scholarly journals Multimedia Communication Security in 5G/6G Coverless Steganography Based on Image Text Semantic Association

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yajing Hao ◽  
Xinrong Yan ◽  
Jianbin Wu ◽  
Huijun Wang ◽  
Linfeng Yuan
Keyword(s):  
Communication Security ◽  
Semantic Matching ◽  
Semantic Association ◽  
Web Crawler ◽  
Training Set ◽  
Covert Communications ◽  
Maximum Payload ◽  
Wide Range ◽  
Carrier Image ◽  
Training Subset

Recently, researchers have shown that coverless steganography is relatively safe. On this basis, to improve the payload of the coverless steganography, a novel semiconstruction coverless steganography algorithm is introduced in the paper. Firstly, web crawler technology is applied to crawl a wide range of small icons and hot news images from the Internet. These icons can be used as the training subset, and the hot news can be designed according to construction rules. Secondly, the Alex-Net network is introduced for training in the algorithm, and the adversarial samples are added to the training set. Thirdly, using the preset template, certain small icons and a hot news image are spliced into a secret carrier image according to the construction principle. The hot news image is in the top half of the carrier, and those small icons are in the bottom half. The image on the upper part of the carrier and the icons of the lower part can be connected by image and text semantics, and the semantic matching can be realized between image semantics and explanatory. The experimental results and analysis show that the proposed algorithm can resist steganalysis tools effectively and has good robustness against various image attacks. Meanwhile, the secret information payload has been greatly improved, the maximum payload can reach 180 bits of a single 512 × 512 image. This promising algorithm can be applied to build covert communications.

Design and characterization of a hybrid soft gripper with active palm pose control

2020 ◽  
Vol 39 (14) ◽  
pp. 1668-1685 ◽  
Author(s):  
Vignesh Subramaniam ◽  
Snehal Jain ◽  
Jai Agarwal ◽  
Pablo Valdivia y Alvarado
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Composite Structure ◽  
Element Model ◽  
Open Loop ◽  
Aspect Ratios ◽  
Maximum Payload ◽  
Wide Range ◽  
Pose Control

The design and characterization of a soft gripper with an active palm to control grasp postures is presented herein. The gripper structure is a hybrid of soft and stiff components to facilitate integration with traditional arm manipulators. Three fingers and a palm constitute the gripper, all of which are vacuum actuated. Internal wedges are used to tailor the deformation of a soft outer reinforced skin as vacuum collapses the composite structure. A computational finite-element model is proposed to predict finger kinematics. Thanks to its active palm, the gripper is capable of grasping a wide range of part geometries and compliances while achieving a maximum payload of 30 N. The gripper natural softness enables robust open-loop grasping even when components are not properly aligned. Furthermore, the grasp pose of objects with various aspect ratios and compliances can be robustly maintained during manipulation at linear accelerations of up to 15 m/s2 and angular accelerations of up to 5.23 rad/s2.

scholarly journals Multi-Tissue Neocortical Transcriptome-Wide Association Study Implicates 8 Genes Across 6 Genomic Loci in Alzheimer’s Disease

2020 ◽  
Author(s):  
Jake Gockley ◽  
Kelsey S. Montgomery ◽  
William L. Poehlman ◽  
Jesse C. Wiley ◽  
Yue Liu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Association Study ◽  
The United States ◽  
Therapeutic Interventions ◽  
Repeated Measure ◽  
Training Set ◽  
Common Genetic Variants ◽  
Wide Range ◽  
Measure Design

