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Bayesian hypergame approach to equilibrium stability and robustness in moving target defense

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arXiv 2024年

作者： Zhang, Hanzheng Cheng, Zhaoyang Chen, Guanpu Johansson, Karl Henrik Shanghai Research Institute for Autonomous Intelligent Systems Tongji University Shanghai China Academy of Mathematics and System Science Chinese Academy of Science University of Chinese Academy of Science Beijing China The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology The Digital Futures Stockholm Sweden

We investigate the equilibrium stability and robustness in a class of moving target defense problems, in which players have both incomplete information and asymmetric cognition. We first establish a Bayesian Stackelberg game model for incomplete information and then employ a hypergame reformulation to address asymmetric cognition. With the core concept of the hyper Bayesian Nash equilibrium (HBNE), a condition for achieving both the strategic and cognitive stability in equilibria can be realized by solving linear equations. Moreover, to deal with players’ underlying perturbed knowledge, we study the equilibrium robustness by presenting a condition of robust HBNE under the given configuration. Experiments evaluate our theoretical results. Copyright © 2024, The Authors. All rights reserved.

关键词： Nash equilibrium

G2Pxy: Generative Open-Set Node Classification on Graphs with Proxy Unknowns

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arXiv 2023年

作者： Zhang, Qin Shi, Zelin Zhang, Xiaolin Chen, Xiaojun Fournier-Viger, Philippe Pan, Shirui Big Data Institute College of Computer Science and Software Engineering Shenzhen University China School of Information and Communication Technology Griffith University Queensland Australia

Node classification is the task of predicting the labels of unlabeled nodes in a graph. State-of-the-art methods based on graph neural networks achieve excellent performance when all labels are available during training. But in real-life, models are often applied on data with new classes, which can lead to massive misclassification and thus significantly degrade performance. Hence, developing open-set classification methods is crucial to determine if a given sample belongs to a known class. Existing methods for open-set node classification generally use transductive learning with part or all of the features of real unseen class nodes to help with open-set classification. In this paper, we propose a novel generative open-set node classification method, i.e., G2Pxy, which follows a stricter inductive learning setting where no information about unknown classes is available during training and validation. Two kinds of proxy unknown nodes, inter-class unknown proxies and external unknown proxies are generated via mixup to efficiently anticipate the distribution of novel classes. Using the generated proxies, a closed-set classifier can be transformed into an open-set one, by augmenting it with an extra proxy classifier. Under the constraints of both cross entropy loss and complement entropy loss, G2Pxy achieves superior effectiveness for unknown class detection and known class classification, which is validated by experiments on benchmark graph datasets. Moreover, G2Pxy does not have specific requirement on the GNN architecture and shows good generalizations. Copyright © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

Counterfactual Inference of Second Opinions

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arXiv 2022年

作者： Corvelo Benz, Nina L. Rodriguez, Manuel Gomez Max Planck Institute for Software Systems Germany Department of Biosystems Science and Engineering ETH Zurich Switzerland

Automated decision support systems that are able to infer second opinions from experts can potentially facilitate a more efficient allocation of resources-they can help decide when and from whom to seek a second opinion. In this paper, we look at the design of this type of support systems from the perspective of counterfactual inference. We focus on a multiclass classification setting and first show that, if experts make predictions on their own, the underlying causal mechanism generating their predictions needs to satisfy a desirable set invariant property. Further, we show that, for any causal mechanism satisfying this property, there exists an equivalent mechanism where the predictions by each expert are generated by independent sub-mechanisms governed by a common noise. This motivates the design of a set invariant Gumbel-Max structural causal model where the structure of the noise governing the sub-mechanisms underpinning the model depends on an intuitive notion of similarity between experts which can be estimated from data. Experiments on both synthetic and real data show that our model can be used to infer second opinions more accurately than its non-causal counterpart. © 2022, CC BY.

关键词： Forecasting

Annotation Along Arranged Bacteria to Detect Gram Positive Cocci from Gram Stained Smears Images

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Annotation Along Arranged Bacteria to Detect Gram Positive C...

IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Ukyo Kashino Mayu Yoshida Kouichi Hirata Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan Department of Artificial Intelligence Kyushu Institute of Technology Iizuka Japan Tokyo Electron Kyushu Ltd.

ISBN: (数字)9798350377903

ISBN: (纸本)9798350377910

Gram positive cocci occur as not only singles but also arrangements of pairs, tetorads, chains and clusters in Gram stained smears images. In this paper, we detect Gram positive cocci based on the annotation along singles, which is an annotation along a single bacterium, and the annotation along arrangements, which is an annotation along arranged bacteria. Here, we avoid to the difference of the number of regions obtained by the annotations both decreasing the number of regions by annotation along singles and increasing the number of regions by annotation along arrangements.

