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International Conference on Computer Vision (ICCV)

作者： Siyuan Liang Baoyuan Wu Yanbo Fan Xingxing Wei Xiaochun Cao Institute of Information Engineering Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Data Science The Chinese University of Hong Kong Shenzhen China Secure Computing Lab of Big Data Shenzhen Research Institute of Big Data Shenzhen China Tencent Shenzhen China Institute of Artificial Intelligence Hangzhou Innovation Institute Beihang University Beijing China

ISBN: (纸本)9781665428132

Object detection has been widely used in many safety- critical tasks, such as autonomous driving. However, its vulnerability to adversarial examples has not been sufficiently studied, especially under the practical scenario of black-box attacks, where the attacker can only access the query feedback of predicted bounding-boxes and top- 1 scores returned by the attacked model. Compared with black-box attack to image classification, there are two main challenges in black-box attack to detection. Firstly, even if one bounding-box is successfully attacked, another sub- optimal bounding-box may be detected near the attacked bounding-box. Secondly, there are multiple bounding- boxes, leading to very high attack cost. To address these challenges, we propose a Parallel Rectangle Flip Attack (PRFA) via random search. We explain the difference between our method with other attacks in Fig. 1. Specifically, we generate perturbations in each rectangle patch to avoid sub-optimal detection near the attacked region. Besides, utilizing the observation that adversarial perturbations mainly locate around objects’ contours and critical points under white-box attacks, the search space of attacked rectangles is reduced to improve the attack efficiency. Moreover, we develop a parallel mechanism of attacking multiple rectangles simultaneously to further accelerate the attack process. Extensive experiments demonstrate that our method can effectively and efficiently attack various popular object detectors, including anchor-based and anchor- free, and generate transferable adversarial examples.

关键词： Costs Perturbation methods Detectors Object detection Predictive models Search problems Task analysis

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The Importance of Artificial Intelligence in Green Innovation

The Importance of Artificial Intelligence in Green Innovatio...

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IEEE International Conference on Control System, computing and Engineering (ICCSCE)

作者： Essam Hussain Al Lawati Musab A. M. Ali Nooritawati Md Tahir College of Economics and Business Administration University of Technology and Applied Sciences Sultanate of Oman College of Computing and Information Sciences University of Technology and Applied Sciences Sultanate of Oman College of Engineering Universiti Teknologi MARA (UiTM) Malaysia Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Malaysia Applied College Princess Nourah Bint Abdul Rahman University (PNU) Riyadh Saudi Arabia

ISBN: (数字)9798350364507

ISBN: (纸本)9798350364514

The study focuses on Artificial Intelligence’s importance in developing green innovation. Artificial intelligence significantly enhances green innovation by enhancing productivity, accelerating environmentally friendly technological advancements, and facilitating better decision-making through energy optimisation, waste reduction, and smart infrastructure support. This research adopted a scoping review approach utilising the Scopus database as a source of documents. The study highlights several aspects, such as publication trend by years, publication source and context analysis. The reviews included documents published since 2021. The method section is derived from the PRISM-ScR checklist table. The highest publisher was the Business Strategy and The Environment journal, and the country with the highest publishing was China. In addition, the study recommends that more efforts should be exerted to increase companies’ awareness of the importance of green innovation and the feasibility of developing green products.

关键词： Waste reduction Technological innovation Reviews Computational modeling Green products Prototypes Air pollution Market research Artificial intelligence Sustainable development

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The robustness of cluster clustering covariance calibration

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Astronomy and Astrophysics 2025年 697卷

作者： Fumagalli, Alessandra Castro, Tiago Borgani, Stefano Valentini, Milena Ludwig-Maximilians-University Schellingstrasse 4 Munich80799 Germany INAF Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 Trieste34143 Italy IFPU Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy INFN Sezione di Trieste Via Valerio 2 Trieste34127 Italy ICSC Centro Nazionale di Ricerca in High Performance Computing Big Data e Quantum Computing Via Magnanelli 2 Bologna Italy Dipartimento di Fisica – Sezione di Astronomia Università di Trieste Via Tiepolo 11 Trieste34131 Italy

