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Convergent Approaches to AI Explainability for HEP Muonic Particles Pattern Recognition

Computing and Software for Big Science 2023年第1期7卷 1-18页

作者： Maglianella, Leandro Nicoletti, Lorenzo Giagu, Stefano Napoli, Christian Scardapane, Simone Department of Computer Control and Management Engineering Antonio Ruberti Sapienza University of Rome Rome Italy Department of Physics Sapienza University of Rome Rome Italy Institute for Systems Analysis and Computer Science Italian National Research Council Rome Italy Department of Computational Intelligence Electronics and Telecommunications Czestochowa University of Technology Czestochowa Poland Department of Information Engineering Electronics and Telecommunications Sapienza University of Rome Rome Italy

Neural networks are commonly defined as ‘black-box’ models, meaning that the mechanism describing how they give predictions and perform decisions is not immediately clear or even understandable by humans. Therefore, Explainable Artificial Intelligence (xAI) aims at overcoming such limitation by providing explanations to Machine Learning (ML) algorithms and, consequently, making their outcomes reliable for users. However, different xAI methods may provide different explanations, both from a quantitative and a qualitative point of view, and the heterogeneity of approaches makes it difficult for a domain expert to select and interpret their result. In this work, we consider this issue in the context of a high-energy physics (HEP) use-case concerning muonic motion. In particular, we explored an array of xAI methods based on different approaches, and we tested their capabilities in our use-case. As a result, we obtained an array of potentially easy-to-understand and human-readable explanations of models’ predictions, and for each of them we describe strengths and drawbacks in this particular scenario, providing an interesting atlas on the convergent application of multiple xAI algorithms in a realistic context. © 2023, The Author(s).

关键词： Explainable Artificial Intelligence High-energy physics Intrinsically interpretable Decision Trees Saliency maps methods Tracing gradient descent

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System for Determining the Shape of the Wavefront of a Nonsinusoidal Periodic Acoustic Wave

System for Determining the Shape of the Wavefront of a Nonsi...

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International Conference on engineering of Modern Electric systems (EMES)

作者： Adrian Codoban Laura Coroiu Eugen Gergely József Sárosi Department Control Systems Engineering and Management University of Oradea Oradea Romania Department of Mechatronics and Automation University of Szeged Szeged Hungary

The shape of the wavefront is important for the most realistic reproduction of the acoustic wave. In acoustics, the wave front (or wave surface) is defined as the totality of points in the space of the propagating medium that have the same instantaneous elongation amplitude. In the ideal case, in a homogeneous and isotropic propagation medium without other disturbances, the wave has a spherical symmetry, i.e. the shape of the wavefront is circular and at large distances from the wave source, due to dispersion, the shape of the wavefront can be considered plan. In practice, however, due to the multiple component harmonics, reflections and possible summations of the primary acoustic wave, the elongation has a non-sinusoidal shape, and the wavefront may have an irregular random shape. Moreover, if the source of the wave or bodies from which it is reflected are in motion, the shape of the wavefront may even be variable in time. The paper presents a method for determining the shape of the wave front of a non-sinusoidal periodic plane acoustic wave, resulting from the addition of several harmonic components, reflected waves, etc. It uses an array of omnidirectional capacitive microphones arranged in line, at equal distances between them. At each of the microphones, the acoustic wave is captured out of phase depending on the position of the microphone in space and the shape of the wave front. These phase shifts placed next to each other give an image of the shape of the wavefront captured by the microphone system. The method presented in this paper reproduces sounds very realistically and can be used in Active Noise Cancel systems, in open spaces, with one or more mobile noise sources. One of the benefits is the increase in hearing comfort in noisy environments. Another is to record sounds with high fidelity.

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Competition and Cooperation Mechanism Between Agency Selling and Wholesale: An Application of the Emerging E-commerce Model

Competition and Cooperation Mechanism Between Agency Selling...

