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Equalizer Zero-Determinant Strategy in Discounted Repeated Stackelberg Asymmetric Game

Journal of Systems science & Complexity 2024年第1期37卷 184-203页

作者： CHENG Zhaoyang CHEN Guanpu HONG Yiguang Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology10044 StockholmSweden. Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 210201China

This paper focuses on the performance of equalizer zero-determinant(ZD)strategies in discounted repeated Stackelberg asymmetric *** the leader-follower adversarial scenario,the strong Stackelberg equilibrium(SSE)deriving from the opponents’best response(BR),is technically the optimal strategy for the ***,computing an SSE strategy may be difficult since it needs to solve a mixed-integer program and has exponential complexity in the number of *** this end,the authors propose an equalizer ZD strategy,which can unilaterally restrict the opponent’s expected *** authors first study the existence of an equalizer ZD strategy with one-to-one situations,and analyze an upper bound of its performance with the baseline SSE *** the authors turn to multi-player models,where there exists one player adopting an equalizer ZD *** authors give bounds of the weighted sum of opponents’s utilities,and compare it with the SSE ***,the authors give simulations on unmanned aerial vehicles(UAVs)and the moving target defense(MTD)to verify the effectiveness of the proposed approach.

关键词： Discounted repeated Stackelberg asymmetric game equalizer zero-determinant strategy strong Stackelberg equilibrium strategy

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Consistent Counterfactual Explanations via Anomaly control and Data Coherence

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第4期6卷 794-804页

作者： Movin, Maria Siciliano, Federico Ferreira, Rui Silvestri, Fabrizio Tolomei, Gabriele Spotify StockholmSE-111 Sweden Sapienza University of Rome Department of Computer Control and Management Engineering Rome00185 Italy Spotify LondonWC2N 6AT United Kingdom Sapienza University of Rome Department of Computer Science Rome00161 Italy

Algorithmic recourses are popular methods to provide individuals impacted by machine learning models with recommendations on feasible actions for a more favorable prediction. Most of the previous algorithmic recourse methods work under the assumption that the predictive model does not change over time. However, in reality, models in deployment may both be periodically retrained and have their architecture changed. Therefore, it is desirable that the recourse should remain valid when such a model update occurs, unless new evidence arises. We call this feature consistency. This article presents anomaly control and data coherence (ACDC), a novel model-agnostic recourse method that generates counterfactual explanations, i.e., instance-level recourses. ACDC is inspired by anomaly detection methods and uses a one-class classifier to aid the search for valid, consistent, and feasible counterfactual explanations. The one-class classifier asserts that the generated counterfactual explanations lie on the data manifold and are not outliers of the target class. We compare ACDC against several state-of-the-art recourse methods across four datasets. Our experiments show that ACDC outperforms baselines both in generating consistent counterfactual explanations, and in generating feasible and plausible counterfactual explanations, while still having proximity measures similar to the baseline methods targeting the data manifold. © 2024 IEEE.

关键词： Machine learning

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Toward quantum unmanned systems

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science China(Technological sciences) 2024年第7期67卷 2277-2280页

作者： CHENG ShuMing QI HongSheng SUN Yue LIU LiJun XIE ShaoRong REN XiaoQiang Department of Control Science and Engineering Tongji UniversityShanghai 200092China Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing 100190China Cipher Cloud Core Ltd. Beijing 100085China College of Mathematics and Computer Science Shanxi Normal UniversityLinfen 041081China School of Computer Engineering and Science Shanghai UniversityShanghai 200444China School of Mechatronic Engineering and Automation Shanghai UniversityShanghai 200444China

1 Quantum information technology Quantum information technology utilizes physical systems at the microscopic level, such as photon, atom, ion, and superconducting, to accomplish information-processing tasks that are impossible for the classical macroscopic world. During the past decade, significant process has been achieved in the pursuit of quantum technology into practical applications,generating great research interest from various domains, with the potential to radically change our information infrastructure [1–3].

关键词： quantum utilize generating

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Static Analysis Techniques for Fixing Software Defects in MPI-Based Parallel Programs

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computers, Materials & Continua 2024年第5期79卷 3139-3173页

作者： Norah Abdullah Al-Johany Sanaa Abdullah Sharaf Fathy Elbouraey Eassa Reem Abdulaziz Alnanih Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Software Engineering and Distributed System Research Group King Abdulaziz UniversityJeddah21589Saudi Arabia

The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed ***, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.