AbstractBackgroundAlzheimer’s disease (AD), an incurable neurodegenerative disease, currently affecting 1.75% of the United States population, with projected growth to 3.46% by 2050. Identifying common genetic variants driving differences in transcript expression that confer AD-risk is necessary to elucidate AD mechanism and develop therapeutic interventions. We modify the FUSION Transcriptome Wide Association Study (TWAS) pipeline to ingest expression from multiple neocortical regions, provide a set of 6780 gene weights which are abstracatable across the neocortex, and leverage these to find 8 genes from six loci with associated AD risk validated through summary mendelian randomization (SMR) utilizing IGAP summary statistics.MethodA combined dataset of 2003 genotypes clustered to Central European (CEU) ancestry was used to construct a training set of 790 genotypes paired to 888 RNASeq profiles across 6 Neo-cortical tissues (TCX=248, FP=50, IFG=41, STG=34, PHG=34, DLPFC=461). Following within-tissue normalization and covariate adjustment, predictive weights to impute expression components based on a gene’s surrounding cis-variants were trained. The FUSION pipeline was modified to support input of pre-scaled expression values and provide support for cross validation with a repeated measure design arising from the presence of multiple transcriptome samples from the same individual across different tissues.ResultsCis-variant architecture alone was informative to train weights and impute expression for 6780 (49.67%) autosomal genes, the majority of which significantly correlated with gene expression; FDR < 5%: N=6775 (99.92%), Bonferroni: N=6716 (99.06%). Validation of weights in 515 matched genotype to RNASeq profiles from the CommonMind Consortium (CMC) was (72.14%) in DLPFC profiles. Association of imputed expression components from all 2003 genotype profiles yielded 8 genes significantly associated with AD (FDR < 0.05); APOC1, EED, CD2AP, CEACAM19, CLPTM1, MTCH2, TREM2, KNOP1.ConclusionWe provide evidence of cis-genetic variation conferring AD risk through 8 genes across six distinct genomic loci. Moreover, we provide expression weights for 6780 genes as a valuable resource to the community, which can be abstracted across the neocortex and a wide range of neuronal phenotypes.

scholarly journals Calculation of the Detonation State of HN3 with Quantum Accuracy

2020 ◽  
Author(s):  
Cong Huy Pham ◽  
Rebecca Lindsey ◽  
Laurence E. Fried ◽  
Nir Goldman
Keyword(s):  
Molecular Dynamics ◽  
Density Functional Theory ◽  
Potential Energy ◽  
Force Field ◽  
Density Functional ◽  
Energy Surface ◽  
Detonation Properties ◽  
Training Set ◽  
Functional Theory ◽  
Wide Range

<div>HN<sub>3</sub> is a unique liquid energetic material that exhibits ultrafast detonation chemistry and a transition to metallic states during detonation. We combine the ChIMES many-body reactive force field and the extended-Lagrangian multiscale shock technique (MSST) molecular dynamics method to calculate the detonation properties of HN<sub>3</sub> with the accuracy of Kohn-Sham density-functional theory. ChIMES is based on a Chebyshev polynomial expansion and can accurately reproduce density-functional theory molecular dynamics (DFT-MD) simulations for a wide range of unreactive and decomposition conditions of liquid HN<sub>3</sub>. We show that addition of random displacement configurations and the energies of gas-phase equilibrium products in the training set allows ChIMES to efficiently explore the complex potential energy surface. Schemes for selecting force field parameters and the inclusion of stress tensor and energy data in the training set are examined. Structural and dynamical properties, as well as chemistry predictions for the resulting models are benchmarked against DFT-MD. We demonstrate that the inclusion of explicit four-body energy terms is necessary to capture the potential energy surface across a wide range of conditions. The present force field, which was fit to a balance of forces, energies, and stress tensors yields excellent agreement with DFT, while exhibiting an orders-of-magnitude increase in computational efficiency over DFT-MD. Our results generally retain the accuracy of DFT-MD while yielding a high degree of computational efficiency, allowing simulations to approach orders of magnitude larger time and spatial scales. The techniques and recipes for MD model creation we present allow for direct simulation of nanosecond shock compression experiments and calculation of the detonation properties of materials with the accuracy of Kohn-Sham density-functional theory.</div>

scholarly journals Analysis of Public Opinion in Colleges and Universities Based on Wireless Web Crawler Technology in the Context of Artificial Intelligence

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
WenNing Wu ◽  
ZhengHong Deng
Keyword(s):  
Artificial Intelligence ◽  
Public Opinion ◽  
Colleges And Universities ◽  
Web Crawler ◽  
The Public ◽  
User Influence ◽  
Proposed Model ◽  
Wide Range ◽  
Different Dimensions ◽  
The Web

