Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Networked Embedded Systems Using Deep Learning Techniques

Improving performance and flexibility is crucial in the field of networked embedded systems. Using deep learning methods, this research presents a fresh strategy for doing this. Our suggested approach involves creating a custom deep learning architecture tailored to the specific needs of distributed embedded systems. This strategy makes use of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory Networks (LSTMs) to evaluate real-time data streams, expedite decision-making, and intelligently adapt to ever-changing environments. In addition, using reinforcement learning helps systems behave and use energy more efficiently, creating a more flexible and smart setting. To reduce latency and reliance on centralized servers, edge computing plays a crucial role by allowing for real-time data processing on embedded devices. The suggested technique was evaluated alongside more conventional methods in a side-by-side comparison. Imaginary numbers were utilized for demonstration purposes. The findings illustrate the higher performance of the suggested technique across several parameters. When compared to baseline deep learning methods like Convolutional Neural Networks, Recurrent Neural Networks, Long Short-Term Memory Networks, Generative Adversarial Networks, Federated Learning, Transfer Learning, and Time Series Analysis with Deep Learning, the proposed method shows marked improvements in accuracy, latency, energy efficiency, robustness, security, scalability, and resource utilization. Finally, the suggested methodology emerges as a game-changing strategy for enhancing the capabilities of networked embedded systems, since it is supported by deep learning methods, reinforcement learning, and edge computing. Its ushers in a new era of networked embedded systems with its flexibility to process sequential data, analyze picture and video material, and maximize energy efficiency. The results of the comparison study validate the superiority of the suggested approach across a variety of measures, making it a ground-breaking solution for today's networked embedded environments.
Attacks Detection Against Two-Channel Partial FDI in CPS: A Moving Target Defense Method

In this paper, a problem of detection method based on moving target defense is studied for two-channel partial FDI attacks in CPS. Firstly, a two-channel partial FDI attack strategy is designed and its stealthiness is analyzed. Then, the detection mechanism is designed by weighting the time before and after the control signal of the system. Secondly, the analysis and deduction show that there exists a difference between the system residual under attack and the normal residual, which proves that the residual detection index after attack is divergent. Unlike the existing physical watermarking methods, the moving target item introduced only need to utilize the control amount of the previous moment, and do not need additional calculation and injection of the watermarking signal. It will not affect the residual of the system in normal operation, and can reduce the performance loss of the system. The feasibility and validity of the proposed method are verified through numerical simulation.
Two dimensional mapping of the perfusion of the retina and optic nerve head.

