Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting COVID-19 Patients in X-Ray Images Based on MAI-Nets

COVID-19 is an infectious disease caused by virus SARS-CoV-2 virus. Early classification of COVID-19 is essential for disease cure and control. Transcription-polymerase chain reaction (RT-PCR) is used widely for the detection of COVID-19. However, its high cost, time-consuming and low sensitivity will significantly reduce the diagnosis efficiency and increase the difficulty of diagnosis for COVID-19. For X-ray images of COVID-19 patients have high inter-class similarity and low intra-class variability, we specifically designed a multi attention interaction enhancement module (MAIE) and proposed a new convolutional neural network, MAI-Net, based on this module. As a lightweight network, MAI-Net has fewer layers and amount of network parameters than other network models, enabling more efficient detection of COVID-19. To verify the performance of the model, MAINet performed a comparison experiment on two open-source datasets. The experimental results show that its overall accuracy and COVID-19 category accuracy are 96.42% and 100%, respectively, and the sensitivity of COVID-19 is 99.02%. Considering the factors such as accuracy rate, the parameters number of network model and the calculation amount, MAI-Net has better practicability. Compared with the existing work, the network structure of MAI-Net is simpler, and the hardware requirements of the equipment are lower, which can be better used in ordinary equipment. (C) 2021 The Authors. Published by Atlantis Press B.V.
Increasing the Size of the Camouflage Area for Remote Sensing Images

Image camouflaging is a hard-to-find replacement of some information in an image with a verisimilar imitation. An increase in the size of the camouflage area entails a degradation in the quality of the results of most camouflage algorithms. The article deals with the effect of the camouflaged area size on the performance of the adversarial neural network when camouflaging remote sensing images. We consider a camouflage adversarial neural network that includes an algorithm for edge analysis and generation. We study the dependence of the qualitative and quantitative characteristics of remote sensing camouflage images on the size of the camouflaged area. We use a quantitative measure of quality based on the image sharpness inside the camouflage area. We compute an estimate of the image sharpness within the camouflage area using a gradient averaged over that area. We average this gradient value over a series of natural camouflage images in our experimental study. Computational experiments prove a high quality of the adversarial network result with an increase in the size of the camouflage areas for natural remote sensing images.
Clustering Single-Cell RNA Sequencing Data by Deep Learning Algorithm

The development of single-cell RNA sequencing (scRNA-seq) technology provides a good opportunity to study cell heterogeneity and diversity. Especially, clustering is an important step in scRNA-seq analysis. With the advance of technology, many scRNA-seq data are available, which develop a lot of clustering methods. However, the existing methods usually employ the gene expression data, ignoring the related information between genes and the structure information in data. Therefore, we propose a new method (NDMgcn) to reconstruct the gene expression data based on the association of gene network, and cluster the data by Variational Autoencoder (V AE) and Graph Convolutional Network (GCN). The V AE learns low-dimensional information and the GCN learns structural information. The experimental results indicate that NDMgcn outperforms other popular algorithms in terms of NMI and ARI metrics. It provides a new insight for clustering scRNA-seq data from the network perspective.
A simulation-based analysis for a transverse guidance for highly automated driving functions

In recent decades, research in both academic and industry has focused on the area of highly automated driving systems and is expected to do so in the near future. The scenario-based analysis is widely used in automated vehicles that requires simulation-based vehicle control estimation. One of the assist system is transverse guidance that is inescapable for highly automated driving system. The transverse guidance assist system deals with the longitudinal and lateral behavior of the vehicle. For a specific use case, merging functions like adaptive cruise control and lane keeping assist system are considered. The paper is focused on the development of transverse guidance that forms the combined lateral and longitudinal control of highly automated driving system like Lane Change maneuver planning in highway scenario. Moreover, the parameters are demonstrated for the model and performance of the model has been shown for the use case scenario. The proposed approach is then evaluated for a scenario in a simulation environment modeled using MATLAB/Simulink.
6-DOF Grasp Detection for Unknown Objects Using Surface Reconstruction