关键词： High-performance computing parallel computing software engineering software defect message passing interface deadlock

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Intelligent Proximate Analysis of Coal Based on Near-Infrared Spectroscopy and Multioutput Deep Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1398-1410页

作者： Zou, Liang Qiao, Jiahui Yu, Xinhui Chen, Xun Lei, Meng School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China

Proximate analysis of coal indicates the moisture, ash, volatile content, and calorific value, which has been widely utilized as the basis for coal characterization. It involves heating the coal under various conditions until a constant weight is obtained. Although it is a relatively simple process that does not require expensive analytical equipment, determining these characteristics is time consuming. An alternative way for proximate analysis is spectral analysis in combination with various machine learning methods. However, most previous works analyze individual characteristics and fail to explore the relationship among them. In this study, we propose a method for proximate analysis based on near-infrared spectroscopy and a multioutput attention Unet (MOA-Unet), which can predict multiple characteristics simultaneously. First, an attention-based Unet is designed as the shared feature extraction subnetwork, including an encoder, a decoder, convolutional block attention modules, and multiscale feature fusion modules, which can improve the representation power of the U-shape network through aggregating features of shallower layers and concatenating features of deeper layers. Second, four individual subnetworks with fully connected layers, designed for four outputs, are utilized for regressing those four characteristics. We employ the gradient normalization algorithm to alleviate the gradient magnitude masking effect caused by training imbalance among different tasks. The proposedMOA-Unet is compared with classical chemometric methods on 670 coal samples from on-site *** experimental results demonstrate that the proposedmodel achieves state-of-the-art performance with correlation coefficients of 0.9015, 0.9538, 0.8986, and 0.8884, corresponding to moisture, ash, volatile content, and calorific value, respectively. Impact Statement-The proximate analysis of coal has been widely utilized as the basis for determining the rank of coal which is in connection with coa

关键词： Feature extraction

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Optimizing the Parameters of Fluidized Bed Dryer 9

Optimizing the Parameters of Fluidized Bed Dryer

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Soni, Binjal Padhiyar, Jay Ragupathy, A.G. Choudhury, Amitava Bharti, Santosh Pandit Deendayal Energy University Department of Computer Science And Engineering Gandhinagar India Zydus Group Department of Digitalization And Automation Ahmedabad India

ISBN: (纸本)9798350394474

The present study centres on the application of an all-encompassing methodology for optimizing the parameters of Fluidized Bed Dryer (FBD). First, a dataset with various FBD attributes are subjected to Principle Component Analysis (PCA). Subsequently, a variety of regression algorithms, including multiple linear regressors, support vector classifiers, decision trees, random forests, and gradient boosting regressors, are employed to analyze the identified key attributes. These attributes comprise the actual values of inlet air flow, inlet air temperature, exhaust air temperature, loss on drying, product temperature, and total drying time. When it comes to predicting the total drying time, the gradient boosting regressor performs better than others. A novel optimization technique called Bayesian optimization is used to find the optimized values of highly correlated features with the total drying time in order further maximize efficiency. This integrated approach uses both sophisticated machine learning algorithms for predictive modelling and optimization techniques to fine-tune specific parameters that are critical for the Fluidized Bed Dryer. The study's findings provide useful information about enhancing FBDs' overall performance and efficacy, which advances drying processes in a variety of industries. © 2024 IEEE.

关键词： Decision trees

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The Societal Implications of False Positives Using Machine Learning 6th

The Societal Implications of False Positives Using Machine L...

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6th International Conference on Recent Innovations in Computing, ICRIC 2023

作者： Dubey, Anurag Mishra, Aryan Tayal, Divyansh Yadav, Aarush Jamshed, Aatif Department of IT ABES Engineering College AKTU Lucknow India Department of Computer Science KIET Group of Institutions Delhi-NCR Ghaziabad India

ISBN: (纸本)9789819734412

The article aims to investigate the societal consequences of false positive detection through a systematic review leveraging ML (machine learning) techniques. It seeks to assess the impact of inaccurate detections on various aspects of society, such as healthcare, security, and privacy, to inform better decision-making and policy development. The study employs a comprehensive dataset encompassing diverse sources and topics to train and validate the model's efficacy. Results showcase promising outcomes, indicating that sentiment analysis can effectively contribute to the identification of misleading information. The proposed approach not only demonstrates robust performance in distinguishing between genuine and fake news but also provides insights into the emotional underpinnings of deceptive content. This research contributes to the evolving field of fake news detection, offering a nuanced perspective that aligns sentiment analysis with the imperative of fostering a more informed and resilient information landscape. The purpose of this work is to propose a notion and methods for identifying misinformation. The process of gathering news items and then utilizing SVM (SVM(Support Vector Machine)) to categorize them as either authentic or fake is accomplished through the utilization of ML (machine learning) and natural language-processing processes. Comparing the results of the proposed model with those of the existing models is the purpose of this comparison. With an accuracy rating of 93.6%, the model that was provided is performing efficiently and accurately identifying the outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Towards a Feasible Hand Gesture Recognition System as Sterile Non-contact Interface in the Operating Room with 3D Convolutional Neural Network

