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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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High-Capacity DWDM Transmission System for Free-Space Optical Network

High-Capacity DWDM Transmission System for Free-Space Optica...

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International Conference on Electrical, control and Instrumentation engineering (ICECIE)

作者： M.T Rahman M.A Jahid S.Y Anha Md. Shohanur Rahman F. Azmi Orpy A. S. M. Bakibillah Department of Computer Science and Engineering University Asia Pacific (UAP) Bangladesh Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo Japan

ISBN: (数字)9798350380040

ISBN: (纸本)9798350380057

This study presents the architecture and performance evaluation of a high-capacity free-space optical (FSO) communication system that makes use of dense wavelength division multiplexing (DWDM) and a 1.28 Tb/s link. The system under consideration comprises 64 individual channels, each of which is capable of transferring data streams at a speed of 20 Gbps. A return-to-zero (RZ) line coding algorithm is incorporated to maximize performance across 2.0 km of FSO link. Important parameters that affect the system's performance, like beam divergence and atmosphere attenuation, are taken into account in the analysis. The signals' bit-error-rate (BER), quality factor (Q), and eye diagrams are calculated and examined to assess the system's performance. For future optical-wireless networks that require significant bandwidth capabilities, cost-effective implementation, and increased flexibility, this novel high-capacity design offers a promising solution. The results aid in the development of dependable and useful high-speed FSO communication systems.

关键词： Q-factor Wireless communication Performance evaluation Free-space optical communication System performance Bit error rate Optical fiber networks Wavelength division multiplexing Encoding Streams

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Convex Equilibrium-Free Stability and Performance Analysis of Discrete-Time Nonlinear Systems

arXiv

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arXiv 2024年

作者： Koelewijn, Patrick J.W. Weiland, Siep Tóth, Roland Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Netherlands Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Hungary

This paper considers the equilibrium-free stability and performance analysis of discrete-time nonlinear systems. We consider two types of equilibrium-free notions. Namely, the universal shifted concept, which considers stability and performance w.r.t. all equilibrium points of the system, and the incremental concept, which considers stability and performance between trajectories of the system. In this paper, we show how universal shifted stability and performance of discrete-time systems can be analyzed by making use of the time-difference dynamics. Moreover, we extend the existing results for incremental dissipativity for discrete-time systems based on dissipativity analysis of the differential dynamics to more general state-dependent storage functions for less conservative results. Finally, we show how both these equilibrium-free notions can be cast as a convex analysis problem by making use of the linear parameter-varying framework, which is also demonstrated by means of an example. Copyright © 2024, The Authors. All rights reserved.

关键词： Nonlinear systems

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AI and data-driven infrastructures for workflow automation and integration in advanced research and industrial applications

AI and data-driven infrastructures for workflow automation a...

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2023 Italia Intelligenza Artificiale - Thematic Workshops, Ital-IA 2023

作者： Forni, Tommaso Vozza, Mario Le Piane, Fabio Lorenzoni, Andrea Baldoni, Matteo Mercuri, Francesco DAIMON Lab CNR-ISMN via P. Gobetti 101 Bologna40129 Italy Department of Control and Computer Engineering Polytechnic University of Turin Corso Castelfidardo 34/d Turin10138 Italy Department of Computer Science and Engineering University of Bologna Viale del Risorgimento 2 Bologna40136 Italy

The use of AI and data-driven technologies and infrastructures for innovation and development of advanced research and industrial applications requires a strong degree of integration across a broad range of tools, disciplines and competences. In spite of a huge disruptive potential, the role of AI for research and development in the context of industrial applications is often hampered by the lack of consolidated and shared practices for transforming domain-specific processes for generating knowledge into added value. These issues are particularly striking for small-medium enterprises (SMEs), which must adopt clear and effective policies for implementing successful technology transfer paths for innovation. The activities of the DAIMON Lab of the CNR-ISMN focus on the design, development, implementation and application of integrated modelling, data-driven and AI methods and infrastructures for innovation in hi-tech applications. Our approach is based on the development of horizontal platforms, which can be applied to a broad range of vertical use-cases. Namely, we target the realisation of high-throughput workflows, related to specific domains and use cases, which are able to collect and process simulations and/or physical data and information. The implementation of an interoperable integration framework is a prerequisite for further application of AI tools for predictivity and automation. With a strong focus on the development of key enabling technologies (KETs), such as advanced materials, the approach pursued is extended to a broad range of application fields and scenarios of interest in industry, including electronic and ICT, advanced and sustainable manufacturing, energy, mobility. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Automation

