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15th International Conference Management of Large-Scale System Development, MLSD 2022

作者： Ivanyuk, Vera Shuvalov, Konstantin Goroshnikova, Tatiana Tereliansky, Pavel Levchenko, Kirill Sunchalin, Andrew Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation Moscow Russia Bauman Moscow State Technical University Department of Higher Mathematics Moscow Russia Lab. of Mgmt. of the Devmt. of Large-scale Syst. V.A. Trapeznikov Inst. of Control Sciences of Ras Moscow Russia Financial University under the Government of the Russian Federation Faculty of International Economic Relations Moscow Russia Plekhanov Russian University of Economics Moscow Russia Financial University under the Government of the Russian Federation Department of Mathematics Moscow Russia

The aim of the work is a comparative analysis of investment portfolios built based on stochastic modeling. We obtained actual stock price data of 80 Russian companies traded on the Moscow Stock Exchange for the last s... 详细信息

ISBN: (数字)9781665497015

ISBN: (纸本)9781665497015

关键词： Stochastic systems

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Development of Modern Forecasting Models 16

Development of Modern Forecasting Models

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16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Ivanyuk, Vera Shuvalov, Konstantin Akhobadze, Gurami Malekova, Victoria Mikhailov, Alexey Levchenko, Kiril Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia Bauman Moscow State Technical University Department of Higher Mathematics Moscow Russia V.A.Trapeznikov Institute of Control Sciences of Ras Laboratory of Management of Large-Scale System Development Moscow Russia V. A.Trapeznikov Institute of Control Sciences of Ras Laboratory of Network Systems Control Moscow Russia Financial University under the Government of the Russian Federation Department of Financial Markets and Financial Engineering Moscow Russia Financial University under the Government of the Russian Federation Department of Mathematics Moscow Russia

ISBN: (纸本)9798350337907

关键词： Forecasting

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Using ERPs for assessing the (sub) conscious perception of noise

Using ERPs for assessing the (sub) conscious perception of n...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Anne K. Porbadnigk Jan-N. Antons Benjamin Blankertz Matthias S. Treder Robert Schleicher Sebastian Möller Gabriel Curio Machine Learning Laboratory Berlin Institute of Technology Berlin Germany Quality and Usability Lab Telekom Laboratories Berlin Germany Intelligent Data Analysis Group Fraunhofer FIRST Berlin Germany Department of Neurology and Clinical Neurophysiology Charite Berlin Germany Department of Neurology Clinical Neurophysiology Charite Berlin Germany

In this paper, we investigate the use of event-related potentials (ERPs) as a quantitative measure for quality assessment of disturbed audio signals. For this purpose, we ran an EEG study (N=11) using an oddball paradigm, during which subjects were presented with the phoneme /a/, superimposed with varying degrees of signal-correlated noise. Based on this data set, we address the question to which degree the degradation of the auditory stimuli is reflected on a neural level, even if the disturbance is below the threshold of conscious perception. For those stimuli that are consciously recognized as being disturbed, we suggest the use of the amplitude and latency of the P300 component for assessing the level of disturbance. For disturbed stimuli for which the noise is not perceived consciously, we show for two subjects that a classifier based on shrinkage LDA can be applied successfully to single out stimuli, for which the noise was presumably processed subconsciously.

关键词： Noise Presses Electroencephalography Training Degradation Noise measurement Pattern recognition

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Support System for Choosing an Individual Educational Trajectory at Vasyl’ Stus Donetsk National University

Support System for Choosing an Individual Educational Trajec...

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International Scientific-Practical Conference Problems of Infocommunications Science and Technology

作者： Pavlo Chaikovskyi Oleh Vietrov Artem Baiev Yana Teletska Maksym Strutovskyi Educational and Practical Laboratory of Information Security Vasyl' Stus Donetsk National University Vinnytsia Ukraine Department of Applied Mathematics Vasyl' Stus Donetsk National University Vinnytsia Ukraine Interdepartmental Laboratory of Machine Learning and Intelligent Data Analysis Vasyl' Stus Donetsk National University Vinnytsia Ukraine

Members of Interdepartmental laboratory of machine learning and intelligent data analysis Vasyl' Stus Donetsk national university are developing a software that helps students choose minors and other additional subjects, as well as proper tooling for teachers and staff. Described process of development, its specifics and addressed direction and possible technologies that may serve the scaling of the system.

