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Comparative sensitivity analysis of single and multiple modifications in multi-criteria decision analysis

Procedia computer science 2024年 246卷 91-102页

作者： Jakub Więckowski Paweł Białoń Wojciech Sałabun Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Żołnierska 49 71-210 Szczecin Poland National Institute of Telecommunications ul. Szachowa 1 04-894 Warsaw Poland

In the area of decision-making, where complexities often exceed experts’ analytical capabilities, this study addresses a critical gap by introducing a novel methodology for sensitivity analysis within the Multi-Criteria Decision Analysis (MCDA) problems. Current analytical frameworks struggle to comprehensively consider potential modifications to values within decision matrices, making it challenging to understand their impact in detail. To address this challenge, decision-makers need enhanced tools, and MCDA methods combined with systematic changes in selected elements of the decision matrix stand out as a promising approach. However, existing studies primarily focus on different criteria-weight scenarios, leaving an unexplored gap in the simultaneous modification of multiple values within the decision matrix. This paper integrates sensitivity analysis within the MCDA methods, extending its role in the comprehensive assessment of decision problems. Sensitivity analysis becomes crucial in offering decision-makers a broader perspective, aiding them in navigating the complexities of decision-making in dynamic environments. Recognizing the unexplored potential in sensitivity analysis, the study proposed a novel approach of simultaneous modification of multiple values in a decision matrix, offering an extension of conventional one-at-a-time modifications. As a Proof of Concept (PoC) research work, the study investigated whether the determined approach provides divergent preference scores compared to the traditional single-modification method across ten different MCDA techniques. The results showed that modifying multiple values simultaneously produced different preference scores of alternatives than in the case of a conventional single change, showing that additional insight knowledge could be extracted from this type of sensitivity analysis.

关键词： Sensitivity analysis Multiple changes Decision making Results robustness MCDA

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Deep Reinforcement Learning and Convex Mean-Variance Optimisation for Portfolio Management

TechRxiv

引用

TechRxiv 2022年

作者： Pretorius, Ruan van Zyl, Terence L. The School of Computer Science and Applied Mathematics University of the Witwatersrand South Africa South Africa The Institute for Intelligent Systems Unversity of Johannesburg Johannesburg South Africa

Traditional portfolio management methods can incorporate specific investor preferences but rely on accurate forecasts of asset returns and covariances. Reinforcement learning (RL) methods do not rely on these explicit forecasts and are better suited for multi-stage decision processes. To address limitations of the evaluated research, experiments were conducted on three markets in different economies with different overall trends. By incorporating specific investor preferences into our RL models’ reward functions, a more comprehensive comparison could be made to traditional methods in risk-return space. Transaction costs were also modelled more realistically by including nonlinear changes introduced by market volatility and trading volume. The results of this study suggest that there can be an advantage to using RL methods compared to traditional convex mean-variance optimisation methods under certain market conditions. Our RL models could significantly outperform traditional single-period optimisation (SPO) and multi-period optimisation (MPO) models in upward trending markets, but only up to specific risk limits. In sideways trending markets, the performance of SPO and MPO models can be closely matched by our RL models for the majority of the excess risk range tested. The specific market conditions under which these models could outperform each other highlight the importance of a more comprehensive comparison of Pareto optimal frontiers in risk-return space. These frontiers give investors a more granular view of which models might provide better performance for their specific risk tolerance or return targets. © 2022, CC BY-SA.

关键词： Investments

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Universal subgradient and proximal bundle methods for convex and strongly convex hybrid composite optimization

arXiv

引用

arXiv 2024年

作者： Guigues, Vincent Liang, Jiaming Monteiro, Renato D.C. School of Applied Mathematics FGV/EMAp Rio de Janeiro22250-900 Brazil Goergen Institute for Data Science Department of Computer Science University of Rochester RochesterNY14620 United States School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA30332 United States

This paper develops two parameter-free methods for solving convex and strongly convex hybrid composite optimization problems, namely, a composite subgradient type method and a proximal bundle type method. Both functional and stationary complexity bounds for the two methods are established in terms of the unknown strong convexity parameter. To the best of our knowledge, the two proposed methods are the first universal methods for solving hybrid strongly convex composite optimization problems that do not rely on any restart scheme nor require the knowledge of the optimal value. © 2024, CC BY.

