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Segmenting Input data to Improve the Quality of Identification of Information Security Events

AUTOMATIC CONTROL AND COMPUTER SCIENCES 2024年第8期58卷 1192-1203页

作者： Sukhoparov, M. E. Lebedev, I. S. Tikhonov, D. D. Russian State Hydrometeorol Univ St Petersburg 192007 Russia Russian Acad Sci St Petersburg Fed Res Ctr St Petersburg 199178 Russia

The processing of information sequences using segmentation of input data, aimed at improving the quality indicators of destructive impact detection using machine learning models is proposed. The basis of the proposed solution is the division of data into segments with different properties of the objects of observation. A method is described that uses a multilevel data processing architecture, where the processes of training, analysis of the achieved values of quality indicators, and assignment of the best models for quality indicators to individual data segments are implemented at various levels. The proposed method allows us to improve the quality indicators of the detection of destructive information impacts through segmentation and assignment of models that have the best indicators in individual segments.

关键词： information security machine learning data sets data sampling data segmentation processing models

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CT evaluation prior to transapical aortic valve replacement: semi-automatic versus manual image segmentation

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INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING 2015年第6期31卷 1233-1242页

作者： Foldyna, Borek Jungert, Camelia Luecke, Christian von Aspern, Konstantin Boehmer-Lasthaus, Sonja Rueth, Eva Maria Grothoff, Matthias Nitzsche, Stefan Gutberlet, Matthias Mohr, Friedrich Wilhelm Lehmkuhl, Lukas Univ Leipzig Ctr Heart Dept Intervent & Diagnost Radiol D-04289 Leipzig Germany Univ Leipzig Ctr Heart Dept Cardiac Surg D-04289 Leipzig Germany Siemens Healthcare Imaging & Therapy Erlangen Germany

To compare the performance of semi-automatic versus manual segmentation for ECG-triggered cardiovascular computed tomography (CT) examinations prior to transcatheter aortic valve replacement (TAVR), with focus on the speed and precision of experienced versus inexperienced observers. The preoperative ECG-triggered CT data of 30 consecutive patients who were scheduled for TAVR were included. All datasets were separately evaluated by two radiologists with 1 and 5 years of experience (novice and expert, respectively) in cardiovascular CT using an evaluation software program with or without a semi-automatic TAVR workflow. The time expended for data loading and all segmentation steps required for the implantation planning were assessed. Inter-software as well as inter-observer reliability analysis was performed. The CT datasets were successfully evaluated, with mean duration between 520.4 +/- A 117.6 s and 693.2 +/- A 159.5 s. The three most time-consuming steps were the 3D volume rendering, the measurement of aorta diameter and the sizing of the aortic annulus. Using semi-automatic segmentation, a novice could evaluate CT data approximately 12.3 % faster than with manual segmentation, and an expert could evaluate CT data approximately 10.3 % faster [mean differences of 85.4 +/- A 83.8 s (p < 0.001) and 59.8 +/- A 101 s (p < 0.001), respectively]. The inter-software reliability for a novice was slightly lower than for an expert;however, the reliability for a novice and expert was excellent (ICC 0.92, 95 % CI 0.75-0.97/ICC 0.96, 95 % CI 0.91-0.98). Automatic aortic annulus detection failed in two patients (6.7 %). The study revealed excellent inter-software and inter-observer reliability, with a mean ICC of 0.95. TAVR evaluation can be accomplished significantly faster with semi-automatic rather than with manual segmentation, with comparable exactness, showing a benefit for experienced and inexperienced observers.

关键词： Transapical valve replacement Aortic valve Computed tomography data segmentation data evaluation Experienced versus inexperienced users Efficiency

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Automated System Identification in Mineral Processing Industries: A Case Study using the Zinc Flotation Cell

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IFAC-PapersOnLine 2018年第18期51卷 132-137页

作者： Shardt, Yuri A.W. Brooks, Kevin Technical University of Ilmenau Ilmenau Canada BluESP 53 Platina St Randburg South Africa

In many industries, including the mineral processing industry, process modelling can be improved by mining the data historian. However, the data in the historian is often contaminated with missing values, unknown operating conditions, and other imperfections. Furthermore, manual segmentation of the data is difficult due to the large number of data points and variables. Thus, there is a need to develop and implement methods that can automatically segment the data set into viable components for identification purposes. One approach uses Laguerre models to segment the data set. However, when used in a multivariate situation, such as in the zinc flotation cell, various issues, such as collinearity, arise. Therefore, the data segmentation algorithm needs to take this into consideration when examining a data set. Using the zinc flotation cell, it is shown that for the multivariate case preselecting the data variables to consider improves the data segmentation. © 2018