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Consensus-Based Optimization Methods Converge Globally

arXiv

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arXiv 2021年

作者： Fornasier, Massimo Klock, Timo Riedl, Konstantin Technical University of Munich School of Computation Information and Technology Department of Mathematics Munich Germany Munich Center for Machine Learning Munich Germany Munich Data Science Institute Munich Germany Simula Research Laboratory Department of Numerical Analysis and Scientific Computing Oslo Norway University of San Diego Department of Mathematics San DiegoCA United States

In this paper, we study consensus-based optimization (CBO), which is a multi-agent metaheuristic derivative-free optimization method that can globally minimize nonconvex nonsmooth functions and is amenable to theoretical analysis. Based on an experimentally supported intuition that, on average, CBO performs a gradient descent of the squared Euclidean distance to the global minimizer, we devise a novel technique for proving the convergence to the global minimizer in mean-field law for a rich class of objective functions. The result unveils internal mechanisms of CBO that are responsible for the success of the method. In particular, we prove that CBO performs a convexification of a large class of optimization problems as the number of optimizing agents goes to infinity. Furthermore, we improve prior analyses by requiring mild assumptions about the initialization of the method and by covering objectives that are merely locally Lipschitz continuous. As a core component of this analysis, we establish a quantitative nonasymptotic Laplace principle, which may be of independent interest. From the result of CBO convergence in mean-field law, it becomes apparent that the hardness of any global optimization problem is necessarily encoded in the rate of the mean-field approximation, for which we provide a novel probabilistic quantitative estimate. The combination of these results allows to obtain probabilistic global convergence guarantees of the numerical CBO *** Codes 65K10, 90C26, 90C56, 35Q90, 35Q84 Copyright © 2021, The Authors. All rights reserved.

关键词： Fokker Planck equation

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Correction to: Potential cyber threats of adversarial attacks on autonomous driving models

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Journal of Computer Virology and Hacking Techniques 2023年第2期20卷 315-315页

作者： Boltachev, Eldar Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation Moscow Russia

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BGTplanner: Maximizing Training Accuracy for Differentially Private Federated Recommenders via Strategic Privacy Budget Allocation

arXiv

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arXiv 2024年

作者： Zhang, Xianzhi Zhou, Yipeng Hu, Miao Wu, Di Liao, Pengshan Guizani, Mohsen Sheng, Michael The School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China The Department of Computing Faculty of Science and Engineering Macquarie University Sydney Australia The Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

To mitigate the rising concern about privacy leakage, the federated recommender (FR) paradigm emerges, in which decentralized clients co-train the recommendation model without exposing their raw user-item rating data. The differentially private federated recommender (DPFR) further enhances FR by injecting differentially private (DP) noises into clients. Yet, current DPFRs, suffering from noise distortion, cannot achieve satisfactory accuracy. Various efforts have been dedicated to improving DPFRs by adaptively allocating the privacy budget over the learning process. However, due to the intricate relation between privacy budget allocation and model accuracy, existing works are still far from maximizing DPFR accuracy. To address this challenge, we develop BGTplanner (Budget Planner) to strategically allocate the privacy budget for each round of DPFR training, improving overall training performance. Specifically, we leverage the Gaussian process regression and historical information to predict the change in recommendation accuracy with a certain allocated privacy budget. Additionally, Contextual Multi-Armed Bandit (CMAB) is harnessed to make privacy budget allocation decisions by reconciling the current improvement and long-term privacy constraints. Our extensive experimental results on real datasets demonstrate that BGTplanner achieves an average improvement of 6.76% in training performance compared to state-of-the-art baselines. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential privacy