Ongoing and upcoming wide-field surveys at different wavelengths will measure the distribution of galaxy clusters with unprecedented precision, demanding accurate models for the two-point correlation function (2PCF) covariance. In this work we assessed a semi-analytical framework for the cluster 2PCF covariance that employs three nuisance parameters to account for non-Poissonian shot noise, residual uncertainties in the halo bias model, and sub-leading noise terms. We calibrated these parameters on a suite of fast approximate simulations generated by PINOCCHIO as well as full N-body simulations from OpenGADGET3. We demonstrate that PINOCCHIO can reproduce the 2PCF covariance measured in OpenGADGET3 at the few percent level, provided the mass functions are carefully rescaled. Resolution tests confirm that high particle counts are necessary to capture shot-noise corrections, especially at high redshifts. We performed the parameter calibration across multiple cosmological models, showing that one of the nuisance parameters, the non-Poissonian shot-noise correction (α), depends mildly on the amplitude of matter fluctuations (σ8). In contrast, the remaining two parameters, β, which controls the bias correction, and γ, which controls the secondary shot-noise correction, exhibit more significant variation with redshift and halo mass. Overall, our results underscore the importance of calibrating covariance models on realistic mock catalogs that replicate the selection function of forthcoming surveys and highlight that approximate methods, when properly tuned, can effectively complement full N-body simulations for precision cluster cosmology. © The Authors 2025.

关键词： Dark Matter

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Hyper-IIoT: A Smart Contract-inspired Access Control Scheme for Resource-constrained Industrial Internet of Things

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable computing 2025年

作者： Li, Dun Li, Hongzhi Crespi, Noel Minerva, Roberto Li, Ming Liang, Wei Li, Kuan-Ching Tsinghua University Department of Industrial Engineering China Institut Polytechnique de Paris Samovar Telecom SudParis Palaiseau91120 France Chizhou University College of Big Data and Artificial Intelligence Chizhou247100 China The Hong Kong Polytechnic University Research Institute for Advanced Manufacturing Hung Hom Hong Kong Hunan University of Science and Technology School of Computer Science and Engineering Hunan University of Science and Technology Sanya Research Institute Hunan Key Laboratory for Service Computing and Novel Software Technology China Providence University Dept. of Computer Science and Information Engineering Taiwan

In recent years, the refinements in industrial processes and the increasing complexity of managing privacy-sensitive data in Industrial Internet of Things (IIoT) devices have highlighted the need for secure, robust, and adaptive data management solutions. In this work, we propose a smart contract-assisted access control scheme for IIoT, which employs the Attribute-Based Access Control (ABAC) model to set access permissions for different industrial components. Besides, we defined a storage model and data format for private data by designing and deploying smart contracts to manage system operations and access policies. In addition, the bloom filter component is deployed to optimize the efficiency of contract management and system performance. Experimental results show that in real-world simulations, Hyper-IIoT demonstrates excellent contract execution time control, stable system throughput, and rapid consensus processes that are capable of handling high throughput and efficient consensus in distributed systems, even in large-scale request scenarios. © 2016 IEEE.

