Reviewerly/exHarmony huggingface dataset:
Efficient GAN-based Method for Extractive Summarization

Background and Objectives: Text summarization plays an essential role in reducing time and cost in many domains such as medicine, engineering, etc. On the other hand, manual summarization requires much time. So, we need an automated system for summarizing. How to select sentences is critical in summarizing. Summarization techniques that have been introduced in recent years are usually greedy in the choice of sentences, which leads to a decrease in the quality of the summary. In this paper, a non-greedily method for selecting essential sentences from a text is presented.Methods: The present paper presents a method based on a generative adversarial network and attention mechanism called GAN-AM for extractive summarization. Generative adversarial networks have two generator and discriminator networks whose parameters are independent of each other. First, the features of the sentences are extracted by two traditional and embedded methods. We extract 12 traditional features. Some of these features are extracted from sentence words and others from the sentence. In addition, we use the well-known Skip-Gram model for embedding. Then, the features are entered into the generator as a condition, and the generator calculates the probability of each sentence in summary. A discriminator is used to check the generated summary of the generator and to strengthen its performance. We introduce a new loss function for discriminator training that includes generator output, real and fake summaries of each document. During training and testing, each document enters the generator with different noises. It allows the generator to see many combinations of sentences that are suitable for quality summaries.Results: We evaluate our results on CNN/Daily Mail and Medical datasets. Summaries produced by the generator show that our model performs better than other methods compared based on the ROUGE metric. We apply different sizes of noise to the generator to check the effect of noise on our model. The results indicate that the noise-free model has poor results.Conclusion: Unlike recent works, in our method, the generator selects sentences non-greedily. Experimental results show that the generator with noise can produce summaries that are related to the main subject.
Tracking Multiple Spawning Targets Using Poisson Multi-Bernoulli Mixtures on Sets of Tree Trajectories

This paper proposes a Poisson multi-Bernoulli mixture (PMBM) filter on the space of sets of tree trajectories for multiple target tracking with spawning targets. A tree trajectory contains all trajectory information of a target and its descendants, which appear due to the spawning process. Each tree contains a set of branches, where each branch has trajectory information of a target or one of the descendants and its genealogy. For the standard dynamic and measurement models with multi-Bernoulli spawning, the posterior is a PMBM density, with each Bernoulli having information on a potential tree trajectory. To enable a computationally efficient implementation, we derive an approximate PMBM filter in which each Bernoulli tree trajectory has multi-Bernoulli branches, obtained by minimising the Kullback-Leibler divergence. The resulting filter improves tracking performance of state-of-the-art algorithms in a simulated scenario.
Construction of genuinely entangled subspaces and the associated bounds on entanglement measures for mixed states

Genuine entanglement is the strongest form of multipartite entanglement. Genuinely entangled pure states contain entanglement in every bipartition and as such can be regarded as a valuable resource in the protocols of quantum information processing. A recent direction of research is the construction of genuinely entangled subspaces -- the class of subspaces consisting entirely of genuinely multipartite entangled pure states. In this paper we present several methods of construction of such subspaces including those of maximal possible dimension. The approach is based on the correspondence between bipartite entangled subspaces and quantum channels of a certain type. The examples include maximal subspaces for systems of three qubits, four qubits, three qutrits. We also provide lower bounds on two entanglement measures for mixed states, the concurrence and the convex-roof extended negativity, which are directly connected with the projection on genuinely entangled subspaces.
Automated interpretation and analysis of bronchoalveolar lavage fluid

The cytological analysis of bronchoalveolar lavage fluid (BALF) plays an essential role in the differential diagnosis of respiratory diseases. In recent years, deep learning has demonstrated excellent performance in image processing and object recognition.We aim to apply deep learning to the automated interpretation and analysis of BALF.Visual images were acquired using an automated biological microscopy platform. We propose a three-step algorithm to automatically interpret BALF cytology based on a convolutional neural network (CNN). The clinical value was evaluated at the patient level.Our model successfully detected most cells in BALF specimens and achieved a sensitivity, precision, and F1 score of over 0.9 for most cell types. In two tests in the clinical context, the algorithm outperformed experienced practitioners.The program can automatically provide the cytological background of BALF and augment clinical decision-making for clinicians.
Evaluation of global solar radiation, cloudiness index and sky view factor as potential indicators of Ghana's solar energy resource