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2023 IEEE International Conference on Industrial engineering and engineering management, IEEM 2023

作者： Wang, Haonan Lee, C.K.M. Ji, Ping Li, Gang The Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong Laboratory for Artificial Intelligence in Design Hong Kong Science Park New Territories Hong Kong Xi'an Jiaotong - Liverpool University China School of Management Xi'an Jiaotong University The State Key Lab for Manufacturing Systems Engineering The Key Lab of the Ministry of Education for Process Control and Efficiency Engineering Xi'an China

ISBN: (纸本)9798350323153

Recently, the platform economy allows enterprises to collaborate with business partners and provide additional channels for various parties. E-commerce platforms have become integral channels for connecting suppliers with consumers. In the platform economy, agency selling and reselling are two predominant sales models that often coexist for the same product types, resulting in a competitive relationship. This study analyzes transaction data for 30,000 Stock Keeping Units (SKUs) provided by the Chinese e-commerce platforms over one month. We employ three models to compare the sales quantity and profits of manufacturers and online platforms and to identify the most efficient mode for stakeholders based on the model establishment and empirical evidence. Our findings suggest that apart from the existing channel to provide products to customers, manufacturers enter the platform which will increase the profit of platform operators. On the other hand, platform operators may also affect the profit of the manufacturers. © 2023 IEEE.

关键词： Channel structure Data-analysis Online platform

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Innovation management Opportunities of Artificial Intelligence Social Enterprises in Romania

Innovation Management Opportunities of Artificial Intelligen...

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International Conference on engineering of Modern Electric systems (EMES)

作者： Marius Vlad Pop Gabriela Tont Andrei Şandor Doctoral School of Engineering Sciences University of Oradea Oradea Romania Control Systems Engineering and Management Department University of Oradea Oradea Romania

This paper is focused on Ai Social Enterprise Opportunities in Romanian Ecosystem, on exploring the emerging grounds of Digital Innovation management. Clarity is needed in order to pursue goal-oriented initiatives, our research strategy is focused on maximizing the current strengths and adapting them to emerging needs in an AI opportunity perspective.

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Quantum Robust control for Time-Varying Noises Based on Adversarial Learning

Quantum Robust Control for Time-Varying Noises Based on Adve...

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IEEE International Conference on systems, Man and Cybernetics

作者： Haotian Ji Sen Kuang Daoyi Dong Chunlin Chen Department of Automation University of Science and Techonology of China Hefei China School of Engineering Australian National University Canberra Australia Department of Control and Systems Engineering School of Management and Engineering Nanjing University Nanjing China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Time-varying noises are one of the reasons that make it difficult for quantum systems to complete control tasks. How to quantify the influence of time-varying noises on control results and how to design a control law that can resist time-varying noises are two important problems. In this paper, the adversarial learning is introduced into quantum control and the loss function under the worst-case noise is used as a way to quantify the impact of time-varying noises on control performance. We utilize the Gradient Ascent Pulse engineering (GRAPE) technique to search the worst-case noise and meanwhile offer a strategy to improve the robustness of the control law. Simulation experiments on a two-qubit system and a four-qubit system show that the found noises indeed can act as worst-case noises. Furthermore, the optimized control laws demonstrate good robustness to time-varying noises in state preparation tasks.

关键词： Robust control Quantum system Noise Pipelines control systems Robustness Adversarial machine learning Superconducting device noise Trajectory Time-varying systems

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Real Time Sound Source Localization Using von-Mises ResNet

Real Time Sound Source Localization Using von-Mises ResNet

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2024 IEEE/SICE International Symposium on System Integration, SII 2024

作者： Bozkurtlar, Mert Yen, Benjamin Itoyama, Katsutoshi Nakadai, Kazuhiro School of Engineering Tokyo Institute of Technology Department of Systems and Control Engineering 2-12-1 Ookayama Meguro-ku Tokyo152-8552 Japan Istanbul Technical University Faculty of Management Department of Industrial Engineering Maçka/Beşiktaş/Harbiye Istanbul34367 Turkey Istanbul Technical University Faculty of Computer and Informatics Engineering Department of Computer Engineering Sariyer/Ayazaga Istanbul34485 Turkey The Nakadai Lab Japan Honda Research Institute Japan Co. Ltd 8-1 Honcho Saitama Wako351-0188 Japan

ISBN: (纸本)9798350312072

This paper addresses the task of learning periodic information using deep neural networks to achieve real-time, environment-independent sound source localization. Previous papers showed phase data is the most significant cue in sound source localization tasks and the proposed vM-B DNN was validated to be able to handle such periodic information using on synthesized data. However, they haven't shown its effectiveness and robustness in realistic use cases. This paper introduces a more complex model based on residual networks and adapts vM-B activation function for convolutional layers for use cases that require real-time predictions in dynamically changing environments. © 2024 IEEE.

关键词： Deep neural networks

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Knowledge management System Evaluation Using Genetic Algorithm and Monte Carlo Method

Knowledge Management System Evaluation Using Genetic Algorit...