关键词： Smart contract

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Governing Online Rumor: An Uncertain Intervention Timing Model for Online Rumor Propagation from the Perspective of Government

Journal of Uncertain Systems

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Journal of Uncertain Systems 2024年第1期17卷

作者： Jin, Meiling Liu, Fengming Ning, Yufu Zhou, Jian School of Management Shanghai University Shanghai 200444 China Business School Shandong Normal University Jinan 250014 China School of Information Engineering Shandong Youth University of Political Science Jinan 250103 China Smart Healthcare Big Data Engineering and Ubiquitous Computing Characteristic Laboratory in Universities of Shandong Jinan 250103 China Confucius Institute University of Bahrain Bahrain

In view of different types of online rumors, when the governance institutions (government departments) intervene to achieve the least cost and the best effect is a core issue of rumor governance. Based on the perspective of the government, under the premise of limited human, material and financial resources, and considering the different impacts of government intervention opportunities on the spread of different types of rumors, this paper establishes an uncertain multi-objective network rumor transmission intervention timing model, so as to achieve the optimal allocation of government intervention opportunities for different types of online rumors, minimize the total cost and maximize the inhibition effect of rumor spreading. Then, the equivalent form of the model is given, and the model is solved. In addition, numerical experiments are conducted on three types of rumors during the COVID-19 pandemic. The experimental results show that when the types of online rumors and the focus of government departments on cost and governance effect are different, the optimal time for intervention will change. Finally, combined with the experimental results, corresponding countermeasures and suggestions are put forward for the supervision of online rumors by government departments. © 2023 World Academic Union. All rights reserved.

关键词： Online rumors rumor propagation timing of intervention uncertain programming

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Coupled Alternating Neural Networks for Solving Multi-Population High-Dimensional Mean-Field Games with Stochasticity

TechRxiv

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TechRxiv 2022年

作者： Wang, Guofang Yao, Wang Zhang, Xiao Niu, Zijia The School of Mathematical Sciences Beihang University Beijing100191 China Key Laboratory of Mathematics Informatics and Behavioral Semantics Ministry of Education Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Peng Cheng Laboratory Guangdong Shenzhen518055 China The Institute of Artificial Intelligence Beihang University Beijing100191 China Key Laboratory of Mathematics Informatics and Behavioral Semantics Ministry of Education Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

Multi-population mean-field game is a critical subclass of mean-field games (MFGs). It is a theoretically feasible multi-agent model for simulating and analyzing the game between multiple heterogeneous populations of interacting massive agents. Due to the factors of game complexity, dimensionality disaster and disturbances should be taken into account simultaneously, how to solve the multi-population high-dimensional stochastic MFG problem is faced with great challenges. We present CA-Net, a coupled alternating neural network approach for tractably solving multi-population high-dimensional MFGs in the stochastic case. First, we provide a universal modeling framework for large-scale heterogeneous multi-agent game systems, which is strictly expressed as a multi-population MFG problem. Next, we generalize the potential variational primal-dual structure that MFGs exhibit, then phrase the multi-population MFG problem as a convex-concave saddle-point problem. Last but not least, we design a generative adversarial network (GAN) with multiple generators and multiple discriminators—the solving network, which parameterizes the value functions and the density functions of multiple populations by two sets of neural networks, respectively. Moreover, numerical experiments demonstrate the feasibility and effectiveness of our approach. © 2022, CC BY.

关键词： Generative adversarial networks

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CausalTester: Measuring the Consistency of Replicated Services via Causality Semantics

CausalTester: Measuring the Consistency of Replicated Servic...

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Asian Test Symposium (ATS)

作者： Yu Tang Le Zhao Wei Yuan Xu Wang Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China Chengdu Aircraft Design & Research Institute Chengdu China

ISBN: (纸本)9781665440523

Cloud and big data systems often replicate data and prefer weak consistency such as eventual consistency for better scalability and availability. Such weak consistency may produce unexpected and harmful system behaviors, for example, stale reads and conflicting writes. In order to measure the consistency levels and help developers understand the harmful degree, we propose a testing framework called CausalTester to evaluate the causality semantics of replicated systems, including 12 real test cases collected from Twitter, Flickr, Amazon, the corresponding benchmark services, and the automatic detection of causality violation with crash injection. We implement the testing framework and measure the consistency of three widely-used distributed databases. The experimental results show that it is effective to detect the consistency violations for the weak consistency and helpful to find consistency-related bugs if the strong consistency is violated.

