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2024 Winter Simulation Conference, WSC 2024

作者： Huang, Wenfei Melzer, Matthias Dünnweber, Jan Faculty of Computer Science and Mathematics OTH Regensburg Technical University of Applied Sciences Bavaria Regensburg93053 Germany

ISBN: (纸本)9798331534202

Smart retail technologies save grocery store operators a lot of work. At the same time, these technologies produce valuable data for building sustainable and economical inventory management strategies. AI models can be trained for sales forecasting using the data. The forecasts support the provisioning of fresh food over the whole week and help reducing food waste. In this paper, we present a Web portal which we developed to allow grocery store operators experiments with AI models revealing interrelations between observed and anticipated customer behavior. Clickable diagrams facilitate the exploration of data sets combining historical data and synthetically generated data. Pricing and ordering can be adapted accordingly to the simulated forecasts. By means of a case study, we show that our simulations are not only useful for predicting future sales but for other smart retail tasks as well. © 2024 IEEE.

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Unsupervised Feature Selection via Multi-Structure Learning and Indicator Matrix

Unsupervised Feature Selection via Multi-Structure Learning ...

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2024 International Conference on New Trends in Computational Intelligence, NTCI 2024

作者： Hu, Zebiao Gao, Xiaoyu Bian, Ziwei Wang, Jian Ablameyko, Sergey V. Qingdao China Qingdao China Belarusian State University Faculty of Applied Mathematics and Computer Science Minsk Belarus

ISBN: (纸本)9798331517021

Traditional feature selection methods usually only consider a specific geometric structure of the original data, which cannot reveal the most essential underlying structure of the data. Apart from this, a regularization constraint with sparsity is often imposed on the mapping matrix, which inevitably leads to additional parameter setting issues. To address these challenges, a novel unsupervised feature selection via multi-structure learning and indicator matrix (UFSMI) is proposed in this study. More concretely, we construct a unified framework that incorporates multi-structure learning and feature selection. UFSMI first explores the global and local geometric structure of high dimensional data in subspace to excavate the intrinsic structure of data, effectively reducing the interference of redundant features. Then, an indicator matrix for feature selection that can explicitly indicate which features should be selected is in-troduced without imposing any regularization constraints. Finally, the most useful subset of features can be obtained based on the indices of the non-zero elements in the indicator matrix. A series of experiments are conducted to demonstrate the effectiveness and superiority of the proposed method on nine real-word high dimensional datasets by comparing with existing state-of-the-art feature selection methods. © 2024 IEEE.

关键词： Unsupervised learning

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Unveiling the Power of Sparse Neural Networks for Feature Selection 27

Unveiling the Power of Sparse Neural Networks for Feature Se...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Atashgahi, Zahra Liu, Tennison Pechenizkiy, Mykola Veldhuis, Raymond Mocanu, Decebal Constantin van der Schaar, Mihaela Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands Department of Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Department of Computer Science University of Luxembourg Luxembourg

ISBN: (纸本)9781643685489

Sparse Neural Networks (SNNs) have emerged as powerful tools for efficient feature selection. Leveraging the dynamic sparse training (DST) algorithms within SNNs has demonstrated promising feature selection capabilities while drastically reducing computational overheads. Despite these advancements, several critical aspects remain insufficiently explored for feature selection. Questions persist regarding the choice of the DST algorithm for network training, the choice of metric for ranking features/neurons, and the comparative performance of these methods across diverse datasets when compared to dense networks. This paper addresses these gaps by presenting a comprehensive systematic analysis of feature selection with sparse neural networks. Moreover, we introduce a novel metric considering sparse neural network characteristics, which is designed to quantify feature importance within the context of SNNs. Our findings show that feature selection with SNNs trained with DST algorithms can achieve, on average, more than 50% memory and 55% FLOPs reduction compared to the dense networks, while outperforming them in terms of the quality of the selected features. © 2024 The Authors.

