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Prospects for the Application of Reinforcement Learning to Network Traffic Classification Tasks

Journal of Physics: Conference Series 2021年第1期2096卷

作者： G D Asyaev Lecturer Department of system programming FSAEIHE SUSU (NRU) 76 Lenin prospect 454080 Chelyabinsk Russia

The basic principles and methods of reinforcement learning are reviewed. The problems and approaches for applying a model based on reinforcement learning in the framework of attack prevention are described. The model is built and the hyperparameters of machine learning for the task of classifying network traffic are selected, and its performance on the test data set is evaluated by such quality metrics as accuracy and completeness. The dataset used to implement an agent for selecting the optimal defense strategy for a particular attack has been finalized. Developed an algorithm for using a reinforcement learning neural network for the traffic classification task. A table of rules and rewards for the problem is generated. An agent has been developed and trained to interact with the system. We describe the application of reinforcement learning to the traffic classification task.

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QUANTUM RANDOM WALKS AND QUANTUM OSCILLATOR IN AN INFINITE-DIMENSIONAL PHASE SPACE

arXiv

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arXiv 2024年

作者： Busovikov, Vladimir Pechen, Alexander Sakbaev, Vsevolod Department of Mathematical Methods for Quantum Technologies Steklov Mathematical Institute of Russian Academy of Sciences Gubkina Str. 8 Moscow Russia University of Science and Technology MISIS Leninskiy prosp. 4 Moscow Russia Ivannikov Institute for System Programming of RAS Alexander Solzhenitsyn Str. 25 Moscow Russia

We consider quantum random walks in an infinite-dimensional phase space constructed using Weyl representation of the coordinate and momentum operators in the space of functions on a Hilbert space which are square integrable with respect to a shift-invariant measure. We study unitary groups of shift operators in the phase space and averaging of such shifts by Gaussian vectors, which form semigroups of self-adjoint contractions: we find conditions for their strong continuity and establish properties of their generators. Significant differences in their properties allow us to show the absence of the Fourier transform as a unitary transformation that implements the unitary equivalence of these compressive semigroups. Next, we prove the Taylor formula for a certain special subset of smooth functions for shifting to a non-finite vector. It allows us to prove convergence of quantum random walks in the coordinate representation to the evolution of a diffusion process, as well as convergence of quantum random walks in both coordinate and momentum representations to the evolution semigroup of a quantum oscillator in an infinite-dimensional phase space. We find the special essential common domain of generators of semigroups arising in averaging of random shift operators both in position and momentum representations. The invariance of this common domain with respect to both semigroups allows to establish properties of a convex combination of both generators. That convex combination are Hamiltonians of infinite-dimensional quantum oscillators. Thus, we obtain that a Weyl representation of a random walk in an infinite dimensional phase space describes the semigroup of self-adjoint contractions whose generator is the Hamiltonian of an infinite dimensional harmonic *** Codes 28C20, 81V73, 43A32, 37K06 Copyright © 2024, The Authors. All rights reserved.

关键词： Hilbert spaces

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Optimizing machine learning algorithms to enhance blood donation for liver patients: a case study

Optimizing machine learning algorithms to enhance blood dona...

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7th IET Smart Cities Symposium (SCS 2023)

作者： Maad M. Mijwil El-Sayed M. El-kenawy Indu Bala Mohammad Aljanabi Mostafa Abotaleb Abdelhameed Ibrahim Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt School of Electrical and Electronics Engineering Lovely Professional University Punjab India Department of Computer Aliraqia University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

Blood is vital for transporting oxygen, nutrients, and hormones to all body parts as it circulates through arteries and veins. It removes carbon dioxide, regulates body temperature, and maintains the body's immune system. Individuals constantly need blood and its derivatives to save their lives and improve their health through medical treatments and surgical operations. Liver diseases are one of the diseases that affects the health of individuals and requires blood to continue living. These diseases cause significant damage to people's health, and early diagnosis plays a crucial role in saving lives. In this paper, machine learning algorithms (support vector machine and random forest) are involved in detecting liver diseases and determining whether donors are suitable to donate blood from blood values. This paper is applied research that found that the performance measures of the random forest algorithm achieved excellent performance in identifying suitable people to donate blood.

