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IEEE Conference on Decision and Control

作者： M. H. W. Engelaar Z. Zhang E. E. Vlahakis D.V. Dimarogonas M. Lazar S. Haesaert Department of Electrical Engineering (Control Systems Group) Eindhoven University of Technology The Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper addresses the control synthesis of heterogeneous stochastic linear multi-agent systems with realtime allocation of signal temporal logic (STL) specifications. Based on previous work, we decompose specifications into subspecifications on the individual agent level. To leverage the efficiency of task allocation, a heuristic filter evaluates potential task allocation based on STL robustness, and subsequently, an auctioning algorithm determines the definitive allocation of specifications. Finally, a control strategy is synthesized for each agent-specification pair using tube-based model predictive control (MPC), ensuring provable probabilistic satisfaction. We demonstrate the efficacy of the proposed methods using a multi-shuttle scenario that highlights a promising extension to automated driving applications like vehicle routing.

关键词： Vehicle routing Stochastic processes Probabilistic logic Prediction algorithms Real-time systems Robustness Resource management Vehicle dynamics Multi-agent systems Predictive control

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Generative Adversarial Networks for Dynamic Malware Behavior: A Comprehensive Review, Categorization, and Analysis

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Gebrehans, Ghebrebrhan Ilyas, Naveed Eledlebi, Khouloud Lunardi, Willian T. Andreoni, Martin Yeun, Chan Yeob Damiani, Ernesto Lab Khalifa University of Science and Technology Abu Dhabi United Arab Emirates University of California Riverside United States Khalifa University of Science and Technology Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Abu Dhabi United Arab Emirates Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Khalifa University of Science and Technology Center for Secure Cyber-physical Systems Abu Dhabi United Arab Emirates University of Milan Dept. of Computer Science Italy

This paper highlights the critical role of Machine Learning (ML) in combating the dynamic nature of cybersecurity threats. Unlike previous studies focusing mainly on static analysis, this work surveys the literature on dynamic analysis-based malware generation and detection. The study addresses the complexities of applying GANs to tabular data with heavy-tailed and multimodal distributions. It also examines the challenges of generating sequential malware behavior data and categorizes GAN-based models and their primary use cases. Furthermore, the paper evaluates adversarial losses and their limitations in generating dynamic malware behavior. Finally, it identifies existing metrics to assess GAN generalization in malware research and suggests future research directions based on identified limitations. © 2020 IEEE.

关键词： Generative adversarial networks

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Enhancing transparency in buyer-driven commodity chains for complex products: a blockchain-based traceability framework demonstrated through an apparel supply chain simulation

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Procedia computer science 2025年 253卷 2703-2712页

作者： Ritwik Takkar Ken Birman H. Oliver Gao Department of Systems Engineering Cornell University Ithaca NY 14853 USA Department of Computer Science Cornell University Ithaca NY 14853 USA School of Civil and Environmental Engineering Cornell University Ithaca NY 14853 USA

Buyer-driven commodity chains are characterized by commercial relationships between buyers and sellers that may obscure accountability due to complexity, thereby undermining sustainability efforts. Conventional methods to trace production, including ineffective human-led audits, risk reorienting global corporate governance towards the interests of private business and away from social benefit by limiting the role of objective data in the process. This study examines the relevant features of private, permissioned blockchain towards harnessing the transparency challenge by demonstrating the efficacy of our proposed framework against a simulation of a real-world multi-tier apparel supply chain. The simulation integrates a set of functional and operational requirements achieved through a combination of programmable smart contracts and underlying blockchain architecture. We then evaluate the framework both qualitatively and quantitatively before discussing the limitations of our work.

关键词： Blockchain Traceability Supply Chain Management Manufacturing

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Dynamic Network Selection for URLLC and eMBB Applications in Sub-6GHz-mmWave-THz Networks

Dynamic Network Selection for URLLC and eMBB Applications in...

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IEEE Conference on Wireless Communications and Networking

作者： Nguyen Cong Luong Feng Shaohan Shimin Gong Dusit Niyato Faculty of Computer Science PHENIKAA University Hanoi Vietnam School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou China School of Computer Science and Engineering Nanyang Technological University Singapore China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

In this paper, we investigate a heterogeneous network (HetNet) including sub-6GHz base stations (BSs), mmWave BSs, and THz BSs to support enhanced mobile broadband (eMBB) users and ultra-reliable low-latency communication (URLLC) users. We particularly investigate the user-centric network in which the users are allowed to locally and dynamically select and switch among the BSs over time to achieve their highest utility. The two types of users have different Quality of Service (QoS) requirements. Thus, we design two types of utility functions specific for the eMBB users and URLLC users. Then, to model the dynamic selection behavior of the users, we propose to use a fractional game with the power-law memory (PLM). The fractional game allows the eMBB users and URLLC users to incorporate their past strategies into their current selection, thus improving their utility. Simulation results show that the total utility obtained by the users with fractional game is higher than that obtained by the users with classical game. Moreover, the type of URLLC users in the network also affects the total utility obtained by the eMBB users.