Wi-Fi-enabled information terminals have become enormously faster and more powerful because of this technology’s rapid advancement. As a result of this, the field of artificial intelligence (AI) was born. Artificial intelligence (AI) has been used in a wide range of societal contexts. It has had a significant impact on the realm of education. Using big data to support multistage views of every subject of opinion helps to recognize the unique characteristics of each aspect and improves social network governance’s suitability. As public opinion in colleges and universities becomes an increasingly important vehicle for expressing public opinion, this paper aims to explore the concepts of public opinion based on the web crawler and CNN (Convolutional Neural Network) model. Web crawler methodology is utilised to gather the data given by students of college and universities and mention them in different dimensions. This CNN has robust data analysis capability; this proposed model uses the CNN to analyse the public opinion. Preprocessing of data is done using the oversampling method to maximize the effect of classification. Through the association of descriptions, comprehensive utilization of image information like user influence, stances of comments, topics, time of comments, etc., to suggest guidance phenomenon for various schemes, helps to enhance the effectiveness and targeted social governance of networks. The overall experimentation was carried out in python here in which the suggested methodology was predicting the positive and negative opinion of the students over the web crawler technology with a low rate of error when compared to other existing methodology.

scholarly journals MRI-Based Radiomics Nomogram To Predict Intraoperative Hemorrhage of Placenta Previa

2021 ◽  
Author(s):  
Ye Song ◽  
Liping Zhu ◽  
Dali Chen ◽  
Yongmei Li ◽  
Qi Xi ◽  
...  
Keyword(s):  
Predictive Accuracy ◽  
Placenta Previa ◽  
Preoperative Evaluation ◽  
Curve Analysis ◽  
Training Set ◽  
Decision Curve Analysis ◽  
Preoperative Prediction ◽  
Mri Features ◽  
Wide Range ◽  
Validation Set

Abstract Background: Placenta previa is associated with higher percentage of intraoperative and postpartum hemorrhage, increased obstetric hysterectomy, significant maternal morbidity and mortality. We aimed to develop and validate a magnetic resonance imaging (MRI)-based nomogram to preoperative prediction of intraoperative hemorrhage (IPH) for placenta previa, which might contribute to adequate assessment and preoperative preparation for the obstetricians.Methods: Between May 2015 and December 2019, a total of 125 placenta previa pregnant women were divided into a training set (n = 80) and a validation set (n = 45). Radiomics features were extracted from MRI images of each patient. A MRI-based model comprising seven features was built for the classification of patients into IPH and non-IPH groups in a training set and validation set. Multivariate nomograms based on logistic regression analyses were built according to radiomics features. Receiver operating characteristic (ROC) curve was used to assess the model. Predictive accuracy of nomogram were assessed by calibration plots and decision curve analysis. Results: In multivariate analysis, placenta position, placenta thickness, cervical blood sinus and placental signals in the cervix were signifcantly independent predictors for IPH (all p < 0.05). The MRI-based nomogram showed favorable discrimination between IPH and non-IPH groups. The calibration curve showed good agreement between the estimated and the actual probability of IPH. Decision curve analysis also showed a high clinical benefit across a wide range of probability thresholds. The AUC was 0.918 ( 95% CI, 0.857-0.979 ) in the training set and 0.866( 95% CI, 0.748-0.985 ) in the validation set by the combination of four MRI features.Conclusions: The MRI-based nomograms might be a useful tool for the preoperative prediction of IPH outcomes for placenta previa. Our study enables obstetricians to perform adequate preoperative evaluation to minimize blood loss and reduce the rate of caesarean hysterectomy.

scholarly journals Investigating the OECD database of per- and polyfluoroalkyl substances – chemical variation and applicability of current fate models

2020 ◽  
Vol 17 (7) ◽  
pp. 498 ◽  
Author(s):  
Ioana C. Chelcea ◽  
Lutz Ahrens ◽  
Stefan Örn ◽  
Daniel Mucs ◽  
Patrik L. Andersson
Keyword(s):  
Environmental Fate ◽  
Chemical Diversity ◽  
Acid Dissociation ◽  
Substantial Part ◽  
Chemical Variation ◽  
Training Set ◽  
Knowledge Gaps ◽  
Diverse Range ◽  
Wide Range ◽  
Polyfluoroalkyl Substances