关键词： YOLO Annotations Bacteria Data augmentation Informatics

Enhancing Dental Caries Identification with Deep Learning: A Study of Convolutional Neural Networks and Transfer Learning Approaches

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SSRN

SSRN 2024年

作者： Mannan, Fayqa Adeel, Muhammad Arif, Erssa Li, Jianqiang Ullah, Amin Nawazish, Samra Mahmood, Tariq Rehman, Amjad Shoukat, Ijaz Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science Government College University Faisalabad38000 Pakistan Department of Computer Science School of Science National Textile University Faisalabad37610 Pakistan Department of Computer Science Faculty of Computing Riphah International University Faisalabad38000 Pakistan Beijing Engineering Research Center for IoT Software and Systems 100124 China Department of Computer Science Faculty of IT and Computer Science University of Central Punjab Lahore Pakistan School of Computer Science and Engineering Southeast University Nanjing China Software Engineering Department University of Engineering and Technology Taxila Pakistan Artificial Intelligence & Data Analytics Lab CCIS Prince Sultan University Riyadh11586 Saudi Arabia Faculty of information Sciences University of Education Vehari Campus Vehari61100 Pakistan

Various deep learning techniques have been employed to diagnose dental caries using X-ray images. In this study, we utilized deep learning models, including Convolutional Neural Networks (CNNs) and transfer learning models such as Visual Geometry Group (VGG16), VGG19, ResNet50, and Inception V3, to identify dental caries in periapical radiographs. The traditional CNN model comprises three sets of 2D convolutional layers, followed by an activation layer, a max-pooling layer, a flatten layer, one dense layer, another activation layer, dropout, another dense layer, and a final activation layer. For the VGG models, ResNet50, and Inception V3, the top convolutional layers were frozen to prevent retraining of the model, with only the last layers trained to produce the required output. The accuracy achieved by CNN, VGG16, VGG19, ResNet50, and Inception V3 was 90%, 96%, 73%, 70%, and 73%, respectively. VGG16 exhibited the highest accuracy among the models. Hyperparameters were carefully selected using a grid search approach, and the results were validated using the Shuffle-Split-Cross (SSC) validation method. These findings underscore the effectiveness of transfer learning in analyzing medical images, particularly in the diagnosis of dental caries. © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

Streaming quantum state purification for general mixed states

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arXiv 2025年

作者： Grier, Daniel Leung, Debbie Li, Zhi Pashayan, Hakop Schaeffer, Luke Department of Mathematics Department of Computer Science and Engineering University of California San Diego United States Institute for Quantum Computing University of Waterloo Canada Perimeter Institute for Theoretical Physics Canada National Research Council of Canada Canada Dahlem Center for Complex Quantum Systems Freie Universität Berlin Germany Research Institute Taiwan Department of Computer Science Institute for Advanced Computer Studies Joint Center for Quantum Information and Computer Science University of Maryland United States

Given multiple copies of a mixed quantum state with an unknown, nondegenerate principal eigenspace, quantum state purification is the task of recovering a quantum state that is closer to the principal eigenstate. A streaming protocol relying on recursive swap tests has been proposed and analysed for noisy depolarized states with arbitrary dimension and noise level. Here, we show that the same algorithm applies much more broadly, enabling the purification of arbitrary mixed states with a nondegenerate principal eigenvalue. We demonstrate this through two approaches. In the first approach, we show that, given the largest two eigenvalues, the depolarized noise is the most difficult noise to purify for the recursive swap tests, thus the desirable bounds on performance and cost follow from prior work. In the second approach, we provide a new and direct analysis for the performance of purification using recursive swap tests for the more general noise. We also derive simple lower bounds on the sample complexity, showing that the recursive swap test algorithm attains optimal sample complexity (up to a constant factor) in the low-noise regime. © 2025, CC BY.

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Intelligent Defect Detection and Classification in Manufacturing Execution systems with IoT

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Intelligent Defect Detection and Classification in Manufactu...

