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[IEEE 2018 26th Telecommunications Forum (TELFOR) - Belgrade, Serbia (2018.11.20-2018.11.21)] 2018 26th Telecommunications Forum (TELFOR) - Security Verification of Artificial Neural Networks Used to Error Correction in Quantum Cryptography
摘要： Error correction in quantum cryptography based on artificial neural networks is a new and promising solution. In this paper the security verification of this method is discussed and results of many simulations with different parameters are presented. The test scenarios assumed partially synchronized neural networks, typical for error rates in quantum cryptography. The results were also compared with scenarios based on the neural networks with random chosen weights to show the difficulty of passive attacks.
关键词： artificial neural networks,security,error correction,machine,learning,cryptography,verification,quantum
更新于2025-09-19 17:15:36
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[IEEE MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM) - Los Angeles, CA, USA (2018.10.29-2018.10.31)] MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM) - On the Defense Against Adversarial Examples Beyond the Visible Spectrum
摘要： Machine learning (ML) models based on RGB images are vulnerable to adversarial attacks, representing a potential cyber threat to the user. Adversarial examples are inputs maliciously constructed to induce errors by ML systems at test time. Recently, researchers also showed that such attacks can be successfully applied at test time to ML models based on multispectral imagery, suggesting this threat is likely to extend to the hyperspectral data space as well. Military communities across the world continue to grow their investment portfolios in multispectral and hyperspectral remote sensing, while expressing their interest in machine learning based systems. This paper aims at increasing the military community's awareness of the adversarial threat and also in proposing ML training strategies and resilient solutions for state of the art artificial neural networks. Specifically, the paper introduces an adversarial detection network that explores domain specific knowledge of material response in the shortwave infrared spectrum, and a framework that jointly integrates an automatic band selection method for multispectral imagery with adversarial training and adversarial spectral rule-based detection. Experiment results show the effectiveness of the approach in an automatic semantic segmentation task using Digital Globe's WorldView-3 satellite 16-band imagery.
关键词： Adversarial Machine Learning,Multispectral Imagery,Adversarial Examples,Defenses
更新于2025-09-19 17:15:36
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Learning-in-Templates Enables Accelerated Discovery and Synthesis of New Stable Double-Perovskites
摘要： In the past three years, Machine Learning (ML) in combination with Density Functional Theory (DFT) has enabled computational screening of compounds with the goal of accelerated materials discovery. Unfortunately, DFT+ML has, until now, either relied on knowledge of the atomic positions at DFT energy minima, which are a priori unknown, or has been limited to chemical spaces of modest size. Here we report a strategy that we term Learning-in-Templates (LiT) wherein we first define a series of space group and stoichiometry templates corresponding to hypothesized compounds and, orthogonally, we allow any list of atoms to take on any template. The LiT approach is deployed in combination with previously-established position-dependent representations and performs best with the representations that rely least on the atomic positions. Since the positions of the atoms in templates are known and do not change, LiT enables us to infer the properties of interest directly; additionally, LiT allows working with increased chemical spaces, since the same elements can take on a large number of templates. Only by using LiT were we able to span a 5x106 double perovskite compounds and achieve an acceleration factor of 700 compared to brute-force DFT, allowing us to predict never-before-screened compounds. Our findings motivated us to synthesize a new BaCuyTa(1-y)S3 perovskite which we show using an Electron Probe Microanalyzer has a 5:3 molar ratio of Cu to Ta, and using powder X-ray Diffraction (XRD) analysis combined with a DFT-based XRD simulation and fitting indicate a new phase having a I4/m space group.
关键词： Atomic Representation,Machine Learning,Density Functional Theory,Double Perovskites
更新于2025-09-19 17:15:36
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Machine learning algorithms enhance the specificity of cancer biomarker detection using SERS-based immunoassays in microfluidic chips
摘要： Specificity is a challenge in liquid biopsy and early diagnosis of various diseases. There are only a few biomarkers that have been approved for use in cancer diagnostics; however, these biomarkers suffer from a lack of high specificity. Moreover, determining the exact type of disorder for patients with positive liquid biopsy tests is difficult, especially when the aberrant expression of one single biomarker can be found in various other disorders. In this study, a SERS-based protein biomarker detection platform in a microfluidic chip and two machine learning algorithms (K-nearest neighbor and classification tree) are used to improve the reproducibility and specificity of the SERS-based liquid biopsy assay. Applying machine learning algorithms to the analysis of the expression level data of 5 protein biomarkers (CA19-9, HE4, MUC4, MMP7, and mesothelin) in pancreatic cancer patients, ovarian cancer patients, pancreatitis patients, and healthy individuals improves the chance of recognition for one specific disorder among the aforementioned diseases with overlapping protein biomarker changes. Our results demonstrate a convenient but highly specific approach for cancer diagnostics using serum samples.