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Examining finite-time behaviors in the fractional Gray–Scott model: Stability, synchronization, and simulation analysis

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International Journal of Cognitive Computing in Engineering 2025年 6卷 380-390页

作者： Momani, Shaher Batiha, Iqbal M. Bendib, Issam Ouannas, Adel Hioual, Amel Mohamed, Dalah Department of Mathematics Faculty of Science The University of Jordan Amman11942 Jordan Ajman University Ajman United Arab Emirates Department of Mathematics Al Zaytoonah University of Jordan Amman11733 Jordan Applied Mathematics & Modeling Laboratory Department of Mathematics Faculty of Exact Sciences University of Brothers Mentouri Constantine25000 Algeria Department of Mathematics and Computer Science University of Oum El Bouaghi Oum El Bouaghi04000 Algeria

This paper investigates the behavior and stability of the fractional-order Gray–Scott model, with a specific focus on achieving finite-time stability and synchronization. It introduces essential concepts, including the Gamma function, the Riemann–Liouville fractional-order integral operator, the Caputo fractional derivative, and the Mittag-Leffler function, to establish a foundational framework for subsequent analysis. Equilibrium points are defined, distinguishing between initial and finite-time equilibria, and the conditions for finite-time stability, including settling time, are precisely outlined. Stability results for this model are presented through theorems with detailed proofs, elucidating the roles of Lyapunov functions, class functions, and other system parameters. Furthermore, the paper explores finite-time synchronization schemes in master–slave systems, providing a mathematical framework for understanding and achieving synchronization within a finite time frame. This framework illuminates synchronization dynamics and their practical implications for controlling complex systems. Additionally, numerical examples illustrate finite-time stability and synchronization within the Gray–Scott reaction–diffusion model. © 2025 The Authors

关键词： Lyapunov functions

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Numerical scheme to solve a class of variable–order Hilfer–Prabhakar fractional differential equations with Jacobi wavelets polynomials

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applied mathematics(A Journal of Chinese Universities) 2022年第1期37卷 35-51页

作者： B.Bagherzadeh Tavasani A.H.Refahi Sheikhani H.Aminikhah Department of Applied Mathematics Faculty of Mathematical SciencesLahijan BranchIslamic Azad UniversityLahijanIran Department of Applied Mathematics and Computer Science Faculty of Mathematical SciencesUniversity of GuilanRashtIran

In this paper,we introduced a numerical approach for solving the fractional differential equations with a type of variable-order Hilfer-Prabhakar derivative of orderμ(t)andν(t).The proposed method is based on the Jacobi wavelet collocation *** to this method,an operational matrix is *** use this operational matrix of the fractional derivative of variable-order to reduce the solution of the linear fractional equations to the system of algebraic *** considerations are ***,some numerical examples are presented to demonstrate the accuracy of the proposed method.

关键词： Hilfer-Prabhakar derivative Jacobi wavelets operational matrices variable order

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Multi-Task Learning Enhanced Physics-Informed Neural Network for Solving Fluid-Structure Interaction Equations 7

Multi-Task Learning Enhanced Physics-Informed Neural Network...

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7th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2024

作者： Zhao, Yihe Yao, Jun Wang, Jian Xie, Xuetao Ablameyko, Sergey V. Qingdao China Qingdao China Belarusian State University Faculty of Applied Mathematics and Computer Science Minsk220030 Belarus

ISBN: (纸本)9798350370782

Physics-informed neural networks (PINNs) incorporate physical constraints into their loss functions, allowing them to efficiently solve Partial Differential Equations (PDEs). In this work, we introduce an innovative network structure based on PINNs, specifically designed to address fluid-structure interaction (FSI) problems in porous media, referred to as M-PINN. Our approach is inspired by fully coupled traditional numerical methods, leveraging a multi-task learning (MTL) framework with soft parameter sharing to enhance the interactions across different physical fields. This alignment leads to an improved representation of multi-physics coupling phenomena. Experiments on the constant pressure production problem demonstrate that our method provides better performance than both traditional and sequential PINN approaches. © 2024 IEEE.

关键词： Liquefied gases

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256 Metaverse Records Dataset 24

256 Metaverse Records Dataset

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32nd ACM International Conference on Multimedia, MM 2024

作者： Steinert, Patrick Wagenpfeil, Stefan Frommholz, Ingo Hemmje, Matthias L. Faculty of Mathematics and Computer Science University of Hagen Hagen Germany Faculty of Business Computing and Software Engineering Pfh University of Applied Sciences Göttingen Germany School of Mathematics and Computer Science University of Wolverhampton Wolverhampton United Kingdom

ISBN: (纸本)9798400706868

The metaverse is a developing field that crosses several areas of multimedia research. In this paper, we introduce the 256-MetaverseRecords dataset, a novel and extensive collection of annotated screen recordings in the form of videos from various virtual worlds of the metaverse. We describe the process of creating the dataset, the quality criteria for the annotations, and the exploration of the dataset. We also show four experiments to evaluate the performance of different feature extraction methods for Metaverse Recordings (MVRs): MVR segmentation, audio event detection, and object and interaction detection based on this dataset. Our results demonstrate that existing methods have limitations and leave challenges in dealing with the diversity and complexity of metaverse data, and that more research is needed to develop metaverse-specific techniques. Our dataset can serve as a valuable resource for the research community and foster the development of new applications and solutions for the metaverse. © 2024 Owner/Author.

