufficient quantity of training data is not feasible. Because of this, an active area of research in machine learning is the field of few sample learning or few shot learning (FSL), with architectures that attempt to build effective models for a low-sample regime. In this paper, we focus on the established few-shot learning algorithm developed by Snell et al.1 We propose an FSL model where the model is produced via traditional encoding with the backend output layer replaced with the prototypical clustering classifier of Snell et al.. We hypothesize that this algorithm’s encoding structure produced by this training may be equivalent to models produced by traditional cross-entropy deep learning optimization. We compare few shot classification performance on unseen classes between models trained using the FSL training paradigm and our hybrid models trained traditionally with softmax, but modified for FSL use. Our empirical results indicate that traditionally trained models can be effectively re-used for few sample classification.
Real time machine learning and decision-making with hardware expert systems and gradient descent trained expert systems

Three previously proposed machine learning techniques (a technique based on applying gradient descent training to a fractional value expert system, the fractional value expert system technique’s real time hardware implementation, and a Boolean expert system hardware implementation) are evaluated for application to defense-relevant real time processing challenges. These techniques are defensible, meaning that their decisions are constrained by logical pathways that can be reviewed by a human prior to a decision being made and acted upon by the system (as opposed to simply explained afterwards). These techniques and several conventional techniques are evaluated under multiple defense-relevant real time processing scenarios.
Evolution of hardware-aware neural architecture search (NAS) on the edge

Neural Architecture Search (NAS) is a method of autonomously designing deep learning models to achieve top performance for tasks such as data classification and data retrieval by using defined search spaces and strategies. These strategies have demonstrated improvements in a variety of tasks over ad-hoc deep neural architectures, but have presented unique challenges related to bias in search spaces, the intensive training requirements of various search strategies, and inefficient model performance evaluation. These challenges have been a primary focus for NAS research until recently. However, artificial intelligence (AI) on the edge has emerged as a significant area of research and producing models that achieve top performance on small devices with limited resources has become a priority. NAS research has primarily been focused on improving models by using more diverse search spaces, improving search strategies, and evaluating models faster. A limitation when applied to edge devices is that NAS has historically been finding superior deep neural networks that have become increasingly more difficult to port to embedded devices due to memory limitations, computational bottlenecks, latency requirements, and power restrictions. In recent years, researchers have begun to consider these limitations and develop methods for porting deep neural networks to these embedded devices, but few methods have incorporated the device itself in the training process efficiently. In this paper, we compile a list of methods actively being explored and discuss their limitations. We also present our evidence in support of the use of genetic algorithms as a method for hardware-aware NAS that efficiently considers hardware, power, and latency requirements during training.
Algorithm for determining the boundary contours of plasma discharges in the formation of surface of memristor structures

The article proposes to control of algorithm for the process of forming a coating with an increased content of an oxide layer resulting from the application of plasma formation of surface films. An implementation of an algorithm for adaptive determination of the contours of plasma discharge boundaries during the formation of films of memristor structures is proposed. The construction of the algorithm is based on the use of a multicriteria data processing method in the function of the boundary detector. An implementation of an adaptive change in the contact mask of the plasma discharge with the surface is proposed. Analysis of the contact size and density influences the shape and rate of formation of the oxide layer. The appearance of such a coating has the ability, when exposed to current, to form a complex curve of a function of a given shape. With the subsequent application of voltage, it can be used as an activation function. Recommendations on control and changes influences are presented. A hardware model implementation of an artificial neuron based on blocks of digital elements is presented. Examples of solving the problem of predicting the movement of an actuating element in the control of robotic complexes based on the formed neurons are given.
Enhancing quantum sensing and interferometry through entanglement