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Modeling of rainfall time series using NAR and ARIMA model over western Himalaya, India

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Arabian Journal of Geosciences 2022年第23期15卷 1-26页

作者： Rawat, Deepa Mishra, Pradeep Ray, Soumik Warnakulasooriya, Himasha Harshani Fernando Sati, Sarswati Prakash Mishra, Gaurav Alkattan, Hussein Abotaleb, Mostafa College of Forestry VCSG Uttarakhand University of Horticulture and Forestry Tehri Garhwal India College of Agriculture Rewa India Centurion University of Technology and Management Paralakhemundi India Department of Statistics & Computer Science Faculty of Science University of Kelaniya Kelaniya Sri Lanka Centre of Excellence on Sustainable Land Management Indian Council of Forestry Research and Education (MoEF & CC Govt. of India) Dehradun India Department of System Programming South Ural State University Chelyabinsk Russia

The high Himalayas in northern India are an essential source of climate generation and maintenance over the entire northern belt of the Indian subcontinent. It also affects extreme weather phenomena such as western disturbances in the region during winter. The work presented here describes the trends in 117-year precipitation changes and their impact on the western Himalayas and suggests some possible explanations in the context of changing rainfall patterns. Under the investigation, the forecasting efficiency and the prediction pattern of artificial neural network (ANN) and seasonal autoregressive integrated moving average (SARIMA) models for rainfall series in the western Himalayan states of India have been assessed. The results revealed significant changes in the monthly, seasonal, and annual rainfall series data for the three states of the Western Himalayan regions from the years 1900 to 2017. The study also concludes that the nonlinear autoregressive neural network (NARNN) models can be used to forecast the western Himalayan region data series well. According to the result interpretation, the highest rainfall may be estimated in August, 1632.63 mm (2023), whereas the lowest rainfall can be obtained in October (0.43 mm) during 2023. The model predicted a gradual decrease in annual rainfall trends in Uttarakhand and Himachal Pradesh from 2018 to 2023 despite heavy rainfall prediction in the monsoon season, whereas Jammu and Kashmir increase in annual rainfall has been predicted from 2018 to 2023. Possible explanations for the change in precipitation over the western Himalayas have also been proposed and explained.

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Scientific Data Lake for High Luminosity LHC project and other data-intensive particle and astro-particle physics experiments 5

Scientific Data Lake for High Luminosity LHC project and oth...

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5th International Conference on Particle Physics and Astrophysics, ICPPA 2020

作者： Alekseev, A. Kiryanov, A. Klimentov, A. Korchuganova, T. Mitsyn, V. Oleynik, D. Smirnov, A. Smirnov, S. Zarochentsev, A. Compiler Technology Department Ivannikov Institute for System Programming of the Ras Moscow109004 Russia Cloud Technologies and Big Data Analytics Laboratory Plekhanov Russian University of Economics Moscow117997 Russia Atlas Group Universidad Andres Bello Santiago7550196 Chile Department of Computing Resources and Technologies Nrc "kurchatov Institute"-PNPI Gatchina188300 Russia Physics Department Brookhaven National Laboratory UptonNY11973 United States Laboratory of Information Technologies Joint Institute for Nuclear Research Dubna141980 Russia Institute of Nuclear Physics and Engineering National Research Nuclear University MEPhI Moscow115409 Russia Laboratory of Ultra-High Energy Physics Saint Petersburg State University Saint Petersburg198504 Russia