关键词： dataset

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Recommender System to Identify Drug-Target Associations Using Matrix Factorization 9

Recommender System to Identify Drug-Target Associations Usin...

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Joint 9th International Conference on Digital Arts, Media and Technology with 7th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2024

作者： Phuangmalai, Sornsawan Suratanee, Apichat Plaimas, Kitiporn Chulalongkorn University Faculty of Science Department of Mathematics and Computer Science Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand Center Department of Mathematics and Computer Science Bangkok Thailand

ISBN: (纸本)9798350318241

Recommender systems, widely used in information filtering systems, demonstrate efficacy in suggesting novel items to users. In this study, we propose the application of a recommender system to predict new drug-target associations. Here, drugs are represented as users, while protein targets are treated as items within the recommender system. However, due to sparse data in a matrix of drugs and protein targets, matrix factorization (MF) techniques are employed to decompose the extensive matrix into smaller matrices. We identify non-negative matrix factorization (NMF) and singular value decomposition (SVD) as top-performing models for this task. Subsequently, we integrated them into the traditional recommender system, encompassing both drug-based collaborative filtering and target-based collaborative filtering, to identify new drug-target associations aligned with observed interactions between drugs and target proteins. Finally, we evaluated the performance of our developed recommender system with matrix factorization for drug-target associations and compared the results with those obtained from a recommender system without matrix factorization. © 2024 IEEE.

关键词： Collaborative filtering

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Integration of Metaverse Recordings in Multimedia Information Retrieval 24

Integration of Metaverse Recordings in Multimedia Informatio...

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13th International Conference on Software and computer Applications, ICSCA 2024

作者： Steinert, Patrick Wagenpfeil, Stefan Frommholz, Ingo Hemmje, Matthias L. University Of Hagen Faculty Of Mathematics And Computer Science Hagen Germany Pfh Private University Of Applied Sciences Faculty Of Business Computing And Software Engineering Göttingen Germany University Of Wolverhampton School Of Mathematics And Computer Science Wolverhampton United Kingdom

ISBN: (纸本)9798400708329

This paper addresses the challenges in integrating Metaverse Recordings in Multimedia Information Retrieval as a new type of multimedia. Specifically, we describe the characteristics of video content and explain the key differences of a metaverse to other videos. We present a specialized process to analyze and index Metaverse Recordings. We demonstrate that existing techniques can be used for metaverse-specific retrieval, but need content-specific adaptions. Furthermore, we evaluate a prototypical implementation based on the proposed methods with respect to the impact on the retrieval process. © 2024 Owner/Author.

关键词： Multimedia systems

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Node Load Rate Calculation Method based on Database Query Statistics in Load Balancing Strategy 5

Node Load Rate Calculation Method based on Database Query St...

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5th Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2024

作者： Song, Qingqing Wu, Zhen Xia, Shaoliang Belarusian State University Faculty of Applied Mathematics and Computer Science Minsk Belarus Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9798350374407

The calculation of node load rate has a crucial impact on the quality of load balancing strategies in distributed systems. The existing commonly used methods for calculating node load rates do not fully consider the current state of nodes, leading to a discrepancy between their calculation results and reality. This paper focuses on distributed information systems, taking databases as the starting point, and proposes a node load rate calculation method based on database query statistics to be used in load balancing strategies. It adopts a method of combining theoretical calculation data with benchmark performance testing data for analysis, fully considering the impact of factors such as data volume in distributed information system databases, peak database access periods, and data table indexes on calculation results. While ensuring the accuracy of node load rate calculation results, it minimizes the need for repetitive testing, conserves computer performance and energy resources, and reduces the time and economic expenses associated with node load rate calculation. The validation results of the algorithm indicate that the average error rate between the calculated database seconds per query (SPQ) and the actual values is as low as 1.7%. © 2024 IEEE.

关键词： Database systems

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