Smart Technologies for Sustainable Development Goals (ICSTSDG), International Conference on

作者： Chitra Sabapathy Ranganathan Aarthy S.T S. Prabha C. Srinivasan M. Rajmohan Malarvizhi S Mphasis Corporation Chennai Tamil Nadu India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science Jawaharlal Nehru College for Women (Arts & Science) Ulundurpet Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India Department of Computer Science and Business Systems S.A. Engineering College (Autonomous) Chennai Tamil Nadu India

ISBN: (数字)9798331516284

ISBN: (纸本)9798331516291

In today's industrial environment, competitiveness and customer happiness depend on product quality. One potential way to improve Manufacturing Execution System (MES) fault identification and categorization is to use Internet of Things (IoT) capabilities in conjunction with sophisticated machine learning algorithms. Using Convolutional Neural Networks (CNNs) and IoT devices, this research proposes a new method for intelligent flaw detection and classification. Throughout production, the proposed system's IoT sensors gather real-time data on product performance and quality. These sensors take images of the produced parts, which are then input into CNNs to identify and classify defects automatically. CNNs have a stellar reputation for their ability to analyze images. It can learn complex patterns and characteristics from simple pixel data, improving their accuracy in defect classification. It provides real-time defect detection, allowing prompt intervention to fix problems and reduce production losses. MES's built-in fault identification and categorization capabilities allow for more efficient coordination and optimization of production processes. This paper proves the effectiveness and feasibility of the proposed method in actual production settings using case studies and empirical validation. The data shows an increase in overall production quality, a decrease in scrap and rework, and an improvement in the precision of defect identification. It supports producers' commitment to providing customers with superior-quality goods and equips them with actionable information for continual process improvement.

关键词： Fault diagnosis Production Real-time systems Product design Sensor systems Quality assessment Manufacturing Internet of Things Intelligent sensors Defect detection

An Ensemble Machine Learning Technique for Stroke Prognosis

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computer systems science & engineering 2023年第10期47卷 413-429页

作者： Mesfer Al Duhayyim Sidra Abbas Abdullah Al Hejaili Natalia Kryvinska Ahmad Almadhor Uzma Ghulam Mohammad Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz University Al-Kharj16273Saudi Arabia Department of Computer Science COMSATS UniversityIslamabad53000Pakistan Computer Science Department Faculty of Computers&Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Information Systems Department Faculty of ManagementComenius University in BratislavaOdbojárovBratislava440Slovakia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan

Stroke is a life-threatening disease usually due to blockage of blood or insufficient blood flow to the *** has a tremendous impact on every aspect of life since it is the leading global factor of disability and *** can range from minor to severe(extensive).Thus,early stroke assessment and treatment can enhance survival *** prediction is extremely time and resource *** prediction methods such as Modern Information and Communication Technologies(ICTs),particularly those inMachine Learning(ML)area,are crucial for the early diagnosis and prognosis of ***,this research proposed an ensemble voting model based on three Machine Learning(ML)algorithms:Random Forest(RF),Extreme Gradient Boosting(XGBoost),and Light Gradient Boosting Machine(LGBM).We apply data preprocessing to manage the outliers and useless instances in the ***,to address the problem of imbalanced data,we enhance the minority class’s representation using the Synthetic Minority Over-Sampling Technique(SMOTE),allowing it to engage in the learning process *** reveal that the suggested model outperforms existing studies and other classifiers with 0.96%accuracy,0.97%precision,0.97%recall,and 0.96%*** experiment demonstrates that the proposed ensemble voting model outperforms state-of-the-art and other traditional approaches.

关键词： Stroke prediction machine learning ensemble model data analysis Synthetic Minority Over-Sampling

Probabilistic Tube-based Control Synthesis of Stochastic Multi-Agent systems under Signal Temporal Logic

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arXiv 2024年

作者： Vlahakis, Eleftherios E. Lindemann, Lars Sopasakis, Pantelis Dimarogonas, Dimos V. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden Thomas Lord Department of Computer Science Viterbi School of Engineering University of Southern California Los AngelesCA90089 United States School of Electronics Electrical Engineering and Computer Science Queen’s University Northern Ireland Belfast United Kingdom

We consider the control design of stochastic discrete-time linear multi-agent systems (MASs) under a global signal temporal logic (STL) specification to be satisfied at a predefined probability. By decomposing the dynamics into deterministic and error components, we construct a probabilistic reachable tube (PRT) as the Cartesian product of reachable sets of the individual error systems driven by disturbances lying in confidence regions (CRs) with a fixed probability. By bounding the PRT probability with the specification probability, we tighten all state constraints induced by the STL specification by solving tractable optimization problems over segments of the PRT, and relax the underlying stochastic problem with a deterministic one. This approach reduces conservatism compared to tightening guided by the STL structure. Additionally, we propose a recursively feasible algorithm to attack the resulting problem by decomposing it into agent-level subproblems, which are solved iteratively according to a scheduling policy. We demonstrate our method on a ten-agent system, where existing approaches are impractical. © 2024, CC BY.

关键词： Stochastic systems