The next phase of LHC Operations-High Luminosity LHC (HL-LHC), which is aimed at ten-fold increase in the luminosity of proton-proton collisions at the energy of 14 TeV, is expected to start operation in 2027-2028 and will deliver an unprecedented scientific data volume of multi-exabyte scale. This amount of data has to be stored and the corresponding storage system should ensure fast and reliable data delivery for processing by scientific groups distributed all over the world. The present LHC computing and data processing model will not be able to provide the required infrastructure growth even taking into account the expected hardware technology evolution. To address this challenge the new state-of-The-Art computing infrastructure technologies are now being developed and are presented here. The possibilities of application of the HL-LHC distributed data handling technique for other particle and astro-particle physics experiments dealing with large-scale data volumes like DUNE, LSST, Belle-II, JUNO, SKAO etc. are also discussed. © Published under licence by IOP Publishing Ltd.

关键词： Luminance

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Preface

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CEUR Workshop Proceedings 2017年 1989卷 5-7页

作者： Pozin, Boris A. Cavalli, Ana Rosa Petrenko, Alexander K. Department of Information and Analytical Systems EC-leasing Co. Russia Department of Network Software TELECOM and Management SudParis France Department of System Engineering Institute of System Programming of RAS Russia

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Intelligent system for Classifying of Acoustic Endoscopic Images of the Pancreas Based the Analysis of Walsh Spectra of Local Windows

Intelligent System for Classifying of Acoustic Endoscopic Im...

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Information Technology (ACIT)

作者： Riad Taha Al-Kasasbeh Sergey Filist Olga Shatalova Dmitry Sergeevich Kondrashov Ashraf Shaqadan Nikolay A. Korenevskiy Osama M. Al-Habahbeh Emad Saleh Tarawneh Evgeny Starkov Mechatronics Engineering Department The University of Jordan Amman Jordan Department of SMART Technologies Moscow Polytechnic University Moscow Russian Federation Department of Biomedical Engineering Southwest State University Kursk Russian Federation Civil Engineering Department Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia South-West State University Russian Federation The University of Jordan Jordan Institute of System Programming of the Russian Academy of Sciences Russian Federation

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

The purpose of the work is to develop and study methods and algorithms for intelligent classification systems for digital medical images. A method for automatic classification of medical images was developed using neural networks and transparency masks obtained by preliminary image segmentation. This allows you to generate descriptors only for those pixels that are selected by the transparency mask. Also algorithmic and software for classifying medical images has been developed. Experimental testing was carried out on ultrasound images of the pancreas. Differential diagnostics was carried out on control samples. The classes "oncology" and "pancreatitis" were divided. The diagnostic sensitivity by class turned out to be at least 0.8, while the diagnostic specificity is at least 0.85.

关键词： Image segmentation Ultrasonic imaging Sensitivity Neural networks Software algorithms Oncology Software Pancreas Intelligent systems Medical diagnostic imaging

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Quantum channels, complex Stiefel manifolds, and optimization

arXiv

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arXiv 2024年

作者： Russkikh, Ivan Volkov, Boris Pechen, Alexander Department of Mathematical Methods for Quantum Technologies Steklov Mathematical Institute of Russian Academy of Sciences 8 Gubkina Str. Moscow119991 Russia University of Science and Technology MISIS 4 Leninsky Prosp. Moscow119049 Russia Ivannikov Institute for System Programming The Russian Academy of Sciences Alexandra Solzhenitsyna str. 25 Moscow109004 Russia

Most general dynamics of an open quantum system is commonly represented by a quantum channel, which is a completely positive trace-preserving map (CPTP or Kraus map). Well-known are the representations of quantum channels by Choi matrices and by Kraus operator-sum representation (OSR). As was shown before, one can use Kraus OSR to parameterize quantum channels by points of a suitable quotient under the action of the unitary group of some complex Stiefel manifold. In this work, we establish a continuity relation (homeomorphism) between the topological space of quantum channels and the quotient of the complex Stiefel manifold. Then the metric on the set of quantum channels induced by the Riemannian metric on the Stiefel manifold is defined. The established relation can be applied to various quantum optimization problems. As an example, we apply it to the analysis of extrema points for a wide variety of quantum control objective functionals defined on the complex Stiefel manifolds, including mean value, generation of quantum gates, thermodynamic quantities involving entropy, *** Codes 81Q93 Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum channel

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Design and Implementation of a QoL Improvement system for Visually Impaired Patients

Design and Implementation of a QoL Improvement System for Vi...