The increasing demand of Ghana's renewable energy sector requires a comprehensive evaluation of sky conditions that may be favourable for the efficient harnessing of solar energy resources. However, data for such evaluation are typically scarce due to instrumentation challenges. Empirical estimates using sunshine duration and cloud cover as input data were used to evaluate the solar energy resources’ potential at 22 synoptic stations distributed across the four climatic zones of Ghana (savannah, transition, forest and coastal) for the period 2015 – 2018. Sunshine duration was recorded using the Campbell-Stokes sunshine recorder while the cloud amount was done manually through visual observation. The modified Ångström-Prescot sunshine model was used to estimate the monthly mean global solar radiation. The cloudiness index was then computed as the ratio of the mean cloud cover to the global solar radiation. A modified Steyn-method, based on sunshine duration, was used to estimate the sky view factor. Ordinary kriging was used to interpolate mean quarterly stationary values of the sky condition parameters into spatial distribution over Ghana. Results show that the dry periods (November to April) are characterised by high solar radiations, clear clouds and visible skies as a result of high temperatures, less cloud cover, and low relative humidity, whereas the opposite conditions are observed in the wet periods (May to October). The savannah and coastal zones recorded higher solar radiations with up to 22 MJ m − 2 day −1 in the dry season compared to the forest and transition zones. Cloudiness index was above 0.3 and below 0.2 in the wet and dry seasons respectively, for the forest, transition and coastal zones, whereas the index was between 0.15 and 0.3 in the wet season and below 0.1 in the dry season for the savannah zone. Sky view factor was consistently between 0.7 and 0.8 in the dry season, with low values below 0.7 in August for all the climatic zones. Spatial interpolation of the sky condition parameters revealed that the savannah, coastal and eastern transition climatic zones observe high solar radiations, clear and visible skies and are therefore more favourable for harnessing solar energy. These locations may be more appropriate for setting up large-scale solar farms, though dust associated with the Harmattan dry season can be a hinderance.
Decisional DNA (DDNA) Based Machine Monitoring and Total Productive Maintenance in Industry 4.0 Framework

The entire manufacturing spectrum is transforming with the advent of Industry 4.0. The features of Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA) were utilized for developing Virtual Engineering Objects (VEO), Virtual Engineering Process (VEP) and Virtual Engineering Factory (VEF), which in turn facilitate the creation of smart factories. In this study, DDNA based Machine Monitoring for Total Maintenance in Industry 4.0 framework is demonstrated. The concept of VEO is used for the Tool and Equipment Monitoring, while for the Plants Operations Monitoring and Quality Monitoring, VEP and VEF are employed. Query extraction feature of DDNA is exploited for Adaptive Control. This study shows that Machine Efficiency (ME) can be monitored along with analysis of machine KPI’s like breakdown time, setting time, and other losses. Moreover, reports can be generated efficiency-wise, breakdown-wise, operator-wise. The data of these reports is used to predict and make future decisions related to machine maintenance.
A Multimodal Approach for the Risk Prediction of Intensive Care and Mortality in Patients with COVID-19

Although several studies have been launched towards the prediction of risk factors for mortality and admission in the intensive care unit (ICU) in COVID-19, none of them focuses on the development of explainable AI models to define an ICU scoring index using dynamically associated biological markers.We propose a multimodal approach which combines explainable AI models with dynamic modeling methods to shed light into the clinical features of COVID-19. Dynamic Bayesian networks were used to seek associations among cytokines across four time intervals after hospitalization. Explainable gradient boosting trees were trained to predict the risk for ICU admission and mortality towards the development of an ICU scoring index.Our results highlight LDH, IL-6, IL-8, Cr, number of monocytes, lymphocyte count, TNF as risk predictors for ICU admission and survival along with LDH, age, CRP, Cr, WBC, lymphocyte count for mortality in the ICU, with prediction accuracy 0.79 and 0.81, respectively. These risk factors were combined with dynamically associated biological markers to develop an ICU scoring index with accuracy 0.9.to our knowledge, this is the first multimodal and explainable AI model which quantifies the risk of intensive care with accuracy up to 0.9 across multiple timepoints.
Lesion Segmentation Framework Based on Convolutional Neural Networks with Dual Attention Mechanism

Computational intelligence has been widely used in medical information processing. The deep learning methods, especially, have many successful applications in medical image analysis. In this paper, we proposed an end-to-end medical lesion segmentation framework based on convolutional neural networks with a dual attention mechanism, which integrates both fully and weakly supervised segmentation. The weakly supervised segmentation module achieves accurate lesion segmentation by using bounding-box labels of lesion areas, which solves the problem of the high cost of pixel-level labels with lesions in the medical images. In addition, a dual attention mechanism is introduced to enhance the network’s ability for visual feature learning. The dual attention mechanism (channel and spatial attention) can help the network pay attention to feature extraction from important regions. Compared with the current mainstream method of weakly supervised segmentation using pseudo labels, it can greatly reduce the gaps between ground-truth labels and pseudo labels. The final experimental results show that our proposed framework achieved more competitive performances on oral lesion dataset, and our framework further extended to dermatological lesion segmentation.
Exemplars and Counterexemplars Explanations for Image Classifiers, Targeting Skin Lesion Labeling