关键词： Cells Automation Cytology data mining Flotation History Identification (control systems) Mineral industry Religious buildings Zinc Automated systems data segmentation Flotation cell Manual segmentation Mineral processing industry Number of datum Operating condition Process modelling

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Improving the Quality of the Identification of the Information Security State Based on Sample segmentation

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AUTOMATIC CONTROL AND COMPUTER SCIENCES 2023年第8期57卷 1071-1075页

作者： Sukhoparov, M. E. Lebedev, I. S. Russian State Hydrometeorol Univ St Petersburg 192007 Russia Russian Acad Sci St Petersburg Fed Res Ctr St Petersburg 199178 Russia

Increasing the quality indicators for identifying the information security (IS) state of individual segments of cyber-physical systems is related to processing large information arrays. A method for improving quality indicators when solving problems of identifying the IS state is proposed. Its implementation is based on the formation of individual sample segments. Analysis of the properties of these segments makes it possible to select and assign algorithms that have the best quality indicators in the current segment. segmentation of a data sample is considered. Using real dataset data as an example, experimental values of the quality indicator for the proposed method are given for various classifiers on individual segments and the entire sample.

关键词： information security machine learning data set data segmentation data sampling

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On automated cloud bursting and hybrid cloud setups using Apache Mesos

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2020年第17期32卷

作者： Haugerud, Harek Xue, Noha Yazidi, Anis Oslo Metropolitan Univ Dept Comp Sci Oslo Norway Oslo Metropolitan Univ POB 4 N-0130 Oslo Norway

Hybrid cloud technology is becoming increasingly popular as it merges private and public clouds to bring the best of two worlds together. However, due to the heterogeneous cloud installation, facilitating a hybrid cloud setup is not simple. Despite some commercial solutions being available to build a hybrid cloud, an open-source implementation is still unavailable. In this paper, we try to bridge the gap by providing an open-source implementation using the power of Apache Mesos. We build a hybrid cloud on top of multiple cloud platforms, private and public. Through comprehensive experimental results, we show that our solution is able to ensure resource bursting by leveraging the power of the public cloud.

关键词： Apache Mesos cloud bursting data segmentation fault tolerance open-source hybrid cloud

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Discussion of 'Detecting possibly frequent change-points: Wild Binary segmentation 2 and steepest-drop model selection'

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JOURNAL OF THE KOREAN STATISTICAL SOCIETY 2020年第4期49卷 1076-1080页

作者： Cho, Haeran Kirch, Claudia Univ Bristol Sch Math Bristol Avon England Otto von Guericke Univ Dept Math Ctr Behav Brain Sci CBBS Magdeburg Germany

We congratulate the author for this interesting paper which introduces a novel method for the data segmentation problem that works well in a classical change point setting as well as in a frequent jump situation. Most notably, the paper introduces a new model selection step based on finding the 'steepest drop to low levels' (SDLL). Since the new model selection requires a complete (or at least relatively deep) solution path ordering the change point candidates according to some measure of importance, a new recursive variant of the Wild Binary segmentation (Fryzlewicz in Ann Stat 42:2243-2281, 2014, WBS) named WBS2, has been proposed for candidate generation.

关键词： Change point detection data segmentation Multiscale procedure MOSUM procedure

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Geometric in-line inspection of profiled strips and welding seams

Geometric in-line inspection of profiled strips and welding ...

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Conference on Image Processing - Machine Vision Applications

作者： Reisinger, Johann Niel, Kurt S. Tratnig, Mark Vatron Gmbh A-4031 Linz Austria Fachhochschule Oberos Studien GMbH A-4600 Wels Austria

ISBN: (纸本)9780819469854

In this paper two in-line measurement systems for the geometric inspection of welded or profiled steel strips are presented. For the first case, the welded strips, the paper presents a light sectioning based measurement system for the in-line inspection of the welding seam. The system measures the vertical and angular misalignment of the welded parts in-line, right after the welding station. Based on the calculated offset of the two welded parts on one hand the welding adjustment can be optimized. On the other hand, quality relevant data is collected. The inspection of the vertical offset is done with an accuracy of +/- 5 mu m (+/- 2 sigma). In addition to the measurement of the vertical and angular offset, the system also inspects the width of the welding seam and the seam's shape and depth, all three with an accuracy of +/- 15 mu m (+/- 2 sigma). The second system presented in the paper is a measurement system for the inspection of the cross section of profiled strips. The system consists of three light sectioning based measurement heads and is mounted right after the profiling plates. Regarding the measurement precision, the shape of the profiled strip can be measured with an accuracy of better than +/- 10 mu m (+/- 2 sigma). The paper for both applications describe the sensor setup, geometric conditions, the used hardware and the calibration and registration approaches.