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International Conference on Advances in Computational Tools for Engineering Applications, ACTEA

作者： Roy Abi Zeid Daou Jad Beaino Johnny Hawat Ramy Ibrahim Ali Hayek Josef Boercsoek Jose Javier Serrano Olmedo Mechatronics department Faculty of Engineering Université La Sagesse Beirut Lebanon MART Learning Education and Research Center Chananir Lebanon Department Lippstadt 2 Hardware Engineering University of Applied Science Hamm-Lippstadt Hamm Department of Computer Architecture and System Programming Faculty of Electrical Engineering/Computer Science University of Kassel Germany Lab of Bioinstrumentation and Nanomedicine Centro de Tecnología Biomédica Universidad Politécnica de Madrid Spain

Visually impaired people encounter several challenges in their mobility and navigation. Their daily activities are obstructed due to their inability to adapt or recognize accurately their surroundings, especially outside their house which they are more familiar with. Thus, it becomes the main reason of accidents, falling off, getting lost in unknown areas, etc. Furthermore, one of the sensory systems that helps the body to process data about the external environment is the visual system. Blind people also lose touch with the outside world, develops poor motor habits, which results in postural problems as a result. This project will assist visually impaired people in their daily life and simplify normal tasks through a system combining two previously designed projects, “Smart Shoes for Blind and Visually Impaired People”, and “Human posture monitoring device”. The multifunctional system is developed with the goal of securing safe movements for visually impaired people as well as maintaining a good back posture by detecting leaning postures (LP). The purpose of the smart shoe is to identify obstacles and protect the user from unintended accidents. A compatible Android application has been developed to alert the user when there is an obstruction or when he is walking on a wet surface. Voice alarms will be used to acoustically alert the user. If the user collapses, a message with their position will be sent right away to a relative. On the other hand, the smart vest will identify the position of the user's back and alert him to maintain a straight posture through the same application as well. As the system is dealing with human health, some safety measurements would be taken into consideration to implement a safe electrical system in order to reduce error and to increase accuracy.

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Data poisoning: issues, challenges, and needs

Data poisoning: issues, challenges, and needs

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7th IET Smart Cities Symposium (SCS 2023)

作者： Mohammad Aljanabi Alaa Hamza Omran Maad M Mijwil Mostafa Abotaleb El-Sayed M. El-kenawy Sahar Yousif Mohammed Abdelhameed Ibrahim Department of computer College of Education Aliarqai university Baghdad Iraq University of information technology and communications Baghdad Iraq Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Science Department Computer Science & Information Technology College Anbar University Anbar Iraq Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

Data poisoning attacks, where adversaries manipulate training data to degrade model performance, are an emerging threat as machine learning becomes widely deployed in sensitive applications. This paper provides a comprehensive overview of data poisoning including attack techniques, adversary incentives, impacts on security and reliability, detection methods, defenses, and key research gaps. We examine label flipping, instance injection, backdoors, and other attack categories that enable malicious outcomes ranging from IP theft to accidents in autonomous systems. Promising detection approaches include statistical tests, robust learning, and forensics. However, significant challenges remain in translating academic defenses like adversarial training and sanitization into practical tools ready for operational use. With safety and trustworthiness at stake, more research on benchmarking evaluations, adaptive attacks, fundamental tradeoffs, and real-world deployment of defenses is urgently needed. Understanding vulnerabilities and developing resilient machine learning pipelines will only grow in importance as data integrity is fundamental to developing safe artificial intelligence.

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