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arXiv 2023年

作者： Wu, Chengzhi Pfrommer, Julius Beyerer, Jürgen Li, Kangning Neubert, Boris Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany Fraunhofer Center for Machine Learning Fraunhofer IOSB Karlsruhe Germany Institute for Visualization and Data Analysis Karlsruhe Institute of Technology Karlsruhe Germany

We present an improved approach for 3D object detection in point clouds data based on the Frustum PointNet (F-PointNet). Compared to the original F-PointNet, our newly proposed method considers the point neighborhood when computing point features. The newly introduced local neighborhood embedding operation mimics the convolutional operations in 2D neural networks. Thus features of each point are not only computed with the features of its own or of the whole point cloud, but also computed especially with respect to the features of its neighbors. Experiments show that our proposed method achieves better performance than the F-Pointnet baseline on 3D object detection tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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machine learning Technologies for Bakery Management Decisions 24

Machine Learning Technologies for Bakery Management Decision...

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24th International Conference on Digital Signal Processing and its Applications, DSPA 2022

作者： Andriyanov, Nikita Dementiev, Vitaly Tashlinsky, Alexandr Danilov, Andrey Financial University under the Government of the Russian Federation Department of the Data Analysis and Machine Learning Moscow Russia Ulyanovsk State Technical University Department of the Telecommunications Ulyanovsk Russia Ulyanovsk State Technical University Department of the Radio Engineering Ulyanovsk Russia Llc Hlebnitsca Department of the Management Izhevsk Russia

ISBN: (纸本)9781665494434

The paper discusses using Deep Stream technologies in tasks for predicting best locations for deployment the bakeries. Such methods provides the calculation of people going through possible bakery and use convolutional neural networks for detection people and some effective algorithms for counting. The proposed solution allows deploying successful bakeries and keeping money in real production. Furthermore a lot of applied data science models were used for data analysis in these conditions. The paper discusses in detail the regression, factorial, cluster and discriminant analysis on the example of real data on the operation of a chain of bakeries with changes to preserve trade secrets. The analysis made it possible to simplify the decision-making process for managers among many factors. Moreover, the proposed method made it possible to predict profitability when opening a new point and explore various models of its development. Comparison results are provided for 3 models. The choice was made in favor of one of them. This choice resulted in the opening of a profitable bakery with a high profit margin for the retail market. The regression, factor and cluster analysis results show good opportunities to apply the results of the analysis for making management decisions when choosing the location of bakeries. © 2022 IEEE.

关键词： Decision making

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Support System for Choosing an Individual Educational Trajectory at Vasyl' Stus Donetsk National University 9

Support System for Choosing an Individual Educational Trajec...

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9th IEEE International Conference on Problems of Infocommunications Science and Technology, PIC S and T 2022

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

ISBN: (纸本)9798350398915

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. © 2022 IEEE.

关键词： Gale-Shapley algorithm ICS individual educational trajectory

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Using Convolutional Neural Network to Classify 2D EEG Scalp Topograms during Visual Task 5

Using Convolutional Neural Network to Classify 2D EEG Scalp ...

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5th Scientific School on Dynamics of Complex Networks and their Applications, DCNA 2021

作者： Korchagin, Sergey Maksimenko, Vladimir Hramov, Alexander Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation Moscow Russia Institute of Information Technologies Mathematics and Mechanics Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia

ISBN: (纸本)9781665442824

we developed an artificial neural network (ANN) classifier to analyze the cortical activity signals during visual information processing. We tested several ANN architectures and chose a convolutional neural network (CNN) with the Resnet50 topology. As a result, the trained CNN classifier achieved an accuracy of 74% on the data of newly recruited subjects. © 2021 IEEE

关键词： Convolutional neural networks

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Forecasting Financial Time Series Based on the Ensemble Method 15

Forecasting Financial Time Series Based on the Ensemble Meth...

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

作者： Ivanyuk, Vera Tsvirkun, Anatoly Soloviev, Vladimir Feklin, Vadim Sunchalina, Anna Kravchenko, Oleg 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 Laboratory of Management of the Development of Large-scale Systems V.A. Trapeznikov Institute of Control Sciences of Ras Moscow Russia

The study considers methods for forecasting financial time series. We construct an ensemble forecast based on the linear forecast, ETS forecast, and neural forecast methods. Thereupon, we evaluate the quality of the f... 详细信息

ISBN: (数字)9781665497015

ISBN: (纸本)9781665497015

关键词： Time series

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On the Impact of Explanations on Understanding of Algorithmic Decision-Making

arXiv

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arXiv 2023年

作者： Schmude, Timothée Koesten, Laura Möller, Torsten Tschiatschek, Sebastian University of Vienna Faculty of Computer Science Research Network Data Science UniVie Doctoral School Computer Science DoCS Austria University of Vienna Faculty of Computer Science Research Group Visualization and Data Analysis Austria University of Vienna Faculty of Computer Science Research Network Data Science Research Group Visualization and Data Analysis Austria University of Vienna Faculty of Computer Science Research Network Data Science Research Group Data Mining and Machine Learning Austria