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A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements 63

A Multi-Player Potential Game Approach for Sensor Network Lo...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Xu, Gehui Chen, Guanpu Fidan, Baris Hong, Yiguang Qi, Hongsheng Parisini, Thomas Johansson, Karl H. Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm100 44 Sweden University of Waterloo Department of Mechanical and Mechatronics Engineering WaterlooONN2L 3G1 Canada Tongji University Department of Control Science and Engineering shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China University of Chinese Academy of Sciences School of Mathematical Sciences Beijing China Aalborg University Department of Electronic Systems Denmark Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom University of Trieste Department of Engineering and Architecture Italy

ISBN: (纸本)9798350316339

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multiplayer non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies. © 2024 IEEE.

关键词： Nash equilibrium

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The Goldilocks Framework: Towards Selecting the Optimal Approach to Conducting AI Projects 22

The Goldilocks Framework: Towards Selecting the Optimal Appr...

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1st International Conference on AI engineering - Software engineering for AI, CAIN 2022

作者： Dzhusupova, Rimman Bosch, Jan Olsson, Helena Holmstrom Control & Safety Systems McDermott Dept. of Electrical & Instrumentation The Hague Netherlands Chalmers University of Technology Department of Computer Science and Engineering Gothenburg Sweden Malmö University Department of Computer Science and Media Technology Malmö Sweden

ISBN: (纸本)9781450392754

Artificial intelligence is increasingly becoming important to businesses since many companies have realized the benefits of applying Machine Learning (ML) and Deep Learning (DL) into their operations. Nevertheless, ML/DL technologies' industrial development and deployment examples are still rare and generally confined within a small cluster of large international companies who are struggling to apply ML more broadly and deploy their use cases at a large scale. Meanwhile, current AI market has started offering various solutions and services. Thus, organizations must understand how to acquire AI technology based on their business strategy and available resources. This paper discusses the industrial experience of developing and deploying ML/DL use cases to support organizations in their transformation towards AI. We identify how various factors, like cost, schedule, and intellectual property, can be affected by the choice of approach towards ML/DL project development and deployment within large international engineering corporations. As a research result, we present a framework that covers the trade-offs between those various factors and can support engineering companies to choose the best approach based on their long-term business strategies and, therefore, would help to accomplish their ML/DL project deployment successfully. © 2022 ACM.

关键词： Commerce

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Decoding Musical Evolution Through Network science

arXiv

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arXiv 2025年

作者： Di Marco, Niccolò Loru, Edoardo Galeazzi, Alessandro Cinelli, Matteo Quattrociocchi, Walter Department of Computer Science Sapienza University of Rome Italy Department of Computer Control and Management Engineering Sapienza University of Rome Italy University of Padova Department of Mathematics Padova Italy

Music has always been central to human culture, reflecting and shaping traditions, emotions, and societal changes. Technological advancements have transformed how music is created and consumed, influencing tastes and the music itself. In this study, we use Network science to analyze musical complexity. Drawing on ≈ 20, 000 MIDI files across six macro-genres spanning nearly four centuries, we represent each composition as a weighted directed network to study its structural properties. Our results show that Classical and Jazz compositions have higher complexity and melodic diversity than recently developed genres. However, a temporal analysis reveals a trend toward simplification, with even Classical and Jazz nearing the complexity levels of modern genres. This study highlights how digital tools and streaming platforms shape musical evolution, fostering new genres while driving homogenization and simplicity. © 2025, CC BY.

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Adversarial Training with Maximal Coding Rate Reduction

Adversarial Training with Maximal Coding Rate Reduction

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Asilomar Conference on Signals, Systems & computers

作者： Hsiang-Yu Chu Hongbo Zhao Markus Flierl Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Control Science and Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

Deep convolutional networks can solve various complex tasks in the field of image processing. However, adversarial attacks have been shown to have the ability of fooling deep learning models. Adversarial training is one commonly used strategy to improve the robustness of deep learning models against adversarial examples, which is performed by incorporating adversarial examples into the training process. Traditionally, during this process, cross-entropy loss is used as the loss function. In order to improve the robustness of deep learning models against adversarial examples, we propose in this paper two new methods of adversarial training by applying the principle of Maximal Coding Rate Reduction (MCR 2 ). We evaluate the performance of different adversarial training methods by comparing the clean accuracy and adversarial accuracy. It is shown that adversarial training with the MCR 2 loss function yields a more robust network than the traditional adversarial training method. In our experiments, adversarial accuracies are improved by up to 10%. The two loss functions are discussed by using a model.