Novel multiphoton entangled states have been developed, which offer utility for sensors. These states are linear combinations of M&N states (LCMNS). Closed form expressions for the coefficients have been derived that result in states offering sensitivities far greater than those found in the literature. Adaptive optics procedures have been conceived that permit these coefficients to be determined. LCMNS can also be used to generate high quality hyperentangled states. Various states generated by this process are developed. The utility of these procedures for quantum interferometry and RF signal detection is examined. The susceptibility to loss of the various interferometry and RF detection approaches is examined using an open systems analysis. The novel quantum interferometer uses adaptive optics, which offers a huge improvement in phase sensitivity over previous interferometers, while greatly reducing the number of photons used. Large numbers of photons can contribute to vibration reducing sensitivity. The approach based on entanglement presented here, even in the presence of loss can reduce the number of photons used significantly, while enhancing phase sensitivity. The new interferometer simultaneously minimizes phase error, maximizes visibility and greatly reduces the number of photons used. When the interferometer employing LCMNS is combined with an atomic RF antenna, E-field sensitivity is increased to a value nearly 400,000 times that found in the literature. Extensive numerical results are provided comparing phase sensitivity, visibility and E-field sensitivity for LCMNS, N00N states, and non-entangled states. LCMNS offer huge improvements in sensitivity compared to other approaches.
Synthetic Generation of Multidimensional Data to Improve Classification Model Validity

This article aims to compare Generative Adversarial Network (GAN) models and feature selection methods for generating synthetic data in order to improve the validity of a classification model. The synthetic data generation technique involves generating new data samples from existing data to increase the diversity of the data and help the model generalize better. The multidimensional aspect of the data refers to the fact that it can have multiple features or variables that describe it. The GAN models have proven to be effective in preserving the statistical properties of the original data. However, the order of data augmentation and feature selection is crucial to build robust and accurate predictive models. By comparing the different GAN models with feature selection methods on multidimensional datasets, this article aims to determine the best combination to support the validity of a classification model in multidimensional data.
Ethics of Personas, Not of People

온라인에서 벌어지는 일은, 그러한 매체 없이 벌어지는 일과 마찬가지로 사람에게 영향을 끼친다. 온라인에서 저질러지는 잘못 또한 사람에게 실제의 해를 끼치는 것은 명백하다. 이런 점에서 온라인상에서 저질러지는 잘못은 가상이 아니라 실제 잘못이다. 온라인에서 저질러지는 실제 윤리적 잘못을 피하고자 필요한 것은 아마도 실제 윤리라고 할 사람이 많이들 있을 것이다. 이 논문은 그들의 주장이 실용적이지도 않으며 또한 현실적이지도 않음을 논증하고자 한다. 이를 위해(1) 서로 모순될 수도 있는 여러 가치가 존재하며,(2) 사람의 윤리를 온라인에 적용하는 것은 온라인 커뮤니티가 제공하는 가장 매혹적 부분을 앗는 결과를 낳을 수 있고, 나아가(3) “가상”의 윤리 곧 페르소나의 윤리를 적용함으로써 온라인에서의 잘못을 방지할 수 있음을 논증한다.The things that we do online impact us just the same as the things that we do offline. The real harm is done to people by the wrongs done online. The wrongs done online are real in that respect. The fact that the wrongs committed online are caused by people, are impressed upon people, and can be helped if the people so wish who take on the personas, seems to suggest that the prevention of the real wrongs done online requires real ethics, viz. ethics of people. In this paper, I argue that the seemingly straightforward suggestion is neither practical nor realistic. For(1) not all values are equal,(2) the implementation of the suggestion risks a very attractive part of what online communities have to offer, and(3) the application of “virtual” ethics -- ‘ethics of personas,’ as I’d call it — could serve as the ethics lab so that the virtual communities enable us to further investigate how best to conduct or lives and also to let us know more about each other.
Secure Distributed Unscented Kalman Filter Against Cyber Attacks