Ethical principles for algorithms are gaining importance as more and more stakeholders are affected by "high-risk" algorithmic decision-making (ADM) systems. Understanding how these systems work enables stakeholders to make informed decisions and to assess the systems’ adherence to ethical values. Explanations are a promising way to create understanding, but current explainable artificial intelligence (XAI) research does not always consider existent theories on how understanding is formed and evaluated. In this work, we aim to contribute to a better understanding of understanding by conducting a qualitative task-based study with 30 participants, including users and affected stakeholders. We use three explanation modalities (textual, dialogue, and interactive) to explain a "high-risk" ADM system to participants and analyse their responses both inductively and deductively, using the "six facets of understanding" framework by Wiggins & McTighe [63]. Our findings indicate that the "six facets" framework is a promising approach to analyse participants’ thought processes in understanding, providing categories for both rational and emotional understanding. We further introduce the "dialogue" modality as a valid explanation approach to increase participant engagement and interaction with the "explainer", allowing for more insight into their understanding in the process. Our analysis further suggests that individuality in understanding affects participants’ perceptions of algorithmic fairness, demonstrating the interdependence between understanding and ADM assessment that previous studies have outlined. We posit that drawing from theories on learning and understanding like the "six facets" and leveraging explanation modalities can guide XAI research to better suit explanations to learning processes of individuals and consequently enable their assessment of ethical values of ADM systems. © 2023, CC BY.

关键词： Decision making

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Multi-Scale Clinical-Guided Binocular Fusion Framework for Predicting New-Onset Hypertension Over a Four-Year Period

Multi-Scale Clinical-Guided Binocular Fusion Framework for P...

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IEEE International Symposium on Biomedical Imaging

作者： Haoshen Li Zifan Chen Jie Zhao Heyun Chen Hexin Dong Mingze Yuan Bin Dong Li Zhang Center for Data Science Peking University China National Engineering Laboratory for Big Data Analysis and Applications Peking University China Peking University Changsha Institute for Computing and Digital Economy China Beijing International Center for Mathematical Research Peking University China Center for Machine Learning Research Peking University China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Hypertension is a major global health concern, linked to various cardiovascular diseases and associated with distinct ocular manifestations. While recent advances in artificial intelligence have enabled accurate diagnosis of current hypertension through fundus images, predicting the future onset of hypertension remains an uncharted domain. In this study, we introduce the multi-scale clinical-guided binocular fusion framework (MCBO), designed to predict the likelihood of developing hypertension within the next four years. MCBO uniquely integrates left and right fundus images and clinical data, utilizing a shared-weight multi-stage Transformer-based encoder. Our multi-scale clinical-guided module (MCM) ensures image feature extraction is clinically contextualized based on clinical information, and our binocular fusion module (BFM) fuses binocular information. Comparative performance against seven baseline models establishes MCBO’s supremacy, with improvements of 6.7% in Area Under Curve (AUC), 6.9% in Accuracy (ACC), 5.1% in Sensitivity (SEN) and 5.5% in Specificity (SPE). This approach offers a promising avenue for proactive hypertension management, underscoring the potential of integrating Deep learning with clinical data for enhanced healthcare outcomes. Our code is available at https://***/HaoshenLi/MCBO.

关键词： Hypertension Deep learning Accuracy Sensitivity Codes Fuses Feature extraction

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Applying Interdisciplinary Frameworks to Understand Algorithmic Decision-Making

arXiv

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arXiv 2023年

We argue that explanations for "algorithmic decision-making" (ADM) systems can profit by adopting practices that are already used in the learning sciences. We shortly introduce the importance of explaining ADM systems, give a brief overview of approaches drawing from other disciplines to improve explanations, and present the results of our qualitative task-based study incorporating the "six facets of understanding" [24] framework. We close with questions guiding the discussion of how future studies can leverage an interdisciplinary approach. © 2023, CC BY.

关键词： Decision making

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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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2023 Seminar on Information Computing and Processing, ICP 2023

作者： Makoveichuk, Krystina A. Olifirov, Alexander V. Petrenko, Sergei A. Makoveichuk, Yan T. Kazak, Anatoliy N. Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia V.I. Vernadsky Crimean Federal University Department of Economics and Finance Yalta Russia Saint Petersburg Electrotechnical University 'LETI' Saint Petersburg Department of Information Security Kazan Russia V.I. Vernadsky Crimean Federal University Department of Management and Tourism Business Yalta Russia

ISBN: (纸本)9798350369830

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. © 2023 IEEE.

关键词： Wine

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Application of the Semantic Segmentation Method in the Assessment of Forest Carbon Balance

Application of the Semantic Segmentation Method in the Asses...

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Inna Tulokhonova Svetlana Mikhaylova Soelma Danilova Department of Software Engineering and Artificial Intelligence East Siberia State University of Technology and Management Ulan-Ude Russia Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation Moscow Russia

This article focuses on the problem of human impact on the natural environment and its solution by machine learning methods. The concept of carbon balance is key in assessing climate change on the planet, which is especially important in connection with global warming. The main causes of global warming are associated with anthropogenic emissions of carbon dioxide as a result of agricultural production, land use and burning of fossil fuels. One of the approaches to mitigate this environmental problem is to estimate the carbon balance based on neural network semantic segmentation of forests of terrestrial ecosystems. The proposed convolutional neural network (CNN) model is based on the U-Net architecture. This architecture surpasses traditional machine learning methods in solving computer vision and pattern recognition problems, and is also quite easy to learn. The input data of the model is a set of satellite images of the forest area. The images were obtained using the Sentinel-2 satellite, which provides high spatial resolution and a large number of spectral channels. A software prototype has been developed to implement the CNN model. Training experiments and calculations on changes in the carbon balance in forests led to the conclusion that the results of the study will contribute to the balanced management of reforestation and carbon deposition.

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