Environmental contextA diverse range of materials contain organofluorine chemicals, some of which are hazardous and widely distributed in the environment. We investigated an inventory of over 4700 organofluorine compounds, characterised their chemical diversity and selected representatives for future testing to fill knowledge gaps about their environmental fate and effects. Fate and property models were examined and concluded to be valid for only a fraction of studied organofluorines. AbstractMany per- and polyfluoroalkyl substances (PFASs) have been identified in the environment, and some have been shown to be extremely persistent and even toxic, thus raising concerns about their effects on human health and the environment. Despite this, little is known about most PFASs. In this study, the comprehensive database of over 4700 PFAS entries recently compiled by the OECD was curated and the chemical variation was analysed in detail. The analysis revealed 3363 individual PFASs with a huge variation in chemical functionalities and a wide range of mixtures and polymers. A hierarchical clustering methodology was employed on the curated database, which resulted in 12 groups, where only half were populated by well-studied compounds thus indicating the large knowledge gaps. We selected both a theoretical and a procurable training set that covered a substantial part of the chemical domain based on these clusters. Several computational models to predict physicochemical and environmental fate related properties were assessed, which indicated their lack of applicability for PFASs and the urgent need for experimental data for training and validating these models. Our findings indicate reasonable predictions of the octanol-water partition coefficient for a small chemical domain of PFASs but large data gaps and uncertainties for water solubility, bioconcentration factor, and acid dissociation factor predictions. Improved computational tools are necessary for assessing risks of PFASs and for including suggested training set compounds in future testing of both physicochemical and effect-related data. This should provide a solid basis for better chemical understanding and future model development purposes.

scholarly journals An Innovative Deep Learning Algorithm for Drowsiness Detection from EEG Signal

Computation ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 13 ◽  
Author(s):  
Francesco Rundo ◽  
Sergio Rinella ◽  
Simona Massimino ◽  
Marinella Coco ◽  
Giorgio Fallica ◽  
...  
Keyword(s):  
Machine Learning ◽  
Visual Information ◽  
Learning Algorithm ◽  
Eeg Signal ◽  
Training Set ◽  
Robust Algorithms ◽  
Physiological Measurement ◽  
Deep Learning Algorithm ◽  
Wide Range ◽  
Electroencephalogram Eeg

The development of detection methodologies for reliable drowsiness tracking is a challenging task requiring both appropriate signal inputs and accurate and robust algorithms of analysis. The aim of this research is to develop an advanced method to detect the drowsiness stage in electroencephalogram (EEG), the most reliable physiological measurement, using the promising Machine Learning methodologies. The methods used in this paper are based on Machine Learning methodologies such as stacked autoencoder with softmax layers. Results obtained from 62 volunteers indicate 100% accuracy in drowsy/wakeful discrimination, proving that this approach can be very promising for use in the next generation of medical devices. This methodology can be extended to other uses in everyday life in which the maintaining of the level of vigilance is critical. Future works aim to perform extended validation of the proposed pipeline with a wide-range training set in which we integrate the photoplethysmogram (PPG) signal and visual information with EEG analysis in order to improve the robustness of the overall approach.

Multi-Strategy Web Service Discovery for Smart Government

2014 ◽  
Vol 536-537 ◽  
pp. 625-631 ◽  
Author(s):  
Yun Yun Du ◽  
Xue Qin
Keyword(s):  
Web Service ◽  
Service Discovery ◽  
Large Scale ◽  
Search Space ◽  
Semantic Matching ◽  
Business Systems ◽  
Keyword Query ◽  
Web Service Discovery ◽  
Wide Range ◽  
Smart Government

Semantic Web Service technology is the solution to system integration and business collaboration for smart government which is cross-border and heterogeneous on a large scale. However the tremendous Web services search space caused by the wide range, large scale and complex e-government business systems is one of the great challenges for smart government. The paper focuses on researches about service discovery in e-government business integration for smart government. In accordance with the application environment and the current technical status of e-government, the author proposes a multi-strategy Web service discovery method on the basis of the proposed semantic model. The discovery process comprises three stages: keyword query with semantic enhancement, IO semantic matching and PE semantic matching. Finally similarity calculating method is proposed to evaluate the matching degree of each candidate service for service selection as well as the conclusions.