关键词： Base stations Enhanced mobile broadband Simulation Games Switches Quality of service Ultra reliable low latency communication

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Modality-Specific Interactive Attack for Vision-Language Pre-Training Models

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5663-5677页

作者： Zhang, Haiqi Tang, Hao Sun, Yanpeng He, Shengfeng Li, Zechao Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China The Hong Kong Polytechnic University Centre for Smart Health Hong Kong Singapore Management University School of Computing and Information Systems Singapore

Recent advances have heightened the interest in the adversarial transferability of Vision-Language Pre-training (VLP) models. However, most existing strategies constrained by two persistent limitations: suboptimal utilization of crossmodal interactive information, and inherent discrepancies across hierarchical textual representation. To address these challenges, we propose the Modality-Specific Interactive Attack (MSIAttack), a novel approach that integrates semantic-level image perturbations with embedding-level text perturbations, all while maintaining minimal inter-modal constraints. In our image attack methodology, we introduce Multi-modal Integrated Gradients (MIG) to guide perturbations toward the core semantics of images, enriched by their associated deeply text information. This technique enhances transferability by capturing consistent features across various models, thereby effectively misleading similar-model perception areas. Additionally, we employ a momentum iteration strategy in conjunction with MIG, which amalgamates current and historical gradients to expedite the perturbation updates. For text attacks, we streamline the perturbation process by operating exclusively at the embedding level. This reduces semantic gaps across hierarchical structures and significantly enhances the generalizability of adversarial text. Moreover, we delve deeper into how semantic perturbations with varying degrees of similarity affect the overall attack effectiveness. Our experimental results on image-text retrieval tasks using the multi-modal datasets Flickr30K and MSCOCO underscore the efficacy of MSI-Attack. Our method achieves superior performance, setting a new state-of-the-art benchmark, all without the need for additional mechanisms. 2005-2012 IEEE.

关键词： Photointerpretation

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AFRODITE - Optimizing Federated Learning in Resource-Constrained Environments Through Data Sampling Techniques

AFRODITE - Optimizing Federated Learning in Resource-Constra...

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Pervasive and Intelligent Computing (PICom), IEEE Conference on

作者： Mayara Aragão Pedro González Claudio de Farias Flávia Delicato Systems Engineering and Computer Science Program Federal University of Rio de Janeiro Fluminense Federal University Rio de Janeiro Brazil

ISBN: (数字)9798331522742

ISBN: (纸本)9798331522759

Federated Learning (FL) has emerged as a promising solution to address challenges in traditional machine learning (ML) regarding data privacy and security. However, training federated models in resource-constrained environments, such as IoT devices, presents challenges due to limited computational resources and complex data. This paper proposes data sampling techniques to optimize federated training in such environments, aiming to reduce training time while maintaining model quality. The study evaluates the impact of data sampling on federated model performance and compares it with traditional approaches. The methodology involves implementing random data selection in client datasets within the context of federated learning and conducting experiments across different configurations to analyze results. The findings provide insights for practical application in real-world scenarios with computational constraints.

关键词： Training Accuracy Federated learning Computational modeling Data models Robustness Proposals Internet of Things Security Standards

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Successful Integration of IoT and Blockchain Technologies Using Several Machine Learning Algorithms

Successful Integration of IoT and Blockchain Technologies Us...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： C. Calarany M Indumathy P. Senthilraja D. Suganya V Gopi Vardhan Yelisetty Shanmukh Rahul Department of Computer Science Tagore Government Arts and Science College Puducherry India Department of Information Technology Rajiv Gandhi College of Engineering & Technology Kirumampakkam India Department of Computer Science K.S.Rangasamy College of Technology Tamil Nadu India Department of Computer Science Rajiv Gandhi College of Engineering & technology Kirumampakkam Data science and business systems SRM Institute of Science and Technology Kattankulathur India