Explainable AI consists in developing mechanisms allowing for an interaction between decision systems and humans by making the decisions of the formers understandable. This is particularly important in sensitive contexts like in the medical domain. We propose a use case study, for skin lesion diagnosis, illustrating how it is possible to provide the practitioner with explanations on the decisions of a state of the art deep neural network classifier trained to characterize skin lesions from examples. Our framework consists of a trained classifier onto which an explanation module operates. The latter is able to offer the practitioner exemplars and counterexemplars for the classification diagnosis thus allowing the physician to interact with the automatic diagnosis system. The exemplars are generated via an adversarial autoencoder. We illustrate the behavior of the system on representative examples.
DANets: Deep Abstract Networks for Tabular Data Classification and Regression

Tabular data are ubiquitous in real world applications. Although many commonly-used neural components (e.g., convolution) and extensible neural networks (e.g., ResNet) have been developed by the machine learning community, few of them were effective for tabular data and few designs were adequately tailored for tabular data structures. In this paper, we propose a novel and flexible neural component for tabular data, called Abstract Layer (AbstLay), which learns to explicitly group correlative input features and generate higher-level features for semantics abstraction. Also, we design a structure re-parameterization method to compress the trained AbstLay, thus reducing the computational complexity by a clear margin in the reference phase. A special basic block is built using AbstLays, and we construct a family of Deep Abstract Networks (DANets) for tabular data classification and regression by stacking such blocks. In DANets, a special shortcut path is introduced to fetch information from raw tabular features, assisting feature interactions across different levels. Comprehensive experiments on seven real-world tabular datasets show that our AbstLay and DANets are effective for tabular data classification and regression, and the computational complexity is superior to competitive methods. Besides, we evaluate the performance gains of DANet as it goes deep, verifying the extendibility of our method. Our code is available at https://github.com/WhatAShot/DANet.
Numerical methods for mean field games based on Gaussian processes and Fourier features

In this article, we propose two numerical methods, the Gaussian Process (GP) method and the Fourier Features (FF) algorithm, to solve mean field games (MFGs). The GP algorithm approximates the solution of a MFG with maximum a posteriori probability estimators of GPs conditioned on the partial differential equation (PDE) system of the MFG at a finite number of sample points. The main bottleneck of the GP method is to compute the inverse of a square gram matrix, whose size is proportional to the number of sample points. To improve the performance, we introduce the FF method, whose insight comes from the recent trend of approximating positive definite kernels with random Fourier features. The FF algorithm seeks approximated solutions in the space generated by sampled Fourier features. In the FF method, the size of the matrix to be inverted depends only on the number of Fourier features selected, which is much less than the size of sample points. Hence, the FF method reduces the precomputation time, saves the memory, and achieves comparable accuracy to the GP method. We give the existence and the convergence proofs for both algorithms. The convergence argument of the GP method does not depend on any monotonicity condition, which suggests the potential applications of the GP method to solve MFGs with non-monotone couplings in future work. We show the efficacy of our algorithms through experiments on a stationary MFG with a non-local coupling and on a time-dependent planning problem. We believe that the FF method can also serve as an alternative algorithm to solve general PDEs.
Globally Optimal Multi-Scale Monocular Hand-Eye Calibration Using Dual Quaternions

In this work, we present an approach for monocular hand-eye calibration from per-sensor ego-motion based on dual quaternions. Due to non-metrically scaled translations of monocular odometry, a scaling factor has to be estimated in addition to the rotation and translation calibration. For this, we derive a quadratically constrained quadratic program that allows a combined estimation of all extrinsic calibration parameters. Using dual quaternions leads to low run-times due to their compact representation. Our problem formulation further allows to estimate multiple scalings simultaneously for different sequences of the same sensor setup. Based on our problem formulation, we derive both, a fast local and a globally optimal solving approach. Finally, our algorithms are evaluated and compared to state-of-the-art approaches on simulated and real-world data, e.g., the EuRoC MAV dataset.
USE OF AUGMENTED AND VIRTUAL REALITY TOOLS IN A GENERAL SECONDARY EDUCATION INSTITUTION IN THE CONTEXT OF BLENDED LEARNING