关键词： strip inspection welding inspection quality control light sectioning data segmentation fitting

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On Automated Cloud Bursting and Hybrid Cloud Setups Using Apache Mesos 3

On Automated Cloud Bursting and Hybrid Cloud Setups Using Ap...

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3rd International Conference of Cloud Computing Technologies and Applications (CloudTech)

作者： Xue, Noha Haugerud, Harek Yazidi, Anis Oslo & Akershus Univ Coll Dept Comp Sci Oslo Norway

ISBN: (纸本)9781538611159

Hybrid cloud technology is becoming increasingly popular as it merges private and public clouds to bring the best of two worlds together. However, due to the heterogeneous cloud installation, facilitating a hybrid cloud setup is not simple. Despite the availability of some commercial solutions to build a hybrid cloud, an open source implementation is still unavailable. In this paper, we try to bridge the gap by providing an open source implementation by leveraging the power of Apache Mesos. We build a hybrid cloud on the top of multiple cloud platforms, private and public. Through comprehensive experimental results, we show that our solution is able to ensure resource bursting by leveraging the power of the public cloud.

关键词： Open Source Hybrid Cloud Apache Mesos Cloud Bursting data segmentation Fault Tolerance

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Randomized Approximations of Operators and their Spectral Decomposition for Diffusion Based Embeddings of Heterogeneous data 3

Randomized Approximations of Operators and their Spectral De...

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3rd International Workshop on Compressed Sensing Theory and its Application to Radar, Sonar, and Remote Sensing (CoSeRa)

作者： Czaja, Wojciech Hafftka, Ariel Manning, Benjamin Weinberg, Dan Univ Maryland Dept Math College Pk MD 20742 USA

ISBN: (纸本)9781479974207

Modern sensing platforms acquire heterogeneous data collected from multiple sensing modalities. We are then faced with the challenge of fusing the already collected heterogeneous data for effective use in such applications as data classification, segmentation, or target detection. data fusion is a non-trivial problem and many existing approaches are either highly dependent on the types of data or fuse the information post classification. When the data is high dimensional and possesses a large number of data points (pixels), such as is the case for Hyperspectral Satellite Imagery, it can be burdensome on even the most efficient algorithms. Previous techniques of data integration have relied harmonic analysis methods to fuse data and produce low dimensional representations of large heterogeneous datasets. These techniques construct data-driven operators whose spectral decomposition creates a representation of the data. The bottlenecks of this approach are in the construction of the operator and its spectral decomposition, which may require a significant computational cost. Thus, we introduce randomized approximations at the most computationally intensive steps to significantly reduce the computational cost with minimal loss of fidelity, thus enabling the practical use of these highly sophisticated and geometrically reliable methods.

关键词： object detection sensor fusion computational cost data classification data fusion data segmentation diffusion based embedding harmonic analysis heterogeneous data hyperspectral satellite imagery sensing platforms target detection Approximation algorithms Approximation methods data integration Eigenvalues and eigenfunctions Laplace equations Laser radar Matrix decomposition

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DYNAMIC FACTOR GRAPHS: A NOVEL FRAMEWORK FOR MULTIPLE FEATURES data FUSION

DYNAMIC FACTOR GRAPHS: A NOVEL FRAMEWORK FOR MULTIPLE FEATUR...

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2010 IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Kampa, Kittipat Principe, Jose C. Slatton, K. Clint Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA

ISBN: (纸本)9781424442966

The Dynamic Tree [1] (DT) Bayesian Network is a powerful analytical tool for image segmentation and object segmentation tasks. Its hierarchical nature makes it possible to analyze and incorporate information from different scales, which is desirable in many applications. Having a flexible structure enables model selection, concurrent with parameter inference. In this paper, we propose a novel framework, dynamic factor graphs (DFG), where data segmentation and fusion tasks are combined in the same framework. Factor graphs (FGs) enable us to have a broader range of modeling applications than Bayesian networks (BNs) since FGs include both directed acyclic and undirected graphs in the same setting. The example in this paper will focus on segmentation and fusion of 2D image features with a linear Gaussian model assumption.

关键词： dynamic factor graphs sum-product algorithm linear Gaussian models data fusion data segmentation

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