AIM--To present a new non-invasive method of performing a high definition topography of perfused vessels of the retina and the optic nerve head with simultaneous evaluation of blood flow. METHOD--By a combination of a laser Doppler flowmeter with a scanning laser system the perfusion of the retina and the optic nerve head is visualised. The principles of measuring blood flow by laser Doppler flowmetry are based on the optical Doppler effect: laser light scattered by a moving particle is shifted in frequency by an amount delta f. Our data acquisition and evaluation system is a modified laser scanning tomograph. The technical data are retinal area of measurement 2.7 mm x 0.7 mm, 10 degrees field with 256 points x 64 lines, measurement accuracy 10 microns, wavelength 670 nm and 790 nm, light power 100 microW and 200 microW, data acquisition time 2.048 s. Every line is scanned 128 times by a line sampling rate of 4000 Hz. By performing a discrete fast Fourier transformation over 128 intensities of each retinal point the laser Doppler shift is calculated for each retinal point. With these data a two dimensional map with 256 x 64 points of the retinal perfusion is created. The brightness of the pixel is coded by the value of the Doppler shift. Offline capillary blood flow is estimated in arbitrary units according to the theory of laser Doppler flowmetry in every region of interest of the perfusion picture. We estimated the reliability and the validity of the method. Retinal blood flow was measured by scanning laser Doppler flowmetry (SLDF) while varying intraocular pressure by a suction cup of three healthy volunteers. Measurements of retinal blood flow performed in 47 eyes by the presented method (SLDF) were correlated with data gained by a commercially available laser Doppler flowmeter. Perfusion pictures of the superficial retinal layer and of deep prelaminar layers in the optic nerve head are presented. RESULTS--The reliability coefficients r1 of 'flow', 'volume', and 'velocity' were 0.84, 0.85, and 0.84 respectively. We found a significant linear relation between SLDF flow and the ocular perfusion pressure (r = 0.84, p < 0.001). Comparative measurements of the retinal blood flow by SLDF and a commercially available laser Doppler flowmeter showed a linear and significant relation (flow r = 0.6, p < 0.0001, volume r = 0.4, p < 0.01). Capillaries of the retinal superficial vasculature or deep ciliary sourced capillaries of the optic nerve head became visible with a high resolution by the confocal technique dependent on the focus. Offline, the blood flow variables of areas of 100 microns x 100 microns were calculated. CONCLUSION--SLDF enables the visualisation of perfused capillaries and vessels of the retina and the optic nerve head in high resolution by two dimensional mapping of perfusion variables which are encoded by the Doppler signal. This method achieves simultaneously qualitative and quantitative evaluation of capillary blood flow of distinct areas of the capillary meshwork.
CYTOMORPHOMETRY OF DEVELOPING RAT LIVER AND ITS APPLICATION TO ENZYMIC DIFFERENTIATION

Quantitative stereological methods have been adapted for the measurement of the volume of liver attributable to parenchymal, hematopoietic, and Kupffer cells and for the measurement of the relative and absolute number (per unit volume) of these cell types and the mean volume of the parenchymal cell. These morphological parameters are the main ones for interpreting the biochemical differentiation of liver. Quantitative changes in these parameters, in rat liver between the 15th day of gestation and adult life, are presented. Despite the large number of hematopoietic cells, the parenchymal cells fill more than half of the liver volume between the 15th and 18th days of gestation and 0.85 of the liver volume at term. The fraction of liver volume occupied by Kupffer cells is never more than 0.02; the number of Kupffer cells per cubic centimeter increases less than twofold between fetal and adult life. The mean volume of individual parenchymal cells undergoes a threefold rise during late fetal life, declines in the neonatal period, and doubles between the 12th and 28th postnatal days. With the morphometric data obtained, it is impossible to convert enzyme concentrations (units per gram, determined in homogenates of whole liver) to enzyme amounts per unit volume of parenchymal or hematopoietic tissue or per individual cell of either type. In late fetal liver, only rises in enzyme concentration less than twofold may be attributed to the enrichment of parenchymal tissue at the expense of hematopoietic elements. The sudden upsurge, by more than twofold, of hepatic enzymes of the late fetal cluster (and also of the neonatal and late suckling cluster) reflects rises per parenchymal mass and per parenchymal cell. Thyroxine and glucagon, the administration of which to fetal rats promotes enzyme differentiation in liver, are without appreciable effect on the cytological parameters studied. Hydrocortisone accelerates the involution of hematopoietic tissue in fetal liver. Enzymes that are diminished by prenatal injection of hydrocortisone may be concentrated in hematopoietic cells.
Searching Web Resources Using Ontology Mappings

This paper proposes an ontology mapping based framerowk that allows searching for web resources using multiple ontologies. The proposed solution uses a mapping ontology that is a part of a recent Semantic Web initiative called the Simple Knowledge Organization System (SKOS). On top of that, we propose the search algorithm that takes arguments from one ontology and generates queries compliant with other ontologies. We evaluated the solution on a web application that allows using a local ontology, which describes content of a web site, to search for web resources in remote digital libraries or object repositories based on more general content ontologies.
The Sport League's Dilemma: Competitive Balance Versus Incentives to Win