Many state-of-the-art grasping approaches are constrained to top-down grasps. Reliable robotic grasping in a human-centric environment requires considering all six degrees of freedom. We use an end-effector mounted depth camera to reconstruct the object’s surface by fusing data gathered along a shaped trajectory. The utilization of a truncated signed distance function and an effective pose refinement algorithm counteract typical sources of error. We propose to use a multi-view deep learning approach to vastly limit the search space of possible grasps and employ robust quality metrics to estimate their chances of success. To evaluate the performance of our approach we conducted extensive real-world experiments and achieved an average success rate of 92.3%.
Hepcidin: Biological Activity, Analytical Methods in Biological Fluids, Clinical Applications and Antagonists. A Short Review

Hepcidin is a small protein involved in iron metabolism that also exhibits antibacterial and antifungal activities. Since its discovery in 2000 hepcidin has been extensively studied in many research centers. Usage of natural and synthetic hepcidin inhibitors can be applied as a powerful therapeutic tool that targets multiple types of anemia. In addition the pro- tein itself has a potential to placate increased absorption of iron in patients with � -thalassemia. Previous studies have char- acterized mechanisms of hepcidin synthesis as well as its biological activity, however methods enabling estimation of its concentration in biological materials remain obscure. The physiological role of hepcidin in iron metabolism as well as methods of its determination in serum and urine samples are described in this paper. Furthermore, the possible role of hepcidin and its molecular antagonists in health improvement is considered.
On the Computational Power of Convolution Pooling: A Theoretical Approach for Deep Learning

Convolutional neural networks (CNNs) have been widely used for image analysis and recognition. For example, LeNet-5 is a 7-layer convectional neural network, which can attain more than 99% test accuracy for classification of handwritten digits. CNNs repeats convolution and pooling operations alternately. However, the computational capability of such operations is not clear. We are curious to know a class of problems that can be solved by CNNs. As a formal approach for this task, we introduce a theoretical parallel computational model of CNNs that we call the convolution-pooling machine. It captures the essence of convolution and pooling operations, and application of non-linear activation functions performed in CNNs. In this paper, we assume the convolution-pooling machine operating on 1-dimensional arrays for simplicity, and focus on the problem of classification of inputs by the distance of two feature points. More specifically, we will design a convolution-pooling machine solving the problem Dk (k≥1), a problem to determine if the distance of the two 1’s is at most k or not. For designing the convolution-pooling machine solving the problem Dk, we generate a mixed-integer linear programming problem (MILP) with constraints and objective functions. We have solved the generated linear programming problem for each Dk (1≤k≤128) by Gurobi optimizer, a commercial MILP solver. We succeeded in finding a solution for all Dk (1 ≤ k ≤ 128) and designing the convolution-pooling machine for solving them. This fact indicates that convolution and pooling operations in CNNs may have the computational capability of classification by the distance of feature points.
Automatic Bio-impedance Signal Analysis: Smoothing Processes Efficacy Evaluation in Determining the Vascular Tone Type

Automatic detection of feature points in bio-impedance signals is an essential step in signal analysis. In some cases, this process requires high degree of accuracy to ensure that the detected feature points result from changing bio-impedance not from inappropriate filter use or noise. To determine vascular tone by electrical bio-impedance, there are three basic feature points to be detected. These points are the first systolic wave peak, the diastolic wave peak, and the incisura. In this work, signal smoothing has been considered using three methods: Savitzky-Golay, Locally Weighted Polynomial Regression Method (LOESS), and Binomial smoothing. Each method has been applied to a specific signal which was recorded from thigh part of human body. The resulting signals have been considered in terms of different parameters. These parameters include clear appearance of feature points, change in feature points time, which results from smoothing process, and number of detected cycles that contain feature points after smoothing. The obtained results have been discussed according to each applied method. An evaluation criterion has been proposed to evaluate the applied smoothing methods. Based on the obtained results and discussions, it is clarified that each method can give better results according to the specified research goal.
Plaster: an Embedded FPGA-based Cluster Orchestrator for Accelerated Distributed Algorithms

The increasing use of real-time data-intensive applications and the growing interest in Heterogeneous Architectures have led to the need for increasingly complex embedded computing systems. An example of this is the research carried out by both the scientific community and companies toward embedded multi-FPGA systems for the implementation of the inference phase of Convolutional Neural Networks.In this paper, we focus on optimizing the management system of these embedded FPGA-based distributed systems. We extend the state-of-the-art FARD framework to data-intensive applications in an embedded scenario. Our orchestration and management infrastructure benefits from compiled language and is accessible to end-users by the means of Python APIs, which provides a simple way to interact with the cluster and design apps to run on the embedded nodes. The proposed prototype system consists of a PYNQ-based cluster of multiple FPGAs and has been evaluated by running an FPGA-based You Only Look Once (YOLO) image classification algorithm.
Kefuri: A novel technological tool for increasing organ donation in Chile