During the past decades, distributed unscented Kalman filter (DUKF) has developed rapidly in the field of position tracking in sensor networks. In this paper, we propose a DUKF with cluster-based attack detection strategy algorithm for state estimation under malicious network attacks. The application of the attack detection algorithm with two layers of cycling mechanism to the state estimation attack detection guarantees the security and reliability of the converged data. A good estimation of the situation in which the information of the secure node is unknown is achieved by measuring 4 steps: update, cluster-based attraction detection, information fusion and prediction. Moreover, the simulation results show that the method is robust to random attacks, FDI attacks, reverse attacks and delayed attacks.
Research on Recognition and Registration Method Based on Deformed Fiducial Markers

To solve the problem of low efficiency of fiducial markers detection and recognition due to the distortion of fiducial marker images caused by shooting angle and obliqued markers in practical applications, this paper proposes a method for registration and recognition of deformed fiducial markers, which combines two deep convolutional neural networks with the Perspective-n-Point (PnP) algorithm to estimate the 6DoF of the deformed ArUco markers. Firstly, ArUco is detected quickly and accurately through the improved U2Net; Secondly, the control points and reference points on deformed marker images are predicted, and the interpolation relationship between the predicted control points and reference points is then used to achieve deformation correction; Finally, the PnP algorithm is used for pose estimation. Through the comparative experimental evaluation on the self-collected dataset, the results have shown that the method proposed in this paper can detect and recognize the deformed ArUco markers with high accuracy and reliability under the complex scenes, and effectively improve the recognition rate of the deformed markers.
Internet of Things Data Aggregation Based on Image Information Hiding

In the era of intelligent information technology of 5G, smart buildings, promoted by the Internet of Things, enhance the ability of indoor monitoring and equipment management. The acquisition and transmission of Architectural Physical Environment Data (APED) is a crucial part of the Internet of Things (IoT). Traditional methods rely on many sensors and multiple types of network channels, resulting in problems such as many data transmissions and increased communication bandwidth requirements. Therefore, a data aggregation strategy based on data hiding technology is proposed. All the temporal data is hidden in the existing image through a customized lightweight codec scheme and communication protocol based on binary coding, and the data is aggregated into image(s) for uploading without reducing the fidelity of the image, to improve the efficiency of data transmission. The paper uses APED to conduct simulation experiments to verify the feasibility of the technique. The scheme can be extended to other similar Internet of Things applications.
A New Approach to Non-Stationary Signals Detection Using Adaptive Matched Filter

In this work, we propose a non-stationary signal detection algorithm based on matched filtering to significantly improve the processing performance of high-speed rendezvous target. We transform the target detection problem into a multivariate hypothesis test problem of target signal duration. We derive the test statistics, and prove that the test statistics can be calculated by adaptive matched filter. By searching for the maximum test statistics under each hypothesis test, we can simultaneously make hypothesis test decisions and estimate target trajectory parameters. The simulation results show that the performance of the constructed detector is consistent with the theoretical analysis results. Compared to the traditional two-step method, the proposed algorithm can reduce the estimation error of the signal duration by two orders of magnitude. And it also brings a significant improvement in the performance of the trajectory parameter estimation.
Characteristics of the Skills of Working with Educational Information in Younger Schoolchildren and Ap the Development of Their Diagnostics Development

The article reveals the essence of the concept of the skill of working with educational information and pedagogical means of monitoring it in younger schoolchildren. The main interpretations of general pedagogical categories are presented through the analysis of “skill” and “educational information”. The periodization of the process of developing the skills of a junior student with educational information is determined, and the indicators of the performance of actions with educational information by younger students on the basis of the Federal State Educational Standard are also determined.
Implementation of the Requirements of the Updated Standard When Introducing New Concepts in Mathematics Lessons

The article is based on the main key ideas of the formation of the planned subject and meta-subject results. The article discusses the methodology for implementing the requirements of the updated standard, taking into account the goals of teaching mathematics, under which conditions are created that ensure the possibility of forming students’ planned results in accordance with the requirements of the standard. An approach is proposed to the formation of individual skills of cognitive universal educational actions of students in teaching mathematics, using learning tasks and learning tasks that develop the ability to solve a wide range of problems.