关键词： cancer biomarkers,SERS,specificity,machine learning,microfluidic
更新于2025-09-19 17:15:36
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Indoor Scene and Position Recognition Based on Visual Landmarks Obtained from Visual Saliency without Human Effect
摘要： Numerous autonomous robots are used not only for factory automation as labor saving devices, but also for interaction and communication with humans in our daily life. Although superior compatibility for semantic recognition of generic objects provides wide applications in a practical use, it is still a challenging task to create an extraction method that includes robustness and stability against environmental changes. This paper proposes a novel method of scene and position recognition based on visual landmarks (VLs) used for an autonomous mobile robot in an environment living with humans. The proposed method provides a mask image of human regions using histograms of oriented gradients (HOG). The VL features are described with accelerated KAZE (AKAZE) after extracting conspicuous regions obtained using saliency maps (SMs). The experimentally obtained results using leave-one-out cross validation (LOOCV) revealed that recognition accuracy of high-saliency feature points was higher than that of low-saliency feature points. We created our original benchmark datasets using a mobile robot. The recognition accuracy evaluated using LOOCV reveals 49.9% for our method, which is 3.2 percentage points higher than the accuracy of the comparison method without HOG detectors. The analysis of false recognition using a confusion matrix examines false recognition occurring in neighboring zones. This trend is reduced according to zone separations.
关键词： visual landmark,machine learning,saliency maps,semantic position recognition,histograms of oriented gradients
更新于2025-09-19 17:15:36
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Retrieval of Maize Leaf Area Index Using Hyperspectral and Multispectral Data
摘要： Field spectra acquired from a handheld spectroradiometer and Sentinel-2 images spectra were used to investigate the applicability of hyperspectral and multispectral data in retrieving the maize leaf area index in low-input crop systems, with high spatial and intra-annual variability, and low yield, in southern Mozambique, during three years. Seventeen vegetation indices, comprising two and three band indices, and nine machine learning regression algorithms (MLRA) were tested for the statistical approach while five cost functions were tested in the look-up-table (LUT) inversion approach. The three band vegetation indices were selected, specifically the modified difference index (mDId: 725; 715; 565) for the hyperspectral dataset and the modified simple ratio (mSRc: 740; 705; 865) for the multispectral dataset of field spectra and the three band spectral index (TBSIb: 665; 865; 783) for the Sentinel-2 dataset. The relevant vector machine was the selected MLRA for the two datasets of field spectra (multispectral and hyperspectral) while the support vector machine was selected for the Sentinel-2 data. When using the LUT inversion technique, the minimum contrast estimation and the Bhattacharyya divergence cost functions were the best performing. The vegetation indices outperformed the other two approaches, with the TBSIb as the most accurate index (RMSE = 0.35). At the field scale, spectral data from Sentinel-2 can accurately retrieve the maize leaf area index in the study area.
关键词： hyperspectral,multispectral,vegetation indices,Sentinel-2,machine learning regression algorithms,PROSAIL,field-spectroradiometer,LUT inversion,leaf area index
更新于2025-09-19 17:15:36
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Detection of pepper fusarium disease using machine learning algorithms based on spectral reflectance
摘要： The development of computerized automated diagnostic systems ensures more effective health screening in plants. In this way, the damage caused by diseases can be reduced by early detection. Light reflections from plant leaves are known to carry information about plant health. In the study, healthy and fusarium diseased peppers (capsicum annuum) was detected from the reflections obtained from the pepper leaves with the aid of spectroradiometer. Reflections were taken from four groups of pepper leaves (healthy, fusarium-diseased, mycorrhizal fungus, fusarium-diseased and mycorrhizal fungus) grown in a closed environment at wavelengths between 350 nm and 2500 nm. Pepper disease detection takes place in two stages. In the first step, the feature vector is obtained. In the second step, the feature vectors of the input data are classified. The feature vector consist of the coefficients of wavelet decomposition and the statistical values of these coefficients. Artificial Neural Networks (ANN), Naive Bayes (NB) and K-nearest Neighbor (KNN) were used for classification. In detection the health case of pepper, the average success rates of different classification algorithms for the first two groups (diseased and healthy peppers) were calculated as 100% for KNN, 97.5% for ANN and 90% for NB. Likewise, these rates for the classification of all groups were calculated as 100% for KNN, 88.125% for ANN and 82% for NB. Overall, the results have shown that leaf reflections can be successfully used in disease detection.