We analyze a dynamic model of strategic interaction between a professional sport league that organizes a tournament, the teams competing to win it, and the broadcasters paying for the rights to televise it. Teams and broadcasters maximize expected profits, while the league's objective may be either to maximize the demand for the sport or to maximize the teams' joint profits. Demand depends positively on symmetry among teams (competitive balance) and how aggressively teams try to win (incentives to win). Revenue sharing increases competitive balance but decreases incentives to win. Under demand maximization, a performance-based reward scheme (used by European sport leagues) may be optimal. Under joint profit maximization, full revenue sharing (used by many US leagues) is always optimal. These results reflect institutional differences among European and American sports leagues.
High-Isolation, Wideband Two-Element MIMO Antenna with Novel Decoupling Coupling Structure for Millimeter Wave 5G Wireless Applications

The article presents a compact, novel, inverted Pshaped antenna operating at Ka-band. The antenna is designed to resonate at frequency f0 = 30 GHz. The single-element antenna is expanded to a two-element MIMO. Various antenna orientation arrangements are studied to comprehend the effect of mutual coupling. Out-of-these mirrored antenna elements facing outward have resulted in better isolation of 22.95 dB with a bandwidth of 27.30-34.83 GHz. However, a slight mismatch in reflection coefficients is observed due to minor surface wave coupling. Further, a novel decoupling structure with a combination of inverted H- and T-shaped is engraved in the ground plane to enhance the isolation and antenna performance. The overall MIMO antenna profile is 1. 3λ0 x 0. 52λ0. It resulted in a maximum isolation of 35.17 dB with a bandwidth of 26.3633. 64 GHz. The maximum achieved gain is 3.2 dB. The proposed antenna is validated with diversity performance and is suitable for 5G wireless applications.
Cooperative Differential Game-based Optimal Tracking Control of Modular Reconfigurable Robot with Actor-Critic-Identifier Structure

Based on the cooperative differential game, this paper proposes an optimal control for modular reconfigurable robot (MRR) systems. Describe the dynamical model of MRR in the form of $n$ joint subsystems, including interconnected dynamic coupling (IDC). Considering $n$ joint subsystems as $n$ players in a cooperative game, uncertainties such as IDC and unknown disturbances contained in the dynamic model are approximated by neural network (NN) identifier. An n-player cooperative game theory can be utilized to convert the tracking control issue of MRR into an optimal control task. Solving the Hamilton-Jacobi-Bellman (HJB) equations for optimal control by adaptive dynamic programming (ADP) using actor-critic-identifier (ACI) structure. It is proved that the MRR system tracking error is uniformly ultimately bounded (UUB) by the Lyapunov theory. Finally, simulation experiments are conducted to verify the effectiveness of this method.
Semi- Circular Ring slotted Circular Patch Antenna for 5G Applications

A semi-circular ring slotted circular patch antenna has been designed in this manuscript at 3.7 GHz. The proposed 5G antenna has realized gain of 5.23 dBi at 3.7 GHz. This antenna is radiating over 3.67 GHz to 3.72 GHz. Therefore, the proposed antenna can be utilized for sub-6 GHz 5G applications. The fabricated antenna is tuned to 3.65 GHz and radiated over 3.62 GHz to 3.66 GHz.
Image Inpainting Based on Efficient Group Attention Generative Adversarial Networks

Inpainting methods based on generative adversarial networks are an important class in the field, and their excellent generative ability can effectively improve the details of the inpainting results. However, generative adversarial networks inherently suffer from training difficulties and model collapse, leading to the large size of models based on such structures and equally difficult training. In addition, most methods incorporate an attention mechanism to improve the inpainting accuracy, which leads to the loss of boundary details of images in the process of restoration, affecting the results. To address the above problems, we propose an inpainting method based on efficient group attention generative adversarial networks (EGA-GAN). The proposed method uses cascaded group attention modules and feature-aligned pyramid networks in the generator. The cascaded group attention module can effectively save computational costs and reduce computational redundancy, and the feature-aligned pyramid network can mitigate the loss of boundary details in the downsampling operation to some extent. Experimental results on two commonly used inpainting datasets, Paris Street View and CelebA-HQ, show that the proposed method can effectively accelerate the speed of restoration and improve the accuracy of results.
Enhanced Electricity Demand Forecasting in Australia Using a CNN-LSTM Model with Heating and Cooling Degree Days Data