关键词： Social networking (online) Image databases Scalability Semantics Multimedia Web sites Computer bugs Blogs

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data-Driven Control Based on Function Approximation with Binomial Coefficients

Data-Driven Control Based on Function Approximation with Bin...

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第33届中国控制与决策会议

作者： Guoliang Zhu Lei Chen Kexin Liu Jinhu Lü School of Automation Science and Electrical Engineering Beihang Uniersity Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Advanced Innovation Center for Big Data and Brain Computing Beihang University

This paper investigates data-driven control methods based on function approximation with binomial coefficients and sampling *** propose two approximate models and corresponding controllers formed from sampled *** shows that the information truncated in the first approximate model is saved in the latter with parameter estimation *** results validate the effectiveness of proposed approaches.

关键词： Sampled-data driven systems Function approximation Approximate theory Adaptive control Binomial coefficients

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ReconBoost: boosting can achieve modality reconcilement 24

ReconBoost: boosting can achieve modality reconcilement

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Proceedings of the 41st International Conference on Machine Learning

作者： Cong Hua Qianqian Xu Shilong Bao Zhiyong Yang Qingming Huang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China and School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China and School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China and Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China and Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores a novel multi-modal alternating learning paradigm pursuing a reconciliation between the exploitation of uni-modal features and the exploration of cross-modal interactions. This is motivated by the fact that current paradigms of multi-modal learning tend to explore multi-modal features simultaneously. The resulting gradient prohibits further exploitation of the features in the weak modality, leading to modality competition, where the dominant modality overpowers the learning process. To address this issue, we study the modality-alternating learning paradigm to achieve reconcilement. Specifically, we propose a new method called ReconBoost to update a fixed modality each time. Herein, the learning objective is dynamically adjusted with a reconcilement regularization against competition with the historical models. By choosing a KL-based reconcilement, we show that the proposed method resembles Friedman's Gradient-Boosting (GB) algorithm, where the updated learner can correct errors made by others and help enhance the overall performance. The major difference with the classic GB is that we only preserve the newest model for each modality to avoid overfitting caused by ensembling strong learners. Furthermore, we propose a memory consolidation scheme and a global rectification scheme to make this strategy more effective. Experiments over six multi-modal benchmarks speak to the efficacy of the method. We release the code at https://***/huacong/ReconBoost.

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A Fast Two-Stage Black-Box Deep Learning Network Attacking Method Based on Cross-Correlation

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Computers, Materials & Continua 2020年第7期64卷 623-635页

作者： Deyin Li Mingzhi Cheng Yu Yang Min Lei Linfeng Shen State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijing100876China College of New Media Beijing Institute of Graphic CommunicationBeijing102600China School of Computing Science Simon Fraser UniversityBurnabyCanada

Deep learning networks are widely used in various systems that require ***,deep learning networks are vulnerable to adversarial *** study on adversarial attacks plays an important role in ***-box attacks require less knowledge about target models than white-box attacks do,which means black-box attacks are easier to launch and more ***,the state-of-arts black-box attacks still suffer in low success rates and large visual distances between generative adversarial images and original *** paper proposes a kind of fast black-box attack based on the cross-correlation(FBACC)*** attack is carried out in two *** the first stage,an adversarial image,which would be missclassified as the target label,is generated by using gradient descending *** far the image may look a lot different than the original ***,in the second stage,visual quality keeps getting improved on the condition that the label keeps being *** using the cross-correlation method,the error of the smooth region is ignored,and the number of iterations is *** with the proposed black-box adversarial attack methods,FBACC achieves a better fooling rate and fewer *** attacking LeNet5 and AlexNet respectively,the fooling rates are 100%and 89.56%.When attacking them at the same time,the fooling rate is 69.78%.FBACC method also provides a new adversarial attack method for the study of defense against adversarial attacks.

关键词： Black-box adversarial attack cross-correlation two-module

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