The study examines the problem of using augmented and virtual reality in the process of blended learning in general secondary education. Analysis of recent research and publications has shown that the use of augmented and virtual reality in the educational process has been considered by scientists. However, the target group in these studies is students of higher education institutions. Most of the works of scientists are devoted to the problem of introducing augmented reality into the traditional educational process. At the same time, the use of augmented and virtual reality technologies in the process of blended learning remains virtually unexplored. The study analyzes the meaning of the concept of "blended learning". The conceptual principles of blended learning are considered. It has been found that scholars differ in their understanding of the concept of "blended learning". Sometimes researchers distinguish between the components of blended learning: full-time and online learning. The study presents the special advantages of blended learning and the taxonomy of blended learning. It was found that there are some difficulties in implementing blended learning. The article outlines the practical use of virtual and augmented reality. The definition of augmented and virtual reality is given. The mixed reality is considered as a separate kind of notion. Separate applications of virtual and augmented reality that can be used in the process of blended learning are considered (MEL Chemistry VR; Anatomyou VR; Google Expeditions; EON-XR). As a result of the study, the authors propose possible ways to use augmented reality in the educational process. The model of using augmented and virtual reality in blended learning in general secondary education institutions was designed. It consists of the following blocks: goal; teacher’s activity; forms of education; teaching methods; teaching aids; organizational forms of education; pupil activity and results. Based on the model, the methodology of using augmented and virtual reality in blended learning in general secondary education was developed. The methodology contains the following components: target component, content component, technological component and resultant component. The methodology is quite universal and can be used for any subject in general secondary education. The types of lessons in which it is expedient to use augmented (AR) and virtual reality(VR) are determined. Recommendations are given at which stage of the lesson it is better to use AR and VR tools (depending on the type of lesson).
Quantum metrology based on symmetry-protected adiabatic transformation: imperfection, finite time duration, and dephasing

The aim of quantum metrology is to estimate target parameters as precisely as possible. In this paper, we consider quantum metrology based on symmetry-protected adiabatic transformation. We introduce a ferromagnetic Ising model with a transverse field as a probe and consider the estimation of a longitudinal field. Without the transverse field, the ground state of the probe is given by the Greenberger-Horne-Zeilinger state, and thus the Heisenberg limit estimation of the longitudinal field can be achieved through parity measurement. In our scheme, full information of the longitudinal field encoded on parity is exactly mapped to global magnetization by symmetry-protected adiabatic transformation, and thus the parity measurement can be replaced with global magnetization measurement. Moreover, this scheme requires neither accurate control of individual qubits nor that of interaction strength. We discuss the effects of the finite transverse field and nonadiabatic transitions as imperfection of adiabatic transformation. By taking into account finite time duration for state preparation, sensing, and readout, we also compare performance of the present scheme with a classical scheme in the absence and presence of dephasing.
Estimation of Vehicle State Based on IMM-AUKF

Establishing a symmetrical model of surrounding vehicles and accurately obtaining the driving state of the surrounding vehicles in the driving environment can improve the safety of driving, which is an important issue that needs to be considered in the automatic driving system or auxiliary driving system. Therefore, we propose an adaptive unscented Kalman filter algorithm based on Interacting Multiple Model (IMM) theory to estimate the state of target vehicle in the high-speed driving environment. To be specific, we use the Constant Turn Rate and Acceleration (CTRA) theory to establish the target vehicle kinematics model, simultaneously, in order to overcome the problem of estimator failure when the yaw rate is close to zero, a simplified version of the CTRA model is also introduced into the estimation process. In addition, the parameter adaptation strategy is added, so the proposed estimator can overcome the uncertainty of the noise model and improve its accuracy. Finally, the effectiveness of proposed state estimation algorithm is verified on the Carsim and Simulink co-simulation platform. The results of simulations and experiments show that the accuracy and stability of IMM-based algorithm is better than the single-model algorithm in different scenarios, and the parameter adaptation strategy brings performance improvement.
Research on Image Denoising Algorithm Based on Improved Wavelet Threshold and Non-local Mean Filtering

Wavelet threshold denoising and non-local mean denoising are traditional image denoising methods, but wavelet hard threshold denoising will produce pseudo Gibbs phenomenon due to some discontinuous wavelet coefficients after wavelet reconstruction; wavelet soft threshold denoising is in After wavelet reconstruction, the image accuracy will be reduced due to the constant deviation between the approximate wavelet coefficients and the original wavelet coefficients; traditional non-local mean filtering will increase the noise of similar local blocks due to the search for weights with the increase of noise, resulting in low confidence The noise denoising effect of the noise ratio is not good. In view of the above situation, this paper proposes an improved wavelet threshold combined with non-local mean filtering to denoise images. The image denoising effect is better than wavelet threshold denoising or non-local mean denoising.