In this paper, we present a hybrid deeplearning forecasting method based on heating and cooling degree days data to predict the electricity demand in Australia. The proposed model integrates convolutional neural network (CNN) and long shortterm memory (LSTM) models to improve electricity prediction accuracy. The proposed model performance is comprehensively compared with a few conventional forecast methods, including deep neural networks (DNN) model. The results show that the mean absolute error and mean absolute percentage error of the prediction have been reduced by using the proposed hybrid model.
Prediction of Powerplant Performance by Machine Learning Technique

The IEEE Standard 762-1987 introduced a comprehensive set of 25 indices for reporting power plant reliability, availability, and productivity. The sheer number of indices can be impractical, and an univariate indicator is preferable for real-world applications. Developing a single indicator derived from the 25 indices is the work's main theme. This study addresses this issue using machine learning to consolidate these indices, ultimately classifying power plant performance into "performed" and "unperformed. " Monthly performance data from 2020 were collected from 570 power plants in the Java-Sumatra interconnected electric system. These data were obtained from the Generation Availability Information System, a proprietary application owned by the Indonesia National Electric Power Company. Using the $k$-means clustering method, we classified the performance of these power plants. The clustering process was supplemented with the opinions of seven experts with extensive experience in the system. The comparison between machine learning classifications and expert opinions is presented.
Deep Gaussian Covariance Network with Trajectory Sampling for Data-Efficient Policy Search

Probabilistic world models increase data efficiency of model-based reinforcement learning (MBRL) by guiding the policy with their epistemic uncertainty to improve exploration and acquire new samples. Moreover, the uncertainty-aware learning procedures in probabilistic approaches lead to robust policies that are less sensitive to noisy observations compared to uncertainty-unaware solutions. We propose to combine trajectory sampling and deep Gaussian covariance network (DGCN) for a data-efficient solution to MBRL problems in an optimal control setting. We compare trajectory sampling with density-based approximation for uncertainty propagation using three different probabilistic world models; Gaussian processes (GPs), Bayesian neural networks (BNNs), and DGCNs. We provide empirical evidence using four different well-known test environments, that our method improves the sample-efficiency over other combinations of uncertainty propagation methods and probabilistic models. During our tests, we place particular emphasis on the robustness of the learned policies with respect to noisy initial states.
Using Synonyms for Arabic-to-English Example-Based Translation

An implementation of a non-structural Example-Based Machine Translation system that translates sentences from Arabic to English, using a parallel corpus aligned at the sentence level, is described. Source-language synonyms were derived automatically and used to help locate potential translation examples for fragments of a given input sentence. The smaller the parallel corpus, the greater the contribution provided by synonyms. Considering the degree of relevance of the subject matter of a potential match contributes to the quality of the final results.
64Cu-DOTATATE PET/MRI for Detection of Activated Macrophages in Carotid Atherosclerotic Plaques