关键词： Optimization algorithms

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Relativistic Digital Twin: Bringing the IoT to the Future

arXiv

引用

arXiv 2023年

作者： Sciullo, Luca De Marchi, Alberto Trotta, Angelo Montori, Federico Bononi, Luciano Felice, Marco Di Department of Computer Science and Engineering University of Bologna Italy Advanced Research Center on Electronic Systems University of Bologna Italy Institute of Applied Mathematics and Scientific Computing Department of Aerospace Engineering University of the Bundeswehr Munich Germany

Complex IoT ecosystems often require the usage of Digital Twins (DTs) of their physical assets in order to perform predictive analytics and simulate what-if scenarios. DTs are able to replicate IoT devices and adapt over time to their behavioral changes. However, DTs in IoT are typically tailored to a specific use case, without the possibility to seamlessly adapt to different scenarios. Further, the fragmentation of IoT poses additional challenges on how to deploy DTs in heterogeneous scenarios characterized by the usage of multiple data formats and IoT network protocols. In this paper, we propose the Relativistic Digital Twin (RDT) framework, through which we automatically generate general-purpose DTs of IoT entities and tune their behavioral models over time by constantly observing their real counterparts. The framework relies on the object representation via the Web of Things (WoT), to offer a standardized interface to each of the IoT devices as well as to their DTs. To this purpose, we extended the W3C WoT standard in order to encompass the concept of behavioral model and define it in the Thing Description (TD) through a new vocabulary. Finally, we evaluated the RDT framework over two disjoint use cases to assess its correctness and learning performance, i.e., the DT of a simulated smart home scenario with the capability of forecasting the indoor temperature, and the DT of a real-world drone with the capability of forecasting its trajectory in an outdoor scenario. Experiments show that the generated DT can estimate the behavior of its real counterpart after an observation stage, regardless of the considered scenario. © 2023, CC BY.

关键词： Internet of things

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Classification of Information Objects with Fuzzy Parameters in E-Learning systems 12

Classification of Information Objects with Fuzzy Parameters ...

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12th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing systems: Technology and Applications, IDAACS 2023

作者： Mukhin, Vadym Zavgorodnii, Valerii Liskin, Viacheslav Syrota, Sergiy Koval, Vasyl Honchar, Liudmyla National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Department of System Design Kiev Ukraine National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Department of Information Systems and Technologies Kiev Ukraine National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Department of Applied Mathematics Kiev Ukraine West Ukrainian National University Department of Information-Computing Systems and Control Ternopil Ukraine West Ukrainian National University Department of Computer Science Ternopil Ukraine

ISBN: (纸本)9798350358056

The paper describes the process of the information objects classification in e-learning systems using the mathematical background of fuzzy logic. This approach can be used to improve the process of searching and organizing information in e-learning systems. As the volume of educational materials grows, it becomes more and more difficult to find and structure information. Fuzzy logic helps to optimize the search, and the use of a neural network can correctly classify materials in e-learning systems, which increases the efficiency and accuracy of search queries. © 2023 IEEE.

关键词： classification e-learning system fuzzy logic information object neural network

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Context Preservation Through Eye Tracking: Adaptive Reading Application Design for an Optimal Reading Experience 25

Context Preservation Through Eye Tracking: Adaptive Reading ...

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Proceedings of the 2025 Symposium on Eye Tracking Research and Applications

作者： Helena Eschricht Jensen Chaudhary Muhammad Aqdus Ilyas Ashkan Tashk Bart Cooreman Sofie Beier Per Bækgaard Denmark Technical University Kgs. Lyngby Denmark Department of Applied Mathematics and Computer Science Section for Cognitive Systems Denmark Technical University Kgs. Lyngby Denmark Centre for Visibility Design Royal Danish Academy Copenhagen Denmark Royal Danish Academy Copenhagen Denmark

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Improvement of computer Recognition of Foam Layer Criteria

Improvement of Computer Recognition of Foam Layer Criteria

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2022 International Conference "Quality Management, Transport and Information Security, Information Technologies", IT and QM and IS 2022

作者： Zatonskiy, Andrey V. Bazhenov, Ruslan I. Orozobekova, Aida K. Beknazarova, Saida S. Gorbunova, Tatiana N. Ledovskikh, Irina A. Perm National Research Polytechnic University Automation of Technological Processes Perm Russia Sholom-Aleichem Priamursky State University Information Systems and Mathematics Birobidzhan Russia Kyrgyz State University of Construction Transport and Architecture Name of N.Isanov Applied Computer Science Bishkek Kyrgyzstan Tashkent University of Information Technologies Named by Muhammad Al-Khwarizmi Audiovisual Technologies Tashkent Uzbekistan Moscow State University of Civil Engineering Applied Mathematics Moscow Russia Pacific National University Mathematics and Information Technology Khabarovsk Russia

ISBN: (数字)9781665488709

ISBN: (纸本)9781665488709

Foam bed flotation processes are common in the poly metallic, potash and food industries. A worker who visually assesses the condition of the foam bed controls the process. This reduces process control and product quality because of the human factor. Using computer vision is based on bubble boundary recognition to control due to poor lighting, low foam contrast, and splash back. Recognition is possible by analyzing the glare from the foam surface. We attempt to improve recognition by accounting for the dark antiglare that takes place due to the bubble deformation. It improved recognition and reduced the noise of the method to a few percents. © 2022 IEEE.