关键词： Training Deep learning Hands Accuracy Image coding Computational modeling Robustness Data models Encoding Convolutional neural networks

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Leveraging GPT-4 Capabilities for Developing Context-Aware, Personalized Chatbot Interfaces in E-commerce Customer Support Systems 10

Leveraging GPT-4 Capabilities for Developing Context-Aware, ...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Santosh, Kathari Kholmukhamedov, Timur Kumar, M. Sandeep Aarif, Mohd Muda, Iskandar Bala, B Kiran Cmr Institute of Technology Bengaluru Department of Mba Bengaluru India Turin Polytechnic University In Tashkent Automatic Control and Computer Engineering Department Tashkent Uzbekistan Publishing Global Research Network Noida India Universitas Sumatera Utara Medan Indonesia K.Ramakrishnan College of Engineering Department of Artificial Intelligence and Data Science Trichy India

ISBN: (纸本)9798350353068

In the realm of e-commerce customer support, the adoption of chatbots is on the rise, driven by a quest for heightened user interactions. This study introduces an inventive approach harnessing the advanced capabilities of GPT-4 to construct chatbot interfaces that are both context-aware and personalized. The main aim of this work is to revolutionize ecommerce customer support by developing intelligent and adaptable chatbot interfaces that are context-aware, personalized, and significantly enhance user experiences. Existing chatbot models struggle with context retention and personalization, limiting effectiveness in dynamic e-commerce environments despite the promise of automation. This innovative approach leverages GPT-4's robust language processing, integrating it with user profiling systems for personalized responses grounded in user preferences and historical interactions. The method gives, diverse e-commerce dataset undergoes collection and pre-processing, followed by fine-tuning GPT-4 with an emphasis on context-awareness and personalized responses. The model is seamlessly integrated with e-commerce backend systems for real-time information, multimodal input support caters to varied user preferences. Results exhibit a significant 95% accuracy improvement, affirming the chatbot's enhanced ability to comprehend user queries. By integrating personalization and advanced context retention, this study enhances user engagement, paving way for revolutionary intelligent and adaptable chatbot interfaces in ecommerce customer support. © 2024 IEEE.

关键词： Electronic commerce

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Fast Disentangled Slim Tensor Learning for Multi-view Clustering

arXiv

引用

arXiv 2024年

作者： Xu, Deng Zhang, Chao Li, Zechao Chen, Chunlin Li, Huaxiong Department of Control Science and Intelligence Engineering Nanjing University Nanjing210093 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210014 China

Tensor-based multi-view clustering has recently received significant attention due to its exceptional ability to explore cross-view high-order correlations. However, most existing methods still encounter some limitations. (1) Most of them explore the correlations among different affinity matrices, making them unscalable to large-scale data. (2) Although some methods address it by introducing bipartite graphs, they may result in sub-optimal solutions caused by an unstable anchor selection process. (3) They generally ignore the negative impact of latent semantic-unrelated information in each view. To tackle these issues, we propose a new approach termed fast Disentangled Slim Tensor Learning (DSTL) for multi-view clustering . Instead of focusing on the multi-view graph structures, DSTL directly explores the high-order correlations among multi-view latent semantic representations based on matrix factorization. To alleviate the negative influence of feature redundancy, inspired by robust PCA, DSTL disentangles the latent low-dimensional representation into a semantic-unrelated part and a semantic-related part for each view. Subsequently, two slim tensors are constructed with tensor-based regularization. To further enhance the quality of feature disentanglement, the semantic-related representations are aligned across views through a consensus alignment indicator. Our proposed model is computationally efficient and can be solved effectively. Extensive experiments demonstrate the superiority and efficiency of DSTL over state-of-the-art approaches. The code of DSTL is available at https://***/dengxu-nju/DSTL. Copyright © 2024, The Authors. All rights reserved.

关键词： Matrix factorization

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