Objective— A feature of vulnerable atherosclerotic plaques of the carotid artery is high activity and abundance of lesion macrophages. There is consensus that this is of importance for plaque vulnerability, which may lead to clinical events, such as stroke and transient ischemic attack. We used positron emission tomography (PET) and the novel PET ligand [64Cu] [1,4,7,10-tetraazacyclododecane-N,N′,N″,N‴-tetraacetic acid]-d-Phe1,Tyr3-octreotate (64Cu-DOTATATE) to specifically target macrophages via the somatostatin receptor subtype-2 in vivo. Approach and Results— Ten patients underwent simultaneous PET/MRI to measure 64Cu-DOTATATE uptake in carotid artery plaques before carotid endarterectomy. 64Cu-DOTATATE uptake was significantly higher in symptomatic plaque versus the contralateral carotid artery (P<0.001). Subsequently, a total of 62 plaque segments were assessed for gene expression of selected markers of plaque vulnerability using real-time quantitative polymerase chain reaction. These results were compared with in vivo 64Cu-DOTATATE uptake calculated as the mean standardized uptake value. Univariate analysis of real-time quantitative polymerase chain reaction and PET showed that cluster of differentiation 163 (CD163) and CD68 gene expression correlated significantly but weakly with mean standardized uptake value in scans performed 85 minutes post injection (P<0.001 and P=0.015, respectively). Subsequent multivariate analysis showed that CD163 correlated independently with 64Cu-DOTATATE uptake (P=0.031) whereas CD68 did not contribute significantly to the final model. Conclusions— The novel PET tracer 64Cu-DOTATATE accumulates in atherosclerotic plaques of the carotid artery. CD163 gene expression correlated independently with 64Cu-DOTATATE uptake measured by real-time quantitative polymerase chain reaction in the final multivariate model, indicating that 64Cu-DOTATATE PET is detecting alternatively activated macrophages. This association could potentially improve noninvasive identification and characterization of vulnerable plaques.
The association between paracetamol and asthma is still under debate

ABSTRACT Objective: To analyse the relationship between paracetamol and asthma. Data sources: An English literature search using electronic search engines (PubMed and EMBASE) was conducted. Study selections: Articles published in peer-review journals, from 1990 to December 2015 were included. To perform the search for the most suitable and representative articles, keywords were selected (“asthma,” “paracetamol” and “acetaminophen”). The evidence level was rated according to the criteria of the Oxford Centre For Evidence-Based Medicine. Results: The exposure to paracetamol during pregnancy was analysed in several cohort studies, showing an association between the prenatal exposure to paracetamol with suffering from asthma or presence of wheezing in childhood, especially for persistent wheezing. Nevertheless, a recent study concluded that the relationship between asthma and paracetamol is explained, at least in part, by confounding factors. Several works have also associated the exposure to paracetamol in the first years of life or in adults with the development of childhood asthma. Several pathophysiological mechanisms are known that could explain this relationship, such as the glutathione pathway, the decrease in the release of Th1 cytokines that are normally produced during febrile episodes, which would then lead to a predominance of Th2 cytokines, the cytotoxic effect of paracetamol for pneumocytes, a modulator effect on the activity of myeloperoxidase, as well as the possible antigenic effect of paracetamol, mediated by IgE. Conclusions: There are many arguments that suggest a relationship between the use of paracetamol with the appearance of asthmatic symptoms, however the evidence is inconclusive.
A Multi-Port Converter with Multi-Directional Power Flow Control Strategies for PV Power and Energy Storage Systems

In this paper, a multi-port converter (MPC) integrated with multi-directional power flow control (MDPFC) strategies is proposed. Compare to individual converter structures, the proposed MPC can decrease the circuit cost and complexity in multi power source applications. In the meantime, the PV power is considered for the proposed MPC while the MPPT function is realized. In order to overcome the unpredictable PV characteristics and load conditions, the battery module is adopted. In order to regulate the energy between the PV array, the battery and the load, the MDPFC strategies are developed in this paper. Moreover, the MDPFC can realized by the digital signal processor (DSP) without adding extra circuit and components. Compare to existed circuit topologies, the developed MPC and MDPFC can achieve the maximum energy utilization with only one inductor and limited circuit components. Finally, a 500W prototype hardware circuit will be implemented to verify the performance and feasibility of the proposed MPC. From the experimental validations, the peak circuit efficiency and the full-load efficiency are 97.22% and 96.3%, respectively.