Organ shortage for transplantation is one of the most challenging problems for modern medicine that affects all the world. While Spain, organ donation leader, registered 48.9 donors per million population on 2019, Chile only registered 10.4, which is less that the globe’s average. While Spain has put its efforts in improving procurement process, Chile has given more importance to try to improve family refusal rates, leading to almost no growth at all. We studied Chilean organ procurement process using different quantitative and qualitative research tools, and discovered some process problems, where the main one was at the first 2 stages, where the possible donor should be identified in an emergency room and notified to the procurement unit. We discovered that 87% of possible donors were lost in those stages, neither they were appropriately identified nor notified, transforming these stages as critical and strategic to improve in order to increase the country’s organ donation rate. So we asked if including technology in the first stages of the procurement process will increase possible organ donor identification and warning, and, therefore, effective organ donors. We designed a first-in-class smartphone app to easily and quickly warn the procurement unit about a possible organ donor, with the aim of automating those two failing stages. After training all health professionals working in emergency and critical care units of a main hospital in Chile and correcting all pitfalls detected by those users, we observed a statistically significant increase in both potential donor detection (93%) and actual donation rate (500%) making the app a very welcome innovation in both clinical units. Conclusions: Even though the process still depends on human criteria, we were able to increase possible organ donor warnings and, even, effective organ donors. Also, the technology helped the personnel to understand an unknown process for them and be aware of possible donors and warning them. Analysing, rethinking failing processes and searching for innovative solutions such as high end technologies could transform a country with poor organ donation rates and improve both end stage solid organ shortage and chronic disease patient survival.
ECG Classification Performing Feature Extraction Automatically Using a Hybrid CNN-SVM Algorithm

In this study, we have presented a hybrid Convolution Neural Network (CNN)-Support Vector Machine (SVM) algorithm which has overcome overtitting for classifying Electrocardiogram (ECG) signals that have been transformed to 2D images using continuous wavelet transform (CWT). We also have suggested ProposedNet that is a kind of convolutional neural network algorithm. Also, it has been trained more than once. Moreover, it has performed feature extraction automatically. We have compared the ProposedNet which has 34 layers, with SVM. Additionally, we also have compared ProposedNet-SVM that is also our suggestion, with these algorithms. Comparison results indicate that ProposedNet, SVM, and ProposedNet-SVM have been achieved accuracy rates of 95.6%, 89.17%, and 99.524% respectively.
Improved Landmark-based Shortest Path Length Estimation in Large Graphs with Distance Correction

The existence of massive network graphs with hundreds of millions of nodes that support many online services such as messaging systems, social networks, routing infrastructures, among others, pose unique challenges to analysis algorithms. Many applications rely on computing distances between node pairs. Computing the shortest distance between a pair of nodes is an essential operation that can take a long time in large graphs using traditional algorithms. In this paper, we study approximate landmark-based methods for shortest path distance that scale to large graphs. These methods rely on selecting a set of landmark nodes used to estimate the distance at running time quickly. Our work offers two main results: we combine existing metrics to select landmarks that offer limited performance improvements but work well in some graphs, and we propose distance correction methods that boost the performance of the previous ones significantly. These methods are new in the literature for this class of distance estimators.
Determining the Choice of Attack Methods Approach

The article analyzes the stages and methods of computer attack (CA) implementation from the intruder point of view. The following stages of the CA were identified from the intruder point of view: achieving the CA goal, theoretical and practical training. Reasonable conclusions about the approach to the specific method choice of conducting CA by the intruder have been formed. The choice of the CA method is determined by the presence and popularity of the CA method in DarkNet, lack of protection measures against CA, monitoring CA methods are not being implemented, absence of visible harm to the CA object. Reasonable conditions for the termination of the spacecraft by the violator are formulated. A spacecraft can be technically terminated by an intruder. So, CA can be terminated if attacker does not have enough skills, methods and tools for CA. CA finished if methods and tools for CA will not be successfully implemented at any of the CA stages. The CA has been stopped by organization, if if's found at the stage of theoretical or practical training.
Design of a Power Management Circuit for an Opto-Electro Stimulator