关键词： Process control

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Multi-bounce resonances in the interaction of walking droplets

arXiv

引用

arXiv 2023年

作者： Zhang, George Christov, Ivan C. Rahman, Aminur Department of Applied Mathematics University of Washington United States School of Mechanical Engineering Purdue University United States Department of Computer Science University of Nicosia Cyprus AI Institute in Dynamic Systems University of Washington United States

Discrete dynamical models of walking droplets ("walkers") have allowed swift numerical experiments revealing heretofore unobserved quantum statistics and related behaviors in a classical hydrodynamic system. We present evidence that one such model of walking droplets exhibits the empirically elusive n-bounce resonances that are traditionally seen in the scattering of solitary waves governed by covariant nonlinear field theories with polynomial self-interaction. A numerical investigation of the chosen model of interacting walking droplets reveals a fractal structure of resonances in the velocity in–velocity out diagram, much like the usual maps constructed for collisions of solitary waves. We suggest avenues for further theoretical analysis of walker collisions, which may connect this discrete model to the field-theoretic setting, as well as directions towards new experimental realizations n-bounce *** Codes 37xx, 76B15, 70Kxx Copyright © 2023, The Authors. All rights reserved.

关键词： Drops

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DSTFormer: 3D Human Pose Estimation with a Dual-scale Spatial and Temporal Transformer Network

DSTFormer: 3D Human Pose Estimation with a Dual-scale Spatia...

引用

International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Shaokun Zhang Xinde Li Chuanfei Hu Jianping Xu Huaping Liu School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Nanjing Center for Applied Mathematics Nanjing China Southeast University Shenzhen Research Institute Shenzhen China Science and Technology on Information Systems Engineering Laboratory Nanjing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Recent transformer-based methods for estimating 3D human pose have gained widespread attention, achieving state-of-the-art results. Previous methods have primarily focused on capturing motion patterns of the human body at a single scale or cascading multiple scales, such as joints, bones, and body-parts. However, they are difficult to simultaneously capture spatial-temporal motion patterns of the human body at different scales due to the complex motion patterns. To address this issue, we propose Dual-scale Spatial and Temporal transFormer (DSTFormer), which can concurrently explore the spatial dependencies and temporal motion patterns of human joints and bones. Additionally, we introduce a Gcn-Spatial Transformer Block (GSTB), which introduces Graph Convolutional Networks (GCN) into transformer to enhance the exploitation of local relationships and global information between adjacent joints or bones. Extensive experiments are conducted on the Human3.6M benchmark dataset, and superior results are reported when comparing to other state-of-the-art methods. More remarkably, our model achieves to-date the best published performance, with P1 errors of 37.9 mm and 15.6 mm, respectively.

关键词： Solid modeling Three-dimensional displays Mechatronics Graph convolutional networks Computational modeling Pose estimation Transformers Bones Joints Robots

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A $(k, n)$-Secret Image Sharing With Steganography Using Generalized Tent Map

A $(k, n)$-Secret Image Sharing With Steganography Using Gen...

引用

International Conference on Modern Circuits and systems Technologies (MOCAST)

作者： Bishoy K. Sharobim Salwa K. Abd-El-Hafiz Ahmed G. Radwan Heba K. Aslan Nanoelectronics Integrated Systems Center (NISC) Nile University Giza Egypt Centre of Informatics Science Faculty of Information Technology and Computer Science Nile University Giza Egypt Engineering Mathematics and Physics Department Faculty of Engineering Cairo University Giza Egypt School of Engineering and Applied Sciences Nile University Giza Egypt

ISBN: (数字)9798350385427

ISBN: (纸本)9798350385434

Secret Image Sharing (SIS) transfers an image to mutually suspicious receivers as $n$ meaningless shares, where $k$ or more shares must be present to recover the secret. This paper proposes a $(k, n)$ -SIS system for any image type using polynomial interpolation based on Lagrange polynomials, where the generated shares are of size $1/k$ of the secret image size. A full encryption system, consisting of substitution and permutation stages, is employed by using the generalized Tent map as a source of randomness. In addition to using a long and sensitive system key, steganography using the Least Significant Bits (LSBs) embedding technique is utilized to improve security. Detailed experimental analysis of the security, robustness and performance of the proposed system is provided, which is more comprehensive than the analyses given in other related works. Security is demonstrated using statistical tests, and robustness against noise and cron attacks is validated.

关键词： Steganography Interpolation Circuits and systems Noise Receivers Robustness Polynomials

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