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International Conference on engineering of Modern Electric systems (EMES)

作者： Andrei şandor Gabriela Tonţ Marius Vlad Pop Doctoral School of Engineering Sciences University of Oradea Oradea Romania Control Systems Engineering and Management Department University of Oradea Oradea Romania

The performance and sustain ability of modern organizations heavily rely on knowledge. In order to be able to manage it, organizations use dedicated tools - knowledge management systems, which enhance knowledge-related processes. The use of this kind of systems can imply a high number of resources and therefore a comprehensive analysis on the performance of the system from the organizational requirements perspective is needed. This article proposes a performance evaluation method based on genetic algorithm and Monte Carlo simulation combining an optimization-based approach with a probabilistic perspective. The main advantage of this method is that it considers a set of very specific requirements or needs, making it a highly customizable tool.

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Differentially Private Online Federated Learning with Correlated Noise

arXiv

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arXiv 2024年

作者： Zhang, Jiaojiao Zhu, Linglingzhi Johansson, Mikael Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-100 44 Sweden Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong

We introduce a novel differentially private algorithm for online federated learning that employs temporally correlated noise to enhance utility while ensuring privacy of continuously released models. To address challenges posed by DP noise and local updates with streaming non-iid data, we develop a perturbed iterate analysis to control the impact of the DP noise on the utility. Moreover, we demonstrate how the drift errors from local updates can be effectively managed under a quasi-strong convexity condition. Subject to an (ϵ, δ)DP budget, we establish a dynamic regret bound over the entire time horizon, quantifying the impact of key parameters and the intensity of changes in dynamic environments. Numerical experiments confirm the efficacy of the proposed algorithm. © 2024, CC BY.

关键词： Federated learning

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Adaptive Decentralized Asymptotic Tracking control for Large-Scale Nonlinear systems With Unknown Strong Interconnections

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 173-186页

作者： Ben Niu Jidong Liu Ding Wang Xudong Zhao Huanqing Wang College of Information Science and Engineering Shandong Normal UniversityJinan 250014China School of Artificial Intelligence Nankai UniversityTianjin 300071China Faculty of Information Technology Beijing University of TechnologyBeijing 100124 State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Control Science and Engineering Dalian University of TechnologyDalian 116023China Department of Mathematical Science and Automatization Institute Bohai UniversityJinzhou 121000China

An adaptive decentralized asymptotic tracking control scheme is developed in this paper for a class of large-scale nonlinear systems with unknown strong interconnections,unknown time-varying parameters,and ***,by employing the intrinsic properties of Gaussian functions for the interconnection terms for the first time,all extra signals in the framework of decentralized control are filtered out,thereby removing all additional assumptions imposed on the interconnec-tions,such as upper bounding functions and matching ***,by introducing two integral bounded functions,asymptotic tracking control is ***,the nonlinear filters with the compensation terms are introduced to circumvent the issue of“explosion of complexity”.It is shown that all the closed-loop signals are bounded and the tracking errors converge to zero *** the end,a simulation example is carried out to demonstrate the effectiveness of the proposed approach.

关键词： Adaptive decentralized control asymptotic tracking control large-scale nonlinear systems unknown strong interconnections

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A Multi-modal Virtual-Real Fusion System for Multi-task Human-Computer Interaction

A Multi-modal Virtual-Real Fusion System for Multi-task Huma...

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IEEE International Conference on Networking, Sensing and control

作者： Xinlei Zhang Jiahui Yu Yuxiang Sun Min Li Yang Song Xianzhong Zhou Department of Control and Systems Engineering School of Management and Engineering Nanjing University Nanjing China Department of Control and Systems Engineering School of Management and Engineering Research Center for New Technology in Intelligent Equipment Nanjing China

ISBN: (纸本)9781665472449

Due to the complexity of the task and the diversification of the scene, the traditional interactive control method can no longer meet the requirements of users. To solve this problem, a multi-modal virtual-real fusion system for multi-task human-computer interaction is proposed in this paper, which integrates eye movement, gesture and voice. Frist, aiming at the phenomenon of multi-task and multi-modal, a task-modal matching model is established. Then, the task-modal matching model is abstracted into a multi-objective optimization problem, and a method for solving this problem is designed and a matching scheme is successfully obtained. Meanwhile, the construction of the system is completed for the virtual-real fusion environment, and the control of unmanned car and virtual car is realized. The system can carry out multi-modal interaction and complete multiple tasks in real scene, virtual scene, parallel system and virtual-real fusion scene. Finally, the experiment proves the stability and reliability of the system.

关键词： Human computer interaction Multitasking Software systems control systems Stability analysis Software reliability Sensors

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