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Informatica (Slovenia) 2022年第1期46卷 1-12页

作者： Salvador, Roy Amante A. Naval, Prospero C. Computer Vision and Machine Intelligence Group Department of Computer Science College of Engineering University of the Philippines Diliman Quezon City Philippines

Operating surgeons are constrained when interacting with computer systems as they traditionally utilize hand-held devices such as keyboard and mouse. Studies have previously proposed and shown the use of hand gestures is an efficient, touchless way of interfacing with such systems to maintain a sterile field. In this paper, we propose a Deep computer Vision-based Hand Gesture Recognition framework to facilitate the interaction. We trained a 3D Convolutional Neural Network with a very large scale dataset to classify hand gestures robustly. This network became the core component of a prototype application requiring intraoperative navigation of medical images of a patient. Usability evaluation with surgeons demonstrates the application would work and a hand gesture lexicon that is germane to Medical Image Navigation was defined. By completing one cycle of usability engineering, we prove the feasibility of using the proposed framework inside the Operating Room. © 2022 Slovene Society Informatika. All rights reserved.

关键词： Classification (of information)

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Reinforcement learning-based unknown reference tracking control of HMASs with nonidentical communication delays

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science China(Information sciences) 2023年第7期66卷 46-57页

作者： Yong XU Zheng-Guang WU Wei-Wei CHE Deyuan MENG School of Automation Beijing Institute of Technology Institute of Cyber-Systems and Control Zhejiang University College of Mathematics and Computer Science Zhejiang Normal University Department of Automation Qingdao University School of Automation Science and Electrical Engineering Beihang University(BUAA)

This paper focuses on the optimal output synchronization control problem of heterogeneous multiagent systems(HMASs) subject to nonidentical communication delays by a reinforcement learning *** with existing studies assuming that the precise model of the leader is globally or distributively accessible to all or some of the followers, the leader's precise dynamical model is entirely inaccessible to all the followers in this paper. A data-based learning algorithm is first proposed to reconstruct the leader's unknown system matrix online. A distributed predictor subject to communication delays is further devised to estimate the leader's state, where interaction delays are allowed to be nonidentical. Then, a learning-based local controller, together with a discounted performance function, is projected to reach the optimal output synchronization. Bellman equations and game algebraic Riccati equations are constructed to learn the optimal solution by developing a model-based reinforcement learning(RL) algorithm online without solving regulator equations, which is followed by a model-free off-policy RL algorithm to relax the requirement of all agents' dynamics faced by the model-based RL algorithm. The optimal tracking control of HMASs subject to unknown leader dynamics and communication delays is shown to be solvable under the proposed RL algorithms. Finally, the effectiveness of theoretical analysis is verified by numerical simulations.

关键词： heterogeneous multiagent systems HMAS reinforcement learning RL optimal output synchronization communication delays

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Pneumonia Detection from X-ray Images Using Convolutional Neural Networks 24

Pneumonia Detection from X-ray Images Using Convolutional Ne...

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24th International Conference on control Systems and computer science, CSCS 2023

作者： Dediu, Viorel Udrea, Andreea Faculty of Automatic Control and Computer Science Univesity Politehnica of Bucharest Department of Automatic Control and Systems Engineering Bucharest Romania

ISBN: (纸本)9798350313390

Pneumonia is one of the top causes of death in Romania and early detection of this disease improves the recovery chances and shortens the length of hospitalization. In this work, we develop a solution for automatic pneumonia detection based on convolutional neural networks. Four network models are investigated. They are trained on 4.163 images from a public dataset and tested on 530 images. The best results are obtained by one of the proposed models conducting to a sensitivity of 98.72%, an accuracy of 89.81%, and ROC 93.46%. Thus, this research proposes a lightweight screening tool that can help triaging the patients with pneumonia. © 2023 IEEE.

关键词： Convolution

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