Fuel effects on range versus payload for modern jet aircraft

2011 ◽  
Vol 115 (1172) ◽  
pp. 627-634 ◽  
Author(s):  
S. Blakey ◽  
C. W. Wilson ◽  
M. Farmery ◽  
R. Midgley
Keyword(s):  
Specific Energy ◽  
Hydrocarbon Fuels ◽  
Fuel Properties ◽  
Maximum Payload ◽  
Wide Range ◽  
Maximum Range ◽  
Fuel Effects

Abstract With changes in the availability and quality of existing aviation fuels anticipated in the next 30 years it is timely to assess how changes in fuel properties would affect the range payload performance of aircraft. The effects on range and payload of a wide range of candidate fuels for aviation are investigated, including changes to the blends of conventional hydrocarbon fuels. Lighter fuels tend to be more desirable for commercial flights, where the flight is as close to the maximum payload as possible. Flights favouring range over payload are better suited by a more dense fuel. The hydrocarbon blends suggest for each aircraft, an optimum fuel may exist for the maximum payload and allowing the maximum range. Specific flight plans below the maximum range of the aircraft may be met with a lower specific energy fuel.

scholarly journals Térdporc szegmentálása MR-felvételekből mesterséges intelligencia segítségével

2021 ◽  
Vol 162 (9) ◽  
pp. 352-360
Author(s):  
Péter Szoldán ◽  
Zsófia Egyed ◽  
Endre Szabó ◽  
János Somogyi ◽  
György Hangody ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Knee Joint ◽  
Cartilage Tissue ◽  
Osteochondral Allograft ◽  
Mr Images ◽  
Training Set ◽  
Mr Image ◽  
The Neural Network ◽  
Wide Range

Összefoglaló. Bevezetés: A térdízületnek ultrafriss osteochondralis allograft segítségével történő részleges ortopédiai rekonstrukciója képalkotó vizsgálatokon alapuló pontos tervezést igényel, mely folyamatban a morfológia felismerésére képes mesterséges intelligencia nagy segítséget jelenthet. Célkitűzés: Jelen kutatásunk célja a porc morfológiájának MR-felvételen történő felismerésére alkalmas mesterséges intelligencia kifejlesztése volt. Módszer: A feladatra legalkalmasabb MR-szekvencia meghatározása és 180 térd-MR-felvétel elkészítése után a mesterséges intelligencia tanításához manuálisan és félautomata szegmentálási módszerrel bejelölt porckontúrokkal tréninghalmazt hoztunk létre. A mély convolutiós neuralis hálózaton alapuló mesterséges intelligenciát ezekkel az adatokkal tanítottuk be. Eredmények: Munkánk eredménye, hogy a mesterséges intelligencia képes a meghatározott szekvenciájú MR-felvételen a porcnak a műtéti tervezéshez szükséges pontosságú bejelölésére, mely az első lépés a gép által végzett műtéti tervezés felé. Következtetés: A választott technológia – a mesterséges intelligencia – alkalmasnak tűnik a porc geometriájával kapcsolatos feladatok megoldására, ami széles körű alkalmazási lehetőséget teremt az ízületi terápiában. Orv Hetil. 2021; 162(9): 352–360. Summary. Introduction: The partial orthopedic reconstruction of the knee joint with an osteochondral allograft requires precise planning based on medical imaging reliant; an artificial intelligence capable of determining the morphology of the cartilage tissue can be of great help in such a planning. Objective: We aimed to develop and train an artificial intelligence capable of determining the cartilage morphology in a knee joint based on an MR image. Method: After having determined the most appropriate MR sequence to use for this project and having acquired 180 knee MR images, we created the training set for the artificial intelligence by manually and semi-automatically segmenting the contours of the cartilage in the images. We then trained the neural network with this dataset. Results: As a result of our work, the artificial intelligence is capable to determine the morphology of the cartilage tissue in the MR image to a level of accuracy that is sufficient for surgery planning, therefore we have made the first step towards machine-planned surgeries. Conclusion: The selected technology – artificial intelligence – seems capable of solving tasks related to cartilage geometry, creating a wide range of application opportunities in joint therapy. Orv Hetil. 2021; 162(9): 352–360.

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