Internet of Things (IoT) produces massive amounts of data that need to be processed and saved securely. The strong features of Blockchain makes it as a best candidate for storing the data received from IoT sensors. However, there is a need of concern to take care of the challenges associated with both IoT and blockchain paradigms. Firstly, the enormous amount of data should be effortlessly handled by Blockchain network, without adding much complexity. Secondly, the heterogeneous nature of data that are received from various IoT Sensors should be stored within the blockchain. In this paper, a conceptual framework based on Multimodal Machine Learning (MML) algorithm is proposed as an interface between the IoT sensors and Blockchain networks. The MML algorithm is used to understand the different modalities of the data and provides a way to manage the heterogeneity in that data. The MML algorithms provides an intelligent data processing that allows for real-time data storage in the blockchain networks. The conceptual frame assumes that audio, video, and text are the three types of data as received from IoT. These inputs are processed in simplistic Recurrent neural network RNN that acts as MML and then passed to Blockchain networks for secure storage. Simulation of the above said conceptual framework is performed assuming all other relevant requirements of IoT, MML and Blockchain are taken into account with default parameters and values. The results obtained provide a promising note for future scope in this direction.

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Adaptive Constraint Partition Based Optimization Framework for Large-Scale Integer Linear Programming 37

Adaptive Constraint Partition Based Optimization Framework f...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Ye, Huigen Wang, Hongyan Xu, Hua Wang, Chengming Jiang, Yu State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing100084 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Meituan Inc. Block F&G Wangjing International R&D Park No.6 Wang Jing East Rd Chaoyang District Beijing100102 China

ISBN: (纸本)9781577358800

Integer programming problems (IPs) are challenging to be solved efficiently due to the NP-hardness, especially for large-scale IPs. To solve this type of IPs, Large neighborhood search (LNS) uses an initial feasible solution and iteratively improves it by searching a large neighborhood around the current solution. However, LNS easily steps into local optima and ignores the correlation between variables to be optimized, leading to compromised performance. This paper presents a general adaptive constraint partition-based optimization framework (ACP) for large-scale IPs that can efficiently use any existing optimization solver as a subroutine. Specifically, ACP first randomly partitions the constraints into blocks, where the number of blocks is adaptively adjusted to avoid local optima. Then, ACP uses a subroutine solver to optimize the decision variables in a randomly selected block of constraints to enhance the variable correlation. ACP is compared with LNS framework with different subroutine solvers on four IPs and a real-world IP. The experimental results demonstrate that in specified wall-clock time ACP shows better performance than SCIP and Gurobi. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Integer programming

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Technological solution of psychological therapies using neural networks for the control and monitoring of anxiety disorder 5

Technological solution of psychological therapies using neur...

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5th IEEE sciences and Humanities International Research Conference, SHIRCON 2021

作者： Trujillo, Roger Fernando Lopez Santos, Nathaly Mercedes Gamarra Armas-Aguirre, Jimmy Systems Engineering and Computer Science School Universidad Peruana de Ciencias Aplicadas Lima Peru

ISBN: (纸本)9781665429146

This article proposes a technological solution for the control and monitoring of anxiety disorder behavior in vulnerable populations that allows receiving symptomatology data, tests and manifestations in real time during psychological therapies The result of this research seeks to optimize the time of attention and to monitor patients in psychological therapies through the use of neural networks and the systematization of psychological tests. This proposal consists of 4 phases: 1. Collection of test results and alerts;2. Reception of the collected information;3. The technological solution was validated in a community mental health center in the district of San Martin de Porres in Lima, Peru. Preliminary results showed that technological solution allows to reduce time of psychological therapies by 25% and has an efficiency of 80% based on the tests performed. © 2021 IEEE.

关键词： Diagnosis

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Uncertainty-Informed Model Selection Method for Nonlinear System Identification and Interpretable Machine Learning

Uncertainty-Informed Model Selection Method for Nonlinear Sy...

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Mediterranean Conference on Control and Automation (MED)

作者： Yuanlin Gu Hua-Liang Wei Division of Computer Science and Maths University of Stirling Stirling United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield Sheffield

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

Modeling uncertainty has been an active and important topic in the fields of data-driven modeling and machine learning. Uncertainty ubiquitously exists in any data modeling process, making it challenging to identify the optimal models among many potential candidates. This article proposes an uncertainty-informed method to address the model selection problem. The performance of the proposed method is evaluated on a dataset generated from a complex system model. The experimental results demonstrate the effectiveness of the proposed method and its superiority over conventional approaches. This method has minimal requirements for the length of training data and model types, making it applicable for various modeling frameworks.

关键词： Data-driven modeling Adaptation models Uncertainty Recurrent neural networks Training data Machine learning Data models

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