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An Evolutionary machine learning Approach to Outlier Detec-Tion Based on Weighted Sum Model with Automatic Weight Discovery by Slsl-Qpso

SSRN

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SSRN 2024年

作者： Kang, Liang Zhang, Xiukai Krakhmalev, Oleg Zeng, Wenlei Engineering Training and Innovation Education Center Shanghai Polytechnic University Shanghai201209 China School of Computer and Information Engineering Shanghai Polytechnic University Shanghai201209 China Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation 4‐th Veshnyakovsky Passage 4 Moscow109456 Russia

In this study, we present a weighted sum methodology to assign a quantifiable value to each data point within a dataset, reflecting its propensity to be an outlier. This value is subsequently trans-formed into a probability via a sigmoid function, denoting the likelihood of the data point being an outlier. However, manually crafting weights for the weighted sum is impractical due to their var-iable significance across different datasets. To address this, an evolutionary optimization algorithm is used, namely Short-Lived Swarm-Layer Quantum-behaved Particle Swarm Optimization (SLSL-QPSO), which is an advanced variant of particle swarm optimization, to autonomously learn these weights. The learning process is guided by a fitness function derived from binary cross-entropy loss. We evaluate the algorithm’s performance across 14 meticulously chosen datasets, which differ in data point distribution, feature count, and outlier proportion. The empirical results demonstrate robust performance on these datasets, suggesting the algorithm’s potential to make contributions to the field. © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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SLSL-QPSO: Quantum-Behaved Particle Swarm Optimization with Short-Lived Swarm Layers

SSRN

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SSRN 2023年

作者： Liang, Kang Xiukai, Zhang Krakhmalev, Oleg Engineering Training and Innovation Education Center Shanghai Polytechnic University Shanghai201209 China School of Computer and Information Engineering Shanghai Polytechnic University Shanghai201209 China Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation 4th Veshnyakovsky Passage 4 Moscow109456 Russia

SLSL-QPSO is a software that can find the optimal value of a function. It improves over the QPSO algorithms by leveraging the concept of living and death as swarm layers, the Lévy mutation, the scoped contradiction-expansion coefficient, and the selection of effective layers. Experimental results demonstrate that SLSL-QPSO has superior performance than QPSO and several other variants thus providing a competitive solution to optimization problems. © 2023, The Authors. All rights reserved.

关键词： Particle swarm optimization (PSO)

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Methodology for Applying IIoT Architecture to Design IIOT Systems in the Agricultural Sector of the Russian Federation

Methodology for Applying IIoT Architecture to Design IIOT Sy...

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Information Computing and Processing (ICP), Seminar on

作者： Krystina A. Makoveichuk Alexander V. Olifirov Sergei A. Petrenko Yan T. Makoveichuk Anatoliy N. Kazak Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation Moscow Russia Department of Economics and Finance V.I. Vernadsky Crimean Federal University Yalta Russia Department of Information Security Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Kazan Russia Department of Management and Tourism Business V.I. Vernadsky Crimean Federal University Yalta Russia

The article presents the results of the study of Industrial Internet of Things (IIoT) technologies for the agricultural sector, taking into account international experience for the period 2004 - 2021, and proposes a methodology for applying IIoT architecture to design IIOT systems in the agricultural sector of Russia. A two-factor classification of IIoT systems in agriculture has been made. A typical conceptual scheme of intelligent IIoT system for viticulture and winemaking has been developed. The architecture of a typical IIoT system is analyzed, taking into account the observance of information security in data processing and transmission. An analysis of the current state and prospects for the implementation of IoT in the Russian wine industry has been carried out. A business process model is presented in the concept of BPME (BPM Everywhere) for the production of a certain brand of wine. The model in a non-decomposed form shows sub-processes with and without IoT, using traditional “manual” methods, as well as taking into account the impact of various business processes on production efficiency and product quality. The reasons for the lag and growth drivers for the use of IIoT in agrosector are identified.

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