This paper presents the design of an integrated power management circuit for use in an implantable opto-electro stimulator. It features an active rectifier with pulse width modulation (PWM) regulation to generate a 3.3 V regulated output, and a 3-stage high voltage charge pump (CP) that generates a 12 V output from a 3.3 V input with a 20 MHz, two-phase non-overlapping clock generator. The circuits were designed in a 0.18-µm CMOS technology requiring a chip area of 0.048 mm2. Simulation results show that the regulating rectifier has a voltage conversion efficiency of 94.3% and 92.8% with an ac input magnitude of 3.5 V and 3.6 V, respectively. The peak power transfer efficiency of the regulated 3.3V output voltage is 70.7% with a maximum output power of 30.3 mW. The CP with an overall on-chip capacitance is 60 pF.
Energy Consumption Prediction of Electric Vehicle Air Conditioning System Using Artificial Intelligence

Electric vehicles (EV) are considered as a solution to the problem of pollution in the transport sector. One of the issues that prevent expanding of EV in use is the difficulty of accurate range prediction. Range prediction is a complicated problem because it is undergoing the influence of many factors: vehicle design, environment, and driver behavior. We apply machine learning techniques to investigate the extent to which these factors affect the range of EVs and estimate the weight of these factors. We focus on the energy consumption associated with Heating Ventilation and Air Conditioning (HVAC) system operation. We considered significant features to use for model training as follows: temperature inside and outside, temperature setting selected by a driver, energy consumption in previous periods of time, and even road inclination. We used a weighted sum of Linear Regression and Random Forest algorithms, and mispredicting was only 65 watts for the 30 min time interval.
Location-aware Verification for Autonomous Truck Platooning Based on Blockchain and Zero-knowledge Proof

Platooning technologies enable trucks to drive cooperatively and automatically, which bring benefits including less fuel consumption, more road capacity and safety. In order to establish trust during dynamic platoon formation, ensure vehicular data integrity, and guard platoons against potential attackers, it is pivotal to verify any given vehicle’s identity information before granting it access to join a platoon. To address this concern in dynamic truck platooning, we present a novel location-aware and privacy-preserving verification protocol based on zero-knowledge proof and permissioned blockchain. By performing the verification process within the spatially-local area defined by a given platoon, our system can provide lower latency and communication overhead compared to a location-agnostic blockchain system. We prototype the proposed system and perform benchmark tests on the Hyperledger platform. The experimental results show that our system is suitable for real-world truck platooning.
Role of Backbone Charge Rearrangement in the Bond-Dipole and Work Function of Molecular Monolayers

Self-assembled organic monolayers serve for modifying the work function of inorganic substrates. We examine the role of the molecular backbone in determining monolayer-adsorbed work function, by considering the adsorption of dithiols with either a partially conjugated or a saturated backbone on the GaAs(001) surface. Using a combination of chemically resolved electrical measurements based on X-ray photoelectron spectroscopy and contact potential difference, together with first principles electronic structure calculations, we are able to distinguish quantitatively between the contributions of the band bending and surface dipole components. We find that the substrates coated by partially conjugated layers possess a larger band-bending, relative to that of the substrates coated by saturated layers. This is associated with an increased density of surface states, likely related to the presence of oxygen. At the same time, the samples coated by partially conjugated layers also possess a larger bond-dipole, with.
Recent Developments in Phase Inversion Emulsification

Emulsions are multiphasic fluid systems in which liquid droplets are dispersed in another immiscible liquid. The main components of an emulsion are the two liquid phases, typically oil and water, and the emulsifier, which stabilizes the interface between the two liquid phases. Emulsifiers can be a variety of molecules, such as polymers, amphiphilic surfactants, and proteins, and they can also be colloidal particles. Emulsion phase inversion is the process of interconversion between two types of simple emulsions: water-in-oil and oil-in-water emulsions. Phase inversion can be induced by shifting the emulsifier affinity from one phase to the other, which is called transitional phase inversion. It can also be triggered by a change in the water-to-oil ratio of the emulsion, which leads to a process known as catastrophic phase inversion. With recent advances in the stabilization of emulsions using colloidal particles and stimuli-responsive surfactants, numerous novel emulsion systems that undergo emulsion phas.