关键词： Wavelet,Spectral reflectance,Machine learning algorithms,Pepper disease detection,Classification
更新于2025-09-19 17:15:36
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[IEEE 2019 23rd International Conference on Mechatronics Technology (ICMT) - SALERNO, Italy (2019.10.23-2019.10.26)] 2019 23rd International Conference on Mechatronics Technology (ICMT) - Toward the Application of Reinforcement Learning to the Intensity Control of a Seeded Free-Electron Laser
摘要： The optimization of particle accelerators is a challenging task, and many different approaches have been proposed in years, to obtain an optimal tuning of the plant and to keep it optimally tuned despite drifts or disturbances. Indeed, the classical model-free approaches (such as Gradient Ascent or Extremum Seeking algorithms) have intrinsic limitations. To overcome those limitations, Machine Learning techniques, in particular, the Reinforcement Learning, are attracting more and more attention in the particle accelerator community. The purpose of this paper is to apply a Reinforcement Learning model-free approach to the alignment of a seed laser, based on a rather general target function depending on the laser trajectory. The study focuses on the alignment of the lasers at FERMI, the free-electron laser facility at Elettra Sincrotrone Trieste. In particular, we employ Q-learning with linear function approximation and report experimental results obtained in two setups, which are the actual setups where the final application has to be deployed. Despite the simplicity of the approach, we report satisfactory preliminary results, that represent the first step toward a fully automatic procedure for seed laser to the electron beam. Such a superimposition is, at present, performed manually.
关键词： Free-Electron Laser,Particle Accelerators,Q-learning,Reinforcement Learning,Machine Learning
更新于2025-09-19 17:13:59
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Riparian trees genera identification based on leaf-on/leaf-off airborne laser scanner data and machine learning classifiers in northern France
摘要： Riparian forests are valuable environments delivering multiples ecological services. Because they face both natural and anthropogenic constraints, riparian forests need to be accurately mapped in terms of genera/species diversity. Previous studies have shown that the Airborne Laser Scanner (ALS) data have the potential to classify trees in di?erent contexts. However, an assessment of important features and classi?cation results for broadleaved deciduous riparian forests mapping using ALS remains to be achieved. The objective of this study was to estimate which features derived from ALS data were important for describing trees genera from a riparian deciduous forest, and provide results of classi?cations using two Machine Learning algorithms. The procedure was applied to 191 trees distributed in eight genera located along the Sélune river in Normandy, northern France. ALS data from two surveys, in the summer and winter, were used. From these data, trees crowns were extracted and global morphology and internal structure features were computed from the 3D points clouds. Five datasets were established, containing for each one an increasing number of genera. This was implemented in order to assess the level of discrimination between trees genera. The most discriminant features were selected using a stepwise Quadratic Discriminant Analysis (sQDA) and Random Forest, allowing the number of features to be reduced from 144 to 3–9, depending on the datasets. The sQDA-selected features highlighted the fact that, with an increasing number of genera in the datasets, internal structure became more discrimi- nant. The selected features were used as variables for classi?cation using Support Vector Machine (SVM) and Random Forest (RF) algorithms. Additionally, Random Forest classi?cations were conducted using all features computed, without selection. The best classi?ca- tion performances showed that using the sQDA-selected features with SVM produced accuracy ranging from 83.15% when using three genera (Oak, Alder and Poplar). A similar result was obtained using RF and all features available for classi?cation. The latter also achieved the best classi?cation performances when using seven and eight genera. The results highlight that ML algorithms are suitable methods to map riparian trees.
关键词： Machine Learning,Riparian forests,tree genera identification,Support Vector Machine (SVM),Airborne Laser Scanner (ALS),Random Forest (RF)
更新于2025-09-19 17:13:59
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A hybrid deep learning architecture for classification of microscopic damage on National Ignition Facility laser optics
摘要： Accurately classifying microscopic damage helps automate the repair and recycling of National Ignition Facility optics and informs the study of damage initiation and growth. This complex 12-class problem previously required human experts to distinguish and label the various damage morphologies. Finding image analysis methods to extract and calculate distinguishing features would be time consuming and challenging, so we sought to automate this task by using convolutional neural networks (CNNs) pretrained on the ImageNet database to take advantage of its automated feature discovery and extraction. We compared three model architectures on this dataset and found the one with highest overall accuracy, 99.17%, was a novel hybrid architecture, one in which we removed the final decision-making layer of the deep learner and replaced it with an ensemble of decision trees (EDT). This combines the power of feature extraction by CNNs with the decision-making strength of EDT. The accuracy of the hybrid architecture over the deep learning alone is shown to be significantly improved. Furthermore, we applied this novel hybrid architecture to an entirely different dataset, one containing images of repaired damage sites, and improved on the previously published findings, also with a demonstrably significant increase in accuracy over using the deep learner alone.
关键词： machine learning,laser optic damage,deep learning,automation
更新于2025-09-19 17:13:59